pwc-archive/papers-with-abstracts huggingface dataset:
Beyond Atoms: Enhancing Molecular Pretrained Representations with 3D Space Modeling
Authors: Shuqi Lu, Xiaohong Ji, Bohang Zhang, Lin Yao, Siyuan Liu, Zhifeng Gao, Linfeng Zhang, Guolin Ke
Date: 2025-03-13 00:00:00
Tasks: Drug Discovery, Representation Learning

Molecular pretrained representations (MPR) has emerged as a powerful approach for addressing the challenge of limited supervised data in applications such as drug discovery and material design. While early MPR methods relied on 1D sequences and 2D graphs, recent advancements have incorporated 3D conformational information to capture rich atomic interactions. However, these prior models treat molecules merely as discrete atom sets, overlooking the space surrounding them. We argue from a physical perspective that only modeling these discrete points is insufficient. We first present a simple yet insightful observation: naively adding randomly sampled virtual points beyond atoms can surprisingly enhance MPR performance. In light of this, we propose a principled framework that incorporates the entire 3D space spanned by molecules. We implement the framework via a novel Transformer-based architecture, dubbed SpaceFormer, with three key components: (1) grid-based space discretization; (2) grid sampling/merging; and (3) efficient 3D positional encoding. Extensive experiments show that SpaceFormer significantly outperforms previous 3D MPR models across various downstream tasks with limited data, validating the benefit of leveraging the additional 3D space beyond atoms in MPR models.
Silicon is the next frontier in plant synthetic biology
Authors: Aniruddha Acharya, Kaitlin Hopkins, Tatum Simms
Date: 2025-03-13 00:00:00

Silicon has striking similarity with carbon and is found in plant cells. However, there is no specific role that has been assigned to silicon in the life cycle of plants. The amount of silicon in plant cells is species specific and can reach levels comparable to macronutrients. Silicon is the central element for artificial intelligence, nanotechnology and digital revolution thus can act as an informational molecule like nucleic acids while the diverse bonding potential of silicon with different chemical species is analogous to carbon and thus can serve as a structural candidate such as proteins. The discovery of large amounts of silicon on Mars and the moon along with the recent developments of enzyme that can incorporate silicon into organic molecules has propelled the theory of creating silicon-based life. More recently, bacterial cytochrome has been modified through directed evolution such that it could cleave silicon-carbon bonds in organo-silicon compounds thus consolidating on the idea of utilizing silicon in biomolecules. In this article the potential of silicon-based life forms has been hypothesized along with the reasoning that autotrophic virus-like particles can be a lucrative candidate to investigate such potential. Such investigations in the field of synthetic biology and astrobiology will have corollary benefit on Earth in the areas of medicine, sustainable agriculture and environmental sustainability. Bibliometric analysis indicates an increasing interest in synthetic biology. Germany leads in research related to plant synthetic biology, while Biotechnology and Biological Sciences Research Council (BBSRC) at UK has highest financial commitments and Chinese Academy of Sciences generates the highest number of publications in the field.
QWENDY: Gene Regulatory Network Inference Enhanced by Large Language Model and Transformer
Authors: Yue Wang, Xueying Tian
Date: 2025-02-22 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Knowing gene regulatory networks (GRNs) is important for understanding various biological mechanisms. In this paper, we present a method, QWENDY, that uses single-cell gene expression data measured at four time points to infer GRNs. Based on a linear gene expression model, it solves the transformation for the covariance matrices. Unlike its predecessor WENDY, QWENDY avoids solving a non-convex optimization problem and produces a unique solution. Then we enhance QWENDY by the transformer neural networks and large language models to obtain two variants: TEQWENDY and LEQWENDY. We test the performance of these methods on two experimental data sets and two synthetic data sets. TEQWENDY has the best overall performance, while QWENDY ranks the first on experimental data sets.
ST-FlowNet: An Efficient Spiking Neural Network for Event-Based Optical Flow Estimation
Authors: Hongze Sun, Jun Wang, Wuque Cai, Duo Chen, Qianqian Liao, Jiayi He, Yan Cui, Dezhong Yao, Daqing Guo
Date: 2025-03-13 00:00:00
Tasks: Event-based Optical Flow, Optical Flow Estimation

