February 2025 arXiv papers — page 42

Shikha Surana, Nathan Grinsztajn, Timothy Atkinson, Paul Duckworth

Machine learning methods can automate the in silico design of biological sequences, aiming to reduce costs and accelerate medical research. Given the limited access to wet labs, in silico design methods commonly use an oracle model to evaluate de novo generated sequences. However, the use of different oracle models across methods makes it challenging to comp

Jean Van Schaftingen, Benoît Van Vaerenbergh

We give direct proofs and constructions of the trace and extension theorems for Sobolev mappings in $W^{1, 1} (M, N)$, where $M$ is Riemannian manifold with compact boundary $\partial M$ and $N$ is a complete Riemannian manifold. The analysis is also applicable to halfspaces and strips. The extension is based on a tiling the domain of the considered applicat

Robyn Larracy, Angkoon Phinyomark, Ala Salehi, Eve MacDonald

Gait refers to the patterns of limb movement generated during walking, which are unique to each individual due to both physical and behavioral traits. Walking patterns have been widely studied in biometrics, biomechanics, sports, and rehabilitation. While traditional methods rely on video and motion capture, advances in plantar pressure sensing technology no

Priya Tripathi, Seung Jae Lee

Particle size and shape are the key 3D particle geometry parameters that govern the complex behavior of granular materials. The effect of particle size and shape has often been examined in isolation, typically through separate analyses of particle size distribution (PSD) and shape distribution, leading to an unaddressed knowledge gap. Beyond size and shape,

Zhicheng Chen

Let $\textbf{SU}$ be the superintuitionistic logic defined by the axiom $\boldsymbol{su} = ((\neg p\to q)\land(\neg q\to p) \rightarrow r \vee s) \to ( p \rightarrow r) \vee(q \rightarrow s)$, or equivalently, by Andrew's axiom. It is easy to check that $\textbf{SU}$ is contained in Medvedev's logic and contains both Kreisel-Putnam logic and Scott logic. We

Qing-Guo Zeng, Xiao-Peng Cui, Xian-Zhe Tao, Jia-Qi Hu

Applying quantum annealing or current quantum-/physics-inspired algorithms for MIMO detection always abandon the direct gray-coded bit-to-symbol mapping in order to obtain Ising form, leading to inconsistency errors. This often results in slow convergence rates and error floor, particularly with high-order modulations. We propose HOPbit, a novel MIMO detecto

Alexandre Arbey, Marco Calzà, Yuber F. Perez-Gonzalez

The calculation of gray-body factors is essential for understanding Hawking radiation and black hole thermodynamics. While the formalism developed by Chandrasekhar is effective for static black holes, it faces significant challenges in Kerr spacetimes, particularly in the superradiant regime, where a specific choice of coordinates introduces numerical inaccu

Tianpeng Li, Jun Liu, Tao Zhang, Yuanbo Fang

We introduce Baichuan-Audio, an end-to-end audio large language model that seamlessly integrates audio understanding and generation. It features a text-guided aligned speech generation mechanism, enabling real-time speech interaction with both comprehension and generation capabilities. Baichuan-Audio leverages a pre-trained ASR model, followed by multi-codeb

Savannah D. Gowen, Elina Ghimire, Charlie A. Lindberg, Ingrid S. Appen

Training has emerged as a promising materials design technique in which function can be acheived through repeated physical modification of an existing material rather than by direct chemical functionalization, cutting or reprocessing. This work investigates both the ability to train for function and then to erase that function on-demand in macroscopic metama

Gabriele Berton, Carlo Masone

Retrieving images from the same location as a given query is an important component of multiple computer vision tasks, like Visual Place Recognition, Landmark Retrieval, Visual Localization, 3D reconstruction, and SLAM. However, existing solutions are built to specifically work for one of these tasks, and are known to fail when the requirements slightly chan

Hogun Kee, Wooseok Oh, Minjae Kang, Hyemin Ahn

In this paper, we present the tidiness score-guided Monte Carlo tree search (TSMCTS), a novel framework designed to address the tabletop tidying up problem using only an RGB-D camera. We address two major problems for tabletop tidying up problem: (1) the lack of public datasets and benchmarks, and (2) the difficulty of specifying the goal configuration of un

Félicien Comtat, Didier Lesesvre, Siu Hang Man

We develop an explicit version of the Kuznetsov trace formula for GSp(4), relating sums of Fourier coefficients to Kloosterman sums. We study the precise analytic behaviour of both the spectral and the arithmetic transforms arising in the Kuznetsov trace formula for GSp(4). We use these results to provide an effective version of the trace formula, and establ

