November 2022 arXiv papers — page 80

A. Dumitrescu, D. S. Delion

We show that the Hartree-Fock-Bogoliubov (HFB) method is able to describe experimental values of alpha decay widths by including a residual nucleon-nucleon Surface Gaussian Interaction (SGI) within the standard procedure used to calculate the nuclear mean field. We call this method the Cluster HFB (CHFB) approach. In this way we correct the deficient asympto

Fatemeh chahshouri, Nahid Talebi

Inelastic interaction of free-electrons with optical near fields has recently attracted attention for manipulating and shaping free-electron wavepackets. Understanding the nature and the dependence of the inelastic cross section on the polarization of the optical near-field is important for both fundamental aspects and the development of new applications in

Zhi-Chao Zhao, Sai Wang

Assuming that the common-spectrum process in the NANOGrav 12.5-year dataset has an origin of scalar-induced gravitational waves, we study the enhancement of primordial curvature perturbations and the mass function of primordial black holes, by performing the Bayesian parameter inference for the first time. We obtain lower limits on the spectral amplitude, i.

Jeshwanth Mohan

As technology has improved, binary neutron star systems have been observed more frequently, in fact, the first gravitational wave to have an electromagnetic counterpart originated from the merger of two neutron stars (GW170817). Detecting these systems prior to merger may help recover essential data for developing an Equation of State for neutron stars. This

Takashi Nakatsukasa

The nuclear energy density functional method at finite temperature is a useful tool for studies of nuclear structure at high excitation, and also for researches of nuclear matter involved in explosive stellar phenomena and neutron stars. However, its unrestricted calculation requires large computational costs for the three-dimensional coordinate-space solver

Anna Vanselow, Lukas Halekotte, Pinaki Pal, Sebastian Wieczorek

Plankton blooms are complex nonlinear phenomena whose occurrence can be described by the two-timescale (fast-slow) phytoplankton-zooplankton model intrpduced by Truscott and Brindley 1994. In their work, they observed that a sufficiently fast rise of the water temperature causes a critical transition from a low phytoplankton concentration to a single outburs

Baikal Collaboration, V. A. Allakhverdyan, A. D. Avrorin, A. V. Avrorin

We report on the first observation of the diffuse cosmic neutrino flux with the Baikal-GVD neutrino telescope. Using cascade-like events collected by Baikal-GVD in 2018--2021, a significant excess of events over the expected atmospheric background is observed. This excess is consistent with the high-energy diffuse cosmic neutrino flux observed by IceCube. Th

Katja Ludwig, Daniel Kienzle, Julian Lorenz, Rainer Lienhart

Analyses based on the body posture are crucial for top-class athletes in many sports disciplines. If at all, coaches label only the most important keypoints, since manual annotations are very costly. This paper proposes a method to detect arbitrary keypoints on the limbs and skis of professional ski jumpers that requires a few, only partly correct segmentati

Tamás Matuszka, Iván Barton, Ádám Butykai, Péter Hajas

Autonomous driving is a popular research area within the computer vision research community. Since autonomous vehicles are highly safety-critical, ensuring robustness is essential for real-world deployment. While several public multimodal datasets are accessible, they mainly comprise two sensor modalities (camera, LiDAR) which are not well suited for adverse

Hidekazu Furusho, Nao Komiyama

We explain the current situation of the relationship between the Kashiwara-Vergne Lie algebra $\mathfrak{krv}$ and the double shuffle Lie algebra $\mathfrak{dmr}$. We also show the validity of Ecalle's senary relation for small depths.

Amirmohammad Farzaneh, Mihai-Alin Badiu, Justin P. Coon

Routing is one of the critical and ongoing challenges in Wireless Sensor Networks. The main challenge has always been to have a routing protocol that reduces the communication overhead, hence saving the energy of the sensors in the network. Hierarchical routing protocols are known to be the most energy-efficient routing protocols for Wireless Sensor Networks

Jiang Zhu, Jun Guo, Zhaofeng Kang

It is challenging to build a model that can correctly and unifiedly account for the deconfinement phase transition and thermodynamics of the hot $SU(N)$ pure Yang-Mills (PYM) system, for any $N$. In this article, we slightly generalize the massive PYM model to the situation with a quasigluon mass $M_g(T)$ varying with temperature, inspired by the quasigluon

Yoshimasa Hidaka, Satoshi Iso, Kengo Shimada

Motivated by the Bose et al.-Matletto-Vedral (BMV) proposal for detecting quantum superposition of spacetime geometries, we study a toy model of a quantum entanglement generation between two spins (qubits) mediated by a relativistic free scalar field. After time evolution, spin correlation is generated through the interactions with the field. Because of the

