December 2024 arXiv papers — page 42

F. S. Benanti, A. Valenti

Let $F$ be a field of characteristic zero and let $ \mathcal V$ be a variety of associative $F$-algebras. In \cite{regev2016} Regev introduced a numerical sequence measuring the growth of the proper central polynomials of a generating algebra of $ \mathcal V$. Such sequence $c_n^\delta(\mathcal V), \, n \ge 1,$ is called the sequence of proper central polyno

Ian Leahy, Andrew Treglia, Gang Cao, Brian Skinner

In studying compensated semimetals, the two-band model has proven extremely useful in capturing electrical conductivity under magnetic field, as a function of density and mobility of electron-like and hole-like carriers. However, it rarely offers practical insight into magneto-thermoelectric properties. Here, we report the field dependence of thermoelectric

Matteo Forconi

Over the past decades, advancements in observational cosmology have introduced us in an era of precision cosmology, dramatically enhancing our understanding of the Universe's history as well as bringing new tensions to light. Observations of the Cosmic Microwave Background, large-scale structure, and distant galaxies have provided unprecedented insights into

Dan Shi, Tianhao Shen, Yufei Huang, Zhigen Li

The rapid development and deployment of large language models (LLMs) have introduced a new frontier in artificial intelligence, marked by unprecedented capabilities in natural language understanding and generation. However, the increasing integration of these models into critical applications raises substantial safety concerns, necessitating a thorough exami

Dennies K. Bor, Edward J. Oughton, Evan A. Peters, Noah Rivera

Space weather risk assessment is constrained by the lack of available asset information needed to model geomagnetically induced currents (GICs) in electricity transmission infrastructure. We propose a systematic method that enables risk analysts to collect their own open-source substation data. Using a web browser platform for annotation, we convert OpenStre

Arnav M. Das, Gantavya Bhatt, Lilly Kumari, Sahil Verma

Retrieval augmentation, the practice of retrieving additional data from large auxiliary pools, has emerged as an effective technique for enhancing model performance in the low-data regime. Prior approaches have employed only nearest-neighbor based strategies for data selection, which retrieve auxiliary samples with high similarity to instances in the target

Karthik V. Myilswamy, Lucas M. Cohen, Suparna Seshadri, Hsuan-Hao Lu

Frequency-bin encoding furnishes a compelling pathway for quantum information processing systems compatible with established lightwave infrastructures based on fiber-optic transmission and wavelength-division multiplexing. Yet although significant progress has been realized in proof-of-principle tabletop demonstrations, ranging from arbitrary single-qubit ga

Sourav Dey, Dorye L. Esteras, José J. Baldoví

The growing interest in 2D van der Waals (vdW) magnetic materials stems from their unique properties and potential applications in spintronics, magnonics and quantum information technologies. Among them, CrSBr is a semiconductor that stands out, owing to its high Curie temperature (TC ~ 146 K), air stability and tunable electronic and magnetic properties. He

Pascal Heid, Paul Houston, Benjamin Stamm, Thomas P. Wihler

This article deals with the stationary Gross-Pitaevskii non-linear eigenvalue problem in the presence of a rotating magnetic field that is used to model macroscopic quantum effects such as Bose-Einstein condensates (BECs). In this regime, the ground-state wave-function can exhibit an a priori unknown number of quantum vortices at unknown locations, which nec

Luca Cafaro, Lorenzo Cipriani, Francesco Fazzini, Farshid Soltani

We explore semiclassical stellar collapse scenarios with pressure within the framework of effective loop quantum gravity. The objective of this work is to generalize existent models of semiclassical dust collapse and examine the role of pressure in the formation of shell-crossing singularities in a semiclassical context. Numerical investigations show that th

Zhe Chen, Xun Lin, Yawen Cui, Zitong Yu

Missing modalities are a common challenge in real-world multimodal learning scenarios, occurring during both training and testing. Existing methods for managing missing modalities often require the design of separate prompts for each modality or missing case, leading to complex designs and a substantial increase in the number of parameters to be learned. As

Jan-Christopher Cohrs, Benjamin Berkels

Recently, the distribution-dependent Mumford-Shah model for hyperspectral image segmentation was introduced. It approximates an image based on first and second order statistics using a data term, that is built of a Mahalanobis distance plus a covariance regularization, and the total variation as spatial regularization. Moreover, to achieve feasibility, the a

