October 2022 arXiv papers — page 11

Won Joon Yun, Hankyul Baek, Joongheon Kim

In recent years, quantum machine learning (QML) has been actively used for various tasks, e.g., classification, reinforcement learning, and adversarial learning. However, these QML studies are unable to carry out complex tasks due to scalability issues on input and output which is currently the biggest hurdle in QML. Therefore, the purpose of this paper is t

Fuping Hu, Zhaohong Deng, Zhenping Xie, Kup-Sze Choi

Fuzzy systems (FSs) have enjoyed wide applications in various fields, including pattern recognition, intelligent control, data mining and bioinformatics, which is attributed to the strong interpretation and learning ability. In traditional application scenarios, FSs are mainly applied to model Euclidean space data and cannot be used to handle graph data of n

Naoki Genra

We prove that the finite $\mathcal{W}$-algebra associated to $\mathfrak{osp}_{1|2n}$ and its principal nilpotent element is isomorphic to Gorelik's ghost center of $\mathfrak{osp}_{1|2n}$, which proves an analog of Kostant's theorem for $\mathfrak{osp}_{1|2n}$.

Yan Yang, LiYuan Pan, Liu Liu, Eric A Stone

This paper aims to predict gene expression from a histology slide image precisely. Such a slide image has a large resolution and sparsely distributed textures. These obstruct extracting and interpreting discriminative features from the slide image for diverse gene types prediction. Existing gene expression methods mainly use general components to filter text

W. G. Wang, C. Ni, A. Ozbay, L. R. Shah

Magnetic tunnel junctions with wide band gap semiconductor ZnTe barrier were fabricated. A very low barrier height and sizable magnetoresistance were observed in the Fe/ZnTe/Fe junctions at room temperature. The nonlinear I-V characteristic curve confirmed the observed magnetoresistance is due to spin-dependent tunneling effect. Temperature dependent study i

Hao Yen, Woojay Jeon

In embedding-matching acoustic-to-word (A2W) ASR, every word in the vocabulary is represented by a fixed-dimension embedding vector that can be added or removed independently of the rest of the system. The approach is potentially an elegant solution for the dynamic out-of-vocabulary (OOV) words problem, where speaker- and context-dependent named entities lik

Jiangxu Huang, Lei Wang

In this paper, the droplet impact dynamic behavior of droplet on Janus-textured heated substrates is numerically investigate by using the thermal Lattice Boltzmann method. The effect of several factors like the wettability, the Jakob number and the Weber number on the droplet impact dynamic behavior on Janus-textured heated surface are studied in detail. The

Hanrui Wang, Pengyu Liu, Jinglei Cheng, Zhiding Liang

Among different quantum algorithms, PQC for QML show promises on near-term devices. To facilitate the QML and PQC research, a recent python library called TorchQuantum has been released. It can construct, simulate, and train PQC for machine learning tasks with high speed and convenient debugging supports. Besides quantum for ML, we want to raise the communit

Rey Mendoza, Minh Nguyen, Judith Weng Zhu, Vincent Dumont

Computed tomography has propelled scientific advances in fields from biology to materials science. This technology allows for the elucidation of 3-dimensional internal structure by the attenuation of x-rays through an object at different rotations relative to the beam. By imaging 2-dimensional projections, a 3-dimensional object can be reconstructed through

Kyu-Won Park, Kwon-Wook Son, Chang-Hyun Ju, Kabgyun Jeong

Park et al. [Phys. Rev. A 106, L031504 (2022)] showed that the Shannon entropy of the probability distribution of a single random variable for far-field profiles (FFPs) in deformed microcavity lasers can efficiently measure the directionality of deformed microcavity lasers. In this study, we instead consider two random variables of FFPs with joint probabilit

Yusuke Imai

By introducing various actions involving color to geometrical objects, we represent a cube and simplex in four or fewer dimensions, the geometrical net of a cube and simplex in five or fewer dimensions, hyperprisms, truncated polytopes, stellated polytopes, and fractals such as Cantor dust and Menger sponge, and propose the "four-dimensional" Menger sponge w

Yan Yang, Md Zakir Hossain, Eric A Stone, Shafin Rahman

Spatial transcriptomics (ST) is essential for understanding diseases and developing novel treatments. It measures gene expression of each fine-grained area (i.e., different windows) in the tissue slide with low throughput. This paper proposes an Exemplar Guided Network (EGN) to accurately and efficiently predict gene expression directly from each window of a