Kazuki Irie, Jürgen Schmidhuber

Short-term memory in standard, general-purpose, sequence-processing recurrent neural networks (RNNs) is stored as activations of nodes or "neurons." Generalising feedforward NNs to such RNNs is mathematically straightforward and natural, and even historical: already in 1943, McCulloch and Pitts proposed this as a surrogate to "synaptic modifications" (in eff

Mareike Dressler, Marina Garrote-López, Guido Montúfar, Johannes Müller

We study the optimization of the expected long-term reward in finite partially observable Markov decision processes over the set of stationary stochastic policies. In the case of deterministic observations, also known as state aggregation, the problem is equivalent to optimizing a linear objective subject to quadratic constraints. We characterize the feasibl

Vineet Kumar, Suresh Sundaram

Handwriting recognition is one of the desirable attributes of document comprehension and analysis. It is concerned with the documents writing style and characteristics that distinguish the authors. The diversity of text images, notably in images with varying handwriting, makes the process of learning good features difficult in cases where little data is avai

Yifeng Xie

While most successful approaches for machine reading comprehension rely on single training objective, it is assumed that the encoder layer can learn great representation through the loss function we define in the predict layer, which is cross entropy in most of time, in the case that we first use neural networks to encode the question and paragraph, then dir

Jan Thoen, George Cordoyiannis, Wanhe Jiang, Georg H. Mehl

Adiabatic scanning calorimetry (ASC) is capable of providing simultaneously the specific enthalpy h(T) and the specific heat capacity cp(T), and is an important tool to determine the order of transitions and to render high-resolution information on pretransitional thermal behavior. Here we report on ASC results on the compound DIO and on mixtures with RM734.

Ojasvi Pal, Bashab Dey, Tarun Kanti Ghosh

We study the linear, second-order nonlinear (NL) current and voltage responses of a two-dimensional gapped semi-Dirac system with merging Dirac nodes along the $x$ direction under the influence of a weak magnetic field ($B$), using the semiclassical Boltzmann formalism. We investigate the effect of band geometric quantities like Berry curvature and orbital m

ATLAS Collaboration

Measurements of joint-polarisation states of $W$ and $Z$ gauge bosons in $W^{\pm}Z$ production are presented. The data set used corresponds to an integrated luminosity of $139$ fb$^{-1}$ of proton-proton collisions at a center-of-mass energy of $13$ TeV recorded by the ATLAS detector at the CERN Large Hadron Collider. The $W^{\pm}Z$ candidate events are reco

Lukas Schwenkel, Johannes Köhler, Matthias A. Müller, Frank Allgöwer

This work provides a framework to compute an upper bound on the robust peak-to-peak gain of discrete-time uncertain linear systems using integral quadratic constraints (IQCs). Such bounds are of particular interest in the computation of reachable sets and the $\ell_1$-norm, as well as when safety-critical constraints need to be satisfied pointwise in time. T

Xuejie Liu, Yue Tan, Xiaoyun Chen, Dianyong Chen

Inspired by the states $T_{c\bar{s}0}^{a}(2900)^{0}$ and $T_{c\bar{s}0}^{a}(2900)^{++}$ reported by the LHCb Collaboration, we carry out a systematical investigation of the charm-strange pentaquark system using resonance group method in the quark delocalization color screening model. The present results predict the existence of some bound states and resonanc

Tao Fang, Xiying Yuan

The Tur\'an number of a graph $H$, denoted by $ex(n, H)$, is the maximum number of edges in any graph on $n$ vertices containing no $H$ as a subgraph. Let $P_{\ell}$ denote the path on $\ell$ vertices, $S_{\ell-1}$ denote the star on $\ell$ vertices and $k_1P_{\ell}\cup k_2S_{\ell-1}$ denote the path-star forest with disjoint union of $k_1$ copies of $P_{\el

Luca Iorio, Jacopo Maria De Ponti, Federico Maspero, Raffaele Ardito

While roton dispersion relations had been restricted to correlated quantum systems at low temperature, recent works show the possibility of obtaining this unusual dispersion in acoustic and elastic metamaterials. Such phenomenon has been demonstrated in periodic structures by means of beyond-nearest-neighbor interactions, following the formulation firstly de

E. A. Kochurin, E. A. Kuznetsov

We present the results of direct numerical simulation of three-dimensional acoustic turbulence in medium with weak positive dispersion. It is shown that at the beginning of the long-wavelength region in the turbulence energy distribution in the $k$-space, there are formed jets in the form of narrow cones. At larger wavenumbers, the cones broaden, and the dis

