pwc-archive/papers-with-abstracts huggingface dataset:
Stability Bounds for Smooth Optimal Transport Maps and their Statistical Implications
Authors: Sivaraman Balakrishnan, Tudor Manole
Date: 2025-02-17 00:00:00

We study estimators of the optimal transport (OT) map between two probability distributions. We focus on plugin estimators derived from the OT map between estimates of the underlying distributions. We develop novel stability bounds for OT maps which generalize those in past work, and allow us to reduce the problem of optimally estimating the transport map to that of optimally estimating densities in the Wasserstein distance. In contrast, past work provided a partial connection between these problems and relied on regularity theory for the Monge-Ampere equation to bridge the gap, a step which required unnatural assumptions to obtain sharp guarantees. We also provide some new insights into the connections between stability bounds which arise in the analysis of plugin estimators and growth bounds for the semi-dual functional which arise in the analysis of Brenier potential-based estimators of the transport map. We illustrate the applicability of our new stability bounds by revisiting the smooth setting studied by Manole et al., analyzing two of their estimators under more general conditions. Critically, our bounds do not require smoothness or boundedness assumptions on the underlying measures. As an illustrative application, we develop and analyze a novel tuning parameter-free estimator for the OT map between two strongly log-concave distributions.
NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generation
Authors: Zhiyuan Liu, Yanchen Luo, Han Huang, Enzhi Zhang, Sihang Li, Junfeng Fang, Yaorui Shi, Xiang Wang, Kenji Kawaguchi, Tat-Seng Chua
Date: 2025-02-18 00:00:00
Tasks: 3D Generation, 3D Molecule Generation, Drug Discovery, Language Modeling, Language Modelling, Transfer Learning

3D molecule generation is crucial for drug discovery and material design. While prior efforts focus on 3D diffusion models for their benefits in modeling continuous 3D conformers, they overlook the advantages of 1D SELFIES-based Language Models (LMs), which can generate 100% valid molecules and leverage the billion-scale 1D molecule datasets. To combine these advantages for 3D molecule generation, we propose a foundation model -- NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generation. NExT-Mol uses an extensively pretrained molecule LM for 1D molecule generation, and subsequently predicts the generated molecule's 3D conformers with a 3D diffusion model. We enhance NExT-Mol's performance by scaling up the LM's model size, refining the diffusion neural architecture, and applying 1D to 3D transfer learning. Notably, our 1D molecule LM significantly outperforms baselines in distributional similarity while ensuring validity, and our 3D diffusion model achieves leading performances in conformer prediction. Given these improvements in 1D and 3D modeling, NExT-Mol achieves a 26% relative improvement in 3D FCD for de novo 3D generation on GEOM-DRUGS, and a 13% average relative gain for conditional 3D generation on QM9-2014. Our codes and pretrained checkpoints are available at https://github.com/acharkq/NExT-Mol.
UniMatch: Universal Matching from Atom to Task for Few-Shot Drug Discovery
Authors: Ruifeng Li, Mingqian Li, Wei Liu, Yuhua Zhou, Xiangxin Zhou, Yuan YAO, Qiang Zhang, Hongyang Chen
Date: 2025-02-18 00:00:00
Tasks: Drug Discovery, Few-Shot Learning, Meta-Learning, molecular representation

Drug discovery is crucial for identifying candidate drugs for various diseases.However, its low success rate often results in a scarcity of annotations, posing a few-shot learning problem. Existing methods primarily focus on single-scale features, overlooking the hierarchical molecular structures that determine different molecular properties. To address these issues, we introduce Universal Matching Networks (UniMatch), a dual matching framework that integrates explicit hierarchical molecular matching with implicit task-level matching via meta-learning, bridging multi-level molecular representations and task-level generalization. Specifically, our approach explicitly captures structural features across multiple levels, such as atoms, substructures, and molecules, via hierarchical pooling and matching, facilitating precise molecular representation and comparison. Additionally, we employ a meta-learning strategy for implicit task-level matching, allowing the model to capture shared patterns across tasks and quickly adapt to new ones. This unified matching framework ensures effective molecular alignment while leveraging shared meta-knowledge for fast adaptation. Our experimental results demonstrate that UniMatch outperforms state-of-the-art methods on the MoleculeNet and FS-Mol benchmarks, achieving improvements of 2.87% in AUROC and 6.52% in delta AUPRC. UniMatch also shows excellent generalization ability on the Meta-MolNet benchmark.
Helix-mRNA: A Hybrid Foundation Model For Full Sequence mRNA Therapeutics
Authors: Matthew Wood, Mathieu Klop, Maxime Allard
Date: 2025-02-19 00:00:00

