May 2024 arXiv papers — page 58

Jacques K. Desmarais, Giovanni Vignale, Kamel Bencheikh, Alessandro Erba

Understanding of bonding is key to modelling materials and predicting properties thereof. A widely adopted indicator of bonds and atomic shells is the electron localization function (ELF). The building blocks of the ELF are also used in the construction of modern density functional approximations. Here we demonstrate that the ELF breaks down when applied bey

Benet Eiximeno, Arnau Miró, Beka Begiashvili, Eusebio Valero

This paper describes the numerical implementation in a high-performance computing environment of an open-source library for model order reduction in fluid dynamics. This library, called pyLOM, contains the algorithms of proper orthogonal decomposition (POD), dynamic mode decomposition (DMD) and spectral proper orthogonal decomposition (SPOD), as well as, eff

Samuel L. Krushkal

The problem of starlikeness of Teichmuller spaces in Bers' embedding was raised in 1974 and is solved (negatively) for Teichmuller spaces of sufficiently large dimensions. The original proof given by the author relies on the existence of conformally rigid domains established by Thurston. Later the author found another proof of non-starlikeness of universal T

Guihua Yan, Paulina Pršlja, Gaofeng Chen, Jiahui Kang

Syngas conversion into higher alcohols represents a promising avenue for transforming coal or biomass into liquid fuels. However, the commercialization of this process has been hindered by the high cost, low activity, and inadequate C$_{2+}$OH selectivity of catalysts. Herein, we have developed Cu/Co carbon wood catalysts, offering a cost-effective and stabl

Haoze He, Juncheng Billy Li, Xuan Jiang, Heather Miller

LoRA and its variants have become popular parameter-efficient fine-tuning (PEFT) methods due to their ability to avoid excessive computational costs. However, an accuracy gap often exists between PEFT methods and full fine-tuning (FT), and this gap has yet to be systematically studied. In this work, we introduce a method for selecting sparse sub-matrices tha

Edward Sanderson, Bogdan J. Matuszewski

It has recently been demonstrated that pretraining backbones in a self-supervised manner generally provides better fine-tuned polyp segmentation performance, and that models with ViT-B backbones typically perform better than models with ResNet50 backbones. In this paper, we extend this recent work to consider generalisability. I.e., we assess the performance

Igor Shilov, Matthieu Meeus, Yves-Alexandre de Montjoye

As Large Language Models (LLMs) become widely adopted, understanding how they learn from, and memorize, training data becomes crucial. Memorization in LLMs is widely assumed to only occur as a result of sequences being repeated in the training data. Instead, we show that LLMs memorize by assembling information from similar sequences, a phenomena we call mosa

Charlie Beil

Internal spacetime geometry was recently introduced to model certain quantum phenomena using spacetime metrics that are degenerate. We use the Ricci tensors of these metrics to derive a ratio of the bare up and down quark masses, obtaining $m_u/m_d = 9604/19683 \approx .4879$. This value is within the lattice QCD value $.473 \pm .023$, obtained at $2 \operat

Huimu Wang, Mingming Li, Dadong Miao, Songlin Wang

Re-ranking is a process of rearranging ranking list to more effectively meet user demands by accounting for the interrelationships between items. Existing methods predominantly enhance the precision of search results, often at the expense of diversity, leading to outcomes that may not fulfill the varied needs of users. Conversely, methods designed to promote

Marco Grasso, Giulia Renda, Marilena Daquino

This article introduces a novel software solution to create a Web portal to align Linked Open Data sources and provide user-friendly interfaces for serendipitous discovery. We present the Polifonia Web portal as a motivating scenario and case study to address research problems such as data reconciliation and serving generous interfaces in the music heritage

Weishuai Zhou, Manhong Yao, Xi Lin, Quan Yu

Confocal microscopy, a critical advancement in optical imaging, is widely applied because of its excellent anti-noise ability. However, it has low imaging efficiency and can cause phototoxicity. Optical-sectioning structured illumination microscopy (OS-SIM) can overcome the limitations of confocal microscopy but still face challenges in imaging depth and sig

T. Berriel Martins, Javier Civera

3D Gaussian Splatting has emerged as a very promising scene representation, achieving state-of-the-art quality in novel view synthesis significantly faster than competing alternatives. However, its use of spherical harmonics to represent scene colors limits the expressivity of 3D Gaussians and, as a consequence, the capability of the representation to genera

Yuyang Xue, Jingshuai Liu, Steven McDonagh, Sotirios A. Tsaftaris

Machine unlearning is a promising paradigm for removing unwanted data samples from a trained model, towards ensuring compliance with privacy regulations and limiting harmful biases. Although unlearning has been shown in, e.g., classification and recommendation systems, its potential in medical image-to-image translation, specifically in image recon-struction

Ludovic Courtès, Timothy Sample, Simon Tournier, Stefano Zacchiroli

The ability to verify research results and to experiment with methodologies are core tenets of science. As research results are increasingly the outcome of computational processes, software plays a central role. GNU Guix is a software deployment tool that supports reproducible software deployment, making it a foundation for computational research workflows.

