November 2024 arXiv papers — page 57

Paul Balmer, Beren Sanders

Let $\hat{R}$ be the $I$-adic completion of a commutative ring $R$ with respect to a finitely generated ideal $I$. We give a necessary and sufficient criterion for the category of perfect complexes over $\hat{R}$ to be equivalent to the subcategory of dualizable objects in the derived category of $I$-complete complexes of $R$-modules. Our criterion is always

Kaike Zhang, Yunfan Wu, Yougang lyu, Du Su

Recommender systems are quintessential applications of human-computer interaction. Widely utilized in daily life, they offer significant convenience but also present numerous challenges, such as the information cocoon effect, privacy concerns, fairness issues, and more. Consequently, this workshop aims to provide a platform for researchers to explore the dev

Kai Lu, Siqi Zhao, Jiguang Wan

Efficient management of storage resources in big data and cloud computing environments requires accurate identification of data's "cold" and "hot" states. Traditional methods, such as rule-based algorithms and early AI techniques, often struggle with dynamic workloads, leading to low accuracy, poor adaptability, and high operational overhead. To address thes

Mohamed Rameez

Under the assumption that they are standard(isable) candles, the lightcurves of Type Ia supernovae have been analyzed in the framework of the standard Friedmann-Lema\^itre-Robertson-Walker cosmology to conclude that the expansion rate of the Universe is accelerating due to dark energy. While the original claims in the late 1990s were made using overlapping s

Pei-Zhe Li, William J. Munro, Kae Nemoto, Nicoló Lo Piparo

Continuous-variable (CV) codes and their application in quantum communication have attracted increasing attention. In particular, one typical CV codes, cat-codes, has already been experimentally created using trapped atoms in cavities with relatively high fidelities. However, when these codes are used in a repeater protocol, the secret key rate (SKR) that ca

Geonho Song, Jip Kim

This paper introduces the 193 bus synthetic Korean power grid (KPG 193), developed using open data sources to address recent challenges of the Korean power system. The KPG 193 test system serves as a valuable platform for decarbonization research, capturing Korean low renewable energy penetration, concentrated urban energy demand, and isolated grid structure

Feng Ding, Jun Zhang, Xinan He, Jianfeng Xu

The high-quality, realistic images generated by generative models pose significant challenges for exposing them.So far, data-driven deep neural networks have been justified as the most efficient forensics tools for the challenges. However, they may be over-fitted to certain semantics, resulting in considerable inconsistency in detection performance across di

Jiuqi Wei, Xiaodong Lee, Zhenyu Liao, Themis Palpanas

Approximate Nearest Neighbor (ANN) search in high-dimensional Euclidean spaces is a fundamental problem with a wide range of applications. However, there is currently no ANN method that performs well in both indexing and query answering performance, while providing rigorous theoretical guarantees for the quality of the answers. In this paper, we first design

Md Kamrujjaman, Kazi Mehedi Mohammad

This study incorporates mathematical analysis, focusing on developing theories and conducting numerical simulations of Influenza virus transmission using real-world data. The terms in the equations introduce parameters which are determined by fitting the model for matching clinical data sets using non-linear least-square method. The purpose is to determine t

Yongxing Zhu

We derive rigorously the reduced dynamical law for quantized vortex dynamics of the coupled nonlinear Schr\"odinger equation without Josephson junction (CNLS) when the core size of vortex $\varepsilon\to 0$. It is proved that when $\varepsilon\to 0$, the vortex motion of one component won't affect the vortex motion on the other component. Moreover, the motio

Nikoo Moradi, André Ferreira, Behrus Puladi, Jens Kleesiek

Radiation therapy (RT) is essential in treating head and neck cancer (HNC), with magnetic resonance imaging(MRI)-guided RT offering superior soft tissue contrast and functional imaging. However, manual tumor segmentation is time-consuming and complex, and therfore remains a challenge. In this study, we present our solution as team TUMOR to the HNTS-MRG24 MIC

Sen Yang, Minyue Jiang, Ziwei Fan, Xiaolu Xie

Recent advances in autonomous driving systems have shifted towards reducing reliance on high-definition maps (HDMaps) due to the huge costs of annotation and maintenance. Instead, researchers are focusing on online vectorized HDMap construction using on-board sensors. However, sensor-only approaches still face challenges in long-range perception due to the r

Kaito Shiku, Kazuya Nishimura, Daiki Suehiro, Kiyohito Tanaka

Patient-level diagnosis of severity in ulcerative colitis (UC) is common in real clinical settings, where the most severe score in a patient is recorded. However, previous UC classification methods (i.e., image-level estimation) mainly assumed the input was a single image. Thus, these methods can not utilize severity labels recorded in real clinical settings

