April 2024 arXiv papers — page 105

Qi Zhang, Lei Wang, Weihua Xu, Hongye Su

The accuracy of the underlying model predictions is crucial for the success of model predictive control (MPC) applications. If the model is unable to accurately analyze the dynamics of the controlled system, the performance and stability guarantees provided by MPC may not be achieved. Learning-based MPC can learn models from data, improving the applicability

Matteo Busi, Riccardo Focardi, Flaminia Luccio

Techniques for verifying or invalidating the security of computer systems have come a long way in recent years. Extremely sophisticated tools are available to specify and formally verify the behavior of a system and, at the same time, attack techniques have evolved to the point of questioning the possibility of obtaining adequate levels of security, especial

Eslam Ahmed, Shun Tamura, Yukio Tanaka, Jorge Cayao

Majorana zero modes have been shown to be the simplest quasiparticles exhibiting pure odd-frequency pairing, an effect that has so far been theoretically established in the static regime. In this work, we investigate the formation of Majorana modes and odd-frequency pairing in $p$-wave spin-polarized superconductors under a time-dependent drive. We first sho

Xiao Wang, Shiao Wang, Yuhe Ding, Yuehang Li

In the post-deep learning era, the Transformer architecture has demonstrated its powerful performance across pre-trained big models and various downstream tasks. However, the enormous computational demands of this architecture have deterred many researchers. To further reduce the complexity of attention models, numerous efforts have been made to design more

Yuexing Han, Ruijie Li, Guanxin Wan, Gan Hu

Cu-Cr-Zr alloys play a crucial role in electronic devices and the electric power industry, where their electrical conductivity and hardness are of great importance. However, due to the scarcity of available samples, there has been a lack of effective studies exploring the relationship between the microstructural images of Cu-Cr-Zr alloys and their key proper

Leping Li, Hongqiang Song, Hardi Peter, Lakshmi Pradeep Chitta

Solar magnetic flux rope (MFR) plays a central role in the physics of coronal mass ejections (CMEs). It mainly includes a cold filament at typical chromospheric temperatures (10000 K) and a hot channel at high coronal temperatures (10 MK). The warm MFR at quiescent coronal temperatures of a million Kelvin is, however, rarely reported. In this study, using mu

Abel Lacabanne, Daniel Tubbenhauer, Pedro Vaz

We discuss formulas for the asymptotic growth rate of the number of summands in tensor powers in certain (finite or infinite) monoidal categories. Our focus is on monoidal categories with infinitely many indecomposable objects, with our main tools being generalized Perron-Frobenius theory alongside techniques from random walks.

Weifeng Chen, Tao Gu, Yuhao Xu, Chengcai Chen

We propose Magic Clothing, a latent diffusion model (LDM)-based network architecture for an unexplored garment-driven image synthesis task. Aiming at generating customized characters wearing the target garments with diverse text prompts, the image controllability is the most critical issue, i.e., to preserve the garment details and maintain faithfulness to t

Jia Zhou, Jun Xu

We have obtained the constraints on the density dependence of the symmetry energy from neutron-skin thickness data by parity-violating electron scatterings and neutron-star observables using a Bayesian approach, based on the standard Skyrme-Hartree-Fock (SHF) model and its extension as well as the relativistic mean-field (RMF) model. While the neutron-skin t

Artur Matysiak, Volker Roeber, Henrik Kalisch, Reinhard König

Artificial neural networks (ANNs) have evolved from the 1940s primitive models of brain function to become tools for artificial intelligence. They comprise many units, artificial neurons, interlinked through weighted connections. ANNs are trained to perform tasks through learning rules that modify the connection weights. With these rules being in the focus o

Chong Peng, Liqiang He, Dan Su

Today, there have been many achievements in learning the association between voice and face. However, most previous work models rely on cosine similarity or L2 distance to evaluate the likeness of voices and faces following contrastive learning, subsequently applied to retrieval and matching tasks. This method only considers the embeddings as high-dimensiona

Yuke Li, Victor Steinberg

Recent research indicates that low-inertia viscoelastic channel flow experiences supercritical non-normal mode elastic instability from laminar to sustained chaotic flow due to finite-size perturbations. The challenge of this study is to elucidate a realization of such a pathway when the intensity of the elastic wave is too low to amplify velocity fluctuatio

