[{"date_updated":"2024-12-10T10:23:49Z","title":"Single-molecule conductance in a unique cross-conjugated tetra(aminoaryl)ethene","day":"01","type":"journal_article","month":"05","article_type":"original","volume":57,"extern":"1","page":"591-594","date_created":"2024-09-09T06:44:58Z","pmid":1,"language":[{"iso":"eng"}],"scopus_import":"1","publisher":"Royal Society of Chemistry","publication_identifier":{"eissn":["1364-548X"],"issn":["1359-7345"]},"year":"2021","status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"17901","OA_type":"closed access","citation":{"mla":"Medina Rivero, Samara, et al. “Single-Molecule Conductance in a Unique Cross-Conjugated Tetra(Aminoaryl)Ethene.” <i>Chemical Communications</i>, vol. 57, no. 5, Royal Society of Chemistry, 2021, pp. 591–94, doi:<a href=\"https://doi.org/10.1039/d0cc07124b\">10.1039/d0cc07124b</a>.","chicago":"Medina Rivero, Samara, Paloma García Arroyo, Liang Li, Suman Gunasekaran, Thijs Stuyver, María José Mancheño, Mercedes Alonso, Latha Venkataraman, José L. Segura, and Juan Casado. “Single-Molecule Conductance in a Unique Cross-Conjugated Tetra(Aminoaryl)Ethene.” <i>Chemical Communications</i>. Royal Society of Chemistry, 2021. <a href=\"https://doi.org/10.1039/d0cc07124b\">https://doi.org/10.1039/d0cc07124b</a>.","short":"S. Medina Rivero, P. García Arroyo, L. Li, S. Gunasekaran, T. Stuyver, M.J. Mancheño, M. Alonso, L. Venkataraman, J.L. Segura, J. Casado, Chemical Communications 57 (2021) 591–594.","ieee":"S. Medina Rivero <i>et al.</i>, “Single-molecule conductance in a unique cross-conjugated tetra(aminoaryl)ethene,” <i>Chemical Communications</i>, vol. 57, no. 5. Royal Society of Chemistry, pp. 591–594, 2021.","ista":"Medina Rivero S, García Arroyo P, Li L, Gunasekaran S, Stuyver T, Mancheño MJ, Alonso M, Venkataraman L, Segura JL, Casado J. 2021. Single-molecule conductance in a unique cross-conjugated tetra(aminoaryl)ethene. Chemical Communications. 57(5), 591–594.","apa":"Medina Rivero, S., García Arroyo, P., Li, L., Gunasekaran, S., Stuyver, T., Mancheño, M. J., … Casado, J. (2021). Single-molecule conductance in a unique cross-conjugated tetra(aminoaryl)ethene. <i>Chemical Communications</i>. Royal Society of Chemistry. <a href=\"https://doi.org/10.1039/d0cc07124b\">https://doi.org/10.1039/d0cc07124b</a>","ama":"Medina Rivero S, García Arroyo P, Li L, et al. Single-molecule conductance in a unique cross-conjugated tetra(aminoaryl)ethene. <i>Chemical Communications</i>. 2021;57(5):591-594. doi:<a href=\"https://doi.org/10.1039/d0cc07124b\">10.1039/d0cc07124b</a>"},"external_id":{"pmid":["33325935"]},"article_processing_charge":"No","publication":"Chemical Communications","oa_version":"None","fulldoi":"https://doi.org/10.1039/d0cc07124b","date_published":"2021-05-01T00:00:00Z","publication_status":"published","issue":"5","author":[{"full_name":"Medina Rivero, Samara","last_name":"Medina Rivero","first_name":"Samara"},{"last_name":"García Arroyo","full_name":"García Arroyo, Paloma","first_name":"Paloma"},{"last_name":"Li","full_name":"Li, Liang","first_name":"Liang"},{"last_name":"Gunasekaran","full_name":"Gunasekaran, Suman","first_name":"Suman"},{"first_name":"Thijs","last_name":"Stuyver","full_name":"Stuyver, Thijs"},{"last_name":"Mancheño","full_name":"Mancheño, María José","first_name":"María José"},{"last_name":"Alonso","full_name":"Alonso, Mercedes","first_name":"Mercedes"},{"id":"9ebb78a5-cc0d-11ee-8322-fae086a32caf","full_name":"Venkataraman, Latha","last_name":"Venkataraman","orcid":"0000-0002-6957-6089","first_name":"Latha"},{"first_name":"José L.","full_name":"Segura, José L.","last_name":"Segura"},{"first_name":"Juan","last_name":"Casado","full_name":"Casado, Juan"}],"intvolume":"        57","abstract":[{"lang":"eng","text":"A 1,1,2,2-tetrakis(4-aminophenyl)ethene with three paths of π-conjugation, linear-cis, linear-trans and a cross-conjugation, has been prepared. The molecule is able to bind to gold electrodes forming molecular junctions for single-molecule conductance measurements. Only two regimes of conduction are found experimentally. The modelling of the conductance allows to assign them to through-bond transmission in the linear case, while the cross-conjugated channel is further assisted by through-space transmission, partially alleviating the destructive quantum interference."}],"doi":"10.1039/d0cc07124b","quality_controlled":"1"},{"_id":"18192","status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","year":"2021","publication":"Physical Review Letters","article_number":"150504","citation":{"ista":"Bohrdt A, Kim S, Lukin A, Rispoli M, Schittko R, Knap M, Greiner M, Leonard J. 2021. Analyzing nonequilibrium quantum states through snapshots with artificial neural networks. Physical Review Letters. 127(15), 150504.","ieee":"A. Bohrdt <i>et al.</i>, “Analyzing nonequilibrium quantum states through snapshots with artificial neural networks,” <i>Physical Review Letters</i>, vol. 127, no. 15. American Physical Society, 2021.","apa":"Bohrdt, A., Kim, S., Lukin, A., Rispoli, M., Schittko, R., Knap, M., … Leonard, J. (2021). Analyzing nonequilibrium quantum states through snapshots with artificial neural networks. <i>Physical Review Letters</i>. American Physical Society. <a href=\"https://doi.org/10.1103/physrevlett.127.150504\">https://doi.org/10.1103/physrevlett.127.150504</a>","mla":"Bohrdt, A., et al. “Analyzing Nonequilibrium Quantum States through Snapshots with Artificial Neural Networks.” <i>Physical Review Letters</i>, vol. 127, no. 15, 150504, American Physical Society, 2021, doi:<a href=\"https://doi.org/10.1103/physrevlett.127.150504\">10.1103/physrevlett.127.150504</a>.","chicago":"Bohrdt, A., S. Kim, A. Lukin, M. Rispoli, R. Schittko, M. Knap, M. Greiner, and Julian Leonard. “Analyzing Nonequilibrium Quantum States through Snapshots with Artificial Neural Networks.” <i>Physical Review Letters</i>. American Physical Society, 2021. <a href=\"https://doi.org/10.1103/physrevlett.127.150504\">https://doi.org/10.1103/physrevlett.127.150504</a>.","short":"A. Bohrdt, S. Kim, A. Lukin, M. Rispoli, R. Schittko, M. Knap, M. Greiner, J. Leonard, Physical Review Letters 127 (2021).","ama":"Bohrdt A, Kim S, Lukin A, et al. Analyzing nonequilibrium quantum states through snapshots with artificial neural networks. <i>Physical Review Letters</i>. 2021;127(15). doi:<a href=\"https://doi.org/10.1103/physrevlett.127.150504\">10.1103/physrevlett.127.150504</a>"},"article_processing_charge":"No","external_id":{"arxiv":["2012.11586"]},"author":[{"first_name":"A.","last_name":"Bohrdt","full_name":"Bohrdt, A."},{"first_name":"S.","full_name":"Kim, S.","last_name":"Kim"},{"first_name":"A.","last_name":"Lukin","full_name":"Lukin, A."},{"last_name":"Rispoli","full_name":"Rispoli, M.","first_name":"M."},{"last_name":"Schittko","full_name":"Schittko, R.","first_name":"R."},{"last_name":"Knap","full_name":"Knap, M.","first_name":"M."},{"last_name":"Greiner","full_name":"Greiner, M.","first_name":"M."},{"first_name":"Julian","last_name":"Leonard","full_name":"Leonard, Julian","id":"b75b3f45-7995-11ef-9bfd-9a9cd02c3577"}],"publication_status":"published","issue":"15","abstract":[{"text":"Current quantum simulation experiments are starting to explore nonequilibrium many-body dynamics in previously inaccessible regimes in terms of system sizes and timescales. Therefore, the question emerges as to which observables are best suited to study the dynamics in such quantum many-body systems. Using machine learning techniques, we investigate the dynamics and, in particular, the thermalization behavior of an interacting quantum system that undergoes a nonequilibrium phase transition from an ergodic to a many-body localized phase. We employ supervised and unsupervised training methods to distinguish nonequilibrium from equilibrium data, using the network performance as a probe for the thermalization behavior of the system. We test our methods with experimental snapshots of ultracold atoms taken with a quantum gas microscope. Our results provide a path to analyze highly entangled large-scale quantum states for system sizes where numerical calculations of conventional observables become challenging.","lang":"eng"}],"intvolume":"       127","fulldoi":"https://doi.org/10.1103/physrevlett.127.150504","date_published":"2021-10-08T00:00:00Z","oa_version":"Preprint","quality_controlled":"1","doi":"10.1103/physrevlett.127.150504","title":"Analyzing nonequilibrium quantum states through snapshots with artificial neural networks","date_updated":"2024-10-08T09:58:03Z","day":"08","oa":1,"main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2012.11586","open_access":"1"}],"arxiv":1,"language":[{"iso":"eng"}],"month":"10","type":"journal_article","date_created":"2024-10-07T11:47:11Z","extern":"1","volume":127,"article_type":"original","publisher":"American Physical Society","publication_identifier":{"issn":["0031-9007","1079-7114"]},"scopus_import":"1"},{"publication_status":"published","author":[{"first_name":"F. A.","full_name":"Palm, F. A.","last_name":"Palm"},{"last_name":"Buser","full_name":"Buser, M.","first_name":"M."},{"first_name":"Julian","id":"b75b3f45-7995-11ef-9bfd-9a9cd02c3577","full_name":"Leonard, Julian","last_name":"Leonard"},{"last_name":"Aidelsburger","full_name":"Aidelsburger, M.","first_name":"M."},{"first_name":"U.","last_name":"Schollwöck","full_name":"Schollwöck, U."},{"first_name":"F.","last_name":"Grusdt","full_name":"Grusdt, F."}],"issue":"16","intvolume":"       103","abstract":[{"text":"Topological states of matter, such as fractional quantum Hall states, are an active field of research due to their exotic excitations. In particular, ultracold atoms in optical lattices provide a highly controllable and adaptable platform to study such new types of quantum matter. However, finding a clear route to realize non-Abelian quantum Hall states in these systems remains challenging. Here we use the density-matrix renormalization-group (DMRG) method to study the Hofstadter-Bose-Hubbard model at filling factor 𝜈=1 and find strong indications that at 𝛼=1/6 magnetic flux quanta per plaquette the ground state is a lattice analog of the continuum non-Abelian Pfaffian. We study the on-site correlations of the ground state, which indicate its paired nature at 𝜈=1, and find an incompressible state characterized by a charge gap in the bulk. We argue that the emergence of a charge density wave on thin cylinders and the behavior of the two- and three-particle correlation functions at short distances provide evidence for the state being closely related to the continuum Pfaffian. The signatures discussed in this letter are accessible in current cold atom experiments and we show that the Pfaffian-like state is readily realizable in few-body systems using adiabatic preparation schemes.","lang":"eng"}],"date_published":"2021-04-15T00:00:00Z","fulldoi":"https://doi.org/10.1103/physrevb.103.l161101","oa_version":"Preprint","doi":"10.1103/physrevb.103.l161101","quality_controlled":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","_id":"18193","year":"2021","publication":"Physical Review B","article_number":"L161101","citation":{"ista":"Palm FA, Buser M, Leonard J, Aidelsburger M, Schollwöck U, Grusdt F. 2021. Bosonic Pfaffian state in the Hofstadter-Bose-Hubbard model. Physical Review B. 103(16), L161101.","apa":"Palm, F. A., Buser, M., Leonard, J., Aidelsburger, M., Schollwöck, U., &#38; Grusdt, F. (2021). Bosonic Pfaffian state in the Hofstadter-Bose-Hubbard model. <i>Physical Review B</i>. American Physical Society. <a href=\"https://doi.org/10.1103/physrevb.103.l161101\">https://doi.org/10.1103/physrevb.103.l161101</a>","ieee":"F. A. Palm, M. Buser, J. Leonard, M. Aidelsburger, U. Schollwöck, and F. Grusdt, “Bosonic Pfaffian state in the Hofstadter-Bose-Hubbard model,” <i>Physical Review B</i>, vol. 103, no. 16. American Physical Society, 2021.","short":"F.A. Palm, M. Buser, J. Leonard, M. Aidelsburger, U. Schollwöck, F. Grusdt, Physical Review B 103 (2021).","mla":"Palm, F. A., et al. “Bosonic Pfaffian State in the Hofstadter-Bose-Hubbard Model.” <i>Physical Review B</i>, vol. 103, no. 16, L161101, American Physical Society, 2021, doi:<a href=\"https://doi.org/10.1103/physrevb.103.l161101\">10.1103/physrevb.103.l161101</a>.","chicago":"Palm, F. A., M. Buser, Julian Leonard, M. Aidelsburger, U. Schollwöck, and F. Grusdt. “Bosonic Pfaffian State in the Hofstadter-Bose-Hubbard Model.” <i>Physical Review B</i>. American Physical Society, 2021. <a href=\"https://doi.org/10.1103/physrevb.103.l161101\">https://doi.org/10.1103/physrevb.103.l161101</a>.","ama":"Palm FA, Buser M, Leonard J, Aidelsburger M, Schollwöck U, Grusdt F. Bosonic Pfaffian state in the Hofstadter-Bose-Hubbard model. <i>Physical Review B</i>. 