March 2024 arXiv papers — page 36

Neha Verma, S. Sivaprasad Kumar

In this paper, we employ a novel second and third-order differential subordination technique to establish the sufficient conditions for functions to belong to the classes $\mathcal{S}^*_s$ and $\mathcal{S}^*_{\rho}$, where $\mathcal{S}^*_s$ is the set of all normalized analytic functions $f$ satisfying $ zf'(z)/f(z)\prec 1+\sin z$ and $\mathcal{S}^*_{\rho}$

Matthieu Saumard, Abir El Haj, Thibault Napoleon

Speech Emotion Recognition (SER) plays a crucial role in advancing human-computer interaction and speech processing capabilities. We introduce a novel deep-learning architecture designed specifically for the functional data model known as the multiple-index functional model. Our key innovation lies in integrating adaptive basis layers and an automated data t

Mohd Halim Mohd Noor, Ayokunle Olalekan Ige

Deep learning, a branch of artificial intelligence, is a data-driven method that uses multiple layers of interconnected units or neurons to learn intricate patterns and representations directly from raw input data. Empowered by this learning capability, it has become a powerful tool for solving complex problems and is the core driver of many groundbreaking t

Runze Li, Chaoxi Cui, Ying Liu, Zhi-Ming Yu

Andreev reflection at a normal-superconductor interface may be accompanied with an anomalous spatial shift. The studies so far are limited to the top incidence configuration. Here, we investigate this effect in the side incidence configuration, with the interface parallel to the principal axis of superconductor. We find that the shift exhibits rich behaviors

Cristian Conde, Niçusor Minculete

The main study of this article is the characterization of Richard's inequality, because it is closely related to Buzano's inequality. Finally, we present a newapproach for Richard's inequality, where we use the Selberg operator.

Neha Gupta, Suhith K N

A neural code on $ n $ neurons is a collection of subsets of the set $ [n]=\{1,2,\dots,n\} $. Curto et al. \cite{curto2013neural} associated a ring $\mathcal{R}_{\mathcal{C}}$ (neural ring) to a neural code $\mathcal{C}$. A special class of ring homomorphisms between two neural rings, called neural ring homomorphism, was introduced by Curto and Youngs \cite{

Mohsen Kian, Zainab Peymani Mazraj

We study the Mercer inequality and its operator extension for superquadratic functions. In particular, we give a more general form of the Mercer inequality by replacing some constants by positive operators. As some consequences, our results produce a Jensen operator inequality for superquadratic functions. Moreover, we present some Mercer inequalities of Her

Jian Yang, Hongcheng Guo, Yuwei Yin, Jiaqi Bai

Multilingual translation supports multiple translation directions by projecting all languages in a shared space, but the translation quality is undermined by the difference between languages in the text-only modality, especially when the number of languages is large. To bridge this gap, we introduce visual context as the universal language-independent repres

Kai Du, Yunzhang Li, Yuyang Ye

In this paper, we construct a type of interacting particle systems to approximate a class of stochastic different equations whose coefficients depend on the conditional probability distributions of the processes given partial observations. After proving the well-posedness and regularity of the particle systems, we establish a quantitative convergence result

Christian Hespe, Adwait Datar, Herbert Werner

A problem with considering correlations in the analysis of multiagent system with stochastic packet loss is that they induce dependencies between agents that are otherwise decoupled, preventing the application of decomposition methods required for efficient evaluation. To circumvent that issue, this paper is proposing an approach based on analysing sets of n

Veronika Grigoreva, Anastasiia Ivanova, Ilseyar Alimova, Ekaterina Artemova

Warning: this work contains upsetting or disturbing content. Large language models (LLMs) tend to learn the social and cultural biases present in the raw pre-training data. To test if an LLM's behavior is fair, functional datasets are employed, and due to their purpose, these datasets are highly language and culture-specific. In this paper, we address a gap

Jianlin Su, Murtadha Ahmed, Wenbo, Luo Ao

Large Language Models (LLMs) have shown promising in-context learning abilities. However, conventional In-Context Learning (ICL) approaches are often impeded by length limitations of transformer architecture, which pose challenges when attempting to effectively integrate supervision from a substantial number of demonstration examples. In this paper, we intro

Maria Krinner, Angel Romero, Leonard Bauersfeld, Melanie Zeilinger

Quadrotor flight is an extremely challenging problem due to the limited control authority encountered at the limit of handling. Model Predictive Contouring Control (MPCC) has emerged as a promising model-based approach for time optimization problems such as drone racing. However, the standard MPCC formulation used in quadrotor racing introduces the notion of

