October 2022 arXiv papers — page 40

Yongkang Liu, Shi Feng, Wei Gao, Daling Wang

Current end-to-end retrieval-based dialogue systems are mainly based on Recurrent Neural Networks or Transformers with attention mechanisms. Although promising results have been achieved, these models often suffer from slow inference or huge number of parameters. In this paper, we propose a novel lightweight fully convolutional architecture, called DialogCon

Chen Luo, Xiang Zhou, Hui-Chang Li, Tai-Lin Zhang

Higher-order topological insulators(HOTIs) is an exciting topic. We constructed a square lattice dipole arrays, it supports out-of-plane and in-plane modes by going beyond conventional scalar coupling. In-plane modes naturally break $\mathrm C_{4}$ symmetry, we only studied the out-of-plane modes that maintain $\mathrm C_{4}$ symmetry. Due to the slowly deca

Yoichi Arai, Taisuke Otsu, Mengshan Xu

The generalized least square (GLS) is one of the most basic tools in regression analyses. A major issue in implementing the GLS is estimation of the conditional variance function of the error term, which typically requires a restrictive functional form assumption for parametric estimation or smoothing parameters for nonparametric estimation. In this paper, w

Fabrizio Fiore, Alejandro Guzman, Riccardo Campana, Yuri Evangelista

HERMES-Pathfinder is a constellation of six 3U nano-satellites hosting simple but innovative X-ray detectors for determining the positions of, and monitoring cosmic high-energy transients such as gamma-ray bursts and the electromagnetic counterparts of gravitational Wave Events. The HERMES Technological Pathfinder project is funded by the Italian Space Agenc

Shibing Chen, Jiakun Liu

In this paper, we establish a regularity theory for the optimal transport problem when the target is composed of two disjoint convex domains. This is an important model in which singularities arise. Even though the singular set does not exhibit any form of convexity a priori, we prove its higher order regularity by developing novel methods, which also have m

Tianqi Chen, Ruizhe Shen, Ching Hua Lee, Bo Yang

The AKLT state is the ground state of an isotropic quantum Heisenberg spin-$1$ model. It exhibits an excitation gap and an exponentially decaying correlation function, with fractionalized excitations at its boundaries. So far, the one-dimensional AKLT model has only been experimentally realized with trapped-ions as well as photonic systems. In this work, we

Roberto Gallotta, Kai Arulkumaran, L. B. Soros

In mixed-initiative co-creation tasks, wherein a human and a machine jointly create items, it is important to provide multiple relevant suggestions to the designer. Quality-diversity algorithms are commonly used for this purpose, as they can provide diverse suggestions that represent salient areas of the solution space, showcasing designs with high fitness a

Yuxuan Chen, David Harbecke, Leonhard Hennig

Prompting pre-trained language models has achieved impressive performance on various NLP tasks, especially in low data regimes. Despite the success of prompting in monolingual settings, applying prompt-based methods in multilingual scenarios has been limited to a narrow set of tasks, due to the high cost of handcrafting multilingual prompts. In this paper, w

Zhenyu Chen, Xiaodie Lin, Zhaohui Wei

Suppose we have an unknown multipartite quantum state, how can we experimentally find out whether it is genuine multipartite entangled or not? Recall that even for a bipartite quantum state whose density matrix is known, it is already NP-Hard to determine whether it is entangled or not. Therefore, it is hard to efficiently solve the above problem generally.

T. Y. S. S Santosh, Shanshan Xu, Oana Ichim, Matthias Grabmair

This work demonstrates that Legal Judgement Prediction systems without expert-informed adjustments can be vulnerable to shallow, distracting surface signals that arise from corpus construction, case distribution, and confounding factors. To mitigate this, we use domain expertise to strategically identify statistically predictive but legally irrelevant inform

Zhenyu Wu, Lin Wang, Wei Wang, Tengfei Shi

Although deep salient object detection (SOD) has achieved remarkable progress, deep SOD models are extremely data-hungry, requiring large-scale pixel-wise annotations to deliver such promising results. In this paper, we propose a novel yet effective method for SOD, coined SODGAN, which can generate infinite high-quality image-mask pairs requiring only a few

Herman Van der Auweraer, Dirk Hartmann

While the digital twin has become an intrinsic part of the product creation process, its true power lies in the connectivity of the digital representation with its physical counterpart. Data acquired on the physical asset can validate, update and enrich the digital twin. The knowledge contained in the digital representation brings value to the physical asset

