March 2023 arXiv papers — page 41

Yiqian Wu, Jing Zhang, Hongbo Fu, Xiaogang Jin

The creation of 2D realistic facial images and 3D face shapes using generative networks has been a hot topic in recent years. Existing face generators exhibit exceptional performance on faces in small to medium poses (with respect to frontal faces) but struggle to produce realistic results for large poses. The distorted rendering results on large poses in 3D

Lai-Bin Qian, Xiao-Bo Yan

Here, we study the controllable optical responses in a two-cavity optomechanical system, especially on the $\mathit{perfect}$ optomechanically induced transparency (OMIT) in the model which has never been studied before. The results show that the perfect OMIT can still occur even with a large mechanical damping rate, and at the perfect transparency window th

Atnafu Lambebo Tonja, Tadesse Destaw Belay, Israel Abebe Azime, Abinew Ali Ayele

This survey delves into the current state of natural language processing (NLP) for four Ethiopian languages: Amharic, Afaan Oromo, Tigrinya, and Wolaytta. Through this paper, we identify key challenges and opportunities for NLP research in Ethiopia. Furthermore, we provide a centralized repository on GitHub that contains publicly available resources for vari

Songqiao Hu, Zeyi Liu, Xiao He

Concept drift detection has attracted considerable attention due to its importance in many real-world applications such as health monitoring and fault diagnosis. Conventionally, most advanced approaches will be of poor performance when the evaluation criteria of the environment has changed (i.e. concept drift), either can only detect and adapt to virtual dri

Chuang-Chieh Lin, Chi-Jen Lu, Po-An Chen, Chih-Chieh Hung

We study organizational elections in which each group nominates one candidate and receives as payoff its members expected utility under a probabilistic winning rule. We empirically justify a standard monotonicity assumption by simulating two- and three-group elections, finding that a candidates aggregate voter utility correlates monotonically with win probab

Muhammad Akhtar Munir, Muhammad Haris Khan, Salman Khan, Fahad Shahbaz Khan

Deep neural networks (DNNs) have enabled astounding progress in several vision-based problems. Despite showing high predictive accuracy, recently, several works have revealed that they tend to provide overconfident predictions and thus are poorly calibrated. The majority of the works addressing the miscalibration of DNNs fall under the scope of classificatio

Anantha Sai Hariharan Vinjarapu, Yorick Broens, Hans Butler, Roland Tóth

Growing demands in today's industry results in increasingly stringent performance and throughput specifications. For accurate positioning of high-precision motion systems, feedforward control plays a crucial role. Nonetheless, conventional model-based feedforward approaches are no longer sufficient to satisfy the challenging performance requirements. An attr

Raunak Joshi, Abhishek Gupta, Himanshu Soni, Ronald Laban

The polycystic ovary syndrome diagnosis is a problem that can be leveraged using prognostication based learning procedures. Many implementations of PCOS can be seen with Machine Learning but the algorithms have certain limitations in utilizing the processing power graphical processing units. The simple machine learning algorithms can be improved with advance

Songjie Yang, Wanting Lyu, Yunis Xanthos, Zhongpei Zhang

Millimeter wave (mmWave) full-duplex (FD) is a promising technique for improving capacity by maximizing the utilization of both time and the rich mmWave frequency resources. Still, it has restrictions due to FD self-interference (SI) and mmWave's limited coverage. Therefore, this study dives into FD mmWave MIMO with the assistance of reconfigurable intellige

Dominic C. Milioto

An efficient method of computing power expansions of algebraic functions is the method of Kung and Traub and is based on exact arithmetic. This paper shows a numeric approach is both feasible and accurate while also introducing a performance improvement to Kung and Traub's method based on the ramification extent of the expansions. A new method is then descri

Jirong Mao, Jiancheng Wang

The emission of gamma-ray burst (GRB) 221009A at 18 TeV has been detected by the large high-altitude air shower observatory (LHAASO). We suggest jitter radiation as a possible explanation for the TeV emission for this energetic GRB. In our scenario, the radiation field is linked to the perturbation field, and the perturbation field is dominated by kinetic tu

Kristina Oganesyan

We obtain estimates for the number $p_d(n)$ of $(d-1)$-dimensional integer partitions of a number $n$. It is known that the two-sided inequality $C_1(d)n^{1-1/d}<\log p_d(n)< C_2(d)n^{1-1/d}$ is always true and that $C_1(d)>1$ whenever $\log n> 3d$. However, establishing the $``$right$"$ dependence of $C_2$ on $d$ remained an open problem. We show that if $d