Mauricio A. Diaz, Srinandan Dasmahapatra, Stefano Moretti

This paper presents hep-aid, a modular Python library conceived for utilising, implementing, and developing parameter scan algorithms. Originally devised for sample-efficient, multi-objective active search approaches in computationally expensive Beyond Standard Model (BSM) phenomenology, the library currently integrates three Machine Learning (ML)-based appr

Michele Cirafici

This note aims to offer a non-technical and self-contained introduction to gravitational algebras and their applications in the nonequilibrium physics of gravitational systems. We begin by presenting foundational concepts from operator algebra theory and exploring their relevance to perturbative quantum gravity. Additionally, we provide a brief overview of t

Zihan Tan, Julian I. U. Peters, Holger Stark

Trypanosoma brucei (T. brucei), a single-celled parasite and natural microswimmer, is responsible for fatal sleeping sickness in infected mammals, including humans. Understanding how T. brucei interacts with fluid environments and navigates through confining spaces is crucial not only for medical and clinical applications but also for a fundamental understan

M. Urbano, P. -A. Duc, T. Saifollahi, E. Sola

Deep surveys reveal tidal debris and associated compact stellar systems. Euclid's unique combination of capabilities (spatial resolution, depth, and wide sky coverage) will make it a groundbreaking tool for galactic archaeology in the local Universe, bringing low surface brightness (LSB) science into the era of large-scale astronomical surveys. Euclid's Earl

Fabrizio Guillaro, Giada Zingarini, Ben Usman, Avneesh Sud

Successful forensic detectors can produce excellent results in supervised learning benchmarks but struggle to transfer to real-world applications. We believe this limitation is largely due to inadequate training data quality. While most research focuses on developing new algorithms, less attention is given to training data selection, despite evidence that pe

Masamune Hattori, Renta Yagi, Shintarou Yanagida

We introduce a superspace analogue of combinatorial Hopf algebras (Aguiar-Bergeron-Sottile, 2006), and show that the Hopf superalgebra of quasi-symmetric (resp. symmetric) functions in superspace (Fishel-Lapointe-Pinto, 2019) is a terminal object in the category of all (resp. cocommutative) combinatorial Hopf superalgebras. We also introduce a superspace ana

Sijbren van Vaals, Yevgen Matusevych, Frank Tsiwah

Broca's aphasia is a type of aphasia characterized by non-fluent, effortful and agrammatic speech production with relatively good comprehension. Since traditional aphasia treatment methods are often time-consuming, labour-intensive, and do not reflect real-world conversations, applying natural language processing based approaches such as Large Language Model

Caroline Brosse, Nicolas Martins, Nicolas Nisse, Rudini Sampaio

The graph coloring game is a famous two-player game (re)introduced by Bodlaender in $1991$. Given a graph $G$ and $k \in \mathbb{N}$, Alice and Bob alternately (starting with Alice) color an uncolored vertex with some color in $\{1,\cdots,k\}$ such that no two adjacent vertices receive a same color. If eventually all vertices are colored, then Alice wins and

Jiatong Shi, Hye-jin Shim, Jinchuan Tian, Siddhant Arora

In this work, we introduce VERSA, a unified and standardized evaluation toolkit designed for various speech, audio, and music signals. The toolkit features a Pythonic interface with flexible configuration and dependency control, making it user-friendly and efficient. With full installation, VERSA offers 65 metrics with 729 metric variations based on differen

Subhodeep Dey, Smarajit Karmakar

Active glasses refer to a class of driven non-equilibrium systems that share remarkably similar dynamical behavior as conventional glass-formers in equilibrium. Glass-like dynamical characteristics have been observed in various biological systems from micro to macro length scales. As activity induces additional fluctuations in the system, studying how they c

Marco Cogoni, Giovanni Busonera, Enrico Gobbetti

We study the evolution of the fastest paths (FP) in transportation networks under increasing congestion. Moving from the common edge-based to a path-based analysis, we examine the directed FPs connecting random origin-destination pairs as traffic grows. We describe their shape through effective length, detour (maximum distance of FP from a straight line), in

Shuzhang Cai, Twumasi Mensah-Boateng, Xander Kuksov, Jing Yuan

Large Language Models (LLMs) have demonstrated exceptional abilities across a broad range of language-related tasks, including generating solutions to complex reasoning problems. An effective technique to enhance LLM performance is in-context learning, which encourages a step-by-step reasoning process by including explanatory examples to guide the model's re

B. Bradji, M. L. Benkhedir

In this paper, we studied the structural and electronic properties of MoSe$_2$ monolayer in its pure and doped forms, using the density functional theory (DFT), and the calculations were performed using Quantum Espresso (QE) software package. The doped systems are a MoSe$_2$ monolayer with a vacancy in Mo site (Mo vacancy system), the MoSe$_2$ monolayer with

