[{"day":"01","author":[{"full_name":"Nahshan, Yury","first_name":"Yury","last_name":"Nahshan"},{"last_name":"Chmiel","full_name":"Chmiel, Brian","first_name":"Brian"},{"first_name":"Chaim","full_name":"Baskin, Chaim","last_name":"Baskin"},{"first_name":"Evgenii","full_name":"Zheltonozhskii, Evgenii","last_name":"Zheltonozhskii"},{"last_name":"Banner","first_name":"Ron","full_name":"Banner, Ron"},{"full_name":"Bronstein, Alexander","first_name":"Alexander","orcid":"0000-0001-9699-8730","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","last_name":"Bronstein"},{"full_name":"Mendelson, Avi","first_name":"Avi","last_name":"Mendelson"}],"issue":"11-12","article_processing_charge":"No","OA_type":"green","OA_place":"repository","oa":1,"page":"3245-3262","publication_status":"published","title":"Loss aware post-training quantization","extern":"1","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"}],"publication_identifier":{"eissn":["1573-0565"],"issn":["0885-6125"]},"publication":"Machine Learning","article_type":"original","year":"2021","language":[{"iso":"eng"}],"external_id":{"arxiv":["1911.07190"]},"related_material":{"link":[{"relation":"software","url":"https://github.com/ynahshan/nn-quantization-pytorch/tree/master/lapq"}]},"type":"journal_article","doi":"10.1007/s10994-021-06053-z","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.1911.07190","open_access":"1"}],"scopus_import":"1","date_created":"2024-10-08T12:57:05Z","volume":110,"date_updated":"2024-10-15T07:33:28Z","quality_controlled":"1","_id":"18233","oa_version":"Preprint","month":"12","publisher":"Springer Nature","arxiv":1,"status":"public","intvolume":"       110","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2021-12-01T00:00:00Z","citation":{"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>","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.","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>","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.","short":"Y. Nahshan, B. Chmiel, C. Baskin, E. Zheltonozhskii, R. Banner, A.M. Bronstein, A. Mendelson, Machine Learning 110 (2021) 3245–3262.","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>."}},{"publisher":"MDPI","arxiv":1,"article_number":"2144","status":"public","intvolume":"         9","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","DOAJ_listed":"1","date_published":"2021-09-02T00:00:00Z","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.","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>.","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>.","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>","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>","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."},"type":"journal_article","doi":"10.3390/math9172144","scopus_import":"1","_id":"18234","month":"09","oa_version":"Published Version","date_updated":"2024-10-15T07:37:39Z","quality_controlled":"1","volume":9,"date_created":"2024-10-08T12:57:24Z","publication_identifier":{"issn":["2227-7390"]},"publication":"Mathematics","year":"2021","article_type":"original","external_id":{"arxiv":["1810.00162"]},"language":[{"iso":"eng"}],"author":[{"last_name":"Baskin","full_name":"Baskin, Chaim","first_name":"Chaim"},{"first_name":"Evgenii","full_name":"Zheltonozhkii, Evgenii","last_name":"Zheltonozhkii"},{"first_name":"Tal","full_name":"Rozen, Tal","last_name":"Rozen"},{"last_name":"Liss","full_name":"Liss, Natan","first_name":"Natan"},{"full_name":"Chai, Yoav","first_name":"Yoav","last_name":"Chai"},{"full_name":"Schwartz, Eli","first_name":"Eli","last_name":"Schwartz"},{"last_name":"Giryes","full_name":"Giryes, Raja","first_name":"Raja"},{"id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","last_name":"Bronstein","orcid":"0000-0001-9699-8730","full_name":"Bronstein, Alexander","first_name":"Alexander"},{"last_name":"Mendelson","full_name":"Mendelson, Avi","first_name":"Avi"}],"day":"02","issue":"17","OA_type":"gold","article_processing_charge":"No","OA_place":"publisher","publication_status":"published","extern":"1","title":"NICE: Noise Injection and Clamping Estimation for neural network quantization","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"}]},{"arxiv":1,"status":"public","intvolume":"       149","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"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>","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.","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.","short":"S. Doveh, E. Schwartz, C. Xue, R. Feris, A.M. Bronstein, R. Giryes, L. Karlinsky, Pattern Recognition Letters 149 (2021) 130–136.","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>.","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>."},"date_published":"2021-09-01T00:00:00Z","publisher":"Elsevier","doi":"10.1016/j.patrec.2021.05.010","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.1912.00412"}],"scopus_import":"1","_id":"18235","month":"09","oa_version":"Preprint","date_updated":"2024-10-15T07:39:56Z","date_created":"2024-10-08T12:57:53Z","quality_controlled":"1","volume":149,"type":"journal_article","year":"2021","article_type":"original","external_id":{"arxiv":["1912.00412"]},"language":[{"iso":"eng"}],"publication_identifier":{"issn":["0167-8655"]},"publication":"Pattern Recognition Letters","publication_status":"published","page":"130-136","extern":"1","title":"MetAdapt: Meta-learned task-adaptive architecture for few-shot classification","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."}],"author":[{"first_name":"Sivan","full_name":"Doveh, Sivan","last_name":"Doveh"},{"full_name":"Schwartz, Eli","first_name":"Eli","last_name":"Schwartz"},{"last_name":"Xue","first_name":"Chao","full_name":"Xue, Chao"},{"first_name":"Rogerio","full_name":"Feris, Rogerio","last_name":"Feris"},{"first_name":"Alexander","full_name":"Bronstein, Alexander","last_name":"Bronstein","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","orcid":"0000-0001-9699-8730"},{"last_name":"Giryes","first_name":"Raja","full_name":"Giryes, Raja"},{"full_name":"Karlinsky, Leonid","first_name":"Leonid","last_name":"Karlinsky"}],"day":"01","OA_type":"green","article_processing_charge":"No","OA_place":"repository","oa":1},{"doi":"10.1073/pnas.2020620118","_id":"18236","oa_version":"Published Version","month":"06","date_created":"2024-10-08T12:58:09Z","date_updated":"2024-10-15T07:43:01Z","quality_controlled":"1","volume":118,"scopus_import":"1","type":"journal_article","status":"public","article_number":"e2020620118","date_published":"2021-06-07T00:00:00Z","citation":{"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>","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.","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.","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>.","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>."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","intvolume":"       118","publisher":"National Academy of Sciences","publication_status":"published","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."}],"extern":"1","title":"Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning–based ECG analysis","issue":"24","author":[{"first_name":"Yonatan","full_name":"Elul, Yonatan","last_name":"Elul"},{"last_name":"Rosenberg","first_name":"Aviv A.","full_name":"Rosenberg, Aviv A."