July 2022 arXiv papers — page 63

Yizheng Huang, Huaizheng Zhang, Yuanming Li, Chiew Tong Lau

The success of today's AI applications requires not only model training (Model-centric) but also data engineering (Data-centric). In data-centric AI, active learning (AL) plays a vital role, but current AL tools 1) require users to manually select AL strategies, and 2) can not perform AL tasks efficiently. To this end, this paper presents an automatic and ef

Sumin Hu, Yeeun Kim, Hyungtae Lim, Alex Junho Lee

Contrary to other standard cameras, event cameras interpret the world in an entirely different manner; as a collection of asynchronous events. Despite event camera's unique data output, many event feature detection and tracking algorithms have shown significant progress by making detours to frame-based data representations. This paper questions the need to d

Ekraam Sabir, Soumyaroop Nandi, Wael AbdAlmageed, Prem Natarajan

Manipulation of biomedical images to misrepresent experimental results has plagued the biomedical community for a while. Recent interest in the problem led to the curation of a dataset and associated tasks to promote the development of biomedical forensic methods. Of these, the largest manipulation detection task focuses on the detection of duplicated region

Nan Zhang, Rami Bahsoon, Nikos Tziritas, Georgios Theodoropoulos

Digital Twins (DT) are essentially dynamic data-driven models that serve as real-time symbiotic "virtual replicas" of real-world systems. DT can leverage fundamentals of Dynamic Data-Driven Applications Systems (DDDAS) bidirectional symbiotic sensing feedback loops for its continuous updates. Sensing loops can consequently steer measurement, analysis and rec

Vladimir Tchuiev, Yakov Miron, Dotan Di-Castro

Object manipulation in cluttered scenes is a difficult and important problem in robotics. To efficiently manipulate objects, it is crucial to understand their surroundings, especially in cases where multiple objects are stacked one on top of the other, preventing effective grasping. We here present DUQIM-Net, a decision-making approach for object manipulatio

T. A. Nauryz

In this article we consider a mathematical model of an initial stage of closure electrical contact that involves a metallic vaporization after instantaneous exploding of contact due to arc ignition with power $P_0$ on fixed face $z=0$ and heat transfer in material with a variable cross section, when the radial component of the temperature gradient can be neg

Tuvy Lemberg, Vadim Indelman

Semantic simultaneous localization and mapping is a subject of increasing interest in robotics and AI that directly influences the autonomous vehicles industry, the army industries, and more. One of the challenges in this field is to obtain object classification jointly with robot trajectory estimation. Considering view-dependent semantic measurements, there

Antonio Blanca, Zongchen Chen, Daniel Štefankovič, Eric Vigoda

We study the identity testing problem for high-dimensional distributions. Given as input an explicit distribution $\mu$, an $\varepsilon>0$, and access to sampling oracle(s) for a hidden distribution $\pi$, the goal in identity testing is to distinguish whether the two distributions $\mu$ and $\pi$ are identical or are at least $\varepsilon$-far apart. When

Yuhang Jiang, Wenping Tong

At present, the identification of agricultural pests and diseases has the problem that the model is not lightweight enough and difficult to apply. Based on MobileNetV3, this paper introduces the Coordinate Attention block. The parameters of MobileNetV3-large are reduced by 22%, the model size is reduced by 19.7%, and the accuracy is improved by 0.92%. The pa

Yusheng Zhao, Jinyu Chen, Chen Gao, Wenguan Wang

Vision-language navigation is the task of directing an embodied agent to navigate in 3D scenes with natural language instructions. For the agent, inferring the long-term navigation target from visual-linguistic clues is crucial for reliable path planning, which, however, has rarely been studied before in literature. In this article, we propose a Target-Drive

Eric Buffenoir

In a recent paper, the author introduced an operational description of physical theories where probabilities are replaced by counterfactual statements belonging to a three-valued (i.e. possibilistic) semantic domain. The complete axiomatic of these Generalized possibilistic Theories is generalized and clarified in the present paper. The problem of bipartite

František Bartoš, Patrícia Martinková

Inter-rater reliability (IRR) is one of the commonly used tools for assessing the quality of ratings from multiple raters. However, applicant selection procedures based on ratings from multiple raters usually result in a binary outcome; the applicant is either selected or not. This final outcome is not considered in IRR, which instead focuses on the ratings

Sheng Chen

In this article, we establish the arithmetic purity of strong approximation for smooth loci of weighted projective spaces. By using this result and the descent method, we also prove that the arithmetic purity of strong approximation with Brauer-Manin obstruction holds for any smooth and complete toric variety.

