October 2024 arXiv papers — page 91

Yihao Liu, Jiaming Zhang, Letian Ai, Jing Tian

Transcranial magnetic stimulation (TMS) is a noninvasive medical procedure that can modulate brain activity, and it is widely used in neuroscience and neurology research. Compared to manual operators, robots may improve the outcome of TMS due to their superior accuracy and repeatability. However, there has not been a widely accepted standard protocol for per

Zhicun Liu, Wenyuan Cui, Jiajia Gu, Jianrong Shi

In this work, we update the catalog of OB stars based on the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) data release 7 and modified the OB stars selection criterion the spectral line indices space. The new catalog includes 37,778 spectra of 27,643 OB stars, of which 3827 OB stars are newly identified. The spectral subclasses of 27,643

Yujia Wu, Bo Yang, Yang Zhao, Elynn Chen

Graph Neural Networks (GNNs) have become the de facto standard for analyzing graph-structured data, leveraging message-passing techniques to capture both structural and node feature information. However, recent studies have raised concerns about the statistical reliability of uncertainty estimates produced by GNNs. This paper addresses this crucial challenge

Yongqin Wang, Rachit Rajat, Jonghyun Lee, Tingting Tang

As cloud-based ML expands, ensuring data security during training and inference is critical. GPU-based Trusted Execution Environments (TEEs) offer secure, high-performance solutions, with CPU TEEs managing data movement and GPU TEEs handling authentication and computation. However, CPU-to-GPU communication overheads significantly hinder performance, as data

Yujia Wu, Bo Yang, Elynn Chen, Yuzhou Chen

Graph classification in medical imaging and drug discovery requires accuracy and robust uncertainty quantification. To address this need, we introduce Conditional Prediction ROC (CP-ROC) bands, offering uncertainty quantification for ROC curves and robustness to distributional shifts in test data. Although developed for Tensorized Graph Neural Networks (TGNN

Zachary Sheldon, Peeyush Kumar

This paper explores the intersection of economic anthropology and generative artificial intelligence (GenAI). It examines how large language models (LLMs) can simulate human decision-making and the inductive biases present in AI research. The study introduces two AI models: C.A.L.L.O.N. (Conventionally Average Late Liberal ONtology) and M.A.U.S.S. (More Accu

Vincent Corlay, Viet-Hoa Nguyen, Nicolas Gresset

Previous studies explained how the 2D positioning problem in indoor non line-of-sight environments can be addressed using ray tracing with noisy angle of arrival (AoA) measurements. In this work, we generalize these results on two aspects. First, we outline how to adapt the proposed methods to address the 3D positioning problem. Second, we introduce efficien

Michael Adeniyi

Infrared (IR) dyes, especially those within the near-infrared (NIR) spectrum, offer substantial advantages for in vivo imaging, owing to their deep tissue penetration and minimal background autofluorescence. Nanoparticles incorporating these IR dyes, such as indocyanine green (ICG) have undergone extensive investigation regarding their pharmacokinetic behavi

R. Z. Jiang, Z. L. Li, Y. J. Li

The momentum distributions of electron-hole (EH) pair production in graphene for two arbitrarily polarized electric fields with a time delay are investigated employing a massless quantum kinetic equation and compared with the results obtained in electron-positron (EP) pair production from vacuum. For a single elliptically polarized electric field, the moment

Ruizhe Shen, Wei Jie Chan, Ching Hua Lee

Recently, it has been revealed that a variety of novel phenomena emerge in hyperbolic spaces, while non-Hermitian physics has significantly enriched the landscape of condensed matter physics. Building on these developments, we construct a geodesic-based method to study the non-Hermitian skin effect (NHSE) in non-reciprocal hyperbolic lattices. Additionally,

Xinyu Qiu, Zhiwei Song, Lin Chen

Quantifying the entanglement generation of a multipartite unitary operation is a key problem in quantum information processing. We introduce the definition of multipartite entangling, assisted entangling, and disentangling power, which is a natural generalization of the bipartite ones. We show that they are assumed at a specified quantum state. We analytical

Olivier Benoist, Claire Voisin

We consider the problem of smoothing algebraic cycles with rational coefficients on smooth projective complex varieties up to homological equivalence. We show that a solution to this problem would be incompatible with the validity of the Hartshorne conjecture on complete intersections in projective space. We also solve unconditionally a symplectic variant of

Robbert Fokkink

Conway and Ryba considered a table of bi-infinite Fibonacci sequences and discovered new interesting patterns. We extend their considerations to tables that are defined by the recurrence $X_{n+1}=dX_n+X_{n-1}$ for natural numbers $d$. In our search for new patterns we run into a Red Wall and exotic numeration systems.

