May 2024 arXiv papers — page 47

Cristian González-Riquelme, Diogo Oliveira e Silva

Sharp restriction theory and the finite field extension problem have both received a great deal of attention in the last two decades, but so far they have not intersected. In this paper, we initiate the study of sharp restriction theory on finite fields. We prove that constant functions maximize the Fourier extension inequality from the parabola $\mathbb{P}^

Mohammed Nowaz Rabbani Chowdhury, Meng Wang, Kaoutar El Maghraoui, Naigang Wang

The sparsely gated mixture of experts (MoE) architecture sends different inputs to different subnetworks, i.e., experts, through trainable routers. MoE reduces the training computation significantly for large models, but its deployment can be still memory or computation expensive for some downstream tasks. Model pruning is a popular approach to reduce infere

Hanwen Liang, Yuyang Yin, Dejia Xu, Hanxue Liang

The availability of large-scale multimodal datasets and advancements in diffusion models have significantly accelerated progress in 4D content generation. Most prior approaches rely on multiple image or video diffusion models, utilizing score distillation sampling for optimization or generating pseudo novel views for direct supervision. However, these method

Sergey Samsonov, Eric Moulines, Qi-Man Shao, Zhuo-Song Zhang

In this paper, we obtain the Berry-Esseen bound for multivariate normal approximation for the Polyak-Ruppert averaged iterates of the linear stochastic approximation (LSA) algorithm with decreasing step size. Moreover, we prove the non-asymptotic validity of the confidence intervals for parameter estimation with LSA based on multiplier bootstrap. This proced

Yuhki Hosoya

We consider a class of economic growth models that includes the classical Ramsey--Cass--Koopmans capital accumulation model and verify that, under several assumptions, the value function of the model is the unique viscosity solution to the Hamilton--Jacobi--Bellman equation. Moreover, we discuss a solution method for these models using differential inclusion

Aneesh Muppidi, Zhiyu Zhang, Heng Yang

A key challenge in lifelong reinforcement learning (RL) is the loss of plasticity, where previous learning progress hinders an agent's adaptation to new tasks. While regularization and resetting can help, they require precise hyperparameter selection at the outset and environment-dependent adjustments. Building on the principled theory of online convex optim

Jone Lopez de Gamiz Zearra, Conchita Martínez Pérez

We generalize to (certain) Artin groups some results previously known for right-angled Artin groups (RAAGs). First, we generalize a result by Droms, B. Servatius, and H. Servatius, and prove that the derived subgroup of an Artin group is free if and only if the group is coherent. Second, coherent Artin groups over non complete graphs split as free amalgamate

Tianyi Bai, Hao Liang, Binwang Wan, Yanran Xu

Multimodal large language models (MLLMs) enhance the capabilities of standard large language models by integrating and processing data from multiple modalities, including text, vision, audio, video, and 3D environments. Data plays a pivotal role in the development and refinement of these models. In this survey, we comprehensively review the literature on MLL

Santanu Das, Jatin Batra, Piyush Srivastava

In contemporary deep learning practice, models are often trained to near zero loss i.e. to nearly interpolate the training data. However, the number of parameters in the model is usually far more than the number of data points n, the theoretical minimum needed for interpolation: a phenomenon referred to as overparameterization. In an interesting piece of wor

Joseph DiCapua, Victor Kolyvagin

We give a classification of power series parametrizing Lubin-Tate trace compatible sequences. This proof answers a question posed in the literature by Berger and Fourquaux. Lubin-Tate trace compatible sequences are a generalization of norm compatible sequences, which arise in Iwasawa theory and local class field theory. The result we prove generalizes the in

Zhixiang Wu

We study ``change of weights'' maps between loci of the stack of $(\varphi,\Gamma)$-modules over the Robba ring with integral Hodge-Tate-Sen weights. We show that in the $\mathrm{GL}_2(\mathbb{Q}_p)$ case these maps can realize translations of $(\varphi,\Gamma)$-modules geometrically. The motivation is to investigate translations of locally analytic represen

Peitian Zhang, Zheng Liu, Shitao Xiao, Ninglu Shao

Compressing lengthy context is a critical but technically challenging problem. In this paper, we propose a new method called UltraGist, which is distinguished for its high-quality compression of lengthy context due to the innovative design of the compression and learning algorithm. UltraGist brings forth the following important benefits. Firstly, it notably

