October 2024 arXiv papers — page 107

Xinyi Yuan, Zhiwei Shang, Zifan Wang, Chenkai Wang

Diffusion models demonstrate superior performance in capturing complex distributions from large-scale datasets, providing a promising solution for quadrupedal locomotion control. However, the robustness of the diffusion planner is inherently dependent on the diversity of the pre-collected datasets. To mitigate this issue, we propose a two-stage learning fram

Kuan-Ying Lee, Qian Zhou, Klara Nahrstedt

Multi-camera systems are indispensable in movies, TV shows, and other media. Selecting the appropriate camera at every timestamp has a decisive impact on production quality and audience preferences. Learning-based view recommendation frameworks can assist professionals in decision-making. However, they often struggle outside of their training domains. The sc

Daniel Arean, David Garcia-Fariña

We construct and study inhomogeneous non-Hermitian strongly coupled holographic field theories. We consider two models: a lattice where in each site there is some inflow/outflow of matter and a Hermitian/non-Hermitian/Hermitian junction. By tuning a complex external gauge field, we find a non-Hermitian model which can be mapped to a Hermitian one via a compl

Neil A. Chriss

This is the third paper in a series concerning the game-theoretic aspects of position-building while in competition. The first paper set forth foundations and laid out the essential goal, which is to minimize implementation costs in light of how other traders are likely to trade. The majority of results in that paper center on the two traders in competition

Nikolaos-Antonios Ypsilantis, Ondřej Chum

This work proposes a novel method for object co-segmentation, i.e. pixel-level localization of a common object in a set of images, that uses no pixel-level supervision for training. Two pre-trained Vision Transformer (ViT) models are exploited: ImageNet classification-trained ViT, whose features are used to estimate rough object localization through intra-cl

Arlyn Reese Madsen

This paper investigates the impact of dynamic range compression (DRC) on music genre classification accuracy. By applying various compression settings to the test set of 200 songs, we aim to determine if compression can enhance the classifier's ability to discern distinct musical genres. A support vector machine (SVM) classifier was trained on the original,

Vishwa Prakash HV, Pratik Ghosal, Prajakta Nimbhorkar, Nithin Varma

We study the problem of finding an envy-free allocation of indivisible goods among agents with additive valuations. We focus on the fairness notion of envy-freeness up to any good (EFX). A central open question in fair division is whether EFX allocations always exist for any number of agents. While EFX has been established for three agents [CGM24] and for an

Zhiqiang Kou, Haoyuan Xuan, Jing Wang, Yuheng Jia

Label Distribution Learning (LDL) is a novel machine learning paradigm that addresses the problem of label ambiguity and has found widespread applications. Obtaining complete label distributions in real-world scenarios is challenging, which has led to the emergence of Incomplete Label Distribution Learning (InLDL). However, the existing InLDL methods overloo

Shitao Li, Minjia Shi, Yang Li, San Ling

Finding a mass formula for a given class of linear codes is a fundamental problem in combinatorics and coding theory. In this paper, we consider the action of the unitary (resp. symplectic) group on the set of all Hermitian (resp. symplectic) linear complementary dual (LCD) codes, prove that all Hermitian (resp. symplectic) LCD codes are on a unique orbit un

Benjamin Leblanc, Mathieu Bazinet, Nathaniel D'Amours, Alexandre Drouin

Both PAC-Bayesian and Sample Compress learning frameworks are instrumental for deriving tight (non-vacuous) generalization bounds for neural networks. We leverage these results in a meta-learning scheme, relying on a hypernetwork that outputs the parameters of a downstream predictor from a dataset input. The originality of our approach lies in the investigat

Simone Rademacher

We consider a Bose gas on the unit torus at zero temperature in the Gross-Pitaevskii regime, known to perform Bose-Einstein condensation: a macroscopic fraction of the bosons occupy the same quantum state, called condensate. We study the Bose gas' quantum depletion, that is the number of bosons outside the condensate, and derive an explicit asymptotic formul

Huangjun Zhu, Chengsi Mao, Changhao Yi

When the local dimension $d$ is an odd prime, the qudit Clifford group is only a 2-design, but not a 3-design, unlike the qubit counterpart. This distinction and its extension to Clifford orbits have profound implications for many applications in quantum information processing. In this work we systematically delve into general qudit Clifford orbits with a fo

François Bienvenu

Models of random phylogenetic networks have been used since the inception of the field, but the introduction and rigorous study of mathematically tractable models is a much more recent topic that has gained momentum in the last 5 years. This manuscript discusses some recent developments in the field through a selection of examples. The emphasis is on the tec