Elham Bagheri, Yalda Mohsenzadeh

Image memorability refers to the phenomenon where certain images are more likely to be remembered than others. It is a quantifiable and intrinsic image attribute, defined as the likelihood of an image being remembered upon a single exposure. Despite advances in understanding human visual perception and memory, it is unclear what features contribute to an ima

Ze Wang, Zekun Wu, Jeremy Zhang, Xin Guan

Model collapse, a phenomenon characterized by performance degradation due to iterative training on synthetic data, has been widely studied. However, its implications for bias amplification, the progressive intensification of pre-existing societal biases in Large Language Models (LLMs), remain significantly underexplored, despite the growing influence of LLMs

Sina Baharlouei, Sadra Sabouri

Fair graph clustering is crucial for ensuring equitable representation and treatment of diverse communities in network analysis. Traditional methods often ignore disparities among social, economic, and demographic groups, perpetuating biased outcomes and reinforcing inequalities. This study introduces fair graph clustering within the framework of the dispara

Alexey Dubinsky, Antonina F. Zinhailo

In light of the recently discovered connection between grey-body factors and quasinormal ringing, we derive analytic expressions for the grey-body factors of generic parametrized spherically symmetric and asymptotically flat black holes. These expressions are presented as expansions in terms of the inverse multipole number and the coefficients of the paramet

Thibault Marin, Vasily Belov, Yanis Chemli, Jinsong Ouyang

Receptor occupancy (RO) studies using PET neuroimaging play a critical role in the development of drugs targeting the central nervous system (CNS). The conventional approach to estimate drug receptor occupancy consists in estimation of binding potential changes between two PET scans (baseline and post-drug injection). This estimation is typically performed s

Yuzhu Li, Hao Li, Weijie Chen, Keelan O'Riordan

Distinguishing between swarming and swimming, the two principal forms of bacterial movement, holds significant conceptual and clinical relevance. This is because bacteria that exhibit swarming capabilities often possess unique properties crucial to the pathogenesis of infectious diseases and may also have therapeutic potential. Here, we report a deep learnin

Nilgun Sungar, John Sharpe, Loic Ijzerman, Jack-William Barotta

Chiral active particles are able to draw energy from the environment to self-propel in the form of rotation. We describe an experimental arrangement wherein chiral objects, spinners, floating on the surface of a vibrated fluid rotate due to emitted capillary waves. We observe that pairs of spinners can assemble at quantized distances via the mutually generat

Shalosh B. Ekhad, Manuel Kauers, Doron Zeilberger

One of the most important sequences in enumerative combinatorics is OEIS sequence A85, the number of involutions of length n. In the Art of Computer Programming, vol. 3, Don Knuth derived the O(1/n) asymptotic formula for these numbers. In this modest tribute to our two heroes, Neil Sloane who just turned 85, and Don Knuth who was 85 a year ago, we go all th

Cheryl S. C. Lau, Maya A. Petkova, Ian A. Bonnell

HII regions powered by ionizing radiation from massive stars drive the dynamical evolution of the interstellar medium. Fast radiative transfer methods for incorporating photoionization effects are thus essential in astrophysical simulations. Previous work by Petkova et al. established a hybrid radiation hydrodynamics (RHD) scheme that couples Smoothed Partic

Hao Chen, Abdul Waheed, Xiang Li, Yidong Wang

The rise of Large Language Models (LLMs) has accentuated the need for diverse, high-quality pre-training data. Synthetic data emerges as a viable solution to the challenges of data scarcity and inaccessibility. While previous literature has focused predominantly on the quality and quantity of real data, our work enables the measurement of diversity in synthe

Harshita Diddee, Daphne Ippolito

Prior work has shown that language models can be tuned to follow user instructions using only a small set of high-quality instructions. This has accelerated the development of methods that filter a large, noisy instruction-tuning datasets down to high-quality subset which works just as well. However, typically, the performance of these methods is not demonst

Zhen Qin, Zhihui Zhu

Tensor train (TT) decomposition represents an $N$-order tensor using $O(N)$ matrices (i.e., factors) of small dimensions, achieved through products among these factors. Due to its compact representation, TT decomposition has found wide applications, including various tensor recovery problems in signal processing and quantum information. In this paper, we stu

Hsiu-Chung Yeh, Aditi Mitra

Recursion methods such as Krylov techniques map complex dynamics to an effective non-interacting problem in one dimension. For example, the operator Krylov space for Floquet dynamics can be mapped to the dynamics of an edge operator of the one-dimensional Floquet inhomogeneous transverse field Ising model (ITFIM), where the latter, after a Jordan-Wigner tran

