November 2022 arXiv papers — page 163

LHCb collaboration

The decay $B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-}$ is studied in proton-proton collisions at a center-of-mass energy of $\sqrt{s}=13$ TeV using data corresponding to an integrated luminosity of 5 $\mathrm{fb}^{-1}$ collected by the LHCb experiment. In the $\Lambda_{c}^+ K^{-}$ system, the $\Xi_{c}(2930)^{0}$ state observed at the BaBar and Bel

Xujia Gong, Wei Wang, Wen Lei, Feng Xiao

First-principles calculations and simulations are conducted to clarify the nonmagnetic insulating ground state of the honeycomb lattice compound Na2RuO3 with 4d^4 electronic configuration and explore the evolutions of crystal structure and electronic property under pressure. We reveal that individual Coulomb correlation or spin-orbit coupling (SOC) effect ca

Xuepeng Chen, Li Sun, Jiancheng Feng, Shiyu Zhang

We present wide-field and high-sensitivity CO(1-0) molecular line observations toward the Cassiopeia region, using the 13.7m millimeter telescope of the Purple Mountain Observatory (PMO). The CO observations reveal a large-scale highly filamentary molecular cloud within the Galactic region of 132\fdg0\,$\geq$\,$l$\,$\geq$\,122\fdg0 and -1\fdg0\,$\leq$\,$b$\,

Serban Stan, Mohammad Rostami

The recent prevalence of deep neural networks has lead semantic segmentation networks to achieve human-level performance in the medical field when sufficient training data is provided. Such networks however fail to generalize when tasked with predicting semantic maps for out-of-distribution images, requiring model re-training on the new distributions. This e

Ting Hua, Yen-Chang Hsu, Felicity Wang, Qian Lou

Singular value decomposition (SVD) is one of the most popular compression methods that approximate a target matrix with smaller matrices. However, standard SVD treats the parameters within the matrix with equal importance, which is a simple but unrealistic assumption. The parameters of a trained neural network model may affect task performance unevenly, whic

Hamid Hosseinianfar, Hami Rabbani, Maite Brandt-Pearce

Visible light positioning has the potential to yield sub-centimeter accuracy in indoor environments, yet conventional received signal strength (RSS)-based localization algorithms cannot achieve this because their performance degrades from optical multipath reflection. However, this part of the optical received signal is deterministic due to the often static

Guillaume Huguet, Alexander Tong, María Ramos Zapatero, Christopher J. Tape

Efficient computation of optimal transport distance between distributions is of growing importance in data science. Sinkhorn-based methods are currently the state-of-the-art for such computations, but require $O(n^2)$ computations. In addition, Sinkhorn-based methods commonly use an Euclidean ground distance between datapoints. However, with the prevalence o

Chenlin Meng, Kristy Choi, Jiaming Song, Stefano Ermon

Representing probability distributions by the gradient of their density functions has proven effective in modeling a wide range of continuous data modalities. However, this representation is not applicable in discrete domains where the gradient is undefined. To this end, we propose an analogous score function called the "Concrete score", a generalization of

Jiachen Yang, Ketan Mittal, Tarik Dzanic, Socratis Petrides

Adaptive mesh refinement (AMR) is necessary for efficient finite element simulations of complex physical phenomenon, as it allocates limited computational budget based on the need for higher or lower resolution, which varies over space and time. We present a novel formulation of AMR as a fully-cooperative Markov game, in which each element is an independent

Francesco Fournier-Facio, Richard D. Wade

For a large class of groups, we exhibit an infinite-dimensional space of homogeneous quasimorphisms that are invariant under the action of the automorphism group. This class includes non-elementary hyperbolic groups, infinitely-ended finitely generated groups, some relatively hyperbolic groups, and a class of graph products of groups that includes all right-

Michel Godefroid, Jörgen Ekman, Per Jönsson

Signs of the different contributions to the isotope shift of an atomic line are discussed in details to clarify some confusing differences between the electronic parameters calculated with RIS (Naz\'e et al. Comput. Phys. Commun. 184 (2013) 2187 ; Ekman et al. Comput. Phys. Commun. 235 (2019) 433) and those appearing in other commonly used expressions.

