March 2023 arXiv papers — page 54

Shubham Kumar, Biman Bagchi

Boltzmann's H-function H(t) holds a venerable place in the history of science. However, it seems never to have been evaluated for particles with orientational degrees of freedom. We generalize Boltzmann's H-function to a gas of molecules that can both rotate and translate and on collision exchange both momentum and angular momentum, obeying conservation laws

Sheng Wang, Yi Zhou

In the paper by Klainerman, Rodnianski and Tao \cite{Klainerman-Rodnianski-Tao}, they give a physical space proof to a classical result of Klainerman and Machedon \cite{Klainerman-Machedon} for the bilinear space-time estimates of null forms. In this paper, we shall give an alternative and very simple physical space proof of the same bilinear estimates by ap

Swarnendu Mandal, Manish Dev Shrimali

Reservoir Computing has found many potential applications in the field of complex dynamics. In this article, we exploit the exceptional capability of the echo-state network (ESN) model to make it learn a unidirectional coupling scheme from only a few time series data of the system. We show that, once trained with a few example dynamics of a drive-response sy

Xiang He, Yang Li, Dongcheng Zhao, Qingqun Kong

Spiking Neural Networks (SNNs) can do inference with low power consumption due to their spike sparsity. ANN-SNN conversion is an efficient way to achieve deep SNNs by converting well-trained Artificial Neural Networks (ANNs). However, the existing methods commonly use constant threshold for conversion, which prevents neurons from rapidly delivering spikes to

Owen Kevin Appadoo, Hugo Rositi, Sylvie Valarier, Marie-Claire Ombret

The release of Microsoft's HoloLens headset addresses new types of issues that would have been difficult to design without such a hardware. This semi-transparent visor headset allows the user who wears it to view the projection of 3D virtual objects placed in its real environment. The user can also interact with these 3D objects, which can interact with each

Xiang He, Dongcheng Zhao, Yang Li, Guobin Shen

Spiking neural networks (SNNs) are rich in spatio-temporal dynamics and are suitable for processing event-based neuromorphic data. However, event-based datasets are usually less annotated than static datasets. This small data scale makes SNNs prone to overfitting and limits their performance. In order to improve the generalization ability of SNNs on event-ba

Xiaoshi Wu, Feng Zhu, Rui Zhao, Hongsheng Li

Open-vocabulary detection (OVD) is an object detection task aiming at detecting objects from novel categories beyond the base categories on which the detector is trained. Recent OVD methods rely on large-scale visual-language pre-trained models, such as CLIP, for recognizing novel objects. We identify the two core obstacles that need to be tackled when incor

Kamal Raj Sharma, Wei-Yang Chiu, Weizhi Meng

Security associated threats are often increased for online social media during a pandemic, such as COVID-19, along with changes in a work environment. For example, employees in many companies and organizations have started to work from home due to the COVID-19 pandemic. Such working style has increased many remote activities and further relied on email for c

Lukasz A. Sterczewski, Mahmood Bagheri

Chip-based optical frequency combs address the demand for compact, bright, coherent light sources of equidistant phase-locked lines. Traditionally, the Fourier Transform Spectroscopy (FTS) technique has been considered a suboptimal choice for resolving comb lines in chip-based sensing applications due to the requirement of long optical delays, and spectral d

Wei-Yang Chiu, Weizhi Meng

Central-managed security mechanisms are often utilized in many organizations, but such server is also a security breaking point. This is because the server has the authority for all nodes that share the security protection. Hence if the attackers successfully tamper the server, the organization will be in trouble. Also, the settings and policies saved on the

Haoyu Tang, Zhaoyi Liu, Chang Zeng, Xinfeng Li

Transformer-based models have recently made significant achievements in the application of end-to-end (E2E) automatic speech recognition (ASR). It is possible to deploy the E2E ASR system on smart devices with the help of Transformer-based models. While these models still have the disadvantage of requiring a large number of model parameters. To overcome the

Sizhe An, Hongyi Xu, Yichun Shi, Guoxian Song

Synthesis and reconstruction of 3D human head has gained increasing interests in computer vision and computer graphics recently. Existing state-of-the-art 3D generative adversarial networks (GANs) for 3D human head synthesis are either limited to near-frontal views or hard to preserve 3D consistency in large view angles. We propose PanoHead, the first 3D-awa

