December 2023 arXiv papers — page 114

Vikram Singh, Vikram Singh

Transcriptomic data is a treasure-trove in modern molecular biology, as it offers a comprehensive viewpoint into the intricate nuances of gene expression dynamics underlying biological systems. This genetic information must be utilised to infer biomolecular interaction networks that can provide insights into the complex regulatory mechanisms underpinning the

Bui Minh Tuan, Diep N. Nguyen, Nguyen Linh Trung, Van-Dinh Nguyen

Wireless communications are particularly vulnerable to eavesdropping attacks due to their broadcast nature. To effectively deal with eavesdroppers, existing security techniques usually require accurate channel state information (CSI), e.g., for friendly jamming (FJ), and/or additional computing resources at transceivers, e.g., cryptography-based solutions, w

Ying Chen, Xi Liu, Zhenhua Chai, Baochang Shi

The Allen-Cahn equation (ACE) inherently possesses two crucial properties: the maximum principle and the energy dissipation law. Preserving these two properties at the discrete level is also necessary in the numerical methods for the ACE. In this paper, unlike the traditional top-down macroscopic numerical schemes which discretize the ACE directly, we first

Kongming Liang, Xinran Wang, Rui Wang, Donghui Gao

Attribute labeling at large scale is typically incomplete and partial, posing significant challenges to model optimization. Existing attribute learning methods often treat the missing labels as negative or simply ignore them all during training, either of which could hamper the model performance to a great extent. To overcome these limitations, in this paper

Chenyang Li, Shuxian Mao, Renke Huang, Francesco A. Evangelista

We present a reduced-cost implementation of the state-averaged driven similarity renormalization group (SA-DSRG) based on the frozen natural orbital (FNO) approach. The natural orbitals (NOs) are obtained by diagonalizing the one-body reduced density matrix from SA-DSRG second-order perturbation theory (SA-DSRG-PT2). Subsequently, the virtual NOs with occupa

Mikhail Mikhailovskii, Maria Poleva, Nikolay Solodovchenko, Mikhail Sidorenko

Efficient trapping of light in nanostructures is essential for the development of optical devices that are based on the interaction between light and matter. In this work, we show theoretically and experimentally that one-dimensional arrays of subwavelength dielectric Mie-resonant particles can support collective resonances with increased $Q$-factors. We dem

Ziqi Zhang, Jingzehua Xu, Zifeng Zhuang, Hongyin Zhang

Proximal Policy Optimization (PPO) has been broadly applied to robotics learning, showcasing stable training performance. However, the fixed clipping bound setting may limit the performance of PPO. Specifically, there is no theoretical proof that the optimal clipping bound remains consistent throughout the entire training process. Meanwhile, previous researc

Zeming Chen, Wenwei Zhang, Xinjiang Wang, Kai Chen

While the pseudo-label method has demonstrated considerable success in semi-supervised object detection tasks, this paper uncovers notable limitations within this approach. Specifically, the pseudo-label method tends to amplify the inherent strengths of the detector while accentuating its weaknesses, which is manifested in the missed detection of pseudo-labe

Yufei Chang, Xiaodan Chen, Shuting Zhang

Let $G$ be a simple graph of order $n$ with degree sequence $(d_1,d_2,\cdots,d_n)$. For an integer $p>1$, let $e_p(G)=\sum_{i=1}^n d^{p}_i$ and let $ex_p(n,H)$ be the maximum value of $e_p(G)$ among all graphs with $n$ vertices that do not contain $H$ as a subgraph (known as $H$-free graphs). Caro and Yuster proposed the problem of determining the exact valu

Zihao Wang, Pablo M. Perez-Piskunow, Calvin Pei Yu Wong, Matthew Holwill

We report intriguing and hitherto overlooked low-field room temperature extremely large magnetoresistance (XMR) patterns in graphene/hexagonal boron nitride (h-BN) superlattices that emerge due to the existence of open orbits within each miniband. This finding is set against the backdrop of the experimental discovery of the Hofstadter butterfly in moir super

Tianyi Li, Alexander Halatsis, Raphael Stern

This paper introduces RACER, the Rational Artificial Intelligence Car-following model Enhanced by Reality, a cutting-edge deep learning car-following model, that satisfies partial derivative constraints, designed to predict Adaptive Cruise Control (ACC) driving behavior while staying theoretically feasible. Unlike conventional models, RACER effectively integ

Ruobing Bai, Mohamed Majdoub, Tarek Saanouni

We investigate the blow-up for a fourth-order Schr\"odinger equation with a mas-critical focusing inhomogeneous nonlinearity. We prove the finite/infinite-time blow-up of non-radial solutions with negative energy. Our result serves as a valuable complement to the existing literature and offers an improvement in our understanding of the subject matter.