Peng Liu, Pengming Zhu, Zhiwen Zeng, Xuekai Qiu

In practical applications, the unpredictable movement of obstacles and the imprecise state observation of robots introduce significant uncertainties for the swarm of robots, especially in cluster environments. However, existing methods are difficult to realize safe navigation, considering uncertainties, complex environmental structures, and robot swarms. Thi

Chengsi Mao, Changhao Yi, Huangjun Zhu

Shadow estimation is a sample-efficient protocol for learning the properties of a quantum system using randomized measurements, but the current understanding of qudit shadow estimation is quite limited compared with the qubit setting. Here we clarify the sample complexity of qudit shadow estimation based on the Clifford group, where the local dimension $d$ i

Guosheng Zhao, Chaojun Ni, Xiaofeng Wang, Zheng Zhu

Closed-loop simulation is essential for advancing end-to-end autonomous driving systems. Contemporary sensor simulation methods, such as NeRF and 3DGS, rely predominantly on conditions closely aligned with training data distributions, which are largely confined to forward-driving scenarios. Consequently, these methods face limitations when rendering complex

Tobias Czempiel, Alfie Roddan, Maria Leiloglou, Zepeng Hu

This study investigates the reconstruction of hyperspectral signatures from RGB data to enhance surgical imaging, utilizing the publicly available HeiPorSPECTRAL dataset from porcine surgery and an in-house neurosurgery dataset. Various architectures based on convolutional neural networks (CNNs) and transformer models are evaluated using comprehensive metric

Friederike Butt, David F. Locher, Katharina Brechtelsbauer, Hans Peter Büchler

Reliable execution of large-scale quantum algorithms requires robust underlying operations and this challenge is addressed by quantum error correction (QEC). Most modern QEC protocols rely on measurements and feed-forward operations, which are experimentally demanding, and often slow and prone to high error rates. Additionally, no single error-correcting cod

Yujian Zhao, Chengru Wu, Yinong Xu, Xuanzheng Du

Cloth-changing person re-identification (CC-ReID), also known as Long-Term Person Re-Identification (LT-ReID) is a critical and challenging research topic in computer vision that has recently garnered significant attention. However, due to the high cost of constructing CC-ReID data, the existing data-driven models are hard to train efficiently on limited dat

Jack Hilliard, Adrian Hilton, Jean-Yves Guillemaut

Recent illumination estimation methods have focused on enhancing the resolution and improving the quality and diversity of the generated textures. However, few have explored tailoring the neural network architecture to the Equirectangular Panorama (ERP) format utilised in image-based lighting. Consequently, high dynamic range images (HDRI) results usually ex

Manjari Shankar, Anna-Maria Hartner, Callum R. K. Arnold, Ezra Gayawan

Mathematical models are established tools to assist in outbreak response. They help characterise complex patterns in disease spread, simulate control options to assist public health authorities in decision-making, and longer-term operational and financial planning. In the context of vaccine-preventable diseases (VPDs), vaccines are one of the most-cost effec

Jooyeol Yun, Davide Abati, Mohamed Omran, Jaegul Choo

Location modeling, or determining where non-existing objects could feasibly appear in a scene, has the potential to benefit numerous computer vision tasks, from automatic object insertion to scene creation in virtual reality. Yet, this capability remains largely unexplored to date. In this paper, we develop a generative location model that, given an object c

Jesus Garcia Fernandez, Nasir Ahmad, Marcel van Gerven

Learning is a fundamental property of intelligent systems, observed across biological organisms and engineered systems. While modern intelligent systems typically rely on gradient descent for learning, the need for exact gradients and complex information flow makes its implementation in biological and neuromorphic systems challenging. This has motivated the

Paul Youssef, Jörg Schlötterer, Christin Seifert

Pre-trained Language Models (PLMs) encode various facts about the world at their pre-training phase as they are trained to predict the next or missing word in a sentence. There has a been an interest in quantifying and improving the amount of facts that can be extracted from PLMs, as they have been envisioned to act as soft knowledge bases, which can be quer

S. A. Tyul'bashev, V. A. Samodurov, A. S. Pozanenko, E. A. Brylyakova

We report the discovery of a bright pulse having a dispersion measure (DM) equal to 134.4 \pm 2 pc cm^{-3}, a peak flux density (S_p) equal to 20 \pm 4 Jy and a half-width (W_e) equal to 211 \pm 6 ms. The excessive DM of the pulse, after taking into account the Milky Way contribution, is 114 pc cm^{-3} that indicates its extragalactic origin. Such value of D