Siyou Lin, Zuoqiang Shi, Yebin Liu

Estimating consistently oriented normals for point clouds enables a number of important applications in computer graphics. While local normal estimation is possible with simple techniques like PCA, orienting them to be globally consistent has been a notoriously difficult problem. Some recent methods exploit various properties of the winding number formula to

ChungYi Lin, Shen-Lung Tung, Hung-Ting Su, Winston H. Hsu

Traditional traffic prediction, limited by the scope of sensor data, falls short in comprehensive traffic management. Mobile networks offer a promising alternative using network activity counts, but these lack crucial directionality. Thus, we present the TeltoMob dataset, featuring undirected telecom counts and corresponding directional flows, to predict dir

Colin Cooper, Alan Frieze

We consider random walks on edge coloured random graphs, where the colour of an edge reflects the cost of using it. In the simplest instance, the edges are coloured red or blue. Blue edges are free to use, whereas red edges incur a unit cost every time they are traversed.

Kritika Garg, Adrish Chakraborty, Ayon Bhattacharjee, Sandip Paul Choudhury

The article studies the different physical, vibrational, nonlinear optical, and thermodynamical properties of higher homologs 5O.m (m = 14,16) liquid crystalline compounds using density functional theory. The optimized structure of 5O.m (m= 14,16) liquid crystals was obtained by using density functional theory (DFT) with B3LYP functional and standard basis s

Qiong Nan, Qiang Sheng, Juan Cao, Beizhe Hu

Fake news detection plays a crucial role in protecting social media users and maintaining a healthy news ecosystem. Among existing works, comment-based fake news detection methods are empirically shown as promising because comments could reflect users' opinions, stances, and emotions and deepen models' understanding of fake news. Unfortunately, due to exposu

Boris Hanin, Alexander Zlokapa

We show at a physics level of rigor that Bayesian inference with a fully connected neural network and a shaped nonlinearity of the form $\phi(t) = t + \psi t^3/L$ is (perturbatively) solvable in the regime where the number of training datapoints $P$ , the input dimension $N_0$, the network layer widths $N$, and the network depth $L$ are simultaneously large.

M. I. Belishev, A. F. Vakulenko

Let ${\Omega}$ be a bounded plane domain. As is known, the spectrum $0<\lambda_1<\lambda_2\leqslant\dots$ of its Dirichlet Laplacian $L=-\Delta{\upharpoonright}[H^2({\Omega})\cap H^1_0({\Omega})]$ does not determine ${\Omega}$ (up to isometry). By this, a reasonable version of the M.Kac problem is to augment the spectrum with relevant data that provide the d

Shaheer U. Saeed, Shiqi Huang, João Ramalhinho, Iani J. M. B. Gayo

Weakly-supervised segmentation (WSS) methods, reliant on image-level labels indicating object presence, lack explicit correspondence between labels and regions of interest (ROIs), posing a significant challenge. Despite this, WSS methods have attracted attention due to their much lower annotation costs compared to fully-supervised segmentation. Leveraging re

Isaac M. Felix Fabiano M. Andrade, Cristiano F. Woellner, Douglas S. Galvao, Raphael M. Tromer

In the present work, we have carried out DFT simulations to investigate the electronic and optical properties of a porphyrin-based 2D crystal named 2D Diboron-Porphyrin (2DDP). We showed that it is possible to use strain to tune the 2DDP electronic properties (from semiconductor to metal) depending on the direction of the applied strain. 2DDP exhibits optica

James R. Beattie, Christoph Federrath, Ralf S. Klessen, Salvatore Cielo

Supersonic magnetohydrodynamic (MHD) turbulence is a ubiquitous state for many astrophysical plasmas. However, even the basic statistics for this type of turbulence remains uncertain. We present results from supersonic MHD turbulence simulations at unparalleled resolutions, with plasma Reynolds numbers of over a million. In the kinetic energy spectrum we fin

Shuvendu Roy, Elham Dolatabadi, Arash Afkanpour, Ali Etemad

We propose Consistency-guided Asynchronous Contrastive Tuning (CoACT), a novel method for continuously tuning foundation models to learn new classes in few-shot settings. CoACT consists of three key components:(i) asynchronous contrastive tuning, which learns new classes by including LoRA modules in the pre-trained encoder while enforcing consistency between

Francesco Carotenuto, Rob Fender, Alexandra J. Tetarenko, Stéphane Corbel

Relativistic discrete ejecta launched by black hole X-ray binaries (BH XRBs) can be observed to propagate up to parsec-scales from the central object. Observing the final deceleration phase of these jets is crucial to estimate their physical parameters and to reconstruct their full trajectory, with implications for the jet powering mechanism, composition and

