November 2022 arXiv papers — page 48

Xiao Li, Yin Zhu, Sichen Liu, Jiangzhou Ju

Numerical reasoning over hybrid data containing tables and long texts has recently received research attention from the AI community. To generate an executable reasoning program consisting of math and table operations to answer a question, state-of-the-art methods use a retriever-generator pipeline. However, their retrieval results are static, while differen

Rong Wen, Ying Zhang, Qiang Li, Youxi Tang

This paper considers transmit beamforming in dual-function radar-communication (DFRC) system, where a DFRC transmitter simultaneously communicates with a communication user and detects a malicious target with the same waveform. Since the waveform is embedded with information, the information is risked to be intercepted by the target. To address this problem,

Ruizhu Chen, Junwei Zhao, Shea Hess Webber, Yang Liu

Accurate modeling of the Sun's coronal magnetic field and solar wind structures require inputs of the solar global magnetic field, including both the near and far sides, but the Sun's far-side magnetic field cannot be directly observed. However, the Sun's far-side active regions are routinely monitored by helioseismic imaging methods, which only require cont

Yunqi Wang, Yang Li, Qingjiang Shi, Yik-Chung Wu

Cooperative beamforming design has been recognized as an effective approach in modern wireless networks to meet the dramatically increasing demand of various wireless data traffics. It is formulated as an optimization problem in conventional approaches and solved iteratively in an instance-by-instance manner. Recently, learning-based methods have emerged wit

Thoudam Vilip Singh, Lenin S. Shagolsem

The effect of topology on the collapse transition and instantaneous shape of an energy polydisperse polymer (a model heteropolymer) is studied by means of computer simulations. In particular, we consider three different chain topology, namely, linear (L), ring (R) and trefoil knot (T). The heteropolymer is modeled by assigning each monomer an interaction par

Constantine E. Theodosiou

We revisit the photoabsorption from the ground state of K through the first ionization limit, and provide recommended values for the optical oscillator strengths. The anomaly in the oscillator strength ratio within each doublet is also revisited and compared with both the experimental data available and the relativistic calculations available.

Andries E. Brouwer, Jan Draisma, Çiçek Güven

We show that the Kneser graph of objects of a fixed type in a building of spherical type has the unique coclique extension property when the corresponding representation has minuscule weight and also when the diagram is simply laced and the representation is adjoint.

Nicholas S. Martis, Danilo M. Marchesini, Adam Muzzin, Chris J. Willot

We present 65 extremely dust-obscured galaxies from the UltraVISTA DR3 survey of the COSMOS field at $1<z<4$. In contrast to other studies of dusty galaxies, we select our sample based on dust attenuation measured by UV-MIR spectral energy distribution (SED) modeling that allows for extreme attenuation levels. We construct our sample by making cuts at $1 \le

Ziv Ran

For every $n\geq 3, g\geq 1$ and all large enough $e$ depending on $n,g$, there exist curves of genus $g$, degree $e$ in a general hypersurface of degree $n$ in $\mathbb P^n$, or in $\mathbb P^n$ itself, whose whose normal bundle $N$ is stable, as is any sufficiently general full-rank subsheaf of $N$. For $g=1$, $N$ is semi-stable. On general hypersurface of

Shiva Mayahi, Marko Vidrih

In today's world of marketing, it is necessary to have visually appealing content. Visual material has become an essential area of focus for every company as a result of the widespread availability of gadgets for mass communication and extended visual advancements. Similarly, artificial intelligence is also gaining ground and it is proving to be the most rev

Gao Yixiao, Lei Xu, Xiang Shen

Resonance coupling in the regime of bound states in the continuum (BICs) provides an efficient method for engineering nanostructure's optical response with various lineshape while maintaining an ultra-narrow linewidth feature, where the quality factor of resonances plays a crucial role. Independent manipulation of the Q factors of BIC resonances enables full

Nalini Joshi, Tomas Lasic Latimer

We investigate a Riemann-Hilbert problem (RHP), whose solution corresponds to a group of $q$-orthogonal polynomials studied earlier by Ismail et al. Using RHP theory we determine new asymptotic results in the limit as the degree of the polynomials approach infinity. The RHP formulation also enables us to obtain further properties. In particular, we consider

Puzuo Wang, Wei Yao, Jie Shao

Reliance on vast annotations to achieve leading performance severely restricts the practicality of large-scale point cloud semantic segmentation. For the purpose of reducing data annotation costs, effective labeling schemes are developed and contribute to attaining competitive results under weak supervision strategy. Revisiting current weak label forms, we i

