January 2022 arXiv papers — page 37

A. A. Aligia

The system of an iron phthalocyanine molecule on the Au(111) surface, has been studied recently due to its peculiar properties. In particular, several surprising results of scanning tunneling spectroscopy changing the position of the molecule and applying magnetic field can be explained by the {\it non-Landau} Fermi liquid state of a 2-channel spin-1 Kondo m

Elsa Abreu, Matteo Savoini, Larissa Boie, Paul Beaud

We use time-resolved hard x-ray diffraction to investigate the structural dynamics of the multiferroic insulator TbMnO$_3$ in the low temperature antiferromagnetic and ferroelectrically ordered phase. The lattice response following photoexcitation at 1.55 eV is detected by measuring the (0 2 4) and (1 3 -5) Bragg reflections. A 0.02% tensile strain, normal t

Andreia F Silva, Tiffany A Wood, Daniel J M Hodgson, John R Royer

The handleability and sensory perception of hand sanitisers by consumers affect the hygiene outcome. Spillage may result in under-dosing and poor sensory properties can lead to under-utilisation. We first propose four principles (low run off, spreadability, smoothness and non-stickiness) for designing the rheology of thickened alcohol-based hand rubs with ac

V. Ibarra-Junquera, D. Radillo-Ochoa, C. A. Terrero-Escalante

A theoretical study of cell evolution is presented here. By using a toolbox containing an intracellular catalytic reaction network model and a mutation-selection process, four distinct phases of self-organization were unveiled. First, the nutrients prevail as the central substrate of the chemical reactions. Second, the cell becomes a small-world. Third, a hi

Diego H. Useche, Oscar A. Bustos-Brinez, Joseph A. Gallego-Mejia, Fabio A. González

Density estimation is a central task in statistics and machine learning. This problem aims to determine the underlying probability density function that best aligns with an observed data set. Some of its applications include statistical inference, unsupervised learning, and anomaly detection. Despite its relevance, few works have explored the application of

Arvind Neelakantan, Tao Xu, Raul Puri, Alec Radford

Text embeddings are useful features in many applications such as semantic search and computing text similarity. Previous work typically trains models customized for different use cases, varying in dataset choice, training objective and model architecture. In this work, we show that contrastive pre-training on unsupervised data at scale leads to high quality

Samya Sen, Randy H. Ewoldt

There is no universal model for thixotropy, and comparing thixotropic effects between different fluids is a subtle yet challenging problem. We introduce a generalized (model-insensitive) framework for comparing thixotropic properties based on thixotropic spectra. A superposition of exponential stress modes distributed over thixotropic timescales is used to q

Ye Gao, Brian Baucom, Karen Rose, Kristina Gordon

In smart computing, the labels of training samples for a specific task are not always abundant. However, the labels of samples in a relevant but different dataset are available. As a result, researchers have relied on unsupervised domain adaptation to leverage the labels in a dataset (the source domain) to perform better classification in a different, unlabe

Zengyi Li, Yubei Chen, Yann LeCun, Friedrich T. Sommer

Given a union of non-linear manifolds, non-linear subspace clustering or manifold clustering aims to cluster data points based on manifold structures and also learn to parameterize each manifold as a linear subspace in a feature space. Deep neural networks have the potential to achieve this goal under highly non-linear settings given their large capacity and

Jonathan N. Blakely, Shawn D. Pethel

Spoofing an electromagnetic signal involves measuring its properties and preparing a spoof signal that is a close enough copy to fool a receiver. A classic application of spoofing is in radar where an airborne target attempts to avoid being tracked by a ground-based radar by emitting pulses indicating a false range or velocity. In certain scenarios it has be

Hans Wenzl

Let $V=\C^N$ with $N$ odd. We construct a $q$-deformation of $\End_{Sp(N-1)}(V^{\otimes n})$ which contains $\End_{U_q\sl_N}(V^{\otimes n})$. It is a quotient of an abstract two-variable algebra which is defined by adding one more generator to the generators of the Hecke algebras $H_n$. These results suggest the existence of module categories of $Rep(U_q\sl_