Dmitrii Khizbullin, Eduardo Rocha de Andrade, Thanh Hau Nguyen, Matheus Pedroza Ferreira

With the recent popularity of neural networks comes the need for efficient serving of inference workloads. A neural network inference workload can be represented as a computational graph with nodes as operators transforming multidimensional tensors. The tensors can be transposed and/or tiled in a combinatorially large number of ways, some configurations lead

Dylan Cope, Peter McBurney

In many situations, communication between agents is a critical component of cooperative multi-agent systems, however, it can be difficult to learn or evolve. In this paper, we investigate a simple way in which the emergence of communication may be facilitated. Namely, we explore the effects of when agents can mimic preexisting, externally generated useful si

Sven Jandura, Laura Pecorari, Guido Pupillo

We consider stabilizer measurements for surface codes with neutral atoms and identify gate protocols that minimize logical error rates in the presence of a fundamental error source -- spontaneous emission from Rydberg states. We demonstrate that logical error rates are minimized by protocols that prevent the propagation of Rydberg leakage errors and not by p

Faical Khennoufa, Khelil Abdellatif, Ferdi Kara, Halim Yanikomeroglu

Uncrewed aerial vehicles (UAVs) have attracted recent attention for sixth-generation (6G) networks due to their low cost and flexible deployment. In order to maximize the ever-increasing data rates, spectral efficiency, and wider coverage, technologies such as reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) are adapted with

Sebastian Neef, Maath Oudeh

Unrestricted file upload (UFU) is a class of web security vulnerabilities that can have a severe impact on web applications if uploaded files are not sufficiently validated or securely handled. A review of related work shows an increased interest in finding new methods to discover such vulnerabilities. However, each publication evaluates its new vulnerabilit

Xiangjing Lai, Zhenheng Lin, Jin-Kao Hao, Qinghua Wu

Continuous p-dispersion problems with and without boundary constraints are NP-hard optimization problems with numerous real-world applications, notably in facility location and circle packing, which are widely studied in mathematics and operations research. In this work, we concentrate on general cases with a non-convex multiply-connected region that are rar

Edward Shuryak

This letter relates theoretical predictions made previously with recently announced RHIC BES-II experimental data on multi-baryon fluctuations. We conclude that new statistically significant effect is observed, and its features are in good agreement with predictions.

José Sanabria, Adolfo Pimienta, Semiramis Zambrano

In this manuscript the idea of soft convex structures is given and some of their properties are investigated. Also, soft convex sets, soft concave sets and soft convex hull operator are defined and their properties are studied. Moreover, the concepts of soft convexly derived operator and soft convex base are studied and their relationship to convex structure

Siddhant Saxena, Shounak Ghatak, Raghu Kolla, Debashis Mukherjee

Message passing on hypergraphs has been a standard framework for learning higher-order correlations between hypernodes. Recently-proposed hypergraph neural networks (HGNNs) can be categorized into spatial and spectral methods based on their design choices. In this work, we analyze the impact of change in hypergraph topology on the suboptimal performance of H

Ajay Chandra, Harprit Singh

We introduce a notion of distributional $k$-forms on $d$-dimensional manifolds which can be integrated against suitably regular $k$-submanifolds. Our approach combines ideas from Whitney's geometric integration [Whi57] with those of sewing approaches to rough integration [Gub04, FdLP06].

A. H. Nzokem

The paper describes the self-decomposable distribution and the background driving L\'evy process (BDLP) associated with the Generalized Tempered Stable (GTS) distribution. Two distributions are provided: the background driving L\'evy process (BDLP) of the GTS distribution and the self-decomposable distribution generated by the GTS distribution as BDLP. The d

G. Pantelis

A preliminary test of the software package RA is presented. The main focus of this test is to assess RA`s reasoning capabilities that are based on the formal system PECR. Particular attention is given to the finite computational resources of the real-world machine that define the environment within which programs are to be executed.

Babooshka Shavazipour, Lovisa Engberg Sundström

Sustainable forest management requires handling uncertainty introduced from various sources, considering different conflicting economic, environmental, and social objectives, and involving multiple decision-making periods. This study proposes an interactive and intuitive decision-support approach for sustainable, robust forest harvest scheduling in multiple

Armando Castañeda, Gregory Chockler, Brijesh Dongol, Ori Lahav

We present a general methodology for establishing the impossibility of implementing certain concurrent objects on different (weak) memory models. The key idea behind our approach lies in characterizing memory models by their mergeability properties, identifying restrictions under which independent memory traces can be merged into a single valid memory trace.