Sukmin Yun, Seong Hyeon Park, Paul Hongsuck Seo, Jinwoo Shin

Vision-language (VL) pre-training has recently gained much attention for its transferability and flexibility in novel concepts (e.g., cross-modality transfer) across various visual tasks. However, VL-driven segmentation has been under-explored, and the existing approaches still have the burden of acquiring additional training images or even segmentation anno

Minghan Li, Shuai Li, Wangmeng Xiang, Lei Zhang

While impressive progress has been achieved, video instance segmentation (VIS) methods with per-clip input often fail on challenging videos with occluded objects and crowded scenes. This is mainly because instance queries in these methods cannot encode well the discriminative embeddings of instances, making the query-based segmenter difficult to distinguish

Xuechen Zheng, Huub Röttgering, Arjen van der Wel, Michele Cappellari

The radio jets of an active galactic nucleus (AGN) can heat up the gas around a host galaxy and quench star formation activity. The presence of a radio jet could be related to the evolutionary path of the host galaxy and may be imprinted in the morphology and kinematics of the galaxy. In this work, we use data from the Sloan Digital Sky Survey's Mapping Near

Juan Lucas Callo, George Chen, Yasiru Jayasooriya, Leo Li

We analyse the set of matrices in SL$_3(\mathbb{F}_7)$ without eigenvalues explicitly, extracting nice bijections between the 18 equally sized conjugacy classes contained within. In doing so, we discover a set of $18$ commuting matrices for which every conjugacy class is represented and tells us how to decide when collections of commuting matrices are simult

Yorick Broens, Hans Butler, Roland Tóth

The demand for high-precision and high-throughput motion control systems has increased significantly in recent years. The use of moving-magnet planar actuators (MMPAs) is gaining popularity due to their advantageous characteristics, such as complete environmental decoupling and reduction of stage mass. Nonetheless, model-based commutation techniques for MMPA

Zhen Zhang, Masaki Takeda, Makoto Iwata

Neural decoding of visual object classification via functional magnetic resonance imaging (fMRI) data is challenging and is vital to understand underlying brain mechanisms. This paper proposed a multi-pooling 3D convolutional neural network (MP3DCNN) to improve fMRI classification accuracy. MP3DCNN is mainly composed of a three-layer 3DCNN, where the first a

Zhengping Ji, Xiao Zhang, Daizhan Cheng

The paper provides a method to approximate a large-scale finite-valued network by a smaller model called the aggregated simulation, which is a combination of aggregation and (bi-)simulation. First, the algebraic state space representation (ASSR) of a transition system is presented. Under output equivalence, the quotient system is obtained, which is called th

Shanghua Gao, Pan Zhou, Ming-Ming Cheng, Shuicheng Yan

Despite its success in image synthesis, we observe that diffusion probabilistic models (DPMs) often lack contextual reasoning ability to learn the relations among object parts in an image, leading to a slow learning process. To solve this issue, we propose a Masked Diffusion Transformer (MDT) that introduces a mask latent modeling scheme to explicitly enhanc

E. A. Illarionov, D. D. Sokoloff

We consider a model of a random media with fixed and finite memory time with abrupt losses of memory (renovation model). Within the memory intervals we can observe either amplification or oscillation of the vector field in a given particle. The cumulative effect of amplifications in many subsequent intervals leads to amplification of the mean field and mean

Bin Yang, Yuming Qin, Alain Miranville, Ke Wang

This paper is concerned with the existence and regularity of global attractor $\mathcal A$ for a Kirchhoff wave equation with strong damping and memory in the weighted time-dependent spaces $\mathcal H$ and $\mathcal H^{1}$, respectively. In order to obtain the existence of $\mathcal A$, we mainly use the energy method in the priori estimations, and then ver

Hwanjun Song, Jihwan Bang

Prompt-OVD is an efficient and effective framework for open-vocabulary object detection that utilizes class embeddings from CLIP as prompts, guiding the Transformer decoder to detect objects in both base and novel classes. Additionally, our novel RoI-based masked attention and RoI pruning techniques help leverage the zero-shot classification ability of the V

Aritra Lahiri, Sang-Jun Choi, Björn Trauzettel

Josephson tunnel junctions exhibit a supercurrent typically proportional to the sine of the superconducting phase difference $\phi$. In general, a term proportional to $\cos(\phi)$ is also present, alongside microscopic electronic retardation effects. We show that voltage pulses sharply varying in time prompt a significant impact of the $\cos(\phi)$ term. It