Shigeki Aoki, Akitaka Ariga, Tomoko Ariga, Nikolaos Charitonidis

Tau neutrino is the least studied lepton of the Standard Model (SM). The NA65/DsTau experiment targets to investigate $D_s$, the parent particle of the $\nu_\tau$, using the nuclear emulsion-based detector and to decrease the systematic uncertainty of $\nu_\tau$ flux prediction from over 50% to 10% for future beam dump experiments. In the experiment, the emu

Du Chen, Jie Liang, Xindong Zhang, Ming Liu

How to generate the ground-truth (GT) image is a critical issue for training realistic image super-resolution (Real-ISR) models. Existing methods mostly take a set of high-resolution (HR) images as GTs and apply various degradations to simulate their low-resolution (LR) counterparts. Though great progress has been achieved, such an LR-HR pair generation sche

Shreyansh S. Dave, Subrata Pal

We study the effects of a static and uniform magnetic field on the evolution of energy density fluctuations present in a medium. By numerically solving the relativistic Boltzmann-Vlasov equation within the relaxation time approximation, we explicitly show that magnetic field can affect the characteristics of energy density fluctuations at the timescale the s

Pinjun Zheng, Xing Liu, Tarig Ballal, Tareq Y. Al-Naffouri

This paper considers the localization problem in a 5G-aided global navigation satellite system (GNSS) based on real-time kinematic (RTK) technique. Specifically, the user's position is estimated based on the hybrid measurements, including GNSS pseudo-ranges, GNSS carrier phases, 5G angle-of-departures, and 5G channel delays. The underlying estimation problem

Rabin Banerjee, Soumya Bhattacharya

In this paper, we discuss Galilean relativistic Proca theory in detail. We first provide a set of mapping relations, derived systematically, that connect the covariant and contravariant vectors in the Lorentz relativistic and Galilean relativistic formulations. Exploiting this map, we construct the two limits of Galilean relativistic Proca theory from usual

Xinnian Liang, Shuangzhi Wu, Hui Huang, Jiaqi Bai

Retrieval augmented methods have shown promising results in various classification tasks. However, existing methods focus on retrieving extra context to enrich the input, which is noise sensitive and non-expandable. In this paper, following this line, we propose a $k$-nearest-neighbor (KNN) -based method for retrieval augmented classifications, which interpo

Ahsan Tanveer, Sarvat Mushtaq Ahmad

In this article, a simplified modeling and system identification procedure for yaw motion of an unmanned surface vehicle (USV) is presented. Two thrusters that allow for both speed and direction control propel the USV. The outputs of the vehicle under inquiry include parameters that define the mobility of the USV in horizontal plane, such as yaw angle and ya

Ahsan Tanveer, Sarvat Mushtaq Ahmad

In this study, a small, inexpensive remotely driven underwater vehicle that can navigate in shallow water for the purpose of monitoring water quality and demonstrating vehicle control algorithms is presented. The vehicle is operated by an onboard micro-controller, and the sensor payload comprises a turbidity sensor for determining the quality of the water, a

Wuyang Luo, Su Yang, Xinjian Zhang, Weishan Zhang

Semantic image editing provides users with a flexible tool to modify a given image guided by a corresponding segmentation map. In this task, the features of the foreground objects and the backgrounds are quite different. However, all previous methods handle backgrounds and objects as a whole using a monolithic model. Consequently, they remain limited in proc

Koji Matsushita

The rank of a $d$-dimensional polytope $P$ is defined by $F-(d+1)$, where $F$ denotes the number of facets of $P$. In this paper, We focus on the toric rings of $(0,1)$-polytopes with small rank. We study their normality, the torsionfreeness of their divisor class groups and the classification of their isomorphism classes.

Zixiao Wang, Yunheng Shen, Wenqian Zhao, Yang Bai

Deep generative models dominate the existing literature in layout pattern generation. However, leaving the guarantee of legality to an inexplicable neural network could be problematic in several applications. In this paper, we propose \tool{DiffPattern} to generate reliable layout patterns. \tool{DiffPattern} introduces a novel diverse topology generation me

Kaoru Teranishi, Kiminao Kogiso

A sample identifying complexity and a sample deciphering time have been introduced in a previous study to capture an estimation error and a computation time of system identification by adversaries. The quantities play a crucial role in defining the security of encrypted control systems and designing a security parameter. This study proposes an optimal securi

Alex Camilo Gormaz-Matamala, Jorge Cuadra, Georges Meynet, Michel Curé

Mass loss due to line-driven winds is central to our understanding of the evolution of massive stars. We extend the evolution models introduced in Paper I, where the mass loss recipe is based on the simultaneous calculation of the wind hydrodynamics and the line-acceleration, by incorporating the effects of stellar rotation. We introduce a grid of self-consi

