November 2024 arXiv papers — page 142

Sabrina Berger, Arianna Lasinski, Vincent MacKay, Eamon Egan

We present results from the first application of the Global Navigation Satellite System (GNSS; e.g., the Global Positioning System, GPS) for radio beam calibration using a commercial GNSS receiver with the Deep Dish Development Array (D3A) at the Dominion Radio Astrophysical Observatory (DRAO). Several GNSS satellites pass through the main and sidelobes of t

Luana Hildever, José Laurentino, José Araújo, Francisco Estrada

Yttrium calcium aluminate, with the formula CaYAl3O7, has been extensively researched due to its remarkable luminescent properties when doped or co-doped. Additionally, it exhibits exceptional piezoelectric properties at high temperatures. However, the potential magnetic properties this material can acquire through doping or co-doping have largely gone unexp

Kaixuan Lu

The recent development of vision language models (VLMs) has led to significant advances in visual-language integration through visual instruction tuning, and they have rapidly evolved in the field of remote sensing image understanding, demonstrating their powerful capabilities. However, existing RSVLMs mainly focus on image-level or frame-level understanding

Francesco Bacchiocchi, Matteo Bollini, Matteo Castiglioni, Alberto Marchesi

We study online Bayesian persuasion problems in which an informed sender repeatedly faces a receiver with the goal of influencing their behavior through the provision of payoff-relevant information. Previous works assume that the sender has knowledge about either the prior distribution over states of nature or receiver's utilities, or both. We relax such unr

Marco Simnacher, Xiangnan Xu, Hani Park, Christoph Lippert

Conditional independence tests (CITs) test for conditional dependence between random variables. As existing CITs are limited in their applicability to complex, high-dimensional variables such as images, we introduce deep nonparametric CITs (DNCITs). The DNCITs combine embedding maps, which extract feature representations of high-dimensional variables, with n

Ganiyu Oladimeji

This review discusses the theoretical frameworks and application prospects of Zero Trust Security (ZTS) in cloud computing context. This is because, as organisations move more of their applications and data to the cloud, the old borders-based security model that many implemented are inadequate, therefore a model that has a trust no one, verify everything app

Ciprian-Octavian Truică, Ana-Teodora Constantinescu, Elena-Simona Apostol

The mental health of social media users has started more and more to be put at risk by harmful, hateful, and offensive content. In this paper, we propose \textsc{StopHC}, a harmful content detection and mitigation architecture for social media platforms. Our aim with \textsc{StopHC} is to create more secure online environments. Our solution contains two modu

Wenbo Wu, Cheng Tan, Kangcheng Yang, Zhishu Shen

Low Earth Orbit (LEO) satellite networks are increasingly essential for space-based artificial intelligence (AI) applications. However, as commercial use expands, LEO satellite networks face heightened cyberattack risks, especially through satellite-to-satellite communication links, which are more vulnerable than ground-based connections. As the number of op

Minjie Tang, Chenyuan Feng, Tony Q. S. Quek

This paper investigates the semantic communication and cooperative tracking control for an UAV swarm comprising a leader UAV and a group of follower UAVs, all interconnected via unreliable wireless multiple-input-multiple-output (MIMO) channels. Initially, we develop a dynamic model for the UAV swarm that accounts for both the internal interactions among the

Ruiyu Li, Peilin Zhao, Guangxia Li, Zhiqiang Xu

Online multi-task learning (OMTL) enhances streaming data processing by leveraging the inherent relations among multiple tasks. It can be described as an optimization problem in which a single loss function is defined for multiple tasks. Existing gradient-descent-based methods for this problem might suffer from gradient vanishing and poor conditioning issues

Vira R. Besaga, Ivan V. Lopushenko, Oleksii Sieryi, Alexander Bykov

We explore quantum-based optical polarimetry as a potential diagnostic tool for biological tissues by developing a theoretical and experimental framework to understand polarization-entangled photon behavior in scattering media. We investigate the mathematical relationship between Wolf's coherency matrix in classical optics and the density matrix formalism of

Wenping Cao, Zirong Zeng, Deng Zhang

We consider the 3D stochastic Navier-Stokes equations (NSE) on torus where the viscosity exponent can be larger than the Lions exponent 5/4. For arbitrarily prescribed divergence-free initial data in $L^{2}_x$, we construct infinitely many probabilistically strong and analytically weak solutions in the class $L^{r}_{\Omega}L_{t}^{\gamma}W_{x}^{s,p}$, where $

Luca Tanganelli Castrillón

We exhibit a family of metrizable manifolds such that any finite group appears as the fundamental group of one of them. These spaces are especially interesting as they can be easily visualized, as opposed to classical examples of spaces with arbitrary fundamental group.

