May 2023 arXiv papers — page 43

Aldi Piroli, Vinzenz Dallabetta, Johannes Kopp, Marc Walessa

Autonomous vehicles rely on LiDAR sensors to perceive the environment. Adverse weather conditions like rain, snow, and fog negatively affect these sensors, reducing their reliability by introducing unwanted noise in the measurements. In this work, we tackle this problem by proposing a novel approach for detecting adverse weather effects in LiDAR data. We ref

Xuming Hu, Junzhe Chen, Zhijiang Guo, Philip S. Yu

Evidence plays a crucial role in automated fact-checking. When verifying real-world claims, existing fact-checking systems either assume the evidence sentences are given or use the search snippets returned by the search engine. Such methods ignore the challenges of collecting evidence and may not provide sufficient information to verify real-world claims. Ai

Inigo V. Slijepcevic, Anna M. M. Scaife, Mike Walmsley, Micah Bowles

In this work, we apply self-supervised learning with instance differentiation to learn a robust, multi-purpose representation for image analysis of resolved extragalactic continuum images. We train a multi-use model which compresses our unlabelled data into a structured, low dimensional representation which can be used for a variety of downstream tasks (e.g.

Miquel Kegeleirs, David Garzón Ramos, Lorenzo Garattoni, Gianpiero Francesca

Automatic off-line design is an attractive approach to implementing robot swarms. In this approach, a designer specifies a mission for the swarm, and an optimization process generates suitable control software for the individual robots through computer-based simulations. Most relevant literature has focused on effectively transferring control software from s

Howard Baer, Vernon Barger, Dakotah Martinez, Shadman Salam

We revisit the various measures of naturalness for models of weak scale supersymmetry including 1. electroweak (EW) naturalness, 2. naturalness via sensitivity to high scale parameters (EENZ/BG), 3. sensitivity of Higgs soft term due to high scale (HS) radiative corrections and 4. stringy naturalness (SN) from the landscape. The EW measure is most conservati

Jiahao Yang, Wufei Ma, Angtian Wang, Xiaoding Yuan

Obtaining accurate 3D object poses is vital for numerous computer vision applications, such as 3D reconstruction and scene understanding. However, annotating real-world objects is time-consuming and challenging. While synthetically generated training data is a viable alternative, the domain shift between real and synthetic data is a significant challenge. In

S. Furuta, S. H. Moody, K. Kado, W. Koshibae

Spatially varying magnetic textures can exhibit electric-current-induced dynamics as a result of the spin-transfer torque effect. When such a magnetic system is electrically driven, an electric field is generated, which is called the emergent electric field. In particular, when magnetic-texture dynamics are induced under the application of an AC electric cur

Johannes Aspman, Gilles Bareilles, Vyacheslav Kungurtsev, Jakub Marecek

The Moment/Sum-of-squares hierarchy provides a way to compute the global minimizers of polynomial optimization problems (POP), at the cost of solving a sequence of increasingly large semidefinite programs (SDPs). We consider large-scale POPs, for which interior-point methods are no longer able to solve the resulting SDPs. We propose an algorithm that combine

Shengwei Li, Zhiquan Lai, Dongsheng Li, Yanqi Hao

Deep learning is experiencing a rise in large-scale models. Training large-scale models is costly, prompting researchers to train large-scale models on commodity servers that more researchers can access. The massive number of parameters necessitates the use of model parallelism training methods. Existing studies focus on training with pipeline model parallel

Jessy Ayala, Joshua Garcia

In open-source projects, anyone can contribute, so it is important to have an active continuous integration and continuous delivery (CI/CD) pipeline in addition to a protocol for reporting security concerns, especially in projects that are widely used and belong to the software supply chain. Many of these projects are hosted on GitHub, where maintainers can

Tao Feng, Weicong Li, Qing Xiang

In this paper, we construct a new family of $(q^4+1)$-tight sets in $Q(24,q)$ or $Q^-(25,q)$ according as $q=3^f$ or $q\equiv 2\pmod 3$. The novelty of the construction is the use of the action of the exceptional simple group $F_4(q)$ on its minimal module over $\F_q$.

