@article{19408, abstract = {Continual learning is a subfield of machine learning, which aims to allow machine learning models to continuously learn on new data, by accumulating knowledge without forgetting what was learned in the past. In this work, we take a step back, and ask: "Why should one care about continual learning in the first place?". We set the stage by examining recent continual learning papers published at four major machine learning conferences, and show that memory-constrained settings dominate the field. Then, we discuss five open problems in machine learning, and even though they might seem unrelated to continual learning at first sight, we show that continual learning will inevitably be part of their solution. These problems are model editing, personalization and specialization, on-device learning, faster (re-)training and reinforcement learning. Finally, by comparing the desiderata from these unsolved problems and the current assumptions in continual learning, we highlight and discuss four future directions for continual learning research. We hope that this work offers an interesting perspective on the future of continual learning, while displaying its potential value and the paths we have to pursue in order to make it successful. This work is the result of the many discussions the authors had at the Dagstuhl seminar on Deep Continual Learning, in March 2023.}, author = {Verwimp, Eli and Aljundi, Rahaf and Ben-David, Shai and Bethge, Matthias and Cossu, Andrea and Gepperth, Alexander and Hayes, Tyler L. and Hüllermeier, Eyke and Kanan, Christopher and Kudithipudi, Dhireesha and Lampert, Christoph and Mundt, Martin and Pascanu, Razvan and Popescu, Adrian and Tolias, Andreas S. and Van De Weijer, Joost and Liu, Bing and Lomonaco, Vincenzo and Tuytelaars, Tinne and Van De Ven, Gido M.}, issn = {2835-8856}, journal = {Transactions on Machine Learning Research}, publisher = {Transactions on Machine Learning Research}, title = {{Continual learning: Applications and the road forward}}, volume = {2024}, year = {2024}, } @article{19446, abstract = {This Comment explores new approaches to enrich large-scale population data, including incorporating macro-environmental and digital health measures.}, author = {Nees, Frauke and Renner, Paul and Holz, Nathalie E. and Polemiti, Elli and Siehl, Sebastian and Hese, Sören and Schepanski, Kerstin and Schumann, Gunter and Walter, Henrik and Heinz, Andreas and Ralser, Markus and Twardziok, Sven and Vaidya, Nilakshi and Bernas, Antoine and Serin, Emin and Jentsch, Marcel and Hitchen, Esther and Kebir, Hedi and Lett, Tristram A. and Roy, Jean Charles and Eils, Roland and Taron, Ulrike Helene and Schütz, Tatjana and Banks, Jamie and Banaschewski, Tobias and Jansone, Karina and Christmann, Nina and Meyer-Lindenberg, Andreas and Tost, Heike and Holz, Nathalie and Schwarz, Emanuel and Stringaris, Argyris and Neidhart, Maja and Seefried, Beke and Aden, Rieke and Andreassen, Ole A. and Westlye, Lars T. and Van Der Meer, Dennis and Fernandez, Sara and Kjelkenes, Rikka and Ask, Helga and Rapp, Michael and Tschorn, Mira and Böttger, Sarah Jane and Marquand, Andre and Novarino, Gaia and Marr, Lena and Slater, Mel and Viapiana, Guillem Feixas and Orosa, Francisco Eiroa and Gallego, Jaime and Pastor, Alvaro and Forstner, Andreas J. and Hoffmann, Per and Nöthen, Markus M. and Claus, Isabelle and Miller, Abigail and Mathey, Carina M. and Heilmann-Heimbach, Stefanie and Sommer, Peter and Patraskaki, Myrto and Wilbertz, Johannes and Schmitt, Karen and Jirsa, Viktor and Petkoski, Spase and Pitel, Séverine and Otten, Lisa and Athanasiadis, Anastasios Polykarpos and Pearmund, Charlie and Spanlang, Bernhard and Alvarez, Elena and Sanchez, Mavi and Giner, Arantxa and Jia, Tianye and Gong, Yanting and Xia, Yunman and Chang, Xiao and Calhoun, Vince and Liu, Jingyu