Qiang Du, Kuang Huang, James Scott, Wen Shen

We propose and study a nonlocal conservation law modelling traffic flow in the existence of inter-vehicle communication. It is assumed that the nonlocal information travels at a finite speed and the model involves a space-time nonlocal integral of weighted traffic density. The well-posedness of the model is established under suitable conditions on the model

Yosuke Higuchi, Tetsuji Ogawa, Tetsunori Kobayashi, Shinji Watanabe

This paper presents InterMPL, a semi-supervised learning method of end-to-end automatic speech recognition (ASR) that performs pseudo-labeling (PL) with intermediate supervision. Momentum PL (MPL) trains a connectionist temporal classification (CTC)-based model on unlabeled data by continuously generating pseudo-labels on the fly and improving their quality.

Yoshiaki Ono, Seiji Fujimoto, Yuichi Harikane, Masami Ouchi

We present our new Atacama Large Millimeter/submillimeter Array (ALMA) observations targeting CO(6-5) emission from three luminous Lyman break galaxies (LBGs) at $z_{\rm spec} = 6.0293$-$6.2037$ found in the Subaru/Hyper Suprime-Cam survey, whose [OIII]$88\mu$m and [CII]$158\mu$m emission have been detected with ALMA. We find a marginal detection of the CO(6

Yosuke Higuchi, Tetsuji Ogawa, Tetsunori Kobayashi, Shinji Watanabe

We present BERT-CTC-Transducer (BECTRA), a novel end-to-end automatic speech recognition (E2E-ASR) model formulated by the transducer with a BERT-enhanced encoder. Integrating a large-scale pre-trained language model (LM) into E2E-ASR has been actively studied, aiming to utilize versatile linguistic knowledge for generating accurate text. One crucial factor

Sajad Movahedi, Azadeh Shakery

While deep learning in the form of recurrent neural networks (RNNs) has caused a significant improvement in neural language modeling, the fact that they are extremely prone to overfitting is still a mainly unresolved issue. In this paper we propose a regularization method based on generative adversarial networks (GANs) and adversarial training (AT), that can

Spilios Evmorfos, Athina P. Petropulu, H. Vincent Poor

The paper studies the problem of designing the Intelligent Reflecting Surface (IRS) phase shifters for Multiple Input Single Output (MISO) communication systems in spatiotemporally correlated channel environments, where the destination can move within a confined area. The objective is to maximize the expected sum of SNRs at the receiver over infinite time ho

Ziyou Ren, Nan Cheng, Ruijin Sun, Xiucheng Wang

Multiple-input multiple-output and orthogonal frequency-division multiplexing (MIMO-OFDM) are the key technologies in 4G and subsequent wireless communication systems. Conventionally, the MIMO-OFDM receiver is performed by multiple cascaded blocks with different functions and the algorithm in each block is designed based on ideal assumptions of wireless chan

Fabio Santos

Selecting an appropriate task is challenging for contributors to Open Source Software (OSS), mainly for those who are contributing for the first time. Therefore, researchers and OSS projects have proposed various strategies to aid newcomers, including labeling tasks. We investigate the automatic labeling of open issues strategy to help the contributors to pi

Hye-Jin Park, Shin-Jeong Kim, Shinna Kim, Maurice H. P. M. van Putten

The distribution of LIGO black hole binaries (BBH) shows an intermediate-mass range consistent with the Salpeter Initial Mass Function (IMF) in black hole formation by core-collapse supernovae, subject to preserving binary association. They are effectively parameterized by mean mass $μ$ with Pearson correlation coefficient $r = 0.93\,\pm\,0.06$ of secondary

Shivam Gupta, Bidisha Bhatt, Meenaxi Sharma, Krishnacharya Khare

Due to the slow dynamics of the wetting ridge, it is challenging to predict the wetting morphology of liquid drops on thin lubricant coated surfaces. It is hypothesized that when a drop sinks on a lubricated surface, quasi-static wetting morphology can be numerically computed only from the knowledge of interfacial energies, lubricant thickness, and drop volu

Leonardo Cruciani

The aim of this thesis is to compare the capacity of different models of neural networks. We start by analysing the problem solving capacity of a single perceptron using a simple combinatorial argument. After some observations on the storage capacity of a basic network, known as an associative memory, we introduce a powerful statistical mechanical approach t

Sudeep Pasricha

This article reviews the landscape of ethical challenges of integrating artificial intelligence (AI) into smart healthcare products, including medical electronic devices. Differences between traditional ethics in the medical domain and emerging ethical challenges with AI-driven healthcare are presented, particularly as they relate to transparency, bias, priv

Flavio Piccoli, Simone Locatelli, Paolo Napoletano, Raimondo Schettini

In this work we propose a software platform for the collection, visualization, management and analysis of heterogeneous and multisource data for soil characteristics estimation. The platform is designed in such a way that it can easily handle proximity, airborne and spaceborne data and provides to the final user all the tools and proper visualizations to per

