April 2024 arXiv papers — page 153

Simon Vrhovec, Blaž Markelj

Cyberattacks pose a significant business risk to organizations. Although there is ample literature focusing on why people pose a major risk to organizational cybersecurity and how to deal with it, there is surprisingly little we know about cyber and information security decision-makers who are essentially the people in charge of setting up and maintaining or

Anthony Burrow, E. Baron, Christopher R. Burns, Eric Y. Hsiao

We present a method of extrapolating the spectroscopic behavior of Type Ia supernovae (SNe Ia) in the near-infrared (NIR) wavelength regime up to 2.30 $\mu$m using optical spectroscopy. Such a process is useful for accurately estimating K-corrections and other photometric quantities of SNe Ia in the NIR. Principal component analysis is performed on data cons

Kurmanbek Kaiyrbekov, Brian A. Camley

Collective response to external directional cues like electric fields plays a pivotal role in processes such as tissue development, regeneration, and wound healing. In this study we focus on the impact of anisotropy in cell shape and local cell alignment on the collective response to electric fields. We model elongated cells that have a different accuracy se

Derui Zhu, Dingfan Chen, Qing Li, Zongxiong Chen

Despite tremendous advancements in large language models (LLMs) over recent years, a notably urgent challenge for their practical deployment is the phenomenon of hallucination, where the model fabricates facts and produces non-factual statements. In response, we propose PoLLMgraph, a Polygraph for LLMs, as an effective model-based white-box detection and for

Patrick Tam

The class of $k$-nearly finitary matroids for some natural number $k$ is a subclass of the class of nearly finitary matroids. A natural question is whether this inclusion is proper. We answer this question affirmatively by constructing a nearly finitary matroid that is not $k$-nearly finitary for any $k \in \mathbb{N}$.

Zhuoxu Huang, Zhenkun Fan, Tao Xu, Jungong Han

Point cloud video representation learning is challenging due to complex structures and unordered spatial arrangement. Traditional methods struggle with frame-to-frame correlations and point-wise correspondence tracking. Recently, partial differential equations (PDE) have provided a new perspective in uniformly solving spatial-temporal data information within

Yik Lun Kei, Jialiang Li, Hangjian Li, Yanzhen Chen

This manuscript studies the unsupervised change point detection problem in time series of graphs using a decoder-only latent space model. The proposed framework consists of learnable prior distributions for low-dimensional graph representations and of a decoder that bridges the observed graphs and latent representations. The prior distributions of the latent

Prasun C Tripathi, Sina Tabakhi, Mohammod N I Suvon, Lawrence Schöb

Pulmonary Arterial Wedge Pressure (PAWP) is an essential cardiovascular hemodynamics marker to detect heart failure. In clinical practice, Right Heart Catheterization is considered a gold standard for assessing cardiac hemodynamics while non-invasive methods are often needed to screen high-risk patients from a large population. In this paper, we propose a mu

Rikard Enberg, Yaşar Hiçyılmaz, Stefano Moretti, Carlos Pérez de los Heros

We explain the ATOMKI anomaly with a very light $Z'$ state that features non-anomalous and non-flavour-universal vector and axial-vector couplings to all leptons. This $Z'$ comes from a theoretical framework with a spontaneously broken $U(1)'$ symmetry in addition to the Standard Model gauge group and is compliant with current measurements of the anomalous m

Rajat Majumder, Alexander Yu. Sokolov

We present a formulation and implementation of second-order quasidegenerate N-electron valence perturbation theory (QDNEVPT2) that provides a balanced and accurate description of spin-orbit coupling and dynamic correlation effects in multiconfigurational electronic states. In our approach, the energies and wavefunctions of electronic states are computed by t

Sasan Kheiri, Hadi Cheraghi, Saeed Mahdavifar, Nicholas Sedlmayr

The bond-dependent Kitaev model offers a playground in which one can search for quantum spin liquids. In these Kitaev materials, a symmetric off-diagonal $\Gamma$ term emerges, hosting a number of remarkable features, which has been particularly challenging to fully understand. One primary question that arises after recognizing a new phase is how information

Elita Lobo, Harvineet Singh, Marek Petrik, Cynthia Rudin

Off-policy Evaluation (OPE) methods are a crucial tool for evaluating policies in high-stakes domains such as healthcare, where exploration is often infeasible, unethical, or expensive. However, the extent to which such methods can be trusted under adversarial threats to data quality is largely unexplored. In this work, we make the first attempt at investiga