Ruijia Chang, Suncheng Xiang, Chengyu Zhou, Kui Su

Chromosome recognition is an essential task in karyotyping, which plays a vital role in birth defect diagnosis and biomedical research. However, existing classification methods face significant challenges due to the inter-class similarity and intra-class variation of chromosomes. To address this issue, we propose a supervised contrastive learning strategy th

Donipolo Ghimire, Solmaz S. Kia

This paper examines the spatial coverage optimization problem for multiple sensors in a known convex environment, where the coverage service of each sensor is heterogeneous and anisotropic. We introduce the Stein Coverage algorithm, a distribution-matching coverage approach that aims to place sensors at positions and orientations such that their collective c

Yuqing Yang, Ethan Chern, Xipeng Qiu, Graham Neubig

Recent research has made significant strides in aligning large language models (LLMs) with helpfulness and harmlessness. In this paper, we argue for the importance of alignment for \emph{honesty}, ensuring that LLMs proactively refuse to answer questions when they lack knowledge, while still not being overly conservative. However, a pivotal aspect of alignme

Jingchun Zhou, Zongxin He, Qiuping Jiang, Kui Jiang

Underwater image enhancement (UIE) is a challenging task due to the complex degradation caused by underwater environments. To solve this issue, previous methods often idealize the degradation process, and neglect the impact of medium noise and object motion on the distribution of image features, limiting the generalization and adaptability of the model. Prev

Takashi Ichikawa

In order to generalize a program by Agostini and others producing new KP solutions, we construct families of quasi-periodic KP solutions which are derived from degenerating Riemann surfaces associated with tropical curves having nontrivial weights. By taking the regularized tropical limits of these solutions, we obtain formulas of general KP solutions which

Pritha Dolai, Aditya Singh Rajput, K. Vijay Kumar

Collections of persistently moving active particles are an example of a nonequilibrium heat bath. One way to study the nature of nonequilibrium fluctuations in such systems is to follow the dynamics of an embedded probe particle. With this aim, we study the dynamics of an anisotropic inclusion embedded in a bath of active particles. By studying various stati

Olgur Celikbas, Toshinori Kobayashi, Brian Laverty, Hiroki Matsui

We prove that the depth formula holds for two finitely generated Tor-independent modules over Cohen-Macaulay local rings if one of the modules considered has finite reducing projective dimension (for example, if it has finite projective dimension, or the ring is a complete intersection). This generalizes a result of Bergh-Jorgensen which shows that the depth

Jinsong Shi, Pan Gao, Jie Qin

Image Quality Assessment (IQA) has long been a research hotspot in the field of image processing, especially No-Reference Image Quality Assessment (NR-IQA). Due to the powerful feature extraction ability, existing Convolution Neural Network (CNN) and Transformers based NR-IQA methods have achieved considerable progress. However, they still exhibit limited ca

Le Tung Bach

One of the main stages for achieving success is the adoption of new technology by its users. Several studies show that Property technology is advantageous for real estate stakeholders. Hence, the purpose of this paper is to investigate the users' engagement behavior to adopt Property technology in the Vietnamese real estate market. To that end, a purposive s

Henry Bae, Aghyad Deeb, Alex Fleury, Kehang Zhu

We present ComplexityNet, a streamlined language model designed for assessing task complexity. This model predicts the likelihood of accurate output by various language models, each with different capabilities. Our initial application of ComplexityNet involves the Mostly Basic Python Problems (MBPP) dataset. We pioneered the creation of the first set of labe

Jichao Yin, Ziming Wen, Shuhao Li, Yaya Zhanga

Integration of machine learning (ML) into the topology optimization (TO) framework is attracting increasing attention, but data acquisition in data-driven models is prohibitive. Compared with popular ML methods, the physics-informed neural network (PINN) can avoid generating enormous amounts of data when solving forward problems and additionally provide bett

Sourav Bhadra, Satyendra Thoudam, Biman B Nath, Prateek Sharma

We show that massive young star clusters may be possible candidates that can accelerate Galactic cosmic rays (CRs) in the range of $10^7\hbox{--}10^9$ GeV (between the `knee' and `ankle'). Various plausible scenarios such as acceleration at the wind termination shock (WTS), supernova shocks inside these young star clusters, etc. have been proposed,since it i