Narjes Ansari, Zhifeng Francis Jing, Antoine Gagelin, Florent Hédin

Predicting the binding affinity between small molecules and target macromolecules while combining both speed and accuracy, is a cornerstone of modern computational drug discovery which is critical for accelerating therapeutic development. Despite recent progresses in molecular dynamics (MD) simulations, such as advanced polarizable force fields and enhanced-

Yi Xu, Chen Gao, Shi-Xue Zhang, Wei-Tian Deng

In relativistic high energy heavy-ion collisions, Chiral Magnetic Effect (CME) could produce a charge separation in QGP. The charge separation could survive into final hadron system during evolution, observed as correlator $\Delta\gamma$. This physics procedure could also occurs in small collision system if CME emerge in the QGP droplet. In this paper, we st

Mourad Halla

The Quantum Fisher Information Matrix (QFIM) plays a crucial role in quantum optimization algorithms such as Variational Quantum Imaginary Time Evolution and Quantum Natural Gradient Descent. However, computing the full QFIM incurs a quadratic computational cost of O(d^2) with respect to the number of parameters d, limiting its scalability for high-dimension

Adarsh Djeacoumar, Felix Mujkanovic, Hans-Peter Seidel, Thomas Leimkühler

Fractal geometry, defined by self-similar patterns across scales, is crucial for understanding natural structures. This work addresses the fractal inverse problem, which involves extracting fractal codes from images to explain these patterns and synthesize them at arbitrary finer scales. We introduce a novel algorithm that optimizes Iterated Function System

Alberto Carta, Anwesha Panda, Claude Ederer

Calculations combining density functional theory (DFT) and dynamical mean-field theory (DMFT) for transition metal (TM) oxides and similar compounds usually focus on improving the description of the TM $d$ states. Here, we emphasize the importance of also accounting for corrections of the ligand $p$ states. We demonstrate that focusing exclusively on an impr

Manoj Kummini, Mandira Mondal

Let $p$ be a prime number, $\Bbbk$ a field of characteristic $p$ and $G$ a finite $p$-group acting on a standard graded polynomial ring $S = \Bbbk[x_1, \ldots, x_n]$ as degree-preserving $\Bbbk$-algebra automorphisms. Assume that $G$ is generated by pseudo-reflections. In our earlier work (\emph{J. Pure Appl. Algebra}, vol. 228, no. 12, 2024) we introduced a

Margaret-Ellen Messinger, Logan Pipes

A new model for domination reconfiguration is introduced which combines the properties of the preexisting token addition/removal (TAR) and token sliding (TS) models. The vertices of the TARS-graph correspond to the dominating sets of $G$, where two vertices are adjacent if and only if they are adjacent via either the TAR reconfiguration rule or the TS reconf

Vijay Kumar Sutrakar, Anjana P K, Sajal Kesharwani, Siddharth Bisariya

The paper presents an innovative methodology for designing frequency selective surface (FSS) based radar absorbing materials using machine learning (ML) technique. In conventional electromagnetic design, unit cell dimensions of FSS are used as input and absorption coefficient is then predicted for a given design. In this paper, absorption coefficient is cons

Ratun Rahman, Pablo Moriano, Samee U. Khan, Dinh C. Nguyen

Electric load forecasting is essential for power management and stability in smart grids. This is mainly achieved via advanced metering infrastructure, where smart meters (SMs) record household energy data. Traditional machine learning (ML) methods are often employed for load forecasting, but require data sharing, which raises data privacy concerns. Federate

Gernot Eichmann, Raul D. Torres

Five-point functions and five-body wave functions play an important role in many areas of nuclear and particle physics, e.g., in 2 -> 3 scattering processes, in the five-gluon vertex, or in the study of pentaquarks. In this work we consider the permutation group S5 to facilitate the description of such objects. We work out the multiplets transforming under i

Vito Pellizzani

Conformal field theories (CFTs) are associated with critical phenomena and phase transitions and also play an essential role in string theory. Solving a CFT is an extremely constrained problem due to conformal invariance -- the task essentially reduces to the computation of a set of numbers called the CFT data -- yet it remains highly nontrivial. In fact, CF

Erik Learned-Miller

We address the problem of producing a lower bound for the mean of a discrete probability distribution, with known support over a finite set of real numbers, from an iid sample of that distribution. Up to a constant, this is equivalent to bounding the mean of a multinomial distribution (with known support) from a sample of that distribution. Our main contribu

