November 2022 arXiv papers — page 151

Kouki Nakamura, Takahiro Miyoshi, Chiho Nonaka, Hiroyuki R. Takahashi

We construct a relativistic resistive magneto-hydrodynamic (RRMHD) numerical simulation code for high-energy heavy-ion collisions. We split the system of differential equations into two parts, a non-stiff and a stiff part. For the non-stiff part, we evaluate the numerical flux using HLL approximated Riemann solver and execute the time integration by the seco

Sándor Demes, François Lique, Alexandre Faure, Floris F. S. van der Tak

Hydronium (H$_3$O$^+$) was first detected in 1986 in interstellar molecular clouds. It was reported in many galactic diffuse and dense regions, as well as in extragalactic sources. H$_3$O$^+$ plays a major role both in interstellar oxygen and water chemistry. However, despite the large number of H$_3$O$^+$ observations, its collisional excitation was investi

Sebastian Babiński, Andrzej Grzesik

In 1959 Erd\H{o}s and Gallai proved the asymptotically optimal bound for the maximum number of edges in graphs not containing a path of a fixed length. Here we study a rainbow version of their theorem, in which one considers $k \geq 1$ graphs on a common set of vertices not creating a path having edges from different graphs and asks for the maximum number of

Kyusik Cho, Suhyeon Lee, Hongje Seong, Euntai Kim

The network trained for domain adaptation is prone to bias toward the easy-to-transfer classes. Since the ground truth label on the target domain is unavailable during training, the bias problem leads to skewed predictions, forgetting to predict hard-to-transfer classes. To address this problem, we propose Cross-domain Moving Object Mixing (CMOM) that cuts s

Andhika Kiswandhi, Tomotaka Ochi, Toshihiro Taen, Mitsuyuki Sato

We performed thermoelectric Hall conductivity $\alpha_{xy}$ measurements on single-crystal graphite in the quantum limit up to 13 T. Both electrical and thermoelectric transport measurements were performed on the same crystal to extract pure $\alpha_{xy}$, avoiding any sample quality dependence. The $\alpha_{xy}$ converges to a plateau in the quantum limit w

J. Vaubaillon

In 2019 a claim was made that the CNEOS 2014-01-08 meteor is interstellar. However, apparent interstellar meteors have been detected for decades. Moreover, they are expected from any meteor observation survey, as a natural consequence of measurement error propagation. Here we examine if enough scientific data were published to identify the orbital and physic

Kunyi Liu, Fei Lu, Yuan Li

We report a strategy to design nanoscale cold-source field-effect transistors (CS-FETs) with bias-independent sub-60 mV/dec subthreshold swing (SS). By first-principles calculations and quantum-transport simulations, we reveal that the energy alignment of density of states (DOS) between the drain and source electrodes is critical to achieving bias-independen

Joram Meron, Victor Guimarães

Semantic Machines (SM) have introduced the use of the dataflow (DF) paradigm to dialogue modelling, using computational graphs to hierarchically represent user requests, data, and the dialogue history [Semantic Machines et al. 2020]. Although the main focus of that paper was the SMCalFlow dataset (to date, the only dataset with "native" DF annotations), they

Hirotoshi Hirai

We propose an excited-state molecular dynamics simulation method based on variational quantum algorithms at a computational cost comparable to that of ground-state simulations. We utilize the feature that excited states can be obtained as metastable states in the restricted variational quantum eigensolver calculation with a hardware-efficient ansatz. To demo

Yin Zhu, Qiuqiang Kong, Junjie Shi, Shilei Liu

Binaural rendering of ambisonic signals is of broad interest to virtual reality and immersive media. Conventional methods often require manually measured Head-Related Transfer Functions (HRTFs). To address this issue, we collect a paired ambisonic-binaural dataset and propose a deep learning framework in an end-to-end manner. Experimental results show that n

Alper Kayabaşı, Gülin Tüfekci, İlkay Ulusoy

Few-shot segmentation aims to devise a generalizing model that segments query images from unseen classes during training with the guidance of a few support images whose class tally with the class of the query. There exist two domain-specific problems mentioned in the previous works, namely spatial inconsistency and bias towards seen classes. Taking the forme

Omid Gheibi, Danny Weyns

Recently, machine learning (ML) has become a popular approach to support self-adaptation. ML has been used to deal with several problems in self-adaptation, such as maintaining an up-to-date runtime model under uncertainty and scalable decision-making. Yet, exploiting ML comes with inherent challenges. In this paper, we focus on a particularly important chal