Zavier Ndum Ndum, Jian Tao, John Ford, Yang Liu

Monte Carlo (MC) simulations, particularly using FLUKA, are essential for replicating real-world scenarios across scientific and engineering fields. Despite the robustness and versatility, FLUKA faces significant limitations in automation and integration with external post-processing tools, leading to workflows with a steep learning curve, which are time-con

Vindula Jayawardana, Baptiste Freydt, Ao Qu, Cameron Hickert

Despite the popularity of multi-agent reinforcement learning (RL) in simulated and two-player applications, its success in messy real-world applications has been limited. A key challenge lies in its generalizability across problem variations, a common necessity for many real-world problems. Contextual reinforcement learning (CRL) formalizes learning policies

Nicolas Cresto, Laurent Freidel

In this paper we construct a non-perturbative action of the higher spin symmetry algebra on the gravitational phase space. We introduce a symmetry algebroid $\mathcal{T}$ which allows us to include radiation in an algebraic framework. We show that $\mathcal{T}$ admits a non-linear realization on the asymptotic phase space generated by a Noether charge define

Junyang He, Ying-Jung Chen, Alireza Jafari, Anushka Idamekorala

There has been active investigation into deep learning approaches for time series analysis, including foundation models. However, most studies do not address significant scientific applications. This paper aims to identify key features in time series by examining hydrology data. Our work advances computer science by emphasizing critical application features

Skye Gunasekaran, Assel Kembay, Hugo Ladret, Rui-Jie Zhu

Accurate time-series forecasting is crucial in various scientific and industrial domains, yet deep learning models often struggle to capture long-term dependencies and adapt to data distribution shifts over time. We introduce Future-Guided Learning, an approach that enhances time-series event forecasting through a dynamic feedback mechanism inspired by predi

Bhanu Prasad Bhowmik, Christopher Ness

The rheology of dense suspensions lacks a universal description due to the involvement of a wide variety of parameters, ranging from the physical properties of solid particles to the nature of the external deformation or applied stress. While the former controls microscopic interactions, spatial variations in the latter induce heterogeneity in the flow, maki

Muhammad Husni Santriaji, Jiaqi Xue, Qian Lou, Yan Solihin

Fully Homomorphic Encryption (FHE) allows computations to be performed directly on encrypted data without needing to decrypt it first. This "encryption-in-use" feature is crucial for securely outsourcing computations in privacy-sensitive areas such as healthcare and finance. Nevertheless, in the context of FHE-based cloud computing, clients often worry about

Winston Hurst, Yasamin Mostofi

This paper studies the use of a multi-attribute auction in a communication system to bring about efficient relaying in a non-cooperative setting. We consider a system where a source seeks to offload data to an access point (AP) while balancing both the timeliness and energy-efficiency of the transmission. A deep fade in the communication channel (due to, e.g

Yusuf Civan, Zakir Deniz, Oleg Duginov, Mehmet Akif Yetim

A biclique in a graph $G$ is a complete bipartite subgraph (not necessarily induced), and the least positive integer $k$ for which the vertex set of $G$ can be partitioned into at most $k$ bicliques is the biclique vertex partition number $bp(G)$ of $G$. We prove that the inequality $reg(S(G))\geq |G|-bp(G)$ holds for every graph $G$, where $S(G)$ is the $1$

Subramanyam Sahoo, Kamlesh Dutta

The relentless pursuit of technological advancements has ushered in a new era where artificial intelligence (AI) is not only a powerful tool but also a critical economic driver. At the forefront of this transformation is Generative AI, which is catalyzing a paradigm shift across industries. Deep generative models, an integration of generative and deep learni

Yaogan Mensah

This paper shows how a family of function spaces (coined as Assiamoua spaces) plays a fundamental role in the Fourier analysis of vector-valued functions compact groups. These spaces make it possible to transcribe the classic results of Fourier analysis in the framework of analysis of vector-valued functions and vector measures. The construction of Sobolev s

Daniel Louzon, Genko T. Genov, Nicolas Staudenmaier, Florian Frank

We propose and demonstrate experimentally continuous phased dynamical decoupling (CPDD), where we apply a continuous field with discrete phase changes for quantum sensing and robust compensation of environmental and amplitude noise. CPDD does not use short pulses, making it particularly suitable for experiments with limited driving power or nuclear magnetic