2021;103(16). doi:<a href=\"https://doi.org/10.1103/physrevb.103.l161101\">10.1103/physrevb.103.l161101</a>"},"external_id":{"arxiv":["2011.02477"]},"article_processing_charge":"No","arxiv":1,"language":[{"iso":"eng"}],"main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2011.02477","open_access":"1"}],"type":"journal_article","month":"04","volume":103,"article_type":"letter_note","extern":"1","date_created":"2024-10-07T11:47:51Z","publisher":"American Physical Society","publication_identifier":{"issn":["2469-9950"],"eissn":["2469-9969"]},"scopus_import":"1","title":"Bosonic Pfaffian state in the Hofstadter-Bose-Hubbard model","date_updated":"2024-10-08T09:55:46Z","day":"15","oa":1},{"quality_controlled":"1","doi":"10.1007/s10994-021-06053-z","fulldoi":"https://doi.org/10.1007/s10994-021-06053-z","oa_version":"Preprint","date_published":"2021-12-01T00:00:00Z","publication_status":"published","author":[{"first_name":"Yury","full_name":"Nahshan, Yury","last_name":"Nahshan"},{"last_name":"Chmiel","full_name":"Chmiel, Brian","first_name":"Brian"},{"full_name":"Baskin, Chaim","last_name":"Baskin","first_name":"Chaim"},{"full_name":"Zheltonozhskii, Evgenii","last_name":"Zheltonozhskii","first_name":"Evgenii"},{"first_name":"Ron","full_name":"Banner, Ron","last_name":"Banner"},{"orcid":"0000-0001-9699-8730","first_name":"Alexander","full_name":"Bronstein, Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","last_name":"Bronstein"},{"full_name":"Mendelson, Avi","last_name":"Mendelson","first_name":"Avi"}],"issue":"11-12","intvolume":"       110","abstract":[{"text":"Neural network quantization enables the deployment of large models on resource-constrained devices. Current post-training quantization methods fall short in terms of accuracy for INT4 (or lower) but provide reasonable accuracy for INT8 (or above). In this work, we study the effect of quantization on the structure of the loss landscape. We show that the structure is flat and separable for mild quantization, enabling straightforward post-training quantization methods to achieve good results. We show that with more aggressive quantization, the loss landscape becomes highly non-separable with steep curvature, making the selection of quantization parameters more challenging. Armed with this understanding, we design a method that quantizes the layer parameters jointly, enabling significant accuracy improvement over current post-training quantization methods. Reference implementation is available at https://github.com/ynahshan/nn-quantization-pytorch/tree/master/lapq.","lang":"eng"}],"citation":{"chicago":"Nahshan, Yury, Brian Chmiel, Chaim Baskin, Evgenii Zheltonozhskii, Ron Banner, Alex M. Bronstein, and Avi Mendelson. “Loss Aware Post-Training Quantization.” <i>Machine Learning</i>. Springer Nature, 2021. <a href=\"https://doi.org/10.1007/s10994-021-06053-z\">https://doi.org/10.1007/s10994-021-06053-z</a>.","mla":"Nahshan, Yury, et al. “Loss Aware Post-Training Quantization.” <i>Machine Learning</i>, vol. 110, no. 11–12, Springer Nature, 2021, pp. 3245–62, doi:<a href=\"https://doi.org/10.1007/s10994-021-06053-z\">10.1007/s10994-021-06053-z</a>.","short":"Y. Nahshan, B. Chmiel, C. Baskin, E. Zheltonozhskii, R. Banner, A.M. Bronstein, A. Mendelson, Machine Learning 110 (2021) 3245–3262.","apa":"Nahshan, Y., Chmiel, B., Baskin, C., Zheltonozhskii, E., Banner, R., Bronstein, A. M., &#38; Mendelson, A. (2021). Loss aware post-training quantization. <i>Machine Learning</i>. Springer Nature. <a href=\"https://doi.org/10.1007/s10994-021-06053-z\">https://doi.org/10.1007/s10994-021-06053-z</a>","ieee":"Y. Nahshan <i>et al.</i>, “Loss aware post-training quantization,” <i>Machine Learning</i>, vol. 110, no. 11–12. Springer Nature, pp. 3245–3262, 2021.","ista":"Nahshan Y, Chmiel B, Baskin C, Zheltonozhskii E, Banner R, Bronstein AM, Mendelson A. 2021. Loss aware post-training quantization. Machine Learning. 110(11–12), 3245–3262.","ama":"Nahshan Y, Chmiel B, Baskin C, et al. Loss aware post-training quantization. <i>Machine Learning</i>. 2021;110(11-12):3245-3262. doi:<a href=\"https://doi.org/10.1007/s10994-021-06053-z\">10.1007/s10994-021-06053-z</a>"},"article_processing_charge":"No","external_id":{"arxiv":["1911.07190"]},"publication":"Machine Learning","year":"2021","_id":"18233","status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","OA_type":"green","OA_place":"repository","scopus_import":"1","publisher":"Springer Nature","publication_identifier":{"issn":["0885-6125"],"eissn":["1573-0565"]},"month":"12","type":"journal_article","extern":"1","date_created":"2024-10-08T12:57:05Z","page":"3245-3262","article_type":"original","volume":110,"main_file_link":[{"url":"https://doi.org/10.48550/arXiv.1911.07190","open_access":"1"}],"language":[{"iso":"eng"}],"arxiv":1,"oa":1,"day":"01","related_material":{"link":[{"url":"https://github.com/ynahshan/nn-quantization-pytorch/tree/master/lapq","relation":"software"}]},"date_updated":"2024-10-15T07:33:28Z","title":"Loss aware post-training quantization"},{"day":"02","date_updated":"2024-10-15T07:37:39Z","DOAJ_listed":"1","title":"NICE: Noise Injection and Clamping Estimation for neural network quantization","scopus_import":"1","OA_place":"publisher","publisher":"MDPI","publication_identifier":{"issn":["2227-7390"]},"type":"journal_article","month":"09","date_created":"2024-10-08T12:57:24Z","extern":"1","article_type":"original","volume":9,"language":[{"iso":"eng"}],"arxiv":1,"citation":{"ista":"Baskin C, Zheltonozhkii E, Rozen T, Liss N, Chai Y, Schwartz E, Giryes R, Bronstein AM, Mendelson A. 2021. NICE: Noise Injection and Clamping Estimation for neural network quantization. Mathematics. 9(17), 2144.","ieee":"C. Baskin <i>et al.</i>, “NICE: Noise Injection and Clamping Estimation for neural network quantization,” <i>Mathematics</i>, vol. 9, no. 17. MDPI, 2021.","apa":"Baskin, C., Zheltonozhkii, E., Rozen, T., Liss, N., Chai, Y., Schwartz, E., … Mendelson, A. (2021). NICE: Noise Injection and Clamping Estimation for neural network quantization. <i>Mathematics</i>. MDPI. <a href=\"https://doi.org/10.3390/math9172144\">https://doi.org/10.3390/math9172144</a>","chicago":"Baskin, Chaim, Evgenii Zheltonozhkii, Tal Rozen, Natan Liss, Yoav Chai, Eli Schwartz, Raja Giryes, Alex M. Bronstein, and Avi Mendelson. “NICE: Noise Injection and Clamping Estimation for Neural Network Quantization.” <i>Mathematics</i>. MDPI, 2021. <a href=\"https://doi.org/10.3390/math9172144\">https://doi.org/10.3390/math9172144</a>.","mla":"Baskin, Chaim, et al. “NICE: Noise Injection and Clamping Estimation for Neural Network Quantization.” <i>Mathematics</i>, vol. 9, no. 17, 2144, MDPI, 2021, doi:<a href=\"https://doi.org/10.3390/math9172144\">10.3390/math9172144</a>.","short":"C. Baskin, E. Zheltonozhkii, T. Rozen, N. Liss, Y. Chai, E. Schwartz, R. Giryes, A.M. Bronstein, A. Mendelson, Mathematics 9 (2021).","ama":"Baskin C, Zheltonozhkii E, Rozen T, et al. NICE: Noise Injection and Clamping Estimation for neural network quantization. <i>Mathematics</i>. 2021;9(17). doi:<a href=\"https://doi.org/10.3390/math9172144\">10.3390/math9172144</a>"},"article_processing_charge":"No","external_id":{"arxiv":["1810.00162"]},"publication":"Mathematics","article_number":"2144","year":"2021","_id":"18234","status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","OA_type":"gold","quality_controlled":"1","doi":"10.3390/math9172144","fulldoi":"https://doi.org/10.3390/math9172144","date_published":"2021-09-02T00:00:00Z","oa_version":"Published Version","publication_status":"published","issue":"17","author":[{"first_name":"Chaim","full_name":"Baskin, Chaim","last_name":"Baskin"},{"first_name":"Evgenii","full_name":"Zheltonozhkii, Evgenii","last_name":"Zheltonozhkii"},{"full_name":"Rozen, Tal","last_name":"Rozen","first_name":"Tal"},{"full_name":"Liss, Natan","last_name":"Liss","first_name":"Natan"},{"full_name":"Chai, Yoav","last_name":"Chai","first_name":"Yoav"},{"last_name":"Schwartz","full_name":"Schwartz, Eli","first_name":"Eli"},{"first_name":"Raja","last_name":"Giryes","full_name":"Giryes, Raja"},{"full_name":"Bronstein, Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","last_name":"Bronstein","orcid":"0000-0001-9699-8730","first_name":"Alexander"},{"full_name":"Mendelson, Avi","last_name":"Mendelson","first_name":"Avi"}],"intvolume":"         9","abstract":[{"text":"Convolutional Neural Networks (CNNs) are very popular in many fields including computer vision, speech recognition, natural language processing, etc. Though deep learning leads to groundbreaking performance in those domains, the networks used are very computationally demanding and are far from being able to perform in real-time applications even on a GPU, which is not power efficient and therefore does not suit low power systems such as mobile devices. To overcome this challenge, some solutions have been proposed for quantizing the weights and activations of these networks, which accelerate the runtime significantly. Yet, this acceleration comes at the cost of a larger error unless spatial adjustments are carried out. The method proposed in this work trains quantized neural networks by noise injection and a learned clamping, which improve accuracy. This leads to state-of-the-art results on various regression and classification tasks, e.g., ImageNet classification with architectures such as ResNet-18/34/50 with as low as 3 bit weights and activations. We implement the proposed solution on an FPGA to demonstrate its applicability for low-power real-time applications. The quantization code will become publicly available upon acceptance.","lang":"eng"}]},{"_id":"18235","status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","OA_type":"green","year":"2021","publication":"Pattern Recognition Letters","citation":{"short":"S. Doveh, E. Schwartz, C. Xue, R. Feris, A.M. Bronstein, R. Giryes, L. Karlinsky, Pattern Recognition Letters 149 (2021) 130–136.","mla":"Doveh, Sivan, et al. “MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification.” <i>Pattern Recognition Letters</i>, vol. 149, Elsevier, 2021, pp. 130–36, doi:<a href=\"https://doi.org/10.1016/j.patrec.2021.05.010\">10.1016/j.patrec.2021.05.010</a>.","chicago":"Doveh, Sivan, Eli Schwartz, Chao Xue, Rogerio Feris, Alex M. Bronstein, Raja Giryes, and Leonid Karlinsky. “MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification.” <i>Pattern Recognition Letters</i>. Elsevier, 2021. <a href=\"https://doi.org/10.1016/j.patrec.2021.05.010\">https://doi.org/10.1016/j.patrec.2021.05.010</a>.","apa":"Doveh, S., Schwartz, E., Xue, C., Feris, R., Bronstein, A. M., Giryes, R., &#38; Karlinsky, L. (2021). MetAdapt: Meta-learned task-adaptive architecture for few-shot classification. <i>Pattern Recognition Letters</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.patrec.2021.05.010\">https://doi.org/10.1016/j.patrec.2021.05.010</a>","ista":"Doveh S, Schwartz E, Xue C, Feris R, Bronstein AM, Giryes R, Karlinsky L. 2021. MetAdapt: Meta-learned task-adaptive architecture for few-shot classification. Pattern Recognition Letters. 149, 130–136.","ieee":"S. Doveh <i>et al.</i>, “MetAdapt: Meta-learned task-adaptive architecture for few-shot classification,” <i>Pattern Recognition Letters</i>, vol. 149. Elsevier, pp. 130–136, 2021.","ama":"Doveh S, Schwartz E, Xue C, et al. MetAdapt: Meta-learned task-adaptive architecture for few-shot classification. <i>Pattern Recognition Letters</i>. 