Giorgio Poggesi

We consider a class of rigidity results in a convex cone $\Sigma \subseteq \mathbb{R}^N$. These include overdetermined Serrin-type problems for a mixed boundary value problem relative to $\Sigma$, Alexandrov's soap bubble-type results relative to $\Sigma$, and a Heintze-Karcher's inequality relative to $\Sigma$. Each rigidity result is obtained by means of a

Baojie Jiang, Jiawen Zhang, Jianguo Zhang

Let $\mathcal{G}$ be a locally compact \'{e}tale groupoid and $\mathscr{L}(L^2(\mathcal{G}))$ be the $C^*$-algebra of adjointable operators on the Hilbert $C^*$-module $L^2(\mathcal{G})$. In this paper, we discover a notion called quasi-locality for operators in $\mathscr{L}(L^2(\mathcal{G}))$, generalising the metric space case introduced by Roe. Our main r

Kazuya Ohata, Shunsuke Kitada, Hitoshi Iyatomi

We propose a simple yet effective image captioning framework that can determine the quality of an image and notify the user of the reasons for any flaws in the image. Our framework first determines the quality of images and then generates captions using only those images that are determined to be of high quality. The user is notified by the flaws feature to

Johannes Buchner

Bayesian inference with nested sampling requires a likelihood-restricted prior sampling method, which draws samples from the prior distribution that exceed a likelihood threshold. For high-dimensional problems, Markov Chain Monte Carlo derivatives have been proposed. We numerically study ten algorithms based on slice sampling, hit-and-run and differential ev

Christof Beierle, Patrick Felke

Let $p$ be a prime and $n$ a positive integer. As the first main result, we present a deterministic algorithm for deciding whether the matrix algebra $\mathbb{F}_p[A_1,\dots,A_t]$ with $A_1,\dots,A_t \in \mathrm{GL}(n,\mathbb{F}_p)$ is a finite field, performing at most $\mathcal{O}(tn^6\log(p))$ elementary operations in $\mathbb{F}_p$. In the affirmative ca

Zoe Kotti, Rafaila Galanopoulou, Diomidis Spinellis

Machine learning (ML) techniques increase the effectiveness of software engineering (SE) lifecycle activities. We systematically collected, quality-assessed, summarized, and categorized 83 reviews in ML for SE published between 2009-2022, covering 6,117 primary studies. The SE areas most tackled with ML are software quality and testing, while human-centered

Dmitry Bogachuk, Jessica Girard, Siddharth Tilala, David Martineau

A sacrificial film of polystyrene nanoparticles was utilized to introduce nano-cavities in mesoporous metal oxide layers. This enabled the growth of larger perovskite crystals inside the oxide scaffold with significantly suppressed non-radiative recombination and improving device performance. This work exemplifies potential applications of such nanoarchitect

Yuzhe Qin, Binghao Huang, Zhao-Heng Yin, Hao Su

We propose a sim-to-real framework for dexterous manipulation which can generalize to new objects of the same category in the real world. The key of our framework is to train the manipulation policy with point cloud inputs and dexterous hands. We propose two new techniques to enable joint learning on multiple objects and sim-to-real generalization: (i) using

G. Torrioli, A. Forrer, M. Beck, P. Carelli

We study the performance of an hot-electron bolometer (HEB) operating at THz frequencies based on superconducting niobium nitride films. We report on the voltage response of the detector over a large optical bandwidth carried out with different THz sources. We show that the impulse response of the fully packaged HEB at 7.5 K has a 3 dB cut-off around 2 GHz.

Ming Yang, Yanhan Wang, Xin Wang, Zhenyong Zhang

Federated learning is a distributed learning that allows each client to keep the original data locally and only upload the parameters of the local model to the server. Despite federated learning can address data island, it remains challenging to train with data heterogeneous in a real application. In this paper, we propose FedSiam-DA, a novel dual-aggregated

Marco Doretti, Minna Genbäck, Elena Stanghellini

With reference to a stratified case-control procedure based on a binary variable of primary interest, we derive the expression of the distortion induced by the sampling design on the parameters of the logistic model of a secondary variable. This is particularly relevant when performing mediation analysis (possibly in a causal framework) with stratified case-

Lukas Hedegaard, Aman Alok, Juby Jose, Alexandros Iosifidis

Adapters are a parameter-efficient alternative to fine-tuning, which augment a frozen base network to learn new tasks. Yet, the inference of the adapted model is often slower than the corresponding fine-tuned model. To improve on this, we propose Structured Pruning Adapters (SPAs), a family of compressing, task-switching network adapters, that accelerate and

