December 2023 arXiv papers — page 33

Sai Sreekar Vankayalapati, Srijanee Mookherji, Vanga Odelu

Internet of Things (IoT) have gained popularity in recent times. With an increase in the number of IoT devices, security and privacy vulnerabilities are also increasing. For sensitive domains like healthcare and industrial sectors, such vulnerabilities can cause havoc. Thus, authentication is an important aspect for establishing a secure communication betwee

Jiaying Chen, Han Wang, Minghui Hu, Ponnuthurai Nagaratnam Suganthan

LiDAR SLAM has become one of the major localization systems for ground vehicles since LiDAR Odometry And Mapping (LOAM). Many extension works on LOAM mainly leverage one specific constraint to improve the performance, e.g., information from on-board sensors such as loop closure and inertial state; prior conditions such as ground level and motion dynamics. In

Therdpong Daengsi, Patsita Sirawongphatsara, Phisit Pornpongtechavanich

This paper aims to propose the quality of experience (QoE) models based on the expectation and/or the perception of 5G users to evaluate for mean opinion score (MOS) for real-time or interactive services/applications with high reliability. Therefore, Based on the fundamental QoE concept, the analytic hierarchy process (AHP) decision making technique has been

Fei Ma, Jinzhi Ouyang, Ping Wang, Haobin Shi

The family of planar graphs is a particularly important family and models many real-world networks. In this paper, we propose a principled framework based on the widely-known Apollonian packing process to generate new planar network, i.e., Type-II Apollonian network $\mathcal{A}_{t}$. The manipulation is different from that of the typical Apollonian network,

Gurusha Juneja, Sukrit Kumar

Diffusion models when conditioned on text prompts, generate realistic-looking images with intricate details. But most of these pre-trained models fail to generate accurate images when it comes to human features like hands, teeth, etc. We hypothesize that this inability of diffusion models can be overcome through well-annotated good-quality data. In this pape

Leo Maxime Brunswic, Yinchuan Li, Yushun Xu, Shangling Jui

GFlowNets is a novel flow-based method for learning a stochastic policy to generate objects via a sequence of actions and with probability proportional to a given positive reward. We contribute to relaxing hypotheses limiting the application range of GFlowNets, in particular: acyclicity (or lack thereof). To this end, we extend the theory of GFlowNets on mea

Fabian Mohn, Fynn Förger, Florian Thieben, Martin Möddel

In Magnetic Particle Imaging, a field-free region is maneuvered throughout the field of view using a time-varying magnetic field known as the drive-field. Human-sized systems operate the drive-field in the kHz range and generate it by utilizing strong currents that can rise to the kA range within a coil called the drive field generator. Matching and tuning b

Deyou Zhang, Sicong Ye, Ming Xiao, Kezhi Wang

Over-the-air computation (AirComp) has emerged as a promising technology for fast wireless data aggregation by harnessing the superposition property of wireless multiple-access channels. This paper investigates a fluid antenna (FA) array-enhanced AirComp system, employing the new degrees of freedom achieved by antenna movements. Specifically, we jointly opti

A. Plati, A. Puglisi, A. Sarracino

A new Thermodynamic Uncertainty Relation (TUR) is derived for systems described by linearly coupled Langevin equations in the presence of non-linear frictional forces. In our scheme, the main variable represents the velocity of a particle, while the other coupled variables describe memory effects which may arise from strongly correlated degrees of freedom wi

Rashik Shrestha, Bishad Koju, Abhigyan Bhusal, Danda Pani Paudel

With the widespread use of NeRF-based implicit 3D representation, the need for camera localization in the same representation becomes manifestly apparent. Doing so not only simplifies the localization process -- by avoiding an outside-the-NeRF-based localization -- but also has the potential to offer the benefit of enhanced localization. This paper studies t

Fazl Barez, Philip Torr

As artificial intelligence (AI) systems become increasingly integrated into various domains, ensuring that they align with human values becomes critical. This paper introduces a novel formalism to quantify the alignment between AI systems and human values, using Markov Decision Processes (MDPs) as the foundational model. We delve into the concept of values a

Jin-Bao Wang, Zhi-Hui Guo, Hai-Qing Zhou

Axion-pion scattering amplitudes at finite temperatures are calculated within chiral perturbation theory up to the one loop level. Unitarization procedure is implemented to these amplitudes in order to extend the applicable range of energy and temperature. The influence of the thermal axion-pion scattering amplitudes on the $a\pi\to\pi\pi$ cross sections and