Karim Gumerov, Samantha Rigg, Richard Mikael Slevinsky

It is well known that matrices with low Hessenberg-structured displacement rank enjoy fast algorithms for certain matrix factorizations. We show how $n\times n$ principal finite sections of the Gram matrix for the orthogonal polynomial measure modification problem has such a displacement structure, unlocking a collection of fast algorithms for computing conn

V. V. Ignatyuk, A. P. Moina

The R\'{e}nyi statistics is applied for a description of finite size effects in the 1D Ising model. We calculate the internal energy of the spin chain and the system temperature using the R\'{e}nyi distribution and postulate them to be equal to their counterparts, obtained in the microcanonical ensemble. It allows us to self-consistently derive the R\'{e}nyi

Jan Gerrit Horstmann, Ehsan Hassanpour, Yannik Zemp, Thomas Lottermoser

Controlling the domain structure of ferroic materials is key to manipulating their functionality. Typically, quasi-static electric, magnetic, or strain fields are exploited to transform or pole ferroic domains. In contrast, metallurgy makes use of fast thermal quenches across phase transitions to create new functional states and domain structures. This appro

Connor Watson

It is well-known that the consistency strength of the GCH failing at a measurable cardinal is the existence of a cardinal $\kappa$ with $o(\kappa)=\kappa^{++}$. As the literature does not contain more than a proof sketch of the lower bound of this equiconsistency, we give an expository proof which fills in the details in order to fill this gap in the literat

Jae S. Hwang, Jin Xu, Aaswath P. Raman

Tuning the spatial extent of directional thermal emission across an arbitrary, and fixed spectral bandwidth is a fundamentally enabling capability for a range of emerging applications such as thermophotovoltaics, thermal imaging, and radiative cooling. However, previous experimental demonstrations were limited to narrow bandwidths, and the resonance frequenc

Amirreza Zamani, Mikael Skoglund

We study a semantic communication problem with a privacy constraint where an encoder consists of two separate parts, e.g., encoder 1 and encoder 2. The first encoder has access to information source $X=(X_1,\ldots,X_N)$ which is arbitrarily correlated with private data $S$. The private data is not accessible by encoder 1, however, the second encoder has acce

Maxim Olshanskii, Henry von Wahl

This paper focuses on identifying the cause and proposing a remedy for the problem of spurious pressure oscillations in a sharp-interface immersed boundary finite element method for incompressible flow problems in moving domains. The numerical method belongs to the class of Eulerian unfitted finite element methods. It employs a cutFEM discretization in space

M. S. Maniuk, A. V. Korotun, V. I. Reva, I. M. Titov

The optical and plasmonic properties of the chains of prolate metallic spheroids of the dielectric substrate are studied in the work using the local field approximation. The case when spheroids are arranged in such a way that their major semi-axis belongs to the substrate plane is considered. The relations for the transverse component of the chain polarizabi

Julien Korinman

We survey various constructions of finite dimensional projective representations of mapping class groups derived from stated skein algebras.

Huaxu He

Spiking Neural Networks (SNNs) are a class of network models capable of processing spatiotemporal information, with event-driven characteristics and energy efficiency advantages. Recently, directly trained SNNs have shown potential to match or surpass the performance of traditional Artificial Neural Networks (ANNs) in classification tasks. However, in object

C. Rudresha, C. Balaji, V. Vidya Shree, S. Maruthamanikandan

Time-periodic electric field modulation of a viscoelastic dielectric fluid layer heated from below and cooled from above is examined using an Oldroyd-B type liquid. On the basis of small amplitudes of modulation, the regular perturbation method can be used to calculate the threshold for correction of the critical Rayleigh number. The dielectric constant is a

Maryam, Matteo Biagiola, Andrea Stocco, Vincenzo Riccio

Test Input Generators (TIGs) are crucial to assess the ability of Deep Learning (DL) image classifiers to provide correct predictions for inputs beyond their training and test sets. Recent advancements in Generative AI (GenAI) models have made them a powerful tool for creating and manipulating synthetic images, although these advancements also imply increase

Francisco de Arriba-Pérez, Silvia García-Méndez

Anxiety and depression are the most common mental health issues worldwide, affecting a non-negligible part of the population. Accordingly, stakeholders, including governments' health systems, are developing new strategies to promote early detection and prevention from a holistic perspective (i.e., addressing several disorders simultaneously). In this work, a