James Freeburn, Brendan O'Connor, Jeff Cooke, Dougal Dobie

GRB$\,$220831A is a gamma-ray burst (GRB) with a duration and spectral peak energy that places it at the interface between the distribution of long-soft and short-hard GRBs. In this paper, we present the multi-wavelength follow-up campaign to GRB$\,$220831A and its optical, near-infrared, X-ray and radio counterparts. Our deep optical and near-infrared obser

Subhasis Dasgupta, Pratik Satpati, Ishika Choudhary, Jaydip Sen

In the recent past, there were several works on the prediction of stock price using different methods. Sentiment analysis of news and tweets and relating them to the movement of stock prices have already been explored. But, when we talk about the news, there can be several topics such as politics, markets, sports etc. It was observed that most of the prior a

J. E. Hirsch, F. Marsiglio

The preceding Comment [1], previously posted as arXiv:2312.04495 [2], on our paper J. Supercond. Nov. Mag. 35, 3141 (2022) [3] provides a welcome opportunity to clarify what we understand to be pervading misconceptions by Eremets, Minkov and coauthors in regard to our analysis [3] of their trapped flux experiments in hydrides under pressure [4]. We hope that

Md. Kamrujjaman, Mayesha Sharmim Tisha

In this study, a spatially distributed reaction-diffusion-advection (RDA) model with harvesting is investigated to signify the outcome of a competition between two competing species in a heterogeneous environment. The study builds upon the concept presented in literature \cite{tisha2}, applying it to river ecology in the context of harvesting activities. We

Tianyi Huang

Designing user-centric mobile applications is increasingly essential in educational technology. However, platforms like MIT App Inventor-one of the world's largest educational app development tools-face inherent limitations in supporting modern UI/UX design. This study introduces the Figma-Enhanced App Design (FEAD) Method, a structured framework that integr

He Jia

A major challenge in extracting information from current and upcoming surveys of cosmological Large-Scale Structure (LSS) is the limited availability of computationally expensive high-fidelity simulations. We introduce calibrated Neural Quantile Estimation (NQE), a new Simulation-Based Inference (SBI) method that leverages a large number of approximate simul

Kelly R. Moran, Richard Payne, Earl Lawrence, David Higdon

The nonlinear matter power spectrum in cosmology describes how matter density fluctuations vary with scale in the universe, providing critical insights into large-scale structure formation. The matter power spectrum includes both smooth regions and highly oscillatory features. Cosmologists rely on noisy, multi-resolution realizations of large N-body simulati

Dipanjan Chaudhuri, Qianni Jiang, Xuefei Guo, Jin Chen

A charge density wave (CDW) is a phase of matter characterized by a periodic modulation of the valence electron density accompanied by a distortion of the lattice structure. The microscopic details of CDW formation are closely tied to the dynamic charge susceptibility, $\chi(q,\omega)$, which describes the behavior of electronic collective modes. Despite dec

Zhuan Khye Koh, Omri Weinstein, Sorrachai Yingchareonthawornchai

We present a nearly linear work parallel algorithm for approximating the Held-Karp bound for the Metric TSP problem. Given an edge-weighted undirected graph $G=(V,E)$ on $m$ edges and $\epsilon>0$, it returns a $(1+\epsilon)$-approximation to the Held-Karp bound with high probability, in $\tilde{O}(m/\epsilon^4)$ work and $\tilde{O}(1/\epsilon^4)$ depth. Whi

Yi Wang, Jiaze Wang, Ziyu Guo, Renrui Zhang

Recently Transformer-based models have advanced point cloud understanding by leveraging self-attention mechanisms, however, these methods often overlook latent information in less prominent regions, leading to increased sensitivity to perturbations and limited global comprehension. To solve this issue, we introduce PointACL, an attention-driven contrastive l

Zhengrui Guo, Conghao Xiong, Jiabo Ma, Qichen Sun

Few-shot learning presents a critical solution for cancer diagnosis in computational pathology (CPath), addressing fundamental limitations in data availability, particularly the scarcity of expert annotations and patient privacy constraints. A key challenge in this paradigm stems from the inherent disparity between the limited training set of whole slide ima

Tanaji Sen

We develop the theory of beam echoes in circular accelerators under several different conditions. We derive detailed expressions for the echo amplitude and pulse width with nonlinear quadrupole and dipole kicks, first without and then with momentum spread. We use the theory with the linearized dipole and quadrupole kicks to solve the diffusion equation for d