Conor Curtin, Rossen Ivanov

The currents in the ocean have a serious impact on ocean dynamics, since they affect the transport of mass and thus the distribution of salinity, nutrients and pollutants. In many physically important situations the current depends quadratic-ally on the depth. We consider a single layer of fluid and study the propagation of the surface waves in the presence

Chandrajit Bajaj, Minh Nguyen, Shubham Bhardwaj

Material segmentation is a complex task, particularly when dealing with aerial data in poor lighting and atmospheric conditions. To address this, hyperspectral data from specialized cameras can be very useful in addition to RGB images. However, due to hardware constraints, high spectral data often come with lower spatial resolution. Additionally, incorporati

Mohammad Rashidujjaman Rifat, Dipto Das, Arpon Podder, Mahiratul Jannat

Despite significant research on online harm, polarization, public deliberation, and justice, CSCW still lacks a comprehensive understanding of the experiences of religious minorities, particularly in relation to fear, as prominently evident in our study. Gaining faith-sensitive insights into the expression, participation, and inter-religious interactions on

Wassim Jabbour, Enzo Benoit-Jeannin, Oscar Bedford, Saif Shahin

Chess involves extensive study and requires players to keep manual records of their matches, a process which is time-consuming and distracting. The lack of high-quality labeled photographs of chess boards, and the tediousness of manual labeling, have hindered the wide application of Deep Learning (DL) to automating this record-keeping process. This paper pro

Anning Wei, Jintao Liang, Kaiyuan Lin, Ziyue Li

Existing multi-agent deep reinforcement learning (MADRL) methods for multi-UAV navigation face challenges in generalization, particularly when applied to unseen complex environments. To address these limitations, we propose a Dual-Transformer Encoder-based Proximal Policy Optimization (DTPPO) method. DTPPO enhances multi-UAV collaboration through a Spatial T

Alexandru Chirvasitu

Let $\mathbb{U}$ be a Banach Lie group and $S\subseteq \mathbb{U}$ an ad-bounded subset thereof, in the sense that there is a uniform bound on the adjoint operators induced by elements of $S$ on the Lie algebra of $\mathbb{U}$. We prove that (1) $S$-valued continuous maps from compact groups to $\mathbb{U}$ sufficiently close to being morphisms are uniformly

W. Górny, J. M. Mazón, J. Toledo

Random walk spaces are a general framework for the study of PDEs. They include as particular cases locally finite weighted connected graphs and nonlocal settings involving symmetric integrable kernels on $\mathbb{R}^N$. We are interested in the study of evolution problems involving two random walk structures so that the associated functionals have different

Nicholas McCleerey

Given a psh function $\varphi\in\mathcal{E}(\Omega)$ and a smooth, bounded $\theta\geq 0$, it is known that one can solve the Monge-Amp\`{e}re equation $\mathrm{MA}(\varphi_\theta)=\theta^n\mathrm{MA}(\varphi)$, with some form of Dirichlet boundary values, by work of Ahag--Cegrell--Czy\.{z}--Hiep. Under some natural conditions, we show that $\varphi_\theta$

Baiyue Wang, Anthony Bloch

We consider learning nonholonomic dynamical systems while discovering the constraints, and describe in detail the case of the rolling disk. A nonholonomic system is a system subject to nonholonomic constraints. Unlike holonomic constraints, nonholonomic constraints do not define a sub-manifold on the configuration space. Therefore, the inverse problem of fin

Durgesh Nandini, Simon Bloethner, Mirco Schoenfeld, Mario Larch

Understanding the complex dynamics of high-dimensional, contingent, and strongly nonlinear economic data, often shaped by multiplicative processes, poses significant challenges for traditional regression methods as such methods offer limited capacity to capture the structural changes they feature. To address this, we propose leveraging the potential of knowl

Guy Mouanda

Information systems and data are necessary resources for several companies and individuals; but they likewise encounter numerous risks and dangers that can threaten their protection and value. Information security and information assurance are two connected expressions of protecting the confidentiality, integrity, and availability of information systems and

Yimin Zhang, Mario de Sousa

The capabilities demonstrated by Large Language Models (LLMs) inspire researchers to integrate them into industrial production and automation. In the field of Programmable Logic Controller (PLC) programming, previous researchers have focused on using LLMs to generate Structured Text (ST) language, and created automatic programming workflows based on it. The

Xianghao Xu, Srinath Sridhar, Daniel Ritchie

We propose a zero-shot text-driven 3D shape deformation system that deforms an input 3D mesh of a manufactured object to fit an input text description. To do this, our system optimizes the parameters of a deformation model to maximize an objective function based on the widely used pre-trained vision language model CLIP. We find that CLIP-based objective func