Joe Davighi, Nakarin Lohitsiri, Napat Poovuttikul

We study the physical consequences of 't Hooft anomalies in the high-temperature limit of relativistic quantum field theories with $SU(2)$, or more generally $USp(2N)$, global symmetry. The global anomaly afflicting these symmetry groups results in new transport phenomena akin to the chiral magnetic and chiral vortical effect, predicting conductivities that

Tarak Nath Maity

Ionization or excitation resulting from the noninstantaneous response of the electron cloud to nuclear recoil is known as the Migdal effect. Dark matter searches utilizing this process set the most stringent bounds on the spin-independent dark matter-nucleon scattering cross section over a large region of the sub-GeV dark matter parameter space, underscoring

Tithi Dwary, K. V. Krishna

In this work, we characterize the class of word-representable graphs with respect to the modular decomposition. Consequently, we determine the representation number of a word-representable graph in terms of the permutation-representation numbers of the modules and the representation number of the associated quotient graph. In this connection, we also obtain

Lijian Li

Multi-view clustering (MVC) has emerged as a powerful technique for extracting valuable insights from data characterized by multiple perspectives or modalities. Despite significant advancements, existing MVC methods struggle with effectively quantifying the consistency and complementarity among views, and are particularly susceptible to the adverse effects o

Ananda Theertha Suresh, Andrew Thangaraj, Aditya Nanda Kishore Khandavally

Given the ease of creating synthetic data from machine learning models, new models can be potentially trained on synthetic data generated by previous models. This recursive training process raises concerns about the long-term impact on model quality. As models are recursively trained on generated data from previous rounds, their ability to capture the nuance

I. Kovačić, A. T. Barnes, F. Bigiel, I. De Looze

As a major cooling line of interstellar gas, the far-infrared 158 {\mu}m line from singly ionised carbon [CII] is an important tracer of various components of the interstellar medium in galaxies across all spatial and morphological scales. Yet, there is still not a strong constraint on the origins of [CII] emission. In this work, we derive the resolved [CII]

Ente Lin, Xujie Zhang, Fuwei Zhao, Yuxuan Luo

Diffusion models for garment-centric human generation from text or image prompts have garnered emerging attention for their great application potential. However, existing methods often face a dilemma: lightweight approaches, such as adapters, are prone to generate inconsistent textures; while finetune-based methods involve high training costs and struggle to

Jing Si, Jianfei Xu

The study uses CSSCI-indexed literature from the China National Knowledge Infrastructure (CNKI) database as the data source. It utilizes the CiteSpace visualization software to draw knowledge graphs on aspects such as institutional collaboration and keyword co-occurrence. This analysis provides insights into the current state of research and emerging trends

Tithi Dwary, K. V. Krishna

A double-arborescence is a treelike comparability graph with an all-adjacent vertex. In this paper, we first give a forbidden induced subgraph characterization of double-arborescences, where we prove that double-arborescences are precisely $P_4$-free treelike comparability graphs. Then, we characterize a more general class consisting of $P_4$-free distance-h

Filipp N. Rybakov, Olle Eriksson, Nikolai S. Kiselev

Magnetic vortices and skyrmions are typically characterized by distinct topological invariants. This work presents a unified approach for the topological classification of these textures, encompassing isolated objects and configurations where skyrmions and vortices coexist. Using homotopy group analysis, we derive topological invariants that form the free ab

Federico Spurio, Emad Bahrami, Gianpiero Francesca, Juergen Gall

In this work, we address unsupervised temporal action segmentation, which segments a set of long, untrimmed videos into semantically meaningful segments that are consistent across videos. While recent approaches combine representation learning and clustering in a single step for this task, they do not cope with large variations within temporal segments of th

Yuchen Wang, Lei Zhao

We show that the number of $\mathbf{S}$-balanced configurations of four bodies in the plane is finite, provided that the symmetric matrix $\mathbf{S}$ is close to a numerical matrix.

