October 2024 arXiv papers — page 106

James MacLaurin

We prove a Large Deviation Principle for {\color{blue} jump-Markov } Processes on sparse large disordered network with disordered connectivity. The network is embedded in a geometric space, with the probability of a connection a (scaled) function of the spatial positions of the nodes. This type of model has numerous applications, including neuroscience, epid

Shengbin Ye, Meng Li

Symbolic regression (SR) is a powerful technique for discovering symbolic expressions that characterize nonlinear relationships in data, gaining increasing attention for its interpretability, compactness, and robustness. However, existing SR methods do not scale to datasets with a large number of input variables (referred to as extreme-scale SR), which is co

Fernando Diaz

Traditional evaluation of information access systems has focused primarily on average utility across a set of information needs (information retrieval) or users (recommender systems). In this work, we argue that evaluating only with average metric measurements assumes utilitarian values not aligned with traditions of information access based on equal access.

Sota Takagi, Miki Saijo, Takumi Ohashi

As the global food system faces increasing challenges from sustainability, climate change, and food security issues, alternative food networks like Community-Supported Agriculture (CSA) play an essential role in fostering stronger connections between consumers and producers. However, understanding consumer engagement with CSA is fragmented, particularly in J

Ruya Zhang, Charles Brooker, Laura L. E. Whitehouse, Neil H. Thomson

The mechanical competence and suturing ability of collagen-based membranes are paramount in Guided Bone Regeneration (GBR) therapy, to ensure damage-free implantation, fixation and space maintenance in vivo. However, contact with the biological medium can induce swelling of collagen molecules, yielding risks of membrane sinking into the bone defect, early lo

Konstantinos Alexopoulos, Bryn Davies, Erik Orvehed Hiltunen

We extend the theory of topological localised interface modes to systems with damping. The spectral problem is formulated as a root-finding problem for the interface impedance function and Rouch\'e's theorem is used to track the zeros when damping is introduced. We show that the localised eigenfrequencies, corresponding to interface modes, remain for non-zer

Zhaolin Wang, Chongjun Ouyang, Yuanwei Liu

The beamforming optimization in continuous aperture array (CAPA)-based multi-user communications is studied. In contrast to conventional spatially discrete antenna arrays, CAPAs can exploit the full spatial degrees of freedom (DoFs) by emitting information-bearing electromagnetic (EM) waves through continuous source current distributed across the aperture. N

Andrey Morozov, Aleksandr Popolitov, Alexei Sleptsov

We prove that normalized colored Alexander polynomial (the $A \rightarrow 1$ limit of colored HOMFLY-PT polynomial) evaluated for one-hook (L-shape) representation R possesses scaling property: it is equal to the fundamental Alexander polynomial with the substitution $q \rightarrow q^{|R|}$. The proof is simple and direct use of Reshetikhin-Turaev formalism

Amit Moryossef, Gerard Sant, Zifan Jiang

We introduce a method for transferring the signer's appearance in sign language skeletal poses while preserving the sign content. Using estimated poses, we transfer the appearance of one signer to another, maintaining natural movements and transitions. This approach improves pose-based rendering and sign stitching while obfuscating identity. Our experiments

Yijun Liang, Shweta Bhardwaj, Tianyi Zhou

Low-quality or scarce data has posed significant challenges for training deep neural networks in practice. While classical data augmentation cannot contribute very different new data, diffusion models opens up a new door to build self-evolving AI by generating high-quality and diverse synthetic data through text-guided prompts. However, text-only guidance ca

Stefan Matijević

We prove that the filtered positive ($S^1$-equivariant) symplectic homology of a convex domain is naturally isomorphic to the filtered singular ($S^1$-equivariant) homology induced by Clarke's dual functional associated with the convex domain. As a result, we prove that the Gutt-Hutchings capacities coincide with the spectral invariants introduced by Ekeland

Manuel Sanchez del Rio, Juan Reyes-Herrera

We introduce a model that can accurately simulate radiation from undulator sources for ray tracing applications. It incorporates several key effects relevant to 4$^\text{th}$ generation synchrotron sources, such as electron emittance, energy spread, and diffraction-limited beam size. This code has been developed as part of SHADOW4, the latest version of the

Varun Gumma, Ananditha Raghunath, Mohit Jain, Sunayana Sitaram

Assessing the capabilities and limitations of large language models (LLMs) has garnered significant interest, yet the evaluation of multiple models in real-world scenarios remains rare. Multilingual evaluation often relies on translated benchmarks, which typically do not capture linguistic and cultural nuances present in the source language. This study provi