2021;149:130-136. doi:<a href=\"https://doi.org/10.1016/j.patrec.2021.05.010\">10.1016/j.patrec.2021.05.010</a>"},"article_processing_charge":"No","external_id":{"arxiv":["1912.00412"]},"publication_status":"published","author":[{"first_name":"Sivan","full_name":"Doveh, Sivan","last_name":"Doveh"},{"full_name":"Schwartz, Eli","last_name":"Schwartz","first_name":"Eli"},{"first_name":"Chao","full_name":"Xue, Chao","last_name":"Xue"},{"first_name":"Rogerio","full_name":"Feris, Rogerio","last_name":"Feris"},{"orcid":"0000-0001-9699-8730","first_name":"Alexander","full_name":"Bronstein, Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","last_name":"Bronstein"},{"first_name":"Raja","last_name":"Giryes","full_name":"Giryes, Raja"},{"last_name":"Karlinsky","full_name":"Karlinsky, Leonid","first_name":"Leonid"}],"abstract":[{"lang":"eng","text":"Recently, great progress has been made in the field of Few-Shot Learning (FSL). While many different methods have been proposed, one of the key factors leading to higher FSL performance is surprisingly simple. It is the backbone network architecture used to embed the images of the few-shot tasks. While first works on FSL resorted to small architectures with just a few convolution layers, recent works show that large architectures pre-trained on the training portion of FSL datasets produce strong features that are more easily transferable to novel few-shot tasks, thus attaining significant gains to methods using them. Despite these observations, little to no work has been done towards finding the right backbone for FSL. In this paper we propose MetAdapt that not only meta-searches for an optimized architecture for FSL using Network Architecture Search (NAS), but also results in a model that can adaptively ‘re-wire’ itself predicting the better architecture for a given novel few-shot task. Using the proposed approach we observe strong results on two popular few-shot benchmarks: miniImageNet and FC100."}],"intvolume":"       149","fulldoi":"https://doi.org/10.1016/j.patrec.2021.05.010","oa_version":"Preprint","date_published":"2021-09-01T00:00:00Z","quality_controlled":"1","doi":"10.1016/j.patrec.2021.05.010","title":"MetAdapt: Meta-learned task-adaptive architecture for few-shot classification","date_updated":"2024-10-15T07:39:56Z","day":"01","oa":1,"main_file_link":[{"url":"https://doi.org/10.48550/arXiv.1912.00412","open_access":"1"}],"arxiv":1,"language":[{"iso":"eng"}],"month":"09","type":"journal_article","page":"130-136","extern":"1","date_created":"2024-10-08T12:57:53Z","volume":149,"article_type":"original","publisher":"Elsevier","publication_identifier":{"issn":["0167-8655"]},"scopus_import":"1","OA_place":"repository"},{"year":"2021","OA_type":"free access","_id":"18236","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","article_processing_charge":"No","external_id":{"pmid":["34099565"]},"citation":{"short":"Y. Elul, A.A. Rosenberg, A. Schuster, A.M. Bronstein, Y. Yaniv, Proceedings of the National Academy of Sciences 118 (2021).","chicago":"Elul, Yonatan, Aviv A. Rosenberg, Assaf Schuster, Alex M. Bronstein, and Yael Yaniv. “Meeting the Unmet Needs of Clinicians from AI Systems Showcased for Cardiology with Deep-Learning–Based ECG Analysis.” <i>Proceedings of the National Academy of Sciences</i>. National Academy of Sciences, 2021. <a href=\"https://doi.org/10.1073/pnas.2020620118\">https://doi.org/10.1073/pnas.2020620118</a>.","mla":"Elul, Yonatan, et al. “Meeting the Unmet Needs of Clinicians from AI Systems Showcased for Cardiology with Deep-Learning–Based ECG Analysis.” <i>Proceedings of the National Academy of Sciences</i>, vol. 118, no. 24, e2020620118, National Academy of Sciences, 2021, doi:<a href=\"https://doi.org/10.1073/pnas.2020620118\">10.1073/pnas.2020620118</a>.","ista":"Elul Y, Rosenberg AA, Schuster A, Bronstein AM, Yaniv Y. 2021. Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning–based ECG analysis. Proceedings of the National Academy of Sciences. 118(24), e2020620118.","apa":"Elul, Y., Rosenberg, A. A., Schuster, A., Bronstein, A. M., &#38; Yaniv, Y. (2021). Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning–based ECG analysis. <i>Proceedings of the National Academy of Sciences</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.2020620118\">https://doi.org/10.1073/pnas.2020620118</a>","ieee":"Y. Elul, A. A. Rosenberg, A. Schuster, A. M. Bronstein, and Y. Yaniv, “Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning–based ECG analysis,” <i>Proceedings of the National Academy of Sciences</i>, vol. 118, no. 24. National Academy of Sciences, 2021.","ama":"Elul Y, Rosenberg AA, Schuster A, Bronstein AM, Yaniv Y. Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning–based ECG analysis. <i>Proceedings of the National Academy of Sciences</i>. 2021;118(24). doi:<a href=\"https://doi.org/10.1073/pnas.2020620118\">10.1073/pnas.2020620118</a>"},"article_number":"e2020620118","publication":"Proceedings of the National Academy of Sciences","fulldoi":"https://doi.org/10.1073/pnas.2020620118","date_published":"2021-06-07T00:00:00Z","oa_version":"Published Version","intvolume":"       118","abstract":[{"lang":"eng","text":"Despite their great promise, artificial intelligence (AI) systems have yet to become ubiquitous in the daily practice of medicine largely due to several crucial unmet needs of healthcare practitioners. These include lack of explanations in clinically meaningful terms, handling the presence of unknown medical conditions, and transparency regarding the system’s limitations, both in terms of statistical performance as well as recognizing situations for which the system’s predictions are irrelevant. We articulate these unmet clinical needs as machine-learning (ML) problems and systematically address them with cutting-edge ML techniques. We focus on electrocardiogram (ECG) analysis as an example domain in which AI has great potential and tackle two challenging tasks: the detection of a heterogeneous mix of known and unknown arrhythmias from ECG and the identification of underlying cardio-pathology from segments annotated as normal sinus rhythm recorded in patients with an intermittent arrhythmia. We validate our methods by simulating a screening for arrhythmias in a large-scale population while adhering to statistical significance requirements. Specifically, our system 1) visualizes the relative importance of each part of an ECG segment for the final model decision; 2) upholds specified statistical constraints on its out-of-sample performance and provides uncertainty estimation for its predictions; 3) handles inputs containing unknown rhythm types; and 4) handles data from unseen patients while also flagging cases in which the model’s outputs are not usable for a specific patient. This work represents a significant step toward overcoming the limitations currently impeding the integration of AI into clinical practice in cardiology and medicine in general."}],"publication_status":"published","author":[{"last_name":"Elul","full_name":"Elul, Yonatan","first_name":"Yonatan"},{"first_name":"Aviv A.","full_name":"Rosenberg, Aviv A.","last_name":"Rosenberg"},{"first_name":"Assaf","full_name":"Schuster, Assaf","last_name":"Schuster"},{"full_name":"Bronstein, Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","last_name":"Bronstein","orcid":"0000-0001-9699-8730","first_name":"Alexander"},{"first_name":"Yael","last_name":"Yaniv","full_name":"Yaniv, Yael"}],"issue":"24","quality_controlled":"1","doi":"10.1073/pnas.2020620118","date_updated":"2024-10-15T07:43:01Z","title":"Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning–based ECG analysis","day":"07","date_created":"2024-10-08T12:58:09Z","extern":"1","pmid":1,"volume":118,"article_type":"original","type":"journal_article","month":"06","language":[{"iso":"eng"}],"scopus_import":"1","OA_place":"publisher","publication_identifier":{"eissn":["1091-6490"],"issn":["0027-8424"]},"publisher":"National Academy of Sciences"},{"OA_type":"green","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","_id":"18237","year":"2021","publication":"ACM Transactions on Computer Systems","external_id":{"arxiv":["1804.10969"]},"article_processing_charge":"No","citation":{"ista":"Baskin C, Liss N, Schwartz E, Zheltonozhskii E, Giryes R, Bronstein AM, Mendelson A. 2021. UNIQ: Uniform Noise Injection for Non-Uniform Quantization of neural networks. ACM Transactions on Computer Systems. 37(1–4), 1–15.","apa":"Baskin, C., Liss, N., Schwartz, E., Zheltonozhskii, E., Giryes, R., Bronstein, A. M., &#38; Mendelson, A. (2021). UNIQ: Uniform Noise Injection for Non-Uniform Quantization of neural networks. <i>ACM Transactions on Computer Systems</i>. Association for Computing Machinery. <a href=\"https://doi.org/10.1145/3444943\">https://doi.org/10.1145/3444943</a>","ieee":"C. Baskin <i>et al.</i>, “UNIQ: Uniform Noise Injection for Non-Uniform Quantization of neural networks,” <i>ACM Transactions on Computer Systems</i>, vol. 37, no. 1–4. Association for Computing Machinery, pp. 1–15, 2021.","mla":"Baskin, Chaim, et al. “UNIQ: Uniform Noise Injection for Non-Uniform Quantization of Neural Networks.” <i>ACM Transactions on Computer Systems</i>, vol. 37, no. 1–4, Association for Computing Machinery, 2021, pp. 1–15, doi:<a href=\"https://doi.org/10.1145/3444943\">10.1145/3444943</a>.","chicago":"Baskin, Chaim, Natan Liss, Eli Schwartz, Evgenii Zheltonozhskii, Raja Giryes, Alex M. Bronstein, and Avi Mendelson. “UNIQ: Uniform Noise Injection for Non-Uniform Quantization of Neural Networks.” <i>ACM Transactions on Computer Systems</i>. Association for Computing Machinery, 2021. <a href=\"https://doi.org/10.1145/3444943\">https://doi.org/10.1145/3444943</a>.","short":"C. Baskin, N. Liss, E. Schwartz, E. Zheltonozhskii, R. Giryes, A.M. Bronstein, A. Mendelson, ACM Transactions on Computer Systems 37 (2021) 1–15.","ama":"Baskin C, Liss N, Schwartz E, et al. UNIQ: Uniform Noise Injection for Non-Uniform Quantization of neural networks. <i>ACM Transactions on Computer Systems</i>. 2021;37(1-4):1-15. doi:<a href=\"https://doi.org/10.1145/3444943\">10.1145/3444943</a>"},"intvolume":"        37","abstract":[{"lang":"eng","text":"We present a novel method for neural network quantization. Our method, named UNIQ, emulates a non-uniform k-quantile quantizer and adapts the model to perform well with quantized weights by injecting noise to the weights at training time. As a by-product of injecting noise to weights, we find that activations can also be quantized to as low as 8-bit with only a minor accuracy degradation. Our non-uniform quantization approach provides a novel alternative to the existing uniform quantization techniques for neural networks. We further propose a novel complexity metric of number of bit operations performed (BOPs), and we show that this metric has a linear relation with logic utilization and power. We suggest evaluating the trade-off of accuracy vs. complexity (BOPs). The proposed method, when evaluated on ResNet18/34/50 and MobileNet on ImageNet, outperforms the prior state of the art both in the low-complexity regime and the high accuracy regime. We demonstrate the practical applicability of this approach, by implementing our non-uniformly quantized CNN on FPGA."