Jonathan J. Y. Kim, Martin Urschler, Patricia J. Riddle, Jorg S. Wicker

With the advancement in robotics, it is becoming increasingly common for large factories and warehouses to incorporate visual SLAM (vSLAM) enabled automated robots that operate closely next to humans. This makes any adversarial attacks on vSLAM components potentially detrimental to humans working alongside them. Loop Closure Detection (LCD) is a crucial comp

Prisha Shroff

Wildfires pose a serious threat to the environment of the world. The global wildfire season length has increased by 19% and severe wildfires have besieged nations around the world. Every year, forests are burned by wildfires, causing vast amounts of carbon dioxide to be released into the atmosphere, contributing to climate change. There is a need for a syste

Caridad Arroyo Arevalo, Sayedeh Leila Noorbakhsh, Yun Dong, Yuan Hong

Federated learning (FL) has been widely studied recently due to its property to collaboratively train data from different devices without sharing the raw data. Nevertheless, recent studies show that an adversary can still be possible to infer private information about devices' data, e.g., sensitive attributes such as income, race, and sexual orientation. To

Guangfeng Jiang, Jun Liu, Yuzhi Wu, Wenlong Liao

Instance segmentation is a fundamental research in computer vision, especially in autonomous driving. However, manual mask annotation for instance segmentation is quite time-consuming and costly. To address this problem, some prior works attempt to apply weakly supervised manner by exploring 2D or 3D boxes. However, no one has ever successfully segmented 2D

Wuqiong Pan, Hongliang Gu

This article describes a lightweight additive homomorphic algorithm with the same encryption and decryption keys. Compared to standard additive homomorphic algorithms like Paillier, this algorithm reduces the computational cost of encryption and decryption from modular exponentiation to modular multiplication, and reduces the computational cost of ciphertext

Thomas Paniagua, Ryan Grainger, Tianfu Wu

The adversarial vulnerability of Deep Neural Networks (DNNs) has been well-known and widely concerned, often under the context of learning top-$1$ attacks (e.g., fooling a DNN to classify a cat image as dog). This paper shows that the concern is much more serious by learning significantly more aggressive ordered top-$K$ clear-box~\footnote{ This is often ref

Julian Frattini, Maximilian Junker, Michael Unterkalmsteiner, Daniel Mendez

Background: The detection and extraction of causality from natural language sentences have shown great potential in various fields of application. The field of requirements engineering is eligible for multiple reasons: (1) requirements artifacts are primarily written in natural language, (2) causal sentences convey essential context about the subject of requ

Daeun Kim, Namyoon Lee

Satellite networks are recognized as an effective solution to ensure seamless connectivity worldwide, catering to a diverse range of applications. However, the broad coverage and broadcasting nature of satellite networks also expose them to security challenges. Despite these challenges, there is a lack of analytical understanding addressing the secrecy perfo

L. O. Castaños-Cervantes, Lorenzo M. Procopio, Marco Enríquez

In this work, we uncover new features on the study of a two-level atom interacting with one of two cavities in a coherent superposition. The James-Cummings model is used to describe the atom-field interaction and to study the effects of quantum indefiniteness on such an interaction. We show that coherent control of the two cavities in an undefined manner all

Chen Zhu, Zhouxiang Zhao, Zejing Shan, Lijie Yang

Target detection is pivotal for modern urban computing applications. While image-based techniques are widely adopted, they falter under challenging environmental conditions such as adverse weather, poor lighting, and occlusion. To improve the target detection performance under complex real-world scenarios, this paper proposes an intelligent integrated optica

Zihe Liang, Sibo Chai, Qinyun Ding, Kai Xiao

Non-Euclidean surfaces are ubiquitous in numerous engineering fields, such as automotive, aerospace, and biomedical engineering domains. Morphing origami has numerous potential engineering applications, including soft robots, mechanical metamaterials, antennas, aerospace structures, and biomedical devices, owing to its intrinsic morphing features from two-di

Pavlos Constas, Vikram Rawal, Matthew Honorio Oliveira, Andreas Constas

We propose a reinforcement learning (RL)-based system that would automatically prescribe a hypothetical patient medication that may help the patient with their mental health-related speech disfluency, and adjust the medication and the dosages in response to zero-cost frequent measurement of the fluency of the patient. We demonstrate the components of the sys

Michael Coons, Yohei Tachiya

The Thue--Morse sequence $\{t(n)\}_{n\geqslant 1}$ is the indicator function of the parity of the number of ones in the binary expansion of positive integers $n$, where $t(n)=1$ (resp. $=0$) if the binary expansion of $n$ has an odd (resp. even) number of ones. In this paper, we generalize a recent result of E.~Miyanohara by showing that, for a fixed Pisot o