and Schwalber, Ameli and Thompson, Paul and Clinton, Nicholas and Desrivières, Sylvane and Young, Allan H. and Stahl, Bernd and Ogoh, George}, issn = {2731-6076}, journal = {Nature Mental Health}, number = {10}, pages = {1124--1127}, publisher = {Springer Nature}, title = {{Large-scale population data enrichment in mental health research}}, doi = {10.1038/s44220-024-00316-z}, volume = {2}, year = {2024}, } @inproceedings{19517, abstract = {In this paper, we present a novel data-free method for merging neural networks in weight space. Differently from most existing works, our method optimizes for the permutations of network neurons globally across all layers. This allows us to enforce cycle consistency of the permutations when merging n ≥ 3 models, allowing circular compositions of permutations to be computed without accumulating error along the path. We qualitatively and quantitatively motivate the need for such a constraint, showing its benefits when merging sets of models in scenarios spanning varying architectures and datasets. We finally show that, when coupled with activation renormalization, our approach yields the best results in the task.}, author = {Crisostomi, Donato and Fumero, Marco and Baieri, Daniele and Bernard, Florian and Rodolà, Emanuele}, booktitle = {38th Conference on Neural Information Processing Systems}, issn = {1049-5258}, location = {Vancouver, Canada}, publisher = {Neural Information Processing Systems Foundation}, title = {{C2M3: Cycle-consistent multi-model merging}}, volume = {37}, year = {2024}, } @inproceedings{19518, abstract = {The rising footprint of machine learning has led to a focus on imposing model sparsity as a means of reducing computational and memory costs. For deep neural networks (DNNs), the state-of-the-art accuracy-vs-sparsity is achieved by heuristics inspired by the classical Optimal Brain Surgeon (OBS) framework [LeCun et al., 1989, Hassibi and Stork, 1992, Hassibi et al., 1993], which leverages loss curvature information to make better pruning decisions. Yet, these results still lack a solid theoretical understanding, and it is unclear whether they can be improved by leveraging connections to the wealth of work on sparse recovery algorithms. In this paper, we draw new connections between these two areas and present new sparse recovery algorithms inspired by the OBS framework that comes with theoretical guarantees under reasonable assumptions and have strong practical performance. Specifically, our work starts from the observation that we can leverage curvature information in OBS-like fashion upon the projection step of classic iterative sparse recovery algorithms such as IHT. We show for the first time that this leads both to improved convergence bounds under standard assumptions. Furthermore, we present extensions of this approach to the practical task of obtaining accurate sparse DNNs, and validate it experimentally at scale for Transformer-based models on vision and language tasks.}, author = {Wu, Diyuan and Modoranu, Ionut-Vlad and Safaryan, Mher and Kuznedelev, Denis and Alistarh, Dan-Adrian}, booktitle = {38th Conference on Neural Information Processing Systems}, issn = {1049-5258}, location = {Vancouver, Canada}, publisher = {Neural Information Processing Systems Foundation}, title = {{The iterative optimal brain surgeon: Faster sparse recovery by leveraging second-order information}}, volume = {37}, year = {2024}, } @inproceedings{19519, abstract = {There has been significant interest in "extreme" compression of large language models (LLMs), i.e. to 1-2 bits per parameter, which allows such models to be executed efficiently on resource-constrained devices. Existing work focused on improved one-shot quantization techniques and weight representations; yet, purely post-training approaches are reaching diminishing returns in terms of the