Therdpong Daengsi, Pongpisit Wuttidittachotti

This research proposes an enhanced measurement method for VoIP quality assessment which provides an improvement to accuracy and reliability. To improve the objective measurement tool called the simplified E-model for the selected codec, G.729, it has been enhanced by utilizing a subjective MOS prediction model based on native Thai users, who use the Thai-ton

Mohamed Shawky Sabae, Hoda Anis Baraka, Mayada Mansour Hadhoud

Learning neural implicit surfaces from volume rendering has become popular for multi-view reconstruction. Neural surface reconstruction approaches can recover complex 3D geometry that are difficult for classical Multi-view Stereo (MVS) approaches, such as non-Lambertian surfaces and thin structures. However, one key assumption for these methods is knowing ac

Sheng Liu, Lu Fan, Zhengyu Li, Qiang Zhou

Research on optimal eavesdropping models under practical conditions will help to evaluate realistic risk when employing quantum key distribution (QKD) system for secure information transmission. Intuitively, fiber loss will lead to the optical energy leaking to the environment, rather than harvested by the eavesdropper, which also limits the eavesdropping ab

Tong Li, Jiale Deng, Yanyan Shen, Luyu Qiu

Heterogeneous graph neural networks (HGNs) are prominent approaches to node classification tasks on heterogeneous graphs. Despite the superior performance, insights about the predictions made from HGNs are obscure to humans. Existing explainability techniques are mainly proposed for GNNs on homogeneous graphs. They focus on highlighting salient graph objects

Javier Torón-Artiles, Daniel Hernández-Sosa, Oliverio J. Santana, Javier Lorenzo-Navarro

This research addresses whether the ball's direction after a soccer free-kick can be accurately predicted solely by observing the shooter's kicking technique. To investigate this, we meticulously curated a dataset of soccer players executing free kicks and conducted manual temporal segmentation to identify the moment of the kick precisely. Our approach invol

Tong Li, Zhaoyang Liu, Yanyan Shen, Xue Wang

Stock price forecasting has remained an extremely challenging problem for many decades due to the high volatility of the stock market. Recent efforts have been devoted to modeling complex stock correlations toward joint stock price forecasting. Existing works share a common neural architecture that learns temporal patterns from individual stock series and th

Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, Xinhao Cheng

In the rapidly evolving landscape of artificial intelligence (AI), generative large language models (LLMs) stand at the forefront, revolutionizing how we interact with our data. However, the computational intensity and memory consumption of deploying these models present substantial challenges in terms of serving efficiency, particularly in scenarios demandi

Maolin Li, Giacomo Tarroni

In the field of medical image analysis, deep learning models have demonstrated remarkable success in enhancing diagnostic accuracy and efficiency. However, the reliability of these models is heavily dependent on the quality of training data, and the existence of label noise (errors in dataset annotations) of medical image data presents a significant challeng

Rahim Kargar

In this paper, we study the $(s, C(s))$-Harnack inequality in a domain $G\subset \mathbb{R}^n$ for $s\in(0,1)$ and $C(s)\geq1$ and present a series of inequalities related to $(s, C(s))$-Harnack functions and the Harnack metric. We also investigate the behavior of the Harnack metric under $K$-quasiconformal and $K$-quasiregular mappings, where $K\geq 1$. Fin

Dongfang Xu, Yiming Xu, Zhiqiang Wei, Shenghui Song

The integration of sensing and communication enables wireless communication systems to serve environment-aware applications. In this paper, we propose to leverage sensing to enhance physical layer security (PLS) in multiuser communication systems in the presence of a suspicious target. To this end, we develop a two-phase framework to first estimate the locat

Max Zimmer, Megi Andoni, Christoph Spiegel, Sebastian Pokutta

Neural Networks can be effectively compressed through pruning, significantly reducing storage and compute demands while maintaining predictive performance. Simple yet effective methods like magnitude pruning remove less important parameters and typically require a costly retraining procedure to restore performance. However, with the rise of LLMs, full retrai

Toluwani Aremu

Private computation of nonlinear functions, such as Rectified Linear Units (ReLUs) and max-pooling operations, in deep neural networks (DNNs) poses significant challenges in terms of storage, bandwidth, and time consumption. To address these challenges, there has been a growing interest in utilizing privacy-preserving techniques that leverage polynomial acti

Federico Siciliano, Luca Maiano, Lorenzo Papa, Federica Baccini

Fake news detection models are critical to countering disinformation but can be manipulated through adversarial attacks. In this position paper, we analyze how an attacker can compromise the performance of an online learning detector on specific news content without being able to manipulate the original target news. In some contexts, such as social networks,