Dan Guo, Jun-Zhang Wang, Dian-Yong Chen, Xiang Liu

Focusing on recent measurement of $B^+ \to D_s^+D_s^-K^+$ process given by the LHCb Collaboration, we propose that this newly observed near the $D_s^+D_s^-$ threshold enhancement can be due to the contribution of the $\chi_{c0}(2P)$, which is a $P$-wave charmonium below the $D_s^+D_s^-$ threshold. By performing a combined fit to the measured $D_s^+D_s^-$, $D

Yu Zheng, Zhangxuan Dang, Chunlei Peng, Chao Yang

Network traffic classification is the basis of many network security applications and has attracted enough attention in the field of cyberspace security. Existing network traffic classification based on convolutional neural networks (CNNs) often emphasizes local patterns of traffic data while ignoring global information associations. In this paper, we propos

Marc Ethier, Patrizio Frosini, Nicola Quercioli, Francesca Tombari

In this paper we exploit the concept of extended Pareto grid to study the geometric properties of the matching distance for $\mathbb{R}^2$-valued regular functions defined on a Riemannian closed manifold. In particular, we prove that in this case the matching distance is realised either at special values or at values corresponding to vertical, horizontal or

James Orme-Rogers, Ajitesh Srivastava

The traditional methods for detecting autism spectrum disorder (ASD) are expensive, subjective, and time-consuming, often taking years for a diagnosis, with many children growing well into adolescence and even adulthood before finally confirming the disorder. Recently, graph-based learning techniques have demonstrated impressive results on resting-state func

Jonathan García, Carlos A. Gómez, Florian Luca

We prove some separation results for the roots of the generalized Fibonacci polynomials and their absolute values

Yilong Chen, Haocheng Hua, Jie Xu

This paper unifies integrated sensing and communication (ISAC) and simultaneous wireless information and power transfer (SWIPT), by investigating a new multi-functional multiple-input multiple-output (MIMO) system integrating wireless sensing, communication, and powering. In this system, one multi-antenna hybrid access point (H-AP) transmits wireless signals

Kevin Reuer, Jonas Landgraf, Thomas Fösel, James O'Sullivan

To realize the full potential of quantum technologies, finding good strategies to control quantum information processing devices in real time becomes increasingly important. Usually these strategies require a precise understanding of the device itself, which is generally not available. Model-free reinforcement learning circumvents this need by discovering co

Kyeong Min Kim

The problem of maximizing the average cross section through a point within a shape is introduced. This idea is extended into arbitrary dimensions. However, the average cross sectional volume cannot be maximized unless the cross sections pass through the centroid of the shape. Therefore, we focus on the shapes with stationary values of average cross section.

Pavol Hell, Jing Huang, Jephian C. -H. Lin

We introduce the class of strong cocomparability graphs, as the class of reflexive graphs whose adjacency matrix can be rearranged by a simultaneous row and column permutation to avoid the submatrix with rows 01, 10, which we call Slash. We provide an ordering characterization, a forbidden structure characterization, and a polynomial-time recognition algorit

Raoni W. Arroyo, Jonnas R. B. Arenhart

`Shallow' and `deep' versions of scientific realism may be distinguished as follows: the shallow realist is satisfied with belief in the existence of the posits of our best scientific theories; by contrast, deep realists claim that realism can be legitimate only if such entities are described in metaphysical terms. We argue that this methodological discussio

Hassaan Hashmi, Spyridon Pougkakiotis, Dionysios S. Kalogerias

Although Intelligent Reflective Surfaces (IRSs) are a cost-effective technology promising high spectral efficiency in future wireless networks, obtaining optimal IRS beamformers is a challenging problem with several practical limitations. Assuming fully-passive, sensing-free IRS operation, we introduce a new data-driven Zeroth-order Stochastic Gradient Ascen

Yanzeng Zhang, Yuzhi Li, Bhuvana Srinivasan, Xian-Zhu Tang

A large family of plasmas has collisional mean-free-path much longer than the non-neutral sheath width, which scales with the plasma Debye length. The plasmas, particularly the electrons, assume strong temperature anisotropy in the sheath. The temperature in the sheath flow direction ($T_{e\parallel}$) is lower and drops towards the wall as a result of the d

Adel Javanmard, Simeng Shao, Jacob Bien

Large datasets make it possible to build predictive models that can capture heterogenous relationships between the response variable and features. The mixture of high-dimensional linear experts model posits that observations come from a mixture of high-dimensional linear regression models, where the mixture weights are themselves feature-dependent. In this p