Aurélien Marc, Timo Fleig

We present a systematic study of the nucleon-electron tensor-pseudotensor (Ne-TPT) interaction in candidate molecules for next-generation experimental searches for new sources of charge-parity violation. The considered molecules are all amenable to assembly from laser-cooled atoms, with the francium-silver (FrAg) molecule previously shown to be the most sens

Jiayang Xu, Yamin Li, Ruxin Su, Saishuang Wu

Mother-child interaction is a highly dynamic process neurally characterized by inter-brain synchrony (IBS) at {\theta} and/or {\alpha} rhythms. However, their establishment, dynamic changes, and roles in mother-child interactions remain unknown. Through dynamic analysis of dual-EEG from 40 mother-child dyads during turn-taking cooperation, we uncover that {\

Amit Moryossef, Rotem Zilberman, Ohad Langer

The lack of automatic evaluation metrics tailored for SignWriting presents a significant obstacle in developing effective transcription and translation models for signed languages. This paper introduces a comprehensive suite of evaluation metrics specifically designed for SignWriting, including adaptations of standard metrics such as \texttt{BLEU} and \textt

Xiutian Zhao, Ke Wang, Wei Peng

Dialogue agents have been receiving increasing attention for years, and this trend has been further boosted by the recent progress of large language models (LLMs). Stance detection and dialogue summarization are two core tasks of dialogue agents in application scenarios that involve argumentative dialogues. However, research on these tasks is limited by the

Shailaja Keyur Sampat, Mutsumi Nakamura, Shankar Kailas, Kartik Aggarwal

Deriving inference from heterogeneous inputs (such as images, text, and audio) is an important skill for humans to perform day-to-day tasks. A similar ability is desirable for the development of advanced Artificial Intelligence (AI) systems. While state-of-the-art models are rapidly closing the gap with human-level performance on diverse computer vision and

Ahmad Abdi, Gérard Cornuéjols, Siyue Liu, Olha Silina

Let $D=(V,A)$ be a digraph whose underlying undirected graph is $2$-edge-connected, and let $P$ be the polytope whose vertices are the incidence vectors of arc sets whose reversal makes $D$ strongly connected. We study the lattice theoretic properties of the integer points contained in a proper face $F$ of $P$ not contained in $\{x:x_a=i\}$ for any $a\in A,i

E. Merhej, J. P. Hague, R. M. Konik, A. J. A. James

Using matrix product state techniques we study the nonequilibrium dynamical response of the half-filled Hubbard ladder when subject to an optical pump. Optical pumping offers a way of producing and manipulating new strongly correlated phenomena by suppressing existing magnetic correlations. The ladder allows the effects of pump directionality to be investiga

Demetris Shianios, Panayiotis Kolios, Christos Kyrkou

The integration of Unmanned Aerial Vehicles (UAVs) with artificial intelligence (AI) models for aerial imagery processing in disaster assessment, necessitates models that demonstrate exceptional accuracy, computational efficiency, and real-time processing capabilities. Traditionally Convolutional Neural Networks (CNNs), demonstrate efficiency in local featur

Shailaja Keyur Sampat, Yezhou Yang, Chitta Baral

Humans observe various actions being performed by other humans (physically or in videos/images) and can draw a wide range of inferences about it beyond what they can visually perceive. Such inferences include determining the aspects of the world that make action execution possible (e.g. liquid objects can undergo pouring), predicting how the world will chang

Reggie C. Pantig, Shubham Kala, Ali Övgün, Nikko John Leo S. Lobos

This study probes spacetime solutions within Einstein-Bumblebee gravity, a modified gravitational framework incorporating spontaneous Lorentz symmetry violation through a vector field mechanism. By introducing a cosmological constant into this model, the research scrutinizes thermodynamic properties of black holes in both anti-de Sitter (AdS) and de Sitter (

V. Bozza, V. Saggese, G. Covone, P. Rota

Modeling of microlensing events poses computational challenges for the resolution of the lens equation and the high dimensionality of the parameter space. In particular, numerical noise represents a severe limitation to fast and efficient calculations of microlensing by multiple systems, which are of particular interest in exoplanetary searches. We present a

Zhong-Jun Yang, Jin-Lei Yang, Shu-Min Zhao, Xing-Gang Wu

In an extension of MSSM with two triplets and a singlet, called the TNMSSM, there are seven neutralinos which can enrich the study of cold dark matter if one expects that the weakly interacting massive particle (WIMP) is responsible for the observation of Planck satellite. Such a model, compared to the MSSM, can naturally offer a solution to the $\mu$ proble