Yash Kurkure, Miles Shamo, Joseph Wiseman, Sainyam Galhotra

The task of extracting a diverse subset from a dataset, often referred to as maximum diversification, plays a pivotal role in various real-world applications that have far-reaching consequences. In this work, we delve into the realm of fairness-aware data subset selection, specifically focusing on the problem of selecting a diverse set of size $k$ from a lar

MAP, Collaboration, :, Valerio Bertone

We present MAPPDFpol1.0, a new determination of the helicity-dependent parton distribution functions (PDFs) of the proton from a set of longitudinally polarised inclusive and semi-inclusive deep-inelastic scattering data. The determination includes, for the first time, next-to-next-to-leading order QCD corrections to both processes, and is carried out in a f

May Abdallah, Mohamad Darwich, Luc Molinet

This paper is devoted to the study of existence and properties of solitary waves of the Benjamin equation. The studied equation includes a parameter $\gamma$ in front of the Benjamin-Ono term. We show the existence, uniqueness, decay and orbital stability of solitary wave solutions obtained as a solution to a certain minimization problem, associated either w

Pawanesh Pawanesh, Charu Sharma, Niteesh Sahni

In this study, we model the Indian stock market as heterogenous scale free network, which is then embedded in a two dimensional hyperbolic space through a machine learning based technique called as coalescent embedding. This allows us to apply the hyperbolic kmeans algorithm on the Poincare disc and the clusters so obtained resemble the original network comm

Daniel Freund, Sébastien Martin, Jiayu Kamessi Zhao

Flexibility is a cornerstone of operations management, crucial to hedge stochasticity in product demands, service requirements, and resource allocation. In two-sided platforms, flexibility is also two-sided and can be viewed as the compatibility of agents on one side with agents on the other side. Platform actions often influence the flexibility on either th

Mingxuan He, Mithuna Thottethodi, T. N. Vijaykumar

Emerging machine learning (ML) models (e.g., transformers) involve memory pin bandwidth-bound matrix-vector (MV) computation in inference. By avoiding pin crossings, processing in memory (PIM) can improve performance and energy for pin-bound workloads, as evidenced by recent commercial efforts in (digital) PIM. Sparse models can improve performance and energ

Carolina Biliotti, Luca Verginer, Massimo Riccaboni

This study exploits COVID-19 as an exogenous shock in biomedical research to show how the emergence of an unexpected new research topic exacerbates gender bias in key authorship positions of scientific publications relevant to new research topics (e.g. Vaccines, Epidemiology). We determine author's gender based on the names listed on their scientific publica

Saswat Das, Subhankar Mishra

There has been an explosion of research on differential privacy (DP) and its various applications in recent years, ranging from novel variants and accounting techniques in differential privacy to the thriving field of differentially private machine learning (DPML) to newer implementations in practice, like those by various companies and organisations such as

Gemma Crowe

In this second paper we solve the twisted conjugacy problem for even dihedral Artin groups, that is, groups with presentation $G(m) = \langle a,b \mid {}_{m}(a,b) = {}_{m}(b,a) \rangle$, where $m \geq 2$ is even, and $_{m}(a,b)$ is the word $abab\dots$ of length $m$. Similar to odd dihedral Artin groups, we prove orbit decidability for all subgroups $A \leq

Ekaterina Kriukova

We study the production of dark photons with masses in range 0.4-1.5\,GeV in elastic proton bremsstrahlung. In contrast to the known approaches to calculating the bremsstrahlung cross section, we consider the non-zero momentum transfer between protons. The numerical estimates show that the refined result agrees well with the generalized Weizsacker-Williams a

Niv Dayan, Ioana-Oriana Bercea, Rasmus Pagh

Filter data structures are widely used in various areas of computer science to answer approximate set-membership queries. In many applications, the data grows dynamically, requiring their filters to expand along with the data. However, existing methods for expanding filters cannot maintain stable performance, memory footprint, and false positive rate (FPR) s

Vesna Gotovac Đogaš, Marcela Mandarić

In this paper we present the methodology for detecting outliers and testing the goodness-of-fit of random sets using topological data analysis. We construct the filtration from level sets of the signed distance function and consider various summary functions of the persistence diagram derived from the obtained persistence homology. The outliers are detected

Siqi Wu, Chenchao Xu, Xiaoqun Wang, Hai-Qing Lin

The spin dynamics and electronic orders of the kagome system at different filling levels stand as an intriguing subject in condensed matter physics. By first-principles calculations and random phase approximation analyses, we investigate the spin fluctuations and superconducting instabilities in kagome phase of CsCr$_3$Sb$_5$ under high pressure. At the fill

Jinyong Hahn, John Ham, Geert Ridder, Shuyang Sheng

We study the estimation of treatment effects using samples stratified by treatment status. Standard estimators of the average treatment effect and the local average treatment effect are inconsistent in this setting. We propose consistent estimators and characterize their asymptotic distributions.