Philippe Heim, Rayna Dimitrova

Two-player graph games have found numerous applications, most notably in the synthesis of reactive systems from temporal specifications, but also in verification. The relevance of infinite-state systems in these areas has lead to significant attention towards developing techniques for solving infinite-state games. We propose novel symbolic semi-algorithms fo

Max Langtry, Chaoqun Zhuang, Rebecca Ward, Nikolas Makasis

The use of monitored data to improve the accuracy of building energy models and operation of energy systems is ubiquitous, with topics such as building monitoring and Digital Twinning attracting substantial research attention. However, little attention has been paid to quantifying the value of the data collected against its cost. This paper argues that witho

Stefano Ferrero, Cecilia Ceccarelli, Piero Ugliengo, Mariona Sodupe

Carbon ($^3$P) atom is a reactive species that, according to laboratory experiments and theoretical calculations, condensates with interstellar ice components. This fact is of uttermost importance for the chemistry in the interstellar medium (ISM) because the condensation reaction is barrierless and the subsequent species formed are still reactive given thei

Zhenxing Wang, Zhenyuan Jia

This study aims to develop a low-cost refractometer for measuring the sucrose content of fruit juice, which is an important factor affecting human health. While laboratory-grade refractometers are expensive and unsuitable for personal use, existing low-cost commercial options lack stability and accuracy. To address this gap, we propose a refractometer that r

Hongzuo Xu, Yijie Wang, Juhui Wei, Songlei Jian

Due to the unsupervised nature of anomaly detection, the key to fueling deep models is finding supervisory signals. Different from current reconstruction-guided generative models and transformation-based contrastive models, we devise novel data-driven supervision for tabular data by introducing a characteristic -- scale -- as data labels. By representing var

Fan Yang, Xingyu Li, Chengshu Li

The Fermi sea topology is characterized by the Euler characteristics $\chi_F$. In this paper, we examine how $\chi_F$ of the metallic state is inhereted by the topological invariant of the superconducting state. We establish a correspondence between the Euler characteristic and the Chern number $C$ of $p$-wave topological superconductors without time-reversa

Tong Zhou

This paper presents a hidden Markov model designed to investigate the complex nature of earnings persistence. The proposed model assumes that the residuals of log-earnings consist of a persistent component and a transitory component, both following general Markov processes. Nonparametric identification is achieved through spectral decomposition of linear ope

A. N. Osipov, I. V. Iorsh, A. V. Yulin, I. A. Shelykh

Light-matter coupling in a planar optical cavity substantially modifies the transport regimes in the system in presence of a short range excitonic disorder. Basing on Master equation for a resonantly coupled exciton-photon system, and treating disorder scattering in the Born-Markov approximation we demonstrate the onset of ballistic and diffusive transport r

Nina R Benway, Jonathan L Preston, Asif Salekin, Yi Xiao

Mispronunciation detection tools could increase treatment access for speech sound disorders impacting, e.g., /r/. We show age-and-sex normalized formant estimation outperforms cepstral representation for detection of fully rhotic vs. derhotic /r/ in the PERCEPT-R Corpus. Gated recurrent neural networks trained on this feature set achieve a mean test particip

Rajesh Tripathi, D. T. Adroja, Y. Muro, Shivani Sharma

We present a detailed investigation of the stoichiometric CeRh$_4$Al$_{15}$ single crystal compound using the temperature dependence of the heat capacity [$C_{\text{P}}$($T$)], electrical resistivity [$\rho$($T$)], magnetic susceptibility [$\chi$($T$)], and magnetization [$M$($H$)] measurements for a magnetic field ($H$) applied in the basal plane and along

Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer

Undersampling is a common method in Magnetic Resonance Imaging (MRI) to subsample the number of data points in k-space, reducing acquisition times at the cost of decreased image quality. A popular approach is to employ undersampling patterns following various strategies, e.g., variable density sampling or radial trajectories. In this work, we propose a metho

Juan Luis Villarreal-Haro, Remy Gardier, Erick J Canales-Rodriguez, Elda Fischi Gomez

Monte-Carlo diffusion simulations are a powerful tool for validating tissue microstructure models by generating synthetic diffusion-weighted magnetic resonance images (DW-MRI) in controlled environments. This is fundamental for understanding the link between micrometre-scale tissue properties and DW-MRI signals measured at the millimetre-scale, optimising ac

Junjie Wang, Yang Yu, Jianbiao Hu, Peng Wen

Let $a,b$ be two positive integers such that $a \le b$ and $a \equiv b$ (mod $2$). We say that a graph $G$ has an $(a,b)$-parity factor if $G$ has a spanning subgraph $F$ such that $d_{F}(v) \equiv b$ (mod $2$) and $a \le d_{F}(v) \le b$ for all $v \in V (G)$. In this paper, we provide a tight spectral radius condition for a graph to have $(a,b)$-parity fact

