November 2024 arXiv papers — page 163

Robert Reynolds

In this work we derive a functional equation in terms of the Hurwitz-Lerch zeta function along with definite integrals in terms of the incomplete gamma and Hurwitz-Lerch zeta functions. The method used in these derivations is contour integration. Special cases in terms of fundamental constants are produced.

SM Abdullah Al Mamun, Samaneh Farokhirad

The dynamics of droplet collisions in microchannels are inherently complex, governed by multiple interdependent physical and geometric factors. Understanding and predicting the outcomes of these collisions-whether coalescence, reverse-back, or pass-over-pose significant challenges, particularly due to the deformability of droplets and the influence of key pa

Quyet V. Do, Junze Li, Tung-Duong Vuong, Zhaowei Wang

Commonsense datasets have been well developed in Natural Language Processing, mainly through crowdsource human annotation. However, there are debates on the genuineness of commonsense reasoning benchmarks. In specific, a significant portion of instances in some commonsense benchmarks do not concern commonsense knowledge. That problem would undermine the meas

Francesca Bucci, Matthias Eller

This article is concerned with the optimal boundary control of the Maxwell system. We consider a Bolza problem, where the quadratic functional to be minimized penalizes the electromagnetic field at a given final time. Since the state is weighted in the energy space topology -- a physically realistic choice --, the property that the optimal cost operator does

Wolfgang Rolke

We present the results of a large number of simulation studies regarding the power of various goodness-of-fit as well as nonparametric two-sample tests for univariate data. This includes both continuous and discrete data. In general no single method can be relied upon to provide good power, any one method may be quite good for some combination of null hypoth

Zhangcheng Qiang, Kerry Taylor, Weiqing Wang

The classical text preprocessing pipeline, comprising Tokenisation, Normalisation, Stop Words Removal, and Stemming/Lemmatisation, has been implemented in many systems for ontology matching (OM). However, the lack of standardisation in text preprocessing creates diversity in the mapping results. In this paper, we investigate the effect of the text preprocess

Yang Zhang, Xuan Ye

We study the Stueckelberg field in de Sitter space, which is a massive vector field with the gauge fixing (GF) term $\frac{1}{2\zeta} (A^\mu\,_{;\, \mu})^2$. We obtain the vacuum stress tensor, which consists of the transverse, longitudinal, temporal, and GF parts, and each contains various UV divergences. By the minimal subtraction rule, we regularize each

Hatef Otroshi Shahreza, Anjith George, Sébastien Marcel

Face recognition systems extract embedding vectors from face images and use these embeddings to verify or identify individuals. Face reconstruction attack (also known as template inversion) refers to reconstructing face images from face embeddings and using the reconstructed face image to enter a face recognition system. In this paper, we propose to use a fa

Xinzheng Zhang, Yuqing Luo, Guopeng Li

Automatic target recognition (ATR) is an important use case for synthetic aperture radar (SAR) image interpretation. Recent years have seen significant advancements in SAR ATR technology based on semi-supervised learning. However, existing semi-supervised SAR ATR algorithms show low recognition accuracy in the case of class imbalance. This work offers a non-

Brian Cunningham

Starting with Hedins equations, simple expressions for the irreducible self-energy are derived. The derivation with vertex effects included in the self-energy results in a number of terms beyond GW such as second-order screened exchange (the term that also gives rise to vertex/excitonic effects in the polarisation) and an infinite series describing correlati

Tiantian Liu, Hongwei Yao, Feng Lin, Tong Wu

Embeddings have become a cornerstone in the functionality of large language models (LLMs) due to their ability to transform text data into rich, dense numerical representations that capture semantic and syntactic properties. These embedding vector databases serve as the long-term memory of LLMs, enabling efficient handling of a wide range of natural language

Yuhang Liu, Xueyu Hu, Shengyu Zhang, Jingyuan Chen

Retrieval-Augmented Generation (RAG) has proven to be an effective method for mitigating hallucination issues inherent in large language models (LLMs). Previous approaches typically train retrievers based on semantic similarity, lacking optimization for RAG. More recent works have proposed aligning retrievers with the preference signals of LLMs. However, the

Fanjin Wang, Maryam Parhizkar, Anthony Harker, Mohan Edirisinghe

Polymeric nano- and micro-scale particles have critical roles in tackling critical healthcare and energy challenges with their miniature characteristics. However, tailoring their synthesis process to meet specific design targets has traditionally depended on domain expertise and costly trial-and-errors. Recently, modeling strategies, particularly Bayesian op

K. Khelifa-Kerfa

We compute the invariant mass of dijets produced in $e^+ e^-$ annihilation processes up to four loops in perturbation theory for both anti-$k_t$ and $k_t$ jet algorithms. The calculations, performed within the eikonal approximation and employing strong-energy ordering, capture the full analytic structure of the leading Abelian and non-Abelian non-global loga

Dean P. Foster, Sergiu Hart

We provide bounds on the tail probabilities for simple procedures that generate random samples _without replacement_, when the probabilities of being selected need not be equal.