Pranab Islam, Shaan Khosla, Arthur Lok, Mudit Saxena

Modern language models leverage increasingly large numbers of parameters to achieve performance on natural language understanding tasks. Ensembling these models in specific configurations for downstream tasks show even further performance improvements. In this paper, we perform an analysis of bagging language models and compare single language models to bagg

Yumeng Wang, Ziwei Zhu, Xuming He

In this paper, we propose a one-shot distributed learning algorithm via refitting bootstrap samples, which we refer to as ReBoot. ReBoot refits a new model to mini-batches of bootstrap samples that are continuously drawn from each of the locally fitted models. It requires only one round of communication of model parameters without much memory. Theoretically,

Yue Gao, Abby Stevens, Rebecca Willet, Garvesh Raskutti

As opaque predictive models increasingly impact many areas of modern life, interest in quantifying the importance of a given input variable for making a specific prediction has grown. Recently, there has been a proliferation of model-agnostic methods to measure variable importance (VI) that analyze the difference in predictive power between a full model trai

Ankit Khandelwal, M Girish Chandra

Quantum Image Processing is a recent highlight in the quantum computing field. All previous methods for representing the images as quantum states were defined using qubits. One Quantum Image Representation (QIR) method using qutrits is present in the literature. Inspired by the qubit methods and the higher state-space available for qutrits, multiple QIR meth

Meng Hua, Qingqing Wu, Wen Chen, Octavia A. Dobre

In this paper, an intelligent reflecting surface (IRS) is leveraged to enhance the physical layer security of an integrated sensing and communication (ISAC) system in which the IRS is deployed to not only assist the downlink communication for multiple users, but also create a virtual line-of-sight (LoS) link for target sensing. In particular, we consider a c

Yuan Xie, Shaohan Huang, Tianyu Chen, Furu Wei

Sparsely Mixture of Experts (MoE) has received great interest due to its promising scaling capability with affordable computational overhead. MoE converts dense layers into sparse experts, and utilizes a gated routing network to make experts conditionally activated. However, as the number of experts grows, MoE with outrageous parameters suffers from overfitt

Hidetoshi Kawaguchi, Yuichi Nakatani, Shogo Okada

As the importance of intrusion detection and prevention systems (IDPSs) increases, great costs are incurred to manage the signatures that are generated by malicious communication pattern files. Experts in network security need to classify signatures by importance for an IDPS to work. We propose and evaluate a machine learning signature classification model w

Naoki Sato

We are developing a positron-electron plasma trap based on a dipole magnetic field generated by a levitated superconducting magnet to investigate the physics of magnetized plasmas with mass symmetry as well as antimatter components. Such laboratory magnetosphere is deemed essential for the understanding of pair plasmas in astrophysical environments, such as

Ayan Chatterjee, Bestoun S. Ahmed

Ongoing research on anomaly detection for the Internet of Things (IoT) is a rapidly expanding field. This growth necessitates an examination of application trends and current gaps. The vast majority of those publications are in areas such as network and infrastructure security, sensor monitoring, smart home, and smart city applications and are extending into

A. D. Kerin, A. M. Martin

We consider a system of two identical fermions of general mass interacting with a third distinguishable particle via a contact interaction within an isotropic three-dimensional harmonic trap. We calculate time-dependent observables of the system after it is quenched in s-wave scattering length. To do this we use exact closed form mass-imbalanced hyperspheric

Mohammadi Zaki, Avinash Mohan, Aditya Gopalan, Shie Mannor

We consider an improper reinforcement learning setting where a learner is given $M$ base controllers for an unknown Markov decision process, and wishes to combine them optimally to produce a potentially new controller that can outperform each of the base ones. This can be useful in tuning across controllers, learnt possibly in mismatched or simulated environ

Long Chen, Yingying Xu, Fangyi Xu, Qian Hu

The deployment of the sensor nodes (SNs) always plays a decisive role in the system performance of wireless sensor networks (WSNs). In this work, we propose an optimal deployment method for practical heterogeneous WSNs which gives a deep insight into the trade-off between the reliability and deployment cost. Specifically, this work aims to provide the optima

Wai Weng Lo, Gayan K. Kulatilleke, Mohanad Sarhan, Siamak Layeghy

In this paper, we propose XG-BoT, an explainable deep graph neural network model for botnet node detection. The proposed model comprises a botnet detector and an explainer for automatic forensics. The XG-BoT detector can effectively detect malicious botnet nodes in large-scale networks. Specifically, it utilizes a grouped reversible residual connection with