Kutay Yılmaz, Matthias Nießner, Anastasiia Kornilova, Alexey Artemov

Recently, significant progress has been achieved in sensing real large-scale outdoor 3D environments, particularly by using modern acquisition equipment such as LiDAR sensors. Unfortunately, they are fundamentally limited in their ability to produce dense, complete 3D scenes. To address this issue, recent learning-based methods integrate neural implicit repr

Jingyu Xu, Binbin Wu, Jiaxin Huang, Yulu Gong

The medical field is one of the important fields in the application of artificial intelligence technology. With the explosive growth and diversification of medical data, as well as the continuous improvement of medical needs and challenges, artificial intelligence technology is playing an increasingly important role in the medical field. Artificial intellige

Suhith K N, Neha Gupta

A neural code on $ n $ neurons is a collection of subsets of the set $ [n]=\{1,2,\dots,n\} $. In this paper, we study some properties of graphs of neural codes. In particular, we study codeword containment graph (CCG) given by Chan et al. (SIAM J. on Dis. Math., 37(1):114-145,2017) and general relationship graph (GRG) given by Gross et al. (Adv. in App. Math

Apurba Biswas, Arnab Pal

We study the Mpemba effect on a non-equilibrium Markov chain that mimics the run and tumble motion of an active particle in a discrete energy landscape. The broken detailed balance, rendered by the activity, gives rise to a unique anomalous relaxation in the system which is distinctly different than the typical equilibrium systems. We observe that the activi

S. Di Luozzo, A. Fronzetti Colladon, M. M. Schiraldi

The current study proposes an innovative methodology for the profiling of psychological traits of Operations Management (OM) and Supply Chain Management (SCM) professionals. We use innovative methods and tools of text mining and social network analysis to map the demand for relevant skills from a set of job descriptions, with a focus on psychological charact

Shun Inadumi, Seiya Kawano, Akishige Yuguchi, Yasutomo Kawanishi

Situated conversations, which refer to visual information as visual question answering (VQA), often contain ambiguities caused by reliance on directive information. This problem is exacerbated because some languages, such as Japanese, often omit subjective or objective terms. Such ambiguities in questions are often clarified by the contexts in conversational

Yuheng Zhao, Yi Qin Gao

Recent first-principle calculations unveiled a distinctive dynamic behavior in water molecule rotation during the melting process of highly confined water, indicating a notable time-scale separation in diffusion. In this short paper, we conducted molecular dynamics (MD) simulations to explore the rotation dynamics during the mono-layer ice nucleation process

Matteo Vorabbi, Paolo Finelli, Carlotta Giusti

In this work we use microscopic Nucleon-Nucleus Optical Potentials (OP) to analyze elastic scattering data for the differential cross section of the $^{78}$Kr (p,p) $^{78}$Kr reaction, with the goal of extracting the matter radius and estimating the neutron skin, quantities that are both needed to determine the slope parameter $L$ of the nuclear symmetry ene

Marius Captari, Remo Sasso, Matthia Sabatelli

Despite the considerable attention given to the questions of \textit{how much} and \textit{how to} explore in deep reinforcement learning, the investigation into \textit{when} to explore remains relatively less researched. While more sophisticated exploration strategies can excel in specific, often sparse reward environments, existing simpler approaches, suc

Astitva Srivastava, Pranav Manu, Amit Raj, Varun Jampani

In this paper, we tackle a new and challenging problem of text-driven generation of 3D garments with high-quality textures. We propose "WordRobe", a novel framework for the generation of unposed & textured 3D garment meshes from user-friendly text prompts. We achieve this by first learning a latent representation of 3D garments using a novel coarse-to-fine t

Masamune Kobayashi, Masato Mita, Mamoru Komachi

Large Language Models (LLMs) have been reported to outperform existing automatic evaluation metrics in some tasks, such as text summarization and machine translation. However, there has been a lack of research on LLMs as evaluators in grammatical error correction (GEC). In this study, we investigate the performance of LLMs in GEC evaluation by employing prom

Jean-Pierre Gazeau, Hamed Pejhan

We revisit the concept of de Sitter (dS) 'tachyonic' scalar fields, characterized by discrete negative squared mass values, and assess their physical significance through a rigorous Wigner-inspired group-theoretical analysis. This perspective demonstrates that such fields, often misinterpreted as inherently unstable due to their mass parameter, are best unde