}],"publication_status":"published","issue":"1-4","author":[{"last_name":"Baskin","full_name":"Baskin, Chaim","first_name":"Chaim"},{"first_name":"Natan","last_name":"Liss","full_name":"Liss, Natan"},{"last_name":"Schwartz","full_name":"Schwartz, Eli","first_name":"Eli"},{"full_name":"Zheltonozhskii, Evgenii","last_name":"Zheltonozhskii","first_name":"Evgenii"},{"full_name":"Giryes, Raja","last_name":"Giryes","first_name":"Raja"},{"last_name":"Bronstein","full_name":"Bronstein, Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","first_name":"Alexander","orcid":"0000-0001-9699-8730"},{"full_name":"Mendelson, Avi","last_name":"Mendelson","first_name":"Avi"}],"date_published":"2021-03-26T00:00:00Z","fulldoi":"https://doi.org/10.1145/3444943","oa_version":"Preprint","doi":"10.1145/3444943","quality_controlled":"1","title":"UNIQ: Uniform Noise Injection for Non-Uniform Quantization of neural networks","date_updated":"2024-10-15T07:47:22Z","day":"26","oa":1,"arxiv":1,"language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.1804.10969"}],"volume":37,"article_type":"original","date_created":"2024-10-08T12:58:26Z","page":"1-15","extern":"1","type":"journal_article","month":"03","publication_identifier":{"eissn":["1557-7333"],"issn":["0734-2071"]},"publisher":"Association for Computing Machinery","scopus_import":"1","OA_place":"repository"},{"scopus_import":"1","OA_place":"publisher","publication_identifier":{"issn":["2071-1050"]},"publisher":"MDPI","extern":"1","date_created":"2024-10-08T12:58:47Z","article_type":"original","volume":13,"month":"01","type":"journal_article","main_file_link":[{"url":"https://doi.org/10.3390/su13020717","open_access":"1"}],"language":[{"iso":"eng"}],"oa":1,"day":"13","date_updated":"2024-10-15T08:17:49Z","title":"Early-stage neural network hardware performance analysis","quality_controlled":"1","doi":"10.3390/su13020717","date_published":"2021-01-13T00:00:00Z","fulldoi":"https://doi.org/10.3390/su13020717","oa_version":"Published Version","abstract":[{"lang":"eng","text":"The demand for running NNs in embedded environments has increased significantly in recent years due to the significant success of convolutional neural network (CNN) approaches in various tasks, including image recognition and generation. The task of achieving high accuracy on resource-restricted devices, however, is still considered to be challenging, which is mainly due to the vast number of design parameters that need to be balanced. While the quantization of CNN parameters leads to a reduction of power and area, it can also generate unexpected changes in the balance between communication and computation. This change is hard to evaluate, and the lack of balance may lead to lower utilization of either memory bandwidth or computational resources, thereby reducing performance. This paper introduces a hardware performance analysis framework for identifying bottlenecks in the early stages of CNN hardware design. We demonstrate how the proposed method can help in evaluating different architecture alternatives of resource-restricted CNN accelerators (e.g., part of real-time embedded systems) early in design stages and, thus, prevent making design mistakes."}],"intvolume":"        13","issue":"2","author":[{"first_name":"Alex","last_name":"Karbachevsky","full_name":"Karbachevsky, Alex"},{"first_name":"Chaim","full_name":"Baskin, Chaim","last_name":"Baskin"},{"full_name":"Zheltonozhskii, Evgenii","last_name":"Zheltonozhskii","first_name":"Evgenii"},{"first_name":"Yevgeny","full_name":"Yermolin, Yevgeny","last_name":"Yermolin"},{"last_name":"Gabbay","full_name":"Gabbay, Freddy","first_name":"Freddy"},{"orcid":"0000-0001-9699-8730","first_name":"Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","full_name":"Bronstein, Alexander","last_name":"Bronstein"},{"first_name":"Avi","last_name":"Mendelson","full_name":"Mendelson, Avi"}],"publication_status":"published","article_processing_charge":"No","citation":{"chicago":"Karbachevsky, Alex, Chaim Baskin, Evgenii Zheltonozhskii, Yevgeny Yermolin, Freddy Gabbay, Alex M. Bronstein, and Avi Mendelson. “Early-Stage Neural Network Hardware Performance Analysis.” <i>Sustainability</i>. MDPI, 2021. <a href=\"https://doi.org/10.3390/su13020717\">https://doi.org/10.3390/su13020717</a>.","mla":"Karbachevsky, Alex, et al. “Early-Stage Neural Network Hardware Performance Analysis.” <i>Sustainability</i>, vol. 13, no. 2, 717, MDPI, 2021, doi:<a href=\"https://doi.org/10.3390/su13020717\">10.3390/su13020717</a>.","short":"A. Karbachevsky, C. Baskin, E. Zheltonozhskii, Y. Yermolin, F. Gabbay, A.M. Bronstein, A. Mendelson, Sustainability 13 (2021).","ieee":"A. Karbachevsky <i>et al.</i>, “Early-stage neural network hardware performance analysis,” <i>Sustainability</i>, vol. 13, no. 2. MDPI, 2021.","ista":"Karbachevsky A, Baskin C, Zheltonozhskii E, Yermolin Y, Gabbay F, Bronstein AM, Mendelson A. 2021. Early-stage neural network hardware performance analysis. Sustainability. 13(2), 717.","apa":"Karbachevsky, A., Baskin, C., Zheltonozhskii, E., Yermolin, Y., Gabbay, F., Bronstein, A. M., &#38; Mendelson, A. (2021). Early-stage neural network hardware performance analysis. <i>Sustainability</i>. MDPI. <a href=\"https://doi.org/10.3390/su13020717\">https://doi.org/10.3390/su13020717</a>","ama":"Karbachevsky A, Baskin C, Zheltonozhskii E, et al. Early-stage neural network hardware performance analysis. <i>Sustainability</i>. 2021;13(2). doi:<a href=\"https://doi.org/10.3390/su13020717\">10.3390/su13020717</a>"},"article_number":"717","publication":"Sustainability","year":"2021","OA_type":"gold","_id":"18238","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public"},{"extern":"1","date_created":"2024-10-08T13:02:34Z","volume":15,"type":"conference","month":"10","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2104.09829"}],"language":[{"iso":"eng"}],"arxiv":1,"OA_place":"repository","scopus_import":"1","publication_identifier":{"eisbn":["9781665428125"]},"publisher":"Institute of Electrical and Electronics Engineers","date_updated":"2024-10-15T08:22:47Z","title":"Detector-free weakly supervised grounding by separation","oa":1,"day":"20","fulldoi":"https://doi.org/10.1109/iccv48922.2021.00182","oa_version":"Preprint","date_published":"2021-10-20T00:00:00Z","intvolume":"        15","abstract":[{"lang":"eng","text":"Nowadays, there is an abundance of data involving images and surrounding free-form text weakly corresponding to those images. Weakly Supervised phrase-Grounding (WSG) deals with the task of using this data to learn to localize (or to ground) arbitrary text phrases in images without any additional annotations. However, most recent SotA methods for WSG assume an existence of a pre-trained object detector, relying on it to produce the ROIs for localization. In this work, we focus on the task of Detector-Free WSG (DF-WSG) to solve WSG without relying on a pre-trained detector. The key idea behind our proposed Grounding by Separation (GbS) method is synthesizing ‘text to image-regions’ associations by random alpha-blending of arbitrary image pairs and using the corresponding texts of the pair as conditions to recover the alpha map from the blended image via a segmentation network. At test time, this allows using the query phrase as a condition for a non-blended query image, thus interpreting the test image as a composition of a region corresponding to the phrase and the complement region. Our GbS shows an 8.5% accuracy improvement over previous DF-WSG SotA, for a range of benchmarks including Flickr30K, Visual Genome, and ReferIt, as well as a complementary improvement (above 7%) over the detector-based approaches for WSG."}],"author":[{"full_name":"Arbelle, Assaf","last_name":"Arbelle","first_name":"Assaf"},{"first_name":"Sivan","full_name":"Doveh, Sivan","last_name":"Doveh"},{"first_name":"Amit","full_name":"Alfassy, Amit","last_name":"Alfassy"},{"first_name":"Joseph","last_name":"Shtok","full_name":"Shtok, Joseph"},{"full_name":"Lev, Guy","last_name":"Lev","first_name":"Guy"},{"last_name":"Schwartz","full_name":"Schwartz, Eli","first_name":"Eli"},{"first_name":"Hilde","last_name":"Kuehne","full_name":"Kuehne, Hilde"},{"first_name":"Hila Barak","last_name":"Levi","full_name":"Levi, Hila Barak"},{"first_name":"Prasanna","last_name":"Sattigeri","full_name":"Sattigeri, Prasanna"},{"last_name":"Panda","full_name":"Panda, Rameswar","first_name":"Rameswar"},{"first_name":"Chun-Fu","full_name":"Chen, Chun-Fu","last_name":"Chen"},{"first_name":"Alexander","orcid":"0000-0001-9699-8730","last_name":"Bronstein","full_name":"Bronstein, Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6"},{"full_name":"Saenko, Kate","last_name":"Saenko","first_name":"Kate"},{"first_name":"Shimon","full_name":"Ullman, Shimon","last_name":"Ullman"},{"last_name":"Giryes","full_name":"Giryes, Raja","first_name":"Raja"},{"full_name":"Feris, Rogerio","last_name":"Feris","first_name":"Rogerio"},{"first_name":"Leonid","last_name":"Karlinsky","full_name":"Karlinsky, Leonid"}],"publication_status":"published","quality_controlled":"1","conference":{"location":"Montreal, Canada","start_date":"2021-10-10","name":"ICCV: International Conference on Computer Vision","end_date":"2021-10-17"},"doi":"10.1109/iccv48922.2021.00182","year":"2021","OA_type":"green","_id":"18239","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","article_processing_charge":"No","external_id":{"arxiv":["2104.09829"]},"citation":{"ama":"Arbelle A, Doveh S, Alfassy A, et al. Detector-free weakly supervised grounding by separation. In: <i>IEEE/CVF International Conference on Computer Vision</i>. Vol 15. Institute of Electrical and Electronics Engineers; 2021. doi:<a href=\"https://doi.org/10.1109/iccv48922.2021.00182\">10.1109/iccv48922.2021.00182</a>","chicago":"Arbelle, Assaf, Sivan Doveh, Amit Alfassy, Joseph Shtok, Guy Lev, Eli Schwartz, Hilde Kuehne, et al. “Detector-Free Weakly Supervised Grounding by Separation.” In <i>IEEE/CVF International Conference on Computer Vision</i>, Vol. 15. Institute of Electrical and Electronics Engineers, 2021. <a href=\"https://doi.org/10.1109/iccv48922.2021.00182\">https://doi.org/10.1109/iccv48922.2021.00182</a>.","mla":"Arbelle, Assaf, et al. “Detector-Free Weakly Supervised Grounding by Separation.” <i>IEEE/CVF International Conference on Computer Vision</i>, vol. 15, Institute of Electrical and Electronics Engineers, 2021, doi:<a href=\"https://doi.org/10.1109/iccv48922.2021.00182\">10.1109/iccv48922.2021.00182</a>.","short":"A. Arbelle, S. Doveh, A. Alfassy, J. Shtok, G. Lev, E. Schwartz, H. Kuehne, H.B. Levi, P. Sattigeri, R. Panda, C.-F. Chen, A.M. Bronstein, K. Saenko, S. Ullman, R. Giryes, R. Feris, L. Karlinsky, in:, IEEE/CVF International Conference on Computer Vision, Institute of Electrical and Electronics Engineers, 2021.","ieee":"A. Arbelle <i>et al.</i>, “Detector-free weakly supervised grounding by separation,” in <i>IEEE/CVF International Conference on Computer Vision</i>, Montreal, Canada, 2021, vol. 15.","ista":"Arbelle A, Doveh S, Alfassy A, Shtok J, Lev G, Schwartz E, Kuehne H, Levi HB, Sattigeri P, Panda R, Chen C-F, Bronstein AM, Saenko K, Ullman S, Giryes R, Feris R, Karlinsky L. 2021. Detector-free weakly supervised grounding by separation. IEEE/CVF International Conference on Computer Vision. ICCV: International Conference on Computer Vision vol. 15.","apa":"Arbelle, A., Doveh, S., Alfassy, A., Shtok, J., Lev, G., Schwartz, E., … Karlinsky, L. (2021). Detector-free weakly supervised grounding by separation. In <i>IEEE/CVF International Conference on Computer Vision</i> (Vol. 15). Montreal, Canada: Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/iccv48922.2021.00182\">https://doi.org/10.1109/iccv48922.2021.00182</a>"},"publication":"IEEE/CVF International Conference on Computer Vision"},{"year":"2021","status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"18240","OA_type":"green","citation":{"ista":"Dahary O, Jacoby M, Bronstein AM. 2021. Digital gimbal: End-to-end deep image stabilization with learnable exposure times. IEEE/CVF Conference on Computer Vision and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition vol. 38.","ieee":"O. Dahary, M. Jacoby, and A. M. Bronstein, “Digital gimbal: End-to-end deep image stabilization with learnable exposure times,” in <i>IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>, Nashville, TN, United States, 2021, vol. 38.","apa":"Dahary, O., Jacoby, M., &#38; Bronstein, A. M. (2021). Digital gimbal: End-to-end deep image stabilization with learnable exposure times. In <i>IEEE/CVF Conference on Computer Vision and Pattern Recognition</i> (Vol. 38). Nashville, TN, United States: Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/cvpr46437.2021.01176\">https://doi.org/10.1109/cvpr46437.2021.01176</a>","chicago":"Dahary, Omer, Matan Jacoby, and Alex M. Bronstein. “Digital Gimbal: End-to-End Deep Image Stabilization with Learnable Exposure Times.” In <i>IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>, Vol. 38. Institute of Electrical and Electronics Engineers, 2021. <a href=\"https://doi.org/10.1109/cvpr46437.2021.01176\">https://doi.org/10.1109/cvpr46437.2021.01176</a>.","mla":"Dahary, Omer, et al. “Digital Gimbal: End-to-End Deep Image Stabilization with Learnable Exposure Times.” <i>IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>, vol. 38, Institute of Electrical and Electronics Engineers, 2021, doi:<a href=\"https://doi.org/10.1109/cvpr46437.2021.01176\">10.1109/cvpr46437.2021.01176</a>.","short":"O. Dahary, M. Jacoby, A.M. Bronstein, in:, IEEE/CVF Conference on Computer Vision and Pattern Recognition, Institute of Electrical and Electronics Engineers, 2021.","ama":"Dahary O, Jacoby M, Bronstein AM. Digital gimbal: End-to-end deep image stabilization with learnable exposure times. In: <i>IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>. Vol 38. Institute of Electrical and Electronics Engineers; 2021. doi:<a href=\"https://doi.org/10.1109/cvpr46437.2021.01176\">10.1109/cvpr46437.2021.01176</a>"},"external_id":{"arxiv":["2012.04515"]},"article_processing_charge":"No","publication":"IEEE/CVF Conference on Computer Vision and Pattern Recognition","oa_version":"Preprint","fulldoi":"https://doi.org/10.1109/cvpr46437.2021.01176","date_published":"2021-06-30T00:00:00Z","publication_status":"published","author":[{"first_name":"Omer","full_name":"Dahary, Omer","last_name":"Dahary"},{"last_name":"Jacoby","full_name":"Jacoby, Matan","first_name":"Matan"},{"orcid":"0000-0001-9699-8730","first_name":"Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","full_name":"Bronstein, Alexander","last_name":"Bronstein"}],"intvolume":"        38","abstract":[{"lang":"eng","text":"Mechanical image stabilization using actuated gimbals enables capturing long-exposure shots without suffering from blur due to camera motion. These devices, however, are often physically cumbersome and expensive, limiting their widespread use. In this work, we propose to digitally emulate a mechanically stabilized system from the input of a fast unstabilized camera. To exploit the trade-off between motion blur at long exposures and low SNR at short exposures, we train a CNN that estimates a sharp high-SNR image by aggregating a burst of noisy short-exposure frames, related by unknown motion. We further suggest learning the burst’s exposure times in an end-to-end manner, thus balancing the noise and blur across the frames. We demonstrate this method’s advantage over the traditional approach of deblurring a single image or denoising a fixed-exposure burst on both synthetic and real data."