Bibhuprasad Mahakud, Bibhuti Parida, Ipsit Panda, Souvik Maity

In order to track and comprehend the academic achievement of students, both private and public educational institutions devote a significant amount of resources and labour. One of the difficult issues that institutes deal with on a regular basis is understanding the exam shortcomings of students. The performance of a student is influenced by a variety of fac

Joon-Ha Kim

The problem of navigating a bipedal robot to a desired destination in various environments is very important. However, it is very difficult to solve the navigation problem in real time because the computation time is very long due to the nature of the biped robot having a high degree of freedom. In order to overcome this, many scientists suggested navigation

Abhishek Vivekanandan, Niels Maier, J. Marius Zoellner

Environmental perception obtained via object detectors have no predictable safety layer encoded into their model schema, which creates the question of trustworthiness about the system's prediction. As can be seen from recent adversarial attacks, most of the current object detection networks are vulnerable to input tampering, which in the real world could

Fengjun Wang, Sarai Mizrachi, Moran Beladev, Guy Nadav

Multi-label image classification is a foundational topic in various domains. Multimodal learning approaches have recently achieved outstanding results in image representation and single-label image classification. For instance, Contrastive Language-Image Pretraining (CLIP) demonstrates impressive image-text representation learning abilities and is robust to

Xuejun Zhao, Ruihao Zhu, William B. Haskell

The rise of big data analytics has automated the decision-making of companies and increased supply chain agility. In this paper, we study the supply chain contract design problem faced by a data-driven supplier who needs to respond to the inventory decisions of the downstream retailer. Both the supplier and the retailer are uncertain about the market demand

Le Xie, Tong Huang, Xiangtian Zheng, Yan Liu

The transition towards carbon-neutral electricity is one of the biggest game changers in addressing climate change since it addresses the dual challenges of removing carbon emissions from the two largest sectors of emitters: electricity and transportation. The transition to a carbon-neutral electric grid poses significant challenges to conventional paradigms

Jaemin Kim, Gleb A. Egorov, George V. Eleftheriades

Over the past decades, many radome designs to extend the angular scan range of phased-array antennas have been devised by utilizing dielectric materials and metamaterials. More recently, metasurface technology such as planar lenses and beam deflectors have been applied to phased arrays, enabling scan-angle enhancers to have a low profile. In this work, a phy

Juntao Wang, Jonathan Lei, Anna Dreber, Michael Gordon

The low replication rate of published studies has long concerned the social science community, making understanding the replicability a critical problem. Several studies have shown that relevant research communities can make predictions about the replicability of individual studies with above-chance accuracy. Follow-up work further indicates that laypeople c

Samuel Dooley

One of the main promises of technology development is for it to be adopted by people, organizations, societies, and governments -- incorporated into their life, work stream, or processes. Often, this is socially beneficial as it automates mundane tasks, frees up more time for other more important things, or otherwise improves the lives of those who use the t

Mostafa Sadeghi, Romain Serizel

Unsupervised speech enhancement based on variational autoencoders has shown promising performance compared with the commonly used supervised methods. This approach involves the use of a pre-trained deep speech prior along with a parametric noise model, where the noise parameters are learned from the noisy speech signal with an expectationmaximization (EM)-ba

Yang Li, Ruinong Wang, Yuanzheng Li, Meng Zhang

In a modern power system with an increasing proportion of renewable energy, wind power prediction is crucial to the arrangement of power grid dispatching plans due to the volatility of wind power. However, traditional centralized forecasting methods raise concerns regarding data privacy-preserving and data islands problem. To handle the data privacy and open

Jianfei Zhou, Tianxing Jiang, Shuwei Song, Ting Chen

In the past few years, several attacks against the vulnerabilities of EOSIO smart contracts have caused severe financial losses to this prevalent blockchain platform. As a lightweight test-generation approach, grey-box fuzzing can open up the possibility of improving the security of EOSIO smart contracts. However, developing a practical grey-box fuzzer for E

Lei Kou, Chuang Liu, Guo-wei Cai, Jia-ning Zhou

A three-phase pulse-width modulation (PWM) rectifier can usually maintain operation when open-circuit faults occur in insulated-gate bipolar transistors (IGBTs), which will lead the system to be unstable and unsafe. Aiming at this problem, based on random forests with transient synthetic features, a data-driven online fault diagnosis method is proposed to lo