Tamara Happé, Chiem van Straaten, Raed Hamed, Fabio D'Andrea

Various regions in the Northern Hemispheric midlatitudes have seen pronounced trends in upper-atmosphere summer circulation and surface temperature extremes over recent decades (since 1979). Several of these regional trends lie outside the range of historic CMIP6 model simulations, and they might constitute a joined dynamic response that is missed by climate

Hamidreza Raei, Elena De Momi, Arash Ajoudani

Although robotic applications increasingly demand versatile and dynamic object handling, most existing techniques are predominantly focused on grasp-based manipulation, limiting their applicability in non-prehensile tasks. To address this need, this study introduces a Deep Deterministic Policy Gradient (DDPG) reinforcement learning framework for efficient no

Francisca Marín, Ijaas Mohamed, Oliver Brandt, Lutz Geelhaar

Semiconductor nanowires are attractive for photovoltaic applications because light absorption can be enhanced compared to planar layers due to the more complex coupling of light with wavelength-scale matter. However, experimentally it is very challenging to investigate light absorption in single nanowires. Here, we employ photoluminescence spectroscopy as a

Weiji Xie, Chenjia Bai, Jiyuan Shi, Junkai Yang

Humans possess delicate dynamic balance mechanisms that enable them to maintain stability across diverse terrains and under extreme conditions. However, despite significant advances recently, existing locomotion algorithms for humanoid robots are still struggle to traverse extreme environments, especially in cases that lack external perception (e.g., vision

Lior Bary-Soroker, Daniele Garzoni, Sasha Sodin

Conditionally on the Riemann hypothesis for certain Dedekind zeta functions, we show that the characteristic polynomial of a class of random tridiagonal matrices of large dimension is irreducible, with probability exponentially close to one; moreover, its Galois group over the rational numbers is either the symmetric or the alternating group. This is the cou

Philip Cardiff, Dylan Armfield, Željko Tuković, Ivan Batistić

This study investigates the efficacy of Jacobian-free Newton-Krylov methods in finite-volume solid mechanics. Traditional Newton-based approaches require explicit Jacobian matrix formation and storage, which can be computationally expensive and memory-intensive. In contrast, Jacobian-free Newton-Krylov methods approximate the Jacobian's action using finite d

Alexander Beiser, David Penz, Nysret Musliu

Large language models (LLMs) achieve astonishing results on a wide range of tasks. However, their formal reasoning ability still lags behind. A promising approach is Neurosymbolic LLM reasoning. It works by using LLMs as translators from natural to formal languages and symbolic solvers for deriving correct results. Still, it remains unclear what the contribu

Shital Saha, Suchandan Kayal

We propose R\'enyi inaccuracy measure based on multivariate copula and multivariate survival copula, respectively dubbed as multivariate cumulative copula R\'enyi inaccuracy measure and multivariate survival copula R\'enyi inaccuracy measure. Bounds of multivariate cumulative copula R\'enyi inaccuracy and multivariate survival copula R\'enyi inaccuracy measu

Boxuan Zhang, Ruqi Zhang

Large language models (LLMs) excel in many tasks but struggle to accurately quantify uncertainty in their generated responses. This limitation makes it challenging to detect misinformation and ensure reliable decision-making. Existing uncertainty quantification (UQ) methods for LLMs are primarily prompt-wise rather than response-wise, often requiring multipl

Jiahe Li, Xin Chen, Fanqi Shen, Junru Chen

Neurological disorders pose major global health challenges, driving advances in brain signal analysis. Scalp electroencephalography (EEG) and intracranial EEG (iEEG) are widely used for diagnosis and monitoring. However, dataset heterogeneity and task variations hinder the development of robust deep learning solutions. This review systematically examines rec

Xander Haijen, Bikram Koirala, Xuanwen Tao, Paul Scheunders

In this paper, we propose a Two-Step Linear Mixing Model (2LMM) that bridges the gap between model complexity and computational tractability. The model achieves this by introducing two distinct scaling steps: an endmember scaling step across the image, and another for pixel-wise scaling. We show that this model leads to only a mildly non-convex optimization

Si-Yu Zhu, Ruo-Xia Yao, De-Xing Kong, S. Y. Lou

This paper explores the rich structure of peakon and pseudo-peakon solutions for a class of higher-order $b$-family equations, referred to as the $J$-th $b$-family ($J$-bF) equations. We propose several conjectures concerning the weak solutions of these equations, including a $b$-independent pseudo-peakon solution, a $b$-independent peakon solution, and a $b

Tomer Raz, Michael Shalyt, Elyasheev Leibtag, Rotem Kalisch

The constant $\pi$ has fascinated scholars throughout the centuries, inspiring numerous formulas for its evaluation, such as infinite sums and continued fractions. Despite their individual significance, many of the underlying connections among formulas remain unknown, missing unifying theories that could unveil deeper understanding. The absence of a unifying