Bin Pei, Lifang Feng, Yunzhang Li, Yong Xu

This paper aims to investigate the non-Markovian dynamics. The governing equations are derived for the probability density functions (PDFs) of non-Markovian stochastic responses to Langevin equation excited by combined fractional Gaussian noise (FGN) and Gaussian white noise (GWN). The main difficulty here is that the Langevin equation excited by FGN cannot

Namrata Pattanayak, Preeti Das, Mihir Ranjan Sahoo, Padmalochan Panda

Glucose monitoring plays a critical role in managing diabetes, one of the most prevalent diseases globally. The development of fast-responsive, cost-effective, and biocompatible glucose sensors is essential for improving patient care. In this study, a comparative analysis is conducted between pristine and Co-doped hematite samples, synthesized via the hydrot

Valentino Dardanoni, Stefano Demichelis

We propose a principled framework for nonparametric empirical Bayes (EB) estimation, based on the idea that the prior should be consistent with the observed posterior and that Bayesian updating should be stable. Focusing on discretized priors, we characterize EB estimators as fixed points of a posterior belief operator. We establish the uniqueness of such fi

Luca Nanni

This paper concerns the study and resolution of wave equations in the space of Schwartz distributions. Wave phenomena are widespread in many branches of physics and chemistry, such as optics, gravitation, quantum mechanics, chemical waves and often arise from instantaneous sources represented by Schwartz distributions f. Hence, there is a need to study the C

Keqin Li, Lipeng Liu, Jiajing Chen, Dezhi Yu

In this paper, how to efficiently find the optimal path in complex warehouse layout and make real-time decision is a key problem. This paper proposes a new method of Proximal Policy Optimization (PPO) and Dijkstra's algorithm, Proximal policy-Dijkstra (PP-D). PP-D method realizes efficient strategy learning and real-time decision making through PPO, and uses

Y. T. Wang, R. Wang, X. Z. Zhang

Exceptional points (EPs) are distinct characteristics of non-Hermitian Hamiltonians that have no counterparts in Hermitian systems. In this study, we focus on EPs in continuous systems rather than discrete non-Hermitian systems, which are commonly investigated in both the experimental and theoretical studies. The non-Hermiticity of the system stems from the

Jing Ma, Jiaming Li, Jia Zhang

Let Zn denote the additive group of residue classes modulo n. Let c(l,m,n) denote the number of cyclic subgroups of Zl *Zm *Zn. For any x > 1, we consider the asymptotic behavior of D3c(x):= \sum_{lmn\leq x} c(l,m,n), obtain an asymptotic formula by complex method, and get an upper bound for the integral mean-square of the error term in that asymptotic formu

Duncan Bossion, Arkaprabha Sarangi, Susanne Aalto, Clarke Esmerian

Context. Cosmic dust is ubiquitous in astrophysical environments, where it significantly influences the chemistry and the spectra. Dust grains are likely to grow through the accretion of atoms and molecules from the gas-phase onto them. Despite their importance, only a few studies compute sticking coefficients for relevant temperatures and species, and their

Evgeny Sedov, Alexey Kavokin

This study investigates the performance of a binarized neuromorphic network leveraging polariton dyads, optically excited pairs of interfering polariton condensates within a microcavity to function as binary logic gate neurons. Employing numerical simulations, we explore various neuron configurations, both linear (NAND, NOR) and nonlinear (XNOR), to assess t

U. Özdem

The magnetic moments of the $B_c$ mesons provide significant insights into their inner structure and geometric shape. Furthermore, a comprehensive understanding of the electromagnetic characteristics of $B_c$ mesons is essential for advancing our knowledge of confinement and heavy flavor effects. In light of this, we proceed to extract the magnetic moments o

Erik J Schlicht

Although misinformation tends to spread online, it can have serious real-world consequences. In order to develop automated tools to detect and mitigate the impact of misinformation, researchers must leverage algorithms that can adapt to the modality (text, images and video), the source, and the content of the false information. However, these characteristics

Yuhan Cheng, Xuecheng Chen, Yixuan Yang, Haoyang Wang

Gas source localization is pivotal for the rapid mitigation of gas leakage disasters, where mobile robots emerge as a promising solution. However, existing methods predominantly schedule robots' movements based on reactive stimuli or simplified gas plume models. These approaches typically excel in idealized, simulated environments but fall short in real-worl