Yuzheng Hu, Tianle Cai, Jinyong Shan, Shange Tang

We consider vertical logistic regression (VLR) trained with mini-batch gradient descent -- a setting which has attracted growing interest among industries and proven to be useful in a wide range of applications including finance and medical research. We provide a comprehensive and rigorous privacy analysis of VLR in a class of open-source Federated Learning

Haitian Zeng, Xin Yu, Jiaxu Miao, Yi Yang

We propose MHR-Net, a novel method for recovering Non-Rigid Shapes from Motion (NRSfM). MHR-Net aims to find a set of reasonable reconstructions for a 2D view, and it also selects the most likely reconstruction from the set. To deal with the challenging unsupervised generation of non-rigid shapes, we develop a new Deterministic Basis and Stochastic Deformati

Jagna Nieuwazny, Karol Nowakowski, Michal Ptaszynski, Fumito Masui

This paper is our attempt at answering a twofold question covering the areas of ethics and authorship analysis. Firstly, since the methods used for performing authorship analysis imply that an author can be recognized by the content he or she creates, we were interested in finding out whether it would be possible for an author identification system to correc

Zhonghua Wu, Yicheng Wu, Guosheng Lin, Jianfei Cai

Weakly supervised point cloud segmentation, i.e. semantically segmenting a point cloud with only a few labeled points in the whole 3D scene, is highly desirable due to the heavy burden of collecting abundant dense annotations for the model training. However, existing methods remain challenging to accurately segment 3D point clouds since limited annotated dat

Motonari Kambara, Komei Sugiura

Domestic service robots that support daily tasks are a promising solution for elderly or disabled people. It is crucial for domestic service robots to explain the collision risk before they perform actions. In this paper, our aim is to generate a caption about a future event. We propose the Relational Future Captioning Model (RFCM), a crossmodal language gen

Bradley Greig, Yuan-Sen Ting, Alexander A. Kaurov

Detecting the 21-cm hyperfine transition from neutral hydrogen in the intergalactic medium is our best probe for understanding the astrophysical processes driving the Epoch of Reionisation (EoR). The primary means for a detection of this 21-cm signal is through a statistical measurement of the spatial fluctuations using the 21-cm power spectrum (PS). However

Wenhao Ding, Haohong Lin, Bo Li, Ding Zhao

As a pivotal component to attaining generalizable solutions in human intelligence, reasoning provides great potential for reinforcement learning (RL) agents' generalization towards varied goals by summarizing part-to-whole arguments and discovering cause-and-effect relations. However, how to discover and represent causalities remains a huge gap that hinders

Hua Ma, Qun Li, Yifeng Zheng, Zhi Zhang

Federated Learning (FL), a distributed machine learning paradigm, has been adapted to mitigate privacy concerns for customers. Despite their appeal, there are various inference attacks that can exploit shared-plaintext model updates to embed traces of customer private information, leading to serious privacy concerns. To alleviate this privacy issue, cryptogr

Arkajit Aich

We have re-investigated Cosmology involving interaction between Dark matter and Dark Energy in the light of a new parametrization. The new parametrization is based on the hypothesis that when Dark matter and Dark Energy will interact, Dark matter will dilute in a different manner than standard non-interacting scenario. We re-built the Cosmological equations

Gopinath Chennupati, Milind Rao, Gurpreet Chadha, Aaron Eakin

Incremental learning is one paradigm to enable model building and updating at scale with streaming data. For end-to-end automatic speech recognition (ASR) tasks, the absence of human annotated labels along with the need for privacy preserving policies for model building makes it a daunting challenge. Motivated by these challenges, in this paper we use a clou

Dhiman Bhowmick, Hao Sun, Bo Yang, Pinaki Sengupta

We report the emergence of time-crystalline behavior in the \pi-Berry phase protected edge states of a Heisenberg ferromagnet in the presence of an external driving field. The magnon amplification due to the external field spontaneously breaks the discrete time-translational symmetry, resulting in a discrete time crystal with a period that is twice that of t

Félix Gaschi, François Plesse, Parisa Rastin, Yannick Toussaint

Some Transformer-based models can perform cross-lingual transfer learning: those models can be trained on a specific task in one language and give relatively good results on the same task in another language, despite having been pre-trained on monolingual tasks only. But, there is no consensus yet on whether those transformer-based models learn universal pat