Huangying Zhan, Jiyang Zheng, Yi Xu, Ian Reid

A high-quality 3D reconstruction of a scene from a collection of 2D images can be achieved through offline/online mapping methods. In this paper, we explore active mapping from the perspective of implicit representations, which have recently produced compelling results in a variety of applications. One of the most popular implicit representations - Neural Ra

Ali Fazeli, Ha H. Nguyen, Halim Yanikomeroglu

This paper develops a low-complexity near-optimal non-coherent receiver for a multi-level energy-based coded modulation system. Inspired by the turbo processing principle, we incorporate the fundamentals of bit-interleaved coded modulation with iterative decoding (BICM-ID) into the proposed receiver design. The resulting system is called bit-interleaved code

Connor Malin

Given smooth manifolds $M$ and $N$, manifold calculus studies the space of embeddings $\operatorname{Emb}(M,N)$ via the "embedding tower", which is constructed using the homotopy theory of presheaves on $M$. The same theory allows us to study the stable homotopy type of $\operatorname{Emb}(M,N)$ via the "stable embedding tower". By analyzing cubes of framed

Haoran Zhan, Yu Liu, Yingcun Xia

Classification and Regression Tree (CART), Random Forest (RF) and Gradient Boosting Tree (GBT) are probably the most popular set of statistical learning methods. However, their statistical consistency can only be proved under very restrictive assumptions on the underlying regression function. As an extension to standard CART, the oblique decision tree (ODT),

J. Martín Ovejero

In this paper, we provide a unified treatment of the Vanna-Volga pricing technique. We derive the value of single and double barriers FX options, as well as closed formulas for the Delta, Vega, Vanna and Volga of those contracts.

Chang Zhang, Raphael St-Gelais

The frequency stability of nanomechanical resonators (NMR) dictates the performance level of many state-of-the-art sensors (e.g., mass, force, temperature, radiation) that relate an external physical perturbation to a resonance frequency shift. While this is obviously of fundamental importance, accurate models and understandings of sources of frequency insta

Ibrahima Ndiour, Nilesh Ahuja, Utku Genc, Omesh Tickoo

This paper presents a fast and principled approach for solving the visual anomaly detection and segmentation problem. In this setup, we have access to only anomaly-free training data and want to detect and identify anomalies of an arbitrary nature on test data. We propose the application of linear statistical dimensionality reduction techniques on the interm

Huangying Zhan, Hamid Rezatofighi, Ian Reid

We propose a robotic learning system for autonomous exploration and navigation in unexplored environments. We are motivated by the idea that even an unseen environment may be familiar from previous experiences in similar environments. The core of our method, therefore, is a process for building, predicting, and using probabilistic layout graphs for assisting

Kyle Lee, Jian-Wei Qiu, George Sterman, Kazuhiro Watanabe

We report the current understanding of heavy quarkonium production at high transverse momentum ($p_T$) in hadronic collisions in terms of QCD factorization. In this presentation, we highlight the role of subleading power corrections to heavy quarkonium production, which are essential to describe the $p_T$ spectrum of quarkonium at a relatively lower $p_T$. W

Xinhang Lu, Jannik Peters, Haris Aziz, Xiaohui Bei

We consider a voting scenario in which the resource to be voted upon may consist of both indivisible and divisible goods. This setting generalizes both the well-studied model of multiwinner voting and the recently introduced model of cake sharing. Under approval votes, we propose two variants of the extended justified representation (EJR) notion from multiwi

Sadi Md. Redwan, Md Palash Uddin, Muhammad Imran Sharif, Anwaar Ulhaq

In the last two decades, functional magnetic resonance imaging (fMRI) has emerged as one of the most effective technologies in clinical research of the human brain. fMRI allows researchers to study healthy and pathological brains while they perform various neuropsychological functions. Beyond task-related activations, the human brain has some intrinsic activ

Sadi Md. Redwan, Md Palash Uddin, Anwaar Ulhaq, Muhammad Imran Sharif

Historically, the analysis of stimulus-dependent time-frequency patterns has been the cornerstone of most electroencephalography (EEG) studies. The abnormal oscillations in high-frequency waves associated with psychotic disorders during sensory and cognitive tasks have been studied many times. However, any significant dissimilarity in the resting-state low-f