accuracy-vs-bit-width trade-off. State-of-the-art quantization methods such as QuIP# and AQLM include fine-tuning (part of) the compressed parameters over a limited amount of calibration data; however, such fine-tuning techniques over compressed weights often make exclusive use of straight-through estimators (STE), whose performance is not well-understood in this setting. In this work, we question the use of STE for extreme LLM compression, showing that it can be sub-optimal, and perform a systematic study of quantization-aware fine-tuning strategies for LLMs.We propose PV-Tuning - a representation-agnostic framework that generalizes and improves upon existing fine-tuning strategies, and provides convergence guarantees in restricted cases.On the practical side, when used for 1-2 bit vector quantization, PV-Tuning outperforms prior techniques for highly-performant models such as Llama and Mistral. Using PV-Tuning, we achieve the first Pareto-optimal quantization for Llama-2 family models at 2 bits per parameter.}, author = {Malinovskii, Vladimir and Mazur, Denis and Ilin, Ivan and Kuznedelev, Denis and Burlachenko, Konstantin and Yi, Kai and Alistarh, Dan-Adrian and Richtarik, Peter}, booktitle = {38th Conference on Neural Information Processing Systems}, isbn = {9798331314385}, issn = {1049-5258}, location = {Vancouver, Canada}, publisher = {Neural Information Processing Systems Foundation}, title = {{PV-tuning: Beyond straight-through estimation for extreme LLM compression}}, volume = {37}, year = {2024}, } @unpublished{19520, abstract = {Vertebrates exhibit a wide range of motor behaviors, ranging from swimming to complex limb-based movements. Here we take advantage of frog metamorphosis, which captures a swim-to-limb-based movement transformation during the development of a single organism, to explore changes in the underlying spinal circuits. We find that the tadpole spinal cord contains small and largely homogeneous populations of motor neurons (MNs) and V1 interneurons (V1s) at early escape swimming stages. These neuronal populations only modestly increase in number and subtype heterogeneity with the emergence of free swimming. In contrast, during frog metamorphosis and the emergence of limb movement, there is a dramatic expansion of MN and V1 interneuron number and transcriptional heterogeneity, culminating in cohorts of neurons that exhibit striking molecular similarity to mammalian motor circuits. CRISPR/Cas9-mediated gene disruption of the limb MN and V1 determinants FoxP1 and Engrailed-1, respectively, results in severe but selective deficits in tail and limb function. Our work thus demonstrates that neural diversity scales exponentially with increasing behavioral complexity and illustrates striking evolutionary conservation in the molecular organization and function of motor circuits across species.}, author = {Vijatovic, David and Toma, Florina Alexandra and Harrington, Zoe P and Sommer, Christoph M and Hauschild, Robert and Trevisan, Alexandra J. and Chapman, Phillip and Julseth, Mara and Brenner-Morton, Susan and Gabitto, Mariano I. and Dasen, Jeremy S. and Bikoff, Jay B. and Sweeney, Lora Beatrice Jaeger}, booktitle = {bioRxiv}, title = {{Spinal neuron diversity scales exponentially with swim-to-limb transformation during frog metamorphosis}}, doi = {10.1101/2024.09.20.614050}, year = {2024}, } @misc{19800, abstract = {This archive contains all the code and data necessary to reproduce the results presented in the "Mapping the attractor landscape of Boolean networks" paper.