Jiahe Zhao, Ruibing Hou, Hong Chang, Xinqian Gu

Current clothes-changing person re-identification (re-id) approaches usually perform retrieval based on clothes-irrelevant features, while neglecting the potential of clothes-relevant features. However, we observe that relying solely on clothes-irrelevant features for clothes-changing re-id is limited, since they often lack adequate identity information and

Khushnaseeb Roshan, Aasim Zafar

The rapid advancement of artificial intelligence within the realm of cybersecurity raises significant security concerns. The vulnerability of deep learning models in adversarial attacks is one of the major issues. In adversarial machine learning, malicious users try to fool the deep learning model by inserting adversarial perturbation inputs into the model d

Yaohua Sun, Ruiwen Li

To enhance coverage and improve service continuity, satellite-terrestrial integrated radio access network (STIRAN) has been seen as an essential trend in the development of 6G. However, there is still a lack of theoretical analysis on its coverage performance. To fill this gap, we first establish a system model to characterize a typical scenario where low-ea

C. A. Bertulani

This is a brief history of photons, both soft and hard, real and virtual. About 150-100 years ago, Maxwell and Einstein discovered intriguing properties of electromagnetic fields and how to understand them both macroscopically and microscopically. Decades later, physicists developed the theory of renormalized quantum electrodynamics (QED), an incredibly accu

Qiangqiang Wu, Antoni B. Chan

Existing deep trackers are typically trained with largescale video frames with annotated bounding boxes. However, these bounding boxes are expensive and time-consuming to annotate, in particular for large scale datasets. In this paper, we propose to learn tracking representations from single point annotations (i.e., 4.5x faster to annotate than the tradition

Rami Katz, Giulia Giordano, Dmitry Batenkov

Given a reaction-diffusion equation with unknown right-hand side, we consider a nonlinear inverse problem of estimating the associated leading eigenvalues and initial condition modes from a finite number of non-local noisy measurements. We define a reconstruction criterion and, for a small enough noise, we prove the existence and uniqueness of the desired ap

Pin Tang, Zhongdao Wang, Guoqing Wang, Jilai Zheng

Vision-based perception for autonomous driving requires an explicit modeling of a 3D space, where 2D latent representations are mapped and subsequent 3D operators are applied. However, operating on dense latent spaces introduces a cubic time and space complexity, which limits scalability in terms of perception range or spatial resolution. Existing approaches

Zhentao He, Chao Ji

In this paper, we first develop the theory of Musielak-Orlicz spaces on locally finite graphs, including completeness, reflexivity, separability, and so on. Then, we give some elementary properties of double phase operators on locally finite graphs. Finally, as applications of previous theory, we prove some existence results of solutions to double phase prob

Junren Wen, Haiqi Gao, Weiming Shi, Shuaibo Feng

Traditional spectral imaging methods are constrained by the time-consuming scanning process, limiting the application in dynamic scenarios. One-shot spectral imaging based on reconstruction has been a hot research topic recently and the primary challenges still lie in both efficient fabrication techniques suitable for mass production and the high-speed, high

Yogesh Verma, Markus Heinonen, Vikas Garg

Climate and weather prediction traditionally relies on complex numerical simulations of atmospheric physics. Deep learning approaches, such as transformers, have recently challenged the simulation paradigm with complex network forecasts. However, they often act as data-driven black-box models that neglect the underlying physics and lack uncertainty quantific

Shuaiying Hou, Hongyu Tao, Junheng Fang, Changqing Zou

Learning 3D human motion from 2D inputs is a fundamental task in the realms of computer vision and computer graphics. Many previous methods grapple with this inherently ambiguous task by introducing motion priors into the learning process. However, these approaches face difficulties in defining the complete configurations of such priors or training a robust

Xinyu Xie, Yawen Cui, Tao Tan, Xubin Zheng

Multimodal image fusion aims to integrate information from different imaging techniques to produce a comprehensive, detail-rich single image for downstream vision tasks. Existing methods based on local convolutional neural networks (CNNs) struggle to capture global features efficiently, while Transformer-based models are computationally expensive, although t

Cenlin Duan, Jianlei Yang, Yiou Wang, Yikun Wang

Bit-level sparsity in neural network models harbors immense untapped potential. Eliminating redundant calculations of randomly distributed zero-bits significantly boosts computational efficiency. Yet, traditional digital SRAM-PIM architecture, limited by rigid crossbar architecture, struggles to effectively exploit this unstructured sparsity. To address this