Haoming Wang

The derivation of zonal polynomials involves evaluating the integral \[ \exp\left( - \frac{1}{2} \operatorname{tr} D_{\beta} Q D_{l} Q \right) \] with respect to orthogonal matrices \(Q\), where \(D_{\beta}\) and \(D_{l}\) are diagonal matrices. The integral is expressed through a polynomial expansion in terms of the traces of these matrices, leading to the

Tomasz Goliński, Gabriel Larotonda, Alice Barbora Tumpach

For a Banach--Lie group $G$ and an embedded Lie subgroup $K$ we consider the homogeneous Banach manifold $\mathcal M=G/K$. In this context we establish the most general conditions for a bounded operator $N$ acting on $Lie(G)$ to define a homogeneous vector bundle map $\mathcal N:T\mathcal M\to T\mathcal M$. In particular our considerations extend all previou

Iván De-Rosende-Celeiro, Virginia Francisco-Gilmartín, Susana Bautista-Blasco, Adriana Ávila-Álvarez

Objective: The objectives encompassed (1) the creation of Recuerdame, a digital app specifically designed for occupational therapists, aiming to support these professionals in the processes of planning, organizing, developing, and documenting reminiscence therapies for older people with dementia, and (2) the evaluation of the designed prototype through a par

N. A. S. Bulkhali, G. Kavya Keerthana, Ranganatha Dasappa

In this paper, we derive a general formula to express the product of three theta functions as a linear combination of other products of three theta functions. Moreover, we use the main formula to deduce a general formula for the product of two theta functions. Furthermore, as applications, we extract several theorems in the theory of representation of intege

Omid Amini, Eduardo Esteves, Eduardo Garcez

A level graph is the data of a pair $(G,\pi)$ consisting of a finite graph $G$ and an ordered partition $\pi$ on the set of vertices of $G$. To each level graph on $n$ vertices we associate a polytope in $\mathbb R^n$ called its residue polytope. We show that residue polytopes are compatible with each other in the sense that if $\pi'$ is a coarsening of $\pi

Yuki Hou, Haruki Tamoto, Qinghua Zhao, Homei Miyashita

Existing retrieval methods in Large Language Models show degradation in accuracy when handling temporally distributed conversations, primarily due to their reliance on simple similarity-based retrieval. Unlike existing memory retrieval methods that rely solely on semantic similarity, we propose SynapticRAG, which uniquely combines temporal association trigge

Romain Gervalle

In the realm of spacetimes governed by Einstein's general relativity and containing only Maxwell's electromagnetic field, stationary black holes are fully characterized by their mass, electric or magnetic charge, and angular momentum -- a property encapsulated in a version of the no-hair theorem. However, the validity of this theorem is contingent on certain

Aser Cortines, Itamar Harel, Dmitry Ioffe, Oren Louidor

A new ``Percolation with Clustering'' (PWC) model is introduced, where (the probabilities of) site percolation configurations on the leaf set of a binary tree are rewarded exponentially according to a generic function, which measures the degree of clustering in the configuration. Conditions on such ``clustering function'' are given for the existence of a lim

Diego López-Alcalá, Ziqi Hu, José J. Baldoví

Carbon chemistry has attracted a lot of attention by chemists, physicists and material scientists in the last decades. The recent discovery of graphullerene provides a promising platform for many applications due to its exceptional electronic properties and the possibility to host molecules or clusters inside the fullerene units. Herein, we introduce graphen

Itai Arad, Miklos Santha

We introduce $k$-local quasi-quantum states: a superset of the regular quantum states, defined by relaxing the positivity constraint. We show that a $k$-local quasi-quantum state on $n$ qubits can be 1-1 mapped to a distribution of assignments over $n$ variables with an alphabet of size $4$, which is subject to non-linear constraints over its $k$-local margi

Guy Blanc, Gregory Valiant

We resolve a fundamental question about the ability to perform a statistical task, such as learning, when an adversary corrupts the sample. Such adversaries are specified by the types of corruption they can make and their level of knowledge about the sample. The latter distinguishes between sample-adaptive adversaries which know the contents of the sample wh

Fabian Hassler

These lecture notes offer a pedagogical yet concise introduction to topological quantum computing. The material focuses on topological superconductors and Majorana qubits. It concludes with a discussion of more general braiding phenomena. In particular, the notes delve into the non-Abelian braiding statistics of Ising and Fibonacci anyons. Although not compr

