October 2024 arXiv papers — page 24

Enxian Chen Bin Wu Hanping Xu

This paper studies the equilibrium properties of the ``obvious strategy profile'' in large finite-player games. Each player in such a strategy profile simply adopts a randomized strategy as she would have used in a symmetric equilibrium of an idealized large game. We show that, under a continuity assumption, (i) obvious strategy profiles constitute a converg

Vishal Kumar, Zeyi Liao, Jaylen Jones, Huan Sun

Although large language models (LLMs) are typically aligned, they remain vulnerable to jailbreaking through either carefully crafted prompts in natural language or, interestingly, gibberish adversarial suffixes. However, gibberish tokens have received relatively less attention despite their success in attacking aligned LLMs. Recent work, AmpleGCG~\citep{liao

Satrio Yudhoatmojo, Utkucan Balci, Jeremy Blackburn

Online communities of known extremist groups like the alt-right and QAnon have been well explored in past work. However, we find that an extremist group called Sovereign Citizens is relatively unexplored despite its existence since the 1970s. Their main belief is delegitimizing the established government with a tactic called paper terrorism, clogging courts

Qi Zhang, Yanjie Zhang, Ao Zhang

This paper investigates a class of multiscale stochastic control problems driven by $\alpha$-stable L\'evy noises, where the controlled dynamics evolve across separate slow and fast time scales. The associated value functions are governed by a family of nonlocal Hamilton-Jacobi-Bellman (HJB) equations subject to singular perturbations. By employing the pertu

Peter P. Pedersen, Didier Queloz, Lionel Garcia, Yannick Schacke

We present the photometric performance of SPIRIT, a ground-based near-infrared InGaAs CMOS-based instrument (1280 by 1024 pixels, 12 micron pitch), using on-sky results from the SPECULOOS-Southern Observatory during 2022 - 2023. SPIRIT was specifically designed to optimise time-series photometric precision for observing late M and L type stars. To achieve th

Çağlar Tozluoğlu, Yuan Liao, Frances Sprei

Activity-based models in transport are crucial for providing a comprehensive and realistic understanding of individuals' activity-travel patterns. Traditionally, travel surveys have been used to develop these models, but they are often costly and have small sample sizes. Mobile phone application data, one example of emerging data sources, offers an alternati

Ruigang Fu, Qingyong Hu, Xiaohu Dong, Yinghui Gao

As a fundamental operation in modern machine vision models, feature upsampling has been widely used and investigated in the literatures. An ideal upsampling operation should be lightweight, with low computational complexity. That is, it can not only improve the overall performance but also not affect the model complexity. Content-aware Reassembly of Features

Alvio Renzini

Early JWST observations have revealed the ubiquitous presence in the early Universe, up to z about 16, of extreme baryon concentrations, namely forming globular clusters, extremely dense galaxies that may or may not be UV bright, and supermassive black holes in relatively low-mass galaxies. This paper is trying to pinpoint which physical conditions may have

Meike Weiß, Alice C. Niemeyer

Simplicial surfaces describe the incidence relations between vertices, edges and faces of triangulated 2-dimensional manifolds in a purely combinatorial way. By considering only the incidences of edges and faces, simplicial surfaces are closely related to cubic graphs. In this paper we investigate how properties of simplicial surfaces and cubic graphs can be

Shaked Brody, Shoval Lagziel

Sequential recommendation systems often struggle to make predictions or take action when dealing with cold-start items that have limited amount of interactions. In this work, we propose SimRec - a new approach to mitigate the cold-start problem in sequential recommendation systems. SimRec addresses this challenge by leveraging the inherent similarity among i

Jintao Tong, Yixiong Zou, Yuhua Li, Ruixuan Li

Cross-domain few-shot segmentation (CD-FSS) is proposed to first pre-train the model on a large-scale source-domain dataset, and then transfer the model to data-scarce target-domain datasets for pixel-level segmentation. The significant domain gap between the source and target datasets leads to a sharp decline in the performance of existing few-shot segmenta

Xiaoniu Song, Zihang Zhong, Rong Chen, Haibo Chen

The promising applications of large language models are often limited by the constrained GPU memory capacity available on edge devices. Mixture-of-Experts (MoE) models help address this issue by activating only a subset of the model's parameters during computation. This approach allows the unused parameters to be offloaded to host memory, thereby reducing th

Raymond Chua, Arna Ghosh, Christos Kaplanis, Blake A. Richards

In Deep Reinforcement Learning (RL), it is a challenge to learn representations that do not exhibit catastrophic forgetting or interference in non-stationary environments. Successor Features (SFs) offer a potential solution to this challenge. However, canonical techniques for learning SFs from pixel-level observations often lead to representation collapse, w

Philipp Berghofer

In quantum foundations, there is growing interest in the program of reconstructing the quantum formalism from clear physical principles. These reconstructions are formulated in an operational framework, deriving the formalism from information-theoretic principles. It has been recognized that this project is in tension with standard Psi-ontic interpretations.