Kazuki Okada, Hidekazu Tanaka, Nobuyuki Kurita, Daisuke Yamamoto

Ba$_3$CoSb$_2$O$_9$ approximates the two-dimensional spin-1/2 triangular-lattice Heisenberg antiferromagnet. This compound displays magnetic-field-induced quantum phase transitions, including the 1/3-magnetization-plateau, but its magnetization processes for the magnetic field $H$ parallel and perpendicular to the $c$ axis are different due to the weak easy-

Rakib Hyder, Ken Shao, Boyu Hou, Panos Markopoulos

Incremental Task learning (ITL) is a category of continual learning that seeks to train a single network for multiple tasks (one after another), where training data for each task is only available during the training of that task. Neural networks tend to forget older tasks when they are trained for the newer tasks; this property is often known as catastrophi

Naoki Takeishi, Marco Edoardo Rosti, Naoto Yokoyama, Luca Brandt

We present a numerical analysis of the rheology of a suspension of red blood cells (RBCs) for different volume fractions in a wall-bounded, effectively inertialess, small amplitude oscillatory shear (SAOS) flow for a wide range of applied frequencies. The RBCs are modeled as biconcave capsules, whose membrane is an isotropic and hyperelastic material followi

Shigeki Inoue, Xiaotian Si, Takashi Okamoto, Moka Nishigaki

We explore the capability of deep learning to classify cosmic structures. In cosmological simulations, cosmic volumes are segmented into voids, sheets, filaments and knots, according to the distribution and kinematics of dark matter (DM), and galaxies are also classified according to the segmentation. However, observational studies cannot adopt this classifi

Yijie Guo, Qiucheng Wu, Honglak Lee

Meta reinforcement learning (meta-RL) aims to learn a policy solving a set of training tasks simultaneously and quickly adapting to new tasks. It requires massive amounts of data drawn from training tasks to infer the common structure shared among tasks. Without heavy reward engineering, the sparse rewards in long-horizon tasks exacerbate the problem of samp

Bytasandram Yaswanth Reddy, Shiv Ram Dubey, Rakesh Kumar Sanodiya, Ravi Ranjan Prasad Karn

Existing data-dependent hashing methods use large backbone networks with millions of parameters and are computationally complex. Existing knowledge distillation methods use logits and other features of the deep (teacher) model and as knowledge for the compact (student) model, which requires the teacher's network to be fine-tuned on the context in parallel wi

Sarit Agami

Measurement error in the covariate of main interest (e.g. the exposure variable, or the risk factor) is common in epidemiologic and health studies. It can effect the relative risk estimator or other types of coefficients derived from the fitted regression model. In order to perform a measurement error analysis, one needs information about the error structure

Thang M. Pham, Seunghyun Yoon, Trung Bui, Anh Nguyen

While contextualized word embeddings have been a de-facto standard, learning contextualized phrase embeddings is less explored and being hindered by the lack of a human-annotated benchmark that tests machine understanding of phrase semantics given a context sentence or paragraph (instead of phrases alone). To fill this gap, we propose PiC -- a dataset of ~28

Sukmin Yun, Jaehyung Kim, Dongyoon Han, Hwanjun Song

Understanding temporal dynamics of video is an essential aspect of learning better video representations. Recently, transformer-based architectural designs have been extensively explored for video tasks due to their capability to capture long-term dependency of input sequences. However, we found that these Video Transformers are still biased to learn spatial

Sumanth Sadu, Shiv Ram Dubey, SR Sreeja

Convolutional neural networks (CNNs) have shown very appealing performance for many computer vision applications. The training of CNNs is generally performed using stochastic gradient descent (SGD) based optimization techniques. The adaptive momentum-based SGD optimizers are the recent trends. However, the existing optimizers are not able to maintain a zero

Felix Dobslaw, Robert Feldt, Francisco de Oliveira Neto

The input domain of software systems can typically be divided into sub-domains for which the outputs are similar. To ensure high quality it is critical to test the software on the boundaries between these sub-domains. Consequently, boundary value analysis and testing has been part of the toolbox of software testers for long and is typically taught early to s

Chuanhao Wei

In this paper, we use non-abelian Hodge Theory to study Kodaira type vanishings and its generalizations. In particular, we generalize Saito vanishing using Mixed Twistor D-modules. We also generalize it to a Kawamata-Viehweg type vanishing using Q-divisors, and we also prove a relative version for a projective morphism.