Xiaoyu Chen, Junliang Guo, Tianyu He, Chuheng Zhang

We introduce Image-GOal Representations (IGOR), aiming to learn a unified, semantically consistent action space across human and various robots. Through this unified latent action space, IGOR enables knowledge transfer among large-scale robot and human activity data. We achieve this by compressing visual changes between an initial image and its goal state in

Hiba Bibi, Marc Soret, Marina Ville

An isometric immersion $X: \Sigma^n \longrightarrow \mathbb{E}^{n+1}$ is biharmonic if $\Delta^2 X = 0$, i.e. if $\Delta H =0$, where $\Delta$ and $H$ are the metric Laplacian and the mean curvature vector field of $\Sigma^n$ respectively. More generally, biconservative hypersurfaces (BCH) are isometric immersions for which only the tangential part of the bi

Sajjad Akherati, Yok Jye Tang, Xinmiao Zhang

Homomorphic encryption (HE) allows computations to be directly carried out on ciphertexts and is essential to privacy-preserving computing, such as neural network inference, medical diagnosis, and financial data analysis. Only addition and 2-input multiplication are defined over ciphertexts in popular HE schemes. However, many HE applications involve non-lin

Anya Nordskova, Michel Van den Bergh

We define a Young subgroup of the braid group as a subgroup generated by an arbitrary subset of the Birman-Ko-Lee generators. We give an intrinsic description of such subgroups which yields, in particular, an easy criterion to decide membership. We also give an algorithm to write an element of a Young subgroup as a product of the generators. Our methods are

Omid Amini, Eduardo Esteves, Eduardo Garcez

We describe the limits of canonical series along families of curves degenerating to a nodal curve which is general for its topology, in the weak sense that the branches over nodes on each of its components are in general position. We define a fan structure on the space of edge lengths on the dual graph of the limit curve, and construct a projective variety p

Zhe Zhang, Xingyu Liu, Yuanzhang Lin, Xiang Gao

Automated unit test generation has been widely studied, with Large Language Models (LLMs) recently showing significant potential. Moreover, in the context of unit test generation, these tools prioritize high code coverage, often at the expense of practical usability, correctness, and maintainability. In response, we propose Property-Based Retrieval Augmentat

Edison P. Velasco-Sánchez, Luis F. Recalde, Guanrui Li, Francisco A. Candelas-Herias

This paper reports on a novel method for LiDAR odometry estimation, which completely parameterizes the system with dual quaternions. To accomplish this, the features derived from the point cloud, including edges, surfaces, and Stable Triangle Descriptor (STD), along with the optimization problem, are expressed in the dual quaternion set. This approach enable

Andrea León, Beatriz Costa, Thomas Heine, Thomas Brumme

Single layers of transition-metal thiophosphates (2D-TMPS$_{3}$) van der Waals magnets are an ideal platform for studying antiferromagnetic interactions in two dimensions. However, the magnetic coupling mechanism between two or more individual layers of these materials remains mostly unexplored. This study presents a density-functional based analysis and ana

Ondřej Straka, Jindřich Havlík

The paper deals with measures of nonlinearity. In state estimation, they are utilized i) to select a suitable state estimation algorithm by assessing the nonlinearity of a system model, ii) to adapt the estimation algorithm structure or parameters, or iii) to indicate the possible effect of strong nonlinearity that leads to estimate credibility loss. This pa

Abhinaba Upadhyay, Arvind Kumar, Harleen Dahiya, Suneel Dutt

This work primarily focusses on determining the magnetic moments of $\frac{1}{2}^-$ baryon resonances in the presence of hot and dense hadronic matter. In the chiral $SU(3)$ quark mean field model approach, we have essentially accounted for the effects on in-medium scalar meson fields to investigate the impact of high densities on the in-medium baryon masses

Jie Xu

Let $ (M, g) $ be a compact manifold or a complete non-compact manifold without boundary, $ \dim M \geqslant 4 $, and not locally conformally flat. In this article, we introduce a new local method to resolve the Yamabe problem on compact manifold for dimensions at least $ 4 $, and the Yamabe problem on non-compact complete manifolds without boundary, which a

Simon D. Fink, Ignaz Rutter, Sandhya T. P

In the Partially Embedded Planarity problem, we are given a graph $G$ together with a topological drawing of a subgraph $H$ of $G$. The task is to decide whether the drawing can be extended to a drawing of the whole graph such that no two edges cross. Angelini et al. gave a linear-time algorithm for solving this problem in 2010 (SODA '10). While their paper

Xiaoye Zhou, Yuqi Liu, Benrui Tang, Junyuan Wang

First-principles molecular dynamics simulations of heat transport in systems with large-scale structural features are challenging due to their high computational cost. Here, using polycrystalline graphene as a case study, we demonstrate the feasibility of simulating heat transport with near first-principles accuracy in systems containing over 1.4 million ato

Yukiyoshi Nakkajima

We construct a fundamental theory of the derived category of non-finite bi-filtered complexes.