Ziyuan Liu, Yuhang Wu, Daniel Zhengyu Huang, Hong Zhang

Neural operators have been validated as promising deep surrogate models for solving partial differential equations (PDEs). Despite the critical role of boundary conditions in PDEs, however, only a limited number of neural operators robustly enforce these conditions. In this paper we introduce semi-periodic Fourier neural operator (SPFNO), a novel spectral op

Gauri Bhardwaj, Yuvaraj Govindarajulu, Sundaraparipurnan Narayanan, Pavan Kulkarni

Medical imaging has revolutionized disease diagnosis, yet the potential is hampered by limited access to diverse and privacy-conscious datasets. Open-source medical datasets, while valuable, suffer from data quality and clinical information disparities. Generative models, such as diffusion models, aim to mitigate these challenges. At Stanford, researchers ex

Bodong Zhang, Hamid Manoochehri, Man Minh Ho, Fahimeh Fooladgar

Histopathological image classification is an important task in medical image analysis. Recent approaches generally rely on weakly supervised learning due to the ease of acquiring case-level labels from pathology reports. However, patch-level classification is preferable in applications where only a limited number of cases are available or when local predicti

Chunmei Zhang, Jian Zhou

The bulk photovoltaic (BPV) effect that converts light into electric current is highly sensitive to the system symmetry and its electronic Bloch wavefunction. To create a sizable net electric current, it is necessary to break the centrosymmetry P in its host material. While prior studies mainly focus on P-broken nonmagnetic (time reversal T-reserved) and col

Zeyu Yang, Hao Wang

The increasing uptake of distributed energy resources (DERs) in smart home prosumers calls for distributed energy management strategies, and the advances in information and communications technology enable peer-to-peer (P2P) energy trading and transactive energy management. Many works attempted to solve the transactive energy management problem using distrib

Harish J. Vallury, Lloyd C. L. Hollenberg

The determination of ground state properties of quantum systems is a fundamental problem in physics and chemistry, and is considered a key application of quantum computers. A common approach is to prepare a trial ground state on the quantum computer and measure observables such as energy, but this is often limited by hardware constraints that prevent an accu

Anubhav Bhatti, Surajsinh Parmar, San Lee

We are introducing SM70, a 70 billion-parameter Large Language Model that is specifically designed for SpassMed's medical devices under the brand name 'JEE1' (pronounced as G1 and means 'Life'). This large language model provides more accurate and safe responses to medical-domain questions. To fine-tune SM70, we used around 800K data entries from the publicl

Jinqiang Yu, Graham Farr, Alexey Ignatiev, Peter J. Stuckey

Widespread use of artificial intelligence (AI) algorithms and machine learning (ML) models on the one hand and a number of crucial issues pertaining to them warrant the need for explainable artificial intelligence (XAI). A key explainability question is: given this decision was made, what are the input features which contributed to the decision? Although a r

Abigail Bodner, Dhruv Balwada, Laure Zanna

Parameterizations of O(1-10)km submesoscale flows in General Circulation Models (GCMs) represent the effects of unresolved vertical buoyancy fluxes in the ocean mixed layer. These submesoscale flows interact non-linearly with mesoscale and boundary layer turbulence, and it is challenging to account for all the relevant processes in physics-based parameteriza

Jie Xiao, Kai Zhu, Han Zhang, Zhiheng Liu

Consistency Models (CMs) have showed a promise in creating visual content efficiently and with high quality. However, the way to add new conditional controls to the pretrained CMs has not been explored. In this technical report, we consider alternative strategies for adding ControlNet-like conditional control to CMs and present three significant findings. 1)

Mourad Oudich, Xianghong Kong, Tan Zhang, Chengwei Qiu

Recent discoveries on Mott insulating and unconventional superconducting states in twisted bilayer graphene with Moir\'e superlattices have reshaped the landscape of ''twistronics'' and paved the way for developing high-temperature superconductors and new devices for quantum computing and sensing. Meanwhile, artificially structured photonic and phononic meta

Zhenyu Xiao, Songqi Cao, Lipeng Zhu, Yanming Liu

Movable antenna (MA) is a new technology with great potential to improve communication performance by enabling local movement of antennas for pursuing better channel conditions. In particular, the acquisition of complete channel state information (CSI) between the transmitter (Tx) and receiver (Rx) regions is an essential problem for MA systems to reap perfo