Vincent W. Hill

This work describes a technique for active rejection of multiple independent and time-correlated stochastic disturbances for a nonlinear flexible inverted pendulum with cart system with uncertain model parameters. The control law is determined through deep reinforcement learning, specifically with a continuous actor-critic variant of deep Q-learning known as

Shifan Zhu, Zixun Xiong, Donghyun Kim

When legged robots perform agile movements, traditional RGB cameras often produce blurred images, posing a challenge for rapid perception. Event cameras have emerged as a promising solution for capturing rapid perception and coping with challenging lighting conditions thanks to their low latency, high temporal resolution, and high dynamic range. However, int

Dan Liu, Wenqing He

The study of precision medicine involves dynamic treatment regimes (DTRs), which are sequences of treatment decision rules recommended by taking patient-level information as input. The primary goal of the DTR study is to identify an optimal DTR, a sequence of treatment decision rules that leads to the best expected clinical outcome. Statistical methods have

Dan Liu, Wenqing He

Dynamic treatment regimes (DTRs) have received an increasing interest in recent years. DTRs are sequences of treatment decision rules tailored to patient-level information. The main goal of the DTR study is to identify an optimal DTR, a sequence of treatment decision rules that yields the best expected clinical outcome. Q-learning has been considered as one

April Yi Wang, Zihan Wu, Christopher Brooks, Steve Oney

Real-time collaborative editing in computational notebooks can improve the efficiency of teamwork for data scientists. However, working together through synchronous editing of notebooks introduces new challenges. Data scientists may inadvertently interfere with each others' work by altering the shared codebase and runtime state if they do not set up a social

Jan Malý, Zdeněk Mihula, Vít Musil, Luboš Pick

We develop a new functional-analytic technique for investigating the degree of noncompactness of an operator defined on a quasinormed space and taking values in a Marcinkiewicz space. The main result is a general principle from which it can be derived that such operators are almost always maximally noncompact in the sense that their ball measure of noncompac

Bonan Liu, Guoyang Zhao, Jianhao Jiao, Guang Cai

A Colored point cloud, as a simple and efficient 3D representation, has many advantages in various fields, including robotic navigation and scene reconstruction. This representation is now commonly used in 3D reconstruction tasks relying on cameras and LiDARs. However, fusing data from these two types of sensors is poorly performed in many existing framework

Poorvi Joshi, Alakesh Kalita, Mohan Gurusamy

Unmanned Aerial Vehicles (UAVs) are integral in various sectors like agriculture, surveillance, and logistics, driven by advancements in 5G. However, existing research lacks a comprehensive approach addressing both data freshness and security concerns. In this paper, we address the intricate challenges of data freshness, and security, especially in the conte

Andres Anabalon, Dumitru Astefanesei, Adolfo Cisterna, Fernando Izaurieta

We present new, exact, rotating and accelerating solutions within the framework of five-dimensional Einstein-Gauss-Bonnet theory at the Chern-Simons point. The rotating solutions describe black holes characterized by a single rotation parameter, a mass parameter, and two extra integration constants that can be interpreted as hairs of a gravitational nature.

Mohammed A. A. Elmaleeh

This paper presents different neural network-based classifier algorithms for diagnosing and classifying Anemia. The study compares these classifiers with established models such as Feed Forward Neural Network (FFNN), Elman network, and Non-linear Auto-Regressive Exogenous model (NARX). Experimental evaluations were conducted using data from clinical laborato

Gianluca Detommaso, Martin Bertran, Riccardo Fogliato, Aaron Roth

This paper proposes the use of "multicalibration" to yield interpretable and reliable confidence scores for outputs generated by large language models (LLMs). Multicalibration asks for calibration not just marginally, but simultaneously across various intersecting groupings of the data. We show how to form groupings for prompt/completion pairs that are corre

Aitor Arrieta, Pablo Valle, Shaukat Ali

Stateflow models are widely used in the industry to model the high-level control logic of Cyber-Physical Systems (CPSs) in Simulink--the defacto CPS simulator. Many approaches exist to test Simulink models, but once a fault is detected, the process to repair it remains manual. Such a manual process increases the software development cost, making it paramount

