October 2022 arXiv papers — page 83

Yue Zhang, Hongliang Fei, Ping Li

Recently, the task of distantly supervised (DS) ultra-fine entity typing has received significant attention. However, DS data is noisy and often suffers from missing or wrong labeling issues resulting in low precision and low recall. This paper proposes a novel ultra-fine entity typing model with denoising capability. Specifically, we build a noise model to

Zhao-Heng Yin, Weirui Ye, Qifeng Chen, Yang Gao

Imitation learning is a class of promising policy learning algorithms that is free from many practical issues with reinforcement learning, such as the reward design issue and the exploration hardness. However, the current imitation algorithm struggles to achieve both high performance and high in-environment sample efficiency simultaneously. Behavioral Clonin

Xiaonan Li, Daya Guo, Yeyun Gong, Yun Lin

Code contrastive pre-training has recently achieved significant progress on code-related tasks. In this paper, we present \textbf{SCodeR}, a \textbf{S}oft-labeled contrastive pre-training framework with two positive sample construction methods to learn functional-level \textbf{Code} \textbf{R}epresentation. Considering the relevance between codes in a large-

Lixin Cheng, Weihao Mao

When optimization theorists consider optimization problems in infinite dimensional spaces, they need to deal with closed convex subsets(usually cones) which mostly have empty interior. These subsets often prevent optimization theorists from applying powerful techniques to study these optimization problems. In this paper, by nonsupport point, we present gener

Jyoti Turi, A. P. Misra

The generation of magnetohydrodynamic (MHD) waves and their instabilities are studied in galactic gaseous rotating plasmas with the effects of the magnetic field, the self gravity, the diffusion-convection of cosmic rays as well as the gas and cosmic-ray pressures. The coupling of the Jeans, Alfv{\'e}n and magnetosonic waves, and the conditions of damping or

Li-Juan Cheng, Anton Thalmaier, Feng-Yu Wang

By methods of stochastic analysis on Riemannian manifolds, we develop two approaches to determine an explicit constant $c(D)$ for an $n$-dimensional compact manifold $D$ with boundary such that $\frac{\lambda}{n}\,\|\phi\|_{\infty} \leq \|{\rm Hess}\ \phi\|_{\infty}\leq c(D)\lambda \,\|\phi\|_{\infty}$ holds for any Dirichlet eigenfunction $\phi$ of $-\Delta

Deeksha Beniwal, Patrick Clearwater, Liam Dunn, Lucy Strang

We present a search for continuous gravitational wave signals from an unidentified pulsar potentially powering HESS~J1427-608, a spatially unresolved TeV point source detected by the High Energy Stereoscopic System (HESS). The search uses a semi-coherent algorithm, which combines the maximum likelihood $\mathcal{F}$~statistic with a hidden Markov model to ef

Pei-Pei Zhang, Xin-Yu He, Wei Liu, Yi-Qing Guo

More and more experiments have identified that the energy spectra of both primary and secondary cosmic-rays exhibit a hardening above $\sim 200$ GV. Most recently, the DAMPE experiment has reported a hardening of boron-to-carbon ratio at $200$ GV. These signs call for modifications of the conventional cosmic-ray (CR) picture. In this work, we propose that th

D. Chatterjee, A. P. Misra, S. Ghosh

The influence of neutrino flavor oscillations on the propagation of magnetohydrodynamic (MHD) waves and instabilities is studied in neutrino-beam driven magnetoplasmas. Using the neutrino MHD model, a general dispersion relation is derived which manifests the resonant interactions of MHD waves, not only with the neutrino beam, but also with the neutrino flav

Christian Kanzow, Alexandra Schwarz, Felix Weiß

We consider the sparse optimization problem with nonlinear constraints and an objective function, which is given by the sum of a general smooth mapping and an additional term defined by the $ \ell_0 $-quasi-norm. This term is used to obtain sparse solutions, but difficult to handle due to its nonconvexity and nonsmoothness (the sparsity-improving term is eve

Jaehoon Oh, Jongwoo Ko, Se-Young Yun

Translation has played a crucial role in improving the performance on multilingual tasks: (1) to generate the target language data from the source language data for training and (2) to generate the source language data from the target language data for inference. However, prior works have not considered the use of both translations simultaneously. This paper

Shahbaz Syed, Dominik Schwabe, Martin Potthast

This paper presents Summary Workbench, a new tool for developing and evaluating text summarization models. New models and evaluation measures can be easily integrated as Docker-based plugins, allowing to examine the quality of their summaries against any input and to evaluate them using various evaluation measures. Visual analyses combining multiple measures