John Cullinan, Santanu Antu

In this paper we build on the work of \cite{kaber} where it was shown that the one-parameter family of Gegenbauer Polynomials (GP) exhibit a Gibbs Phenomenon at a jump discontinuity. We show that the one-parameter family of Generalized Laguerre Polynomials (GLP) also exhibit a Gibbs Phenomenon. Among many differences, a major one is that the GLP are orthogon

Zach Beever, David Caratelli, Angela Fava, Francesco Pietropaolo

The microphysics of electron and photon propagation in liquid argon is a key component of detector design and calibrations needed to construct and perform measurements within a wide range of particle physics experiments. As experiments grow in scale and complexity, and as the precision of their intended measurements increases, the development of tools to inv

Chang Liu, Xuemeng Liu, Zhiqiang Wei, Derrick Wing Kwan Ng

Beamforming design for intelligent reflecting surface (IRS)-assisted multi-user communication (IRS-MUC) systems critically depends on the acquisition of accurate channel state information (CSI). However, channel estimation (CE) in IRS-MUC systems causes a large signaling overhead for training due to the large number of IRS elements. In this paper, taking int

C. A. Bertulani

The hypertriton is predicted to have a small binding energy (a weighted average of about 150 keV), consistent with a large matter radius (~ 10 fm), larger than the historical 11Li halo discovered more than 35 years ago. But the reported experimental values of the binding energy of the hypertriton range from 70 to 400 keV. In this work I discuss the electroma

Justin Van Ee, Christian Hagen, David Pavlacky, Kent Fricke

Integrated models are a popular tool for analyzing species of conservation concern. Species of conservation concern are often monitored by multiple entities that generate several datasets. Individually, these datasets may be insufficient for guiding management due to low spatio-temporal resolution, biased sampling, or large observational uncertainty. Integra

Aashna Jena, Vivek Gupta, Manish Shrivastava, Julian Martin Eisenschlos

Creating challenging tabular inference data is essential for learning complex reasoning. Prior work has mostly relied on two data generation strategies. The first is human annotation, which yields linguistically diverse data but is difficult to scale. The second category for creation is synthetic generation, which is scalable and cost effective but lacks inv

Shahryar Zehtabi, Seyyedali Hosseinalipour, Christopher G. Brinton

Federated learning (FL) is a popular technique for distributing machine learning (ML) across a set of edge devices. In this paper, we study fully decentralized FL, where in addition to devices conducting training locally, they carry out model aggregations via cooperative consensus formation over device-to-device (D2D) networks. We introduce asynchronous, eve

Theodora Bourni, Mat Langford, Stephen Lynch

We make rigorous an old idea of using mean curvature flow to prove a theorem of Richard Hamilton on the compactness of proper hypersurfaces with pinched, bounded curvature.

Zhou Lu, Nataly Brukhim, Paula Gradu, Elad Hazan

In the framework of online convex optimization, most iterative algorithms require the computation of projections onto convex sets, which can be computationally expensive. To tackle this problem HK12 proposed the study of projection-free methods that replace projections with less expensive computations. The most common approach is based on the Frank-Wolfe met

Paul Barry

We give conjectures on the form of families of integer sequences whose Hankel transforms are, respectively, $(\alpha, \beta)$ Somos $4$ sequences, $(\alpha, 0, \gamma)$ Somos $6$ sequences, and $(\alpha, \beta, \gamma, \delta)$ Somos $8$ sequences, for particular values of $\alpha$, $\beta$, $\gamma$, $\delta$ which we describe. The sequences involved can be

Wei Zhou, Ruizeng Zhang, Leida Li, Hantao Liu

Image dehazing aims to restore spatial details from hazy images. There have emerged a number of image dehazing algorithms, designed to increase the visibility of those hazy images. However, much less work has been focused on evaluating the visual quality of dehazed images. In this paper, we propose a Reduced-Reference dehazed image quality evaluation approac

Ahmad Peyvan, Khemraj Shukla, Jesse Chan, George Karniadakis

We compare high-order methods including spectral difference (SD), flux reconstruction (FR), the entropy-stable discontinuous Galerkin spectral element method (ES-DGSEM), modal discontinuous Galerkin methods, and WENO to select the best candidate to simulate strong shock waves characteristic of hypersonic flows. We consider several benchmarks, including the L