Erik J Schlicht

Generative Artificial Intelligence offers a powerful tool for adversaries who wish to engage in influence operations, such as the Chinese Spamouflage operation and the Russian Internet Research Agency effort that both sought to interfere with recent US election cycles. Therefore, this study seeks to investigate the propensity of current generative AI models

Sanchar Palit, Sathya Veera Reddy Dendi, Mallikarjuna Talluri, Raj Narayana Gadde

Vision Transformers and U-Net architectures have been widely adopted in the implementation of Diffusion Models. However, each architecture presents specific challenges while realizing them on-device. Vision Transformers require positional embedding to maintain correspondence between the tokens processed by the transformer, although they offer the advantage o

D Weihs, M Ringel

Cetacean Calves keep up with their mothers while rapidly swimming, by a hydrodynamical effect called drafting. This has been observed in the wild and enclosed areas, and has been mathematically analyzed in the past, but no quantitative measures of the actual forces involved have been made. We built wind tunnel models of Mother-Calf pairs and present force me

Ahmad Bazzi, Marwa Chafii

The following paper presents a reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system model scenario, where a base station communicates with a user, and a bi-static sensing unit, i.e. the passive radar (PR), senses targets using downlink signals. Given that the RIS aids with communication and sensing tasks, this pap

Soham De, Michiel A. Bakker, Jay Baxter, Martin Saveski

X's Community Notes, a crowd-sourced fact-checking system, allows users to annotate potentially misleading posts. Notes rated as helpful by a diverse set of users are prominently displayed below the original post. While demonstrably effective at reducing misinformation's impact when notes are displayed, there is an opportunity for notes to appear on many mor

Jungwoo Kim, Taesik Lee

By the end of 2021, COVID-19 had spread to over 230 countries, with over 5.4 million deaths. To contain its spread, many countries implemented non-pharmaceutical interventions, notably contact tracing and self-quarantine policies. However, these measures came with significant social costs, highlighting the need for more sustainable approaches that minimize d

Francesco Regazzoni, Corrado Poggesi, Cecilia Ferrantini

The left ventricular end-systolic pressure-volume relationship (ESPVr) is a key indicator of cardiac contractility. Despite its established importance, several studies suggested that the mechanical mode of contraction, such as isovolumetric or ejecting contractions, may affect the ESPVr, challenging the traditional notion of a single, consistent relationship

Amirhossein Mashghdoust, Stephane Durocher

Depth measures quantify central tendency in the analysis of statistical and geometric data. Selecting a depth measure that is simple and efficiently computable is often important, e.g., when calculating depth for multiple query points or when applied to large sets of data. In this work, we introduce \emph{Hyperplane Distance Depth (HDD)}, which measures the

Ruixiang Wu, Xudong Wang, Tongxin Li

With the rapid development of electric vehicles (EVs) and vehicle-to-grid (V2G) technology, detecting malicious EV drivers is becoming increasingly important for the reliability and efficiency of smart grids. To address this challenge, machine learning (ML) algorithms are employed to predict user behavior and identify patterns of non-cooperation. However, th

Jiayin Wang, Xiaoyu Zhang, Weizhi Ma, Zhiqiang Guo

Recommendation model interpretation aims to reveal the relationships between inputs, model internal representations and outputs to enhance the transparency, interpretability, and trustworthiness of recommendation systems. However, the inherent complexity and opacity of deep learning models pose challenges for model-level interpretation. Moreover, most existi

Fan Ding, Xuewen Luo, Gaoxuan Li, Hwa Hui Tew

To tackle the twin challenges of limited battery life and lengthy charging durations in electric vehicles (EVs), this paper introduces an Energy-efficient Hybrid Model Predictive Planner (EHMPP), which employs an energy-saving optimization strategy. EHMPP focuses on refining the design of the motion planner to be seamlessly integrated with the existing autom

Hambur Wang

Air pollution has emerged as a serious challenge for China, posing a threat to public health and hindering the progress of sustainable economic development. In response to air pollution and other environmental issues, the Chinese government introduced a new Environmental Protection Law in 2015. This paper investigates the impact of the new Environmental Prot

Dripto Biswas, Igor Pesando

We show that on shell DDF amplitudes are on shell lightcone amplitudes and that Mandelstam maps emerge naturally with a precise normalization and are intrinsic to the DDF states. Off shell DDF and Mandelstam amplitudes \`a la Kaku-Kikkawa differ. Underway we give a very explicit formula for the conformal transformation of a generic vertex in the form of a co