Kota Nishida, Yoshihiro Midoh, Noriyuki Miura, Satoshi Kawakami

Silicon Photonics-based AI Accelerators (SPAAs) have been considered as promising AI accelerators achieving high energy efficiency and low latency. While many researchers focus on improving SPAAs' energy efficiency and latency, their physical security has only recently received attention. While it is essential to deliver strong optical neural network inferen

Gregory Beylkin

We present an unconditionally stable algorithm for applying matrix transfer function of a linear time invariant system (LTI) in time domain. The state matrix of an LTI system used for modeling long range dependencies in state space models (SSMs) has eigenvalues close to $1$. The standard recursion defining LTI system becomes unstable if the $m\times m$ state

Xin Yu, Ze Yuan, Yuan-Chen Guo, Ying-Tian Liu

While high-quality texture maps are essential for realistic 3D asset rendering, few studies have explored learning directly in the texture space, especially on large-scale datasets. In this work, we depart from the conventional approach of relying on pre-trained 2D diffusion models for test-time optimization of 3D textures. Instead, we focus on the fundament

Simon Lupart, Zahra Abbasiantaeb, Mohammad Aliannejadi

The Interactive Knowledge Assistant Track (iKAT) 2024 focuses on advancing conversational assistants, able to adapt their interaction and responses from personalized user knowledge. The track incorporates a Personal Textual Knowledge Base (PTKB) alongside Conversational AI tasks, such as passage ranking and response generation. Query Rewrite being an effecti

Jiashuo Liang, Guancheng Li, Yang Yu

Large language models (LLMs) have been widely adopted in applications such as automated content generation and even critical decision-making systems. However, the risk of prompt injection allows for potential manipulation of LLM outputs. While numerous attack methods have been documented, achieving full control over these outputs remains challenging, often r

Zhe Zhao, Jingping Xu, Ce Wang, Yaping Yang

Analytic continuation aims to reconstruct real-time spectral functions from imaginary-time Green's functions; however, this process is notoriously ill-posed and challenging to solve. We propose a novel neural network architecture, named the Feature Learning Network (FL-net), to enhance the prediction accuracy of spectral functions, achieving an improvement o

Xiaomin Li, Junyi Sha

Identifying key product features that influence consumer preferences is essential in the fashion industry. In this study, we introduce a robust methodology to ascertain the most impactful features in fashion product images, utilizing past market sales data. First, we propose the metric called "influence score" to quantitatively assess the importance of produ

El Mehdi Zahraoui, Patricio Maturana-Russel, Willem van Straten, Renate Meyer

Globally, Pulsar Timing Array (PTA) experiments have revealed evidence supporting an existing gravitational wave background (GWB) signal in the PTA data set. Apart from acquiring more observations, the sensitivity of PTA experiments can be increased by improving the accuracy of the noise modeling. In PTA data analysis, noise modeling is conducted primarily u

Yusen Su, Jorge A. Navas, Arie Gurfinkel, Isabel Garcia-Contreras

Relational object invariants (or representation invariants) are relational properties held by the fields of a (memory) object throughout its lifetime. For example, the length of a buffer never exceeds its capacity. Automatic inference of these invariants is particularly challenging because they are often broken temporarily during field updates. In this paper

Sonu Prasad Keshri, Guang-Yu Guo

We investigate the superconducting (SC) properties of experimentally realised $\gamma$-BiPd by solving the anisotropic Migdal-Eliashberg equations in conjunction with {\it ab initio} relativistic calculations of the electron and phonon band structures as well as electron-phonon coupling (EPC) matrix elements. Our study reveals that $\gamma$-BiPd possesses a

Donghyeon Yi, Seoyoung Lee, Jongho Kim, Junyoung Kim

Encoder-based transformers, powered by self-attention layers, have revolutionized machine learning with their context-aware representations. However, their quadratic growth in computational and memory demands presents significant bottlenecks. Analog-Mixed-Signal Process-in-Memory (AMS-PiM) architectures address these challenges by enabling efficient on-chip

Jingwen Zhou, Yunsong Fu, Ling Liu, Chungen Liu

Electrochemical processes play a crucial role in energy storage and conversion systems, yet their computational modeling remains a significant challenge. Accurately incorporating the effects of electric potential has been a central focus in theoretical electrochemistry. Although constant-potential ab initio molecular dynamics (CP-AIMD) has provided valuable

Majidkhan Azizov

In this work, we obtain the description of all homogeneous anti-Rota-Baxter operators on Witt and Virasoro algebras. Moreover, we describe anti-Rota-Baxter operators on three-dimensional simple Lie algebra $sl_2.$

