March 2024 arXiv papers — page 116

Louis Owen, Vishesh Tripathi, Abhay Kumar, Biddwan Ahmed

The recent breakthroughs in Large Language Models (LLMs) have mostly focused on languages with easily available and sufficient resources, such as English. However, there remains a significant gap for languages that lack sufficient linguistic resources in the public domain. Our work introduces Komodo-7B, 7-billion-parameter Large Language Models designed to a

Pengfei He, Jin-Kao Hao, Qinghua Wu

The capacitated location-routing problem involves determining the depots from a set of candidate capacitated depot locations and finding the required routes from the selected depots to serve a set of customers whereas minimizing a cost function that includes the cost of opening the chosen depots, the fixed utilization cost per vehicle used, and the total cos

Xavier Zambrana-Puyalto, Søren Raza

We show analytically that the quality ($Q$) factor of magnetic and electric Mie modes in a lossless dielectric spherical resonator with high refractive index ($n \gg 1$) scales as $n^{2j+1}$ and $n^{2j+3}$ respectively, where $j$ denotes the multipolar order. We numerically validate these results and show that our high-$n$ analytical relation is accurate for

Dinh Phat Do, Taehoon Kim, Jaemin Na, Jiwon Kim

Domain adaptation for object detection typically entails transferring knowledge from one visible domain to another visible domain. However, there are limited studies on adapting from the visible to the thermal domain, because the domain gap between the visible and thermal domains is much larger than expected, and traditional domain adaptation can not success

Jeongmin Hong, Sungjun Cho, Geonwoo Park, Wonhyuk Yang

We propose overcoming the memory capacity limitation of GPUs with high-capacity Storage-Class Memory (SCM) and DRAM cache. By significantly increasing the memory capacity with SCM, the GPU can capture a larger fraction of the memory footprint than HBM for workloads that oversubscribe memory, achieving high speedups. However, the DRAM cache needs to be carefu

Long Zhang, Halvin Yang, Yizhe Zhao, Jie Hu

Integrated data and energy transfer (IDET) has been of fundamental importance for providing both wireless data transfer (WDT) and wireless energy transfer (WET) services towards low-power devices. Fluid antenna (FA) is capable of exploiting the huge spatial diversity of the wireless channel to enhance the receive signal strength, which is more suitable for t

Tongtong Li, Guohuan Qiu

For any $\alpha $ small, we construct infinitely many $C^{1,\alpha}$ very weak solutions to the 2-Hessian equation with prescribed boundary value. The proof relies on the convex integration method and cut-off technique.

Alois Schiessl

The purpose of this paper is to present series expansions for even powers of the number $\pi$. This is accomplished by generalizing Euler's method for solving the Basel Problem, which was published in 1735. We employ elementary symmetric polynomials, transform them into nested sums, and thereby derive nice series formulas for even powers of the number $\pi$

Hanyu Chen, Zhixiu Hao, Lin Guo, Liying Xiao

Sparse-view Computed Tomography (CT) image reconstruction is a promising approach to reduce radiation exposure, but it inevitably leads to image degradation. Although diffusion model-based approaches are computationally expensive and suffer from the training-sampling discrepancy, they provide a potential solution to the problem. This study introduces a novel

Patrick Johansen Sarsfield, Aitor Garcia-Ruiz, Vladimir I. Fal'ko

Polytypes of tetralayer graphene (TLG: Bernal, rhombohedral and mixed stacking) are crystalline structures with different symmetries. Among those, mixed-stacking tetralayers lack inversion symmetry, which allows for intrinsic spontaneous out-of-plane electrical polarisation, inverted in the mirror-image pair, ABCB and ABAC stackings. Here, we compare the int

Qian Ding, Jie Yang, Yang Luo, Chunbo Luo

This letter aims to provide a fundamental analytical comparison for the two major types of relaying methods: intelligent reflecting surfaces and full-duplex relays, particularly focusing on unmanned aerial vehicle communication scenarios. Both amplify-and-forward and decode-and-forward relaying schemes are included in the comparison. In addition, optimal 3D

Samuel Pagliarini, Aikata Aikata, Malik Imran, Sujoy Sinha Roy

Significant research efforts have been dedicated to designing cryptographic algorithms that are quantum-resistant. The motivation is clear: robust quantum computers, once available, will render current cryptographic standards vulnerable. Thus, we need new Post-Quantum Cryptography (PQC) algorithms, and, due to the inherent complexity of such algorithms, ther