Timofey Atnashev, Veronika Ganeeva, Roman Kazakov, Daria Matyash

The vast majority of existing datasets for Named Entity Recognition (NER) are built primarily on news, research papers and Wikipedia with a few exceptions, created from historical and literary texts. What is more, English is the main source for data for further labelling. This paper aims to fill in multiple gaps by creating a novel dataset "Razmecheno", gath

Cash Costello, Eugene Yang, Dawn Lawrie, James Mayfield

While there are high-quality software frameworks for information retrieval experimentation, they do not explicitly support cross-language information retrieval (CLIR). To fill this gap, we have created Patapsco, a Python CLIR framework. This framework specifically addresses the complexity that comes with running experiments in multiple languages. Patapsco is

Hajime Fukushima, Hidenobu Yajima

We study the formation of star clusters in molecular clouds by performing three-dimensional radiation hydrodynamics simulations with far ultraviolet (FUV; $6 ~{\rm eV} \leqq h \nu \leqq 13.6 ~{\rm eV}$) and extreme ultraviolet (EUV; $h\nu \geqq 13.6~{\rm eV}$) radiative feedback. We find that the FUV feedback significantly suppresses the star formation in di

W. A. da Silva, S. H. Hassanzadeh, A. Simis

The main goal of this paper is to size up the minimal graded free resolution of a homogeneous ideal in terms of its generating degrees. By and large, this is too ambitious an objective. As understood, sizing up means looking closely at the two available parameters: the shifts and the Betti numbers. Since, in general, bounds for the shifts can behave quite st

Ivan P. Costa e Silva, José L. Flores, Kledilson P. R. Honorato

Conditions for the existence of closed geodesics is a classic, much-studied subject in Riemannian geometry, with many beautiful results and powerful techniques. However, many of the techniques that work so well in that context are far less effective in Lorentzian geometry. In revisiting this problem here, we introduce the notion of timelike geodesic homotopy

Dawn Lawrie, James Mayfield, Douglas Oard, Eugene Yang

HC4 is a new suite of test collections for ad hoc Cross-Language Information Retrieval (CLIR), with Common Crawl News documents in Chinese, Persian, and Russian, topics in English and in the document languages, and graded relevance judgments. New test collections are needed because existing CLIR test collections built using pooling of traditional CLIR runs h

Hussin Albahboh, Harry Gingold, Jocelyn Quaintance

Given a postulated set of points, an algebraic system of axioms is proposed for an "arrow space'". An arrow is defined to be an ordered set of two points <T, H>, named respectively Tail and Head. The set of arrows is an arrow space. The arrow space is axiomatically endowed with an arrow space "pre-inner product" which is analogous to the inner product of a E

Piyush Gupta, Vaibhav Srivastava

We study optimal fidelity selection for a human operator servicing a queue of homogeneous tasks. The agent can service a task with a normal or high fidelity level, where fidelity refers to the degree of exactness and precision while servicing the task. Therefore, high-fidelity servicing results in higher-quality service but leads to larger service times and

Suchuan Dong, Jielin Yang

We present a method for solving linear and nonlinear PDEs based on the variable projection (VarPro) framework and artificial neural networks (ANN). For linear PDEs, enforcing the boundary/initial value problem on the collocation points leads to a separable nonlinear least squares problem about the network coefficients. We reformulate this problem by the VarP

A. Mathews, J. L. Terry, S. G. Baek, J. W. Hughes

The role of turbulence in setting boundary plasma conditions is presently a key uncertainty in projecting to fusion energy reactors. To robustly diagnose edge turbulence, we develop and demonstrate a technique to translate brightness measurements of HeI line radiation into local plasma fluctuations via a novel integrated deep learning framework that combines

A. V. Boltachev, A. Yu. Savin

We construct a periodic cyclic cocycle on the symbol algebra of Boutet de Monvel operators and use it to interpret the index formula for elliptic pseudodifferential boundary value problems due to Fedosov as the Chern--Connes pairing of the classes in $K$-theory of elliptic symbols with this cyclic cocycle. We also consider the equivariant case. Namely, we co