Zikun Zhou, Kaige Mao, Wenjie Pei, Hongpeng Wang

This paper aims to solve the video object segmentation (VOS) task in a scribble-supervised manner, in which VOS models are not only trained by the sparse scribble annotations but also initialized with the sparse target scribbles for inference. Thus, the annotation burdens for both training and initialization can be substantially lightened. The difficulties o

Tarun Dalal

In this article, we explicitly construct a canonical basis for the space of certain weakly holomorphic Drinfeld modular forms for $\Gamma_0(T)$ (resp., for $\Gamma_0^+(T)$) and compute the generating function satisfied by the basis elements. We also give an explicit expression for the action of the $\Theta$-operator, which depends on the divisor of meromorph

Yichen Xie, Han Lu, Junchi Yan, Xiaokang Yang

Given the large-scale data and the high annotation cost, pretraining-finetuning becomes a popular paradigm in multiple computer vision tasks. Previous research has covered both the unsupervised pretraining and supervised finetuning in this paradigm, while little attention is paid to exploiting the annotation budget for finetuning. To fill in this gap, we for

Phuong D. Nguyen, Thinh D. Le, Duong Q. Nguyen, Thanh Q. Nguyen

This study explores the potential of a fully convolutional mesh autoencoder model for regenerating 3D nature faces with the presence of imperfect areas. We utilize deep learning approaches in graph processing and analysis to investigate the capabilities model in recreating a filling part for facial scars. Our approach in dataset creation is able to generate

Diana Thongjaomayum, Prabodh Shukla

Notwithstanding great strides that statistical mechanics has made in recent decades, an analytic solution of arguably the simplest model of relaxation dynamics, the Ising model in an applied external field remains elusive even in $1d$. Extant studies are based on numerics using single-spin-flip Glauber dynamics. There is no reason why this algorithm should l

Hemant Rathi, Dibakar Roychowdhury

We present a JT gravity set up in the presence of projected ModMax corrections in two dimensions. Our starting point is the Einstein's gravity in four dimensions accompanied by the ModMax Lagrangian. The 2D gravity action is obtained following a suitable dimensional reduction which contains a 2D image of the 4D ModMax Lagrangian. We carry out a perturbative

Juntian Zheng, Qingyuan Zheng, Lixing Fang, Yun Liu

In this work, we focus on a novel task of category-level functional hand-object manipulation synthesis covering both rigid and articulated object categories. Given an object geometry, an initial human hand pose as well as a sparse control sequence of object poses, our goal is to generate a physically reasonable hand-object manipulation sequence that performs

Kwonyoung Ryu, Soonmin Hwang, Jaesik Park

Despite the increasing popularity of LiDAR sensors, perception algorithms using 3D LiDAR data struggle with the 'sensor-bias problem'. Specifically, the performance of perception algorithms significantly drops when an unseen specification of LiDAR sensor is applied at test time due to the domain discrepancy. This paper presents a fast and flexible LiDAR augm

Chenchen Xu, Min Zhou, Tiezheng Ge, Yuning Jiang

Layout is essential for graphic design and poster generation. Recently, applying deep learning models to generate layouts has attracted increasing attention. This paper focuses on using the GAN-based model conditioned on image contents to generate advertising poster graphic layouts, which requires an advertising poster layout dataset with paired product imag

Hongyu Sun, Yongcai Wang, Xudong Cai, Xuewei Bai

Recently, a growing number of work design unsupervised paradigms for point cloud processing to alleviate the limitation of expensive manual annotation and poor transferability of supervised methods. Among them, CrossPoint follows the contrastive learning framework and exploits image and point cloud data for unsupervised point cloud understanding. Although th

Le-Anh Tran, Dong-Chul Park

An application of the POCS-based clustering algorithm (POCS stands for Projection Onto Convex Set), a novel clustering technique, for feature embedding clustering problems is proposed in this paper. The POCS-based clustering algorithm applies the POCS's convergence property to clustering problems and has shown competitive performance when compared with that

Rui Zhang, Yajing Sun, Jingyuan Yang, Wei Peng

Frame semantics-based approaches have been widely used in semantic parsing tasks and have become mainstream. It remains challenging to disambiguate frame representations evoked by target lexical units under different contexts. Pre-trained Language Models (PLMs) have been used in semantic parsing and significantly improve the accuracy of neural parsers. Howev

Anindita Das, Udit Pant, Cuong Cao, Rakesh S. Moirangthem

Surface enhanced Raman spectroscopy (SERS) has established itself as a promising tool in optical sensing technology. Efforts have been made to improve practicalities of the technology with regards to costs of production, stability, reproducibility, flexibility and robustness. Here, we demonstrate a method to fabricate Ag/Au bimetallic inverted nanopyramid (i