Elias Hossain, Tasfia Nuzhat, Shamsul Masum, Shahram Rahimi

Accurate classification of cancer-related biomedical abstracts is critical for advancing cancer informatics and supporting decision-making in healthcare research. Yet progress in this domain is often constrained by limited availability of labeled corpora and the high computational demands of transformer-based approaches. To address these challenges, we propo

Evgeniya Usenko, Anastasiia Svidzerska, Alexander Glamazda, Vladimir Valeev

The present work is devoted to the study of the thermostability of native DNA during binding to TiO$_{2}$ nanoparticles (NPs) at various concentrations under conditions close to physiological (0.1 M Na $^+$, pH 7) using thermal denaturation method and dynamic light scattering (DLS). The analysis of the DNA melting curves in the presence of TiO$_{2}$ NPs reve

Barbora Benešová, Šárka Nečasová, Jan Scherz, Anja Schlömerkemper

We analyze a model of the evolution of a (solid) magnetoelastic material. More specifically, the model we consider describes the evolution of a compressible magnetoelastic material with a non-convex energy and coupled to a gradient flow equation for the magnetization in the quasi-static setting. The viscous dissipation considered in this model induces an ext

Caio Almeida, Maria Grith, Ratmir Miftachov, Zijin Wang

We analyze the first and second moment risk premia in the Bitcoin market based on options and realized returns and contrast them to the premia embedded in the main US stock index market. First, Bitcoin is much more volatile and has a higher variance risk premium than the S&P 500. By decomposing the return premium into different regions of the return state sp

Sayed Abu Sayeed, Mir Mehedi Rahman, Samiul Alam, Naresh Kshetri

The financial sector's dependence on digital infrastructure increases its vulnerability to cybersecurity threats, requiring strong IT security protocols with other entities. This collaboration, however, is often identified as the most vulnerable link in the chain of cybersecurity. Adopting both symbolic and substantive measures lessens the impact of IT secur

Aleksandr V. Pukhlikov

We prove that a general three-dimensional quartic $V$ in the complex projective space ${\mathbb P}^4$, the only singularity of which is a double point of rank 3, is a birationally rigid variety. Its group of birational self-maps is, up to the finite subgroup of biregular automorphisms, a free product of 25 cyclic groups of order 2. It follows that the comple

Shubhagata Bhaumik, V. Gayathri, Imre Bartos, Jeremiah Anglin

Orbital eccentricity in compact binary mergers carries crucial information about the binary's formation and environment. There are emerging signs that some of the mergers detected by the LIGO and Virgo gravitational wave detectors could indeed be eccentric. Nevertheless, the identification of eccentricity via gravitational waves remains challenging, to a lar

Tanmaya Mishra, Shubhagata Bhaumik, V. Gayathri, Marek J. Szczepańczyk

Burst searches identify gravitational-wave (GW) signals in the detector data without use of a specific signal model, unlike the matched-filter searches that correlate data with simulated signal waveforms (templates). While matched filters are optimal for detection of known signals in the Gaussian noise, the burst searches can be more efficient in finding unu

A. V. Nikolaev, I. I. Vlasov, L. L. Tao

A general method for calculating magnetic susceptibility ($\chi$) in dielectrics within a single choice of magnetic gauge for the whole crystal is presented. On the basis of the method, accounting for all contributions to the Van Vleck paramagnetism and Langevin (Larmore) diamagnetism, a full-scale ab initio calculation of $\chi$ in diamond is performed. Unf

Catalin Vrabie

Recent technological advancements have significantly impacted the public sector's service delivery. Romanian city halls are embracing digitalization as part of their development strategies, aiming to deploy web-based platforms for public services, enhancing efficiency and accessibility for citizens. The COVID-19 pandemic has expedited this digital shift, pro

Guibin Chen

This paper proposes a data-driven solution for Volt-VAR control problem in active distribution system. As distribution system models are always inaccurate and incomplete, it is quite difficult to solve the problem. To handle with this dilemma, this paper formulates the Volt-VAR control problem as a constrained Markov decision process (CMDP). By synergistical

Dhrubajyoti Ghosh, Tucker McElroy, Soumendra Lahiri

Higher-order spectra (or polyspectra), defined as the Fourier Transform of a stationary process' autocumulants, are useful in the analysis of nonlinear and non Gaussian processes. Polyspectral means are weighted averages over Fourier frequencies of the polyspectra, and estimators can be constructed from analogous weighted averages of the higher-order periodo