Alessia Caponera, Maurizia Rossi, María Dolores Ruiz Medina

In this note we investigate geometric properties of invariant spatio-temporal random fields $X:\mathbb M^d\times \mathbb R\to \mathbb R$ defined on a compact two-point homogeneous space $\mathbb M^d$ in any dimension $d\ge 2$, and evolving over time. In particular, we focus on chi-squared distributed random fields, and study the large time behavior (as $T\to

Jiahao Chen, Yipeng Qin, Lingjie Liu, Jiangbo Lu

Neural Radiance Field (NeRF) has been widely recognized for its excellence in novel view synthesis and 3D scene reconstruction. However, their effectiveness is inherently tied to the assumption of static scenes, rendering them susceptible to undesirable artifacts when confronted with transient distractors such as moving objects or shadows. In this work, we p

Paramita Mirza, Viju Sudhi, Soumya Ranjan Sahoo, Sinchana Ramakanth Bhat

State-of-the-art intent classification (IC) and slot filling (SF) methods often rely on data-intensive deep learning models, limiting their practicality for industry applications. Large language models on the other hand, particularly instruction-tuned models (Instruct-LLMs), exhibit remarkable zero-shot performance across various natural language tasks. This

Wei Bian, Xiaojun Chen

In this paper, we focus on a class of convexly constrained nonsmooth convex-concave saddle point problems with cardinality penalties. Although such nonsmooth nonconvex-nonconcave and discontinuous min-max problems may not have a saddle point, we show that they have a local saddle point and a global minimax point, and some local saddle points have the lower b

Santiago Herrera, Caio Corro, Sylvain Kahane

Descriptive grammars are highly valuable, but writing them is time-consuming and difficult. Furthermore, while linguists typically use corpora to create them, grammar descriptions often lack quantitative data. As for formal grammars, they can be challenging to interpret. In this paper, we propose a new method to extract and explore significant fine-grained g

Edvards Scukins, Markus Klein, Lars Kroon, Petter Ögren

Creating new air combat tactics and discovering novel maneuvers can require numerous hours of expert pilots' time. Additionally, for each different combat scenario, the same strategies may not work since small changes in equipment performance may drastically change the air combat outcome. For this reason, we created a reinforcement learning environment to he

Yilin Wang, Minghao Hu, Zhen Huang, Dongsheng Li

The goal of knowledge graph completion (KGC) is to predict missing facts among entities. Previous methods for KGC re-ranking are mostly built on non-generative language models to obtain the probability of each candidate. Recently, generative large language models (LLMs) have shown outstanding performance on several tasks such as information extraction and di

Rejin John Varghese, Man-Yan Tong, Isabella Szczech, Peter Bryan

Spinal cord injuries generally result in sensory and mobility impairments, with torso instability being particularly debilitating. Existing torso stabilisers are often rigid and restrictive. We present an early investigation into a non-restrictive 1 degree-of-freedom (DoF) mechanical torso stabiliser inspired by devices such as centrifugal clutches and seat-

Eva Pachetti, Sotirios A. Tsaftaris, Sara Colantonio

Background and objective: Employing deep learning models in critical domains such as medical imaging poses challenges associated with the limited availability of training data. We present a strategy for improving the performance and generalization capabilities of models trained in low-data regimes. Methods: The proposed method starts with a pre-training phas

Hafsa Ouajdi, Oussama Hadder, Modan Tailleur, Mathieu Lagrange

With the ever-rising quality of deep generative models, it is increasingly important to be able to discern whether the audio data at hand have been recorded or synthesized. Although the detection of fake speech signals has been studied extensively, this is not the case for the detection of fake environmental audio. We propose a simple and efficient pipeline

Chihiro Yano, Akihiko Fukuchi, Shoko Fukasawa, Hideyuki Tachibana

Prior work on multilingual sentence embedding has demonstrated that the efficient use of natural language inference (NLI) data to build high-performance models can outperform conventional methods. However, the potential benefits from the recent ``exponential'' growth of language models with billions of parameters have not yet been fully explored. In this pap

Dan McCabe, Xuegang, Ban, Balazs Kulcsar

Transit agencies that operate battery-electric buses must carefully manage fast-charging infrastructure to extend daily bus range without degrading on-time performance. To support this need, we propose a mixed-integer linear programming model to schedule opportunity charging that minimizes the amount of departure delay in all trips served by electric buses.

