October 2022 arXiv papers — page 93

Josep Elgueta

A notion of {\em normal submonoid} of a monoid $M$ is introduced that generalizes the normal subgroups of a group. When ordered by inclusion, the set $\mathsf{NorSub}(M)$ of normal submonoids of $M$ is a complete lattice. Joins are explicitly described, and the lattice is computed for the finite full transformation monoids $T_n$, $n\geq 1$. It is also shown

Steven Finch

Equilibrium G/M/1-FIFO waiting times are exponentially distributed, as first proved by Smith (1953). For other client-sorting policies, such generality is not feasible. Assume that interarrival times are constant. Symbolics for the D/M/1-LIFO density are completely known; numerics for D/M/1-SIRO arise via an unpublished recursion due to Burke (1967). Conside

Xiangeng Zhou, Shou Lin

Let $\mathcal{I}$ be an ideal on $\mathbb{N}$. A mapping $f:X\to Y$ is called an $\mathcal{I}$-covering mapping provided a sequence $\{y_{n}\}_{n\in\mathbb N}$ is $\mathcal{I}$-converging to a point $y$ in $Y$, there is a sequence $\{x_{n}\}_{n\in\mathbb N}$ converging to a point $x$ in $X$ such that $x\in f^{-1}(y)$ and each $x_n\in f^{-1}(y_n)$. In this pa

A. Vershik

We describe one-dimensional central measures on numberings (tableaux) of ideals of partially ordered sets (posets). As the main example, we study the poset ${\Bbb Z}_+^d$ and the graph of its finite ideals, multidimensional Young tableaux; for $d=2$, it is the ordinary Young graph. The central measures are stratified by dimension; in the paper we give a comp

Malvina Marku, Vera Pancaldi

Inference of gene regulatory networks has been an active area of research for around 20 years, leading to the development of sophisticated inference algorithms based on a variety of assumptions and approaches. With the always increasing demand for more accurate and powerful models, the inference problem remains of broad scientific interest. The abstract repr

Fabian Horst, Djordje Slijepcevic, Matthias Zeppelzauer, Anna-Maria Raberger

State-of-the-art machine learning (ML) models are highly effective in classifying gait analysis data, however, they lack in providing explanations for their predictions. This "black-box" characteristic makes it impossible to understand on which input patterns, ML models base their predictions. The present study investigates whether Explainable Artificial Int

Sushil Sharma, Deepak Kumar, Pawel Moskal

Entanglement of photons originating from the electron-positron annihilation has not been proven experimentally. Though the independent experiments performed so far unanimously confirm that correlation between the linear polarizations of back-to-back photons from the electron-positron annihilation is consistent with the assumption that these photons are entan

Djordje Slijepcevic, Fabian Horst, Marvin Simak, Sebastian Lapuschkin

Machine learning (ML) models have proven effective in classifying gait analysis data, e.g., binary classification of young vs. older adults. ML models, however, lack in providing human understandable explanations for their predictions. This "black-box" behavior impedes the understanding of which input features the model predictions are based on. We investiga

Rui Zhang, Xiaoyan Zhao, Bayu Distiawan Trisedya, Min Yang

The task of entity alignment between knowledge graphs (KGs) aims to identify every pair of entities from two different KGs that represent the same entity. Many machine learning-based methods have been proposed for this task. However, to our best knowledge, existing methods all require manually crafted seed alignments, which are expensive to obtain. In this p

A. Ribak, M. Buzzi, D. Nicoletti, R. Singla

Coherent optical excitation of certain phonon modes in YBa$_2$Cu$_3$O$_{6+x}$ has been shown to induce superconducting-like interlayer coherence at temperatures higher than $T_c$. Recent work has associated these phenomena to a parametric excitation and amplification of Josephson plasma polaritons, which are overdamped above $T_c$ but are made coherent by th

Herkulaas MvE Combrink, Vukosi Marivate, Benjamin Rosman

Understanding which student support strategies mitigate dropout and improve student retention is an important part of modern higher educational research. One of the largest challenges institutions of higher learning currently face is the scalability of student support. Part of this is due to the shortage of staff addressing the needs of students, and the sub

Sergio F. Yapur

Data-driven OKID-ERA identification of the open-loop Tennessee Eastman plant is performed to obtain a linear model for control design purposes. Analysis such as numerical conditioning, output response errors, and zero-pole mappings highlight some definite advantages of the OKID-ERA approach when compared with models derived from typical linearization techniq