Tianrui Wang, Long Zhou, Ziqiang Zhang, Yu Wu

Recent research shows a big convergence in model architecture, training objectives, and inference methods across various tasks for different modalities. In this paper, we propose VioLA, a single auto-regressive Transformer decoder-only network that unifies various cross-modal tasks involving speech and text, such as speech-to-text, text-to-text, text-to-spee

Shimin Li, Xiaotian Zhang, Yanjun Zheng, Linyang Li

Dialogue data in real scenarios tend to be sparsely available, rendering data-starved end-to-end dialogue systems trained inadequately. We discover that data utilization efficiency in low-resource scenarios can be enhanced by mining alignment information uncertain utterance and deterministic dialogue state. Therefore, we innovatively implement dual learning

Kang Li, Pengcheng Zhu, Yan Wang, Fu-Chun Zheng

Ultra-reliable and low-latency communications (URLLC) is firstly proposed in 5G networks, and expected to support applications with the most stringent quality-of-service (QoS). However, since the wireless channels vary dynamically, the transmit power for ensuring the QoS requirements of URLLC may be very high, which conflicts with the power limitation of a r

Konstantinos Mavreas, David Waxman

In this work we present a systematic mathematical approximation scheme that exposes the way that information, about the evolutionary forces of selection and random genetic drift, is encoded in gene-frequency trajectories. We determine approximate, time-dependent, gene-frequency trajectory statistics, assuming additive selection. We use the probability of fix

Zijia Zhao, Longteng Guo, Tongtian Yue, Sihan Chen

Building general-purpose models that can perceive diverse real-world modalities and solve various tasks is an appealing target in artificial intelligence. In this paper, we present ChatBridge, a novel multimodal language model that leverages the expressive capabilities of language as the catalyst to bridge the gap between various modalities. We show that onl

Xinyi Wu, Amir Ajorlou, Zihui Wu, Ali Jadbabaie

Oversmoothing in Graph Neural Networks (GNNs) refers to the phenomenon where increasing network depth leads to homogeneous node representations. While previous work has established that Graph Convolutional Networks (GCNs) exponentially lose expressive power, it remains controversial whether the graph attention mechanism can mitigate oversmoothing. In this wo

Mohammadreza Moeini, Mohsen Akbari, Mohammad Mirsadeghi, Hamid Reza Naeij

Quantum random number generators (QRNGs) produce random numbers based on the intrinsic probabilistic nature of quantum mechanics, making them true random number generators (TRNGs). In this paper, we design and fabricate an embedded QRNG that produces random numbers based on fluctuations of spontaneous emission and absorption in a Light-Emitting Diode (LED).

M. Falla Luza, F. Loray

We study the problem of classifying local projective structures in dimension two having non trivial Lie symmetries. In particular we obtain a classification of flat projective structures having positive dimensional Lie algebra of projective vector fields.

Louis Leconte, Van Minh Nguyen, Eric Moulines

In this paper, we propose a novel centralized Asynchronous Federated Learning (FL) framework, FAVANO, for training Deep Neural Networks (DNNs) in resource-constrained environments. Despite its popularity, ``classical'' federated learning faces the increasingly difficult task of scaling synchronous communication over large wireless networks. Moreover, clients

Demi Allen, Felipe A. Ramirez

We study (inhomogeneous) approximation for systems of linear forms using integer points which satisfy additional primitivity constraints. The first family of primitivity constraints we consider were introduced in 2015 by Dani, Laurent, and Nogueira, and are associated to partitions of the coordinate directions. Our results in this setting strengthen a theore

Anastasia Mantziou, Mihai Cucuringu, Victor Meirinhos, Gesine Reinert

In economic and financial applications, there is often the need for analysing multivariate time series, comprising of time series for a range of quantities. In some applications such complex systems can be associated with some underlying network describing pairwise relationships among the quantities. Accounting for the underlying network structure for the an

Yu-Shen Lin, Ryosuke Takahashi

We showed that a sequence of ALH*-gravitational instantons from pairs consisting of a weak del Pezzo surface and a smooth anti-canonical divisor towards a large complex structure limit introduced by Collins, Jacobs and the first author collapsing to a punctured plane with a special Kahler metric, which can be viewed as a non-compact version of the collapsing

Parvaneh Bahrami, Saeed Nosratabadi, Khodayar Palouzian, Szilard Hegedus

The purpose of this study was to model the impact of mentoring on women's work-life balance. Indeed, this study considered mentoring as a solution to create a work-life balance of women. For this purpose, semi-structured interviews with both mentors and mentees of Tehran Municipality were conducted and the collected data were analyzed using constructivist gr