Mayla R. Boguslav, Adam Kiehl, David Kott, G. Joseph Strecker

Veterinary medical records represent a large data resource for application to veterinary and One Health clinical research efforts. Use of the data is limited by interoperability challenges including inconsistent data formats and data siloing. Clinical coding using standardized medical terminologies enhances the quality of medical records and facilitates thei

Lukas Brunke, Yanni Zhang, Ralf Römer, Jack Naimer

Ensuring safe interactions in human-centric environments requires robots to understand and adhere to constraints recognized by humans as "common sense" (e.g., "moving a cup of water above a laptop is unsafe as the water may spill" or "rotating a cup of water is unsafe as it can lead to pouring its content"). Recent advances in computer vision and machine lea

Oussama Boussif, Léna Néhale Ezzine, Joseph D Viviano, Michał Koziarski

As trajectories sampled by policies used by reinforcement learning (RL) and generative flow networks (GFlowNets) grow longer, credit assignment and exploration become more challenging, and the long planning horizon hinders mode discovery and generalization. The challenge is particularly pronounced in entropy-seeking RL methods, such as generative flow networ

Gabriela Hinojosa, Alberto Verjovsky, Juan Pablo Díaz

Starting with a smooth, non-trivial $n$-dimensional knot $K\subset\bS^{n+2}$, and a beaded $n$-dimensional necklace subordinated to $K$, we construct a wild knot with a Cantor set of wild points (\ie the knot is not locally flat in these points). The construction uses the conformal Schottky group acting on $\bS^{n+2}$, generated by inversions on the spheres

Xiaobo Guo, Neil Potnis, Melody Yu, Nabeel Gillani

The ability for individuals to constructively engage with one another across lines of difference is a critical feature of a healthy pluralistic society. This is also true in online discussion spaces like social media platforms. To date, much social media research has focused on preventing ills -- like political polarization and the spread of misinformation.

Lingyu Zhang, Zhengran Ji, Nicholas R Waytowich, Boyuan Chen

The recent rapid advancement of machine learning has been driven by increasingly powerful models with the growing availability of training data and computational resources. However, real-time decision-making tasks with limited time and sparse learning signals remain challenging. One way of improving the learning speed and performance of these agents is to le

Xin Liu, Weijia Zhang, Min-Ling Zhang

In survival analysis, subjects often face competing risks; for example, individuals with cancer may also suffer from heart disease or other illnesses, which can jointly influence the prognosis of risks and censoring. Traditional survival analysis methods often treat competing risks as independent and fail to accommodate the dependencies between different con

Russel Arbore, Xavier Routh, Abdul Rafae Noor, Akash Kothari

Hyperdimensional Computing (HDC), a technique inspired by cognitive models of computation, has been proposed as an efficient and robust alternative basis for machine learning. HDC programs are often manually written in low-level and target specific languages targeting CPUs, GPUs, and FPGAs -- these codes cannot be easily retargeted onto HDC-specific accelera

Ignacio Bono Parisi, Inés Pacharoni

In the theory of matrix-valued orthogonal polynomials, there exists a longstanding problem known as the Matrix Bochner Problem: the classification of all $N \times N$ weight matrices $W(x)$ such that the associated orthogonal polynomials are eigenfunctions of a second-order differential operator. In [4], Casper and Yakimov made an important breakthrough in t

Gokul Puthumanaillam, Paulo Padrao, Jose Fuentes, Leonardo Bobadilla

Autonomous vehicles performing navigation tasks in complex environments face significant challenges due to uncertainty in state estimation. In many scenarios, such as stealth operations or resource-constrained settings, accessing high-precision localization comes at a significant cost, forcing robots to rely primarily on less precise state estimates. Our key

Behnam Darvish, Nima Chartab, Zahra Sattari, Sina Taamoli

We spectroscopically confirm a new protocluster in the COSMOS field at $z$=2.24430 with Keck/MOSFIRE, dubbed CC2.2B, which is in the immediate vicinity of CC2.2A protocluster, originally presented in \cite{Darvish20}. CC2.2B and CC2.2A centroids are separated by $\sim$5.5 Mpc(angular) and $\sim$16 comoving Mpc(radial). CC2.2B and CC2.2A have similar properti

Lincen Bai, Hedi Tabia, Raúl Santos-Rodríguez

Neural network pruning has traditionally focused on weight-based criteria to achieve model compression, frequently overlooking the crucial balance between adversarial robustness and accuracy. Existing approaches often fail to preserve robustness in pruned networks, leaving them more susceptible to adversarial attacks. This paper introduces Module Robustness

