December 2024 arXiv papers — page 25

Hyeonseok Moon, Jaehyung Seo, Seungyoon Lee, Chanjun Park

One of the key strengths of Large Language Models (LLMs) is their ability to interact with humans by generating appropriate responses to given instructions. This ability, known as instruction-following capability, has established a foundation for the use of LLMs across various fields and serves as a crucial metric for evaluating their performance. While nume

Shuo Wang, Chihang Wang, Jia Gao, Zhen Qi

This study proposes a knowledge distillation algorithm based on large language models and feature alignment, aiming to effectively transfer the knowledge of large pre-trained models into lightweight student models, thereby reducing computational costs while maintaining high model performance. Different from the traditional soft label distillation method, thi

Ali Akbar Estaji, Maryam Taha

Let $\mathcal C_{c}(L):= \{\alpha\in \mathcal{R}(L) \mid R_{\alpha} \, \text{ is a countable subset of } \, \mathbb R \}$, where $R_\alpha:=\{r\in\mathbb R \mid {\mathrm{coz}}(\alpha-r)\neq\top\}$ for every $\alpha\in\mathcal R (L).$ By using idempotent elements, it is going to prove that ${{\mathrm{Coz}}}_c[L]:= \{{\mathrm{coz}}(\alpha) \mid \alpha\in\mathc

Simon Lyakhovich, Nikita Sinelnikov

We consider a classical field theory whose equations of motion follow from the least action principle, but the class of admissible trajectories is restricted by differential equations. The key element of the proposed construction is the complete gauge symmetry of these additional equations. The unfree variation of the trajectories reduces to the infinitesima

Glenn Bruda

Defining a family of recurrences, we generalize Comtet's formula for the generating function of the enumeration of indecomposable permutations. Consequently, we generalize Panaitopol's asymptotic expansion for the prime counting function, obtaining asymptotic expansions salient to the first Hardy-Littlewood conjecture.

Jin Heo, Vic Wang, Ketan Bhardwaj, Ada Gavrilovska

In distributed multimedia applications, content is often delivered to users in a degraded form due to network-induced lossy compression. Real-time and interactive use cases like cloud gaming, which render content on the fly, require low latency and are hosted at resource-constrained edge servers. We present a new insight: when rendered content is delivered o

Murad Ali, Shaaban Khalil, Stefano Moretti, Shoaib Munir

We investigate the phenomenology of scalar diquarks with sub-TeV masses within the framework of the $E_6$ Supersymmetric Standard Model (E$_6$SSM) at the Large Hadron Collider (LHC). Focusing on the lightest of the six diquarks predicted by the model, we select some representative low masses for them in a parameter space region consistent with experimental c

Zainab Malik

Recently, a correspondence between quasinormal modes and grey-body factors of black holes has been established. This correspondence is known to be exact in the eikonal regime for a large class of asymptotically flat black holes and approximate when the multipole number \( \ell \) is small. In this work, we demonstrate that there exists a regime where the cor

Haoyang Li, Yiming Li, Anxin Tian, Tianhao Tang

Large Language Models (LLMs) have revolutionized a wide range of domains such as natural language processing, computer vision, and multi-modal tasks due to their ability to comprehend context and perform logical reasoning. However, the computational and memory demands of LLMs, particularly during inference, pose significant challenges when scaling them to re

Mansour El Alami, Nouhaila Innan, Muhammad Shafique, Mohamed Bennai

As financial fraud becomes increasingly complex, effective detection methods are essential. Quantum Machine Learning (QML) introduces certain capabilities that may enhance both accuracy and efficiency in this area. This study examines how different quantum feature maps and ansatz configurations affect the performance of three QML-based classifiers, the Varia

Kai Yan, Kangjie Chu, Peng Hua, Pengbo Wei

Elastocaloric cooling with shape memory alloys (SMAs) is emerging as a promising candidate for next-generation, environmentally friendly refrigeration. However, its development is hindered by the large driving force and low efficiency associated with uniaxial loading modes. In response, we present an innovative elastocaloric air cooling approach that utilize

Junjie Zhang, Zhimin Zong, Lin Gu, Shenghan Su

The evolution of colour vision is captivating, as it reveals the adaptive strategies of extinct species while simultaneously inspiring innovations in modern imaging technology. In this study, we present a simplified model of visual transduction in the retina, introducing a novel opsin layer. We quantify evolutionary pressures by measuring machine vision reco