Michael Kounavis, Ousmane Dia, Ilqar Ramazanli

Machine learning systems such as large scale recommendation systems or natural language processing systems are usually trained on billions of training points and are associated with hundreds of billions or trillions of parameters. Improving the learning process in such a way that both the training load is reduced and the model accuracy improved is highly des

Shaolei Zhang, Yang Feng

End-to-end simultaneous speech translation (SimulST) outputs translation while receiving the streaming speech inputs (a.k.a. streaming speech translation), and hence needs to segment the speech inputs and then translate based on the current received speech. However, segmenting the speech inputs at unfavorable moments can disrupt the acoustic integrity and ad

Sergio Moreschini, Shahrzad Pour, Ivan Lanese, Daniel Balouek-Thomert

The use of AI in microservices (MSs) is an emerging field as indicated by a substantial number of surveys. However these surveys focus on a specific problem using specific AI techniques, therefore not fully capturing the growth of research and the rise and disappearance of trends. In our systematic mapping study, we take an exhaustive approach to reveal all

Tahira Iqbal, Hina Anwar, Syazwanie Filzah, Mohammad Gharib

The purpose of requirements engineering (RE) is to make sure that the expectations and needs of the stakeholders of a software system are met. Emotional needs can be captured as emotional requirements that represent how the end user should feel when using the system. Differently from functional and quality (non-functional) requirements, emotional requirement

Niklas Sapountzoglou, Yassine Tahraoui, Guy Vallet, Aleksandra Zimmermann

We consider obstacle problems for nonlinear stochastic evolution equations. More precisely, the leading operator in our equation is a nonlinear, second order pseudomonotone operator of Leray-Lions type. The multiplicative noise term is given by a stochastic integral with respect to a Q-Wiener process. We show well-posedness of the associated initial value pr

Qiuyu Ren

We determine the quantum filtration structure of the Lee homology of all torus links. In particular, this determines the $s$-invariant of a torus link equipped with any orientation. In the special case $T(n,n)$, our result confirms a conjecture of Pardon, as well as a conjecture of Manolescu-Marengon-Sarkar-Willis which establishes an adjunction-type inequal

Saeed Nosratabadi, Thabit Atobishi, Szilard HegedHus

In the EU-27 countries, the importance of social sustainability of digital transformation (SOSDIT) is heightened by the need to balance economic growth with social cohesion. By prioritizing SOSDIT, the EU can ensure that its citizens are not left behind in the digital transformation process and that technology serves the needs of all Europeans. Therefore, th

Henry Förster, Michael Kaufmann, Laura Merker, Sergey Pupyrev

A linear layout of a graph $ G $ consists of a linear order $\prec$ of the vertices and a partition of the edges. A part is called a queue (stack) if no two edges nest (cross), that is, two edges $ (v,w) $ and $ (x,y) $ with $ v \prec x \prec y \prec w $ ($ v \prec x \prec w \prec y $) may not be in the same queue (stack). The best known lower and upper boun

Shiqi Zhang, Renchi Yang, Jing Tang, Xiaokui Xiao

Given a graph $\mathcal{G}$, the spanning centrality (SC) of an edge $e$ measures the importance of $e$ for $\mathcal{G}$ to be connected. In practice, SC has seen extensive applications in computational biology, electrical networks, and combinatorial optimization. However, it is highly challenging to compute the SC of all edges (AESC) on large graphs. Exist

Nina R Benway, Yashish M Siriwardena, Jonathan L Preston, Elaine Hitchcock

Acoustic-to-articulatory speech inversion could enhance automated clinical mispronunciation detection to provide detailed articulatory feedback unattainable by formant-based mispronunciation detection algorithms; however, it is unclear the extent to which a speech inversion system trained on adult speech performs in the context of (1) child and (2) clinical

Sangmin Lee, Jong Chul Ye

This paper investigates the relationship between the universal approximation property of deep neural networks and topological characteristics of datasets. Our primary contribution is to introduce data topology-dependent upper bounds on the network width. Specifically, we first show that a three-layer neural network, applying a ReLU activation function and ma

Simone Mucci, Alexander Mitsos, Dominik Bongartz

The synthesis of methanol from captured carbon dioxide and green hydrogen could be a promising replacement for the current fossil-based production. The major energy input and cost driver for such a process is the electricity for hydrogen production. Time-variable electricity cost or availability thus motivates flexible operation. However, it is unclear if ea

Asta Heinesen

Bulk flow velocities are typically estimated in the idealised picture where observers are moving within a perfectly homogeneous and isotropic space-time. This picture is consistent within standard perturbation theory up to relativistic effects that lead to correction terms of order $v z$, where $z$ is the redshift of observation, and $v$ is the amplitude of