Zoubeir Mlika, Soumaya Cherkaoui

This paper studies the problem of minimizing the age of information (AoI) in cellular vehicle-to-everything communications. To provide minimal AoI and high reliability for vehicles' safety information, NOMA is exploited. We reformulate a resource allocation problem that involves half-duplex transceiver selection, broadcast coverage optimization, power alloca

Sima Roy, Amar P. Misra

The evolution of electromagnetic (EM) solitons due to nonlinear coupling of circularly polarized intense laser pulses with low-frequency electron-acoustic perturbations is studied in relativistic degenerate dense astrophysical plasmas with two groups of electrons: a sparse population of classical relativistic electrons and a dense population of relativistic

Narinder Singh, Anikesh Pal

The effect of the rotation on the turbulent mixing of two miscible fluids of small contrasting density, produced by Faraday instability, is investigated using direct numerical simulations (DNS). We demonstrate that at lower forcing amplitudes, the t.k.e. increases with an increase in f till (f/\omega\right)^2<0.25, where \omega is the forcing frequency, duri

Mark Ebert

There is a known connection between the osp(1|2n) polynomial knot invariant $J_K^n$ and the so(2n+1) knot invariant ${}_{so} J_K^n$ studied by Clark in arXiv:1509.03533 and Blumen in arXiv:0901.3232. In the rank one case, the uncolored $U_{q}(osp(1|2))$ link invariant is equal to the $U_{t^{-1}q}(sl_2)$ link invariant where $t^2=-1$. We define a skein relati

Muhammad Abdul-Mageed, Chiyu Zhang, AbdelRahim Elmadany, Houda Bouamor

We describe findings of the third Nuanced Arabic Dialect Identification Shared Task (NADI 2022). NADI aims at advancing state of the art Arabic NLP, including on Arabic dialects. It does so by affording diverse datasets and modeling opportunities in a standardized context where meaningful comparisons between models and approaches are possible. NADI 2022 targ

Hong Wang, Joshua Zahl

A Kakeya set is a compact subset of $\mathbb{R}^n$ that contains a unit line segment pointing in every direction. The Kakeya conjecture asserts that such sets must have Hausdorff and Minkowski dimension $n$. There is a special class of Kakeya sets, called sticky Kakeya sets. Sticky Kakeya sets exhibit an approximate multi-scale self-similarity, and sets of t

John Musgrave, Temesguen Messay-Kebede, David Kapp, Anca Ralescu

In this study we have presented a novel feature representation for malicious programs that can be used for malware classification. We have shown how to construct the features in a bottom-up approach, and analyzed the overlap of malicious and benign programs in terms of their components. We have shown that our method of analysis offers an increase in feature

Abhishek Gupta, Aldo Pacchiano, Yuexiang Zhai, Sham M. Kakade

Reinforcement learning provides an automated framework for learning behaviors from high-level reward specifications, but in practice the choice of reward function can be crucial for good results -- while in principle the reward only needs to specify what the task is, in reality practitioners often need to design more detailed rewards that provide the agent w

Wataru Yoshida, Kei Hirose

We consider the problem of forecasting multivariate time series by a Seemingly Unrelated Time Series Equations (SUTSE) model. The SUTSE model usually assumes that error variables are correlated. A crucial issue is that the model estimation requires heavy computational loads because of a large matrix computation, especially for high-dimensional data. To allev

Prakash Chandra Chhipa, Richa Upadhyay, Rajkumar Saini, Lars Lindqvist

This work presents a novel self-supervised representation learning method to learn efficient representations without labels on images from a 3DPM sensor (3-Dimensional Particle Measurement; estimates the particle size distribution of material) utilizing RGB images and depth maps of mining material on the conveyor belt. Human annotations for material categori

Vance Faber, Jonathan Keegan

In 2020, a paper [arXiv:2010.13443] appeared in the arXiv claiming to prove that a Moore graph of diameter 2 and degree 57 does not exist. (The paper is in Russian; we include a link to a translation of this paper kindly provided to us by Konstantin Selivanov.) The proof technique is reasonable. It employs the fact that such a graph must be distance regular

Zhiyuan Wang, Chenglang Yang

We derive an explicit formula for the connected $(n,m)$-point functions associated to an arbitrary diagonal tau-function of the $2$-BKP hierarchy using computation of neutral fermions and boson-fermion correspondence of type $B$, and then apply this formula to the computation of connected spin double Hurwitz numbers. This is the type $B$ analogue of \cite{wy

Jihua Wang

This paper is concerned with the analytic behaviors (monotonicity, isochronicity and the number of critical points) of period function for potential system $\ddot{x}+g(x)=0$.We give some sufficient criteria to determine the monotonicity and upper bound to the number of critical periods. The conclusion is based on the semi-group properties of (Riemann-Liouvil