mRNA-based vaccines have become a major focus in the pharmaceutical industry. The coding sequence as well as the Untranslated Regions (UTRs) of an mRNA can strongly influence translation efficiency, stability, degradation, and other factors that collectively determine a vaccine's effectiveness. However, optimizing mRNA sequences for those properties remains a complex challenge. Existing deep learning models often focus solely on coding region optimization, overlooking the UTRs. We present Helix-mRNA, a structured state-space-based and attention hybrid model to address these challenges. In addition to a first pre-training, a second pre-training stage allows us to specialise the model with high-quality data. We employ single nucleotide tokenization of mRNA sequences with codon separation, ensuring prior biological and structural information from the original mRNA sequence is not lost. Our model, Helix-mRNA, outperforms existing methods in analysing both UTRs and coding region properties. It can process sequences 6x longer than current approaches while using only 10% of the parameters of existing foundation models. Its predictive capabilities extend to all mRNA regions. We open-source the model (https://github.com/helicalAI/helical) and model weights (https://huggingface.co/helical-ai/helix-mRNA).
Structural determinants of soft memory in recurrent biological networks
Authors: Maria Sol Vidal-Saez, Jordi Garcia-Ojalvo
Date: 2025-02-19 00:00:00

Recurrent neural networks are frequently studied in terms of their information-processing capabilities. The structural properties of these networks are seldom considered, beyond those emerging from the connectivity tuning necessary for network training. However, real biological networks have non-contingent architectures that have been shaped by evolution over eons, constrained partly by information-processing criteria, but more generally by fitness maximization requirements. Here we examine the topological properties of existing biological networks, focusing in particular on gene regulatory networks in bacteria. We identify structural features, both local and global, that dictate the ability of recurrent networks to store information on the fly and process complex time-dependent inputs.
Freezing of Gait as a Complication of Pallidal Deep Brain Stimulation in DYT- KMT2B Patients with Evidence of Striatonigral Degeneration
Authors: Laura Cif, Diane Demailly, Xavier Vasques, Delphine de Verbizier, Philippe Coubes, Kathleen Gorman, Manju A Kurian
Date: 2025-02-19 00:00:00

Background: Mutations in KMT2B are a recognized cause of early-onset complex dystonia, with deep brain stimulation (DBS) of the internal globus pallidus (GPi-DBS) being an effective treatment. However, gait impairment, particularly freezing of gait (FOG), remains a significant challenge in DYT-KMT2B patients post-DBS. Objectives: To characterize the emergence of FOG in DYT-KMT2B patients treated with GPi-DBS and explore potential underlying mechanisms, including striatonigral degeneration. Methods: Five patients (four females) with KMT2B-related dystonia and protein-truncating variants (PTVs) were retrospectively analyzed. Clinical progression, response to GPi-DBS, and the presence of FOG were documented. Dopaminergic function was assessed using DaTscan (SPECT for ^123I-ioflupane) in four patients. Results: FOG developed in all patients, with onset ranging from 1 to 15.5 years post-DBS. DaTscan abnormalities, indicative of bilateral striatal dopaminergic denervation, were observed in four cases. Prior to DBS, all patients exhibited dystonia unresponsive to L-dopa, and post-DBS, FOG remained refractory to dopaminergic treatment in most cases. Despite initial improvements in gait post-DBS, only one patient maintained independent ambulation at the last follow-up. Conclusions: FOG is an emerging complication in DYT-KMT2B patients with PTVs undergoing GPi-DBS, potentially linked to underlying striatonigral degeneration. The findings suggest a need for long-term motor surveillance and consideration of alternative therapeutic strategies, including dopaminergic trials, in this patient population. Further studies are required to elucidate the precise mechanisms driving DBS-related hypokinetic gait disturbances in DYT-KMT2B dystonia.
Emergence of the Primacy Effect in Structured State-Space Models
Authors: Takashi Morita
Date: 2025-02-19 00:00:00
Tasks: State Space Models

