October 2022 arXiv papers — page 18

Ravshan Ashurov, Shakarova Marjona

The inverse problem of determining the right-hand side of the subdiffusion equation with the fractional Caputo derivative is considered. The right-hand side of the equation has the form $f(x)g(t)$ and the unknown is function $f(x)$. The condition $ u (x,t_0)= \psi (x) $ is taken as the over-determination condition, where $t_0$ is some interior point of the c

Masahiro Ibe, Masataka Mishima, Yuhei Nakayama, Satoshi Shirai

The Wino is an $\mathrm{SU}(2)_{L}$ triplet Majorana fermion and a well-motivated dark matter candidate. The mass difference between the charged and the neutral Winos is small thanks to the $\mathrm{SU}(2)_{L}$ symmetry. The small mass difference makes the charged Wino meta-stable, which provides disappearing charged track signatures at collider experiments.

Changjie Lu

Statistical machine learning algorithms have achieved state-of-the-art results on benchmark datasets, outperforming humans in many tasks. However, the out-of-distribution data and confounder, which have an unpredictable causal relationship, significantly degrade the performance of the existing models. Causal Representation Learning (CRL) has recently been a

Masato Kikuchi, Tadachika Ozono

The universal-set naive Bayes classifier (UNB)~\cite{Komiya:13}, defined using likelihood ratios (LRs), was proposed to address imbalanced classification problems. However, the LR estimator used in the UNB overestimates LRs for low-frequency data, degrading the classification performance. Our previous study~\cite{Kikuchi:19} proposed an effective LR estimato

Junyi Peng, Themos Stafylakis, Rongzhi Gu, Oldřich Plchot

Recently, the pre-trained Transformer models have received a rising interest in the field of speech processing thanks to their great success in various downstream tasks. However, most fine-tuning approaches update all the parameters of the pre-trained model, which becomes prohibitive as the model size grows and sometimes results in overfitting on small datas

Wei Li, Xue Xu, Xinyan Xiao, Jiachen Liu

Diffusion generative models have recently greatly improved the power of text-conditioned image generation. Existing image generation models mainly include text conditional diffusion model and cross-modal guided diffusion model, which are good at small scene image generation and complex scene image generation respectively. In this work, we propose a simple ye

G. T. Roberg-Clark, P. Xanthopoulos, G. G. Plunk

We present a stellarator configuration optimized for a large threshold (``critical gradient'') for the onset of the ion temperature gradient (ITG) driven mode, which achieves the largest critical gradient we have seen in any stellarator. Above this threshold, gyrokinetic simulations show that the configuration has low turbulence levels over an experimentally

Zhiyi Wang, Shaoguang Mao, Wenshan Wu, Yan Xia

This work introduces an approach to assessing phrase break in ESL learners' speech with pre-trained language models (PLMs). Different with traditional methods, this proposal converts speech to token sequences, and then leverages the power of PLMs. There are two sub-tasks: overall assessment of phrase break for a speech clip; fine-grained assessment of every

Yuke Lin, Xiaoyi Qin, Huahua Cui, Zhenyi Zhu

The success of automatic speaker verification shows that discriminative speaker representations can be extracted from neutral speech. However, as a kind of non-verbal voice, laughter should also carry speaker information intuitively. Thus, this paper focuses on exploring speaker verification about utterances containing non-verbal laughter segments. We collec

Max Pascher, Til Franzen, Kirill Kronhardt, Jens Gerken

Nowadays, robots are found in a growing number of areas where they collaborate closely with humans. Enabled by lightweight materials and safety sensors, these cobots are gaining increasing popularity in domestic care, supporting people with physical impairments in their everyday lives. However, when cobots perform actions autonomously, it remains challenging

Masato Kikuchi, Yuhi Kusakabe, Tadachika Ozono

A naive likelihood ratio (LR) estimation using the observed frequencies of events can overestimate LRs for infrequent data. One approach to avoid this problem is to use a frequency threshold and set the estimates to zero for frequencies below the threshold. This approach eliminates the computation of some estimates, thereby making practical tasks using LRs m

Julian Kern

This paper presents a gentle and informal introduction to the Skorokhod topologies. Focus is on motivating examples and concepts.