Zhenwei Zhu, Liying Yang, Xuxin Lin, Chaohao Jiang

Deep learning technology has made great progress in multi-view 3D reconstruction tasks. At present, most mainstream solutions establish the mapping between views and shape of an object by assembling the networks of 2D encoder and 3D decoder as the basic structure while they adopt different approaches to obtain aggregation of features from several views. Amon

Christian Bargetz, Emir Medjic, Katriin Pirk

We study the generic behavior of the method of successive approximations for set-valued mappings in separable Banach spaces. We consider the case of nonexpansive mappings with convex and compact point images and show that for the typical such mapping and typical points of its domain the sequence of successive approximations is unique and converges to a fixed

Kepeng Xu, Li Xu, Gang He, Chang Wu

HDR(High Dynamic Range) video can reproduce realistic scenes more realistically, with a wider gamut and broader brightness range. HDR video resources are still scarce, and most videos are still stored in SDR (Standard Dynamic Range) format. Therefore, SDRTV-to-HDRTV Conversion (SDR video to HDR video) can significantly enhance the user's video viewing experi

Ziyan Yin, Zhe Wang, Jun Li, Ming Ding

The explosive growth of dynamic and heterogeneous data traffic brings great challenges for 5G and beyond mobile networks. To enhance the network capacity and reliability, we propose a learning-based dynamic time-frequency division duplexing (D-TFDD) scheme that adaptively allocates the uplink and downlink time-frequency resources of base stations (BSs) to me

Zihan Zang, Haoqiang Wang, Yunpeng Xu

The miniaturization and mobility of computer vision systems are limited by the heavy computational burden and the size of optical lenses. Here, we propose to use a ultra-thin diffractive optical element to implement passive optical convolution. A division adjoint opto-electronic co-design method is also proposed. In our simulation experiments, the first few

Tao Yu, Kento Kajiwara, Kiyomichi Araki, Kei Sakaguchi

One of the key enablers for the realization of a variety of unmanned aerial vehicle (UAV)-based systems is the high-performance communication system linking many UAVs and ground station. We have proposed a spectrum-efficient full-duplex directional-antennas-equipped multi-UAV communication system with low hardware complexity to address the issues of low spec

E. Iani, A. Zanella, J. Vernet, J. Richard

The origin of the Lyman-${\alpha}$ (Ly${\alpha}$) emission in galaxies is a long-standing issue: despite several processes known to originate this line (e.g. AGN, star formation, cold accretion, shock heating), it is difficult to discriminate among these phenomena based on observations. Recent studies have suggested that the comparison of the ultraviolet (UV

Feng Hou, Yao Zhang, Yang Liu, Jin Yuan

Due to domain shift, deep neural networks (DNNs) usually fail to generalize well on unknown test data in practice. Domain generalization (DG) aims to overcome this issue by capturing domain-invariant representations from source domains. Motivated by the insight that only partial parameters of DNNs are optimized to extract domain-invariant representations, we

Karthik Dharmarajan, Will Panitch, Muyan Jiang, Kishore Srinivas

Vascular shunt insertion is a fundamental surgical procedure used to temporarily restore blood flow to tissues. It is often performed in the field after major trauma. We formulate a problem of automated vascular shunt insertion and propose a pipeline to perform Automated Vascular Shunt Insertion (AVSI) using a da Vinci Research Kit. The pipeline uses a learn

Jiehua Zhang, Xueyang Zhang, Zhuo Su, Zitong Yu

Developing lightweight Deep Convolutional Neural Networks (DCNNs) and Vision Transformers (ViTs) has become one of the focuses in vision research since the low computational cost is essential for deploying vision models on edge devices. Recently, researchers have explored highly computational efficient Binary Neural Networks (BNNs) by binarizing weights and

Inwoo Hwang, Sangjun Lee, Yunhyeok Kwak, Seong Joon Oh

Neural networks trained with ERM (empirical risk minimization) sometimes learn unintended decision rules, in particular when their training data is biased, i.e., when training labels are strongly correlated with undesirable features. To prevent a network from learning such features, recent methods augment training data such that examples displaying spurious

Vesna Iršič, Bojan Mohar, Alexandra Wesolek

The recently introduced variation of the game of cops and robber is played on geodesic spaces. In this paper we establish some general strategies for the players, in particular the generalized radial strategy and the covering space strategy. Those strategies are then applied to the game on the $n$-dimensional ball, the sphere, and the torus.