Srinivas Sagar, Athira Subhash, Chen-Feng Liu, Ahmed Elzanaty

Promising technologies such as massive multiple-input and multiple-output, reconfigurable intelligent reflecting surfaces, non-terrestrial networks, millimetre wave communication, ultra-reliable lowlatency communication are envisioned as the enablers for next-generation (NG) networks. In contrast to conventional communication systems meeting specific average

DeepSeek-AI, Aixin Liu, Bei Feng, Bing Xue

We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers

Seong Jin Lee, Will Wei Sun, Yufeng Liu

Reinforcement learning from human feedback (RLHF) has become a cornerstone for aligning large language models with human preferences. However, the heterogeneity of human feedback, driven by diverse individual contexts and preferences, poses significant challenges for reward learning. To address this, we propose a Low-rank Contextual RLHF (LoCo-RLHF) framewor

Dmitry K. Demskoi

For the finite (non-periodic) systems obtained from a lattice introduced by Ferapontov and independently by Shabat and Yamilov, we present a quadrature-free general solution and a recurrent formula for the characteristic integrals. The derivation of these formulae relies on the underlying determinantal equations. We illustrate the results using a two-compone

Yuanpeng He, Wenjie Song, Lijian Li, Tianxiang Zhan

Capturing feature information effectively is of great importance in the field of computer vision. With the development of convolutional neural networks (CNNs), concepts like residual connection and multiple scales promote continual performance gains in diverse deep learning vision tasks. In this paper, we propose a novel CNN architecture that it consists of

Inasa Nakamura, Jumpei Yasuda

A knitted surface is a surface with or without closed components smoothly properly embedded in $D^2 \times B^2$, which is a generalization of a braided surface. A knitted surface is called a 2-dimensional knit if its boundary is the closure of a trivial braid. From a 2-dimensional knit $S$, we obtain a surface-link in $\mathbb{R}^4$ by taking the closure of

M. I. Naher, M. Montasir, M. Y. H. Khan, M. A. Ali

The physical properties and their pressure dependence of recently synthesized Ti2AlX (X = B, C, and N) MAX phases are investigated for the very first time applying density functional theory (DFT).

Masaki Okada

We show that the Mathieu groups $M_{24}$ and $M_{23}$ in the isometry group of the odd Leech lattice do not lift to subgroups of the automorphism group of its lattice vertex operator (super)algebra. In other words, the subgroups $2^{24}.M_{24}$ and $2^{23}.M_{23}$ of the automorphism group of the odd Leech lattice vertex operator algebra are non-split extens

Imteaz Rahaman, Botong Li, Bobby Duersch, Hunter D. Ellis

Germanium dioxide (r-GeO2) is an emerging new ultrawide bandgap (UWBG) semiconductor with significant potential for power electronics, thanks to its large-size substrate compatibility and ambipolar doping capability. However, phase segregation during metal-organic chemical vapor deposition (MOCVD) on substrates like r-TiO2 has posed a significant barrier to

Tianhao Zeng, Ran Li, Dingshi Li

This article addresses the issue of uniform measure attractors for non-autonomous McKean-Vlasov stochastic reaction-diffusion equations defined on unbounded thin domains. Initially, the concept of uniform measure attractors is recalled, and thereafter, the existence and uniqueness of such attractors are demonstrated. Uniform tail estimates are employed to es

Hai-Jun Li

The recently proposed dark dimension scenario reveals that axions can be localized on the Standard Model brane, thereby predicting the quantum chromodynamics (QCD) axion decay constant from the Weak Gravity Conjecture: $f_a\lesssim M_5 \sim 10^{9}-10^{10}\, \rm GeV$, where $M_5$ is the five-dimensional Planck mass. When combined with observational lower boun

Chuan-Shen Hu, Xiang Liu, Kelin Xia

Normal mode analysis (NMA) provides a mathematical framework for exploring the intrinsic global dynamics of molecules through the definition of an energy function, where normal modes correspond to the eigenvectors of the Hessian matrix derived from the second derivatives of this function. The energy required to 'trigger' each normal mode is proportional to t

Chathura Rajapakse, Wathsala Ariyarathna, Shanmugalingam Selvakan

This study investigates Sri Lankan ICT teachers' readiness to teach AI in schools, focusing on self-efficacy. A survey of over 1,300 teachers assessed their self-efficacy using a scale developed based on Bandura's theory. PLS-SEM analysis revealed that teachers' self-efficacy was low, primarily influenced by emotional and physiological states and imaginary e

Alberto Maté, Mariella Dimiccoli

This paper proposes a method for long-term action anticipation (LTA), the task of predicting action labels and their duration in a video given the observation of an initial untrimmed video interval. We build on an encoder-decoder architecture with parallel decoding and make two key contributions. First, we introduce a bi-directional action context regularize