Andrew Olsen, Yolanda Hu, Vidya Ganapati

In computational imaging, hardware for signal sampling and software for object reconstruction are designed in tandem for improved capability. Examples of such systems include computed tomography (CT), magnetic resonance imaging (MRI), and superresolution microscopy. In contrast to more traditional cameras, in these devices, indirect measurements are taken an

Erwan pannier, Taemin Yong, Christophe Laux, Mark A. Cappelli

The efficiency of the conversion of CO$_2$ into CO with nanosecond repetitively pulsed discharges (NRP) is investigated in a high pressure batch reactor. Stable discharges are obtained at up to 12~bar. By-products of CO$_2$ splitting are measured with gas chromatography. The energy efficiency is determined for a range of processing times, pulse energy, and f

Carlos E. Pérez De Jesús, Michael D. Graham

Reduced order models (ROMs) that capture flow dynamics are of interest for decreasing computational costs for simulation as well as for model-based control approaches. This work presents a data-driven framework for minimal-dimensional models that effectively capture the dynamics and properties of the flow. We apply this to Kolmogorov flow in a regime consist

Wenqiang Yang, Wenyuan Wu, Greg Reid

To find consistent initial data points for a system of differential-algebraic equations, requires the identification of its missing constraints. An efficient class of structural methods exploiting a dependency graph for this task was initiated by Pantiledes. More complete methods rely on differential-algebraic geometry but suffer from other issues (e.g. high

Tarik Aougab, Max Lahn, Marissa Loving, Nicholas Miller

We prove that every closed orientable surface S of negative Euler characteristic admits a pair of finite-degree covers which are length isospectral over S but generically not simple length isospectral over S. To do this, we first characterize when two finite-degree covers of a connected, orientable surface of negative Euler characteristic are isomorphic in t

Yue Wang, Zhi Tian, Xin Fan, Yan Huo

With the proliferation of versatile Internet of Things (IoT) services, smart IoT devices are increasingly deployed at the edge of wireless networks to perform collaborative machine learning tasks using locally collected data, giving rise to the edge learning paradigm. Due to device restrictions and resource constraints, edge learning among massive IoT device

Abhishek Srivastava, Debesh Jha, Bulent Aydogan, Mohamed E. Abazeed

Head and Neck (H\&N) organ-at-risk (OAR) and tumor segmentations are essential components of radiation therapy planning. The varying anatomic locations and dimensions of H\&N nodal Gross Tumor Volumes (GTVn) and H\&N primary gross tumor volume (GTVp) are difficult to obtain due to lack of accurate and reliable delineation methods. The downstream effect of in

Nazish Tahir, Ramviyas Parasuraman

Resource-constrained mobile robots that lack the capability to be completely autonomous can rely on a human or AI supervisor acting at a remote site (e.g., control station or cloud) for their control. Such a supervised autonomy or cloud-based control of a robot poses high networking and computing capabilities requirements at both sites, which are not easy to

Jonathan DeWitt

Anosov automorphisms with Jordan blocks are not periodic data rigid. We introduce a refinement of the periodic data and show that this refined periodic data characterizes $C^{1+}$ conjugacy for Anosov automorphisms of the four dimensional torus with a Jordan block.

Arsham Ghavasieh, Manlio De Domenico

The network density matrix formalism allows for describing the dynamics of information on top of complex structures and it has been successfully used to analyze from system's robustness to perturbations to coarse graining multilayer networks from characterizing emergent network states to performing multiscale analysis. However, this framework is usually limi

Yuri G. Rubo

I discuss similitude and differences of spin-orbital effects for electrons in quantum wells with the Rashba coupling and for polaritons in semiconductor microcavities with TE-TM splitting. Contrary to the case of electron, the ground state of polariton in the trap can be non-degenerate and can possess specific polarization structure. For the case of azimutha

A. Savaş Arapoğlu, A. Emrah Yükselci

We investigate first-order vacuum phase transitions in the presence of a non-minimally coupled scalar field starting with the coupling effect on the initial dynamics of phase transitions by defining an effective potential for the scalar field and then performing three dimensional numerical simulations to observe any possible distinction in gravitational wave

J. S. Harms, H. Y. Yuan, Rembert A. Duine

Magnons are the quanta of collective spin excitations in magnetically-ordered systems and manipulation of magnons for computing and information processing has witnessed the development of ``magnonics". A magnon corresponds to an excitation of the magnetic system from its ground state and the creation of a magnon thus increases the total energy of the system.