Bingsong Long

We consider two-dimensional Riemann boundary value problems of Euler equations for the Chaplygin gas with two piecewise constant initial data outside a convex cornered wedge. In self-similar coordinates, when the flow at the wedge corner is subsonic, this problem can be reformulated as a boundary value problem for nonlinear degenerate elliptic equations in c

Yingcong Sun, Laijun Chen, Yue Chen, Mingrui Tang

Shared energy storage systems (ESS) present a promising solution to the temporal imbalance between energy generation from renewable distributed generators (DGs) and the power demands of prosumers. However, as DG penetration rates rise, spatial energy imbalances become increasingly significant, necessitating the integration of peer-to-peer (P2P) energy tradin

Zhaorui Tan, Xi Yang, Tan Pan, Tianyi Liu

Variations in medical imaging modalities and individual anatomical differences pose challenges to cross-modality generalization in multi-modal tasks. Existing methods often concentrate exclusively on common anatomical patterns, thereby neglecting individual differences and consequently limiting their generalization performance. This paper emphasizes the crit

Bingsong Long

In this paper, we consider the 3-D steady potential flow for a compressible gas with pressure satisfying $p'(\rho)=\rho^{\gamma-1}$, where $\rho$ is the density and $\gamma\geq-1$ is a constant. In spherical coordinates, the potential equation is of mixed type in the unit sphere. We establish a strong comparison principle for elliptic solutions of the equati

Pratyoosh Kumar, Manali Sajjan

In this article, we investigate the behavior of solutions \( u(x,t) \) to the fractional Schr\"odinger equation on rank symmetric spaces of non-compact type. We proved that as time \( t \) approaches $0$, then $u(x,t)$ converges pointwise almost everywhere to the initial radial data \( f \), provided that \( f \in H^s(\mathbb{X}) \) with \( s > \frac{1}{2} \

Sung Joon Maeng, Ismail Guvenc

The growing demand for aerial connectivity with unmanned aerial vehicles (UAVs) across diverse settings, ranging from urban to rural scenarios, requires developing a better understanding of spectrum occupancy at aerial corridors. In particular, understanding the altitude-dependent behavior of spectrum occupancy in cellular networks, which could be used in th

Jie Jiang, Haining Xie, Siqi Shen, Yu Shen

With the proliferation of Large Language Models (LLMs) in Business Intelligence (BI), existing solutions face critical challenges in industrial deployments: functionality deficiencies from legacy systems failing to meet evolving LLM-era user demands, interaction limitations from single-round SQL generation paradigms inadequate for multi-round clarification,

Tianmai M. Zhang, Neil F. Abernethy

Reference errors, such as citation and quotation errors, are common in scientific papers. Such errors can result in the propagation of inaccurate information, but are difficult and time-consuming to detect, posing a significant challenge to scientific publishing. To support automatic detection of reference errors, this work evaluated the ability of large lan

Xianghong Zou, Jianping Li, Weitong Wu, Fuxun Liang

Wearable laser scanning (WLS) system has the advantages of flexibility and portability. It can be used for determining the user's path within a prior map, which is a huge demand for applications in pedestrian navigation, collaborative mapping, augmented reality, and emergency rescue. However, existing LiDAR-based global localization methods suffer from insuf

Yuanxiu Wang

As a key task in machine learning, data classification is essentially to find a suitable coordinate system to represent data features of different classes of samples. This paper proposes the mutual-energy inner product optimization method for constructing a feature coordinate system. First, by analyzing the solution space and eigenfunctions of partial differ

Ishika Joshi, Simra Shahid, Shreeya Venneti, Manushree Vasu

Ensuring large language models' (LLMs) responses align with prompt instructions is crucial for application development. Based on our formative study with industry professionals, the alignment requires heavy human involvement and tedious trial-and-error especially when there are many instructions in the prompt. To address these challenges, we introduce CoProm

Yuhan Pan, Yanan Sun, Wei Gong

Long-Tailed (LT) recognition has been widely studied to tackle the challenge of imbalanced data distributions in real-world applications. However, the design of neural architectures for LT settings has received limited attention, despite evidence showing that architecture choices can substantially affect performance. This paper aims to bridge the gap between