Wenkai Chen, Hongzhuo Liang, Zhaopeng Chen, Fuchun Sun

Currently, task-oriented grasp detection approaches are mostly based on pixel-level affordance detection and semantic segmentation. These pixel-level approaches heavily rely on the accuracy of a 2D affordance mask, and the generated grasp candidates are restricted to a small workspace. To mitigate these limitations, we first construct a novel affordance-base

Jianing Wang, Wenkang Huang, Qiuhui Shi, Hongbin Wang

Knowledge-enhanced Pre-trained Language Model (PLM) has recently received significant attention, which aims to incorporate factual knowledge into PLMs. However, most existing methods modify the internal structures of fixed types of PLMs by stacking complicated modules, and introduce redundant and irrelevant factual knowledge from knowledge bases (KBs). In th

Gayal Kuruppu, Bumuthu Dilshan, Shehan Samarasinghe, Nipuna Madhushan

We present an end-to-end virtual try-on pipeline, that can fit different clothes on a personalized 3-D human model, reconstructed using a single RGB image. Our main idea is to construct an animatable 3-D human model and try-on different clothes in a 3-D virtual environment. The existing frame by frame volumetric reconstruction of 3-D human models are highly

Luc Vinet

This text offers a personal account of the scientific legacy of two giants of mathematical physics at the turn of the Millenium and their heritage in Canada, their land of adoption.

Tolson Bell, Alan Frieze

We show that the threshold for having a rainbow copy of a power of a Hamilton cycle in a randomly edge colored copy of $G_{n,p}$ is within a constant factor of the uncolored threshold. Our proof requires $(1+\varepsilon)$ times the minimum number of colors.

Yao-Yao Xu, Xiao-Bing Deng, Xiao-Chun Duan, Lu-Shuai Cao

We report a hyperfine-states related weak equivalence principle (WEP) test which searches for possible WEP violation signal in single atom interferometer. With the ground hyperfine states $\left|F=1\right\rangle$ and $\left|F=2\right\rangle$ of $^{87}$Rb atoms simultaneously scanned over different paths in a Raman Mach-Zehnder interferometer (MZI), the diffe

Manu Joseph, Harsh Raj, Anubhav Yadav, Aaryamann Sharma

Querying databases for the right information is a time consuming and error-prone task and often requires experienced professionals for the job. Furthermore, the user needs to have some prior knowledge about the database. There have been various efforts to develop an intelligence which can help business users to query databases directly. However, there has be

Paris A. Karakasis, Athanasios P. Liavas, Nicholas D. Sidiropoulos, Panagiotis G. Simos

Functional magnetic resonance imaging (fMRI) is one of the most popular methods for studying the human brain. Task-related fMRI data processing aims to determine which brain areas are activated when a specific task is performed and is usually based on the Blood Oxygen Level Dependent (BOLD) signal. The background BOLD signal also reflects systematic fluctuat

Fernando Lucatelli Nunes, Matthijs Vákár

We present a simple technique for semantic, open logical relations arguments about languages with recursive types, which, as we show, follows from a principled foundation in categorical semantics. We demonstrate how it can be used to give a very straightforward proof of correctness of practical forward- and reverse-mode dual numbers style automatic different

Yuyuan Zeng, Bowen Zhao, Shanzhao Qiu, Tao Dai

Deep models have been widely and successfully used in image manipulation detection, which aims to classify tampered images and localize tampered regions. Most existing methods mainly focus on extracting global features from tampered images, while neglecting the relationships of local features between tampered and authentic regions within a single tampered im

Herkulaas MvE Combrink, Vukosi Marivate, Benjamin Rosman

The ability to generate synthetic data has a variety of use cases across different domains. In education research, there is a growing need to have access to synthetic data to test certain concepts and ideas. In recent years, several deep learning architectures were used to aid in the generation of synthetic data but with varying results. In the education con

Saraswati Pandey, B. K. Singh

In this study, we systematically investigate deformed Xe-Xe collisions at 5.44 TeV center of mass energy. We exploit the Monte Carlo HYDJET++ model to compute transverse momentum ($p_{T}$) distribution, nuclear modification factor $R_{AA}$ and relative suppression in terms of $R_{CP}$ as a function of transverse momentum and centrality of collision of charge

Hoi H. Nguyen, Melanie Matchett Wood

In this paper we study the cokernels of various random integral matrix models, including random symmetric, random skew-symmetric, and random Laplacian matrices. We provide a systematic method to establish universality under very general randomness assumption. Our highlights include both local and global universality of the cokernel statistics of all these mo