Chao Li, Jiahao Li, Jinwei Zhang, Eddy Solomon

Purpose: To develop an MRI technique for free-breathing 3D whole-liver quantification of water T1, water T2, proton density fat fraction (PDFF), R2*. Methods: An Eight-echo spoiled gradient echo pulse sequence with spiral readout was developed by interleaving inversion recovery and T2 magnetization preparation. We propose a neural network based on a 4D and a

Srijan Saket, Mohit Agarwal, Rishabh Mehrotra

The rise in popularity of social media platforms, has resulted in millions of new, content pieces being created every day. This surge in content creation underscores the need to pay attention to our design choices as they can greatly impact how long content remains relevant. In today's landscape where regularly recommending new content is crucial, particular

Wu Zhou, Zengqi Chen, Kaihang Lu, Hao Chen

The demand for structured light with a reconfigurable spatial and polarization distribution has been increasing across a wide range of fundamental and advanced photonics applications, including microscopy, imaging, sensing, communications, and quantum information processing. Nevertheless, the unique challenge in manipulating light structure after optical fib

Qianlei Jia, Witold Pedrycz

Linguistic fuzzy information evolution is crucial in understanding information exchange among agents. However, different agent weights may lead to different convergence results in the classic DeGroot model. Similarly, in the Hegselmann-Krause bounded confidence model (HK model), changing the confidence threshold values of agents can lead to differences in th

Johannes Testorf

In a recent paper, the discrete Gabor transform was connected to a Gabor transform with a time frequency domain given by the flat torus. We show that the corresponding Bargmann spaces can be expressed as theta line bundles on Abelian varieties. We give applications of this viewpoint to frame results for the discrete Gabor transform. In particular, we get res

Marek T. Malinowski

This paper contains a study on stochastic Volterra integral equations with fuzzy sets-values and involving on a constant retardation. Moreover, the form of the equation is symmetric in the sense that fuzzy stochastic integrals are placed on both sides of the equation. We show that the considered initial value problem formulated in terms of symmetric fuzzy st

Xiutian Zhao, Ke Wang, Wei Peng

Modern large language models (LLMs) have exhibited cooperative synergy on complex task-solving, and collective decision-making (CDM) is a pivotal component in LLM-based multi-agent collaboration frameworks. Our survey on 52 recent such systems uncovers a severe lack of diversity, with a heavy reliance on dictatorial and plurality voting for CDM. Through the

Elijah Bodish, Jonathan Brundan, Ben Elias

We introduce a new family of monoidal categories which are cyclotomic quotients of the nil-Brauer category. We construct a monoidal functor from the cyclotomic nil-Brauer category to another monoidal category constructed from singular Soergel bimodules of type D. We conjecture that our functor is an equivalence of categories. Although we can prove neither fu

Alexandre Belloni, Yan Chen, Matthew Harding

We consider the problem of estimating assortment probabilities, which is common in operations management applications, including product bundling, advertising, etc. Existing approaches typically model each assortment as a category and apply multinomial models to estimate the choice probabilities; while computationally convenient, these methods do not exploit

Yinhan He, Zaiyi Zheng, Patrick Soga, Yaozhen Zhu

In recent years, Graph Neural Networks (GNNs) have become successful in molecular property prediction tasks such as toxicity analysis. However, due to the black-box nature of GNNs, their outputs can be concerning in high-stakes decision-making scenarios, e.g., drug discovery. Facing such an issue, Graph Counterfactual Explanation (GCE) has emerged as a promi

Jingxuan Chen, Derek Yuen, Bin Xie, Yuhao Yang

Smartphone agents are increasingly important for helping users control devices efficiently, with (Multimodal) Large Language Model (MLLM)-based approaches emerging as key contenders. Fairly comparing these agents is essential but challenging, requiring a varied task scope, the integration of agents with different implementations, and a generalisable evaluati

Timothy Wei, Annabelle Miin, Anastasia Miin

Large Language Models (LLMs) have recently demonstrated impressive capabilities across various real-world applications. However, due to the current text-in-text-out paradigm, it remains challenging for LLMs to handle dynamic and complex application constraints, let alone devise general solutions that meet predefined system goals. Current common practices lik

Shao-Yu Fu, Dong Xu, Wei-Hua Lei, Antonio de Ugarte Postigo

Ultra-long gamma-ray bursts (ULGRBs) are characterized by exceptionally long-duration central engine activities, with characteristic timescales exceeding 1000 seconds. We present ground-based optical afterglow observations of the ultra-long gamma-ray burst GRB 211024B, detected by \textit{Swift}. Its X-ray light curve exhibits a characteristic ``internal pla