Yuying Liu, Aleksei Sholokhov, Hassan Mansour, Saleh Nabi

Koopman operator theory is receiving increased attention due to its promise to linearize nonlinear dynamics. Neural networks that are developed to represent Koopman operators have shown great success thanks to their ability to approximate arbitrarily complex functions. However, despite their great potential, they typically require large training data-sets ei

Alexander Kuznetsov, Evgeny Shinder

In this paper we investigate homologically finite-dimensional objects in the derived category of a given small dg-enhanced triangulated category. Using these we define reflexivity, hfd-closedness, and the Gorenstein property for triangulated categories, and discuss crepant categorical contractions. We illustrate the introduced notions on examples of categori

Alessandro De Luca, Gabriele Fici

We exhibit combinatorial results on Christoffel words and binary balanced words that are motivated by their geometric interpretation as approximations of digital segments. We give a closed formula for counting the exact number of balanced words with $a$ zeroes and $b$ ones. We also study minimal non-balanced words.

Vitaly A. Krasikov

We review results of papers written on the topic of polynomial amoebas with an emphasis on computational aspects of the topic. The polynomial amoebas have a lot of applications in various domains of science. Computation of the amoeba for a given polynomial and describing its properties is in general a problem of formidable complexity. We describe the main al

Merav Parter, Asaf Petruschka

We present near-optimal algorithms for detecting small vertex cuts in the CONGEST model of distributed computing. Despite extensive research in this area, our understanding of the vertex connectivity of a graph is still incomplete, especially in the distributed setting. To this date, all distributed algorithms for detecting cut vertices suffer from an inhere

Giovanni Guerrieri

The top quark is the heaviest known elementary particle. Its large mass, close to the scale of electroweak symmetry breaking, hints at a unique role in the Standard Model of particle physics. The study of top quark-antiquark ($t\bar{t}$) production is an important part of the physics programme of the ATLAS experiment at the CERN Large Hadron Collider (LHC).

Vadim Borokhov

In the presence of non-convexities, the power market may not have an equilibrium price for power that provides economic stability of the centralized dispatch outcome. In this case, to achieve an economically stable outcome, the uplift payments to the market players are introduced as part of the pricing principle. Given the general pricing principle that invo

Xun Gong, Yu Wu, Jinyu Li, Shujie Liu

Traditional automatic speech recognition~(ASR) systems usually focus on individual utterances, without considering long-form speech with useful historical information, which is more practical in real scenarios. Simply attending longer transcription history for a vanilla neural transducer model shows no much gain in our preliminary experiments, since the pred

Sema Kucuksucu, Mustafa Yigit, Nils Paar

The $(n,\alpha)$ reactions play an important role for the energy generation and the synthesis of chemical elements in the stars, as well as for nuclear engineering and medical applications. The aim of this study is to explore the evolution of $(n,\alpha)$ reactions in Fe and Sn isotope chains in order to assess their properties with the increase of neutrons

Yeongjun Kim, Tilen Čadež, Alexei Andreanov, Sergej Flach

We consider manifolds of tunable all-band flat (ABF) lattices in dimensions d = 1, 2, parametrized by a manifold angle parameter {\theta}. We study localization properties of eigenstates in the presence of weak magnetic flux disorder and weak spin-orbit disorder. We demonstrate that weakly disordered ABF lattices are described by effective scale-free models

Seyed Hesam Odin Hashemi, Mohammad-Hassan Majidi, Saeed Khorashadizadeh

In this paper, a deep learning color image steganography scheme combining convolutional autoencoders and ResNet architecture is proposed. Traditional steganography methods suffer from some critical defects such as low capacity, security, and robustness. In recent decades, image hiding and image extraction were realized by autoencoder convolutional neural net

Adam Burgess, Marian Florescu

In this article, we explore the dynamical decoherence of the chromophores within a green fluorescent protein when coupled to a finite-temperature dielectric environment. Such systems are of significant interest due to their anomalously long coherence lifetimes compared to other biomolecules. We work within the spin-boson model and employ the Hierarchical Equ

Hyeong-Seok Choi, Jinhyeok Yang, Juheon Lee, Hyeongju Kim

Various applications of voice synthesis have been developed independently despite the fact that they generate "voice" as output in common. In addition, most of the voice synthesis models still require a large number of audio data paired with annotated labels (e.g., text transcription and music score) for training. To this end, we propose a unified framework

Sheng Guo, Zengxiang Li, Hui Liu, Shubao Zhao

Intelligent fault diagnosis is essential to safe operation of machinery. However, due to scarce fault samples and data heterogeneity in field machinery, deep learning based diagnosis methods are prone to over-fitting with poor generalization ability. To solve the problem, this paper proposes a personalized federated learning framework, enabling multi-task fa