Lishuai Jin, Yueying Yang, Bryan O. Torres Maldonado, Sebastian David Lee

Soft robots have a myriad of potentials because of their intrinsically compliant bodies, enabling safe interactions with humans and adaptability to unpredictable environments. However, most of them have limited actuation speeds, require complex control systems, and lack sensing capabilities. To address these challenges, here we geometrically design a class o

M. R. Ahan, Robin Lehmann, Richard Blythman

Recent developments in machine learning have shown that successful models do not rely only on huge amounts of data but the right kind of data. We show in this paper how this data-centric approach can be facilitated in a decentralized manner to enable efficient data collection for algorithms. Face detectors are a class of models that suffer heavily from bias

Sihao Yu, Fei Sun, Jiafeng Guo, Ruqing Zhang

Machine unlearning aims to erase the impact of specific training samples upon deleted requests from a trained model. Re-training the model on the retained data after deletion is an effective but not efficient way due to the huge number of model parameters and re-training samples. To speed up, a natural way is to reduce such parameters and samples. However, s

Jonathan Svirsky, Ofir Lindenbaum

We propose a novel voice activity detection (VAD) model in a low-resource environment. Our key idea is to model VAD as a denoising task, and construct a network that is designed to identify nuisance features for a speech classification task. We train the model to simultaneously identify irrelevant features while predicting the type of speech event. Our model

Chris Fields, James F. Glazebrook, Antonino Marciano

We show in this pedagogical review that far from being "an apparent law of physics that stands by itself" (R. Bousso, Rev. Mod. Phys. 74 (2002), 825-874), the holographic principle (HP) is a straightforward consequence of the quantum information theory of separable systems. It provides a basis for the theories of measurement, time, and scattering. Principles

Joe Zhiyu Chen, Amol Upadhye, Yvonne Y. Y. Wong

Velocity dispersion of the massive neutrinos presents a daunting challenge for non-linear cosmological perturbation theory. We consider the neutrino population as a collection of non-linear fluids, each with uniform initial momentum, through an extension of the Time Renormalization Group perturbation theory. Employing recently-developed Fast Fourier Transfor

Ya-Zheng Tao, Hong-Bo Jin, Yue-Liang Wu

In space-based gravitational wave detection, the estimation of far-field wavefront error of the distorted beam is the precondition for the noise reduction. Zernike polynomials is used to describe the wavefront error of the transmitted distorted beam. The propagation of a laser beam between two telescope apertures is calculated numerically. Far-field wavefron

A. Chalil, T. J. Mertzimekis, A. Zyriliou, P. Vasileiou

The nucleus $^{180}$Hf is one of the most primary of examples of an axially symmetric prolate rotor. Combined with the presence of high-$K$ isomers, spectroscopic studies can provide important information on the nature of its single-particle levels. Precise measurements are essential for constraining nuclear models and interpreting the nature of such isomeri

Anastasia Drozdova, Polina Guseva, Ekaterina Trofimova, Anna Scherbakova

Program code as a data source is gaining popularity in the data science community. Possible applications for models trained on such assets range from classification for data dimensionality reduction to automatic code generation. However, without annotation number of methods that could be applied is somewhat limited. To address the lack of annotated datasets,

Qiong-Ao Huang, Wei Jiang, Jerry Zhijian Yang, Cheng Yuan

This paper establishes a structure-preserving numerical scheme for the Cahn--Hilliard equation with degenerate mobility. First, by applying a finite volume method with upwind numerical fluxes to the degenerate Cahn--Hilliard equation rewritten by the scalar auxiliary variable (SAV) approach, we creatively obtain an unconditionally bound-preserving, energy-st

Nicolas Boullé

Discovering hidden partial differential equations (PDEs) and operators from data is an important topic at the frontier between machine learning and numerical analysis. This doctoral thesis introduces theoretical results and deep learning algorithms to learn Green's functions associated with linear partial differential equations and rigorously justify PDE lea

R. N. Faustov, V. O. Galkin, E. M. Savchenko

Masses of the ground and excited (1P, 2S, 1D, 2P, 3S) states of the fully-heavy tetraquarks, composed of charm ($c$) and bottom ($b$) quarks and antiquarks, are calculated in the diquark-antidiquark picture within the relativistic quark model based on the quasipotential approach and quantum chromodynamics. The quasipotentials of the quark-quark and diquark-a

Alexander Oestreicher, Lodovico Capuano, Sabino Matarrese, Lavinia Heisenberg

Non-linear cosmic structures contain valuable information on the expansion history of the background space-time, the nature of dark matter, and the gravitational interaction. The recently developed kinetic field theory of cosmic structure formation (KFT) allows to accurately calculate the non-linear power spectrum of cosmic density fluctuations up to wave nu

Ian D. Roberts, Maojin Lang, Daria Trotsenko, Ashley Bemis

With MaNGA integral field spectroscopy, we present a resolved analysis of star formation for 29 jellyfish galaxies in nearby clusters, identified from radio continuum imaging taken by the Low Frequency Array. Simulations predict enhanced star formation on the "leading half" of galaxies undergoing ram pressure stripping, and in this work we report observation