Aled Williams, Daiki Haijima

In this paper we study the (classical) Frobenius problem, namely the problem of finding the largest integer that cannot be represented as a nonnegative integral combination of given relatively prime (strictly) positive integers (known as the Frobenius number). The main contribution of this paper are observations regarding a previously known upper bound on th

Sergey G. Kolesnikov

This article is the second in the series and is devoted to the type G_2. The work consists of two parts. In the first part we calculate the structure constants of the complex simple Lie algebra of type G_2. All structure constants are represented as functions of the structure constants corresponding to extraspecial pairs. The results obtained are used to cal

Sania Ashraf, Cristina Bicchieri, Upasak Das, Alex Shpenev

Open defecation, which is linked to poor health outcomes and lower cognitive ability has been widespread in India. Improved sanitation practice generates local health externalities, which implies that the returns to private toilet usage depend on community wide compliance. Therefore, beliefs about others toilet usage behavior may influence both sanitation ch

Tomer Berg, Or Ordentlich, Ofer Shayevitz

The problem of statistical inference in its various forms has been the subject of decades-long extensive research. Most of the effort has been focused on characterizing the behavior as a function of the number of available samples, with far less attention given to the effect of memory limitations on performance. Recently, this latter topic has drawn much int

Xinyan Chen, Jiaxin Ge, Tianjun Zhang, Jiaming Liu

Diffusion models have shown impressive performance in many domains. However, the model's capability to follow natural language instructions (e.g., spatial relationships between objects, generating complex scenes) is still unsatisfactory. In this work, we propose Iterative Prompt Relabeling (IPR), a novel algorithm that aligns images to text through iterative

Jijia Liu, Chao Yu, Jiaxuan Gao, Yuqing Xie

AI agents powered by Large Language Models (LLMs) have made significant advances, enabling them to assist humans in diverse complex tasks and leading to a revolution in human-AI coordination. LLM-powered agents typically require invoking LLM APIs and employing artificially designed complex prompts, which results in high inference latency. While this paradigm

Quanjun Zhang, Chunrong Fang, Yang Xie, Yaxin Zhang

Software Engineering (SE) is the systematic design, development, maintenance, and management of software applications underpinning the digital infrastructure of our modern world. Very recently, the SE community has seen a rapidly increasing number of techniques employing Large Language Models (LLMs) to automate a broad range of SE tasks. Nevertheless, existi

Sania Ashraf, Cristina Bicchieri, Upasak Das, Tanu Gupta

Prevalence of open defecation is associated with adverse health effects, detrimental not only for the individual but also the community. Therefore, neighborhood characteristics can influence collective progressive behavior such as improved sanitation practices. This paper uses primary data collected from rural and urban areas of Bihar to study the relationsh

Ondrej Kubicek, Neil Burch, Viliam Lisy

Search in test time is often used to improve the performance of reinforcement learning algorithms. Performing theoretically sound search in fully adversarial two-player games with imperfect information is notoriously difficult and requires a complicated training process. We present a method for adding test-time search to an arbitrary policy-gradient algorith

Jialu Zhang, Xiaoying Yang, Wentao He, Jianfeng Ren

Object detection in aerial imagery presents a significant challenge due to large scale variations among objects. This paper proposes an evolutionary reinforcement learning agent, integrated within a coarse-to-fine object detection framework, to optimize the scale for more effective detection of objects in such images. Specifically, a set of patches potential

Xiong Zhang, Miao Zhang

Deep learning enhances earthquake monitoring capabilities by mining seismic waveforms directly. However, current neural networks, trained within specific areas, face challenges in generalizing to diverse regions. Here, we employ a data recombination method to create generalized earthquakes occurring at any location with arbitrary station distributions for ne

Minxin Lu, Annie Green Howard, Penny Gordon-Larsen, Katie A. Meyer

Optimal individualized treatment decision-making has improved health outcomes in recent years. The value function is commonly used to evaluate the goodness of an individualized treatment decision rule. Despite recent advances, comparing value functions between different treatment decision rules or constructing confidence intervals around value functions rema

JinDe Liu, Chenglong Qin, Xi He, Gang Jiang

The essence of atomic structure theory, quantum chemistry, and computational materials science is solving the multi-electron stationary Schr\"odinger equation. The Quantum Monte Carlo-based neural network wave function method has surpassed traditional post-Hartree-Fock methods in precision across various systems. However, its energy uncertainty is limited to

