Miracle-dz/UniFAR-FTUs huggingface dataset:
Neuron-Astrocyte Associative Memory

Astrocytes, a unique type of glial cell, are thought to play a significant role in memory due to their involvement in modulating synaptic plasticity. Nonetheless, no existing theories explain how neurons, synapses, and astrocytes could collectively contribute to memory function. To address this, we propose a biophysical model of neuron-astrocyte interactions that unifies various viewpoints on astrocyte function in a principled, biologically-grounded framework. A key aspect of the model is that astrocytes mediate long-range interactions between distant tripartite synapses. This effectively creates ``multi-neuron synapses"where more than two neurons interact at the same synapse. Such multi-neuron synapses are ubiquitous in models of Dense Associative Memory (also known as Modern Hopfield Networks) and are known to lead to superlinear memory storage capacity, which is a desirable computational feature. We establish a theoretical relationship between neuron-astrocyte networks and Dense Associative Memories and demonstrate that neuron-astrocyte networks have a larger memory storage capacity per compute unit compared to previously published biological implementations of Dense Associative Memories. This theoretical correspondence suggests the exciting hypothesis that memories could be stored, at least partially, within astrocytes instead of in the synaptic weights between neurons. Importantly, the many-neuron synapses can be influenced by feedforward signals into the astrocytes, such as neuromodulators, potentially originating from distant neurons.
Updated Big Bang Nucleosynthesis Bounds on Long-lived Particles from Dark Sectors

As electromagnetic showers may alter the abundance of Helium, Lithium, and Deuterium, we can place severe constraints on the lifetime and amount of electromagnetic energy injected by long-lived particles. Considering up-to-date measurements of the light element abundances that point to $Y_p=0.245\pm 0.003$,$({\rm D/H})= (2.527\pm 0.03)\times 10^{-5}$,$(^7{\rm Li/H})=1.58 ^{+0.35}_{-0.28} \times 10^{-10}$, $(^6{\rm Li/^7 Li})=0.05$, and the baryon-to-photon ratio obtained from the Cosmic Microwave Background data, $\eta=6.104 \times 10^{-10}$, we derive upper limits on the fraction of electromagnetic energy produced by long-lived particles. Our findings apply to decaying dark matter models, long-lived gravitinos, and other non-thermal processes that occurred in the early universe between $10^2-10^{10}$ seconds.
Investigating the influence of two different flow routing algorithms on soil–water–vegetation interactions using the dynamic ecosystem model LPJ‐GUESS

This paper compares two flow routing algorithms' influences on ecohydrological estimations in a northern peatland catchment, within the framework of an arctic‐enabled version of the dynamic ecosystem model LPJ‐GUESS. Accurate hydrological estimations are needed to fully capture vegetation dynamics and carbon fluxes in the subarctic peatland enviroment. A previously proposed distributed hydrological method based on the single flow (SF) algorithm extracted topographic indices has shown to improve runoff estimations in LPJ‐GUESS. This paper investigates model performance differences caused by two flow routing algorithms, and importantly both permafrost processes and peatland hydrology are included in the model. The newly developed triangular form‐based multiple flow (TFM) is selected due to its improved consideration of flow continuity and more realistic flow estimation over flat surfaces. A variety of measured data is included to assess both hydrological and ecological accuracy, and the results demonstrate that the choice of flow algorithm does matter for mesoscale ecohydrology applications. The allowance of flow convergence and consideration of flow partition differences from different terrain forms in the TFM algorithm yield better correspondence with the observed hydrological processes and also carbon fluxes. By directing flow to only one downslope cell together with its poorer depiction of flow over flat areas, the SF algorithm can result in too high runoff estimations for low‐flat regions and overestimate carbon uptake and release in the peatland. The results of this study also highlight the need for care when selecting flow routing algorithms for biogeochemical estimations, especially within hydrologically and climatically sensitive environments. Copyright © 2014 John Wiley & Sons, Ltd.
Non-affinity of liquid networks and bicontinuous mesophases