Yuto Hayakawa, Yusuke Imai, Hiroshi Kohno

We theoretically study the Dzyaloshinskii-Moriya interaction (DMI) mediated by band electrons with strong spin-orbit coupling (SOC). We first derive a general formula for the coefficient ${\bm D}_i$ of the DMI in free energy in terms of Green's functions, and examine its variations in relation to physical quantities. In general, the DMI coefficient can vary

Jacob Franklin, Jacob Pfund, Joshua Bedard, Weiguo Zhang

The weak ferromagnet FeBO$_3$ is well known for being a unique system for modelling and testing magnetic dynamics primarily due to relatively simple and localized magnetic structure and its interesting spin wave dynamics. At room temperature, it has slightly canted iron moments lying in the a-b plane that result in a strong antiferromagnetic moment and a wea

MohammadTaghi Hajiaghayi, Max Springer, Hadi Yami

We here address the problem of fairly allocating indivisible goods or chores to $n$ agents with weights that define their entitlement to the set of indivisible resources. Stemming from well-studied fairness concepts such as envy-freeness up to one good (EF1) and envy-freeness up to any good (EFX) for agents with equal entitlements, we present, in this study,

Teffo Phomolo Nicrocia, Owolawi Pius Adewale, Pholo Moanda Diana

The adoption of digital transformation was not expressed in building an African face shape classifier. In this paper, an approach is presented that uses k-means to classify African women images. African women rely on beauty standards recommendations, personal preference, or the newest trends in hairstyles to decide on the appropriate hairstyle for them. In t

A. Kumari, D. J. Price, F. Daei, J. Pomoell

Data-driven time-dependent magnetofrictional modelling (TMFM) of active region magnetic fields has been proven to be a useful tool to study the corona. The input to the model is the photospheric electric field that is inverted from a time series of the photospheric magnetic field. Constraining the complete electric field, that is, including the non-inductive

Birgit Jacob, Lukas Vorberg, Christian Wyss

A novel algorithm for the computation of the quadratic numerical range is presented and exemplified yielding much better results in less time compared to the random vector sampling method. Furthermore, a bound on the probability for the random vector sampling method to produce a point exceeding a neighborhood of the expectation value in dependence on norm an

Feiyan Min, Gao Wang, Xueqin Chen

To eliminate the static error, overshoot, and vibration of the series elastic actuator (SEA) position control, the resonance ratio control (RRC) algorithm is improved based on L1 adaptive control(L1AC)method. Based on the analysis of the factors affecting the control performance of SEA, the algorithm schema is proposed, the stability is proved, and the main

Philippe Arnault, Julien Racine, Jean-Pierre Raucourt, Augustin Blanchet

In average atom (AA) model, the entropy provides a route to compute thermal electronic contributions to the equation of state (EOS). The complete EOS comprises in many modelings an additional 0K-isotherm and a thermal ionic part. Even at low temperature, the AA model is believed to be the best practical approach. However, when it comes to determine the therm

Yuanchao Li, Peter Bell, Catherine Lai

Holistic perception of affective attributes is an important human perceptual ability. However, this ability is far from being realized in current affective computing, as not all of the attributes are well studied and their interrelationships are poorly understood. In this work, we investigate the relationship between two affective attributes: personality and

Gabriele Nava, Daniele Pucci

Failure detection and fault tolerant control are fundamental safety features of any aerial vehicle. With the emergence of complex, multi-body flying systems such as jet-powered humanoid robots, it becomes of crucial importance to design fault detection and control strategies for these systems, too. In this paper we propose a fault detection and control frame

Yiliu Wang, Wei Chen, Milan Vojnović

We consider a combinatorial multi-armed bandit problem for maximum value reward function under maximum value and index feedback. This is a new feedback structure that lies in between commonly studied semi-bandit and full-bandit feedback structures. We propose an algorithm and provide a regret bound for problem instances with stochastic arm outcomes according

Chris Barrett

The Functional Machine Calculus (FMC) was recently introduced as a generalization of the lambda-calculus to include higher-order global state, probabilistic and non-deterministic choice, and input and output, while retaining confluence. The calculus can encode both the call-by-name and call-by-value semantics of these effects. This is enabled by two independ

Tian Pu, Shuhang Wang, Zhenming Peng, Qingsong Zhu

Uneven light image enhancement is a highly demanded task in many industrial image processing applications. Many existing enhancement methods using physical lighting models or deep-learning techniques often lead to unnatural results. This is mainly because: 1) the assumptions and priors made by the physical lighting model (PLM) based approaches are often viol