Claus Hertling, Matija Vujic

Finite games in normal form and their mixed extensions are a corner stone of noncooperative game theory. Often generic finite games and their mixed extensions are considered. But the properties which one expects in generic games and the existence of games with these properties are often treated only in passing. The paper considers strong properties and prove

Kuangzhi Ge, Lingjun Chen, Kevin Zhang, Yulin Luo

Recently, significant advances have been made in Video Large Language Models (Video LLMs) in both academia and industry. However, methods to evaluate and benchmark the performance of different Video LLMs, especially their fine-grained, temporal visual capabilities, remain very limited. On one hand, current benchmarks use relatively simple videos (e.g., subti

Zhijie Li, Xi Kong, Haoyu Sun, Guanyu Qu

Water exhibits complex behaviors as a result of hydrogen bonding, and low-dimensional confined water plays a key role in material science, geology, and biology science. Conventional techniques like STM, TEM, and AFM enable atomic-scale observations but face limitations under ambient conditions and surface topographies. NV center magnetic resonance technology

Maximilian Roithner, Paola Velasco Herrejon, Koen van Greevenbroek, Aleksander Grochowicz

To meet its commitments under the Paris Agreement and reduce its dependency on energy imports, the pace, and scale of renewable energy deployment across Europe must increase dramatically over the next decade. Such a steep change in the net-zero transition will inevitably necessitate trade-offs with other societal priorities. Here we investigate a case study

Hao Li, Minghan Qin, Zhengyu Zou, Diqi He

Applying Gaussian Splatting to perception tasks for 3D scene understanding is becoming increasingly popular. Most existing works primarily focus on rendering 2D feature maps from novel viewpoints, which leads to an imprecise 3D language field with outlier languages, ultimately failing to align objects in 3D space. By utilizing masked images for feature extra

Mohammadreza Saemian, Livia Del Balzo, Djamal Gacemi, Yanko Todorov

We report room temperature heterodyne detection of a quantum cascade laser beaten with a local oscillator on a unipolar quantum photodetector in two different atmospheric windows, at 4.8 $\mu$m and 9 $\mu$m. A noise equivalent power of few pW is measured by employing an active stabilization technique in which the local oscillator and the signal are locked in

Renyang Liu, Ziyu Lyu, Wei Zhou, See-Kiong Ng

In the rapidly evolving field of Artificial Intelligence Generated Content (AIGC), a central challenge is distinguishing AI-synthesized images from natural ones. Despite the impressive capabilities of advanced generative models in producing visually compelling images, significant discrepancies remain when compared to natural images. To systematically investi

Jianguo Huang, Yue Yu

A robust $C^0$-continuous nonconforming virtual element method (VEM) is developed for a boundary value problem arising from strain gradient elasticity in two dimensions, with the family of polygonal meshes satisfying a very general geometric assumption given in Brezzi et al. (2009) and Chen and Huang (2018). The stability condition of the VEMs is derived by

Jiamin Xu, Yuxin Zheng, Zelong Li, Chi Wang

Achieving high-quality shadow removal with strong generalizability is challenging in scenes with complex global illumination. Due to the limited diversity in shadow removal datasets, current methods are prone to overfitting training data, often leading to reduced performance on unseen cases. To address this, we leverage the rich visual priors of a pre-traine

Kaichen Ouyang, Zong Ke, Shengwei Fu, Lingjie Liu

Evolutionary algorithms (EAs) simulate natural selection but have two main limitations: (1) they rarely update individuals based on global correlations, limiting comprehensive learning; (2) they struggle with balancing exploration and exploitation, where excessive exploitation causes premature convergence, and excessive exploration slows down the search. Mor

Claus Hertling, Matija Vujic

The number of Nash equilibria of the mixed extension of a generic finite game in normal form is finite and odd. This raises the question how large the number can be, depending on the number of players and the numbers of their pure strategies. Here we present a lower bound for the maximal possible number in the case of m-player games where each player has two

Arthur Hubert, Gamal Elghazaly, Raphael Frank

Neural Radiance Fields (NeRF) revolutionized novel view synthesis in recent years by offering a new volumetric representation, which is compact and provides high-quality image rendering. However, the methods to edit those radiance fields developed slower than the many improvements to other aspects of NeRF. With the recent development of alternative radiance

M. Kake, S. I. V. Hontinfinde, M. Karimou, R. Houenou

The exact recursion relations are used to study the mixed half-integer spin-(3/2, 7/2) Blume-Capel Ising ferrimagnetic system on the Bethe lattice. Ground-state phase diagrams are computed in the $({D_{A}}/{q|J|}, {D_{B}}/{q|J|})$ plane to reveal different possible ground states of the model. Using the thermal changes of the order-parameters, interesting tem

Yang Xu, Yi Wang, Hengguan Huang, Hao Wang

Understanding training dynamics and feature evolution is crucial for the mechanistic interpretability of large language models (LLMs). Although sparse autoencoders (SAEs) have been used to identify features within LLMs, a clear picture of how these features evolve during training remains elusive. In this study, we (1) introduce SAE-Track, a novel method for