Jiaxin Gao, Wenbo Hu, Yuntian Chen

Revisiting PCA for Time Series Reduction in Temporal Dimension; Jiaxin Gao, Wenbo Hu, Yuntian Chen; Deep learning has significantly advanced time series analysis (TSA), enabling the extraction of complex patterns for tasks like classification, forecasting, and regression. Although dimensionality reduction has traditionally focused on the variable space-achie

Chen Li, Yoshihiro Yamanishi

De novo generation of hit-like molecules is a challenging task in the drug discovery process. Most methods in previous studies learn the semantics and syntax of molecular structures by analyzing molecular graphs or simplified molecular input line entry system (SMILES) strings; however, they do not take into account the drug responses of the biological system

Da-Wei Wang, Ling Zhou, Yu-xi Liu

We study an adiabatic topological passage of two Su-Schrieffer-Heeger (SSH) chains mediated by a giant atom. When two finite SSH chains are in the topological phase and the frequency of the giant atom is equal to the center frequency of the SSH chains, the system is reduced to a subsystem that describes the coupling of a giant atom to the edge states of two

Xuan Li, Tingyi Ruan, Yankaiqi Li, Quanchao Lu

This paper proposes a frequent itemset mining algorithm based on the Boolean matrix method, aiming to solve the storage and computational bottlenecks of traditional frequent pattern mining algorithms in high-dimensional and large-scale transaction databases. By representing the itemsets in the transaction database as Boolean matrices, the algorithm uses Bool

Yuanpeng He, Lijian Li, Tianxiang Zhan, Wenpin Jiao

Weakly supervised temporal action localization (WS-TAL) is a task of targeting at localizing complete action instances and categorizing them with video-level labels. Action-background ambiguity, primarily caused by background noise resulting from aggregation and intra-action variation, is a significant challenge for existing WS-TAL methods. In this paper, we

Shu Zhao, Tan Yu, Xiaoshuai Hao, Wenchao Ma

Deep hashing has been widely used for large-scale approximate nearest neighbor search due to its storage and search efficiency. However, existing deep hashing methods predominantly rely on abundant training data, leaving the more challenging scenario of low-resource adaptation for deep hashing relatively underexplored. This setting involves adapting pre-trai

Zhong-Hua Zhang, Xu-Guang Huang, Francesco Becattini, Xin-Li Sheng

We derive expressions for the vector and tensor components of the spin polarization of massive vector bosons at local thermodynamic equilibrium up to second order in the space-time gradients of the thermodynamic fields pertaining to the canonical stress-energy tensor and spin tensor of the free Proca field. A set of Feynman rules is devised to calculate the

Jinchao Huang, Sibo Wang

This paper addresses the Poisson $\pi$ps sampling problem, a topic of significant academic interest in various domains and with practical data mining applications, such as influence maximization. The problem includes a set $\mathcal{S}$ of $n$ elements, where each element $v$ is assigned a weight $w(v)$ reflecting its importance. The goal is to generate a ra

Chenxu Liu, Karl Gebhardt, Erin Mentuch Cooper, Dustin Davis

We present the Active Galactic Nuclei (AGN) catalog from the fourth data release (HDR4) of the Hobby-Eberly Telescope Dark Energy Experiment Survey (HETDEX). HETDEX is an untargeted spectroscopic survey. HDR4 contains 345,874 Integral Field Unit (IFU) observations from January 2017 to August 2023 covering an effective area of 62.9 deg2. With no imaging pre-s

Bi'an Du, Wei Hu, Renjie Liao

Consistency Models (CMs) have significantly accelerated the sampling process in diffusion models, yielding impressive results in synthesizing high-resolution images. To explore and extend these advancements to point-cloud-based 3D shape generation, we propose a novel Multi-scale Latent Point Consistency Model (MLPCM). Our MLPCM follows a latent diffusion fra

Jiangwei Ren, Xingyu Jiang, Zizhuo Li, Dingkang Liang

Image matching for both cross-view and cross-modality plays a critical role in multimodal perception. In practice, the modality gap caused by different imaging systems/styles poses great challenges to the matching task. Existing works try to extract invariant features for specific modalities and train on limited datasets, showing poor generalization. In this

Yongli Hou, Yanqiu Wang

We propose a boundary value correction method for the Brezzi-Douglas-Marini mixed finite element discretization of the Darcy flow with non-homogeneous Neumann boundary condition on 2D curved domains. The discretization is defined on a body-fitted triangular mesh, i.e. the boundary nodes of the mesh lie on the curved physical boundary. However, the boundary e