Lopamudra Praharaj, Deepti Gupta, Maanak Gupta

The agriculture sector is increasingly adopting innovative technologies to meet the growing food demands of the global population. To optimize resource utilization and minimize crop losses, farmers are joining cooperatives to share their data and resources among member farms. However, while farmers benefit from this data sharing and interconnection, it expos

Gabriel Santos, Rita Julia, Marcelo Nascimento

Clustering data using prior domain knowledge, starting from a partially labeled set, has recently been widely investigated. Often referred to as semi-supervised clustering, this approach leverages labeled data to enhance clustering accuracy. To maximize algorithm performance, it is crucial to ensure the safety of this prior knowledge. Methods addressing this

Junyang Chen, Yang Lu, Mengke Li, Cuie Yang

Clustering complex data in the form of attributed graphs has attracted increasing attention, where powerful graph representation is a critical prerequisite. However, the well-known Over-Smoothing (OS) effect makes Graph Convolutional Networks tend to homogenize the representation of graph nodes, while the existing OS solutions focus on alleviating the homoge

Xiangyu Zhang

The generation of drug-like molecules is crucial for drug design. Existing reinforcement learning (RL) methods often overlook structural information. However, feature engineering-based methods usually merely focus on binding affinity prediction without substantial molecular modification. To address this, we present Graph-based Topological Reinforcement Learn

Feng Chen, Chenhui Gou, Jing Liu, Yang Yang

As multimodal large language models (MLLMs) advance rapidly, rigorous evaluation has become essential, providing further guidance for their development. In this work, we focus on a unified and robust evaluation of \textbf{vision perception} abilities, the foundational skill of MLLMs. We find that existing perception benchmarks, each focusing on different que

Hironobu Yoshida, Niclas Heinsdorf, Hosho Katsura

We explore the phase diagram of the extended attractive SU($3$) Hubbard chain with two-body hopping and nearest-neighbor attraction at half-filling. In the large on-site attraction limit, we identify three different phases: phase separation (PS), Tomonaga-Luttinger liquid (TLL), and charge density wave (CDW). Our analysis reveals that the $\eta$-clustering s

Minhyeok Lee, Suhwan Cho, Jungho Lee, Sunghun Yang

Open-vocabulary semantic segmentation aims to assign pixel-level labels to images across an unlimited range of classes. Traditional methods address this by sequentially connecting a powerful mask proposal generator, such as the Segment Anything Model (SAM), with a pre-trained vision-language model like CLIP. But these two-stage approaches often suffer from h

Kierstin Torres, Joon Young Park, Victoria A. Posey, Michael E. Ziebel

The recent discovery of the van der Waals (vdW) layered heavy fermion antiferromagnetic metal CeSiI offers promising potential for achieving accessible quantum criticality in the two-dimensional (2D) limit. CeSiI exhibits both heavy fermion behavior and antiferromagnetic (AFM) ordering, while the exact magnetic structure and phase diagram have yet to be dete

I. A. Taimanov

The connections between Euler's equations on central extensions of Lie algebras and Euler's equations on the original, extended algebras are described. A special infinite sequence of central extensions of nilpotent Lie algebras constructed from the Lie algebra of formal vector fields on the line is considered, and the orbits of coadjoint representations for

Luhang Sun, Varsha Pendyala, Yun-Shiuan Chuang, Shanglin Yang

This paper leverages large-language models (LLMs) to experimentally determine optimal strategies for scaling up social media content annotation for stance detection on HPV vaccine-related tweets. We examine both conventional fine-tuning and emergent in-context learning methods, systematically varying strategies of prompt engineering across widely used LLMs a

Jiani Zhu, Xi Lin, Yuxin Qi, Qinghua Mao

Graph Prompt Learning (GPL) represents an innovative approach in graph representation learning, enabling task-specific adaptations by fine-tuning prompts without altering the underlying pre-trained model. Despite its growing prominence, the privacy risks inherent in GPL remain unexplored. In this study, we provide the first evaluation of privacy leakage in G

Binqian Xu, Xiangbo Shu, Haiyang Mei, Guosen Xie

Multimodal Large Language Models (MLLMs) have made significant advancements, demonstrating powerful capabilities in processing and understanding multimodal data. Fine-tuning MLLMs with Federated Learning (FL) allows for expanding the training data scope by including private data sources, thereby enhancing their practical applicability in privacy-sensitive do