Ivan Cherednik, Bradley Hicks

The main aim is to obtain integral formulas for DAHA coinvariants and the corresponding inner products for any values of the DAHA parameters. In the compact case, our approach is similar to the procedure of ``picking up residues" due to Arthur, Heckman, Opdam and others; the resulting formula is a sum of integrals over double affine residual subtori. A s

P. Ballester

The municipal actors of the Catalan capital wish to obtain a reversal of the negative representations concerning the lack of attractiveness of the district of Poblenou considered as one of the poorest and most marginalized districts of the city while having as identity the expression of "Manchester southern Europe", recalling its role as a factory fo

Da Chen, Nima Emami, Shahed Rezaei, Philipp L. Rosendahl

The local geometrical randomness of metal foams brings complexities to the performance prediction of porous structures. Although the relative density is commonly deemed as the key factor, the stochasticity of internal cell sizes and shapes has an apparent effect on the porous structural behaviour but the corresponding measurement is challenging. To address t

Gordon H. Y. Li, Christian R. Leefmans, James Williams, Alireza Marandi

Cellular automata are a class of computational models based on simple rules and algorithms that can simulate a wide range of complex phenomena. However, when using conventional computers, these 'simple' rules are only encapsulated at the level of software. This can be taken one step further by simplifying the underlying physical hardware. Here, we pr

Feisi Fu, Panagiota Kiourti, Wenchao Li

We present a novel methodology for neural network backdoor attacks. Unlike existing training-time attacks where the Trojaned network would respond to the Trojan trigger after training, our approach inserts a Trojan that will remain dormant until it is activated. The activation is realized through a specific perturbation to the network's weight parameters

M. Sharif, Komal Hassan

In this paper, we develop two anisotropic solutions for static self-gravitating spherical structure in the presence of electromagnetic field through gravitational decoupling approach in $f(G,T)$ theory, where $G$ and $T$ denote the Gauss-Bonnet term and trace of the energy-momentum tensor, respectively. The extra source with isotropic seed sector is responsi

Yifei Zhang, Chang Liu, Yu Zhou, Weiping Wang

Contrastive self-supervised learning (CSL) based on instance discrimination typically attracts positive samples while repelling negatives to learn representations with pre-defined binary self-supervision. However, vanilla CSL is inadequate in modeling sophisticated instance relations, limiting the learned model to retain fine semantic structure. On the one h

Lei Yu, Vincent Y. F. Tan

Common information (CI) is ubiquitous in information theory and related areas such as theoretical computer science and discrete probability. However, because there are multiple notions of CI, a unified understanding of the deep interconnections between them is lacking. This monograph seeks to fill this gap by leveraging a small set of mathematical techniques

Jacopo Guidolin, Vyacheslav Kungurtsev, Ondřej Kuželka

Bayesian methods of sampling from a posterior distribution are becoming increasingly popular due to their ability to precisely display the uncertainty of a model fit. Classical methods based on iterative random sampling and posterior evaluation such as Metropolis-Hastings are known to have desirable long run mixing properties, however are slow to converge. G

Hoang Ky Nguyen

Some sixty years ago Buchdahl pioneered a program in search of static spherically symmetric vacua for pure $R^{2}$ gravity (Nuovo Cimento, Vol 23, No 1, pp 141-157 (1962); [https://link.springer.com/article/10.1007/BF02733549]). Surpassing several obstacles, his work culminated in a non-linear second-order ordinary differential equation (ODE) which required

Arkajyoti Saha, Daniela Witten, Jacob Bien

We consider testing whether a set of Gaussian variables, selected from the data, is independent of the remaining variables. We assume that this set is selected via a very simple approach that is commonly used across scientific disciplines: we select a set of variables for which the correlation with all variables outside the set falls below some threshold. Un

Diego Martinez-Taboada, Dino Sejdinovic

The counterfactual distribution models the effect of the treatment in the untreated group. While most of the work focuses on the expected values of the treatment effect, one may be interested in the whole counterfactual distribution or other quantities associated to it. Building on the framework of Bayesian conditional mean embeddings, we propose a Bayesian

A. C. Caligula do E. S. Pedreira, N. Fraija, A. Galvan-Gamez, B. Betancourt Kamenetskaia

As we further our studies on Gamma-ray bursts (GRBs), both on theoretical models and observational tools, more and more options begin to open for exploration of its physical properties. As transient events primarily dominated by synchrotron radiation, it is expected that the synchrotron photons emitted by GRBs should present some degree of polarization throu

Alessandro Verra

After an Introduction to the themes of Enriques surfaces and Rationality questions, the Artin-Mumford counterexample to Lueroth problem is revisited. A construction of it is given, which is related in an explicit way to the geometry of Enriques surfaces, more precisely to the special family of Reye congruences and their classical geometry.