Konstanty Subbotko, Wojciech Jablonski, Piotr Bilinski

Neural Architecture Search (NAS) has been widely adopted to design neural networks for various computer vision tasks. One of its most promising subdomains is differentiable NAS (DNAS), where the optimal architecture is found in a differentiable manner. However, gradient-based methods suffer from the discretization error, which can severely damage the process

Hebert Pérez-Rosés

The change-making problem consists of representing a certain amount of money with the least possible number of coins, from a given, pre-established set of denominations. The greedy algorithm works by choosing the coins of largest possible denomination first. This greedy strategy does not always produce the least number of coins, except when the set of denomi

Pol Timmer, Koen Minartz, Vlado Menkovski

Crystallization processes at the mesoscopic scale, where faceted, dendritic growth, and multigrain formation can be observed, are of particular interest within materials science and metallurgy. These processes are highly nonlinear, stochastic, and sensitive to small perturbations of system parameters and initial conditions. Methods for the simulation of thes

Donsung Lee

In 1974, Birman posed the question of under what conditions a matrix with Laurent polynomial entries is in the image of the Burau representation. In 1984, Squier observed that the matrices in the image are contained in a unitary group. In 2021, Salter formulated a specific question: whether the central quotient of the Burau image group is the central quotien

Chen Ling, Zhuofeng Li, Yuntong Hu, Zheng Zhang

Textual-edge Graphs (TEGs), characterized by rich text annotations on edges, are increasingly significant in network science due to their ability to capture rich contextual information among entities. Existing works have proposed various edge-aware graph neural networks (GNNs) or let language models directly make predictions. However, they often fall short o

Dongchen Han, Ziyi Wang, Zhuofan Xia, Yizeng Han

Mamba is an effective state space model with linear computation complexity. It has recently shown impressive efficiency in dealing with high-resolution inputs across various vision tasks. In this paper, we reveal that the powerful Mamba model shares surprising similarities with linear attention Transformer, which typically underperform conventional Transform

Oliver Brock

This paper proposes a specific conceptualization of intelligence as computation. This conceptualization is intended to provide a unified view for all disciplines of intelligence research. Already, it unifies several conceptualizations currently under investigation, including physical, neural, embodied, morphological, and mechanical intelligences. To achieve

Stefaan Vaes, Lise Wouters

Several recent articles in operator algebras make a nontrivial use of the theory of measurable fields of von Neumann algebras $(M_x)_{x \in X}$ and related structures. This includes the associated field $(\text{Aut}\ M_x)_{x \in X}$ of automorphism groups and more general measurable fields of Polish groups with actions on Polish spaces. Nevertheless, a fully

Edouard F. Bonneville, Jan Beyersmann, Ruth H. Keogh, Jonathan W. Bartlett

The Fine-Gray model for the subdistribution hazard is commonly used for estimating associations between covariates and competing risks outcomes. When there are missing values in the covariates included in a given model, researchers may wish to multiply impute them. Assuming interest lies in estimating the risk of only one of the competing events, this paper

Vanshaj Khattar, Yuhao Ding, Bilgehan Sel, Javad Lavaei

Meta-reinforcement learning has widely been used as a learning-to-learn framework to solve unseen tasks with limited experience. However, the aspect of constraint violations has not been adequately addressed in the existing works, making their application restricted in real-world settings. In this paper, we study the problem of meta-safe reinforcement learni

Junfeng Jiao, Saleh Afroogh, Kevin Chen, David Atkinson

The integration of Generative Artificial Intelligence (GAI) and Large Language Models (LLMs) in academia has spurred a global discourse on their potential pedagogical benefits and ethical considerations. Positive reactions highlight some potential, such as collaborative creativity, increased access to education, and empowerment of trainers and trainees. Howe

Qizao Wang, Xuelin Qian, Bin Li, Yanwei Fu

With the continuous expansion of intelligent surveillance networks, lifelong person re-identification (LReID) has received widespread attention, pursuing the need of self-evolution across different domains. However, existing LReID studies accumulate knowledge with the assumption that people would not change their clothes. In this paper, we propose a more pra

C. J. Barton, W. Xu, S. R. Elliott, R. Massarczyk

For next-generation neutrinoless double beta decay experiments, extremely low backgrounds are necessary. An understanding of in-situ cosmogenic backgrounds is critical to the design effort. In-situ cosmogenic backgrounds impose a depth requirement and especially impact the choice of host laboratory. Often, simulations are used to understand background effect