Ziyuan Qu, Omkar Vengurlekar, Mohamad Qadri, Kevin Zhang

Differentiable 3D-Gaussian splatting (GS) is emerging as a prominent technique in computer vision and graphics for reconstructing 3D scenes. GS represents a scene as a set of 3D Gaussians with varying opacities and employs a computationally efficient splatting operation along with analytical derivatives to compute the 3D Gaussian parameters given scene image

Taminul Islam, Md. Alif Sheakh, Mst. Sazia Tahosin, Most. Hasna Hena

Breast cancer has rapidly increased in prevalence in recent years, making it one of the leading causes of mortality worldwide. Among all cancers, it is by far the most common. Diagnosing this illness manually requires significant time and expertise. Since detecting breast cancer is a time-consuming process, preventing its further spread can be aided by creat

Sen Yang, Chuchuan Hong, Guodong Zhu, Theodore H. Anyika

Optofluidics is dedicated to achieving integrated control of particle and fluid motion, particularly on the micrometer scale, by utilizing light to direct fluid flow and particle motion. The field has seen significant growth recently, driven by the concerted efforts of researchers across various scientific disciplines, notably for its successful applications

Brooke Perreault, Johanna Lee, Ropafadzo Shava, Eni Mustafaraj

Google Search is an important way that people seek information about politics, and Google states that it is ``committed to providing timely and authoritative information on Google Search to help voters understand, navigate, and participate in democratic processes.'' This paper studies the extent to which government-maintained web domains are represented in t

Nelson R. F. Braga, Octavio C. Junqueira

We study the confinement/deconfinement transition of QCD matter for a quark-gluon plasma at finite density using AdS/QCD duality. In order to determine the critical temperature and its dependence on the quark chemical potential, the semi-classical Hawking-Page approach is considered for a charged AdS black hole. The result obtained is consistent with the QCD

Gui-Jun Ding, Si-Yi Jiang, Stephen F. King, Jun-Nan Lu

The flavor structure of quarks and leptons and quark-lepton unification are studied in the framework of Pati-Salam models with $A_4$ modular symmetry. The three generations of the left-handed and right-handed fermions are assigned to be triplet or singlets of $A_4$. The light neutrino masses are generated through the type-I seesaw mechanism. We perform a sys

Yeda Song, Dongwook Lee, Gunhee Kim

Offline reinforcement learning (RL) is a compelling framework for learning optimal policies from past experiences without additional interaction with the environment. Nevertheless, offline RL inevitably faces the problem of distributional shifts, where the states and actions encountered during policy execution may not be in the training dataset distribution.

Chunhui Chen, Xueyan Niu, Wenhao Ye, Hao Wu

The information rate-distortion-perception (RDP) function characterizes the three-way trade-off between description rate, average distortion, and perceptual quality measured by discrepancy between probability distributions and has been applied to emerging areas in communications empowered by generative modeling. We study several variants of the RDP functions

G. Araujo-Pardo, C. Dalfó, M. A. Fiol, N. López

A bipartite graph $G=(V,E)$ with $V=V_1\cup V_2$ is biregular if all the vertices of each stable set, $V_1$ and $V_2$, have the same degree, $r$ and $s$, respectively. This paper studies difference sets derived from both Abelian and non-Abelian groups. From them, we propose some constructions of bipartite biregular graphs with diameter $d=3$ and asymptotical

Yi-Kai Li, Athina Petropulu

In dual-function radar-communication (DFRC) systems the probing signal contains information intended for the communication users, which makes that information vulnerable to eavesdropping by the targets. We study the security of a DFRC system aided by an intelligent reflecting surface (IRS) from the physical layer security (PLS) perspective. The IRS helps ove

Gabriel S. Rocha, Isabella Danhoni, Kevin Ingles, Gabriel S. Denicol

Using an analytical result for the eigensystem of the linearized collision term for a classical system of massless scalar particles with quartic self-interactions, we show that the shear-stress linear response function possesses a branch-cut singularity that covers the whole positive imaginary semi-axis. This is demonstrated in two ways: (1) by truncating th

Philipp Andelfinger, Justin N. Kreikemeyer

Recently proposed gradient estimators enable gradient descent over stochastic programs with discrete jumps in the response surface, which are not covered by automatic differentiation (AD) alone. Although these estimators' capability to guide a swift local search has been shown for certain problems, their applicability to models relevant to real-world applica

Siyu Chen, Kangcheng Liu, Chen Wang, Shenghai Yuan

Visual Odometry (VO) is vital for the navigation of autonomous systems, providing accurate position and orientation estimates at reasonable costs. While traditional VO methods excel in some conditions, they struggle with challenges like variable lighting and motion blur. Deep learning-based VO, though more adaptable, can face generalization problems in new e