Victor F. Los

A projection operator is introduced, which exactly transforms the inhomogeneous Nakajima--Zwanzig generalized master equation for the relevant part of a system +bath statistical operator, containing the inhomogeneous irrelevant term comprising the initial corrrelations, into the homogeneous equation accounting for initial correlations in the kernel governing

Hannah Eichhorn, Veronika Spieker, Kerstin Hammernik, Elisa Saks

Purpose: T2* quantification from gradient echo magnetic resonance imaging is particularly affected by subject motion due to the high sensitivity to magnetic field inhomogeneities, which are influenced by motion and might cause signal loss. Thus, motion correction is crucial to obtain high-quality T2* maps. Methods: We extend our previously introduced learnin

Ze Zhang, Kewei Song, Kayo Hirose, Jianxian He

With the increasing demand for personalized health monitoring, wearable sensors have gained attention in medical diagnostics and physiological tracking. Hydrogels, known for their mechanical properties and similarity to biological tissues, are ideal for flexible sensing. However, conventional hydrogels face challenges in stability, biocompatibility, adhesion

M. T. Atemkeng, C. Chuma, S. Zaza, C. D. Nunhokee

This work proposes a saliency-based attribution framework to evaluate and compare 10 state-of-the-art explainability methods for deep learning models in astronomy, focusing on the classification of radio galaxy images. While previous work has primarily emphasized classification accuracy, we prioritize model interpretability. Qualitative assessments reveal th

Andrew DiGiugno, Ausif Mahmood

Transformer models typically calculate attention matrices using dot products, which have limitations when capturing nonlinear relationships between embedding vectors. We propose Neural Attention, a technique that replaces dot products with feed-forward networks, enabling a more expressive representation of relationships between tokens. This approach modifies

Jie Zeng, Qianyu He, Qingyu Ren, Jiaqing Liang

Real-world instructions with multiple constraints pose a significant challenge to existing large language models (LLMs). An observation is that the LLMs exhibit dramatic performance fluctuation when disturbing the order of the incorporated constraints. Yet, none of the existing works has systematically investigated this position bias problem in the field of

Mingxing Weng, Zhiping Mao, Jie Shen

Neural networks have shown significant potential in solving partial differential equations (PDEs). While deep networks are capable of approximating complex functions, direct one-shot training often faces limitations in both accuracy and computational efficiency. To address these challenges, we propose an adaptive method that uses single-hidden-layer neural n

Jaime S. Buruaga, Ruben B. Méndez, Juan P. Brito, Vicente Martin

Shor's algorithm efficiently solves factoring and discrete logarithm problems using quantum computers, compromising all public key schemes used today. These schemes rely on assumptions on their computational complexity, which quantum computers can easily bypass. The solutions have to come from new algorithms - called Post-Quantum Cryptography (PQC) - or from

V. V. Belokurov, E. T. Shavgulidze, N. E. Shavgulidze

In the paper, we give the proof of the polar decomposition of the Wiener measure according to the orbits of the group of diffeomorphisms.

Bei Zhang, Daxiong Piao

We investigate the spectral structure of multi-frequency quasi-periodic CMV matrices with Verblunsky coefficients defined by shifts on the $d$-dimensional torus. Under the positive Lyapunov exponent regime and standard Diophantine frequency conditions, we establish that the spectrum of these operators contains intervals on the unit circle.

Kento Taniguchi, Atsushi Noguchi, Takashi Oka

Strongly driven nonlinear systems are frequently encountered in physics, yet their accurate control is generally challenging due to the intricate dynamics. In this work, we present a non-perturbative, semi-analytical framework for tailoring such systems. The key idea is heuristically extending the Floquet theory to nonlinear differential equations using the

Wiktor Zuba, Oded Lachish, Solon P. Pissis

Minimizers sampling is one of the most widely-used mechanisms for sampling strings. Let $S=S[0]\ldots S[n-1]$ be a string over an alphabet $\Sigma$. In addition, let $w\geq 2$ and $k\geq 1$ be two integers and $\rho=(\Sigma^k,\leq)$ be a total order on $\Sigma^k$. The minimizer of window $X=S[i\mathinner{.\,.} i+w+k-2]$ is the smallest position in $[i,i+w-1]

Baptiste Chopin, Tashvik Dhamija, Pranav Balaji, Yaohui Wang

We propose Dimitra, a novel framework for audio-driven talking head generation, streamlined to learn lip motion, facial expression, as well as head pose motion. Specifically, we train a conditional Motion Diffusion Transformer (cMDT) by modeling facial motion sequences with 3D representation. We condition the cMDT with only two input signals, an audio-sequen