Viet Hung Hoang

We construct harmonic functions in the quarter plane for discrete Laplace operators. In particular, the functions are conditioned to vanish on the boundary and the Laplacians admit coefficients associated with transition probabilities of non-symmetric random walks. By solving a boundary value problem for generating functions of harmonic functions, we deduce

Marco Cavaglia

We present a new method, based on fractal analysis, to characterize the output of a physical detector that is in the form of a set of real-valued, discrete physical measurements. We apply the method to gravitational-wave data from the latest observing run of the Laser Interferometer Gravitational-wave Observatory. We show that a measure of the fractal dimens

Asaf Farhi, Aristide Dogariu

Both spontaneous emission and resonant energy transfer can be enhanced significantly when the emitter is placed in the vicinity of metallic or crystal structures. This enhancement can be described using the electromagnetic Green tensor and is determined by the dominant surface modes of the structure. Here we use the eigenpermittivity formalism to derive the

Alessandro Perotti

We prove some formulas relating Cauchy-Riemann operators defined on hypercomplex subspaces of an alternative *-algebra to a differential operator associated with the concept of slice-regularity and to the spherical Dirac operator. These results in particular allow to introduce a definition of locally slice-regular function and open the path for local slice a

Aftab Ahmad, Ali Murad

We study the dynamical chiral symmetry breaking/restoration for the various numbers of light quarks flavors $N_f$ and colors $N_c$, using the Nambu-Jona-Lasinio (NJL) model of quarks, dressed with a color-flavor dependence of effective coupling. Initially, we set $N_f = 2$, and varying the number of colors $N_c$, we find that the dynamical chiral symmetry is

Liang Lu, Jinyu Li, Yifan Gong

Streaming end-to-end multi-talker speech recognition aims at transcribing the overlapped speech from conversations or meetings with an all-neural model in a streaming fashion, which is fundamentally different from a modular-based approach that usually cascades the speech separation and the speech recognition models trained independently. Previously, we propo

Aleksandr Shkoruta, Bumsoo Park, Sandipan Mishra

While considerable progress has recently been made in real-time melt pool monitoring for laser powder bed fusion (LPBF), results in in-situ melt pool control are relatively sparse, a major reason being lack of suitable control-oriented models. This study demonstrates an empirical control-oriented model of geometry-dependent melt pool behavior, and subsequent

Gabriel M. Nascimento, Elton Ogoshi, Adalberto Fazzio, Carlos Mera Acosta

The development of spintronic devices demands the existence of materials with some kind of spin splitting (SS). In this Data Descriptor, we build a database of ab initio calculated SS in 2D materials. More than that, we propose a workflow for materials design integrating an inverse design approach and a Bayesian inference optimization. We use the prediction

Milad Asgari Mehrabadi, Seyed Amir Hossein Aqajari, Amir Hosein Afandizadeh Zargari, Nikil Dutt

Continuous monitoring of blood pressure (BP)can help individuals manage their chronic diseases such as hypertension, requiring non-invasive measurement methods in free-living conditions. Recent approaches fuse Photoplethysmograph (PPG) and electrocardiographic (ECG) signals using different machine and deep learning approaches to non-invasively estimate BP; h

Jihong Zhu, Michael Gienger, Jens Kober

Moving away from repetitive tasks, robots nowadays demand versatile skills that adapt to different situations. Task-parameterized learning improves the generalization of motion policies by encoding relevant contextual information in the task parameters, hence enabling flexible task executions. However, training such a policy often requires collecting multipl

Zachary Eberhart, Collin McMillan

In source code search, a common information-seeking strategy involves providing a short initial query with a broad meaning, and then iteratively refining the query using terms gleaned from the results of subsequent searches. This strategy requires programmers to spend time reading search results that are irrelevant to their development needs. In contrast, wh

Ruyi Qu, Shukai Huang, Jiexuan Zhou, ChenXi Fan

At present, a major challenge for the application of automatic driving technology is the accurate prediction of vehicle trajectory. With the vigorous development of computer technology and the emergence of convolution depth neural network, the accuracy of prediction results has been improved. But, the depth, width of the network and image resolution are stil