Xinyue Sun, Qingqing Ye, Haibo Hu, Jiawei Duan

Local differential privacy (LDP), which enables an untrusted server to collect aggregated statistics from distributed users while protecting the privacy of those users, has been widely deployed in practice. However, LDP protocols for frequency estimation are vulnerable to poisoning attacks, in which an attacker can poison the aggregated frequencies by manipu

Samuel Pawel

The Bayes factor, the data-based updating factor of the prior to posterior odds of two hypotheses, is a natural measure of statistical evidence for one hypothesis over the other. We show how Bayes factors can also be used for parameter estimation. The key idea is to consider the Bayes factor as a function of the parameter value under the null hypothesis. Thi

Pardeep Singh, Rabindra Lamsal, Monika Singh, Satish Chand

COVID-19 pandemic has brought unprecedented challenges to the world, and vaccination has been a key strategy to combat the disease. Since Twitter is one of the most widely used public microblogging platforms, researchers have analysed COVID-19 vaccines and vaccination Twitter discourse to explore the conversational dynamics around the topic. While contributi

Rong Du

For a compact K\"{a}hler-Einstein manifold $M$ of dimension $n\ge 2$, we explicitly write the expression $-c_1^n(M)+\frac{2(n+1)}{n}c_2(M)c_1^{n-2}(M)$ in the form of certain integral on the holomorphic sectional curvature and its average at a fixed point in $M$ using the invariant theory. As applications, we get a reverse Yau's inequality and improve the cl

Ao Sun, Weilin Zhao, Xu Han, Cheng Yang

Effective attention modules have played a crucial role in the success of Transformer-based large language models (LLMs), but the quadratic time and memory complexities of these attention modules also pose a challenge when processing long sequences. One potential solution for the long sequence problem is to utilize distributed clusters to parallelize the comp

Hao Zhang, Wenqi Shao, Hong Liu, Yongqiang Ma

Large Vision-Language Models (LVLMs) have shown significant progress in responding well to visual-instructions from users. However, these instructions, encompassing images and text, are susceptible to both intentional and inadvertent attacks. Despite the critical importance of LVLMs' robustness against such threats, current research in this area remains limi

Jeffrey Galkowski, Zhen Huang, Maciej Zworski

We show that for the Lindblad evolution defined using (at most) quadratically growing classical Hamiltonians and (at most) linearly growing classical jump functions (quantized into jump operators assumed to satisfy certain ellipticity conditions and modeling interaction with a larger system), the evolution of a quantum observable remains close to the classic

Hmrishav Bandyopadhyay, Ayan Kumar Bhunia, Pinaki Nath Chowdhury, Aneeshan Sain

We propose SketchINR, to advance the representation of vector sketches with implicit neural models. A variable length vector sketch is compressed into a latent space of fixed dimension that implicitly encodes the underlying shape as a function of time and strokes. The learned function predicts the $xy$ point coordinates in a sketch at each time and stroke. D

Igor Reis, Emmanuel Moulin, Aion Viana, Victor P. Goncalves

Huge efforts have been deployed to detect dark matter (DM) in the GeV-TeV mass range involving various detection techniques, and led to strong constraints in the available parameter space. We compute here the sensitivity to sub-GeV DM that can be probed from the inevitable cosmic-ray scattering onto DM particles populating the Milky Way halo. Inelastic scatt

Elena R. Loubenets, Louis Hanotel

The geometric quantum discord of a two-qudit state has been studied in many papers, however, its exact analytical value in the explicit form is known only for a general two-qubit state, a general qubit-qudit state and some special families of two-qudit states. Based on the general Bloch vectors formalism [J. Phys. A: Math. Theor. 54 195301 (2021)], we find t

Ling-Da Kong, Long Ji, Andrea Santangelo, Meng-Lei Zhou

Utilizing NICER observations, we present an analysis of the soft X-ray re-brightening event of GRS 1915+105 observed in 2021. During this event, we observed the emergence of a stable, long-lasting low-frequency quasi-periodic oscillation (LFQPO) with frequencies ranging from 0.17 to 0.21 Hz. Through a careful spectral analysis, we demonstrate that a low-temp

Maximilian Frank, Julian-Steven Schilling, Theresa Zorn, Philipp Kessler

This paper investigates the thermoelectric properties of solid polymer electrolytes (SPE) containing lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) and sodium bis(trifluoromethanesulfonyl)imide (NaTFSI) salts, along with carbon-based additives of various dimensionalities. Increasing salt concentration leads to higher Seebeck coefficients as a result of