Martin Markl

We show that the functor which assigns to an A-infinity morphism between isotopy classes of A-infinity algebras whose linear part is a chain homotopy equivalence its underlying chain map is a discrete Grothendieck bifibration. We then generalize our results to P-infinity structures over a field of characteristic zero, for any quadratic Koszul operad P. An im

Sicong Zang, Zhijun Fang

When benefiting graphic sketch representation with sketch drawing orders, recent studies have linked sketch patches as graph edges by drawing orders in accordance to a temporal-based nearest neighboring strategy. However, such constructed graph edges may be unreliable, since the contextual relationships between patches may be inconsistent with the sequential

Yuang Qi, Kejiang Chen, Kai Zeng, Weiming Zhang

Recent research in provably secure neural linguistic steganography has overlooked a crucial aspect: the sender must detokenize stegotexts to avoid raising suspicion from the eavesdropper. The segmentation ambiguity problem, which arises when using language models based on subwords, leads to occasional decoding failures in all neural language steganography im

C. Wetterich

We adapt the precise definition of the flowing effective action in order to obtain a functional flow equation with simple properties close to physical intuition. The simplified flow equation is invariant under local gauge transformations and suitable for both euclidean and Minkowski signature and analytic continuation. The cutoff always removes fluctuations

Jan Moser

In this paper new $\Gamma$-functional is constructed upon the basis of the set of almost linear increments of the Hardy-Littlewood integral. This functional generates a $\Gamma$-equivalent of the Fermat-Wiles theorem and also new set of factorization formulae for Euler's $\Gamma$-function.

Anna V. Maiorova, Dmitry Karlovets, Stephan Fritzsche, Andrey Surzhykov

Coulomb excitation of hydrogen atoms by vortex protons is theoretically investigated within the framework of the non--relativistic first--Born approximation and the density matrix approach. Special attention is paid to the magnetic sublevel population of excited atoms and, consequently, to the angular distribution of the fluorescence radiation. We argue that

Xiangyu Yin, Wenjie Ruan

Adversarial training is extensively utilized to improve the adversarial robustness of deep neural networks. Yet, mitigating the degradation of standard generalization performance in adversarial-trained models remains an open problem. This paper attempts to resolve this issue through the lens of model complexity. First, We leverage the Fisher-Rao norm, a geom

Caitlin I. O'Neil, Zhenhai Hu, Naoki Kikugawa, Dmitry A. Sokolov

We report on a new technique for measuring the dynamic Young's modulus, $E$, of quantum materials at low temperatures as a function of static tuning strain, $\epsilon$, in piezoactuator-driven pressure cells. In addition to a static tuning of stress and strain, we apply a small-amplitude, finite-frequency a.c. (1 Hz$ \lesssim \omega \lesssim $1000 Hz) uniaxi

Mustafa Alfarhan, Matteo Ravasi, Fuqiang Chen, Tariq Alkhalifah

Full Waveform Inversion (FWI) is a technique widely used in geophysics to obtain high-resolution subsurface velocity models from waveform seismic data. Due to its large computation cost, most flavors of FWI rely only on the computation of the gradient of the loss function to estimate the update direction, therefore ignoring the contribution of the Hessian. D

Ayan Roychowdhury, Madan Rao, Lev Truskinovsky

Active matter, exemplified by adaptive living materials such as the actomyosin cytoskeleton, can navigate material parameter space dynamically, leading to unconventional mechanical responses. In particular, it can self-drive toward elastic spinodal regimes, where inhomogeneous floppy modes induce elastic degeneracy and enable a controlled interplay between r

Xinpei Zhao, Jingyuan Sun, Shaonan Wang, Jing Ye

Decoding continuous language from brain activity is a formidable yet promising field of research. It is particularly significant for aiding people with speech disabilities to communicate through brain signals. This field addresses the complex task of mapping brain signals to text. The previous best attempt reverse-engineered this process in an indirect way:

Yotam Gafni, Ronen Gradwohl, Moshe Tennenholtz

Two firms are engaged in a competitive prediction task. Each firm has two sources of data -- labeled historical data and unlabeled inference-time data -- and uses the former to derive a prediction model, and the latter to make predictions on new instances. We study data-sharing contracts between the firms. The novelty of our study is to introduce and highlig

Michael Neri, Archontis Politis, Daniel Krause, Marco Carli

Distance estimation from audio plays a crucial role in various applications, such as acoustic scene analysis, sound source localization, and room modeling. Most studies predominantly center on employing a classification approach, where distances are discretized into distinct categories, enabling smoother model training and achieving higher accuracy but impos