Chaoya Jiang, Haiyang Xu, Mengfan Dong, Jiaxing Chen

Multi-modal large language models (MLLMs) have been shown to efficiently integrate natural language with visual information to handle multi-modal tasks. However, MLLMs still face a fundamental limitation of hallucinations, where they tend to generate erroneous or fabricated information. In this paper, we address hallucinations in MLLMs from a novel perspecti

Antonios-Alexandros Robotis

We study admissible subcategories of the derived categories of smooth noncommutative (nc) curves as classified by Reiten-van den Bergh. We prove that any admissible subcategory of the derived category of a smooth nc curve is again the derived category of a smooth nc curve. We use this result to classify semiorthogonal decompositions in derived categories of

Xiaoli Xu, Yong Zeng

Channel knowledge map (CKM) has been recently proposed to enable environment-aware communications by utilizing historical or simulation generated wireless channel data. This paper studies the construction of one particular type of CKM, namely channel gain map (CGM), by using a finite number of measurements or simulation-generated data, with model-based spati

Nikhil Kashyap, Manas Satish Bedmutha, Prerit Chaudhary, Brian Wood

Vision-based human activity recognition (HAR) has made substantial progress in recognizing predefined gestures but lacks adaptability for emerging activities. This paper introduces a paradigm shift by harnessing generative modeling and large language models (LLMs) to enhance vision-based HAR. We propose utilizing LLMs to generate descriptive textual represen

Huaqing Cheng, Zhixing Ling, Chen Zhang, Xiaojin Sun

We report on results of the on-ground X-ray calibration of the Lobster Eye Imager for Astronomy (LEIA), an experimental space wide-field (18.6*18.6 square degrees) X-ray telescope built from novel lobster eye mirco-pore optics. LEIA was successfully launched on July 27, 2022 onboard the SATech-01 satellite. To achieve full characterisation of its performance

Koji Shimizu, Parth Bahuguna, Shigeo Mori, Akitoshi Hayashi

Understanding the atomistic mechanism of ion conduction in solid electrolytes is critical for the advancement of all-solid-state batteries. Glass-ceramics, which undergo crystallization from a glass state, frequently exhibit unique properties including enhanced ionic conductivities compared to both the original crystalline and glass forms. Despite these dist

Joseph Eremondi

Ordinals can help prove termination for dependently typed programs. Brouwer trees are a particular ordinal notation that make it very easy to assign sizes to higher order data structures. They extend natural numbers with a limit constructor, so a function's size can be the supremum of the sizes of values from its image. These can then be used to define well-

Sanja Lazarević, Miroslav D. Filipović, Shi Dai, Roland Kothes

We report the discovery of a bow-shock pulsar wind nebula (PWN), named Potoroo, and the detection of a young pulsar J1638-4713 that powers the nebula. We present a radio continuum study of the PWN based on 20-cm observations obtained from the Australian Square Kilometre Array Pathfinder (ASKAP) and MeerKAT. PSR J1638-4713 was identified using Parkes radio te

Utkarsh Mall, Cheng Perng Phoo, Meilin Kelsey Liu, Carl Vondrick

We introduce a method to train vision-language models for remote-sensing images without using any textual annotations. Our key insight is to use co-located internet imagery taken on the ground as an intermediary for connecting remote-sensing images and language. Specifically, we train an image encoder for remote sensing images to align with the image encoder

Jingfei Sun, Xiaohu Li, Fujun Du, Yao Wang

Organic features lead to two distinct types of Class 0/I low-mass protostars: hot corino sources, and warm carbon-chain chemistry (WCCC) sources. Some observations suggest that the chemical variations between WCCC sources and hot corino sources are associated with local environments, as well as the luminosity of protostars. We conducted gas-grain chemical si

Henrik Skibbe, Michal Byra, Akiya Watakabe, Tetsuo Yamamori

We introduce "PatchMorph," an new stochastic deep learning algorithm tailored for unsupervised 3D brain image registration. Unlike other methods, our method uses compact patches of a constant small size to derive solutions that can combine global transformations with local deformations. This approach minimizes the memory footprint of the GPU during training,

Qing-xin Meng, Jian-wei Liu

Centered around solving the Online Saddle Point problem, this paper introduces the Online Convex-Concave Optimization (OCCO) framework, which involves a sequence of two-player time-varying convex-concave games. We propose the generalized duality gap (Dual-Gap) as the performance metric and establish the parallel relationship between OCCO with Dual-Gap and On

Ray Han

Phylogeny can be inferred using two sources of data from an organism: morphological data and molecular data. Historically, phylogenies were usually inferred using morphological characters, but some morphological features may not necessarily indicate shared heritage. With the introduction of molecular phylogenies, the base sequence of genes, or amino acid seq