Ruyi Qu, Yi Yang, Yuwei Wang

Computer aided diagnosis (CAD) increases diagnosis efficiency, helping doctors providing a quick and confident diagnosis, it has played an important role in the treatment of COVID19. In our task, we solve the problem about abnormality detection and classification. The dataset provided by Kaggle platform and we choose YOLOv5 as our model. We introduce some me

Juan Felipe Carmona, Alf Onshuus

We introduce the notions of definable amenability and extreme definable amenability for groups in continuous structures and conduct an extensive analysis of them, drawing parallels with the classical first-order case. We characterize both notions using fixed-point properties. We show that stable and ultracompact groups are definably amenable and prove that,

Yijia Ma, Xinyu Zhou, Wei Wu

Time warping function provides a mathematical representation to measure phase variability in functional data. Recent studies have developed various approaches to estimate optimal warping between functions and provide non-Euclidean models. However, a principled, linear, generative model on time warping functions is still under-explored. This is a highly chall

Mikołaj Bojańczyk, Bartek Klin, Julian Salamanca

One of the main reasons for the correspondence of regular languages and monadic second-order logic is that the class of regular languages is closed under images of surjective letter-to-letter homomorphisms. This closure property holds for structures such as finite words, finite trees, infinite words, infinite trees, elements of the free group, etc. Such stru

Corinne Stucker, Bingxin Ke, Yuanwen Yue, Shengyu Huang

High-resolution optical satellite sensors, combined with dense stereo algorithms, have made it possible to reconstruct 3D city models from space. However, these models are, in practice, rather noisy and tend to miss small geometric features that are clearly visible in the images. We argue that one reason for the limited quality may be a too early, heuristic

Zilong Zhao, Jiyue Huang, Stefanie Roos, Lydia Y. Chen

Generative Adversarial Networks (GANs) are increasingly adopted by the industry to synthesize realistic images. Due to data not being centrally available, Multi-Discriminator (MD)-GANs training framework employs multiple discriminators that have direct access to the real data. Distributedly training a joint GAN model entails the risk of free-riders, i.e., pa

Ke Yahan, Ruyi Qu, Lu Xiaoxia

Over the last few years, Text classification is one of the fundamental tasks in natural language processing (NLP) in which the objective is to categorize text documents into one of the predefined classes. The news is full of our life. Therefore, news headlines classification is a crucial task to connect users with the right news. The news headline classifica

Pedro Valdeira, Cláudia Soares, João Xavier

Expectation Maximization (EM) is the standard method to learn Gaussian mixtures. Yet its classic, centralized form is often infeasible, due to privacy concerns and computational and communication bottlenecks. Prior work dealt with data distributed by examples, horizontal partitioning, but we lack a counterpart for data scattered by features, an increasingly

B. Miao, L. Feder, J. E. Shrock, H. M. Milchberg

Bessel beams generated with non-ideal axicons are affected by aberrations. We introduce a method to retrieve the complex amplitude of a Bessel beam from intensity measurements alone, and then use this information to correct the wavefront and intensity profile using a deformable mirror.

Caleb I. Cañas, Shubham Kanodia, Chad F. Bender, Suvrath Mahadevan

We confirm the planetary nature of two gas giants discovered by TESS to transit M dwarfs. TOI-3714 ($V=15.24,~J=11.74$) is an M2 dwarf hosting a hot Jupiter ($M_p=0.70 \pm 0.03~\mathrm{M_J}$ and $R_p=1.01 \pm 0.03~\mathrm{R_J}$) on an orbital period of $2.154849 \pm 0.000001$ days with a resolved white dwarf companion. TOI-3629 ($V=14.63,~J=11.42$) is an M1

Sebastian Schlegel Mejia

We give a formula comparing the E-series of the moduli stacks of rank 2 degree 0 semistable Higgs bundles in genus $g \geq 2$ to intersection E-polynomials of its coarse moduli space. A parellel formula holds in various 2-Calabi-Yau settings, for example for sheaves on K3 surfaces, or preprojective algebras of $g$-loop quivers. As a consequence we provide ev

F. C. Leary

If $R$ is a ring with 1, we call a unital left $R$-module $M$ co-Hopfian (Hopfian) in the category of left $R$-modules if any monic (epic) endomorphism of $M$ is an automorphism. For commutative Noetherian $R$ we use results of Matlis to show that in a certain context every submodule of a co-Hopfian injective module is co-Hopfian. For these same $R,$ we char