Joe Zhiyu Chen, Markus R. Mosbech, Amol Upadhye, Yvonne Y. Y. Wong

Simulation of the cosmic clustering of massive neutrinos is a daunting task, due both to their large velocity dispersion and to their weak clustering power becoming swamped by Poisson shot noise. We present a new approach, the multi-fluid hybrid-neutrino simulation, which partitions the neutrino population into multiple flows, each of which is characterised

Maksud Sharipov, Ollabergan Yuldashov

In this paper we present a rule-based stemming algorithm for the Uzbek language. Uzbek is an agglutinative language, so many words are formed by adding suffixes, and the number of suffixes is also large. For this reason, it is difficult to find a stem of words. The methodology is proposed for doing the stemming of the Uzbek words with an affix stripping appr

Ivo Steinbrecher, Nora Hagmeyer, Christoph Meier, Alexander Popp

The present article proposes a novel computational method for coupling arbitrarily curved 1D fibers with a 2D surface as defined, e.g., by the 2D surfaces of a 3D solid body or by 2D shell formulations. The fibers are modeled as 1D Cosserat continua (beams) with six local degrees of freedom, three positional and three rotational ones. A kinematically consist

Hisanori Oshima, Yohei Fuji

We study a (1+1)-dimensional quantum circuit consisting of Haar-random unitary gates and projective measurements that conserve a total $U(1)$ charge and thus have $U(1)$ symmetry. In addition to a measurement-induced entanglement transition between a volume-law and an area-law entangled phase, we find a phase transition between two phases characterized by bi

Hiromichi Takagi

We classified prime $\mathbb{Q}$-Fano $3$-folds $X$ with only $1/2(1,1,1)$-singularities and with $h^{0}(-K_{X})\geq 4$ a long time ago. The classification was undertaken by blowing up each $X$ at one $1/2(1,1,1)$-singularity and constructing a Sarkisov link. The purpose of this paper is to reveal the geometries behind the Sarkisov links for $X$ in 5 classes

Lin Dai, Yi Fang, Yong Liang Guan, Mohsen Guizani

This paper investigates the bit-interleaved coded generalized spatial modulation (BICGSM) with iterative decoding (BICGSM-ID) for multiple-input multiple-output (MIMO) visible light communications (VLC). In the BICGSM-ID scheme, the information bits conveyed by the signal-domain (SiD) symbols and the spatial-domain (SpD) light emitting diode (LED)-index patt

Maksud Sharipov, Ogabek Sobirov

Lemmatization is one of the core concepts in natural language processing, thus creating a lemmatization tool is an important task. This paper discusses the construction of a lemmatization algorithm for the Uzbek language. The main purpose of the work is to remove affixes of words in the Uzbek language by means of the finite state machine and to identify a le

Lorenzo Stasi, Patrik Caspar, Tiff Brydges, Hugo Zbinden

Heralded single-photon sources (HSPS) intrinsically suffer from multiphoton emission, leading to a trade-off between the source's quality and the heralding rate. A solution to this problem is to use photon-number-resolving (PNR) detectors to filter out the heralding events where more than one photon pair is created. Here, we demonstrate the use of a high-eff

Mehdi Talbi, Nizar Touzi, Jianfeng Zhang

This paper analyzes the convergence of the finite population optimal stopping problem towards the corresponding mean field limit. Building on the viscosity solution characterization of the mean field optimal stopping problem of our previous papers [Talbi, Touzi & Zhang 2021 & 2022], we prove the convergence of the value functions by adapting the Barles-Souga

Simon Dräger, Jannik Dunkelau

The loss function is arguably among the most important hyperparameters for a neural network. Many loss functions have been designed to date, making a correct choice nontrivial. However, elaborate justifications regarding the choice of the loss function are not made in related work. This is, as we see it, an indication of a dogmatic mindset in the deep learni

Tobias Lindroth, Axel Svensson, Niklas Åkerblom, Mitra Pourabdollah

Today, there is an ongoing transition to more sustainable transportation, for which an essential part is the switch from combustion engine vehicles to battery electric vehicles (BEVs). BEVs have many advantages from a sustainability perspective, but issues such as limited driving range and long recharge times slow down the transition from combustion engines.