}],"doi":"10.1109/cvpr46437.2021.01176","conference":{"end_date":"2021-06-25","start_date":"2021-06-20","name":"CVPR: Conference on Computer Vision and Pattern Recognition","location":"Nashville, TN, United States"},"quality_controlled":"1","date_updated":"2024-10-15T08:42:37Z","title":"Digital gimbal: End-to-end deep image stabilization with learnable exposure times","oa":1,"day":"30","type":"conference","month":"06","volume":38,"date_created":"2024-10-08T13:02:53Z","extern":"1","language":[{"iso":"eng"}],"arxiv":1,"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2012.04515"}],"OA_place":"repository","scopus_import":"1","publisher":"Institute of Electrical and Electronics Engineers","publication_identifier":{"eisbn":["9781665445092"]}},{"date_updated":"2024-10-16T09:41:11Z","title":"Joint optimization of system design and reconstruction in MIMO radar imaging","oa":1,"day":"01","month":"10","type":"conference","volume":4,"date_created":"2024-10-08T13:03:09Z","extern":"1","arxiv":1,"language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2110.03218"}],"OA_place":"repository","scopus_import":"1","publisher":"Institute of Electrical and Electronics Engineers","publication_identifier":{"eisbn":["9781728163383"]},"year":"2021","status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"18241","OA_type":"green","citation":{"ama":"Weiss T, Peretz N, Vedula S, Feuer A, Bronstein AM. Joint optimization of system design and reconstruction in MIMO radar imaging. In: <i>31st International Workshop on Machine Learning for Signal Processing</i>. Vol 4. Institute of Electrical and Electronics Engineers; 2021. doi:<a href=\"https://doi.org/10.1109/mlsp52302.2021.9596168\">10.1109/mlsp52302.2021.9596168</a>","short":"T. Weiss, N. Peretz, S. Vedula, A. Feuer, A.M. Bronstein, in:, 31st International Workshop on Machine Learning for Signal Processing, Institute of Electrical and Electronics Engineers, 2021.","mla":"Weiss, Tomer, et al. “Joint Optimization of System Design and Reconstruction in MIMO Radar Imaging.” <i>31st International Workshop on Machine Learning for Signal Processing</i>, vol. 4, Institute of Electrical and Electronics Engineers, 2021, doi:<a href=\"https://doi.org/10.1109/mlsp52302.2021.9596168\">10.1109/mlsp52302.2021.9596168</a>.","chicago":"Weiss, Tomer, Nissim Peretz, Sanketh Vedula, Arie Feuer, and Alex M. Bronstein. “Joint Optimization of System Design and Reconstruction in MIMO Radar Imaging.” In <i>31st International Workshop on Machine Learning for Signal Processing</i>, Vol. 4. Institute of Electrical and Electronics Engineers, 2021. <a href=\"https://doi.org/10.1109/mlsp52302.2021.9596168\">https://doi.org/10.1109/mlsp52302.2021.9596168</a>.","ieee":"T. Weiss, N. Peretz, S. Vedula, A. Feuer, and A. M. Bronstein, “Joint optimization of system design and reconstruction in MIMO radar imaging,” in <i>31st International Workshop on Machine Learning for Signal Processing</i>, Gold Coast, Australia, 2021, vol. 4.","ista":"Weiss T, Peretz N, Vedula S, Feuer A, Bronstein AM. 2021. Joint optimization of system design and reconstruction in MIMO radar imaging. 31st International Workshop on Machine Learning for Signal Processing. MLSP: Machine Learning for Signal Processing vol. 4.","apa":"Weiss, T., Peretz, N., Vedula, S., Feuer, A., &#38; Bronstein, A. M. (2021). Joint optimization of system design and reconstruction in MIMO radar imaging. In <i>31st International Workshop on Machine Learning for Signal Processing</i> (Vol. 4). Gold Coast, Australia: Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/mlsp52302.2021.9596168\">https://doi.org/10.1109/mlsp52302.2021.9596168</a>"},"external_id":{"arxiv":["2110.03218"]},"article_processing_charge":"No","publication":"31st International Workshop on Machine Learning for Signal Processing","date_published":"2021-10-01T00:00:00Z","fulldoi":"https://doi.org/10.1109/mlsp52302.2021.9596168","oa_version":"Preprint","publication_status":"published","author":[{"first_name":"Tomer","full_name":"Weiss, Tomer","last_name":"Weiss"},{"first_name":"Nissim","full_name":"Peretz, Nissim","last_name":"Peretz"},{"last_name":"Vedula","full_name":"Vedula, Sanketh","first_name":"Sanketh"},{"last_name":"Feuer","full_name":"Feuer, Arie","first_name":"Arie"},{"last_name":"Bronstein","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","full_name":"Bronstein, Alexander","first_name":"Alexander","orcid":"0000-0001-9699-8730"}],"abstract":[{"lang":"eng","text":"Multiple-input multiple-output (MIMO) radar is one of the leading depth sensing modalities. However, the usage of multiple receive channels lead to relative high costs and prevent the penetration of MIMOs in many areas such as the automotive industry. Over the last years, few studies concentrated on designing reduced measurement schemes and image reconstruction schemes for MIMO radars, however these problems have been so far addressed separately. On the other hand, recent works in optical computational imaging have demonstrated growing success of simultaneous learning-based design of the acquisition and reconstruction schemes, manifesting significant improvement in the reconstruction quality. Inspired by these successes, in this work, we propose to learn MIMO acquisition parameters in the form of receive (Rx) antenna elements locations jointly with an image neural-network based reconstruction. To this end, we propose an algorithm for training the combined acquisition-reconstruction pipeline end-to-end in a differentiable way. We demonstrate the significance of using our learned acquisition parameters with and without the neural-network reconstruction. Code and datasets will be released upon publication."}],"intvolume":"         4","doi":"10.1109/mlsp52302.2021.9596168","conference":{"start_date":"2021-10-25","name":"MLSP: Machine Learning for Signal Processing","location":"Gold Coast, Australia","end_date":"2021-10-28"},"quality_controlled":"1"},{"abstract":[{"lang":"eng","text":"Fiber tractography is an important tool of computational neuroscience that enables reconstructing the spatial connectivity and organization of white matter of the brain. Fiber tractography takes advantage of diffusion Magnetic Resonance Imaging (dMRI) which allows measuring the apparent diffusivity of cerebral water along different spatial directions. Unfortunately, collecting such data comes at the price of reduced spatial resolution and substantially elevated acquisition times, which limits the clinical applicability of dMRI. This problem has been thus far addressed using two principal strategies. Most of the efforts have been extended towards improving the quality of signal estimation for any, yet fixed sampling scheme (defined through the choice of diffusion-encoding gradients). On the other hand, optimization over the sampling scheme has also proven to be effective. Inspired by the previous results, the present work consolidates the above strategies into a unified estimation framework, in which the optimization is carried out with respect to both estimation model and sampling design concurrently. The proposed solution offers substantial improvements in the quality of signal estimation as well as the accuracy of ensuing analysis by means of fiber tractography. While proving the optimality of the learned estimation models would probably need more extensive evaluation, we nevertheless claim that the learned sampling schemes can be of immediate use, offering a way to improve the dMRI analysis without the necessity of deploying the neural network used for their estimation. We present a comprehensive comparative analysis based on the Human Connectome Project data. Code and learned sampling designs available at https://github.com/tomer196/Learned_dMRI."}],"author":[{"first_name":"Tomer","full_name":"Weiss, Tomer","last_name":"Weiss"},{"first_name":"Sanketh","full_name":"Vedula, Sanketh","last_name":"Vedula"},{"last_name":"Senouf","full_name":"Senouf, Ortal","first_name":"Ortal"},{"full_name":"Michailovich, Oleg","last_name":"Michailovich","first_name":"Oleg"},{"id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","full_name":"Bronstein, Alexander","last_name":"Bronstein","orcid":"0000-0001-9699-8730","first_name":"Alexander"}],"publication_status":"published","oa_version":"Preprint","fulldoi":"https://doi.org/10.1007/978-3-030-73018-5_2","date_published":"2021-09-30T00:00:00Z","quality_controlled":"1","conference":{"name":"MICCAI: Conference on Medical Image Computing and Computer-Assisted Intervention","start_date":"2020-10-08","location":"Lima, Peru/Virtual","end_date":"2020-10-08"},"doi":"10.1007/978-3-030-73018-5_2","OA_type":"green","_id":"18242","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","year":"2021","publication":"Computational Diffusion MRI","article_processing_charge":"No","external_id":{"arxiv":["2009.03008"]},"citation":{"ama":"Weiss T, Vedula S, Senouf O, Michailovich O, Bronstein AM. Towards learned optimal q-space sampling in diffusion MRI. In: Gyori N, Hutter J, Nath V, Palombo M, Pizzolato M, Zhang F, eds. <i>Computational Diffusion MRI</i>. Cham: Springer Nature; 2021:13-28. doi:<a href=\"https://doi.org/10.1007/978-3-030-73018-5_2\">10.1007/978-3-030-73018-5_2</a>","chicago":"Weiss, Tomer, Sanketh Vedula, Ortal Senouf, Oleg Michailovich, and Alex M. Bronstein. “Towards Learned Optimal Q-Space Sampling in Diffusion MRI.” In <i>Computational Diffusion MRI</i>, edited by Noemi Gyori, Jana Hutter, Vishwesh Nath, Marco Palombo, Marco Pizzolato, and Fan Zhang, 13–28. Cham: Springer Nature, 2021. <a href=\"https://doi.org/10.1007/978-3-030-73018-5_2\">https://doi.org/10.1007/978-3-030-73018-5_2</a>.","mla":"Weiss, Tomer, et al. “Towards Learned Optimal Q-Space Sampling in Diffusion MRI.” <i>Computational Diffusion MRI</i>, edited by Noemi Gyori et al., Springer Nature, 2021, pp. 13–28, doi:<a href=\"https://doi.org/10.1007/978-3-030-73018-5_2\">10.1007/978-3-030-73018-5_2</a>.","short":"T. Weiss, S. Vedula, O. Senouf, O. Michailovich, A.M. Bronstein, in:, N. Gyori, J. Hutter, V. Nath, M. Palombo, M. Pizzolato, F. Zhang (Eds.), Computational Diffusion MRI, Springer Nature, Cham, 2021, pp. 13–28.","apa":"Weiss, T., Vedula, S., Senouf, O., Michailovich, O., &#38; Bronstein, A. M. (2021). Towards learned optimal q-space sampling in diffusion MRI. In N. Gyori, J. Hutter, V. Nath, M. Palombo, M. Pizzolato, &#38; F. Zhang (Eds.), <i>Computational Diffusion MRI</i> (pp. 13–28). Cham: Springer Nature. <a href=\"https://doi.org/10.1007/978-3-030-73018-5_2\">https://doi.org/10.1007/978-3-030-73018-5_2</a>","ieee":"T. Weiss, S. Vedula, O. Senouf, O. Michailovich, and A. M. Bronstein, “Towards learned optimal q-space sampling in diffusion MRI,” in <i>Computational Diffusion MRI</i>, N. Gyori, J. Hutter, V. Nath, M. Palombo, M. Pizzolato, and F. Zhang, Eds. Cham: Springer Nature, 2021, pp. 13–28.","ista":"Weiss T, Vedula S, Senouf O, Michailovich O, Bronstein AM. 2021.Towards learned optimal q-space sampling in diffusion MRI. In: Computational Diffusion MRI. Mathematics and Visualization, , 13–28."