Genjia Liu, Yue Hu, Chenxin Xu, Weibo Mao

Vehicle-to-everything-aided autonomous driving (V2X-AD) has a huge potential to provide a safer driving solution. Despite extensive researches in transportation and communication to support V2X-AD, the actual utilization of these infrastructures and communication resources in enhancing driving performances remains largely unexplored. This highlights the nece

Xiaoyu Chen, Junbin Dong

Let ${\bf G}$ be a connected reductive algebraic group defined over the finite field $\mathbb{F}_q$ with $q$ elements,where $q$ is a power of a prime number $p$. Let $\Bbbk$ be a field and we study the extensions of certain $\bk\bg$-modules in this paper. We show that the extensions of any modules in $\mathscr{O}(\bg)$ by a finite-dimensional $\bk\bg$-module

Junfan Li, Zheshun Wu, Zenglin Xu, Irwin King

We consider online model selection with decentralized data over $M$ clients, and study the necessity of collaboration among clients. Previous work proposed various federated algorithms without demonstrating their necessity,while we answer the question from a novel perspective of computational constraints. We prove lower bounds on the regret, and propose a fe

Shishir Maheshwari, Kandala N V P S Rajesh, Vivek Kanhangad, U Rajendra Acharya

Attention deficit hyperactivity disorder (ADHD) is one of the common neurodevelopmental disorders in children. This paper presents an automated approach for ADHD detection using the proposed entropy difference (EnD)- based encephalogram (EEG) channel selection approach. In the proposed approach, we selected the most significant EEG channels for the accurate

Yangyifan Xu, Jinliang Lu, Jiajun Zhang

Ensembling different large language models (LLMs) to unleash their complementary potential and harness their individual strengths is highly valuable. Nevertheless, vocabulary discrepancies among various LLMs have constrained previous studies to either selecting or blending completely generated outputs. This limitation hinders the dynamic correction and enhan

Sungwon Han, Jinsung Yoon, Sercan O Arik, Tomas Pfister

Large Language Models (LLMs), with their remarkable ability to tackle challenging and unseen reasoning problems, hold immense potential for tabular learning, that is vital for many real-world applications. In this paper, we propose a novel in-context learning framework, FeatLLM, which employs LLMs as feature engineers to produce an input data set that is opt

Minji Kim, Dongyoon Han, Taekyung Kim, Bohyung Han

We propose a novel framework for video understanding, called Temporally Contextualized CLIP (TC-CLIP), which leverages essential temporal information through global interactions in a spatio-temporal domain within a video. To be specific, we introduce Temporal Contextualization (TC), a layer-wise temporal information infusion mechanism for videos, which 1) ex

Panu Lahti, Khanh Nguyen

In this paper, we study the existence of limits at infinity along almost every infinite curve for the upper and lower approximate limits of bounded variation functions on complete unbounded metric measure spaces. We prove that if the measure is doubling and supports a $1$-Poincar\'e inequality, then for every bounded variation function $f$ and for $1$-a.e. i

Hisatoshi Kodani, Yuji Terashima

We give a generalization of Goncharov's Hodge correlator twistor connection. Our generalized version is a connection 1-form with values in a DG Lie algebra of uni-trivalent graphs which may have loops and satisfies some Maurer--Cartan equation. This connection and the Maurer--Cartan equation can be viewed as an arithmetic analogue of effective action and qua

Xue Dong, Xi Cao, Wen-Long Li, Guofeng Zhang

The control of flying qubits carried by itinerant photons is ubiquitous in quantum networks. Beside their logical states, the shape of flying qubits must also be tailored for high-efficiency information transmission. In this paper, we introduce quantum optimal control theory to the shaping of flying qubits. Building on the flying-qubit control model establis

Kaixin Li, Yuchen Tian, Qisheng Hu, Ziyang Luo

Programming often involves converting detailed and complex specifications into code, a process during which developers typically utilize visual aids to more effectively convey concepts. While recent developments in Large Multimodal Models have demonstrated remarkable abilities in visual reasoning and mathematical tasks, there is little work on investigating

Yong-Geun Oh, Yat-Hin Suen

Let $\mathfrak{Fuk}(T^*M)$ be the Fukaya category in the Fukaya's immersed Lagrangian Floer theory \cite{fukaya:immersed} which is generated by immersed Lagrangian submanifolds with clean self-intersections. This category is monoidal in that the product of two such immersed Lagrangian submanifolds remains to be a Lagrangian immersion with clean self-intersec