Robin Matzner, Akanksha Ahuja, Rasoul Sadeghi, Michael Doherty

Topology Bench is a comprehensive topology dataset designed to accelerate benchmarking studies in optical networks. The dataset, focusing on core optical networks, comprises publicly accessible and ready-to-use topologies, including (a) 105 georeferenced real-world optical networks and (b) 270,900 validated synthetic topologies. Prior research on real-world

Jan T. Schneider, Atsushi Ueda, Yifan Liu, Andreas M. Läuchli

We set up an effective field theory formulation for the renormalization flow of matrix product states (MPS) with finite bond dimension, focusing on systems exhibiting finite-entanglement scaling close to a conformally invariant critical fixed point. We show that the finite MPS bond dimension $\chi$ is equivalent to introducing a perturbation by a relevant op

Ling-Feng Yu, Wei-Lin Li, Xue-Jia Yu, Zhi Li

We study the quench dynamics in a $Z_3$ symmetric chiral clock model (CCM). The results reveal that chiral phases can lead to the emergence of dynamical quantum phase transition (DQPT). By analyzing Lee-Yang-Fisher zeros' distribution in the complex plane, we uncover the relation between the chiral phase and the emergence of DQPT. In concrete terms, only by

Charlie Jeudy, Michel Rouleux

The irreps $(SU(2),{\cal H},U)$ of SU(2) of dimension $(2S+1)^N$, i.e. operators acting on the space ${\cal H}={\cal H}_N={\bf C}^{(2S+1)^N}$ of $N$ identical particles with spin $S$, are described by Clebsch-Gordan decomposition into inequivalent irreps. In the special case $S=1/2$, Dirac \cite{Dir1} discovered that there is another rep given by $({\cal S}(

Abhinav Kumar, Pratham Singla, Aayan Yadav

Image steganography is the technique of embedding secret information within images. The development of deep learning has led to significant advances in this field. However, existing methods often struggle to balance image quality, embedding capacity, and security. This paper proposes a novel approach to image steganography by enhancing an encoder-decoder arc

William Talbot, Julian Nubert, Turcan Tuna, Cesar Cadena

Accurate, efficient, and robust state estimation is more important than ever in robotics as the variety of platforms and complexity of tasks continue to grow. Historically, discrete-time filters and smoothers have been the dominant approach, in which the estimated variables are states at discrete sample times. The paradigm of continuous-time state estimation

Yue Cao, Naihuan Jing, Yiling Wang

We study generalized monogamy and polygamy relations for concurrence of assistance and negativity of assistance using parametrized bounds in general multi-partite quantum systems. The new method overcomes the shortcomings of previous studies where a method is only good at a particular region. We provide detailed examples to show why the new approach is effec

Luca Persichetti, Giacomo Giorgi, Luca Lozzi, Maurizio Passacantando

We investigated the reactivity of layered GeAs in the presence of oxygen and/or water using synchrotron-based X-ray photoelectron spectroscopy and ab initio calculations.

Felipe Marra, Lucas N. Ferreira

This paper investigates the capabilities of text-to-audio music generation models in producing long-form music with prompts that change over time, focusing on soundtrack generation for Tabletop Role-Playing Games (TRPGs). We introduce Babel Bardo, a system that uses Large Language Models (LLMs) to transform speech transcriptions into music descriptions for c

Levent Michael Dasar, Victoria Gould, Craig Miller

A monoid $S$ is said to be weakly right coherent if every finitely generated right ideal of $S$ is finitely presented as a right $S$-act. It is known that $S$ is weakly right coherent if and only if it satisfies the following conditions: $S$ is right ideal Howson, meaning that the intersection of any two finitely generated right ideals of $S$ is finitely gen

Roberto O. Zurita, Cauê M. Kersul, Nick J. Schilder, Gustavo S. Wiederhecker

We introduce a novel optomechanical microgear cavity for both optical and mechanical isotropic materials, featuring a single etch configuration. The design leverages a conjunction of phononic and photonic crystal-like structures to achieve remarkable confinement of both optical and mechanical fields. The microgear cavity we designed in amorphous silicon nitr