Harald Leisenberger, Christian Knoll, Franz Pernkopf

The Bethe free energy approximation provides an effective way for relaxing NP-hard problems of probabilistic inference. However, its accuracy depends on the model parameters and particularly degrades if a phase transition in the model occurs. In this work, we analyze when the Bethe approximation is reliable and how this can be verified. We argue and show by

Libo Chen

This study revisits the modeling of seismic fragility curves by applying ordinal regression models, offering an alternative to the commonly used log-normal distribution function. It compares various ordinal regression approaches, including Cumulative, Sequential, and Adjacent Category models, along with extensions that account for category-specific effects a

Marc Oedingen, Raphael C. Engelhardt, Robin Denz, Maximilian Hammer

In recent times, large language models (LLMs) have made significant strides in generating computer code, blurring the lines between code created by humans and code produced by artificial intelligence (AI). As these technologies evolve rapidly, it is crucial to explore how they influence code generation, especially given the risk of misuse in areas like highe

David White

After explaining the importance of model categories in abstract homotopy theory, we provide concrete examples demonstrating that various categories of manifolds do not have all finite colimits, and hence cannot be model categories. We then consider various enlargements of our categories of manifolds, culminating in categories of presheaves. We explain how to

Simone Billi, Stevell Muller, Tomasz Wawak

We give a classification of finite groups of symplectic birational automorphisms on a manifold of K3^[3]-type with stable and stably saturated cohomological action. We describe the group of polarized automorphisms of a smooth double EPW-cube. Using this description, we exhibit examples of projective hyperkaehler manifolds of K3^[3]-type of maximal Picard ran

Angeliki Kamoutsi, Peter Schmitt-Förster, Tobias Sutter, Volkan Cevher

This work studies discrete-time discounted Markov decision processes with continuous state and action spaces and addresses the inverse problem of inferring a cost function from observed optimal behavior. We first consider the case in which we have access to the entire expert policy and characterize the set of solutions to the inverse problem by using occupat

Olivia Jullian Parra, Julián García Pardiñas, Lorenzo Del Pianta Pérez, Maximilian Janisch

Data Quality Monitoring (DQM) is a crucial task in large particle physics experiments, since detector malfunctioning can compromise the data. DQM is currently performed by human shifters, which is costly and results in limited accuracy. In this work, we provide a proof-of-concept for applying human-in-the-loop Reinforcement Learning (RL) to automate the DQM

Oleh Melnyk, Michael Quellmalz, Gabriele Steidl, Noah Jaitner

In this paper, we propose mathematical models for reconstructing the optical flow in time-harmonic elastography. In this image acquisition technique, the object undergoes a special time-harmonic oscillation with known frequency so that only the spatially varying amplitude of the velocity field has to be determined. This allows for a simpler multi-frame optic

Vinh Tong, Hoang Trung-Dung, Anji Liu, Guy Van den Broeck

Diffusion Probabilistic Models (DPMs) are generative models showing competitive performance in various domains, including image synthesis and 3D point cloud generation. Sampling from pre-trained DPMs involves multiple neural function evaluations (NFEs) to transform Gaussian noise samples into images, resulting in higher computational costs compared to single

Hao Yang, Zexu Sun, Hongteng Xu, Xu Chen

As a promising individualized treatment effect (ITE) estimation method, counterfactual regression (CFR) maps individuals' covariates to a latent space and predicts their counterfactual outcomes. However, the selection bias between control and treatment groups often imbalances the two groups' latent distributions and negatively impacts this method's performan

Chris Ferrie

Whether you're a CEO strategizing the future of your company, a tech enthusiast debating your next career move, a high school teacher eager to enlighten your students, or simply tired of the relentless quantum hype, this is crafted just for you. Cutting through the complex jargon to deliver the straight facts on quantum computing, peeling away the layers of