Shivajay Saxena, Swagatam Islam Sarkar, Prabhat Kumar

Python is a low-cost and open-source substitute for the MATLAB programming language. This paper presents ``\texttt{PyTOPress}", a compact Python code meant for pedagogical purposes for topology optimization for structures subjected to design-dependent fluidic pressure loads. \texttt{PyTOPress}, based on the ``\texttt{TOPress}" MATLAB code \cite{kumar2023topr

Jorge Fandinno, Lute Lillo

This paper introduces a general framework for generate-and-test-based solvers for epistemic logic programs that can be instantiated with different generator and tester programs, and we prove sufficient conditions on those programs for the correctness of the solvers built using this framework. It also introduces a new generator program that incorporates the p

Cory Hymel, Sida Peng, Kevin Xu, Charath Ranganathan

In recent years, with the rapid advancement of large language models (LLMs), multi-agent systems have become increasingly more capable of practical application. At the same time, the software development industry has had a number of new AI-powered tools developed that improve the software development lifecycle (SDLC). Academically, much attention has been pa

Sunghwan Hong, Jaewoo Jung, Heeseong Shin, Jisang Han

We consider the problem of novel view synthesis from unposed images in a single feed-forward. Our framework capitalizes on fast speed, scalability, and high-quality 3D reconstruction and view synthesis capabilities of 3DGS, where we further extend it to offer a practical solution that relaxes common assumptions such as dense image views, accurate camera pose

Amareshwara Sainadh Chamarthi

The paper proposes a physically consistent numerical discretization approach for simulating viscous compressible multicomponent flows. It has two main contributions. First, a contact discontinuity (and material interface) detector is developed. In those regions of contact discontinuities, the THINC (Tangent of Hyperbola for INterface Capturing) approach is u

Anders J. E. Bjerrum, Ulrik L. Andersen, Peter Rabl

We extend the controlled displacement interaction between a qubit and a harmonic oscillator to the multi-qubit (qudit) case. We define discrete quadratures of the qudit and show how the qudit state can be displaced in these quadratures controlled by an oscillator quadrature. Using this interaction, a periodic repetition of the state encoded in the qudit, can

Marvin Manalastas, Muhammad Umar Bin Farooq, Syed Muhammad Asad Zaidi, Haneya Naeem Qureshi

Simulators are indispensable parts of the research and development necessary to advance countless industries, including cellular networks. With simulators, the evaluation, analysis, testing, and experimentation of novel designs and algorithms can be executed in a more cost-effective and convenient manner without the risk of real network service disruption. A

Hyeong-Tark Han, Jae Sung Lee, Jae-Hyung Jeon

Thermodynamic uncertainty relations (TURs) delineate tradeoff relations between the thermodynamic cost and the magnitude of an observable's fluctuation. While TURs have been established for various nonequilibrium systems, their applicability to systems influenced by active noise remains largely unexplored. Here, we present an explicit expression of TUR for s

David Farrell, Fedor Sukochev, Fulin Yang, Dmitriy Zanin

From the viewpoint of $*$-homomorphism on $C^{*}$-algebras, we establish the principal symbol mapping for filtered manifolds which are locally isomorphic to stratified Lie groups. Let $\mathbb{G}$ be a stratified Lie group, and let $M$ be a filtered manifold with a $\mathbb{G}$-atlas and a smooth positive density $\nu$. For the $C^{*}$-algebra bundle $E_{hom

Pin-Yen Huang, Szu-Wei Fu, Yu Tsao

State-of-the-art (SOTA) semi-supervised learning techniques, such as FixMatch and it's variants, have demonstrated impressive performance in classification tasks. However, these methods are not directly applicable to regression tasks. In this paper, we present RankUp, a simple yet effective approach that adapts existing semi-supervised classification techniq

Christian Janos Lebeda, Jakub Tětek

Suppose we have a sample from a distribution $D$ and we want to test whether $D = D^*$ for a fixed distribution $D^*$. Specifically, we want to reject with constant probability, if the distance of $D$ from $D^*$ is $\geq \varepsilon$ in a given metric. In the case of continuous distributions, this has been studied thoroughly in the statistics literature. Nam