Anthony J. Pahl, Alice Shapley, Charles C. Steidel, Naveen A. Reddy

The connection between the escape fraction of ionizing radiation ($f_{esc}$) and the properties of galaxies, such as stellar mass (M*), age, star-formation rate (SFR), and dust content, are key inputs for reionization models, but many of these relationships remain untested at high redshift. We present an analysis of a sample of 96 z~3 galaxies from the Keck

Niklas Smedemark-Margulies, Basak Celik, Tales Imbiriba, Aziz Kocanaogullari

We study the problem of inferring user intent from noninvasive electroencephalography (EEG) to restore communication for people with severe speech and physical impairments (SSPI). The focus of this work is improving the estimation of posterior symbol probabilities in a typing task. At each iteration of the typing procedure, a subset of symbols is chosen for

Marino Badiale, Michela Guida, Sergio Rolando

In this note we complete a previous study, where we got existence results for the quasilinear elliptic equation \begin{equation*} -\Delta w+ V\left( \left| x\right| \right) w - w \left( \Delta w^2 \right)= K(|x|) g(w) \quad \text{in }\mathbb{R}^{N}, \end{equation*} with singular or vanishing continuous radial potentials $V(r)$, $K(r)$. In our previuos study

Zhuangzhuang Cui, Sofie Pollin

When beamforming is applied at the transmitter, only part of reconfigurable intelligent surfaces (RISs) will be active, and it becomes indispensable to study the impact of RIS geometry. This letter aims at evaluating RIS geometry, ranging from linear (1D), and planar (2D), to cylindrical (3D) structures. We first derive the effective illuminated surface of d

Florent Capelli, Nicolas Crosetti, Joachim Niehren, Jan Ramon

In this paper, we study the problem of optimizing a linear program whose variables are the answers to a conjunctive query. For this we propose the language LP(CQ) for specifying linear programs whose constraints and objective functions depend on the answer sets of conjunctive queries. We contribute an efficient algorithm for solving programs in a fragment of

Donald V. Reames

We revisit the full variety of observed temporal and spatial distributions of energetic solar protons in "gradual" solar energetic-particle (SEP) events resulting from the spatial variations in the shock waves that accelerate them. Differences in the shock strength at the solar longitude of a spacecraft and at the footpoint of its connecting magnetic field l

Rakshith Subramanyam, Kowshik Thopalli, Spring Berman, Pavan Turaga

The problem of adapting models from a source domain using data from any target domain of interest has gained prominence, thanks to the brittle generalization in deep neural networks. While several test-time adaptation techniques have emerged, they typically rely on synthetic data augmentations in cases of limited target data availability. In this paper, we c

Guyue Huang, Yang Bai, Liu Liu, Yuke Wang

Pipelining between data loading and computation is a critical tensor program optimization for GPUs. In order to unleash the high performance of latest GPUs, we must perform a synergetic optimization of multi-stage pipelining across the multi-level buffer hierarchy of GPU. Existing frameworks rely on hand-written libraries such as cuBLAS to perform pipelining

Holger Boche, Rafael F. Schaefer, H. Vincent Poor, Frank H. P. Fitzek

As we are more and more dependent on the communication technologies, resilience against any attacks on communication networks is important to guarantee the digital sovereignty of our society. New developments of communication networks tackle the problem of resilience by in-network computing approaches for higher protocol layers, while the physical layer rema

Valerio Faraoni, El Mokhtar Z. R. Mokkedem

Existing literature implements the Dominant Energy Condition for dissipative fluids in general relativity. It is pointed out that this condition fails to forbid superluminal flows, which is what it is ultimately supposed to do. Tilted perfect fluids, which formally have the stress-energy tensor of imperfect fluids, are discussed for comparison.