Asghar Ullah, Marco Cattaneo, Özgür E. Müstecaplıoğlu

We study the performance of single-qubit probes for temperature estimation in the presence of collective baths. We consider a system of two qubits, each locally dissipating into its own bath while being coupled to a common bath. In this setup, we investigate different scenarios for temperature estimation of both the common and local baths. First, we explore

Guillaume Jeanneret, Loïc Simon, Frédéric Jurie

Visual transformers have achieved remarkable performance in image classification tasks, but this performance gain has come at the cost of interpretability. One of the main obstacles to the interpretation of transformers is the self-attention mechanism, which mixes visual information across the whole image in a complex way. In this paper, we propose Hindered

Hongyu Zhou, Zorah Lähner

With the rising popularity of 3D Gaussian splatting and the expanse of applications from rendering to 3D reconstruction, there comes also a need for geometry processing applications directly on this new representation. While considering the centers of Gaussians as a point cloud or meshing them is an option that allows to apply existing algorithms, this might

Shanuja Sasi, Onur Günlü

Distributed computing enables scalable machine learning by distributing tasks across multiple nodes, but ensuring privacy in such systems remains a challenge. This paper introduces a novel private coded distributed computing model that integrates privacy constraints to keep task assignments hidden. By leveraging placement delivery arrays (PDAs), we design an

Yutong Duan, Joel Nagloo

Let $(x_1,y_1),\ldots,(x_n,y_n)$ be distinct non-constant and non-degenerate solutions of the classical Lotka-Volterra system \begin{equation}\notag \begin{split} x'&= axy + bx\\ y'&= cxy + dy, \end{split} \end{equation} where $a,b,c,d\in\mathbb{C}\setminus\{0\}$. We show that if $d$ and $b$ are linearly independent over $\mathbb{Q}$, then the solutions are

Salah Chaib, Ana Cristina Ferreira, Abdelghani Zeghib

Let $G$ be a connected, simply connected three-dimensional Lie group (unimodular or non-unimodular) equipped with a left-invariant (Riemannian or Lorentzian) metric $g$. By definition, the isometry group $\mathrm{Isom}(G, g)$ contains $G$ itself, acting by left translations. It turns out that, generically, $\mathrm{Isom}(G, g)$ is actually equal to $G$, and

Fredrik Parnefjord Gustafsson, Daniel Pęcak, Tomasz Sowiński

We study the stability of bound antiprotons in close proximity to the atomic nucleus. Using experimental data from X-ray spectroscopy measurements of antiprotonic atom transitions, we tune a minimal theoretical framework and estimate parameter ranges where the strong or electromagnetic decay channels govern the stability of the deepest bound states. In this

Andrea De Girolamo, Giuseppe Magnifico, Cosmo Lupo

We study entanglement percolation in qubit-based planar quantum network models of arbitrary topology, where neighboring nodes are initially connected by pure states with quenched disorder in their entanglement. To address this, we develop a physics-informed heuristic algorithm designed to find a sequence of entanglement swapping and distillation operations t

Alex Havrilla, David Alvarez-Melis, Nicolo Fusi

Large language models ($\textbf{LLMs}$) have emerged as a powerful method for discovery. Instead of utilizing numerical data, LLMs utilize associated variable $\textit{semantic metadata}$ to predict variable relationships. Simultaneously, LLMs demonstrate impressive abilities to act as black-box optimizers when given an objective $f$ and sequence of trials.

Clara Wassner, Tommaso Guaita, Jens Eisert, Jose Carrasco

Holonomic quantum computation exploits the geometric evolution of eigenspaces of a degenerate Hamiltonian to implement unitary evolution of computational states. In this work we introduce a framework for performing scalable quantum computation in atom experiments through a universal set of fully holonomic adiabatic gates. Through a detailed differential geom

Andrei Chernov

Recently, Large Language Models (LLMs) with Mixture of Experts (MoE) layers have gained significant attention. Currently, state-of-the-art LLMs utilize this architecture. There is a substantial amount of research on how to train such models and how to select hyperparameters for this architecture. However, there is a lack of studies focusing on post-evaluatio

Yan Dolinksy, Xin Zhang

In this paper, we consider scaling limits of exponential utility indifference prices for European contingent claims in the Bachelier model. We show that the scaling limit can be represented in terms of the \emph{specific relative entropy}, and in addition we construct asymptotic optimal hedging strategies. To prove the upper bound for the limit, we formulate