Shuqiang Huang, Cuiyu Tan, Jinzhen Zheng, Zhugu Huang

Background: RNA guanine-7 methyltransferase (RNMT) is one of the main regulators of N7-methylguanosine, and the deregulation of RNMT correlated with tumor development and immune metabolism. However, the specific function of RNMT in pan-cancer remains unclear. Methods: RNMT expression in different cancers was analyzed using multiple databases, including Cance

Haoran You, Zhanyi Sun, Huihong Shi, Zhongzhi Yu

Vision Transformers (ViTs) have achieved state-of-the-art performance on various vision tasks. However, ViTs' self-attention module is still arguably a major bottleneck, limiting their achievable hardware efficiency. Meanwhile, existing accelerators dedicated to NLP Transformers are not optimal for ViTs. This is because there is a large difference between Vi

Chao Hu, Weibin Qiu, Weijie Wu, Liqiang Zhu

Video anomaly detection aims to discover abnormal events in videos, and the principal objects are target objects such as people and vehicles. Each target in the video data has rich spatio-temporal context information. Most existing methods only focus on the temporal context, ignoring the role of the spatial context in anomaly detection. The spatial context i

Tomohiro Nishiyama

Minimizing divergence measures under a constraint is an important problem. We derive a sufficient condition that binary divergence measures provide lower bounds for symmetric divergence measures under a given triangular discrimination or given means and variances. Assuming this sufficient condition, the former bounds are always tight, and the latter bounds a

Kelimar Diaz, Baxi Chong, Steven Tarr, Eva Erickson

Study of the locomotion of a centipede (L. forficatus) at the air-water interface reveals that it does not predominantly use its 14 leg pairs to locomote; unlike most swimmers which propagate head-to-tail body bending waves, this species propels via tail-to-head waves. Its low mass and body-fluid contact yield locomotion dynamics in which fluid wave drag for

Jean Y. Song, Sangwook Lee, Jisoo Lee, Mina Kim

Despite the common use of rule-based tools for online content moderation, human moderators still spend a lot of time monitoring them to ensure that they work as intended. Based on surveys and interviews with Reddit moderators who use AutoModerator, we identified the main challenges in reducing false positives and false negatives of automated rules: not being

M. Dajczer, C. -R. Onti, Th. Vlachos

Let the warped product $M^n=L^m\times_\varphi F^{n-m}$, $n\geq m+3\geq 8$, of Riemannian manifolds be an Einstein manifold with Ricci curvature $\rho$ that admits an isometric immersion into Euclidean space with codimension two. Under the assumption that $L^m$ is also Einstein, but not of constant sectional curvature, it is shown that $\rho=0$ and that the s

Liudmyla Kryvonos

For a function $f$, continuous on a compact convex set $K$ and analytic in its interior we construct a sequence of almost optimal polynomials that converge with a geometric rate at points of analyticity of $f$.

Pu Hua, Yubei Chen, Huazhe Xu

The low-level sensory and motor signals in deep reinforcement learning, which exist in high-dimensional spaces such as image observations or motor torques, are inherently challenging to understand or utilize directly for downstream tasks. While sensory representations have been extensively studied, the representations of motor actions are still an area of ac

Patrick Huber, Giuseppe Carenini

Discourse analysis and discourse parsing have shown great impact on many important problems in the field of Natural Language Processing (NLP). Given the direct impact of discourse annotations on model performance and interpretability, robustly extracting discourse structures from arbitrary documents is a key task to further improve computational models in NL

Lorena Bociu, Paul Manns, Marvin Severitt, Sarah Strikwerda

We revisit a class of integer optimal control problems for which a trust-region method has been proposed and analyzed in arXiv:2106.13453v3 [math.OC]. While the algorithm proposed in arXiv:2106.13453v3 [math.OC] successfully solves the class of optimization problems under consideration, its convergence analysis requires restrictive regularity assumptions. Th

Patrick Huber, Giuseppe Carenini

Discourse parsing is an essential upstream task in Natural Language Processing with strong implications for many real-world applications. Despite its widely recognized role, most recent discourse parsers (and consequently downstream tasks) still rely on small-scale human-annotated discourse treebanks, trying to infer general-purpose discourse structures from

Decheng Liu, Zhan Dang, Chunlei Peng, Yu Zheng

With the continuous development of deep learning in the field of image generation models, a large number of vivid forged faces have been generated and spread on the Internet. These high-authenticity artifacts could grow into a threat to society security. Existing face forgery detection methods directly utilize the obtained public shared or centralized data f