Zhanxuan Mei, Yun-Cheng Wang, Xingze He, Yong Yan

Blind image quality assessment (BIQA) is a task that predicts the perceptual quality of an image without its reference. Research on BIQA attracts growing attention due to the increasing amount of user-generated images and emerging mobile applications where reference images are unavailable. The problem is challenging due to the wide range of content and mixed

Vaibhav Jindal, Albert Liang, Aarti Singh, Shirley Ho

Finding the initial conditions that led to the current state of the universe is challenging because it involves searching over an intractable input space of initial conditions, along with modeling their evolution via tools such as N-body simulations which are computationally expensive. Recently, deep learning has emerged as a surrogate for N-body simulations

Subtain Malik, Muhammad Tariq Saeed, Marya Jabeen Zia, Shahzad Rasool

In this paper, we present a review of the recent work in deep learning methods for user interface design. The survey encompasses well known deep learning techniques (deep neural networks, convolutional neural networks, recurrent neural networks, autoencoders, and generative adversarial networks) and datasets widely used to design user interface applications.

Anton Glushchenko, Konstantin Lastochkin

The scope of this study is to develop a novel sensorless adaptive vibration suppression controller for two-mass systems with joint estimation of states and controller parameters. Unlike existing solutions, we simultaneously: (i) propose an analytically proved, unified and singularity-issue-free scheme of parameters adjustment of a control law with additional

Luigi Montoro, Luigi Muglia, Berardino Sciunzi

We provide a classification result for positive solutions to $\Delta u = u^{-\gamma}$ in the half space, under zero Dirichlet boundary condition.

Hongyang Du, Zonghang Li, Dusit Niyato, Jiawen Kang

As Metaverse emerges as the next-generation Internet paradigm, the ability to efficiently generate content is paramount. AIGenerated Content (AIGC) emerges as a key solution, yet the resource intensive nature of large Generative AI (GAI) models presents challenges. To address this issue, we introduce an AIGC-as-a-Service (AaaS) architecture, which deploys AI

Shengyang Sun, Xiaojin Gong

Increasing scene-awareness is a key challenge in video anomaly detection (VAD). In this work, we propose a hierarchical semantic contrast (HSC) method to learn a scene-aware VAD model from normal videos. We first incorporate foreground object and background scene features with high-level semantics by taking advantage of pre-trained video parsing models. Then

Xiaoxu Ren, Minrui Xu, Dusit Niyato, Jiawen Kang

Web 3.0 pursues the establishment of decentralized ecosystems based on blockchain technologies to drive the digital transformation of physical commerce and governance. Through consensus algorithms and smart contracts in blockchain, which are based on cryptography technologies, digital identity, digital asset management, decentralized autonomous organization,

Jae-Keun Kim, Kun-Rok Jeon, Pranava K. Sivakumar, Jaechun Jeon

Non-reciprocal electronic transport in a spatially homogeneous system arises from the simultaneous breaking of inversion and time-reversal symmetries. Superconducting and Josephson diodes, a key ingredient for future non-dissipative quantum devices, have recently been realized. Only a few examples of a vertical superconducting diode effect have been reported

Harald Havir, Subhomoy Haldar, Waqar Khan, Sebastian Lehmann

Quantum dots are frequently used as charge sensitive devices in low temperature experiments to probe electric charge in mesoscopic conductors where the current running through the quantum dot is modulated by the nearby charge environment. Recent experiments have been operating these detectors using reflectometry measurements up to GHz frequencies rather than

Le Yu, Leilei Sun, Bowen Du, Weifeng Lv

We propose DyGFormer, a new Transformer-based architecture for dynamic graph learning. DyGFormer is conceptually simple and only needs to learn from nodes' historical first-hop interactions by: (1) a neighbor co-occurrence encoding scheme that explores the correlations of the source node and destination node based on their historical sequences; (2) a patchin

Jian Sang, Jifeng Lan, Mingyong Zhou, Boning Gao

Reconfigurable intelligent surface (RIS) has aroused a surge of interest in recent years. In this paper, we investigate the joint phase alignment and phase quantization on discrete phase shift designs for RIS-assisted single-input single-output (SISO) system. Firstly, the phenomena of phase distribution in far field and near field are respectively unveiled,

Hongyu Liu, Catharine W. K. Lo

In this paper, we consider the inverse problem of determining some coefficients within a coupled nonlinear parabolic system, through boundary observation of its non-negative solutions. In the physical setup, the non-negative solutions represent certain probability densities in different contexts. We innovate the successive linearisation method by further dev