}, author = {trinh, Van Giang and Park, Kyu Hyong and Pastva, Samuel and Rozum, Jordan}, publisher = {Zenodo}, title = {{Mapping the attractor landscape of Boolean networks}}, doi = {10.5281/ZENODO.13854759}, year = {2024}, } @misc{19884, abstract = {This is Marlin, a Mixed Auto-Regressive Linear kernel (and the name of one of the planet's fastest fish), an extremely optimized FP16xINT4 matmul kernel aimed at LLM inference that can deliver close to ideal (4x) speedups up to batchsizes of 16-32 tokens (in contrast to the 1-2 tokens of prior work with comparable speedup). Additionally, it includes Sparse-Marlin, an extension of the MARLIN kernels adding support to 2:4 weight sparsity, achieving 5.3x speedups on NVIDIA GPUs (Ampere/Ada).}, author = {Frantar, Elias and Castro, Roberto and Chen, Jiale and Hoefler, Torsten and Alistarh, Dan-Adrian}, publisher = {Zenodo}, title = {{MARLIN: Mixed-precision auto-regressive parallel inference on Large Language Models}}, doi = {10.5281/ZENODO.14213091}, year = {2024}, } @article{20039, abstract = {This Comment presents a high-level protocol for data harmonization within large cohorts, in which it postulates four main steps including (1) expert review, (2) pre-statistical harmonization, (3) statistical harmonization, and (4) validation.}, author = {Neidhart, Maja and Kjelkenes, Rikka and Jansone, Karina and Rehák Bučková, Barbora and Holz, Nathalie and Nees, Frauke and Walter, Henrik and Schumann, Gunter and Rapp, Michael A. and Banaschewski, Tobias and Schwarz, Emanuel and Marquand, Andre and Ogoh, George and Stahl, Bernd and Young, Allan H. and Desrivières, Sylvane and Clinton, Nicholas and Thompson, Paul and Schwalber, Ameli and Liu, Jingyu and Calhoun, Vince and Chang, Xiao and Xia, Yunman and Gong, Yanting and Jia, Tianye and Renner, Paul and Hese, Sören and Giner, Arantxa and Sanchez, Mavi and Alvarez, Elena and Spanlang, Bernhard and Pearmund, Charlie and Athanasiadis, Anastasios Polykarpos and Otten, Lisa and Pitel, Séverine and Petkoski, Spase and Jirsa, Viktor and Schmitt, Karen and Wilbertz, Johannes and Patraskaki, Myrto and Sommer, Peter and Heilmann-Heimbach, Stefanie and Mathey, Carina M. and Miller, Abigail and Claus, Isabelle and Nöthen, Markus M. and Hoffmann, Per and Forstner, Andreas J. and Pastor, Alvaro and Gallego, Jaime and Orosa, Francisco Eiroa and Viapiana, Guillem Feixas and Slater, Mel and Marr, Lena and Novarino, Gaia and Böttger, Sarah Jane and Tschorn, Mira and Rapp, Michael and Ask, Helga and Fernandez, Sara and Van Der Meer, Dennis and Westlye, Lars T. and Andreassen, Ole A. and Aden, Rieke and Seefried, Beke and Siehl, Sebastian and Nees, Frauke and Stringaris, Argyris and Tost, Heike and Meyer-Lindenberg, Andreas and Christmann, Nina and Banks, Jamie and Schepanski, Kerstin and Schütz, Tatjana and Taron, Ulrike Helene and Eils, Roland and Roy, Jean Charles and Lett, Tristram A. and Kebir, Hedi and Polemiti, Elli and Hitchen, Esther and Jentsch, Marcel and Serin, Emin and Bernas, Antoine and Vaidya, Nilakshi and Twardziok, Sven and Ralser, Markus and Heinz, Andreas}, issn = {2731-6076}, journal = {Nature Mental Health}, number = {10}, pages = {1134--1137}, publisher = {Springer Nature}, title = {{A protocol for data harmonization in large cohorts}}, doi = {10.1038/s44220-024-00315-0}, volume = {2}, year = {2024}, } @misc{20057, abstract = {In article number 2305128, Qing Sun, Shang Wang, Yanhong Tian, Andreu Cabot, and co-workers report an investigation of the energy-storage mechanism of a layered Bi2Te3-based cathode for aqueous zinc-ion batteries (ZIBs). They demonstrate that the zinc ion is not inserted into the cathode as previously assumed; in contrast, proton charge-storage dominates the process. They also demonstrate the great application prospects of aqueous ZIBs in flexible electronics via jet printing technology.