Tyler Evans, Scott Norris

Ion-beam irradiation of an amorphizable material such as Si or Ge may lead to spontaneous pattern formation, rather than flat surfaces, for irradiation beyond some critical angle against the surface normal. It is observed experimentally that this critical angle varies according to many factors, including beam energy, ion species and target material. In this

Tyler Evans, Scott Norris

Ion-beam irradiation of an amorphizable material such as Si or Ge may lead to spontaneous pattern formation beyond some critical angle of the beam versus the surface. It is known from experimental results that this critical angle varies according to beam energy, ion species and target material. However, most prevailing theoretical analyses predict a critical

Andrea Crovetto, Thomas Unold, Andriy Zakutayev

Despite the recent surge in interest in Cu$_{3-x}$P for catalysis, batteries, and plasmonics, the electronic nature of Cu$_{3-x}$P remains unclear. Some studies have shown evidence of semiconducting behavior, whereas others have argued that Cu$_{3-x}$P is a metallic compound. Here, we attempt to resolve this dilemma on the basis of combinatorial thin-film ex

Massimo Ventrucci, Alessandro Vagheggini

Phase II basket trials are popular tools to evaluate efficacy of a new treatment targeting genetic alteration common to a set of different cancer histologies. Efficient designs are obtained by pooling data from the different arms (e.g., cancer histologies) via Bayesian hierarchical modelling, with a variance parameter controlling the strength of shrinkage of

Rahim Moosa

This is a write-up of some lectures I gave in the Fall of 2021 at the Fields Institute in Toronto, as part of the Thematic Programme on Trends in Pure and Applied Model Theory. The goal of the module was to give a quick introduction to the model theory of differential fields that puts differential-algebraic geometry at the center. I focus here on the biratio

Stephen A. Walsh, Marco A. R. Ferreira, Dave Higdon, Stephanie Zick

Tropical cyclones present a serious threat to many coastal communities around the world. Many numerical weather prediction models provide deterministic forecasts with limited measures of their forecast uncertainty. Standard postprocessing techniques may struggle with extreme events or use a 30-day training window that will not adequately characterize the unc

Guanqiang Zhou, Ping Xu, Yue Wang, Zhi Tian

In distributed learning systems, robustness issues may arise from two sources. On one hand, due to distributional shifts between training data and test data, the trained model could exhibit poor out-of-sample performance. On the other hand, a portion of working nodes might be subject to byzantine attacks which could invalidate the learning result. Existing w

Mohamed Ghattassi, Xiaokai Huo, Nader Masmoudi

In this paper, we study the diffusive limit of the steady state radiative heat transfer system for non-homogeneous Dirichlet boundary conditions in a bounded domain with flat boundaries. A composite approximate solution is constructed using asymptotic analysis taking into account of the boundary layers. The convergence to the approximate solution in the diff

Eric J. Carlson, Joshua R. Smith

This paper presents a complete closed-form analytical model for determining the per-cycle energy consumption of stepwise adiabatic drivers used for driving a capacitive load such as a power FET gate. The model takes into account the number of steps used, the stepwise driver tank capacitance, the load capacitance, and the stepwise driver switch resistance and

Elena Farahbakhsh Touli, Hoang Nguyen, Olha Bodnar

In this paper, we study the connection between the companies in the Swedish capital market. We consider 28 companies included in the determination of the market index OMX30. The network structure of the market is constructed using different methods to determine the distance between the companies. We use hierarchical clustering methods to find the relation am

Hiroyuki Masuyama, Hiroshige Dan, Shunji Umetani

This paper investigates the intractability of large-scale optimization with multi-start methods. For the theoretical performance analysis, we focus on random multi-start (RMS), which is one of the representative multi-start methods, including RMS local search and greedy randomized adaptive search procedure (GRASP). Our primary theoretical contribution is to

Nadav Drukker, Elise Paznokas, Dominik Schrimpel

We study the twisting fault emerging in circular knitting and its relation to the mathematical concepts of framing curves and the Gauss linking integral. We create three knitted bands with framing zero, one, and negative two, and use three different techniques to compute the framing using the Gauss linking integral. We also briefly mention the connection to

Kilian Fraboulet

The core of this thesis is the path-integral formulation of quantum field theory and its ability to describe strongly-coupled quantum many-body systems of finite size. Collective behaviors can be efficiently described in such systems through the implementation of spontaneous symmetry breaking (SSB) in mean-field approaches. However, as the thermodynamic limi

Korosh Mahmoodi, Scott E. Kerick, Paolo Grigolini, Piotr J. Franaszczuk

The observational ubiquity of inverse power law spectra (IPL) in complex phenomena entails theory for dynamic fractal phenomena capturing their fractal dimension, dynamics, and statistics. These and other properties are consequences of the complexity resulting from nonlinear dynamic networks collectively summarized for biomedical phenomena as the Network Eff