Abel Luis Peralta

We discuss the possibility of constructing a function that validates the definition or not definition of the partial recursive functions of one variable. This is a topic in computability theory, which was first approached by Alan M. Turing in 1936 in his foundational work "On Computable Numbers". Here we face it using the Model of computability of the recurs

Zhiwei Min, Xu Xiao, Jiacheng Ding, Liang Xiao

We propose a lightweight deep convolutional neural network (lCNN) to estimate cosmological parameters from simulated three-dimensional dark matter (DM) halo distributions and associated statistics. The training dataset comprises 2000 realizations of a cubic box with a side length of 1000 $h^{-1}{\rm Mpc}$, and interpolated over a cubic grid of $300^3$ voxels

Kyungmin Kim, Yeong-Bok Bae, Yoon-Hyun Ryu

Microlensing is one of the most promising tools for discovering stellar-mass black holes (BHs) in the Milky Way because it allows us to probe dark or faint celestial compact objects. While the existence of stellar-mass BHs has been confirmed through observation of X-ray binaries within our galaxy and gravitational waves from extragalactic BH binaries, a conc

Yekai Li, Rufan Zhang, Wenxin Rong, Xianghang Mi

In this study, we introduce SpamDam, a SMS spam detection framework designed to overcome key challenges in detecting and understanding SMS spam, such as the lack of public SMS spam datasets, increasing privacy concerns of collecting SMS data, and the need for adversary-resistant detection models. SpamDam comprises four innovative modules: an SMS spam radar t

Kyubyung Chae, Jaepill Choi, Yohan Jo, Taesup Kim

A primary challenge in abstractive summarization is hallucination -- the phenomenon where a model generates plausible text that is absent in the source text. We hypothesize that the domain (or topic) of the source text triggers the model to generate text that is highly probable in the domain, neglecting the details of the source text. To alleviate this model

David Atkinson, Jacob Morrison

For the first time, this paper presents a taxonomy of legal risks associated with generative AI (GenAI) by breaking down complex legal concepts to provide a common understanding of potential legal challenges for developing and deploying GenAI models. The methodology is based on (1) examining the legal claims that have been filed in existing lawsuits and (2)

A. V. Kotikov, I. A. Kotikov

Anomalous dimension of the shortest single-trace operator is calculated in a simple form for the fishnet limit of the twisted ABJM model in 3D.

Yun Xia

Task offloading plays a pivotal role in mobile edge computing, enabling terminal devices to enhance task execution efficiency and conserve energy. However, servers are reluctant to offer services without compensation. Currently, pricing mechanisms are commonly employed to incentivize servers to serve terminal devices, with servers earning revenue through pay

Zou Zhen, Yu Hu, Zhao Feng

Images corrupted by rain streaks often lose vital frequency information for perception, and image deraining aims to solve this issue which relies on global and local degradation modeling. Recent studies have witnessed the effectiveness and efficiency of Mamba for perceiving global and local information based on its exploiting local correlation among patches,

Byeongkeun Kang, Sinhae Cha, Yeejin Lee

Weakly-supervised learning approaches have gained significant attention due to their ability to reduce the effort required for human annotations in training neural networks. This paper investigates a framework for weakly-supervised object localization, which aims to train a neural network capable of predicting both the object class and its location using onl

Alexander Vedernikov, Puneet Kumar, Haoyu Chen, Tapio Seppanen

Engagement analysis finds various applications in healthcare, education, advertisement, services. Deep Neural Networks, used for analysis, possess complex architecture and need large amounts of input data, computational power, inference time. These constraints challenge embedding systems into devices for real-time use. To address these limitations, we presen

Aman Sinha, Priyanshu Raj Mall, Dwaipayan Roy

Quantifying bias in retrieval functions through document retrievability scores is vital for assessing recall-oriented retrieval systems. However, many studies investigating retrieval model bias lack validation of their query generation methods as accurate representations of retrievability for real users and their queries. This limitation results from the abs

Ajinkya Gaikwad, Hitendra Kumar, Soumen Maity

We study the parameterized complexity of transforming graphs into Uniform Cluster graphs, where each component is an equal-sized clique. We consider Uniform Cluster Vertex Deletion (UCVD), Uniform Cluster Edge Deletion (UCED), Uniform Cluster Edge Addition (UCEA), Uniform Cluster Edge Editing (UCEE), Uniform Cluster Exclusive Vertex Splitting (UCEVS), and Un