Ryan Campbell, Nelson Lojo, Kesava Viswanadha, Christoffer Grondal Tryggestad

In-Context Learning (ICL) is a phenomenon where task learning occurs through a prompt sequence without the necessity of parameter updates. ICL in Multi-Headed Attention (MHA) with absolute positional embedding has been the focus of more study than other sequence model varieties. We examine implications of architectural differences between GPT-2 and LLaMa as

Rémy Mosseri, Yasir Iqbal, Roger Vogeler, Julien Vidal

We study the Kitaev model on the trivalent heptagonal Hurwitz hyperbolic tiling. The presence of odd-length loops in the tiling is responsible for a spontaneous time-reversal symmetry breaking. Interestingly, at the isotropic point, the two degenerate ground states (Kramers pair) are shown to be gapped chiral spin liquids and the elementary excitations are n

Roberto Metere, Kangfeng Ye, Yue Gu, Zhi Zhang

As Open Radio Access Networks (O-RAN) continue to expand, AI-driven applications (xApps) are increasingly being deployed enhance network management. However, developing xApps without formal verification risks introducing logical inconsistencies, particularly in balancing energy efficiency and service availability. In this paper, we argue that prior to their

Robert E. Gaunt, Zixin Ye

Asymptotic expansions are derived for the tail distribution of the product of two correlated normal random variables with non-zero means and arbitrary variances, and more generally the sum of independent copies of such random variables. Asymptotic approximations are also given for the quantile function. Numerical results are given to test the performance of

Joseph Arul Raj, Linglong Qian, Zina Ibrahim

Missing values are pervasive in large-scale time-series data, posing challenges for reliable analysis and decision-making. Many neural architectures have been designed to model and impute the complex and heterogeneous missingness patterns of such data. Most existing methods are end-to-end, rendering imputation tightly coupled with downstream predictive tasks

C. J. Krüger, H. Sotani

We investigate shear and interface modes excited in neutron stars with an elastic crust in the full general relativistic framework and compare them to the results obtained within the relativistic Cowling approximation. We observe that the Cowling approximation has virtually no impact on the frequencies or the eigenfunctions of the shear modes; in contrast, t

Leandro A. Machado, Lucas Madeira, Mônica A. Caracanhas, Vanderlei S. Bagnato

Collective modes are coherent excitations in Bose-Einstein condensates (BECs), and their study provides insight into the macroscopic quantum phenomena that govern these systems. Collective mode frequencies can be used to probe the properties of BECs, such as the trap geometry, the interatomic interactions, and the presence of defects; hence, it is essential

Yueran Duan, Shahan Ali Memon, Bedoor AlShebli, Qing Guan

Postdoctoral training is a career stage often described as a demanding and anxiety-laden time when many promising PhDs see their academic dreams slip away due to circumstances beyond their control. We use a unique data set of academic publishing and careers to chart the more or less successful postdoctoral paths. We build a measure of academic success on the

Alberto Carta, Iurii Timrov, Peter Mlkvik, Alexander Hampel

Several methods have been developed to improve the predictions of density functional theory (DFT) in the case of strongly correlated electron systems. Out of these approaches, DFT+$U$, which corresponds to a static treatment of the local interaction, and DFT combined with dynamical mean field theory (DFT+DMFT), which considers local fluctuations, have both p

Cai Ze-Wei, Zheng Wei, Bao Yi-Ru, Chu Hai-Long

Traditional CNC technology mostly uses the method of increasing the degree of interpolation polynomial when constructing $C^2$ continuous NURBS curves, but this often leads to the appearance of Runge phenomenon in interpolation curves. Alternatively,the method of adding boundary conditions at the endpoints can often make it difficult to control the error ran

Khasmamad Shabanovi, Lukas Wiest, Vladimir Golkov, Daniel Cremers

Post-training quantization is widely employed to reduce the computational demands of neural networks. Typically, individual substructures, such as layers or blocks of layers, are quantized with the objective of minimizing quantization errors in their pre-activations by fine-tuning the corresponding weights. Deriving this local objective from the global objec

D. Carbone, A. Spatafora, D. Calvo, F. Guerra

First prototypes of large area, p-n junction, silicon carbide (SiC) detectors have been produced as part of an ongoing programme to develop a new particle identification wall for the focal plane detector of the MAGNEX magnetic spectrometer, in preparation for future NUMEN experimental campaigns. First characterizations of sensors from two wafers obtained wit