Il-Young Jeong, Jeongsoo Park

This paper describes a pipeline for collecting acoustic scene data by using crowdsourcing. The detailed process of crowdsourcing is explained, including planning, validation criteria, and actual user interfaces. As a result of data collection, we present CochlScene, a novel dataset for acoustic scene classification. Our dataset consists of 76k samples collec

Léopold Gramaize, Adam C. Schneider, Federico Marocco, Jacqueline K. Faherty

We present the discovery of CWISE J151044.74$-$524923.5, a wide low-mass companion to the nearby ($\sim$24.7 pc) system L 262-74, which was identified through the Backyard Worlds: Planet 9 citizen science project. We detail the properties of the system, and we assess that this companion is a mid-L dwarf, which will need to be verified spectroscopically. With

Junya Hara, Yuichi Tanaka

In this paper, we consider multi-channel sampling (MCS) for graph signals. We generally encounter full-band graph signals beyond the bandlimited one in many applications, such as piecewise constant/smooth and union of bandlimited graph signals. Full-band graph signals can be represented by a mixture of multiple signals conforming to different generation mode

Ubaid Ullah Hafeez, Martin Maas, Mustafa Uysal, Richard McDougall

Data processing frameworks such as Apache Beam and Apache Spark are used for a wide range of applications, from logs analysis to data preparation for DNN training. It is thus unsurprising that there has been a large amount of work on optimizing these frameworks, including their storage management. The shift to cloud computing requires optimization across all

Masahiko Ueda

Imitation is simple behavior which uses successful actions of others in order to deal with one's own problems. Because success of imitation generally depends on whether profit of an imitating agent coincides with those of other agents or not, game theory is suitable for specifying situations where imitation can be successful. One of the concepts describing s

Dong Hoon Lee, Sungik Choi, Hyunwoo Kim, Sae-Young Chung

This paper proposes Mutual Information Regularized Assignment (MIRA), a pseudo-labeling algorithm for unsupervised representation learning inspired by information maximization. We formulate online pseudo-labeling as an optimization problem to find pseudo-labels that maximize the mutual information between the label and data while being close to a given model

Zixuan Xiao, Shengshi Yao, Jincheng Dai, Sixian Wang

In this paper, we propose a new class of high-efficiency semantic coded transmission methods for end-to-end speech transmission over wireless channels. We name the whole system as deep speech semantic transmission (DSST). Specifically, we introduce a nonlinear transform to map the speech source to semantic latent space and feed semantic features into source-

Lillian B. Hughes, Zhiran Zhang, Chang Jin, Simon A. Meynell

Systems of spins engineered with tunable density and reduced dimensionality enable a number of advancements in quantum sensing and simulation. Defects in diamond, such as nitrogen-vacancy (NV) centers and substitutional nitrogen (P1 centers), are particularly promising solid-state platforms to explore. However, the ability to controllably create coherent, tw

Mingyu Zhu, Jiapeng Luo, Wendong Mao, Zhongfeng Wang

Deep Forest is a prominent machine learning algorithm known for its high accuracy in forecasting. Compared with deep neural networks, Deep Forest has almost no multiplication operations and has better performance on small datasets. However, due to the deep structure and large forest quantity, it suffers from large amounts of calculation and memory consumptio

Abhishek Kumar Mehta

A class of wormhole solutions is constructed that has restricted polar degrees of freedom to achieve a gateway-like configuration. This compels the use of distribution-valued metrics and connections which further compels the use of neutrix product of distributions, to define distribution-valued curvature, Einstein tensor, and other relevant quantities. The s

Yuna Morishima, Hiroshi Sudou, Aya Yamauchi, Yoshiaki Taniguchi

We present observational results of water vapor maser emission with our high-sensitivity 22 GHz VLBI imaging of the Seyfert galaxy NGC 1068. In this galaxy, there are the following four nuclear radio sources; NE, C, S1, and S2. Among them, the S1 component has been identified as the nucleus while the C component has been considered as attributed to the radio

Bunyo Hatsukade, Tetsuya Hashimoto, Yuu Niino, Tzu-Yin Hsu

We report the properties of molecular gas in a sample of six host galaxies of fast radio bursts (FRBs) obtained from CO observations with the Atacama Large Millimeter/submillimeter Array (FRBs 20180924B, 20190102C, and 20190711A) and results of one non-detection in a dwarf galaxy (FRB20121102A) and two events detected in M81 (FRB20200120E) and the Milky Way