Sambit Mitra, Álvaro Jiménez-Galán, Marcel Neuhaus, Rui E F Silva

Stacking and twisting atom-thin sheets create superlattice structures with unique emergent properties, while tailored light fields can manipulate coherent electron transport on ultrafast timescales. The unification of these two approaches may lead to ultrafast creation and manipulation of band structure properties, which is a crucial objective for the advanc

Shujun Wang, Yongqiang Tian, Dengcheng He

OpenAPI indicates a behavior where producers offer Application Programming Interfaces (APIs) to help end-users access their data, resources, and services. Generally, API has many parameters that need to be entered. However, it is challenging for users to understand and document these parameters correctly. This paper develops an API workbench to help users le

Baifeng Shi, Trevor Darrell, Xin Wang

Current attention algorithms (e.g., self-attention) are stimulus-driven and highlight all the salient objects in an image. However, intelligent agents like humans often guide their attention based on the high-level task at hand, focusing only on task-related objects. This ability of task-guided top-down attention provides task-adaptive representation and hel

A. R. Rao

Tata Institute of Fundamental Research (TIFR) has a very long tradition of conducting space astronomy experiments. Within a few years of the discovery of the first non-solar X-ray source in 1962, TIFR leveraged its expertise in balloon technology to make significant contributions to balloon-borne hard X-ray astronomy. This initial enthusiasm led to extremely

Shujun Wang, Yongqiang Tian, Dengcheng He

Generating and maintaining API documentation with integrity and consistency can be time-consuming and expensive for evolving APIs. To solve this problem, several approaches have been proposed to automatically generate high-quality API documentation based on a combination of knowledge from different web sources. However, current researches are weak in handlin

Han-Cheol Cho, Won Young Jhoo, Wooyoung Kang, Byungseok Roh

Recent open-vocabulary detection methods aim to detect novel objects by distilling knowledge from vision-language models (VLMs) trained on a vast amount of image-text pairs. To improve the effectiveness of these methods, researchers have utilized datasets with a large vocabulary that contains a large number of object classes, under the assumption that such d

X. Q. Shao, F. Liu, X. W. Xue, W. L. Mu

Greenberger-Horne-Zeilinger and W states feature genuine tripartite entanglement that cannot be converted into each other by local operations and classical communication. Here, we present a dissipative protocol for deterministic interconversion between Greenberger-Horne-Zeilinger and W states of three neutral $^{87}$Rb atoms arranged in an equilateral triang

Andreas Dechant, Jerome Garnier-Brun, Shin-ichi Sasa

We derive a variational expression for the correlation time of physical observables in steady-state diffusive systems. As a consequence of this variational expression, we obtain lower bounds on the correlation time, which provide speed limits on the self-averaging of observables. In equilibrium, the bound takes the form of a tradeoff relation between the lon

Md Sadman Sakib Rahman, Xilin Yang, Jingxi Li, Bijie Bai

Under spatially-coherent light, a diffractive optical network composed of structured surfaces can be designed to perform any arbitrary complex-valued linear transformation between its input and output fields-of-view (FOVs) if the total number (N) of optimizable phase-only diffractive features is greater than or equal to ~2 Ni x No, where Ni and No refer to t

Shawn Priore, Meeko Oishi

We propose an open loop methodology based on sample statistics to solve chance constrained stochastic optimal control problems with probabilistic safety guarantees for linear systems where the additive Gaussian noise has unknown mean and covariance. We consider a joint chance constraint for time-varying polytopic target sets under assumptions that the distur

Yu-Neng Chuang, Ruixiang Tang, Xiaoqian Jiang, Xia Hu

Electronic health records (EHRs) store an extensive array of patient information, encompassing medical histories, diagnoses, treatments, and test outcomes. These records are crucial for enabling healthcare providers to make well-informed decisions regarding patient care. Summarizing clinical notes further assists healthcare professionals in pinpointing poten

Alaleh Ahmadianshalchi, Syrine Belakaria, Janardhan Rao Doppa

This paper addresses the problem of constrained multi-objective optimization over black-box objective functions with practitioner-specified preferences over the objectives when a large fraction of the input space is infeasible (i.e., violates constraints). This problem arises in many engineering design problems including analog circuits and electric power sy

Meng Wang, Lianyu Wang, Xinxing Xu, Ke Zou

Deep learning models have shown promising performance in the field of diabetic retinopathy (DR) staging. However, collaboratively training a DR staging model across multiple institutions remains a challenge due to non-iid data, client reliability, and confidence evaluation of the prediction. To address these issues, we propose a novel federated uncertainty-a