Han Zhang, Medhat Elsayed, Majid Bavand, Raimundas Gaigalas

Federated learning (FL) is an innovative distributed artificial intelligence (AI) technique. It has been used for interdisciplinary studies in different fields such as healthcare, marketing and finance. However the application of FL in wireless networks is still in its infancy. In this work, we first overview benefits and concerns when applying FL to wireles

Harin Lee, Min-hwan Oh

In this work, we close the fundamental gap of theory and practice by providing an improved regret bound for linear ensemble sampling. We prove that with an ensemble size logarithmic in $T$, linear ensemble sampling can achieve a frequentist regret bound of $\tilde{O}(d^{3/2}\sqrt{T})$, matching state-of-the-art results for randomized linear bandit algorithms

Jinyang Lin, Zhenghao Zhu, Renyi Ma, Anna Bonaldi

Building the radio sky template are crucial for detecting the 21 cm emission line signal from the Epoch of Reionization (EoR), as well as for other cosmological research endeavors. Utilizing data from the LOFAR Two-meter Sky Survey (LoTSS) at 150 MHz, we recalibrated the luminosity function for various types of radio sources, including High Excitation Radio

Niccolò Bosio, Markus Kuba, Benedikt Stufler

This work is devoted to the analysis of a Gibbs partition model, also known as a composition scheme. We consider a natural new condition on the component weights. It leads to a new behavior for the total number of components. We discover a condensation phenomenon, producing a unique giant component comprising almost the entire mass. Additionally, we prove a

Luca Crugnola, Christian Vergara

When studying the dynamics of incompressible fluids in bounded domains the only available data often provide average flow rate conditions on portions of the domain's boundary. In engineering applications a common practice to complete these conditions is to prescribe a Dirichlet condition by assuming a-priori a spatial profile for the velocity field. However,

Weipeng Guan, Fuling Lin, Peiyu Chen, Peng Lu

Event cameras show great potential for visual odometry (VO) in handling challenging situations, such as fast motion and high dynamic range. Despite this promise, the sparse and motion-dependent characteristics of event data continue to limit the performance of feature-based or direct-based data association methods in practical applications. To address these

Gianmarco Sperone

The steady motion of a viscous incompressible fluid through a sieve (that is, a wall perforated with a large number of small holes), in a pipe of finite length, is modeled through the Navier-Stokes equations under mixed boundary conditions involving the Bernoulli pressure and the tangential velocity on the inlet and outlet of the tube, while the pressure dro

Tao Liu, Dapeng Man, Jiguang Lv, Chen Xu

Federated learning (FL) is widely used in Internet-of-Things (IoT) systems, but its distributed training process also exposes it to backdoor attacks. Existing studies mainly consider single-target or centralized multi-target settings, while coordinated distributed multi-target attacks remain underexplored. In practical IoT scenarios, one adversarial entity m

Debbie Lim, Yixian Qiu, Patrick Rebentrost, Qisheng Wang

Logistic regression, the Support Vector Machine (SVM), and least squares are well-studied methods in the statistical and computer science community, with various practical applications. High-dimensional data arriving on a real-time basis makes the design of online learning algorithms that produce sparse solutions essential. The seminal work of \hyperlink{cit

Cangxiong Chen, Vinay P. Namboodiri, Julia E. Sero

The spatio-temporal nature of live-cell microscopy data poses challenges in the analysis of cell states which is fundamental in bioimaging. Deep-learning based segmentation or tracking methods rely on large amount of high quality annotations to work effectively. In this work, we explore an alternative solution: using feature maps obtained from self-supervise

Aaditya K. Singh, Muhammed Yusuf Kocyigit, Andrew Poulton, David Esiobu

Hampering the interpretation of benchmark scores, evaluation data contamination has become a growing concern in the evaluation of LLMs, and an active area of research studies its effects. While evaluation data contamination is easily understood intuitively, it is surprisingly difficult to define precisely which samples should be considered contaminated and,

Lyuhong Wang, Jiawei Jiang, Yang Zhao

We introduce an innovative framework that leverages advanced big data techniques to analyze dynamic co-movement between stocks and their underlying fundamentals using high-frequency stock market data. Our method identifies leading co-movement stocks through four distinct regression models: Forecast Error Variance Decomposition, transaction volume-normalized