Jun-hao, Xu

Numerous studies have been proposed to detect fake news focusing on multi-modalities based on machine and/or deep learning. However, studies focusing on graph-based structures using geometric deep learning are lacking. To address this challenge, we introduce the Multimodal Adaptive Graph-based Intelligent Classification (aptly referred to as MAGIC) for fake

Yikang Liu, Yeting Shen, Hongao Zhu, Lilong Xu

We present ZhoBLiMP, the largest linguistic minimal pair benchmark for Chinese, with over 100 paradigms, ranging from topicalization to the \textit{Ba} construction. We then train from scratch a suite of Chinese language models (LMs) with different tokenizers, parameter sizes, and token volumes, to study the learning curves of LMs on Chinese. To mitigate the

Nigar Hashimzade, Limor Hatsor, Artyom Jelnov

Recent antitrust regulations in several countries have granted exemptions for collusion aimed at achieving environmental goals. Firms can apply for exemptions if collusion helps to develop or to implement costly clean technology, particularly in sectors like renewable energy, where capital costs are high and economies of scale are significant. However, if th

Qiyuan Xu, David Sanan, Zhe Hou, Xiaokun Luan

Foundational verification considers the functional correctness of programming languages with formalized semantics and uses proof assistants (e.g., Coq, Isabelle) to certify proofs. The need for verifying complex programs compels it to involve expressive Separation Logics (SLs) that exceed the scopes of well-studied automated proof theories, e.g., symbolic he

Prasad Sonar, Ashish Bhateja, Ishan Sharma

We investigate granular flows over an inclined rigid base, which is vibrated externally in a direction normal to itself, through discrete element simulations. We vary the base inclination angle theta, vibration frequency f, and amplitude A to study changes in the granular flow profile and the mass flow rate Q. We find that the flow velocity profiles for the

Seongmin Jeon, Arshak Petrosyan

In this paper, we study the regularity of the "regular" part of the free boundary for almost minimizers in the parabolic Signorini problem with zero thin obstacle. This work is a continuation of our earlier research on the regularity of almost minimizers. We first establish the Weiss-type monotonicity formula by comparing almost minimizers with parabolically

Kaixuan Lu, Ruiqian Zhang, Xiao Huang, Yuxing Xie

Recent self-supervised learning (SSL) methods have demonstrated impressive results in learning visual representations from unlabeled remote sensing images. However, most remote sensing images predominantly consist of scenographic scenes containing multiple ground objects without explicit foreground targets, which limits the performance of existing SSL method

Aya Abdelsalam Ismail, Tuomas Oikarinen, Amy Wang, Julius Adebayo

We introduce Concept Bottleneck Protein Language Models (CB-pLM), a generative masked language model with a layer where each neuron corresponds to an interpretable concept. Our architecture offers three key benefits: i) Control: We can intervene on concept values to precisely control the properties of generated proteins, achieving a 3 times larger change in

Miaomiao Li, Yunzhang Li, Bin Pei, Yong Xu

In this paper, we investigate the averaging principle for a class of semilinear slow-fast partial differential equations driven by finite-dimensional rough multiplicative noise. Specifically, the slow component is driven by a general random $\gamma$-H\"{o}lder rough path for some $\gamma \in (1/3,1/2)$, while the fast component is driven by a Brownian rough

Ramesh Kumar, Mukhtiyar Singh

It is quite intriguing to investigate the transition from a topological insulator (TI) phase to topological crystalline insulator (TCI) phase in a material as the latter has an advantage over the former in controlled device applications. This work investigates the existence of this dual topological behavior in Sn-based ternary chalcogenides family PbSnX2 (X=

Jia Quan Loh, Xuewen Luo, Fan Ding, Hwa Hui Tew

With the advancements of sensor hardware, traffic infrastructure and deep learning architectures, trajectory prediction of vehicles has established a solid foundation in intelligent transportation systems. However, existing solutions are often tailored to specific traffic networks at particular time periods. Consequently, deep learning models trained on one

Naoki Kobayashi

The decidability of the reachability problem for finitary PCF has been used as a theoretical basis for fully automated verification tools for functional programs. The reachability problem, however, often becomes undecidable for a slight extension of finitary PCF with side effects, such as exceptions, algebraic effects, and references, which hindered the exte

Jinglong Zhu, Hiroyuki Umeeda

In this work, nontrivial analytic structure of the quark propagator is discussed for $B$-meson inclusive decays. Attributed to invalidity of the standard K\"{a}ll\'{e}n-Lehman spectral representation, complex conjugate poles alter the evaluation of decay rates, which lead to violation of quark-hadron duality. As phenomenological observables, widths in $B\to