Hao Jiang, Rushan Zhang, Yanning Zhou, Yumeng Wang

Cell instance segmentation in cytology images has significant importance for biology analysis and cancer screening, while remains challenging due to 1) the extensive overlapping translucent cell clusters that cause the ambiguous boundaries, and 2) the confusion of mimics and debris as nuclei. In this work, we proposed a De-overlapping Network (DoNet) in a de

Smruti Rekha Swain, Deepika Saxena, Jatinder Kumar, Ashutosh Kumar Singh

This letter proposes a novel Cloud Radio Access Network (C-RAN) traffic analysis and management model that estimates probable RAN traffic congestion and mitigate its effect by adopting a suitable handling mechanism. A computation approach is introduced to classify heterogeneous RAN traffic into distinct traffic states based on bandwidth consumption and execu

Hao Xu, Tengfei Xue, Dongnan Liu, Fan Zhang

White matter (WM) tract segmentation based on diffusion magnetic resonance imaging (dMRI) plays an important role in the analysis of human health and brain diseases. However, the annotation of WM tracts is time-consuming and needs experienced neuroanatomists. In this study, to explore tract segmentation in the challenging setting of minimal annotations, we p

Cheng Su, Xu-Tao Zeng, Kaitong Sun, Denis Sheptyakov

Magnetic double perovskite compounds provide a fertile playground to explore interesting electronic and magnetic properties. By complementary macroscopic characterizations, neutron powder diffraction measurements and first-principles calculations, we have performed comprehensive studies on the magnetic ordering in the double perovskite compound Sr$_2$NiWO$_6

Peng Jin, Jinfa Huang, Pengfei Xiong, Shangxuan Tian

Contrastive learning-based video-language representation learning approaches, e.g., CLIP, have achieved outstanding performance, which pursue semantic interaction upon pre-defined video-text pairs. To clarify this coarse-grained global interaction and move a step further, we have to encounter challenging shell-breaking interactions for fine-grained cross-mod

Vinoj Jayasundara, Amit Agrawal, Nicolas Heron, Abhinav Shrivastava

We present FlexNeRF, a method for photorealistic freeviewpoint rendering of humans in motion from monocular videos. Our approach works well with sparse views, which is a challenging scenario when the subject is exhibiting fast/complex motions. We propose a novel approach which jointly optimizes a canonical time and pose configuration, with a pose-dependent m

Arqum Hashmi, Shunsuke Yamada, Kazuhiro Yabana, Tomohito Otobe

Here we proposed the valley selective excitations via a two-color (\ensuremath{\omega} + \ensuremath{2\omega}) laser field, made by superimposing two linearly polarized pulses at frequencies \ensuremath{\omega} and \ensuremath{2\omega}. We have studied the intensity ratio between a few-cycle pulse of \ensuremath{\omega} and \ensuremath{2\omega} laser, and it

Akira R. Kinjo, Daphne Teck Ching Lai

With the membership function being strictly positive, the conventional fuzzy c-means clustering method sometimes causes imbalanced influence when clusters of vastly different sizes exist. That is, an outstandingly large cluster drags to its center all the other clusters, however far they are separated. To solve this problem, we propose a hybrid fuzzy-crisp c

Junqing Chen, Zehao Long

Conductivity reconstruction in an inverse eddy current problem is considered in the present paper. With the electric field measurement on part of domain boundary, we formulate the reconstruction problem to a constrained optimization problem with total variation regularization. Existence and stability are proved for the solution to the optimization problem. T

C. C. N. Kuhn, G. Calbert, I. Garanovich, T. Weir

In large organisations and companies, making investment decisions is a complex and challenging task. In the Australian Department of Defence (Defence), the complexity is even higher because defence capabilities are public goods and do not have a financial return \textit{per se}. In this work we mathematically define Defence's investment portfolio problem as

Dillon Chen, Felipe Trevizan, Sylvie Thiébaux

Heuristic search is a powerful approach that has successfully been applied to a broad class of planning problems, including classical planning, multi-objective planning, and probabilistic planning modelled as a stochastic shortest path (SSP) problem. Here, we extend the reach of heuristic search to a more expressive class of problems, namely multi-objective

Prashanta Garain, Wontae Kim, Juha Kinnunen

We establish existence results for a class of mixed anisotropic and nonlocal $p$-Laplace equation with singular nonlinearities. We consider both constant and variable singular exponents. Our argument is based on an approximation method. To this end, we also discuss the necessary regularity properties of weak solutions of the associated non-singular problems.