He Liu, Yu-Heng Liu, Yong-Hang Yang, Min Ju

Using a three-flavor Nambu--Jona-Lasinio model to describe the charge-parity violating effects through axion field, we investigate the axion effects on quark matter and quark-matter cores in massive hybrid stars. The properties of quark matter vary with the scaled axion field $a/f_a$ in a periodic manner, with a period of $2\pi$. Within the range from 0 to $

Yong Lu, Qi Shen, JiaXu Zhong

Let $\Phi=(G,U(\mathbb{Q}),\varphi)$ be a quaternion unit gain graph (or $U(\mathbb{Q})$-gain graph). The adjacency matrix of $\Phi$ is denoted by $A(\Phi)$ and the left row rank of $\Phi$ is denoted by $r(\Phi)$. If $\Phi$ has at least one cycle, then the length of the shortest cycle in $\Phi$ is the girth of $\Phi$, denoted by $g$. In this paper, we prove

Shiyuan Qu, Fenglian Dong, Zhiwei Wei, Chao Shang

In this paper, we describe a novel unsupervised learning scheme for accelerating the solution of a family of mixed integer programming (MIP) problems. Distinct substantially from existing learning-to-optimize methods, our proposal seeks to train an autoencoder (AE) for binary variables in an unsupervised learning fashion, using data of optimal solutions to h

Parham Rezaei, Farzan Farnia, Cheuk Ting Li

The availability of multiple training algorithms and architectures for generative models requires a selection mechanism to form a single model over a group of well-trained generation models. The selection task is commonly addressed by identifying the model that maximizes an evaluation score based on the diversity and quality of the generated data. However, s

He Liu, Kai-Jia Sun, Peng-Cheng Chu

Using an extended Polyakov-looped Nambu--Jona-Lasinio (PNJL) model to describe the baryon density fluctuations of quark matter along the isentropic trajectories corresponding to different $s/\rho_B$ values extracted from Au+Au collisions at energies $\sqrt{s_{NN}} = 7.7-200$ GeV, we investigate the effects of the first-order phase transition on the light nuc

Daniel Iľkovič, Jared León, Xichao Shu

We study a controlled random graph process introduced by Frieze, Krivelevich, and Michaeli. In this model, the edges of a complete graph are randomly ordered and revealed sequentially to a builder. For each edge revealed, the builder must irrevocably decide whether to purchase it. The process is subject to two constraints: the number of observed edges $t$ an

Fenfang Tao, Guo-Sen Xie, Fang Zhao, Xiangbo Shu

Few-shot anomaly detection (FSAD) aims to detect unseen anomaly regions with the guidance of very few normal support images from the same class. Existing FSAD methods usually find anomalies by directly designing complex text prompts to align them with visual features under the prevailing large vision-language model paradigm. However, these methods, almost al

Carmen Cârlan, Francesca Gomez, Yohan Mathew, Ketana Krishna

Frontier artificial intelligence (AI) systems present both benefits and risks to society. Safety cases - structured arguments supported by evidence - are one way to help ensure the safe development and deployment of these systems. Yet the evolving nature of AI capabilities, as well as changes in the operational environment and understanding of risk, necessit

Zixuan Shangguan, Yanjie Dong, Song Guo, Victor C. M. Leung

Facial expressions convey human emotions and can be categorized into macro-expressions (MaEs) and micro-expressions (MiEs) based on duration and intensity. While MaEs are voluntary and easily recognized, MiEs are involuntary, rapid, and can reveal concealed emotions. The integration of facial expression analysis with Internet-of-Thing (IoT) systems has signi

Renzo Bruera, Jezabel Curbelo, Guillermo Garcia-Sanchez, Ana M. Mancho

Vertical motions across the ocean are central to processes, like CO$_2$ fixation, heat removal or pollutant transport, which are essential to the Earth's climate. This work explores 3D conveyor routes {associated with} the Atlantic Meridional Overturning Circulation (AMOC). Our findings show the geometry of mixing structures in the upper and deep ocean layer

Herve Debar, Sven Dietrich, Pavel Laskov, Emil C. Lupu

Large language models (LLMs) have achieved record adoption in a short period of time across many different sectors including high importance areas such as education [4] and healthcare [23]. LLMs are open-ended models trained on diverse data without being tailored for specific downstream tasks, enabling broad applicability across various domains. They are com

Lawrence Wang, Stephen J. Roberts

Traditional analyses of gradient descent optimization show that, when the largest eigenvalue of the loss Hessian - often referred to as the sharpness - is below a critical learning-rate threshold, then training is 'stable' and training loss decreases monotonically. Recent studies, however, have suggested that the majority of modern deep neural networks achie