Fengjiao Zou, Hsien-Wen Deng, Tsing-Un Iunn, Jennifer Harper Ogle

Pedestrian occlusion is challenging for autonomous vehicles (AVs) at midblock locations on multilane roadways because an AV cannot detect crossing pedestrians that are fully occluded by downstream vehicles in adjacent lanes. This paper tests the capability of vehicle-to-vehicle (V2V) communication between an AV and its downstream vehicles to share midblock p

Cheng Guo, Leidong Fan, Ziyu Xue, and Xiuhua Jiang

In media industry, the demand of SDR-to-HDRTV up-conversion arises when users possess HDR-WCG (high dynamic range-wide color gamut) TVs while most off-the-shelf footage is still in SDR (standard dynamic range). The research community has started tackling this low-level vision task by learning-based approaches. When applied to real SDR, yet, current methods t

Davood Pirayesh Neghab, Mucahit Cevik, M. I. M. Wahab

The complexity and ambiguity of financial and economic systems, along with frequent changes in the economic environment, have made it difficult to make precise predictions that are supported by theory-consistent explanations. Interpreting the prediction models used for forecasting important macroeconomic indicators is highly valuable for understanding relati

Wen Chang, Min Huang, Jian-Rong Li

In this paper, we study quasi-homomorphisms of quantum cluster algebras, which are quantum analogy of quasi-homomorphisms of cluster algebras introduced by Fraser. For a quantum Grassmannian cluster algebra $\mathbb{C}_q[{\rm Gr}(k,n)]$, we show that there is an associated braid group and each generator $\sigma_i$ of the braid group preserves the quasi-commu

Maryam Babaie, Ayaz Akram, Jason Lowe-Power

The increasing growth of applications' memory capacity and performance demands has led the CPU vendors to deploy heterogeneous memory systems either within a single system or via disaggregation. For instance, systems like Intel's Knights Landing and Sapphire Rapids can be configured to use high bandwidth memory as a cache to main memory. While there is signi

Surendra Singh

Neutron reflectometry (NR) has emerged as a unique technique for the investigation of structure and magnetism of thin films of both biologically relevant and magnetic materials. The advantage of NR with respect to many other surface-sensitive techniques is its sub-nanometer resolution that enables structural characterizations at the molecular level. While in

Shoichi Koyama, Keisuke Kimura, Natsuki Ueno

Two sound field reproduction methods, weighted pressure matching and weighted mode matching, are theoretically and experimentally compared. The weighted pressure and mode matching are a generalization of conventional pressure and mode matching, respectively. Both methods are derived by introducing a weighting matrix in the pressure and mode matching. The wei

Maryam Babaie, Ayaz Akram, Jason Lowe-Power

To accommodate the growing memory footprints of today's applications, CPU vendors have employed large DRAM caches, backed by large non-volatile memories like Intel Optane (e.g., Intel's Cascade Lake). The existing computer architecture simulators do not provide support to model and evaluate systems which use DRAM devices as a cache to the non-volatile main m

E. Aguilar-Ruiz, N. Fraija, A. Galván-Gámez

Blazars have been pointed out as promising high-energy (HE) neutrinos sources, although the mechanism is still under debate. The blazars with a hard-TeV spectrum, which leptonic models can hardly explain, can be successfully interpreted in the hadronic scenarios. Recently, Aguilar et al. proposed a lepto-hadronic two-zone model to explain the multi-wavelengt

Hamid Ghaderi, Brandon Foreman, Amin Nayebi, Sindhu Tipirneni

Determining clinically relevant physiological states from multivariate time series data with missing values is essential for providing appropriate treatment for acute conditions such as Traumatic Brain Injury (TBI), respiratory failure, and heart failure. Utilizing non-temporal clustering or data imputation and aggregation techniques may lead to loss of valu

Su Chen, Xianwei Liu, Lei Fu, Suyang Wang

Ground-motion model (GMM) is the basis of many earthquake engineering studies. In this study, a novel physics-informed symbolic learner (PISL) method based on the Nest Generation Attenuation-West2 database is proposed to automatically discover mathematical equation operators as symbols. The sequential threshold ridge regression algorithm is utilized to disti

Jianhua Xian, Ziqi Wang

This study presents an importance sampling formulation based on adaptively relaxing parameters from the indicator function and/or the probability density function. The formulation embodies the prevalent mathematical concept of relaxing a complex problem into a sequence of progressively easier sub-problems. Due to the flexibility in constructing relaxation pa