},{"full_name":"Schuster, Assaf","first_name":"Assaf","last_name":"Schuster"},{"full_name":"Bronstein, Alexander","first_name":"Alexander","orcid":"0000-0001-9699-8730","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","last_name":"Bronstein"},{"first_name":"Yael","full_name":"Yaniv, Yael","last_name":"Yaniv"}],"day":"07","OA_place":"publisher","OA_type":"free access","article_processing_charge":"No","year":"2021","article_type":"original","pmid":1,"external_id":{"pmid":["34099565"]},"language":[{"iso":"eng"}],"publication_identifier":{"issn":["0027-8424"],"eissn":["1091-6490"]},"publication":"Proceedings of the National Academy of Sciences"},{"issue":"1-4","day":"26","author":[{"first_name":"Chaim","full_name":"Baskin, Chaim","last_name":"Baskin"},{"last_name":"Liss","full_name":"Liss, Natan","first_name":"Natan"},{"last_name":"Schwartz","full_name":"Schwartz, Eli","first_name":"Eli"},{"full_name":"Zheltonozhskii, Evgenii","first_name":"Evgenii","last_name":"Zheltonozhskii"},{"last_name":"Giryes","first_name":"Raja","full_name":"Giryes, Raja"},{"orcid":"0000-0001-9699-8730","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","last_name":"Bronstein","first_name":"Alexander","full_name":"Bronstein, Alexander"},{"full_name":"Mendelson, Avi","first_name":"Avi","last_name":"Mendelson"}],"OA_place":"repository","oa":1,"article_processing_charge":"No","OA_type":"green","page":"1-15","publication_status":"published","abstract":[{"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.","lang":"eng"}],"title":"UNIQ: Uniform Noise Injection for Non-Uniform Quantization of neural networks","extern":"1","publication_identifier":{"issn":["0734-2071"],"eissn":["1557-7333"]},"publication":"ACM Transactions on Computer Systems","article_type":"original","year":"2021","language":[{"iso":"eng"}],"external_id":{"arxiv":["1804.10969"]},"type":"journal_article","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.1804.10969"}],"doi":"10.1145/3444943","date_created":"2024-10-08T12:58:26Z","quality_controlled":"1","date_updated":"2024-10-15T07:47:22Z","volume":37,"month":"03","_id":"18237","oa_version":"Preprint","scopus_import":"1","publisher":"Association for Computing Machinery","status":"public","arxiv":1,"date_published":"2021-03-26T00:00:00Z","citation":{"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>","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.","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>","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.","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.","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>."},"intvolume":"        37","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"},{"publication_identifier":{"issn":["2071-1050"]},"publication":"Sustainability","article_type":"original","year":"2021","language":[{"iso":"eng"}],"day":"13","author":[{"first_name":"Alex","full_name":"Karbachevsky, Alex","last_name":"Karbachevsky"},{"last_name":"Baskin","full_name":"Baskin, Chaim","first_name":"Chaim"},{"first_name":"Evgenii","full_name":"Zheltonozhskii, Evgenii","last_name":"Zheltonozhskii"},{"first_name":"Yevgeny","full_name":"Yermolin, Yevgeny","last_name":"Yermolin"},{"full_name":"Gabbay, Freddy","first_name":"Freddy","last_name":"Gabbay"},{"full_name":"Bronstein, Alexander","first_name":"Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","last_name":"Bronstein","orcid":"0000-0001-9699-8730"},{"first_name":"Avi","full_name":"Mendelson, Avi","last_name":"Mendelson"}],"issue":"2","article_processing_charge":"No","OA_type":"gold","OA_place":"publisher","oa":1,"publication_status":"published","title":"Early-stage neural network hardware performance analysis","extern":"1","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."}],"publisher":"MDPI","article_number":"717","status":"public","intvolume":"        13","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"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>","ieee":"A. Karbachevsky <i>et al.</i>, “Early-stage neural network hardware performance analysis,” <i>Sustainability</i>, vol. 13, no. 2. MDPI, 2021.","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>","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.","short":"A. Karbachevsky, C. Baskin, E. Zheltonozhskii, Y. Yermolin, F. Gabbay, A.M. Bronstein, A. Mendelson, Sustainability 13 (2021).","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>."},"date_published":"2021-01-13T00:00:00Z","type":"journal_article","doi":"10.3390/su13020717","main_file_link":[{"url":"https://doi.org/10.3390/su13020717","open_access":"1"}],"scopus_import":"1","quality_controlled":"1","date_created":"2024-10-08T12:58:47Z","date_updated":"2024-10-15T08:17:49Z","volume":13,"month":"01","_id":"18238","oa_version":"Published Version"},{"scopus_import":"1","_id":"18239","month":"10","oa_version":"Preprint","volume":15,"quality_controlled":"1","date_updated":"2024-10-15T08:22:47Z","date_created":"2024-10-08T13:02:34Z","doi":"10.1109/iccv48922.2021.00182","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2104.09829","open_access":"1"}],"type":"conference","conference":{"start_date":"2021-10-10","location":"Montreal, Canada","name":"ICCV: International Conference on Computer Vision","end_date":"2021-10-17"},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","intvolume":"        15","date_published":"2021-10-20T00:00:00Z","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>","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>","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.","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.","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>."},"arxiv":1,"status":"public","publisher":"Institute of Electrical and Electronics Engineers","extern":"1","title":"Detector-free weakly supervised grounding by separation","abstract":[{"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.","lang":"eng"}],"publication_status":"published","OA_type":"green","article_processing_charge":"No","OA_place":"repository","oa":1,"author":[{"last_name":"Arbelle","full_name":"Arbelle, Assaf","first_name":"Assaf"},{"full_name":"Doveh, Sivan","first_name":"Sivan","last_name":"Doveh"},{"first_name":"Amit","full_name":"Alfassy, Amit","last_name":"Alfassy"},{"last_name":"Shtok","first_name":"Joseph","full_name":"Shtok, Joseph"},{"full_name":"Lev, Guy","first_name":"Guy","last_name":"Lev"},{"last_name":"Schwartz","first_name":"Eli","full_name":"Schwartz, Eli"},{"full_name":"Kuehne, Hilde","first_name":"Hilde","last_name":"Kuehne"},{"first_name":"Hila Barak","full_name":"Levi, Hila Barak","last_name":"Levi"},{"full_name":"Sattigeri, Prasanna","first_name":"Prasanna","last_name":"Sattigeri"},{"full_name":"Panda, Rameswar","first_name":"Rameswar","last_name":"Panda"},{"full_name":"Chen, Chun-Fu","first_name":"Chun-Fu","last_name":"Chen"},{"id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","last_name":"Bronstein","orcid":"0000-0001-9699-8730","first_name":"Alexander","full_name":"Bronstein, Alexander"},{"last_name":"Saenko","full_name":"Saenko, Kate","first_name":"Kate"},{"last_name":"Ullman","full_name":"Ullman, Shimon","first_name":"Shimon"},{"last_name":"Giryes","first_name":"Raja","full_name":"Giryes, Raja"},{"last_name":"Feris","full_name":"Feris, Rogerio","first_name":"Rogerio"},{"first_name":"Leonid","full_name":"Karlinsky, Leonid","last_name":"Karlinsky"}],"day":"20","external_id":{"arxiv":["2104.09829"]},"language":[{"iso":"eng"}],"year":"2021","publication":"IEEE/CVF International Conference on Computer Vision","publication_identifier":{"eisbn":["9781665428125"]}},{"day":"30","author":[{"first_name":"Omer","full_name":"Dahary, Omer","last_name":"Dahary"},{"first_name":"Matan","full_name":"Jacoby, Matan","last_name":"Jacoby"},{"orcid":"0000-0001-9699-8730","last_name":"Bronstein","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","full_name":"Bronstein, Alexander","first_name":"Alexander"}],"oa":1,"OA_place":"repository","article_processing_charge":"No","OA_type":"green","publication_status":"published","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."