Jahid Hasan

This paper presents a comprehensive analysis of quantization techniques for optimizing Large Language Models (LLMs), specifically focusing on Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT). Through empirical evaluation across models ranging from 10M to 1B parameters, we demonstrate that quantization can achieve up to 68% reduction in

Pitu Sarkar, Nita Tamang

In this paper, we obtain a restricted decomposition formula for interpolated multiple zeta values using t-stuffle product. We then derive a recursive formula of t-stuffle product, which also provides a route to the same formula. In both cases, combinatorial description of t-stuffle product is our basic tool. We also provide alternative proofs by mathematical

Yi Zeng, Mingguang Han, Xiaoguang Li, Tiejun Li

Channel estimation and extrapolation are fundamental issues in MIMO communication systems. In this paper, we proposed the quasi-Newton orthogonal matching pursuit (QNOMP) approach to overcome these issues with high efficiency while maintaining accuracy. The algorithm consists of two stages on the super-resolution recovery: we first performed a cheap on-grid

Dongmin Gang, Heeyeon Kim, Byoungyoon Park, Spencer Stubbs

It is known that a large class of characters of 2d conformal field theories (CFTs) can be written in the form of a Nahm sum. In \cite{Zagier:2007knq}, D. Zagier identified a list of Nahm sum expressions that are modular functions under a congruence subgroup of $SL(2,\mathbb{Z})$ and can be thought of as candidates for characters of rational CFTs. Motivated b

Sayyed Faraz Mohseni, Hamid R. Arian, Jean-François Bégin

Portfolio diversification, traditionally measured through asset correlations and volatilitybased metrics, is fundamental to managing financial risk. However, existing diversification metrics often overlook non-numerical relationships between assets that can impact portfolio stability, particularly during market stresses. This paper introduces the lexical rat

Kentaro Yoshioka, Shimpei Ando, Satomi Miyagi, Yung-Chin Chen

This paper presents a tutorial and review of SRAM-based Compute-in-Memory (CIM) circuits, with a focus on both Digital CIM (DCIM) and Analog CIM (ACIM) implementations. We explore the fundamental concepts, architectures, and operational principles of CIM technology. The review compares DCIM and ACIM approaches, examining their respective advantages and chall

Shriyank Somvanshi, Syed Aaqib Javed, Md Monzurul Islam, Diwas Pandit

This systematic review explores the theoretical foundations, evolution, applications, and future potential of Kolmogorov-Arnold Networks (KAN), a neural network model inspired by the Kolmogorov-Arnold representation theorem. KANs distinguish themselves from traditional neural networks by using learnable, spline-parameterized functions instead of fixed activa

Taher A. Ghaleb, Osamah Abduljalil, Safwat Hassan

Continuous Integration and Continuous Delivery (CI/CD) is a well-established practice that automatically builds, tests, packages, and deploys software systems. To adopt CI/CD, software developers need to configure their projects using dedicated YML configuration files. Mobile apps have distinct characteristics with respect to CI/CD practices, such as testing

Aleksandr Simonyan

This paper introduces BreakGPT, a novel large language model (LLM) architecture adapted specifically for time series forecasting and the prediction of sharp upward movements in asset prices. By leveraging both the capabilities of LLMs and Transformer-based models, this study evaluates BreakGPT and other Transformer-based models for their ability to address t

Ahmad Sheykhi, Ava Shahbazi Sooraki

Using thermodynamics-gravity conjecture, we present the formal derivation of the modified Friedmann equations inspired by the Barrow entropy, $S\sim A ^{1+\delta/2}$, where $0\leq\delta\leq 1$ is the Barrow exponent and $A$ is the horizon area. We then constrain the exponent $\delta$ by using Big-Bang Nucleosynthesis (BBN) observational data. In order to imp

Kaixuan Lu, Ruiqian Zhang, Xiao Huang, Yuxing Xie

Recently, large vision language models (VLMs) have made significant strides in visual language capabilities through visual instruction tuning, showing great promise in the field of remote sensing image interpretation. However, existing remote sensing vision language models (RSVLMs) often fall short in capturing the complex characteristics of remote sensing s

Dan Pagendam, Jeff Baldock, David Clifford, Ryan Farquharson

A statistical framework we call CQUESST (Carbon Quantification and Uncertainty from Evolutionary Soil STochastics), which models carbon sequestration and cycling in soils, is applied to a long-running agricultural experiment that controls for crop type, tillage, and season. The experiment, known as the Millenium Tillage Trial (MTT), ran on 42 field-plots for