Jinzhen Zhu

Simulating large proteins using traditional molecular dynamics (MD) is computationally demanding. To address this challenge, we propose a novel tree-structured coarse-grained model that efficiently captures protein dynamics. By leveraging a hierarchical protein representation, our model accurately reconstructs high-resolution protein structures, with sub-ang

Haoran Liu, Mingzhe Liu, Peng Li, Jiahui Wu

Improving the efficiency of current neural networks and modeling them in biological neural systems have become popular research directions in recent years. Pulse-coupled neural network (PCNN) is a well applicated model for imitating the computation characteristics of the human brain in computer vision and neural network fields. However, differences between t

Danai Roumelioti, Stelios Stefas, George Zoupanos

Based on the observation that the dimension of the tangent space is not necessarily equal to the dimension of the corresponding curved manifold and on the known fact that gravitational theories can be formulated in a gauge theoretic way, we discuss how to describe all known interactions in a unified manner. This is achieved by enlarging the tangent group of

Lukas Baumann

Basket trials are a new type of clinical trial in which a treatment is investigated in several subgroups. For the analysis of these trials, information is shared between the subgroups based on the observed data to increase the power. Many approaches for the analysis of basket trials have been suggested, but only a few have been implemented in open source sof

Sascha Kurz

Linear codes play a central role in coding theory and have applications in several branches of mathematics. For error correction purposes the minimum Hamming distance should be as large as possible. Linear codes related to applications in Galois Geometry often require a certain divisibility of the occurring weights. In this paper we present an algorithmic fr

Modan Tailleur, Junwon Lee, Mathieu Lagrange, Keunwoo Choi

This paper explores whether considering alternative domain-specific embeddings to calculate the Fr\'echet Audio Distance (FAD) metric can help the FAD to correlate better with perceptual ratings of environmental sounds. We used embeddings from VGGish, PANNs, MS-CLAP, L-CLAP, and MERT, which are tailored for either music or environmental sound evaluation. The

Bangchen Yin, Yue Yin, Yuda W. Tang, Hai Xiao

Machine learning force fields (MLFFs) are a promising approach to balance the accuracy of quantum mechanics with the efficiency of classical potentials, yet selecting an optimal model amid increasingly diverse architectures that delivers reliable force predictions and stable simulations remains a core pratical challenge. Here we introduce EL-MLFFs, an ensemb

Silvia Bonettini, Luca Calatroni, Danilo Pezzi, Marco Prato

We propose an unfolded accelerated projected-gradient descent procedure to estimate model and algorithmic parameters for image super-resolution and molecule localization problems in image microscopy. The variational lower-level constraint enforces sparsity of the solution and encodes different noise statistics (Gaussian, Poisson), while the upper-level cost

Nicolas Bousquet

This article provides a critical review of the main methods used to produce conservative estimators of probabilities of rare events, or critical failures, for reliability and certification studies in the broadest sense. These probabilities must theoretically be calculated from simulations of (certified) numerical models, but which typically suffer from prohi

Aishwarjya Gogoi, Jadav Chandra Mandal, Amitabh Saraf

The paper presents a new approach of stability evaluation of the approximate Riemann solvers based on the direct Lyapunov method. The present methodology offers a detailed understanding of the origins of numerical shock instability in the approximate Riemann solvers. The pressure perturbation feeding the density and transverse momentum perturbations is ident

Huiping Zhuang, Run He, Kai Tong, Ziqian Zeng

Class-incremental learning (CIL) under an exemplar-free constraint has presented a significant challenge. Existing methods adhering to this constraint are prone to catastrophic forgetting, far more so than replay-based techniques that retain access to past samples. In this paper, to solve the exemplar-free CIL problem, we propose a Dual-Stream Analytic Learn

Dihan Zheng, Yihang Zou, Xiaowen Zhang, Chenglong Bao

The data bottleneck has emerged as a fundamental challenge in learning based image restoration methods. Researchers have attempted to generate synthesized training data using paired or unpaired samples to address this challenge. This study proposes SeNM-VAE, a semi-supervised noise modeling method that leverages both paired and unpaired datasets to generate

Erik J. Gustafson, Florian Herren, Ruth S. Van de Water, Raynette van Tonder

In this contribution we present a novel, model-independent description of semileptonic $B\rightarrow D \pi \ell \nu$ decays. In addition, we discuss recent developments in the understanding of coupled-channel $D \pi$-$D \eta$-$D_s K$ S-wave scattering and, for the first time, apply them to semileptonic decays. We not only obtain model-independent predictions