Retnaningsih Retnaningsih, Agus Budiyono, Rifky Ismail, Dodik Tugasworo

Robotic technology has the potential to revolutionize the field of neurology by providing new methods for diagnosis, treatment, and rehabilitation of neurological disorders. In recent years, there has been an increasing interest in the development of robotics applications for neurology, driven by advances in sensing, actuation, and control systems. This revi

Yang Xu, Yongqiang Yao, Yufan Huang, Mengnan Qi

Instruction tuning, a specialized technique to enhance large language model (LLM) performance via instruction datasets, relies heavily on the quality of employed data. Existing quality improvement methods alter instruction data through dataset expansion or curation. However, the expansion method risks data redundancy, potentially compromising LLM performance

Jingchun Zhou, Qilin Gai, Kin-man Lam, Xianping Fu

In underwater environments, variations in suspended particle concentration and turbidity cause severe image degradation, posing significant challenges to image enhancement (IE) and object detection (OD) tasks. Currently, in-air image enhancement and detection methods have made notable progress, but their application in underwater conditions is limited due to

Feng Sun, Aijun Hong

The mobility formula based on deformation potential (DP) theory is of great importance in semiconductor physics. However, the related calculations for the DP constant are controversial. It is necessary to redo in-depth and comprehensive research on the mobility of single crystal silicon and the related parameters such as the effective mass and the DP constan

Kasra Rouhi, Filippo Capolino, Alexander Figotin

An exceptional point of degeneracy (EPD) occurs when both the eigenvalues and the corresponding eigenvectors of a square matrix coincide and the matrix has a nontrivial Jordan block structure. It is not easy to achieve an EPD exactly. In our prior studies, we synthesized simple conservative (lossless) circuits with evolution matrices featuring EPDs by using

Gokce Basar

We analyze the trajectory of the Lee-Yang edge singularities of the QCD equation of state in the complex baryon chemical potential ($\mu_B$) plane for different values of the temperature by using the recent lattice results for the Taylor expansion coefficients up to eighth order in $\mu_B$ and various resummation techniques that blend in Pade expansions and

Daniel Hutchings

This thesis is dedicated to the study of (super)spin projection operators and their applications on maximally symmetric (super)space backgrounds in three and four dimensions. On such backgrounds, the irreducible representations of the associated isometry (super)algebra may be realised on the space of tensor (super)fields satisfying certain differential const

Ke Hu, WeiDong Qiu, Peng Tang

In the field of federated learning, addressing non-independent and identically distributed (non-i.i.d.) data remains a quintessential challenge for improving global model performance. This work introduces the Feature Norm Regularized Federated Learning (FNR-FL) algorithm, which uniquely incorporates class average feature norms to enhance model accuracy and c

Thong Nguyen, Xiaobao Wu, Xinshuai Dong, Khoi Le

Fully fine-tuning pretrained large-scale transformer models has become a popular paradigm for video-language modeling tasks, such as temporal language grounding and video-language summarization. With a growing number of tasks and limited training data, such full fine-tuning approach leads to costly model storage and unstable training. To overcome these short

Wei Suo, Weiwei Zhang

Numerical simulation is dominant in solving partial difference equations (PDEs), but balancing fine-grained grids with low computational costs is challenging. Recently, solving PDEs with neural networks (NNs) has gained interest, yet cost-effectiveness and high accuracy remains a challenge. This work introduces a novel paradigm for solving PDEs, called multi

Tejes Gaertner, Jared Reiten

In the first part of this work, we demonstrate how the metric space structure induced by the energy mover's distance can be leveraged for the unsupervised tagging of jets according to their progenitor. Namely, we focus on the task of tagging jets initiated by a top quark from a background of jets initiated by light quarks and gluons. By examining the local n

Kangneng Zhou, Daiheng Gao, Xuan Wang, Jie Zhang

3D-aware portrait editing has a wide range of applications in multiple fields. However, current approaches are limited due that they can only perform mask-guided or text-based editing. Even by fusing the two procedures into a model, the editing quality and stability cannot be ensured. To address this limitation, we propose \textbf{MaTe3D}: mask-guided text-b

Jingchun Zhou, Tianyu Liang, Dehuan Zhang, Zongxin He

Neural Radiance Field (NeRF) technology demonstrates immense potential in novel viewpoint synthesis tasks, due to its physics-based volumetric rendering process, which is particularly promising in underwater scenes. Addressing the limitations of existing underwater NeRF methods in handling light attenuation caused by the water medium and the lack of real Gro