Charles F. Manski, Eytan Sheshinski

We study the subtlety of optimal paternalism when a utilitarian planner has the power to design a discrete choice set for a heterogeneous population with bounded rationality. We first consider the planning problem in abstraction. We show that the policy that most effectively constrains or influences choices depends multiplicatively on the preferences of the

Kirill Antonov, Marijn Siemons, Niki van Stein, Thomas H. W. Bäck

This work addresses the critical challenge of optimal filter selection for a novel trace gas measurement device. This device uses photonic crystal filters to retrieve trace gas concentrations affected by photon and read noise. The filter selection directly influences the accuracy and precision of the gas retrieval and, therefore, is a crucial performance dri

Li Tu, Yi Zhou

We give a simpler proof for the local well-posedness of the modified Korteweg-de Vries equations and modified Benjamin-Ono equation in $H^{\frac{1}{4}}(\mathbb{R})$ and $H^{\frac{1}{2}}(\mathbb{R})$, respectively. The proof is based on the Strichartz estimate, dyadic decomposition and a bilinear estimate given by a new type of div-curl lemma.

Amir Sivan, Meir Orenstein

We show here that the photonic states emitted by ensembles of multilevel atoms via a superradiance process exhibit entanglement in the modal (frequency) degree of freedom, making this collective emission process a favorable candidate for a fast, bright and deterministic source of entangled photons. This entanglement is driven by two mechanisms: (i) selective

Philipp Wicke, Marianna M. Bolognesi

Political debates are a peculiar type of political discourse, in which candidates directly confront one another, addressing not only the the moderator's questions, but also their opponent's statements, as well as the concerns of voters from both parties and undecided voters. Therefore, language is adjusted to meet specific expectations and achieve persuasion

Alejandro O. Majadas-Moure, David Mosquera-Lois

We lift the Lefschetz number from an algebraic invariant of maps between spaces to an invariant of morphisms of data over the spaces.

Shelby Cox, Igor Makhlin

The type A cluster configuration space, commonly known as $\mathcal M_{0,n}$, is the very affine part of the binary geometry associated with the associahedron. The tropicalization of $\mathcal M_{0,n}$ can be realized as the space of phylogenetic trees and its signed tropicalizations as the dual-associahedron subfans. We give a concise overview of this const

Shailaja Keyur Sampat, Maitreya Patel, Yezhou Yang, Chitta Baral

An ability to learn about new objects from a small amount of visual data and produce convincing linguistic justification about the presence/absence of certain concepts (that collectively compose the object) in novel scenarios is an important characteristic of human cognition. This is possible due to abstraction of attributes/properties that an object is comp

C. R. García, G. Illiano, D. F. Torres, A. Papitto

We compute and apply the minimum spanning tree (MST) of the binary millisecond pulsar population, and discuss aspects of the known phenomenology of these systems in this context. We find that the MST effectively separates different classes of spider pulsars, eclipsing radio pulsars in tight binary systems either with a companion with a mass in the range of a

Tianyi Zhang, Atta Norouzian, Aanchan Mohan, Frederick Ducatelle

In virtual assistant (VA) systems it is important to reject or redirect user queries that fall outside the scope of the system. One of the most accurate approaches for out-of-scope (OOS) rejection is to combine it with the task of intent classification on in-scope queries, and to use methods based on the similarity of embeddings produced by transformer-based

Yuling Gu, Oyvind Tafjord, Hyunwoo Kim, Jared Moore

Large language models (LLMs) are increasingly tested for a "Theory of Mind" (ToM) - the ability to attribute mental states to oneself and others. Yet most evaluations stop at explicit belief attribution in classical toy stories or stylized tasks, leaving open the questions of whether LLMs can implicitly apply such knowledge to predict human behavior, or to j

Ying Li, Zichen Song, Zijie Gong, Sitan Huang

With the development of social economy and the improvement of people's attention to health, the growth and development of children and adolescents has become an important indicator to measure the level of national health. Therefore, accurate and timely assessment of children's growth and development has become increasingly important. At the same time, global

Luke Mortimer, Donato Farina, Grazia Di Bello, David Jansen

Estimating the steady-state properties of open many-body quantum systems is a fundamental challenge in quantum science and technologies. In this work, we present a scalable approach based on semi-definite programming to derive certified bounds on the expectation value of an arbitrary observable in the steady state of Lindbladian dynamics. We illustrate our m

Hagen Holthusen, Tim Brepols, Kevin Linka, Ellen Kuhl

Soft biological tissues exhibit a tendency to maintain a preferred state of tensile stress, known as tensional homeostasis, which is restored even after external mechanical stimuli. This macroscopic behavior can be described using the theory of kinematic growth, where the deformation gradient is multiplicatively decomposed into an elastic part and a part rel