Jiahao Yu, Li Chen

Recent medical image segmentation methods apply implicit neural representation (INR) to the decoder for achieving a continuous coordinate decoding to tackle the drawback of conventional discrete grid-based data representations. However, the INR-based decoder cannot well handle the feature misalignment problem brought about by the naive latent code acquisitio

Rishov Sarkar, Rachel Paul, Cong Hao

High-Level Synthesis (HLS) enables rapid prototyping of complex hardware designs by translating C or C++ code to low-level RTL code. However, the testing and evaluation of HLS designs still typically rely on slow RTL-level simulators that can take hours to provide feedback, especially for complex designs. A recent work, LightningSim, helps to solve this prob

Akshansh Mishra

Architected materials with their unique topology and geometry offer the potential to modify physical and mechanical properties. Machine learning can accelerate the design and optimization of these materials by identifying optimal designs and forecasting performance. This work presents LatticeML, a data-driven application for predicting the effective Young's

Dmitry Ignatov, Andrey Ignatov, Radu Timofte

We present ANYU, a new virtually augmented version of the NYU depth v2 dataset, designed for monocular depth estimation. In contrast to the well-known approach where full 3D scenes of a virtual world are utilized to generate artificial datasets, ANYU was created by incorporating RGB-D representations of virtual reality objects into the original NYU depth v2

Yichi Zhang, Zhuo Chen, Lingbing Guo, Yajing Xu

Multi-modal knowledge graph completion (MMKGC) aims to discover unobserved knowledge from given knowledge graphs, collaboratively leveraging structural information from the triples and multi-modal information of the entities to overcome the inherent incompleteness. Existing MMKGC methods usually extract multi-modal features with pre-trained models, resulting

Jiansong Xu

In the search for political-economic tools for greenhouse gas mitigation, carbon pricing, which includes carbon tax and cap-and-trade, is implemented by many governments. However, the inflating food prices in carbon-pricing countries, such as Canada, have led many to believe such policies harm food affordability. This study aims to identify changes in food p

Ming Cheng, Ziyi Zhou, Bowen Zhang, Ziyu Wang

In the landscape of spatio-temporal data analytics, effective trajectory representation learning is paramount. To bridge the gap of learning accurate representations with efficient and flexible mechanisms, we introduce Efflex, a comprehensive pipeline for transformative graph modeling and representation learning of the large-volume spatio-temporal trajectori

Yujia Yan, Zhiyao Duan

The neural semi-Markov Conditional Random Field (semi-CRF) framework has demonstrated promise for event-based piano transcription. In this framework, all events (notes or pedals) are represented as closed time intervals tied to specific event types. The neural semi-CRF approach requires an interval scoring matrix that assigns a score for every candidate inte

Edward Y. Chang

This paper explores the integration of human-like emotions and ethical considerations into Large Language Models (LLMs). We first model eight fundamental human emotions, presented as opposing pairs, and employ collaborative LLMs to reinterpret and express these emotions across a spectrum of intensity. Our focus extends to embedding a latent ethical dimension

Yandan Yang, Baoxiong Jia, Peiyuan Zhi, Siyuan Huang

With recent developments in Embodied Artificial Intelligence (EAI) research, there has been a growing demand for high-quality, large-scale interactive scene generation. While prior methods in scene synthesis have prioritized the naturalness and realism of the generated scenes, the physical plausibility and interactivity of scenes have been largely left unexp

Abinash Sahu

We adopt a continuous weak measurement tomography protocol to explore the signatures of chaos in the quantum system(s). We generate the measurement record as a series of expectation values of an observable evolving under the desired dynamics, which can show a transition from integrability to chaos. We find that the rate of information gain depends on the deg

Debayan Mandal, Lei Zou, Rohan Singh Wilkho, Joynal Abedin

In an era of increased climatic disasters, there is an urgent need to develop reliable frameworks and tools for evaluating and improving community resilience to climatic hazards at multiple geographical and temporal scales. Defining and quantifying resilience in the social domain is relatively subjective due to the intricate interplay of socioeconomic factor

Sara Ha, Simona Onori

COBRAPRO is a new open-source physics-based battery modeling software with the capability to conduct closed-loop parameter optimization using experimental data. Physics-based battery models require systematic parameter calibration to accurately predict battery behavior across different usage scenarios. While parameter calibration is essential to predict the

Masanori Hirano

Derivative hedging and pricing are important and continuously studied topics in financial markets. Recently, deep hedging has been proposed as a promising approach that uses deep learning to approximate the optimal hedging strategy and can handle incomplete markets. However, deep hedging usually requires underlying asset simulations, and it is challenging to