Yu. M. Zinoviev

In this paper we consider massless spin 2 interacting with the massive arbitrary spin fermions in d=3. First of all, we study all possible deformations for the massive fermion unfolded equations in the massless spin 2 background. We find three linearly independent solutions one of which corresponds to the standard gravitational interactions. Then for all thr

Jiayi Zhang, Xiaoshan Chen, Zhongxi Qiu, Mingming Yang

Hard exudates (HE) is the most specific biomarker for retina edema. Precise HE segmentation is vital for disease diagnosis and treatment, but automatic segmentation is challenged by its large variation of characteristics including size, shape and position, which makes it difficult to detect tiny lesions and lesion boundaries. Considering the complementary fe

Chinmaya Kausik, Kevin Tan, Ambuj Tewari

We present an algorithm for learning mixtures of Markov chains and Markov decision processes (MDPs) from short unlabeled trajectories. Specifically, our method handles mixtures of Markov chains with optional control input by going through a multi-step process, involving (1) a subspace estimation step, (2) spectral clustering of trajectories using "pairwise d

Yue Feng, Katharina Schratz

We establish the improved uniform error bounds on a Lawson-type exponential integrator Fourier pseudospectral (LEI-FP) method for the long-time dynamics of sine-Gordon equation where the amplitude of the initial data is $O(\varepsilon)$ with $0 < \varepsilon \ll 1$ a dimensionless parameter up to the time at $O(1/\varepsilon^2)$. The numerical scheme combine

Hung-Chieh Fang, Kuo-Han Hung, Chao-Wei Huang, Yun-Nung Chen

Open-domain conversational question answering can be viewed as two tasks: passage retrieval and conversational question answering, where the former relies on selecting candidate passages from a large corpus and the latter requires better understanding of a question with contexts to predict the answers. This paper proposes ConvADR-QA that leverages historical

Silpa Muralidharan, Kenji Toyoda

We demonstrate the site-dependent control of polaritons in the Jaynes Cummings Hubbard (JCH) model with trapped ions. In a linear ion crystal under illumination by optical beams nearly resonant to the red-sideband (RSB) transition for the radial vibrational direction, quasiparticles called polaritonic excitations or polaritons, each being a superposition of

Hongsheng Cai, Guoyuan Liu, Peiqi Qiu, Guangfu Luo

Minimum energy path (MEP) search is a vital but often very time-consuming method to predict the transition states of versatile dynamic processes in chemistry, physics, and materials science. In this study, we reveal that the chemical bond lengths in the MEP structures, including those directly involved in the dynamical processes, largely resemble those in th

Jia Tian, Yingyu Yang

We continue our study about the half-wormhole proposal. By generalizing the original proposal of half-wormhole we propose a new way to detect half-wormholes. The crucial idea is to decompose the observables into self-averaged sector and non-self-averaged sectors. We find the contributions from different sectors have interesting statistics in the semi-classic

Hasti Seifi, Steven A. Vasquez, Hyunyoung Kim, Pooyan Fazli

A wide variety of robotic hands have been designed to date. Yet, we do not know how users perceive these hands and feel about interacting with them. To inform hand design for social robots, we compiled a dataset of 73 robot hands and ran an online study, in which 160 users rated their impressions of the hands using 17 rating scales. Next, we developed 17 reg

V. I. Korobov, D. Bakalov

We present the first systematic calculation of the electric dipole forbidden transitions in the homonuclear molecular ion H$_2^+$. We get that the transition rate from the ground "ortho" $(v\!=\!0,N\!=\!1,J\!=\!1/2)$ state to the ground "para" $(v\!=\!0,N\!=\!0,J\!=\!1/2)$ state is $4.9\!\times\!10^{-14}$ s$^{-1}$ that corresponds to the lifetime of $6.4\!\t

Son T. Duong, Ha H. Nguyen, Ebrahim Bedeer

This paper investigates non-coherent detection of single-input multiple-output (SIMO) systems over block Rayleigh fading channels. Using the Kullback-Leibler divergence as the design criterion, we formulate a multiple-symbol constellation optimization problem, which turns out to have high computational complexity to construct and detect. We exploit the struc

Ran Zhou, Xin Li, Lidong Bing, Erik Cambria

Cross-lingual named entity recognition (NER) suffers from data scarcity in the target languages, especially under zero-shot settings. Existing translate-train or knowledge distillation methods attempt to bridge the language gap, but often introduce a high level of noise. To solve this problem, consistency training methods regularize the model to be robust to