Yusuke Iguchi, Huiyuan Man, S. M. Thomas, Filip Ronning

The spin-triplet superconductor UTe$_2$ shows spontaneous time-reversal symmetry breaking and multiple superconducting phases in some crystals, implying chiral superconductivity. Here we microscopically image the local magnetic fields and magnetic susceptibility near the surface of UTe$_2$, observing a homogeneous superfluid density $n_s$ and homogeneous pin

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Based on $e^+e^-$ collision data corresponding to an integrated luminosity of 4.5 $fb^{-1}$ collected at the center-of-mass energies between 4.600 and 4.699 GeV with the BESIII detector at BEPCII, the absolute branching fraction of the inclusive decay $\Lambda^{+}_{c}\to n + X$, where $X$ refers to any possible final state particles, is measured. The absolut

Yeseul Jeon, Won Chang, Seonghyun Jeong, Sanghoon Han

Convolutional neural networks (CNNs) provide flexible function approximations for a wide variety of applications when the input variables are in the form of images or spatial data. Although CNNs often outperform traditional statistical models in prediction accuracy, statistical inference, such as estimating the effects of covariates and quantifying the predi

Patrick Huber, Giuseppe Carenini

With a growing need for robust and general discourse structures in many downstream tasks and real-world applications, the current lack of high-quality, high-quantity discourse trees poses a severe shortcoming. In order the alleviate this limitation, we propose a new strategy to generate tree structures in a task-agnostic, unsupervised fashion by extending a

Gitaek Kwon, Eunjin Kim, Sunho Kim, Seongwon Bak

Recently, diabetic retinopathy (DR) screening utilizing ultra-wide optical coherence tomography angiography (UW-OCTA) has been used in clinical practices to detect signs of early DR. However, developing a deep learning-based DR analysis system using UW-OCTA images is not trivial due to the difficulty of data collection and the absence of public datasets. By

Alister W. Graham, Nandini Sahu

We recently revealed that bulges and elliptical galaxies broadly define distinct, super-linear relations in the $M_{\rm bh}$-$M_{\rm *,sph}$ diagram, with the order-of-magnitude lower $M_{\rm bh}/M_{\rm *,sph}$ ratios in the elliptical galaxies due to major (disc-destroying, elliptical-building) dry mergers. Here we present a more nuanced picture. Galaxy mer

Xin-Yi Gao, Kai Li, Ya-Wen Cai, Ya-Ni Guo

The photometric analysis and spectroscopic study of the long period low mass ratio deep contact binary KN Per were executed. The light curves of BV(RI)$_c$-band were from the Ningbo Bureau of Education and Xinjiang Observatory Telescope (NEXT) at the Xingming Observatory. Through the analysis of Wilson-Devinney (W-D) program, KN Per was found as an A-type lo

Chen Wang, Yuchen Liu, Boxing Chen, Jiajun Zhang

End-to-end Speech Translation (ST) aims at translating the source language speech into target language text without generating the intermediate transcriptions. However, the training of end-to-end methods relies on parallel ST data, which are difficult and expensive to obtain. Fortunately, the supervised data for automatic speech recognition (ASR) and machine

C. Chock, K. Flood, L. Macchiarulo, I. Mostafanezhad

We present the first results from the HPSoC ASIC designed for readout of Ultra-fast Silicon Detectors. The 4-channel ASIC manufactured in 65 nm CMOS by TSMC has been optimized for 50 um thick AC-LGAD. The evaluation of the analog front end with \b{eta}-particles impinging on 3x3 AC-LGAD arrays (500 um pitch, 200x200 um2 metal) confirms a fast output rise tim

Abida Sanjana Shemonti, Emanuele Plebani, Natalia P. Biscola, Deborah M. Jaffey

A thorough understanding of the neuroanatomy of peripheral nerves is required for a better insight into their function and the development of neuromodulation tools and strategies. In biophysical modeling, it is commonly assumed that the complex spatial arrangement of myelinated and unmyelinated axons in peripheral nerves is random, however, in reality the ax

Ashish Uppala, Domenic Rosati, Josh M. Nicholson, Milo Mordaunt

Despite being a key element in the process of disseminating scientific knowledge, editorial notices are often obscured and not clearly linked to the papers to which they refer. In the present paper, we describe established methods of aggregating notice data, and introduce a novel method of finding editorial notices in the scientific literature. Specifically,

Mingqing Chen, Jianguo Huang, Xuehai Huang

A robust nonconforming mixed finite element method is developed for a strain gradient elasticity (SGE) model. In two and three dimensional cases, a lower order $C^0$-continuous $H^2$-nonconforming finite element is constructed for the displacement field through enriching the quadratic Lagrange element with bubble functions. This together with the linear Lagr