Martin Zach, Florian Knoll, Thomas Pock

Data-driven approaches recently achieved remarkable success in magnetic resonance imaging (MRI) reconstruction, but integration into clinical routine remains challenging due to a lack of generalizability and interpretability. In this paper, we address these challenges in a unified framework based on generative image priors. We propose a novel deep neural net

Jaehee Jang, Heonseok Ha, Dahuin Jung, Sungroh Yoon

Personalized federated learning is aimed at allowing numerous clients to train personalized models while participating in collaborative training in a communication-efficient manner without exchanging private data. However, many personalized federated learning algorithms assume that clients have the same neural network architecture, and those for heterogeneou

Guohui Guan, Zongxia Liang, Yilun Song

This paper studies the continuous-time pre-commitment KMM problem proposed by Klibanoff, Marinacci and Mukerji (2005) in incomplete financial markets, which concerns with the portfolio selection under smooth ambiguity. The decision maker (DM) is uncertain about the dominated priors of the financial market, which are characterized by a second-order distributi

Chen Zhang, Luis Fernando D'Haro, Qiquan Zhang, Thomas Friedrichs

Recent model-based reference-free metrics for open-domain dialogue evaluation exhibit promising correlations with human judgment. However, they either perform turn-level evaluation or look at a single dialogue quality dimension. One would expect a good evaluation metric to assess multiple quality dimensions at the dialogue level. To this end, we are motivate

Carlo Paolo Sasso, Giovanni Mana, Enrico Massa

The observation of neutron interference by using a crystal interferometer having a separate analyser opens the way to the construction and operation of interferometers with vast arm separation and length. Setting the design specifications requires a three-dimensional dynamical-theory model of their operation. In this paper, we develop the needed three-dimens

Eduard Gorbunov, Adrien Taylor, Samuel Horváth, Gauthier Gidel

Algorithms for min-max optimization and variational inequalities are often studied under monotonicity assumptions. Motivated by non-monotone machine learning applications, we follow the line of works [Diakonikolas et al., 2021, Lee and Kim, 2021, Pethick et al., 2022, B\"ohm, 2022] aiming at going beyond monotonicity by considering the weaker negative comono

Wenceslao Arroyo-Machado, Daniel Torres-Salinas, Rodrigo Costas

Wikipedia is one of the most visited websites in the world and is also a frequent subject of scientific research. However, the analytical possibilities of Wikipedia information have not yet been analyzed considering at the same time both a large volume of pages and attributes. The main objective of this work is to offer a methodological framework and an open

Han Meng, Xiaosong He, Zexing Chen, Feng Zhou

Some Natural Language Generation (NLG) tasks require both faithfulness and diversity. The decoding strategy is intensively related to the quality of the generated text. Strategies such as beam search, greedy search, etc., perform with low diversity and high repetition. On the other hand, guided decoding, the solution towards diversity, may generate unfaithfu

Peng-Bo Song, Zhiwei Hu, Su-Yang Hsu, Jin-Ming Chen

Single crystals of Sr3NaIrO6 and Sr3AgIrO6 have been successfully grown using hydroxides flux. Sr3NaIrO6 and Sr3AgIrO6 crystallize in the K4CdCl6-type structure with the space group R-3c (No.167). Sr3NaIrO6 and Sr3AgIrO6 are electrically insulating with estimated activation gaps of 0.68 eV and 0.80 eV, respectively. Sr3NaIrO6 and Sr3AgIrO6 show paramagnetic

Xulong Zhang, Jianzong Wang, Ning Cheng, Mengyuan Zhao

The recent emergence of joint CTC-Attention model shows significant improvement in automatic speech recognition (ASR). The improvement largely lies in the modeling of linguistic information by decoder. The decoder joint-optimized with an acoustic encoder renders the language model from ground-truth sequences in an auto-regressive manner during training. Howe

Li Yu, Wenshuai Chang, Shiyu Wu, Moncef Gabbouj

Convolutional neural networks have achieved excellent results in compressed video quality enhancement task in recent years. State-of-the-art methods explore the spatiotemporal information of adjacent frames mainly by deformable convolution. However, offset fields in deformable convolution are difficult to train, and its instability in training often leads to

Lizhao Liu, Kunyang Lin, Shangxin Huang, Zhongli Li

Stroke is the basic element of Chinese character and stroke extraction has been an important and long-standing endeavor. Existing stroke extraction methods are often handcrafted and highly depend on domain expertise due to the limited training data. Moreover, there are no standardized benchmarks to provide a fair comparison between different stroke extractio