Rodrigo A. Castro, José M. Díaz-Báñez, Marco A. Heredia, Jorge Urrutia

Given a graph $G=(V,E)$ with costs on its edges, the minimum-cost edge cover problem consists of finding a subset of $E$ covering all vertices in $V$ at minimum cost. If $G$ is bipartite, this problem can be solved in time $O(|V|^3)$ via a well-known reduction to a maximum-cost matching problem on $G$. If in addition $V$ is a set of points on the Euclidean l

Hao Wang, Yaliang Gui, Chaobo Dong, Salem Altaleb

Topological insulators have shown great potential for future optoelectronic technology due to their extraordinary optical and electrical properties. Photodetectors, as one of the most widely used optoelectronic devices, are crucial for sensing, imaging, communication, and optical computing systems to convert optical signals to electrical signals. Here we exp

Arup Chattopadhyay, Guixiang Hong, Chandan Pradhan, Samya Kumar Ray

The existence of isometric embedding of $S_q^m$ into $S_p^n$, where $1\leq p\neq q\leq \infty$ and $m,n\geq 2$ has been recently studied in \cite{JFA22}. In this article, we extend the study of isometric embeddability beyond the above mentioned range of $p$ and $q$. More precisely, we show that there is no isometric embedding of the commutative quasi-Banach

Zhifeng Qiu, Wanxin Zeng, Dahua Liao, Ning Gui

Feature selection is an important process in machine learning. It builds an interpretable and robust model by selecting the features that contribute the most to the prediction target. However, most mature feature selection algorithms, including supervised and semi-supervised, fail to fully exploit the complex potential structure between features. We believe

Gabriele Benelli, Thomas Y. Chen, Javier Duarte, Matthew Feickert

The growing role of data science (DS) and machine learning (ML) in high-energy physics (HEP) is well established and pertinent given the complex detectors, large data, sets and sophisticated analyses at the heart of HEP research. Moreover, exploiting symmetries inherent in physics data have inspired physics-informed ML as a vibrant sub-field of computer scie

Nan Song, Chi Zhang, Guosheng Lin

Few-shot open-set recognition aims to classify both seen and novel images given only limited training data of seen classes. The challenge of this task is that the model is required not only to learn a discriminative classifier to classify the pre-defined classes with few training data but also to reject inputs from unseen classes that never appear at trainin

Rafael C. Cardoso, Angelo Ferrando, Fabio Papacchini, Mehrnoosh Askarpour

This volume contains the proceedings of the Second Workshop on Agents and Robots for reliable Engineered Autonomy (AREA 2022), co-located with the 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI-ECAI 2022). The AREA workshop brings together researchers from autonomous agents, s

Liu Yang, Keping Yu, Simon X. Yang, Chinmay Chakraborty

5G edge computing enabled Internet of Medical Things (IoMT) is an efficient technology to provide decentralized medical services while Device-to-device (D2D) communication is a promising paradigm for future 5G networks. To assure secure and reliable communication in 5G edge computing and D2D enabled IoMT systems, this paper presents an intelligent trust clou

Li Chen, Yadong Wu

Quantum dissipation arises from the unavoidable coupling between a quantum system and its surrounding environment, which is known as a major obstacle in the quantum processing of information. Apart from its existence, how to trace the dissipation from observational data is a crucial topic that may stimulate manners to suppress the dissipation. In this paper,

Wentong Li, Wenyu Liu, Jianke Zhu, Miaomiao Cui

In contrast to the fully supervised methods using pixel-wise mask labels, box-supervised instance segmentation takes advantage of the simple box annotations, which has recently attracted a lot of research attentions. In this paper, we propose a novel single-shot box-supervised instance segmentation approach, which integrates the classical level set model wit

Arjuna Madanayake, Viduneth Ariyarathna, Suresh Madishetty, Sravan Pulipati

Millimeter wave communications require multibeam beamforming in order to utilize wireless channels that suffer from obstructions, path loss, and multi-path effects. Digital multibeam beamforming has maximum degrees of freedom compared to analog phased arrays. However, circuit complexity and power consumption are important constraints for digital multibeam sy

Xiongkun Linghu, Yan Bai, Yihang Lou, Shengsen Wu

Few-Shot Classification(FSC) aims to generalize from base classes to novel classes given very limited labeled samples, which is an important step on the path toward human-like machine learning. State-of-the-art solutions involve learning to find a good metric and representation space to compute the distance between samples. Despite the promising accuracy per

Farid Jokar

A positive integer $n$ is said to be $k$-layered if its divisors can be partitioned into $k$ sets with equal sum. In this paper, we start the systematic study of these class of numbers. In particular, we state some algorithms to find some even $k$-layered numbers $n$ such that $2^{\alpha}n$ is a $k$-layered number for every positive integer $\alpha$. We also