N. Boulanger, F. Buisseret, F. Dierick, O. White

The two-thirds power law is a link between angular speed $\omega$ and curvature $\kappa$ observed in voluntary human movements: $\omega$ is proportional to $\kappa^{2/3}$. Squared jerk is known to be a Lagrangian leading to the latter law. We propose that a broader class of higher-derivative Lagrangians leads to the two-thirds power law and we perform the Ha

Avishek Singh, Nirmal Ganguli

Solving the Schr\"{o}dinger equation for interacting many-body quantum systems faces computational challenges due to exponential scaling with system size. This complexity limits the study of important phenomena in materials science and physics. We develop an Artificial Neural Network (ANN)-driven algorithm to simulate fermionic systems on lattices. Our metho

Gina Angelo, Liana Klivansky, Jeremy G. Philbrick, Tai Kong

Functionalities of solid-state materials are usually considered to be dependent on their crystal structures. The limited structural types observed in the emerged high-entropy oxides put constraints on exploration of their physical properties and potential applications. Herein, we synthesized the first high-entropy oxide in a trirutile structure, (Mn$_{0.2}$F

Aleksei Leonov, Aleksei Zakharov, Sergey Koshelev, Maxim Pisov

Automatic ribs segmentation and numeration can increase computed tomography assessment speed and reduce radiologists mistakes. We introduce a model for multilabel ribs segmentation with hierarchical loss function, which enable to improve multilabel segmentation quality. Also we propose postprocessing technique to further increase labeling quality. Our model

J. W. Maluf, F. Lessa Carneiro, S. C. Ulhoa, J. F. da Rocha-Neto

We address the issue of gravitational radiation in the context of the Bondi-Sachs space-time, and consider the expression for the gravitational energy of the radiation obtained in the realm of the teleparallel equivalent of general relativity (TEGR). This expression is independent of the radial distance (i.e., of powers of $1/r$) and depends exclusively on t

Alexandre Benatti, Roberto M. Cesar, Luciano da F. Costa

Complex systems have motivated continuing interest from the scientific community, leading to new concepts and methods. Growing systems represent a case of particular interest, as their topological, geometrical, and also dynamical properties change along time, as new elements are incorporated into the existing structure. In the present work, an approach is th

Anna Maddux, Reda Ouhamma, Maryam Kamgarpour

This paper investigates the convergence time of log-linear learning to an $\epsilon$-efficient Nash equilibrium in potential games, where an efficient Nash equilibrium is defined as the maximizer of the potential function. Previous literature provides asymptotic convergence rates to efficient Nash equilibria, and existing finite-time rates are limited to pot

Robert Fulsche

In this short note, we discuss essential positivity of Toeplitz operators on the Fock space, as motivated by a recent question of Per\"al\"a and Virtanen. We give a proper characterization of essential positivity in terms of limit operators. A conjectured characterization of essential positivity of Per\"al\"a and Virtanen is disproven when the assumption of

T. B. van Sluijs, S. K. F. Stoter, E. H. van Brummelen

Surface-active agents (surfactants) release potential energy as they migrate from one of two adjacent fluids onto their fluid-fluid interface, a process that profoundly impacts the system's energy and entropy householding. The continuum thermodynamics underlying such a surfactant-enriched binary-fluid system has not yet been explored comprehensively. In this

Zhengbao He, Tao Li, Xinwen Cheng, Zhehao Huang

Machine unlearning (MU) aims to eliminate information that has been learned from specific training data, namely forgetting data, from a pre-trained model. Currently, the mainstream of existing MU methods involves modifying the forgetting data with incorrect labels and subsequently fine-tuning the model. While learning such incorrect information can indeed re

Xi Yu, Benjamin Wilhelm, Danielle Holmes, Arjen Vaartjes

High-dimensional quantum systems are a valuable resource for quantum information processing. They can be used to encode error-correctable logical qubits, which has been demonstrated using continuous-variable states in microwave cavities or the motional modes of trapped ions. For example, high-dimensional systems can be used to realise `Schr\"{o}dinger cat' s

Liu Zhiwei, Yu Wangbing

In order to address the challenge of traditional sliding mode controllers struggling to balance between suppressing system jitter and accelerating convergence speed, a deep neural network (DNN)-based sliding mode control strategy is proposed in this paper. The strategy achieves dynamic adjustment of parameters by modelling and learning the system through dee

Yota Otachi, Akira Suzuki, Yuma Tamura

In the last decade, algorithmic frameworks based on a structural graph parameter called mim-width have been developed to solve generally NP-hard problems. However, it is known that the frameworks cannot be applied to the Clique problem, and the complexity status of many problems of finding dense induced subgraphs remains open when parameterized by mim-width.