Jaehyeok Bae, Jae-Han Lee, Seyun Kim

Because anomalous samples cannot be used for training, many anomaly detection and localization methods use pre-trained networks and non-parametric modeling to estimate encoded feature distribution. However, these methods neglect the impact of position and neighborhood information on the distribution of normal features. To overcome this, we propose a new algo

Ben Adcock, Simone Brugiapaglia, Nick Dexter, Sebastian Moraga

The past decade has seen increasing interest in applying Deep Learning (DL) to Computational Science and Engineering (CSE). Driven by impressive results in applications such as computer vision, Uncertainty Quantification (UQ), genetics, simulations and image processing, DL is increasingly supplanting classical algorithms, and seems poised to revolutionize sc

Vinay Kothapally, John H. L. Hansen

Several speech processing systems have demonstrated considerable performance improvements when deep complex neural networks (DCNN) are coupled with self-attention (SA) networks. However, the majority of DCNN-based studies on speech dereverberation that employ self-attention do not explicitly account for the inter-dependencies between real and imaginary featu

Yunzhe Zhou, Peiru Xu, Giles Hooker

Model distillation has been a popular method for producing interpretable machine learning. It uses an interpretable "student" model to mimic the predictions made by the black box "teacher" model. However, when the student model is sensitive to the variability of the data sets used for training even when keeping the teacher fixed, the corresponded interpretat

Jawad Ettayb

Let $E$ be an ultrametric Banach space over $\mathbb{C}_{p}.$ In this note, we obtain a new necessary and sufficient conditions on the resolvent of $A$ such that for each $m\in\mathbb{N},\;\|A^{m}\|\leq1.$

Tao Gu, Robin Piedeleu, Fabio Zanasi

We propose a calculus of string diagrams to reason about satisfiability of Boolean formulas, and prove it to be sound and complete. We then showcase our calculus in a few case studies. First, we consider SAT-solving. Second, we consider Horn clauses, which leads us to a new decision method for propositional logic programs equivalence under Herbrand model sem

Peiyuan Fang, Weiqi Zhang, Lu Xiong, Nan Li

This paper introduces the Generalized Action Governor (AG), a supervisory scheme that augments a nominal closed-loop system with the capability to enforce state and input constraints through online action adjustment. We develop a generalized AG theory for discrete-time systems under bounded uncertainties, and relax the usual requirement of positive invarianc

Fatemeh Nouri, Robert Bergevin

It is crucial to choose actions from an appropriate distribution while learning a sequential decision-making process in which a set of actions is expected given the states and previous reward. Yet, if there are more than two latent variables and every two variables have a covariance value, learning a known prior from data becomes challenging. Because when th

David Blaschke, Alexander Ayriyan, Mateusz Cierniak, Ana Gabriela Grunfeld

We present a systematic investigation of the possible locations for the special point (SP), a unique feature of hybrid neutron stars in the mass-radius diagram. The study is performed within the two-phase approach where the high-density (quark matter) phase is described by the covariant nonlocal Nambu--Jona-Lasinio (nlNJL) model equation of state (EOS) which

Xiaoyan Wu, Tammo van der Heide, Thomas Frauenheim, Sergei Tretiak

Heterogeneous catalysis of adsorbates on metallic surfaces mediated by plasmon has potential high photoelectric conversion efficiency and controllable reaction selectivity. Theoretical modeling of dynamical reaction processes provides in-depth analyses complementing experimental investigations. Especially for plasmon-mediated chemical transformations, light

Zifan Wang, Nan Ding, Tomer Levinboim, Xi Chen

Recent research in robust optimization has shown an overfitting-like phenomenon in which models trained against adversarial attacks exhibit higher robustness on the training set compared to the test set. Although previous work provided theoretical explanations for this phenomenon using a robust PAC-Bayesian bound over the adversarial test error, related algo

Vinay Kothapally, J. H. L. Hansen

With the advancements in deep learning approaches, the performance of speech enhancing systems in the presence of background noise have shown significant improvements. However, improving the system's robustness against reverberation is still a work in progress, as reverberation tends to cause loss of formant structure due to smearing effects in time and freq

Hao He, William R. F. Dent, Christine Wilson

The ALMA observatory is now putting more focus on high-frequency observations (frequencies from 275-950 GHz). However, high-frequency observations often suffer from rapid variations in atmospheric opacity that directly affect the system temperature $T_{sys}$. Current observations perform discrete atmospheric calibrations (Atm-cals) every few minutes, with ty