Dangli Gao, Zhigang Wang, Xiangyu Zhang, Qing Pang

Persistent phosphor has emerged as a promising candidate for information storage due to the rapid accessibility and low-energy requirements. However, the low storage capacity has limited its practical application. Herein, we skillfully designed and developed NaGdGeO4:Pb2+,Tb3+ stimulated phosphor by trace doped Sm3+. As expected, this phosphor demonstrates t

Emily J. Griffith, David W. Hogg, Sten Hasselquist, James W. Johnson

Many nucleosynthetic channels create the elements, but two-parameter models characterized by $\alpha$ and Fe nonetheless predict stellar abundances in the Galactic disk to accuracies of 0.02 to 0.05 dex for most measured elements, near the level of current abundance uncertainties. It is difficult to make individual measurements more precise than this to inve

Barbara Hoffmann, Jana Vatter, Ruben Mayer

The European Union's Artificial Intelligence Act (AI Act) introduces comprehensive guidelines for the development and oversight of Artificial Intelligence (AI) and Machine Learning (ML) systems, with significant implications for Graph Neural Networks (GNNs). This paper addresses the unique challenges posed by the AI Act for GNNs, which operate on complex gra

Zhi Chang, Chukwudi Obite, Shuang Zhou, Shiwei Lan

Motivated by deep neural networks, the deep Gaussian process (DGP) generalizes the standard GP by stacking multiple layers of GPs. Despite the enhanced expressiveness, GP, as an $L_2$ regularization prior, tends to be over-smooth and sub-optimal for inhomogeneous subjects, such as images with edges. Recently, Q-exponential process (Q-EP) has been proposed as

Elisabeth Kirsten, Ivan Habernal, Vedant Nanda, Muhammad Bilal Zafar

Last few years have seen unprecedented advances in capabilities of Large Language Models (LLMs). These advancements promise to benefit a vast array of application domains. However, due to their immense size, performing inference with LLMs is both costly and slow. Consequently, a plethora of recent work has proposed strategies to enhance inference efficiency,

Valentina Bais

We present a proof of the fact that a closed orientable 4-manifold is parallelizable if and only if its second Stiefel-Whitney class, first Pontryagin class and Euler characteristics vanish. This follows from a stronger result due to Dold and Whitney on the classification of oriented sphere bundles over a 4-complex. The contribution of this note is to outlin

Alexander Osterkorn, Yuta Murakami, Tatsuya Kaneko, Zhiyuan Sun

We theoretically study dynamical excitonic condensates occurring in bilayers with an imposed chemical potential difference and in photodoped semiconductors. We show that optical spectroscopy can experimentally identify phase-trapped and phase-delocalized dynamical regimes of condensation. In the weak-bias regime, the trapped dynamics of the order parameter's

Elsa Andres, Romualdo Pastor-Satorras, Michele Starnini, Márton Karsai

Behavioral adoptions are influenced by peers in different ways. While some individuals may change after a single incoming influence, others need multiple cumulated attempts. These two mechanism, known as the simple and the complex contagions, often occur together in social phenomena alongside personal factors determining individual adoptions. Here we aim to

Qiuhao Wang, Shaohang Xu, Chin Pang Ho, Marek Petrik

We develop a generic policy gradient method with the global optimality guarantee for robust Markov Decision Processes (MDPs). While policy gradient methods are widely used for solving dynamic decision problems due to their scalable and efficient nature, adapting these methods to account for model ambiguity has been challenging, often making it impractical to

Ildus Sadrtdinov, Maxim Kodryan, Eduard Pokonechny, Ekaterina Lobacheva

It is generally accepted that starting neural networks training with large learning rates (LRs) improves generalization. Following a line of research devoted to understanding this effect, we conduct an empirical study in a controlled setting focusing on two questions: 1) how large an initial LR is required for obtaining optimal quality, and 2) what are the k

Haonan Tong, Haopeng Li, Hongyang Du, Zhaohui Yang

This paper studies an efficient multimodal data communication scheme for video conferencing. In our considered system, a speaker gives a talk to the audiences, with talking head video and audio being transmitted. Since the speaker does not frequently change posture and high-fidelity transmission of audio (speech and music) is required, redundant visual video

Chaohong Wang, Alberto Pérez de Alba Ortíz, Marjolein Dijkstra

Optimizing the synthesis of zeolites and exploring novel frameworks offer pivotal opportunities and challenges in materials design. While inverse design proves highly effective for simpler crystals, its application to intricate structures like zeolites poses severe challenges. Here, we introduce an innovative inverse design workflow tailored to efficiently r

