@unpublished{18999, abstract = {Exploring the shape of point configurations has been a key driver in the evolution of TDA (short for topological data analysis) since its infancy. This survey illustrates the recent efforts to broaden these ideas to model spatial interactions among multiple configurations, each distinguished by a color. It describes advances in this area and prepares the ground for further exploration by mentioning unresolved questions and promising research avenues while focusing on the overlap with discrete geometry.}, author = {Cultrera di Montesano, Sebastiano and Draganov, Ondrej and Edelsbrunner, Herbert and Saghafian, Morteza}, booktitle = {arXiv}, title = {{Chromatic topological data analysis}}, doi = {10.48550/ARXIV.2406.04102}, year = {2024}, } @inproceedings{19028, abstract = {The stochastic nature of modern Monte Carlo (MC) rendering methods inevitably produces noise in rendered images for a practical number of samples per pixel. The problem of denoising these images has been widely studied, with most recent methods relying on data-driven, pretrained neural networks. In contrast, in this paper we propose a statistical approach to the denoising problem, treating each pixel as a random variable and reasoning about its distribution. Considering a pixel of the noisy rendered image, we formulate fast pair-wise statistical tests—based on online estimators—to decide which of the nearby pixels to exclude from the denoising filter. We show that for symmetric pixel weights and normally distributed samples, the classical Welch t-test is optimal in terms of mean squared error. We then show how to extend this result to handle non-normal distributions, using more recent confidence-interval formulations in combination with the Box-Cox transformation. Our results show that our statistical denoising approach matches the performance of state-of-the-art neural image denoising without having to resort to any computation-intensive pretraining. Furthermore, our approach easily generalizes to other quantities besides pixel intensity, which we demonstrate by showing additional applications to Russian roulette path termination and multiple importance sampling.}, author = {Sakai, Hiroyuki and Freude, Christian and Auzinger, Thomas and Hahn, David and Wimmer, Michael}, booktitle = {Proceedings - SIGGRAPH Asia 2024 Conference Papers}, isbn = {9798400711312}, location = {Tokyo, Japan}, publisher = {Association for Computing Machinery}, title = {{A statistical approach to Monte Carlo denoising}}, doi = {10.1145/3680528.3687591}, year = {2024}, } @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}, } @article{19470, abstract = {When food is freely available, eating occurs without energy deficit. While agouti-related peptide (AgRP) neurons are likely involved, their activation is thought to require negative energy balance. To investigate this, we implemented long-term, continuous in vivo fiber-photometry recordings in mice. We discovered new forms of AgRP neuron regulation, including fast pre-ingestive decreases in activity and unexpectedly rapid activation by fasting. Furthermore, AgRP neuron activity has a circadian rhythm that peaks concurrent with the daily feeding onset. Importantly, this rhythm persists when nutrition is provided via constant-rate gastric infusions. Hence, it is not secondary to a circadian feeding rhythm. The AgRP neuron rhythm is driven by the circadian clock, the suprachiasmatic nucleus (SCN), as SCN ablation abolishes the circadian rhythm in AgRP neuron activity and feeding. The SCN activates AgRP neurons via excitatory afferents from thyrotrophin-releasing hormone-expressing neurons in the dorsomedial hypothalamus (DMHTrh neurons) to drive daily feeding rhythms.}, author = {Douglass, Amelia May Barnett and Kucukdereli, Hakan and Madara, Joseph C. and Wang, Daqing and Wu, Chen and Lowenstein, Elijah D. and Tao, Jenkang and Lowell, Bradford B.}, issn = {1550-4131}, journal = {Cell Metabolism}, number = {3}, pages = {708--722.e5}, publisher = {Elsevier}, title = {{Acute and circadian feedforward regulation of agouti-related peptide hunger neurons}}, doi = {10.1016/j.cmet.2024.11.009}, volume = {37}, year = {2024}, } @article{19486, abstract = {Consider the family of elliptic curves En:y2=x3+n2, where n varies over positive cubefree integers. There is a rational 3-isogeny ϕ from En to E^n:y2=x3−27n2 and a dual isogeny ϕ^:E^n→En. We show that for almost all n, the rank of Selϕ(En) is 0, and the rank of Selϕ^(E^n) is determined by the number of prime factors of n that are congruent to 2mod3 and the congruence class of nmod9.}, author = {Chan, Yik Tung}, issn = {1687-0247}, journal = {International Mathematics Research Notices}, number = {9}, pages = {7571--7593}, publisher = {Oxford University Press}, title = {{The 3-isogeny selmer groups of the elliptic curves y2=x3+n2}}, doi = {10.1093/imrn/rnad266}, volume = {2024}, 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}, } @article{19809, abstract = {In many physical situations in which many-body assemblies exist at temperature T, a characteristic quantum-mechanical time scale of approximately h/kbT can be identified in both theory and experiment, leading to speculation that it may be the shortest meaningful time in such circumstances. This behavior can be investigated by probing the scattering rate of electrons in a broad class of materials often referred to as “strongly correlated metals”. It is clear that in some cases only electron–electron scattering can be its cause, while in others it arises from high-temperature scattering of electrons from quantized lattice vibrations, i.e., phonons. In metallic oxides, which are among the most studied materials, analysis of electrical transport does not satisfactorily identify the relevant scattering mechanism at “high” temperatures near room temperature. We therefore employ a contactless optical method to measure thermal diffusivity in two Ru-based layered perovskites, Sr3Ru2O7 and Sr2RuO4, and use the measurements to extract the dimensionless Lorenz ratio. By comparing our results to the literature data on both conventional and unconventional metals, we show how the analysis of high-temperature thermal transport can both give important insight into dominant scattering mechanisms and be offered as a stringent test of theories attempting to explain anomalous scattering.