Hanqing Zhang, Dawei Song

Prompt learning with immensely large Casual Language Models (CLMs) has been shown promising for attribute-controllable text generation (CTG). However, vanilla prompt tuning tends to imitate training corpus characteristics beyond the control attributes, resulting in a poor generalization ability. Moreover, it is less able to capture the relationship between d

Zheng Ma, Shi Zong, Mianzhi Pan, Jianbing Zhang

In recent years, vision and language pre-training (VLP) models have advanced the state-of-the-art results in a variety of cross-modal downstream tasks. Aligning cross-modal semantics is claimed to be one of the essential capabilities of VLP models. However, it still remains unclear about the inner working mechanism of alignment in VLP models. In this paper,

Nathan J. Dawson, Yunli Lu, Zoe Lowther, Jacob Abell

We present and implement a design for an automated system that fabricates multilayer photonic crystal structures. The device is constructed with low-cost materials. A polystyrene/cellulose acetate multilayer Bragg reflector was fabricated to confirm the device's capability. A distributed feedback laser was also fabricated and characterized. The system has al

Ruijun Li, Weihua Li, Yi Yang, Hanyu Wei

Recently, diffusion models have been proven to perform remarkably well in text-to-image synthesis tasks in a number of studies, immediately presenting new study opportunities for image generation. Google's Imagen follows this research trend and outperforms DALLE2 as the best model for text-to-image generation. However, Imagen merely uses a T5 language model

Zhe Ding, Yumeng Sun, Ningchong Zheng, Xingyue Ma

Non-collinear spin order that breaks space inversion symmetry and allows efficient electric-field control of magnetism makes BiFeO$_3$ a promising candidate for applications in low-power spintronic devices. Epitaxial strain effects have been intensively studied and exhibit significant modulation of the magnetic order in BiFeO$_3$, but tuning its spin structu

Masoud Zargar

Each signature $\underline{\lambda}(n)=(\lambda_1(n),\dots,\lambda_n(n))$, where $\lambda_1(n)\geq\dots\geq\lambda_n(n)$ are integers, gives an irreducible representation $\pi_{\underline{\lambda}(n)}:U(n)\rightarrow\text{GL}(V_{\underline{\lambda}(n)})$ of the unitary group $U(n)$. Suppose $X$ is a finite-area cusped hyperbolic surface, $\chi$ is a random s

Xinhai Chen, Jie Liu, Junjun Yan, Zhichao Wang

Mesh generation remains a key technology in many areas where numerical simulations are required. As numerical algorithms become more efficient and computers become more powerful, the percentage of time devoted to mesh generation becomes higher. In this paper, we present an improved structured mesh generation method. The method formulates the meshing problem

Zhiyuan Zhang, Lingjuan Lyu, Xingjun Ma, Chenguang Wang

Deep Neural Networks (DNNs) are known to be vulnerable to backdoor attacks. In Natural Language Processing (NLP), DNNs are often backdoored during the fine-tuning process of a large-scale Pre-trained Language Model (PLM) with poisoned samples. Although the clean weights of PLMs are readily available, existing methods have ignored this information in defendin

A. A. Kocharyan, M. Samsonyan, V. G. Gurzadyan

We show that gravitational waves can act as waveguides for electromagnetic radiation, that is if the latter is initially aligned with the gravitational waves, then the alignment will survive during the propagation. The analysis is performed using the Hamiltonian formalism and the Jacobi equation for null geodesics and conditions for certain cases of polariza

Zifu Wang, Liyang Luo, Di Xia, Siqi Lu

Broadband Kerr combs with a flat comb spectral profile are expected in a number of applications, such as high-capacity optical communication. Here, we propose novel concentric dual-ring microresonators (DRMs) for advanced dispersion engineering to tailor the comb spectral profile. The dispersion can be flexibly engineered not only by the cross-section of the

Nikolaos Kidonakis, Nodoka Yamanaka

We study soft-gluon corrections for the associated production of a single top quark and a $Z$ boson ($tqZ$ production) at hadron colliders. We find that the radiative corrections are dominated by soft-gluon emission. We calculate the approximate NNLO (aNNLO) cross section at LHC energies, including uncertainties from scale dependence and from parton distribu

Han Gil Choi, Tao Yang, Hyung Mok Lee

During its inspiral stage, a binary black hole (BBH) produces characteristic gravitational wave (GW) signals. The waveform of the GW signals can be described by the physical parameters of BBH, such as the masses of the black holes and the orbital eccentricity. Precise and accurate estimation of these parameters is crucial for GW astrophysics. In the aspect o