Haiming Zhang, Wending Zhou, Yiyao Zhu, Xu Yan

This paper introduces VisionPAD, a novel self-supervised pre-training paradigm designed for vision-centric algorithms in autonomous driving. In contrast to previous approaches that employ neural rendering with explicit depth supervision, VisionPAD utilizes more efficient 3D Gaussian Splatting to reconstruct multi-view representations using only images as sup

Yijun Fan, Yiwei Ma, Jiayi Ji, Xiaoshuai Sun

Recent progress in 3D object generation has been fueled by the strong priors offered by diffusion models. However, existing models are tailored to specific tasks, accommodating only one modality at a time and necessitating retraining to change modalities. Given an image-to-3D model and a text prompt, a naive approach is to convert text prompts to images and

Shan-shan Li, Chun-qian Li, Chang-hua Li, Dong-wei Fan

We utilize a hybrid approach that integrates the traditional cross-correlation function (CCF) and machine learning to detect spectroscopic multi-systems, specifically focusing on double-line spectroscopic binary (SB2). Based on the ninth data release (DR9) of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), which includes a medium-reso

Chenxu Zhu, Shigang Quan, Bo Chen, Jianghao Lin

CTR prediction plays a vital role in recommender systems. Recently, large language models (LLMs) have been applied in recommender systems due to their emergence abilities. While leveraging semantic information from LLMs has shown some improvements in the performance of recommender systems, two notable limitations persist in these studies. First, LLM-enhanced

Mobin Alipour, Yiran Li, Haoyu Liu, Amir A. Pahlavan

Understanding how colloids move in crowded environments is key for gaining control over their transport in applications such as drug delivery, filtration, contaminant/microplastic remediation and agriculture. The classical models of colloid transport in porous media rely on geometric characteristics of the medium, and hydrodynamic/non-hydrodynamic equilibriu

Shuming Liang, Yu Ding, Zhidong Li, Bin Liang

This paper explores the ability of Graph Neural Networks (GNNs) in learning various forms of information for link prediction, alongside a brief review of existing link prediction methods. Our analysis reveals that GNNs cannot effectively learn structural information related to the number of common neighbors between two nodes, primarily due to the nature of s

Binghui Ding, Ling Chen, Chuandong Li, Tingwen Huang

In this article, we propose a memristor-based selective convolutional (MSC) circuit for salt-and-pepper (SAP) noise removal. We implement its algorithm using memristors in analog circuits. In experiments, we build the MSC model and benchmark it against a ternary selective convolutional (TSC) model. Results show that the MSC model effectively restores images

IlKwon Sohn, Boseon Kim, Kwangil Bae, Wooyeong Song

Quantum networks aim to facilitate the fault-tolerant and secure transmission of quantum states across distant devices. The widely adopted quantum teleportation scheme requires multiple rounds of entanglement swapping and purification, leading to significant resource overhead and operational complexity. In this study, we propose a novel fault-tolerant and se

Tong Fang, Rongxi Bi, Hui Zhang, You Zhou

The solar system planets are benchmarks for the planet formation theory. Yet two paradigms coexist for the four terrestrial planets: the prolonged collisional growth among planetesimals lasting $>100$ million years (Myr) and the fast formation via planetesimals accreting pebbles within 10 Myr. Despite their dramatic difference, we can hardly tell which theor

Eric Tang, Bangding Yang, Xingyou Song

With the rise of large language models (LLMs) for flexibly processing information as strings, a natural application is regression, specifically by preprocessing string representations into LLM embeddings as downstream features for metric prediction. In this paper, we provide one of the first comprehensive investigations into embedding-based regression and de

Inhwi Hwang

This paper presents an estimator-based control framework for hybrid flying capacitor multilevel (FCML) converters, achieving high-bandwidth control and reduced computational complexity. Utilizing a hybrid estimation method that combines closed-loop and open-loop dynamics, the proposed approach enables accurate and fast flying capacitor voltage estimation wit

Yuxiang Tuo, Yifeng Geng, Liefeng Bo

As the text-to-image (T2I) domain progresses, generating text that seamlessly integrates with visual content has garnered significant attention. However, even with accurate text generation, the inability to control font and color can greatly limit certain applications, and this issue remains insufficiently addressed. This paper introduces AnyText2, a novel m

F. Tang, D. Han

In this paper, we propose an new the CUSUM sequential test (control chart, stopping time) with the observation-adjusted control limits (CUSUM-OAL) for monitoring quickly and adaptively the change in distribution of a sequential observations. We give the estimation of the in-control and the out-of-control average run lengths (ARLs) of the CUSUM-OAL test. The