S. Izak Ghasemian, F. Reuter, C. D. Ohl

While the ultrasound and cavitation based therapies have mushroomed over the years, there is a lack of online monitoring the cavitation bubble dynamics in biological tissue. {\color{black}Here we demonstrate that with ultrasonic plane wave imaging the fast dynamics of single cavitation bubbles can be resolved non-invasively in a tissue mimicking material}. D

Pengqi Li, Lantian Li, Askar Hamdulla, Dong Wang

Visualization is of great value in understanding the internal mechanisms of neural networks. Previous work found that LayerCAM is a reliable visualization tool for deep speaker models. In this paper, we use LayerCAM to analyze the widely-adopted data augmentation (DA) approach, to understand how it leads to model robustness. We conduct experiments on the Vox

Armin Reimers, Ala Bouhanguel, Erik Greve, Morten Möller

Particulate air pollution is taking a huge toll on modern society, being associated with more than three million deaths per year. In addition, airborne infectious microorganism can spread dangerous diseases, further elevating the problem. A common way to mitigate the risks of airborne particles is by air filtration. However, conventional air filters usually

N. S. Manton, Boan Zhao

We study abelian BPS vortices on a surface $S$ with boundary, which satisfy the Neumann boundary condition on the norm of the scalar field, or equivalently, that the current along the boundary vanishes. These vortices have quantised magnetic flux and quantised energy. Existence of such vortices is manifest when $S$ is the quotient by a reflection of a smooth

Raymond Centner, Raymond Cheng, Christopher Felder

This work studies optimal polynomial approximants (OPAs) in the classical Hardy spaces on the unit disk, $H^p$ ($1 < p < \infty$). In particular, we uncover some estimates concerning the OPAs of degree zero and one. It is also shown that if $f \in H^p$ is an inner function, or if $p>2$ is an even integer, then the roots of the nontrivial OPA for $1/f$ are bo

Vinzenz Stummer, Tobias Flöry, Matthias Schneller, Edgar Kaksis

Multi-photon resonant spectroscopies require tunable narrowband excitation to deliver spectral selectivity and, simultaneously, high temporal intensity to drive a nonlinear-optical process. These contradictory requirements are achievable with bursts of ultrashort pulses, which provides both high intensity and tunable narrowband peaks in the frequency domain

Sanket Thakur, Cigdem Beyan, Pietro Morerio, Vittorio Murino

In this technical report, we describe the Guided-Attention mechanism based solution for the short-term anticipation (STA) challenge for the EGO4D challenge. It combines the object detections, and the spatiotemporal features extracted from video clips, enhancing the motion and contextual information, and further decoding the object-centric and motion-centric

Yuanchao Li, Zeyu Zhao, Ondrej Klejch, Peter Bell

In Speech Emotion Recognition (SER), textual data is often used alongside audio signals to address their inherent variability. However, the reliance on human annotated text in most research hinders the development of practical SER systems. To overcome this challenge, we investigate how Automatic Speech Recognition (ASR) performs on emotional speech by analyz

Ning-Liu Wei, Yu-Bo Li, Hai-Liang Wu

In this paper, motivated by the work of Chapman, Vsemirnov and Sun et al., we investigate some arithmetic properties of the generalized Legendre matrices over finite fields. For example, letting $a_1,\cdots,a_{(q-1)/2}$ be all non-zero squares in the finite field $\mathbb{F}_q$ which contains $q$ elements with $2\nmid q$, we give the explicit value of $D_{(q

Mohsen Raoufi, Pawel Romanczuk, Heiko Hamann

Inter-individual differences are studied in natural systems, such as fish, bees, and humans, as they contribute to the complexity of both individual and collective behaviors. However, individuality in artificial systems, such as robotic swarms, is undervalued or even overlooked. Agent-specific deviations from the norm in swarm robotics are usually understood

A. M. Tsvelik

The paper describes a simple mechanism for superconducting pairing with finite wave vector (Pair Density Wave) which is illustrated with a quasi one-dimensional model. Within this model pair and charge density wave order parameters are intertwined emerging as components of the same matrix field and are related by particle-hole symmetry. Experimental applicat

Zhengyuan Shi, Hongyang Pan, Sadaf Khan, Min Li

Circuit representation learning aims to obtain neural representations of circuit elements and has emerged as a promising research direction that can be applied to various EDA and logic reasoning tasks. Existing solutions, such as DeepGate, have the potential to embed both circuit structural information and functional behavior. However, their capabilities are