Sihao Zhao, Xiaowei Cui, Mingquan Lu

A Global Navigation Satellite System (GNSS) receiver usually needs a sufficient number of full pseudorange measurements to obtain a position solution. However, it is time-consuming to acquire full pseudorange information from only the satellite broadcast signals due to the navigation data features of GNSS. In order to realize fast positioning during a cold o

Harit Vishwakarma, Heguang Lin, Frederic Sala, Ramya Korlakai Vinayak

Creating large-scale high-quality labeled datasets is a major bottleneck in supervised machine learning workflows. Threshold-based auto-labeling (TBAL), where validation data obtained from humans is used to find a confidence threshold above which the data is machine-labeled, reduces reliance on manual annotation. TBAL is emerging as a widely-used solution in

Ebba Mark, Ryan Rafaty, Moritz Schwarz

The United States, much like other countries around the world, faces significant obstacles to achieving a rapid decarbonization of its economy. Crucially, decarbonization disproportionately affects the communities that have been historically, politically, and socially embedded in the nation's fossil fuel production. However, this effect has rarely been quant

I. Manthos, S. Aune, J. Bortfeldt, F. Brunbauer

Timing information in current and future accelerator facilities is important for resolving objects (particle tracks, showers, etc.) in extreme large particles multiplicities on the detection systems. The PICOSEC Micromegas detector has demonstrated the ability to time 150\,GeV muons with a sub-25\,ps precision. Driven by detailed simulation studies and a phe

Elisabetta Nocerino, Irene San Lorenzo, Konstantinos Papadopulos, Marisa Medarde

The quasi-2D Pt-based rare earth intermetallic material LaPt$_2$Si$_2$ has attracted attention as it exhibits strong interplay between charge density wave (CDW) and and superconductivity (SC). However, the most of the results reported on this material come from theoretical calculations, preliminary bulk investigations and powder samples, which makes it diffi

Saheed Bolarinwa

This report highlights our work on improving GPU parallelization by supporting compute nodes with multiple GPUs. However, since the default support for multi-GPUs in OpenACC is limited[6], the current implementation allows each MPI process to access only a single GPU. Thus, the only way to take full advantage of multi-GPU nodes in the current version is to l

Weiyan Shi, Emily Dinan, Adi Renduchintala, Daniel Fried

Existing approaches built separate classifiers to detect nonsense in dialogues. In this paper, we show that without external classifiers, dialogue models can detect errors in their own messages introspectively, by calculating the likelihood of replies that are indicative of poor messages. For example, if an agent believes its partner is likely to respond "I

Fang Yu, Mikhail Tikhonov

Understanding computational capabilities of simple biological circuits, such as the regulatory circuits of single-cell organisms, remains an active area of research. Recent theoretical work has shown that a simple regulatory architecture based on end-product inhibition can exhibit predictive behavior by learning fluctuation statistics of one or two environme

Xiangyu Jin, Jinyi Yang, Xiaohui Fan, Feige Wang

Cosmic reionization was the last major phase transition of hydrogen from neutral to highly ionized in the intergalactic medium (IGM). Current observations show that the IGM is significantly neutral at $z>7$, and largely ionized by $z\sim5.5$. However, most methods to measure the IGM neutral fraction are highly model-dependent, and are limited to when the vol

Changxiao Cai, T. Tony Cai, Hongzhe Li

Motivated by a range of applications, we study in this paper the problem of transfer learning for nonparametric contextual multi-armed bandits under the covariate shift model, where we have data collected on source bandits before the start of the target bandit learning. The minimax rate of convergence for the cumulative regret is established and a novel tran

Mark Van Raamsdonk

Time-dependent scalar fields provide a candidate explanation for the dark energy. For these to vary on cosmological time scales, the derivative of the scalar potential in Planck units should have roughly the same magnitude as the potential itself. We emphasize that scalars with this property are present in any four-dimensional gravitational effective theory

Bryan Lim, Manon Flageat, Antoine Cully

Exploration is a key challenge in Reinforcement Learning, especially in long-horizon, deceptive and sparse-reward environments. For such applications, population-based approaches have proven effective. Methods such as Quality-Diversity deals with this by encouraging novel solutions and producing a diversity of behaviours. However, these methods are driven by

Yi Sun, Jacob N. Sanders, K. N. Houk

We explored how steric effects influence the rate of hydrogen atom transfer (HAT) reactions between oxyradicals and alkanes. Quantum chemical computations of transition states show that activation barriers and reaction enthalpies are both influenced by bulky substituents on the radical, but less so by substituents on the alkane. The activation barriers corre