Jianggang Zhu, Zheng Wang, Jingjing Chen, Yi-Ping Phoebe Chen

Real-world data typically follow a long-tailed distribution, where a few majority categories occupy most of the data while most minority categories contain a limited number of samples. Classification models minimizing cross-entropy struggle to represent and classify the tail classes. Although the problem of learning unbiased classifiers has been well studied

Ryohei Matsumiya, Hiroaki Akatsuka, Chris P. Bidinosti, Charles A. Davis

The TRIUMF Ultra-Cold Advanced Neutron (TUCAN) collaboration aims at a precision neutron electric dipole moment (nEDM) measurement with an uncertainty of $10^{-27}\,e\cdot\mathrm{cm}$, which is an order-of-magnitude better than the current nEDM upper limit and enables us to test Supersymmetry. To achieve this precision, we are developing a new high-intensity

Menglin Yang, Zhihao Li, Min Zhou, Jiahong Liu

Considering the prevalence of the power-law distribution in user-item networks, hyperbolic space has attracted considerable attention and achieved impressive performance in the recommender system recently. The advantage of hyperbolic recommendation lies in that its exponentially increasing capacity is well-suited to describe the power-law distributed user-it

Ali Ayub, Chrystopher L. Nehaniv, Kerstin Dautenhahn

Assistive robots operating in household environments would require items to be available in the house to perform assistive tasks. However, when these items run out, the assistive robot must remind its user to buy the missing items. In this paper, we present a computational architecture that can allow a robot to learn personalized contextual knowledge of a ho

Xulong Shi, Zhi Qi, Jiaxuan Cai, Keqi Fu

Binary neural network (BNN) is an extreme quantization version of convolutional neural networks (CNNs) with all features and weights mapped to just 1-bit. Although BNN saves a lot of memory and computation demand to make CNN applicable on edge or mobile devices, BNN suffers the drop of network performance due to the reduced representation capability after bi

Chengxu Liu, Huan Yang, Jianlong Fu, Xueming Qian

Video frame interpolation (VFI) aims to synthesize an intermediate frame between two consecutive frames. State-of-the-art approaches usually adopt a two-step solution, which includes 1) generating locally-warped pixels by flow-based motion estimations, 2) blending the warped pixels to form a full frame through deep neural synthesis networks. However, due to

Zhen-Ye Huang, Xin-Yi Fan, Jianwen Zhou, Hai-Jun Zhou

Predictive coding is a promising theoretical framework in neuroscience for understanding information transmission and perception. It posits that the brain perceives the external world through internal models and updates these models under the guidance of prediction errors. Previous studies on predictive coding emphasized top-down feedback interactions in hie

Lei Tan, Pingyang Dai, Rongrong Ji, Yongjian Wu

Although person re-identification has achieved an impressive improvement in recent years, the common occlusion case caused by different obstacles is still an unsettled issue in real application scenarios. Existing methods mainly address this issue by employing body clues provided by an extra network to distinguish the visible part. Nevertheless, the inevitab

Fei Pan, Sungsu Hur, Seokju Lee, Junsik Kim

Open compound domain adaptation (OCDA) considers the target domain as the compound of multiple unknown homogeneous subdomains. The goal of OCDA is to minimize the domain gap between the labeled source domain and the unlabeled compound target domain, which benefits the model generalization to the unseen domains. Current OCDA for semantic segmentation methods

Aboozar Moradi, Mohammad Reza Yousefi

In this paper, a three-dimensional finite element method is developed to simulate the heat distribution in the human eye with different types of tumors to understand the effect of tumors on heat distribution in the human eye. The human eye is modeled as a composition of several homogeneous regions and the physical and thermal properties of each region used i

Sunghyun Hong

We prove the invariant of the symplectic capacity for the Zakharov system on a torus. If the Zakharov solution map is well-defined, then it can be regarded as a symplectomorphism. Thus, we first show the global well-posedness via the local well-posedness and the conservation law. The invariant of the symplectic capacity can be obtained using an approximation

Jia Sun, Robert A. Gutermuth, Hongchi Wang, Shuinai Zhang

Young stellar clusters are believed to inherit the spatial distribution like hierarchical structures of their natal molecular cloud during their formation. However, the change of the structures between the cloud and the young clusters is not well constrained observationally. We select the W40 - Serpens South region (~ 7 $\times$ 9 pc$^{2}$) of the Aquila Rif