Shao-Yuan Lo, Poojan Oza, Sumanth Chennupati, Alejandro Galindo

Unsupervised Domain Adaptation (UDA) of semantic segmentation transfers labeled source knowledge to an unlabeled target domain by relying on accessing both the source and target data. However, the access to source data is often restricted or infeasible in real-world scenarios. Under the source data restrictive circumstances, UDA is less practical. To address

Xu Zheng, Jinjing Zhu, Yexin Liu, Zidong Cao

The ability of scene understanding has sparked active research for panoramic image semantic segmentation. However, the performance is hampered by distortion of the equirectangular projection (ERP) and a lack of pixel-wise annotations. For this reason, some works treat the ERP and pinhole images equally and transfer knowledge from the pinhole to ERP images vi

Thai Son Doan

This paper is devoted to study stability of Lyapunov exponents and simplicity of Lyapunov spectrum for bounded random compact operators on a separable infinite-dimensional Hilbert space from a generic point of view generated by the essential supremum norm. Firstly, we show the density of both the set of bounded random compact operators having finite number L

Ying-Chen Lin, Vincent S. Tseng

Recently, Transformer-based methods have been utilized to improve the performance of human action recognition. However, most of these studies assume that multi-view data is complete, which may not always be the case in real-world scenarios. Therefore, this paper presents a novel Multi-view Knowledge Distillation Transformer (MKDT) framework that consists of

Xiaoxiao He, Chaowei Tan, Bo Liu, Liping Si

Federated Learning has gained popularity among medical institutions since it enables collaborative training between clients (e.g., hospitals) without aggregating data. However, due to the high cost associated with creating annotations, especially for large 3D image datasets, clinical institutions do not have enough supervised data for training locally. Thus,

Kaige Wang

The interference phenomenon of light is a common but most important effect in physics. In this article, we compare and analyse the interference effects of both coherent and incoherent light; in particular, the interference of spatially incoherent light sources is described. Thermal light and a two-photon entangled source may both be regarded as incoherent so

Chia-Hsiang Lai, Li-Hsiang Shen, Kai-Ten Feng

The open radio access network (O-RAN) architecture offers a cost-effective and scalable solution for internet service providers to optimize their networks using machine learning algorithms. The architecture's open interfaces enable network function virtualization, with the O-RAN serving as the primary communication device for users. However, the limited freq

Justin R. David, Edi Gava, Rajesh Kumar Gupta, K. S. Narain

The partition functions of free bosons as well as fermions on $AdS_2$ are not smooth as a function of their masses. For free bosons, the partition function on $AdS_2$ is not smooth when the mass saturates the Breitenlohner-Freedman bound. We show that the expectation value of the scalar bilinear on $AdS_2$ exhibits a kink at the BF bound and the change in sl

Ting-Hui Wang, Li-Hsiang Shen, Kai-Ten Feng

The innovation of Wi-Fi 6, IEEE 802.11ax, was be approved as the next sixth-generation (6G) technology of wireless local area networks (WLANs) by improving the fundamental performance of latency, throughput, and so on. The main technical feature of orthogonal frequency division multiple access (OFDMA) supports multi-users to transmit respective data concurre

Zalan Fabian, Berk Tinaz, Mahdi Soltanolkotabi

Diffusion models have established new state of the art in a multitude of computer vision tasks, including image restoration. Diffusion-based inverse problem solvers generate reconstructions of exceptional visual quality from heavily corrupted measurements. However, in what is widely known as the perception-distortion trade-off, the price of perceptually appe

Wen Liu, An-Hung Hsiao, Li-Hsiang Shen, Kai-Ten Feng

The demand for device-free indoor localization using commercial Wi-Fi devices has rapidly increased in various fields due to its convenience and versatile applications. However, random frequency offset (RFO) in wireless channels poses challenges to the accuracy of indoor localization when using fluctuating channel state information (CSI). To mitigate the RFO

Yi-Neng Zhou, Tian-Gang Zhou, Pengfei Zhang

The spectral form factor (SFF) can probe the eigenvalue statistic at different energy scales as its time variable varies. In closed quantum chaotic systems, the SFF exhibits a universal dip-ramp-plateau behavior, which reflects the spectrum rigidity of the Hamiltonian. In this work, we explore the universal properties of SFF in open quantum systems. We find

Chih-Yu Chen, Li-Hsiang Shen, Kai-Ten Feng, Lie-Liang Yang

Low-Earth orbit (LEO) satellites have been prosperously deployed for various Earth observation missions due to its capability of collecting a large amount of image or sensor data. However, traditionally, the data training process is performed in the terrestrial cloud server, which leads to a high transmission overhead. With the recent development of LEO, it

