November 2022 arXiv papers — page 114

D. E. Morosan, J. Pomoell, A. Kumari, R. Vainio

Context. Some of of the most prominent sources for energetic particles in our Solar System are huge eruptions of magnetised plasma from the Sun called coronal mass ejections (CMEs), which usually drive shocks that accelerate charged particles up to relativistic energies. In particular, energetic electron beams can generate radio bursts through the plasma emi

Jiashu Lou, Leyi Cui, Wenxuan Qiu

In this paper, we tested several sparse optimization algorithms based on the public dataset of the DREAM5 Gene Regulatory Network Inference Challenge. And we find that introducing 20% of the regulatory network as a priori known data can provide a basis for parameter selection of inference algorithms, thus improving prediction efficiency and accuracy. In addi

Matthew Bailes

Fast radio bursts (FRBs) are millisecond-timescale bursts of coherent radio emission that are luminous enough to be detectable at cosmological distances. In this review I describe the discovery of FRBs, subsequent advances in our understanding of them, and future prospects. Thousands of potentially observable FRBs reach Earth every day; they probably origina

Sachin Vaidya, Christina Jörg, Kyle Linn, Megan Goh

Waves propagating in certain one-dimensional quasiperiodic lattices are known to exhibit a sharp localization transition. We theoretically predict and experimentally observe that the localization of light in one-dimensional photonic quasicrystals may be followed by a second delocalization transition for some states on increasing quasiperiodic modulation stre

Haotian Tian, Xiaochun Fang

In this paper, we give some properties of the fixed point algebra and the crossed product of a unital separable simple infinite dimensional C*-algebra by an action of a second-countable compact group with the tracial Rokhlin property with comparison that could be deduced from the properties of its original algebra: (1) stable rank one; (2) real rank zero; (3

Ziyi Xu, Xue Cheng

This paper studies the influences of a high-frequency trader (HFT) on a large trader whose future trading is predicted by the former. We conclude that HFT always front-runs and the large trader is benefited when: (1) there is sufficient high-speed noise trading; (2) HFT's prediction is vague enough. Besides, we find surprisingly that (1) making HFT's predict

Mateusz Urbańczyk

A categorical approach to study model comparison games in terms of comonads was recently initiated by Abramsky et al. In this work, we analyse games that appear naturally in the context of description logics and supplement them with suitable game comonads. More precisely, we consider expressive sublogics of ALCSelfIbO, namely, the logics that extend ALC with

Yuxi Liu, Shaowen Qin, Antonio Jimeno Yepes, Wei Shao

Predicting the health risks of patients using Electronic Health Records (EHR) has attracted considerable attention in recent years, especially with the development of deep learning techniques. Health risk refers to the probability of the occurrence of a specific health outcome for a specific patient. The predicted risks can be used to support decision-making

Rathish Das, John Iacono, Yakov Nekrich

The $B^{\epsilon}$-tree [Brodal and Fagerberg 2003] is a simple I/O-efficient external-memory-model data structure that supports updates orders of magnitude faster than B-tree with a query performance comparable to the B-tree: for any positive constant $\epsilon<1$ insertions and deletions take $O(\frac{1}{B^{1-\epsilon}}\log_{B}N)$ time (rather than $O(\log

Alexander N. Poddubny

We study theoretically quantum states of two repelling spinless particles in a one-dimensional tight-binding model with simple periodic lattice and open boundary conditions. We demonstrate, that when the particles are not identical, their interaction drives nontrivial correlated two-particle states, such as bound states, edge states as well as interaction-in

David Criens, Mikhail Urusov

The separating time for two probability measures on a filtered space is an extended stopping time which captures the phase transition between equivalence and singularity. More specifically, two probability measures are equivalent before their separating time and singular afterwards. In this paper, we investigate the separating time for two laws of general on

Tianrui Wang, Xie Chen, Zhuo Chen, Shu Yu

In recent years, self-supervised learning (SSL) has achieved tremendous success in various speech tasks due to its power to extract representations from massive unlabeled data. However, compared with tasks such as speech recognition (ASR), the improvements from SSL representation in speech separation (SS) and enhancement (SE) are considerably smaller. Based

Pascal Maier, Yung Chen, Yilin Xu, Yiyang Bao

Combining optical gain in direct-bandgap III-V materials with tunable optical feedback offered by advanced photonic integrated circuits is key to chip-scale external-cavity lasers (ECL), offering wideband tunability along with low optical linewidths. External feedback circuits can be efficiently implemented using low-loss silicon nitride (Si$_{3}$N$_{4}$) wa