},"main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2009.03008","open_access":"1"}],"arxiv":1,"language":[{"iso":"eng"}],"date_created":"2024-10-08T13:03:26Z","extern":"1","page":"13-28","month":"09","type":"book_chapter","publication_identifier":{"eisbn":["9783030730185"],"issn":["1612-3786"],"isbn":["9783030730178"]},"publisher":"Springer Nature","alternative_title":["Mathematics and Visualization"],"OA_place":"repository","scopus_import":"1","place":"Cham","title":"Towards learned optimal q-space sampling in diffusion MRI","date_updated":"2024-10-16T09:51:45Z","related_material":{"link":[{"relation":"software","url":"https://github.com/tomer196/Learned_dMRI"}]},"editor":[{"full_name":"Gyori, Noemi","last_name":"Gyori","first_name":"Noemi"},{"first_name":"Jana","full_name":"Hutter, Jana","last_name":"Hutter"},{"first_name":"Vishwesh","last_name":"Nath","full_name":"Nath, Vishwesh"},{"last_name":"Palombo","full_name":"Palombo, Marco","first_name":"Marco"},{"first_name":"Marco","last_name":"Pizzolato","full_name":"Pizzolato, Marco"},{"last_name":"Zhang","full_name":"Zhang, Fan","first_name":"Fan"}],"day":"30","oa":1},{"quality_controlled":"1","conference":{"end_date":"2021-08-25","start_date":"2021-08-23","name":"APWeb-WAIM: International Joint Conference on Asia-Pacific Web and Web-Age Information Management","location":"Guangzhou, China"},"doi":"10.1007/978-3-030-85896-4_4","date_published":"2021-08-19T00:00:00Z","fulldoi":"https://doi.org/10.1007/978-3-030-85896-4_4","oa_version":"None","publication_status":"published","author":[{"last_name":"Hermanns","full_name":"Hermanns, Judith","first_name":"Judith"},{"last_name":"Tsitsulin","full_name":"Tsitsulin, Anton","first_name":"Anton"},{"last_name":"Munkhoeva","full_name":"Munkhoeva, Marina","first_name":"Marina"},{"full_name":"Bronstein, Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","last_name":"Bronstein","orcid":"0000-0001-9699-8730","first_name":"Alexander"},{"last_name":"Mottin","full_name":"Mottin, Davide","first_name":"Davide"},{"first_name":"Panagiotis","last_name":"Karras","full_name":"Karras, Panagiotis"}],"issue":"Part I","abstract":[{"text":"What is the best way to match the nodes of two graphs? This graph alignment problem generalizes graph isomorphism and arises in applications from social network analysis to bioinformatics. Existing solutions either require auxiliary information such as node attributes, or provide a single-scale view of the graph by translating the problem into aligning node embeddings.\r\n\r\nIn this paper, we transfer the shape-analysis concept of functional maps from the continuous to the discrete case, and treat the graph alignment problem as a special case of the problem of finding a mapping between functions on graphs. We present GRASP, a method that captures multiscale structural characteristics from the eigenvectors of the graph’s Laplacian and uses this information to align two graphs.Our experimental study, featuring noise levels higher than anything used in previous studies, shows that GRASP outperforms state-of-the-art methods for graph alignment across noise levels and graph types.","lang":"eng"}],"intvolume":"     12858","citation":{"ama":"Hermanns J, Tsitsulin A, Munkhoeva M, Bronstein AM, Mottin D, Karras P. GRASP: Graph alignment through spectral signatures. In: <i>International Joint Conference on Asia-Pacific Web and Web-Age Information Management</i>. Vol 12858. Springer Nature; 2021:44-52. doi:<a href=\"https://doi.org/10.1007/978-3-030-85896-4_4\">10.1007/978-3-030-85896-4_4</a>","short":"J. Hermanns, A. Tsitsulin, M. Munkhoeva, A.M. Bronstein, D. Mottin, P. Karras, in:, International Joint Conference on Asia-Pacific Web and Web-Age Information Management, Springer Nature, 2021, pp. 44–52.","mla":"Hermanns, Judith, et al. “GRASP: Graph Alignment through Spectral Signatures.” <i>International Joint Conference on Asia-Pacific Web and Web-Age Information Management</i>, vol. 12858, no. Part I, Springer Nature, 2021, pp. 44–52, doi:<a href=\"https://doi.org/10.1007/978-3-030-85896-4_4\">10.1007/978-3-030-85896-4_4</a>.","chicago":"Hermanns, Judith, Anton Tsitsulin, Marina Munkhoeva, Alex M. Bronstein, Davide Mottin, and Panagiotis Karras. “GRASP: Graph Alignment through Spectral Signatures.” In <i>International Joint Conference on Asia-Pacific Web and Web-Age Information Management</i>, 12858:44–52. Springer Nature, 2021. <a href=\"https://doi.org/10.1007/978-3-030-85896-4_4\">https://doi.org/10.1007/978-3-030-85896-4_4</a>.","ieee":"J. Hermanns, A. Tsitsulin, M. Munkhoeva, A. M. Bronstein, D. Mottin, and P. Karras, “GRASP: Graph alignment through spectral signatures,” in <i>International Joint Conference on Asia-Pacific Web and Web-Age Information Management</i>, Guangzhou, China, 2021, vol. 12858, no. Part I, pp. 44–52.","ista":"Hermanns J, Tsitsulin A, Munkhoeva M, Bronstein AM, Mottin D, Karras P. 2021. GRASP: Graph alignment through spectral signatures. International Joint Conference on Asia-Pacific Web and Web-Age Information Management. APWeb-WAIM: International Joint Conference on Asia-Pacific Web and Web-Age Information Management, LNCS, vol. 12858, 44–52.","apa":"Hermanns, J., Tsitsulin, A., Munkhoeva, M., Bronstein, A. M., Mottin, D., &#38; Karras, P. (2021). GRASP: Graph alignment through spectral signatures. In <i>International Joint Conference on Asia-Pacific Web and Web-Age Information Management</i> (Vol. 12858, pp. 44–52). Guangzhou, China: Springer Nature. <a href=\"https://doi.org/10.1007/978-3-030-85896-4_4\">https://doi.org/10.1007/978-3-030-85896-4_4</a>"},"article_processing_charge":"No","publication":"International Joint Conference on Asia-Pacific Web and Web-Age Information Management","year":"2021","_id":"18243","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","status":"public","scopus_import":"1","alternative_title":["LNCS"],"publisher":"Springer Nature","publication_identifier":{"issn":["0302-9743","1611-3349"],"isbn":["9783030858957","9783030858964"]},"month":"08","type":"conference","date_created":"2024-10-08T13:03:44Z","extern":"1","page":"44 - 52","volume":12858,"language":[{"iso":"eng"}],"day":"19","date_updated":"2025-01-29T09:57:31Z","title":"GRASP: Graph alignment through spectral signatures"},{"date_updated":"2024-12-12T10:10:29Z","title":"Do we need depth in state-uf-the-art face authentication?","oa":1,"day":"19","date_created":"2024-10-08T13:04:02Z","extern":"1","month":"01","type":"conference","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2003.10895","open_access":"1"}],"arxiv":1,"language":[{"iso":"eng"}],"scopus_import":"1","publication_identifier":{"eissn":["2475-7888"],"isbn":["9781728181295"]},"publisher":"IEEE","year":"2021","_id":"18244","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","status":"public","article_processing_charge":"No","external_id":{"arxiv":["2003.10895"]},"citation":{"ama":"Livne A, Aviv Z, Grofit S, Bronstein AM, Kimmel R. Do we need depth in state-uf-the-art face authentication? In: <i>2020 International Conference on 3D Vision (3DV)</i>. IEEE; 2021. doi:<a href=\"https://doi.org/10.1109/3dv50981.2020.00099\">10.1109/3dv50981.2020.00099</a>","apa":"Livne, A., Aviv, Z., Grofit, S., Bronstein, A. M., &#38; Kimmel, R. (2021). Do we need depth in state-uf-the-art face authentication? In <i>2020 International Conference on 3D Vision (3DV)</i>. Fukuoka, Japan: IEEE. <a href=\"https://doi.org/10.1109/3dv50981.2020.00099\">https://doi.org/10.1109/3dv50981.2020.00099</a>","ista":"Livne A, Aviv Z, Grofit S, Bronstein AM, Kimmel R. 2021. Do we need depth in state-uf-the-art face authentication? 2020 International Conference on 3D Vision (3DV). 8th International Conference on 3D Vision, 9320359.","ieee":"A. Livne, Z. Aviv, S. Grofit, A. M. Bronstein, and R. Kimmel, “Do we need depth in state-uf-the-art face authentication?,” in <i>2020 International Conference on 3D Vision (3DV)</i>, Fukuoka, Japan, 2021.","short":"A. Livne, Z. Aviv, S. Grofit, A.M. Bronstein, R. Kimmel, in:, 2020 International Conference on 3D Vision (3DV), IEEE, 2021.","chicago":"Livne, Amir, Ziv Aviv, Shahaf Grofit, Alex M. Bronstein, and Ron Kimmel. “Do We Need Depth in State-Uf-the-Art Face Authentication?” In <i>2020 International Conference on 3D Vision (3DV)</i>. IEEE, 2021. <a href=\"https://doi.org/10.1109/3dv50981.2020.00099\">https://doi.org/10.1109/3dv50981.2020.00099</a>.","mla":"Livne, Amir, et al. “Do We Need Depth in State-Uf-the-Art Face Authentication?” <i>2020 International Conference on 3D Vision (3DV)</i>, 9320359, IEEE, 2021, doi:<a href=\"https://doi.org/10.1109/3dv50981.2020.00099\">10.1109/3dv50981.2020.00099</a>."},"article_number":"9320359","publication":"2020 International Conference on 3D Vision (3DV)","date_published":"2021-01-19T00:00:00Z","fulldoi":"https://doi.org/10.1109/3dv50981.2020.00099","oa_version":"Preprint","abstract":[{"lang":"eng","text":"Some face recognition methods are designed to utilize geometric information extracted from depth sensors to overcome the weaknesses of single-image based recognition technologies. However, the accurate acquisition of the depth profile is an expensive and challenging process. Here, we introduce a novel method that learns to recognize faces from stereo camera systems without the need to explicitly compute the facial surface or depth map. The raw face stereo images along with the location in the image from which the face is extracted allow the proposed CNN to improve the recognition task while avoiding the need to explicitly handle the geometric structure of the face. This way, we keep the simplicity and cost efficiency of identity authentication from a single image, while enjoying the benefits of geometric data without explicitly reconstructing it. We demonstrate that the suggested method outperforms both existing single-image and explicit depth based methods on largescale benchmarks, and even capable of recognize spoofing attacks. We also provide an ablation study that shows that the suggested method uses the face locations in the left and right images to encode informative features that improve the overall performance."}],"publication_status":"published","author":[{"last_name":"Livne","full_name":"Livne, Amir","first_name":"Amir"},{"first_name":"Ziv","full_name":"Aviv, Ziv","last_name":"Aviv"},{"first_name":"Shahaf","full_name":"Grofit, Shahaf","last_name":"Grofit"},{"last_name":"Bronstein","full_name":"Bronstein, Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","first_name":"Alexander","orcid":"0000-0001-9699-8730"},{"first_name":"Ron","last_name":"Kimmel","full_name":"Kimmel, Ron"}],"quality_controlled":"1","doi":"10.1109/3dv50981.2020.00099","conference":{"name":"8th International Conference on 3D Vision","start_date":"2020-11-25","location":"Fukuoka, Japan","end_date":"2020-11-28"}},{"day":"01","oa":1,"das_tickbox":"1","title":"Microscopic conservation laws for integrable lattice models","date_updated":"2026-06-30T07:03:12Z","publication_identifier":{"issn":["0026-9255"],"eissn":["1436-5081"]},"publisher":"Springer Nature","scopus_import":"1","OA_place":"repository","arxiv":1,"language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2012.04782"}],"volume":196,"article_type":"original","page":"477-504","extern":"1","date_created":"2026-06-19T08:12:18Z","month":"11","type":"journal_article","publication":"Monatshefte für Mathematik","external_id":{"arxiv":["2012.04782"]},"article_processing_charge":"No","citation":{"apa":"Harrop-Griffiths, B., Killip, R., &#38; Vişan, M. (2021). Microscopic conservation laws for integrable lattice models. <i>Monatshefte Für Mathematik</i>. Springer Nature. <a href=\"https://doi.org/10.1007/s00605-021-01529-5\">https://doi.org/10.1007/s00605-021-01529-5</a>","ieee":"B. Harrop-Griffiths, R. Killip, and M. Vişan, “Microscopic conservation laws for integrable lattice models,” <i>Monatshefte für Mathematik</i>, vol. 196, no. 3. Springer Nature, pp. 477–504, 2021.","ista":"Harrop-Griffiths B, Killip R, Vişan M. 2021. Microscopic conservation laws for integrable lattice models. Monatshefte für Mathematik. 