Li-Hsiang Shen, Yun Ho, Kai-Ten Feng, Lie-Liang Yang

Low Earth orbit (LEO) satellite constellation is capable of providing global coverage area with high-rate services in the next sixth-generation (6G) non-terrestrial network (NTN). Due to limited onboard resources of operating power, beams, and channels, resilient and efficient resource management has become compellingly imperative under complex interference

Mohsin Yousufi, Charlotte Alexander, Nassim Parvin

This paper brings attention to epistemic injustice, an issue that has not received much attention in the design of technology and policy. Epistemic injustices occur when individuals are treated unfairly or harmed specifically in relation to their role as knowers or possessors of knowledge. Drawing on the case of making heat complaints in New York City, this

Jun Cen, Shiwei Zhang, Xiang Wang, Yixuan Pei

Open-set action recognition is to reject unknown human action cases which are out of the distribution of the training set. Existing methods mainly focus on learning better uncertainty scores but dismiss the importance of feature representations. We find that features with richer semantic diversity can significantly improve the open-set performance under the

Wei-Yu Chung, Li-Hsiang Shen, Kai-Ten Feng, Yuan-Chun Lin

Channel State Information (CSI) is widely adopted as a feature for indoor localization. Taking advantage of the abundant information from the CSI, people can be accurately sensed even without equipped devices. However, the positioning error increases severely in non-line-of-sight (NLoS) regions. Reconfigurable intelligent surface (RIS) has been introduced to

Christopher J. Jensen, Alberto Quintana, Patrick Quarterman, Alexander J. Grutter

Electric field control of the exchange bias effect across ferromagnet/antiferromagnet (FM/AF) interfaces has offered exciting potentials for low-energy-dissipation spintronics. In particular, the solid state magneto-ionic means is highly appealing as it may allow reconfigurable electronics by transforming the all-important FM/AF interfaces through ionic migr

Yujia Li, Jiong Shi, S. Kevin Zhou

Clinical decision making requires counterfactual reasoning based on a factual medical image and thus necessitates causal image synthesis. To this end, we present a novel method for modeling the causality between demographic variables, clinical indices and brain MR images for Alzheimer's Diseases. Specifically, we leverage a structural causal model to depict

Fengyin Lin, Mingkang Li, Da Li, Timothy Hospedales

This paper studies the problem of zero-short sketch-based image retrieval (ZS-SBIR), however with two significant differentiators to prior art (i) we tackle all variants (inter-category, intra-category, and cross datasets) of ZS-SBIR with just one network (``everything''), and (ii) we would really like to understand how this sketch-photo matching operates (`

Ertai Liu, Josephine Monica, Kaitlin Gold, Lance Cadle-Davidson

Autonomous navigation is the key to achieving the full automation of agricultural research and production management (e.g., disease management and yield prediction) using agricultural robots. In this paper, we introduced a vision-based autonomous navigation framework for agriculture robots in trellised cropping systems such as vineyards. To achieve this, we

Sanbao Su, Songyang Han, Yiming Li, Zhili Zhang

Object detection and multiple object tracking (MOT) are essential components of self-driving systems. Accurate detection and uncertainty quantification are both critical for onboard modules, such as perception, prediction, and planning, to improve the safety and robustness of autonomous vehicles. Collaborative object detection (COD) has been proposed to impr

Han Lin, Guangxing Han, Jiawei Ma, Shiyuan Huang

Vision Transformers (ViTs) emerge to achieve impressive performance on many data-abundant computer vision tasks by capturing long-range dependencies among local features. However, under few-shot learning (FSL) settings on small datasets with only a few labeled data, ViT tends to overfit and suffers from severe performance degradation due to its absence of CN

Lina Wang, Mingzhu Zhang, Hongjiong Tian, Lijun Yi

We introduce and analyze an $hp$-version $C^1$-continuous Petrov-Galerkin (CPG) method for nonlinear initial value problems of second-order ordinary differential equations. We derive a-priori error estimates in the $L^2$-, $L^\infty$-, $H^1$- and $H^2$-norms that are completely explicit in the local time steps and local approximation degrees. Moreover, we sh

Chang Gao

This article provides a general iterative approximation to partial differential equations, and thus establish existence of smooth solution. The heart of the method is to contract (or expand) the boundary conditions uniformly in the domain, then using local and global correspondence to transform discrete step function to successive integration on the domain.