Mario Raciti

The widespread integration of Internet of Things (IoT) devices in households generates extensive digital footprints, notably within Smart Home ecosystems. These IoT devices, brimming with data about residents, inadvertently offer insights into human activities, potentially embodying even criminal acts, such as a murder. As technology advances, so does the co

Gerson Gutierrez, Emilio A. Lauret, Juan Pablo Rossetti

We prove that the spectrum of the Kohn Laplacian does not determine the equivalence classes of CR manifolds. We construct pairs of odd-dimensional elliptic manifolds that are not equivalent as CR manifolds but whose Kohn Laplacians have the same spectrum. These manifolds are endowed with the CR structures inherited from the canonical CR structure on the sphe

Pablo Montero de Hijes, Christoph Dellago, Ryosuke Jinnouchi, Bernhard Schmiedmayer

In this paper we investigate the performance of different machine learning potentials (MLPs) in predicting key thermodynamic properties of water using RPBE+D3. Specifically, we scrutinize kernel-based regression and high-dimensional neural networks trained on a highly accurate dataset consisting of about 1,500 structures, as well as a smaller data set, about

P. F. Gora, Ewa Gudowska-Nowak

Under normal operations, memristive devices undergo variability in time and space and have internal dynamics. Interplay of memory and stochastic signal processing in memristive devices makes them candidates for performing bio-inspired tasks of information transduction and transformation, where intrinsic random behavior can be harnessed for high performance o

Dávid Péter Kovács, J. Harry Moore, Nicholas J. Browning, Ilyes Batatia

Classical empirical force fields have dominated biomolecular simulation for over 50 years. Although widely used in drug discovery, crystal structure prediction, and biomolecular dynamics, they generally lack the accuracy and transferability required for first-principles predictive modeling. In this paper, we introduce MACE-OFF, a series of short range transf

Patrick E. Farrell, Giovanni Russo, Umberto Zerbinati

Nematic ordering describes the phenomenon where anisotropic molecules tend to locally align, like matches in a matchbox. This ordering can arise in solids (as nematic elastomers), liquids (as liquid crystals), and in gases. In the 1940s, Onsager described how nematic ordering can arise in dilute colloidal suspensions from the molecular point of view. However

Avgerinos Delkos, Marianna Girlando

When evaluating a counterfactual statement, it is often convenient to specify conditions that ought to be kept unchanged. Formally, this can be done by associating to each counterfactual a ceteris paribus set of formulas, specifying the facts that "ought to be kept unchanged". Ceteris paribus counterfactuals originate in the debate between D. Lewis and Fine

Qi Hu, Yangqiu Song

Recommender systems can be privacy-sensitive. To protect users' private historical interactions, federated learning has been proposed in distributed learning for user representations. Using federated recommender (FedRec) systems, users can train a shared recommendation model on local devices and prevent raw data transmissions and collections. However, the re

Qiangchang Ju, Lei Li, Zhengce Zhang

We justify rigorously the non-equilibrium-diffusion limit of the compressible Euler model coupled with a radiative transfer equation arising in radiation hydrodynamics. For general initial data, we establish the uniform existence of the solution to the coupled model in $\mathbb{T}^{3}$ and prove the convergence of the solutions to the limiting system in the

Ivica Nakić, Marinela Pilj Vidaković, Zoran Tomljanović

We consider a vibrational system control problem over a finite time horizon. The performance measure of the system is taken to be $p$-mixed $H_2$ norm which generalizes the standard $H_2$ norm. We present an algorithm for efficient calculation of this norm in the case when the system is parameter dependent and the number of inputs or outputs of the system is

J. Dörner, J. Becker Tjus, P. -S. Blomenkamp, H. Fichtner

The very high energy (VHE) emission of the Central Molecular Zone (CMZ) is rarely modelled in 3D. Most approaches describe the morphology in 1D or simplify the diffusion to the isotropic case. In this work we show the impact of a realistic 3D magnetic field configuration and gas distribution on the VHE gamma-ray distribution of the CMZ. We solve the 3D cosmi

Anna Kiriliouk, Jeongjin Lee, Johan Segers

Regular vine sequences permit the organisation of variables in a random vector along a sequence of trees. Regular vine models have become greatly popular in dependence modelling as a way to combine arbitrary bivariate copulas into higher-dimensional ones, offering flexibility, parsimony, and tractability. In this project, we use regular vine structures to de