Félix del Teso, Julio D. Rossi

We extend the classical mean value property for the Laplacian operator to address a nonlinear and non-homogeneous problem related to the $p$-Laplacian operator for $p>2$. Specifically, we characterize viscosity solutions to the $p$-Laplace equation $\Delta_p u:=\nabla\cdot(|\nabla u|^{p-2} \nabla u) = f$ with a nontrivial right-hand side $f$, through novel a

Benjamin Biggs, Daniel J. Stilwell, Harun Yetkin, James McMahon

We present the results of experiments performed using a team of small autonomous underwater vehicles (AUVs) to determine the location of an isobath. The primary contributions of this work are (1) the development of a novel objective function for level set estimation that utilizes a rigorous assessment of uncertainty, and (2) a description of the practical ch

Nailya Ganiyeva, Bruno J. Barros, Álvaro de la Cruz-Dombriz, Francisco S. N. Lobo

In this work, we focus on the dynamics of a massive one-form field, \textbf{B}, often referred to simply as a vector field, that is minimally coupled to standard Einstein gravity. In the framework of four-dimensional spacetimes, the theory of a massive one-form propagates three massive vector degrees of freedom. The inclusion of a self-interacting potential

Tomonari Mizoguchi, Hiroyasu Matsuura, Masao Ogata

We study the orbital magnetic susceptibility of tilted massless Dirac fermions in two dimensions. It is well-known that the type-I massless Dirac fermions exhibit divergingly-large diamagnetic susceptibility, whereas less is known about the types II and III cases. We first clarify that the orbital magnetic susceptibility is vanishing for the types II and III

Jiaqi Fan, Jianhua Wu, Jincheng Gao, Jianhao Yu

Multimodal large language models (MLLMs) have shown satisfactory effects in many autonomous driving tasks. In this paper, MLLMs are utilized to solve joint semantic scene understanding and risk localization tasks, while only relying on front-view images. In the proposed MLLM-SUL framework, a dual-branch visual encoder is first designed to extract features fr

Samuel J. Harris

We prove that, to each synchronous non-local game $\mathcal{G}=(I,O,\lambda)$ with $|I|=n$ and $|O|=m \geq 3$, there is an associated graph $G_{\lambda}$ for which approximate winning strategies for the game $\mathcal{G}$ and the $3$-coloring game for $G_{\lambda}$ are preserved. That is, using a similar graph to previous work of the author (Ann. Henri Poinc

Xuefeng Yang, Shiheng Zhang, Jian Guan, Feiyang Xiao

This study is based on the ICASSP 2025 Signal Processing Grand Challenge's Accelerometer-Based Person-in-Bed Detection Challenge, which aims to determine bed occupancy using accelerometer signals. The task is divided into two tracks: "in bed" and "not in bed" segmented detection, and streaming detection, facing challenges such as individual differences, post

Fumiyasu Makinoshima, Tatsuya Mitomi, Fumiya Makihara, Eigo Segawa

Discrete choice models are essential for modelling various decision-making processes in human behaviour. However, the specification of these models has depended heavily on domain knowledge from experts, and the fully automated but interpretable modelling of complex human behaviours has been a long-standing challenge. In this paper, we introduce the different

Xiong Hu, Xuebing Hao, Baode Li

Let $0<\alpha<1$ and $\frac{1}{q}=1-\alpha$. We first obtain that the function $\omega :\mathbb{Z} \rightarrow (0,\infty)$ belongs to weight class of $\mathcal{A} (1,q)(\mathbb{Z})$ if and only if discrete fractional maximal operator $M_{\alpha}$ or discrete Riesz potential $I_\alpha$ is bounded from $l_{\omega}^{1}(\mathbb{Z})$ to $l_{\omega^q}^{q,weak}(\ma

Max T. M. Ng, Hani S. Mahmassani, Draco Tong, Omer Verbas

Shared autonomous vehicles (SAVs) bring competition to traditional transit services but redesigning multimodal transit network can utilize SAVs as feeders to enhance service efficiency and coverage. This paper presents an optimization framework for the joint multimodal transit frequency and SAV fleet size problem, a variant of the transit network frequency s

Shi-Zheng Yang, Xin-Qing Xie, Shi Pu, Jian-Hua Gao

We compute the $00$ element of the spin density matrix, denoted as $\rho_{00}$ and called the spin alignment, up to the second order of the gradient expansion in local equilibrium by Zubarev's approach. In the first order, we obtain $\rho_{00}=1/3$, meaning that the contributions from thermal vorticity and shear stress tensor are vanishing. The non-vanishing

