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The Stroop effect refers to cognitive interference in a color-naming task: When the color and the word do not match, the response is slower and more likely to be incorrect. The Stroop task is used to assess cognitive flexibility, selective attention, and executive function. This paper implements the Stroop task with self-organizing maps (SOMs): Target color and the competing word are inputs for the semantic and lexical maps, associative connections bring color information to the lexical map, and lateral connections combine their effects over time. The model achieved an overall accuracy of 84.2%, with significantly fewer errors and faster responses in congruent compared to no-input and incongruent conditions. The model's effect is a side effect of optimizing response times, and can thus be seen as a cost associated with overall efficient performance. The model can further serve studying neurologically-inspired cognitive control and related phenomena., Comment: 7 pages, 6 figures

We present an interactive visualization of the Cell Map for AI Talent Knowledge Graph (CM4AI TKG), a detailed semantic space comprising approximately 28,000 experts and 1,000 datasets focused on the biomedical field. Our tool leverages transformer-based embeddings, WebGL visualization techniques, and generative AI, specifically Large Language Models (LLMs), to provide a responsive and user-friendly interface. This visualization supports the exploration of around 29,000 nodes, assisting users in identifying potential collaborators and dataset users within the health and biomedical research fields. Our solution transcends the limitations of conventional graph visualization tools like Gephi, particularly in handling large-scale interactive graphs. We utilize GPT-4o to furnish detailed justifications for recommended collaborators and dataset users, promoting informed decision-making. Key functionalities include responsive search and exploration, as well as GenAI-driven recommendations, all contributing to a nuanced representation of the convergence between biomedical and AI research landscapes. In addition to benefiting the Bridge2AI and CM4AI communities, this adaptable visualization framework can be extended to other biomedical knowledge graphs, fostering advancements in medical AI and healthcare innovation through improved user interaction and data exploration. The demonstration is available at: https://jiawei-alpha.vercel.app/., Comment: Accepted by GenAI for Health Workshop @ NeurIPS 2024, Vancouver

High-quality biomedical datasets are essential for medical research and disease treatment innovation. The NIH-funded Bridge2AI project strives to facilitate such innovations by uniting top-tier, diverse teams to curate datasets designed for AI-driven biomedical research. We examined 1,699 dataset papers from the Nucleic Acids Research (NAR) database issues and the Bridge2AI Talent Knowledge Graph. By treating each paper's authors as a team, we explored the relationship between team attributes (team power and fairness) and dataset paper quality, measured by scientific impact (Relative Citation Ratio percentile) and clinical translation power (APT, likelihood of citation by clinical trials and guidelines). Utilizing the SHAP explainable AI framework, we identified correlations between team attributes and the success of dataset papers in both citation impact and clinical translation. Key findings reveal that (1) PI (Principal Investigator) leadership and team academic prowess are strong predictors of dataset success; (2) team size and career age are positively correlated with scientific impact but show inverse patterns for clinical translation; and (3) higher female representation correlates with greater dataset success. Although our results are correlational, they offer valuable insights into forming high-performing data generation teams. Future research should incorporate causal frameworks to deepen understanding of these relationships., Comment: Accepted by JCDL 2024

ObitoNet employs a Cross Attention mechanism to integrate multimodal inputs, where Vision Transformers (ViT) extract semantic features from images and a point cloud tokenizer processes geometric information using Farthest Point Sampling (FPS) and K Nearest Neighbors (KNN) for spatial structure capture. The learned multimodal features are fed into a transformer-based decoder for high-resolution point cloud reconstruction. This approach leverages the complementary strengths of both modalities rich image features and precise geometric details ensuring robust point cloud generation even in challenging conditions such as sparse or noisy data.

We prove that for a finitely generated group G with a free factor system and an injective endomorphism that preserves the free factor system, the ascending HNN extension of G is hyperbolic relative to a collection of maximal parabolic subgroups. As a corollary, we see that if an injective endomorphism of a finite rank free group F is exponentially growing, the ascending HNN extension of F is relatively hyperbolic., Comment: 19 pages

Network performance modeling presents important challenges in modern computer networks due to increasing complexity, scale, and diverse traffic patterns. While traditional approaches like queuing theory and packet-level simulation have served as foundational tools, they face limitations in modeling complex traffic behaviors and scaling to large networks. This project presents an extended implementation of RouteNet-Fermi, a Graph Neural Network (GNN) architecture designed for network performance prediction, with additional recurrent neural network variants. We improve the the original architecture by implementing Long Short-Term Memory (LSTM) cells and Recurrent Neural Network (RNN) cells alongside the existing Gated Recurrent Unit (GRU) cells implementation. This work contributes to the understanding of recurrent neural architectures in GNN-based network modeling and provides a flexible framework for future experimentation with different cell types.