Abhilash Nandy, Yash Kulkarni, Pawan Goyal, Niloy Ganguly

In this paper, we propose sequence-based pretraining methods to enhance procedural understanding in natural language processing. Procedural text, containing sequential instructions to accomplish a task, is difficult to understand due to the changing attributes of entities in the context. We focus on recipes, which are commonly represented as ordered instruct

Samiran Bairagi, Marcus Lorentzon, Firat Angay, Roger Magnusson

We present the first report on fabrication of Hafnium aluminum nitride chiral sculptured thin films (CSTFs) using reactive magnetron sputtering in a glancing angle deposition configuration, and the analysis of its optical polarization properties. The resulting CSTFs were designed to give interference extrema or so-called circular Bragg (CB) resonances at des

Nalin Jayaweera, Andrea Bonfante, Mark Schamberger, Amir Mehdi Ahmadian Tehrani

The legacy beam management (BM) procedure in 5G introduces higher measurement and reporting overheads for larger beam codebooks resulting in higher power consumption of user equipment (UEs). Hence, the 3rd generation partnership project (3GPP) studied the use of artificial intelligence (AI) and machine learning (ML) in the air interface to reduce the overhea

R. M. Oliveira, R. C. de Lamare

In this paper, we present an adaptive reweighted sparse belief propagation (AR-SBP) decoder for polar codes. The AR-SBP technique is inspired by decoders that employ the sum-product algorithm for low-density parity-check codes. In particular, the AR-SBP decoding strategy introduces reweighting of the exchanged log-likelihood-ratio in order to refine the mess

Honghu Chen, Yuxin Yao, Juyong Zhang

In this paper, we introduce Neural-ABC, a novel parametric model based on neural implicit functions that can represent clothed human bodies with disentangled latent spaces for identity, clothing, shape, and pose. Traditional mesh-based representations struggle to represent articulated bodies with clothes due to the diversity of human body shapes and clothing

Sergei Khlebnikov

Description of electrons in a dielectric as solitons of the polarization field requires that the interaction between the solitons (prior to their coupling to electromagnetism) is short-range. We present an analytical study of the mechanism by which this is achieved. The mechanism is unusual in that it enables screening of electrically neutral soliton cores b

Nicolas Yax, Pierre-Yves Oudeyer, Stefano Palminteri

This paper introduces PhyloLM, a method adapting phylogenetic algorithms to Large Language Models (LLMs) to explore whether and how they relate to each other and to predict their performance characteristics. Our method calculates a phylogenetic distance metric based on the similarity of LLMs' output. The resulting metric is then used to construct dendrograms

Peter Abbamonte, Jörg Fink

The dynamic charge susceptibility, $\chi(q,\omega)$, is a fundamental observable of all materials, in one, two, and three dimensions, quantifying the collective charge modes, the ability of a material to screen charge, as well as its electronic compressibility. Here, we review the current state of efforts to measure this quantity using inelastic electron sca

Roopa Mayya, Vivekanand Venkataraman, Anwesh P R, Narayana Darapaneni

Introduction: Music generation is a complex task that has received significant attention in recent years, and deep learning techniques have shown promising results in this field. Objectives: While extensive work has been carried out on generating Piano and other Western music, there is limited research on generating classical Indian music due to the scarcity

Anantha Prabhu, David Pratap, Narayana Darapeni, Anwesh P R

Introduction: Video Quality Assessment (VQA) is one of the important areas of study in this modern era, where video is a crucial component of communication with applications in every field. Rapid technology developments in mobile technology enabled anyone to create videos resulting in a varied range of video quality scenarios. Objectives: Though VQA was pres

Anurag Singh, Siu Lun Chau, Shahine Bouabid, Krikamol Muandet

Out-of-distribution (OOD) generalisation is challenging because it involves not only learning from empirical data, but also deciding among various notions of generalisation, e.g., optimising the average-case risk, worst-case risk, or interpolations thereof. While this choice should in principle be made by the model operator like medical doctors, this informa

Xiaoyu Chen, Xiongxin Yang, Yitong Yin, Xinyuan Zhang

We study how to establish $\textit{spectral independence}$, a key concept in sampling, without relying on total influence bounds, by applying an $\textit{approximate inverse}$ of the influence matrix. Our method gives constant upper bounds on spectral independence for two foundational Gibbs distributions known to have unbounded total influences: $\bullet$ Th