Ryan Ridden-Harper, Michele T. Bannister, Sophie E. Deam, Thomas Nordlander

We present starkiller, an open-source Python package for forward-modeling flux retrieval from integral field unit spectrograph (IFU) datacubes. Starkiller simultaneously provides stellar spectral classification, relative velocity, and line-of-sight extinction for all sources in a catalog, alongside a source-subtracted datacube. It performs synthetic differen

Zengbao Sun, Ming Zhao, Gaorui Liu, André Kaup

Remote sensing cross-modal text-image retrieval (RSCTIR) has gained attention for its utility in information mining. However, challenges remain in effectively integrating global and local information due to variations in remote sensing imagery and ensuring proper feature pre-alignment before modal fusion, which affects retrieval accuracy and efficiency. To a

Linqi Lu, Xianshi Yu, Akhil Perumal Reddy

This study investigates the interplay of visual and textual features in online discussions about cannabis edibles and their impact on user engagement. Leveraging the CLIP model, we analyzed 42,743 images from Facebook (March 1 to August 31, 2021), with a focus on detecting food-related visuals and examining the influence of image attributes such as colorfuln

Wen-Xu Lin, Sheng-Bang Qian, Li-Ying Zhu, Wen-Ping Liao

Asteroseismology has emerged as a powerful tool to unravel the intricate relationships between evolved stars and their planetary systems. In this study, we leverage this technique to investigate the evolutionary stages of five exoplanet host stars, each exhibiting solar-like oscillations. Building on our previous work that identified two host stars as red cl

Cesar Damian, Oscar Loaiza-Brito

We study the dynamics of a coherent state of closed type II string gravitons within the framework of the Steepest Entropy Ascent Quantum Thermodynamics, an effective model where the quantum evolution is driven by a maximal increase of entropy. We find that by perturbing the pure coherent state of gravitons by the presence of other coherent fields in the stri

Kum Yew Loke, Sherwin Stephen Chan, Mingyuan Lei, Henry Johan

With the increasing use of assistive robots in rehabilitation and assisted mobility of human patients, there has been a need for a deeper understanding of human-robot interactions particularly through simulations, allowing an understanding of these interactions in a digital environment. There is an emphasis on accurately modelling personalised 3D human digit

Yuanbo Chen, Kedi Zheng, Yuxuan Gu, Jianxiao Wang

The grid-connected electric vehicles (EVs) serve as a promising regulating resource in the distribution grid with Vehicle-to-Grid (V2G) facilities. In the day-ahead stage, electric vehicle batteries (EVBs) need to be precisely dispatched and controlled to ensure high efficiency and prevent degradation. This article focuses on considering a refined battery mo

Wenqi Zhao, Chong Han, Ho-Jin Song, Emil Björnson

Terahertz (THz) communication is envisioned as a key technology for 6G and beyond wireless systems owing to its multi-GHz bandwidth. To maintain the same aperture area and the same link budget as the lower frequencies, ultra-massive multi-input and multi-output (UM-MIMO) with hybrid beamforming is promising. Nevertheless, the hardware imperfections particula

Xunyu Zhu, Jian Li, Can Ma, Weiping Wang

Large Language Models (LLMs) demonstrate exceptional reasoning capabilities, often achieving state-of-the-art performance in various tasks. However, their substantial computational and memory demands, due to billions of parameters, hinder deployment in resource-constrained environments. A promising solution is knowledge distillation, where LLMs transfer reas

Pierre Briaud, Riddhi Ghosal, Aayush Jain, Paul Lou

In this work, we propose a new way to (non-interactively, verifiably) demonstrate Quantum Advantage by solving the average-case $\mathsf{NP}$ search problem of finding a solution to a system of (underdetermined) multivariate quadratic equations over the finite field $\mathbb{F}_2$ drawn from a specified distribution. In particular, we design a distribution o

Jinglei Cheng, Ruilin Zhou, Yuhang Gan, Chen Qian

We present a quantum-inspired algorithm that utilizes Quantum Hamiltonian Descent (QHD) for efficient community detection. Our approach reformulates the community detection task as a Quadratic Unconstrained Binary Optimization (QUBO) problem, and QHD is deployed to identify optimal community structures. We implement a multi-level algorithm that iteratively r

Hao Chen, Francois Bremond, Nicu Sebe, Shiliang Zhang

Regular unsupervised domain adaptive person re-identification (ReID) focuses on adapting a model from a source domain to a fixed target domain. However, an adapted ReID model can hardly retain previously-acquired knowledge and generalize to unseen data. In this paper, we propose a Dual-level Joint Adaptation and Anti-forgetting (DJAA) framework, which increm