Manuel Garcia-Villalba, Blanca Fuentes, Jan Dusek, Manuel Moriche

We present a simple modification of the direct-forcing immersed boundary method (IBM) proposed by Uhlmann [J. Comput. Phys, 2005] in order to enable it to be applied to particulate flows with solid-to-fluid density ratios around unity. The main difference with respect to the original formulation lies in the particle velocity update which is performed directl

Xiaodan Xing, Federico Felder, Yang Nan, Giorgos Papanastasiou

Synthetic images generated from deep generative models have the potential to address data scarcity and data privacy issues. The selection of synthesis models is mostly based on image quality measurements, and most researchers favor synthetic images that produce realistic images, i.e., images with good fidelity scores, such as low Fr\'echet Inception Distance

Jingbo Sun, Fei Wang

The diffusive-viscous wave equation is an advancement in wave equation theory, as it accounts for both diffusion and viscosity effects. This has a wide range of applications in geophysics, such as the attenuation of seismic waves in fluid-saturated solids and frequency-dependent phenomena in porous media. Therefore, the development of an efficient numerical

Jimin Li, Zongping Gong

We study the long-range hopping limit of a one-dimensional array of $N$ equal-distanced quantum emitters in free space, where the hopping amplitude of emitter excitation is proportional to the inverse of the distance and equals the lattice dimension. For two species of emitters in an alternating arrangement, the single excitation sector exhibits non-Hermitia

Zhenyu Zhu, Ang Li, Jinniu Hu, Hong Shen

The equation of state of neutron-rich nuclear matter is of interest to both nuclear physics and astrophysics. We have demonstrated the consistency between laboratory and astrophysical nuclear matter in neutron stars by considering low-density nuclear physics constraints (from $^{208}$Pb neutron-skin thickness) and high-density astrophysical constraints (from

Chih-Yuan Li, Navya Martin Kollapally, Soon Ae Chun, James Geller

While the world has been combating COVID-19 for over three years, an ongoing "Infodemic" due to the spread of fake news regarding the pandemic has also been a global issue. The existence of the fake news impact different aspect of our daily lives, including politics, public health, economic activities, etc. Readers could mistake fake news for real news, and

Shuo Yu, Hongyan Xue, Xiang Ao, Feiyang Pan

In the field of quantitative trading, it is common practice to transform raw historical stock data into indicative signals for the market trend. Such signals are called alpha factors. Alphas in formula forms are more interpretable and thus favored by practitioners concerned with risk. In practice, a set of formulaic alphas is often used together for better m

Ranga Shaarad Ayyagari, Revanth Raj Eega, Ambedkar Dukkipati

Reinforcement Learning Algorithms are predominantly developed for stationary environments, and the limited literature that considers nonstationary environments often involves specific assumptions about changes that can occur in transition probability matrices and reward functions. Considering that real-world applications involve environments that continuousl

Cheng Guo, Sajid Ahmed, Mohamed-Slim Alouini

The state-of-the-art cardiovascular disease diagnosis techniques use machine-learning algorithms based on feature extraction and classification. In this work, in contrast to a conventional single Electrocardiogram (ECG) lead, two leads are used, and autoregressive (AR) coefficients and statistical parameters are extracted to be used as features. Four machine

Vagner R. de Bessa, Anderson L. P. Porto, Pavel A. Zalesskii

A finitely generated residually finite group $G$ is an $\widehat{OE}$-group if any action of its profinite completion $\widehat G$ on a profinite tree with finite edge stabilizers admits a global fixed point. In this paper, we study the profinite genus of free products $G_1*_HG_2$ of $\widehat{OE}$-groups $G_1,G_2$ with finite amalgamation $H$. Given such $G

Samantha Durdy, Michael W. Gaultois, Vladimir Gusev, Danushka Bollegala

Clustering is a common task in machine learning, but clusters of unlabelled data can be hard to quantify. The application of clustering algorithms in chemistry is often dependant on material representation. Ascertaining the effects of different representations, clustering algorithms, or data transformations on the resulting clusters is difficult due to the d

Curran Kalha, Laura E. Ratcliff, Giorgio Colombi, Christoph Schlueter

Hydrogen as a fuel plays a crucial role in driving the transition to net zero greenhouse gas emissions. To realise its potential, obtaining a means of efficient storage is paramount. One solution is using metal hydrides, owing to their good thermodynamical absorption properties and effective hydrogen storage. Although metal hydrides appear simple compared to

Nikita Tsoy, Nikola Konstantinov

Collaborative learning techniques have significantly advanced in recent years, enabling private model training across multiple organizations. Despite this opportunity, firms face a dilemma when considering data sharing with competitors -- while collaboration can improve a company's machine learning model, it may also benefit competitors and hence reduce prof