Yusen Xie, Zhenmin Huang, Kai Chen, Lei Zhu

Structure from Motion (SfM) and visual localization in indoor texture-less scenes and industrial scenarios present prevalent yet challenging research topics. Existing SfM methods designed for natural scenes typically yield low accuracy or map-building failures due to insufficient robust feature extraction in such settings. Visual markers, with their artifici

Zheng Zhai, Jialu Xu, Mingxin Wu, Xiaohui Li

This paper introduces a regularized projection matrix approximation framework designed to recover cluster information from the affinity matrix. The model is formulated as a projection approximation problem, incorporating an entry-wise penalty function. We investigate three distinct penalty functions, each specifically tailored to address bounded, positive, a

Qizao Wang, Xuelin Qian, Bin Li, Lifeng Chen

Cloth-changing person re-identification aims at recognizing the same person with clothing changes across non-overlapping cameras. Advanced methods either resort to identity-related auxiliary modalities (e.g., sketches, silhouettes, and keypoints) or clothing labels to mitigate the impact of clothes. However, relying on unpractical and inflexible auxiliary mo

Runyi Li, Xuanyu Zhang, Zhipei Xu, Yongbing Zhang

With the advent of personalized generation models, users can more readily create images resembling existing content, heightening the risk of violating portrait rights and intellectual property (IP). Traditional post-hoc detection and source-tracing methods for AI-generated content (AIGC) employ proactive watermark approaches; however, these are less effectiv

Henry Taylor, Leonardo Stella

A major challenge in decision making domains with large state spaces is to effectively select actions which maximize utility. In recent years, approaches such as reinforcement learning (RL) and search algorithms have been successful to tackle this issue, despite their differences. RL defines a learning framework that an agent explores and interacts with. Sea

Mehrdad Pournaderi, Yu Xiang

Conformal prediction methodology has recently been extended to the covariate shift setting, where the distribution of covariates differs between training and test data. While existing results ensure that the prediction sets from these methods achieve marginal coverage above a nominal level, their coverage rate conditional on the training dataset (referred to

A. J. Ross, J. Aguilar, S. Ahlen, S. Alam

We present the technical details on how large-scale structure (LSS) catalogs are constructed from redshifts measured from spectra observed by the Dark Energy Spectroscopic Instrument (DESI). The LSS catalogs provide the information needed to determine the relative number density of DESI tracers as a function of redshift and celestial coordinates and, e.g., d

Véronique Bazier-Matte, Ralf Schiffler

To every knot (or link) diagram K, we associate a cluster algebra A that contains a cluster x with the property that every cluster variable in x specializes to the Alexander polynomial of K. We call x the knot cluster of A. Furthermore, there exists a cluster automorphism of A of order two that maps the initial cluster to the cluster x. We realize this conne

Qijie Wang, Guandu Liu, Bin Wang

Recent advances in vision-language foundational models, such as CLIP, have demonstrated significant strides in zero-shot classification. However, the extensive parameterization of models like CLIP necessitates a resource-intensive fine-tuning process. In response, TIP-Adapter and SuS-X have introduced training-free methods aimed at bolstering the efficacy of

N. A. Schwadron, Stuart D. Bale, J. Bonnell, A. Case

We present an event observed by Parker Solar Probe at $\sim$0.2 au on March 2, 2022 in which imaging and \emph{in situ} measurements coincide. During this event, PSP passed through structures on the flank of a streamer blowout CME including an isolated flux tube in front of the CME, a turbulent sheath, and the CME itself. Imaging observations and \emph{in si

Harkirat Singh, David L. Henann

Many dense granular systems are non-monodisperse, consisting of particles of different sizes, and will segregate based on size during flow. This phenomenon is an important aspect of many industrial and geophysical processes, necessitating predictive continuum models. This paper systematically studies a key aspect of the three-dimensional nature of segregatio

Anjie Liu, Jianhong Wang, Haoxuan Li, Xu Chen

In human-AI interaction, a prominent goal is to attain human`s desirable outcome with the assistance of AI agents, which can be ideally delineated as a problem of seeking the optimal Nash Equilibrium that matches the human`s desirable outcome. However, reaching the outcome is usually challenging due to the existence of multiple Nash Equilibria that are relat