Hao-Tao Zhu, Yizhi Huang, Wen-Xin Pan, Chao-Wu Zhou

Quantum key distribution is a cornerstone of quantum technology, offering information-theoretical secure keys for remote parties. With many quantum communication networks established globally, the mode-pairing protocol stands out for its efficacy over inter-city distances using simple setups, emerging as a promising solution. In this study, we employ the mod

Tao Huang, Xiaohuan Pei, Shan You, Fei Wang

Recent advancements in state space models, notably Mamba, have demonstrated significant progress in modeling long sequences for tasks like language understanding. Yet, their application in vision tasks has not markedly surpassed the performance of traditional Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). This paper posits that the key

Jose Brox, Carla Rizzo

We study the differential identities of the algebra $M_k(F)$ of $k\times k$ matrices over a field $F$ of characteristic zero when its full Lie algebra of derivations, $L=\mbox{Der}(M_k(F))$, acts on it. We determine a set of 2 generators of the ideal of differential identities of $M_k(F)$ for $k\geq 2$. Moreover, we obtain the exact values of the correspondi

M. Athanasakis-Kaklamanakis, S. G. Wilkins, P. Lassègues, L. Lalanne

The radiative lifetime of the $A$ $^2 \Pi_{1/2}$ (v=0) state in radium monofluoride (RaF) is measured to be 35(1) ns. The lifetime of this state and the related decay rate $\Gamma = 2.86(8) \times 10^7$ $s^{-1}$ are of relevance to the laser cooling of RaF via the optically closed $A$ $^2 \Pi_{1/2} \leftarrow X$ $^2\Sigma_{1/2}$ transition, which makes the m

Carlo Bellingeri, Peter K. Friz, Sylvie Paycha

The Euler-Maclaurin formula which relates a discrete sum with an integral, is generalised to the setting of Riemann-Stieltjes sums and integrals on stochastic processes whose paths are a.s. rectifiable, namely, continuous and with bounded variation. For this purpose, new variants of the signature are introduced, such as the flip and the sawtooth signature. T

Uriel Singer, Amit Zohar, Yuval Kirstain, Shelly Sheynin

We introduce Emu Video Edit (EVE), a model that establishes a new state-of-the art in video editing without relying on any supervised video editing data. To develop EVE we separately train an image editing adapter and a video generation adapter, and attach both to the same text-to-image model. Then, to align the adapters towards video editing we introduce a

Yufei Zhan, Shurong Zheng, Yousong Zhu, Hongyin Zhao

Large Vision Language Models have achieved fine-grained object perception, but the limitation of image resolution remains a significant obstacle to surpassing the performance of task-specific experts in complex and dense scenarios. Such limitation further restricts the model's potential to achieve nuanced visual and language referring in domains such as GUI

Richard E. Zeebe, Margriet L. Lantink

The planets' gravitational interaction causes rhythmic changes in Earth's orbital parameters (also called Milankovi\'c cycles), which have powerful applications in geology and astrochronology. For instance, the primary astronomical eccentricity cycle due to the secular frequency term (g2-g5) (~405 kyr in the recent past) utilized in deep-time analyses is dom

D0 Collaboration

We measure proton structure parameters sensitive primarily to valence quarks using 8.6 fb$^{-1}$ of data collected by the D0 detector in $\sqrt{s}=1.96$ TeV $p\bar{p}$ collisions at the Fermilab Tevatron. We exploit the property of the forward-backward asymmetry in dilepton events to be factorized in to distinct structure parameters and electroweak quark-lev

Gui Zhou, Moritz Garkisch, Zhendong Peng, Cunhua Pan

We propose a novel integrated sensing and communication (ISAC) system that leverages sensing to assist communication, ensuring fast initial access, seamless user tracking, and uninterrupted communication for millimeter wave (mmWave) wideband systems. True-time-delayers (TTDs) are utilized to generate frequency-dependent radar rainbow beams by controlling the

Eilon Bilinsky, Moti Gitik

We use indecomposable ultrafilters to answer some questions of Hayut, Karagila paper "Spectra of uniformity". It is shown that the bound on the strength by T. Usuba "A note on uniform ultrafilters in choiceless context" is optimal.