Maozhen Wang, Aykut Ozgun Onol, Philip Long, Taskin Padir

We present a contact-implicit planning approach that can generate contact-interaction trajectories for non-prehensile manipulation problems without tuning or a tailored initial guess and with high success rates. This is achieved by leveraging the concept of state-triggered constraints (STCs) to capture the hybrid dynamics induced by discrete contact modes wi

Eli Bronstein, Mark Palatucci, Dominik Notz, Brandyn White

We demonstrate the first large-scale application of model-based generative adversarial imitation learning (MGAIL) to the task of dense urban self-driving. We augment standard MGAIL using a hierarchical model to enable generalization to arbitrary goal routes, and measure performance using a closed-loop evaluation framework with simulated interactive agents. W

Yan Gao, Weikang Wu, Ben-Chao Gong, Huan-Cheng Yang

The intrinsic ferromagnetic (FM) axion insulators and Weyl semimetals (WSMs) with only single pair of Weyl points have drawn intensive attention but so far remain rare and elusive in real materials. Here, we propose a new class of Mn\emph{X}$_{2}$\emph{B}$_{2}$\emph{T}$_{6}$-B (\emph{X}=Ge, Sn, or Pb; \emph{B}=Sb or Bi; \emph{T}=Se or Te) family that is the

Ming Li, Ruihong Huang

Complex feature extractors are widely employed for text representation building. However, these complex feature extractors make the NLP systems prone to overfitting especially when the downstream training datasets are relatively small, which is the case for several discourse parsing tasks. Thus, we propose an alternative lightweight neural architecture that

Alessandra Corsi, Anna Y. Q. Ho, S. Bradley Cenko, Shrinivas R. Kulkarni

The dividing line between gamma-ray bursts (GRBs) and ordinary stripped-envelope core-collapse supernovae (SNe) is yet to be fully understood. Observationally mapping the variety of ejecta outcomes (ultra-relativistic, mildly-relativistic or non-relativistic) in SNe of Type Ic with broad lines (Ic-BL) can provide a key test to stellar explosion models. Howev

Lingxiao Zhao, Saurabh Sawlani, Arvind Srinivasan, Leman Akoglu

Graph-based anomaly detection finds numerous applications in the real-world. Thus, there exists extensive literature on the topic that has recently shifted toward deep detection models due to advances in deep learning and graph neural networks (GNNs). A vast majority of prior work focuses on detecting node/edge/subgraph anomalies within a single graph, with

Naoyuki Kamiyama

Robust subsets of matroids were introduced by Huang and Sellier to propose approximate kernels for the matroid-constrained maximum vertex cover problem. In this paper, we prove that the bound for robust subsets of transversal matroids given by Huang and Sellier can be improved.

Taichi Terashima, Hishiro T. Hirose, Yoshitaka Matsushita, Shinya Uji

Both cuprates and iron-based superconductors demonstrate nematicity, defined as the spontaneous breaking of rotational symmetry in electron systems. The nematic state can play a role in the high-transition-temperature superconductivity of these compounds. However, the microscopic mechanism responsible for the transport anisotropy in iron-based compounds rema

Shengjie Zheng, Ling Liu, Junjie Yang, Jianwei Zhang

The development of artificial intelligence (AI) and robotics are both based on the tenet of "science and technology are people-oriented", and both need to achieve efficient communication with the human brain. Based on multi-disciplinary research in systems neuroscience, computer architecture, and functional organic materials, we proposed the concept of using

Meredith Neyer, Jemma Wolcott-Green

We study the impact of neutral hydrogen absorption on ${\rm H_2}$ photodissociation in protogalactic haloes exposed to soft-UV radiation. Lyman-series absorption can significantly deplete dissociating photons as line overlap with the ${\rm H_2}$ Lyman-Werner bands occurs for neutral column densities exceeding $10^{22}$ ${\rm cm^{-2}}$, but this effect has no

Xiaoyuan Liu, Ilya Tyagin, Hayato Ushijima-Mwesigwa, Indradeep Ghosh

With the rapid development of machine learning, improving its explainability has become a crucial research goal. We study the problem of making the clusters more explainable by investigating the cluster descriptors. Given a set of objects $S$, a clustering of these objects $\pi$, and a set of tags $T$ that have not participated in the clustering algorithm. E

Junjie Yang, Ling Liu, Shengjie Zheng, Lang Qian

In brain-machine interface (BMI) applications, a key challenge is the low information content and high noise level in neural signals, severely affecting stable robotic control. To address this challenge, we proposes a cooperative shared control framework based on brain-inspired intelligence, where control signals are decoded from neural activity, and the rob