196(3), 477–504.","short":"B. Harrop-Griffiths, R. Killip, M. Vişan, Monatshefte Für Mathematik 196 (2021) 477–504.","mla":"Harrop-Griffiths, Benjamin, et al. “Microscopic Conservation Laws for Integrable Lattice Models.” <i>Monatshefte Für Mathematik</i>, vol. 196, no. 3, Springer Nature, 2021, pp. 477–504, doi:<a href=\"https://doi.org/10.1007/s00605-021-01529-5\">10.1007/s00605-021-01529-5</a>.","chicago":"Harrop-Griffiths, Benjamin, Rowan Killip, and Monica Vişan. “Microscopic Conservation Laws for Integrable Lattice Models.” <i>Monatshefte Für Mathematik</i>. Springer Nature, 2021. <a href=\"https://doi.org/10.1007/s00605-021-01529-5\">https://doi.org/10.1007/s00605-021-01529-5</a>.","ama":"Harrop-Griffiths B, Killip R, Vişan M. Microscopic conservation laws for integrable lattice models. <i>Monatshefte für Mathematik</i>. 2021;196(3):477-504. doi:<a href=\"https://doi.org/10.1007/s00605-021-01529-5\">10.1007/s00605-021-01529-5</a>"},"OA_type":"green","status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"22063","year":"2021","doi":"10.1007/s00605-021-01529-5","quality_controlled":"1","intvolume":"       196","abstract":[{"lang":"eng","text":"We consider two discrete completely integrable evolutions: the Toda Lattice and the Ablowitz–Ladik system. The principal thrust of the paper is the development of microscopic conservation laws that witness the conservation of the perturbation determinant under these dynamics. In this way, we obtain discrete analogues of objects that we found essential in our recent analyses of KdV, NLS, and mKdV. In concert with this, we revisit the classical topic of microscopic conservation laws attendant to the (renormalized) trace of the Green’s function."}],"issue":"3","author":[{"full_name":"Harrop-Griffiths, Benjamin","last_name":"Harrop-Griffiths","first_name":"Benjamin"},{"last_name":"Killip","full_name":"Killip, Rowan","first_name":"Rowan"},{"first_name":"Monica","last_name":"Visan","id":"056daca0-b8d1-11f0-964f-f91054abf8ca","full_name":"Visan, Monica"}],"publication_status":"published","fulldoi":"https://doi.org/10.1007/s00605-021-01529-5","date_published":"2021-11-01T00:00:00Z","oa_version":"Preprint"},{"date_published":"2021-01-01T00:00:00Z","fulldoi":"https://doi.org/10.1137/20m1381824","oa_version":"Preprint","publication_status":"published","issue":"5","author":[{"full_name":"Killip, Rowan","last_name":"Killip","first_name":"Rowan"},{"last_name":"Murphy","full_name":"Murphy, Jason","first_name":"Jason"},{"first_name":"Monica","last_name":"Visan","full_name":"Visan, Monica","id":"056daca0-b8d1-11f0-964f-f91054abf8ca"}],"intvolume":"        53","abstract":[{"text":"We consider the nonlinear Schrödinger equation in three space dimensions with combined focusing cubic and defocusing quintic nonlinearity. This problem was considered previously by Killip et al. [Arch. Ration. Mech. Anal., 225 (2017), pp. 469--548], who proved scattering for the whole region of the mass/energy plane where the virial quantity is guaranteed to be positive. In this paper, we prove scattering in a slightly larger region where, in particular, the virial quantity is no longer guaranteed to be sign definite.","lang":"eng"}],"mathsc":["35Q55"],"quality_controlled":"1","doi":"10.1137/20m1381824","keyword":["NLS","scattering","viral"],"year":"2021","_id":"22084","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","OA_type":"green","citation":{"short":"R. Killip, J. Murphy, M. Vişan, SIAM Journal on Mathematical Analysis 53 (2021) 5803–5812.","chicago":"Killip, Rowan, Jason Murphy, and Monica Vişan. “Scattering for the Cubic-Quintic NLS: Crossing the Virial Threshold.” <i>SIAM Journal on Mathematical Analysis</i>. Society for Industrial &#38; Applied Mathematics, 2021. <a href=\"https://doi.org/10.1137/20m1381824\">https://doi.org/10.1137/20m1381824</a>.","mla":"Killip, Rowan, et al. “Scattering for the Cubic-Quintic NLS: Crossing the Virial Threshold.” <i>SIAM Journal on Mathematical Analysis</i>, vol. 53, no. 5, Society for Industrial &#38; Applied Mathematics, 2021, pp. 5803–12, doi:<a href=\"https://doi.org/10.1137/20m1381824\">10.1137/20m1381824</a>.","ista":"Killip R, Murphy J, Vişan M. 2021. Scattering for the cubic-quintic NLS: Crossing the virial threshold. SIAM Journal on Mathematical Analysis. 53(5), 5803–5812.","ieee":"R. Killip, J. Murphy, and M. Vişan, “Scattering for the cubic-quintic NLS: Crossing the virial threshold,” <i>SIAM Journal on Mathematical Analysis</i>, vol. 53, no. 5. Society for Industrial &#38; Applied Mathematics, pp. 5803–5812, 2021.","apa":"Killip, R., Murphy, J., &#38; Vişan, M. (2021). Scattering for the cubic-quintic NLS: Crossing the virial threshold. <i>SIAM Journal on Mathematical Analysis</i>. Society for Industrial &#38; Applied Mathematics. <a href=\"https://doi.org/10.1137/20m1381824\">https://doi.org/10.1137/20m1381824</a>","ama":"Killip R, Murphy J, Vişan M. Scattering for the cubic-quintic NLS: Crossing the virial threshold. <i>SIAM Journal on Mathematical Analysis</i>. 2021;53(5):5803-5812. doi:<a href=\"https://doi.org/10.1137/20m1381824\">10.1137/20m1381824</a>"},"article_processing_charge":"No","external_id":{"arxiv":["2007.07406"]},"publication":"SIAM Journal on Mathematical Analysis","month":"01","type":"journal_article","date_created":"2026-06-19T08:28:23Z","page":"5803-5812","extern":"1","article_type":"original","volume":53,"main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2007.07406","open_access":"1"}],"language":[{"iso":"eng"}],"arxiv":1,"scopus_import":"1","OA_place":"repository","publisher":"Society for Industrial & Applied Mathematics","publication_identifier":{"eissn":["1095-7154"],"issn":["0036-1410"]},"date_updated":"2026-07-01T07:37:46Z","title":"Scattering for the cubic-quintic NLS: Crossing the virial threshold","das_tickbox":"1","oa":1,"day":"01"},{"doi":"10.1353/ajm.2021.0014","quality_controlled":"1","date_published":"2021-04-01T00:00:00Z","fulldoi":"https://doi.org/10.1353/ajm.2021.0014","oa_version":"Preprint","intvolume":"       143","abstract":[{"text":"We prove that solutions of the cubic nonlinear Schrödinger equation on $\\Bbb{R}^2$ can be approximated by a finite-dimensional Hamiltonian system, uniformly on bounded sets of initial data. This is despite the wealth of non-compact symmetries: scaling, translation, and Galilei boosts.\r\n\r\nComplementing this approximation result, we show that all solutions of the finite-dimensional Hamiltonian system we use can be approximated by the full PDE.\r\n\r\nA key ingredient in these results is the development of a general methodology for transfering uniform global space-time bounds to suitable Fourier truncations of dispersive PDE models.\r\n\r\nAs an application, we prove symplectic non-squeezing (in the sense of Gromov) for the cubic NLS on $\\Bbb{R}^2$. This is the first symplectic non-squeezing result for a Hamiltonian PDE in infinite volume. It is also the first unconditional symplectic non-squeezing result in a scaling-critical setting.\r\n\r\nFinally, we discuss implications of non-squeezing on the nature of scattering.","lang":"eng"}],"publication_status":"published","author":[{"last_name":"Killip","full_name":"Killip, Rowan","first_name":"Rowan"},{"first_name":"Monica","full_name":"Visan, Monica","id":"056daca0-b8d1-11f0-964f-f91054abf8ca","last_name":"Visan"},{"first_name":"Xiaoyi","full_name":"Zhang, Xiaoyi","last_name":"Zhang"}],"issue":"2","external_id":{"arxiv":["1606.07738"]},"article_processing_charge":"No","citation":{"ama":"Killip R, Vişan M, Zhang X. Finite-dimensional approximation and non-squeezing for the cubic nonlinear Schrödinger equation on ℝ2. <i>American Journal of Mathematics</i>. 2021;143(2):613-680. doi:<a href=\"https://doi.org/10.1353/ajm.2021.0014\">10.1353/ajm.2021.0014</a>","ieee":"R. Killip, M. Vişan, and X. Zhang, “Finite-dimensional approximation and non-squeezing for the cubic nonlinear Schrödinger equation on ℝ2,” <i>American Journal of Mathematics</i>, vol. 143, no. 2. Johns Hopkins University Press, pp. 613–680, 2021.","ista":"Killip R, Vişan M, Zhang X. 2021. Finite-dimensional approximation and non-squeezing for the cubic nonlinear Schrödinger equation on ℝ2. American Journal of Mathematics. 143(2), 613–680.","apa":"Killip, R., Vişan, M., &#38; Zhang, X. (2021). Finite-dimensional approximation and non-squeezing for the cubic nonlinear Schrödinger equation on ℝ2. <i>American Journal of Mathematics</i>. Johns Hopkins University Press. <a href=\"https://doi.org/10.1353/ajm.2021.0014\">https://doi.org/10.1353/ajm.2021.0014</a>","short":"R. Killip, M. Vişan, X. Zhang, American Journal of Mathematics 143 (2021) 613–680.","mla":"Killip, Rowan, et al. “Finite-Dimensional Approximation and Non-Squeezing for the Cubic Nonlinear Schrödinger Equation on ℝ2.” <i>American Journal of Mathematics</i>, vol. 143, no. 2, Johns Hopkins University Press, 2021, pp. 613–80, doi:<a href=\"https://doi.org/10.1353/ajm.2021.0014\">10.1353/ajm.2021.0014</a>.","chicago":"Killip, Rowan, Monica Vişan, and Xiaoyi Zhang. “Finite-Dimensional Approximation and Non-Squeezing for the Cubic Nonlinear Schrödinger Equation on ℝ2.” <i>American Journal of Mathematics</i>. Johns Hopkins University Press, 2021. <a href=\"https://doi.org/10.1353/ajm.2021.0014\">https://doi.org/10.1353/ajm.2021.0014</a>."},"publication":"American Journal of Mathematics","year":"2021","OA_type":"green","status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"22087","scopus_import":"1","OA_place":"repository","publication_identifier":{"eissn":["1080-6377"]},"publisher":"Johns Hopkins University Press","article_type":"original","volume":143,"page":"613-680","date_created":"2026-06-19T08:46:12Z","extern":"1","type":"journal_article","month":"04","arxiv":1,"language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.1606.07738"}],"oa":1,"day":"01","date_updated":"2026-07-01T12:38:11Z","das_tickbox":"1","title":"Finite-dimensional approximation and non-squeezing for the cubic nonlinear Schrödinger equation on ℝ2"},{"month":"10","type":"journal_article","page":"51373–51382","date_created":"2021-11-21T23:01:30Z","pmid":1,"acknowledgement":"This work was supported by the European Regional Development Funds. M.L., Y.Z., X.H., and K.X. thank the China Scholarship Council for scholarship support. M. I. has been financially supported by IST Austria and the Werner Siemens Foundation. Y.L. acknowledges funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 754411. J.L. is a Serra Húnter fellow and is grateful to ICREA Academia program and projects MICINN/FEDER RTI2018-093996-B-C31 and GC 2017 SGR 128. ICN2 acknowledges funding from Generalitat de Catalunya 2017 SGR 327 and the Spanish MINECO project NANOGEN (PID2020-116093RB-C43). ICN2 was supported by the Severo Ochoa program from Spanish MINECO (grant no. SEV-2017-0706) and was funded by the CERCA Programme/Generalitat de Catalunya. X.H. thanks China Scholarship Council for scholarship support (201804910551). Part of the present work was performed in the framework of Universitat Autònoma de Barcelona Materials Science Ph.D. program.","article_type":"original","volume":13,"main_file_link":[{"url":"https://upcommons.upc.edu/bitstream/2117/363528/1/Pb%20mengyao.pdf","open_access":"1"}],"department":[{"_id":"MaIb"}],"language":[{"iso":"eng"}],"corr_author":"1","scopus_import":"1","publisher":"American Chemical Society","ec_funded":1,"publication_identifier":{"eissn":["1944-8252"],"issn":["1944-8244"]},"date_updated":"2026-07-06T12:15:05Z","title":"PbS–Pb–CuxS composites for thermoelectric application","das_tickbox":"1","oa":1,"day":"19","project":[{"_id":"260C2330-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","name":"ISTplus - Postdoctoral Fellowships","grant_number":"754411"},{"name":"HighTE: The Werner Siemens Laboratory for the High Throughput Discovery of Semiconductors for Waste Heat Recovery","_id":"9B8F7476-BA93-11EA-9121-9846C619BF3A"}],"fulldoi":"https://doi.org/10.1021/acsami.1c15609","date_published":"2021-10-19T00:00:00Z","oa_version":"Submitted