Hang Xu, Kaihong Lu, Yu-Long Wang, Qixin Zhu

In this paper, the mixed equilibrium problem with coupled inequality constraints in dynamic environments is solved by employing a multi-agent system, where each agent only has access to its own bifunction, its own constraint function, and can only communicate with its immediate neighbors via a time-varying digraph. At each time, the goal of agents is to coop

J. M. Hall, M. G. Guenza

We present a generalized Einstein relation for the friction coefficients associated with an underlying memory kernel in terms of observable time correlation functions. There is considerable freedom in the correlations involved, and this allows the expression to be tailored to the particular system to achieve numerical stability. We demonstrate this by recove

Haruki Kono

Extending the results of Nardi (2015), this note establishes an existence and uniqueness result for second-order uniformly elliptic PDEs in divergence form with Neumann boundary conditions. A Schauder estimate is also derived.

Kiran Koshy Thekumparampil, Gaurush Hiranandani, Kousha Kalantari, Shoham Sabach

We study learning of human preferences from a limited comparison feedback. This task is ubiquitous in machine learning. Its applications such as reinforcement learning from human feedback, have been transformational. We formulate this problem as learning a Plackett-Luce model over a universe of $N$ choices from $K$-way comparison feedback, where typically $K

Nima Moradi, Niloufar Mirzavand Boroujeni, Navid Aftabi, Amin Aslani

Multi-echelon parcel delivery systems using electric vehicles (EVs) are crucial for managing urban logistics complexity and promoting sustainability. In multi-echelon systems, particularly within two-stage systems, larger vehicles transport parcels from a central depot to satellite hubs, where smaller EVs pick up the parcels and carry out last-mile deliverie

Jianshuo Dong, Ziyuan Zhang, Qingjie Zhang, Tianwei Zhang

Auto-regressive large language models (LLMs) have yielded impressive performance in many real-world tasks. However, the new paradigm of these LLMs also exposes novel threats. In this paper, we explore their vulnerability to inference cost attacks, where a malicious user crafts Engorgio prompts to intentionally increase the computation cost and latency of the

Ulrich Heinz, Björn Schenke

We review the history and success of applying relativistic hydrodynamics to high-energy heavy-ion collisions. We emphasize the important role hydrodynamics has played in the discovery of the quark-gluon plasma and its quantitative exploration.

Liad Lea Didi, Tomer Gafni, Kobi Cohen

We address the problem of searching for a change point in an anomalous process among a finite set of M processes. Specifically, we address a composite hypothesis model in which each process generates measurements following a common distribution with an unknown parameter (vector). This parameter belongs to either a normal or abnormal space depending on the cu

Eugene Choi, Julian Rodriguez, Edmund Young

Domain adaptation is an active area of research driven by the growing demand for robust machine learning models that perform well on real-world data. Adversarial learning for deep neural networks (DNNs) has emerged as a promising approach to improving generalization ability, particularly for image classification. In this paper, we implement a specific advers

Andrew M. Lydner

Due to the multidisciplinary nature of wearable technology, the industry faces potential limitations in innovation. The wearable technology industry is still in its infancy and increased applicable use faces stagnation despite the plethora of technologies that have been largely wrist worn. This could be a result of the lack of multidisciplinary expert knowle

Kaiyu Zhang, Wladimir Zholobenko, Andreas Stegmeir, Konrad Eder

Plasma turbulence in the edge of magnetic confinement devices is customarily treated as full-$f$ due to large fluctuations. For computational efficiency, field-aligned coordinates are employed, separating the magnetic field into equilibrium $B_0$ and delta-f perturbations which are handled by the magnetic flutter operators. Evolving the full-$f$ pressure wit

Kazumasa Nomura, Paul Terwilliger

In this paper, we describe the nucleus of the Johnson graph $\Gamma = J(N,D)$ with $N > 2D$. Let $X$ denote the vertex set of $\Gamma$. Let $A \in \text{Mat}_X({\mathbb C})$ denote the adjacency matrix of $\Gamma$. Let $\{E_i\}_{i=0}^D$ denote the $Q$-polynomial ordering of the primitive idempotents of $A$. Fix $x \in X$, and consider the corresponding dual

Ulises Hernandez-Vera

Recently obtained black hole solutions within the framework of beyond-Horndeski theories, which have the advantage of featuring primary hair, are generalized in the presence of two axionic fields. In order to induce a momentum dissipation, the axionic field solutions are homogeneously distributed along the horizon coordinates of the planar base manifold. We