Hiroki Matsuda, Michio Otsuki

We numerically investigate the stress distribution in pastes after horizontal rotation by using an elasto-plastic model. Residual stress remains as a memory of rotation. The stress in the circumferential direction increases after the rotation, whereas that in the radial direction decreases. The residual stress is analytically related to the plastic deformati

Kejun Ren, Xin Wu, Lianming Xu, Li Wang

Unmanned Aerial Vehicle (UAV) remote sensing, with its advantages of rapid information acquisition and low cost, has been widely applied in scenarios such as emergency response. However, due to the long imaging distance and complex imaging mechanisms, targets in remote sensing images often face challenges such as small object size, dense distribution, and lo

Carla M. Brunner, Nicolas Y. Joly

Optical fibers constitute an attractive platform for the realization of nonlinear and quantum optics processes. Here we show, through theoretical considerations, how polarization effects of both third-order parametric down-conversion and four-wave-mixing in optical fibers may be exploited to enhance detection schemes. We apply our general framework specifica

Barbara Roessle, Norman Müller, Lorenzo Porzi, Samuel Rota Bulò

We propose L3DG, the first approach for generative 3D modeling of 3D Gaussians through a latent 3D Gaussian diffusion formulation. This enables effective generative 3D modeling, scaling to generation of entire room-scale scenes which can be very efficiently rendered. To enable effective synthesis of 3D Gaussians, we propose a latent diffusion formulation, op

Zhaoyang Mul, Aoming Liang, Mingming Ge, Dashuai Chen

The interaction of waves with structural barriers such as dams breaking plays a critical role in flood defense and tsunami disasters. In this work, we explore the dynamic changes in wave surfaces impacting various structural shapes, e.g., circle, triangle, and square, by using deep learning techniques. We introduce the DamFormer, a novel transformer-based mo

Qi Cheng, Hongru Cao, Sian-Jheng Lin

The evolving $k$-threshold secret sharing scheme allows the dealer to distribute the secret to many participants such that only no less than $k$ shares together can restore the secret. In contrast to the conventional secret sharing scheme, the evolving scheme allows the number of participants to be uncertain and even ever-growing. In this paper, we consider

Aditya Mallick, Rahul L R, Albert Shaiju, Satya Deepika Neelapala

Clinicians generally diagnose cardiovascular diseases (CVDs) using standard 12-Lead electrocardiogram (ECG). However, for smartphone-based public healthcare systems, a reduced 3-lead system may be preferred because of (i) increased portability, and (ii) reduced requirement for power, storage and bandwidth. Subsequently, clinicians require accurate 3-lead to

Niek Kerssies, Jose Segovia Martin, James Winters

Like other social animals and biological systems, human groups constantly exchange information. Network models provide a way of quantifying this process by representing the pathways of information propagation between individuals. Existing approaches to studying these networks largely hypothesize network formation to be a result of cognitive biases and choice

Muhammad Saad Nawaz, Thomas Dallmann, Torsten Schoen, Dirk Heberling

For the validation and verification of automotive radars, datasets of realistic traffic scenarios are required, which, how ever, are laborious to acquire. In this paper, we introduce radar scene synthesis using GANs as an alternative to the real dataset acquisition and simulation-based approaches. We train a PointNet++ based GAN model to generate realistic r

Tetsuro Kubo, Hiroshi Yasuoka, Deepa Kasinathan, K. M. Ranjith

Nuclear magnetic resonance (NMR) techniques have been used to study the static and dynamic microscopic properties of the Weyl semimetal NbP. From a complete analysis of the angular dependence of the $^{93}$Nb-NMR spectra in a single crystal, the parameters for the electric quadrupole interactions and the magnetic hyperfine interactions were determined to be

Justin Cano, Julien Queyrel, Laurent Castanet, Michel Bousquet

This paper presents the design and implementation of the Scintillation Filter by Kalman-colored algorithm (SciFi), which is used to remove tropospheric scintillation from Q/V bands total attenuation data series. In contrast to the classical methods using low-pass filters, the SciFi algorithm allows to estimate both the attenuation, its slope and a confidence