}],"title":"Digital gimbal: End-to-end deep image stabilization with learnable exposure times","extern":"1","publication_identifier":{"eisbn":["9781665445092"]},"publication":"IEEE/CVF Conference on Computer Vision and Pattern Recognition","year":"2021","language":[{"iso":"eng"}],"external_id":{"arxiv":["2012.04515"]},"conference":{"name":"CVPR: Conference on Computer Vision and Pattern Recognition","location":"Nashville, TN, United States","end_date":"2021-06-25","start_date":"2021-06-20"},"type":"conference","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2012.04515"}],"doi":"10.1109/cvpr46437.2021.01176","quality_controlled":"1","volume":38,"date_created":"2024-10-08T13:02:53Z","date_updated":"2024-10-15T08:42:37Z","_id":"18240","month":"06","oa_version":"Preprint","scopus_import":"1","publisher":"Institute of Electrical and Electronics Engineers","status":"public","arxiv":1,"citation":{"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>","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>","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.","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.","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.","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>."},"date_published":"2021-06-30T00:00:00Z","intvolume":"        38","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"},{"year":"2021","external_id":{"arxiv":["2110.03218"]},"language":[{"iso":"eng"}],"publication_identifier":{"eisbn":["9781728163383"]},"publication":"31st International Workshop on Machine Learning for Signal Processing","publication_status":"published","abstract":[{"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.","lang":"eng"}],"extern":"1","title":"Joint optimization of system design and reconstruction in MIMO radar imaging","author":[{"last_name":"Weiss","full_name":"Weiss, Tomer","first_name":"Tomer"},{"last_name":"Peretz","first_name":"Nissim","full_name":"Peretz, Nissim"},{"last_name":"Vedula","first_name":"Sanketh","full_name":"Vedula, Sanketh"},{"full_name":"Feuer, Arie","first_name":"Arie","last_name":"Feuer"},{"first_name":"Alexander","full_name":"Bronstein, Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","last_name":"Bronstein","orcid":"0000-0001-9699-8730"}],"day":"01","OA_place":"repository","oa":1,"OA_type":"green","article_processing_charge":"No","status":"public","arxiv":1,"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>","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.","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>","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.","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.","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>.","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>."},"date_published":"2021-10-01T00:00:00Z","intvolume":"         4","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"Institute of Electrical and Electronics Engineers","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2110.03218"}],"doi":"10.1109/mlsp52302.2021.9596168","oa_version":"Preprint","_id":"18241","month":"10","quality_controlled":"1","date_updated":"2024-10-16T09:41:11Z","date_created":"2024-10-08T13:03:09Z","volume":4,"scopus_import":"1","conference":{"name":"MLSP: Machine Learning for Signal Processing","location":"Gold Coast, Australia","end_date":"2021-10-28","start_date":"2021-10-25"},"type":"conference"},{"conference":{"start_date":"2020-10-08","end_date":"2020-10-08","name":"MICCAI: Conference on Medical Image Computing and Computer-Assisted Intervention","location":"Lima, Peru/Virtual"},"type":"book_chapter","related_material":{"link":[{"url":"https://github.com/tomer196/Learned_dMRI","relation":"software"}]},"month":"09","_id":"18242","oa_version":"Preprint","date_created":"2024-10-08T13:03:26Z","date_updated":"2024-10-16T09:51:45Z","quality_controlled":"1","scopus_import":"1","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2009.03008"}],"doi":"10.1007/978-3-030-73018-5_2","publisher":"Springer Nature","citation":{"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.","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>","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>.","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.","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>.","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."},"date_published":"2021-09-30T00:00:00Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","arxiv":1,"alternative_title":["Mathematics and Visualization"],"oa":1,"OA_place":"repository","OA_type":"green","article_processing_charge":"No","place":"Cham","author":[{"last_name":"Weiss","full_name":"Weiss, Tomer","first_name":"Tomer"},{"first_name":"Sanketh","full_name":"Vedula, Sanketh","last_name":"Vedula"},{"last_name":"Senouf","first_name":"Ortal","full_name":"Senouf, Ortal"},{"first_name":"Oleg","full_name":"Michailovich, Oleg","last_name":"Michailovich"},{"full_name":"Bronstein, Alexander","first_name":"Alexander","orcid":"0000-0001-9699-8730","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","last_name":"Bronstein"}],"day":"30","abstract":[{"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.","lang":"eng"}],"extern":"1","title":"Towards learned optimal q-space sampling in diffusion MRI","publication_status":"published","page":"13-28","publication":"Computational Diffusion MRI","editor":[{"last_name":"Gyori","full_name":"Gyori, Noemi","first_name":"Noemi"},{"full_name":"Hutter, Jana","first_name":"Jana","last_name":"Hutter"},{"full_name":"Nath, Vishwesh","first_name":"Vishwesh","last_name":"Nath"},{"full_name":"Palombo, Marco","first_name":"Marco","last_name":"Palombo"},{"last_name":"Pizzolato","first_name":"Marco","full_name":"Pizzolato, Marco"},{"last_name":"Zhang","first_name":"Fan","full_name":"Zhang, Fan"}],"publication_identifier":{"isbn":["9783030730178"],"issn":["1612-3786"],"eisbn":["9783030730185"]},"external_id":{"arxiv":["2009.03008"]},"language":[{"iso":"eng"}],"year":"2021"},{"language":[{"iso":"eng"}],"year":"2021","publication":"International Joint Conference on Asia-Pacific Web and Web-Age Information Management","publication_identifier":{"issn":["0302-9743","1611-3349"],"isbn":["9783030858957","9783030858964"]},"extern":"1","title":"GRASP: Graph alignment through spectral signatures","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"}],"publication_status":"published","page":"44 - 52","article_processing_charge":"No","author":[{"last_name":"Hermanns","full_name":"Hermanns, Judith","first_name":"Judith"},{"last_name":"Tsitsulin","first_name":"Anton","full_name":"Tsitsulin, Anton"},{"first_name":"Marina","full_name":"Munkhoeva, Marina","last_name":"Munkhoeva"},{"first_name":"Alexander","full_name":"Bronstein, Alexander","orcid":"0000-0001-9699-8730","last_name":"Bronstein","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6"},{"full_name":"Mottin, Davide","first_name":"Davide","last_name":"Mottin"},{"first_name":"Panagiotis","full_name":"Karras, Panagiotis","last_name":"Karras"}],"day":"19","issue":"Part I","intvolume":"     12858","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","date_published":"2021-08-19T00:00:00Z","citation":{"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.","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>","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>","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>.","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>.","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."