Matthew M. Dobson, Megan E. Schwamb, Alan Fitzsimmons, Michael S. P. Kelley

We utilize serendipitous observations from the Asteroid Terrestrial-impact Last Alert System (ATLAS) and the Zwicky Transient Facility (ZTF) in addition to targeted follow-up observations from the Las Cumbres Observatory (LCO) and Liverpool Telescope to analyze the first observed instance of cometary activity by the newly-discovered Jupiter-family comet C/20

Chenyu Wang, Masatoshi Uehara, Yichun He, Amy Wang

Recent studies have demonstrated the strong empirical performance of diffusion models on discrete sequences across domains from natural language to biological sequence generation. For example, in the protein inverse folding task, conditional diffusion models have achieved impressive results in generating natural-like sequences that fold back into the origina

Daniel Orr, Milo Bechtloff Weising

The modified Macdonald functions $\widetilde{H}_{\mu}$ are fundamental objects in modern algebraic combinatorics. Haiman showed that there is a correspondence between the $(\mathbb{C}^{*})^2$-fixed points $I_{\mu}$ of the Hilbert schemes $\mathrm{Hilb}_{n}(\mathbb{C}^2)$ and the functions $\widetilde{H}_{\mu}$ realizing a derived equivalence between $(\mathb

Sabit Hassan, Anthony Sicilia, Malihe Alikhani

Ensuring that Large Language Models (LLMs) generate text representative of diverse sub-populations is essential, particularly when key concepts related to under-represented groups are scarce in the training data. We address this challenge with a novel clustering-based active learning framework, enhanced with knowledge distillation. The proposed framework tra

Yiming Wang, Pei Zhang, Baosong Yang, Derek F. Wong

LLM self-evaluation relies on the LLM's own ability to estimate response correctness, which can greatly improve its deployment reliability. In this research track, we propose the Chain-of-Embedding (CoE) in the latent space to enable LLMs to perform output-free self-evaluation. CoE consists of all progressive hidden states produced during the inference time,

Siwei Wu, Zhongyuan Peng, Xinrun Du, Tuney Zheng

Enabling Large Language Models (LLMs) to handle a wider range of complex tasks (e.g., coding, math) has drawn great attention from many researchers. As LLMs continue to evolve, merely increasing the number of model parameters yields diminishing performance improvements and heavy computational costs. Recently, OpenAI's o1 model has shown that inference strate

Girish Narayanswamy, Xin Liu, Kumar Ayush, Yuzhe Yang

Wearable sensors have become ubiquitous thanks to a variety of health tracking features. The resulting continuous and longitudinal measurements from everyday life generate large volumes of data; however, making sense of these observations for scientific and actionable insights is non-trivial. Inspired by the empirical success of generative modeling, where la

Alexandra Bazarova, Evgenia Romanenkova, Alexey Zaytsev

Change point detection (CPD) methods aim to identify abrupt shifts in the distribution of input data streams. Accurate estimators for this task are crucial across various real-world scenarios. Yet, traditional unsupervised CPD techniques face significant limitations, often relying on strong assumptions or suffering from low expressive power due to inherent m

Anthony Sicilia, Malihe Alikhani

Conversation forecasting tasks a model with predicting the outcome of an unfolding conversation. For instance, it can be applied in social media moderation to predict harmful user behaviors before they occur, allowing for preventative interventions. While large language models (LLMs) have recently been proposed as an effective tool for conversation forecasti

Yabo Dong, Kun Wang, Jingya Zhu

Motivated by the possible 95 GeV diphoton excess, we investigate the capability of the Type-I Two-Higgs-Doublet Model (2HDM-I) to explain this signal under current theoretical and experimental constraints. Using full Monte Carlo (MC) simulations for the process of $pp \to t(\to W^+ b)\bar{t}(\to W^- \bar{b})h(\to \gamma\gamma)$, we evaluate the discovery pot

Lourenço Beirão Da Veiga, Franco Dassi, Sergio Gómez

We carry out a stability and convergence analysis for the fully discrete scheme obtained by combining a finite or virtual element spatial discretization with the upwind-discontinuous Galerkin time-stepping applied to the time-dependent advection-diffusion equation. A space-time streamline-upwind Petrov-Galerkin term is used to stabilize the method. More prec

Victor Alexandrov

The article contributes to the theory of infinitesimal bendings of smooth surfaces in Euclidean 3-space. We derive a linear differential equation of the first order, which previously did not appear in the literature and which is satisfied by any Darboux rotation field of a smooth surface. We show that, for some surfaces, this additional equation is functiona