}, author = {Sun, Fei and Mishra, Simli and Stockert, Ulrike and Daou, Ramzy and Kikugawa, Naoki and Perry, Robin S. and Hassinger, Elena and Hartnoll, Sean A. and Mackenzie, Andrew P. and Sunko, Veronika}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences}, number = {35}, publisher = {National Academy of Sciences}, title = {{The Lorenz ratio as a guide to scattering contributions to transport in strongly correlated metals}}, doi = {10.1073/pnas.2318159121}, volume = {121}, year = {2024}, } @article{19816, abstract = {Understanding and manipulating emergent phases, which are themes at the forefront of quantum-materials research, rely on identifying their underlying symmetries. This general principle has been particularly prominent in materials with coupled electronic and magnetic degrees of freedom, in which magnetic order influences the electronic band structure and can lead to exotic topological effects. However, identifying symmetry of a magnetically ordered phase can pose a challenge, particularly in the presence of small domains. Here we introduce a multimodal approach for determining magnetic structures, which combines symmetry-sensitive optical probes, scattering, and group-theoretical analysis. We apply it to EuIn2⁢As2, a material that has received attention as a candidate axion insulator. While first-principles calculations predict this state on the assumption of a simple collinear antiferromagnetic structure, subsequent neutron-scattering measurements reveal a much more intricate magnetic ground state characterized by two coexisting magnetic wave vectors reached by successive thermal phase transitions. The proposed high- and low-temperature phases are a spin helix and a state with interpenetrating helical and Néel antiferromagnetic order termed a “broken helix,” respectively. Employing a multimodal approach, we identify the magnetic structure associated with these two phases of EuIn2⁢As2. We find that the higher-temperature phase is characterized by a variation of the magnetic moment amplitude from layer to layer, with the moment vanishing entirely in every third Eu layer. The lower-temperature structure is similar to the broken helix, with one important difference: Because of local strain, the relative orientation of the magnetic structure and the lattice is not fixed. Consequently, the symmetry required to protect the axion phase is not generically protected in EuIn2⁢As2, but we show that it can be restored if the magnetic structure is tuned with uniaxial strain. Finally, we present a spin Hamiltonian that identifies the spin interactions that account for the complex magnetic order in EuIn2⁢As2. Our work highlights the importance of a multimodal approach in determining the symmetry of complex order parameters.}, author = {Donoway, E. and Trevisan, T. V. and Liebman-Peláez, A. and Day, R. P. and Yamakawa, K. and Sun, Y. and Soh, J. R. and Prabhakaran, D. and Boothroyd, A. T. and Fernandes, R. M. and Analytis, J. G. and Moore, J. E. and Orenstein, J. and Sunko, Veronika}, issn = {2160-3308}, journal = {Physical Review X}, number = {3}, publisher = {American Physical Society}, title = {{Multimodal approach reveals the symmetry-breaking pathway to the broken helix in EuIn2As2}}, doi = {10.1103/physrevx.14.031013}, volume = {14}, 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}, } @article{20060, abstract = {Un fenómeno a menudo asociado con el autismo es un modo atípico de función ejecutiva, cuyas manifestaciones incluyen dificultad para iniciar tareas. En algunos casos, esto va acompañado de sentimientos de inercia y sensaciones que pueden describirse como inquietud y parálisis simultáneas. En consecuencia, la dificultad para iniciar las tareas puede dar lugar a la procrastinación, ya sea simplemente posponiendo el trabajo en la tarea objetivo o realizando otras tareas no relacionadas antes de dedicarse a la tarea objetivo. Curiosamente, sin embargo, también está documentado que, una vez iniciada una tarea, los autistas pueden centrarse en ella intensamente y durante periodos prolongados de tiempo, especialmente cuando les resulta interesante. Este trabajo utiliza el procesamiento predictivo y la inferencia activa para modelar la relación entre la función ejecutiva, la procrastinación y la hiperfocalización en el autismo. Este modelo integra las causas conocidas y propuestas de los déficits en la función ejecutiva y el papel que desempeña el interés en la regulación de la atención y la motivación. El modelo propone que la procrastinación es el resultado de procesos diferenciales de minimización de errores de predicción, como la ponderación de estímulos sensoriales. Se discuten los vínculos con modelos propuestos previamente, como la coherencia central débil (CCC), y la teoría de los priores altos e inflexibles de los errores de predicción en el autismo (HIPPEA).}, author = {Carls-Diamante, Sidney and Laciny, Alice}, issn = {1316-693X}, journal = {Lógoi. Revista de Filosofía}, number = {45}, pages = {88--114}, publisher = {Universidad Católica Andrés Bello}, title = {{Stuck in uncertainty: A predictive processing/ active inference account of procrastination-like behaviour in autism}}, doi = {10.62876/lr.vi45.6481}, 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}, }