Felipe Galarce, Diego Rivera, Douglas Pacheco, Alfonso Caiazzo

This article presents and assesses a framework for estimating temperature fields in real time for food-freezing applications, significantly reducing computational load while ensuring accurate temperature monitoring, which represents a promising technological tool for optimizing and controlling food engineering processes. The strategy is based on (i) a mathem

Junoh Jung, Rutvij Bhagwat, Aaron Towne

We develop an optimal resolvent-based estimator and controller to predict and attenuate unsteady vortex shedding fluctuations in the laminar wake of a NACA 0012 airfoil at an angle of attack of 6.5 degrees, chord-based Reynolds number of 5000, and Mach number of 0.3. The resolvent-based estimation and control framework offers several advantages over standard

Shengyi Wang, Mengying Pan, Andrew W. Appel

To prove the functional correctness of a P4 program running in a programmable network switch or smart NIC, prior works have focused mainly on verifiers for the &#34;control block&#34; (match-action pipeline). But to verify that a switch handles packets according to a desired specification, proving the control block is not enough. We demonstrate a new compreh

Jorge Antonio Cruz Chapital, Tatsuya Goto, Yusuke Hayashi, Takashi Yamazoe

We consider combining the definition of a cardinal invariant and the notion of an infinite game. We focus on the splitting number $\mathfrak{s}$ since the corresponding cardinal invariants behave in an interesting way. We introduce three kinds of games as reasonable realizations of the combination of the notions of splitting and infinite games. Then, we cons

Zhaonan Dong, Emmanuil H. Georgoulis, Philip J. Herbert

We propose a new stabilised finite element method for the classical Kolmogorov equation. The latter serves as a basic model problem for large classes of kinetic-type equations and, crucially, is characterised by degenerate diffusion. The stabilisation is constructed so that the resulting method admits a \emph{numerical hypocoercivity} property, analogous to

Naihuan Jing, Li Zheng, Jian Zhang

We introduce the quantum Berezinian for the quantum affine superalgebra $\mathrm{U}_q(\widehat{\mathfrak{gl}}_{M|N})$ and show that the coefficients of the quantum Berezinian belong to the center of $\mathrm{U}_q(\widehat{\gl}_{M|N})$. We also construct another family of central elements which can be expressed in the quantum Berezinian by a Liouville-type th

Ellis Solaiman, Jorge Robins

Q&A websites rely on user-generated responses, with incentives such as reputation scores or monetary rewards often offered. While some users may find it intrinsically rewarding to assist others, studies indicate that payment can improve the quality and speed of answers. However, traditional payment processors impose minimum thresholds that many Q&A inquiries

Md Isfakul Anam, Tuyen Vu, Jianhua Zhang

As power systems become more complex with the continuous integration of intelligent distributed energy resources (DERs), new risks and uncertainties arise. Consequently, to enhance system resiliency, it is essential to account for various uncertain events when implementing the optimization problem for the energy management system (EMS). This paper presents a

David A. Broadway, Mykhailo Flaks, Adrien E. E. Dubois, Patrick Maletinsky

Spatial imaging of magnetic stray fields from magnetic materials is a useful tool for identifying the underlying magnetic configurations of the material. However, transforming the magnetic image into a magnetization image is an ill-poised problem, which can result in artefacts that limit the inferences that can be made on the material under investigation. In

Sean Cox

Deconstructibility is an often-used sufficient condition on a class $\mathcal{C}$ of modules that allows one to carry out homological algebra \emph{relative to $\mathcal{C}$}. The principle \textbf{Maximum Deconstructibility (MD)} asserts that a certain necessary condition for a class to be deconstructible is also sufficient. MD implies, for example, that th

R. Della Picca, J. M. Randazzo, S. D. López, M. F. Ciappina

We theoretically study atomic laser-assisted photoelectric emission (LAPE) beyond the electric dipole approximation. We present a theoretical description for first-order nondipole corrections ($O(c^{-1})$ where $c$ is the speed of light) to the nonrelativistic description of the laser-atom interaction for a strong circularly polarized infrared (IR) laser fie

Jianhai Bao, Mateusz B. Majka, Jian Wang

As a well-known fact, the classical Euler scheme works merely for SDEs with coefficients of linear growth. In this paper, we study a general framework of modified Euler schemes, which is applicable to SDEs with super-linear drifts and encompasses numerical methods such as the tamed Euler scheme and the truncated Euler scheme. On the one hand, by exploiting a

Shuang Bai, Zhao Liu, Diyi Cheng, Bingyu Lu

To unlock the full potential of lithium metal batteries, a deep understanding of lithium metal reactivity and its solid electrolyte interphase is essential. Correlative imaging, combining focused ion beam and electron microscopy offers a powerful approach for multi-scale characterization. However, the extreme reactivity of lithium metal and its SEI presents