Version","isi":1,"publication_status":"published","issue":"43","author":[{"full_name":"Li, Mengyao","last_name":"Li","first_name":"Mengyao"},{"last_name":"Liu","id":"2A70014E-F248-11E8-B48F-1D18A9856A87","full_name":"Liu, Yu","first_name":"Yu","orcid":"0000-0001-7313-6740"},{"first_name":"Yu","last_name":"Zhang","full_name":"Zhang, Yu"},{"last_name":"Han","full_name":"Han, Xu","first_name":"Xu"},{"full_name":"Xiao, Ke","last_name":"Xiao","first_name":"Ke"},{"first_name":"Mehran","last_name":"Nabahat","full_name":"Nabahat, Mehran"},{"first_name":"Jordi","last_name":"Arbiol","full_name":"Arbiol, Jordi"},{"full_name":"Llorca, Jordi","last_name":"Llorca","first_name":"Jordi"},{"id":"43C61214-F248-11E8-B48F-1D18A9856A87","full_name":"Ibáñez, Maria","last_name":"Ibáñez","orcid":"0000-0001-5013-2843","first_name":"Maria"},{"first_name":"Andreu","full_name":"Cabot, Andreu","last_name":"Cabot"}],"abstract":[{"text":"Composite materials offer numerous advantages in a wide range of applications, including thermoelectrics. Here, semiconductor–metal composites are produced by just blending nanoparticles of a sulfide semiconductor obtained in aqueous solution and at room temperature with a metallic Cu powder. The obtained blend is annealed in a reducing atmosphere and afterward consolidated into dense polycrystalline pellets through spark plasma sintering (SPS). We observe that, during the annealing process, the presence of metallic copper activates a partial reduction of the PbS, resulting in the formation of PbS–Pb–CuxS composites. The presence of metallic lead during the SPS process habilitates the liquid-phase sintering of the composite. Besides, by comparing the transport properties of PbS, the PbS–Pb–CuxS composites, and PbS–CuxS composites obtained by blending PbS and CuxS nanoparticles, we demonstrate that the presence of metallic lead decisively contributes to a strong increase of the charge carrier concentration through spillover of charge carriers enabled by the low work function of lead. The increase in charge carrier concentration translates into much higher electrical conductivities and moderately lower Seebeck coefficients. These properties translate into power factors up to 2.1 mW m–1 K–2 at ambient temperature, well above those of PbS and PbS + CuxS. Additionally, the presence of multiple phases in the final composite results in a notable decrease in the lattice thermal conductivity. Overall, the introduction of metallic copper in the initial blend results in a significant improvement of the thermoelectric performance of PbS, reaching a dimensionless thermoelectric figure of merit ZT = 1.1 at 750 K, which represents about a 400% increase over bare PbS. Besides, an average ZTave = 0.72 in the temperature range 320–773 K is demonstrated.","lang":"eng"}],"intvolume":"        13","quality_controlled":"1","doi":"10.1021/acsami.1c15609","keyword":["CuxS","PbS","energy conversion","nanocomposite","nanoparticle","solution synthesis","thermoelectric"],"year":"2021","_id":"10327","status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"ieee":"M. Li <i>et al.</i>, “PbS–Pb–CuxS composites for thermoelectric application,” <i>ACS Applied Materials and Interfaces</i>, vol. 13, no. 43. American Chemical Society, pp. 51373–51382, 2021.","apa":"Li, M., Liu, Y., Zhang, Y., Han, X., Xiao, K., Nabahat, M., … Cabot, A. (2021). PbS–Pb–CuxS composites for thermoelectric application. <i>ACS Applied Materials and Interfaces</i>. American Chemical Society. <a href=\"https://doi.org/10.1021/acsami.1c15609\">https://doi.org/10.1021/acsami.1c15609</a>","ista":"Li M, Liu Y, Zhang Y, Han X, Xiao K, Nabahat M, Arbiol J, Llorca J, Ibáñez M, Cabot A. 2021. PbS–Pb–CuxS composites for thermoelectric application. ACS Applied Materials and Interfaces. 13(43), 51373–51382.","mla":"Li, Mengyao, et al. “PbS–Pb–CuxS Composites for Thermoelectric Application.” <i>ACS Applied Materials and Interfaces</i>, vol. 13, no. 43, American Chemical Society, 2021, pp. 51373–51382, doi:<a href=\"https://doi.org/10.1021/acsami.1c15609\">10.1021/acsami.1c15609</a>.","chicago":"Li, Mengyao, Yu Liu, Yu Zhang, Xu Han, Ke Xiao, Mehran Nabahat, Jordi Arbiol, Jordi Llorca, Maria Ibáñez, and Andreu Cabot. “PbS–Pb–CuxS Composites for Thermoelectric Application.” <i>ACS Applied Materials and Interfaces</i>. American Chemical Society, 2021. <a href=\"https://doi.org/10.1021/acsami.1c15609\">https://doi.org/10.1021/acsami.1c15609</a>.","short":"M. Li, Y. Liu, Y. Zhang, X. Han, K. Xiao, M. Nabahat, J. Arbiol, J. Llorca, M. Ibáñez, A. Cabot, ACS Applied Materials and Interfaces 13 (2021) 51373–51382.","ama":"Li M, Liu Y, Zhang Y, et al. PbS–Pb–CuxS composites for thermoelectric application. <i>ACS Applied Materials and Interfaces</i>. 2021;13(43):51373–51382. doi:<a href=\"https://doi.org/10.1021/acsami.1c15609\">10.1021/acsami.1c15609</a>"},"article_processing_charge":"No","external_id":{"pmid":["34665616"],"isi":["000715852100070"]},"publication":"ACS Applied Materials and Interfaces"},{"author":[{"full_name":"Li, Mengyao","last_name":"Li","first_name":"Mengyao"},{"orcid":"0000-0001-7313-6740","first_name":"Yu","full_name":"Liu, Yu","id":"2A70014E-F248-11E8-B48F-1D18A9856A87","last_name":"Liu"},{"first_name":"Yu","full_name":"Zhang, Yu","last_name":"Zhang"},{"first_name":"Xu","last_name":"Han","full_name":"Han, Xu"},{"last_name":"Zhang","full_name":"Zhang, Ting","first_name":"Ting"},{"last_name":"Zuo","full_name":"Zuo, Yong","first_name":"Yong"},{"first_name":"Chenyang","last_name":"Xie","full_name":"Xie, Chenyang"},{"full_name":"Xiao, Ke","last_name":"Xiao","first_name":"Ke"},{"first_name":"Jordi","last_name":"Arbiol","full_name":"Arbiol, Jordi"},{"first_name":"Jordi","full_name":"Llorca, Jordi","last_name":"Llorca"},{"last_name":"Ibáñez","full_name":"Ibáñez, Maria","id":"43C61214-F248-11E8-B48F-1D18A9856A87","first_name":"Maria","orcid":"0000-0001-5013-2843"},{"first_name":"Junfeng","full_name":"Liu, Junfeng","last_name":"Liu"},{"full_name":"Cabot, Andreu","last_name":"Cabot","first_name":"Andreu"}],"issue":"3","publication_status":"published","intvolume":"        15","abstract":[{"text":"Cu2–xS has become one of the most promising thermoelectric materials for application in the middle-high temperature range. Its advantages include the abundance, low cost, and safety of its elements and a high performance at relatively elevated temperatures. However, stability issues limit its operation current and temperature, thus calling for the optimization of the material performance in the middle temperature range. Here, we present a synthetic protocol for large scale production of covellite CuS nanoparticles at ambient temperature and atmosphere, and using water as a solvent. The crystal phase and stoichiometry of the particles are afterward tuned through an annealing process at a moderate temperature under inert or reducing atmosphere. While annealing under argon results in Cu1.8S nanopowder with a rhombohedral crystal phase, annealing in an atmosphere containing hydrogen leads to tetragonal Cu1.96S. High temperature X-ray diffraction analysis shows the material annealed in argon to transform to the cubic phase at ca. 400 K, while the material annealed in the presence of hydrogen undergoes two phase transitions, first to hexagonal and then to the cubic structure. The annealing atmosphere, temperature, and time allow adjustment of the density of copper vacancies and thus tuning of the charge carrier concentration and material transport properties. In this direction, the material annealed under Ar is characterized by higher electrical conductivities but lower Seebeck coefficients than the material annealed in the presence of hydrogen. By optimizing the charge carrier concentration through the annealing time, Cu2–xS with record figures of merit in the middle temperature range, up to 1.41 at 710 K, is obtained. We finally demonstrate that this strategy, based on a low-cost and scalable solution synthesis process, is also suitable for the production of high performance Cu2–xS layers using high throughput and cost-effective printing technologies.","lang":"eng"}],"fulldoi":"https://doi.org/10.1021/acsnano.0c09866","date_published":"2021-03-01T00:00:00Z","isi":1,"oa_version":"Submitted Version","doi":"10.1021/acsnano.0c09866","quality_controlled":"1","status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"9235","year":"2021","keyword":["General Engineering","General Physics and Astronomy","General Materials Science"],"publication":"ACS Nano","citation":{"ieee":"M. Li <i>et al.</i>, “Effect of the annealing atmosphere on crystal phase and thermoelectric properties of copper sulfide,” <i>ACS Nano</i>, vol. 15, no. 3. American Chemical Society, pp. 4967–4978, 2021.","apa":"Li, M., Liu, Y., Zhang, Y., Han, X., Zhang, T., Zuo, Y., … Cabot, A. (2021). Effect of the annealing atmosphere on crystal phase and thermoelectric properties of copper sulfide. <i>ACS Nano</i>. American Chemical Society. <a href=\"https://doi.org/10.1021/acsnano.0c09866\">https://doi.org/10.1021/acsnano.0c09866</a>","ista":"Li M, Liu Y, Zhang Y, Han X, Zhang T, Zuo Y, Xie C, Xiao K, Arbiol J, Llorca J, Ibáñez M, Liu J, Cabot A. 2021. Effect of the annealing atmosphere on crystal phase and thermoelectric properties of copper sulfide. ACS Nano. 15(3), 4967–4978.","chicago":"Li, Mengyao, Yu Liu, Yu Zhang, Xu Han, Ting Zhang, Yong Zuo, Chenyang Xie, et al. “Effect of the Annealing Atmosphere on Crystal Phase and Thermoelectric Properties of Copper Sulfide.” <i>ACS Nano</i>. American Chemical Society, 2021. <a href=\"https://doi.org/10.1021/acsnano.0c09866\">https://doi.org/10.1021/acsnano.0c09866</a>.","mla":"Li, Mengyao, et al. “Effect of the Annealing Atmosphere on Crystal Phase and Thermoelectric Properties of Copper Sulfide.” <i>ACS Nano</i>, vol. 15, no. 3, American Chemical Society, 2021, pp. 4967–4978, doi:<a href=\"https://doi.org/10.1021/acsnano.0c09866\">10.1021/acsnano.0c09866</a>.","short":"M. Li, Y. Liu, Y. Zhang, X. Han, T. Zhang, Y. Zuo, C. Xie, K. Xiao, J. Arbiol, J. Llorca, M. Ibáñez, J. Liu, A. Cabot, ACS Nano 15 (2021) 4967–4978.","ama":"Li M, Liu Y, Zhang Y, et al. Effect of the annealing atmosphere on crystal phase and thermoelectric properties of copper sulfide. <i>ACS Nano</i>. 2021;15(3):4967–4978. doi:<a href=\"https://doi.org/10.1021/acsnano.0c09866\">10.1021/acsnano.0c09866</a>"},"external_id":{"isi":["000634569100106"],"pmid":["33645986"]},"article_processing_charge":"No","language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1","url":"https://upcommons.upc.edu/bitstream/handle/2117/363528/Pb%20mengyao.pdf?sequence=1&isAllowed=y"}],"department":[{"_id":"MaIb"}],"type":"journal_article","month":"03","volume":15,"article_type":"original","acknowledgement":"This work was supported by the European Regional Development Funds. M.Y.L., X.H., T.Z., and K.X. thank the China Scholarship Council for scholarship support. M.I. acknowledges financial support from IST Austria. J.L. acknowledges support from the National Natural Science Foundation of China (No. 22008091), the funding for scientific research startup of Jiangsu University (No. 19JDG044), and Jiangsu Provincial Program for High-Level Innovative and Entrepreneurial Talents Introduction. J.L. is a Serra Húnter fellow and is grateful to the ICREA Academia program and projects MICINN/FEDER RTI2018-093996-B-C31 and GC 2017 SGR 128. ICN2 acknowledges funding from Generalitat de Catalunya 2017 SGR 327 and the Spanish MINECO ENE2017-85087-C3. ICN2 is supported by the Severo Ochoa program from Spanish MINECO (Grant No. SEV-2017-0706) and is funded by the CERCA Programme/Generalitat de Catalunya. Part of the present work has been performed in the framework of Universitat Autònoma de Barcelona Materials Science PhD program. T.Z. has received funding from the CSC-UAB PhD scholarship program.","page":"4967–4978","pmid":1,"date_created":"2021-03-10T20:12:45Z","publisher":"American Chemical Society","publication_identifier":{"eissn":["1936-086X"],"issn":["1936-0851"]},"scopus_import":"1","corr_author":"1","title":"Effect of the annealing atmosphere on crystal phase and thermoelectric properties of copper sulfide","das_tickbox":"1","date_updated":"2026-07-06T12:15:39Z","day":"01","oa":1}]