Amphiphiles self-assemble into a variety of bicontinuous mesophases whose equilibrium structures take the form of high-symmetry cubic networks. Here, we show that the symmetry-breaking distortions in these systems give rise to anomalously large, non-affine collective deformations, which we argue to be a generic consequence of mass equilibration within deformed networks. We propose and study a minimal liquid network model of bicontinuous networks, in which acubic distortions are modeled by the relaxation of residually-stressed mechanical networks with constant-tension bonds. We show that non-affinity is strongly dependent on the valency of the network as well as the degree of strain-softening/stiffening force in the bonds. Taking diblock copolymer melts as a model system, liquid network theory captures quantitative features of two bicontinuous phases based on comparison with self-consistent field theory predictions and direct experimental characterization of acubic distortions, which are likely to be pronounced in soft amphiphilic systems more generally.
Analysis of a tensor POD-ROM for parameter dependent parabolic problems

A space-time-parameters structure of the parametric parabolic PDEs motivates the application of tensor methods to define reduced order models (ROMs). Within a tensor-based ROM framework, the matrix SVD -- a traditional dimension reduction technique -- yields to a low-rank tensor decomposition (LRTD). Such tensor extension of the Galerkin proper orthogonal decomposition ROMs (POD-ROMs) benefits both the practical efficiency of the ROM and its amenability for the rigorous error analysis when applied to parametric PDEs. The paper addresses the error analysis of the Galerkin LRTD-ROM for an abstract linear parabolic problem that depends on multiple physical parameters. An error estimate for the LRTD-ROM solution is proved, which is uniform with respect to problem parameters and extends to parameter values not in a sampling/training set. The estimate is given in terms of discretization and sampling mesh properties, and LRTD accuracy. The estimate depends on the smoothness rather than on the Kolmogorov n-widths of the parameterized manifold of solutions. Theoretical results are illustrated with several numerical experiments.
GMTR: Graph Matching Transformers

Vision transformers (ViTs) have recently been used for visual matching beyond object detection and segmentation. However, the original grid dividing strategy of ViTs neglects the spatial information of the keypoints, limiting the sensitivity to local information. Therefore, we propose QueryTrans (Query Transformer), which adopts a cross-attention module and keypoints-based center crop strategy for better spatial information extraction. We further integrate the graph attention module and devise a transformer-based graph matching approach GMTR (Graph Matching TRansformers) whereby the combinatorial nature of GM is addressed by a graph transformer neural GM solver. On standard GM benchmarks, GMTR shows competitive performance against the SOTA frameworks. Specifically, on Pascal VOC, GMTR achieves $\mathbf{83.6\%}$ accuracy, $\mathbf{0.9\%}$ higher than the SOTA framework. On Spair-71k, GMTR shows great potential and outperforms most of the previous works. Meanwhile, on Pascal VOC, QueryTrans improves the accuracy of NGMv2 from $80.1\%$ to $\mathbf{83.3\%}$, and BBGM from $79.0\%$ to $\mathbf{84.5\%}$. On Spair-71k, QueryTrans improves NGMv2 from $80.6\%$ to $\mathbf{82.5\%}$, and BBGM from $82.1\%$ to $\mathbf{83.9\%}$. Source code will be made publicly available.
Upper Ocean Water Masses and Transports in the Western Tropical Pacific (165°E)

Abstract As part of the international TOGA program, the ORSTOM Center in Noumea (New Caledonia) initiated in January 1984 a series of semi-annual cruises along the 165°E meridian from 20°S to 10°N, across the equatorial current system of the western Pacific. This paper presents an analysis of the first six hydrographic (0-1000 m) and current (0-600 m) sections. A detailed description of “typical” January 1986 vertical structures of temperature, salinity and zonal measured velocity is offered. Differences are noted with structures previously obtained in the tropical Pacific. Compared to the central and eastern Pacific, the 165°E dataset evidences a much weaker equatorial upwelling and deeper surface isothermal layer and subsurface currents. Compared to the few western Pacific measurements, the two speed cores of the Equatorial Undercurrent (EUC) previously reported at 100 and 200 m are not observed here. Special attention is given to the eastward equatorial jet (2°S-2°N; 0-75 m) measured in January 1985 wh.
Stochastic Optimization of Large-Scale Parametrized Dynamical Systems