Nuno F. Loureiro

A transport-like framework for the study of magnetic reconnection mediated by self-driven turbulence is proposed, based on timescale separation between the reconnection time and the characteristic timescale of the turbulent fluctuations which arise in the reconnection layer. We argue that the mean fields remain on MHD scales even in collisionless cases. Thes

Isaac Dobes, Naihuan Jing

In this paper, we represent $n$-qubits as hypermatrices and consider various applications to quantum entanglement. In particular, we use the higher-order singular value decomposition of hypermatrices to prove that the $\pi$-transpose is an LU invariant. Additionally, through our construction we show that the matrix representation of the combinatorial hyperde

Hua Sun, Hui-Xiang Chen, Yinhuo Zhang

In this paper, we show that there are infinitely many semisimple tensor (or monoidal) categories of rank two over an algebraically closed field $\mathbb F$.

Ryan Greenblatt, Buck Shlegeris, Kshitij Sachan, Fabien Roger

As large language models (LLMs) become more powerful and are deployed more autonomously, it will be increasingly important to prevent them from causing harmful outcomes. Researchers have investigated a variety of safety techniques for this purpose, e.g. using models to review the outputs of other models, or red-teaming techniques to surface subtle failure mo

MAhdi Abolghasemi, Odkhishig Ganbold, Kristian Rotaru

This study investigates the forecasting accuracy of human experts versus Large Language Models (LLMs) in the retail sector, particularly during standard and promotional sales periods. Utilizing a controlled experimental setup with 123 human forecasters and five LLMs, including ChatGPT4, ChatGPT3.5, Bard, Bing, and Llama2, we evaluated forecasting precision t

Jacob Fein-Ashley, Tian Ye, Rajgopal Kannan, Viktor Prasanna

Synthetic Aperture Radar SAR Automatic Target Recognition ATR is a key technique of remote-sensing image recognition which can be supported by deep neural networks The existing works of SAR ATR mostly focus on improving the accuracy of the target recognition while ignoring the systems performance in terms of speed and storage which is critical to real-world

Wan-Guan Chang, Chia-Yi Ju, Guang-Yin Chen, Yueh-Nan Chen

To store quantum information, quantum memory plays a central intermediate ingredient in a network. The minimal criterion for a reliable quantum memory is the maintenance of the entangled state, which can be described by the non-entanglement-breaking (non-EB) channel. In this work, we show that all single-qubit quantum memory can be quantified without trustin

Satoshi Koike, Laurentiu Paunescu

In this paper we investigate the behaviour of the geometric directional bundles, associated to arbitrary subsets in R^n, under bi-Lipschitz homeomorphisms, and give conditions under which their bi-Lipschitz type is preserved. The most general sets we consider satisfy the sequence selection property (SSP) and, consequently, we investigate the behaviour of suc

Gautam Goel, Peter Bartlett

Transformers are a class of autoregressive deep learning architectures which have recently achieved state-of-the-art performance in various vision, language, and robotics tasks. We revisit the problem of Kalman Filtering in linear dynamical systems and show that Transformers can approximate the Kalman Filter in a strong sense. Specifically, for any observabl

Gavin Gosling, Ivan-teofil Catovic, Ghazal Bangash, Daniel MacCormick

In the realm of music therapy, Virtual Reality (VR) has a long-standing history of enriching human experiences through immersive applications, spanning entertainment games, serious games, and professional training in various fields. However, the untapped potential lies in using VR games to support mindfulness through music. We present a new approach utilizin

Maciej Głuchowski, Georg Menz

The main focus of this article is the study of ergodicity of Interacting Particle Systems (IPS). We present a simple lemma showing that scaling time is equivalent to taking the convex combination of the transition matrix of the IPS with the identity. As a consequence, the ergodic properties of IPS are invariant under this transformation. Surprisingly, this s

Dongliang Luo, Yuliang Liu, Rui Yang, Xianjin Liu

With the surge in realistic text tampering, detecting fraudulent text in images has gained prominence for maintaining information security. However, the high costs associated with professional text manipulation and annotation limit the availability of real-world datasets, with most relying on synthetic tampering, which inadequately replicates real-world tamp

Joseph M. Marcinik, Martín A. Toderi, Dolores Bozovic

Biophysical models describing complex, cellular phenomena typically include systems of nonlinear differential equations with many free parameters. While experimental measurements can fix some parameters, those describing internal cellular processes frequently remain inaccessible. Hence, a proliferation of free parameters risks overfitting the data, limiting

Julia Huiming Wang, Dexter Tsin, Tatiana Engel

Variational autoencoders (VAEs) have been used extensively to discover low-dimensional latent factors governing neural activity and animal behavior. However, without careful model selection, the uncovered latent factors may reflect noise in the data rather than true underlying features, rendering such representations unsuitable for scientific interpretation.