Yi-Chun Chen, Gaoji Hu, Xiangqian Yang

A principal who values an object allocates it to one or more agents. Agents learn private information (signals) from an information designer about the allocation payoff to the principal. Monetary transfer is not available but the principal can costly verify agents' private signals. The information designer can influence the agents' signal distributions, base

Samaneh Manavi Roodsari, Sara Freund, Martin Angelmahr, Georg Rauter

Fiber optic shape sensors have enabled unique advances in various navigation tasks, from medical tool tracking to industrial applications. Eccentric fiber Bragg gratings (FBG) are cheap and easy-to-fabricate shape sensors that are often interrogated with simple setups. However, using low-cost interrogation systems for such intensity-based quasi-distributed s

Zeyu Wang, Haibin Shen, Changyou Men, Quan Sun

Image inpainting has achieved fundamental advances with deep learning. However, almost all existing inpainting methods aim to process natural images, while few target Thermal Infrared (TIR) images, which have widespread applications. When applied to TIR images, conventional inpainting methods usually generate distorted or blurry content. In this paper, we pr

Ayman Beghdadi, Malik Mallem, Lotfi Beji

The robustness of object detection algorithms plays a prominent role in real-world applications, especially in uncontrolled environments due to distortions during image acquisition. It has been proven that the performance of object detection methods suffers from in-capture distortions. In this study, we present a performance evaluation framework for the stat

Rajesh Kumar Gupta, Meenu

Non-relativistic conformal field theory is significant to understand various aspects of an ultra-cold system. In this paper, we study a non-relativistic system of two-component fermions interacting with a complex boson with Yukawa-like interactions near $d=4$-spatial dimensions in the presence of a quenched disorder. The homogeneous theory flows to an intera

Ching Lam Choi, Farzan Farnia

Despite their great success in image recognition tasks, deep neural networks (DNNs) have been observed to be susceptible to universal adversarial perturbations (UAPs) which perturb all input samples with a single perturbation vector. However, UAPs often struggle in transferring across DNN architectures and lead to challenging optimization problems. In this w

Binyi Su, Hua Zhang, Jingzhi Li, Zhong Zhou

Open-set object detection (OSOD) aims to detect the known categories and reject unknown objects in a dynamic world, which has achieved significant attention. However, previous approaches only consider this problem in data-abundant conditions, while neglecting the few-shot scenes. In this paper, we seek a solution for the generalized few-shot open-set object

Takayuki Kawamata, Shin Saito, Naruki Tsuji, Takuya Sumura

We have performed Compton scattering measurements on as-grown and reduced single crystals of the electron-doped T'-cuprate Pr1.3-xLa0.7CexCuO4 (x = 0.10) to investigate the effect of reduction annealing on the electronic state. The obtained results have revealed that the numbers of electrons in the O2p orbital in the Zhang-Rice singlet band, the Cu3dx2-y2 or

Agnes Beaudry, Irina Bobkova, Paul G. Goerss, Hans-Werner Henn

We compute the continuous cohomology of the Morava stabilizer group with coefficients in Morava $E$-theory, $H^*(\mathbb{G}_2, E_t)$, at $p=2$, for $0\leq t < 12$, using the Algebraic Duality Spectral Sequence. Furthermore, in that same range, we compute the $d_3$-differentials in the homotopy fixed point spectral sequence for the $K(2)$-local sphere spectru

Viviana Casasola, Simone Bianchi, Laura Magrini, Aleksandr V. Mosenkov

We perform a homogeneous analysis of an unprecedented set of spatially resolved scaling relations (SRs) between ISM components and other properties in the range of scales 0.3-3.4 kpc. We also study some ratios: dust-to-stellar, dust-to-gas, and dust-to-metal. We use a sample of 18 large, spiral, face-on DustPedia galaxies. All the SRs are moderate/strong cor

Yunqing Wu

We classify simply-connected, complete Willmore surfaces with vanishing Gaussian curvature. We also study the Willmore cones and give a classification. As an application, we give a Bernstein-type theorem.

Robin K. S. Hankin

In this short article I introduce the mvp package, which provides some functionality for handling multivariate polynomials. The package uses the C++ Standard Template Library's map class to store and retrieve elements; it conforms to disordR discipline for coefficients. The package is available on CRAN at https://CRAN.R-project.org/package=mvp.