Yang Luo, Shaoxiang Sheng, Michele Pisarra, Alberto Martin Jimenez

The capability to excite, probe, and manipulate vibrational modes is essential for understanding and controlling chemical reactions at the molecular level. Recent advancements in tip-enhanced Raman spectroscopies have enabled the probing of vibrational fingerprints in a single molecule with Angstrom-scale spatial resolution. However, achieving controllable e

Andrew Wang, Mike Davies

Ill-posed image reconstruction problems appear in many scenarios such as remote sensing, where obtaining high quality images is crucial for environmental monitoring, disaster management and urban planning. Deep learning has seen great success in overcoming the limitations of traditional methods. However, these inverse problems rarely come with ground truth d

Duotun Wang, Hengyu Meng, Zeyu Cai, Zhijing Shao

Current text-to-avatar methods often rely on implicit representations (e.g., NeRF, SDF, and DMTet), leading to 3D content that artists cannot easily edit and animate in graphics software. This paper introduces a novel framework for generating stylized head avatars from text guidance, which leverages locally learnable mesh deformation and 2D diffusion priors

Helen Meyer, Ulf-G. Meißner

We present an improved calculation of the light element abundances in the framework of Big Bang nucleosynthesis as a function of the Higgs vacuum expectation value $v$. We compare the methods of our calculation to previous literature including the recently published work of Burns et al. [1]. The PDG result for the $^4$He abundance can be explained within $2\

Clément Erignoux, Alexandre Roget, Assaf Shapira, Marielle Simon

We investigate a $2d$-conservative lattice gas exhibiting a dynamical active-absorbing phase transition with critical density $\rho_c$. We derive the hydrodynamic equation for this model, showing that all critical exponents governing the large scale behavior near criticality can be obtained from two independent ones. We show that as the supercritical density

Jiaqing Zhang, Mingxiang Cao, Weiying Xie, Jie Lei

Multimodal image fusion and object detection are crucial for autonomous driving. While current methods have advanced the fusion of texture details and semantic information, their complex training processes hinder broader applications. Addressing this challenge, we introduce E2E-MFD, a novel end-to-end algorithm for multimodal fusion detection. E2E-MFD stream

Anca Hangan, Dragos Lazea, Tudor Cioara

IoT devices have become indispensable components of our lives, and the advancement of AI technologies will make them even more pervasive, increasing the vulnerability to malfunctions or cyberattacks and raising privacy concerns. Encryption can mitigate these challenges; however, most existing anomaly detection techniques decrypt the data to perform the analy

Zulfidin Khodzhaev

The paper summarizes spectrogram and gives practical application of spectrogram in signal processing. For analysis, finger-snapping is recorded with a sampling rate of 441000 Hz and 96000 Hz. The effects of the number of segments on the Power Spectral Density (PSD) and spectrogram are analyzed and visualized.

Konstantin Gaul, Ronald F. Garcia Ruiz, Robert Berger

Direct laser-coolability and a comparatively simple electronic structure render alkaline-earth metal monofluoride molecules versatile laboratories for precision tests of fundamental physics. In this theoretical work, a route for efficient stopping and cooling of high-energy hot beams of mass-selected alkaline-earth metal monofluorides via their anions is exp

Jiating Yao, Benliang Zhou, Xiaoying Zhou, Xianbo Xiao

We theoretically study the quantum scattering and transport of electrons with Mexican-hat dispersion through both step and rectangular potential barriers by using the transfer matrix method. Owing to the torus-like iso-energy lines of the Mexican-hat dispersion, we observe the presence of double reflections and double transmissions in both two different barr

Sheng-Yao Wu, Run-Ze Li, Yan-Qi Song, Su-Juan Qin

Neural network is a powerful learning paradigm for data feature learning in the era of big data. However, most neural network models are deterministic models that ignore the uncertainty of data. Fuzzy neural networks are proposed to address this problem. FDNN is a hierarchical deep neural network that derives information from both fuzzy and neural representa

Ding-Tao Huang, En-Te Lin, Lipeng Chen, Li-Fu Liu

Despite the success in 6D pose estimation in bin-picking scenarios, existing methods still struggle to produce accurate prediction results for symmetry objects and real world scenarios. The primary bottlenecks include 1) the ambiguity keypoints caused by object symmetries; 2) the domain gap between real and synthetic data. To circumvent these problem, we pro

Gilles Chabrier, Pierre Dumond

We examine various physical processes that may explain the shallow high-mass slope of the IMF as well as the low SFR in star-forming molecular clouds (MCs) in the Central Molecular Zone (CMZ). We show that the strong tidal field and the tidal shear experienced by the CMZ have opposite effects on the collapse of density fluctuations and nearly compensate, but

René Groh, Nina Goes, Andreas M. Kist

Benchmarking plays a pivotal role in assessing and enhancing the performance of compact deep learning models designed for execution on resource-constrained devices, such as microcontrollers. Our study introduces a novel, entirely artificially generated benchmarking dataset tailored for speech recognition, representing a core challenge in the field of tiny de