Many relevant problems in the area of systems and control, such as controller synthesis, observer design and model reduction, can be viewed as optimization problems involving dynamical systems: for instance, maximizing performance in the synthesis setting or minimizing error in the reduction setting. When the involved dynamics are large-scale (e.g., high-dimensional semi-discretizations of partial differential equations), the optimization becomes computationally infeasible. Existing methods in literature lack computational scalability or solve an approximation of the problem (thereby losing guarantees with respect to the original problem). In this paper, we propose a novel method that circumvents these issues. The method is an extension of Stochastic Gradient Descent (SGD) which is widely used in the context of large-scale machine learning problems. The proposed SGD scheme minimizes the $\mathcal{H}_2$ norm of a (differentiable) parametrized dynamical system, and we prove that the scheme is guaranteed to preserve stability with high probability under boundedness conditions on the step size. Conditioned on the stability preservation, we also obtain probabilistic convergence guarantees to local minimizers. The method is also applicable to problems involving non-realizable dynamics as it only requires frequency-domain input-output samples. We demonstrate the potential of the approach on two numerical examples: fixed-order observer design for a large-scale thermal model and controller tuning for an infinite-dimensional system.
Reinforcement Learning for Solving Stochastic Vehicle Routing Problem

This study addresses a gap in the utilization of Reinforcement Learning (RL) and Machine Learning (ML) techniques in solving the Stochastic Vehicle Routing Problem (SVRP) that involves the challenging task of optimizing vehicle routes under uncertain conditions. We propose a novel end-to-end framework that comprehensively addresses the key sources of stochasticity in SVRP and utilizes an RL agent with a simple yet effective architecture and a tailored training method. Through comparative analysis, our proposed model demonstrates superior performance compared to a widely adopted state-of-the-art metaheuristic, achieving a significant 3.43% reduction in travel costs. Furthermore, the model exhibits robustness across diverse SVRP settings, highlighting its adaptability and ability to learn optimal routing strategies in varying environments. The publicly available implementation of our framework serves as a valuable resource for future research endeavors aimed at advancing RL-based solutions for SVRP.
Activity Sparsity Complements Weight Sparsity for Efficient RNN Inference

Artificial neural networks open up unprecedented machine learning capabilities at the cost of ever growing computational requirements. Sparsifying the parameters, often achieved through weight pruning, has been identified as a powerful technique to compress the number of model parameters and reduce the computational operations of neural networks. Yet, sparse activations, while omnipresent in both biological neural networks and deep learning systems, have not been fully utilized as a compression technique in deep learning. Moreover, the interaction between sparse activations and weight pruning is not fully understood. In this work, we demonstrate that activity sparsity can compose multiplicatively with parameter sparsity in a recurrent neural network model based on the GRU that is designed to be activity sparse. We achieve up to $20\times$ reduction of computation while maintaining perplexities below $60$ on the Penn Treebank language modeling task. This magnitude of reduction has not been achieved previously with solely sparsely connected LSTMs, and the language modeling performance of our model has not been achieved previously with any sparsely activated recurrent neural networks or spiking neural networks. Neuromorphic computing devices are especially good at taking advantage of the dynamic activity sparsity, and our results provide strong evidence that making deep learning models activity sparse and porting them to neuromorphic devices can be a viable strategy that does not compromise on task performance. Our results also drive further convergence of methods from deep learning and neuromorphic computing for efficient machine learning.
GreekT5: A Series of Greek Sequence-to-Sequence Models for News Summarization