Zhuo Diao, Hayato Yamashita, Masayuki Abe

We present the development of an automated scanning probe microscopy (SPM) measurement system using an advanced large-scale language model (LLM). This SPM system can receive instructions via social networking services (SNS), and the integration of SNS and LLMs enables real-time, language-agnostic control of SPM operations, thereby improving accessibility and

Anurag Mashruwala

The emergence of distributed systems has revolutionized the financial technology (Fintech) landscape, offering unprecedented opportunities for enhancing security, scalability, and efficiency in financial operations. This paper explores the role of distributed systems in Fintech, analyzing their architecture, benefits, challenges, and applications. It examine

Yeqing Lin, Minji Lee, Zhao Zhang, Mohammed AlQuraishi

Protein diffusion models have emerged as a promising approach for protein design. One such pioneering model is Genie, a method that asymmetrically represents protein structures during the forward and backward processes, using simple Gaussian noising for the former and expressive SE(3)-equivariant attention for the latter. In this work we introduce Genie 2, e

Avishek Singh, Nirmal Ganguli

The simulation of quantum many-body systems poses a significant challenge in physics due to the exponential scaling of Hilbert space with the number of particles. Traditional methods often struggle with large system sizes and frustrated lattices. In this research article, we present a novel algorithm that leverages the power of deep neural networks combined

X. H. Wu, P. W. Zhao

The principal component analysis approach is employed to extract the principal components contained in nuclear mass models for the first time. The effects coming from different nuclear mass models are reintegrated and reorganized in the extracted principal components. These extracted principal components are recombined to build new mass models, which achieve

Qing Guo, Siyu Chen, Xiangquan Zeng

The proliferation of internet technology has catalyzed the rapid development of digital finance, significantly impacting the optimization of resource allocation in China and exerting a substantial and enduring influence on the structure of employment and income distribution. This research utilizes data sourced from the Chinese General Social Survey and the D

Siyuan Guo, Aniket Didolkar, Nan Rosemary Ke, Anirudh Goyal

We are beginning to see progress in language model assisted scientific discovery. Motivated by the use of LLMs as a general scientific assistant, this paper assesses the domain knowledge of LLMs through its understanding of different mathematical skills required to solve problems. In particular, we look at not just what the pre-trained model already knows, b

Jun-Xiang Huang, Hou-Jun Lü, Jared Rice, En-Wei Liang

Magnetars are potential energy sources or central engines for numerous transient phenomena in the Universe. How newborn magnetars evolve in different environments remains an open question. Based on both observed and candidate magnetars, it is found that the periods of all magnetars or candidates appear as a bimodal distribution, and are defined as the ``long

Sonika, Sunil Gangwar, Pankaj Kumar, A. Taraphder

1T-VSe$_2$ is a narrow band transition metal chalcogenide that shows charge density wave (CDW) state below $T_{CDW}$ = 110 K. Here, we have explored the relevance of Kohler's rule and the thermal transport properties of VSe$_2$ across the CDW state. The magnetoresistance (MR) follows Kohler's rule above $T_{CDW}$, while an extended Kohler's rule is employed

Victor G. Lopez, Matthias A. Müller, Paolo Rapisarda

We illustrate a novel version of Willems' lemma for data-based representation of continuous-time systems. The main novelties compared to previous works are two. First, the proposed framework relies only on measured input-output trajectories from the system and no internal (state) information is required. Second, our system representation makes use of exact s

Jialin Zhao, Yingtao Zhang, Xinghang Li, Huaping Liu

The growing demands on GPU memory posed by the increasing number of neural network parameters call for training approaches that are more memory-efficient. Previous memory reduction training techniques, such as Low-Rank Adaptation (LoRA) and ReLoRA, face challenges, with LoRA being constrained by its low-rank structure, particularly during intensive tasks lik

Emanuele Troiani, Yatin Dandi, Leonardo Defilippis, Lenka Zdeborová

Multi-index models - functions which only depend on the covariates through a non-linear transformation of their projection on a subspace - are a useful benchmark for investigating feature learning with neural nets. This paper examines the theoretical boundaries of efficient learnability in this hypothesis class, focusing on the minimum sample complexity requ

G. Bastien, Q. Courtade, A. Eliáš, T. Haidamak

We report ferromagnetic ordering at $T_\mathrm {C}=1.3\,$K in the quasi-two dimensional magnet EuAl$_{12}$O$_{19}$ with large spins $S=7/2$. This ferromagnetic state was characterized by magnetization and specific heat measurements and the experimental results were compared with classical Monte Carlo simulations. They reveal a strong single ion anisotropy le