Data compression continues to evolve, with traditional information theory methods being widely used for compressing text, images, and videos. Recently, there has been growing interest in leveraging Generative AI for predictive compression techniques. This paper introduces a lossless text compression approach using a Large Language Model (LLM). The method involves two key steps: first, prediction using a dense neural network architecture, such as a transformer block; second, compressing the predicted ranks with standard compression algorithms like Adaptive Huffman, LZ77, or Gzip. Extensive analysis and benchmarking against conventional information-theoretic baselines demonstrate that neural compression offers improved performance.

Mathematical modelling has served a central role in studying how infectious disease transmission manifests at the population level. These models have demonstrated the importance of population-level factors like social network heterogeneity on structuring epidemic risk and are now routinely used in public health for decision support. One barrier to broader utility of mathematical models is that the existing canon does not readily accommodate the social determinants of health as distinct, formal drivers of transmission dynamics. Given the decades of empirical support for the organizational effect of social determinants on health burden more generally and infectious disease risk more specially, addressing this modelling gap is of critical importance. In this study, we build on prior efforts to integrate social forces into mathematical epidemiology by introducing several new metrics, principally structural causal influence (SCI). Here, SCI leverages causal analysis to provide a measure of the relative vulnerability of subgroups within a susceptible population, which are crafted by differences in healthcare, exposure to disease, and other determinants. We develop our metrics using a general case and apply it to specific one of public health importance: Hepatitis C virus in a population of persons who inject drugs. Our use of the SCI reveals that, under specific parameters in a multi-community model, the "less vulnerable" community may sustain a basic reproduction number below one when isolated, ensuring disease extinction. However, even minimal transmission between less and more vulnerable communities can elevate this number, leading to sustained epidemics within both communities. Summarizing, we reflect on our findings in light of conversations surrounding the importance of social inequalities and how their consideration can influence the study and practice of mathematical epidemiology., Comment: 8 figures, 8 tables

Let $\eta_{1}\ge \eta_{2}\ge\cdots\ge \eta_{n}$ be the eigenavalues of $\mathcal{ABS}$ matrix. In this paper, we characterize connected graphs with $\mathcal{ABS}$ eigenvalue $\eta_{n}>-1$. As a result, we determine all connected graphs with exactly two distinct $\mathcal{ABS}$ eigenvalues. We show that a connected bipartite graph has three distinct $\mathcal{ABS}$ eigenvalues if and only if it is a complete bipartite graph. Furthermore, we present some bounds for the $\mathcal{ABS}$ spectral radius (resp. $\mathcal{ABS}$ energy) and characterize extremal graphs. Also, we obtain a relation between $\mathcal{ABC}$ energy and $\mathcal{ABS}$ energy. Finally, the chemical importance of $\mathcal{ABS}$ energy is investigated and it shown that the $\mathcal{ABS}$ energy is useful in predicting certain properties of molecules.

We propose a lesion-aware graph neural network (LEGNet) to predict language ability from resting-state fMRI (rs-fMRI) connectivity in patients with post-stroke aphasia. Our model integrates three components: an edge-based learning module that encodes functional connectivity between brain regions, a lesion encoding module, and a subgraph learning module that leverages functional similarities for prediction. We use synthetic data derived from the Human Connectome Project (HCP) for hyperparameter tuning and model pretraining. We then evaluate the performance using repeated 10-fold cross-validation on an in-house neuroimaging dataset of post-stroke aphasia. Our results demonstrate that LEGNet outperforms baseline deep learning methods in predicting language ability. LEGNet also exhibits superior generalization ability when tested on a second in-house dataset that was acquired under a slightly different neuroimaging protocol. Taken together, the results of this study highlight the potential of LEGNet in effectively learning the relationships between rs-fMRI connectivity and language ability in a patient cohort with brain lesions for improved post-stroke aphasia evaluation., Comment: Accepted at MICCAI 2024 International Workshop on Machine Learning in Clinical Neuroimaging (MLCN)