Narges Khakpour, David Parker

Network systems often contain vulnerabilities that remain unfixed in a network for various reasons, such as the lack of a patch or knowledge to fix them. With the presence of such residual vulnerabilities, the network administrator should properly react to the malicious activities or proactively prevent them, by applying suitable countermeasures that minimiz

Song-Ying Li

In this paper, the author solves the long term open problem of Kerzman on sup-norm estimate for Cauchy-Riemann equation on polydisc in $n$-dimensional complex space. The problem has been open since 1971. He also extends and solves the problem on a bounded product domain $Ω^n$, where $Ω$ either is simply connected with $C^{1,α}$ boundary or satisfies a unifor

Ziming Shao, Noah Schnitzer, Jacob Ruf, Oleg Y. Gorobtsov

Exploiting the emerging nanoscale periodicities in epitaxial, single-crystal thin films is an exciting direction in quantum materials science: confinement and periodic distortions induce novel properties. The structural motifs of interest are ferroelastic, ferroelectric, multiferroic, and, more recently, topologically protected magnetization and polarization

Minjie Lei, Ka Vang Tsang, Sean Gasiorowski, Chuan Li

Optical photons are used as signal in a wide variety of particle detectors. Modern neutrino experiments employ hundreds to tens of thousands of photon detectors to observe signal from millions to billions of scintillation photons produced from energy deposition of charged particles. These neutrino detectors are typically large, containing kilotons of target

Renato Pelessoni, Paolo Vicig

We investigate how basic probability inequalities can be extended to an imprecise framework, where (precise) probabilities and expectations are replaced by imprecise probabilities and lower/upper previsions. We focus on inequalities giving information on a single bounded random variable $X$, considering either convex/concave functions of $X$ (Jensen's in

H. Ning, O. Mehio, C. Lian, X. Li

Elemental tellurium is a strongly spin-orbit coupled Peierls-distorted semiconductor whose band structure features topologically protected Weyl nodes. Using time-dependent density functional theory calculations, we show that impulsive optical excitation can be used to transiently control the amplitude of the Peierls distortion, realizing a mechanism to switc

Wenxuan Zhou, David Held

A simple gripper can solve more complex manipulation tasks if it can utilize the external environment such as pushing the object against the table or a vertical wall, known as "Extrinsic Dexterity." Previous work in extrinsic dexterity usually has careful assumptions about contacts which impose restrictions on robot design, robot motions, and the var

Ming Zhou, Andrew V. Knyazev, Klaus Neymeyr

Convergence rates of block iterations for solving eigenvalue problems typically measure errors of Ritz values approximating eigenvalues. The errors of the Ritz values are commonly bounded in terms of principal angles between the initial or iterative subspace and the invariant subspace associated with the target eigenvalues. Such bounds thus cannot be applied

Dennis Wei, Rahul Nair, Amit Dhurandhar, Kush R. Varshney

Interpretable and explainable machine learning has seen a recent surge of interest. We focus on safety as a key motivation behind the surge and make the relationship between interpretability and safety more quantitative. Toward assessing safety, we introduce the concept of maximum deviation via an optimization problem to find the largest deviation of a super

Yu Zhang, Mitchell Bucklew

In this paper, we introduce Max Markov Chain (MMC), a novel representation for a useful subset of High-order Markov Chains (HMCs) with sparse correlations among the states. MMC is parsimony while retaining the expressiveness of HMCs. Even though parameter optimization is generally intractable as with HMC approximate models, it has an analytical solution, bet

A. Botteon, R. J. van Weeren, G. Brunetti, F. Vazza

The hot plasma within merging galaxy clusters is predicted to be filled with shocks and turbulence that may convert part of their kinetic energy into relativistic electrons and magnetic fields generating synchrotron radiation. Analyzing Low Frequency Array (LOFAR) observations of the galaxy cluster Abell 2255, we show evidence of radio synchrotron emission d

Tyler J. Slade, Nao Furukawa, Tanner R. Smith, Juan Schmidt

We present the growth and basic magnetic and transport properties of Cr$_{1+x}$Pt$_{5-x}$P. We show that single crystals can readily be grown from a high-temperature solution created by adding dilute quantities of Cr to Pt-P based melts. Like other 1-5-1 compounds, Cr$_{1+x}$Pt$_{5-x}$P adopts a tetragonal P4/mmm structure composed face-sharing CrPt$_3$ like