Haodi Jiang, Qin Li, Zhihang Hu, Nian Liu

Solar activity is usually caused by the evolution of solar magnetic fields. Magnetic field parameters derived from photospheric vector magnetograms of solar active regions have been used to analyze and forecast eruptive events such as solar flares and coronal mass ejections. Unfortunately, the most recent solar cycle 24 was relatively weak with few large fla

Quy Pham, Thang Pham, Chun-Yen Shen

In this paper, we provide estimates for the additive discretized energy of \[\sum_{c\in C} |\{(a_1, a_2, b_1, b_2)\in A^2\times B^2: |(a_1 +cb_1) - (a_2 + cb_2)|\le \delta\}|_{\delta},\] that depend on non-concentration conditions of the sets. Our proof follows the Guth-Katz-Zahl approach (2021) with appropriate changes along the way clarifying and optimizin

Girish Kumar

We discuss new physics (NP) contributions to kaon mixing parameter $\varepsilon_K$, direct CP violation parameter $\varepsilon^\prime/\varepsilon$ of $K\to \pi\pi$, and rare decays $K^+\to \pi^+\nu\bar\nu$, $K_L\to \pi^0\nu\bar\nu$ and $K_{L, S}\to \mu^+ \mu^- $ in the context of general two Higgs doublet model. We focus on contributions of top quark related

Xinyi Fang, Yusuke Nakayama, Peng Ren

In this paper, we characterize homogeneous arithmetically Cohen-Macaulay (ACM) bundles over exceptional Grassmannians in terms of their associated data. We show that there are only finitely many irreducible homogeneous ACM bundles by twisting line bundles over exceptional Grassmannians. As a consequence, we prove that some exceptional Grassmannians are of wi

Anne Kohlbrenner, Ben Kaiser, Kartikeya Kandula, Rebecca Weiss

Empirical technology and society research is in a methodological crisis. Problems increasingly involve closed platforms, targeted content, and context-specific behavior. Prevailing research methods, such as surveys, tasks, and web crawls, pose design and ecological validity limitations. Deploying studies in participant browsers and devices is a promising dir

Hiroshi Shiraishi, Tomoshige Nakamura, Ryotato Shibuki

We discuss an application of Generalized Random Forests (GRF) proposed by Athey et al.(2019) to quantile regression for time series data. We extracted the theoretical results of the GRF consistency for i.i.d. data to time series data. In particular, in the main theorem, based only on the general assumptions for time series data in Davis and Nielsen (2020), a

Ali Borji

We show that it is possible to predict which deep network has generated a given logit vector with accuracy well above chance. We utilize a number of networks on a dataset, initialized with random weights or pretrained weights, as well as fine-tuned networks. A classifier is then trained on the logit vectors of the trained set of this dataset to map the logit

Jan Harold Alcantara, Ching-pei Lee

We consider the projected gradient algorithm for the nonconvex best subset selection problem that minimizes a given empirical loss function under an $\ell_0$-norm constraint. Through decomposing the feasible set of the given sparsity constraint as a finite union of linear subspaces, we present two acceleration schemes with global convergence guarantees, one

Martin J. Falk, Jiayi Wu, Ayanna Matthews, Vedant Sachdeva

Evolution in time-varying environments naturally leads to adaptable biological systems that can easily switch functionalities. Advances in the synthesis of environmentally-responsive materials therefore open up the possibility of creating a wide range of synthetic materials which can also be trained for adaptability. We consider high-dimensional inverse prob

Changyuan Qiu, Winston Wu, Xinliang Frederick Zhang, Lu Wang

Prior work on ideology prediction has largely focused on single modalities, i.e., text or images. In this work, we introduce the task of multimodal ideology prediction, where a model predicts binary or five-point scale ideological leanings, given a text-image pair with political content. We first collect five new large-scale datasets with English documents a

Gonzalo Muñoz, David Salas, Anton Svensson

We study linear bilevel programming problems whose lower-level objective is given by a random cost vector with known distribution. We consider the case where this distribution is nonatomic, allowing to reformulate the problem of the leader using the Bayesian approach in the sense of Salas and Svensson (2023), with a decision-dependent distribution that conce

Dilhan Manawadu, Timothy N. Georges, William Barford

We describe our dynamical simulations of the excited states of the carotenoid, neurosporene, following its photoexcitation into the 'bright' (nominally $1^1B_u^+$) state. We employ the adaptive tDMRG method on the UV model of $\pi$-conjugated electrons and use the Ehrenfest equations of motion to simulate the coupled nuclei dynamics. To account for the exper