Yuanyuan Gao, Yalun Dai, Hao Li, Weicai Ye

3D Gaussian Splatting (3DGS) has demonstrated impressive performance in scene reconstruction. However, most existing GS-based surface reconstruction methods focus on 3D objects or limited scenes. Directly applying these methods to large-scale scene reconstruction will pose challenges such as high memory costs, excessive time consumption, and lack of geometri

Qi-Bin Sun, Sheng-Bang Qian, Li-Ying Zhu, Qin-Mei Li

Dwarf novae are semi-detached binaries, where a white dwarf accretes material from a cool main-sequence companion via an accretion disk, and are known for their intermittent outbursts, making them key systems for studying accretion physics. The accumulation of large survey datasets has challenged traditional models, which assumed that the disk remains hot an

Chau Pham, Hoang Phan, David Doermann, Yunjie Tian

The personalization model has gained significant attention in image generation yet remains underexplored for large vision-language models (LVLMs). Beyond generic ones, with personalization, LVLMs handle interactive dialogues using referential concepts (e.g., ``Mike and Susan are talking.'') instead of the generic form (e.g., ``a boy and a girl are talking.''

Fabrizio Frasca, Fabian Jogl, Moshe Eliasof, Matan Ostrovsky

To develop a preliminary understanding towards Graph Foundation Models, we study the extent to which pretrained Graph Neural Networks can be applied across datasets, an effort requiring to be agnostic to dataset-specific features and their encodings. We build upon a purely structural pretraining approach and propose an extension to capture feature informatio

Ugo Bruzzo, Peter Dalakov

We obtain explicit formulae for the Donagi-Markman (Bryant-Griffiths, Yukawa) cubic for Hitchin systems of type $A_2$, $B_2$ and $G_2$. This is achieved by evaluating the quadratic residues in the Balduzzi-Pantev formula, using a previous result of ours. For $G_2$ we also recover earlier results of Hitchin.

Risa Shinoda, Kuniaki Saito, Shohei Tanaka, Tosho Hirasawa

Building a large-scale figure QA dataset requires a considerable amount of work, from gathering and selecting figures to extracting attributes like text, numbers, and colors, and generating QAs. Although recent developments in LLMs have led to efforts to synthesize figures, most of these focus primarily on QA generation. Additionally, creating figures direct

Christopher Mudry, Ömer M. Aksoy, Claudio Chamon, Akira Furusaki

The anisotropic quantum spin-1/2 XY model on a linear chain was solved by Lieb, Schultz, and Mattis in 1961 and shown to display a continuous quantum phase transition at the O(2) symmetric point separating two gapped phases with competing Ising long-range order. For the square lattice, the following is known. The two competing Ising ordered phases extend to

Bryan Verhoef, Rutger Hermsen, Joost de Graaf

Bacterial colonies can form a wide variety of shapes and structures based on ambient and internal conditions. To help understand the mechanisms that determine the structure of and the diversity within these colonies, various numerical modeling techniques have been applied. The most commonly used ones are continuum models, agent-based models, and lattice mode

Xiangfei Qiu, Xiuwen Li, Ruiyang Pang, Zhicheng Pan

Time series forecasting has important applications across diverse domains. EasyTime, the system we demonstrate, facilitates easy use of time-series forecasting methods by researchers and practitioners alike. First, EasyTime enables one-click evaluation, enabling researchers to evaluate new forecasting methods using the suite of diverse time series datasets c

Daniel Graf, Alex J. W. Thom

Non-orthogonal configuration interaction (NOCI) is a generalization of the standard orthogonal configuration interaction (CI) method and offers a highly flexible framework for describing ground and excited electronic states. However, this flexibility also comes with challenges, as there is still no clear or generally accepted approach for constructing a comp

Jiaqi Ma, Guo-Sen Xie, Fang Zhao, Zechao Li

Few-shot learning aims to recognize novel concepts by leveraging prior knowledge learned from a few samples. However, for visually intensive tasks such as few-shot semantic segmentation, pixel-level annotations are time-consuming and costly. Therefore, in this paper, we utilize the more challenging image-level annotations and propose an adaptive frequency-aw

Vincent Lahoche, Dine Ousmane Samary, Parham Radpay

To gain a deeper understanding of the glassy phase in $p$-spin quantum models, this paper examines the dynamics of the $N$-vector $\bm{x} \in \mathbb{R}^N$ through the framework of renormalization group theory. First, we focus on perturbation theory, which is more suitable than nonperturbative techniques due to the specific temporal non-locality of the model