Achraf Zinihi, Mostafa Tahiri, Moulay Rchid Sidi Ammi

In this chapter, we consider a reaction-diffusion SVIR infection model with dis-tributed delay and nonlinear incidence rate. The wellposedness of the proposed model is proved. By means of Lyapunov functionals, we show that the disease-free equilibrium state is globally asymptotically stable when the basic reproduction number is less or equal than one, and th

Panagiotis Agrafiotis, Łukasz Janowski, Dimitrios Skarlatos, Begüm Demir

Accurate, detailed, and high-frequent bathymetry, coupled with complex semantic content, is crucial for the undermapped shallow seabed areas facing intense climatological and anthropogenic pressures. Current methods exploiting remote sensing images to derive bathymetry or seabed classes mainly exploit non-open data. This lack of openly accessible benchmark a

Lijie Hu, Chenyang Ren, Zhengyu Hu, Hongbin Lin

Concept Bottleneck Models (CBMs) have garnered much attention for their ability to elucidate the prediction process through a humanunderstandable concept layer. However, most previous studies focused on cases where the data, including concepts, are clean. In many scenarios, we often need to remove/insert some training data or new concepts from trained CBMs f

Eduard Zamfir, Zongwei Wu, Nancy Mehta, Danda Pani Paudel

Reconstructing missing details from degraded low-quality inputs poses a significant challenge. Recent progress in image restoration has demonstrated the efficacy of learning large models capable of addressing various degradations simultaneously. Nonetheless, these approaches introduce considerable computational overhead and complex learning paradigms, limiti

Hanlin Gu, Gongxi Zhu, Jie Zhang, Xinyuan Zhao

In recent years, Federated Learning (FL) has garnered significant attention as a distributed machine learning paradigm. To facilitate the implementation of the right to be forgotten, the concept of federated machine unlearning (FMU) has also emerged. However, current FMU approaches often involve additional time-consuming steps and may not offer comprehensive

Cencheng Shen

Graph encoder embedding, a recent technique for graph data, offers speed and scalability in producing vertex-level representations from binary graphs. In this paper, we extend the applicability of this method to a general graph model, which includes weighted graphs, distance matrices, and kernel matrices. We prove that the encoder embedding satisfies the law

Xiaoyu Zhang, Shibo He, Chuanhou Gao, Denis Dochain

Linear conjugacy offers a new perspective to broaden the scope of stable biochemical reaction networks to the systems linearly conjugated to the well-established complex balanced mass action systems ($\ell$cCBMASs). This paper addresses the challenge posed by time delay, which can disrupt the linear conjugacy relationship and complicate stability analysis fo

Emily Cheng, Diego Doimo, Corentin Kervadec, Iuri Macocco

A language model (LM) is a mapping from a linguistic context to an output token. However, much remains to be known about this mapping, including how its geometric properties relate to its function. We take a high-level geometric approach to its analysis, observing, across five pre-trained transformer-based LMs and three input datasets, a distinct phase chara

Wen Ji, Xueyuan Sun, Chunna Li, Xuyi Jia

In this study, a method for predicting unsteady aerodynamic forces under different initial conditions using a limited number of samples based on transfer learning is proposed, aiming to avoid the need for large-scale high-fidelity aerodynamic simulations. First, a large number of training samples are acquired through high-fidelity simulation under the initia

Anlaug Amanda Djupvik, João L. Yun, Fernando Comerón

We investigate the star formation occurring in the Planck Galactic cold clump PGCC 120.69+2.66. Near-infrared JHKs images and K-band spectroscopy obtained with NOTCam at the Nordic Optical Telescope complemented with archive data are used to study the stellar content. In addition, millimetre line CO and CS spectra were obtained with the Onsala 20 m telescope

Abhishek Goswami, Aru Ranjan Singh, Francesco Banterle, Kurt Debattista

The range of real-world scene luminance is larger than the capture capability of many digital camera sensors which leads to details being lost in captured images, most typically in bright regions. Inverse tone mapping attempts to boost these captured Standard Dynamic Range (SDR) images back to High Dynamic Range (HDR) by creating a mapping that linearizes th

Orhan Donmez

In the region where the gravitational field is strong, we have examined the influence of different gravities on the accretion disk formed due to spherical accretion. To achieve this, we obtain numerical solutions of the GRH equations, utilizing Schwarzschild, Kerr, Einstein-Gauss-Bonnet, and Hartle-Thorne spacetime metrics. We investigate the impact of the r