Sarah Kaakai, Anis Matoussi, Achraf Tamtalini

We present a framework for constructing multivariate risk measures that is inspired from univariate Optimized Certainty Equivalent (OCE) risk measures. We show that this new class of risk measures verifies the desirable properties such as convexity, monotonocity and cash invariance. We also address numerical aspects of their computations using stochastic alg

Ekaterina Morozova, Vladimir Panov

In this paper, we present a new bivariate model for the joint description of the Bitcoin prices and the media attention to Bitcoin. Our model is based on the class of the L\'evy processes and is able to realistically reproduce the jump-type dynamics of the considered time series. We focus on the low-frequency setup, which is for the L\'evy - based models ess

Nankai Lin, Hongyan Wu, Sihui Fu, Shengyi Jiang

Chinese spelling check is a task to detect and correct spelling mistakes in Chinese text. Existing research aims to enhance the text representation and use multi-source information to improve the detection and correction capabilities of models, but does not pay too much attention to improving their ability to distinguish between confusable words. Contrastive

Alireza Talebian, Seyed Ali Hosseini Mansoori, Hassan Firouzjahi

We study a model of inflation with multiple pseudo-scalar fields coupled to a $U(1)$ gauge field through Chern-Simons interactions. Because of parity violating interactions, one polarization of the gauge field is amplified yielding to enhanced curvature perturbation power spectrum. Inflation proceeds in multiple stages as each pseudo-scalar field rolls towar

Zhenyu Wu, Shuai Li, Chenglizhao Chen, Hong Qin

Recent research advances in salient object detection (SOD) could largely be attributed to ever-stronger multi-scale feature representation empowered by the deep learning technologies. The existing SOD deep models extract multi-scale features via the off-the-shelf encoders and combine them smartly via various delicate decoders. However, the kernel sizes in th

Xulong Zhang, Jianzong Wang, Ning Cheng, Jing Xiao

Recent advances in pre-trained language models have improved the performance for text classification tasks. However, little attention is paid to the priority scheduling strategy on the samples during training. Humans acquire knowledge gradually from easy to complex concepts, and the difficulty of the same material can also vary significantly in different lea

Shikhar Nilabh, Fidel Grandia

Numerous polluted groundwater sites across the globe require an active remediation strategy to restore natural environmental conditions and local ecosystem. The Engineered Nano-particles (ENPs) have emerged as an efficient reactive agent for the in-situ degradation of groundwater contaminants. While the performance of these ENPs has been highly promising on

Thomas Kosch, Julian Rasch, Albrecht Schmidt, Sebastian Feger

Understanding electronics is a critical area in the maker scene. Many of the makers' projects require electronics knowledge to connect microcontrollers with sensors and actuators. Yet, learning electronics is challenging, as internal component processes remain invisible, and students often fear personal harm or component damage. Augmented Reality (AR) applic

Jongho Park, Juan Carlos Algaba

The magnetic field is believed to play a critical role in the bulk acceleration and propagation of jets produced in active galactic nuclei (AGN). Polarization observations of AGN jets provide valuable information about their magnetic fields. As a result of radiative transfer, jet structure, and stratification, among other factors, it is not always straightfo

Xulong Zhang, Jianzong Wang, Ning Cheng, Jing Xiao

Most previous neural text-to-speech (TTS) methods are mainly based on supervised learning methods, which means they depend on a large training dataset and hard to achieve comparable performance under low-resource conditions. To address this issue, we propose a semi-supervised learning method for neural TTS in which labeled target data is limited, which can a

Pierre-Loïc Méliot, Ashkan Nikeghbali, Gabriele Visentin

In this article, we provide an extension of the Chen-Stein inequality for Poisson approximation in the total variation distance for sums of independent Bernoulli random variables in two ways. We prove that we can improve the rate of convergence (hence the quality of the approximation) by using explicitly constructed signed or positive probability measures, a

Alban Farchi, Marcin Chrust, Marc Bocquet, Patrick Laloyaux

Recent studies have demonstrated that it is possible to combine machine learning with data assimilation to reconstruct the dynamics of a physical model partially and imperfectly observed. Data assimilation is used to estimate the system state from the observations, while machine learning computes a surrogate model of the dynamical system based on those estim

Joris Bierkens, Andrew Duncan

When performing Bayesian computations in practice, one is often faced with the challenge that the constituent model components and/or the data are only available in a distributed fashion, e.g. due to privacy concerns or sheer volume. While various methods have been proposed for performing posterior inference in such federated settings, these either make very