Spiking Neural Networks (SNNs) have emerged as a promising tool for event-based optical flow estimation tasks due to their ability to leverage spatio-temporal information and low-power capabilities. However, the performance of SNN models is often constrained, limiting their application in real-world scenarios. In this work, we address this gap by proposing a novel neural network architecture, ST-FlowNet, specifically tailored for optical flow estimation from event-based data. The ST-FlowNet architecture integrates ConvGRU modules to facilitate cross-modal feature augmentation and temporal alignment of the predicted optical flow, improving the network's ability to capture complex motion dynamics. Additionally, to overcome the challenges associated with training SNNs, we introduce a novel approach to derive SNN models from pre-trained artificial neural networks (ANNs) through ANN-to-SNN conversion or our proposed BISNN method. Notably, the BISNN method alleviates the complexities involved in biological parameter selection, further enhancing the robustness of SNNs in optical flow estimation tasks. Extensive evaluations on three benchmark event-based datasets demonstrate that the SNN-based ST-FlowNet model outperforms state-of-the-art methods, delivering superior performance in accurate optical flow estimation across a diverse range of dynamic visual scenes. Furthermore, the inherent energy efficiency of SNN models is highlighted, establishing a compelling advantage for their practical deployment. Overall, our work presents a novel framework for optical flow estimation using SNNs and event-based data, contributing to the advancement of neuromorphic vision applications.
Increased GM-WM in a prefrontal network and decreased GM in the insula and the precuneus are associated with reappraisal usage: A data fusion approach
Authors: Alessandro Grecucci, Parisa Ahmadi Ghomroudi, Carmen Morawetz, Valerie Lesk, Irene Messina
Date: 2025-03-13 00:00:00

Emotion regulation plays a crucial role in mental health, and difficulties in regulating emotions can contribute to psychological disorders. While reappraisal and suppression are well-studied strategies, the combined contributions of gray matter (GM) and white matter (WM) to these strategies remain unclear due to methodological limitations in previous studies. To address this, we applied a data fusion approach using Parallel Independent Component Analysis (Parallel ICA) to GM and WM MRI images from 165 individuals. Parallel ICA identified two networks associated with reappraisal usage. Network 1 included a large lateral and medial prefrontal cortical network, overlapping with the default mode network (DMN) and adjacent WM regions. Higher reappraisal frequency was associated with greater GM-WM density within this network, and this network was negatively correlated with perceived stress. Network 2 included the insula, precuneus, sub-gyral, and lingual gyri in its GM portion, showing a negative association with reappraisal usage. The WM portion, adjacent to regions of the central executive network (CEN), was positively associated with reappraisal usage. Regarding suppression, no significant network was associated with this strategy. This study provides new insights into individual differences in reappraisal use, showing a positive association between reappraisal frequency and increased gray and white matter concentration in a large frontal network, including regions of the frontal DMN and the CEN. Conversely, subcortical areas exhibited reduced gray and white concentration.
Mechanoreceptive A$β$ primary afferents discriminate naturalistic social touch inputs at a functionally relevant time scale
Authors: Shan Xu, Steven C. Hauser, Saad S. Nagi, James A. Jablonski, Merat Rezaei, Ewa Jarocka, Andrew G. Marshall, Håkan Olausson, Sarah McIntyre, Gregory J. Gerling
Date: 2025-03-12 00:00:00