Hirokuni Miyamoto, Futo Asano, Koutarou Ishizawa, Wataru Suda

Effective biological utilization of wood biomass is necessary worldwide. Since several insect larvae can use wood biomass as a nutrient source, studies on their digestive mechanism are expected to speculate a novel rule in wood biomass processing. Here, the relationships of inhabitant bacteria involved in carbon and nitrogen metabolism in the intestine of be

Blair W. Lebert, Subin Kim, Danil A. Prishchenko, Alexander A. Tsirlin

Acoustic phonons have recently been posited as playing an integral role in explaining the half-quantized thermal Hall effect in $\alpha$-RuCl$_3$. Therefore, we present much needed inelastic x-ray scattering measurements of its acoustic phonon dispersion, along with calculations using the frozen-phonon method. We also discuss a temperature study which conclu

Fausto Arpino, Giorgio Grossi, Gino Cortellessa, Alex Mikszewski

In this paper the risk of infection from SARS-CoV-2 Delta variant of passengers sharing a car cabin with an infected subject for a 30-min journey is estimated through an integrated approach combining a recently developed predictive emission-to-risk approach and a validated CFD numerical model numerically solved using the open-source OpenFOAM software. Differ

Tso-Kang Wang, Kourosh Shoele

The flow-induced flutter has a significant role in aircraft stability, renewable energy extraction, animal locomotion, among many other applications. While being a ubiquitous phenomenon, the control of the flutter response has been primarily limited to simplified systems and, often, with the help of linear inviscid flow theories. In this paper, we numericall

Tomer Laor, Naif Mehanna, Antonin Durey, Vitaly Dyadyuk

Browser fingerprinting aims to identify users or their devices, through scripts that execute in the users' browser and collect information on software or hardware characteristics. It is used to track users or as an additional means of identification to improve security. In this paper, we report on a new technique that can significantly extend the tracking ti

Utkarsh Gupta, Hessam Mahdavifar

We consider the problem of binary string reconstruction from the multiset of its substring compositions, i.e., referred to as the substring composition multiset, first introduced and studied by Acharya et al. We introduce a new algorithm for the problem of string reconstruction from its substring composition multiset which relies on the algebraic properties

Hassan Anifowose, Wei Yan, Manish Dixit

Architectural Education faces limitations due to its tactile approach to learning in classrooms with only 2-D and 3-D tools. At a higher level, virtual reality provides a potential for delivering more information to individuals undergoing design learning. This paper investigates a hypothesis establishing grounds towards a new research in Building Information

Tatiana Castro Vélez, Raffi Khatchadourian, Mehdi Bagherzadeh, Anita Raja

Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code that supports symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development tends to produce DL code that is error-prone, non-intu

Aditya Saxena, Shamsheer Pal Singh

The COVID-19 (coronavirus) is an ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus was first identified in mid-December 2019 in the Hubei province of Wuhan, China and by now has spread throughout the planet with more than 75.5 million confirmed cases and more than 1.67 million deaths. With limited number of CO

Michele Invernizzi, Michele Parrinello

In adaptive-bias enhanced sampling methods, a bias potential is added to the system to drive transitions between metastable states. The bias potential is a function of a few collective variables and is gradually modified according to the underlying free energy surface. We show that when the collective variables are suboptimal, there is an exploration-converg

Fiona Kirk

The Cabibbo Angle Anomaly, an apparent deficit in first-row CKM unitarity, can be addressed by leptophilic Standard Model extensions that generate new contributions to the Fermi constant and affect the determination of the CKM element $V_{ud}$. We focus on simplified models with this property, including the Standard Model extended by vectorlike leptons, by t

Egbert Castro, Abhinav Godavarthi, Julian Rubinfien, Kevin B. Givechian

The development of powerful natural language models have increased the ability to learn meaningful representations of protein sequences. In addition, advances in high-throughput mutagenesis, directed evolution, and next-generation sequencing have allowed for the accumulation of large amounts of labeled fitness data. Leveraging these two trends, we introduce