}, author = {Zeng, Guifang and Sun, Qing and Horta, Sharona and Wang, Shang and Lu, Xuan and Zhang, Chao Yue and Li, Jing and Li, Junshan and Ci, Lijie and Tian, Yanhong and Ibáñez, Maria and Cabot, Andreu}, booktitle = {Advanced Materials}, issn = {1521-4095}, number = {1}, publisher = {Wiley}, title = {{A layered Bi2Te3@PPy cathode for aqueous Zinc‐Ion batteries: Mechanism and application in printed flexible batteries}}, doi = {10.1002/adma.202470004}, volume = {36}, year = {2024}, } @misc{20121, abstract = {PyDaddy is an open source package which is a key contribution of the manuscript Nabeel et al, arXiv:2205.02645. The basic scientific premise for this package is to discover the nature of stochasticity in ecological time series datasets. It is well known that the stochasticity can affect the dynamics of ecological systems in counter-intuitive ways. Without understanding the equations (typically, in the form of stochastic differential equations or SDEs, in short) that govern the dynamics of populations or ecosystems, it's challenging to determine the impact of randomness on real datasets. In this manuscript and accompanying package, we introduce a methodology for discovering equations (SDEs) that transforms time series data of state variables into stochastic differential equations. This approach merges traditional stochastic calculus with modern equation-discovery techniques. We showcase the generality of our method through various applications and discuss its limitations and potential pitfalls, offering diagnostic measures to address these challenges.}, author = {Nabeel, Arshed and Karichannavar, Ashwin and Palathingal, Shuaib and Jhawar, Jitesh and Brückner, David and Danny Raj, Masila and Guttal, Vishwesha}, publisher = {Zenodo}, title = {{PyDaddy: A Python Package for Discovering SDEs from Time Series Data}}, doi = {10.5281/ZENODO.7137151}, year = {2024}, } @article{20156, abstract = {Integrative analyses that incorporate different levels of ‘-omics’ data represent a powerful tool for deciphering the biological mechanisms that underlie environmental influences on mental health and disease. This Comment highlights various aspects of such multi-omics approaches, using the example of the EU-funded environMENTAL project.}, author = {Desrivières, Sylvane and Miller, Abigail and Mathey, Carina M. and Yu, Xinyang and Chen, Di and Agunbiade, Kofoworola and Heilmann-Heimbach, Stefanie and Forstner, Andreas J. and Schumann, Gunter and Hoffmann, Per and Nöthen, Markus M. and Ogoh, George and Stahl, Bernd and Young, Allan H. and Clinton, Nicholas and Thompson, Paul and Schwalber, Ameli and Liu, Jingyu and Calhoun, Vince and Chang, Xiao and Xia, Yunman and Gong, Yanting and Jia, Tianye and Renner, Paul and Hese, Sören and Giner, Arantxa and Sanchez, Mavi and Alvarez, Elena and Spanlang, Bernhard and Pearmund, Charlie and Athanasiadis, Anastasios Polykarpos and Otten, Lisa and Pitel, Séverine and Petkoski, Spase and Jirsa, Viktor and Schmitt, Karen and Wilbertz, Johannes and Patraskaki, Myrto and Sommer, Peter and Claus, Isabelle and Pastor, Alvaro and Gallego, Jaime and Orosa, Francisco Eiroa and Viapiana, Guillem Feixas and Slater, Mel and Marr, Lena and Novarino, Gaia and Marquand, Andre and Böttger, Sarah Jane and Tschorn, Mira and Rapp, Michael and Ask, Helga and Kjelkenes, Rikka and Fernandez, Sara and Van Der Meer, Dennis and Westlye, Lars T. and Andreassen, Ole A. and Aden, Rieke and Seefried, Beke and Siehl, Sebastian and Nees, Frauke and Neidhart, Maja and Stringaris, Argyris and Schwarz, Emanuel and Holz, Nathalie and Tost, Heike and Meyer-Lindenberg, Andreas and Christmann, Nina and Jansone, Karina and Banaschewski, Tobias and Banks, Jamie and Schepanski, Kerstin and Schütz, Tatjana and Taron, Ulrike Helene and Eils, Roland and Roy, Jean Charles and Lett, Tristram A. and Kebir, Hedi and Polemiti, Elli and Hitchen, Esther and Jentsch, Marcel and Serin, Emin and Bernas, Antoine and Vaidya, Nilakshi and Twardziok, Sven and Ralser, Markus and Heinz, Andreas and Walter, Henrik}, issn = {2731-6076}, journal = {Nature Mental Health}, number = {10}, pages = {1131--1133}, publisher = {Springer Nature}, title = {{Multi-omics analyses of the environMENTAL project provide insights into mental health and disease}}, doi = {10.1038/s44220-024-00317-y}, volume = {2}, year = {2024}, } @article{20157, abstract = {The focus of much of contemporary research ethics is on compliance with established protocols. However, large data-driven neuroscience research raises new ethical concerns that have no agreed-upon solution. Here we reflect on these challenges and propose better integration of public and patient involvement in this evolving landscape.