Shan Lin, Albert J. Miao, Jingpei Lu, Shunkai Yu

Accurate and robust tracking and reconstruction of the surgical scene is a critical enabling technology toward autonomous robotic surgery. Existing algorithms for 3D perception in surgery mainly rely on geometric information, while we propose to also leverage semantic information inferred from the endoscopic video using image segmentation algorithms. In this

Marco Buratti

After extending the classic notion of a tight Heffter array H$(m,n)$ to any group of order $2mn+1$, we give direct constructions for elementary abelian tight Heffter arrays, hence in particular for prime tight Heffter arrays. If $q=2mn+1$ is a prime power, we say that an elementary abelian H$(m,n)$ is ``over $\mathbb{F}_q$" since, for its construction, we ex

Gabriele Dian

This thesis describes progresses made by the author and collaborators in the positive geometry description of superamplitudes and supercorrelators in planar N = 4 SYM.

Nairouz Shehata, Wulfie Bain, Ben Glocker

Graph neural networks have emerged as a promising approach for the analysis of non-Euclidean data such as meshes. In medical imaging, mesh-like data plays an important role for modelling anatomical structures, and shape classification can be used in computer aided diagnosis and disease detection. However, with a plethora of options, the best architectural ch

N. Bartel, M. S. Burgin, E. N. Fadeev, M. V. Popov

We observed the scintillation pattern of nine bright pulsars at 324 MHz and three at 1.68 GHz and analyzed the wavenumber spectrum which is related to electron density variations of the plasma turbulence of the interstellar medium. For all pulsars the frequency section of the autocorrelation function of the dynamic spectra to at least 45\% of the maximum cor

Kamal K. Saha, Walter Robson, Connor Howington, In-Saeng Suh

The Poisson equation has many applications across the broad areas of science and engineering. Most quantum algorithms for the Poisson solver presented so far either suffer from lack of accuracy and/or are limited to very small sizes of the problem, and thus have no practical usage. Here we present an advanced quantum algorithm for solving the Poisson equatio

Hamid Amiriara, Farid Ashtiani, Mahtab Mirmohseni, Masoumeh Nasiri-Kenari

This paper concentrates on the problem of associating an intelligent reflecting surface (IRS) to multiple users in a multiple-input single-output (MISO) downlink wireless communication network. The main objective of the paper is to maximize the sum-rate of all users by solving the joint optimization problem of the IRS-user association, IRS reflection, and BS

Mihai-Silviu Lazorec, Marius Tărnăuceanu

Let $o(G)$ be the average order of a finite group $G$. We show that if $o(G)<c$, where $c\in \lbrace \frac{13}{6}, \frac{11}{4}\rbrace$, then $G$ is an elementary abelian 2-group or a solvable group, respectively. Also, we prove that the set containing the average orders of all finite groups is not dense in $[a, \infty)$, for all $a\in [0, \frac{13}{6}]$. We

Felix Finster, Margarita Kraus

We give a novel construction of global solutions to the linearized field equations for causal variational principles. The method is to glue together local solutions supported in lens-shaped regions. As applications, causal Green's operators and cone structures are introduced.

Anatoli Juditsky, Arkadi Nemirovski

The subject of this paper is regularity-preserving aggregation of regular norms on finite-dimensional linear spaces. Regular norms were introduced in [5] and are closely related to ``type 2'' spaces [9, Chapter 9] playing important role in 1) high-dimensional convex geometry and probability in Banach spaces [0.9.12.13.15], and in 2) design of proximal first-

Yosuke Higuchi, Brian Yan, Siddhant Arora, Tetsuji Ogawa

This paper presents BERT-CTC, a novel formulation of end-to-end speech recognition that adapts BERT for connectionist temporal classification (CTC). Our formulation relaxes the conditional independence assumptions used in conventional CTC and incorporates linguistic knowledge through the explicit output dependency obtained by BERT contextual embedding. BERT-

Christos N. Efrem, Ioannis Krikidis

In this paper, we study an intelligent reflecting surface (IRS) assisted communication system with single-antenna transmitter and receiver, under imperfect channel state information (CSI). More specifically, we deal with the robust selection of binary (on/off) states of the IRS elements in order to maximize the worst-case energy efficiency (EE), given a boun

Ivelisse Rubio, Jaziel Torres

A unifying theoretical framework is presented, in which the connections among Costas sequences, circular Costas sequences, Costas polynomials, the shifting property, and Welch sequences are extended to the multidimensional context. Several conjectures on multidimensional periodic Costas arrays by J. Ortiz-Ubarri et al. are proved. Furthermore, a conjecture o