Vinayak Trivedi, Tushar Gurjar, Sumaiya Shaikh, Saketh Maddamsetty

In systems owned by normal end-users, many times security attacks are mounted by sneaking in malicious applications or exploiting existing software vulnerabilities through security non-conforming actions of users. Virtualization approaches can address this problem by providing a quarantine environment for applications, malicious devices, and device drivers,

Stephen MacNeil, Andrew Tran, Arto Hellas, Joanne Kim

Advances in natural language processing have resulted in large language models (LLMs) that are capable of generating understandable and sensible written text. Recent versions of these models, such as OpenAI Codex and GPT-3, can generate code and code explanations. However, it is unclear whether and how students might engage with such explanations. In this pa

Anushka Jha, Preetika Karnal, Joelle Frechette

Elastomers swollen with non-polar fluids show potential as anti-adhesive materials. We study the effect of oil fraction and contact time on the adhesion between swollen spherical probes of PDMS (polydimethylsiloxane) and flat glass surfaces. The PDMS probes are swollen with pre-determined amount of 10 cSt silicone oil to span the range where the PDMS is flui

Nusrat Jahan Prottasha, Saydul Akbar Murad, Abu Jafar Md Muzahid, Masud Rana

Machine learning is the study of computer algorithms that can automatically improve based on data and experience. Machine learning algorithms build a model from sample data, called training data, to make predictions or judgments without being explicitly programmed to do so. A variety of wellknown machine learning algorithms have been developed for use in the

Uicheol Jang, Hongsu Kim

Relativistic jets are observed around accreting black holes, from stellar mass to super massive black holes. But Its origin has not been fully understood. Although Blanford-Payne process has been considered as most reliable theory of jet production It has no observational proof or supports. In this problem, We suggested the way to display that Bardeen-Petter

Tomoki Tokuda, Hiromichi Nagao

The detection of earthquakes is a fundamental prerequisite for seismology and contributes to various research areas, such as forecasting earthquakes and understanding the crust/mantle structure. Recent advances in machine learning technologies have enabled the automatic detection of earthquakes from waveform data. In particular, various state-of-the-art deep

Caitao Zhan, Himanshu Gupta

A quantum sensor (QS) is able to measure various physical phenomena with extreme sensitivity. QSs have been used in several applications such as atomic interferometers, but few applications of a quantum sensor network (QSN) have been proposed or developed. We look at a natural application of QSN -- localization of an event (in particular, of a wireless signa

Lei Wu, Xia Wang, Ting Wang, Jun Jiang

The energy levels and electric dipole ($E1$) matrix elements of the ground state and low-lying excited states of Be atoms are calculated using the relativistic configuration interaction plus core polarization (RCICP) method. The static and dynamic $E1$, magnetic dipole ($M1$) and electric quadrupole ($E2$) polarizabilities as well as the hyperpolarizabilitie

Nikita Evseev

We study continuous mappings on the Heisenberg group that up to a time change preserve horizontal Brownian motion. It is proved that only harmonic morphisms possess this property.

N. Rahman, M. Kalam, A. Das, S. Islam

Einstein-Gauss-Bonnet gravity is a generalization of the general relativity to higher dimensions in which the first and second-order terms correspond to general relativity and Einstein-Gauss-Bonnet gravity respectively. We construct a new class of five-dimensional (5D) thin-shell wormholes by the `Cut-Paste' technique from black holes in Einstein-Gauss-Bonne

Guy Blanc, Caleb Koch, Jane Lange, Carmen Strassle

In the certification problem, the algorithm is given a function $f$ with certificate complexity $k$ and an input $x^\star$, and the goal is to find a certificate of size $\le \text{poly}(k)$ for $f$'s value at $x^\star$. This problem is in $\mathsf{NP}^{\mathsf{NP}}$, and assuming $\mathsf{P} \ne \mathsf{NP}$, is not in $\mathsf{P}$. Prior works, dating back

Zhengyong Huang, Sijuan Zou, Guoshuai Wang, Zixiang Chen

Achieving accurate and automated tumor segmentation plays an important role in both clinical practice and radiomics research. Segmentation in medicine is now often performed manually by experts, which is a laborious, expensive and error-prone task. Manual annotation relies heavily on the experience and knowledge of these experts. In addition, there is much i

Kaiwen Hou, Guillaume Rabusseau

Various forms of regularization in learning tasks strive for different notions of simplicity. This paper presents a spectral regularization technique, which attaches a unique inductive bias to sequence modeling based on an intuitive concept of simplicity defined in the Chomsky hierarchy. From fundamental connections between Hankel matrices and regular gramma