He Huang, Lizhi Hou, Qi Yang, Yiling Xu

In the current Video-based Dynamic Mesh Coding (V-DMC) standard, inter-frame coding is restricted to mesh frames with constant topology. Consequently, temporal redundancy is not fully leveraged, resulting in suboptimal compression efficacy. To address this limitation, this paper introduces a novel coarse-to-fine scheme to generate anchor meshes for frames wi

Ian Poey, Jiajun Liu, Qishuai Zhong, Adrien Chenailler

Real-time detection of out-of-context LLM outputs is crucial for enterprises looking to safely adopt RAG applications. In this work, we train lightweight models to discriminate LLM-generated text that is semantically out-of-context from retrieved text documents. We preprocess a combination of summarisation and semantic textual similarity datasets to construc

R. James Cotton, Bryant A. Seamon, Richard L. Segal, Randal D. Davis

Precision rehabilitation offers the promise of an evidence-based approach for optimizing individual rehabilitation to improve long-term functional outcomes. Emerging techniques, including those driven by artificial intelligence, are rapidly expanding our ability to quantify the different domains of function during rehabilitation, other encounters with health

Nana Lin, Youxiang Zhu, Xiaohui Liang, John A. Batsis

Mild cognitive impairment (MCI) is a major public health concern due to its high risk of progressing to dementia. This study investigates the potential of detecting MCI with spontaneous voice assistant (VA) commands from 35 older adults in a controlled setting. Specifically, a command-generation task is designed with pre-defined intents for participants to f

Yuta Nozaki, David Palmer, Yuya Koda

Nozaki et.~al.\ gave a homotopy classification of the knotted defects of ordered media in three-dimensional space by considering continuous maps from complements of spatial graphs to the order parameter space modulo a certain equivalence relation. We extend their result by giving a classification scheme for ordered media in handlebodies, where defects are al

Weiran Ding, Jianquan Ge, Fagui Li

In this paper we give a pinching theorem of the Simon conjecture in the case s=3 and also give a new proof of the cases s=1 and s=2 by some Simons-type integral inequalities.

Estefanio Kesto, Michael J. Dominguez, Vanessa Sih

Electron-nuclear spin interactions by pulsed optical pumping have been found to polarize the nuclear spin system, leading to the nuclei building up an intrinsic magnetic field known as the Overhauser field. Studies have indicated an Overhauser field hysteresis effect dependent on the sweep direction of an externally applied magnetic field in negatively detun

Youchuang Chao, Hansol Jeon, Stefan Karpitschka

Soft materials are ubiquitous in technological applications that require deformability, for instance, in flexible, water-repellent coatings. However, the wetting properties of pre-strained soft materials are only beginning to be explored. Here we study the sliding dynamics of droplets on pre-strained soft silicone gels, both in tension and in compression. In

Hengzhu Liu, Tianqing Zhu, Lefeng Zhang, Ping Xiong

With the extensive use of machine learning technologies, data providers encounter increasing privacy risks. Recent legislation, such as GDPR, obligates organizations to remove requested data and its influence from a trained model. Machine unlearning is an emerging technique designed to enable machine learning models to erase users' private information. Altho

Leonid O. Chekhov

For a Riemann surface with holes, we propose a variant of the action on a circum\-ference-$P$ boundary component with $n$ bordered cusps attached (a "fool's crown") that is decoration-invariant and generates finite volumes $V^{\text{crown}}_{n,P}$ of the corresponding moduli spaces when integrated against the volume form obtained by inverting the Fenchel--Ni

Maxime Varese, Vianney Lebouteiller, Lise Ramambason, Frédéric Galliano

Star formation in galaxies is regulated by dynamical and thermal processes. The photoelectric effect on small dust grains usually dominates the heating of the star-forming neutral atomic gas reservoir in metal-rich galaxies, while the lower dust-to-gas mass ratio and the higher luminosity of X-ray sources in metal-poor galaxies suggest that other heating mec

Erik Kuulkers

The detection of Type I X-ray bursts is attributed to those seen by the Astronomical Netherlands Satellite (ANS) in September 1975 from the globular cluster NGC6624 containing the X-ray source 4U1820-303. I revisit these X-ray bursts, by re-analysing data from the Soft X-ray Experiment (SXX) onboard ANS, which were stored on microfiche. Earlier accounts of X

Joel Poncha Lemayian, Ghyslain Gagnon, Kaiwen Zhang, Pascal Giard

The SECP256K1 elliptic curve algorithm is fundamental in cryptocurrency wallets for generating secure public keys from private keys, thereby ensuring the protection and ownership of blockchain-based digital assets. However, the literature highlights several successful side-channel attacks on hardware wallets that exploit SECP256K1 to extract private keys. Th