Xinyu Xiong, Churan Wang, Wenxue Li, Guanbin Li

Accurate identification of breast masses is crucial in diagnosing breast cancer; however, it can be challenging due to their small size and being camouflaged in surrounding normal glands. Worse still, it is also expensive in clinical practice to obtain adequate pixel-wise annotations for training deep neural networks. To overcome these two difficulties with

Jiawen Chen, Xiaoquan Yu

We study Onsager vortex clustered states in a shell-shaped superfluid containing a large number of quantum vortices. In the incompressible limit and at low temperatures, the relevant problem can be boiled down to the statistical mechanics of neutral point vortices confined on a sphere. We analyze rotation free vortex clustered states within the mean field th

Martin Aubard, László Antal, Ana Madureira, Erika Ábrahám

In this paper we present YOLOX-ViT, a novel object detection model, and investigate the efficacy of knowledge distillation for model size reduction without sacrificing performance. Focused on underwater robotics, our research addresses key questions about the viability of smaller models and the impact of the visual transformer layer in YOLOX. Furthermore, we

Angelo Pasquale, Mohammad-Javad Kazemzadeh-Parsi, Daniele Di Lorenzo, Victor Champaney

In the present work, a new methodology is proposed for building surrogate parametric models of engineering systems based on modular assembly of pre-solved modules. Each module is a generic parametric solution considering parametric geometry, material and boundary conditions. By assembling these modules and satisfying continuity constraints at the interfaces,

Javad B. Ebrahimi, Mehri Oghbaei Bonab

In this paper, we introduce and study the problem of \textit{binary stretch embedding} of edge-weighted graph. This problem is closely related to the well-known \textit{addressing problem} of Graham and Pollak. Addressing problem is the problem of assigning the shortest possible length strings (called ``addresses") over the alphabet $\{0,1,*\}$ to the vertic

Christian Hirsch, Daniel Willhalm

We study large deviations in the context of stochastic gradient descent for one-hidden-layer neural networks with quadratic loss. We derive a quenched large deviation principle, where we condition on an initial weight measure, and an annealed large deviation principle for the empirical weight evolution during training when letting the number of neurons and t

James Lindesay

An extension of the Lorentz group that includes generators $\Gamma^\mu$ carrying a space-time index has been previously demonstrated to \emph{explicitly} construct the Minkowski metric \emph{within} the internal group space as a consequence of the non-vanishing commutation relations between those generators. Fields that transform under representations of thi

Arul Selvam Periyasamy, Sven Behnke

Cluttered bin-picking environments are challenging for pose estimation models. Despite the impressive progress enabled by deep learning, single-view RGB pose estimation models perform poorly in cluttered dynamic environments. Imbuing the rich temporal information contained in the video of scenes has the potential to enhance models ability to deal with the ad

Cathy Mengying Fang, Krzysztof Zieliński, Pattie Maes, Joe Paradiso

Programming a robotic is a complex task, as it demands the user to have a good command of specific programming languages and awareness of the robot's physical constraints. We propose a framework that simplifies robot deployment by allowing direct communication using natural language. It uses large language models (LLM) for prompt processing, workspace unders

Soroush Seifi, Daniel Olmeda Reino, Fabien Despinoy, Rahaf Aljundi

Semantic Segmentation is one of the most challenging vision tasks, usually requiring large amounts of training data with expensive pixel level annotations. With the success of foundation models and especially vision-language models, recent works attempt to achieve zeroshot semantic segmentation while requiring either large-scale training or additional image/

Gubio G. de Lima, Sinara S. Dourado

Since the development of lasers, we have continuously sought to advance techniques and theory to obtain beams with a high degree of coherence, as natural light sources provide incoherent light. However, there are applications where it is advantageous to use partially coherent (PC) beams in a controlled manner, such as in propagation through turbulent media.

Ayoub Si-ahmed, Mohammed Ali Al-Garadi, Narhimene Boustia

The Internet of Medical Things transcends traditional medical boundaries, enabling a transition from reactive treatment to proactive prevention. This innovative method revolutionizes healthcare by facilitating early disease detection and tailored care, particularly in chronic disease management, where IoMT automates treatments based on real-time health data

Idil Ozdamar, Doganay Sirintuna, Robin Arbaud, Arash Ajoudani

For mobile robots, navigating cluttered or dynamic environments often necessitates non-prehensile manipulation, particularly when faced with objects that are too large, irregular, or fragile to grasp. The unpredictable behavior and varying physical properties of these objects significantly complicate manipulation tasks. To address this challenge, this manusc