Sun Zhaole, Jihong Zhu, Robert B. Fisher

Deformable linear object (DLO) manipulation is needed in many fields. Previous research on deformable linear object (DLO) manipulation has primarily involved parallel jaw gripper manipulation with fixed grasping positions. However, the potential for dexterous manipulation of DLOs using an anthropomorphic hand is under-explored. We present DexDLO, a model-fre

Kang Yang, Xinjun Mao, Shangwen Wang, Tanghaoran Zhang

Pre-trained code models have emerged as crucial tools in various code intelligence tasks. However, their effectiveness depends on the quality of the pre-training dataset, particularly the human reference comments, which serve as a bridge between the programming language and natural language. One significant challenge is that such comments can become inconsis

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

The ratio of production cross-sections of $\psi(2S)$ over $J/\psi$ mesons as a function of charged-particle multiplicity in proton-proton collisions at a centre-of-mass energy $\sqrt{s}=13$ TeV is measured with a data sample collected by the LHCb detector, corresponding to an integrated luminosity of 658 pb$^{-1}$. The ratio is measured for both prompt and n

Keyan Chen, Chengyang Liu, Wenyuan Li, Zili Liu

Change detection, a prominent research area in remote sensing, is pivotal in observing and analyzing surface transformations. Despite significant advancements achieved through deep learning-based methods, executing high-precision change detection in spatio-temporally complex remote sensing scenarios still presents a substantial challenge. The recent emergenc

Xin-Wei Jin, Zhan-Ying Yang, Yanan Liu, Guangyin Jing

Manipulation of magnons in artificial magnonic crystals (MCs) leads to fascinating nonlinear wave phenomena such as the generation of gap solitons, which has been mostly limited to one-dimensional systems. Here, we propose a model system for the magnetization in two-dimensional MCs subjected to a periodic external magnetic field, describing the dynamics of m

Farzaneh Koohestani, Nader Karimi, Shadrokh Samavi

In digital imaging, enhancing visual content in poorly lit environments is a significant challenge, as images often suffer from inadequate brightness, hidden details, and an overall reduction in quality. This issue is especially critical in applications like nighttime surveillance, astrophotography, and low-light videography, where clear and detailed visual

Yan Leng, Yuan Yuan

As LLMs increasingly take on roles in human-AI interactions and autonomous AI systems, understanding their social behavior becomes important for informed use and continuous improvement. However, their behaviors in social interactions with humans and other agents, as well as the mechanisms shaping their responses, remain underexplored. To address this gap, we

Xize Cheng, Rongjie Huang, Linjun Li, Tao Jin

Direct speech-to-speech translation achieves high-quality results through the introduction of discrete units obtained from self-supervised learning. This approach circumvents delays and cascading errors associated with model cascading. However, talking head translation, converting audio-visual speech (i.e., talking head video) from one language into another,

Martina Karl, Philipp Eller

We present a novel method for identifying transients suitable for both strong signal-dominated and background-dominated objects. By employing the unsupervised machine learning algorithm known as Expectation Maximization, we achieve computing time reductions of over $10^4$ on a single CPU compared to conventional brute-force methods. Furthermore, this approac

Xianjie Zhang, Jiahao Sun, Chen Gong, Kai Wang

The emergence of on-demand ride pooling services allows each vehicle to serve multiple passengers at a time, thus increasing drivers' income and enabling passengers to travel at lower prices than taxi/car on-demand services (only one passenger can be assigned to a car at a time like UberX and Lyft). Although on-demand ride pooling services can bring so many

Hengrui Gu, Kaixiong Zhou, Xiaotian Han, Ninghao Liu

Multi-hop question answering (MQA) is one of the challenging tasks to evaluate machine's comprehension and reasoning abilities, where large language models (LLMs) have widely achieved the human-comparable performance. Due to the dynamics of knowledge facts in real world, knowledge editing has been explored to update model with the up-to-date facts while avoi

R. Soni, A. K. Pathak, P. Vellaisamy

In this paper, we present a probabilistic extension of the Fubini polynomials and numbers associated with a random variable satisfying some appropriate moment conditions. We obtain the exponential generating function and an integral representation for it. The higher order Fubini polynomials and recurrence relations are also derived. A probabilistic generaliz

Lai Jiang

In this paper, we first present a simple lemma which allows us to estimate the box dimension of graphs of given functions by the associated oscillation sums and oscillation vectors. Then we define vertical scaling matrices of generalized affine fractal interpolation surfaces (FISs). By using these matrices, we establish relationships between oscillation vect