Yulin Zhu, Liang Tong, Gaolei Li, Xiapu Luo

Graph Neural Networks (GNNs) are vulnerable to data poisoning attacks, which will generate a poisoned graph as the input to the GNN models. We present FocusedCleaner as a poisoned graph sanitizer to effectively identify the poison injected by attackers. Specifically, FocusedCleaner provides a sanitation framework consisting of two modules: bi-level structura

Kohei Ohgane, Yusuke Masaki, Hiroaki Matsueda

We study quasi-particle dynamics in a quasi-periodic Ising model with temporally fluctuating transverse fields. Specifically, we calculate the dynamical exponents of the standard deviation of a quasi-particle spreading under a field chosen randomly from binary values $\pm h$ at every time interval. We find that the short-time behavior of the dynamical expone

Suvigya Kaushik, Siva Sankar Nemala, Mukesh Kumar, Devesh Negi

Liquid-phase exfoliation of two-dimensional materials is very attractive for large-scale applications. Although used extensively, isolating MoS2 layers (<10) with high efficiency is reported to be extremely difficult. Further, the importance of soaking has not yet been studied, and the surfactants' role in stabilizing MoS2 nanosheets is poorly understood1. H

Amadou Tall

It is known that the Scholz conjecture on addition chains is true for all integers $n$ with $\ell(2n) = \ell(n)+1$. There exists infinitely many integers with $\ell(2n) \leq \ell(n)$ and we don't know if the conjecture still holds for them. The conjecture is also proven to hold for integers $n$ with $v(n) \leq 5$ and for infinitely many integers with $v(n)=6

Xulong Zhang, Jianzong Wang, Ning Cheng, Jing Xiao

Metaverse expands the physical world to a new dimension, and the physical environment and Metaverse environment can be directly connected and entered. Voice is an indispensable communication medium in the real world and Metaverse. Fusion of the voice with environment effects is important for user immersion in Metaverse. In this paper, we proposed using the v

Rizhao Cai, Haoliang Li, Alex Kot

Filter pruning has been widely used for compressing convolutional neural networks to reduce computation costs during the deployment stage. Recent studies have shown that filter pruning techniques can achieve lossless compression of deep neural networks, reducing redundant filters (kernels) without sacrificing accuracy performance. However, the evaluation is

Yuuki Shida, Masami Sugawara, Ryosuke Tsumura, Haruaki Chiba

Purpose: Conventional robotic ultrasound systems were utilized with patients in supine positions. Meanwhile, the limitation of the systems is that it is difficult to evacuate the patients in case of emergency (e.g., patient discomfort and system failure) because the patients are restricted between the robot system and bed. Then, it is ideal that the patient

Hadi Ghasemi, Tayebe Lal Shateri

In the present paper, we investigate some properties of duals of continuous frames in Hilbert C*-modules. In particular, we give requirements so that by removing some elements of a continuous frame, it does not remain a continuous frame and when the remaining set still remains a continuous frame.

Jana Orszaghova, Siane Lemoine, Harrif Santo, Paul H. Taylor

Since intermittent and highly variable power supply is undesirable, quantifying power yield fluctuations of wave energy converters (WECs) aids with assessment of potential deployment sites. This paper presents analysis of 3-hourly, monthly, seasonal, and inter-annual variability of power output of the M4 WEC. We compare expected performance from deployment a

Lina Yang, Quan Zhang, Gengkai Hu, Nuo Yang

The thermal management have been widely focused due to broad applications. Generally, the deformation can largely tune the thermal transport. The main challenge of flexible electronics/ materials is to maintain thermal conductance under large deformation. This work investigates the thermal conductance of a nano-designed Si metamaterial constructed with curve

Xulong Zhang, Jianzong Wang, Ning Cheng, Kexin Zhu

Recovering the masked speech frames is widely applied in speech representation learning. However, most of these models use random masking in the pre-training. In this work, we proposed two kinds of masking approaches: (1) speech-level masking, making the model to mask more speech segments than silence segments, (2) phoneme-level masking, forcing the model to

Francesca Biagini, Andrea Mazzon, Thilo Meyer-Brandis, Katharina Oberpriller

In this paper we study the evolution of asset price bubbles driven by contagion effects spreading among investors via a random matching mechanism in a discrete-time version of the liquidity based model of [25]. To this scope, we extend the Markov conditionally independent dynamic directed random matching of [13] to a stochastic setting to include stochastic

Xulong Zhang, Jianzong Wang, Ning Cheng, Jing Xiao

In this paper, we proposed Adapitch, a multi-speaker TTS method that makes adaptation of the supervised module with untranscribed data. We design two self supervised modules to train the text encoder and mel decoder separately with untranscribed data to enhance the representation of text and mel. To better handle the prosody information in a synthesized voic