Yi Du

Let $\omega$ be a Kahler form on $M$, which is a torus $T^4$, a $K3$ surface or an Enriques surface, let $M\#n\overline{\mathbb{CP}^2}$ be $n-$point Kahler blowup of $M$. Suppose that $\kappa=[\omega]$ satisfies certain irrationality condition. Applying techniques related to deformation of complex objects, we extend the guage-theoretic invariant on closed Ka

Liang Yu, Haoyu Fang, Goran Strbac, Dawei Qiu

Ensuring resilience in multi-energy systems (MESs) has become increasingly urgent and challenging due to the growing frequency and severity of extreme events, such as natural disasters, extreme weather, and cyber-physical attacks. Among the various approaches to enhancing MES resilience, hydrogen integration offers significant potential in cross-temporal, cr

Marco Bertola, Alexander Tovbis

We consider the family of (poly)continua $\K$ in the upper half-plane ${\mathbb H} $ that contain a preassigned finite {\it anchor} set $E\in\mathbb H$. For a given harmonic external field we define a Dirichlet energy functional $\mathcal I(\mathcal K)$ and show that within each ``connectivity class'' of the family, there exists a minimizing compact $\mathca

Adamantios Ntakaris, Gbenga Ibikunle

High-frequency trading (HFT) has transformed modern financial markets, making reliable short-term price forecasting models essential. In this study, we present a novel approach to mid-price forecasting using Level 1 limit order book (LOB) data from NASDAQ, focusing on 100 U.S. stocks from the S&P 500 index during the period from September to November 2022. E

Martin Rosenlyst

We show that the mass of a self-interacting dark matter candidate, specifically a Dirac fermion, can be generated by composite dynamics, with a light scalar mediator emerging alongside the Higgs itself as composite particles. These novel models naturally explain the halo structure problems at various scales and alleviates the Standard Model naturalness probl

Gopi Raju Matta, Reddypalli Trisha, Kaushik Mitra

Novel view synthesis has been greatly enhanced by the development of radiance field methods. The introduction of 3D Gaussian Splatting (3DGS) has effectively addressed key challenges, such as long training times and slow rendering speeds, typically associated with Neural Radiance Fields (NeRF), while maintaining high-quality reconstructions. In this work (Be

Bhubanjyoti Bhattacharya, Suneth Jayawardana, Nausheen R. Shah

The Standard Model (SM) of particle physics fails to explain the observed hierarchy in fermion masses or the origin of fermion-flavor structure. We construct a model to explain these observations in the quark sector. We introduce a spectrum of new particles consisting of six of each -- massive singlet vector-like quarks (VLQs), singlet scalars, and SU(2)-dou

François Gay-Balmaz, Meng Wu

We derive variational integrators for stochastic Hamiltonian systems on Lie groups using a discrete version of the stochastic Hamiltonian phase space principle. The structure-preserving properties of the resulting scheme, such as symplecticity, preservation of the Lie-Poisson structure, preservation of the coadjoint orbits, and conservation of Casimir functi

Huihui Chen, Darinka Dentcheva, Yang Lin, Gregory J. Stock

This paper focuses on vector-valued composite functionals, which may be nonlinear in probability. Our primary goal is to establish central limit theorems for these functionals when mixed estimators are employed. Our study is relevant to the evaluation and comparison of risk in decision-making contexts and extends to functionals that arise in machine learning

Antonio Álvarez-López, Borjan Geshkovski, Domènec Ruiz-Balet

We study an approximate controllability problem for the continuity equation and its application to constructing transport maps with normalizing flows. Specifically, we construct time-dependent controls $\theta=(w, a, b)$ in the vector field $x\mapsto w(a^\top x + b)_+$ to approximately transport a known base density $\rho_{\mathrm{B}}$ to a target density $\

Ernest Greene

There is perceptual and physiological evidence that the retina registers and signals luminance and luminance contrast using dual-channel mechanisms. This process begins in the retina, wherein the luminance of a uniform zone and differentials of luminance in neighboring zones determine the degree of brightness or darkness of the zones. The neurons that proces

Elisa Postinghel, Artie Prendergast-Smith

We introduce bilinear secant varieties and joins of subvarieties of products of projective spaces, as a generalisation of the classical secant varieties and joins of projective varieties. We show that the bilinear secant varieties of certain rational normal curves of $\mathbb P^n \times \mathbb P^{n+1}$ play a central role in the study of the birational geom