Anna Vettoruzzo, Mohamed-Rafik Bouguelia, Thorsteinn Rögnvaldsson

Federated learning has emerged as a promising approach for training machine learning models on decentralized data sources while preserving data privacy. However, challenges such as communication bottlenecks, heterogeneity of client devices, and non-i.i.d. data distribution pose significant obstacles to achieving optimal model performance. We propose a novel

Ming Cheng, Xingjian Diao, Shitong Cheng, Wenjun Liu

Speech anonymization and de-identification have garnered significant attention recently, especially in the healthcare area including telehealth consultations, patient voiceprint matching, and patient real-time monitoring. Speaker identity classification tasks, which involve recognizing specific speakers from audio to learn identity features, are crucial for

Tao Yin, Lu Zhang, Weiying Zheng, Xiaopeng Zhu

This paper proposes a new Helmholtz decomposition based windowed Green function (HD-WGF) method for solving the time-harmonic elastic scattering problems on a half-space with Dirichlet boundary conditions in both 2D and 3D. The Helmholtz decomposition is applied to separate the pressure and shear waves, which satisfy the Helmholtz and Helmholtz/Maxwell equat

Zhuangzhuang Cui, Achiel Colpaert, Sofie Pollin

Non-Terrestrial Network (NTN) has been envisioned as a key component of the sixth-generation (6G) mobile communication system. Meanwhile, unmanned aerial vehicles (UAVs) play an important role in enabling and deploying NTNs. In this paper, we focus on massive multi-input multi-output (MaMIMO) supported UAV communications, where channel state information (CSI

Jiayu Li, Zilong Zhao, Milad Abdollahzadeh, Biplab Sikdar

Relational databases (RDBs) are widely used by corporations and governments to store multiple related tables. Their relational schemas pose unique challenges to synthetic data generation for privacy-preserving data sharing, e.g., for collaborative analytical and data mining tasks, as well as software testing at various scales. Relational schemas typically in

Juncheng Jia, Ji Liu, Chendi Zhou, Hao Tian

While data is distributed in multiple edge devices, Federated Learning (FL) is attracting more and more attention to collaboratively train a machine learning model without transferring raw data. FL generally exploits a parameter server and a large number of edge devices during the whole process of the model training, while several devices are selected in eac

Ziyang Ma, Zhisheng Zheng, Jiaxin Ye, Jinchao Li

We propose emotion2vec, a universal speech emotion representation model. emotion2vec is pre-trained on open-source unlabeled emotion data through self-supervised online distillation, combining utterance-level loss and frame-level loss during pre-training. emotion2vec outperforms state-of-the-art pre-trained universal models and emotion specialist models by o

Shuoran Jiang, Qingcai Chen, Youchen Pan, Yang Xiang

Lowering the memory requirement in full-parameter training on large models has become a hot research area. MeZO fine-tunes the large language models (LLMs) by just forward passes in a zeroth-order SGD optimizer (ZO-SGD), demonstrating excellent performance with the same GPU memory usage as inference. However, the simulated perturbation stochastic approximati

Albert Munyeshyaka, Joseph Ntahompagaze, Tom Mutabazi, Manasse. R Mbonye

We investigate cosmological perturbations of f(G) gravity in the presence of a scalar field. Using the 1 + 3 covariant formalism, we present the energy overdensity perturbation equations responsible for large scale structure formation. After applying harmonic decomposition method together with the redshift transformation technique, we obtain the fully pertur

Haonan Wang, Peng Cao, Xiaoli Liu, Jinzhu Yang

Most state-of-the-art methods for medical image segmentation adopt the encoder-decoder architecture. However, this U-shaped framework still has limitations in capturing the non-local multi-scale information with a simple skip connection. To solve the problem, we firstly explore the potential weakness of skip connections in U-Net on multiple segmentation task

Ankita Maity, Anubhav Sharma, Rudra Dhar, Tushar Abhishek

Lack of diverse perspectives causes neutrality bias in Wikipedia content leading to millions of worldwide readers getting exposed by potentially inaccurate information. Hence, neutrality bias detection and mitigation is a critical problem. Although previous studies have proposed effective solutions for English, no work exists for Indian languages. First, we

Anusuya Pal, Anupam Sengupta, Miho Yanagisawa

Sessile drying droplets in various bio-relevant systems, encompassing passive bio-colloids like DNA, proteins, and blood to active microbes, gain considerable attention due to intricate interplay among different convective flows, droplet pinning, mechanical stress, wettability, and the emergence of distinctive patterns. Chlamydomonas reinhardtii, or chlamys,