Xiaochen Zheng

Automated animal censuses with aerial imagery are a vital ingredient towards wildlife conservation. Recent models are generally based on supervised learning and thus require vast amounts of training data. Due to their scarcity and minuscule size, annotating animals in aerial imagery is a highly tedious process. In this project, we present a methodology to re

Herbert Owen, Oriol Lehmkuhl, Pasqua D'Ambra, Fabio Durastante

In this paper, we describe an upgrade of the Alya code with up-to-date parallel linear solvers capable of achieving reliability, efficiency and scalability in the computation of the pressure field at each time step of the numerical procedure for solving a Large Eddy Simulation formulation of the incompressible Navier-Stokes equations. We developed a software

Sung-Lin Yeh, Hao Tang

While discrete latent variable models have had great success in self-supervised learning, most models assume that frames are independent. Due to the segmental nature of phonemes in speech perception, modeling dependencies among latent variables at the frame level can potentially improve the learned representations on phonetic-related tasks. In this work, we

Tom Tirer, Haoxiang Huang, Jonathan Niles-Weed

Training deep neural networks for classification often includes minimizing the training loss beyond the zero training error point. In this phase of training, a "neural collapse" behavior has been observed: the variability of features (outputs of the penultimate layer) of within-class samples decreases and the mean features of different classes approach a cer

Namiko Matsumoto, Arya Mazumdar, Soumyabrata Pal

One-bit compressed sensing (1bCS) is an extremely quantized signal acquisition method that has been proposed and studied rigorously in the past decade. In 1bCS, linear samples of a high dimensional signal are quantized to only one bit per sample (sign of the measurement). Assuming the original signal vector to be sparse, existing results in 1bCS either aim t

Jiachen Liu, Fan Lai, Yinwei Dai, Aditya Akella

Federated learning (FL) is an emerging machine learning (ML) paradigm that enables heterogeneous edge devices to collaboratively train ML models without revealing their raw data to a logically centralized server. However, beyond the heterogeneous device capacity, FL participants often exhibit differences in their data distributions, which are not independent

Piotr Jaworski, Damian Jelito, Marcin Pitera

It is well known that while the independence of random variables implies zero correlation, the opposite is not true. Namely, uncorrelated random variables are not necessarily independent. In this note we show that the implication could be reversed if we consider the localised version of the correlation coefficient. More specifically, we show that if random v

Kiriko Terai, Jonathon L. Yuly, Peng Zhang, David N. Beratan

Studies of nanoscale biological transport often use a mean-field approximation that is exact only when the system is at equilibrium and there are no interactions between particles on different sites in the network. We explore the limitations of this approximation to describe many-particle transport in the context of enzyme function and biological transport n

Anton N. Vetlugin, Filippo Martinelli, Shuyu Dong, Cesare Soci

Common methods to achieve photon number resolution rely on fast on-off single-photon detectors in conjunction with temporal or spatial mode multiplexing. Yet, these methods suffer from an inherent trade-off between the efficiency of photon number discrimination and photon detection rate. Here, we introduce a method of photon number resolving detection that o

Richard Blythman, Mohamed Arshath, Jakub Smékal, Hithesh Shaji

AI requires heavy amounts of storage and compute. As a result, AI developers are regular users of centralised cloud services such as AWS, GCP and Azure, compute environments such as Jupyter and Colab notebooks, and AI Hubs such as HuggingFace and ActiveLoop. There services are associated with certain benefits and limitations that stem from the underlying inf

Shubham Singh, Jayanta Roy, Bhaswati Bhattacharyya, Ujjwal Panda

The fast Fourier transform (FFT) based periodicity search methods provide an efficient way to search for millisecond and binary pulsars but encounter significant sensitivity degradation while searching for long period and short duty cycle pulsars. An alternative to FFT-based search methods called the Fast Folding Algorithm (FFA) search, provides superior sen

Philipp Wegner, Sebastian Schaaf, Mischa Uebachs, Marcus Grobe-Einsler

Motivation: Visualization of data is a crucial step to understanding and deriving hypotheses from clinical data. However, for clinicians, visualization often comes with great effort due to the lack of technical knowledge about data handling and visualization. The application offers an easy-to-use solution with an intuitive design that enables various kinds o

İbrahim Semiz

The general analytical solution for the static spherically symmetric metric supported by a perfect fluid with proportional-equation-of-state $p = w \rho$ is not known at the time of this writing, except for the trivial cases $w=0$ and $w=-1$; for $w=-1/3$, and the recently reported $w=-1/5$. We show that the case $w=-1/6$ is also analytically solvable, as pr