}, author = {Stahl, Bernd and Ogoh, George and Schumann, Gunter and Walter, Henrik and Stahl, Bernd and Young, Allan H. and Desrivières, Sylvane and Clinton, Nicholas and Thompson, Paul and Schwalber, Ameli and Liu, Jingyu and Calhoun, Vince and Chang, Xiao and Xia, Yunman and Gong, Yanting and Jia, Tianye and Renner, Paul and Hese, Sören and Giner, Arantxa and Sanchez, Mavi and Alvarez, Elena and Spanlang, Bernhard and Pearmund, Charlie and Athanasiadis, Anastasios Polykarpos and Otten, Lisa and Pitel, Séverine and Petkoski, Spase and Jirsa, Viktor and Schmitt, Karen and Wilbertz, Johannes and Patraskaki, Myrto and Sommer, Peter and Heilmann-Heimbach, Stefanie and Mathey, Carina M. and Miller, Abigail and Claus, Isabelle and Nöthen, Markus M. and Hoffmann, Per and Forstner, Andreas J. and Pastor, Alvaro and Gallego, Jaime and Orosa, Francisco Eiroa and Viapiana, Guillem Feixas and Slater, Mel and Marr, Lena and Novarino, Gaia and Marquand, Andre and Böttger, Sarah Jane and Tschorn, Mira and Rapp, Michael and Ask, Helga and Kjelkenes, Rikka and Fernandez, Sara and Van Der Meer, Dennis and Westlye, Lars T. and Andreassen, Ole A. and Aden, Rieke and Seefried, Beke and Siehl, Sebastian and Nees, Frauke and Neidhart, Maja and Stringaris, Argyris and Schwarz, Emanuel and Holz, Nathalie and Tost, Heike and Meyer-Lindenberg, Andreas and Christmann, Nina and Jansone, Karina and Banaschewski, Tobias and Banks, Jamie and Schepanski, Kerstin and Schütz, Tatjana and Taron, Ulrike Helene and Eils, Roland and Roy, Jean Charles and Lett, Tristram A. and Kebir, Hedi and Polemiti, Elli and Hitchen, Esther and Jentsch, Marcel and Serin, Emin and Bernas, Antoine and Vaidya, Nilakshi and Twardziok, Sven and Ralser, Markus and Heinz, Andreas and Walter, Henrik}, issn = {2731-6076}, journal = {Nature Mental Health}, number = {10}, publisher = {Springer Nature}, title = {{Rethinking ethics in interdisciplinary and big data-driven neuroscience projects}}, doi = {10.1038/s44220-024-00320-3}, volume = {2}, year = {2024}, } @article{17235, abstract = {We demonstrate ion irradiation by argon or gallium as a wafer-scale post-processing method to increase disorder in superconducting thin films. We study several widely used superconductors, both single-elements and compounds. We show that ion irradiation increases normal-state resistivity in all our films, which is expected to enable tuning their superconducting properties, for example, toward a higher kinetic inductance. We observe an increase in superconducting transition temperature for Al and MoSi and a decrease for Nb, NbN, and TiN. In MoSi, ion irradiation also improves the mixing of the two materials. We demonstrate the fabrication of an amorphous and homogeneous film of MoSi with uniform thickness, which is promising, for example, for superconducting nanowire single-photon detectors.