Adway Mitra, Palash Dey

In district-based multi-party elections, electors cast votes in their respective districts. In each district, the party with maximum votes wins the corresponding seat in the governing body. Election Surveys try to predict the election outcome (vote shares and seat shares of parties) by querying a random sample of electors. However, the survey results are oft

M. S. Mirmoosa, T. Setälä, A. Norrman

Modulating macroscopic parameters of materials in time offers innovative avenues for manipulating electromagnetic waves. Due to such enticing prospects, the general research subject of time-varying systems is expanding today in different branches of electromagnetism and optics. However, compared with the research efforts and progresses that have taken place

Ruiqi Li, John W. Simpson-Porco, Stephen L. Smith

We propose a data-driven receding-horizon control method dealing with the chance-constrained output-tracking problem of unknown stochastic linear time-invariant (LTI) systems with partial state observation. The proposed method takes into account the statistics of the process noise, the measurement noise and the uncertain initial condition, following an analo

Yuta Shiohira, Yuka Fujii, Hajime Kita, Tomoki Kimura

Magnetized exoplanets can serve as the source of auroral radio emissions, allowing us to characterize the magnetospheric properties of these planets. Successful detections of auroral radio emissions from brown dwarfs, as well as from Jupiter, suggest that Jupiter-like planets in distant orbits may also generate radio emissions through a similar mechanism. In

Vijay Kag, Venkatesh Gopinath

In this work, we present the physics-informed neural network (PINN) model applied particularly to dynamic problems in solid mechanics. We focus on forward and inverse problems. Particularly, we show how a PINN model can be used efficiently for material identification in a dynamic setting. In this work, we assume linear continuum elasticity. We show results f

Xingqin Lin

The 3rd generation partnership project (3GPP) initiated 5G-Advanced in Release 18, laying a solid foundation for the further evolution of 5G-Advanced. Release 19-the next wave of 5G-Advanced-will primarily focus on commercial deployment needs while serving as a bridge toward 6G. In this article, we provide an in-depth overview of the 5G-Advanced evolution in

Zongxia Liang, Jianming Xia, Keyu Zhang

This paper considers a class of stochastic control problems with implicitly defined objective functions, which are the sources of time-inconsistency. We study the closed-loop equilibrium solutions in a general controlled diffusion framework. First, we provide a sufficient and necessary condition for a strategy to be an equilibrium. Then, we apply the result

Aishan Liu, Xinwei Zhang, Yisong Xiao, Yuguang Zhou

Pre-trained vision models (PVMs) have become a dominant component due to their exceptional performance when fine-tuned for downstream tasks. However, the presence of backdoors within PVMs poses significant threats. Unfortunately, existing studies primarily focus on backdooring PVMs for the classification task, neglecting potential inherited backdoors in down

Pengmiao Zhang, Neelesh Gupta, Rajgopal Kannan, Viktor K. Prasanna

Attention-based Neural Networks (NN) have demonstrated their effectiveness in accurate memory access prediction, an essential step in data prefetching. However, the substantial computational overheads associated with these models result in high inference latency, limiting their feasibility as practical prefetchers. To close the gap, we propose a new approach

Sabyasachi Tiwari, Maarten Van de Put, Bart Soree, Christopher Hinkle

Using Hubbard U corrected density functional theory calculations, lattice Monte-Carlo, and spin-Monte-Carlo simulations, we investigate the impact of dopant clustering on the magnetic properties of WSe2~doped with period four transition metals. We use manganese (Mn) and iron (Fe) as candidate n-type dopants and vanadium (V) as the candidate p-type dopants, s

Yang Li, Huaqiang Jiang, Yangkai Wu

Text-to-image generation is conducted through Generative Adversarial Networks (GANs) or transformer models. However, the current challenge lies in accurately generating images based on textual descriptions, especially in scenarios where the content and theme of the target image are ambiguous. In this paper, we propose a method that utilizes artificial intell

John F Kam, Haiyue Kang, Charles D Hill, Gary J Mooney

As quantum technology advances and the size of quantum computers grow, it becomes increasingly important to understand the extent of quality in the devices. As large-scale entanglement is a quantum resource crucial for achieving quantum advantage, the challenge in its generation makes it a valuable benchmark for measuring the performance of universal quantum

Hao Zhou, Li Zhang, Zhilei Wang, Hewei Gao

Purpose: Fast kV-switching (FKS) and dual-layer flat-panel detector (DL-FPD) technologies have been actively studied as promising dual-energy solutions for FPD-based cone-beam computed tomography (CBCT). However, CBCT spectral imaging is known to face challenges in obtaining accurate and robust material discrimination performance due to the limited energy se