Shuoguang Yang, Yuhao Yan, Xiuneng Zhu, Qiang Sun

Sparse regression has been a popular approach to perform variable selection and enhance the prediction accuracy and interpretability of the resulting statistical model. Existing approaches focus on offline regularized regression, while the online scenario has rarely been studied. In this paper, we propose a novel online sparse linear regression framework for

Mohammed Tobrej, Binay Rai, Manoj Ghising, Ruchi Tamang

We report X-ray observations of the High Mass X-ray Binary (HMXB) pulsar 4U 1907+09. Spectral and Timing analysis of the source has been performed using NuSTAR observation. Timing analysis of the photon events revealed the coherent X-ray pulsation of the source with a pulse period of $442.92\;\pm\;0.03$ s. It is observed that the source is spinning down at a

A. Karmakar, P. Datta, Soumik Bhattacharya, Shabir Dar

The spectroscopic studies have identified few Actinide and Lanthanide nuclei of the periodic table, which can assume the pear shape. The low frequency collective rotation of these nuclei has been established by determining the band structure of the excited levels and their gamma decay rates. In this article, we report the rotation of a pear-shaped nucleus in

W. Yao, A. Higginson, J. -R. Marquès, P. Antici

The propagation and energy coupling of intense laser beams in plasmas are critical issues in laser-driven inertial confinement fusion. Applying magnetic fields to such a setup has been evoked to enhance fuel confinement and heating, and mitigate laser energy losses. Here we report on experimental measurements demonstrating improved transmission and increased

Marcin Pruszczyk, Paweł Jakubczyk

We consider the thermal Casimir effect in ideal Bose gases, where the dispersion relation involves both terms quadratic and quartic in momentum. We demonstrate that if macroscopic objects are immersed in such a fluid in spatial dimensionality $d\in\{3,7, 11, \dots\}$ and at the critical temperature $T_c$, the Casimir force acting between them is characterize

Ke Liao, Wei Wang, Armagan Elibol, Lingzhong Meng

Machine learning has been widely used in healthcare applications to approximate complex models, for clinical diagnosis, prognosis, and treatment. As deep learning has the outstanding ability to extract information from time series, its true capabilities on sparse, irregularly sampled, multivariate, and imbalanced physiological data are not yet fully explored

Yuzhou Gu, Zhao Song

Semidefinite programming is a fundamental tool in optimization and theoretical computer science. It has been extensively used as a black-box for solving many problems, such as embedding, complexity, learning, and discrepancy. One natural setting of semidefinite programming is the small treewidth setting. The best previous SDP solver under small treewidth set

Xie-Yu Li, Wei-Yang Yu, Ming-Chung Chang

For unstructured experimental units, the minimum aberration due to Fries and Hunter (1980) is a popular criterion for choosing regular fractional factorial designs. Following which, many related studies have focused on multi-stratum factorial designs, in which multiple error terms arise from the complicated structures of experimental units. Chang and Cheng (

Jiawei Sun, Chengran Yuan, Shuo Sun, Zhiyang Liu

Accurately predicting interactive road agents' future trajectories and planning a socially compliant and human-like trajectory accordingly are important for autonomous vehicles. In this paper, we propose a planning-centric prediction neural network, which takes surrounding agents' historical states and map context information as input, and outputs the joint

Alistair Moffat, Joel Mackenzie

In a dynamic retrieval system, documents must be ingested as they arrive, and be immediately findable by queries. Our purpose in this paper is to describe an index structure and processing regime that accommodates that requirement for immediate access, seeking to make the ingestion process as streamlined as possible, while at the same time seeking to make th

Shuyu Tan, Quentin A. Parker, Albert Zijlstra, Andreas Ritter

This is the first in a series of papers that present sets of different results for 136 compact, known planetary nebulae within a 10 $\times$ 10 degree region of the Galactic bulge. We use a large, previously unpublished sample of our own extensive ESO 8 m VLT deep imaging and spectroscopic data. This is combined with archival deep $\textit{HST}$ imaging wher

Yuhao Yi, Liren Shan, Shijie Wang, Philip E. Paré

This paper studies efficient algorithms for dynamic curing policies and the corresponding network design problems to guarantee the fast extinction of epidemic spread in a susceptible-infected-susceptible (SIS) model. We consider a Markov process-based SIS epidemic model. We provide a computationally efficient curing algorithm based on the curing policy propo

Hao Zheng, Hui Lin, Rong Zhao, Luping Shi

The binding problem is one of the fundamental challenges that prevent the artificial neural network (ANNs) from a compositional understanding of the world like human perception, because disentangled and distributed representations of generative factors can interfere and lead to ambiguity when complex data with multiple objects are presented. In this paper, w