Chenzi Jin, Yanir A. Rubinstein

The Chebyshev potential of a K\"ahler potential on a projective variety, introduced by Witt Nystr\"om, is a convex function defined on the Okounkov body. It is a generalization of the symplectic potential of a torus-invariant K\"ahler potential on a toric variety, introduced by Guillemin, that is a convex function on the Delzant polytope. A folklore conjectu

Guanhui Ye, Jiashi Gao, Wei Xie, Bo Yin

Image watermarking is a technique for hiding information into images that can withstand distortions while requiring the encoded image to be perceptually identical to the original image. Recent work based on deep neural networks (DNN) has achieved impressive progression in digital watermarking. Higher robustness under various distortions is the eternal pursui

Melanie Sclar, Peter West, Sachin Kumar, Yulia Tsvetkov

We present Referee, a novel framework for sentence summarization that can be trained reference-free (i.e., requiring no gold summaries for supervision), while allowing direct control for compression ratio. Our work is the first to demonstrate that reference-free, controlled sentence summarization is feasible via the conceptual framework of Symbolic Knowledge

Paul Gavrikov, Janis Keuper

Recent work has investigated the distributions of learned convolution filters through a large-scale study containing hundreds of heterogeneous image models. Surprisingly, on average, the distributions only show minor drifts in comparisons of various studied dimensions including the learned task, image domain, or dataset. However, among the studied image doma

Hikaru Takeda, Jiancong Mai, Masatoshi Akazawa, Kyo Tamura

We investigate both the longitudinal thermal conductivity ($\kappa_{xx}$) and the planar thermal Hall conductivity ($\kappa_{xy}$) in the Kitaev spin liquid candidate of Co-based honeycomb antiferromagnet Na$_2$Co$_2$TeO$_6$ in a magnetic field ($B$) applied along the $a$ and $a^*$ axes. A finite $\kappa_{xy}$ is resolved for both field directions in the ant

Dequan Wang, Yifan Duan, Xiaoran Fan, Chengzhen Meng

In this letter, we propose MAROAM, a millimeter wave radar-based SLAM framework, which employs a two-step feature selection process to build the global consistent map. Specifically, we first extract feature points from raw data based on their local geometric properties to filter out those points that violate the principle of millimeter-wave radar imaging. Th

Zhendong Chen

In this paper, we show the shock formation of the solutions to the 3-dimensional (3D) compressible isentropic and irrotational Euler equations with damping for the initial short pulse data which was first introduced by D.Christodoulou\cite{christodoulou2007}. Due to the damping effect, the largeness of the initial data is necessary for the shock formation an

Zehua Zhang, Shilin Sun, Guixiang Ma, Caiming Zhong

Link prediction tasks focus on predicting possible future connections. Most existing researches measure the likelihood of links by different similarity scores on node pairs and predict links between nodes. However, the similarity-based approaches have some challenges in information loss on nodes and generalization ability on similarity indexes. To address th

Klaus Zollner, Paulo E. Faria Junior, Jaroslav Fabian

We investigate the twist-angle and gate dependence of the proximity-induced exchange coupling in the monolayer transition-metal dichalcogenides (TMDCs) MoSe$_2$ and WSe$_2$ due to the vdW coupling to the ferromagnetic semiconductor CrI$_3$, from first-principles calculations. A model Hamiltonian, that captures the relevant band edges at the $K/K^{\prime}$ va

Li Ma, Luke S. Trainor, Gavin G. G. King, Harald G. L. Schwefel

We showcase an erbium-doped whispering-gallery-mode resonator with optical modes that display intrinsic quality factors better than $10^8$ (linewidths less than 2 MHz), and coupling strengths to collective erbium transitions of up to 2$\pi\times$1.2 GHz - enough to reach the ensemble strong coupling regime. Our optical cavity sits inside a microwave resonato

Noam Soker

I examine the assumption that the jets that shape the axisymmetrical morphological features of core collapse supernova (CCSN) remnants are post-kick jets, i.e., the neutron star (NS) launches these jets after the explosion and after it acquired its natal kick velocity. I find that this assumption implies that the pre-collapse cores of CCSN progenitors have s

Mi Qian, Yao Ge, Miaowen Wen, Fei Ji

As a promising technique for high-mobility wireless communications, orthogonal time frequency space (OTFS) has been proved to enjoy excellent advantages with respect to traditional orthogonal frequency division multiplexing (OFDM). However, a challenging problem is to design efficient systems to further improve the performance. In this paper, we propose a no