Anne Lauscher, Debora Nozza, Archie Crowley, Ehm Miltersen

As 3rd-person pronoun usage shifts to include novel forms, e.g., neopronouns, we need more research on identity-inclusive NLP. Exclusion is particularly harmful in one of the most popular NLP applications, machine translation (MT). Wrong pronoun translations can discriminate against marginalized groups, e.g., non-binary individuals (Dev et al., 2021). In thi

Christian J. Eckhardt, Patrick Kappl, Anna Kauch, Karsten Held

The parquet equation is an exact field-theoretic equation known since the 60s that underlies numerous approximations to solve strongly correlated Fermion systems. Its derivation previously relied on combinatorial arguments classifying all diagrams of the two-particle Green's function in terms of their (ir)reducibility properties. In this work we provide a de

Lantian Li, Xiaolou Li, Haoyu Jiang, Chen Chen

Audio-visual person recognition (AVPR) has received extensive attention. However, most datasets used for AVPR research so far are collected in constrained environments, and thus cannot reflect the true performance of AVPR systems in real-world scenarios. To meet the request for research on AVPR in unconstrained conditions, this paper presents a multi-genre A

Zhifeng Li, Yifan Fan, Bowei Zou, Yu Hong

Leveraging external knowledge to enhance the reasoning ability is crucial for commonsense question answering. However, the existing knowledge bases heavily rely on manual annotation which unavoidably causes deficiency in coverage of world-wide commonsense knowledge. Accordingly, the knowledge bases fail to be flexible enough to support the reasoning over div

Lanwei Zhang, Jamie Evans, Jingge Zhu

Compute-forward multiple access (CFMA) is a multiple access transmission scheme based on Compute-and-Forward (CF) which allows the receiver to first decode linear combinations of the transmitted signals and then solve for individual messages. This paper extends the CFMA scheme to a two-user Gaussian multiple-input multiple-output (MIMO) multiple access chann

Miao He, Yunfeng Jiang

The notion of a crosscap state, a special conformal boundary state first defined in 2d CFT, was recently generalized to 2d massive integrable quantum field theories and integrable spin chains. It has been shown that the crosscap states preserve integrability. In this work, we first generalize this notion to the Lieb-Liniger model, which is a prototype of int

Romain Dalidet, Anthony Martin, Mattis Riesner, Sidi-Ely Ahmedou

Since the first proof-of-principle experiments 25 years ago, quantum metrology has matured from fundamental concepts to versatile and powerful tools in a large variety of research branches, such as gravitational-wave detection, atomic clocks, plasmonic sensing, and magnetometry. At the same time, two-photon interferometry, which underpins the possibility of

Gehua Ma, Rui Yan, Huajin Tang

$\textbf{Formal version available at}$ https://cell.com/patterns/fulltext/S2666-3899(23)00200-3 Networks of spiking neurons underpin the extraordinary information-processing capabilities of the brain and have become pillar models in neuromorphic artificial intelligence. Despite extensive research on spiking neural networks (SNNs), most studies are establishe

Jiaying Wang, Xianglong Wang, Namin Wang, Lantian Li

Modern speaker recognition systems represent utterances by embedding vectors. Conventional embedding vectors are dense and non-structural. In this paper, we propose an ordered binary embedding approach that sorts the dimensions of the embedding vector via a nested dropout and converts the sorted vectors to binary codes via Bernoulli sampling. The resultant o

Patrick Ebel, Christoph Lingenfelder, Andreas Vogelsang

Driver assistance systems are designed to increase comfort and safety by automating parts of the driving task. At the same time, modern in-vehicle information systems with large touchscreens provide the driver with numerous options for entertainment, information, or communication, and are a potential source of distraction. However, little is known about how

Marc Jourdan, Rémy Degenne, Emilie Kaufmann

We propose EB-TC$\varepsilon$, a novel sampling rule for $\varepsilon$-best arm identification in stochastic bandits. It is the first instance of Top Two algorithm analyzed for approximate best arm identification. EB-TC$\varepsilon$ is an *anytime* sampling rule that can therefore be employed without modification for fixed confidence or fixed budget identifi

Martti Vainio, Antti Suni, Juraj Šimko, Sofoklis Kakouros

Parliamentary recordings provide a rich source of data for studying how politicians use speech to convey their messages and influence their audience. This provides a unique context for studying how politicians use speech, especially prosody, to achieve their goals. Here we analyzed a corpus of parliamentary speeches in the Finnish parliament between the year