Matyas Barczy, Zsolt Páles

We introduce the notions of generalized and weighted generalized $\psi$-estimators as unique points of sign change of some appropriate functions, and we give necessary as well as sufficient conditions for their existence. We also derive a set of sufficient conditions under which the so-called $\psi$-expectation function has a unique point of sign change. We

Tushar Kanti Bhowmik, Saswata Halder, T. P. Sinha

We have presented the synthesis, structural, and magnetic properties from the experimental point of view. Then we verified our experimental observation by studying the electronic and magnetic properties of Al-doped LaMnO3 from the first principle density functional theory (DFT) and Monte-Carlo simulation. We have synthesized the LaAlxMn1-xO3 (x= 0.05, 0.15,

Lingtong Kong, Jinfeng Liu, Jie Yang

Recently, flow-based frame interpolation methods have achieved great success by first modeling optical flow between target and input frames, and then building synthesis network for target frame generation. However, above cascaded architecture can lead to large model size and inference delay, hindering them from mobile and real-time applications. To solve thi

Hyolim Kang, Hanjung Kim, Joungbin An, Minsu Cho

Temporal Action Localization (TAL) methods typically operate on top of feature sequences from a frozen snippet encoder that is pretrained with the Trimmed Action Classification (TAC) tasks, resulting in a task discrepancy problem. While existing TAL methods mitigate this issue either by retraining the encoder with a pretext task or by end-to-end fine-tuning,

Noboru Ito

By integrating curvatures multiplied non-trivial densities, we introduce an integral expression of the Arnold strangeness that is a celebrated plane curve invariant. The key is a partition function by Shumakovitch to reformulate Arnold strangeness. Our integrating curvatures suggests a quantized Arnold strangeness which Taylor expansion includes the rotation

D. Fernández-Arenas, E. Carrasco, R. Terlevich, E. Terlevich

We present a spatially resolved spectroscopic study for the metal poor HII galaxy J084220+115000 using MEGARA Integral Field Unit observations at the Gran Telescopio Canarias. We estimated the gas metallicity using the direct method for oxygen, nitrogen and helium and found a mean value of 12+$\log$(O/H)=$8.03\pm$0.06, and integrated electron density and tem

Rickie Xian, Sarah Rauscher

The paradigm that the primary amino acid sequence prescribes structure and thus function has for a long time been central to the understanding of protein science. Though the theory is supported by the behaviour of most structured proteins, it loses much of its applicability when discussing intrinsically disordered proteins (IDPs). These peculiar proteins, wh

Florian Breuer, Fabien Pazuki

We obtain an explicit upper bound on the size of the coefficients of the elliptic modular polynomials $\Phi_N$ for any $N\geq1$. These polynomials vanish at pairs of $j$-invariants of elliptic curves linked by cyclic isogenies of degree $N$. The main term in the bound is asymptotically optimal as $N$ tends to infinity.

Lingtong Kong, Jie Yang

Recent works have shown that optical flow can be learned by deep networks from unlabelled image pairs based on brightness constancy assumption and smoothness prior. Current approaches additionally impose an augmentation regularization term for continual self-supervision, which has been proved to be effective on difficult matching regions. However, this metho

Zhibin Zou, Maqsood Careem, Aveek Dutta, Ngwe Thawdar

The high mobility, density and multi-path evident in modern wireless systems makes the channel highly non-stationary. This causes temporal variation in the channel distribution that leads to the existence of time-varying joint interference across multiple degrees of freedom (DoF, e.g., users, antennas, frequency and symbols), which renders conventional preco

Ke-Jung Chen, Ching-Yao Tang, Daniel J. Whalen, Meng-Yuan Ho

Massive Pop III stars can die as energetic supernovae that enrich the early universe with metals and determine the properties of the first galaxies. With masses of about $10^9$ Msun at $z \gtrsim 10$, these galaxies are believed to be the ancestors of the Milky Way. This paper investigates the impact of Pop III supernova remnants (SNRs) from both Salpeter-li

Xiaoping Wu, Ying Fu, Changzheng Qu

Considered herein is the reducibility of the quasi-periodically time dependent linear dynamical system with a diophantine frequency vector $\omega \in \mathcal{O}_0 \subset \mathbb{R}^{\nu}$. This system is derived from linearizing the dispersive Camassa-Holm equation with unbounded perturbations at a small amplitude quasi-periodic function. It is shown that