Jarah Evslin, Alberto García Martín-Caro

Many kink solutions enjoy internal excitations, called shape modes. In some 1+1d scalar models, such as the $\phi^4$ double-well model, when a kink's shape mode is excited twice it may decay to a ground state kink plus a meson. We analytically calculate the decay rates of both the twice-excited shape mode and also a coherent state corresponding to the classi

Helmut Gfrerer, Alexander Y. Kruger

The paper extends the 2003 radius of metric regularity theorem by Dontchev, Lewis & Rockafellar by providing an exact formula for the radius with respect to Lipschitz continuous perturbations in general Asplund spaces, thus, answering affirmatively an open question raised twenty years ago by Ioffe. In the non-Asplund case, we give natural upper bounds for th

Sanjay Adhikesaven

Workplace accidents are a critical problem that causes many deaths, injuries, and financial losses. Climate change has a severe impact on industrial workers, partially caused by global warming. To reduce such casualties, it is important to proactively find unsafe environments where injuries could occur by detecting the use of personal protective equipment (P

Mohit, Ranjana Jain

In this article, the relationship between Birkhoff-James orthogonality of elementary tensors in certain tensor product spaces with the Birkhoff-James orthogonality of individual elements in their respective spaces is studied.

Pierre Lairez

Twenty years after the discovery of the F5 algorithm, Gr\"obner bases with signatures are still challenging to understand and to adapt to different settings. This contrasts with Buchberger's algorithm, which we can bend in many directions keeping correctness and termination obvious. I propose an axiomatic approach to Gr\"obner bases with signatures with the

Cameron Gordon

The hierarchical nature of corporate information processing is a topic of great interest in economic and management literature. Firms are characterised by a need to make complex decisions, often aggregating partial and uncertain information, which greatly exceeds the attention capacity of constituent individuals. However, the efficient transmission of these

Dominic Hirtler, Christof Gattringer

We study the massless Schwinger model with an additional 4-fermi interaction and a topological term. For topological angle $\theta = \pi$ charge conjugation symmetry is implemented in a non-trivial way and we study the possibility of its spontaneous breaking. For the lattice discretization we use staggered fermions and the Villain action for the gauge fields

Xinyu Cheng, Jiao Xu

In this paper we are interested in the global well-posedness of the 3D Klein-Gordon-Zakharov equations with small initial data. We show the uniform boundedness of the energy for the global solution without any compactness assumptions on the initial data. The main novelty of our proof is to apply a modified Alinhac's ghost weight method together with a newly

Ziyang Lyu

Semi-supervised learning (SSL) approaches have been successfully applied in a wide range of engineering and scientific fields. This paper investigates the generative model framework with a missingness mechanism for unclassified observations, as introduced by Ahfock and McLachlan(2020). We show that in a partially classified sample, a classifier using Bayes r

Longwen Zhou

A $d$-dimensional, $n$th-order topological insulator or superconductor has localized eigenmodes at its $(d-n)$-dimensional boundaries ($n\leq d$). In this work, we apply periodic driving fields to two-dimensional superconductors, and obtain a wide variety of Floquet second-order topological superconducting (SOTSC) phases with many Majorana corner modes at bo

Eitan Wagner, Renana Keydar, Amit Pinchevski, Omri Abend

The task of topical segmentation is well studied, but previous work has mostly addressed it in the context of structured, well-defined segments, such as segmentation into paragraphs, chapters, or segmenting text that originated from multiple sources. We tackle the task of segmenting running (spoken) narratives, which poses hitherto unaddressed challenges. As

Chenyang Zhao, Chuanfei Hu, Hang Shao, Zhe Wang

An automatic vision-based sewer inspection plays a key role of sewage system in a modern city. Recent advances focus on utilizing deep learning model to realize the sewer inspection system, benefiting from the capability of data-driven feature representation. However, the inherent uncertainty of sewer defects is ignored, resulting in the missed detection of

Natalie Coleman, Amir Esmalian, Cheng-Chun Lee, Eulises Gonzales

The energy sector in the United States is ever more vulnerable to extreme climatic hazards, leading to multiple hazard-induced and managed power outages. Utility companies reportedly prioritize the restoration of power systems based on the number of outages and the size of affected populations. This approach fails to account for unequal impacts. Research in