Jia Sun, Robert A. Gutermuth, Hongchi Wang, Miaomiao Zhang

Active star forming regions are excellent laboratories for studying the origins and evolution of young stellar object (YSO) clustering. The W40 - Serpens South region is such a region, and we compile a super near-and-mid-infrared catalog of point sources in it, based on deep NIR observations of CFHT in combination with 2MASS, UKIDSS, and Spitzer catalogs. Fr

Philip Caesar Flores, Eric A. Galapon

The tunneling time problem earlier studied in Phys. Rev. Lett 108 170402 (2012) using a non-relativistic time-of-arrival (TOA) operator predicted that tunneling time is instantaneous. This raises the question on whether instantaneous tunneling time is a consequence of using a non-relativistic theory. Here, we extend the analysis by proposing a formalism on t

Peter Athron, Csaba Balazs, Adriano Cherchiglia, Douglas H. J. Jacob

GM2Calc is a leading tool for calculating precise contributions to $a_\mu$ in the Minimal Supersymmetric Standard Model. In this proceeding we detail GM2Calc version 2 where it is extended so it can calculate two-loop contributions to $a_\mu$ in the Two-Higgs Doublet Model (2HDM), based on the work in Ref. [1]. The 2HDM is a simple model, yet it is one of th

Tu N. Nguyen, Kashyab J. Ambarani, My T. Thai

5G radio access network (RAN) with network slicing methodology plays a key role in the development of the next-generation network system. RAN slicing focuses on splitting the substrate's resources into a set of self-contained programmable RAN slices. Leveraged by network function virtualization (NFV), a RAN slice is constituted by various virtual network fun

Jiaqi Li, Lei Feng, Chinmayee Panigrahi, Jiarong Hong

Current experiment techniques for vorticity measurement suffer from limited spatial and temporal resolution to resolve the small-scale eddy dynamics in turbulence. In this study, we develop a new method for direct vorticity measurement in fluid flows based on digital inline holography (DIH). The DIH system utilizes a collimated laser beam to illuminate the t

Gauthier Mukerjee, Kay Joerg Wiese

There are two main universality classes for depinning of elastic interfaces in disordered media: quenched Edwards-Wilkinson (qEW), and quenched Kardar-Parisi-Zhang (qKPZ). The first class is relevant as long as the elastic force between two neighboring sites on the interface is purely harmonic, and invariant under tilting. The second class applies when the e

David Roodman

Duflo (2001) exploits a 1970s schooling expansion in Indonesia to estimate impacts on schooling and labor outcomes, as well as the returns to schooling. I correct data errors, adjust for potential sources of bias, follow up later in life, and check sensitivity to two specification choices that are not explained in the original text and that are applied to so

Jiashu Wu, Hao Dai, Yang Wang, Yong Zhang

In this paper, we study a data caching problem in the cloud environment, where multiple frequently co-utilised data items could be packed as a single item being transferred to serve a sequence of data requests dynamically with reduced cost. To this end, we propose an online algorithm with respect to a homogeneous cost model, called PackCache, that can levera

Tu N. Nguyen, Dung H. P. Nguyen, Dang H. Pham, Bing-Hong Liu

There will be a fast-paced shift from conventional network systems to novel quantum networks that are supported by the quantum entanglement and teleportation, key technologies of the quantum era, to enable secured data transmissions in the next-generation of the Internet. Despite this prospect, migration to quantum networks cannot be done at once, especially

Joshua Baines, Matt Visser

Despite some 60 years of work on the subject of the Kerr rotating black hole there is as yet no widely accepted physically based and pedagogically viable ansatz suitable for deriving the Kerr solution without significant computational effort. (Typically involving computer-aided symbolic algebra.) Perhaps the closest one gets in this regard is the Newman-Jani

Wanchaloem Wunkaew, Yuqing Liu, Kirill V. Golubnichiy

In the paper written by Klibanov et al, it proposes a novel method to calculate implied volatility of a European stock options as a solution to ill-posed inverse problem for the Black-Scholes equation. In addition, it proposes a trading strategy based on the difference between implied volatility of the option and the volatility of the underlying stock. In ad

Sovanlal Mondal

In this paper, we will introduce the `grid method' to prove that the extreme case of oscillation occurs for the averages obtained by sampling a flow along the sequence of times of the form $\{n^\alpha: n\in \mathbb{N}\}$, where $\alpha$ is a positive non-integer rational number. Such behavior of a sequence is known as the `strong sweeping out property'. By u

Christopher Wiedeman, Ge Wang

Artificial intelligence has made great progress in medical data analysis, but the lack of robustness and trustworthiness has kept these methods from being widely deployed. As it is not possible to train networks that are accurate in all scenarios, models must recognize situations where they cannot operate confidently. Bayesian deep learning methods sample th

Chengfei Xie, Gennian Ge

In this paper, we give some new lower bounds for the kissing number of $\ell_p$-spheres. These results improve the previous work due to Xu (2007). Our method is based on coding theory.