Sören Bartels, Bernd Schmidt, Philipp Tscherner

A numerical scheme is proposed to identify low energy configurations of a F\"oppl-von K\'arm\'an model for bilayer plates. The dependency of the corresponding elastic energy on the in-plane displacement $u$ and the out-of-plane deflection $w$ leads to a practical minimization of the functional via a decoupled gradient flow. In particular, the energies of the

Yuming Yang, Yang Nan, Junjie Ye, Shihan Dou

Data diversity is crucial for the instruction tuning of large language models. Existing studies have explored various diversity-aware data selection methods to construct high-quality datasets and enhance model performance. However, the fundamental problem of precisely defining and measuring data diversity remains underexplored, limiting clear guidance for da

Fatemeh Chahshouri, Nahid Talebi

The interaction between free electrons and laser-induced near-fields provides a platform to study ultrafast processes and quantum phenomena while enabling precise manipulation of electron wavefunctions through linear and orbital momentum transfer. Here, by introducing phase offset between two orthogonally polarized laser pulses exciting a gold nanorod, we ge

Mohak Sharma, Chandan Kumar, Shikhar Arora, Arvind

We investigate the role of quadrature squeezing in the quantum teleportation protocol for coherent states, using non-Gaussian resource states. For the two-mode systems, the non-Gaussian resource states that we use are obtained by an experimentally realizable scheme of photon subtraction, photon addition, and photon catalysis, on the two-mode squeezed vacuum,

Justin Cano, Jonathan Israël, Laurent Féral

Satellite communication systems are shifting to higher frequency bands (Ka, Q/V, W) to support more data-intensive services and alleviate spectral congestion. However, the use of Extremely High Frequencies, typically above 20 GHz, causes significant tropospheric impairments, such as rain attenuation, which can causes system outages. To mitigate these effects

G. M. Voit

How much energy is required to unbind baryons from the cosmological structures that originally bind them? This tutorial article explains why trying to answer this question using just a halo model can be misleading. Instead, it recommends parsing the universe into ``bound domains,'' which are the gravitationally bound structures that ultimately become widely

Friðrik Freyr Gautason, Vyshnav Mohan, Lárus Thorlacius

The holographic complexity of a static spherically symmetric black hole, defined as the volume of an extremal surface, grows linearly with time at late times in general relativity. The growth comes from a region at a constant transverse area inside the black hole and continues forever in the classical theory. In this region the volume complexity of any spher

G. Kishore, Nishant K. Singh

Using direct numerical simulations of forced rotating turbulence, we study the effect of rotation on the growth rate and the saturation level of the small-scale dynamo. For slow rotation rates, increasing the rotation rate reduces both the growth rate and the saturation level. Once the rotation rate crosses a threshold, large-scale vortices are formed which

Utsav Akhaury, Pascale Jablonka, Frédéric Courbin, Jean-Luc Starck

With the advent of surveys like Euclid and Vera C. Rubin, astrophysicists will have access to both deep, high-resolution images and multiband images. However, these two types are not simultaneously available in any single dataset. It is therefore vital to devise image deconvolution algorithms that exploit the best of both worlds and that can jointly analyze

Patrick Schnider, Pablo Soberón

In a mass partition problem, we are interested in finding equitable partitions of smooth measures in $\mathbb{R}^d$. In this manuscript, we study the problem of finding simultaneous bisections of measures using scaled copies of a prescribed set $K$. We distinguish the problem when we are allowed to use scaled and translated copies of $K$ and the problem when

Raymond Zhang, Hedi Hadiji, Richard Combes

We consider linear stochastic bandits where the set of actions is an ellipsoid. We provide the first known minimax optimal algorithm for this problem. We first derive a novel information-theoretic lower bound on the regret of any algorithm, which must be at least $\Omega(\min(d \sigma \sqrt{T} + d \|\theta\|_{A}, \|\theta\|_{A} T))$ where $d$ is the dimensio

Xinzhe Luo, Yingzhen Li, Chen Qin

Accelerated magnetic resonance imaging involves reconstructing fully sampled images from undersampled k-space measurements. Current state-of-the-art approaches have mainly focused on either end-to-end supervised training inspired by compressed sensing formulations, or posterior sampling methods built on modern generative models. However, their efficacy heavi

Xueru Wen, Jie Lou, Zichao Li, Yaojie Lu

Reward models (RMs) are crucial for aligning large language models (LLMs) with human preferences. However, most RM research is centered on English and relies heavily on synthetic resources, which leads to limited and less reliable datasets and benchmarks for Chinese. To address this gap, we introduce CheemsBench, a fully human-annotated RM evaluation benchma