Shikui Shang

Let $k$ be a field of characteristic $0$. For a superspace $V=V_\bar{0}\oplus V_\bar{1}$ over $k$, we call the vector $(\dim_k V_\bar{0} ,\dim_k V_\bar{1})$ the (${\mathbb Z}_2$-)graded dimension of $V$. Let $J(D_1|D_2)$ be the free Jordan superalgebra generated by $D_1$ even generators and $D_2$ odd generators. In this paper, we study the graded dimensions

Wisal Khan, Muhammad Turab, Waqas Ahmad, Syed Hasnat Ahmad

Data dimension reduction (DDR) is all about mapping data from high dimensions to low dimensions, various techniques of DDR are being used for image dimension reduction like Random Projections, Principal Component Analysis (PCA), the Variance approach, LSA-Transform, the Combined and Direct approaches, and the New Random Approach. Auto-encoders (AE) are used

Fuzhou Gong, Zigeng Xia

In this paper, we focus on the interpretability of deep neural network. Our work is motivated by the renormalization group (RG) in statistical mechanics. RG plays the role of a bridge connecting microscopical properties and macroscopic properties, the coarse graining procedure of it is quite similar with the calculation between layers in the forward propagat

Mingyang Ren, Yaoming Zhen, Junhui Wang

Tensor Gaussian graphical models (GGMs), interpreting conditional independence structures within tensor data, have important applications in numerous areas. Yet, the available tensor data in one single study is often limited due to high acquisition costs. Although relevant studies can provide additional data, it remains an open question how to pool such hete

Tyron Offerman, Robert Blinde, Christoph Johann Stettina, Joost Visser

Many organizations adopt DevOps practices and tools in order to break down silos within the organization, improve software quality and delivery, and increase customer satisfaction. However, the impact of the individual practices on the performance of the organization is not well known. In this paper, we collect evidence on the effects of DevOps practices and

Dazhi Gao, Rongyang Li, Hongbo Wang, Lingfeng Mao

The shield machine (SM) is a complex mechanical device used for tunneling. However, the monitoring and deciding were mainly done by artificial experience during traditional construction, which brought some limitations, such as hidden mechanical failures, human operator error, and sensor anomalies. To deal with these challenges, many scholars have studied SM

Sixia Yu, Ya-Li Mao, Chang Niu, Hu Chen

In this work we establish rigorously a measurement uncertainty relation (MUR) for three unbiased qubit observables, which was previously shown to hold true under some presumptions. The triplet MUR states that the uncertainty, which is quantified by the total statistic distance between the target observables and the jointly implemented observables, is lower b

Ya-Li Mao, Hu Chen, Chang Niu, Zheng-Da Li

Heisenberg's measurement uncertainty relations (MUR) of two quantum observables are essential for contemporary researches in quantum foundations and quantum information science. Going beyond, here we report the first experimental test of MURs for three quantum observables. Following the proposal of Bush, Lahti, and Werner [Phys. Rev. A 89, 012129 (2014)], we

James Thorne

Document retrieval is a core component of many knowledge-intensive natural language processing task formulations such as fact verification and question answering. Sources of textual knowledge, such as Wikipedia articles, condition the generation of answers from the models. Recent advances in retrieval use sequence-to-sequence models to incrementally predict

Rohit Singh, Sanjay Puri

The dynamic critical behavior at the paramagnetic-antiferromagnetic (PM-AFM) transition in manganites has recently been studied experimentally [Niermann et al., Phys. Rev. Lett. {\bf 114}, 037204 (2015)]. We extend the Hamiltonian of Paper I by incorporating an energy field, and study the corresponding Model C of critical dynamics. We use the dynamic renorma

Zehui Chen, Zhenyu Li, Shiquan Zhang, Liangji Fang

3D object detection from multiple image views is a fundamental and challenging task for visual scene understanding. Owing to its low cost and high efficiency, multi-view 3D object detection has demonstrated promising application prospects. However, accurately detecting objects through perspective views is extremely difficult due to the lack of depth informat

Lee Hyun, Taehyun Kim, Hyolim Kang, Minjoo Ki

Commercial adoption of automatic music composition requires the capability of generating diverse and high-quality music suitable for the desired context (e.g., music for romantic movies, action games, restaurants, etc.). In this paper, we introduce combinatorial music generation, a new task to create varying background music based on given conditions. Combin

Rohit Singh, Sanjay Puri

We use a model Hamiltonian to study critical phenomena in manganites. This Hamiltonian includes long-range strain interactions, and a coupling between the magnetic order parameter and the strain field. We perform a perturbative renormalization group (RG) analysis and calculate the static critical exponents, correct to the one-loop level. We compare our RG re