Ruofan Liang, Huiting Chen, Chunlin Li, Fan Chen

Recent advances in neural rendering have shown great potential for reconstructing scenes from multiview images. However, accurately representing objects with glossy surfaces remains a challenge for existing methods. In this work, we introduce ENVIDR, a rendering and modeling framework for high-quality rendering and reconstruction of surfaces with challenging

Yanchao Li, Mingying Zhong

The pointwise space-time behaviors of the Green's function and the global solution to the modified Vlasov- Poisson-Boltzmann (mVPB) system in one-dimensional space are studied in this paper. It is shown that, the Green's function admits the diffusion wave, the Huygens's type sound wave, the singular kinetic wave and the remainder term decaying exponentially

Yunsong Zhou, Quan Liu, Hongzi Zhu, Yunzhe Li

Monocular 3D object detection (Mono3D) in mobile settings (e.g., on a vehicle, a drone, or a robot) is an important yet challenging task. Due to the near-far disparity phenomenon of monocular vision and the ever-changing camera pose, it is hard to acquire high detection accuracy, especially for far objects. Inspired by the insight that the depth of an object

Jeongrak Son, Nelly H. Y. Ng

Thermal operations are a generic description for allowed state transitions under thermodynamic restrictions. However, the quest for simpler methods to encompass all these processes remains unfulfilled. We resolve this challenge through the catalytic use of thermal baths, which are assumed to be easily accessible. We select two sets of simplified operations:

Yue Hu, Yifan Lu, Runsheng Xu, Weidi Xie

Camera-only 3D detection provides an economical solution with a simple configuration for localizing objects in 3D space compared to LiDAR-based detection systems. However, a major challenge lies in precise depth estimation due to the lack of direct 3D measurements in the input. Many previous methods attempt to improve depth estimation through network designs

Jinnan Piao, Dong Li, Jindi Liu, Xueting Yu

Minimum weight distribution (MWD) is an important metric to calculate the first term of union bound called minimum weight union bound (MWUB). In this paper, we first prove the maximum likelihood (ML) performance approaches MWUB as signal-to-noise ratio (SNR) goes to infinity and provide the deviation when MWD and SNR are given. Then, we propose a nested reli

Yunsong Zhou, Hongzi Zhu, Quan Liu, Shan Chang

Mobile monocular 3D object detection (Mono3D) (e.g., on a vehicle, a drone, or a robot) is an important yet challenging task. Existing transformer-based offline Mono3D models adopt grid-based vision tokens, which is suboptimal when using coarse tokens due to the limited available computational power. In this paper, we propose an online Mono3D framework, call

Phakhinkon Napp Phunphayap, Passawan Noppakaew, Prapanpong Pongsriiam

We introduce a new arc in directed graphs of integers. Among other things, we determine the positive integers that have arcs to all except a finite number of positive integers. We also propose some possible research problems at the end of this article.

Elijah Wyckoff, Ronan Reza, Fernando Moreu

Human-machine interaction (HMI) and human-robot interaction (HRI) can assist structural monitoring and structural dynamics testing in the laboratory and field. In vibratory experimentation, one mode of generating vibration is to use electrodynamic exciters. Manual control is a common way of setting the input of the exciter by the operator. To measure the str

Marc Katzef, Andrew C. Cullen, Tansu Alpcan, Christopher Leckie

The analysis of distributed techniques is often focused upon their efficiency, without considering their robustness (or lack thereof). Such a consideration is particularly important when devices or central servers can fail, which can potentially cripple distributed systems. When such failures arise in wireless communications networks, important services that

Fangfu Liu, Chubin Zhang, Yu Zheng, Yueqi Duan

In this paper, we aim to learn a semantic radiance field from multiple scenes that is accurate, efficient and generalizable. While most existing NeRFs target at the tasks of neural scene rendering, image synthesis and multi-view reconstruction, there are a few attempts such as Semantic-NeRF that explore to learn high-level semantic understanding with the NeR

Nan Gao, Zeyu Zhao, Zhi Zeng, Shuwu Zhang

Gesture synthesis has gained significant attention as a critical research field, aiming to produce contextually appropriate and natural gestures corresponding to speech or textual input. Although deep learning-based approaches have achieved remarkable progress, they often overlook the rich semantic information present in the text, leading to less expressive