Xiangxiang Dai, Jin Li, Xutong Liu, Anqi Yu

With the rapid advancement of large language models (LLMs), the diversity of multi-LLM tasks and the variability in their pricing structures have become increasingly important, as costs can vary greatly between different LLMs. To tackle these challenges, we introduce the \textit{C2MAB-V}, a \underline{C}ost-effective \underline{C}ombinatorial \underline{M}ul

Yuta Inoue, Ken-ichi Kawarabayashi, Atsuyuki Miyashita, Bojan Mohar

We prove that every cyclically 4-edge-connected cubic graph that can be embedded in the projective plane, with the single exception of the Petersen graph, is 3-edge-colorable. In other words, the only (non-trivial) snark that can be embedded in the projective plane is the Petersen graph. This implies that a 2-connected cubic (multi)graph that can be embedded

Yuhang Chen, Wenke Huang, Mang Ye

Federated learning (FL) has emerged as a new paradigm for privacy-preserving collaborative training. Under domain skew, the current FL approaches are biased and face two fairness problems. 1) Parameter Update Conflict: data disparity among clients leads to varying parameter importance and inconsistent update directions. These two disparities cause important

Yuxin Wang, Ivory Yang, Saeed Hassanpour, Soroush Vosoughi

Mental manipulation, a significant form of abuse in interpersonal conversations, presents a challenge to identify due to its context-dependent and often subtle nature. The detection of manipulative language is essential for protecting potential victims, yet the field of Natural Language Processing (NLP) currently faces a scarcity of resources and research on

Rui Bao, Zhiwei Fang, Jian Liu, Zhaoxiang Liu

We demonstrate high-power thin film lithium niobate (TFLN) erbium-doped waveguide amplifier (EDWA) with a maximum on-chip output power of 113 mW and a gain of 16 dB. The on-chip integrated EDWA is composed of large mode area (LMA) waveguide structures with a total length of 7 cm and a footprint of 1x1 cm2. Particularly, we connect segmented LMA waveguides wi

Joshua Offergeld, Marcel van Gerven, Nasir Ahmad

Improving the efficiency of neural network inference is undeniably important in a time where commercial use of AI models increases daily. Node pruning is the art of removing computational units such as neurons, filters, attention heads, or even entire layers to significantly reduce inference time while retaining network performance. In this work, we propose

Clarissa Astuto, Armando Coco, Umberto Zerbinati

In this paper, a comparative study between the Coco-Russo scheme (based on finite-difference scheme) and the $\mathghost$-FEM (based on finite-element method) is presented when solving the Poisson equation in arbitrary domains. The comparison between the two numerical methods is carried out by presenting analytical results from the literature \cite{cocoStiss

Itai Shufaro, Nadav Merlis, Nir Weinberger, Shie Mannor

In many sequential decision problems, an agent performs a repeated task. He then suffers regret and obtains information that he may use in the following rounds. However, sometimes the agent may also obtain information and avoid suffering regret by querying external sources. We study the trade-off between the information an agent accumulates and the regret it

Yusaku Ando, Miya Nakajima, Takahiro Saitoh, Tsuyoshi Kato

In recent years, the deterioration of artificial materials used in structures has become a serious social issue, increasing the importance of inspections. Non-destructive testing is gaining increased demand due to its capability to inspect for defects and deterioration in structures while preserving their functionality. Among these, Laser Ultrasonic Visualiz

Shanghaoran Quan

Constructing high-quality query-response pairs from custom corpus is crucial for supervised fine-tuning (SFT) large language models (LLMs) in many applications, like creating domain-specific AI assistants or roleplaying agents. However, sourcing this data through human annotation is costly, and existing automated methods often fail to capture the diverse ran

Jerome Wiesemann, Jan Krause, Devashish Tupkary, Norbert Lütkenhaus

In recent years, quantum key distribution (QKD) has evolved from a scientific research field to a commercially available security solution, supported by mathematically formulated security proofs. However, since the knowledge required for a full understanding of a security proof is scattered across numerous publications, it has proven difficult to gain a comp

Tianyu Xie, Yu Zhu, Longlin Yu, Tong Yang

Continuous normalizing flows (CNFs) learn an ordinary differential equation to transform prior samples into data. Flow matching (FM) has recently emerged as a simulation-free approach for training CNFs by regressing a velocity model towards the conditional velocity field. However, on constrained domains, the learned velocity model may lead to undesirable flo

Mykola Pratsiovytyi, Dmytro Karvatskyi

In this paper, we study the fractal properties of the boundary of the Cantorval connected with Guthrie-Nymann's series. In particular, we prove that such a Cantorval can be represented as a union of open intervals and a Cantor set having zero Lebesgue measure and a fractional Hausdorff dimension. Moreover, we extend the result to a countable family of Cantor