Minki Kang, Dongchan Min, Sung Ju Hwang

There has been a significant progress in Text-To-Speech (TTS) synthesis technology in recent years, thanks to the advancement in neural generative modeling. However, existing methods on any-speaker adaptive TTS have achieved unsatisfactory performance, due to their suboptimal accuracy in mimicking the target speakers' styles. In this work, we present Grad-St

Sichao Huang, Ziwei Wang, Jie Zhou, Jiwen Lu

Object packing by autonomous robots is an im-portant challenge in warehouses and logistics industry. Most conventional data-driven packing planning approaches focus on regular cuboid packing, which are usually heuristic and limit the practical use in realistic applications with everyday objects. In this paper, we propose a deep hierarchical reinforcement lea

Zhiyun Fan, Zhenlin Liang, Linhao Dong, Yi Liu

In multi-talker scenarios such as meetings and conversations, speech processing systems are usually required to segment the audio and then transcribe each segmentation. These two stages are addressed separately by speaker change detection (SCD) and automatic speech recognition (ASR). Most previous SCD systems rely solely on speaker information and ignore the

Lawson Oliveira Lima, Julien Rosenberger, Esteban Antier, Frederic Magoules

Many Partial Differential Equations (PDEs) do not have analytical solution, and can only be solved by numerical methods. In this context, Physics-Informed Neural Networks (PINN) have become important in the last decades, since it uses a neural network and physical conditions to approximate any functions. This paper focuses on hypertuning of a PINN, used to s

Jihyun Lee, Chaebin Lee, Yunsu Kim, Gary Geunbae Lee

In dialogue state tracking (DST), labeling the dataset involves considerable human labor. We propose a new self-training framework for few-shot generative DST that utilize unlabeled data. Our self-training method iteratively improves the model by pseudo labeling and employs Purpose Preserving Augmentation (PPAug) to prevent overfitting. We increaese the few-

Savera Sarwar, Muhammad Turab, Danish Channa, Aisha Chandio

One of the most important senses in human life is vision, without it life is totally filled with darkness. According to WHO globally millions of people are visually impaired estimated there are 285 million, of whom some millions are blind. Unfortunately, there are around 2.4 million people are blind in our beloved country Pakistan. Human are a crucial part o

Ce Hao, Chen Tang, Eric Bergkvist, Catherine Weaver

Autonomous racing has become a popular sub-topic of autonomous driving in recent years. The goal of autonomous racing research is to develop software to control the vehicle at its limit of handling and achieve human-level racing performance. In this work, we investigate how to approach human expert-level racing performance with model-based planning and contr

Gus Lehrer, Mengfan Lyu

In an earlier work, we defined a ``generalised Temperley-Lieb algebra'' $TL_{r,1,n}$ corresponding to the imprimitive reflection group $G(r,1,n)$ as a quotient of the cyclotomic Hecke algebra. In this work we introduce the generalised Temperley-Lieb algebra $TL_{r,p,n}$ which corresponds to the complex reflection group $G(r,p,n)$. Our definition identifies $

Sumit Kumar, B. Anshuman, Linus Ruettimann, Richard H. R. Hahnloser

Event detection improves when events are captured by two different modalities rather than just one. But to train detection systems on multiple modalities is challenging, in particular when there is abundance of unlabelled data but limited amounts of labeled data. We develop a novel self-supervised learning technique for multi-modal data that learns (hidden)

Jiaheng Liu, Tong He, Honghui Yang, Rui Su

Previous top-performing methods for 3D instance segmentation often maintain inter-task dependencies and the tendency towards a lack of robustness. Besides, inevitable variations of different datasets make these methods become particularly sensitive to hyper-parameter values and manifest poor generalization capability. In this paper, we address the aforementi

Junjie Huang, Chenglong Wang, Jipeng Zhang, Cong Yan

Code generation models can benefit data scientists' productivity by automatically generating code from context and text descriptions. An important measure of the modeling progress is whether a model can generate code that can correctly execute to solve the task. However, due to the lack of an evaluation dataset that directly supports execution-based model ev

Meduri Venkata Shivaditya, José Alves, Francesca Bugiotti, Frederic Magoules

Current simulation of metal forging processes use advanced finite element methods. Such methods consist of solving mathematical equations, which takes a significant amount of time for the simulation to complete. Computational time can be prohibitive for parametric response surface exploration tasks. In this paper, we propose as an alternative, a Graph Neural