Sam Olin, Wei-Cheng Lee

Recently, Floquet systems have attracted a great deal of interest as they offer unprecedented ability to engineer topological states through the tuning of an external time-periodic drive. Consequentially, seeking new driving protocols that allow for more exotic topological phases and transitions becomes imperative for the Floquet engineer. In this paper, we

Vladislav Morozov

We develop a methodology for conducting inference on extreme quantiles of unobserved individual heterogeneity (e.g., heterogeneous coefficients, treatment effects) in panel data and meta-analysis settings. Inference is challenging in such settings: only noisy estimates of heterogeneity are available, and central limit approximations perform poorly in the tai

Surangika Ranathunga, Nisansa de Silva

Linguistic disparity in the NLP world is a problem that has been widely acknowledged recently. However, different facets of this problem, or the reasons behind this disparity are seldom discussed within the NLP community. This paper provides a comprehensive analysis of the disparity that exists within the languages of the world. We show that simply categoris

Zhi-Tian Zou, Wen-Sheng Fang, Xin Liu, Ying Li

LHCb Collaboration studied the resonant structure of $B_s\to \overline{D}^0K^-\pi^+$ decays using the Dalitz plot analysis technique, based on a data sample corresponding to an integrated luminosity of $3.0{\rm fb}^{-1}$ of $pp$ collision. The $K^-\pi^+$ components have been analyzed in the amplitude model, where the decay amplitude is modeled to be the reso

Shengxi Li, Xinyi Zhao, Ljubisa Stankovic, Danilo Mandic

The success of convolution neural networks (CNN) has been revolutionising the way we approach and use intelligent machines in the Big Data era. Despite success, CNNs have been consistently put under scrutiny owing to their \textit{black-box} nature, an \textit{ad hoc} manner of their construction, together with the lack of theoretical support and physical me

Rui Li, Guodong Ma, Dexin Zhao, Ranran Zeng

SpecAugment is a very effective data augmentation method for both HMM and E2E-based automatic speech recognition (ASR) systems. Especially, it also works in low-resource scenarios. However, SpecAugment masks the spectrum of time or the frequency domain in a fixed augmentation policy, which may bring relatively less data diversity to the low-resource ASR. In

Pengchong Qiao, Zhidan Wei, Yu Wang, Zhennan Wang

Semi-supervised learning (SSL) essentially pursues class boundary exploration with less dependence on human annotations. Although typical attempts focus on ameliorating the inevitable error-prone pseudo-labeling, we think differently and resort to exhausting informative semantics from multiple probably correct candidate labels. In this paper, we introduce Fu

Giacomo Brunello

In this thesis we elaborate on two different aspects of the Effective Field Theory (EFT) approach to a Binary Coalescing system in General Relativity (GR). First, we consider the issue of hereditary effects in the Post-Newtonian (PN) perturbative scheme, and we compute hereditary diagrams in the far zone region up to 5PN order, using both Feynman and Schwing

Xiantong Zhao, Yinan Han, Shengjing Tian, Jian Liu

Although recent Siamese network-based trackers have achieved impressive perceptual accuracy for single object tracking in LiDAR point clouds, they usually utilized heavy correlation operations to capture category-level characteristics only, and overlook the inherent merit of arbitrariness in contrast to multiple object tracking. In this work, we propose a ra

Hossein Rahmati, Stefan Czesla, Sara Khalafinejad, Paul Mollière

Transmission spectroscopy is a prime technique to study the chemical composition and structure of exoplanetary atmospheres. Strong excess absorption signals have been detected in the optical Na I D1, 2 Fraunhofer lines during transits of hot Jupiters, which are attributed to the planetary atmospheres and allow us to constrain its structure. We study the atmo

Sebastian M. Cioabă, Vishal Gupta

In this paper, we obtain a lower bound for the smallest eigenvalue of a regular graph containing many copies of a smaller fixed subgraph. This generalizes a result of Aharoni, Alon, and Berger in which the subgraph is a triangle. We apply our results to obtain a lower bound on the smallest eigenvalue of the associahedron graph, and we prove that this bound g

Rosa M. Miró-Roig, Martí Salat-Moltó

In this paper, we introduce the notion of a Klyachko diagram for a monomial ideal $I$ in a certain multi-graded polynomial ring, namely the Cox ring $R$ of a smooth complete toric variety, with irrelevant maximal ideal $B$. We present procedures to compute the Klyachko diagram of $I$ from its monomial generators, and to retrieve the $B-$saturation $I^{\mathr