Alvaro Andres Alvarez Peralta, Priya Desai, Somalee Datta

This manuscript explores linking real-world patient data with external death data in the context of research Clinical Data Warehouses (r-CDWs). We specifically present the linking of Electronic Health Records (EHR) data for Stanford Health Care (SHC) patients and data from the Social Security Administration (SSA) Limited Access Death Master File (LADMF) made

Zachary Zhou, Alisha Zachariah, Devin Conathan, Jeffery Kline

Natural language models are often summarized through a high-dimensional set of descriptive metrics including training corpus size, training time, the number of trainable parameters, inference times, and evaluation statistics that assess performance across tasks. The high dimensional nature of these metrics yields challenges with regard to objectively compari

R. Giampaoli, J. L. Figueiredo, J. D. Rodrigues, J. A. Rodrigues

The statistical properties of physical systems in thermal equilibrium are blatantly different from their far-from-equilibrium counterparts. In the latter, fluctuations often dominate the dynamics and might cluster in ordered patterns in the form of dissipative coherent structures. Here, we study the transition of a cold atomic cloud, driven close to a sharp

Johannes Kruse, Eike Cramer, Benjamin Schäfer, Dirk Witthaut

The operation of power systems is affected by diverse technical, economic and social factors. Social behaviour determines load patterns, electricity markets regulate the generation and weather-dependent renewables introduce power fluctuations. Thus, power system dynamics must be regarded as a non-autonomous system whose parameters vary strongly with time. Ho

Aleksandra Kalinowska, Elnaz Davoodi, Florian Strub, Kory W Mathewson

While it is known that communication facilitates cooperation in multi-agent settings, it is unclear how to design artificial agents that can learn to effectively and efficiently communicate with each other. Much research on communication emergence uses reinforcement learning (RL) and explores unsituated communication in one-step referential tasks -- the task

Joel Shor, Nick Johnston

Compression is essential to storing and transmitting medical videos, but the effect of compression on downstream medical tasks is often ignored. Furthermore, systems in practice rely on standard video codecs, which naively allocate bits between medically relevant frames or parts of frames. In this work, we present an empirical study of some deficiencies of c

Quan Vuong, Aviral Kumar, Sergey Levine, Yevgen Chebotar

In offline RL, constraining the learned policy to remain close to the data is essential to prevent the policy from outputting out-of-distribution (OOD) actions with erroneously overestimated values. In principle, generative adversarial networks (GAN) can provide an elegant solution to do so, with the discriminator directly providing a probability that quanti

Lawrence Li, Sushant Sachdeva

We demonstrate that for expander graphs, for all $ε> 0,$ there exists a data structure of size $\widetilde{O}(nε^{-1})$ which can be used to return $(1 + ε)$-approximations to effective resistances in $\widetilde{O}(1)$ time per query. Short of storing all effective resistances, previous best approaches could achieve $\widetilde{O}(nε^{-2})$ size and $\widet

Xinliang Frederick Zhang, Nick Beauchamp, Lu Wang

Stance detection is typically framed as predicting the sentiment in a given text towards a target entity. However, this setup overlooks the importance of the source entity, i.e., who is expressing the opinion. In this paper, we emphasize the need for studying interactions among entities when inferring stances. We first introduce a new task, entity-to-entity

Ben Kenwright

We present a novel approach for solving articulated inverse kinematic problems (e.g., character structures) by means of an iterative dual-quaternion and exponentialmapping approach. As dual-quaternions are a break from the norm and offer a straightforward and computationally efficient technique for representing kinematic transforms (i.e., position and transl

Yanyan Shi

In this paper, we study the charged-particle dynamics under strong magnetic field in a toroidal axi-symmetric geometry. Using modulated Fourier expansions of the exact and numerical solutions, the long-term drift motion of the exact solution in toroidal geometry is derived and the error analysis of the large-stepsize modified Boris algorithm over long time s

Luke Strgar, David Harwath

We apply transfer learning to the task of phoneme segmentation and demonstrate the utility of representations learned in self-supervised pre-training for the task. Our model extends transformer-style encoders with strategically placed convolutions that manipulate features learned in pre-training. Using the TIMIT and Buckeye corpora we train and test the mode

Cristian Tellez-Piñerez, Leonardo Trujillo, Andrés Gutiérrez-Rojas, Juan Sosa

We propose a novel methodology relating item response theory methods with small area estimation strategies in the presence of missing data. Specifically, we propose an unbiased estimator for the average ability parameter of three-parameter logistic models. Thus, we carry out an extensive simulation study in order to compare our estimator with the well-known