}, author = {Kohopää, Katja and Ronzani, Alberto and Jabdaraghi, Robab Najafi and Bera, Arijit and Ribeiro, Mário and Hazra, Dibyendu and Senior, Jorden L and Prunnila, Mika and Govenius, Joonas and Lehtinen, Janne S. and Kemppinen, Antti}, issn = {2166-532X}, journal = {APL Materials}, number = {7}, publisher = {AIP Publishing}, title = {{Effect of ion irradiation on superconducting thin films}}, doi = {10.1063/5.0202851}, volume = {12}, year = {2024}, } @article{17291, abstract = {Bacterial cell walls are gigadalton-large cross-linked polymers with a wide range of motional amplitudes, including rather rigid as well as highly flexible parts. Magic-angle spinning NMR is a powerful method to obtain atomic-level information about intact cell walls. Here we investigate sensitivity and information content of different homonuclear 13Csingle bond13C and heteronuclear 1Hsingle bond15N, 1Hsingle bond13C and 15Nsingle bond13C correlation experiments. We demonstrate that a CPMAS CryoProbe yields ca. 8-fold increased signal-to-noise over a room-temperature probe, or a ca. 3–4-fold larger per-mass sensitivity. The increased sensitivity allowed to obtain high-resolution spectra even on intact bacteria. Moreover, we compare resolution and sensitivity of 1H MAS experiments obtained at 100 kHz vs. 55 kHz. Our study provides useful hints for choosing experiments to extract atomic-level details on cell-wall samples.}, author = {Vallet, Alicia and Ayala, Isabel and Perrone, Barbara and Hassan, Alia and Simorre, Jean-Pierre and Bougault, Catherine and Schanda, Paul}, issn = {1090-7807}, journal = {Journal of Magnetic Resonance}, publisher = {Elsevier}, title = {{MAS NMR experiments of corynebacterial cell walls: Complementary 1H- and CPMAS CryoProbe-enhanced 13C-detected experiments}}, doi = {10.1016/j.jmr.2024.107708}, volume = {364}, year = {2024}, } @misc{17344, abstract = {This file contains the Mathematica notebook associated with the paper Effect of assortative mating and sexual selection on polygenic barriers to gene flow. It contains the numerical approximations, analyses, and simulations used in the study. }, author = {Surendranadh, Parvathy and Sachdeva, Himani}, publisher = {Institute of Science and Technology Austria}, title = {{Mathematica notebook for 'Effect of assortative mating and sexual selection on polygenic barriers to gene flow'}}, doi = {10.15479/AT:ISTA:17344}, year = {2024}, } @inbook{18167, abstract = {Holdase chaperones are essential in the mitochondrial membrane-protein biogenesis as they stabilize preproteins and keep them in an import-competent state as they travel through the aqueous cytosol and intermembrane space. The small TIM chaperones of the mitochondrial intermembrane space function within a fine balance of client promiscuity and high affinity binding, while being also able to release their client proteins without significant energy barrier to the downstream insertases/translocases. The tendency of the preproteins to aggregate and the dynamic nature of the preprotein—chaperone complexes makes the preparation of these complexes challenging. Here we present two optimized methods for complex formation of highly hydrophobic precursor proteins and chaperones: a pull-down approach and an in-vitro translation strategy. In the former, attaching the client protein to an affinity resin keeps the individual client protein copies apart from each other and decreases the client self-aggregation probability, thereby favouring complex formation. In the latter approach, a purified chaperone, added to the cell-free protein synthesis, captures the nascent precursor protein. The choice of method will depend on the desired client-chaperone complex amount, or the need for specific labeling scheme.