Human and animal memory for sequentially presented items is well-documented to be more accurate for those at the beginning and end of the sequence, phenomena known as the primacy and recency effects, respectively. By contrast, artificial neural network (ANN) models are typically designed with a memory that decays monotonically over time. Accordingly, ANNs are expected to show the recency effect but not the primacy effect. Contrary to this theoretical expectation, however, the present study reveals a counterintuitive finding: a recently developed ANN architecture, called structured state-space models, exhibits the primacy effect when trained and evaluated on a synthetic task that mirrors psychological memory experiments. Given that this model was originally designed for recovering neuronal activity patterns observed in biological brains, this result provides a novel perspective on the psychological primacy effect while also posing a non-trivial puzzle for the current theories in machine learning.
Long-term follow-up of DYT1 dystonia patients treated by deep brain stimulation: an open-label study
Authors: Laura Cif, Xavier Vasques, Victoria Gonzalez, Patrice Ravel, Brigitte Biolsi, Gwenaelle Collod-Beroud, Sylvie Tuffery-Giraud, Hassan Elfertit, Mireille Claustres, Philippe Coubes
Date: 2025-02-19 00:00:00

Long-term efficacy of internal globus pallidus (GPi) deep-brain stimulation (DBS) in DYT1 dystonia and disease progression under DBS was studied. Twenty-six patients of this open-label study were divided into two groups: (A) with single bilateral GPi lead, (B) with a second bilateral GPi lead implanted owning to subsequent worsening of symptomatology. Dystonia was assessed with the Burke Scale. Appearance of new symptoms and distribution according to body region were recorded. In the whole cohort, significant decreases in motor and disability subscores (P < 0.0001) were observed at 1 year and maintained up to 10 years. Group B showed worsening of the symptoms. At 1 year, there were no significant differences between Groups A (without subsequent worsening) and B; at 5 years, a significant difference was found for motor and disability scores. Within Group B, four patients exhibited additional improvement after the second DBS surgery. In the 26 patients, significant difference (P = 0.001) was found between the number of body regions affected by dystonia preoperatively and over the whole follow-up. DBS efficacy in DYT1 dystonia can be maintained up to 10 years (two patients). New symptoms appear with long-term follow-up and may improve with additional leads in a subgroup of patients.
LaVCa: LLM-assisted Visual Cortex Captioning
Authors: Takuya Matsuyama, Shinji Nishimoto, Yu Takagi
Date: 2025-02-19 00:00:00

Understanding the property of neural populations (or voxels) in the human brain can advance our comprehension of human perceptual and cognitive processing capabilities and contribute to developing brain-inspired computer models. Recent encoding models using deep neural networks (DNNs) have successfully predicted voxel-wise activity. However, interpreting the properties that explain voxel responses remains challenging because of the black-box nature of DNNs. As a solution, we propose LLM-assisted Visual Cortex Captioning (LaVCa), a data-driven approach that uses large language models (LLMs) to generate natural-language captions for images to which voxels are selective. By applying LaVCa for image-evoked brain activity, we demonstrate that LaVCa generates captions that describe voxel selectivity more accurately than the previously proposed method. Furthermore, the captions generated by LaVCa quantitatively capture more detailed properties than the existing method at both the inter-voxel and intra-voxel levels. Furthermore, a more detailed analysis of the voxel-specific properties generated by LaVCa reveals fine-grained functional differentiation within regions of interest (ROIs) in the visual cortex and voxels that simultaneously represent multiple distinct concepts. These findings offer profound insights into human visual representations by assigning detailed captions throughout the visual cortex while highlighting the potential of LLM-based methods in understanding brain representations. Please check out our webpage at https://sites.google.com/view/lavca-llm/
Dynamic directed functional connectivity as a neural biomarker for objective motor skill assessment
Authors: Anil Kamat, Rahul Rahul, Anirban Dutta, Lora Cavuoto, Uwe Kruger, Harry Burke, Matthew Hackett, Jack Norfleet, Steven Schwaitzberg, Suvranu De
Date: 2025-02-19 00:00:00
Tasks: EEG, Functional Connectivity, Specificity