Harshmohan Kulkarni, Om Khare, Ninad Barve, Sunil Mane

This research paper proposes a novel methodology for image-to-image style transfer on objects utilizing a single deep convolutional neural network. The proposed approach leverages the You Only Look Once version 8 (YOLOv8) segmentation model and the backbone neural network of YOLOv8 for style transfer. The primary objective is to enhance the visual appeal of

Shihan Huang, Dongkun Han, John Zhen Fu Pang, Yue Chen

With the rapid growth of electric vehicles (EVs), EV aggregators have been playing a increasingly vital role in power systems by not merely providing charging management but also participating in wholesale electricity markets. This work studies the optimal real-time bidding strategy for an EV aggregator. Since the charging process of EVs is time-coupled, it

Weiwei Xu, Weijie Shen, Wen Li, Weiguo Gao

Generalized singular values (GSVs) play an essential role in the comparative analysis. In the real world data for comparative analysis, both data matrices are usually numerically low-rank. This paper proposes a randomized algorithm to first approximately extract bases and then calculate GSVs efficiently. The accuracy of both basis extration and comparative a

Xiangrui Liu, Xinju Wu, Pingping Zhang, Shiqi Wang

Gaussian splatting, renowned for its exceptional rendering quality and efficiency, has emerged as a prominent technique in 3D scene representation. However, the substantial data volume of Gaussian splatting impedes its practical utility in real-world applications. Herein, we propose an efficient 3D scene representation, named Compressed Gaussian Splatting (C

Xiaoke Wang, Titi Li, Xixi Zhang, Yaxin Wang

Magnesium ion batteries (MIBs) are a potential field for the energy storage of the future but are restricted by insufficient rate capability and rapid capacity degradation. Magnesium-sodium hybrid ion batteries (MSHBs) are an effective way to address these problems. Here, we report a new type of MSHBs that use layered sodium vanadate ((Na, Mn)V8O20 5H2O, Mn-

Jongmin Park, Seunghoon Han, Soohwan Jeong, Sungsu Lim

Most previous heterogeneous graph embedding models represent elements in a heterogeneous graph as vector representations in a low-dimensional Euclidean space. However, because heterogeneous graphs inherently possess complex structures, such as hierarchical or power-law structures, distortions can occur when representing them in Euclidean space. To overcome t

Amaael Antonini, Rita Gimelshein, Richard D. Wesel

Posterior matching uses variable-length encoding of the message controlled by noiseless feedback of the received symbols to achieve high rates for short average blocklengths. Traditionally, the feedback of a received symbol occurs before the next symbol is transmitted. The transmitter optimizes the next symbol transmission with full knowledge of every past r

Sepehr Dehdashtian, Bashir Sadeghi, Vishnu Naresh Boddeti

When building classification systems with demographic fairness considerations, there are two objectives to satisfy: 1) maximizing utility for the specific task and 2) ensuring fairness w.r.t. a known demographic attribute. These objectives often compete, so optimizing both can lead to a trade-off between utility and fairness. While existing works acknowledge

Paras Varshney, Niral Desai, Uzair Ahmed

This research endeavors to address the pervasive issue of light pollution through an interdisciplinary approach, leveraging data science and machine learning techniques. By analyzing extensive datasets and research findings, we aim to develop predictive models capable of estimating the degree of sky glow observed in various locations and times. Our research

Xiaojie Wu, Qiming Sun, Zhichen Pu, Tianze Zheng

We describe our contribution as industrial stakeholders to the existing open-source GPU4PySCF project (https: //github.com/pyscf/gpu4pyscf), a GPU-accelerated Python quantum chemistry package. We have integrated GPU acceleration into other PySCF functionality including Density Functional Theory (DFT), geometry optimization, frequency analysis, solvent models

Sua Choi, Dahyun Kang, Minsu Cho

We address the problem of generalized category discovery (GCD) that aims to partition a partially labeled collection of images; only a small part of the collection is labeled and the total number of target classes is unknown. To address this generalized image clustering problem, we revisit the mean-shift algorithm, i.e., a classic, powerful technique for mod

Alexander Russell, Qiang Tang, Jiadong Zhu

The Feistel construction is a fundamental technique for building pseudorandom permutations and block ciphers. This paper shows that a simple adaptation of the construction is resistant, even to algorithm substitution attacks -- that is, adversarial subversion -- of the component round functions. Specifically, we establish that a Feistel-based construction wi