},"alternative_title":["LNCS"],"status":"public","publisher":"Springer Nature","scopus_import":"1","_id":"18243","month":"08","oa_version":"None","date_updated":"2025-01-29T09:57:31Z","quality_controlled":"1","date_created":"2024-10-08T13:03:44Z","volume":12858,"doi":"10.1007/978-3-030-85896-4_4","type":"conference","conference":{"end_date":"2021-08-25","location":"Guangzhou, China","name":"APWeb-WAIM: International Joint Conference on Asia-Pacific Web and Web-Age Information Management","start_date":"2021-08-23"}},{"scopus_import":"1","date_updated":"2024-12-12T10:10:29Z","quality_controlled":"1","date_created":"2024-10-08T13:04:02Z","_id":"18244","month":"01","oa_version":"Preprint","doi":"10.1109/3dv50981.2020.00099","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2003.10895","open_access":"1"}],"type":"conference","conference":{"start_date":"2020-11-25","end_date":"2020-11-28","location":"Fukuoka, Japan","name":"8th International Conference on 3D Vision"},"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","date_published":"2021-01-19T00:00:00Z","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>","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.","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.","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","arxiv":1,"status":"public","publisher":"IEEE","title":"Do we need depth in state-uf-the-art face authentication?","extern":"1","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","article_processing_charge":"No","oa":1,"day":"19","author":[{"last_name":"Livne","full_name":"Livne, Amir","first_name":"Amir"},{"last_name":"Aviv","first_name":"Ziv","full_name":"Aviv, Ziv"},{"full_name":"Grofit, Shahaf","first_name":"Shahaf","last_name":"Grofit"},{"id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","last_name":"Bronstein","orcid":"0000-0001-9699-8730","first_name":"Alexander","full_name":"Bronstein, Alexander"},{"first_name":"Ron","full_name":"Kimmel, Ron","last_name":"Kimmel"}],"language":[{"iso":"eng"}],"external_id":{"arxiv":["2003.10895"]},"year":"2021","publication":"2020 International Conference on 3D Vision (3DV)","publication_identifier":{"isbn":["9781728181295"],"eissn":["2475-7888"]}},{"quality_controlled":"1","date_created":"2020-04-26T22:00:45Z","volume":33,"date_updated":"2025-05-14T10:49:57Z","month":"01","_id":"7685","oa_version":"Preprint","project":[{"grant_number":"694227","call_identifier":"H2020","name":"Analysis of quantum many-body systems","_id":"25C6DC12-B435-11E9-9278-68D0E5697425"}],"scopus_import":"1","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2001.00497"}],"doi":"10.1142/S0129055X20600065","type":"journal_article","date_published":"2021-01-01T00:00:00Z","citation":{"mla":"Boccato, Chiara. “The Excitation Spectrum of the Bose Gas in the Gross-Pitaevskii Regime.” <i>Reviews in Mathematical Physics</i>, vol. 33, no. 1, 2060006, World Scientific Publishing, 2021, doi:<a href=\"https://doi.org/10.1142/S0129055X20600065\">10.1142/S0129055X20600065</a>.","short":"C. Boccato, Reviews in Mathematical Physics 33 (2021).","chicago":"Boccato, Chiara. “The Excitation Spectrum of the Bose Gas in the Gross-Pitaevskii Regime.” <i>Reviews in Mathematical Physics</i>. World Scientific Publishing, 2021. <a href=\"https://doi.org/10.1142/S0129055X20600065\">https://doi.org/10.1142/S0129055X20600065</a>.","ista":"Boccato C. 2021. The excitation spectrum of the Bose gas in the Gross-Pitaevskii regime. Reviews in Mathematical Physics. 33(1), 2060006.","apa":"Boccato, C. (2021). The excitation spectrum of the Bose gas in the Gross-Pitaevskii regime. <i>Reviews in Mathematical Physics</i>. World Scientific Publishing. <a href=\"https://doi.org/10.1142/S0129055X20600065\">https://doi.org/10.1142/S0129055X20600065</a>","ieee":"C. Boccato, “The excitation spectrum of the Bose gas in the Gross-Pitaevskii regime,” <i>Reviews in Mathematical Physics</i>, vol. 33, no. 1. World Scientific Publishing, 2021.","ama":"Boccato C. The excitation spectrum of the Bose gas in the Gross-Pitaevskii regime. <i>Reviews in Mathematical Physics</i>. 2021;33(1). doi:<a href=\"https://doi.org/10.1142/S0129055X20600065\">10.1142/S0129055X20600065</a>"},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","intvolume":"        33","ec_funded":1,"status":"public","arxiv":1,"article_number":"2060006","department":[{"_id":"RoSe"}],"publisher":"World Scientific Publishing","abstract":[{"lang":"eng","text":"We consider a gas of interacting bosons trapped in a box of side length one in the Gross–Pitaevskii limit. We review the proof of the validity of Bogoliubov’s prediction for the ground state energy and the low-energy excitation spectrum. This note is based on joint work with C. Brennecke, S. Cenatiempo and B. Schlein."}],"title":"The excitation spectrum of the Bose gas in the Gross-Pitaevskii regime","publication_status":"published","oa":1,"article_processing_charge":"No","issue":"1","day":"01","author":[{"full_name":"Boccato, Chiara","first_name":"Chiara","last_name":"Boccato","id":"342E7E22-F248-11E8-B48F-1D18A9856A87"}],"language":[{"iso":"eng"}],"external_id":{"isi":["000613313200007"],"arxiv":["2001.00497"]},"article_type":"original","year":"2021","publication":"Reviews in Mathematical Physics","publication_identifier":{"issn":["0129-055X"]},"isi":1},{"article_processing_charge":"No","oa":1,"day":"01","author":[{"orcid":"0000-0002-1071-6091","id":"3DE6C32A-F248-11E8-B48F-1D18A9856A87","last_name":"Benedikter","first_name":"Niels P","full_name":"Benedikter, Niels P"}],"issue":"1","title":"Bosonic collective excitations in Fermi gases","abstract":[{"lang":"eng","text":"Hartree–Fock theory has been justified as a mean-field approximation for fermionic systems. However, it suffers from some defects in predicting physical properties, making necessary a theory of quantum correlations. Recently, bosonization of many-body correlations has been rigorously justified as an upper bound on the correlation energy at high density with weak interactions. We review the bosonic approximation, deriving an effective Hamiltonian. We then show that for systems with Coulomb interaction this effective theory predicts collective excitations (plasmons) in accordance with the random phase approximation of Bohm and Pines, and with experimental observation."