Fan Liu, Liang Yao, Chuanyi Zhang, Ting Wu

Detecting objects from Unmanned Aerial Vehicles (UAV) is often hindered by a large number of small objects, resulting in low detection accuracy. To address this issue, mainstream approaches typically utilize multi-stage inferences. Despite their remarkable detecting accuracies, real-time efficiency is sacrificed, making them less practical to handle real app

Paul Revenant, Hadrien Cambazard, Nicolas Catusse

In the order picking problem, a picker has to collect a number of products in a warehouse with a minimum length tour. Ratliff and Rosenthal gave a linear algorithm solving the order picking problem in the case where the warehouse has two cross aisles. Their algorithm allow the tour to double cross an entire aisle. We prove that, in rectangular warehouses, th

Zicheng Wang, Zhenghao Chen, Yiming Wu, Zhen Zhao

Point cloud analysis has seen substantial advancements due to deep learning, although previous Transformer-based methods excel at modeling long-range dependencies on this task, their computational demands are substantial. Conversely, the Mamba offers greater efficiency but shows limited potential compared with Transformer-based methods. In this study, we int

R Kervazo, A Congar, G Perin, L Lablonde

We present a compact InGaN fiber Bragg grating (FBG) semiconductor laser diode operating below 400 nm in the single-mode emission regime. This compact coherent laser source exhibits an intrinsic linewidth of 14 kHz in the near-UV range and a side-mode suppression ratio reaching up to 40 dB accompanied by a mW-level output power. Furthermore, the properties o

Hongshen Yang, Avinash Malik

This research proposes a novel arbitrage approach in multivariate pair trading, termed the Optimal Trading Technique (OTT). We present a method for selectively forming a "bucket" of fiat currencies anchored to cryptocurrency for monitoring and exploiting trading opportunities simultaneously. To address quantitative conflicts from multiple trading signals, a

Hao Liu, Yi Shen, Chang Zhou, Yuelin Zou

This paper addresses the challenge of collision-free motion planning in automated navigation within complex environments. Utilizing advancements in Deep Reinforcement Learning (DRL) and sensor technologies like LiDAR, we propose the TD3-DWA algorithm, an innovative fusion of the traditional Dynamic Window Approach (DWA) with the Twin Delayed Deep Determinist

Luca Arnaboldi, Yatin Dandi, Florent Krzakala, Luca Pesce

Neural networks can identify low-dimensional relevant structures within high-dimensional noisy data, yet our mathematical understanding of how they do so remains scarce. Here, we investigate the training dynamics of two-layer shallow neural networks trained with gradient-based algorithms, and discuss how they learn pertinent features in multi-index models, t

Hongyi Peng, Han Yu, Xiaoli Tang, Xiaoxiao Li

Federated learning (FL) enables collaborative machine learning across distributed data owners, but data heterogeneity poses a challenge for model calibration. While prior work focused on improving accuracy for non-iid data, calibration remains under-explored. This study reveals existing FL aggregation approaches lead to sub-optimal calibration, and theoretic

Elisabetta Brocchieri, Laurent Desvillettes, Helge Dietert

We study the existence, regularity and uniqueness for a general class of triangular reaction-cross-diffusion systems coming from the study of starvation driven behavior for two species in competition. This study involves an equivalent system in non-divergence form, for which existence can be obtained thanks to Schauder's fixed point theorem.

J. P. Wang, Y. Q. Ji, L. P. Yang, C. Q. Wang

Conversion between different types of entangled states is an interesting problem in quantum mechanics. But research on the conversion between Greenberger-Horne-Zeilinger (GHZ) state and Knill-Laflamme-Milburn (KLM) state in atomic system is absent. In this paper, we propose a scheme to realize the interconversion (one-step) between GHZ state and KLM state wi

Jan Głowacki

This paper presents a research program aimed at establishing relational foundations for relativistic quantum physics. Although the formalism is still under development, we believe it has matured enough to be shared with the broader scientific community. Our approach seeks to integrate Quantum Field Theory on curved backgrounds and scenarios with indefinite c

Munief Hassan Tahir, Sana Shams, Layba Fiaz, Farah Adeeba

Large Language Models (LLMs) pre-trained on multilingual data have revolutionized natural language processing research, by transitioning from languages and task specific model pipelines to a single model adapted on a variety of tasks. However majority of existing multilingual NLP benchmarks for LLMs provide evaluation data in only few languages with little l