Lin Luo, Xin Wang, Bojia Zi, Shihao Zhao

Large pre-trained Vision-Language Models (VLMs) such as Contrastive Language-Image Pre-training (CLIP) have been shown to be susceptible to adversarial attacks, raising concerns about their deployment in safety-critical applications like autonomous driving and medical diagnosis. One promising approach for robustifying pre-trained VLMs is Adversarial Prompt T

Kedi Zheng, Hongye Guo, Qixin Chen

This paper studies the pool strategy for price-makers under imperfect information. In this occasion, market participants cannot obtain essential transmission parameters of the power system. Thus, price-makers should estimate the market results with respect to their offer curves using available historical information. The linear programming model of economic

Reinis Cirpons, James East, James D. Mitchell

We obtain formulae for the minimum transformation degrees of the most well-studied families of finite diagram monoids, including the partition, Brauer, Temperley--Lieb and Motzkin monoids. For example, the partition monoid $P_n$ has degree $1 + \frac{B(n+2)-B(n+1)+B(n)}2$ for $n\geq2$, where these are Bell numbers. The proofs involve constructing explicit fa

Naoki Masuda, Kazuyuki Aihara

Dynamics of class II neurons, firing frequencies of which are strongly regulated by the inherent neuronal property, have been extensively studied since the formulation of the Hodgkin--Huxley model in 1952. However, how class II neurons process stimulus information and what kind of external information and internal structure firing patterns of neurons represe

Hansol Lim, Jee Won Lee, Jonathan Boyack, Jongseong Brad Choi

An onboard prediction of dynamic parameters (e.g. Aerodynamic drag, rolling resistance) enables accurate path planning for EVs. This paper presents EV-PINN, a Physics-Informed Neural Network approach in predicting instantaneous battery power and cumulative energy consumption during cruising while generalizing to the nonlinear dynamics of an EV. Our method le

Nima Dehghani, Michael Levin

The pursuit of creating artificial intelligence (AI) mirrors our longstanding fascination with understanding our own intelligence. From the myths of Talos to Aristotelian logic and Heron's inventions, we have sought to replicate the marvels of the mind. While recent advances in AI hold promise, singular approaches often fall short in capturing the essence of

Paolo Glorioso, Quentin Anthony, Yury Tokpanov, Anna Golubeva

In this technical report, we present the Zamba2 series -- a suite of 1.2B, 2.7B, and 7.4B parameter hybrid Mamba2-transformer models that achieve state of the art performance against the leading open-weights models of their class, while achieving substantial gains in inference latency, throughput, and memory efficiency. The Zamba2 series builds upon our init

Faezeh Yazdi, Derek Bingham, Daniel Williamson

Computer models are used as a way to explore complex physical systems. Stationary Gaussian process emulators, with their accompanying uncertainty quantification, are popular surrogates for computer models. However, many computer models are not well represented by stationary Gaussian processes models. Deep Gaussian processes have been shown to be capable of c

Yong Zheng

With the development of recommender systems (RSs), several promising systems have emerged, such as context-aware RS, multi-criteria RS, and group RS. Multi-criteria recommender systems (MCRSs) are designed to provide personalized recommendations by considering user preferences in multiple attributes or criteria simultaneously. Unlike traditional RSs that typ

AJ Piergiovanni, Dahun Kim, Michael S. Ryoo, Isaac Noble

Generating automatic dense captions for videos that accurately describe their contents remains a challenging area of research. Most current models require processing the entire video at once. Instead, we propose an efficient, online approach which outputs frequent, detailed and temporally aligned captions, without access to future frames. Our model uses a no

Cunzhong Lou, Chushun Tian, Zhixing Zou, Tao Shi

Entanglement fluctuations associated with Schr\"{o}dinger evolution of wavefunctions offer a unique perspective on various fundamental issues ranging from quantum thermalization to state preparation in quantum devices. Very recently, a subset of present authors have shown that in a class of free-fermion lattice models and interacting spin chains, entanglemen

Sho Katayama

We consider the Lane-Emden equation with a supercritical nonlinearity with an inhomogeneous Dirichlet boundary condition on an infinite cone. Under suitable conditions for the boundary data and the exponent of nonlinearity, we give a complete classification of the existence/nonexistence of a solution with respect to the size of boundary data. Moreover, we gi

Xinyi Hong, Ning-Chen Sun, Zexi Niu, Junjie Wu

The progenitors of Type II-P supernovae (SN) have been confirmed to be red supergiants. However, the upper mass limit of the directly probed progenitors is much lower than that predicted by current theories, and the accurate determination of the progenitor masses is key to understand the final fate of massive stars. Located at a distance of only 6.72 Mpc, th