Vahagn Aslanyan

In this paper we survey the history of, and recent developments on, two major conjectures originating in Zilber's model-theoretic work on complex exponentiation -- Existential Closedness and Zilber-Pink. The main focus is on the modular versions of these conjectures and specifically on novel variants incorporating the derivatives of modular functions. The fu

Antonia Calvi, Théophile Gaudin, Dominik Miketa, Dominique Sydow

Assessing whether a molecule can be synthesised is a primary task in drug discovery. It enables computational chemists to filter for viable compounds or bias molecular generative models. The notion of synthesisability is dynamic as it evolves depending on the availability of key compounds. A common approach in drug discovery involves exploring the chemical s

Yu Cai, Hao Chen, Kwang-Ting Cheng

Medical anomaly detection aims to identify abnormal findings using only normal training data, playing a crucial role in health screening and recognizing rare diseases. Reconstruction-based methods, particularly those utilizing autoencoders (AEs), are dominant in this field. They work under the assumption that AEs trained on only normal data cannot reconstruc

Robert Jewsbury, Ruoyu Wang, Abhir Bhalerao, Nasir Rajpoot

Stain normalization algorithms aim to transform the color and intensity characteristics of a source multi-gigapixel histology image to match those of a target image, mitigating inconsistencies in the appearance of stains used to highlight cellular components in the images. We propose a new approach, StainFuser, which treats this problem as a style transfer t

Xinnan Zhang, Yuanbo Cheng, Xiaolei Shang, Jun Liu

We consider a mixed analog-to-digital converter (ADC) based architecture consisting of high-precision and one-bit ADCs with the antenna-varying threshold for direction of arrival (DOA) estimation using a uniform linear array (ULA), which utilizes fixed but different thresholds for one-bit ADCs across different receive antennas. The Cram{\'e}r-Rao bound (CRB)

Ehsan Mokhtarian, Sepehr Elahi, Sina Akbari, Negar Kiyavash

Causal discovery, i.e., learning the causal graph from data, is often the first step toward the identification and estimation of causal effects, a key requirement in numerous scientific domains. Causal discovery is hampered by two main challenges: limited data results in errors in statistical testing and the computational complexity of the learning task is d

Isambard Goodbody

We characterise reflexive DG-categories, as introduced by Kuznetsov and Shinder, as the reflexive objects in the closed symmetric monoidal category of DG-categories localised at Morita equivalences. As consequences, we show that the Hochschild cohomology and the derived Picard group of a reflexive DG-category coincide with those of its derived category of co

Anna Kruspe

This paper addresses the challenges and advancements in speech recognition for singing, a domain distinctly different from standard speech recognition. Singing encompasses unique challenges, including extensive pitch variations, diverse vocal styles, and background music interference. We explore key areas such as phoneme recognition, language identification

Will Simmons, Aleks Kissinger

The $\mathrm{Caus}[-]$ construction takes a base category of ``raw materials'' and builds a category of higher order causal processes, that is a category whose types encode causal (a.k.a. signalling) constraints between collections of systems. Notable examples are categories of higher-order stochastic maps and higher-order quantum channels. Well-typedness in

Yu-Chu Yu, Chi-Pin Huang, Jr-Jen Chen, Kai-Po Chang

Large-scale vision-language models (VLMs) have shown a strong zero-shot generalization capability on unseen-domain data. However, adapting pre-trained VLMs to a sequence of downstream tasks often leads to the forgetting of previously learned knowledge and a reduction in zero-shot classification performance. To tackle this problem, we propose a unique Selecti

Peter Sjögårde, Per Ahlgren

In this contribution, we deal with seed-based information retrieval in networks of research publications. Using systematic reviews as a baseline, and publication data from the NIH Open Citation Collection, we compare the performance of the three citation-based approaches direct citation, co-citation, and bibliographic coupling with respect to recall and prec

Qingqiu Li, Xiaohan Yan, Jilan Xu, Runtian Yuan

Learning medical visual representations through vision-language pre-training has reached remarkable progress. Despite the promising performance, it still faces challenges, i.e., local alignment lacks interpretability and clinical relevance, and the insufficient internal and external representation learning of image-report pairs. To address these issues, we p

Chuchu Chen, Jialin Hong, Ge Liang

It is known from the monograph [1, Chapter 5] that the weak convergence analysis of numerical schemes for stochastic Maxwell equations is an unsolved problem. This paper aims to fill the gap by establishing the long-time weak convergence analysis of the semi-implicit Euler scheme for stochastic Maxwell equations. Based on analyzing the regularity of transfor