}],"publication_status":"published","publication":"Reviews in Mathematical Physics","isi":1,"publication_identifier":{"issn":["0129-055X"],"eissn":["1793-6659"]},"language":[{"iso":"eng"}],"external_id":{"isi":["000613313200010"],"arxiv":["1910.08190"]},"article_type":"original","year":"2021","type":"journal_article","project":[{"call_identifier":"H2020","grant_number":"694227","_id":"25C6DC12-B435-11E9-9278-68D0E5697425","name":"Analysis of quantum many-body systems"}],"scopus_import":"1","volume":33,"quality_controlled":"1","date_created":"2020-05-28T16:47:55Z","date_updated":"2025-05-14T10:49:46Z","month":"01","_id":"7900","oa_version":"Preprint","doi":"10.1142/s0129055x20600090","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1910.08190"}],"department":[{"_id":"RoSe"}],"publisher":"World Scientific Publishing","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","intvolume":"        33","date_published":"2021-01-01T00:00:00Z","citation":{"ista":"Benedikter NP. 2021. Bosonic collective excitations in Fermi gases. Reviews in Mathematical Physics. 33(1), 2060009.","chicago":"Benedikter, Niels P. “Bosonic Collective Excitations in Fermi Gases.” <i>Reviews in Mathematical Physics</i>. World Scientific Publishing, 2021. <a href=\"https://doi.org/10.1142/s0129055x20600090\">https://doi.org/10.1142/s0129055x20600090</a>.","short":"N.P. Benedikter, Reviews in Mathematical Physics 33 (2021).","mla":"Benedikter, Niels P. “Bosonic Collective Excitations in Fermi Gases.” <i>Reviews in Mathematical Physics</i>, vol. 33, no. 1, 2060009, World Scientific Publishing, 2021, doi:<a href=\"https://doi.org/10.1142/s0129055x20600090\">10.1142/s0129055x20600090</a>.","ama":"Benedikter NP. Bosonic collective excitations in Fermi gases. <i>Reviews in Mathematical Physics</i>. 2021;33(1). doi:<a href=\"https://doi.org/10.1142/s0129055x20600090\">10.1142/s0129055x20600090</a>","ieee":"N. P. Benedikter, “Bosonic collective excitations in Fermi gases,” <i>Reviews in Mathematical Physics</i>, vol. 33, no. 1. World Scientific Publishing, 2021.","apa":"Benedikter, N. P. (2021). Bosonic collective excitations in Fermi gases. <i>Reviews in Mathematical Physics</i>. World Scientific Publishing. <a href=\"https://doi.org/10.1142/s0129055x20600090\">https://doi.org/10.1142/s0129055x20600090</a>"},"article_number":"2060009","arxiv":1,"ec_funded":1,"status":"public"},{"oa":1,"article_processing_charge":"Yes (via OA deal)","author":[{"first_name":"Niels P","full_name":"Benedikter, Niels P","orcid":"0000-0002-1071-6091","last_name":"Benedikter","id":"3DE6C32A-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Nam","first_name":"Phan Thành","full_name":"Nam, Phan Thành"},{"full_name":"Porta, Marcello","first_name":"Marcello","last_name":"Porta"},{"full_name":"Schlein, Benjamin","first_name":"Benjamin","last_name":"Schlein"},{"full_name":"Seiringer, Robert","first_name":"Robert","id":"4AFD0470-F248-11E8-B48F-1D18A9856A87","last_name":"Seiringer","orcid":"0000-0002-6781-0521"}],"ddc":["510"],"day":"03","abstract":[{"text":"We derive rigorously the leading order of the correlation energy of a Fermi gas in a scaling regime of high density and weak interaction. The result verifies the prediction of the random-phase approximation. Our proof refines the method of collective bosonization in three dimensions. We approximately diagonalize an effective Hamiltonian describing approximately bosonic collective excitations around the Hartree–Fock state, while showing that gapless and non-collective excitations have only a negligible effect on the ground state energy.","lang":"eng"}],"title":"Correlation energy of a weakly interacting Fermi gas","publication_status":"published","page":"885-979","acknowledgement":"We thank Christian Hainzl for helpful discussions and a referee for very careful reading of the paper and many helpful suggestions. NB and RS were supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 694227). Part of the research of NB was conducted on the RZD18 Nice–Milan–Vienna–Moscow. NB thanks Elliott H. Lieb and Peter Otte for explanations about the Luttinger model. PTN has received funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy (EXC-2111-390814868). MP acknowledges financial support from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (ERC StG MaMBoQ, grant agreement No. 802901). BS gratefully acknowledges financial support from the NCCR SwissMAP, from the Swiss National Science Foundation through the Grant “Dynamical and energetic properties of Bose-Einstein condensates” and from the European Research Council through the ERC-AdG CLaQS (grant agreement No. 834782). All authors acknowledge support for workshop participation from Mathematisches Forschungsinstitut Oberwolfach (Leibniz Association). NB, PTN, BS, and RS acknowledge support for workshop participation from Fondation des Treilles.","publication":"Inventiones Mathematicae","publication_identifier":{"eissn":["1432-1297"],"issn":["0020-9910"]},"file":[{"file_id":"11386","relation":"main_file","file_name":"2021_InventMath_Benedikter.pdf","checksum":"f38c79dfd828cdc7f49a34b37b83d376","date_updated":"2022-05-16T12:23:40Z","file_size":1089319,"date_created":"2022-05-16T12:23:40Z","success":1,"creator":"dernst","content_type":"application/pdf","access_level":"open_access"}],"isi":1,"external_id":{"arxiv":["2005.08933"],"isi":["000646573600001"]},"language":[{"iso":"eng"}],"year":"2021","article_type":"original","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"has_accepted_license":"1","type":"journal_article","month":"05","_id":"7901","oa_version":"Published Version","quality_controlled":"1","date_created":"2020-05-28T16:48:20Z","volume":225,"date_updated":"2025-04-14T07:27:00Z","scopus_import":"1","project":[{"_id":"B67AFEDC-15C9-11EA-A837-991A96BB2854","name":"IST Austria Open Access Fund"},{"call_identifier":"H2020","grant_number":"694227","_id":"25C6DC12-B435-11E9-9278-68D0E5697425","name":"Analysis of quantum many-body systems"}],"doi":"10.1007/s00222-021-01041-5","file_date_updated":"2022-05-16T12:23:40Z","license":"https://creativecommons.org/licenses/by/4.0/","department":[{"_id":"RoSe"}],"publisher":"Springer","citation":{"ama":"Benedikter NP, Nam PT, Porta M, Schlein B, Seiringer R. Correlation energy of a weakly interacting Fermi gas. <i>Inventiones Mathematicae</i>. 2021;225:885-979. doi:<a href=\"https://doi.org/10.1007/s00222-021-01041-5\">10.1007/s00222-021-01041-5</a>","apa":"Benedikter, N. P., Nam, P. T., Porta, M., Schlein, B., &#38; Seiringer, R. (2021). Correlation energy of a weakly interacting Fermi gas. <i>Inventiones Mathematicae</i>. Springer. <a href=\"https://doi.org/10.1007/s00222-021-01041-5\">https://doi.org/10.1007/s00222-021-01041-5</a>","ieee":"N. P. Benedikter, P. T. Nam, M. Porta, B. Schlein, and R. Seiringer, “Correlation energy of a weakly interacting Fermi gas,” <i>Inventiones Mathematicae</i>, vol. 225. Springer, pp. 885–979, 2021.","ista":"Benedikter NP, Nam PT, Porta M, Schlein B, Seiringer R. 2021. Correlation energy of a weakly interacting Fermi gas. Inventiones Mathematicae. 225, 885–979.","mla":"Benedikter, Niels P., et al. “Correlation Energy of a Weakly Interacting Fermi Gas.” <i>Inventiones Mathematicae</i>, vol. 225, Springer, 2021, pp. 885–979, doi:<a href=\"https://doi.org/10.1007/s00222-021-01041-5\">10.1007/s00222-021-01041-5</a>.","short":"N.P. Benedikter, P.T. Nam, M. Porta, B. Schlein, R. Seiringer, Inventiones Mathematicae 225 (2021) 885–979.","chicago":"Benedikter, Niels P, Phan Thành Nam, Marcello Porta, Benjamin Schlein, and Robert Seiringer. “Correlation Energy of a Weakly Interacting Fermi Gas.” <i>Inventiones Mathematicae</i>. Springer, 2021. <a href=\"https://doi.org/10.1007/s00222-021-01041-5\">https://doi.org/10.1007/s00222-021-01041-5</a>."