Che Liu, Zhongwei Wan, Haozhe Wang, Yinda Chen

Medical Vision-Language Pre-training (MedVLP) has made significant progress in enabling zero-shot tasks for medical image understanding. However, training MedVLP models typically requires large-scale datasets with paired, high-quality image-text data, which are scarce in the medical domain. Recent advancements in Large Language Models (LLMs) and diffusion mo

Alec McClean, Yiting Li, Sunjae Bae, Mara A. McAdams-DeMarco

Comparing outcomes across treatments is essential in medicine and public policy. To do so, researchers typically estimate a set of parameters, possibly counterfactual, with each targeting a different treatment. Treatment-specific means are commonly used, but their identification requires a positivity assumption, that every subject has a non-zero probability

Germano R. Mugambi, Nicolae Darii, Hesam Khazraj, Oscar S. Romano

The development of larger Offshore Wind Power Plants (OWPPs) is moving towards multi-vendor setups, ultimately aiming to establish Energy hubs. These structures are characterized by installations from different vendors sharing the same connection or closely interconnected points. Control interactions among Wind Turbine (WT) converters and power systems have

Matteo Bollini, Francesco Bacchiocchi, Matteo Castiglioni, Alberto Marchesi

We study principal-agent problems where a farsighted agent takes costly actions in an MDP. The core challenge in these settings is that agent's actions are hidden to the principal, who can only observe their outcomes, namely state transitions and their associated rewards. Thus, the principal's goal is to devise a policy that incentives the agent to take acti

Doyon Kim

We give a formula for a birational map on the Schubert cell associated to each Weyl group element of $G=\text{GL}(n)$. The map simplifies the UDL decomposition of matrices, providing structural insight into the Schubert cell decomposition of the flag variety $G/B$, where $B$ is a Borel subgroup. An application of the formula includes a new proof of the exist

Beatriz Villarroel, Kevin Krisciunas

Unidentified Anomalous Phenomena (UAP) have historically been stigmatized and regarded as pseudoscience due to a general lack of robust evidence. Recently, however, the subject has gained interest among astronomers and the military. This review explores how astronomers can enhance our understanding of these enigmatic phenomena by focusing on empirical tests

Hervé Dutrieux, Thibaud Meisgny, Cédric Mezrag, Hervé Moutarde

The unique experimental connection to the QCD energy-momentum tensor offered by generalised parton distributions has been strongly highlighted in the past few years with attempts to extract the pressure and shear forces distributions within the nucleon. If, in principle, this can be performed in a model independent way from experimental data, in practice, th

Meghdoot Biswas, Daewook Kim

New Interferometric Testing Utility (NITU) is a newly developed Python package for analyzing and visualizing interferometric data. It provides Zernike decomposition, interactive visualization, time series analysis, and additional features for optical manufacturing and testing.

Virgile Rennard, Christos Xypolopoulos, Michalis Vazirgiannis

Large language models (LLMs) inherit biases from their training data and alignment processes, influencing their responses in subtle ways. While many studies have examined these biases, little work has explored their robustness during interactions. In this paper, we introduce a novel approach where two instances of an LLM engage in self-debate, arguing opposi

Marco Spinaci, Marek Polewczyk, Johannes Hoffart, Markus C. Kohler

Self-supervised learning on tabular data seeks to apply advances from natural language and image domains to the diverse domain of tables. However, current techniques often struggle with integrating multi-domain data and require data cleaning or specific structural requirements, limiting the scalability of pre-training datasets. We introduce PORTAL (Pretraini

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

The beta decay of the lightest charmed baryon $\Lambda_c^+$ provides unique insights into the fundamental mechanism of strong and electro-weak interactions, serving as a testbed for investigating non-perturbative quantum chromodynamics and constraining the Cabibbo-Kobayashi-Maskawa (CKM) matrix parameters. This article presents the first observation of the C

Efimia Panagiotaki, Georgi Pramatarov, Lars Kunze, Daniele De Martini

Testing and validating Autonomous Vehicle (AV) performance in safety-critical and diverse scenarios is crucial before real-world deployment. However, manually creating such scenarios in simulation remains a significant and time-consuming challenge. This work introduces a novel method that generates dynamic temporal scene graphs corresponding to diverse traff

Dwijendra Dubey, A. Mishra, Subrata Ghosh, M. V. Reddy

Battery geometry (shape and size) is one of the important parameters which governs the battery capacity and thermal behavior. In the dynamic conditions or during the operation, the performance of batteries become much more complex. Herein, the changes in thermal behavior of lithium-ion battery (LIB)by altering the geometry i.e., length to diameter ratio (l/d