Jianxin Dai, Jin Ge, Kangda Zhi, Cunhua Pan

This paper investigates the performance of a two-timescale transmission design for uplink reconfigurable intelligent surface (RIS)-aided cell-free massive multiple-input multiple-output (CF-mMIMO) systems. We consider the Rician channel model and design the passive beamforming of RISs based on the long-time statistical channel state information (CSI), while

Nicholas D. Sidiropoulos, Paris Karakasis, Aritra Konar

We consider the problem of finding the smallest or largest entry of a tensor of order N that is specified via its rank decomposition. Stated in a different way, we are given N sets of R-dimensional vectors and we wish to select one vector from each set such that the sum of the Hadamard product of the selected vectors is minimized or maximized. We show that t

Lidan Wang

In this paper, we study the nonlinear Schr\"{o}dinger equation $$ -\Delta u+(V(x)- \frac{\rho}{(|x|^2+1)})u=f(x,u) $$ on the lattice graph $\mathbb{Z}^N$ with $N\geq 3$, where $V$ is a bounded periodic potential and $0$ lies in a spectral gap of the Schr\"{o}dinger operator $-\Delta+V$. Under some assumptions on the nonlinearity $f$, we prove the existence a

Yujin Guo, Yan Li, Yong Luo

This paper is concerned with ground states of attractive Bose gases confined in an anharmonic trap $V(x)=\omega(|x|^2+k|x|^4)$ rotating at the velocity $\Omega>0$, where $\omega>0$ denotes the trapping frequency, and $k>0$ represents the strength of the quartic term. It is known that for any $\Omega>0$, ground states exist in such traps if and only if $0<a<a

Chujie Zheng, Jinfeng Zhou, Yinhe Zheng, Libiao Peng

Dialogue contradiction is a critical issue in open-domain dialogue systems. The contextualization nature of conversations makes dialogue contradiction detection rather challenging. In this work, we propose a benchmark for Contradiction Detection in Chinese Conversations, namely CDConv. It contains 12K multi-turn conversations annotated with three typical con

M. L. L. Dantas, R. Smiljanic, R. Boesso, H. J. Rocha-Pinto

We report the identification of a set of old super metal-rich dwarf stars with orbits of low eccentricity that reach a maximum height from the Galactic plane between ~0.5-1.5 kpc. We discuss their properties to understand their origins. We use data from the internal data release 6 of the Gaia-ESO Survey. We selected stars observed at high resolution with abu

Ying-Xuan Chen, Jia-Hui Huang, Haoxiang Jiang

The spherical photon orbits around a black hole with constant radii are particular important in astrophysical observations of the black hole. In this paper, the equatorial and non-equatorial spherical photon orbits around Kerr-Newman black holes are studied. The radii of these orbits satisfy a sextic polynomial equation with three parameters, the rotation pa

Nika Mansouri Ghiasi, Mohammad Sadrosadati, Geraldo F. Oliveira, Konstantinos Kanellopoulos

Recent nano-technological advances enable the Monolithic 3D (M3D) integration of multiple memory and logic layers in a single chip, allowing for fine-grained connections between layers and significantly alleviating main memory bottlenecks. We show for a variety of workloads, on a state-of-the-art M3D-based system, that the performance and energy bottlenecks

Maximilian Harmel, Roger Andrew Sauer

This work proposes four novel hybrid quadrature schemes for the efficient and accurate evaluation of weakly singular boundary integrals (1/r kernel) on arbitrary smooth surfaces. Such integrals appear in boundary element analysis for several partial differential equations including the Stokes equation for viscous flow and the Helmholtz equation for acoustics

Azhan Mohammed

Currently, a significant amount of research has been done in field of Remote Sensing with the use of deep learning techniques. The introduction of Marine Debris Archive (MARIDA), an open-source dataset with benchmark results, for marine debris detection opened new pathways to use deep learning techniques for the task of debris detection and segmentation. Thi

Leo Herr

We introduce new notions of log jet spaces. Mildly singular spaces are ``smooth'' in log geometry, so their log jet spaces behave like the jet spaces of smooth varieties. Myriad examples contrast log jet spaces with the usual jet spaces of schemes. We then compute the log K\"ahler differentials of the log jet and arc spaces after de Fernex and Docampo. We ob

Ieng Wai Un, Subhajit Sarkar, Yonatan Sivan

Low electron density Drude (LEDD) materials such as indium tin oxide (ITO) are receiving considerable attention because of their combination of CMOS compatibility, unique epsilon-near-zero (ENZ) behavior, and giant ultrafast nonlinear thermo-optic response. However, the understanding of the electronic and optical response of LEDD materials is so far based on