},"date_published":"2021-05-03T00:00:00Z","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","intvolume":"       225","status":"public","ec_funded":1,"arxiv":1},{"tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"has_accepted_license":"1","type":"journal_article","doi":"10.1007/s00454-020-00206-y","_id":"7905","month":"06","oa_version":"Published Version","date_created":"2020-05-30T10:26:04Z","date_updated":"2025-04-15T06:53:15Z","volume":65,"quality_controlled":"1","scopus_import":"1","project":[{"name":"IST Austria Open Access Fund","_id":"B67AFEDC-15C9-11EA-A837-991A96BB2854"}],"publisher":"Springer Nature","file_date_updated":"2020-11-25T09:06:41Z","department":[{"_id":"HeEd"}],"corr_author":"1","status":"public","arxiv":1,"citation":{"ieee":"A. Brown and B. Wang, “Sheaf-theoretic stratification learning from geometric and topological perspectives,” <i>Discrete and Computational Geometry</i>, vol. 65. Springer Nature, pp. 1166–1198, 2021.","apa":"Brown, A., &#38; Wang, B. (2021). Sheaf-theoretic stratification learning from geometric and topological perspectives. <i>Discrete and Computational Geometry</i>. Springer Nature. <a href=\"https://doi.org/10.1007/s00454-020-00206-y\">https://doi.org/10.1007/s00454-020-00206-y</a>","ama":"Brown A, Wang B. Sheaf-theoretic stratification learning from geometric and topological perspectives. <i>Discrete and Computational Geometry</i>. 2021;65:1166-1198. doi:<a href=\"https://doi.org/10.1007/s00454-020-00206-y\">10.1007/s00454-020-00206-y</a>","short":"A. Brown, B. Wang, Discrete and Computational Geometry 65 (2021) 1166–1198.","chicago":"Brown, Adam, and Bei Wang. “Sheaf-Theoretic Stratification Learning from Geometric and Topological Perspectives.” <i>Discrete and Computational Geometry</i>. Springer Nature, 2021. <a href=\"https://doi.org/10.1007/s00454-020-00206-y\">https://doi.org/10.1007/s00454-020-00206-y</a>.","mla":"Brown, Adam, and Bei Wang. “Sheaf-Theoretic Stratification Learning from Geometric and Topological Perspectives.” <i>Discrete and Computational Geometry</i>, vol. 65, Springer Nature, 2021, pp. 1166–98, doi:<a href=\"https://doi.org/10.1007/s00454-020-00206-y\">10.1007/s00454-020-00206-y</a>.","ista":"Brown A, Wang B. 2021. Sheaf-theoretic stratification learning from geometric and topological perspectives. Discrete and Computational Geometry. 65, 1166–1198."},"date_published":"2021-06-01T00:00:00Z","intvolume":"        65","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","author":[{"last_name":"Brown","id":"70B7FDF6-608D-11E9-9333-8535E6697425","first_name":"Adam","full_name":"Brown, Adam"},{"full_name":"Wang, Bei","first_name":"Bei","last_name":"Wang"}],"ddc":["510"],"day":"01","oa":1,"article_processing_charge":"Yes (via OA deal)","publication_status":"published","page":"1166-1198","acknowledgement":"Open access funding provided by Institute of Science and Technology (IST Austria). This work was partially supported by NSF IIS-1513616 and NSF ABI-1661375. The authors would like to thank the anonymous referees for their insightful comments.","abstract":[{"lang":"eng","text":"We investigate a sheaf-theoretic interpretation of stratification learning from geometric and topological perspectives. Our main result is the construction of stratification learning algorithms framed in terms of a sheaf on a partially ordered set with the Alexandroff topology. We prove that the resulting decomposition is the unique minimal stratification for which the strata are homogeneous and the given sheaf is constructible. In particular, when we choose to work with the local homology sheaf, our algorithm gives an alternative to the local homology transfer algorithm given in Bendich et al. (Proceedings of the 23rd Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1355–1370, ACM, New York, 2012), and the cohomology stratification algorithm given in Nanda (Found. Comput. Math. 20(2), 195–222, 2020). Additionally, we give examples of stratifications based on the geometric techniques of Breiding et al. (Rev. Mat. Complut. 31(3), 545–593, 2018), illustrating how the sheaf-theoretic approach can be used to study stratifications from both topological and geometric perspectives. This approach also points toward future applications of sheaf theory in the study of topological data analysis by illustrating the utility of the language of sheaf theory in generalizing existing algorithms."}],"title":"Sheaf-theoretic stratification learning from geometric and topological perspectives","publication_identifier":{"issn":["0179-5376"],"eissn":["1432-0444"]},"file":[{"relation":"main_file","file_id":"8803","file_name":"2020_DiscreteCompGeometry_Brown.pdf","checksum":"487a84ea5841b75f04f66d7ebd71b67e","date_created":"2020-11-25T09:06:41Z","file_size":1013730,"date_updated":"2020-11-25T09:06:41Z","creator":"dernst","success":1,"access_level":"open_access","content_type":"application/pdf"}],"isi":1,"publication":"Discrete and Computational Geometry","year":"2021","article_type":"original","external_id":{"isi":["000536324700001"],"arxiv":["1712.07734"]},"language":[{"iso":"eng"}]},{"type":"journal_article","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"has_accepted_license":"1","doi":"10.1007/s11590-020-01603-1","project":[{"name":"Discrete Optimization in Computer Vision: Theory and Practice","_id":"25FBA906-B435-11E9-9278-68D0E5697425","grant_number":"616160","call_identifier":"FP7"},{"_id":"B67AFEDC-15C9-11EA-A837-991A96BB2854","name":"IST Austria Open Access Fund"}],"scopus_import":"1","volume":15,"quality_controlled":"1","date_created":"2020-06-04T11:28:33Z","date_updated":"2024-11-04T13:52:35Z","_id":"7925","month":"09","oa_version":"Published Version","publisher":"Springer Nature","department":[{"_id":"VlKo"}],"file_date_updated":"2024-03-07T14:58:51Z","ec_funded":1,"corr_author":"1","status":"public","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","intvolume":"        15","date_published":"2021-09-01T00:00:00Z","citation":{"ista":"Shehu Y, Gibali A. 2021. New inertial relaxed method for solving split feasibilities. Optimization Letters. 15, 2109–2126.","mla":"Shehu, Yekini, and Aviv Gibali. “New Inertial Relaxed Method for Solving Split Feasibilities.” <i>Optimization Letters</i>, vol. 15, Springer Nature, 2021, pp. 2109–26, doi:<a href=\"https://doi.org/10.1007/s11590-020-01603-1\">10.1007/s11590-020-01603-1</a>.","chicago":"Shehu, Yekini, and Aviv Gibali. “New Inertial Relaxed Method for Solving Split Feasibilities.” <i>Optimization Letters</i>. Springer Nature, 2021. <a href=\"https://doi.org/10.1007/s11590-020-01603-1\">https://doi.org/10.1007/s11590-020-01603-1</a>.","short":"Y. Shehu, A. Gibali, Optimization Letters 15 (2021) 2109–2126.","ama":"Shehu Y, Gibali A. New inertial relaxed method for solving split feasibilities. <i>Optimization Letters</i>. 2021;15:2109-2126. doi:<a href=\"https://doi.org/10.1007/s11590-020-01603-1\">10.1007/s11590-020-01603-1</a>","apa":"Shehu, Y., &#38; Gibali, A. (2021). New inertial relaxed method for solving split feasibilities. <i>Optimization Letters</i>. Springer Nature. <a href=\"https://doi.org/10.1007/s11590-020-01603-1\">https://doi.org/10.1007/s11590-020-01603-1</a>","ieee":"Y. Shehu and A. Gibali, “New inertial relaxed method for solving split feasibilities,” <i>Optimization Letters</i>, vol. 15. Springer Nature, pp. 2109–2126, 2021."