Wenxue Zhang, Muhammad Shaeer Moeed, Andrew Bright, Tobias Serwatka

In this work, we propose a Path Integral Monte Carlo (PIMC) approach based on discretized continuous degrees of freedom and rejection-free Gibbs sampling. The ground state properties of a chain of planar rotors with dipole-dipole interactions are used to illustrate the approach. Energetic and structural properties are computed and compared to exact diagonali

Katsuki Aoki, Andrea Cristofoli, Yu-tin Huang

We develop an on-shell approach to study black hole mergers. Since, asymptotically, the initial and final states can be described by point-like spinning particles, we propose a massive three-point amplitude for the merger of two Schwarzschild black holes into a Kerr black hole. This three-point amplitude and the spectral function of the final state are fully

Adrian Padellaro, Sanjaye Ramgoolam, Ryo Suzuki

Multi-matrix invariants, and in particular the scalar multi-trace operators of $\mathcal{N}=4$ SYM with $U(N)$ gauge symmetry, can be described using permutation centraliser algebras (PCA), which are generalisations of the symmetric group algebras and independent of $N$. Free-field two-point functions define an $N$-dependent inner product on the PCA, and bas

E. Fable, G. Tardini, L. Giannone, the ASDEX Upgrade Team

A new axisymmetric equilibrium solver has been written, called FEQIS (Flexible EQuIlibrium Solver), which purpose is to be used inside integrated modeling of tokamak plasmas. The FEQIS code solves the Grad-Shafranov equation and the "circuit" equations for the external coils and passive conducting structures that are toroidally connected. The code has been s

Tommaso Lorenzi, Kevin J Painter, Chiara Villa

Populations are heterogeneous, deviating in numerous ways. Phenotypic diversity refers to the range of traits or characteristics across a population, where for cells this could be the levels of signalling, movement and growth activity, etc. Clearly, the phenotypic distribution -- and how this changes over time and space -- could be a major determinant of pop

Ahmet Oguz Sakin, Ali Murat Demirtas, Hamza Kurt, Mehmet Unlu

Ultrafast pulses, particularly those with durations under 100 femtoseconds, are crucial in achieving unprecedented precision and control in light-matter interactions. However, conventional on-chip photonic platforms are not inherently designed for ultrafast time-domain operations, posing a significant challenge in achieving essential parameters such as high

Bethan Turner

Higher Time Derivative Theories are generated by considering space-time rotated KdV and mKdV systems. These systems are then studied to see if/how instabilities, usually associated with higher time derivative theories, manifest on the classical level by presenting both analytic and numerical solutions. For a linearised version of these space-time rotated sys

Frédéric Dux

lightcurver is a photometric pipeline for time series astronomical imaging data, designed for the semi-automatic extraction of precise light curves from small, blended targets. Such targets include, but are not limited to, lensed quasars, supernovae, or Cepheids in crowded fields. lightcurver leverages STARRED (Michalewicz et al., 2023; Millon et al., 2024)

Daji Landis, Nikolaj I. Schwartzbach

We propose a new, more potent attack on decentralized exchanges. This attack leverages absolute commitments, which are commitments that can condition on the strategies made by other agents. This attack allows an adversary to charge monopoly prices by committing to undercut those other miners that refuse to charge an even higher fee. This allows the miner to

Hao Kang, Tevin Wang, Chenyan Xiong

Dense embeddings deliver strong retrieval performance but often lack interpretability and controllability. This paper introduces a novel approach using sparse autoencoders (SAE) to interpret and control dense embeddings via the learned latent sparse features. Our key contribution is the development of a retrieval-oriented contrastive loss, which ensures the

Fatemeh Banani Ardecani, Amit Kumar, Sepehr Sabeti, Omidreza Shoghli

Despite the research and implementation efforts involving various safety strategies, protocols, and technologies, work zone crashes and fatalities continue to occur at an alarming rate each year. This study investigates the neurophysiological responses to Augmented Reality safety warnings in roadway work zones under varying workload conditions. Using electro

Joonhyeon Song, Seohwan Yun, Seongho Yoon, Joohyeok Kim

This work proposes a novel approach beyond supervised learning for effective pathological image analysis, addressing the challenge of limited robust labeled data. Pathological diagnosis of diseases like cancer has conventionally relied on the evaluation of morphological features by physicians and pathologists. However, recent advancements in compute-aided di

Shrishti Saha Shetu, Naveen Kumar Desiraju, Wolfgang Mack, Emanuël A. P. Habets

The successful deployment of deep learning-based acoustic echo and noise reduction (AENR) methods in consumer devices has spurred interest in developing low-complexity solutions, while emphasizing the need for robust performance in real-life applications. In this work, we propose a hybrid approach to enhance the state-of-the-art (SOTA) ULCNet model by integr

Asish Mukhopadhyay, Daniel John, Lucas Sarweh

In this note, we consider the problem of generating $k$-factorable graphic sequences with connected (resp. no connected) $k$-factors.