Yuya Kodama, Akihiro Takano

For virtual knot theory, the virtual braid group was defined by generalizing the braid group. It was proved that any virtual link can be obtained by the closure of a virtual braid. On the other hand, due to work by Jones et al., it is known that any (oriented) link is constructed from an element of Thompson's group $F$. In this paper, we define the ``virtual

The LHAASO Collaboration, Zhen Cao, F. Aharonian, Q. An

The Kilometer Square Array~(KM2A) of the Large High Altitude Air Shower Observatory (LHAASO) aims at surveying the northern gamma-ray sky at energies above 10 TeV with unprecedented sensitivity. Gamma-ray observations have long been one of the most powerful tools for dark matter searches, as e.g., high-energy gamma-rays could be produced by the decays of hea

Leyi Zhao, Yi Li

Self-supervised pre-training models have been used successfully in several machine learning domains. However, only a tiny amount of work is related to music. In our work, we treat a spectrogram of music as a series of patches and design a self-supervised model that captures the features of these sequential patches: Patchifier, which makes good use of self-su

Ryuichi Yamamoto, Reo Yoneyama, Tomoki Toda

This paper describes the design of NNSVS, an open-source software for neural network-based singing voice synthesis research. NNSVS is inspired by Sinsy, an open-source pioneer in singing voice synthesis research, and provides many additional features such as multi-stream models, autoregressive fundamental frequency models, and neural vocoders. Furthermore, N

Seungeun Oh, Jihong Park, Sihun Baek, Hyelin Nam

Recently, vision transformer (ViT) has started to outpace the conventional CNN in computer vision tasks. Considering privacy-preserving distributed learning with ViT, federated learning (FL) communicates models, which becomes ill-suited due to ViT' s large model size and computing costs. Split learning (SL) detours this by communicating smashed data at a cut

Erik Bryhn Myklebust, Ernesto Jimenez-Ruiz, Jiaoyan Chen, Raoul Wolf

Extrapolation of adverse biological (toxic) effects of chemicals is an important contribution to expand available hazard data in (eco)toxicology without the use of animals in laboratory experiments. In this work, we extrapolate effects based on a knowledge graph (KG) consisting of the most relevant effect data as domain-specific background knowledge. An effe

Kirill A. Buzaverov, Aleksandr S. Baburin, Evgeny V. Sergeev, Sergey S. Avdeev

Low-loss photonic integrated circuits (PICs) are the key elements in future quantum technologies, nonlinear photonics and neural networks. The low-loss photonic circuits technology targeting C-band application is well established across multi-project wafer (MPW) fabs, whereas near-infrared (NIR) PICs suitable for the state-of-the-art single-photon sources ar

Fabrizio Tamburini, Ignazio Licata

From new results presented in the literature we discuss the hypothesis that the ultrafast periodic spectral modulations at $f_S \simeq 0.607$ THz found in the spectra of $236$ stars of the Sloan Digital Sky Survey (SDSS) [1] were due to oscillations induced by dark matter (DM) cores in their centers [2] behaving as oscillating boson stars [3,4]. Two addition

Sebastian P. Bayerl, Dominik Wagner, Florian Hönig, Tobias Bocklet

Specially adapted speech recognition models are necessary to handle stuttered speech. For these to be used in a targeted manner, stuttered speech must be reliably detected. Recent works have treated stuttering as a multi-class classification problem or viewed detecting each dysfluency type as an isolated task; that does not capture the nature of stuttering,

K. Welz, M. Gerken, B. Zhu, E. Lippi

We theoretically investigate three-body losses in a single-component Fermi gas near a $p$-wave Feshbach resonance in the interacting, non-unitary regime. We extend the cascade model introduced by Waseem \textit{et al.} [M. Waseem, J. Yoshida, T. Saito, and T. Mukaiyama, Phys. Rev. A \textbf{99}, 052704 (2019)] to describe the elastic and inelastic collision

Sandipan Sengupta

When tetrad (metric) fields are not invertible, the standard canonical formulation of gravity cannot be adopted as it is. Here we develop a Hamiltonian theory of gravity for non-invertible tetrad. In contrast to Einstein gravity, this phase is found to exhibit three local degrees of freedom. This reflects a discrete discontinuity in the limit of a vanishing

N. Karjanto, M. J. Acelajado

The objective of this article is to investigate the effect of active-learning pedagogy on learners' academic achievement and their attitude toward mathematics using both quantitative and qualitative methods. We cultivated sustainable learning in mathematics education for college freshmen ($n = 55$) by exposing them to both the conventional teaching method (C

Tamás Grósz, Mittul Singh, Sudarsana Reddy Kadiri, Hemant Kathania

The events of recent years have highlighted the importance of telemedicine solutions which could potentially allow remote treatment and diagnosis. Relatedly, Computational Paralinguistics, a unique subfield of Speech Processing, aims to extract information about the speaker and form an important part of telemedicine applications. In this work, we focus on tw

Yan Wang, Xin Luo, Zhen-Duo Chen, Peng-Fei Zhang

Despite the great success achieved, existing video moment retrieval (VMR) methods are developed under the assumption that data are centralizedly stored. However, in real-world applications, due to the inherent nature of data generation and privacy concerns, data are often distributed on different silos, bringing huge challenges to effective large-scale train