Haohao Sheng, Zhong Fang, Zhijun Wang

Ferroelectricity and metallicity cannot coexist due to the screening effect of conducting electrons, and a large number of stable monolayers with 1T/1T$^{\prime}$ phase lack spontaneous polarization due to inversion symmetry. In this work, we have constructed the $\pi$-bilayer structures for transition metal dichalcogenides ($M$Te$_2,M =$ Pt, Pd, and Ni) wit

Steven Coyne, Keisuke Sakaguchi, Diana Galvan-Sosa, Michael Zock

GPT-3 and GPT-4 models are powerful, achieving high performance on a variety of Natural Language Processing tasks. However, there is a relative lack of detailed published analysis of their performance on the task of grammatical error correction (GEC). To address this, we perform experiments testing the capabilities of a GPT-3.5 model (text-davinci-003) and a

Murray Z. Frank, Jing Gao, Keer Yang

Standard forecast efficiency tests interpret violations as evidence of behavioral bias. We show theoretically and empirically that rational forecasters using optimal regularization systematically violate these tests. Machine learning forecasts show near zero bias at one year horizon, but strong overreaction at two years, consistent with predictions from a mo

Yifu Ding, Haotong Qin, Qinghua Yan, Zhenhua Chai

Vision transformer emerges as a potential architecture for vision tasks. However, the intense computation and non-negligible delay hinder its application in the real world. As a widespread model compression technique, existing post-training quantization methods still cause severe performance drops. We find the main reasons lie in (1) the existing calibration

Carlos Allende Prieto

Stellar atmospheres separate the hot and dense stellar interiors from the emptiness of space. Radiation escapes from the outermost layers of a star, carrying direct physical information. Underneath the atmosphere, the very high opacity keeps radiation thermalized and resembling a black body with the local temperature. In the atmosphere the opacity drops, and

Yuya Nagase, Masahiro Manago, Junichi Hayashi, Keiki Takeda

We present synthesis and $^{75}$As-nuclear quadrupole resonance (NQR) measurements for the noncentrosymmetric superconductor CaPtAs with a superconducting transition temperature $T_c$ of $\sim 1.5$ K. We discovered two different forms of CaPtAs during synthesis; one is a high-temperature tetragonal form that was previously reported, and the other is a low-te

Dusko Pavlovic, Temra Pavlovic

Hilbert and Ackermann asked for a method to consistently extend incomplete theories to complete theories. G\"odel essentially proved that any theory capable of encoding its own statements and their proofs contains statements that are true but not provable. Hilbert did not accept that G\"odel's construction answered his question, and in his late writings and

Revanth Gangi Reddy, Daniel Lee, Yi R. Fung, Khanh Duy Nguyen

Timely and comprehensive understanding of emerging events is crucial for effective decision-making; automating situation report generation can significantly reduce the time, effort, and cost for intelligence analysts. In this work, we identify intelligence analysts' practices and preferences for AI assistance in situation report generation to guide the desig

Reuben Kirkham

This paper proposes the concept of 'research through litigation', where a HCI researcher would bring a claim in the legal system in order to understand judicial attitudes towards technologies. Based on my seven years of experience of bringing legal cases as a computer scientist in Tribunals, I demonstrate the value of this approach by presenting multiple cas

Guojin Chen, Haoyu Yang, Bei Yu

Multiple patterning lithography (MPL) is regarded as one of the most promising ways of overcoming the resolution limitations of conventional optical lithography due to the delay of next-generation lithography technology. As the feature size continues to decrease, layout decomposition for multiple patterning lithography (MPLD) technology is becoming increasin

Kyle Lo, Joseph Chee Chang, Andrew Head, Jonathan Bragg

Scholarly publications are key to the transfer of knowledge from scholars to others. However, research papers are information-dense, and as the volume of the scientific literature grows, the need for new technology to support the reading process grows. In contrast to the process of finding papers, which has been transformed by Internet technology, the experi

Luca Zancato, Alessandro Achille, Tian Yu Liu, Matthew Trager

We introduce Train/Test-Time Adaptation with Retrieval (${\rm T^3AR}$), a method to adapt models both at train and test time by means of a retrieval module and a searchable pool of external samples. Before inference, ${\rm T^3AR}$ adapts a given model to the downstream task using refined pseudo-labels and a self-supervised contrastive objective function whos

Ashwin Kumar, Yevgeniy Vorobeychik, William Yeoh

State-of-the-art order dispatching algorithms for ridesharing batch passenger requests and allocate them to a fleet of vehicles in a centralized manner, optimizing over the estimated values of each passenger-vehicle matching using integer linear programming (ILP). Using good estimates of future values, such ILP-based approaches are able to significantly incr

Ru Zheng, Rong-Qiang He, Zhong-Yi Lu

Using the natural orbitals renormalization group, we studied the problem of a localized spin- 1/2 impurity coupled to two helical liquids via the Kondo interaction in a quantum spin Hall insulator, based on the Kane-Mele model defined in a finite zigzag graphene nanoribbon. We investigated the influence of the Kondo couplings with the helical liquids on both