Objective motor skill assessment plays a critical role in fields such as surgery, where proficiency is vital for certification and patient safety. Existing assessment methods, however, rely heavily on subjective human judgment, which introduces bias and limits reproducibility. While recent efforts have leveraged kinematic data and neural imaging to provide more objective evaluations, these approaches often overlook the dynamic neural mechanisms that differentiate expert and novice performance. This study proposes a novel method for motor skill assessment based on dynamic directed functional connectivity (dFC) as a neural biomarker. By using electroencephalography (EEG) to capture brain dynamics and employing an attention-based Long Short-Term Memory (LSTM) model for non-linear Granger causality analysis, we compute dFC among key brain regions involved in psychomotor tasks. Coupled with hierarchical task analysis (HTA), our approach enables subtask-level evaluation of motor skills, offering detailed insights into neural coordination that underpins expert proficiency. A convolutional neural network (CNN) is then used to classify skill levels, achieving greater accuracy and specificity than established performance metrics in laparoscopic surgery. This methodology provides a reliable, objective framework for assessing motor skills, contributing to the development of tailored training protocols and enhancing the certification process.
Neuro-oscillatory models of cortical speech processing
Authors: Olesia Dogonasheva, Denis Zakharov, Anne-Lise Giraud, Boris Gutkin
Date: 2025-02-18 00:00:00
Tasks: speech-recognition, Speech Recognition

In this review, we examine computational models that explore the role of neural oscillations in speech perception, spanning from early auditory processing to higher cognitive stages. We focus on models that use rhythmic brain activities, such as gamma, theta, and delta oscillations, to encode phonemes, segment speech into syllables and words, and integrate linguistic elements to infer meaning. We analyze the mechanisms underlying these models, their biological plausibility, and their potential applications in processing and understanding speech in real time, a computational feature that is achieved by the human brain but not yet implemented in speech recognition models. Real-time processing enables dynamic adaptation to incoming speech, allowing systems to handle the rapid and continuous flow of auditory information required for effective communication, interactive applications, and accurate speech recognition in a variety of real-world settings. While significant progress has been made in modeling the neural basis of speech perception, challenges remain, particularly in accounting for the complexity of semantic processing and the integration of contextual influences. Moreover, the high computational demands of biologically realistic models pose practical difficulties for their implementation and analysis. Despite these limitations, these models provide valuable insights into the neural mechanisms of speech perception. We conclude by identifying current limitations, proposing future research directions, and suggesting how these models can be further developed to achieve a more comprehensive understanding of speech processing in the human brain.
Mind the Gap: Aligning the Brain with Language Models Requires a Nonlinear and Multimodal Approach
Authors: Danny Dongyeop Han, Yunju Cho, Jiook Cha, Jay-Yoon Lee
Date: 2025-02-18 00:00:00
Tasks: Prediction