V. Kozlov, D. Vovchuk, P. Ginzburg

Modern radar systems are designed to have high Doppler tolerance to detect fast-moving targets. This means range and Doppler estimations are inevitably coupled, opening pathways to concealing objects by imprinting artificial Doppler signatures on the reflected echoes. Proper temporal control of the backscattered phase can cause the investigating radar to est

William Whitehead, Zachary Nelson, Kerem Y. Camsari, Luke Theogarajan

Massively parallel annealing processors may offer superior performance for a wide range of sampling and optimization problems. A key component dictating the size of these processors is the neuron update circuit, ideally implemented using special stochastic nanodevices. We leverage photon statistics using single photon avalanche diodes (SPADs) and temporal fi

Sam Nelson, Migiwa Sakurai

We introduce a new infinite family of enhancements of the biquandle homset invariant called biquandle arrow weights. These invariants assign weights in an abelian group to intersections of arrows in a Gauss diagram representing a classical or virtual knot depending on the biquandle colors associated to the arrows. We provide examples to show that the enhance

Ravi Kashikar, Abduljelili Popoola, Sergey Lisenkov, A. Stroppa

Persistent spin textures are highly desirable for applications in spintronics as they may allow for long carrier spin lifetimes. However, they are also rare as only four point groups can host such textures, and even for these four groups, the emergence of persistent spin textures requires a delicate balance between coupling parameters, which control the stre

Tejas Khot, Nataliya Shapovalova, Silviu Andrei, Walterio Mayol-Cuevas

This work focuses on low bitrate video streaming scenarios (e.g. 50 - 200Kbps) where the video quality is severely compromised. We present a family of novel deep generative models for enhancing perceptual video quality of such streams by performing super-resolution while also removing compression artifacts. Our model, which we call SuperTran, consumes as inp

Robert M. Alaniz, Bin Fu, Timothy Gomez, Elise Grizzell

The popularity of molecular computation has given rise to several models of abstraction, one of the more recent ones being Chemical Reaction Networks (CRNs). These are equivalent to other popular computational models, such as Vector Addition Systems and Petri-Nets, and restricted versions are equivalent to Population Protocols. This paper continues the work

Per Sebastian Skardal

Random walks represent an important tool for probing the structural and dynamical properties of networks and modeling transport and diffusion processes on networks. However, when individuals' movement becomes dictated by more complicated factors, e.g., scenarios that involve complex decision making, the linear paradigm of classical random walks lack the abil

Semyon Dorokhin, Pavel Lysov, Anastasia Aderkina, Vladimir Lyashev

Reconfigurable Intelligent Surface (RIS) is a planar array that can control reflection and thus can implement the concept of partially controllable propagation environment. RIS received a lot of attention from industry and academia, but the majority of the researchers who study RIS-assisted systems use simple Rician model. Though it is suitable for theoretic

C. Peltekis, D. Filippas, G. Dimitrakopoulos, C. Nicopoulos

Convolutional Neural Networks (CNNs) are the state-of-the-art solution for many deep learning applications. For maximum scalability, their computation should combine high performance and energy efficiency. In practice, the convolutions of each CNN layer are mapped to a matrix multiplication that includes all input features and kernels of each layer and is co

Adam E. Rubinstein, Nicole Karnath, Alice C. Quillen, Samuel Federman

We present a two-epoch Hubble Space Telescope (HST) study of NGC 2071 IR highlighting HOPS 361-C, a protostar producing an arced 0.2 parsec-scale jet. Proper motions for the brightest knots decrease from 350 to 100 km/s with increasing distance from the source. The [Fe II] and Pa$\beta$ emission line intensity ratio gives a velocity jump through each knot of

Ben Adcock, Daan Huybrechs, Cécile Piret

The computation of global radial basis function (RBF) approximations requires the solution of a linear system which, depending on the choice of RBF parameters, may be ill-conditioned. We study the stability and accuracy of approximation methods using the Gaussian RBF in all scaling regimes of the associated shape parameter. The approximation is based on disc

Bolarinwa Olayemi Saheed

There are existing standard solvers for tackling discrete optimization problems. However, in practice, it is uncommon to apply them directly to the large input space typical of this class of problems. Rather, the input is preprocessed to look for simplifications and to extract the core subset of the problem space, which is called the Kernel. This pre-process