ATLAS Collaboration

This report presents a comprehensive collection of searches for new physics performed by the ATLAS Collaboration during the Run 2 period of data taking at the Large Hadron Collider, from 2015 to 2018, corresponding to about 140 fb$^{-1}$ of $\sqrt{s}=13$ TeV proton-proton collision data. These searches cover a variety of beyond-the-standard model topics such

Vijay Ganesh Sadhasivam, Fumika Suzuki, Bin Yan, Nikolai A. Sinitsyn

Advances in atomic physics have led to the possibility of a coherent transformation between ultra-cold atoms and molecules including between completely bosonic condensates. Such transformations are enabled by the magneto-association of atoms at a Feshbach resonance which results in a passage through a quantum critical point. In this study, we show that the p

Liangrui Pan, Yijun Peng, Yan Li, Xiang Wang

Accurately predicting the survival rate of cancer patients is crucial for aiding clinicians in planning appropriate treatment, reducing cancer-related medical expenses, and significantly enhancing patients' quality of life. Multimodal prediction of cancer patient survival offers a more comprehensive and precise approach. However, existing methods still grapp

Anirban Mukherjee, Hannah Hanwen Chang

Theory of Mind (ToM) refers to the ability to attribute mental states, such as beliefs, desires, intentions, and knowledge, to oneself and others, and to understand that these mental states can differ from one's own and from reality. We investigate ToM in environments with multiple, distinct, independent AI agents, each possessing unique internal states, inf

Zhixuan Shen, Haonan Luo, Sijia Li, Tianrui Li

Scene-Text Visual Question Answering (ST-VQA) aims to understand scene text in images and answer questions related to the text content. Most existing methods heavily rely on the accuracy of Optical Character Recognition (OCR) systems, and aggressive fine-tuning based on limited spatial location information and erroneous OCR text information often leads to in

Tyler Franke, Thomas Quella

We revisit the question of string order and hidden symmetry breaking in the q-deformed AKLT model, an example of a spin chain that possesses generalized symmetry. We first argue that the non-local Kennedy-Tasaki duality transformation that was previously proposed to relate the string order to a local order parameter leads to a non-local Hamiltonian and thus

Sonia Marrara, David Bronte Ciriza, Alessandro Magazzu, Roberto Caruso

Recently, acoustic tweezers based on an array of ultrasonic transducers have been reported taking inspiration from holographic optical tweezers. In the latter technique, the calibration of the optical trap is an essential procedure to obtain the trap stiffnesses. On the contrary, in the case of acoustic tweezers the calibration of the acoustic forces is seld

Tim Schreiter, Tiago Rodrigues de Almeida, Yufei Zhu, Eduardo Gutierrez Maestro

We present a new large dataset of indoor human and robot navigation and interaction, called TH\"OR-MAGNI, that is designed to facilitate research on social navigation: e.g., modelling and predicting human motion, analyzing goal-oriented interactions between humans and robots, and investigating visual attention in a social interaction context. TH\"OR-MAGNI wa

Xu Yang, Jiyuan Feng, Songyue Guo, Ye Wang

Personalized federated learning becomes a hot research topic that can learn a personalized learning model for each client. Existing personalized federated learning models prefer to aggregate similar clients with similar data distribution to improve the performance of learning models. However, similaritybased personalized federated learning methods may exacer

Yuxiang Wang, Minhao Zhao, Jinglei Zhang, Wenbin Wu

The Mott-Ioffe-Regel limit sets the lower bound of carrier mean free path for coherent quasiparticle transport. Metallicity beyond this limit is of great interest because it is often closely related to quantum criticality and unconventional superconductivity. Progress along this direction mainly focuses on the strange-metal behaviors originating from the evo

Luca Alasio, Jessica Guerand, Simon Schulz

We establish regularity and, under suitable assumptions, convergence to stationary states for weak solutions of a parabolic equation with a non-linear non-local drift term; this equation was derived from a model of active Brownian particles with repulsive interactions in a previous work, which incorporates advection-diffusion processes both in particle posit

Artur L. Gower, Tom Shearer, Pasquale Ciarletta, Michel Destrade

A large variety of materials, widely encountered both in engineering applications and in the biological realm, are characterised by a non-vanishing internal stress distribution, even in the absence of external deformations or applied forces. These initial stresses are due to initial strains or microstructural changes, such as thermal expansion, manufacturing

Emanuele Borgonovo, Marco Artusa, Elmar Plischke, Francesco Viganò

We evidence a family $\mathcal{X}$ of square matrices over a field $\mathbb{K}$, whose elements will be called X-matrices. We show that this family is shape invariant under multiplication as well as transposition. We show that $\mathcal{X}$ is a (in general non-commutative) subring of $GL(n,\mathbb{K})$. Moreover, we analyse the condition for a matrix $A \in