Edoardo Allegrini, Edoardo Di Paolo, Marinella Petrocchi, Angelo Spognardi

Social media platforms face an ongoing challenge in combating the proliferation of social bots, automated accounts that are also known to distort public opinion and support the spread of disinformation. Over the years, social bots have evolved greatly, often becoming indistinguishable from real users, and more recently, families of bots have been identified

Anna Felikson, Pavel Tumarkin

We provide a classification of positive integral friezes on marked bordered surfaces in the style of Conway and Coxeter. More precisely, we prove that positive integral friezes are in one-to-one correspondence with ideal triangulations supplied with a collection of rescaling constants assigned to punctures. For every triangulation the set of the collections

Aditya Sharma, Aman Dalmia, Mehran Kazemi, Amal Zouaq

Geometry problem-solving demands advanced reasoning abilities to process multimodal inputs and employ mathematical knowledge effectively. Vision-language models (VLMs) have made significant progress in various multimodal tasks. Yet, they still struggle with geometry problems and are significantly limited by their inability to perform mathematical operations

Sana Ahmadi, Francois Paugam, Tristan Glatard, Pierre Lune Bellec

The optimal training of a vision transformer for brain encoding depends on three factors: model size, data size, and computational resources. This study investigates these three pillars, focusing on the effects of data scaling, model scaling, and high-performance computing on brain encoding results. Using VideoGPT to extract efficient spatiotemporal features

Xinze Li, Sen Mei, Zhenghao Liu, Yukun Yan

Retrieval-Augmented Generation (RAG) has proven its effectiveness in mitigating hallucinations in Large Language Models (LLMs) by retrieving knowledge from external resources. To adapt LLMs for the RAG systems, current approaches use instruction tuning to optimize LLMs, improving their ability to utilize retrieved knowledge. This supervised fine-tuning (SFT)

Michael Cuntz, Thorsten Holm, Peter Jorgensen

This paper studies a non-commutative generalisation of Coxeter friezes due to Berenstein and Retakh. It generalises several earlier results to this situation: A formula for frieze determinants, a $T$-path formula expressing the Laurent phenomenon, and results on gluing friezes together. One of our tools is a non-commutative version of the weak friezes introd

Dongkun Han, Shihan Huang, Pak Kei Abia Hui, Yue Chen

Innovation and development of renewable energy devices are crucial for reaching a sustainable and environmentally conscious future. This work focuses on the development of a new type of renewable energy devices in the context of Smart Garden at the Chinese University of Hong Kong, which aims to design a bladeless wind turbine for urban areas, addressing the

Xiliang Yang, Dong Hoon Shin, Kenji Watanabe, Takashi Taniguchi

Crystal defects in hexagonal boron nitride (hBN) are emerging as versatile nanoscale optical probes with a wide application profile, spanning the fields of nanophotonics, biosensing, bioimaging and quantum information processing. However, generating these crystal defects as reliable optical emitters remains challenging due to the need for deterministic defec

Nicolas Wagner, Mario Botsch, Ulrich Schwanecke

Due to the increasing use of virtual avatars, the animation of head-hand interactions has recently gained attention. To this end, we present a novel volumetric and physics-based interaction simulation. In contrast to previous work, our simulation incorporates temporal effects such as collision paths, respects anatomical constraints, and can detect and simula

Andreas Opedal, Haruki Shirakami, Bernhard Schölkopf, Abulhair Saparov

Large language models (LLMs) can solve arithmetic word problems with high accuracy, but little is known about how well they generalize to more complex problems. This is difficult to study, as (i) much of the available evaluation data has already been seen by the most capable models during training, and (ii) existing benchmarks do not capture how problem proo

Yoav Alon, Cristina David

Large Language Models (LLMs) were shown to struggle with long-term planning, which may be caused by the limited way in which they explore the space of possible solutions. We propose an architecture where a Reinforcement Learning (RL) Agent guides an LLM's space exploration: (1) the Agent has access to domain-specific information, and can therefore make decis

Dominik Hirner, Friedrich Fraundorfer

Stereo estimation has made many advancements in recent years with the introduction of deep-learning. However the traditional supervised approach to deep-learning requires the creation of accurate and plentiful ground-truth data, which is expensive to create and not available in many situations. This is especially true for remote sensing applications, where t