Keyuan Cheng, Muhammad Asif Ali, Shu Yang, Gang Lin

Multi-hop Question Answering (MQA) under knowledge editing (KE) is a key challenge in Large Language Models (LLMs). While best-performing solutions in this domain use a plan and solve paradigm to split a question into sub-questions followed by response generation, we claim that this approach is sub-optimal as it fails for hard to decompose questions, and it

Yiming Wu, Hangfei Li, Fangfang Wang, Yilong Zhang

In the domain of language-based fashion image retrieval, pinpointing the desired fashion item using both a reference image and its accompanying textual description is an intriguing challenge. Existing approaches lean heavily on static fusion techniques, intertwining image and text. Despite their commendable advancements, these approaches are still limited by

Noah H. Oldfield, Christoph Laaber, Tao Yue, Shaukat Ali

Quantum computing promises polynomial and exponential speedups in many domains, such as unstructured search and prime number factoring. However, quantum programs yield probabilistic outputs from exponentially growing distributions and are vulnerable to quantum-specific faults. Existing quantum software testing (QST) approaches treat quantum superpositions as

Sayan Bhattacharya, Din Carmon, Martín Costa, Shay Solomon

Vizing's theorem states that any $n$-vertex $m$-edge graph of maximum degree $\Delta$ can be {\em edge colored} using at most $\Delta + 1$ different colors [Diskret.~Analiz, '64]. Vizing's original proof is algorithmic and shows that such an edge coloring can be found in $\tilde{O}(mn)$ time. This was subsequently improved to $\tilde O(m\sqrt{n})$, independe

Khushbu Singh, K. Kaladhar

In the current study, we took into account a model of nonlinear ``predator-prey'' interactions including the ``Allee effect'' on both populations and disease in the predator population. The population as a whole is split into three: the prey population, susceptible predator, and diseased predator. The ``Takagi-Sugeno (T-S) impulsive control model'' and the F

Georg Nawratil

The famous example of the double-Watt mechanism given by Connelly and Servatius raises some problems concerning the classical definitions of higher-order flexibility and rigidity, respectively, as they attest the cusp configuration of the mechanism a third-order rigidity, which conflicts with its continuous flexion. Some attempts were done to resolve the dil

Drago Plecko, Elias Bareinboim

Investigating fairness and equity of automated systems has become a critical field of inquiry. Most of the literature in fair machine learning focuses on defining and achieving fairness criteria in the context of prediction, while not explicitly focusing on how these predictions may be used later on in the pipeline. For instance, if commonly used criteria, s

Satyanu Bhadra, Anit Sane, Akash Ghosh, Shankar Ghosh

We investigate the phenomena of crater formation and gas release caused by projectile impact on underwater beds, which occurs in many natural, geophysical, and industrial applications. The bed in our experiment is constructed of hydrophobic particles, which trap a substantial amount of air in its pores. In contrast to dry beds, the air-water interface in a s

Patryk Krukowski, Anna Bielawska, Kamil Książek, Paweł Wawrzyński

Recently, a new Continual Learning (CL) paradigm was presented to control catastrophic forgetting, called Interval Continual Learning (InterContiNet), which relies on enforcing interval constraints on the neural network parameter space. Unfortunately, InterContiNet training is challenging due to the high dimensionality of the weight space, making intervals d

Drago Plecko, Elias Bareinboim

Systems based on machine learning may exhibit discriminatory behavior based on sensitive characteristics such as gender, sex, religion, or race. In light of this, various notions of fairness and methods to quantify discrimination were proposed, leading to the development of numerous approaches for constructing fair predictors. At the same time, imposing fair

Ali Rasekh, Reza Heidari, Amir Hosein Haji Mohammad Rezaie, Parsa Sharifi Sedeh

With the increasing availability of diverse data types, particularly images and time series data from medical experiments, there is a growing demand for techniques designed to combine various modalities of data effectively. Our motivation comes from the important areas of predicting mortality and phenotyping where using different modalities of data could sig

Jie Wang, March Boedihardjo, Yao Xie

Optimal transport has been very successful for various machine learning tasks; however, it is known to suffer from the curse of dimensionality. Hence, dimensionality reduction is desirable when applied to high-dimensional data with low-dimensional structures. The kernel max-sliced (KMS) Wasserstein distance is developed for this purpose by finding an optimal