Hanxuan Yang, Zhaoxin Yu, Qingchao Kong, Wei Liu

Graph representation learning is a fundamental research issue in various domains of applications, of which the inductive learning problem is particularly challenging as it requires models to generalize to unseen graph structures during inference. In recent years, graph neural networks (GNNs) have emerged as powerful graph models for inductive learning tasks

Daniel Pȩcak, Agata Zdanowicz, Nicolas Chamel, Piotr Magierski

We present a new numerical tool designed to probe the dense layers of neutron star crusts. It is based on the time-dependent Hartree-Fock-Bogoliubov theory with generalized Skyrme nuclear energy-density functionals of the Brussels-Montreal family. We use it to study the time evolution of a nucleus accelerating through superfluid neutron medium in the inner c

Ch. Morawe, P. Bras, S. Labouré, F. Perrin

The surface figure of an x-ray mirror was improved by differential deposition of WSi$_2$ layers. DC magnetron sputtering through beam-defining apertures was applied on moving substrates to generate thin films with arbitrary longitudinal thickness variations. The required velocity profiles were calculated using a deconvolution algorithm. Height errors were ev

Philipp Sadler, Sherzod Hakimov, David Schlangen

In collaborative goal-oriented settings, the participants are not only interested in achieving a successful outcome, but do also implicitly negotiate the effort they put into the interaction (by adapting to each other). In this work, we propose a challenging interactive reference game that requires two players to coordinate on vision and language observation

Yusuke Takimoto, Hikari Takehara, Hiroyuki Sato, Zihao Zhu

In the film and gaming industries, achieving a realistic hair appearance typically involves the use of strands originating from the scalp. However, reconstructing these strands from observed surface images of hair presents significant challenges. The difficulty in acquiring Ground Truth (GT) data has led state-of-the-art learning-based methods to rely on pre

Jonas Blenninger, David Bucher, Giorgio Cortiana, Kumar Ghosh

In this project summary paper, we summarize the key results and use-cases explored in the German Federal Ministry of Education and Research (BMBF) funded project "Q-GRID" which aims to assess potential quantum utility optimization applications in the electrical grid. The project focuses on two layers of optimization problems relevant to decentralized energy

Emad Efatinasab, Francesco Marchiori, Alessandro Brighente, Mirco Rampazzo

Predicting and classifying faults in electricity networks is crucial for uninterrupted provision and keeping maintenance costs at a minimum. Thanks to the advancements in the field provided by the smart grid, several data-driven approaches have been proposed in the literature to tackle fault prediction tasks. Implementing these systems brought several improv

Dino Rossegger, Theodore Slaman, Tomasz Steifer

We investigate natural variations of behaviourally correct learning and explanatory learning -- two learning paradigms studied in algorithmic learning theory -- that allow us to ``learn'' equivalence relations on Polish spaces. We give a characterization of the learnable equivalence relations in terms of their Borel complexity and show that the behaviorally

Xue-Ying Han, Jun Hua, Xiangdong Ji, Cai-Dian Lü

Lightcone distribution amplitudes (LCDAs) of heavy meson within heavy quark effective theory (HQET) are crucial for predicting physical observables in $B$ decays, but unfortunately there is no first-principle result due to severe challenges. After analyzing these challenges, we propose a realistic method to determine heavy meson LCDA. We utilize equal-time c

Xinyu Ning, Yutong Zhao, Yitong Liu, Hongwen Yang

The method of training language models based on domain datasets has obtained significant achievements in the task of generating scientific paper abstracts. However, such models face problems of generalization and expensive training costs. The use of large language models (LLMs) to solve the task of generating paper abstracts saves the cost of model training.

Thomas Bouchet

Let $K$ be a field of characteristic $0$. We present an explicit algorithm that, given the invariants of a generic homogeneous polynomial $f$ under the linear action of $\mathrm{GL}_n$ or $\mathrm{SL}_n$, returns a polynomial differing from $f$ only by a linear change of variables with coefficients in a finite extension of $K$. Our approach uses the theory o

N. Pétrélis, F. Pétrélis

We consider the random deposition of objects of variable width and height over a line. The successive additions of these structures create a random interface. We focus on the regime of heavy tailed distributions of the structure width. When the structure center is chosen at random, the problem is exactly solvable and we prove that the interfaces generically