Nicolas Wittler, Shai Machnes, Frank K. Wilhelm

In the current NISQ era, there is demand for functional quantum devices to solve relevant computational problems, which motivates a utilitarian perspective on device design: The goal is to create a device that is able to run a given algorithm with state-of-the-art performance. In this work, we use optimal control tools to derive the gate set required by a to

Yiming Shi, Jiwei Wei, Yujia Wu, Ran Ran

The rapid growth of model scale has necessitated substantial computational resources for fine-tuning. Existing approach such as Low-Rank Adaptation (LoRA) has sought to address the problem of handling the large updated parameters in full fine-tuning. However, LoRA utilize random initialization and optimization of low-rank matrices to approximate updated weig

Tao Gui, Hongsheng Hu, Minhua Liu

For any crystallographic root system, let $W$ be the associated Weyl group, and let $\mathit{WP}$ be the weight polytope (also known as the $W$-permutohedron) associated with an arbitrary strongly dominant weight. The action of $W$ on $\mathit{WP}$ induces an action on the toric variety $X(\mathit{WP})$ associated with the normal fan of $\mathit{WP}$, and he

Kristina Telegraph, Christos Kyrkou

This work presents advancements in multi-class vehicle detection using UAV cameras through the development of spatiotemporal object detection models. The study introduces a Spatio-Temporal Vehicle Detection Dataset (STVD) containing 6, 600 annotated sequential frame images captured by UAVs, enabling comprehensive training and evaluation of algorithms for hol

Jiri Filip, Filip Dechterenko, Filipp Schmidt, Jiri Lukavsky

The world is abundant with diverse materials, each possessing unique surface appearances that play a crucial role in our daily perception and understanding of their properties. Despite advancements in technology enabling the capture and realistic reproduction of material appearances for visualization and quality control, the interoperability of material prop

Hongbo Zeng

In this paper, we study properties of sensitivity, transitivity and chaos for non-autonomous discrete systems(NDS). Firstly, we present some different sufficient conditions for NDS to be chaotic. Then, we relate the transitivity with the sensitivity of NDS and give several sufficient conditions for NDS to be sensitive. We obtain that transitivity and dense p

Xinjie Zhang, Zhening Liu, Yifan Zhang, Xingtong Ge

4D Gaussian Splatting (4DGS) has recently emerged as a promising technique for capturing complex dynamic 3D scenes with high fidelity. It utilizes a 4D Gaussian representation and a GPU-friendly rasterizer, enabling rapid rendering speeds. Despite its advantages, 4DGS faces significant challenges, notably the requirement of millions of 4D Gaussians, each wit

Dongkun Han, Boyuan Shao

Mobile robots have shown immense potential and are expected to be widely used in the service industry. The importance of automatic navigation and voice cloning cannot be overstated as they enable functional robots to provide high-quality services. The objective of this work is to develop a control algorithm for the automatic navigation of a humanoid mobile r

Shaikat Galib, Shanshan Wang, Guanshuo Xu, Pascal Pfeiffer

Smaller vision-language models (VLMs) are becoming increasingly important for privacy-focused, on-device applications due to their ability to run efficiently on consumer hardware for processing enterprise commercial documents and images. These models require strong language understanding and visual capabilities to enhance human-machine interaction. To addres

Yakun Zhu, Shaohang Wei, Xu Wang, Kui Xue

Integrating tools into Large Language Models (LLMs) has facilitated the widespread application. Despite this, in specialized downstream task contexts, reliance solely on tools is insufficient to fully address the complexities of the real world. This particularly restricts the effective deployment of LLMs in fields such as medicine. In this paper, we focus on

Patrik Okanovic, Andreas Kirsch, Jannes Kasper, Torsten Hoefler

We introduce MODEL SELECTOR, a framework for label-efficient selection of pretrained classifiers. Given a pool of unlabeled target data, MODEL SELECTOR samples a small subset of highly informative examples for labeling, in order to efficiently identify the best pretrained model for deployment on this target dataset. Through extensive experiments, we demonstr