Text summarization (TS) is a natural language processing (NLP) subtask pertaining to the automatic formulation of a concise and coherent summary that covers the major concepts and topics from one or multiple documents. Recent advancements in deep learning have led to the development of abstractive summarization transformer-based models, which outperform classical approaches. In any case, research in this field focuses on high resource languages such as English, while the corresponding work for low resource languages is still underdeveloped. Taking the above into account, this paper proposes a series of novel TS models for Greek news articles. The proposed models were thoroughly evaluated on the same dataset against GreekBART, which is the state-of-the-art model in Greek abstractive news summarization. Our evaluation results reveal that most of the proposed models significantly outperform GreekBART on various evaluation metrics. We make our evaluation code public, aiming to increase the reproducibility of this work and facilitate future research in the field.
Slice monogenic theta series

In this paper we introduce a generalization of theta series in the context of the slice monogenic function theory in $\mathbb{R}^{n+1}$ where me make use of the so-called $*$-exponential function in a hypercomplex variable. Together with the Eisenstein and Poincar\'e series that we introduced in a previous paper, the theta series construction in this paper completes the fundament of the basic theory of modular forms in the slice monogenic setting. We introduce a suitable generalized Poisson summation formula in this framework and we apply a properly adapted Fourier transform. As a direct application we prove a transformation formula for slice monogenic theta series. Then we introduce a family of conjugated theta functions. These are used to construct a slice monogenic generalization of the third power of the Dedekind eta function and of the modular discriminant. We also investigate their transformation behavior. Finally, we show that these theta series are special solutions to a generalization of the heat equation associated with the slice derivative. We round off by discussing the monogenic case.
The GōMartini Approach: Revisiting the Concept of Contact Maps and the Modelling of Protein Complexes

We present a review of a series of contact maps for the determination of native interactions in proteins and nucleic acids based on a distance-threshold. Such contact maps are mostly based on physical and chemical construction, and yet they are sensitive to some parameters (e.g. distances or atomic radii) and can neglect some key interactions. Furthermore, we also comment on a new class of contact maps that only requires geometric arguments. The contact map is a necessary ingredient to build a robust G\=oMartini model for proteins and their complexes in the Martini 3 force field. We present the extension of a popular structure-based G\=o-like approach for the study of protein-sugar complexes, and also limitations of this approach are discussed. The G\=oMartini approach was first introduced by Poma et al. J. Chem. Theory Comput. 2017, 13(3), 1366-1374 in Martini 2 force field and recently, it has gained the status of gold-standard for protein simulation undergoing conformational changes in Martini 3 force field. We discuss several studies that have provided support to this approach in the context of the biophysical community.
Magnetogenesis from Anisotropic Universe

The existence of large-scale anisotropy can not be ruled out by the cosmic microwave background (CMB) radiation. Over the years, several models have been proposed in the context of anisotropic inflation to account for CMB's cold spot and hemispheric asymmetry. However, any small-scale anisotropy, if exists during inflation, is not constrained due to its nonlinear evolution in the subsequent phase. This small-scale anisotropy during inflation can play a non-trivial role in giving rise to the cosmic magnetic field, which is the subject of our present study. Assuming a particular phenomenological form of an anisotropic inflationary universe, we have shown that it can generate a large-scale magnetic field at $1$-Mpc scale with a magnitude $\sim 4\times 10^{-20}~G$, within the observed bound. Because of the anisotropy, the conformal flatness property is lost, and the Maxwell field is generated even without explicit coupling. This immediately resolves the strong coupling problem in the standard magnetogenesis scenario. In addition, assuming very low conductivity during the reheating era, we can further observe the evolution of the electromagnetic field with the equation of state (EoS) $\omega_{eff}$ and its effects on the present-day magnetic field.
Modeling Complex Disease Trajectories using Deep Generative Models with Semi-Supervised Latent Processes