Feiran Zhao, Ruohan Leng, Linbin Huang, Huanhai Xin

Power electronic converters are becoming the main components of modern power systems due to the increasing integration of renewable energy sources. However, power converters may become unstable when interacting with the complex and time-varying power grid. In this paper, we propose an adaptive data-driven control method to stabilize power converters by using

Tristan Aurégan, Luc Deike

We experimentally investigate the effect of salinity and atmospheric humidity on the drainage and lifetime of thin liquid films motivated by conditions relevant to air-sea exchanges. We show that the drainage is independent of humidity and that the effect of a change in salinity is only reflected through the associated change in viscosity. On the other hand,

M. Simões, P. Vaz, A. F. V. Cortez

X-ray imaging is an invaluable tool for noninvasive analysis in many fields ranging from basic science to medicine and security. The development of low-dose large area imaging solutions still represents an important challenge for various applications. One solution to the imaging of large areas lies in the development of novel computational imaging systems th

David Maria Schmidt, Mohammad Fazleh Elahi, Philipp Cimiano

In this paper, we examine the impact of lexicalization on Question Answering over Linked Data (QALD). It is well known that one of the key challenges in interpreting natural language questions with respect to SPARQL lies in bridging the lexical gap, that is mapping the words in the query to the correct vocabulary elements. We argue in this paper that lexical

Antoine Sédillot

In this article, we introduce pseudo-absolute values, which generalise usual absolute values. Roughly speaking, a pseudo-absolute value on a field $K$ is a map $|\cdot| : K \to [0,+\infty]$ satisfying axioms similar to those of usual absolute values. This notion allows to include "pathological" absolute values one can encounter trying to incorporate the anal

Vikas S Bhat, Rounak Chatterjee, Kiran Bajar, Sushil Mujumdar

We explore the consequences of varying the pump beam waist that illuminates a nonlinear crystal, realizing spontaneous parametric down-conversion (SPDC). The coherence is transferred from the marginal one-photon wavefunction to the two-photon wavefunction where it manifests into entanglement in the form of spatial correlation. We interpret this as a conseque

Yiying Chen, Peidong Wang, Zizhu Wang

Causality is one of the most fundamental notions in physics. Generalized probabilistic theories (GPTs) and the process matrix framework incorporate it in different forms. However, a direct connection between these frameworks remains unexplored. By demonstrating the duality between no-signaling principle and classical processes in tripartite classical systems

Haruki Kanaya, Ryota Eguchi, Taisho Sasada, Michiko Inoue

The population protocol model is a computational model for passive mobile agents. We address the leader election problem, which determines a unique leader on arbitrary communication graphs starting from any configuration. Unfortunately, self-stabilizing leader election is impossible to be solved without knowing the exact number of agents; thus, we consider l

Nikita S. Tsegelnik, Vadym Voronyuk, Evgeni E. Kolomeitsev

The strange baryon production in Bi+Bi collisions at $\sqrt{s_{NN}}=9.0$\,GeV is studied using the PHSD transport model. Hyperon and anti-hyperon yields, transverse momentum spectra, and rapidity spectra are calculated, their centrality dependence and the effect of rapidity and transverse momentum cuts are studied. The rapidity distributions for $\overline{L

Zhuorui Ye, Farzan Farnia

Saliency maps have been widely used to interpret the decisions of neural network classifiers and discover phenomena from their learned functions. However, standard gradient-based maps are frequently observed to be highly sensitive to the randomness of training data and the stochasticity in the training process. In this work, we study the role of Gaussian smo

Oliver Knitter, Dan Zhao, James Stokes, Martin Ganahl

Neural-network quantum states (NQS) has emerged as a powerful application of quantum-inspired deep learning for variational Monte Carlo methods, offering a competitive alternative to existing techniques for identifying ground states of quantum problems. A significant advancement toward improving the practical scalability of NQS has been the incorporation of

Xin Xu

The Barzilai-Borwein (BB) method is an effective gradient descent algorithm for solving unconstrained optimization problems. Based on the observation of two classical BB step sizes, by constructing an interpolated least squares model, we propose a novel class of BB step sizes, each of which still retains the quasi-Newton property, with the original two BB st

Devvrat Tiwari, Samyadeb Bhattacharya, Subhashish Banerjee

In this work, we develop a panoramic schematic of quantum thermal analogs of electric circuits in the steady state regime. We establish the foundations of said premise by defining the analogs of Kirchhoff's laws for heat currents and temperature gradients, as well as a quantum thermal step transformer. Using this, we develop two novel quantum thermal circuit