Dyke Ferber, Omar S. M. El Nahhas, Georg Wölflein, Isabella C. Wiest

Multimodal artificial intelligence (AI) systems have the potential to enhance clinical decision-making by interpreting various types of medical data. However, the effectiveness of these models across all medical fields is uncertain. Each discipline presents unique challenges that need to be addressed for optimal performance. This complexity is further increa

Sungmun Cho, Yuchan Lee

We provide the explicit formula for orbital integrals associated with elliptic regular semisimple elements in $\mathrm{GL}_n(F) \cap \mathrm{M}_n(\mathfrak{o})$ and associated with arbitrary elements of the spherical Hecke algebra of $\mathrm{GL}_n(F)$ when $n=2, 3$, using results of [CKL]. Here $F$ is a non-Archimedean local field of any characteristic with

Lingzhi Liu, Haiyang Zhang, Chengwei Tang, Tiantian Zhang

The memory dictionary-based contrastive learning method has achieved remarkable results in the field of unsupervised person Re-ID. However, The method of updating memory based on all samples does not fully utilize the hardest sample to improve the generalization ability of the model, and the method based on hardest sample mining will inevitably introduce fal

Jonas Knoch, Markus Gahn, Maria Neuss-Radu

In this paper, we investigate an effective model for reactive transport in elastically deformable perforated media. This model was derived by formal asymptotic expansions in [25], starting from a microscopic model consisting of a linear elasticity problem on a fixed domain, i.e. in the Lagrangian framework, and a problem for reactive transport on the current

Arne Schmidt, Pablo Morales-Álvarez, Lee A. D. Cooper, Lee A. Newberg

Active Learning (AL) has the potential to solve a major problem of digital pathology: the efficient acquisition of labeled data for machine learning algorithms. However, existing AL methods often struggle in realistic settings with artifacts, ambiguities, and class imbalances, as commonly seen in the medical field. The lack of precise uncertainty estimations

Chuqin Geng, Zhaoyue Wang, Haolin Ye, Xujie Si

Formal verification is only as good as the specification of a system, which is also true for neural network verification. Existing specifications follow the paradigm of data as specification, where the local neighborhood around a reference data point is considered correct or robust. While these specifications provide a fair testbed for assessing model robust

Tianle Pu, Changjun Fan, Mutian Shen, Yizhou Lu

Many complex problems encountered in both production and daily life can be conceptualized as combinatorial optimization problems (COPs) over graphs. Recent years, reinforcement learning (RL) based models have emerged as a promising direction, which treat the COPs solving as a heuristic learning problem. However, current finite-horizon-MDP based RL models hav

Shipeng Xu

The study of ecological networks is crucial for modern conservation biology, addressing habitat fragmentation and biodiversity loss, especially in complex regions. These networks, including corridors, sources, and nodes, are key for species movement and ecosystem functioning. The Periphery Analysis Model (PAM) is introduced as a new approach to study the per

Changjiang Gao, Hongda Hu, Peng Hu, Jiajun Chen

Despite their strong ability to retrieve knowledge in English, current large language models show imbalance abilities in different languages. Two approaches are proposed to address this, i.e., multilingual pretraining and multilingual instruction tuning. However, whether and how do such methods contribute to the cross-lingual knowledge alignment inside the m

V. J. Ervin

In this paper, motivated by the analysis of the fractional Laplace equation on the unit disk in $\mathbb{R}^{2}$, we establish a characterization of the weighted Sobolev space $H_{\beta}^{s}(\Omega)$ in terms of the decay rate of Fourier-Jacobi coefficients. This framework is then used to give a precise analysis of the solution to the fractional Laplace equa

James E. Robinson, Alan Fitzsimmons, David R. Young, Michele Bannister

Sparse and serendipitous asteroid photometry obtained by wide field surveys such as the Asteroid Terrestrial-impact Last Alert System (\ATLAS) is a valuable resource for studying the properties of large numbers of small Solar System bodies. We have gathered a large database of \ATLAS photometry in wideband optical cyan and orange filters, consisting of 9.6\e

Seungjae Jung, Gunsoo Han, Daniel Wontae Nam, Kyoung-Woon On

In real-world services such as ChatGPT, aligning models based on user feedback is crucial for improving model performance. However, due to the simplicity and convenience of providing feedback, users typically offer only basic binary signals, such as 'thumbs-up' or 'thumbs-down'. Most existing alignment research, on the other hand, relies on preference-based

J. Brandão, P. C. Carvalho, I. P. Miranda, T. J. A. Mori

To develop new devices based on synthetic ferrimagnetic heterostructures, understanding the material's physical properties is pivotal. Here, the induced magnetic moment (IMM), magnetic exchange coupling, and spin textures are investigated in Pt(1 nm)/Co(1.5 nm)/Gd(1 nm) multilayers using a multiscale approach. The magnitude and direction of the IMM are inter