Xuming Hu, Shiao Meng, Chenwei Zhang, Xiangli Yang

Information Extraction (IE) aims to extract structured information from heterogeneous sources. IE from natural language texts include sub-tasks such as Named Entity Recognition (NER), Relation Extraction (RE), and Event Extraction (EE). Most IE systems require comprehensive understandings of sentence structure, implied semantics, and domain knowledge to perf

Ehsan-Ul Haq, Lik-Hang Lee, Gareth Tyson, Reza Hadi Mogavi

There has been a significant expansion in the use of online social networks (OSNs) to support people experiencing mental health issues. This paper studies the role of Instagram influencers who specialize in coaching people with mental health issues. Using a dataset of 97k posts, we characterize such users' linguistic and behavioural features. We explore how

Yuchong Yao, Nandakishor Desai, Marimuthu Palaniswami

Masked image modelling (e.g., Masked AutoEncoder) and contrastive learning (e.g., Momentum Contrast) have shown impressive performance on unsupervised visual representation learning. This work presents Masked Contrastive Representation Learning (MACRL) for self-supervised visual pre-training. In particular, MACRL leverages the effectiveness of both masked im

Yuanyuan Tian, Wenwen Li

Knowledge graph technology is considered a powerful and semantically enabled solution to link entities, allowing users to derive new knowledge by reasoning data according to various types of reasoning rules. However, in building such a knowledge graph, events modeling, such as that of disasters, is often limited to single, isolated events. The linkages among

Li Zeng, Stein B. Jacobsen

We explain the overall equilibrium-temperature-dependent trend in the exoplanet mass-radius diagram, using the escape mechanisms of hydrogen and relevant volatiles, and the chemical equilibrium calculation of molecular hydrogen (H$_2$) break-up into atomic hydrogen (H). We identify two Cosmic Hydrogen and Ice Loss Lines (CHILLs) in the mass-radius diagram. G

Na Lei, Zezeng Li, Zebin Xu, Ying Li

Intelligent Mesh Generation (IMG) represents a novel and promising field of research, utilizing machine learning techniques to generate meshes. Despite its relative infancy, IMG has significantly broadened the adaptability and practicality of mesh generation techniques, delivering numerous breakthroughs and unveiling potential future pathways. However, a not

M. Laine

Studies of dark matter annihilation through an $s$-channel resonance are often based on recipes such as a narrow width approximation or real intermediate state subtraction. We review a recipe-free formalism that can be implemented at the NLO level in the full theory, and ensures the cancellation of mass singularities. Its basic ingredients can be formulated

Tatiana Likhomanenko, Ronan Collobert, Navdeep Jaitly, Samy Bengio

Continuous pseudo-labeling (PL) algorithms such as slimIPL have recently emerged as a powerful strategy for semi-supervised learning in speech recognition. In contrast with earlier strategies that alternated between training a model and generating pseudo-labels (PLs) with it, here PLs are generated in end-to-end manner as training proceeds, improving trainin

Grace L. Causer, Alfonso Chacon, André Heinemann, Christian Pfleiderer

Magnetic small-angle neutron scattering (SANS) is ideally suited to provide direct, reciprocal-space information of long-wavelength magnetic modulations, such as helicoids, solitons, merons, or skyrmions. SANS of such structures in thin films or micro-structured bulk materials is strongly limited by the tiny scattering volume vis a vis the prohibitively larg

Kathryn Grasha

The TYPHOON program is producing an atlas of spectroscopic data cubes of 44 large-angular-sized galaxies with complete spatial coverage from 3650-9000 A. This survey provides an unparalleled opportunity to study variations in the interstellar medium (ISM) properties within individual HII regions across the entire star-forming disks of nearby galaxies. This c

Sonain Jamil, Md. Jalil Piran, Oh-Jin Kwon

As a special type of transformer, Vision Transformers (ViTs) are used to various computer vision applications (CV), such as image recognition. There are several potential problems with convolutional neural networks (CNNs) that can be solved with ViTs. For image coding tasks like compression, super-resolution, segmentation, and denoising, different variants o

V. Ugrinovskii, M. R. James

This paper introduces a $H_\infty$-like methodology of coherent filtering for equalization of passive linear quantum systems to help mitigate degrading effects of quantum communication channels. For such systems, which include a wide range of linear quantum optical devices and signals, we seek to find a near optimal equalizing filter which is itself a passiv

Heng Yu, Xiaolan Hou

Hierarchical clustering is a common algorithm in data analysis. It is unique among many clustering algorithms in that it draws dendrograms based on the distance of data under a certain metric, and group them. It is widely used in all areas of astronomical research, covering various scales from asteroids and molecular clouds, to galaxies and galaxy cluster. T