Interpersonal touch is an important channel of social emotional interaction. How these physical skin-to-skin touch expressions are processed in the peripheral nervous system is not well understood. From microneurography recordings in humans, we evaluated the capacity of six subtypes of cutaneous mechanoreceptive afferents to differentiate human-delivered social touch expressions. Leveraging statistical and classification analyses, we found that single units of multiple mechanoreceptive A$\beta$ subtypes, especially slowly adapting type II (SA-II) and fast adapting hair follicle afferents (HFA), can reliably differentiate social touch expressions at accuracies similar to human recognition. We then identified the most informative firing patterns of SA-II and HFA afferents, which indicate that average durations of 3-4 s of firing provide sufficient discriminative information. Those two subtypes also exhibit robust tolerance to spike-timing shifts of up to 10-20 ms, varying with touch expressions due to their specific firing properties. Greater shifts in spike-timing, however, can change a firing pattern's envelope to resemble that of another expression and drastically compromise an afferent's discrimination capacity. Altogether, the findings indicate that SA-II and HFA afferents differentiate the skin contact of social touch at time scales relevant for such interactions, which are 1-2 orders of magnitude longer than those for non-social touch.
Backward Stochastic Differential Equations-guided Generative Model for Structural-to-functional Neuroimage Translator
Authors: Zengjing Chen, Lu Wang, Yongkang Lin, Jie Peng, Zhiping Liu, Jie Luo, Bao Wang, Yingchao Liu, Nazim Haouchine, Xu Qiao
Date: 2025-02-23 00:00:00

A Method for structural-to-functional neuroimage translator
Technical Insights and Legal Considerations for Advancing Federated Learning in Bioinformatics
Authors: Daniele Malpetti, Marco Scutari, Francesco Gualdi, Jessica van Setten, Sander van der Laan, Saskia Haitjema, Aaron Mark Lee, Isabelle Hering, Francesca Mangili
Date: 2025-03-12 00:00:00
Tasks: Federated Learning, Translation

Federated learning leverages data across institutions to improve clinical discovery while complying with data-sharing restrictions and protecting patient privacy. As the evolution of biobanks in genetics and systems biology has proved, accessing more extensive and varied data pools leads to a faster and more robust exploration and translation of results. More widespread use of federated learning may have the same impact in bioinformatics, allowing access to many combinations of genotypic, phenotypic and environmental information that are undercovered or not included in existing biobanks. This paper reviews the methodological, infrastructural and legal issues that academic and clinical institutions must address before implementing it. Finally, we provide recommendations for the reliable use of federated learning and its effective translation into clinical practice.
Climate land use and other drivers impacts on island ecosystem services: a global review
Authors: Aristides Moustakas, Shiri Zemah-Shamir, Mirela Tase, Savvas Zotos, Nazli Demirel, Christos Zoumides, Irene Christoforidi, Turgay Dindaroglu, Tamer Albayrak, Cigdem Kaptan Ayhan, Mauro Fois, Paraskevi Manolaki, Attila D. Sandor, Ina Sieber, Valentini Stamatiadou, Elli Tzirkalli, Ioannis N. Vogiatzakis, Ziv Zemah-Shamir, George Zittis
Date: 2025-03-13 00:00:00
Tasks: Diversity

Islands are diversity hotspots and vulnerable to environmental degradation, climate variations, land use changes and societal crises. These factors can exhibit interactive impacts on ecosystem services. The study reviewed a large number of papers on the climate change-islands-ecosystem services topic worldwide. Potential inclusion of land use changes and other drivers of impacts on ecosystem services were sequentially also recorded. The study sought to investigate the impacts of climate change, land use change, and other non-climatic driver changes on island ecosystem services. Explanatory variables examined were divided into two categories: environmental variables and methodological ones. Environmental variables include sea zone geographic location, ecosystem, ecosystem services, climate, land use, other driver variables, Methodological variables include consideration of policy interventions, uncertainty assessment, cumulative effects of climate change, synergistic effects of climate change with land use change and other anthropogenic and environmental drivers, and the diversity of variables used in the analysis. Machine learning and statistical methods were used to analyze their effects on island ecosystem services. Negative climate change impacts on ecosystem services are better quantified by land use change or other non-climatic driver variables than by climate variables. The synergy of land use together with climate changes is modulating the impact outcome and critical for a better impact assessment. Analyzed together, there is little evidence of more pronounced for a specific sea zone, ecosystem, or ecosystem service. Climate change impacts may be underestimated due to the use of a single climate variable deployed in most studies. Policy interventions exhibit low classification accuracy in quantifying impacts indicating insufficient efficacy or integration in the studies.
Vector encoding of phylogenetic trees by ordered leaf attachment
Authors: Harry Richman, Cheng Zhang, Frederick A. Matsen IV
Date: 2025-03-13 00:00:00