Hong-Quan Li, Sheng-Chen Mao, Ye Zhang

In this work, we establish the uniform heat kernel asymptotics as well as sharp bounds for its derivatives on the free step-two Carnot group with $3$ generators. As a by-product, on this highly non-trivial toy model, we completely solve the Gaveau-Brockett problem, in other words, we obtain the expression of the squared Carnot-Carath\'eodory distance, as exp

Qurrat-Ul-Ain Nadeem, Anas Chaaban

This work studies a multi-cell one-bit massive multiple-input multiple-output (MIMO) system that employs one-bit analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) at each base station (BS). We utilize Bussgang decomposition to derive downlink signal-to-quantization-plus-interference-plus-noise ratio (SQINR) and ergodic achievable ra

Dahyun Kim, Chanjun Park, Sanghoon Kim, Wonsung Lee

We introduce SOLAR 10.7B, a large language model (LLM) with 10.7 billion parameters, demonstrating superior performance in various natural language processing (NLP) tasks. Inspired by recent efforts to efficiently up-scale LLMs, we present a method for scaling LLMs called depth up-scaling (DUS), which encompasses depthwise scaling and continued pretraining.

Aaron Gerding, Nicholas G. Reich, Benjamin Rogers, Evan L. Ray

Recent years have seen increasing efforts to forecast infectious disease burdens, with a primary goal being to help public health workers make informed policy decisions. However, there has only been limited discussion of how predominant forecast evaluation metrics might indicate the success of policies based in part on those forecasts. We explore one possibl

Abdiel de Jesús Espinosa-Champo, Gerardo G. Naumis

\textit{Holey Graphene} (HG) is a widely used graphene material for the synthesis of high-purity and highly crystalline materials. In this work, we explore the electronic properties of a periodic distribution of lattice holes, demonstrating the emergence of flat bands with compact localized states. It is shown that the holes break the bipartite sublattice an

Simon Spaar, Pierre Zhang

We present cosmological constraints from a joint analysis including the power spectrum and bispectrum of BOSS galaxies based on the Effective Field Theory of Large-Scale Structure predictions at one-loop order, in combination with CMB data from Planck, Supernovae from Pantheon+, and BAO from eBOSS and 6dF/MGS. Limits on $\Lambda$CDM parameters are in good ag

Elizabeth Akinyi Ondula, Bhaskar Krishnamachari

Epidemic modeling, encompassing deterministic and stochastic approaches, is vital for understanding infectious diseases and informing public health strategies. This research adopts a prescriptive approach, focusing on reinforcement learning (RL) to develop strategies that balance minimizing infections with maximizing in-person interactions in educational set

Ning Wang, Jiajun Deng, Mingbo Jia

We present that visual grounding and image captioning, which perform as two mutually inverse processes, can be bridged together for collaborative training by careful designs. By consolidating this idea, we introduce CyCo, a cyclic-consistent learning framework to ameliorate the independent training pipelines of visual grounding and image captioning. The prop

Vahid Ahmadi Kalkhorani, Qingquan Zhang, Guanqun Song, Ting Zhu

Video smmarization is a crucial method to reduce the time of videos which reduces the spent time to watch/review a long video. This apporach has became more important as the amount of publisehed video is increasing everyday. A single or multiple videos can be summarized into a relatively short video using various of techniques from multimodal audio-visual te

Shun-ichi Azuma, Dai Takakura, Ryo Ariizumi, Toru Asai

Chemical AI is chemically synthesized artificial intelligence that has the ability of learning in addition to information processing. A research project on chemical AI, called the Molecular Cybernetics Project, was launched in Japan in 2021 with the goal of creating a molecular machine that can learn a type of conditioned reflex through the process called cl

Md Saiful Islam, Srijita Das, Sai Krishna Gottipati, William Duguay

Recent advances in reinforcement learning (RL) and Human-in-the-Loop (HitL) learning have made human-AI collaboration easier for humans to team with AI agents. Leveraging human expertise and experience with AI in intelligent systems can be efficient and beneficial. Still, it is unclear to what extent human-AI collaboration will be successful, and how such te

Hongzheng Chen, Jiahao Zhang, Yixiao Du, Shaojie Xiang

Recent advancements in large language models (LLMs) boasting billions of parameters have generated a significant demand for efficient deployment in inference workloads. The majority of existing approaches rely on temporal architectures that reuse hardware units for different network layers and operators. However, these methods often encounter challenges in a