Riccardo Della Vecchia, Alena Shilova, Philippe Preux, Riad Akrour

Deep Reinforcement Learning (Deep RL) has had incredible achievements on high dimensional problems, yet its learning process remains unstable even on the simplest tasks. Deep RL uses neural networks as function approximators. These neural models are largely inspired by developments in the (un)supervised machine learning community. Compared to these learning

Ben Zandonati, Adrian Alan Pol, Maurizio Pierini, Olya Sirkin

Model compression is vital to the deployment of deep learning on edge devices. Low precision representations, achieved via quantization of weights and activations, can reduce inference time and memory requirements. However, quantifying and predicting the response of a model to the changes associated with this procedure remains challenging. This response is n

Wenbin Chen, Jianyu Jing, Hao Wu

We propose and analyze a first-order finite difference scheme for the functionalized Cahn-Hilliard (FCH) equation with a logarithmic Flory-Huggins potential. The semi-implicit numerical scheme is designed based on a suitable convex-concave decomposition of the FCH free energy. We prove unique solvability of the numerical algorithm and verify its unconditiona

Betty van Aken, Jens-Michalis Papaioannou, Marcel G. Naik, Georgios Eleftheriadis

The use of deep neural models for diagnosis prediction from clinical text has shown promising results. However, in clinical practice such models must not only be accurate, but provide doctors with interpretable and helpful results. We introduce ProtoPatient, a novel method based on prototypical networks and label-wise attention with both of these abilities.

Belay B Yimer, Mark Lunt, Marcus Beasley, Gary J Macfarlane

The past decade has seen an explosion of research in causal mediation analysis. However, most analytic tools developed so far rely on frequentist methods which may not be robust in the case of small sample sizes. In this paper, we propose a Bayesian approach for causal mediation analysis based on Bayesian g-formula. We created BayesGmed, an R-package for fit

K. Yanase, N. Shimizu, K. Higashiyama, N. Yoshinaga

We study the nuclear Schiff moments of $^{129}$Xe and $^{199}$Hg induced by the nucleon electric dipole moment using large-scale shell model calculations. For $^{129}$Xe, we find a linear relation between the leading-order contribution and magnetic moment, which would be useful in reducing the theoretical uncertainty. The conventional model space does not co

Alessandro Venerandi, Luca Maria Aiello, Sergio Porta

The COVID-19 pandemic generated a considerable debate in relation to urban density. This is an old debate, originated in mid 19th century's England with the emergence of public health and urban planning disciplines. While popularly linked, evidence suggests that such relationship cannot be generally assumed. Furthermore, urban density has been investigated i

Petar Radanliev, David De Roure

With the increased digitalisation of our society, new and emerging forms of data present new values and opportunities for improved data driven multimedia services, or even new solutions for managing future global pandemics (i.e., Disease X). The results are somewhat unexpected. Despite the special relationship between the US and the UK, there is not much evi

Marko Maljkovic, Gustav Nilsson, Nikolas Geroliminis

Due to the ever-increasing popularity of ride-hailing services and the indisputable shift towards alternative fuel vehicles, the intersection of the ride-hailing market and smart electric mobility provides an opportunity to trade different services to achieve societal optimum. In this work, we present a hierarchical, game-based, control mechanism for balanci

Vahid Jamali, Walid Ghanem, Robert Schober, H. Vincent Poor

The performance characterization of communication systems assisted by large reconfigurable intelligent surfaces (RISs) significantly depends on the adopted models for the underlying channels. Under unrealistic channel models, the system performance may be over- or under-estimated which yields inaccurate conclusions for the system design. In this paper, we re

Xi Lin, Zhiyuan Yang, Xiaoyuan Zhang, Qingfu Zhang

Expensive multi-objective optimization problems can be found in many real-world applications, where their objective function evaluations involve expensive computations or physical experiments. It is desirable to obtain an approximate Pareto front with a limited evaluation budget. Multi-objective Bayesian optimization (MOBO) has been widely used for finding a

Constantin Octavian Puiu

K-FAC (arXiv:1503.05671, arXiv:1602.01407) is a tractable implementation of Natural Gradient (NG) for Deep Learning (DL), whose bottleneck is computing the inverses of the so-called ``Kronecker-Factors'' (K-factors). RS-KFAC (arXiv:2206.15397) is a K-FAC improvement which provides a cheap way of estimating the K-factors inverses. In this paper, we exploit th

Wenjie Luo, Qun Song, Zhenyu Yan, Rui Tan

Indoor self-localization is a highly demanded system function for smartphones. The current solutions based on inertial, radio frequency, and geomagnetic sensing may have degraded performance when their limiting factors take effect. In this paper, we present a new indoor simultaneous localization and mapping (SLAM) system that utilizes the smartphone's built-