Jiayi Tian, Chao Fang, Haonan Wang, Zhongfeng Wang

Pre-trained BERT models have achieved impressive accuracy on natural language processing (NLP) tasks. However, their excessive amount of parameters hinders them from efficient deployment on edge devices. Binarization of the BERT models can significantly alleviate this issue but comes with a severe accuracy drop compared with their full-precision counterparts

Masaya Kawamura, Yuma Shirahata, Ryuichi Yamamoto, Kentaro Tachibana

We propose a lightweight end-to-end text-to-speech model using multi-band generation and inverse short-time Fourier transform. Our model is based on VITS, a high-quality end-to-end text-to-speech model, but adopts two changes for more efficient inference: 1) the most computationally expensive component is partially replaced with a simple inverse short-time F

Ren Sasaki, Yutaro Tanaka, Shuichi Murakami

We construct a general theory of $Z_2$ topological phase transitions in two-dimensional systems with time-reversal symmetry. We investigate the possibilities of $Z_2$ topological phase transitions at band inversions at all high-symmetry points in $k$-space in all the 80 layer groups. We exclude the layer groups with inversion symmetry because the $Z_2$ topol

Kevin van Liebergen, Juan Caballero, Platon Kotzias, Chris Gates

Online scanners analyze user-submitted files with a large number of security tools and provide access to the analysis results. As the most popular online scanner, VirusTotal (VT) is often used for determining if samples are malicious, labeling samples with their family, hunting for new threats, and collecting malware samples. We analyze 328M VT reports for 2

Yan Zhang, Xiyuan Gao, Qingyan Duan, Jiaxu Leng

Very high-resolution (VHR) remote sensing (RS) image classification is the fundamental task for RS image analysis and understanding. Recently, transformer-based models demonstrated outstanding potential for learning high-order contextual relationships from natural images with general resolution (224x224 pixels) and achieved remarkable results on general imag

Peng He, Xue-ping Wang

In this article, we first characterize pseudocomplemented inductive modular lattices by using their two 0-sublattices. Then we use two 0-sublattices of a subgroup lattice to describe all locally cyclic abelian groups. In particular, we show that a locally cyclic abelian group can be characterized by its three subgroups.

Maria Alejandra Ramirez, Matteo Smerlak, Arne Traulsen, Jürgen Jost

Social dilemmas are situations in which collective welfare is at odds with individual gain. One widely studied example, due to the conflict it poses between human behaviour and game theoretic reasoning, is the Traveler's Dilemma. The dilemma relies on the players' incentive to undercut their opponent at the expense of losing a collective high payoff. Such in

Mohamed A. Shabara, Ossama Abdelkhalik

In the recently introduced Variable-Shape heaving wave energy converters, the buoy changes its shape actively in response to changing incident waves. In this study, a Lagrangian approach for the dynamic modeling of a spherical Variable-Shape Wave Energy Converter is described. The classical bending theory is used to write the stress-strain equations for the

T. A. Wang, C. Guo, X. H. Liang, L. Wang

Silicon photomultiplier (SiPM), a new type of photosensor, is considered a substitute for traditional photomultiplier tube (PMT) in the next generation of dark matter and neutrino detectors, especially in noble gas detectors like liquid argon. However, the design of compact SiPM arrays and their cryogenic electronics that can work in liquid argon is barely d

Walid Al Hajj, S. Morteza Hosseini

In this short paper, we conjecture a correspondence between the creation of the universe in entangled pairs and eternal black holes. We shall see that this correspondence will restore the matter-antimatter asymmetry at the beginning of the universe and provide a new cosmological model that can be used to map the physics of the entire universe to the physics

Geon Lee, Tae-Kyoung Kim, Hyun-Gyoon Kim, Jeonggyu Huh

In finance, implied volatility is an important indicator that reflects the market situation immediately. Many practitioners estimate volatility using iteration methods, such as the Newton--Raphson (NR) method. However, if numerous implied volatilities must be computed frequently, the iteration methods easily reach the processing speed limit. Therefore, we em

Jakub Bernat, Paulina Superczynska, Piotr Gajewski, Agnieszka Marcinkowska

This study examines the concept of axisymmetric actuator based on the magnetorheological membrane, electromagnet and permanent magnet. The construction of the actuator enables its application in wide range of practical devices like pumping, loudspeaker or varying-stiffness button. This work will highlight its working principle especially the influence of per