As part of work to connect phylogenetics with machine learning, there has been considerable recent interest in vector encodings of phylogenetic trees. We present a simple new "ordered leaf attachment" (OLA) method for uniquely encoding a binary, rooted phylogenetic tree topology as an integer vector. OLA encoding and decoding take linear time in the number of leaf nodes, and the set of vectors corresponding to trees is a simply-described subset of integer sequences. The OLA encoding is unique compared to other existing encodings in having these properties. The integer vector encoding induces a distance on the set of trees, and we investigate this distance in relation to the NNI and SPR distances.
Maintaining diversity in structured populations
Authors: David A. Brewster, Jakub Svoboda, Dylan Roscow, Krishnendu Chatterjee, Josef Tkadlec, Martin A. Nowak
Date: 2025-03-12 00:00:00
Tasks: Diversity

We examine population structures for their ability to maintain diversity in neutral evolution. We use the general framework of evolutionary graph theory and consider birth-death (bd) and death-birth (db) updating. The population is of size $N$. Initially all individuals represent different types. The basic question is: what is the time $T_N$ until one type takes over the population? This time is known as consensus time in computer science and as total coalescent time in evolutionary biology. For the complete graph, it is known that $T_N$ is quadratic in $N$ for db and bd. For the cycle, we prove that $T_N$ is cubic in $N$ for db and bd. For the star, we prove that $T_N$ is cubic for bd and quasilinear ($N\log N$) for db. For the double star, we show that $T_N$ is quartic for bd. We derive upper and lower bounds for all undirected graphs for bd and db. We also show the Pareto front of graphs (of size $N=8$) that maintain diversity the longest for bd and db. Further, we show that some graphs that quickly homogenize can maintain high levels of diversity longer than graphs that slowly homogenize. For directed graphs, we give simple contracting star-like structures that have superexponential time scales for maintaining diversity.
Genome evolution in an endangered freshwater mussel
Authors: Rebekah L. Rogers, John P. Wares, Jeffrey T. Garner
Date: 2025-03-12 00:00:00

Nearly neutral theory predicts that evolutionary processes will differ in small populations compared to large populations, a key point of concern for endangered species. The nearly-neutral threshold, the span of neutral variation, and the adaptive potential from new mutations all differ depending on N_e. To determine how genomes respond in small populations, we have created a reference genome for a US federally endangered IUCN Red List freshwater mussel, Elliptio spinosa, and compare it to genetic variation for a common and successful relative, Elliptio crassidens. We find higher rates of background duplication rates in E. spinosa consistent with proposed theories of duplicate gene accumulation according to nearly-neutral processes. Along with these changes we observe fewer cases of adaptive gene family amplification in this endangered species. However, TE content is not consistent with nearly-neutral theory. We observe substantially less recent TE proliferation in the endangered species with over 500 Mb of newly copied TEs in Elliptio crassidens. These results suggest a more complex interplay between TEs and duplicate genes than previously proposed for small populations. They further suggest that TEs and duplications require greater attention in surveys of genomic health for endangered species.
Kalman Filter in the Problem of the Exchange and the Inflation Rates Adequacy To Determining Factors
Authors: N. S. Gonchar, W. H. Kozyrski, A. S. Zhokhin, O. P. Dovzhyk
Date: 2025-03-13 00:00:00
Tasks: Relation

Using introduced concept of the exchange and inflation rates adequacy, the relevance of them to the determining factors is found. We established close positive relation between hryvnia / dollar exchange and inflation rates, fiscal deficit, price level of energy sources, and money supply. On this basis, we give proposals for state macroeconomic policy to stabilize Ukrainian economy.
AgentDAM: Privacy Leakage Evaluation for Autonomous Web Agents
Authors: Arman Zharmagambetov, Chuan Guo, Ivan Evtimov, Maya Pavlova, Ruslan Salakhutdinov, Kamalika Chaudhuri
Date: 2025-03-12 00:00:00