In this paper, we propose a deep generative time series approach using latent temporal processes for modeling and holistically analyzing complex disease trajectories. We aim to find meaningful temporal latent representations of an underlying generative process that explain the observed disease trajectories in an interpretable and comprehensive way. To enhance the interpretability of these latent temporal processes, we develop a semi-supervised approach for disentangling the latent space using established medical concepts. By combining the generative approach with medical knowledge, we leverage the ability to discover novel aspects of the disease while integrating medical concepts into the model. We show that the learned temporal latent processes can be utilized for further data analysis and clinical hypothesis testing, including finding similar patients and clustering the disease into new sub-types. Moreover, our method enables personalized online monitoring and prediction of multivariate time series including uncertainty quantification. We demonstrate the effectiveness of our approach in modeling systemic sclerosis, showcasing the potential of our machine learning model to capture complex disease trajectories and acquire new medical knowledge.
Impact of zonal wind on latitudinal variation of total columnar ozone over the Indian Peninsula

Latitudinal and seasonal variability of total columnar ozone from September 2007 to August 2008 across the Indian longitude sector within 10.5° N to 34.5° N and 70.5° E to 94.5° E using satellite data obtained from Aura Ozone Monitoring Instrument (OMI) of National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) is presented. The total column ozone (TCO) over the area of study shows a gradually varying pattern throughout the year. In the post-monsoon (autumn) and winter months, maximum TCO is observed in the north-western part of the subcontinent while the minimum is often observed towards the east at about the same latitudes. A west–east spatial gradient is clearly observed in autumn months. As winter approaches, a north–south spatial gradient becomes more prominent than the east–west gradient. It has been further observed that TCO does not vary significantly over the entire subcontinent in monsoon.
The Impact of Adversarial Node Placement in Decentralized Federated Learning Networks

As Federated Learning (FL) grows in popularity, new decentralized frameworks are becoming widespread. These frameworks leverage the benefits of decentralized environments to enable fast and energy-efficient inter-device communication. However, this growing popularity also intensifies the need for robust security measures. While existing research has explored various aspects of FL security, the role of adversarial node placement in decentralized networks remains largely unexplored. This paper addresses this gap by analyzing the performance of decentralized FL for various adversarial placement strategies when adversaries can jointly coordinate their placement within a network. We establish two baseline strategies for placing adversarial node: random placement and network centrality-based placement. Building on this foundation, we propose a novel attack algorithm that prioritizes adversarial spread over adversarial centrality by maximizing the average network distance between adversaries. We show that the new attack algorithm significantly impacts key performance metrics such as testing accuracy, outperforming the baseline frameworks by between $9\%$ and $66.5\%$ for the considered setups. Our findings provide valuable insights into the vulnerabilities of decentralized FL systems, setting the stage for future research aimed at developing more secure and robust decentralized FL frameworks.
Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback

In this work, we study the low-rank MDPs with adversarially changed losses in the full-information feedback setting. In particular, the unknown transition probability kernel admits a low-rank matrix decomposition \citep{REPUCB22}, and the loss functions may change adversarially but are revealed to the learner at the end of each episode. We propose a policy optimization-based algorithm POLO, and we prove that it attains the $\widetilde{O}(K^{\frac{5}{6}}A^{\frac{1}{2}}d\ln(1+M)/(1-\gamma)^2)$ regret guarantee, where $d$ is rank of the transition kernel (and hence the dimension of the unknown representations), $A$ is the cardinality of the action space, $M$ is the cardinality of the model class, and $\gamma$ is the discounted factor. Notably, our algorithm is oracle-efficient and has a regret guarantee with no dependence on the size of potentially arbitrarily large state space. Furthermore, we also prove an $\Omega(\frac{\gamma^2}{1-\gamma} \sqrt{d A K})$ regret lower bound for this problem, showing that low-rank MDPs are statistically more difficult to learn than linear MDPs in the regret minimization setting. To the best of our knowledge, we present the first algorithm that interleaves representation learning, exploration, and exploitation to achieve the sublinear regret guarantee for RL with nonlinear function approximation and adversarial losses.