Rajesh B, Keerthana V, Narayana Darapaneni, Anwesh Reddy P

Introduction: Music provides an incredible avenue for individuals to express their thoughts and emotions, while also serving as a delightful mode of entertainment for enthusiasts and music lovers. Objectives: This paper presents a comprehensive approach to enhancing the user experience through the integration of emotion recognition, music recommendation, and

Ziang Guo, Stepan Perminov, Mikhail Konenkov, Dzmitry Tsetserukou

Many established vision perception systems for autonomous driving scenarios ignore the influence of light conditions, one of the key elements for driving safety. To address this problem, we present HawkDrive, a novel perception system with hardware and software solutions. Hardware that utilizes stereo vision perception, which has been demonstrated to be a mo

Suman Sourabh, Murugappan Valliappan, Narayana Darapaneni, Anwesh R P

Introduction: The present study on the development and evaluation of an automated brain tumor segmentation technique based on deep learning using the 3D U-Net model. Objectives: The objective is to leverage state-of-the-art convolutional neural networks (CNNs) on a large dataset of brain MRI scans for segmentation. Methods: The proposed methodology applies p

Agostino Cembalo, Patrick Coirault, Jacques Borée, Clément Dumand

The growing focus on reducing energy consumption, particularly in electric vehicles with limited autonomy, has prompted innovative solutions. In this context, we propose a real-time flap-based control system aimed at improving aerodynamic drag in real driving conditions. Employing a Recursive Subspace based Predictive Control (RSPC) approach, we conducted wi

Simone Tedeschi, Felix Friedrich, Patrick Schramowski, Kristian Kersting

When building Large Language Models (LLMs), it is paramount to bear safety in mind and protect them with guardrails. Indeed, LLMs should never generate content promoting or normalizing harmful, illegal, or unethical behavior that may contribute to harm to individuals or society. This principle applies to both normal and adversarial use. In response, we intro

Prashantkumar Patel

This note aims to present novel positive linear operators involving the Wright function. Furthermore, the present research established the moments of these newly defined operators and estimated the convergence rate using the classical modulus of continuity. Additionally, the convergence rate in the Lipschitz spaces, their A-statistical convergence property,

Xiefan Guo, Jinlin Liu, Miaomiao Cui, Jiankai Li

Recent strides in the development of diffusion models, exemplified by advancements such as Stable Diffusion, have underscored their remarkable prowess in generating visually compelling images. However, the imperative of achieving a seamless alignment between the generated image and the provided prompt persists as a formidable challenge. This paper traces the

G. Mustafa, Faisal Javed, S. K. Maurya, Abdelghani Errehymy

In this letter, we obtain two new embedded WH solutions by using the class-I approach in the background of newly fourth-order Ricci inverse gravity. We show that the combination of these newly calculated shape functions and Ricci inverse gravity provides us with the possibility of obtaining traversable wormholes. All the required wormhole properties are disc

Francesco Marchiori, Mauro Conti

The growing integration of vehicles with external networks has led to a surge in attacks targeting their Controller Area Network (CAN) internal bus. As a countermeasure, various Intrusion Detection Systems (IDSs) have been suggested in the literature to prevent and mitigate these threats. With the increasing volume of data facilitated by the integration of V

Shizhan Gong, Qi Dou, Farzan Farnia

Gradient-based saliency maps have been widely used to explain the decisions of deep neural network classifiers. However, standard gradient-based interpretation maps, including the simple gradient and integrated gradient algorithms, often lack desired structures such as sparsity and connectedness in their application to real-world computer vision models. A fr

Andreea I. Bordianu, Mircea Cimpoeas

Let $I$ be a squarefree monomial ideal of $S=K[x_1,\ldots,x_n]$. We prove that if $\operatorname{hdepth}(S/I)\leq 6$ of $n\leq 9$ then $\operatorname{hdepth}(I)\geq \operatorname{hdepth}(S/I)$, giving a positive answer to a problem putted in arxiv:2403.17078

Yingting Li, Rishabh Bhardwaj, Ambuj Mehrish, Bo Cheng

Neural speech synthesis, or text-to-speech (TTS), aims to transform a signal from the text domain to the speech domain. While developing TTS architectures that train and test on the same set of speakers has seen significant improvements, out-of-domain speaker performance still faces enormous limitations. Domain adaptation on a new set of speakers can be achi