Maximilian Berbig, Juan Herrero-Garcia, Giacomo Landini

A dynamical mechanism, based on a confining non-abelian dark symmetry, which generates Majorana masses for hypercharge-less fermions, is proposed. We apply it to the inverse seesaw scenario, which allows to generate light neutrino masses from the interplay of TeV-scale Pseudo-Dirac mass terms and a small explicit breaking of lepton number. A single generatio

Kazumasa Okabayashi, Naritaka Oshita

The spectral amplitude of the merger-ringdown gravitational wave (GW) emitted by a comparable mass-ratio black hole merger is modeled by the greybody factor of the remnant black hole. We also include the post-Newtonian correction to the greybody factor model. Our model includes only a few fitting parameters, which could evade the overfitting issue. We perfor

Cong-Duy Nguyen, Thong Nguyen, Xiaobao Wu, Anh Tuan Luu

Previous work on multimodal sentence embedding has proposed multimodal contrastive learning and achieved promising results. However, by taking the rest of the batch as negative samples without reviewing when forming contrastive pairs, those studies encountered many suspicious and noisy negative examples, significantly affecting the methods' overall performan

Qihong Sun, Shuangxiang Zhou, Ronghang Chen, Guanru Feng

This paper outlines an alternative approach to teaching quantum computing at the high school level, tailored for students with limited prior knowledge in advanced mathematics and physics. This approach diverges from traditional methods by building upon foundational concepts in classical computing before gradually introducing quantum mechanics, thereby simpli

Kitaro Taniguchi, Shinta Takagishi, Ryotaro Kase

We study black hole (BH) solutions in extended Einstein-Maxwell-scalar theories, which are classified in a subclass of the $U(1)$ gauge-invariant scalar-vector-tensor theories. The scalar field is coupled to the vector field, which has electric and magnetic charges. For the static and spherically symmetric spacetime, we investigate modifications to the Reiss

Robert C. Myers, Shan-Ming Ruan, Tomonori Ugajin

We employ double holography to examine a system of two entangled gravitating universes that live on two codimension-one branes in an asymptotically AdS$_3$ spacetime with two disjoint conformal boundaries. There are distinct brane configurations depending on the temperature of the thermofield double (TFD) state between the left and right systems. The topolog

Kazuki Maeda, Bo Lu, Keiji Yada, Yukio Tanaka

We theoretically study the tunneling conductance of a junction consisting of a two-dimensional unconventional $p$-wave magnet (UPM) and a superconductor (SC) for various pairing symmetries. The zero bias conductance peaks arising from the dispersionless surface Andreev bound states (SABSs) in $d_{xy}$-wave and $p_{x}$-wave superconductor junctions are insens

Lu Lin, Ze Chen

The existing Fr\'echet regression is actually defined within a linear framework, since the weight function in the Fr\'echet objective function is linearly defined, and the resulting Fr\'echet regression function is identified to be a linear model when the random object belongs to a Hilbert space. Even for nonparametric and semiparametric Fr\'echet regression

Rahul Vaze, Jayakrishnan Nair

An online non-convex optimization problem is considered where the goal is to minimize the flow time (total delay) of a set of jobs by modulating the number of active servers, but with a switching cost associated with changing the number of active servers over time. Each job can be processed by at most one fixed speed server at any time. Compared to the usual

Chanwut Kittivorawong

Legible labels should not overlap with other labels and other marks in a chart. When a chart contains a large number of data points, manually positioning these labels for each data point in the chart is a tedious task. A labeling algorithm is necessary to automatically layout the labels for a chart with a large number of data points. The state-of-the-art lab

Morteza Zakeri-Nasrabadi, Saeed Parsa

Requirements form the basis for defining software systems' obligations and tasks. Testable requirements help prevent failures, reduce maintenance costs, and make it easier to perform acceptance tests. However, despite the importance of measuring and quantifying requirements testability, no automatic approach for measuring requirements testability has been pr

Will Johnson

We show that C-minimal fields (i.e., C-minimal expansions of ACVF) have the exchange property, answering a question of Haskell and Macpherson. Additionally, we strengthen some theorems of Cubides Kovacsics and Delon on C-minimal fields. First, we show that definably complete C-minimal fields of characteristic 0 have generic differentiability. Second, we show

Chuhan Jiao, Yao Wang, Guanhua Zhang, Mihai Bâce

We present DiffGaze, a novel method for generating realistic and diverse continuous human gaze sequences on 360{\deg} images based on a conditional score-based denoising diffusion model. Generating human gaze on 360{\deg} images is important for various human-computer interaction and computer graphics applications, e.g. for creating large-scale eye tracking