Tao Song, Yicheng Wu, Minhao Hu, Xiangde Luo

Multimodal MR image synthesis aims to generate missing modality images by effectively fusing and mapping from a subset of available MRI modalities. Most existing methods adopt an image-to-image translation paradigm, treating multiple modalities as input channels. However, these approaches often yield sub-optimal results due to the inherent difficulty in achi

Minsung Go, Myoungwon Jeon, Yumi Choi, Nitya Kallivayalil

Reproducing the physical characteristics of ultra-faint dwarf galaxies (UFDs) in cosmological simulations is challenging, particularly with respect to stellar metallicity and galaxy size. To investigate these difficulties in detail, we conduct high-resolution simulations ($M_{\rm gas} \sim 60 \, M_{\odot}$, $M_{\rm DM} \sim 370 \, M_{\odot}$ ) on six UFD ana

Saran Vijayan, Fei Zhou

In this article, we study quantum critical phenomena in surfaces of symmetry-protected topological matter, i.e. surface topological quantum criticality. A generic phase boundary of gapless surfaces in a symmetry-protected state shall be a co-dimension one manifold in an interaction parameter space of dimension $D_p$ (where $p$ refers to the parameter space)

Ji Guo, Peihong Chen, Wenbo Jiang, Xiaolei Wen

Multimodal diffusion models for image editing generate outputs conditioned on both textual instructions and visual inputs, aiming to modify target regions while preserving the rest of the image. Although diffusion models have been shown to be vulnerable to backdoor attacks, existing efforts mainly focus on unimodal generative models and fail to address the u

Matthew Spellings, Maya Martirossyan, Julia Dshemuchadse

Recent work has proven that training large language models with self-supervised tasks and fine-tuning these models to complete new tasks in a transfer learning setting is a powerful idea, enabling the creation of models with many parameters, even with little labeled data; however, the number of domains that have harnessed these advancements has been limited.

Tengjie Zheng, Haipeng Chen, Lin Cheng, Shengping Gong

Learning dynamical models from data is not only fundamental but also holds great promise for advancing principle discovery, time-series prediction, and controller design. Among various approaches, Gaussian Process State-Space Models (GPSSMs) have recently gained significant attention due to their combination of flexibility and interpretability. However, for

Xinyu Shi

This paper analyzes the motion of solutions to non-homogeneous linear differential equations. It further clarifies that a proportional-integral-derivative (PID) controller essentially comprises two parts: a homogeneous controller and a disturbance observer, which are responsible for stabilizing the homogeneous system and compensating for the lumped disturban

Xiaoxu Tian, Daniel Holdaway, Daryl Kleist

The use of machine learning (ML) models in meteorology has attracted significant attention for their potential to improve weather forecasting efficiency and accuracy. GraphCast and NeuralGCM, two promising ML-based weather models, are at the forefront of this innovation. However, their suitability for data assimilation (DA) systems, particularly for four-dim

Jason Yang

We present an $O^*\left(|\mathbb{F}|^{(R-n_*)\left(\sum_d n_d\right)+n_*}\right)$-time algorithm for determining whether a tensor of shape $n_0\times\dots\times n_{D-1}$ over a finite field $\mathbb{F}$ has rank $\le R$, where $n_*:=\max_d n_d$; we assume without loss of generality that $\forall d:n_d\le R$. We also extend this problem to its border rank ana

Henry J. Kantrow, Elizabeth Gutiérrez-Meza, Hongmo Li, Qiao He

Photophysical aggregates are ubiquitous in many solid-state microstructures adopted by conjugated polymers, in which $\pi$ electrons interact with those in other polymer chains or those in other chromophores along the chain. These interactions fundamentally define the electronic and optical properties of the polymer film. While valuable insight can be gained

Khai Nguyen, Peter Mueller

Existing methods to summarize posterior inference for mixture models focus on identifying a point estimate of the implied random partition for clustering, with density estimation as a secondary goal (Wade and Ghahramani, 2018; Dahl et al., 2022). We propose a novel approach for summarizing posterior inference in nonparametric Bayesian mixture models, priorit

Abhisek Sahu, Mark Van Raamsdonk

We describe and study a holographic construction of big-bang / big-crunch cosmological spacetimes where the matter consists of a lattice of black holes. The cosmological spacetime is dual to an entangled state of a collection of holographic CFTs associated with the second asymptotic regions of the black holes. For a cosmology with spatial slice geometry $\Si