Atul Kumar Shriwastva, R. S. Selvaraj

Given $[n]=\{1,2,\ldots,n\}$, a poset order $\preceq$ on $[n]$, a label map $\pi : [n] \rightarrow \mathbb{N}$ defined by $\pi(i)=k_i$ with $\sum_{i=1}^{n}\pi (i) = N$, and a weight function $w$ on $\mathbb{F}_{q}$, let $\mathbb{F}_{q}^N$ be the vector space of $N$-tuples over the field $\mathbb{F}_{q}$ equipped with $(P,w,\pi)$-metric where $ \mathbb{F}_q^N

Linli Yao, Weijing Chen, Qin Jin

Automatically generating textual descriptions for massive unlabeled images on the web can greatly benefit realistic web applications, e.g. multimodal retrieval and recommendation. However, existing models suffer from the problem of generating ``over-generic'' descriptions, such as their tendency to generate repetitive sentences with common concepts for diffe

Yinan He, Guo Chen

In this report, we present the transferring pretrained video mask autoencoders(VideoMAE) to egocentric tasks for Ego4d Looking at me Challenge. VideoMAE is the data-efficient pretraining model for self-supervised video pre-training and can easily transfer to downstream tasks. We show that the representation transferred from VideoMAE has good Spatio-temporal

Hugo S. Tavares, Bruno Magacho, Luca Moriconi, Juliana B. R. Loureiro

We propose a single-step simplified lattice Boltzmann algorithm capable of performing magnetohydrodynamic (MHD) flow simulations in pipes for very small values of magnetic Reynolds numbers $R_m$. In some previous works, most lattice Boltzmann simulations are performed with values of $R_m$ close to the Reynolds numbers for flows in simplified rectangular geom

Ryo Imazawa, Mahito Sasada, Natsuko Hazama, Yasushi Fukazawa

We have obtained simultaneous and continuous photo-polarization observations of the blazar BL Lacertae in optical and near-infrared (NIR) bands during a historical outburst from 2020 to 2021. In total, fourteen nights of observations were performed where ten observations show microvariability on timescales of a few minutes to several hours. This suggests a c

H. T. Wu, Tai Min, Z. X. Guo, X. R. Wang

Butterfly magnetoresistance (BMR) and antisymmetric magnetoresistance (ASMR) are about a butterfly-cross curve and a curve with one peak and one valley when a magnetic field is swept up and down along a fixed direction. Other than the parallelogram-shaped magnetoresistance-curve (MR-curve) often observed in magnetic memory devices, BMR and ASMR are two ubiqu

Atul Kumar Shriwastva, R. S. Selvaraj

In this paper, we establish the Singleton bound for pomset block codes ($(Pm,\pi)$-codes) of length $N$ over the ring $\mathbb{Z}_m$. We give a necessary condition for a code to be MDS in the pomset (block) metric and prove that every MDS $(Pm,\pi)$-code is an MDS $(P,\pi)$-code. Then we proceed on to find $I$-perfect and $r$-perfect codes. Further, given an

Lukas Tiefenthaler, Siegfried Kollotzek, Michael Gatchell, Klavs Hansen

Neon cluster ions Ne$_s^+$ grown in pre-ionized, mass-to-charge selected helium nanodroplets (HNDs) reveal a strong enrichment of the heavy isotope $^{22}$Ne that depends on cluster size s and the experimental conditions. For small sizes the enrichment is much larger than previously reported for bare neon clusters grown in nozzle expansions and subsequently

Lu Zhu, Li Xi

For decades, transition to turbulence in viscoelastic parallel shear flows was believed to require nonlinear instabilities. We provide numerical evidences for a new wall-mode linear instability that directly triggers the transition to elastoinertial turbulence in channel flow. The instability is 2D but 3D features become important as nonlinear effects grow.

Xin Yuan, Robin Feng, Mingming Ye

While deep learning-based text-to-speech (TTS) models such as VITS have shown excellent results, they typically require a sizable set of high-quality <text, audio> pairs to train, which is expensive to collect. So far, most languages in the world still lack the training data needed to develop TTS systems. This paper proposes two improvement methods for the t

Ishaan Mishra

Large-payload deep space missions are impractical with current rocket propulsion technologies in use. Chemical thrusters yield a high thrust but low efficiency while ion thrusters are efficient but provide too little thrust for large satellites and manned spacecraft. Plasma propulsion is a viable alternative with a higher thrust than electric ion thrusters a

Defu Cao, Yousef El-Laham, Loc Trinh, Svitlana Vyetrenko

In electronic trading markets, limit order books (LOBs) provide information about pending buy/sell orders at various price levels for a given security. Recently, there has been a growing interest in using LOB data for resolving downstream machine learning tasks (e.g., forecasting). However, dealing with out-of-distribution (OOD) LOB data is challenging since