Lucas Backes, Davor Dragicevic, Masakazu Onitsuka, Mihaly Pituk

We introduce the notion of conditional Lipschitz shadowing, which does not aim to shadow every pseudo-orbit, but only those which belong to a certain prescribed set. We establish two types of sufficient conditions under which certain non\-auto\-nomous ordinary differential equations have such a property. The first criterion applies to a semilinear differenti

Paweł Lorek

In this short note we provide some algebraic identity with a proof exploiting its probabilistic interpretation. We show several consequences of the identity, in particular we obtain a new representation of a Stirling number of second kind, $$ S(n,d)={1\over d!} \sum_{1\leq j_1<j_2<\ldots<j_{d-1}< n} 1\cdot2^{j_{d-1}-j_{d-2}}\cdots d^{j_1}$$ for integers $n\g

Animesh Chaturvedi, Aruna Tiwari, Nicolas Spyratos

System Evolution Analytics on a system that evolves is a challenge because it makes a State Series SS = {S1, S2... SN} (i.e., a set of states ordered by time) with several inter-connected entities changing over time. We present stability characteristics of interesting evolution rules occurring in multiple states. We defined an evolution rule with its stabili

Yuma Shirahata, Ryuichi Yamamoto, Eunwoo Song, Ryo Terashima

Several fully end-to-end text-to-speech (TTS) models have been proposed that have shown better performance compared to cascade models (i.e., training acoustic and vocoder models separately). However, they often generate unstable pitch contour with audible artifacts when the dataset contains emotional attributes, i.e., large diversity of pronunciation and pro

Koichi Fujii, Sunyoung Kim, Masakazu Kojima, Hans D. Mittelmann

Tai256c is the largest unsolved quadratic assignment problem (QAP) instance in QAPLIB; a 1.48\% gap remains between the best known feasible objective value and lower bound of the unknown optimal value. This paper shows that the instance can be converted into a 256 dimensional binary quadratic optimization problem (BQOP) with a single cardinality constraint w

Borko Bošković, Jana Herzog, Janez Brest

A low-autocorrelation binary sequences problem with a high figure of merit factor represents a formidable computational challenge. An efficient parallel computing algorithm is required to reach the new best-known solutions for this problem. Therefore, we developed the $\mathit{sokol}_{\mathit{skew}}$ solver for the skew-symmetric search space. The developed

Joonas Jälkö, Lukas Prediger, Antti Honkela, Samuel Kaski

Differentially private (DP) release of multidimensional statistics typically considers an aggregate sensitivity, e.g. the vector norm of a high-dimensional vector. However, different dimensions of that vector might have widely different magnitudes and therefore DP perturbation disproportionately affects the signal across dimensions. We observe this problem i

Yiqiang Cai, Shengchen Li

Acoustic Scene Classification (ASC) algorithms are usually expected to be deployed in resource-constrained systems. Existing works reduce the complexity of ASC algorithms by pruning some components, e.g. pruning channels in neural network. In practice, neural networks are often trained with sparsification such that unimportant channels can be found and furth

Gabriele Santin

In this paper we show how ideas from spline theory can be used to construct a local basis for the space of translates of a general iterated Brownian Bridge kernel $k_{\beta,\varepsilon}$ for $\beta\in\mathbb{N}$, $\varepsilon\geq 0$. In the simple case $\beta=1$, we derive an explicit formula for the corresponding Lagrange basis, which allows us to solve int

Lars A. L. Janssen, Bart Besselink, Rob H. B. Fey, Nathan van de Wouw

Many complex engineering systems consist of multiple subsystems that are developed by different teams of engineers. To analyse, simulate and control such complex systems, accurate yet computationally efficient models are required. Modular model reduction, in which the subsystem models are reduced individually, is a practical and an efficient method to obtain

Gagan Acharya, Sebastian F. Ruf, Erfan Nozari

Neurostimulation technologies have seen a recent surge in interest from the neuroscience and controls communities alike due to their proven potential to treat conditions such as Parkinson's Disease, and depression. The provided stimulation can be of different types, such as electric, and optogenetic, and is generally applied to a specific region of the brain

Sohir Maskey, Ali Parviz, Maximilian Thiessen, Hannes Stärk

Graph neural networks (GNNs) are the primary tool for processing graph-structured data. Unfortunately, the most commonly used GNNs, called Message Passing Neural Networks (MPNNs) suffer from several fundamental limitations. To overcome these limitations, recent works have adapted the idea of positional encodings to graph data. This paper draws inspiration fr

Yoshisuke Ban, Kimihiko Kato, Shota Iizuka, Shigenori Murakami

To realize high-temperature operation of Si qubits, deep impurity levels with large confinement energy, which are hardly thermally excited, have been introduced into Si wafers. Group II impurity Zn and group VI impurities S and Se, which are known to form deep levels, were introduced into the Si substrates by ion implantation. These samples were analyzed for