Tingfei Li, Yuekai Song, Liang Zhang

Recently, the concept of generating function has been employed in one-loop reduction. For one-loop integrals encompassing arbitrary tensor ranks and higher-pole contributions, the generating function can be decomposed into a tensor part and a higher-pole part. While the tensor component has been thoroughly addressed in recent studies, there remains a lack of

Gaurav Singh, Sanket Kalwar, Md Faizal Karim, Bipasha Sen

Efficiently generating grasp poses tailored to specific regions of an object is vital for various robotic manipulation tasks, especially in a dual-arm setup. This scenario presents a significant challenge due to the complex geometries involved, requiring a deep understanding of the local geometry to generate grasps efficiently on the specified constrained re

Ashok Mondal, Satyam Singh

In recent years, large-scale adoption of cloud storage solutions has revolutionized the way we think about digital data storage. However, the exponential increase in data volume, especially images, has raised environmental concerns regarding power and resource consumption, as well as the rising digital carbon footprint emissions. The aim of this research is

A. A. Kon'kov, A. E. Shishkov

We study systems of the differential inequalities $$ \left\{ \begin{aligned} & - \operatorname{div} A_1 (x, \nabla u_1) \ge F_1 (x, u_2) & \mbox{in } {\mathbb R}^n, & - \operatorname{div} A_2 (x, \nabla u_2) \ge F_2 (x, u_1) & \mbox{in } {\mathbb R}^n, \end{aligned} \right. $$ where $n \ge 2$ and $A_i$ are Caratheodory functions such that $$ C_1 |\xi|^{p_i}

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

We search for the di-photon decay of a light pseudoscalar axion-like particle, $a$, in radiative $J/\psi$ decays, using 10 billion $J/\psi$ events collected with the BESIII detector. We find no evidence of a signal and set upper limits at the $95\%$ confidence level on the product branching fraction $\mathcal{B}(J/\psi \to \gamma a) \times \mathcal{B}(a \to

Christian Kuehn, Chiara Piazzola, Elisabeth Ullmann

In this work we consider the Allen--Cahn equation, a prototypical model problem in nonlinear dynamics that exhibits bifurcations corresponding to variations of a deterministic bifurcation parameter. Going beyond the state-of-the-art, we introduce a random coefficient in the linear reaction part of the equation, thereby accounting for random, spatially-hetero

Elisa Rubegni, Omran Ayoub, Stefania Maria Rita Rizzo, Marco Barbero

The widespread use of Artificial Intelligence-based tools in the healthcare sector raises many ethical and legal problems, one of the main reasons being their black-box nature and therefore the seemingly opacity and inscrutability of their characteristics and decision-making process. Literature extensively discusses how this can lead to phenomena of over-rel

Wei Bu, Sean Seet

We propose a systematic approach to celestial holography in massless theories beginning by studying the implications of properly incorporating field configurations built using the eigenstates of central interest: massless conformal primary wavefunctions that diagonalize the dilatation generator. Due to their singular behaviour on the locus $k\cdot x=0$, they

Joachim Winther Pedersen, Erwan Plantec, Eleni Nisioti, Milton Montero

Artificial neural networks used for reinforcement learning are structurally rigid, meaning that each optimized parameter of the network is tied to its specific placement in the network structure. It also means that a network only works with pre-defined and fixed input- and output sizes. This is a consequence of having the number of optimized parameters being

Huiyang Zhang, Shuokai Yan, Qinghua Zhang

This paper focuses on the global solvability for the Boussinesq system with fractional Laplacian $(-\Delta)^{\alpha}$ in $\mathbb{R}^{n}$ for $n\geq3$. It proves the existence of a small positive number $\varepsilon=\varepsilon(n,\alpha)$ such that for each $0<T<\infty$, if $\frac{1}{2}<\alpha<\frac{2+n}{4}$ and $\|u_{0}\|_{\dot{H}^{s_{0}}}+T^{1/2}\|\theta_{

Nagullas KS, Vivekanand. V, Narayana Darapaneni, Anwesh R P

Introduction: Automated Lung X-Ray Abnormality Detection System is the application which distinguish the normal x-ray images from infected x-ray images and highlight area considered for prediction, with the recent pandemic a need to have a non-conventional method and faster detecting diseases, for which X ray serves the purpose. Obectives: As of current situ

David Kerr, Grigoris Kopsacheilis, Spyridon Petrakos

We show that, for every minimal action of a countably infinite discrete group on a compact metrizable space, if the extreme boundary of the simplex of invariant Borel probability measures is closed and has finite covering dimension then the action has the small boundary property.