Marlon Sproesser Mathias, Marcello Augusto Faraco de Medeiros

The Rossiter modes of an open cavity were studied using global linear analysis and nonlinear numerical simulations. The length over depth ratio was two and the Reynolds numbers based on cavity depth were close to 1000. The effect of Mach on such cavities was studied. The global analysis revealed that, in the Mach range 0.1 to 0.9, for thick boundary layers,

Michael Günther, Andreas Brendel, Walter Kellermann

In multichannel signal processing with distributed sensors, choosing the optimal subset of observed sensor signals to be exploited is crucial in order to maximize algorithmic performance and reduce computational load, ideally both at the same time. In the acoustic domain, signal cross-correlation is a natural choice to quantify the usefulness of microphone s

Ke Yue, Junjing Deng, Yi Jiang, Youssef Nashed

X-ray ptychography imaging at synchrotron facilities like the Advanced Photon Source (APS) involves controlling instrument hardwares to collect a set of diffraction patterns from overlapping coherent illumination spots on extended samples, managing data storage, reconstructing ptychographic images from acquired diffraction patterns, and providing the visuali

Marta Sewiło, Martin Cordiner, Steven B. Charnley, Joana M. Oliveira

The Large Magellanic Cloud (LMC) is the nearest laboratory for detailed studies on the formation and survival of complex organic molecules (COMs), including biologically important ones, in low-metallicity environments--typical for earlier cosmological epochs. We report the results of 1.2 mm continuum and molecular line observations of three fields in the sta

Muhammad Rizwanur Rahman, Li Shen, James P. Ewen, Daniele Dini

The evolution of the liquid-vapour interface of a Lennard-Jones fluid is examined with molecular dynamics simulations using the intrinsic sampling method. Results suggest, in agreement with capillary wave theory, clear damping of the density profiles as the temperature is increased. We identify a linear variation of the space-filling nature (fractal dimensio

Oscar Barrera, Imre Bartos

GW190521 was the most massive black hole merger discovered by LIGO/Virgo so far, with masses in tension with stellar evolution models. A possible explanation of such heavy black holes is that they themselves are the remnants of previous mergers of lighter black holes. Here we estimate the masses of the ancestral black holes of GW190521, assuming it is the en

Fujie Tang, Zhenglu Li, Chunyi Zhang, Steven G. Louie

X-ray absorption spectroscopy (XAS) is a powerful experimental technique to probe the local order in materials with core electron excitations. Experimental interpretation requires supporting theoretical calculations. For water, these calculations are very demanding and, to date, could only be done with major approximations that limited the accuracy of the ca

Aakash Tyagi, Addison Crump, Ahmad-Reza Sadeghi, Garrett Persyn

The increasing complexity of modern processors poses many challenges to existing hardware verification tools and methodologies for detecting security-critical bugs. Recent attacks on processors have shown the fatal consequences of uncovering and exploiting hardware vulnerabilities. Fuzzing has emerged as a promising technique for detecting software vulnerabi

Adrian Ebert, Peter Kritzer, Friedrich Pillichshammer

In this paper we consider $L_p$-approximation, $p \in \{2,\infty\}$, of periodic functions from weighted Korobov spaces. In particular, we discuss tractability properties of such problems, which means that we aim to relate the dependence of the information complexity on the error demand $\varepsilon$ and the dimension $d$ to the decay rate of the weight sequ

Meng Wang, Mokhtar Adda-Bedia, John M. Kolinski, Jay Fineberg

Griffith's energetic criterion, or `energy balance', has for a century formed the basis for fracture mechanics; the energy flowing into a crack front is precisely balanced by the dissipation (fracture energy) at the front. If the crack front structure is not properly accounted for, energy balance will either appear to fail or lead to unrealistic results. Her

Marc Josien, Claudia Raithel, Mathias Schäffner

In this contribution we are interested in the quantitative homogenization properties of linear elliptic equations with homogeneous Dirichlet boundary data in polygonal domains with corners. To begin our study of this situation, we consider the setting of an angular sector in 2 dimensions : Unlike in the whole-space, on such a sector there exist non-smooth ha