Ryan Babbush, Dominic W. Berry, Robin Kothari, Rolando D. Somma

We present a quantum algorithm for simulating the classical dynamics of $2^n$ coupled oscillators (e.g., $2^n$ masses coupled by springs). Our approach leverages a mapping between the Schr\"odinger equation and Newton's equation for harmonic potentials such that the amplitudes of the evolved quantum state encode the momenta and displacements of the classical

Jung-Chao Ban, Wen-Guei Hu, Guan-Yu Lai

In this paper, we first concentrate on the possible values and dense property of entropies for isotropic and anisotropic axial products of subshifts of finite type (SFTs) on $\mathbb{N}^d$ and $d$-tree $\mathcal{T}_d$. We prove that the entropies of isotropic and anisotropic axial products of SFTs on $\mathbb{N}^d$ are dense in $[0,\infty)$, and the same res

Jinqi Luo, Zhaoning Wang, Chen Henry Wu, Dong Huang

In practice, metric analysis on a specific train and test dataset does not guarantee reliable or fair ML models. This is partially due to the fact that obtaining a balanced, diverse, and perfectly labeled dataset is typically expensive, time-consuming, and error-prone. Rather than relying on a carefully designed test set to assess ML models' failures, fairne

Dohwan Ko, Joonmyung Choi, Hyeong Kyu Choi, Kyoung-Woon On

Foundation models have shown outstanding performance and generalization capabilities across domains. Since most studies on foundation models mainly focus on the pretraining phase, a naive strategy to minimize a single task-specific loss is adopted for fine-tuning. However, such fine-tuning methods do not fully leverage other losses that are potentially benef

Chia-Min Lin

In this work, we consider homogeneous oscillations of the inflaton field after inflation. In particular, we obtain an analytical result for the (average) equation of state for the oscillating inflaton field for the simplest $\alpha$-attractor T-model. We also study the post-inflationary evolution before inflaton decay. It is possible that during inflaton fie

Feng Lin, Valery I. Levitas, Krishan K. Pandey, Sorb Yesudhas

Severe plastic deformations under high pressure are used to produce nanostructured materials but were studied ex-situ. We introduce rough diamond anvils to reach maximum friction equal to yield strength in shear and perform the first in-situ study of the evolution of the pressure-dependent yield strength and nanostructural parameters for severely pre-deforme

Manuel Kansy, Anton Raël, Graziana Mignone, Jacek Naruniec

Face recognition models embed a face image into a low-dimensional identity vector containing abstract encodings of identity-specific facial features that allow individuals to be distinguished from one another. We tackle the challenging task of inverting the latent space of pre-trained face recognition models without full model access (i.e. black-box setting)

Zhendong Yang, Ailing Zeng, Zhe Li, Tianke Zhang

Knowledge Distillation (KD) uses the teacher's prediction logits as soft labels to guide the student, while self-KD does not need a real teacher to require the soft labels. This work unifies the formulations of the two tasks by decomposing and reorganizing the generic KD loss into a Normalized KD (NKD) loss and customized soft labels for both target class (i

Zesheng Ye, Jing Du, Lina Yao

Conditional Neural Processes~(CNPs) formulate distributions over functions and generate function observations with exact conditional likelihoods. CNPs, however, have limited expressivity for high-dimensional observations, since their predictive distribution is factorized into a product of unconstrained (typically) Gaussian outputs. Previously, this could be

Zhihang Yuan, Jiawei Liu, Jiaxiang Wu, Dawei Yang

Post-training quantization (PTQ) is a popular method for compressing deep neural networks (DNNs) without modifying their original architecture or training procedures. Despite its effectiveness and convenience, the reliability of PTQ methods in the presence of some extrem cases such as distribution shift and data noise remains largely unexplored. This paper f

Xianchao Wu

We enhance the vanilla adversarial training method for unsupervised Automatic Speech Recognition (ASR) by a diffusion-GAN. Our model (1) injects instance noises of various intensities to the generator's output and unlabeled reference text which are sampled from pretrained phoneme language models with a length constraint, (2) asks diffusion timestep-dependent

Chenyang Lyu, Manh-Duy Nguyen, Van-Tu Ninh, Liting Zhou

Recent years have witnessed an increasing amount of dialogue/conversation on the web especially on social media. That inspires the development of dialogue-based retrieval, in which retrieving videos based on dialogue is of increasing interest for recommendation systems. Different from other video retrieval tasks, dialogue-to-video retrieval uses structured q

Edward S. Hu, Richard Chang, Oleh Rybkin, Dinesh Jayaraman

Dropped into an unknown environment, what should an agent do to quickly learn about the environment and how to accomplish diverse tasks within it? We address this question within the goal-conditioned reinforcement learning paradigm, by identifying how the agent should set its goals at training time to maximize exploration. We propose "Planning Exploratory Go