LLM-powered AI agents are an emerging frontier with tremendous potential to increase human productivity. However, empowering AI agents to take action on their user's behalf in day-to-day tasks involves giving them access to potentially sensitive and private information, which leads to a possible risk of inadvertent privacy leakage when the agent malfunctions. In this work, we propose one way to address that potential risk, by training AI agents to better satisfy the privacy principle of data minimization. For the purposes of this benchmark, by "data minimization" we mean instances where private information is shared only when it is necessary to fulfill a specific task-relevant purpose. We develop a benchmark called AgentDAM to evaluate how well existing and future AI agents can limit processing of potentially private information that we designate "necessary" to fulfill the task. Our benchmark simulates realistic web interaction scenarios and is adaptable to all existing web navigation agents. We use AgentDAM to evaluate how well AI agents built on top of GPT-4, Llama-3 and Claude can limit processing of potentially private information when unnecessary, and show that these agents are often prone to inadvertent use of unnecessary sensitive information. We finally propose a prompting-based approach that reduces this.
A New Benchmark for Few-Shot Class-Incremental Learning: Redefining the Upper Bound
Authors: Shiwon Kim, Dongjun Hwang, Sungwon Woo, Rita Singh
Date: 2025-03-13 00:00:00
Tasks: class-incremental learning, Class Incremental Learning, Few-Shot Class-Incremental Learning, Incremental Learning

Class-incremental learning (CIL) aims to continuously adapt to emerging classes while retaining knowledge of previously learned ones. Few-shot class-incremental learning (FSCIL) presents an even greater challenge which requires the model to learn incremental classes with only a limited number of samples. In conventional CIL, joint training is widely considered the upper bound, serving as both a benchmark and a methodological guide. However, we find that joint training fails to be a meaningful upper bound in FSCIL due to the inherent difficulty of inter-task class separation (ICS) caused by severe class imbalance. In this work, we introduce a new joint training benchmark tailored for FSCIL by integrating imbalance-aware techniques, effectively bridging the performance gap between base and incremental classes. Furthermore, we point out inconsistencies in the experimental setup and evaluation of existing FSCIL methods. To ensure fair comparisons between different FSCIL approaches and joint training, we standardize training conditions and propose a unified evaluation protocol that simultaneously considers the validation set and computational complexity. By establishing a reliable upper bound and a standardized evaluation framework for FSCIL, our work provides a clear benchmark and a practical foundation for future research.
Advanced Tool Learning and Selection System (ATLASS): A Closed-Loop Framework Using LLM
Authors: Mohd Ariful Haque, Justin Williams, Sunzida Siddique, Md. Hujaifa Islam, Hasmot Ali, Kishor Datta Gupta, Roy George
Date: 2025-03-13 00:00:00

The combination of LLM agents with external tools enables models to solve complex tasks beyond their knowledge base. Human-designed tools are inflexible and restricted to solutions within the scope of pre-existing tools created by experts. To address this problem, we propose ATLASS, an advanced tool learning and selection system designed as a closed-loop framework. It enables the LLM to solve problems by dynamically generating external tools on demand. In this framework, agents play a crucial role in orchestrating tool selection, execution, and refinement, ensuring adaptive problem-solving capabilities. The operation of ATLASS follows three phases: The first phase, Understanding Tool Requirements, involves the Agents determining whether tools are required and specifying their functionality; the second phase, Tool Retrieval/Generation, involves the Agents retrieving or generating tools based on their availability; and the third phase, Task Solving, involves combining all the component tools necessary to complete the initial task. The Tool Dataset stores the generated tools, ensuring reusability and minimizing inference cost. Current LLM-based tool generation systems have difficulty creating complex tools that need APIs or external packages. In ATLASS, we solve the problem by automatically setting up the environment, fetching relevant API documentation online, and using a Python interpreter to create a reliable, versatile tool that works in a wider range of situations. OpenAI GPT-4.0 is used as the LLM agent, and safety and ethical concerns are handled through human feedback before executing generated code. By addressing the limitations of predefined toolsets and enhancing adaptability, ATLASS serves as a real-world solution that empowers users with dynamically generated tools for complex problem-solving.