Mohamed H. Abdullah, Gillian Wilson, Anatoly Klypin, Tomoaki Ishiyama

The cluster mass-richness relation (MRR) is an observationally efficient and potentially powerful cosmological tool for constraining the mean matter density of the universe and the amplitude of fluctuations using the cluster abundance technique. We derive the MRR relation using GalWCat19, a publicly available galaxy cluster catalog we created from the Sloan

Yiming Li, Zhifang Guo, Zhirong Ye, Xiangdong Wang

In this paper, we describe in detail our system for DCASE 2022 Task4. The system combines two considerably different models: an end-to-end Sound Event Detection Transformer (SEDT) and a frame-wise model, Metric Learning and Focal Loss CNN (MLFL-CNN). The former is an event-wise model which learns event-level representations and predicts sound event categorie

Mai Phuoc Binh, Nguyen Thu Hang, Truong Thi Hien, Tran Nam Trung

Let R = K[x1,...,xr] be a polynomial ring over a field K. Let G be a graph with vertex set {1,...,r} and let J be the cover ideal of G. We give a sharp bound for the stability index of symbolic depth function sdstab(J). In the case G is bipartite, it yields a sharp bound for the stability index of depth function dstab(J) and this bound is exact if G is a for

Murthy N Mittinty, John Lynch

The Risk Ratio (RR) is the ratio of the outcome among the exposed to risk of the outcome among the unexposed. This is a simple concept, which makes one wonder why it has not gained the same popularity as the odds ratio. Using logistic regression to estimate the odds ratio is quite common in epidemiology and interpreting the odds ratio as a risk ratio, under

Stefano Garcia, Timothy Van Reeth, Joris De Ridder, Conny Aerts

Context. Gamma Doradus (hereafter $\gamma$~Dor) stars are gravity-mode pulsators whose periods carry information about the internal structure of the star. These periods are especially sensitive to the internal rotation and chemical mixing, two processes that are currently not well constrained in the theory of stellar evolution. Aims. We aim to identify the p

Ghanesh Narasimhan, Dennice F. Gayme, Charles Meneveau

Large Eddy Simulations (LES) are used to study the effects of veer (the height-dependent lateral deflection of wind velocity due to Coriolis acceleration) on the evolution of wind turbine wakes. Specifically, this work focuses on turbines that are yawed with respect to the mean incoming wind velocity, which produces laterally deflected wakes that have a curl

Zuheng Kang, Jianzong Wang, Junqing Peng, Jing Xiao

Estimating age from a single speech is a classic and challenging topic. Although Label Distribution Learning (LDL) can represent adjacent indistinguishable ages well, the uncertainty of the age estimate for each utterance varies from person to person, i.e., the variance of the age distribution is different. To address this issue, we propose selective varianc

Fadi AlMahamid, Katarina Grolinger

Energy companies often implement various demand response (DR) programs to better match electricity demand and supply by offering the consumers incentives to reduce their demand during critical periods. Classifying clients according to their consumption patterns enables targeting specific groups of consumers for DR. Traditional clustering algorithms use stand

Benjamin Jaye, Manasa N. Vempati

We prove the existence of a $(d-2)$-dimensional purely unrectifiable set upon which a family of \emph{even} singular integral operators is bounded.

Lingxiao Zhao, Louis Härtel, Neil Shah, Leman Akoglu

Message passing neural networks (MPNNs) have become a dominant flavor of graph neural networks (GNNs) in recent years. Yet, MPNNs come with notable limitations; namely, they are at most as powerful as the 1-dimensional Weisfeiler-Leman (1-WL) test in distinguishing graphs in a graph isomorphism testing frame-work. To this end, researchers have drawn inspirat

Likun Cao, Ziwen Chen, James Evans

Innovation or the creation and diffusion of new material, social and cultural things in society has been widely studied in sociology and across the social sciences, with investigations sufficiently diverse and dispersed to make them unnavigable. This complexity results from innovation's importance for society, but also the fundamental paradox underlying inno

Fadi AlMahamid, Hanan Lutfiyya, Katarina Grolinger

This paper introduces the Virtual Sensor Middleware (VSM), which facilitates distributed sensor data processing on multiple fog nodes. VSM uses a Virtual Sensor as the core component of the middleware. The virtual sensor concept is redesigned to support functionality beyond sensor/device virtualization, such as deploying a set of virtual sensors to represent

Lisa Dunlap, Clara Mohri, Devin Guillory, Han Zhang

It is expensive to collect training data for every possible domain that a vision model may encounter when deployed. We instead consider how simply verbalizing the training domain (e.g. "photos of birds") as well as domains we want to extend to but do not have data for (e.g. "paintings of birds") can improve robustness. Using a multimodal model with a joint i