Yacov Manevich, Hagar Meir, Kaoutar Elkhiyaoui, Yoav Tock

Arma is a Byzantine Fault Tolerant (BFT) consensus system designed to achieve horizontal scalability across all hardware resources: network bandwidth, CPU, and disk I/O. As opposed to preceding BFT protocols, Arma separates the dissemination and validation of client transactions from the consensus process, restricting the latter to totally ordering only meta

Peter Richtárik, Simone Maria Giancola, Dymitr Lubczyk, Robin Yadav

We contribute to the growing body of knowledge on more powerful and adaptive stepsizes for convex optimization, empowered by local curvature information. We do not go the route of fully-fledged second-order methods which require the expensive computation of the Hessian. Instead, our key observation is that, for some problems (e.g., when minimizing the sum of

Along He, Tao Li, Yanlin Wu, Ke Zou

Limited labeled data hinder the application of deep learning in medical domain. In clinical practice, there are sufficient unlabeled data that are not effectively used, and semi-supervised learning (SSL) is a promising way for leveraging these unlabeled data. However, existing SSL methods ignore frequency domain and region-level information and it is importa

N. Shavlakadze, N. Odishelidze, F. Criado-Aldeanueva

The problem of constructing an exact solution of singular integro-differential equations related to problems of adhesive interaction between elastic thin semi-infinite homogeneous patch and elastic plate is investigated. For the patch loaded with horizontal forces the usual model of the uniaxial stress state is valid. Using the methods of the theory of analy

Mingyang Song, Yi Feng, Liping Jing

Pre-trained large language models can perform natural language processing downstream tasks by conditioning on human-designed prompts. However, a prompt-based approach often requires "prompt engineering" to design different prompts, primarily hand-crafted through laborious trial and error, requiring human intervention and expertise. It is a challenging proble

Francesca Babiloni, Alexandros Lattas, Jiankang Deng, Stefanos Zafeiriou

We propose ID-to-3D, a method to generate identity- and text-guided 3D human heads with disentangled expressions, starting from even a single casually captured in-the-wild image of a subject. The foundation of our approach is anchored in compositionality, alongside the use of task-specific 2D diffusion models as priors for optimization. First, we extend a fo

Jonathan Weitsman

We use recent progress on Chern-Simons gauge theory in three dimensions [18] to give explicit, closed form formulas for the star product on some functions on the affine space ${\mathcal A}(\Sigma)$ of (smooth) connections on the trivialized principal $G$-bundle on a compact, oriented two manifold $\Sigma.$ These formulas give a close relation between knot in

Vaibhav Tiwari

Nested sampling is often used in Bayesian statistics problems in astronomy. It operates with a set of live points, iteratively replacing the point with the lowest likelihood with a new point of higher likelihood. Each iteration reduces the enclosed volume by a known factor. The estimated sampling density and the likelihood values of both new and old live poi

Minseon Kim, Hyomin Lee, Boqing Gong, Huishuai Zhang

Recent AI systems have shown extremely powerful performance, even surpassing human performance, on various tasks such as information retrieval, language generation, and image generation based on large language models (LLMs). At the same time, there are diverse safety risks that can cause the generation of malicious contents by circumventing the alignment in

CMS Collaboration

A search for the production of a W boson and a Higgs boson through vector boson scattering (VBS) is presented, using CMS data from proton-proton collisions at $\sqrt{s}$ = 13 TeV collected from 2016 to 2018. The integrated luminosity of the data sample is 138 fb$^{-1}$. Selected events must be consistent with the presence of two jets originating from VBS, th

Yichun Hu, Nathan Kallus, Xiaojie Mao, Yanchen Wu

Contextual linear optimization (CLO) uses predictive contextual features to reduce uncertainty in random cost coefficients in the objective and thereby improve decision-making performance. A canonical example is the stochastic shortest path problem with random edge costs (e.g., travel time) and contextual features (e.g., lagged traffic, weather). While exist

Yannick Limmer, Anastasis Kratsios, Xuwei Yang, Raeid Saqur

An inherent challenge in computing fully-explicit generalization bounds for transformers involves obtaining covering number estimates for the given transformer class $T$. Crude estimates rely on a uniform upper bound on the local-Lipschitz constants of transformers in $T$, and finer estimates require an analysis of their higher-order partial derivatives. Unf