Linh Le, Tu N. Nguyen, Kun Suo, Jing He

5G radio access network (RAN) slicing aims to logically split an infrastructure into a set of self-contained programmable RAN slices, with each slice built on top of the underlying physical RAN (substrate) is a separate logical mobile network, which delivers a set of services with similar characteristics. Each RAN slice is constituted by various virtual netw

Charles F. Jekel, Dane M. Sterbentz, Sylvie Aubry, Youngsoo Choi

Richtmyer-Meshkov Instability (RMI) is a complicated phenomenon that occurs when a shockwave passes through a perturbed interface. Over a thousand hydrodynamic simulations were performed to study the formation of RMI for a parameterized high velocity impact. Deep learning was used to learn the temporal mapping of initial geometric perturbations to the full-f

Y. Shen, A. V. Artemyev, X. -J. Zhang, V. Angelopoulos

Electron injections are critical processes associated with magnetospheric substorms, which deposit significant electron energy into the ionosphere. Although wave scattering of $<$10 keV electrons during injections has been well studied, the link between magnetotail electron injections and energetic ($\geq$100 keV) electron precipitation remains elusive. Usin

Joonho Lee, Adam Rettig, Xintian Feng, Evgeny Epifanovsky

In this work, we developed and showcased the occ-RI-K algorithm to compute the exact exchange contribution in density functional calculations of solids near the basis set limit. Within the gaussian planewave (GPW) density fitting, our algorithm achieves a 1-2 orders of magnitude speedup compared to conventional GPW algorithms. Since our algorithm is well-sui

Sara E. Duval, Arthur D. Bosman, Edwin A. Bergin

The chemical composition of the inner region of protoplanetary disks can trace the composition of planetary building material. The exact elemental composition of the inner disk has not yet been measured and tensions between models and observations still exist. Recent advancements have shown UV-shielding to be able to increase emission of organics. Here, we e

Rubem Mondaini, Sabyasachi Tarat, Richard T. Scalettar

Initial characterizations of the fermion sign problem focused on its evolution with spatial lattice size $L$ and inverse temperature $\beta$, emphasizing the implications of the exponential nature of the decay of the average sign $\langle {\cal S} \rangle$ for the complexity of its solution and associated limitations of quantum Monte Carlo studies of strongl

Nirmalya Thakur, Chia Y. Han

This paper presents a multifunctional interdisciplinary framework that makes four scientific contributions towards the development of personalized ambient assisted living, with a specific focus to address the different and dynamic needs of the diverse aging population in the future of smart living environments. First, it presents a probabilistic reasoning-ba

Jiaming Liang, Vincent Guigues, Renato D. C. Monteiro

This paper considers optimization problems where the objective is the sum of a function given by an expectation and a closed convex composite function, and proposes stochastic composite proximal bundle (SCPB) methods for solving it. Complexity guarantees are established for them without requiring knowledge of parameters associated with the problem instance.

Georg Raithel, Alisher Duspayev, Bineet Dash, Sebastian C. Carrasco

We present possible design concepts for a tractor atom interferometer (TAI) based on three-dimensional confinement and transport of ultracold atoms. The confinement reduces device size and wave-packet dispersion, enables arbitrary holding times, and facilitates control to create complex trajectories that allow for optimization to cancel unwanted sensitivity,

Bozhi Wu, Shangqing Liu, Ruitao Feng, Xiaofei Xie

With the rapid increasing number of open source software (OSS), the majority of the software vulnerabilities in the open source components are fixed silently, which leads to the deployed software that integrated them being unable to get a timely update. Hence, it is critical to design a security patch identification system to ensure the security of the utili

Zeyan Li, Nengwen Zhao, Mingjie Li, Xianglin Lu

Fault localization is challenging in an online service system due to its monitoring data's large volume and variety and complex dependencies across or within its components (e.g., services or databases). Furthermore, engineers require fault localization solutions to be actionable and interpretable, which existing research approaches cannot satisfy. Therefore