Self-supervised language and audio models effectively predict brain responses to speech. However, traditional prediction models rely on linear mappings from unimodal features, despite the complex integration of auditory signals with linguistic and semantic information across widespread brain networks during speech comprehension. Here, we introduce a nonlinear, multimodal prediction model that combines audio and linguistic features from pre-trained models (e.g., LLAMA, Whisper). Our approach achieves a 17.2% and 17.9% improvement in prediction performance (unnormalized and normalized correlation) over traditional unimodal linear models, as well as a 7.7% and 14.4% improvement, respectively, over prior state-of-the-art models. These improvements represent a major step towards future robust in-silico testing and improved decoding performance. They also reveal how auditory and semantic information are fused in motor, somatosensory, and higher-level semantic regions, aligning with existing neurolinguistic theories. Overall, our work highlights the often neglected potential of nonlinear and multimodal approaches to brain modeling, paving the way for future studies to embrace these strategies in naturalistic neurolinguistics research.
Time-series attribution maps with regularized contrastive learning
Authors: Steffen Schneider, Rodrigo González Laiz, Anastasiia Filippova, Markus Frey, Mackenzie Weygandt Mathis
Date: 2025-02-17 00:00:00
Tasks: Contrastive Learning, Time Series

Gradient-based attribution methods aim to explain decisions of deep learning models but so far lack identifiability guarantees. Here, we propose a method to generate attribution maps with identifiability guarantees by developing a regularized contrastive learning algorithm trained on time-series data plus a new attribution method called Inverted Neuron Gradient (collectively named xCEBRA). We show theoretically that xCEBRA has favorable properties for identifying the Jacobian matrix of the data generating process. Empirically, we demonstrate robust approximation of zero vs. non-zero entries in the ground-truth attribution map on synthetic datasets, and significant improvements across previous attribution methods based on feature ablation, Shapley values, and other gradient-based methods. Our work constitutes a first example of identifiable inference of time-series attribution maps and opens avenues to a better understanding of time-series data, such as for neural dynamics and decision-processes within neural networks.
Noumenal Labs White Paper: How To Build A Brain
Authors: Maxwell J. D. Ramstead, Candice Pattisapu, Jason Fox, Jeff Beck
Date: 2025-02-16 00:00:00
Tasks: Time Series Analysis

This white paper describes some of the design principles for artificial or machine intelligence that guide efforts at Noumenal Labs. These principles are drawn from both nature and from the means by which we come to represent and understand it. The end goal of research and development in this field should be to design machine intelligences that augment our understanding of the world and enhance our ability to act in it, without replacing us. In the first two sections, we examine the core motivation for our approach: resolving the grounding problem. We argue that the solution to the grounding problem rests in the design of models grounded in the world that we inhabit, not mere word models. A machine super intelligence that is capable of significantly enhancing our understanding of the human world must represent the world as we do and be capable of generating new knowledge, building on what we already know. In other words, it must be properly grounded and explicitly designed for rational, empirical inquiry, modeled after the scientific method. A primary implication of this design principle is that agents must be capable of engaging autonomously in causal physics discovery. We discuss the pragmatic implications of this approach, and in particular, the use cases in realistic 3D world modeling and multimodal, multidimensional time series analysis.
Stability of difference equations with interspecific density dependence, competition, and maturation delays
Authors: Geoffrey R. Hosack, Maud El-Hachem, Nicholas J. Beeton
Date: 2025-02-19 00:00:00

A general system of difference equations is presented for multispecies communities with density dependent population growth and delayed maturity. Interspecific competition, mutualism, predation, commensalism, and amensalism are accommodated. A sufficient condition for the local asymptotic stability of a coexistence equilibrium in this system is then proven. Using this system, the generalisation of the Beverton-Holt and Leslie-Gower models of competition to multispecies systems with possible maturation delays is presented and shown to yield interesting stability properties. The stability of coexistence depends on the relative abundances of the species at the unique interior equilibrium. A sufficient condition for local stability is derived that only requires intraspecific competition to outweigh interspecific competition. The condition does not depend on maturation delays. The derived stability properties are used to develop a novel estimation approach for the coefficients of interspecific competition. This approach finds an optimal configuration given the conjecture that coexisting species strive to outcompete competitors and are most likely observed near the stable interior equilibrium with the greatest dampening of perturbations. The optimal solution is compared to estimates of niche overlap using an empirical example of malaria mosquito vectors with delayed maturity in the Anopheles gambiae sensu lato species complex.