Zhongjiang Yan, Bo Li, Mao Yang

The network protocol architecture not only can be designed from the traditional view of layers, but also can be designed from the view of planes, i.e., the data, control and management planes. Media access control (MAC) is a function of the data link layer, and thus the MAC protocols involve of both the data and control planes. However, although the internat

Shuo Xiao, Wen-Xi Peng, Shuang-Nan Zhang, Shao-Lin Xiong

The precursors of short and long Gamma Ray Bursts (SGRBs and LGRBs) can serve as probes of their progenitors, as well as shedding light on the physical processes of mergers or core-collapse supernovae. Some models predict the possible existence of Quasi-Periodically Oscillations (QPO) in the precursors of SGRBs. Although many previous studies have performed

Jinshan Zeng, Ruiying Xu, Yu Wu, Hongwei Li

Zero-shot Chinese character recognition has attracted rising attention in recent years. Existing methods for this problem are mainly based on either certain low-level stroke-based decomposition or medium-level radical-based decomposition. Considering that the stroke- and radical-level decompositions can provide different levels of information, we propose an

Z. Y. Gao, P. Wang, M. Y. Yang

The nonlocal chiral effective theory is applied to investigate the electromagnetic and strange form factors of nucleon. The bubble and tadpole diagrams are included in the calculation. With the contributions from bubble and tadpole diagrams, the obtained electromagnetic form factors are close to the results without these contributions as long as the low ener

Samuel Rey, Victor M. Tenorio, Antonio G. Marques

When facing graph signal processing tasks, the workhorse assumption is that the graph describing the support of the signals is known. However, in many relevant applications the available graph suffers from observation errors and perturbations. As a result, any method relying on the graph topology may yield suboptimal results if those imperfections are ignore

Yuanyuan Qiao, Jiaxin Yin, Wei Wang, Fábio Duarte

Within the next several years, there will be a high level of autonomous technology that will be available for widespread use, which will reduce labor costs, increase safety, save energy, enable difficult unmanned tasks in harsh environments, and eliminate human error. Compared to software development for other autonomous vehicles, maritime software developme

Tianyu Liu, Jie Lu, Zheng Yan, Guangquan Zhang

As a powerful Bayesian non-parameterized algorithm, the Gaussian process (GP) has performed a significant role in Bayesian optimization and signal processing. GPs have also advanced online decision-making systems because their posterior distribution has a closed-form solution. However, its training and inference process requires all historic data to be store

Hongjiang Chen, Yang Wang, Leibo Liu, Shaojun Wei

Deep learning applications are being transferred from the cloud to edge with the rapid development of embedded computing systems. In order to achieve higher energy efficiency with the limited resource budget, neural networks(NNs) must be carefully designed in two steps, the architecture design and the quantization policy choice. Neural Architecture Search(NA

Yijun Gong, Shupei Liu, Xiao-Lei Zhang

Conventional sound source localization methods are mostly based on a single microphone array that consists of multiple microphones. They are usually formulated as the estimation of the direction of arrival problem. In this paper, we propose a deep-learning-based end-to-end sound source localization method with ad-hoc microphone arrays, where an ad-hoc microp

Mingwang Zhao

In this paper, based on the controllable canonical form and the Hurwitz matrix of the Hurwitz stability criterion, an analytical volume computing method for the smooth controllability zonotope for the linear continuous-time(LCT) systems, without of help of the eigenvalue computing of the systems, is presented. And then, the computing method is generlized to

Tobias König

This note is concerned with the Bianchi-Egnell inequality, which quantifies the stability of the Sobolev inequality, and its generalization to fractional exponents $s \in (0, \frac{d}{2})$. We prove that in dimension $d \geq 2$ the best constant \[ c_{BE}(s) = \inf_{f \in \dot{H}^s(\mathbb R^d) \setminus \mathcal M} \frac{\|(-\Delta)^{s/2} f\|_{L^2(\mathbb R

Peggy Tang, Kun Hu, Lei Zhang, Jiebo Luo

Multimodal summarisation with multimodal output is drawing increasing attention due to the rapid growth of multimedia data. While several methods have been proposed to summarise visual-text contents, their multimodal outputs are not succinct enough at an extreme level to address the information overload issue. To the end of extreme multimodal summarisation,