Rabia Laghmach, Nicolas Candau, Laurent Chazeau, Thierry Biben

In this article, we introduce a framework to investigate the growth of nano-crystallites in a polymer matrix numerically. This framework combines the Flory theory of entropic elasticity with phase-field approaches commonly used to model crystal growth. We investigate in particular the growth kinetics of a crystallite in the presence of topological constraint

Rodrigo Francisquini, Ana Carolina Lorena, Mariá C. V. Nascimento

Several applications have a community structure where the nodes of the same community share similar attributes. Anomaly or outlier detection in networks is a relevant and widely studied research topic with applications in various domains. Despite a significant amount of anomaly detection frameworks, there is a dearth on the literature of methods that conside

David Piorkowski, John Richards, Michael Hind

In reaction to growing concerns about the potential harms of artificial intelligence (AI), societies have begun to demand more transparency about how AI models and systems are created and used. To address these concerns, several efforts have proposed documentation templates containing questions to be answered by model developers. These templates provide a us

Ramakrishna Kakarala, Jun Wei

Although the theory of constrained least squares (CLS) estimation is well known, it is usually applied with the view that the constraints to be imposed are unavoidable. However, there are cases in which constraints are optional. For example, in camera color calibration, one of several possible color processing systems is obtained if a constraint on the row s

Dianxin Luan, John Thompson

Research on machine learning for channel estimation, especially neural network solutions for wireless communications, is attracting significant current interest. This is because conventional methods cannot meet the present demands of the high speed communication. In the paper, we deploy a general residual convolutional neural network to achieve channel estim

Yingying Zhao, Yuhu Chang, Yutian Lu, Yujiang Wang

Emotion recognition in smart eyewear devices is highly valuable but challenging. One key limitation of previous works is that the expression-related information like facial or eye images is considered as the only emotional evidence. However, emotional status is not isolated; it is tightly associated with people's visual perceptions, especially those sentimen

Nathanael Jo, Sina Aghaei, Andrés Gómez, Phebe Vayanos

The increasing use of machine learning in high-stakes domains -- where people's livelihoods are impacted -- creates an urgent need for interpretable, fair, and highly accurate algorithms. With these needs in mind, we propose a mixed integer optimization (MIO) framework for learning optimal classification trees -- one of the most interpretable models -- that

Zheng Li, Rajiv Ranjan

By seamlessly integrating everyday objects and by changing the way we interact with our surroundings, Internet of Things (IoT) is drastically improving the life quality of households and enhancing the productivity of businesses. Given the unique IoT characteristics, IoT applications have emerged distinctively from the mainstream application types. Inspired b

Septimiu Crivei, Derya Keskin Tütüncü, Simona Maria Radu, Rachid Tribak

We investigate relative CS-Baer objects in abelian categories in relationship with other relevant classes of objects such as relative Baer objects, extending objects, objects having certain summand intersection properties and relative CS-Rickart objects. Dual results are automatically obtained by applying the duality principle in abelian categories. We also

Jose Agudelo, Brooke Dippold, Ian Klein, Alex Kokot

We consider practical aspects of reconstructing planar curves with prescribed Euclidean or affine curvatures. These curvatures are invariant under the special Euclidean group and the equi-affine groups, respectively, and play an important role in computer vision and shape analysis. We discuss and implement algorithms for such reconstruction, and give estimat

Luca Bortolussi, Giuseppe Maria Gallo, Jan Křetínský, Laura Nenzi

We introduce a similarity function on formulae of signal temporal logic (STL). It comes in the form of a kernel function, well known in machine learning as a conceptually and computationally efficient tool. The corresponding kernel trick allows us to circumvent the complicated process of feature extraction, i.e. the (typically manual) effort to identify the

Arega Getaneh Abate, Rossana Riccardi, Carlos Ruiz

The interplay between risk aversion and financial derivatives has received increasing attention since the advent of electricity market liberalization. One important challenge in this context is how to develop economically efficient and cost-effective models to integrate renewable energy sources (RES) in the electricity market, which constitutes a relatively

Andrés E. Piatti

We confirm the existence of a second Large Magellanic Cloud (LMC) star cluster, KMHK 1592, with an age that falls in the middle of the so-called LMC star cluster age gap, a long period of time (~ 4 - 11 Gyr) where no star cluster had been uncovered, except ESO121-SC03. The age (8.0+-0.5 Gyr) and the metallicity ([Fe/H]=-1.0+-0.2 dex) of KMHK1592 were derived

Amir Mafi, Dler Naderi, Parasto Soufivand

There are two motivation questions in \cite{MTS, MTS1} about Castelnuovo-Mumford regularity and vertex decomposable of simple graph $G$. In this paper, we disprove the questions by providing of two counterexamples.