Rahimanuddin Shaik, Katikela Sreeharsha Kishore

Text generation is the automated process of producing written or spoken language using computational methods. It involves generating coherent and contextually relevant text based on predefined rules or learned patterns. However, challenges in text generation arise from maintaining coherence, ensuring diversity and creativity, and avoiding biases or inappropr

Tatsuya Hiraoka, Kentaro Inui

This paper introduces repetition neurons, regarded as skill neurons responsible for the repetition problem in text generation tasks. These neurons are progressively activated more strongly as repetition continues, indicating that they perceive repetition as a task to copy the previous context repeatedly, similar to in-context learning. We identify these repe

Chen Yu, Yichu Yang, Tianlin Liu, Yangwei You

We propose a state estimation method that can accurately predict the robot's privileged states to push the limits of quadruped robots in executing advanced skills such as jumping in the wild. In particular, we present the State Estimation Transformers (SET), an architecture that casts the state estimation problem as conditional sequence modeling. SET outputs

Vikash Kumar Dubey, Suman Chakraborty, Sagar Chakraborty

The concept of evolutionarily stability and its relation with the fixed points of the replicator equation are important aspects of evolutionary game dynamics. In the light of the fact that oscillating state of a population and individuals (or players) of different roles are quite natural occurrences, we ask the question how the concept of evolutionarily stab

Javier Cárcamo, Antonio Cuevas, Luis A. Rodríguez

We provide necessary and sufficient conditions for the uniqueness of the k-means set of a probability distribution. This uniqueness problem is related to the choice of k: depending on the underlying distribution, some values of this parameter could lead to multiple sets of k-means, which hampers the interpretation of the results and/or the stability of the a

Mitchell R. Whittam, Lukas Rebholz, Benedikt Zerulla, Carsten Rockstuhl

Deciding on appropriate materials and designs for use in light sails, like the one proposed in the Breakthrough Starshot Initiative, is a topic that requires much care and forethought. Here, we offer a feasible option in the form of metasurfaces made of periodically arranged homogeneous and core-shell spheres. Using the re-normalized T-matrix from Mie theory

Alessandro Micheli, Mélodie Monod

This paper tackles the challenge of learning non-Markovian optimal execution strategies in dynamic financial markets. We introduce a novel actor-critic algorithm based on Deep Deterministic Policy Gradient (DDPG) to address this issue, with a focus on transient price impact modeled by a general decay kernel. Through numerical experiments with various decay k

Ignacio A. Perez, Cristian E. La Rocca

Extensive research has focused on studying the robustness of interdependent non-directed networks and the design of mitigation strategies aimed at reducing disruptions caused by cascading failures. However, real systems such as power and communication networks are directed, which underscores the necessity of broadening the analysis by including directed netw

Sy-Yun Pu, Andrew P. Cooper, Robert J. J. Grand, Facundo A. Gómez

Ongoing large stellar spectroscopic surveys of the Milky Way seek to reconstruct the major events in the assembly history of the Galaxy. Chemical and kinematic observations can be used to separate the contributions of different progenitor galaxies to the present-day stellar halo. Here we compute the number of progenitors that contribute to the accreted stell

Ke Duan, Kai Yang, Houde Liu, Xueqian Wang

In reinforcement learning, agents collect state information and rewards through environmental interactions, essential for policy refinement. This process is notably time-consuming, especially in complex robotic simulations and real-world applications. Traditional algorithms usually re-engage with the environment after processing a single batch of samples, th

Moritz Schneider, Daniele Lain, Ivan Puddu, Nicolas Dutly

The implementations of most hardened cryptographic libraries use defensive programming techniques for side-channel resistance. These techniques are usually specified as guidelines to developers on specific code patterns to use or avoid. Examples include performing arithmetic operations to choose between two variables instead of executing a secret-dependent b

Dibyanayan Bandyopadhyay, Mohammed Hasanuzzaman, Asif Ekbal

Detecting offensive memes is crucial, yet standard deep neural network systems often remain opaque. Various input attribution-based methods attempt to interpret their behavior, but they face challenges with implicitly offensive memes and non-causal attributions. To address these issues, we propose a framework based on a Structural Causal Model (SCM). In this

Pedro Zuidberg Dos Martires, Vincent Derkinderen, Luc De Raedt, Marcus Krantz

Recent developments in AI have reinvigorated pursuits to advance the (life) sciences using AI techniques, thereby creating a renewed opportunity to bridge different fields and find synergies. Headlines for AI and the life sciences have been dominated by data-driven techniques, for instance, to solve protein folding with next to no expert knowledge. In contra