Sebastián Muñoz-Thon

We study the scattering rigidity problem for standard stationary manifolds using timelike geodesics with a fixed momentum. Taking advantage of the symmetry of this manifolds, we use Hamiltonian reduction to show that this problem is related to scattering rigidity for $\mathcal{MP}$-systems, a problem studied before. This gives several new rigidity results (u

Samira Ghanbarian, Arpan Mukhopadhyay, Ravi R. Mazumdar, Fabrice M. Guillemin

A large proportion of jobs submitted to modern computing clusters and data centers are parallelizable and capable of running on a flexible number of computing cores or servers. Although allocating more servers to such a job results in a higher speed-up in the job's execution, it reduces the number of servers available to other jobs, which in the worst case,

Wen-Ning Sun, Mei Qin

For solving large consistent linear systems by iteration methods, inspired by the maximum residual Kaczmarz method and the randomized block Kaczmarz method, we propose the maximum residual block Kaczmarz method, which is designed to preferentially eliminate the largest block in the residual vector $r_{k}$ at each iteration. At the same time, in order to furt

Zhongrui Gui, Shuyang Sun, Runjia Li, Jianhao Yuan

Continual segmentation has not yet tackled the challenge of improving open-vocabulary segmentation models with training data for accurate segmentation across large, continually expanding vocabularies. We discover that traditional continual training results in severe catastrophic forgetting, failing to outperform a zero-shot segmentation baseline. We introduc

Yuki Osawa, Kuan-Nan Lin, Yasusada Nambu, Masahiro Hotta

Flying mirrors with appropriate trajectories have been recognized as an analog system that mimics black hole Hawking evaporation and have been widely investigated. It has recently been suggested that the partner mode of the analog Hawking radiation emitted from a moving mirror would manifest itself through a final burst when the mirror executes a sudden stop

Jenny Sheng, Matthieu Lin, Andrew Zhao, Kevin Pruvost

This paper presents an exploration of preference learning in text-to-motion generation. We find that current improvements in text-to-motion generation still rely on datasets requiring expert labelers with motion capture systems. Instead, learning from human preference data does not require motion capture systems; a labeler with no expertise simply compares t

Jun-Bin Wang, Dongchen Huang, Yi-feng Yang

Solving quantum impurity problems may advance our understanding of strongly correlated electron physics, but its development in multi-impurity systems has been greatly hindered due to the presence of shared bath. Here, we propose a general operation strategy to disentangle the shared bath into multiple auxiliary baths and relate the problem to a spectral dec

Jaeyeon Jang, Diego Klabjan, Veena Mendiratta, Fanfei Meng

Federated learning is an emerging paradigm for decentralized training of machine learning models on distributed clients, without revealing the data to the central server. Most existing works have focused on horizontal or vertical data distributions, where each client possesses different samples with shared features, or each client fully shares only sample in

Alexander Russell, Qiang Tang, Moti Yung, Hong-Sheng Zhou

The random oracle methodology has proven to be a powerful tool for designing and reasoning about cryptographic schemes. In this paper, we focus on the basic problem of correcting faulty or adversarially corrupted random oracles, so that they can be confidently applied for such cryptographic purposes. We prove that a simple construction can transform a "subve

Haowei Shi, Quntao Zhuang

A quantum transducer converts an input signal to an output probe at a distant frequency band while maintaining the quantum information with high fidelity, which is crucial for quantum networking and distributed quantum sensing and computing. In terms of microwave-optical quantum transduction, the state-of-the-art quantum transducers suffer low transduction e

Sergei Nedić, Karin Yamamura, Angus Gale, Igor Aharonovich

Hexagonal boron nitride (hBN) holds promise as a solid state, van der Waals host of single photon emitters for on-chip quantum photonics. The B-centre defect emitting at 436 nm is particularly compelling as it can be generated by electron beam irradiation. However, the emitter generation mechanism is unknown, the robustness of the method is variable, and it

Zhaobo Tom Han

Let $\mathcal{F}$ be a coherent sheaf on a complex variety $X$ that has a locally free resolution $E^{\bullet}$. In [19], the authors constructed a pseudomeromorphic current whose support is contained in $supp(E^{\bullet})$ that represents products of Chern classes of $\mathcal{F}.$ In this paper, we show that their construction works for general de-Rham cha