S. Guchhait, D. V. Ambika, S. Mohanty, Y. Furukawa

The structural and magnetic properties of a spin-$\frac{1}{2}$ averievite (CsBr)Cu$_5$V$_2$O$_{10}$ are investigated by means of temperature-dependent x-ray diffraction, magnetization, heat capacity, and $^{51}$V nuclear magnetic resonance (NMR) measurements. The crystal structure (trigonal, $P\bar{3}$) features a frustrated capped-kagome lattice of the magn

Jiyul Ham, Yonggon Jung, Jun-Geol Baek

Zero-shot anomaly detection (ZSAD) is crucial for detecting anomalous patterns in target datasets without using training samples, specifically in scenarios where there are distributional differences between the target domain and training data or where data scarcity arises because of restricted access. Although recently pretrained vision-language models demon

Zehong Wang, Zheyuan Zhang, Nitesh V Chawla, Chuxu Zhang

Inspired by the success of foundation models in applications such as ChatGPT, as graph data has been ubiquitous, one can envision the far-reaching impacts that can be brought by Graph Foundation Models (GFMs) with broader applications in the areas such as scientific research, social network analysis, drug discovery, and e-commerce. Despite the significant pr

Alireza Masoumian, James R. Wright

In standard RL, a learner attempts to learn an optimal policy for a Markov Decision Process whose structure (e.g. state space) is known. In online model selection, a learner attempts to learn an optimal policy for an MDP knowing only that it belongs to one of $M >1$ model classes of varying complexity. Recent results have shown that this can be feasibly acco

Yury Tokpanov, Paolo Glorioso, Quentin Anthony, Beren Millidge

In this technical report, we present Zyda-2: a five trillion token dataset for language model pretraining. Zyda-2 was used to train our Zamba2 series of models which are state-of-the-art for their weight class. We build Zyda-2 by collating high-quality open-source tokens such as FineWeb and DCLM, then distilling them to the highest-quality subset via cross-d

Yuanbo Chen, Yixiao Kang, Yukun Song, Cyrus Vachha

In this system, we discuss methods to stylize a scene of 3D primitive objects into a higher fidelity 3D scene using novel 3D representations like NeRFs and 3D Gaussian Splatting. Our approach leverages existing image stylization systems and image-to-3D generative models to create a pipeline that iteratively stylizes and composites 3D objects into scenes. We

Muneera Bano, Didar Zowghi, Fernando Mourao, Sarah Kaur

Artificial Intelligence (AI) systems for online recruitment markets have the potential to significantly enhance the efficiency and effectiveness of job placements and even promote fairness or inclusive hiring practices. Neglecting Diversity and Inclusion (D&I) in these systems, however, can perpetuate biases, leading to unfair hiring practices and decreased

Chengqi Dong, Zhiyuan Cao, S Kevin Zhou, Jia Liu

Stock price prediction is of significant importance in quantitative investment. Existing approaches encounter two primary issues: First, they often overlook the crucial role of capturing short-term stock fluctuations for predicting high-volatility returns. Second, mainstream methods, relying on graphs or attention mechanisms, inadequately explore the tempora

Haibo Sun, Naoki Otani, Hannah Kim, Dan Zhang

Conversational Recommender Systems (CRS) engage users in interactive dialogues to gather preferences and provide personalized recommendations. While existing studies have advanced conversational strategies, they often rely on predefined attributes or expensive, domain-specific annotated datasets, which limits their flexibility in handling diverse user prefer

Yueqi Wang, Richard Craster, Guanglian Li

Photonic crystals (PhCs) are periodic dielectric structures that exhibit unique electromagnetic properties, such as the creation of band gaps where electromagnetic wave propagation is inhibited. Accurately predicting dispersion relations, which describe the frequency and direction of wave propagation, is vital for designing innovative photonic devices. Howev

Di Wu, Pengkun Wang, Shiming Zhou, Bochun Zhang

Determining the atomic-level structure of crystalline solids is critically important across a wide array of scientific disciplines. The challenges associated with obtaining samples suitable for single-crystal diffraction, coupled with the limitations inherent in classical structure determination methods that primarily utilize powder diffraction for most poly

Ryan Chown, Yoko Okada, Els Peeters, Ameek Sidhu

(Abridged) JWST continues to deliver incredibly detailed infrared (IR) images of star forming regions in the Milky Way and beyond. IR emission from star-forming regions is very spectrally rich due to emission from gas-phase atoms, ions, and polycyclic aromatic hydrocarbons (PAHs). Physically interpreting IR images of these regions relies on assumptions about