Carolin Benjamins, Elena Raponi, Anja Jankovic, Koen van der Blom

Bayesian Optimization (BO) is a powerful, sample-efficient technique to optimize expensive-to-evaluate functions. Each of the BO components, such as the surrogate model, the acquisition function (AF), or the initial design, is subject to a wide range of design choices. Selecting the right components for a given optimization task is a challenging task, which

Vishak Prasad C, Colin White, Paarth Jain, Sibasis Nayak

A majority of recent developments in neural architecture search (NAS) have been aimed at decreasing the computational cost of various techniques without affecting their final performance. Towards this goal, several low-fidelity and performance prediction methods have been considered, including those that train only on subsets of the training data. In this wo

Xingzhou Tang, Ali Mozaffari, Noe Atzin, Soumik Das

Solitons in nematic liquid crystals offer intriguing opportunities for transport and sensing in microfluidic systems. Little is known about the elementary conditions that are needed to create solitons in nematic materials. In this work, theory, simulations and experiments are used to study the generation and propagation of solitary waves (or "solitons&#3

Michał Fabisiak, Jan Peszek

We prove that certain types of measure-valued mappings are monokinetic i.e. the distribution of velocity is concentrated in a Dirac mass. These include weak measure-valued solutions to the strongly singular Cucker-Smale model with singularity of order $α$ greater or equal to the dimension of the ambient space. Consequently, we are able to answer a couple of

Pasin Manurangsi

We study the complexity of computing (and approximating) VC Dimension and Littlestone's Dimension when we are given the concept class explicitly. We give a simple reduction from Maximum (Unbalanced) Biclique problem to approximating VC Dimension and Littlestone's Dimension. With this connection, we derive a range of hardness of approximation results

Patrick Steinmüller, Tobias Schulz, Ferdinand Graf, Daniel Herr

Parametrized Quantum Circuits (PQCs) enable a novel method for machine learning (ML). However, from a computational point of view they present a challenge to existing eXplainable AI (xAI) methods. On the one hand, measurements on quantum circuits introduce probabilistic errors which impact the convergence of these methods. On the other hand, the phase space

Qianyi Zhang, Antoine Boniface, Virendra K. Parashar, Martin A. M. Gijs

The recent development of light-based 3D printing technologies has marked a turning point in additive manufacturing. Through photopolymerization, liquid resins can be solidified into complex objects. Usually, the polymerization is triggered by exciting a photoinitiator with ultraviolet (UV) or blue light. In two-photon printing (TPP), the excitation is done

Léa Ferellec, Colin Snodgrass, Alan Fitzsimmons, Agata Rożek

Main Belt Comets (MBCs) exhibit sublimation-driven activity while occupying asteroid-like orbits in the Main Asteroid Belt. MBCs and candidates show stronger clustering of their longitudes of perihelion around 15° than other objects from the Outer Main Belt (OMB). This potential property of MBCs could facilitate the discovery of new candidates by observing o

Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alex Bronstein

We introduce a spectral notion of graph complexity derived from the Weyl's law. We experimentally demonstrate its correlation to how well the graph can be embedded in a low-dimensional Euclidean space.

Qin Dai, Benjamin Heinzerling, Kentaro Inui

Bi-encoder architectures for distantly-supervised relation extraction are designed to make use of the complementary information found in text and knowledge graphs (KG). However, current architectures suffer from two drawbacks. They either do not allow any sharing between the text encoder and the KG encoder at all, or, in case of models with KG-to-text attent

Daniel A. Spencer, David Bolin, Amanda F. Mejia

Task functional magnetic resonance imaging (fMRI) is a type of neuroimaging data used to identify areas of the brain that activate during specific tasks or stimuli. These data are conventionally modeled using a massive univariate approach across all data locations, which ignores spatial dependence at the cost of model power. We previously developed and valid

Jean-Marc Deshouillers, Michael Drmota, Clemens Müllner, Andrei Shubin

The purpose of this paper is to study subsequences of synchronizing $k$-automatic sequences $a(n)$ along Piatetski-Shapiro sequences $\lfloor n^c \rfloor$ with non-integer $c>1$. In particular, we show that $a(\lfloor n^c \rfloor)$ satisfies a prime number theorem of the form $\sum_{n\le x} Λ(n)a(\lfloor n^c \rfloor) \sim C\, x$, and, furthermore, that it is