J. C. Phillips

Structural Maintenance of Chromosomes, SMCs, proteins have long rod like structures immersed in water. Here we use our hydroanalytic methods based on amino acid sequences to discuss their dynamics at multiple length scales identified by evolution. The length scales are 10 to 100 times longer than used in normal studies of sequence evolution. Their hydropathi

Sidhika Balachandar, Adrien Poulenard, Congyue Deng, Leonidas Guibas

Equivariant networks have been adopted in many 3-D learning areas. Here we identify a fundamental limitation of these networks: their ambiguity to symmetries. Equivariant networks cannot complete symmetry-dependent tasks like segmenting a left-right symmetric object into its left and right sides. We tackle this problem by adding components that resolve symme

Yiling Xie, Yiling Luo, Xiaoming Huo

Computing the empirical Wasserstein distance in the Wasserstein-distance-based independence test is an optimal transport (OT) problem with a special structure. This observation inspires us to study a special type of OT problem and propose a modified Hungarian algorithm to solve it exactly. For the OT problem involving two marginals with $m$ and $n$ atoms ($m

Darshan Singh S, Anchit Gupta, C. V. Jawahar, Makarand Tapaswi

Over the last decade, online lecture videos have become increasingly popular and have experienced a meteoric rise during the pandemic. However, video-language research has primarily focused on instructional videos or movies, and tools to help students navigate the growing online lectures are lacking. Our first contribution is to facilitate research in the ed

Roshan Sharma, Bhiksha Raj

Transformers are among the state of the art for many tasks in speech, vision, and natural language processing, among others. Self-attentions, which are crucial contributors to this performance have quadratic computational complexity, which makes training on longer input sequences challenging. Prior work has produced state-of-the-art transformer variants with

Roshan Sharma, Hira Dhamyal, Bhiksha Raj, Rita Singh

Traditionally, in paralinguistic analysis for emotion detection from speech, emotions have been identified with discrete or dimensional (continuous-valued) labels. Accordingly, models that have been proposed for emotion detection use one or the other of these label types. However, psychologists like Russell and Plutchik have proposed theories and models that

M. Bertaina, D. Barghini, M. Battisti, A. Belov

The trigger logic of the Tracking Ultraviolet Setup (TUS) and Multiwavelength Imaging New Instrument for the Extreme Universe Space Observatory (Mini-EUSO) space-based projects of the Joint Experiment Missions - EUSO (JEM-EUSO) program is summarized. The performance results on the search for ultra-high energy cosmic rays are presented.

Robert R. Tucci

Determining a causal DAG (directed acyclic graph) for a problem under consideration, is a major roadblock when doing Judea Pearl's Causal Inference (CI) in Statistics. The same problem arises when doing CI in Artificial Intelligence (AI) and Machine Learning (ML). As with many problems in Science, we think Nature has found an effective solution to this probl

Lingwei Meng, Di Dong, Xin Chen, Mengjie Fang

Objective: Radiomics, an emerging tool for medical image analysis, is potential towards precisely characterizing gastric cancer (GC). Whether using one-slice 2D annotation or whole-volume 3D annotation remains a long-time debate, especially for heterogeneous GC. We comprehensively compared 2D and 3D radiomic features' representation and discrimination capaci

Yihua Cheng, Anton Arapin, Ziyi Zhang, Qizheng Zhang

Across many real-time video applications, we see a growing need (especially in long delays and dynamic bandwidth) to allow clients to decode each frame once any (non-empty) subset of its packets is received and improve quality with each new packet. We call it data-scalable delivery. Unfortunately, existing techniques (e.g., FEC, RS and Fountain Codes) fall s

Javier Casas-de la Rosa, William Chen-Mertens, Sergio Garcia-Balan

In this paper, we investigate what selection principles properties are possessed by small (with respect to the bounding and dominating numbers) unions of spaces with certain (star) selection principles.. Furthermore, we give several results about iterations of these properties and weaker properties than paracompactness. In addition, we study the behaviour of

Yu Fei, Ping Nie, Zhao Meng, Roger Wattenhofer

Recent work has demonstrated that pre-trained language models (PLMs) are zero-shot learners. However, most existing zero-shot methods involve heavy human engineering or complicated self-training pipelines, hindering their application to new situations. In this work, we show that zero-shot text classification can be improved simply by clustering texts in the