Lior Dikstein, Ariel Lapid, Arnon Netzer, Hai Victor Habi

Zero-shot quantization (ZSQ) using synthetic data is a key approach for post-training quantization (PTQ) under privacy and security constraints. However, existing data generation methods often struggle to effectively generate data suitable for hardware-friendly quantization, where all model layers are quantized. We analyze existing data generation methods ba

Jonas Ellert, Paweł Gawrychowski, Adam Górkiewicz, Tatiana Starikovskaya

The classical pattern matching asks for locating all occurrences of one string, called the pattern, in another, called the text, where a string is simply a sequence of characters. Due to the potential practical applications, it is desirable to seek approximate occurrences, for example by bounding the number of mismatches. This problem has been extensively st

Zheyuan Liu, Guangyao Dou, Mengzhao Jia, Zhaoxuan Tan

Generative models such as Large Language Models (LLM) and Multimodal Large Language models (MLLMs) trained on massive web corpora can memorize and disclose individuals' confidential and private data, raising legal and ethical concerns. While many previous works have addressed this issue in LLM via machine unlearning, it remains largely unexplored for MLLMs.

Tijmen Vermeij, Amit Sharma, Douglas Steinbach, Jun Lou

We present a methodology for in situ Transmission Kikuchi Diffraction (TKD) tensile testing that enables nanoscale characterization of the evolution of complex plasticity mechanisms. By integrating a modified in situ scanning electron microscope nanoindenter with a microscale push-to-pull device and a conventional Electron Backscatter Diffraction (EBSD) dete

Jinli Chen, Chaohan Cui, Ben Lawrie, Yongzhou Xue

Solid-state single-photon emitters (SPEs) are attracting significant attention as fundamental components in quantum computing, communication, and sensing. Low-dimensional materials-based SPEs (LD-SPEs) have drawn particular interest due to their high photon extraction efficiency, ease of integration with photonic circuits, and strong coupling with external f

Yunjie He, Bo Xiong, Daniel Hernández, Yuqicheng Zhu

Predicting answers to queries over knowledge graphs is called a complex reasoning task because answering a query requires subdividing it into subqueries. Existing query embedding methods use this decomposition to compute the embedding of a query as the combination of the embedding of the subqueries. This requirement limits the answerable queries to queries h

Dmitriy Bilyk, Peter Grabner

We study positive definiteness of kernels $K(x,y)$ on two-point homogeneous spaces. As opposed to the classical case, which has been developed and studied in the existing literature, we allow the kernel to have an (integrable) singularity for $x=y$. Specifically, the Riesz kernel $d(x,y)^{-s}$ (where $d$ denotes some distance on the space) is a prominent exa

Qiuchi Li, Christina Lioma

The matching of competences, such as skills, occupations or knowledges, is a key desiderata for candidates to be fit for jobs. Automatic extraction of competences from CVs and Jobs can greatly promote recruiters' productivity in locating relevant candidates for job vacancies. This work presents the first model that jointly extracts and classifies competence

Arpitha P. Bharathi, Monaldo Mastrolilli

We consider the polynomial Ideal Membership Problem (IMP) for ideals encoding combinatorial problems that are instances of CSPs over a finite language. In this paper, the input polynomial $f$ has degree at most $d=O(1)$ (we call this problem IMP$_d$). We bridge the gap in \cite{MonaldoMastrolilli2019} by proving that the IMP$_d$ for Boolean combinatorial ide

Imad Ali Shah, Jiarong Li, Martin Glavin, Edward Jones

Hyperspectral Imaging (HSI) is known for its advantages over traditional RGB imaging in remote sensing, agriculture, and medicine. Recently, it has gained attention for enhancing Advanced Driving Assistance Systems (ADAS) perception. Several HSI datasets such as HyKo, HSI-Drive, HSI-Road, and Hyperspectral City have been made available. However, a comprehens

Mingzhe Li, Bo Gao, Kentaroh Toyoda, Yechao Yang

Traditional blockchain systems, such as Ethereum, typically rely on a \emph{single volatile cryptocurrency for transaction fees}. This leads to fluctuating transaction fee prices and limits the flexibility of users' payment options. To address these issues, we propose MStableChain, which leverage multiple stablecoins as native tokens for transaction fee sett

Yui Lo, Yuqian Chen, Dongnan Liu, Jon Haitz Legarreta

Brain imaging studies have demonstrated that diffusion MRI tractography geometric shape descriptors can inform the study of the brain's white matter pathways and their relationship to brain function. In this work, we investigate the possibility of utilizing a deep learning model to compute shape measures of the brain's white matter connections. We introduce