Vladimir Pastukhov

In this paper we introduce and study fused lasso nearly-isotonic signal approximation, which is a combination of fused lasso and generalized nearly-isotonic regression. We show how these three estimators relate to each other, derive solution to the general problem, show that it is computationally feasible and provides a trade-off between piecewise monotonici

Ryu Hirai, Atsumoto Ohashi, Ao Guo, Hideki Shiroma

To improve the interactive capabilities of a dialogue system, e.g., to adapt to different customers, the Dialogue Robot Competition (DRC2022) was held. As one of the teams, we built a dialogue system with a pipeline structure containing four modules. The natural language understanding (NLU) and natural language generation (NLG) modules were GPT-2 based model

David Fisher, Karin Melnick

The main result is a classification of smooth actions of $SL(n,{\bf R})$, $n \geq 3$, or connected groups locally isomorphic to it, on closed $n$-manifolds, extending a theorem of Uchida. We construct new exotic actions of $SL(n,{\bf Z})$ on the $n$-torus and connected sums of $n$-tori, and we formulate a conjectural classification of actions of lattices in

Saket Dingliwal, Monica Sunkara, Sravan Bodapati, Srikanth Ronanki

End-to-end speech recognition models trained using joint Connectionist Temporal Classification (CTC)-Attention loss have gained popularity recently. In these models, a non-autoregressive CTC decoder is often used at inference time due to its speed and simplicity. However, such models are hard to personalize because of their conditional independence assumptio

Dong Chen, Xinda Qi, Yu Zheng, Yuzhen Lu

Weed management plays an important role in many modern agricultural applications. Conventional weed control methods mainly rely on chemical herbicides or hand weeding, which are often cost-ineffective, environmentally unfriendly, or even posing a threat to food safety and human health. Recently, automated/robotic weeding using machine vision systems has seen

Zhigang Li, Jiarui Jia, Wenjing Jiang, Wen Ou

The implementation of terahertz (THz) wafer-level metadevices is critical to advance the science for applications including (I) integrated focal plane array which can image for biology and (II) integrated narrowband absorbers for high spectral resolution THz spectroscopy. Substantial progress has been made in the development of THz metamaterials; however, a

Zillur Rahman, Md. Sabir Hossain, Mohammad Hasan, Ahmed Imteaj

Clustering is one of the widely used techniques to find out patterns from a dataset that can be applied in different applications or analyses. K-means, the most popular and simple clustering algorithm, might get trapped into local minima if not properly initialized and the initialization of this algorithm is done randomly. In this paper, we propose a novel a

A. Ali Heydari, Naghmeh Rezaei, Daniel J. McDuff, Javier L. Prieto

We propose a novel formulation of the triplet objective function that improves metric learning without additional sample mining or overhead costs. Our approach aims to explicitly regularize the distance between the positive and negative samples in a triplet with respect to the anchor-negative distance. As an initial validation, we show that our method (calle

Liang Jin, Shixuan Gu, Donglai Wei, Jason Ken Adhinarta

Automatic rib labeling and anatomical centerline extraction are common prerequisites for various clinical applications. Prior studies either use in-house datasets that are inaccessible to communities, or focus on rib segmentation that neglects the clinical significance of rib labeling. To address these issues, we extend our prior dataset (RibSeg) on the bina

Alexia Jolicoeur-Martineau, Alex Lamb, Vikas Verma, Aniket Didolkar

We propose a novel regularizer for supervised learning called Conditioning on Noisy Targets (CNT). This approach consists in conditioning the model on a noisy version of the target(s) (e.g., actions in imitation learning or labels in classification) at a random noise level (from small to large noise). At inference time, since we do not know the target, we ru

Jia-Wei Ji, Faezeh Kimiaee Asadi, Khabat Heshami, Christoph Simon

We propose a quantum repeater architecture that can operate without cryogenics. Each node in our architecture builds on a cell of hot alkali atoms and noble-gas spins which offer a storage time as long as a few hours. Such a cell of hybrid gases is placed in a ring cavity, which allows us to suppress the detrimental four-wave mixing (FWM) noise in the system

Han Xu, Xiaorui Liu, Yuxuan Wan, Jiliang Tang

Fair classification aims to stress the classification models to achieve the equality (treatment or prediction quality) among different sensitive groups. However, fair classification can be under the risk of poisoning attacks that deliberately insert malicious training samples to manipulate the trained classifiers' performance. In this work, we study the pois

Ziyang Lyu, A. H. Welsh

Estimating characteristics of domains (referred to as small areas) within a population from sample surveys of the population is an important problem in survey statistics. In this paper, we consider model-based small area estimation under the nested error regression model. We discuss the construction of mixed model estimators (empirical best linear unbiased p