Baiqiang Zhu, Pierre Meystre, Weiping Zhang, Keye Zhang

By integrating molecular optomechanics with molecular switches, we propose a scheme for a molecular quantum heat engine that operates autonomously through hysteretic feedback without external driving or modulation. Through a comparative analysis conducted within both semiclassical and fully quantum frameworks, we reveal the influence of quantum properties em

Enrico Specogna, William Giarè, Eleonora Di Valentino

We present the results from a series of analyses on two parametric tests of gravity that modify the growth of linear, sub-horizon matter perturbations in the $\Lambda$CDM model. The first test, known as the $(\mu,\Sigma)$ framework, modifies the Poisson and lensing equations from General Relativity (GR). The second test introduces the growth index $\gamma$,

Allison Keith, Antonio Rojas Castro, Hanno Ehrlicher, Kerstin Jung

In theatre, playwrights use the portrayal of characters to explore culturally based gender norms. In this paper, we develop quantitative methods to study gender depiction in the non-religious works (comedias) of Pedro Calder\'on de la Barca, a prolific Spanish 17th century author. We gather insights from a corpus of more than 100 plays by using a gender clas

Amir Hossein Namdar, Nastaran Zoghi, Aline Miller, Alberto Saiani

The structures of fibre networks can vary greatly due to fibre interactions during formation. We have modified the steps of generating Mikado networks to create two new model classes by altering the spatial distribution and relative orientation of their fibres to mimic the structures of self-assembling peptide hydrogels (SAPHs), whose physical properties dep

Edgar Medina-Segura, Paola C. Obando, Light Mkhumbuza, Enrique J. Galvez

Maxwell's demon (MD) has proven an instructive vehicle by which to explore the relationship between information theory and thermodynamics, fueling the possibility of information driven machines. A long standing debate has been the concern of entropy violation, now resolved by the introduction of a quantum MD, but this theoretical suggestion has proven experi

Yue Huang, Shuzheng Wang, Yuming Huang, Gareth Tyson

As a key enabler of Web3, Ethereum has long faced scalability challenges. The recent EIP-4844 upgrade aims to alleviate the scalability issue by introducing the ''blob'', a new data structure for Layer-2 rollups that enables off-chain storage with much reduced costs. Yet, this new mechanism's impact on Ethereum, and the wider Web3 ecosystem, remains largely

S. Ali, A. S. Ryzhikov, D. A. Derkach, F. D. Ratnikov

In the realm of high-energy physics, the longevity of calorimeters is paramount. Our research introduces a deep learning strategy to refine the calibration process of calorimeters used in particle physics experiments. We develop a Wasserstein GAN inspired methodology that adeptly calibrates the misalignment in calorimeter data due to aging or other factors.

Vasily Bolbachan

Chow polylogarithms are some special functions arising in explicit description of the Beilinson regulator map. The most interesting functional equation for this function reflects its vanishing on the boundary in the Bloch's cycle complex. We show that this functional equation formally follows from more simple ones, namely skew-symmetry, functoriality and mul

Minh Duc Bui, Katharina von der Wense, Anne Lauscher

Warning: this paper contains content that may be offensive or upsetting Hate speech moderation on global platforms poses unique challenges due to the multimodal and multilingual nature of content, along with the varying cultural perceptions. How well do current vision-language models (VLMs) navigate these nuances? To investigate this, we create the first mul

Islam M. Tanash, Ayush Kumar Dwivedi, Taneli Riihonen

This paper investigates a high-altitude platform (HAP) network enhanced with reconfigurable intelligent surfaces (RISs). The arbitrary placement of HAPs and RISs is modeled using stochastic geometry, specifically as homogeneous Poisson point processes. The HAP--RIS links are assumed to follow Rician fading, while the RIS--user links experience shadowed-Ricia

Denis Newman-Griffis, Bonnielin Swenor, Rupa Valdez, Gillian Mason

Data are the medium through which individuals' identities and experiences are filtered in contemporary states and systems, and AI is increasingly the layer mediating between people, data, and decisions. The history of data and AI is often one of disability exclusion, oppression, and the reduction of disabled experience; left unchallenged, the current prolife

Zhijian Zhuo, Ya Wang, Yutao Zeng, Xiaoqing Li

Transformers have found extensive applications across various domains due to the powerful fitting capabilities. This success can be partially attributed to their inherent nonlinearity. Thus, in addition to the ReLU function employed in the original transformer architecture, researchers have explored alternative modules such as GeLU and SwishGLU to enhance no