Osama A. Marzouk

This work presents a multidisciplinary mathematical model, as a set of coupled governing equations and auxiliary relations describing the fluid-flow, thermal, and electric fields of partially-ionized plasma with low magnetic Reynolds numbers. The model is generic enough to handle three-dimensionality, Hall effect, compressibility, and variability of fluid, t

Yuxin Huang, Khee-Gan Lee, Noam I. Libeskind, Sunil Simha

The Local Universe (D < 120 Mpc) has been intensely studied for decades, with highly complete galaxy redshift surveys now publicly available. These data have driven density reconstructions of the underlying matter density field, as well as constrained simulations that aim to reproduce the observed structures. In this paper, we introduce a dispersion measure

Giuseppe Del Vecchio Del Vecchio, Satya N. Majumdar

We study a simple one dimensional sluggish random walk model with subdiffusive growth. In the continuum hydrodynamic limit, the model corresponds to a particle diffusing on a line with a space dependent diffusion constant D(x)= |x|^{-\alpha} and a drift potential U(x)=|x|^{-\alpha}, where \alpha\geq 0 parametrizes the model. For \alpha=0 it reduces to the st

Grégoire Le Lay, Sarah Layani, Adrian Daerr, Michael Berhanu

It is well known that two permanent magnets of fixed orientation will either always repel or attract one another regardless of the distance between them. However, if one magnet is rotated at sufficient speed, a stable position at a given equilibrium distance can exist for a second free magnet. The equilibrium is produced by magnetic forces alone, which are s

Eugene Levin

The main goal of the paper is to show that we can treat the $1/N_c$ QCD corrections in the Pomeron calculus. We develop the one dimensional model which is a simplification of the QCD approach that includes $\pom \to 2 \pom$, $2 \pom \to \pom$ and $ 2 \pom \to 2 \pom$ vertices and gives the description of the high energy interaction, both in the framework of

José María Pérez-Poyatos, Veronica Sanz

We investigate the production of dark radiation (DR) from axions and axion-like particles (ALPs) as potential origins of dark matter. Focusing on the dark matter misalignment mechanism, we examine non-thermal, pre-inflationary scenarios that could lead to the generation of DR. A key part of our analysis involves a Bayesian approach to confront ALP parameter

Maximilian Bloor, José Torraca, Ilya Orson Sandoval, Akhil Ahmed

PC-Gym is an open-source tool for developing and evaluating reinforcement learning (RL) algorithms in chemical process control. It features environments that simulate various chemical processes, incorporating nonlinear dynamics, disturbances, and constraints. The tool includes customizable constraint handling, disturbance generation, reward function design,

Yue Kuai, Changcheng Chen, Pengfei Lu

In contrast to lead-based perovskites, double perovskites have attracted considerable interest due to their ability to modulate photovoltaic properties and high stability through elemental control. However, most double perovskites are mainly faced with large band gap ranges and indirect band gaps. Here, we report the structural, electronic, and optical prope

Takahiro Aoi

In this paper, we directly prove that if the limit of microscopic stability thresholds introduced by Berman for a polarized manifold satisfies some condition, then there exists a unique constant scalar curvature K\"{a}hler metric. This is an analogue of K.Zhang's result which is proved by the delta-invariant introduced by Fujita-Odaka. This work is motivated

Xinyue Feng, Shuxin Zhong, Jinquan Hang, Wenjun Lyu

Customer expansion, i.e., growing a business existing customer base by acquiring new customers, is critical for scaling operations and sustaining the long-term profitability of logistics companies. Although state-of-the-art works model this task as a single-node classification problem under a heterogeneous graph learning framework and achieve good performanc

Parvez Zamil, Gollam Rabby, Md. Sadekur Rahman, Sören Auer

The growing volume of biomedical scholarly document abstracts presents an increasing challenge in efficiently retrieving accurate and relevant information. To address this, we introduce a novel approach that integrates an optimized topic modelling framework, OVB-LDA, with the BI-POP CMA-ES optimization technique for enhanced scholarly document abstract categ

Qi Wang, Pu Ren, Hao Zhou, Xin-Yang Liu

When solving partial differential equations (PDEs), classical numerical methods often require fine mesh grids and small time stepping to meet stability, consistency, and convergence conditions, leading to high computational cost. Recently, machine learning has been increasingly utilized to solve PDE problems, but they often encounter challenges related to in

Glenn Wagner, Titus Neupert, Ronny Thomale, Andrzej Szczerbakow

Chiral symmetry is a fundamental property with profound implications for the properties of elementary particles, that implies a spectral symmetry (i.e. E => -E ) in their dispersion relation. In condensed matter physics, chiral symmetry is frequently associated with superconductors or materials hosting Dirac fermions such as graphene or topological insulator