Jiazhuo Cheng, Qiru Wang

In this paper, we study the initial-boundary value problem for a pseudo-parabolic equation in magnetic fractional Orlicz-Sobolev spaces. First, by employing the imbedding theorems, the theory of potential wells and the Galerkin method, we prove the existence and uniqueness of global solutions with subcritical initial energy, critical initial energy and super

Jie Han, Yi Zhao

In this paper we study a multi-partite version of the Erd\H{o}s--Stone theorem. Given integers $r<k$ and $t\ge 1$, let $\text{ex}_k(n, K_{r+1}(t))$ be the maximum number of edges of $K_{r+1}(t)$-free $k$-partite graphs with $n$ vertices in each part, where $K_{r+1}(t)$ is the complete $(r+1)$-partite graph with $t$ vertices in each part. We determine the exa

Yongxian Wei, Zixuan Hu, Li Shen, Zhenyi Wang

Data-Free Meta-Learning (DFML) aims to derive knowledge from a collection of pre-trained models without accessing their original data, enabling the rapid adaptation to new unseen tasks. Current methods often overlook the heterogeneity among pre-trained models, which leads to performance degradation due to task conflicts. In this paper, we empirically and the

Koya Sakamoto, Daichi Azuma, Taiki Miyanishi, Shuhei Kurita

Embodied Question Answering (EQA) serves as a benchmark task to evaluate the capability of robots to navigate within novel environments and identify objects in response to human queries. However, existing EQA methods often rely on simulated environments and operate with limited vocabularies. This paper presents a map-based modular approach to EQA, enabling r

Xin Liu, Di Luo, Zhicheng Luo, Shizhuo Li

The reference-frame-independent quantum key distribution (RFI QKD) protocol enables QKD systems to function effectively despite slowly varying reference frames, offering a distinct advantage in practical scenarios, particularly in mobile platforms. In this study, we successfully distribute secure key bits over a 250-km optical fiber distance by developing an

Chun-Kai Huang, Yi-Hsien Hsieh, Ta-Jung Chien, Li-Cheng Chien

Multivariate time series (MTS) data, when sampled irregularly and asynchronously, often present extensive missing values. Conventional methodologies for MTS analysis tend to rely on temporal embeddings based on timestamps that necessitate subsequent imputations, yet these imputed values frequently deviate substantially from their actual counterparts, thereby

Manuel Santos Gutiérrez, Mickaël David Chekroun, Ilan Koren

Cloud microphysics studies include how tiny cloud droplets grow, and become rain. This is crucial for understanding cloud properties like size, lifespan, and impact on climate through radiative effects. Small, weak-updraft clouds near the haze-to-cloud transition are especially difficult to measure and understand. They are abundant but hard to capture by sat

Zhaozhi Wang, Yue Liu, Yunjie Tian, Yunfan Liu

Visual representation models leveraging attention mechanisms are challenged by significant computational overhead, particularly when pursuing large receptive fields. In this study, we aim to mitigate this challenge by introducing the Heat Conduction Operator (HCO) built upon the physical heat conduction principle. HCO conceptualizes image patches as heat sou

Minghan Chu, Weicheng Qian

Engineering design and scientific analysis rely upon computer simulations of turbulent fluid flows using turbulence models. These turbulence models are empirical and approximate, leading to large uncertainties in their predictions that hamper scientific and engineering advances. We outline a Physics Constrained Deep Learning framework to estimate turbulence

Li Peng, Junqiao Pan, Su Yi, Tao Shi

We investigate the ground-state properties of the quasi-one-dimensional dipolar gases using continuous matrix product states techniques. Making use of the first- and second-order correlation functions, we find that the system supports the superfluid, super-Tonks-Girardeau, and quasicrystal phases according to the Luttinger liquid theory. We also map out the

Ziqin Luo, Haixia Han, Haokun Zhao, Guochao Jiang

Existing Large Language Models (LLMs) generate text through unidirectional autoregressive decoding methods to respond to various user queries. These methods tend to consider token selection in a simple sequential manner, making it easy to fall into suboptimal options when encountering uncertain tokens, referred to as chaotic points in our work. Many chaotic

Dylan Janssen, Wayne Pullan, Alan Wee-Chung Liew

Differential Evolution (DE) is a highly successful population based global optimisation algorithm, commonly used for solving numerical optimisation problems. However, as the complexity of the objective function increases, the wall-clock run-time of the algorithm suffers as many fitness function evaluations must take place to effectively explore the search sp