Nithin Parthasarathy, James Soetedjo, Saarang Panchavati, Nitya Parthasarathy

Amyotrophic lateral sclerosis (ALS), a progressive neuromuscular degenerative disease, severely restricts patient communication capacity within a few years of onset, resulting in a significant deterioration of quality of life. The P300 speller brain computer interface (BCI) offers an alternative communication medium by leveraging a subject's EEG response to

Tiefeng Jiang, Yongcheng Qi

Consider a high-dimensional Wishart matrix $\bd{W}=\bd{X}^T\bd{X}$ where the entries of $\bd{X}$ are i.i.d. random variables with mean zero, variance one, and a finite fourth moment $\eta$. Motivated by problems in signal processing and high-dimensional statistics, we study the maximum of the largest eigenvalues of any two-by-two principal minors of $\bd{W}$

Moamen Jbara, Oleg Zgadzai, Wolfgang Harneit, Aharon Blank

The quest to enhance the sensitivity of electron spin resonance (ESR) is an ongoing challenge. One potential strategy involves increasing the frequency, for instance, moving from Q-band (approximately 35 GHz) to W-band (approximately 94 GHz). However, this shift typically results in higher transmission and switching losses, as well as increased noise in sign

Prajol Shrestha, Mikhail Kulyabin, Aline Sindel, Hilde R. Pedersen

Accurate detection and segmentation of cone cells in the retina are essential for diagnosing and managing retinal diseases. In this study, we used advanced imaging techniques, including confocal and non-confocal split detector images from adaptive optics scanning light ophthalmoscopy (AOSLO), to analyze photoreceptors for improved accuracy. Precise segmentat

R. Sharma, A. Jain, M. Kumawat, J. K. Deegwal

A systematic investigation of superheavy nuclei in the isotopic chains of proton numbers Z=106, 114, 120, and 126 together with isotonic chains of neutron numbers N=162, 172, and 184 is presented in the theoretical framework of relativistic mean-field density functionals based on density-dependent meson-nucleon couplings. Ground-state properties, including b

Khaled Nakhleh, Ceyhun Eksin, Sabit Ekin

This paper proposes an agent-based optimistic policy iteration (OPI) scheme for learning stationary optimal stochastic policies in multi-agent Markov Decision Processes (MDPs), in which agents incur a Kullback-Leibler (KL) divergence cost for their control efforts and an additional cost for the joint state. The proposed scheme consists of a greedy policy imp

Zhaoxian Wu, Quan Xiao, Tayfun Gokmen, Hsinyu Tsai

Aiming to accelerate the training of large deep neural networks (DNN) in an energy-efficient way, analog in-memory computing (AIMC) emerges as a solution with immense potential. AIMC accelerator keeps model weights in memory without moving them from memory to processors during training, reducing overhead dramatically. Despite its efficiency, scaling up AIMC

Manjil P. Saikia, Abhishek Sarma

In this short note, we prove several new congruences for the overcubic partition triples function, using both elementary techniques and the theory of modular forms. These extend the recent list of such congruences given by Nayaka, Dharmendra, and Kumar (2024). We also generalize overcubic partition triples to overcubic partition $k$-tuples and prove a few ar

Yin Li, Liangwei Wang, Shiyuan Piao, Boo-Ho Yang

Large Language Models (LLMs) have demonstrated significant potential in code generation. However, in the factory automation sector, particularly motion control, manual programming, alongside inefficient and unsafe debugging practices, remains prevalent. This stems from the complex interplay of mechanical and electrical systems and stringent safety requiremen

Ruihan Wu, Chhavi Yadav, Russ Salakhutdinov, Kamalika Chaudhuri

Machine unlearning has emerged as an important component in developing safe and trustworthy models. Prior work on fact unlearning in LLMs has mostly focused on removing a specified target fact robustly, but often overlooks its deductive connections to other knowledge. We propose a new setting for fact unlearning, deep unlearning, where the goal is not only t

Stanley Eshiemogie, Peace Aielumoh, Tobechukwu Okamkpa, Miracle Jude

This research proposes a framework for modeling and comparing two electricity scenarios for Nigeria by 2050, focusing on the inclusion and exclusion of electricity storage technologies. A Central Composite Design (CCD) was used to generate a design matrix for data collection, with EnergyPLAN software used to create energy system simulations on the CCD data f

Illya M. Karabash

Motivated by engineering and photonics research on resonators in random or uncertain environments, we study rigorous randomizations of boundary conditions for wave equations of the acoustic-type in Lipschitz domains $\mathcal{O}$. First, a parametrization of essentially all m-dissipative boundary condition by contraction operators in the boundary $L^2$-space