Paul Balister, Béla Bollobás, Robert Morris, Julian Sahasrabudhe

A covering system is a finite collection of arithmetic progressions whose union is the set of integers. The study of these objects was initiated by Erdős in 1950, and over the following decades he asked many questions about them. Most famously, he asked whether there exist covering systems with distinct moduli whose minimum modulus is arbitrarily large. This

Ruddarraju Amrutha, Pratyusha Chattopadhyay

A.A. Suslin proved a normality theorem for an elementary linear group, which says that an elementary linear group of size bigger than or equal to 3 over a commutative ring with unity is normal in the general linear group of same size. Subsequently, V.I. Kopeiko extended this result of Suslin for a symplectic group defined with respect to the standard skew-sy

Gissell Estrada-Rodriguez, Diane Peurichard, Xinran Ruan

In this work we first prove, by formal arguments, that the diffusion limit of nonlinear kinetic equations, where both the transport term and the turning operator are density-dependent, leads to volume-exclusion chemotactic equations. We generalise an asymptotic preserving scheme for such nonlinear kinetic equations based on a micro-macro decomposition. By pr

Prateek Anand, Ganesh Subramanian

We study the inertial migration of a torque-free neutrally buoyant sphere in wall-bounded plane Couette flow over a wide range of channel Reynolds numbers, $Re_c$, in the limit of small particle Reynolds number\,($Re_p\ll1$) and confinement ratio\,($λ\ll1$). Here, $Re_c = V_\text{wall}H/ν$ where $H$ denotes the separation between the channel walls, $V_\text{

Cem Ates Musluoglu, Alexander Bertrand

Wireless sensor networks consist of sensor nodes that are physically distributed over different locations. Spatial filtering procedures exploit the spatial correlation across these sensor signals to fuse them into a filtered signal satisfying some optimality condition. However, gathering the raw sensor data in a fusion center to solve the problem in a centra

Eric Chen, Adam Ge, Andrew Kalashnikov, Tanya Khovanova

In this paper, we generalize a lot of facts from John Conway and Alex Ryba's paper, \textit{The extra Fibonacci series and the Empire State Building}, where we replace the Fibonacci sequence with the Tribonacci sequence. We study the Tribonacci array, which we also call \textit{the Trithoff array} to emphasize the connection to the Wythoff array. We desc

Mohamed Y. Nada, Tarek Mealy, Md Shafiqul Islam, Ilya Vitebskiy

We design a three-way silicon optical waveguide with the Bloch dispersion relation supporting a stationary inflection point (SIP). The SIP is a third order exceptional point of degeneracy (EPD) where three Bloch modes coalesce forming the frozen mode with greatly enhanced amplitude. The proposed design consists of a coupled resonators optical waveguide (CROW

Daniel Finstad, Laurel V. White, Duncan A. Brown

Gravitational-wave observations of neutron star mergers can probe the nuclear equation of state by measuring the imprint of the neutron star's tidal deformability on the signal. We investigate the ability of future gravitational-wave observations to produce a precise measurement of the equation of state from binary neutron star inspirals. Since measurabi

Ryan Dungee, Jennifer van Saders, Eric Gaidos, Mark Chun

We present stellar rotation periods for late K- and early M-dwarf members of the 4 Gyr old open cluster M67 as calibrators for gyrochronology and tests of stellar spin-down models. Using Gaia EDR3 astrometry for cluster membership and Pan-STARRS (PS1) photometry for binary identification, we build this set of rotation periods from a campaign of monitoring M6

Oussama Messai, Aladine Chetouani

Deep learning-based quality metrics have recently given significant improvement in Image Quality Assessment (IQA). In the field of stereoscopic vision, information is evenly distributed with slight disparity to the left and right eyes. However, due to asymmetric distortion, the objective quality ratings for the left and right images would differ, necessitati

Maryam Toloubidokhti, Nilesh Kumar, Zhiyuan Li, Prashnna K. Gyawali

Prior knowledge about the imaging physics provides a mechanistic forward operator that plays an important role in image reconstruction, although myriad sources of possible errors in the operator could negatively impact the reconstruction solutions. In this work, we propose to embed the traditional mechanistic forward operator inside a neural function, and fo

Liam Cronin, Soheil Sadeghi Eshkevari, Thomas J. Matarazzo, Sebastiano Milardo

The knowledge gap in the expected and actual conditions of bridges has created worldwide deficits in infrastructure service and funding challenges. Despite rapid advances over the past four decades, sensing technology is still not a part of bridge inspection protocols. Every time a vehicle with a mobile device passes over a bridge, there is an opportunity to