Mengxin Wang, Dennis J. Zhang, Heng Zhang

Large Language Models (LLMs) have transformed artificial intelligence by excelling in complex natural language processing tasks. Their ability to generate human-like text has opened new possibilities for market research, particularly in conjoint analysis, where understanding consumer preferences is essential but often resource-intensive. Traditional survey-b

Leonardo Gabriel Ferreira Rodrigues, Danilo Ferreira da Silva, Larissa Ferreira Rodrigues, João Fernando Mari

Coronavirus Disease 2019 (COVID-19) pandemic rapidly spread globally, impacting the lives of billions of people. The effective screening of infected patients is a critical step to struggle with COVID-19, and treating the patients avoiding this quickly disease spread. The need for automated and scalable methods has increased due to the unavailability of accur

Jiaao Chen, Diyi Yang

We present Dynamic Skill Adaptation (DSA), an adaptive and dynamic framework to adapt novel and complex skills to Large Language Models (LLMs). Compared with previous work which learns from human-curated and static data in random orders, we propose to first automatically generate and organize the training data by mimicking the learning pathways of human and

Rodrigo Moreira, Larissa Ferreira Rodrigues, Pedro Frosi Rosa, Flávio de Oliveira Silva

The network traffic classification allows improving the management, and the network services offer taking into account the kind of application. The future network architectures, mainly mobile networks, foresee intelligent mechanisms in their architectural frameworks to deliver application-aware network requirements. The potential of convolutional neural netw

Brian C. Kiedrowski, Emily H. Vu

The correspondence between the telegraph random process and transport within a binary stochastic Markovian mixture is established. This equivalence is used to derive the distribution function for the transit length, defined as the distance a particle moving along a straight-line trajectory travels through a specific material zone within the random mixture. A

Mukul Bhattacharya, Kohta Murase, Kazumi Kashiyama

Among more than 1000 known fast radio bursts (FRBs), only five sources - FRBs 20121102A, 20190520B, 20201124A, 20240114A and 20190417A - have confirmed associations with persistent radio sources (PRS). The observed quasi-steady emission is consistent with synchrotron radiation from a composite of magnetar wind nebula (MWN) and supernova (SN) ejecta. Using a

Arthur D. Adams, Christopher Colose, Aronne Merrelli, Margaret Turnbull

Earth-like planets in the circumstellar habitable zone (HZ) may have dramatically different climate outcomes depending on their spin-orbit parameters, altering their habitability for life as we know it. We present a suite of 93 ROCKE-3D general circulation models (GCMs) for planets with the same surface conditions and average annual insolation as Earth, but

Marcel Guzman, Felipe Martins, Menachem Stern, Andrea J. Liu

In physical networks trained using supervised learning, physical parameters are adjusted to produce desired responses to inputs. An example is electrical contrastive local learning networks of nodes connected by edges that are resistors that adjust their conductances during training. When an edge conductance changes, it upsets the current balance of every no

Shaozhi Li, M Sabbir Salek, Mashrur Chowdhury, Yao Wang

Although quantum machine learning has shown great promise, the practical application of quantum computers remains constrained in the noisy intermediate-scale quantum era. To take advantage of quantum machine learning, we investigate the underlying mathematical principles of these quantum models and find that the quantum neural network with amplitude encoding

Yu Qiao, Apurba Adhikary, Kitae Kim, Eui-Nam Huh

Federated learning (FL) is a distributed training technology that enhances data privacy in mobile edge networks by allowing data owners to collaborate without transmitting raw data to the edge server. However, data heterogeneity and adversarial attacks pose challenges to develop an unbiased and robust global model for edge deployment. To address this, we pro

Chen Shen, Siamak Attarian, Yixuan Zhang, Hongbin Zhang

Molten salts are crucial for clean energy applications, yet exploring their thermophysical properties across diverse chemical space remains challenging. We present the development of a machine learning interatomic potential (MLIP) called SuperSalt, which targets 11-cation chloride melts and captures the essential physics of molten salts with near-DFT accurac

Xiangyi Zhu, Yizhe Zhu

We establish central limit theorems (CLTs) for the linear spectral statistics of the adjacency matrix of inhomogeneous random graphs across all sparsity regimes, providing explicit covariance formulas under the assumption that the variance profile of the random graphs converges to a graphon limit. Two types of CLTs are derived for the (non-centered) adjacenc

Sang-gil Lee, Zhifeng Kong, Arushi Goel, Sungwon Kim

Recent years have seen significant progress in Text-To-Audio (TTA) synthesis, enabling users to enrich their creative workflows with synthetic audio generated from natural language prompts. Despite this progress, the effects of data, model architecture, training objective functions, and sampling strategies on target benchmarks are not well understood. With t