},"day":"01","ddc":["510"],"author":[{"first_name":"Yekini","full_name":"Shehu, Yekini","id":"3FC7CB58-F248-11E8-B48F-1D18A9856A87","last_name":"Shehu","orcid":"0000-0001-9224-7139"},{"full_name":"Gibali, Aviv","first_name":"Aviv","last_name":"Gibali"}],"article_processing_charge":"Yes (via OA deal)","oa":1,"acknowledgement":"Open access funding provided by Institute of Science and Technology (IST Austria). The authors are grateful to the referees for their insightful comments which have improved the earlier version of the manuscript greatly. The first author has received funding from the European Research Council (ERC) under the European Union’s Seventh Framework Program (FP7-2007-2013) (Grant agreement No. 616160).","page":"2109-2126","publication_status":"published","title":"New inertial relaxed method for solving split feasibilities","abstract":[{"text":"In this paper, we introduce a relaxed CQ method with alternated inertial step for solving split feasibility problems. We give convergence of the sequence generated by our method under some suitable assumptions. Some numerical implementations from sparse signal and image deblurring are reported to show the efficiency of our method.","lang":"eng"}],"isi":1,"file":[{"checksum":"63c5f31cd04626152a19f97a2476281b","relation":"main_file","file_id":"15089","file_name":"2021_OptimizationLetters_Shehu.pdf","access_level":"open_access","content_type":"application/pdf","date_updated":"2024-03-07T14:58:51Z","date_created":"2024-03-07T14:58:51Z","file_size":2148882,"creator":"kschuh","success":1}],"publication_identifier":{"issn":["1862-4472"],"eissn":["1862-4480"]},"publication":"Optimization Letters","article_type":"original","year":"2021","language":[{"iso":"eng"}],"external_id":{"isi":["000537342300001"]}},{"department":[{"_id":"DaAl"}],"publisher":"Springer Nature","citation":{"ama":"Censor-Hillel K, Dory M, Korhonen J, Leitersdorf D. Fast approximate shortest paths in the congested clique. <i>Distributed Computing</i>. 2021;34:463-487. doi:<a href=\"https://doi.org/10.1007/s00446-020-00380-5\">10.1007/s00446-020-00380-5</a>","apa":"Censor-Hillel, K., Dory, M., Korhonen, J., &#38; Leitersdorf, D. (2021). Fast approximate shortest paths in the congested clique. <i>Distributed Computing</i>. Springer Nature. <a href=\"https://doi.org/10.1007/s00446-020-00380-5\">https://doi.org/10.1007/s00446-020-00380-5</a>","ieee":"K. Censor-Hillel, M. Dory, J. Korhonen, and D. Leitersdorf, “Fast approximate shortest paths in the congested clique,” <i>Distributed Computing</i>, vol. 34. Springer Nature, pp. 463–487, 2021.","ista":"Censor-Hillel K, Dory M, Korhonen J, Leitersdorf D. 2021. Fast approximate shortest paths in the congested clique. Distributed Computing. 34, 463–487.","mla":"Censor-Hillel, Keren, et al. “Fast Approximate Shortest Paths in the Congested Clique.” <i>Distributed Computing</i>, vol. 34, Springer Nature, 2021, pp. 463–87, doi:<a href=\"https://doi.org/10.1007/s00446-020-00380-5\">10.1007/s00446-020-00380-5</a>.","short":"K. Censor-Hillel, M. Dory, J. Korhonen, D. Leitersdorf, Distributed Computing 34 (2021) 463–487.","chicago":"Censor-Hillel, Keren, Michal Dory, Janne Korhonen, and Dean Leitersdorf. “Fast Approximate Shortest Paths in the Congested Clique.” <i>Distributed Computing</i>. Springer Nature, 2021. <a href=\"https://doi.org/10.1007/s00446-020-00380-5\">https://doi.org/10.1007/s00446-020-00380-5</a>."},"date_published":"2021-12-01T00:00:00Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","intvolume":"        34","corr_author":"1","status":"public","arxiv":1,"type":"journal_article","related_material":{"record":[{"status":"public","relation":"earlier_version","id":"6933"}]},"volume":34,"date_created":"2020-06-07T22:00:54Z","quality_controlled":"1","date_updated":"2026-06-18T19:28:41Z","_id":"7939","oa_version":"Published Version","month":"12","project":[{"name":"IST Austria Open Access Fund","_id":"B67AFEDC-15C9-11EA-A837-991A96BB2854"}],"scopus_import":"1","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1007/s00446-020-00380-5"}],"doi":"10.1007/s00446-020-00380-5","publication":"Distributed Computing","publication_identifier":{"eissn":["1432-0452"],"issn":["0178-2770"]},"isi":1,"language":[{"iso":"eng"}],"external_id":{"isi":["000556444600001"],"arxiv":["1903.05956"]},"article_type":"original","year":"2021","oa":1,"article_processing_charge":"Yes (via OA deal)","day":"01","ddc":["000"],"author":[{"last_name":"Censor-Hillel","full_name":"Censor-Hillel, Keren","first_name":"Keren"},{"last_name":"Dory","full_name":"Dory, Michal","first_name":"Michal"},{"id":"C5402D42-15BC-11E9-A202-CA2BE6697425","last_name":"Korhonen","first_name":"Janne","full_name":"Korhonen, Janne"},{"last_name":"Leitersdorf","first_name":"Dean","full_name":"Leitersdorf, Dean"}],"abstract":[{"text":"We design fast deterministic algorithms for distance computation in the Congested Clique model. Our key contributions include:\r\n    A (2+ϵ)-approximation for all-pairs shortest paths in O(log2n/ϵ) rounds on unweighted undirected graphs. With a small additional additive factor, this also applies for weighted graphs. This is the first sub-polynomial constant-factor approximation for APSP in this model.\r\n    A (1+ϵ)-approximation for multi-source shortest paths from O(n−−√) sources in O(log2n/ϵ) rounds on weighted undirected graphs. This is the first sub-polynomial algorithm obtaining this approximation for a set of sources of polynomial size.\r\n\r\nOur main techniques are new distance tools that are obtained via improved algorithms for sparse matrix multiplication, which we leverage to construct efficient hopsets and shortest paths. Furthermore, our techniques extend to additional distance problems for which we improve upon the state-of-the-art, including diameter approximation, and an exact single-source shortest paths algorithm for weighted undirected graphs in O~(n1/6) rounds. ","lang":"eng"}],"title":"Fast approximate shortest paths in the congested clique","page":"463-487","publication_status":"published","acknowledgement":"Open access funding provided by Institute of Science and Technology (IST Austria). We thank Mohsen Ghaffari, Michael Elkin and Merav Parter for fruitful discussions. This project has received funding from the European Union’s Horizon 2020 Research And Innovation Program under Grant Agreement No. 755839."},{"corr_author":"1","status":"public","date_published":"2021-01-01T00:00:00Z","citation":{"ista":"Truckenbrodt SM, Rizzoli SO. 2021.Simple multi-color super-resolution by X10 microscopy. In: Methods in Cell Biology. vol. 161, 33–56.","mla":"Truckenbrodt, Sven M., and Silvio O. Rizzoli. “Simple Multi-Color Super-Resolution by X10 Microscopy.” <i>Methods in Cell Biology</i>, vol. 161, Elsevier, 2021, pp. 33–56, doi:<a href=\"https://doi.org/10.1016/bs.mcb.2020.04.016\">10.1016/bs.mcb.2020.04.016</a>.","short":"S.M. Truckenbrodt, S.O. Rizzoli, in:, Methods in Cell Biology, Elsevier, 2021, pp. 33–56.","chicago":"Truckenbrodt, Sven M, and Silvio O. Rizzoli. “Simple Multi-Color Super-Resolution by X10 Microscopy.” In <i>Methods in Cell Biology</i>, 161:33–56. Elsevier, 2021. <a href=\"https://doi.org/10.1016/bs.mcb.2020.04.016\">https://doi.org/10.1016/bs.mcb.2020.04.016</a>.","ama":"Truckenbrodt SM, Rizzoli SO. Simple multi-color super-resolution by X10 microscopy. In: <i>Methods in Cell Biology</i>. Vol 161. Elsevier; 2021:33-56. doi:<a href=\"https://doi.org/10.1016/bs.mcb.2020.04.016\">10.1016/bs.mcb.2020.04.016</a>","apa":"Truckenbrodt, S. M., &#38; Rizzoli, S. O. (2021). Simple multi-color super-resolution by X10 microscopy. In <i>Methods in Cell Biology</i> (Vol. 161, pp. 33–56). Elsevier. <a href=\"https://doi.org/10.1016/bs.mcb.2020.04.016\">https://doi.org/10.1016/bs.mcb.2020.04.016</a>","ieee":"S. M. Truckenbrodt and S. O. Rizzoli, “Simple multi-color super-resolution by X10 microscopy,” in <i>Methods in Cell Biology</i>, vol. 161, Elsevier, 2021, pp. 33–56."