Simone Müller, Dieter Kranzlmüller

Machine perception is an important prerequisite for safe interaction and locomotion in dynamic environments. This requires not only the timely perception of surrounding geometries and distances but also the ability to react to changing situations through predefined, learned but also reusable skill endings of a robot so that physical damage or bodily harm can

Zhiqi Bu, Xiaomeng Jin, Bhanukiran Vinzamuri, Anil Ramakrishna

Machine unlearning has been used to remove unwanted knowledge acquired by large language models (LLMs). In this paper, we examine machine unlearning from an optimization perspective, framing it as a regularized multi-task optimization problem, where one task optimizes a forgetting objective and another optimizes the model performance. In particular, we intro

Andrew Vlasic, Payal Solanki, Anh Pham

This manuscript introduces a computationally efficient method to calculate the nonlinearity of a quantum feature map, as well as a method for determining whether a quantum feature map will have a high concentration of quantum states. The technique analyzes quantum operators, through an extension of the functions of von Neumann entropy and state-transition ps

Mio Tomoyoshi, Kento Masuda, Teruyuki Hirano, Yui Kasagi

In single-lined spectroscopic binaries (SB1s) where flux variations due to tidal deformation of the primary star (ellipsoidal variations, EVs) are detected, the binary mass can be determined by combining EVs with the primary's radial velocity (RV) variations from orbital motion and information about the primary's radius. This method has been used for mass es

Shaozhen Shi, Yevgen Matusevych, Malvina Nissim

This study presents our submission to the Strict-Small Track of the 2nd BabyLM Challenge. We use a teacher-student distillation setup with the BabyLLaMa model (Timiryasov and Tastet, 2023) as a backbone. To make the student's learning process more focused, we replace the objective function with a reverse Kullback-Leibler divergence, known to cause mode-seeki

Manu Drijvers, Tim Gretler, Yotam Harchol, Tobias Klenze

Byzantine fault tolerant (BFT) protocol descriptions often assume application-layer networking primitives, such as best-effort and reliable broadcast, which are impossible to implement in practice in a Byzantine environment as they require either unbounded buffering of messages or giving up liveness, under certain circumstances. However, many of these protoc

Zhoujie Xu

Human pose estimation on medium and small scales has long been a significant challenge in this field. Most existing methods focus on restoring high-resolution feature maps by stacking multiple costly deconvolutional layers or by continuously aggregating semantic information from low-resolution feature maps while maintaining high-resolution ones, which can le

Yik San Cheng, Runkai Zhao, Heng Wang, Hanchuan Peng

Reconstructing neuron morphology from 3D light microscope imaging data is critical to aid neuroscientists in analyzing brain networks and neuroanatomy. With the boost from deep learning techniques, a variety of learning-based segmentation models have been developed to enhance the signal-to-noise ratio of raw neuron images as a pre-processing step in the reco

Jonathan Feldstein, Paulius Dilkas, Vaishak Belle, Efthymia Tsamoura

Integrating symbolic techniques with statistical ones is a long-standing problem in artificial intelligence. The motivation is that the strengths of either area match the weaknesses of the other, and $\unicode{x2013}$ by combining the two $\unicode{x2013}$ the weaknesses of either method can be limited. Neuro-symbolic AI focuses on this integration where the

Osama A. Marzouk

The main objective of this paper is to present a general mathematical model and an associated numerical algorithm applicable to an arbitrary fixed-wing fixed-mass aircraft undergoing an arbitrary maneuver, based on the 3D nonlinear coupled differential-algebraic equations of motion, including force, moment, kinematic and constraint equations. The model is fo

Luca Jiang-Tao Yu, Running Zhao, Sijie Ji, Edith C. H. Ngai

Speech enhancement is crucial for ubiquitous human-computer interaction. Recently, ultrasound-based acoustic sensing has emerged as an attractive choice for speech enhancement because of its superior ubiquity and performance. However, due to inevitable interference from unexpected and unintended sources during audio-ultrasound data acquisition, existing solu

M. M. Akash, Rahul Deb Mohalder, Md. Al Mamun Khan, Laboni Paul

Yoga has recently become an essential aspect of human existence for maintaining a healthy body and mind. People find it tough to devote time to the gym for workouts as their lives get more hectic and they work from home. This kind of human pose estimation is one of the notable problems as it has to deal with locating body key points or joints. Yoga-82, a ben

Jian Ding, Ewain Gwynne, Zijie Zhuang

We prove that the set of thick points of the log-correlated Gaussian field contains an unbounded path in sufficiently high dimensions. This contrasts with the two-dimensional case, where Aru, Papon, and Powell (2023) showed that the set of thick points is totally disconnected. This result has an interesting implication for the exponential metric of the log-c

Malte Algren, Tobias Golling, Christopher Pollard, John Andrew Raine

In this paper, we present a novel method for pile-up removal of $pp$ interactions using variational inference with diffusion models, called vipr. Instead of using classification methods to identify which particles are from the primary collision, a generative model is trained to predict the constituents of the hard-scatter particle jets with pile-up removed.