Thomas Jacumin, Andreas Langer

In this paper, we propose an adaptive finite difference scheme in order to numerically solve total variation type problems for image processing tasks. The automatic generation of the grid relies on indicators derived from a local estimation of the primal-dual gap error. This process leads in general to a non-uniform grid for which we introduce an adjusted fi

Guangda Ji, Silvan Weder, Francis Engelmann, Marc Pollefeys

Neural network performance scales with both model size and data volume, as shown in both language and image processing. This requires scaling-friendly architectures and large datasets. While transformers have been adapted for 3D vision, a `GPT-moment' remains elusive due to limited training data. We introduce ARKit LabelMaker, a large-scale real-world 3D dat

Lina Gong, Qi Gao, Peng Li, Mingqiang Wei

Current automatic deep learning (i.e., AutoDL) frameworks rely on training feedback from actual runs, which often hinder their ability to provide quick and clear performance predictions for selecting suitable DL systems. To address this issue, we propose EfficientDL, an innovative deep learning board designed for automatic performance prediction and componen

Jiahao Lu, Jiacheng Deng, Ruijie Zhu, Yanzhe Liang

Dynamic scenes rendering is an intriguing yet challenging problem. Although current methods based on NeRF have achieved satisfactory performance, they still can not reach real-time levels. Recently, 3D Gaussian Splatting (3DGS) has garnered researchers attention due to their outstanding rendering quality and real-time speed. Therefore, a new paradigm has bee

Wen-Wei Li

For metaplectic groups over a local field of characteristic zero, we define the Arthur packet attached to any Arthur parameter $\psi$ as a multi-set of unitary genuine irreducible representations, characterized by endoscopic character relations. Over number fields, we obtain a multiplicity formula for the genuine discrete $L^2$-automorphic spectrum in terms

Clayton Souza Leite, Henry Mauranen, Aziza Zhanabatyrova, Yu Xiao

Transformers have excelled in natural language processing and computer vision, paving their way to sensor-based Human Activity Recognition (HAR). Previous studies show that transformers outperform their counterparts exclusively when they harness abundant data or employ compute-intensive optimization algorithms. However, neither of these scenarios is viable i

Lukas Eberhard, Thorsten Ruprechter, Denis Helic

Narrative-driven recommenders aim to provide personalized suggestions for user requests expressed in free-form text such as "I want to watch a thriller with a mind-bending story, like Shutter Island." Although large language models (LLMs) have been shown to excel in processing general natural language queries, their effectiveness for handling such recommenda

D. Kamenskyi, K. Vasin, L. Prodan, K. Kutko

Generation of the monochromatic electromagnetic radiation in the terahertz (THz) range of frequencies for many decades remanes a chellenging task. Here we demonstrate the emission of monochromatic sub-THz radiation by coherent optical phonons in dielectric material KY(MoO4)2. The layered crystal structure of KY(MoO4)2 leads to infrared-active shear lattice v

Luyao Zou, Yu Min Park, Chu Myaet Thwal, Yan Kyaw Tun

Low Earth orbit (LEO) satellites are capable of gathering abundant Earth observation data (EOD) to enable different Internet of Things (IoT) applications. However, to accomplish an effective EOD processing mechanism, it is imperative to investigate: 1) the challenge of processing the observed data without transmitting those large-size data to the ground beca

Guofang Wang, Chao Xia, Xiqiang Zhang

In the paper we establish an optimal logarithmic Sobolev inequality for complete, non-compact, properly embedded self-shrinkers in the Euclidean space, which generalizes a recent result of Brendle \cite{Brendle22} for closed self-shrinkers. We first provide a proof for the logarithmic Sobolev inequality in the Euclidean space by using the Alexandrov-Bakelman

Lucio Centrone, Plamen Koshlukov, Kauê Pereira

Let $A=A_1\oplus\cdots\oplus A_r$ be a decomposition of the algebra $A$ as a direct sum of vector subspaces. If for every choice of the indices $1\le i_j\le r$ there exist $a_{i_j}\in A_{i_j}$ such that the product $a_{i_1}\cdots a_{i_n}\ne 0$, and for every $1\le i,j\le r$ there is a constant $\beta(i,j)\ne 0$ with $a_ia_j=\beta(i,j) a_ja_i$ for $a_i\in A_i

Shrishti Saha Shetu, Emanuël A. P. Habets, Andreas Brendel

Enhancing speech quality under adverse SNR conditions remains a significant challenge for discriminative deep neural network (DNN)-based approaches. In this work, we propose DisCoGAN, which is a time-frequency-domain generative adversarial network (GAN) conditioned by the latent features of a discriminative model pre-trained for speech enhancement in low SNR