Yong Hong, Deren Li, Shupei Luo, Xin Chen

This study proposes an improved end-to-end multi-target tracking algorithm that adapts to multi-view multi-scale scenes based on the self-attentive mechanism of the transformer's encoder-decoder structure. A multi-dimensional feature extraction backbone network is combined with a self-built semantic raster map, which is stored in the encoder for correlation

Fam Le Kien, Sile Nic Chormaic, Thomas Busch

We study the directional dependence of the coupling between a nanofiber-guided light field and a two-level atom with an electric quadrupole transition. We examine the situation where the atom lies on the fiber transverse axis $x$, the quantization axis for the atomic internal states is the other orthogonal transverse axis $y$, the atomic upper and lower leve

Nikhil Biju, Huazhen Fang

Advanced battery management is to lithium-ion battery systems as the brain is to the human body. Its performance rests on the use of battery models that are both fast and accurate. However, mainstream equivalent circuit models and electrochemical models have yet to meet this need well, due to struggle with either predictive accuracy or computational complexi

Jing Lu, Eric Y. Hsiao, Mark M. Phillips, Christopher R. Burns

We present the largest and most homogeneous collection of near-infrared (NIR) spectra of Type Ia supernovae (SNe Ia): 339 spectra of 98 individual SNe obtained as part of the Carnegie Supernova Project-II. These spectra, obtained with the FIRE spectrograph on the 6.5 m Magellan Baade telescope, have a spectral range of 0.8--2.5 $\mu$m. Using this sample, we

Zeyu Hu, Xuyang Bai, Runze Zhang, Xin Wang

We propose LiDAL, a novel active learning method for 3D LiDAR semantic segmentation by exploiting inter-frame uncertainty among LiDAR frames. Our core idea is that a well-trained model should generate robust results irrespective of viewpoints for scene scanning and thus the inconsistencies in model predictions across frames provide a very reliable measure of

Markus Pollnau

We obtain the conditions constituting a thermal equilibrium between two energy levels: (i) the total energy is equal in both levels; (ii) the temperature is equal for all particles. Exploiting these conditions, we derive a differential equation of thermal equilibrium that holds for all particles. Integration delivers the Boltzmann distribution, suggesting th

Logan Morrison, Stefano Profumo, Bibhushan Shakya

We provide a comprehensive study of observable spectra from dark matter pair-annihilation or decay into sterile (right-handed) neutrinos. This occurs, for instance, in neutrino portal dark matter models, where a sterile neutrino acts as the portal between dark matter and the Standard Model sector. The subsequent decays of right-handed neutrinos produce detec

Yuanzhe Jia, Weixuan Wu, Feiqi Cao, Soyeon Caren Han

In-game toxic language becomes the hot potato in the gaming industry and community. There have been several online game toxicity analysis frameworks and models proposed. However, it is still challenging to detect toxicity due to the nature of in-game chat, which has extremely short length. In this paper, we describe how the in-game toxic language shared task

Linmei Hu, Zeyi Liu, Ziwang Zhao, Lei Hou

Pre-trained Language Models (PLMs) which are trained on large text corpus via self-supervised learning method, have yielded promising performance on various tasks in Natural Language Processing (NLP). However, though PLMs with huge parameters can effectively possess rich knowledge learned from massive training text and benefit downstream tasks at the fine-tu

Go Ogiya, Frank C. van den Bosch, Andreas Burkert, Xi Kang

It has been suggested that a trail of diffuse galaxies, including two dark matter deficient galaxies (DMDGs), in the vicinity of NGC1052 formed because of a high-speed collision between two gas-rich dwarf galaxies, one bound to NGC1052 and the other one on an unbound orbit. The collision compresses the gas reservoirs of the colliding galaxies, which in turn

Debdipta Goswami

This paper considers the problem of data-driven prediction of partially observed systems using a recurrent neural network. While neural network based dynamic predictors perform well with full-state training data, prediction with partial observation during training phase poses a significant challenge. Here a predictor for partial observations is developed usi

Shanshan Song, Jiangyun Li, Jing Wang, Yuanxiu Cai

There is a key problem in the medical visual question answering task that how to effectively realize the feature fusion of language and medical images with limited datasets. In order to better utilize multi-scale information of medical images, previous methods directly embed the multi-stage visual feature maps as tokens of same size respectively and fuse the