Bin Yin, Chong-Yi Liu, Liya Fu, Jinkun Zhang

Affective computing has made significant strides in emotion recognition and generation, yet current approaches mainly focus on short-term pattern recognition and lack a comprehensive framework to guide affective agents toward long-term human well-being. To address this, we propose a teleology-driven affective computing framework that unifies major emotion th

Jia-Jia Luo, Sagarika Basak, Han Pu, Xi-Wen Guan

The Drude weight (DW) is an essential quantity that characterizes the quantum transport properties of many-body systems. However, a rigorous understanding and exact computation of DWs, particularly for strongly correlated systems with doping, still remain elusive. In this Letter, taking advantage of the quantum integrability, we calculate exactly the DWs and

Bertrand Meyer

Software engineering concepts and processes are worthy of formal study; and yet we seldom formalize them. This "research ideas" article explores what a theory of software engineering could and should look like. Software engineering research has developed formal techniques of specification and verification as an application of mathematics to specify and verif

Damien Sileo

Large language models demonstrate promising long context processing capabilities, with recent models touting context windows close to one million tokens. However, the evaluations supporting these claims often involve simple retrieval tasks or synthetic tasks padded with irrelevant text, which the models may easily detect and discard. In this work, we generat

Yanzhen Liu, Zhijin Qin, Yongxu Zhu, Geoffrey Ye Li

The pursuit of carbon-neutral wireless networks is increasingly constrained by the escalating energy demands of deep learning-based signal processing. Here, we introduce SpikACom (Spiking Adaptive Communications), a neuromorphic computing framework that synergizes brain-inspired spiking neural networks (SNNs) with wireless signal processing to deliver sustai

Hamidreza Mazandarani, Masoud Shokrnezhad, Tarik Taleb

The Metaverse holds the potential to revolutionize digital interactions through the establishment of a highly dynamic and immersive virtual realm over wireless communications systems, offering services such as massive twinning and telepresence. This landscape presents novel challenges, particularly efficient management of multiple access to the frequency spe

Huanghai Liu, Quzhe Huang, Qingjing Chen, Yiran Hu

In recent years, Large Language Models (LLMs) have been widely applied to legal tasks. To enhance their understanding of legal texts and improve reasoning accuracy, a promising approach is to incorporate legal theories. One of the most widely adopted theories is the Four-Element Theory (FET), which defines the crime constitution through four elements: Subjec

Sill Verberne, Elena Maria Rossi, Sergey E. Koposov, Zephyr Penoyre

The origin of the cluster of S-stars located in the Galactic Centre is tied to the supermassive black hole Sagittarius A*, but exactly how is still debated. In this paper, we investigate whether the Hills mechanism can simultaneously reproduce both the S-star cluster's properties and the observed number of hypervelocity stars. To do so, we forward-model the

Yanbo Zhan, Yangshuai Wang, Hao Wang

The atomistic-to-continuum (a/c) coupling methods, also known as the quasicontinuum (QC) methods, are a important class of concurrent multisacle methods for modeling and simulating materials with defects. The a/c methods aim to balance the accuracy and efficiency by coupling a molecular mechanics model (also termed as the atomistic model) in the vicinity of

María Andrea Cruz Blandón, Jayasimha Talur, Bruno Charron, Dong Liu

Automatic evaluation of retrieval augmented generation (RAG) systems relies on fine-grained dimensions like faithfulness and relevance, as judged by expert human annotators. Meta-evaluation benchmarks support the development of automatic evaluators that correlate well with human judgement. However, existing benchmarks predominantly focus on English or use tr

M. Giersz, A. Askar, A Hypki, J. Hong

Recent findings show that, in some Milky Way globular clusters (GCs), pristine red giant branch (RGB) stars are more centrally concentrated than enriched RGB stars. This contradicts most multiple-population formation scenarios, which predict that the enriched population 2p should initially be more concentrated than the pristine population 1P. We analyze a MO

Michael Koenig, Jakob Rauch, Martin Woerter

Understanding the effects of economic shocks on firms is critical for analyzing economic growth and resilience. We introduce a Web-Based Affectedness Indicator (WAI), a general-purpose tool for real-time monitoring of economic disruptions across diverse contexts. By leveraging Large Language Model (LLM) assisted classification and information extraction on t

Yuli Wu, Fucheng Liu, Rüveyda Yilmaz, Henning Konermann

Fr\'echet Inception Distance (FID), computed with an ImageNet pretrained Inception-v3 network, is widely used as a state-of-the-art evaluation metric for generative models. It assumes that feature vectors from Inception-v3 follow a multivariate Gaussian distribution and calculates the 2-Wasserstein distance based on their means and covariances. While FID eff