Mingwang Zhao

In this paper, the works on the analytical volume analysis for the controllable regions of the linear discrete-time (LDT) systems in papers \cite{zhaomw202001} and \cite {zhaomw202004} are discussed further and a new theorem on the analytical computing for the finite-time controllability zonotope (controllable region) of LDT systems are proven. And then, thr

Takumi Otani

For a stability condition $\sigma$ on a triangulated category, Dimitrov-Katzarkov introduced the notion of a $\sigma$-exceptional collection. In this paper, we study full $\sigma$-exceptional collections in the derived category of an acyclic quiver. In particular, we prove that any stability condition $\sigma$ on the derived category of a Dynkin quiver admit

Baijun Ji, Tong Zhang, Yicheng Zou, Bojie Hu

Multimodal machine translation (MMT) aims to improve translation quality by equipping the source sentence with its corresponding image. Despite the promising performance, MMT models still suffer the problem of input degradation: models focus more on textual information while visual information is generally overlooked. In this paper, we endeavor to improve MM

Hyung-Kwon Ko, Gwanmo Park, Hyeon Jeon, Jaemin Jo

Large-scale Text-to-image Generation Models (LTGMs) (e.g., DALL-E), self-supervised deep learning models trained on a huge dataset, have demonstrated the capacity for generating high-quality open-domain images from multi-modal input. Although they can even produce anthropomorphized versions of objects and animals, combine irrelevant concepts in reasonable wa

Salman Beigi

Kernel method in machine learning consists of encoding input data into a vector in a Hilbert space called the feature space and modeling the target function as a linear map on the feature space. Given a cost function, computing such an optimal linear map requires computation of a kernel matrix whose entries equal the inner products of feature vectors. In the

Jinming Zhao, Hao Yang, Gholamreza Haffari, Ehsan Shareghi

Pre-trained speech Transformers in speech translation (ST) have facilitated state-of-the-art (SotA) results; yet, using such encoders is computationally expensive. To improve this, we present a novel Reducer Adaptor block, RedApt, that could be seamlessly integrated within any Transformer-based speech encoding architecture. Integrating the pretrained wav2vec

Giulio Morpurgo

The Polignac's Conjecture, first formulated by Alphonse de Polignac in 1849, asserts that, for any even number M, there exist infinitely many couples of prime numbers P, P+M. When M = 2, this reduces to the Twin Primes Conjecture. Despite numerical evidence, and many theoretical progresses, the conjecture has resisted a formal proof since. In the first part

Bohong Wu, Hai Zhao

Though offering amazing contextualized token-level representations, current pre-trained language models take less attention on accurately acquiring sentence-level representation during their self-supervised pre-training. However, contrastive objectives which dominate the current sentence representation learning bring little linguistic interpretability and no

Shihao Shao, Qinghua Cui

This paper presents the 1st place solution for the Google Universal Images Embedding Competition on Kaggle. The highlighted part of our solution is based on 1) A novel way to conduct training and fine-tuning; 2) The idea of a better ensemble in the pool of models that make embedding; 3) The potential trade-off between fine-tuning on high-resolution and overl

Chao Zhou, Yuan-Gen Wang, Guopu Zhu

Deep neural networks are facing severe threats from adversarial attacks. Most existing black-box attacks fool target model by generating either global perturbations or local patches. However, both global perturbations and local patches easily cause annoying visual artifacts in adversarial example. Compared with some smooth regions of an image, the object reg

Jian Song, Di Liang, Rumei Li, Yuntao Li

Transformer-based pre-trained models like BERT have achieved great progress on Semantic Sentence Matching. Meanwhile, dependency prior knowledge has also shown general benefits in multiple NLP tasks. However, how to efficiently integrate dependency prior structure into pre-trained models to better model complex semantic matching relations is still unsettled.

Diego Stucchi, Luca Frittoli, Giacomo Boracchi

We introduce Class Distribution Monitoring (CDM), an effective concept-drift detection scheme that monitors the class-conditional distributions of a datastream. In particular, our solution leverages multiple instances of an online and nonparametric change-detection algorithm based on QuantTree. CDM reports a concept drift after detecting a distribution chang

Bingyi Chen

Let $\pi:X\rightarrow Z$ be a Fano type fibration with $\dim X-\dim Z=d$ and let $(X,B)$ be an $\epsilon$-lc pair with $K_X+B\sim_{\RR} 0/Z$. The canonical bundle formula gives $(Z,B_Z+M_Z)$ where $B_Z$ is the discriminant divisor and $M_Z$ is the moduli divisor which is determined up to $\RR$-linear equivalence. Shokurov conjectured that one can choose $M_Z