Nachuan Xiao, Kuangyu Ding, Xiaoyin Hu, Kim-Chuan Toh

In this paper, we consider the minimization of a nonsmooth nonconvex objective function $f(x)$ over a closed convex subset $\mathcal{X}$ of $\mathbb{R}^n$, with additional nonsmooth nonconvex constraints $c(x) = 0$. We develop a unified framework for developing Lagrangian-based methods, which takes a single-step update to the primal variables by some subgrad

Jirat Pasuksmit, Patanamon Thongtanunam, Shanika Karunasekera

Background: Accurate effort estimation is crucial for planning in Agile iterative development. Agile estimation generally relies on consensus-based methods like planning poker, which require less time and information than other formal methods (e.g., COSMIC) but are prone to inaccuracies. Understanding the common reasons for inaccurate estimations and how pro

Abraham P. Punnen, Navpreet Kaur

In this paper, we present several new linearizations of a quadratic binary optimization problem (QBOP), primarily using the method of aggregations. Although aggregations were studied in the past in the context of solving system of Diophantine equations in non-negative variables, none of the approaches developed produced practical models, particularly due to

Shanshan Shan, Yang Gao, David E. J. Waddington, Hongli Chen

MRI-Linac systems require fast image reconstruction with high geometric fidelity to localize and track tumours for radiotherapy treatments. However, B0 field inhomogeneity distortions and slow MR acquisition potentially limit the quality of the image guidance and tumour treatments. In this study, we develop an interpretable unrolled network, referred to as R

Yan-Han Yang, Xue Yang, Xing-Zhou Zheng, Ming-Xing Luo

Quantum coherence plays a crucial role in manipulating and controlling quantum systems, leading to breakthroughs in various fields such as quantum information, quantum sensing, and the detection of gravitational waves. Most coherence witnesses rely on the assumption of being able to control quantum states. Here we report a device-independent coherence model

Zongpeng Ding, Yuanqiu Huang, Fengming Dong

A graph $G$ is said to be crossing-critical if $cr(G-e)< cr(G)$ for every edge $e$ of $G$, where $cr(G)$ is the crossing number of $G$. Richter and Thomassen [Journal of Combinatorial Theory, Series B 58 (1993), 217-224] constructed an infinite family of 4-regular crossing-critical graphs with crossing number $3$. In this article, we present a new infinite f

Shizheng Xu

Mandarin Chinese is the official language in China, Taiwan, and Singapore. It is also the main non-official language spoken predominantly at home in Toronto and Vancouver. This article employs the audio software Audacity and leverages theoretical knowledge to conduct a comprehensive analysis of Mandarin Chinese. The study initiates with an overview of the fu

Chengfeng Liu, Mai Xu, Qunliang Xing, Xin Zou

Lossy image compression is essential for Mars exploration missions, due to the limited bandwidth between Earth and Mars. However, the compression may introduce visual artifacts that complicate the geological analysis of the Martian surface. Existing quality enhancement approaches, primarily designed for Earth images, fall short for Martian images due to a la

Lan Chen, Yufei Ji, Xichen Yao, Hengshu Zhu

This paper explores the evolution of occupations within the context of industry and technology life cycles, highlighting the critical yet underexplored intersection between occupational trends and broader economic dynamics. Introducing the Occupation Life Cycle (OLC) model, we delineate five stages (i.e., growth, peak, fluctuation, maturity, and decline) to

Shuo Wang, David C. Anastasiu, Zheng Tang, Ming-Ching Chang

The eighth AI City Challenge highlighted the convergence of computer vision and artificial intelligence in areas like retail, warehouse settings, and Intelligent Traffic Systems (ITS), presenting significant research opportunities. The 2024 edition featured five tracks, attracting unprecedented interest from 726 teams in 47 countries and regions. Track 1 dea

Bonan Ding, Jin Xie, Jing Nie, Jiale Cao

Pseudo-LiDAR has become a promising paradigm for monocular 3D object detection by transforming monocular images into point cloud representations that can be processed by LiDAR-based 3D object detectors. Recent vision foundation models provide powerful geometric and semantic priors, creating new opportunities for improving the quality of pseudo-LiDAR generati

Sreyes Venkatesh, Razvan Marinescu, Jason K. Eshraghian

Weight quantization is used to deploy high-performance deep learning models on resource-limited hardware, enabling the use of low-precision integers for storage and computation. Spiking neural networks (SNNs) share the goal of enhancing efficiency, but adopt an 'event-driven' approach to reduce the power consumption of neural network inference. While extensi