Mandar R. Nalavade, Gaurav S. Kasbekar, Vivek S. Borkar

We address the problem of user association in a dense millimeter wave (mmWave) network, in which each arriving user brings a file containing a random number of packets and each time slot is divided into multiple mini-slots. This problem is an instance of the restless multi-armed bandit problem, and is provably hard to solve. Using a technique introduced by W

Xiuqin Zhong, Shengyuan Yan, Gongqi Lin, Hongguang Fu

In the context of online education, designing an automatic solver for geometric problems has been considered a crucial step towards general math Artificial Intelligence (AI), empowered by natural language understanding and traditional logical inference. In most instances, problems are addressed by adding auxiliary components such as lines or points. However,

Roey Bokobza, Yisroel Mirsky

Our paper presents a novel defence against black box attacks, where attackers use the victim model as an oracle to craft their adversarial examples. Unlike traditional preprocessing defences that rely on sanitizing input samples, our stateless strategy counters the attack process itself. For every query we evaluate a counter-sample instead, where the counter

Rinki Imada, Thomas C. Hull, Jason S. Ku, Tomohiro Tachi

Non-periodic folding of periodic crease patterns paves the way to novel nonlinear phenomena that cannot be feasible through periodic folding. This paper focuses on the non-periodic folding of recursive crease patterns generalized from Spidron. Although it is known that the Spidron has a $1$-DOF isotropic rigid folding motion, its general kinematics and depen

Michael Assis

It is well known that the set of origami constructible numbers is larger than the classical straight-edge and compass constructible numbers. However, the Huzita-Justin-Hatori origami constructible numbers remain algebraic so that the transcendental number $\pi$ can only be approximated using a finite number of straight line folds. Using these methods we give

Davide Perego, Matteo Tarocchi

In this paper, we prove the rationality of the gluing relation of edge replacement systems, which were introduced for studying rearrangement groups of fractals. More precisely, we describe an algorithmic procedure for building a finite state automaton that recognizes pairs of equivalent sequences that are glued in the fractal. This fits in recent interest to

Matteo Nerini, Shanpu Shen, Bruno Clerckx

Beyond diagonal reconfigurable intelligent surface (BD-RIS) extends conventional RIS through novel architectures, such as group-connected RIS, with scattering matrix not restricted to being diagonal. However, it remains unexplored how to optimally group the elements in group-connected RISs to maximize the performance while maintaining a low-complexity circui

Mingyuan Sun, Donghao Zhang, Zongyuan Ge, Jiaxu Wang

Event camera, a novel bio-inspired vision sensor, has drawn a lot of attention for its low latency, low power consumption, and high dynamic range. Currently, overfitting remains a critical problem in event-based classification tasks for Spiking Neural Network (SNN) due to its relatively weak spatial representation capability. Data augmentation is a simple bu

Ashleigh R. Arendt, Yvette C. Perrott, Ana Contreras-Santos, Daniel de Andres

We present a novel approach to identify galaxy clusters that are undergoing a merger using a deep learning approach. This paper uses massive galaxy clusters spanning $0 \leq z \leq 2$ from \textsc{The Three Hundred} project, a suite of hydrodynamic re-simulations of 324 large galaxy clusters. Mock, idealised Compton-{\it y} and X-ray maps were constructed fo

Maximilian Stargardt, David Kress, Heidi Heinrichs, Jörn-Christian Meyer

Decarbonizing the global energy system requires significant expansions of renewable energy technologies. Given that cost-effective renewable sources are not necessarily situated in proximity to the largest energy demand centers globally, the maritime transportation of low-carbon energy carriers, such as renewable-based hydrogen or ammonia, will be needed. Ho

John M. Dudley, Jeanne Magnin, Luc Froehly, Jerome Salvi

Recently uncovered archives at the University of Franche-Comt\'e in Besan\c{c}on (France) reveal a rich history of research and teaching in physics since the Faculty of Science was first established in 1845. Here, we describe a selection of notable activities conducted by the named Chairs of Physics during the period 1845-1970. We uncover a long tradition of

Hyuckjin Choi, Ly V. Nguyen, Junil Choi, A. Lee Swindlehurst

A reconfigurable intelligent surface (RIS) is a prospective wireless technology that enhances wireless channel quality. An RIS is often equipped with passive array of elements and provides cost and power-efficient solutions for coverage extension of wireless communication systems. Without any radio frequency (RF) chains or computing resources, however, the R