Jielun Chen, E. M. Stoudenmire, Steven R. White

The Quantum Fourier Transform (QFT) is a key component of many important quantum algorithms, most famously as being the essential ingredient in Shor's algorithm for factoring products of primes. Given its remarkable capability, one would think it can introduce large entanglement to qubit systems and would be difficult to simulate classically. While early res

A. R. R. Almeida, C. J. A. P. Martins

Cosmic string networks form during cosmological phase transitions as a consequence of the Kibble mechanism. The evolution of the simplest networks is accurately described by the canonical Velocity Dependent One-Scale (VOS) model. However, numerical simulations have demonstrated the existence of significant quantities of short-wavelength propagation modes on

Joan Quirant

In this thesis we explore the vacua structure of type IIA orientifold (CY) compactifications with fluxes, from the 4d and the 10d point of view. We start by reviewing type IIA Calabi-Yau orientifold compactifications with fluxes. We consider RR and NSNS fluxes, and then add (non)-geometric fluxes. We recall how the potential can be written as a bilinear expr

Hong Chen, Rujun Han, Te-Lin Wu, Hideki Nakayama

Story visualization advances the traditional text-to-image generation by enabling multiple image generation based on a complete story. This task requires machines to 1) understand long text inputs and 2) produce a globally consistent image sequence that illustrates the contents of the story. A key challenge of consistent story visualization is to preserve ch

Xuan Gong, Liangchen Song, Rishi Vedula, Abhishek Sharma

Federated Learning (FL) is a machine learning paradigm where many local nodes collaboratively train a central model while keeping the training data decentralized. This is particularly relevant for clinical applications since patient data are usually not allowed to be transferred out of medical facilities, leading to the need for FL. Existing FL methods typic

Ankur Betageri

In this paper I examine the process of getting affected by and the process of making sense of non-language sounds and propose the idea of the contextual cognitive apparatus or exo-brain. We are affected by a singing voice even when we do not fully understand the sounds produced by it. When the listener starts hearing words in the sung song and/or starts inte

Xiaoqiang Wang, Jiaojiao Wang, Chengju Li, Yansheng Wu

BCH codes and their dual codes are two special subclasses of cyclic codes and are the best linear codes in many cases. A lot of progress on the study of BCH cyclic codes has been made, but little is known about the minimum distances of the duals of BCH codes. Recently, a new concept called dually-BCH code was introduced to investigate the duals of BCH codes

Ori Kelner, Or Weinstein, Ehud Rivlin, Roman Goldenberg

We propose a two-stage unsupervised approach for parsing videos into phases. We use motion cues to divide the video into coarse segments. Noisy segment labels are then used to weakly supervise an appearance-based classifier. We show the effectiveness of the method for phase detection in colonoscopy videos.

Wenxia Li, Zhiqiang Wang

In this paper, we study the spectrality of infinite convolutions in $\mathbb{R}^d$, where the spectrality means the corresponding square integrable function space admits a family of exponential functions as an orthonormal basis. Suppose that the infinite convolutions are generated by a sequence of admissible pairs in $\mathbb{R}^d$. We give two sufficient co

Jonathan Wilton, Abigail M. Y. Koay, Ryan K. L. Ko, Miao Xu

The need to learn from positive and unlabeled data, or PU learning, arises in many applications and has attracted increasing interest. While random forests are known to perform well on many tasks with positive and negative data, recent PU algorithms are generally based on deep neural networks, and the potential of tree-based PU learning is under-explored. In

Thomas D. Honeychurch, Daniel S. Kosov

We investigate the effects of alternating voltage on nonequilibrium quantum systems with localised phonon modes. Nonequilibrium Green's functions are utilised, with electron-phonon coupling being considered with the $GD$ approximation (self-consistent Born approximation). Using a Floquet approach, we assume periodicity of the dynamics. This approach allows u

Hong Chen, Duc Minh Vo, Hiroya Takamura, Yusuke Miyao

Existing automatic story evaluation methods place a premium on story lexical level coherence, deviating from human preference. We go beyond this limitation by considering a novel \textbf{Story} \textbf{E}valuation method that mimics human preference when judging a story, namely \textbf{StoryER}, which consists of three sub-tasks: \textbf{R}anking, \textbf{R}

Tao Tang, Changlin Li, Guangrun Wang, Kaicheng Yu

Automatic data augmentation (AutoAugment) strategies are indispensable in supervised data-efficient training protocols of vision transformers, and have led to state-of-the-art results in supervised learning. Despite the success, its development and application on self-supervised vision transformers have been hindered by several barriers, including the high s