Shi Chen, Yuya Tanizaki

Solitonic symmetry has been believed to follow the homotopy-group classification of topological solitons. Here, we point out a more sophisticated algebraic structure when solitons of different dimensions coexist in the spectrum. We uncover this phenomenon in a concrete quantum field theory, the $4$d $\mathbb{C}P^1$ model. This model has two kinds of solitoni

Alessandro Alla, Peter M. Dower, Vincent Liu

Reachability analysis is a powerful tool when it comes to capturing the behaviour, thus verifying the safety, of autonomous systems. However, general-purpose methods, such as Hamilton-Jacobi approaches, suffer from the curse of dimensionality. In this paper, we mitigate this problem for systems of moderate dimension and we propose a new algorithm based on a

Rifki Afina Putri, Alice Oh

Machine Reading Comprehension (MRC) has become one of the essential tasks in Natural Language Understanding (NLU) as it is often included in several NLU benchmarks (Liang et al., 2020; Wilie et al., 2020). However, most MRC datasets only have answerable question type, overlooking the importance of unanswerable questions. MRC models trained only on answerable

David Wadden, Kyle Lo, Bailey Kuehl, Arman Cohan

While research on scientific claim verification has led to the development of powerful systems that appear to approach human performance, these approaches have yet to be tested in a realistic setting against large corpora of scientific literature. Moving to this open-domain evaluation setting, however, poses unique challenges; in particular, it is infeasible

Eric Ngondiep

This paper develops a robust three-level time split high-order Leapfrog/Crank-Nicolson technique for solving the two-dimensional unsteady sobolev and regularized long wave equations arising in fluid mechanics. A deep analysis of the stability and error estimates of the proposed approach is considered using the $L^{\infty}(0,T;H^{2})$-norm. Under a suitable t

Fude Li, Junjie Wang, Dianzhen Cui, K. Xue

We investigate the Bloch bands and develop a linear response theory for nonlinear systems, where the interplay between topological parameters and nonlinearity leads to new band structures. The nonlinear system under consideration is described by the Qi-Wu-Zhang model with Kerr-type nonlinearity, which can be treated as a nonlinear version of Chern insulator.

Qingyuan Zhang, Utkarsh Sharma, Jordan A. Dennis, Andrea Scifo

Annually-resolved measurements of the radiocarbon content in tree-rings have revealed rare sharp rises in carbon-14 production. These 'Miyake events' are likely produced by rare increases in cosmic radiation from the Sun or other energetic astrophysical sources. The radiocarbon produced is not only circulated through the Earth's atmosphere and oceans, but al

Sarthak Mittal, Guillaume Lajoie, Stefan Bauer, Arash Mehrjou

Diffusion-based generative models learn to iteratively transfer unstructured noise to a complex target distribution as opposed to Generative Adversarial Networks (GANs) or the decoder of Variational Autoencoders (VAEs) which produce samples from the target distribution in a single step. Thus, in diffusion models every sample is naturally connected to a rando

M. Kuzlu, F. O. Catak, S. Sarp, U. Cali

With the rapid development and integration of artificial intelligence (AI) methods in next-generation networks (NextG), AI algorithms have provided significant advantages for NextG in terms of frequency spectrum usage, bandwidth, latency, and security. A key feature of NextG is the integration of AI, i.e., self-learning architecture based on self-supervised

Zhaoji Zhang, Qinghua Guo, Ying Li, Ming Jin

Tailor-made for massive connectivity and sporadic access, grant-free random access has become a promising candidate access protocol for massive machine-type communications (mMTC). Compared with conventional grant-based protocols, grant-free random access skips the exchange of scheduling information to reduce the signaling overhead, and facilitates sharing of

Bálint Máté, François Fleuret

Consider a one-parameter family of Boltzmann distributions $p_t(x) = \tfrac{1}{Z_t}e^{-S_t(x)}$. This work studies the problem of sampling from $p_{t_0}$ by first sampling from $p_{t_1}$ and then applying a transformation $\Psi_{t_1}^{t_0}$ so that the transformed samples follow $p_{t_0}$. We derive an equation relating $\Psi$ and the corresponding family of

Hui Lu, Disong Wang, Xixin Wu, Zhiyong Wu

We propose an unsupervised learning method to disentangle speech into content representation and speaker identity representation. We apply this method to the challenging one-shot cross-lingual voice conversion task to demonstrate the effectiveness of the disentanglement. Inspired by $\beta$-VAE, we introduce a learning objective that balances between the inf