Kai Liu, Zhibin Yi, Guangfu Luo

Doping asymmetry is a notable phenomenon with semiconductors and a particularly longstanding challenge limiting the applications of most wide-band-gap semiconductors, which are inherent of spontaneous heavy n- or p-type doping because of their extreme band edges. This study theoretically shows that by applying a proper external voltage on materials during th

Ratchaphat Nakarachinda, Chatchai Promsiri, Lunchakorn Tannukij, Pitayuth Wongjun

The nonextensive nature of black holes is one of the most intriguing discoveries. In fact, the black hole entropy is a nonextensive quantity that scales by its surface area at the event horizon. In our work, we extend the thermodynamic phase space of black holes by treating the nonextensive parameter of the R\'enyi entropy as the thermodynamic variable. Usin

Philippe Lewalle, Leigh S. Martin, Emmanuel Flurin, Song Zhang

The Zeno effect, in which repeated observation freezes the dynamics of a quantum system, stands as an iconic oddity of quantum mechanics. When a measurement is unable to distinguish between states in a subspace, the dynamics within that subspace can be profoundly altered, leading to non-trivial behavior. Here we show that such a measurement can turn a non-in

Yang Li, Canran Xu, Guodong Long, Tao Shen

Recently, prefix-tuning was proposed to efficiently adapt pre-trained language models to a broad spectrum of natural language classification tasks. It leverages soft prefix as task-specific indicators and language verbalizers as categorical-label mentions to narrow the formulation gap from pre-training language models. However, when the label space increases

Swati Sharma, Aditi Partap, Maria Angels de Luis Balaguer, Sara Malvar

Agriculture is at the heart of the solution to achieve sustainability in feeding the world population, but advancing our understanding on how agricultural output responds to climatic variability is still needed. Precision Agriculture (PA), which is a management strategy that uses technology such as remote sensing, Geographical Information System (GIS), and m

Danial Kamali, Joseph Romain, Huiyi Liu, Wei Peng

Nowadays, the spread of misinformation is a prominent problem in society. Our research focuses on aiding the automatic identification of misinformation by analyzing the persuasive strategies employed in textual documents. We introduce a novel annotation scheme encompassing common persuasive writing tactics to achieve our objective. Additionally, we provide a

Xiaoyue Wang, Linfeng Song, Xin Liu, Chulun Zhou

Simile recognition involves two subtasks: simile sentence classification that discriminates whether a sentence contains simile, and simile component extraction that locates the corresponding objects (i.e., tenors and vehicles). Recent work ignores features other than surface strings. In this paper, we explore expressive features for this task to achieve more

Lifeng Zhou, Kaifeng Wei, Yuke Li, Yiya Hao

A large number of pornographic audios publicly available on the Internet seriously threaten the mental and physical health of children, but these audios are rarely detected and filtered. In this paper, we firstly propose a convolutional neural networks (CNN) based model for acoustic pornography recognition. Then, we research a collection of refinements and v

Jie Yang, Chao-Kai Wen, Xi Yang, Jing Xu

Simultaneous localization and mapping (SLAM) provides user tracking and environmental mapping capabilities, enabling communication systems to gain situational awareness. Advanced communication networks with ultra-wideband, multiple antennas, and a large number of connections present opportunities for deep integration of sensing and communications. First, the

Ngoc Dang Nguyen, Lan Du, Wray Buntine, Changyou Chen

Domain adaptation is an effective solution to data scarcity in low-resource scenarios. However, when applied to token-level tasks such as bioNER, domain adaptation methods often suffer from the challenging linguistic characteristics that clinical narratives possess, which leads to unsatisfactory performance. In this paper, we present a simple yet effective h

Yilin Zhuang, Zhuobin Zhou, Burak Alakent, Mehmet Mercangöz

We present the development of a semi-supervised regression method using variational autoencoders (VAE), which is customized for use in soft sensing applications. We motivate the use of semi-supervised learning considering the fact that process quality variables are not collected at the same frequency as other process variables leading to many unlabelled reco

Hui Wang, Yue-Xin Huang, Huiying Liu, Xiaolong Feng

Recent experiments reported an antisymmetric planar Hall effect, where the Hall current is odd in the in-plane magnetic field and scales linearly with both electric and magnetic fields applied. Existing theories rely exclusively on a spin origin, which requires spin-orbit coupling to take effect. Here, we develop a general theory for the intrinsic planar Hal

Changrong Liu, S. Suvorova, R. J. Evans, B. Moran

The problem of detecting a sinusoidal signal with randomly varying frequency has a long history. It is one of the core problems in signal processing, arising in many applications including, for example, underwater acoustic frequency line tracking, demodulation of FM radio communications, laser phase drift in optical communications and, recently, continuous g