Jongbhin Woo, Hyeonggon Ryu, Youngjoon Jang, Jae Won Cho

Video Temporal Grounding (VTG) aims to identify visual frames in a video clip that match text queries. Recent studies in VTG employ cross-attention to correlate visual frames and text queries as individual token sequences. However, these approaches overlook a crucial aspect of the problem: a holistic understanding of the query sentence. A model may capture c

Yida Xiong, Kun Li, Jiameng Chen, Hongzhi Zhang

Molecular optimization (MO) is a crucial stage in drug discovery in which task-oriented generated molecules are optimized to meet practical industrial requirements. Existing mainstream MO approaches primarily utilize external property predictors to guide iterative property optimization. However, learning all molecular samples in the vast chemical space is un

Kunal Pal, Uwe R. Fischer

We consider the quantum dynamics of Bose-Einstein condensates at absolute zero, and demonstrate that an analogue gravity model going beyond the standard linearized analogue gravity paradigm \`a la Unruh must take into account the backreaction of quasiparticle excitations onto the condensate background. This requires that one expands to second order in pertur

Ze-Zhong Liang, Jing Wang, Hua Gao, Luis C. Ho

Non-axisymmetric structures, such as bars and spiral arms, are known to concentrate molecular gas and star formation in galaxy centers, actively building up the pseudo-bulges. However, a direct link between the neutral (i.e., molecular and atomic) gas distribution and the exerted torque forces over a broader radial range of galactic disks still remains to be

Vladimir Pimonov, Said Tahir, Vincent Jourdain

This study addresses the challenge of analyzing the growth kinetics of carbon nanotubes using in-situ homodyne polarization microscopy (HPM) by developing an automated deep learning (DL) approach. A Mask-RCNN architecture, enhanced with a ResNet-50 backbone, was employed to recognize and track individual nanotubes in microscopy videos, significantly improvin

Richard Ehrenborg

We are interested in conjecturing the sign of the pizza quantity P(H,B(a,R)) for the irreducible Coxeter arrangements H of type A_n, where n=2,3 bmod 4, and type D_n, where n is odd. Our approach is to express the pizza quantity in terms of the pizza quantity of subarrangements known as 2-structures, and we obtain the first non-zero term in the multivariate

Wei Liu, Kerem Delikoyun, Qianyu Chen, Alperen Yildiz

Off-axis digital holographic microscopy is a high-throughput, label-free imaging technology that provides three-dimensional, high-resolution information about samples, particularly useful in large-scale cellular imaging. However, the hologram reconstruction process poses a significant bottleneck for timely data analysis. To address this challenge, we propose

D. C. dos Santos

The Auger Engineering Radio Array (AERA), part of the Pierre Auger Observatory, is a facility designed to detect radio emissions from extensive air showers at high energies. Consisting of 153 autonomous radio-detector stations spread over 17 km$^2$, it detects radio waves in the frequency range of 30 to 80 MHz. Accurate characterization of the detector respo

Arianna Dionigi, Massimo Giulietti, Marco Timpanella

The study of algebraic curves $\cX$ with numerous automorphisms in relation to their genus $g(\cX)$ is a well-established area in Algebraic Geometry. In 1995, Irokawa and Sasaki \cite{Sasaki} gave a complete classification of curves over $\mathbb{C}$ with an automorphism of order $N \geq 2g(\mathcal{X}) + 1$. Precisely, such curves are either hyperelliptic w

Laura Castilla-Castellano, Angelo Lucia

The problem of determining the existence of a spectral gap in a lattice quantum spin system was previously shown to be undecidable for one [J. Bausch et al., "Undecidability of the spectral gap in one dimension", Physical Review X 10 (2020)] or more dimensions [T. S. Cubitt et al., "Undecidability of the spectral gap", Nature 528 (2015)]. In these works, fam

Haipeng Li, Jiangxia Cao, Yiwen Gao, Yunhuai Liu

Cross-Domain Sequential Recommendation (CDSR) is a hot topic in sequence-based user interest modeling, which aims at utilizing a single model to predict the next items for different domains. To tackle the CDSR, many methods are focused on domain overlapped users' behaviors fitting, which heavily relies on the same user's different-domain item sequences colla

Daniel Leal, Ngoc Hung Nguyen, Alex Skvortsov, Sanjeev Arulampalam

This paper investigates the application of game-theoretic principles combined with advanced Kalman filtering techniques to enhance maritime target tracking systems. Specifically, the paper presents a two-player, imperfect information, non-cooperative, sequential game framework for optimal decision making for a tracker and an evader. The paper also investigat