},"intvolume":"       161","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"Elsevier","department":[{"_id":"JoDa"}],"doi":"10.1016/bs.mcb.2020.04.016","month":"01","_id":"7941","oa_version":"None","date_created":"2020-06-07T22:00:55Z","volume":161,"date_updated":"2024-10-09T20:59:36Z","quality_controlled":"1","scopus_import":"1","type":"book_chapter","year":"2021","pmid":1,"external_id":{"pmid":["33478696"]},"language":[{"iso":"eng"}],"publication_identifier":{"isbn":["978012820807-6"],"issn":["0091-679X"]},"publication":"Methods in Cell Biology","publication_status":"published","page":"33-56","abstract":[{"text":"Expansion microscopy is a recently developed super-resolution imaging technique, which provides an alternative to optics-based methods such as deterministic approaches (e.g. STED) or stochastic approaches (e.g. PALM/STORM). The idea behind expansion microscopy is to embed the biological sample in a swellable gel, and then to expand it isotropically, thereby increasing the distance between the fluorophores. This approach breaks the diffraction barrier by simply separating the emission point-spread-functions of the fluorophores. The resolution attainable in expansion microscopy is thus directly dependent on the separation that can be achieved, i.e. on the expansion factor. The original implementation of the technique achieved an expansion factor of fourfold, for a resolution of 70–80 nm. The subsequently developed X10 method achieves an expansion factor of 10-fold, for a resolution of 25–30 nm. This technique can be implemented with minimal technical requirements on any standard fluorescence microscope, and is more easily applied for multi-color imaging than either deterministic or stochastic super-resolution approaches. This renders X10 expansion microscopy a highly promising tool for new biological discoveries, as discussed here, and as demonstrated by several recent applications.","lang":"eng"}],"title":"Simple multi-color super-resolution by X10 microscopy","author":[{"last_name":"Truckenbrodt","id":"45812BD4-F248-11E8-B48F-1D18A9856A87","full_name":"Truckenbrodt, Sven M","first_name":"Sven M"},{"last_name":"Rizzoli","first_name":"Silvio O.","full_name":"Rizzoli, Silvio O."}],"day":"01","article_processing_charge":"No"},{"month":"02","_id":"8196","oa_version":"Published Version","date_created":"2020-08-03T14:29:57Z","volume":22,"date_updated":"2024-11-04T13:52:38Z","quality_controlled":"1","scopus_import":"1","project":[{"_id":"B67AFEDC-15C9-11EA-A837-991A96BB2854","name":"IST Austria Open Access Fund"},{"_id":"25FBA906-B435-11E9-9278-68D0E5697425","name":"Discrete Optimization in Computer Vision: Theory and Practice","call_identifier":"FP7","grant_number":"616160"}],"doi":"10.1007/s11081-020-09544-5","has_accepted_license":"1","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"type":"journal_article","date_published":"2021-02-25T00:00:00Z","citation":{"ama":"Shehu Y, Dong Q-L, Liu L-L, Yao J-C. New strong convergence method for the sum of two maximal monotone operators. <i>Optimization and Engineering</i>. 2021;22:2627-2653. doi:<a href=\"https://doi.org/10.1007/s11081-020-09544-5\">10.1007/s11081-020-09544-5</a>","apa":"Shehu, Y., Dong, Q.-L., Liu, L.-L., &#38; Yao, J.-C. (2021). New strong convergence method for the sum of two maximal monotone operators. <i>Optimization and Engineering</i>. Springer Nature. <a href=\"https://doi.org/10.1007/s11081-020-09544-5\">https://doi.org/10.1007/s11081-020-09544-5</a>","ieee":"Y. Shehu, Q.-L. Dong, L.-L. Liu, and J.-C. Yao, “New strong convergence method for the sum of two maximal monotone operators,” <i>Optimization and Engineering</i>, vol. 22. Springer Nature, pp. 2627–2653, 2021.","ista":"Shehu Y, Dong Q-L, Liu L-L, Yao J-C. 2021. New strong convergence method for the sum of two maximal monotone operators. Optimization and Engineering. 22, 2627–2653.","mla":"Shehu, Yekini, et al. “New Strong Convergence Method for the Sum of Two Maximal Monotone Operators.” <i>Optimization and Engineering</i>, vol. 22, Springer Nature, 2021, pp. 2627–53, doi:<a href=\"https://doi.org/10.1007/s11081-020-09544-5\">10.1007/s11081-020-09544-5</a>.","chicago":"Shehu, Yekini, Qiao-Li Dong, Lu-Lu Liu, and Jen-Chih Yao. “New Strong Convergence Method for the Sum of Two Maximal Monotone Operators.” <i>Optimization and Engineering</i>. Springer Nature, 2021. <a href=\"https://doi.org/10.1007/s11081-020-09544-5\">https://doi.org/10.1007/s11081-020-09544-5</a>.","short":"Y. Shehu, Q.-L. Dong, L.-L. Liu, J.-C. Yao, Optimization and Engineering 22 (2021) 2627–2653."},"intvolume":"        22","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","status":"public","corr_author":"1","ec_funded":1,"file_date_updated":"2020-08-03T15:24:39Z","department":[{"_id":"VlKo"}],"publisher":"Springer Nature","abstract":[{"text":"This paper aims to obtain a strong convergence result for a Douglas–Rachford splitting method with inertial extrapolation step for finding a zero of the sum of two set-valued maximal monotone operators without any further assumption of uniform monotonicity on any of the involved maximal monotone operators. Furthermore, our proposed method is easy to implement and the inertial factor in our proposed method is a natural choice. Our method of proof is of independent interest. Finally, some numerical implementations are given to confirm the theoretical analysis.","lang":"eng"}],"title":"New strong convergence method for the sum of two maximal monotone operators","publication_status":"published","page":"2627-2653","acknowledgement":"Open access funding provided by Institute of Science and Technology (IST Austria). The project of Yekini Shehu has received funding from the European Research Council (ERC) under the European Union’s Seventh Framework Program (FP7—2007–2013) (Grant Agreement No. 616160). The authors are grateful to the anonymous referees and the handling Editor for their comments and suggestions which have improved the earlier version of the manuscript greatly.","oa":1,"article_processing_charge":"Yes (via OA deal)","author":[{"last_name":"Shehu","id":"3FC7CB58-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-9224-7139","first_name":"Yekini","full_name":"Shehu, Yekini"},{"last_name":"Dong","full_name":"Dong, Qiao-Li","first_name":"Qiao-Li"},{"full_name":"Liu, Lu-Lu","first_name":"Lu-Lu","last_name":"Liu"},{"last_name":"Yao","full_name":"Yao, Jen-Chih","first_name":"Jen-Chih"}],"day":"25","ddc":["510"],"external_id":{"isi":["000559345400001"]},"language":[{"iso":"eng"}],"year":"2021","article_type":"original","publication":"Optimization and Engineering","publication_identifier":{"issn":["1389-4420"],"eissn":["1573-2924"]},"file":[{"file_id":"8197","relation":"main_file","file_name":"2020_OptimizationEngineering_Shehu.pdf","date_updated":"2020-08-03T15:24:39Z","date_created":"2020-08-03T15:24:39Z","file_size":2137860,"success":1,"creator":"dernst","content_type":"application/pdf","access_level":"open_access"}],"isi":1}]