Domagoj Bradač, Patryk Morawski, Benny Sudakov, Yuval Wigderson

Given a vertex-ordered graph $G$, the ordered Ramsey number $r_<(G)$ is the minimum integer $N$ such that every $2$-coloring of the edges of the complete ordered graph $K_N$ contains a monochromatic ordered copy of $G$. Motivated by a similar question posed by Erd\H{o}s and Graham in the unordered setting, we study the problem of bounding the ordered Ramsey

Jan Prüser

We propose a large structural VAR which is identified by higher moments without the need to impose economically motivated restrictions. The model scales well to higher dimensions, allowing the inclusion of a larger number of variables. We develop an efficient Gibbs sampler to estimate the model. We also present an estimator of the deviance information criter

Guillermo Garcia-Sanchez, Ana M. Mancho, Antonio G. Ramos, Josep Coca

The chaotic nature of ocean motion is a major challenge that hinders the discovery of spatio-temporal current routes that govern the transport of material. Certain material, such as oil spills, pose significant environmental threats and these are enhanced by the fact that they evolve in a chaotic sea, in a way which still nowadays is far from being systemati

Kai Ruan, Xuan Wang, Jixiang Hong, Peng Wang

While Large Language Models (LLMs) demonstrate remarkable capabilities in scientific tasks such as literature analysis and experimental design (e.g., accurately extracting key findings from papers or generating coherent experimental procedures), existing evaluation benchmarks primarily assess performance using rich contextual inputs. We introduce LiveIdeaBen

Long Bai, Beilei Cui, Liangyu Wang, Yanheng Li

Deep learning can predict depth maps and capsule ego-motion from capsule endoscopy videos, aiding in 3D scene reconstruction and lesion localization. However, the collisions of the capsule endoscopies within the gastrointestinal tract cause vibration perturbations in the training data. Existing solutions focus solely on vision-based processing, neglecting ot

Qingxin Dong, Pengtao Yang, Zhihao Liu, Yuzhi Wang

Magnetoresistance effect lays the foundation for spintronics, magnetic sensors and hard drives. The pursuit of magnetic materials with colossal magnetoresistance (CMR) and/or angular magnetoresistance (AMR) has attracted enduring research interest and extensive investigations over past decades. Here we report on the discovery of field-induced record-breaking

Chengbing Wang, Yang Zhang, Fengbin Zhu, Jizhi Zhang

Leveraging Large Language Models (LLMs) to harness user-item interaction histories for item generation has emerged as a promising paradigm in generative recommendation. However, the limited context window of LLMs often restricts them to focusing on recent user interactions only, leading to the neglect of long-term interests involved in the longer histories.

Rachael L. Fleurence, Dalia Dawoud, Jiang Bian, Mitchell K. Higashi

Introduction: Generative artificial intelligence (AI), particularly large language models (LLMs), holds significant promise for Health Economics and Outcomes Research (HEOR). However, standardized reporting guidance for LLM-assisted research is lacking. This article introduces the ELEVATE GenAI framework and checklist - reporting guidelines specifically desi

Ziqian Peng, Rachel Bawden, François Yvon

Transformer architectures are increasingly effective at processing and generating very long chunks of texts, opening new perspectives for document-level machine translation (MT). In this work, we challenge the ability of MT systems to handle texts comprising up to several thousands of tokens. We design and implement a new approach designed to precisely measu

Jiawen Qin, Pengfeng Huang, Qingyun Sun, Cheng Ji

Graph is a prevalent data structure employed to represent the relationships between entities, frequently serving as a tool to depict and simulate numerous systems, such as molecules and social networks. However, real-world graphs usually suffer from the size-imbalanced problem in the multi-graph classification, i.e., a long-tailed distribution with respect t

CMS Collaboration

"Soft" muons with a transverse momentum below 10 GeV are featured in many processes studied by the CMS experiment, such as decays of heavy-flavor hadrons or rare tau lepton decays. Maximizing the selection efficiency for these muons, while simultaneously suppressing backgrounds from long-lived light-flavor hadron decays, is therefore important for the succes

Yanheng He, Jiahe Jin, Shijie Xia, Jiadi Su

Imagine a world where AI can handle your work while you sleep - organizing your research materials, drafting a report, or creating a presentation you need for tomorrow. However, while current digital agents can perform simple tasks, they are far from capable of handling the complex real-world work that humans routinely perform. We present PC Agent, an AI sys