Abdurahman Maarouf, Nicolas Pröllochs, Stefan Feuerriegel

Online hate speech is responsible for violent attacks such as, e.g., the Pittsburgh synagogue shooting in 2018, thereby posing a significant threat to vulnerable groups and society in general. However, little is known about what makes hate speech on social media go viral. In this paper, we collect N = 25,219 cascades with 65,946 retweets from X (formerly kno

Jerin Geo James, Devansh Jain, Ajit Rajwade

Videos shot by laymen using hand-held cameras contain undesirable shaky motion. Estimating the global motion between successive frames, in a manner not influenced by moving objects, is central to many video stabilization techniques, but poses significant challenges. A large body of work uses 2D affine transformations or homography for the global motion. Howe

Xingting Yao, Fanrong Li, Zitao Mo, Jian Cheng

Spiking Neural Networks (SNNs) have been studied over decades to incorporate their biological plausibility and leverage their promising energy efficiency. Throughout existing SNNs, the leaky integrate-and-fire (LIF) model is commonly adopted to formulate the spiking neuron and evolves into numerous variants with different biological features. However, most L

Stefan Vlaski, Soummya Kar, Ali H. Sayed, José M. F. Moura

The article reviews significant advances in networked signal and information processing, which have enabled in the last 25 years extending decision making and inference, optimization, control, and learning to the increasingly ubiquitous environments of distributed agents. As these interacting agents cooperate, new collective behaviors emerge from local decis

Yingtian Chi, Kentaro Yokoo, Hironori Nakajima, Kohei Ito

Inhomogeneous current and temperature distributions are harmful to the durability of the solid oxide electrolysis cell (SOEC). Segmented SOEC experiments reveal that a high steam utilization, which is favorable for system efficiency, leads to local steam starvation and enhanced the inhomogeneity. It is necessary to consider inhomogeneity and efficiency joint

Robert C. Kirby, Xiaoyu Wei, Andreas Kloeckner

Morse and Ingard give a coupled system of time-harmonic equations for the temperature and pressure of an excited gas. These equations form a critical aspect of modeling trace gas sensors. Like other wave propagation problems, the computational problem must be closed with suitable far-field boundary conditions. Working in a scattered-field formulation, we ada

Ce Ju, Cuntai Guan

The motor imagery (MI) classification has been a prominent research topic in brain-computer interfaces based on electroencephalography (EEG). Over the past few decades, the performance of MI-EEG classifiers has seen gradual enhancement. In this study, we amplify the geometric deep learning-based MI-EEG classifiers from the perspective of time-frequency analy

Anjan Paul, Devendra Sharma

The kinetic damping mechanism of low frequency transverse perturbations propagating parallel to the magnetic field in a magnetized warm electron plasma is simulated by means of electromagnetic (EM) Vlasov simulations. The short-time-scale damping of the electron magnetohydrodynamic whistler perturbations and underlying physics of finite electron temperature

Zhiying Xu, Francis Y. Yan, Rachee Singh, Justin T. Chiu

The rapid expansion of global cloud wide-area networks (WANs) has posed a challenge for commercial optimization engines to efficiently solve network traffic engineering (TE) problems at scale. Existing acceleration strategies decompose TE optimization into concurrent subproblems but realize limited parallelism due to an inherent tradeoff between run time and

Qingyang Wang, Defu Lian, Chenwang Wu, Enhong Chen

Recommender systems are often susceptible to well-crafted fake profiles, leading to biased recommendations. The wide application of recommender systems makes studying the defense against attack necessary. Among existing defense methods, data-processing-based methods inevitably exclude normal samples, while model-based methods struggle to enjoy both generaliz

Lei Wang, Xu Chen, Quanyu Dai, Zhenhua Dong

Recommender system has been deployed in a large amount of real-world applications, profoundly influencing people's daily life and production.Traditional recommender models mostly collect as comprehensive as possible user behaviors for accurate preference estimation. However, considering the privacy, preference shaping and other issues, the users may not want

Ziren Xiao, Ruxin Xiao, Chang Liu, Honghao Gao

Dijkstra's algorithm is one of the most popular classic path planning algorithms, achieving optimal solutions across a wide range of challenging tasks. However, it only calculates the shortest distance from one vertex to another, which is hard to directly apply to the Dynamic Multi-Sources to Single-Destination (DMS-SD) problem. This paper proposes a modifie

Oleg Rybakov, Fadi Biadsy, Xia Zhang, Liyang Jiang

We present a fully on-device streaming Speech2Speech conversion model that normalizes a given input speech directly to synthesized output speech. Deploying such a model on mobile devices pose significant challenges in terms of memory footprint and computation requirements. We present a streaming-based approach to produce an acceptable delay, with minimal los