Chia-Jung Yang, Michael Woerner, Oliver Stockert, Hilbert v. Loehneysen

In strongly correlated systems such as heavy-fermion materials, the coherent superposition of localized and mobile spin states leads to the formation of Kondo resonant states, which on a dense, periodic array of Kondo ions develop lattice coherence. Characteristically, these quantum-coherent superposition states respond to a terahertz (THz) excitation by a d

Hongchao Shi, Heng Zhu, Bing Tang, Chao Yang

When a Heisenberg honeycomb ferromagnet is irradiated by high frequency circularly polarized light, the underlying uncharged magnons acquire a time dependent Aharonov Casher phase, which makes it a Floquet topological magnon insulator. In this context, we investigate the many body interaction effects of Floquet magnons in laser irradiated Heisenberg honeycom

Senthil Kumar Jagatheesaperumal, Mohamed Rahouti, Kaiqi Xiong, Abdellah Chehri

Unmanned Aerial Vehicles (UAVs), previously favored by enthusiasts, have evolved into indispensable tools for effectively managing disasters and responding to emergencies. For example, one of their most critical applications is to provide seamless wireless communication services in remote rural areas. Thus, it is substantial to identify and consider the diff

Takeshi Kato

Reducing wealth inequality and disparity is a global challenge. The economic system is mainly divided into (1) gift and reciprocity, (2) power and redistribution, (3) market exchange, and (4) mutual aid without reciprocal obligations. The current inequality stems from a capitalist economy consisting of (2) and (3). To sublimate (1), which is the human econom

Fatimah Alzamzami, Abdulmotaleb El Saddik

Resources in high-resource languages have not been efficiently exploited in low-resource languages to solve language-dependent research problems. Spanish and French are considered high resource languages in which an adequate level of data resources for informal online social behavior modeling, is observed. However, a machine translation system to access thos

Fatemeh Dehghani, Loutfouz Zaman

Emotion detection is a crucial component of Games User Research (GUR), as it allows game developers to gain insights into players' emotional experiences and tailor their games accordingly. However, detecting emotions in Virtual Reality (VR) games is challenging due to the Head-Mounted Display (HMD) that covers the top part of the player's face, namely, their

Yu Fu, Yufei Li, Wen Xiao, Cong Liu

Recent developments in balancing the usefulness and safety of Large Language Models (LLMs) have raised a critical question: Are mainstream NLP tasks adequately aligned with safety consideration? Our study, focusing on safety-sensitive documents obtained through adversarial attacks, reveals significant disparities in the safety alignment of various NLP tasks.

Wentao Liu, Hanglei Hu, Jie Zhou, Yuyang Ding

In recent years, there has been remarkable progress in leveraging Language Models (LMs), encompassing Pre-trained Language Models (PLMs) and Large-scale Language Models (LLMs), within the domain of mathematics. This paper conducts a comprehensive survey of mathematical LMs, systematically categorizing pivotal research endeavors from two distinct perspectives

Zhao Zhang, Xinpeng Li, Menghan Li, Jiayu Zhai

The uncertainty quantification of Darcy flows using history matching is important for the evaluation and prediction of subsurface reservoir performance. Conventional methods aim to obtain the maximum a posterior or maximum likelihood estimate (MLE) using gradient-based, heuristic or ensemble-based methods. These methods can be computationally expensive for h

Sha-Sha Wang, Hai-Ling Liu, Yong-Mei Li, Fei Gao

The Quantum Alternating Operator Ansatz (QAOA+) is one of the Variational Quantum Algorithm (VQA) specifically developed to tackle combinatorial optimization problems by exploring the feasible space in search of a target solution. For constrained optimization problems with unconstrained variables, which we call Unconstrained-Variables Problems (UVPs), the mi

Hiroshi Ishimoto

In our previous paper we gave proofs of Ibukiyama's correspondences on holomorphic Siegel modular forms of degree 2 of half-integral weight and integral weight. In this paper, we formulate and prove similar correspondences on automorphic forms on $\operatorname{Mp}_4(\mathbb{A}_\mathbb{Q})$ or $\operatorname{SO}_5(\mathbb{A}_\mathbb{Q})$ generating large dis