Jiancheng An, Chao Xu, Qingqing Wu, Derrick Wing Kwan Ng

Reconfigurable intelligent surfaces (RIS) is a revolutionary technology to cost-effectively improve the performance of wireless networks. We first review the existing framework of channel estimation and passive beamforming (CE & PBF) in RIS-assisted communication systems. To reduce the excessive pilot signaling overhead and implementation complexity of the C

Qiang Li, Qiao Xiang, Derui Liu, Yuxin Wang

In this paper, we conduct systematic measurement studies to show that the high memory bandwidth consumption of modern distributed applications can lead to a significant drop of network throughput and a large increase of tail latency in high-speed RDMA networks.We identify its root cause as the high contention of memory bandwidth between application processes

San-Dong Guo, Yu-Ling Tao, Guang-Zhao Wang, Shaobo Chen

Valleytronic materials can provide new degrees of freedom to future electronic devices. In this work, the concepts of the ferrovalley metal (FVM) and valley gapless semiconductor (VGS) are proposed, which can be achieved in valleytronic bilayer systems by electric-field tuning, where the interaction between out-of-plane ferroelectricity and A-type antiferrom

Kyle Broder, James Stanfield

It is shown that many results, previously believed to be properties of the Lichnerowicz Ricci curvature, hold for the Ricci curvature of all Gauduchon connections. We prove the existence of $t$--Gauduchon Ricci-flat metrics on the suspension of a compact Sasaki--Einstein manifold, for all $t \in (-\infty,1)$; in particular, for the Bismut, Minimal, and Hermi

Peng Jia, Ruiqi Sun, Nan Li, Yu Song

Strong lensing in galaxy clusters probes properties of dense cores of dark matter halos in mass, studies the distant universe at flux levels and spatial resolutions otherwise unavailable, and constrains cosmological models independently. The next-generation large scale sky imaging surveys are expected to discover thousands of cluster-scale strong lenses, whi

Baichuan Jiang, Keshuai Xu, Abhay Moghekar, Peter Kazanzides

Ultrasound-guided spine interventions, such as lumbar-puncture procedures, often suffer from the reduced visibility of key anatomical features such as the inter-spinous process space, due to the complex shape of the self-shadowing vertebra. Therefore, we propose to design a wearable 3D ultrasound device capable of imaging the vertebra from multiple insonific

Jiashu Lou, Jie zou, Baohua Wang

With the improvement of arithmetic power and algorithm accuracy of personal devices, biological features are increasingly widely used in personal identification, and palm vein recognition has rich extractable features and has been widely studied in recent years. However, traditional recognition methods are poorly robust and susceptible to environmental influ

Sounak Mukherjee, Zi-Huai Zhang, Daniel G. Oblinsky, Mitchell O. de Vries

Color centers in diamond are promising platforms for quantum technologies. Most color centers in diamond discovered thus far emit in the visible or near-infrared wavelength range, which are incompatible with long-distance fiber communication and unfavorable for imaging in biological tissues. Here, we report the experimental observation of a new color center

Adrian Dumitrescu, Géza Tóth

Given a set of $n$ labeled points in general position in the plane, we remove all of its points one by one. At each step, one point from the convex hull of the remaining set is erased. In how many ways can the process be carried out? The answer obviously depends on the point set. If the points are in convex position, there are exactly $n!$ ways, which is the

Motoi Omachi, Brian Yan, Siddharth Dalmia, Yuya Fujita

The black-box nature of end-to-end speech translation (E2E ST) systems makes it difficult to understand how source language inputs are being mapped to the target language. To solve this problem, we would like to simultaneously generate automatic speech recognition (ASR) and ST predictions such that each source language word is explicitly mapped to a target l

Mariya Romanova, Guorong Weng, Arsineh Apelian, Vojtech Vlcek

We introduce an approach to treat localized correlated electronic states in the otherwise weakly correlated host medium. Here, the environment is dynamically downfolded on the correlated subspace. It is captured via renormalization of one and two quasiparticle interaction terms which are evaluated using many-body perturbation theory. We outline the strategy

Harsh Patel, Nicole Schneider, Hanan Samet

Visualizations such as bar charts, scatter plots, and objects on geographical maps often convey critical information, including exact and relative numeric values, using shapes. The choice of shape and method of encoding information is often arbitrarily, or based on convention. However, past studies have shown that the human eye can be fooled by visual repres