Mingyang Song, Haiyun Jiang, Shuming Shi, Songfang Yao

The emergence of ChatGPT has recently garnered significant attention from the computational linguistics community. To demonstrate its capabilities as a keyphrase generator, we conduct a preliminary evaluation of ChatGPT for the keyphrase generation task. We evaluate its performance in various aspects, including keyphrase generation prompts, keyphrase generat

Bashima Islam, Yubo Luo, Shahriar Nirjon

Intermittent computing systems undergo frequent power failure, hindering necessary data sample capture or timely on-device computation. These missing samples and deadlines limit the potential usage of intermittent computing systems in many time-sensitive and fault-tolerant applications. However, a group/swarm of intermittent nodes may amalgamate to sense and

Chaoqun You, Kun Guo, Gang Feng, Peng Yang

Federated Learning (FL) can be used in mobile edge networks to train machine learning models in a distributed manner. Recently, FL has been interpreted within a Model-Agnostic Meta-Learning (MAML) framework, which brings FL significant advantages in fast adaptation and convergence over heterogeneous datasets. However, existing research simply combines MAML a

Samrat Sahoo, Nitin Paul, Agam Shah, Andrew Hornback

Non-Fungible Tokens (NFTs) are a type of digital asset that represents a proof of ownership over a particular digital item such as art, music, or real estate. Due to the non-fungible nature of NFTs, duplicate tokens should not possess the same value. However, with the surge of new blockchains and a massive influx of NFTs being created, a wealth of NFT data i

Yande Li, Mingjie Wang, Minglun Gong, Yonggang Lu

The ever-increasing demands for intuitive interactions in Virtual Reality has triggered a boom in the realm of Facial Expression Recognition (FER). To address the limitations in existing approaches (e.g., narrow receptive fields and homogenous supervisory signals) and further cement the capacity of FER tools, a novel multifarious supervision-steering Transfo

Jin Sima, Chao Pan, Olgica Milenkovic

Balanced and swap-robust minimal trades, introduced in [1], are important for studying the balance and stability of server access request protocols under data popularity changes. Constructions of such trades have so far relied on paired sets obtained through iterative combining of smaller sets that have provable stability guarantees, coupled with exhaustive

Takefumi Nosaka

We establish a new approach to obtain 3-manifold invariants via Dehn surgery. For this, we introduce skew-racks with good involution and Property FR, and define cocycle invariants as 3-manifold invariants. We also define some link invariants in the 3-sphere which are invariant up to link-homotopic.

Yaozhong Hu, Xiong Wang, Panqiu Xia, Jiayu Zheng

In this paper, long time and high order moment asymptotics for super-Brownian motions (sBm's) are studied. By using a moment formula for sBm's (e.g. Theorem 3.1, Hu et al. Ann. Appl. Probab. 2023+), precise upper and lower bounds for all positive integer moments and for all time of sBm's for certain initial conditions are achieved. Then, the moment asymptoti

Kuofeng Gao, Yang Bai, Jindong Gu, Yong Yang

Backdoor defenses have been studied to alleviate the threat of deep neural networks (DNNs) being backdoor attacked and thus maliciously altered. Since DNNs usually adopt some external training data from an untrusted third party, a robust backdoor defense strategy during the training stage is of importance. We argue that the core of training-time defense is t

Xiang Li, Ge Wu, Lingfeng Yang, Wenhai Wang

New knowledge originates from the old. The various types of elements, deposited in the training history, are a large amount of wealth for improving learning deep models. In this survey, we comprehensively review and summarize the topic--``Historical Learning: Learning Models with Learning History'', which learns better neural models with the help of their le

Jay K. Kueny, Colin Orion Chandler, Maxime Devogèle, Nicholas Moskovitz

(155140) 2005 UD has a similar orbit to (3200) Phaethon, an active asteroid in a highly eccentric orbit thought to be the source of the Geminid meteor shower. Evidence points to a genetic relationship between these two objects, but we have yet to fully understand how 2005 UD and Phaethon could have separated into this associated pair. Presented herein are ne

Jin Sima, Yun-Han Li, Ilan Shomorony, Olgica Milenkovic

Motivated by applications in polymer-based data storage we introduced the new problem of characterizing the code rate and designing constant-weight binary $B_2$-sequences. Binary $B_2$-sequences are collections of binary strings of length $n$ with the property that the real-valued sums of all distinct pairs of strings are distinct. In addition to this defini