Deepika Venkattu, Peter Lundqvist, Miguel Pérez Torres, Etienne Bonnassieux

We present the first LOFAR image of the centre of M31 at a frequency of 150 MHz. We clearly detect three supernova remnants, which, along with archival VLA data at 3 GHz and other published radio and X-ray data allows us to characterize them in detail. Our observations also allow us to obtain upper limits the historical SN 1885A which is undetected even at a

Yue-Hui Yao, Tingyuan Jiang, Yong Tang

Axion or axionlike particles are one of the leading candidates for dark matter. Because of its tiny coupling with photons, axion dark matter in the background can induce distinct phase velocities for light with different parity, an effect known as birefringence. Here, we propose a modification to the polarization state of the interspacecraft laser link in LI

Adi Simhi, Jonathan Herzig, Idan Szpektor, Yonatan Belinkov

Large language models (LLMs) are susceptible to hallucinations -- factually incorrect outputs -- leading to a large body of work on detecting and mitigating such cases. We argue that it is important to distinguish between two types of hallucinations: ones where the model does not hold the correct answer in its parameters, which we term HK-, and ones where th

Qizhi Chen, Delin Qu, Junli Liu, Yiwen Tang

Reconstructing controllable Gaussian splats for articulated objects from monocular video is especially challenging due to its inherently insufficient constraints. Existing methods address this by relying on dense masks and manually defined control signals, limiting their real-world applications. In this paper, we propose an annotation-free method, FreeGaussi

Nikolaos Tsilivis, Eitan Gronich, Julia Kempe, Gal Vardi

We study the implicit bias of the general family of steepest descent algorithms with infinitesimal learning rate in deep homogeneous neural networks. We show that: (a) an algorithm-dependent geometric margin starts increasing once the networks reach perfect training accuracy, and (b) any limit point of the training trajectory corresponds to a KKT point of th

Jukka-Pekka Humaloja, Nikolaos Bekiaris-Liberis

We develop a backstepping control design for a class of continuum systems of linear hyperbolic PDEs, described by a coupled system of an ensemble of rightward transporting PDEs and a (finite) system of $m$ leftward transporting PDEs. The key analysis challenge of the design is to establish well-posedness of the resulting ensemble of kernel equations, since t

Zhuorui Ye, Jinhan Li, Rongwu Xu

Translating lyrics for musicals presents unique challenges due to the need to ensure high translation quality while adhering to singability requirements such as length and rhyme. Existing song translation approaches often prioritize these singability constraints at the expense of translation quality, which is crucial for musicals. This paper aims to enhance

Jordi-Lluís Figueras, Aron Persson, Lauri Viitasaari

We consider the projected normal distribution, with isotropic variance, on the 2-sphere using intrinsic statistics. We show that in this case, the expectation commutes with the projection and that the covariance of the normal variable has a 1-1 correspondence with the intrinsic covariance of the projected normal distribution. This allows to estimate, after m

Vu C. Dinh, Lam Si Tung Ho, Cuong V. Nguyen

We analyze the error rates of the Hamiltonian Monte Carlo algorithm with leapfrog integrator for Bayesian neural network inference. We show that due to the non-differentiability of activation functions in the ReLU family, leapfrog HMC for networks with these activation functions has a large local error rate of $\Omega(\epsilon)$ rather than the classical err

Shaojie Liu, Tengji Xu, Benshan Wang, Dongliang Wang

Microring resonators (MRRs) are essential components in large-scale photonic integrated circuits (PICs), but programming these circuits with high precision and efficiency remains an unsolved challenge. Conventional methods rely on complex calibration processes that are both time-consuming and often inaccurate, limiting the scalability of PICs. This work intr

G. Bourdarot, F. Eisenhauer

Infrared interferometry has seen a revolution over the last few years. The advent of GRAVITY+ is about to enable high-contrast observations, all-sky coverage and faint science up to K=21, with the implementation on 8m-class telescope of extreme adaptive optics, wide-field observations, and soon laser guide stars, following a long-term vision of technological

Ziqing Zhu, Shuyang Zhu, Siqi Bu

This paper addresses the challenges of power flow calculation in large scale power systems with high renewable penetration, focusing on computational efficiency and generalization. Traditional methods, while accurate, struggle with scalability for large power systems. Existing data driven deep learning approaches, despite their speed, require extensive train