Dinara Valiolda, Daniyar Janseitov, Vladimir Melezhik

We investigate the breakup of the ^{11}Be halo nuclei on a light target ^{12}C within quantum-quasiclassical approach in a wide range of the beam energy (5-67 MeV/nucleon) including bound states and low-lying resonances in different partial and spin states of ^{11}Be. The obtained results are in good agreement with existing experimental data at 67 MeV/nucleo

Zichen Geng, Caren Han, Zeeshan Hayder, Jian Liu

Text-driven human motion generation is an emerging task in animation and humanoid robot design. Existing algorithms directly generate the full sequence which is computationally expensive and prone to errors as it does not pay special attention to key poses, a process that has been the cornerstone of animation for decades. We propose KeyMotion, that generates

Wenquan Dong, Edward T. A. Mitchard, Yuwei Chen, Man Chen

Large-scale high spatial resolution aboveground biomass (AGB) maps play a crucial role in determining forest carbon stocks and how they are changing, which is instrumental in understanding the global carbon cycle, and implementing policy to mitigate climate change. The advent of the new space-borne LiDAR sensor, NASA's GEDI instrument, provides unparalleled

Luis Morales-Navarro, Yasmin B. Kafai, Ha Nguyen, Kayla DesPortes

TikTok, a popular short video sharing application, emerged as the dominant social media platform for young people, with a pronounced influence on how young women and people of color interact online. The application has become a global space for youth to connect with each other, offering not only entertainment but also opportunities to engage with artificial

Candace Edwards

In higher education, accreditation is a quality assurance process, where an institution demonstrates a commitment to delivering high quality programs and services to their students. For business schools nationally and internationally the Association to Advance Collegiate Schools of Business (AACSB) accreditation is the gold standard. For a business school to

Yuzhe Song, Timothy A. D. Paglione, Ekaterina Ilin

Flares from magnetically active dwarf stars should produce relativistic particles capable of creating gamma-rays. So far, the only isolated main sequence star besides the Sun to have been detected in gamma-rays is TVLM 513-46546. Detecting gamma-ray flares from more dwarf stars can improve our understanding of their magnetospheric properties, and could also

Álvaro Becerra, Javier Irigoyen, Roberto Daza, Ruth Cobos

In this article, we explore computer vision approaches to detect abnormal head pose during e-learning sessions and we introduce a study on the effects of mobile phone usage during these sessions. We utilize behavioral data collected from 120 learners monitored while participating in a MOOC learning sessions. Our study focuses on the influence of phone-usage

Maciej Pylak, Mariusz Gajda, Paweł Zin

We investigate beyond-mean-field corrections to the energy of an elongated homogeneous Bose gas strongly confined in two directions, with dipoles aligned along the long axis of the system. When the dipolar interaction reaches its critical strength, the mean-field approach predicts instability. However, similar to the free-space case, beyond-mean-field effect

Wei Dong, Han Zhou, Ruiyi Wang, Xiaohong Liu

Image dehazing, a pivotal task in low-level vision, aims to restore the visibility and detail from hazy images. Many deep learning methods with powerful representation learning capability demonstrate advanced performance on non-homogeneous dehazing, however, these methods usually struggle with processing high-resolution images (e.g., $4000 \times 6000$) due

MohammadAmin Vakilifard, Tim Düe, Mohammad Rihan, Maik Röper

The rapid growth of non-terrestrial communication necessitates its integration with existing terrestrial networks, as highlighted in 3GPP Releases 16 and 17. This paper analyses the concept of functional splits in 3D-Networks. To manage this complex structure effectively, the adoption of a Radio Access Network (RAN) architecture with Functional Split (FS) of

Hong-Ping Yin, Ling-Xiong Han, Feng Qi

In the paper, by convolution theorem of the Laplace transforms, a monotonicity rule for the ratio of two Laplace transforms, Bernstein's theorem for completely monotonic functions, and other analytic techniques, the authors verify decreasing property of a ratio between three derivatives of a function involving trigamma function and find necessary and suffici

Jannis Kurtz, Ş. İlker Birbil, Dick den Hertog

The concept of counterfactual explanations (CE) has emerged as one of the important concepts to understand the inner workings of complex AI systems. In this paper, we translate the idea of CEs to linear optimization and propose, motivate, and analyze three different types of CEs: strong, weak, and relative. While deriving strong and weak CEs appears to be co

David Boetius, Stefan Leue

Naively trained Deep Reinforcement Learning agents may fail to satisfy vital safety constraints. To avoid costly retraining, we may desire to repair a previously trained reinforcement learning agent to obviate unsafe behaviour. We devise a counterexample-guided repair algorithm for repairing reinforcement learning systems leveraging safety critics. The algor