Chloé Bourquin, Jonathan Porée, Brice Rauby, Vincent Perrot

A rise in blood flow velocity variations (i.e., pulsatility) in the brain, caused by the stiffening of upstream arteries, is associated with cognitive impairment and neurodegenerative diseases. The study of this phenomenon requires brain-wide pulsatility measurements, with high penetration depth and high spatiotemporal resolution. The development of Dynamic

Miao Hu, Zhenxiao Luo, Amirmohammad Pasdar, Young Choon Lee

Edge computing has been getting a momentum with ever-increasing data at the edge of the network. In particular, huge amounts of video data and their real-time processing requirements have been increasingly hindering the traditional cloud computing approach due to high bandwidth consumption and high latency. Edge computing in essence aims to overcome this hin

Alireza Bakhshi, Erfan Hassannayebi, Amir Hossein Sadeghi

Process mining can help acquire insightful knowledge and heighten the system's performance. In this study, we surveyed the trajectories of 1050 sepsis patients in a regional hospital in the Netherlands from the registration to the discharge phase. Based on this real-world case study, the event log comprises events and activities related to the emergency ward

Josiah Foster

For a projective $2n$-dimensional irreducible holomorphic symplectic manifold $Y$ of generalized Kummer deformation type and $j$ the smallest prime number dividing $n+1$, we prove the Lefschetz standard conjectures in degrees $<2(n+1)(j-1)/j$. We show that the restriction homomorphism from the cohomology of a projective deformation of a moduli space of Giese

Gaopei Pan, Yuan Da Liao, Weilun Jiang, Jonathan D'Emidio

There is no doubt that the information hidden in entanglement entropy (EE), for example, the $n$-th order R\'enyi EE, i.e., $S^{A}_n=\frac{1}{1-n}\ln \Tr (\rho_A^n)$ where $\rho_A=\mathrm{Tr}_{\overline{A}}\rho$ is the reduced density matrix, can be used to infer the organizing principle of 2D interacting fermion systems, ranging from spontaneous symmetry br

Vladimir Batagelj

In the analysis of large/big data sets, aggregation (replacing values of a variable over a group by a single value) is a standard way of reducing the size (complexity) of the data. Data analysis programs provide different aggregation functions. Recently some books dealing with the theoretical and algorithmic background of traditional aggregation functions we

Xukun Zhou, Jiwei Li, Tianwei Zhang, Lingjuan Lyu

Backdoor attack aims at inducing neural models to make incorrect predictions for poison data while keeping predictions on the clean dataset unchanged, which creates a considerable threat to current natural language processing (NLP) systems. Existing backdoor attacking systems face two severe issues:firstly, most backdoor triggers follow a uniform and usually

Gang Wu, Junjun Jiang, Yuanchao Bai, Xianming Liu

In recent years, the use of large convolutional kernels has become popular in designing convolutional neural networks due to their ability to capture long-range dependencies and provide large receptive fields. However, the increase in kernel size also leads to a quadratic growth in the number of parameters, resulting in heavy computation and memory requireme

M. Chrysler, J. Gabel, T. -L. Lee, Z. Zhu

Charge redistribution across heterojunctions has long been utilized to induce functional response in materials systems. Here we examine how the composition of the terminating surface affects charge transfer across a heterojunction consisting of Si and the crystalline complex oxide SrTiO3. Itinerant electrons in Si migrate across the interface toward the surf

Usman Nazir, Wadood Islam, Sara Khalid, Murtaza Taj

Land-use monitoring is fundamental for spatial planning, particularly in view of compound impacts of growing global populations and climate change. Despite existing applications of deep learning in land use monitoring, standard convolutional kernels in deep neural networks limit the applications of these networks to the Euclidean domain only. Considering the

Daniel Lemire

We sometimes need to compute the most significant digits of the product of small integers with a multiplier requiring much storage: e.g., a large integer (e.g., $5^{100}$) or an irrational number ($\pi$). We only need to access the most significant digits of the multiplier-as long as the integers are sufficiently small. We provide an efficient algorithm to c

Hao Zhou, Melike Erol-Kantarci, Yuanwei Liu, H. Vincent Poor

Reconfigurable intelligent surfaces (RISs) have received considerable attention as a key enabler for envisioned 6G networks, for the purpose of improving the network capacity, coverage, efficiency, and security with low energy consumption and low hardware cost. However, integrating RISs into the existing infrastructure greatly increases the network managemen