Laura Cabello, Carmen Martin-Turrero, Uchenna Akujuobi, Anders Søgaard

Question answering is a natural language understanding task that involves reasoning over both explicit context, and unstated relevant domain knowledge. Despite the high cost of training, large language models (LLMs) -- the backbone of most modern question-answering systems -- still struggle to reliably capture the nuanced relationships between concepts that

Thomas Tony

Every closed connected Riemannian spin manifold of non-zero $\hat{A}$-genus or non-zero Hitchin invariant with non-negative scalar curvature admits a parallel spinor, in particular is Ricci-flat. In this note, we generalize this result to closed connected spin manifolds of non-vanishing Rosenberg index. This provides a criterion for the existence of a parall

Timo Breuer

Considering query variance in information retrieval (IR) experiments is beneficial for retrieval effectiveness. Especially ranking ensembles based on different topically related queries retrieve better results than rankings based on a single query alone. Recently, generative instruction-tuned Large Language Models (LLMs) improved on a variety of different ta

Luis Mendonça, Thomas S. Weigel, Theo Zapata

Using methods of associative algebras, Lie theory, group cohomology, and modular representation theory, we construct profinite $p$-adic analytic groups such that the centralizer of each of their non-trivial elements is abelian. The paper answers questions of P.~Shumyatsky, P.~Zalesskii, and T.~Zapata in the Israel J. Math., v.~230, 2019.

Markus Weißenhofer, Philipp Rieger, M. S. Mrudul, Luca Mikadze

Chiral phonons are desirable for applications in spintronics but their generation and control remains a challenge.Here we demonstrate the emergence of truly chiral phonons from selective magnon-phonon coupling in inversion-symmetric magnetic systems. Considering bcc Fe as example, we quantitatively calculate hybridized magnon-phonon quasiparticle states acro

Liam Lysaght, Timothée Goubault, Patrick Sinnott, Shane Mansfield

We present qubit logic on qudits (QLOQ), a compression scheme in which the qubits from a hardware agnostic circuit are divided into groups of various sizes, and each group is mapped to a physical qudit for computation. QLOQ circuits have qubit-logic inputs, outputs, and gates, making them compatible with existing qubit-based algorithms and Hamiltonians. We s

Kiran Purohit, Venktesh V, Raghuram Devalla, Krishna Mohan Yerragorla

Answering reasoning-based complex questions over text and hybrid sources, including tables, is a challenging task. Recent advances in large language models (LLMs) have enabled in-context learning (ICL), allowing LLMs to acquire proficiency in a specific task using only a few demonstration samples (exemplars). A critical challenge in ICL is the selection of o

Feibo Jiang, Siwei Tu, Li Dong, Cunhua Pan

The rapid development of generative Artificial Intelligence (AI) continually unveils the potential of Semantic Communication (SemCom). However, current talking-face SemCom systems still encounter challenges such as low bandwidth utilization, semantic ambiguity, and diminished Quality of Experience (QoE). This study introduces a Large Generative Model-assiste

Robin Strässer, Julian Berberich, Frank Allgöwer

In this paper, we propose a novel controller design approach for unknown nonlinear systems using the Koopman operator. In particular, we use the recently proposed stability- and feedback-oriented extended dynamic mode decomposition (SafEDMD) architecture to generate a data-driven bilinear surrogate model with certified error bounds. Then, by accounting for t

Roni Chatterjee, Monoj Adhikari, Smarajit Karmakar

We investigate the effects of random pinning, where we freeze the relaxation degrees of freedom for a fraction of randomly selected particles, on the yielding transition under oscillatory shear through extensive computer simulations. Using Kob-Anderson model as our model glass former, we pin a fraction of the particles. These pinned particles can move affine

Italo Belli, Florian van Melis, J. Micah Prendergast, Ajay Seth

Robotic devices provide a great opportunity to assist in delivering physical therapy and rehabilitation movements, yet current robot-assisted methods struggle to incorporate biomechanical metrics essential for safe and effective therapy. We introduce BATON, a Biomechanics-Aware Trajectory Optimization approach to online robotic Navigation of human musculoske

Sam Azadi, Kühne

We employ real-space variational quantum Monte Carlo methods with resonating valence bond many-body wave functions to investigate electron correlation effects in the Kagome system $Zn_xCu_{4-x}O_6$. Using three trial wave functions of the Slater-Jastrow type, where (i) only the Jastrow correlation factor is optimized and the orbitals obtained by density func