Jonathan Zheng, Ashutosh Baheti, Tarek Naous, Wei Xu

We present Stanceosaurus, a new corpus of 28,033 tweets in English, Hindi, and Arabic annotated with stance towards 251 misinformation claims. As far as we are aware, it is the largest corpus annotated with stance towards misinformation claims. The claims in Stanceosaurus originate from 15 fact-checking sources that cover diverse geographical regions and cul

Artem Khodzitskii

The intensive study of Rota-Baxter operators on the polynomial algebra $F[x]$ has been started with the work of S.H. Zheng, L. Guo, and M. Rosenkranz (2015). We deal with the case of two variables and monomial Rota-Baxter operators of nonzero weight. The family of such operators arisen from homomorphic averaging operators on $F[x,y]$ is described.

Abhishek Mund, Amit K Nayse, Arindam Das

Clathrate hydrate is a naturally occurring ice-like solid which forms in water phase under suitable temperature and pressure conditions, in the presence of one or more hydrophobic molecules. It also forms inside the oil and gas pipes leading to higher pumping cost, flow blockage and even catastrophic accidents. Engineered surfaces with low hydrate adhesion c

Louis Bahrman, Marina Krémé, Paul Magron, Antoine Deleforge

Signal inpainting is the task of restoring degraded or missing samples in a signal. In this paper we address signal inpainting when Fourier magnitudes are observed. We propose a mathematical formulation of the problem that highlights its connection with phase retrieval, and we introduce two methods for solving it. First, we derive an alternating minimization

Huajian Si, Zeyong Wei, Zhe Zhu, Honghua Chen

Bilateral filter (BF) is a fast, lightweight and effective tool for image denoising and well extended to point cloud denoising. However, it often involves continual yet manual parameter adjustment; this inconvenience discounts the efficiency and user experience to obtain satisfied denoising results. We propose LBF, an end-to-end learnable bilateral filtering

Shrajan Bhandary, Zahra Babaiee, Dejan Kostyszyn, Tobias Fechter

Despite the success of convolutional neural networks for 3D medical-image segmentation, the architectures currently used are still not robust enough to the protocols of different scanners, and the variety of image properties they produce. Moreover, access to large-scale datasets with annotated regions of interest is scarce, and obtaining good results is thus

Ligen Shi, Chang Liu, Di He, Xing Zhao

A major challenge for matching-based depth estimation is to prevent mismatches in occlusion and smooth regions. An effective matching window satisfying three characteristics: texture richness, disparity consistency and anti-occlusion should be able to prevent mismatches to some extent. According to these characteristics, we propose matching entropy in the sp

Liangchen Song, Anpei Chen, Zhong Li, Zhang Chen

Visually exploring in a real-world 4D spatiotemporal space freely in VR has been a long-term quest. The task is especially appealing when only a few or even single RGB cameras are used for capturing the dynamic scene. To this end, we present an efficient framework capable of fast reconstruction, compact modeling, and streamable rendering. First, we propose t

Ada Gonzalez-Torres

Media around the world is disseminated at the national level as well as at the local level. While the capacity of media to shape preferences and behavior has been widely recognized, less is known about the differential impacts of local media. Local media may have particularly important effects on social norms due to the provision of locally relevant informat

Q. Fable, A. Chbihi, J. D. Frankland, P. Napolitani

We investigate the isospin transport with 40,48 Ca+ 40,48 Ca reactions at 35 MeV/nucleon, measured with the coupling of the VAMOS high acceptance spectrometer and the INDRA charged particle multidetector. Using the quasi-projectile remnant measured with VAMOS and carefully selected light-charged-particles measured in coincidence with INDRA, a reconstruction

Zihan Zhang, Jinfeng Li, Ning Shi, Bo Yuan

Despite of the superb performance on a wide range of tasks, pre-trained language models (e.g., BERT) have been proved vulnerable to adversarial texts. In this paper, we present RoChBERT, a framework to build more Robust BERT-based models by utilizing a more comprehensive adversarial graph to fuse Chinese phonetic and glyph features into pre-trained represent

Jongwoo Park, Kumara Kahatapitiya, Donghyun Kim, Shivchander Sudalairaj

Vision Transformers (ViTs) have recently become the state-of-the-art across many computer vision tasks. In contrast to convolutional networks (CNNs), ViTs enable global information sharing even within shallow layers of a network, i.e., among high-resolution features. However, this perk was later overlooked with the success of pyramid architectures such as Sw