Lise Aabel, Sven Jacobsson, Mikael Coldrey, Frida Olofsson

We present the uplink and downlink of a time-division duplex distributed multiple-input multiple-output (D-MIMO) testbed, based on a 1-bit radio-over-fiber architecture, which is low-cost and scalable. The proposed architecture involves a central unit (CU) that is equipped with 1-bit digital-to-analog and analog-to-digital converters, operating at 10 GS/s. T

Robert C. Myers, Shan-Ming Ruan

Recent investigations [arXiv:2111.02429][arXiv:2210.09647][arXiv:2304.05453] have introduced an infinite class of novel gravitational observables in Asymptotically anti-de Sitter (AdS) space that reside on codimension-one or -zero regions of the bulk spacetime. These observables encompass well-established holographic complexity measures such as the maximum v

Thomas Blomme, Gurvan Mével

Block and G\"ottsche introduced a Laurent polynomial multiplicity to count tropical curves. Itenberg and Mikhalkin then showed that this multiplicity leads to invariant counts called tropical refined invariants. Recently, Brugall\'e and Jaramillo-Puentes studied the polynomiality properties of the coefficients of these invariants and showed that for fixed ge

Haiyang Zhang, Qiuyi Chen, Yuanjie Zou, Yushan Pan

The Document Set Expansion (DSE) task involves identifying relevant documents from large collections based on a limited set of example documents. Previous research has highlighted Positive and Unlabeled (PU) learning as a promising approach for this task. However, most PU methods rely on the unrealistic assumption of knowing the class prior for positive samp

Pascal Bianchi, Walid Hachem, Victor Priser

We consider a discrete-time system of n coupled random vectors, a.k.a. interacting particles. The dynamics involve a vanishing step size, some random centered perturbations, and a mean vector field which induces the coupling between the particles. We study the doubly asymptotic regime where both the number of iterations and the number n of particles tend to

Arnaud Guillin, D I Lu, Boris Nectoux, Liming Wu

In this paper, we prove in a very weak regularity setting existence and uniqueness of quasi-stationary distributions as well as exponential conver- gence towards the quasi-stationary distribution for the generalized Langevin and the Nos{\'e}-Hoover processes, two processes which are widely used in molecular dynamics. The case of singular potentials is consid

Guillaume Coulaud, Maxime Le, Régis Duvigneau

Physics-Informed Neural Networks (PINNs) have recently emerged as a novel approach to simulate complex physical systems on the basis of both data observations and physical models. In this work, we investigate the use of PINNs for various applications in aerodynamics and we explain how to leverage their specific formulation to perform some tasks effectively.

Pascal Auscher, Cyril Imbert, Lukas Niebel

We show the existence and uniqueness of fundamental solution operators to Kolmo\-gorov-Fokker-Planck equations with rough (measurable) coefficients and local or integral diffusion on finite and infinite time strips. In the local case, that is to say when the diffusion operator is of differential type, we prove $\L^2$ decay using Davies' method and the conser

Shao-Qun Zhang, Zong-Yi Chen, Yong-Ming Tian, Xun Lu

Past decades have witnessed a great interest in the distinction and connection between neural network learning and kernel learning. Recent advancements have made theoretical progress in connecting infinite-wide neural networks and Gaussian processes. Two predominant approaches have emerged: the Neural Network Gaussian Process (NNGP) and the Neural Tangent Ke

Abdessalam Benhari, Denis Trystram, Fanny Dufossé, Yves Denneulin

The demand in computing power has never stopped growing over the years. Today, the performance of the most powerful systems exceeds the exascale and the number of petascale systems continues to grow. Unfortunately, this growth also goes hand in hand with ever-increasing energy costs, which in turn means a significant carbon footprint. In view of the environm

Yunpeng Luo, Junlong Du, Ke Yan, Shouhong Ding

The evolution of Diffusion Models has dramatically improved image generation quality, making it increasingly difficult to differentiate between real and generated images. This development, while impressive, also raises significant privacy and security concerns. In response to this, we propose a novel Latent REconstruction error guided feature REfinement meth

Pascal Auscher, Cyril Imbert, Lukas Niebel

We prove existence, uniqueness and regularity of weak solutions of Kolmogorov--Fokker--Planck equations with either local or non-local diffusion in the velocity variable and rough diffusion coefficients or kernels. Our results cover the Cauchy problem and allow a broad class of source terms under minimal assumptions. The core of the analysis is a set of shar