Purpose: Estimation of multi-compartment intravoxel flow in fD in ml/100g/min with multi-b-value diffusion weighted imaging and a multi-Gaussian model in the kidneys. Theory and Methods: A multi-Gaussian model of intravoxel flow using water transport time to quantify fD is presented and simulated. Multi-compartment anisotropic DWI signal is simulated analyzed with (1) a rigid bi-exponential, (2) a rigid tri-exponential, and (3) diffusion spectrum imaging model of intravoxel incoherent motion (spectral diffusion). The application is demonstrated in a two-center study of 54 kidney allografts with 9 b-value advanced DWI that were split by function (CKD-EPI 2021 eGFR<45ml/min/1.73m2) and fibrosis (Banff 2017 interstitial fibrosis and tubular atrophy score 0-6). Results: Spectral diffusion demonstrated strong correlation to truth for simulated three-compartment anisotropic diffusion (y=1.08x+0.1, R2=0.71) and two-compartment anisotropic diffusion (y=0.91x+0.6, R2=0.74), outperforming rigid models in cases of variable compartment number. Use of a fixed regularization parameter set to {\lambda}=0.1 increased computation up to 208-fold and agreed with voxel-wise cross-validated regularization (concordance correlation coefficient=0.99). Spectral diffusion of renal allografts showed significant increase in tissue parenchyma compartment fD (f-stat=3.86, p=0.02). Tubular fD was significantly decreased in allografts with impaired function (Mann-Whitney Utest t-stat=-2.14, p=0.04). Conclusions: Quantitative multi-compartment intravoxel flow can be estimated in ml/100g/min with fD from multi-Gaussian diffusion, even with moderate anisotropy such as in kidneys. The use of spectral diffusion with a multi-Gaussian model and a fixed regularization parameter shows promise in organs such as the kidney with variable numbers of physiologic compartments.

On the advent of the slow death of Moore's law, the silicon industry is moving towards a new era of chiplets. The automotive industry is experiencing a profound transformation towards software-defined vehicles, fueled by the surging demand for automotive compute chips, expected to reach 20-22 billion by 2030. High-performance compute (HPC) chips become instrumental in meeting the soaring demand for computational power. Various strategies, including centralized electrical and electronic architecture and the innovative Chiplet Systems, are under exploration. The latter, breaking down System-on-Chips (SoCs) into functional units, offers unparalleled customization and integration possibilities. The research accentuates the crucial open Chiplet ecosystem, fostering collaboration and enhancing supply chain resilience. In this paper, we address the unique challenges that arise when attempting to leverage chiplet-based architecture to design a holistic silicon solution for the automotive industry. We propose a throughput-oriented micro-architecture for ADAS and infotainment systems alongside a novel methodology to evaluate chiplet architectures. Further, we develop in-house simulation tools leveraging the gem5 framework to simulate latency and throughput. Finally, we perform an extensive design of thermally-aware chiplet placement and develop a micro-fluids-based cooling design.

Chiral molecule sensing is currently performed using chromatography, electrophoresis, enzymatic-assays, mass spectrometry, and chiroptical sensing techniques. Currently polarimetry is the only method having in-vivo sensing capabilities, while other techniques analyze chiral molecules in body fluids. Polarimetry demonstrated in-vivo performance upto depths of 1 mm while using UV-Visbile light, beyond which light scattering tends to be dominant. We hypothesize that photoacoustic sensing while operating at the Near-Infrared (NIR)-II window can allow for deep-tissue sensing, due to reduced scattering/autofluorescence effects. Herein, a Photoacoustic based Polarization Enhanced Optical Rotation Sensing (PAPEORS) system was developed for the first time to estimate optical rotation parameter using the recorded PA signals at larger depths. This optical rotation was then used to correlate with the chiral molecular concentration at depths of about 3.5 mm. Experimental evaluations were performed with aqueous glucose solution, naproxen drug, and serum-based glucose samples. PAPEORS has achieved a detection limit of 80 mg/dL using circular incidence polarization with serum-based glucose samples. Further ex-vivo experiments were performed to demonstrate the ability of PAPEORS for deep-tissue sensing, which can be extended for in-vivo chiral molecular sensing. PAPEORS uses single wavelength for sensing chiral biomolecules, hence can be miniaturized, allowing for many point-of-care applications., Comment: 30 pages, 5 figures

Detector studies for future experiments rely on advanced software tools to estimate performance and optimize their design and technology choices. The Key4hep project provides a flexible turnkey solution for the full experiment life-cycle based on established community tools such as ROOT, Geant4, DD4hep, Gaudi, podio and spack. Members of the CEPC, CLIC, EIC, FCC, and ILC communities have joined to develop this framework and have merged, or are in the progress of merging, their respective software environments into the Key4hep stack. These proceedings will give an overview over the recent progress in the Key4hep project: covering the developments towards adaptation of state-of-the-art tools for simulation (DD4hep, Gaussino), track and calorimeter reconstruction (ACTS, CLUE), particle flow (PandoraPFA), analysis via RDataFrame, and visualization with Phoenix, as well as tools for testing and validation., Comment: Proceedings of CHEP 2023