Sunrit Chakraborty, Saptarshi Roy, Ambuj Tewari

We consider the stochastic linear contextual bandit problem with high-dimensional features. We analyze the Thompson sampling algorithm using special classes of sparsity-inducing priors (e.g., spike-and-slab) to model the unknown parameter and provide a nearly optimal upper bound on the expected cumulative regret. To the best of our knowledge, this is the fir

Chunyan Zeng, Jiaxiang Ye, Zhifeng Wang, Nan Zhao

Most Deep Learning (DL) based Compressed Sensing (DCS) algorithms adopt a single neural network for signal reconstruction, and fail to jointly consider the influences of the sampling operation for reconstruction. In this paper, we propose unified framework, which jointly considers the sampling and reconstruction process for image compressive sensing based on

Baichuan Jiang, Keshuai Xu, Ahbay Moghekar, Peter Kazanzides

Clear identification of bone structures is crucial for ultrasound-guided lumbar interventions, but it can be challenging due to the complex shapes of the self-shadowing vertebra anatomy and the extensive background speckle noise from the surrounding soft tissue structures. Therefore, we propose to use a patch-like wearable ultrasound solution to capture the

Chengrui Li, Anqi Wu

The state-of-the-art dimensionality reduction approaches largely rely on complicated optimization procedures. On the other hand, closed-form approaches requiring merely eigen-decomposition do not have enough sophistication and nonlinearity. In this paper, we propose a novel nonlinear dimensionality reduction method -- Inverse Kernel Decomposition (IKD) -- ba

Jiaxing Xu, Yunhan Yang, David Tse Jung Huang, Sophi Shilpa Gururajapathy

This paper presents a comprehensive and quality collection of functional human brain network data for potential research in the intersection of neuroscience, machine learning, and graph analytics. Anatomical and functional MRI images have been used to understand the functional connectivity of the human brain and are particularly important in identifying unde

Lucas Gagnon

This paper constructs a novel Hopf algebra $\mathsf{cf}(\mathrm{UT}_{\bullet})$ on the class functions of the unipotent upper triangular groups $\mathrm{UT}_{n}(\mathbb{F}_{q})$ over a finite field. This construction is representation theoretic in nature and uses the machinery of Hopf monoids in the category of vector species. In contrast with a similar know

Amir-Salar Esteki, Hossein Moradian, Solmaz S. Kia

In this letter, we study the problem of accelerating reaching average consensus over connected graphs in a discrete-time communication setting. Literature has shown that consensus algorithms can be accelerated by increasing the graph connectivity or optimizing the weights agents place on the information received from their neighbors. Here, instead of alterin

Amir Pouran Ben Veyseh, Minh Van Nguyen, Franck Dernoncourt, Thien Huu Nguyen

Event Detection (ED) is the task of identifying and classifying trigger words of event mentions in text. Despite considerable research efforts in recent years for English text, the task of ED in other languages has been significantly less explored. Switching to non-English languages, important research questions for ED include how well existing ED models per

Christopher-Lloyd Simon

The modular group PSL(2;Z) acts on the hyperbolic plane HP with quotient the modular surface M, whose unit tangent bundle U is a 3-manifold homeomorphic to the complement of the trefoil knot in the 3-sphere. The hyperbolic conjugacy classes of PSL(2;Z) correspond to the closed oriented geodesics in M. Those lift to the periodic orbits for the geodesic flow i

Tian-Ying Yao, Rui-Yun Guo, Xin-Yue Zhao

We constrain two dynamical dark energy models that are parametrized by the logarithm form of $w(z)=w_{0}+w_{1}\left(\frac{\ln (2+z)}{1+z}-\ln 2\right)$ and the oscillating form of $w(z)=w_{0}+w_{1}\left(\frac{\sin(1+z)}{1+z}-\sin(1)\right)$. Comparing with the Chevallier-Polarski-Linder (CPL) model, the two parametrizations for dark energy can explore the wh

Amir Pouran Ben Veyseh, Javid Ebrahimi, Franck Dernoncourt, Thien Huu Nguyen

Event Extraction (EE) is one of the fundamental tasks in Information Extraction (IE) that aims to recognize event mentions and their arguments (i.e., participants) from text. Due to its importance, extensive methods and resources have been developed for Event Extraction. However, one limitation of current research for EE involves the under-exploration for no

Yilin Guo, Haolei Weng, Arian Maleki

Since its development, the minimax framework has been one of the corner stones of theoretical statistics, and has contributed to the popularity of many well-known estimators, such as the regularized M-estimators for high-dimensional problems. In this paper, we will first show through the example of sparse Gaussian sequence model, that the theoretical results