Ulrik Thinggaard Hansen, Frederik Ravn Klausen, Peter Wildemann

We consider the graphical representations of the Ising model on tree-like graphs. We construct a class of graphs on which the loop $\mathrm{O}(1)$ model and the single random current exhibit a non-unique phase transition with respect to the inverse temperature, highlighting the non-monotonicity of both models. It follows from the construction that there exis

Paolo Marimon, Michael Pinsker

We classify the possible types of minimal operations above an arbitrary permutation group. Above the trivial group, a theorem of Rosenberg yields that there are five types of minimal operations. We show that above any non-trivial permutation group there are at most four such types. Indeed, except above Boolean groups acting freely on a set, there are only th

Shutong Jin, Ruiyu Wang, Kuangyi Chen, Florian T. Pokorny

Scene rearrangement, like table tidying, is a challenging task in robotic manipulation due to the complexity of predicting diverse object arrangements. Web-scale trained generative models such as Stable Diffusion can aid by generating natural scenes as goals. To facilitate robot execution, object-level representations must be extracted to match the real scen

Ross Knapman, Maria Azhar, Alessandro Pignedoli, Louis Gallard

To gain deeper insight into the complex, stable, and robust configurations of magnetic textures, topological characterisation has proven essential. In particular, while the skyrmion number is a well-established topological invariant for 2D magnetic textures, the Hopf index serves as a key topological descriptor for 3D magnetic structures. In this work, we pr

Zijiang Liu, Xiaoyu Liu, Linhao Qu, Yonghong Shi

Accurate segmentation of brain metastases (BMs) in MR image is crucial for the diagnosis and follow-up of patients. Methods based on deep convolutional neural networks (CNNs) have achieved high segmentation performance. However, due to the loss of critical feature information caused by convolutional and pooling operations, CNNs still face great challenges in

Yulong Wang, Chang Zuo, Yin Xuan, Hong Li

Fine-tuning large language models (LLMs) has become essential for adapting pretrained models to specific downstream tasks. In this paper, we propose Linear Chain Transformation (LinChain), a novel approach that introduces a sequence of linear transformations during fine-tuning to enrich optimization dynamics. By incorporating multiple linear transformations

Ryoya Ogura, Tomoya Nishida, Yohei Kawaguchi

This paper proposes a method for unsupervised anomalous sound detection (UASD) and captioning the reason for detection. While there is a method that captions the difference between given normal and anomalous sound pairs, it is assumed to be trained and used separately from the UASD model. Therefore, the obtained caption can be irrelevant to the differences t

Tomasz Goliński, Gabriel Larotonda, Alice Barbora Tumpach

For a unital non-simple $C^*$-algebra $\mathcal A$ we consider its Banach--Lie group $G$ of invertible elements. For a given closed ideal $\mathfrak k$ in $\mathcal A$, we consider the embedded Banach--Lie subgroup $K$ of $G$ of elements differing from the unit element by an element in $\mathfrak k$. We study vector bundle maps of the tangent space of the ho

Kiarash Firouzi, Mohammad Jelodari Mamaghani

Due to the increasing popularity of futures trading among financial market participants, the risk management of these instruments is crucial. In this paper, we introduce a model for estimating the ideal time for leaving a trading position on a stock. Also, using ergodic theorems, we investigate the European call option pricing problem using a stochastic irra

Kush Kaushik, Jiban Mondal, Ritesh Kumar Bag, Shagun Sharma

Room temperature single photon sources (SPS) are crucial for developing the next generation quantum technologies. Quantum dots (QDs), recently, have been reported as promising materials as SPS at room temperature. By optimizing the single particle optical properties of a series of water-soluble, $CdTe_{x}Se_{1-x}$, here we provide an efficient SPS with incre

Lothar Banz, Miriam Schönauer, Andreas Schröder

In this paper, we derive a priori error estimates for variational inequalities of the first kind in an abstract framework. This is done by combining the first Strang Lemma and the Falk Theorem. The main application consists in the derivation of a priori error estimates for Galerkin methods, in which "variational crimes" may perturb the underlying variational

Mats W. J. van Es, Chetan Gohil, Andrew J. Quinn, Mark W. Woolrich

We describe OHBA Software Library for the analysis of electrophysiological data (osl-ephys). This toolbox builds on top of the widely used MNE-Python package and provides unique analysis tools for magneto-/electro-encephalography (M/EEG) sensor and source space analysis, which can be used modularly. In particular, it facilitates processing large amounts of d