Kaiqi Jiang, Dhruv Malik, Yuanzhi Li

Adaptive optimization methods are well known to achieve superior convergence relative to vanilla gradient methods. The traditional viewpoint in optimization, particularly in convex optimization, explains this improved performance by arguing that, unlike vanilla gradient schemes, adaptive algorithms mimic the behavior of a second-order method by adapting to t

Omer Atasi, Mithun Ravisankar, Dominique Legendre, Roberto Zenit

Bubbles appear when a carbonated drink is poured in a glass. Very stable bubble chains are clearly observed in champagne, showing an almost straight line from microscopic nucleation sites from which they are continuously formed. In some other drinks such as soda, such chains are not straight (not stable). Considering pair interactions for spherical clean bub

Yoshiharu Mori

A new scheme of low energy negative muon source with the muon catalyzed fusion (MuCF) is described. In the MuCF reaction process, muonic helium ions ($\mu$He$^+$) are created. By re-accelerating and stripping $\mu$He$^+$ ions, a low emittance negative muon beam is generated.

Jonathan P. Marshall, Steve Ertel, Eric Birtcil, Eva Villaver

The persistence of planetary systems after their host stars evolve into their post-main sequence phase is poorly constrained by observations. Many young white dwarf systems exhibit infrared excess emission and/or spectral absorption lines associated with a reservoir of dust (or planetesimals) and its accretion. However, most white dwarfs are too cool to suff

Bandhav Veluri, Justin Chan, Malek Itani, Tuochao Chen

We present the first neural network model to achieve real-time and streaming target sound extraction. To accomplish this, we propose Waveformer, an encoder-decoder architecture with a stack of dilated causal convolution layers as the encoder, and a transformer decoder layer as the decoder. This hybrid architecture uses dilated causal convolutions for process

Eric Gautier, Christiern Rose

We propose and implement an approach to inference in linear instrumental variables models which is simultaneously robust and computationally tractable. Inference is based on self-normalization of sample moment conditions, and allows for (but does not require) many (relative to the sample size), weak, potentially invalid or potentially endogenous instruments,

Mutsumi Minoguchi, Shin-ichi Sasa

When a spatially localized stress is applied to a growing one-dimensional interface, the interface deforms. This deformation is described by the effective surface tension representing the stiffness of the interface. We present that the stiffness exhibits divergent behavior in the large system size limit for a growing interface with thermal noise, which has n

Junghyun Koo, Marco A. Martínez-Ramírez, Wei-Hsiang Liao, Stefan Uhlich

We propose an end-to-end music mixing style transfer system that converts the mixing style of an input multitrack to that of a reference song. This is achieved with an encoder pre-trained with a contrastive objective to extract only audio effects related information from a reference music recording. All our models are trained in a self-supervised manner from

Om Rajput, Suyash Nigam, M. J. Chowdhury, Kayalvizhi Jayavel

Currently, the IoT ecosystem is comprised of fully connected smart devices that exchange data to provide more automated, precise, and fast decisions. This idealised situation can only be accomplished if a system for data transactions is processed efficiently and security is ensured with high scalability and practicability. The integrity of data must be maint

Rana Salal Ali, Benjamin Zi Hao Zhao, Hassan Jameel Asghar, Tham Nguyen

Named entity recognition models (NER), are widely used for identifying named entities (e.g., individuals, locations, and other information) in text documents. Machine learning based NER models are increasingly being applied in privacy-sensitive applications that need automatic and scalable identification of sensitive information to redact text for data shari

Alex Junho Lee, Younggun Cho, Hyun Myung

Robotics has been widely applied in smart construction for generating the digital twin or for autonomous inspection of construction sites. For example, for thermal inspection during concrete curing, continual monitoring of the concrete temperature is required to ensure concrete strength and to avoid cracks. However, buildings are typically too large to be mo

Zheng Sun, Zhiqi Wang, Junjia Liu, Miao Li

Bimanual activities like coffee stirring, which require coordination of dual arms, are common in daily life and intractable to learn by robots. Adopting reinforcement learning to learn these tasks is a promising topic since it enables the robot to explore how dual arms coordinate together to accomplish the same task. However, this field has two main challeng

Zhixin Zhang, Zhiyong Chen

This paper investigates the asymptotical cooperative cruise fault tolerant control problem for multiple high-speed trains consisting of multiple carriages in the presence of actuator faults. A distributed state-fault observer utilizing the structural information of faults is designed to achieve asymptotical estimation of states and faults of each carriage. T

Sadamichi Maekawa, Takashi Kikkawa, Hiroyuki Chudo, Jun'ichi Ieda

Along with the progress of spin science and spintronics research, the flow of electron spins, (i.e. spin current), has attracted interest. New phenomena and electronic states were explained in succession using the concept of spin current. Moreover, as many of the conventionally known spintronics phenomena became well organized based on spin current, it has r

Sheng Cheng

Inferring the quality of network services is the vital basis of optimization for network operators. However, prevailing real-time video streaming applications adopt encryption for security, leaving it a problem to extract Quality of Service (QoS) indicators of real-time video. In this paper, we propose DaI, a traffic-based real-time video quality estimator.