Joseph Walker, Joseph Aylett-Bullock, Difu Shi, Allen Gidraf Kahindo Maina

Contact matrices are an important ingredient in age-structured epidemic models to inform the simulated spread of the disease between sub-groups of the population. These matrices are generally derived using resource-intensive diary-based surveys and few exist in the Global South or tailored to vulnerable populations. In particular, no contact matrices exist f

Kuang Bai, Jane J. Ye

The directional subdifferential of the value function gives an estimate on how much the optimal value changes under a perturbation in a certain direction. In this paper we derive upper estimates for the directional limiting and singular subdifferential of the value function for a very general parametric optimization problem. We obtain a characterization for

Ravikiran Attota

With the rapid growth in the semiconductor industry, it is becoming critical to detect and classify increasingly smaller patterned defects. Recently machine learning, including deep learning, has come to aid in this endeavor in a big way. However, the literature shows that it is challenging to successfully classify defect types at the 6 nm node with 100% acc

Kang Wang, Subhashis Ghosal

We consider the nonparametric regression problem with multiple predictors and an additive error, where the regression function is assumed to be coordinatewise nondecreasing. We propose a Bayesian approach to make an inference on the multivariate monotone regression function, obtain the posterior contraction rate, and construct a universally consistent Bayesi

A. C. Aguilar, F. De Soto, M. N. Ferreira, J. Papavassiliou

Continuum and lattice analyses have revealed the existence of a mass-scale in the gluon two-point Schwinger function. It has long been conjectured that this expresses the action of a Schwinger mechanism for gauge boson mass generation in quantum chromodynamics (QCD). For such to be true, it is necessary and sufficient that a dynamically-generated, massless,

Orlando Oliveira, Lee C. Loveridge, Paulo J. Silva

The lattice Landau gauge photon propagator for the pure gauge theory is revisited using large lattices. For the confined case we show that it has an associated linearly growing potential, it has a mass gap, that is related to the presence of monopoles, and its spectral function violates positivity. In the deconfined phase, our simulations suggest that a free

Sheehan Olver

We introduce an algorithm to decompose orthogonal matrix representations of the symmetric group over the reals into irreducible representations, which as a by-product also computes the multiplicities of the irreducible representations. The algorithm applied to a $d$-dimensional representation of $S_n$ is shown to have a complexity of $O(n^2 d^3)$ operations

Andrey Chetverikov, Árni Kristjánsson

Current theories of perception suggest that the brain represents features of the world as probability distributions, but can such uncertain foundations provide the basis for everyday vision? Perceiving objects and scenes requires knowing not just how features (e.g., colors) are distributed but also where they are and which other features they are combined wi

Vinay Kothapally, Yong Xu, Meng Yu, Shi-Xiong Zhang

While current deep learning (DL)-based beamforming techniques have been proved effective in speech separation, they are often designed to process narrow-band (NB) frequencies independently which results in higher computational costs and inference times, making them unsuitable for real-world use. In this paper, we propose DL-based mel-subband spatio-temporal

Robert M. Alaniz, David Caballero, Sonya C. Cirlos, Timothy Gomez

Tile Automata is a recently defined model of self-assembly that borrows many concepts from cellular automata to create active self-assembling systems where changes may be occurring within an assembly without requiring attachment. This model has been shown to be powerful, but many fundamental questions have yet to be explored. Here, we study the state complex

Wenhu Chen, Xueguang Ma, Xinyi Wang, William W. Cohen

Recently, there has been significant progress in teaching language models to perform step-by-step reasoning to solve complex numerical reasoning tasks. Chain-of-thoughts prompting (CoT) is by far the state-of-art method for these tasks. CoT uses language models to perform both reasoning and computation in the multi-step `thought' process. To disentangle comp

Ali Kaan Kurbanzade, Julia Gaudio

In typical applications of facility location problems, the location of demand is assumed to be an input to the problem. The demand may be fixed or dynamic, but ultimately outside the optimizers control. In contrast, there are settings, especially in humanitarian contexts, in which the optimizer decides where to locate a demand node. In this work, we introduc

Sabah Al-Fedaghi

This paper is an exploration of the ontological foundations of conceptual modeling that addresses the concept of events and related notions. Development models that convey how things change over space and time demand continued attention in systems and software engineering. In this context, foundational matters in modeling systems include the definition of an