Fanhu Zeng, Haiyang Guo, Fei Zhu, Li Shen

Fine-tuning pre-trained models with custom data leads to numerous expert models on specific tasks. Merging models into one universal model to empower multi-task ability refraining from data leakage has gained popularity. With the expansion in data and model size, parameter-efficient tuning becomes the common practice for obtaining task-specific models effici

Debarshi Dey

The aim of this thesis is twofold: a) A comprehensive study of the thermoelectric response in QGP in the absence and presence of a background magnetic field, b) Exploring the dynamics of heavy quarks traversing in QGP in the presence of a weak background magnetic field. We have evaluated the strength of the thermoelectric response in QGP quantified by the Se

Canyu Zhao, Yanlong Sun, Mingyu Liu, Huanyi Zheng

This paper's primary objective is to develop a robust generalist perception model capable of addressing multiple tasks under constraints of computational resources and limited training data. We leverage text-to-image diffusion models pre-trained on billions of images and successfully introduce our DICEPTION, a visual generalist model. Exhaustive evaluations

Debanjan Balui, Joydeep Chakrabortty, Debmalya Dey, Subhendra Mohanty

We show that the long-standing problem of gauge dependence of the effective potential arises due to the factorisation of the determinant of operators, which is invalid when we take the zeta-regularised trace of the operators. We show by correcting for this assumption by computing the multiplicative anomaly, the gauge-dependent terms of the effective potentia

Gonzalo Javier Alvarez, Pablo Dmitruk, Branislav Rabatin, David C. Collins

Three-dimensional direct numerical simulations (DNS) are implemented to investigate the energy cascade rate in compressible isothermal magnetohydrodynamic (MHD) turbulence. Utilizing an exact law derived from the K\'arm\'an-Howarth equation, we examine the contributions of flux and non-flux terms to the cascade rate across a broad range of sonic and Alfv\'en

Mustafa Yurdakul, Kubra Uyar, Sakir Tasdemir

Glaucoma is a prevalent eye disease that progresses silently without symptoms. If not detected and treated early, it can cause permanent vision loss. Computer-assisted diagnosis systems play a crucial role in timely and efficient identification. This study introduces MaxGlaViT, a lightweight model based on the restructured Multi-Axis Vision Transformer (MaxV

A. I. Poteryaev, Z. V. Pchelkina, S. V. Streltsov, Y. Long

Recently synthesized quadruple perovskite CuCu$_3$Fe$_2$Re$_2$O$_{12}$ possesses strong ferromagnetism and unusual electron properties, including enhanced electronic specific heat. Application of the first principles electronic structure approaches unambiguously shows importance of the many-body effects in this compound. While CuCu$_3$Fe$_2$Re$_2$O$_{12}$ is

Meihua Jin, Shihao Meng, Yunhua Zhou

In this paper, we investigate some dynamical properties near a nonhyperbolic fixed point. Under some conditions on the higher nonlinear terms, we establish a stable manifold theorem and a degenerate Hartman theorem. Furthermore, the finite shadowing property also be discussed.

Andrea Bertazzi, Tim Johnston, Gareth O. Roberts, Alain Durmus

This paper aims to provide differential privacy (DP) guarantees for Markov chain Monte Carlo (MCMC) algorithms. In a first part, we establish DP guarantees on samples output by MCMC algorithms as well as Monte Carlo estimators associated with these methods under assumptions on the convergence properties of the underlying Markov chain. In particular, our resu

Bertrand Meyer, Reto Weber

A re-construction of the fundamentals of programming as a small mathematical theory (PRISM) based on elementary set theory. Highlights: $\bullet$ Zero axioms. No properties are assumed, all are proved (from standard set theory). $\bullet$ A single concept covers specifications and programs. $\bullet$ Its definition only involves one relation and one set. $\b

Tatsuro Kawakami, Kenta Sato

We prove the logarithmic extension theorem for one-forms on strongly $F$-regular singularities. Additionally, we establish the logarithmic extension theorem for one-forms on three-dimensional klt singularities in characteristic $p>41$. To this end, we reduce the problem to the logarithmic extension theorem for two-dimensional klt singularities with imperfect

Paolo Antonelli, Didier Bresch, Stefano Spirito

We prove the global existence of weak solutions of the one-dimensional Navier-Stokes-Korteweg (NSK) equations when the viscosity and the capillarity coefficients are power functions of the density, which may be zero on a set with positive measure. The proofs are based on a truncation argument combined with the Energy estimate and BD Entropy. Notably, we do n