Jingshu Li, Aaditya Patwari, Yi-Chieh Lee

Rises in the number of animal abuse cases are reported around the world. While chatbots have been effective in influencing their users' perceptions and behaviors, little if any research has hitherto explored the design of chatbots that embody animal identities for the purpose of eliciting empathy toward animals. We therefore conducted a mixed-methods experim

Jian Zhang, Xiang Zhang, Jingchen Huang, Jilin Zhang

Developing asynchronous neuromorphic hardware to meet the demands of diverse real-life edge scenarios remains significant challenges. These challenges include constraints on hardware resources and power budgets while satisfying the requirements for real-time responsiveness, reliable inference accuracy, and so on. Besides, the existing system-level simulators

John H. Caporaletti, J. P. Kestner

A charge qubit couples to environmental electric field fluctuations through its dipole moment, resulting in fast decoherence. We propose the p orbital (pO) qubit, formed by the single electron, p-like valence states of a five-electron Si quantum dot, which couples to charge noise through the quadrupole moment. We demonstrate that the pO qubit offers distinct

Dun Tang

In this paper, we define the virtual fundamental cycle of a global Kuranishi chart as an element in the (analytic) orbispace K-homology of the virtual orbifold and verify that it defines the same invariants as those in \cite{Abouzaid23}.

Quentin Fruytier, Aryan Mokhtari, Sujay Sanghavi

Classical Mixtures of Experts (MoE) are Machine Learning models that involve partitioning the input space, with a separate "expert" model trained on each partition. Recently, MoE-based model architectures have become popular as a means to reduce training and inference costs. There, the partitioning function and the experts are both learnt jointly via gradien

Xinran Liu, Yikun Bai, Rocío Díaz Martín, Kaiwen Shi

Efficient comparison of spherical probability distributions becomes important in fields such as computer vision, geosciences, and medicine. Sliced optimal transport distances, such as spherical and stereographic spherical sliced Wasserstein distances, have recently been developed to address this need. These methods reduce the computational burden of optimal

Qiqi Wu, M. Scialpi, Shilong Liao, F. Mannucci

Context. A series of studies have demonstrated that the Gaia multipeak method (GMP) is a very efficient technique to select active galactic nucleus (AGN) pair candidates. The number of candidates is determined by the size of the input AGN catalogs, usually limited to spectroscopically-confirmed objects. Aims. The objective of this work is to compile a larger

Dun Tang

In this paper, we prove an analog of Dijkgraaf-Witten's theorem for $g=1$ invariants in quantum K-theory.

Hongfeng Liu, Xiangjing Liu, Qian Chen, Yixian Qiu

We report NMR scattering circuit experiments that reveal causal structure. The scattering circuit involves interacting a probe qubit with the system of interest and finally measuring the probe qubit. The scattering circuit thereby implements a coarse-grained projective measurement. Causal structure refers to which events influence others and in the quantum c

Hongfeng Liu, Xiangjing Liu, Qian Chen, Yixian Qiu

We probe the foundations of causal structure inference experimentally. The causal structure concerns which events influence other events. We probe whether causal structure can be determined without intervention in quantum systems. Intervention is commonly used to determine causal structure in classical scenarios, but in the more fundamental quantum theory, t

Benjamín Barrios

Let $X$ be a smooth projective variety defined over a number field $K$. We give an upper bound for the generalized greatest common divisor of a point $x\in X$ with respect to an irreducible subvariety $Y\subseteq X$ also defined over $K$. To prove the result, we stablish a rather uniform Riemann--Roch type inequality.

John Schreck, Yingkai Sha, William Chapman, Dhamma Kimpara

Recent advancements in artificial intelligence (AI) for numerical weather prediction (NWP) have significantly transformed atmospheric modeling. AI NWP models outperform traditional physics-based systems, such as the Integrated Forecast System (IFS), across several global metrics while requiring fewer computational resources. However, existing AI NWP models f

Fatemeh Shiri, Xiao-Yu Guo, Mona Golestan Far, Xin Yu

Large Multimodal Models (LMMs) have achieved strong performance across a range of vision and language tasks. However, their spatial reasoning capabilities are under-investigated. In this paper, we construct a novel VQA dataset, Spatial-MM, to comprehensively study LMMs' spatial understanding and reasoning capabilities. Our analyses on object-relationship and