Preeti Cowan, Ian A. Bond, Napoleon H. Reyes

Asteroids are an indelible part of most astronomical surveys though only a few surveys are dedicated to their detection. Over the years, high cadence microlensing surveys have amassed several terabytes of data while scanning primarily the Galactic Bulge and Magellanic Clouds for microlensing events and thus provide a treasure trove of opportunities for scien

A. C. Bezerra, F. Manfio

In this paper we establish conditions on the length of the traceless part of the second fundamental form of a complete constant mean curvature hypersurface immersed in a space of constant sectional curvature in order to show that it is totally umbilical.

Jamie Fravel, Robert Hildebrand, Laurel Travis

We study continuous, equality knapsack problems with uniform separable, non-convex objective functions that are continuous, antisymmetric about a point, and have concave and convex regions. For example, this model captures a simple allocation problem with the goal of optimizing an expected value where the objective is a sum of cumulative distribution functio

Yongle Yu, Hailin Luo

We elucidate the microscopic quantum mechanism of superfluid $^4$He by uncovering a novel characteristic of its many-body energy levels. At temperature below the transition point, the system's low-lying levels exhibit a fundamental grouping behavior, wherein each level belongs exclusively to a single group. In a superflow state, the system establishes therma

Daniel Varivoda, Rongzhi Dong, Sadman Sadeed Omee, Jianjun Hu

Uncertainty quantification (UQ) has increasing importance in building robust high-performance and generalizable materials property prediction models. It can also be used in active learning to train better models by focusing on getting new training data from uncertain regions. There are several categories of UQ methods each considering different types of unce

Zhipeng Huang, Kevin S. Xu

Kidney transplantation is the preferred treatment for people suffering from end-stage renal disease. Successful kidney transplants still fail over time, known as graft failure; however, the time to graft failure, or graft survival time, can vary significantly between different recipients. A significant biological factor affecting graft survival times is the

Yifang Chen, Karthik Sankararaman, Alessandro Lazaric, Matteo Pirotta

Active learning with strong and weak labelers considers a practical setting where we have access to both costly but accurate strong labelers and inaccurate but cheap predictions provided by weak labelers. We study this problem in the streaming setting, where decisions must be taken \textit{online}. We design a novel algorithmic template, Weak Labeler Active

Linsong Liu, Kejia Chen, Zheng Liu

Directed graphs model asymmetric relationships between nodes and research on directed graph embedding is of great significance in downstream graph analysis and inference. Learning source and target embedding of nodes separately to preserve edge asymmetry has become the dominant approach, but also poses challenge for learning representations of low or even ze

Krishan Rana, Ming Xu, Brendan Tidd, Michael Milford

Skill-based reinforcement learning (RL) has emerged as a promising strategy to leverage prior knowledge for accelerated robot learning. Skills are typically extracted from expert demonstrations and are embedded into a latent space from which they can be sampled as actions by a high-level RL agent. However, this skill space is expansive, and not all skills ar

Shunsuke Horii

We study the problem of sequential experimental design to estimate the parametric component of a partially linear model with a Gaussian process prior. We consider an active learning setting where an experimenter adaptively decides which data to collect to achieve their goal efficiently. The experimenter's goals may vary, such as reducing the classification e

Yuta Murotani, Natsuki Kanda, Tomohiro Fujimoto, Takuya Matsuda

We experimentally elucidate the origin of the anomalous Hall conductivity in a three-dimensional Dirac semimetal, Cd$_3$As$_2$, driven by circularly polarized light. Using time-resolved terahertz Faraday rotation spectroscopy, we determine the transient Hall conductivity spectrum with special attention to its sign. Our results clearly show the dominance of d

Yinan Hu, Quanyan Zhu

The detection and discrimination of quantum states serve a crucial role in quantum signal processing, a discipline that studies methods and techniques to process signals that obey the quantum mechanics frameworks. However, just like classical detection, evasive behaviors also exist in quantum detection. In this paper, we formulate an adversarial quantum dete