Tamás P. Papp, Chris Sherlock

There has been a recent surge of interest in coupling methods for Markov chain Monte Carlo algorithms: they facilitate convergence quantification and unbiased estimation, while exploiting embarrassingly parallel computing capabilities. Motivated by these, we consider the design and analysis of couplings of the random walk Metropolis algorithm which scale wel

Vasileios Sitokonstantinou

Big streams of Earth images from satellites or other platforms (e.g., drones and mobile phones) are becoming increasingly available at low or no cost and with enhanced spatial and temporal resolution. This thesis recognizes the unprecedented opportunities offered by the high quality and open access Earth observation data of our times and introduces novel mac

Emi Aoki, Arkajyoti Sinha, Charles Thompson, Kavitha Chandra

The design of data-driven dashboards that inform municipalities on ongoing changes in infections within their community is addressed in this research. Daily reports of Covid-19 infections published by the state of Wisconsin as the initial surge in the pandemic ensued during the October 2020 to September 2021 time frame is considered as a case study. Of parti

Iliyas Noman, Yuan Yao

A set of points $S$ in $d$-dimensional Euclidean space $\mathbb{R}^d$ is called a 2-distance set if the set of pairwise distances between the points has cardinality two. The 2-distance set is called spherical if its points lie on the unit sphere in $\mathbb{R}^{d}$. We characterize the spherical 2-distance sets using the spectrum of the adjacency matrix of a

Chris Cameron, Jason Hartford, Taylor Lundy, Tuan Truong

We introduce Monte Carlo Forest Search (MCFS), a class of reinforcement learning (RL) algorithms for learning policies in {tree MDPs}, for which policy execution involves traversing an exponential-sized tree. Examples of such problems include proving unsatisfiability of a SAT formula; counting the number of solutions of a satisfiable SAT formula; and finding

Samuel Duffield, Sumeetpal S. Singh

Sequential Monte Carlo samplers represent a compelling approach to posterior inference in Bayesian models, due to being parallelisable and providing an unbiased estimate of the posterior normalising constant. In this work, we significantly accelerate sequential Monte Carlo samplers by adopting the L-BFGS Hessian approximation which represents the state-of-th

Taras Banakh, Yaryna Stelmakh

A topological space $X$ is $strongly$ $rigid$ if each non-constant continuous map $f:X\to X$ is the identity map of $X$. A Hausdorff topological space $X$ is called $Brown$ if for any nonempty open sets $U,V\subseteq X$ the intersection $\bar U\cap\bar V$ is infinite. We prove that every second-countable Brown Hausdorff space $X$ admits a stronger topology $

Bhargav Ganguly, Vaneet Aggarwal

Federated Learning (FL) is an emerging domain in the broader context of artificial intelligence research. Methodologies pertaining to FL assume distributed model training, consisting of a collection of clients and a server, with the main goal of achieving optimal global model with restrictions on data sharing due to privacy concerns. It is worth highlighting

Kishan KC, Rui Li, Paribesh Regmi, Anne R. Haake

Heterogeneous molecular entities and their interactions, commonly depicted as a network, are crucial for advancing our systems-level understanding of biology. With recent advancements in high-throughput data generation and a significant improvement in computational power, graph neural networks (GNNs) have demonstrated their effectiveness in predicting biomed

Daniel Sofer, Gilad Yossifon, Touvia Miloh

The electro-rotation (EROT) and electro-orientation (EOR) behavior of metallodielectric (MD) spherical Janus particles (JP) are studied analytically and verified experimentally. This stands in contrast to previous either heuristic or numerically computed models of JP dipoles. First, we obtain frequency-dependent analytic expressions for the corresponding dip

Wael Fatnassi, Haitham Khedr, Valen Yamamoto, Yasser Shoukry

In this paper, we present BERN-NN as an efficient tool to perform bound propagation of Neural Networks (NNs). Bound propagation is a critical step in wide range of NN model checkers and reachability analysis tools. Given a bounded input set, bound propagation algorithms aim to compute tight bounds on the output of the NN. So far, linear and convex optimizati

Thomas Donlon, Heidi Jo Newberg

We find that the chemical abundances and dynamics of APOGEE and GALAH stars in the local stellar halo are inconsistent with a scenario in which the inner halo is primarily composed of debris from a single, massive, ancient merger event, as has been proposed to explain the Gaia-Enceladus/Gaia Sausage (GSE) structure. The data contains trends of chemical compo