}, author = {Guillerm, Undina and Sučec, Iva and Schanda, Paul}, booktitle = {Methods in Enzymology}, issn = {0076-6879}, pages = {391--422}, publisher = {Elsevier}, title = {{Generation of TIM chaperone substrate complexes}}, doi = {10.1016/bs.mie.2024.07.051}, volume = {707}, year = {2024}, } @unpublished{20701, abstract = {A Proof of Exponentiation (PoE) allows a prover to efficiently convince a verifier that 𝑦 = 𝑥 𝑒 in some group of unknown order. PoEs are the basis for practical constructions of Verifiable Delay Functions (VDFs), which, in turn, are important for various higher-level protocols in distributed computing. In applications such as distributed consensus, many PoEs are generated regularly, motivating protocols for secure aggregation of batches of statements into a few statements to improve the efficiency for both parties. Rotem (TCC 2021) recently presented two such generic batch PoEs. In this work, we introduce two batch PoEs that outperform both proposals of Rotem and we evaluate their practicality. First, we show that the two batch PoEs of Rotem can be combined to improve the overall efficiency by at least a factor of two. Second, we revisit the work of Bellare, Garay, and Rabin (EUROCRYPT 1998) on batch verification of digital signatures and show that, under the low order assumption, their bucket test can be securely adapted to the setting of groups of unknown order. The resulting batch PoE quickly outperforms the state of the art in the expected number of group multiplications with the growing number of instances, and it decreases the cost of batching by an order of magnitude already for hundreds of thousands of instances. Importantly, it is the first batch PoE that significantly decreases both the proof size and complexity of verification. Our experimental evaluations show that even a nonoptimized implementation achieves such improvements, which would match the demands of real-life systems requiring large-scale PoE processing. Finally, even though our proof techniques are conceptually similar to Rotem, we give an improved analysis of the application of the low order assumption towards secure batching of PoE instances, resulting in a tight reduction, which is important when setting the security parameter in practice.}, author = {Hoffmann, Charlotte and Hubáček, Pavel and Ivanova, Svetlana}, booktitle = {Cryptology ePrint Archive}, publisher = {International Association for Cryptologic Research}, title = {{Practical batch proofs of exponentiation}}, year = {2024}, } @unpublished{19425, abstract = {We demonstrate that periodically driven quantum rotors provide a promising and broadly applicable platform to implement multi-gap topological phases, where groups of bands can acquire topological invariants due to non-Abelian braiding of band degeneracies. By adiabatically varying the periodic kicks to the rotor we find nodal-line braiding, which causes sign flips of topological charges of band nodes and can prevent them from annihilating, indicated by non-zero values of the %non-Abelian patch Euler class. In particular, we report on the emergence of an anomalous Dirac string phase arising in the strongly driven regime, a truly out-of-equilibrium phase of the quantum rotor. This phase emanates from braiding processes involving all (quasienergy) gaps and manifests itself with edge states at zero angular momentum. Our results reveal direct applications in state-of-the-art experiments of quantum rotors, such as linear molecules driven by periodic far-off-resonant laser pulses or artificial quantum rotors in optical lattices, whose extensive versatility offers precise modification and observation of novel non-Abelian topological properties. }, author = {Karle, Volker and Lemeshko, Mikhail and Bouhon, Adrien and Slager, Robert-Jan and Ünal, F. Nur}, booktitle = {arXiv}, title = {{Anomalous multi-gap topological phases in periodically driven quantum rotors}}, doi = {10.48550/arXiv.2408.16848}, year = {2024}, } @article{19051, abstract = {This paper corrects an error in an earlier work of the author.}, author = {Browning, Timothy D}, issn = {1687-0247}, journal = {International Mathematics Research Notices}, number = {13}, pages = {10165--10168}, publisher = {Oxford University Press}, title = {{The polynomial sieve and equal sums of like polynomials}}, doi = {10.1093/imrn/rnae066}, volume = {2024}, year = {2024}, }