Ju-ho Kim, Jungwoo Heo, Hyun-seo Shin, Chan-yeong Lim

The advent of hyper-scale and general-purpose pre-trained models is shifting the paradigm of building task-specific models for target tasks. In the field of audio research, task-agnostic pre-trained models with high transferability and adaptability have achieved state-of-the-art performances through fine-tuning for downstream tasks. Nevertheless, re-training

Qiang Li, Tian Miao, Huimin Zhang, Weiyan Lin

Strongly correlated materials often exhibit an electronic phase separation (EPS) phenomena whose domain pattern is random in nature. The ability to control the spatial arrangement of the electronic phases at microscopic scales is highly desirable for tailoring their macroscopic properties and/or designing novel electronic devices. Here we report the formatio

Viranjan Bhattacharyya, Ardalan Vahidi

A new motion planning framework for automated highway merging is presented in this paper. To plan the merge and predict the motion of the neighboring vehicle, the ego automated vehicle solves a joint optimization of both vehicle costs over a receding horizon. The non-convex nature of feasible regions and lane discipline is handled by introducing integer deci

Alessandro Lovato, Travis Dore, Robert D. Pisarski, Bjoern Schenke

Since the release of the 2015 Long Range Plan in Nuclear Physics, major events have occurred that reshaped our understanding of quantum chromodynamics (QCD) and nuclear matter at large densities, in and out of equilibrium. The US nuclear community has an opportunity to capitalize on advances in astrophysical observations and nuclear experiments and engage in

Chunming Jiang, Yilei Zhang

Spiking neural networks (SNNs) attract great attention due to their low power consumption, low latency, and biological plausibility. As they are widely deployed in neuromorphic devices for low-power brain-inspired computing, security issues become increasingly important. However, compared to deep neural networks (DNNs), SNNs currently lack specifically desig

Kenny Young, Aditya Ramesh, Louis Kirsch, Jürgen Schmidhuber

Model-Based Reinforcement Learning (RL) is widely believed to have the potential to improve sample efficiency by allowing an agent to synthesize large amounts of imagined experience. Experience Replay (ER) can be considered a simple kind of model, which has proved effective at improving the stability and efficiency of deep RL. In principle, a learned paramet

Wei Ren, Gang Yang

For any group $G$, the Gorenstein homological dimension ${\rm Ghd}_RG$ is defined to be the Gorenstein flat dimension of the coefficient ring $R$, which is considered as an $RG$-module with trivial group action. We prove that ${\rm Ghd}_RG < \infty$ if and only if the Gorenstein flat dimension of any $RG$-module is finite, if and only if there exists an $R$-

Masaaki Tsubouchi, Nobuhisa Ishii, Yuji Kagotani, Rumi Shimizu

We perform a beat-frequency-resolved analysis for two-dimensional electronic spectroscopy using a high-speed and stable 2D electronic spectrometer and few-cycle visible laser pulses to disentangle the vibrational coherences in an artificial fluorescent protein. We develop a highly stable ultrashort light source that generates 5.3-fs visible pulses with a pul

Chengcheng Ma, Yang Liu, Jiankang Deng, Lingxi Xie

Pretrained vision-language models (VLMs) such as CLIP have shown impressive generalization capability in downstream vision tasks with appropriate text prompts. Instead of designing prompts manually, Context Optimization (CoOp) has been recently proposed to learn continuous prompts using taskspecific training data. Despite the performance improvements on down

Suraj Yerramilli, Akshay Iyer, Wei Chen, Daniel W. Apley

Real engineering and scientific applications often involve one or more qualitative inputs. Standard Gaussian processes (GPs), however, cannot directly accommodate qualitative inputs. The recently introduced latent variable Gaussian process (LVGP) overcomes this issue by first mapping each qualitative factor to underlying latent variables (LVs), and then uses

Aya Bamba, Brian J. Williams

Although only a small fraction of stars end their lives as supernovae, all supernovae leave behind a supernova remnant (SNR), an expanding shock wave that interacts with the surrounding medium, heating the gas and seeding the cosmos with elements forged in the progenitor In this chapter, we introduce the basic properties of galactic and extragalactic SNRs (S

Lu Liu, Zheng-Wen Long

In this paper, we study the interaction of spin 1/2 Dirac particles with the Hylleraas potential based on the noncommutative space framework. Solving the first-order correction of the energy level caused by the noncommutation parameter $\theta$ in the wave equation. The problem of energy level correction under the action of additional weak electric field is