Chi H. Mak

Performing full-resolution atomistic simulations of nucleic acid folding has remained a challenge for biomolecular modeling. Understanding how nucleic acids fold and how they transition between different folded structures as they unfold and refold has important implications for biology. This paper reports a theoretical model and computer simulation of the ab

S. Gevorkyan, I. Larin, R. Miskimen, E. Smith

We explore the photoproduction mechanisms for charged and neutral pion pairs off a heavy nucleus at threshold. We calculate the production of charged pairs in the Coulomb field of the nucleus in the Born approximation using explicit expressions for the differential cross sections and their connection with the total cross-section. The production of sigma meso

G. Sharma, Snehashish Nandy, Karthik V. Raman, Sumanta Tewari

A series of recent papers have claimed that intranode scattering, alone, can contribute to positive longitudinal magnetoconductance (LMC) due to chiral anomaly (CA) in Weyl semimetals (WSMs). We revisit the problem of CA induced LMC in WSMs, and show that intranode scattering, by itself, does not result in enhancement of LMC. In the limit of zero internode s

U. Niedermayer, K. Leedle, P. Musumeci, S. A. Schmid

We discuss recent developments and challenges of beam dynamics in Dielectric Laser Acceleration (DLA), for both high and low energy electron beams. Starting from ultra-low emittance nanotip sources the paper follows the beam path of a tentative DLA light source concept. Acceleration in conjuction with focusing is discussed in the framework of Alternating Pha

Alexandr Karpenko, Sergey P. Vyatchanin

An analysis is given for the Fabry-Perot cavity having a combination of dissipative and dispersive optomechanical coupling. It is established that the combined coupling leads to optical rigidity. At the same time, this rigidity appears in systems with the combined coupling on the resonant pump, which is not typical for pure dispersive and dissipative couplin

Bo Xiong, Nico Potyka, Trung-Kien Tran, Mojtaba Nayyeri

Recently, increasing efforts are put into learning continual representations for symbolic knowledge bases (KBs). However, these approaches either only embed the data-level knowledge (ABox) or suffer from inherent limitations when dealing with concept-level knowledge (TBox), i.e., they cannot faithfully model the logical structure present in the KBs. We prese

Ratul Biswas, Wei-Kuo Chen, Arnab Sen

We study a diluted mean-field spin glass model with a quadratic Hamiltonian. Our main result establishes the limiting free energy in terms of an integral of a family of random variables that are the weak limits of the quenched variances of the spins in the system with varying edge connectivity. The key ingredient in our argument is played by the identificati

Ninareh Mehrabi, Cyprien de Lichy, John McKay, Cynthia He

Federated Learning (FL) has emerged as a result of data ownership and privacy concerns to prevent data from being shared between multiple parties included in a training procedure. Although issues, such as privacy, have gained significant attention in this domain, not much attention has been given to satisfying statistical fairness measures in the FL setting.

Rainer Engelken, Alessandro Ingrosso, Ramin Khajeh, Sven Goedeke

Neural circuits exhibit complex activity patterns, both spontaneously and evoked by external stimuli. Information encoding and learning in neural circuits depend on how well time-varying stimuli can control spontaneous network activity. We show that in firing-rate networks in the balanced state, external control of recurrent dynamics, i.e., the suppression o

Anjali Gupta, Smita Mathur, Josh Kingsbury, Sanskriti Das

The X-ray bright bubbles at the Galactic Center provide an opportunity to understand the effects of feedback on galaxy evolution. The shells of the eROSITA bubbles show enhanced X-ray emission over the sky background. Previously, these shells were assumed to have a single temperature component and to trace the shock-heated lower-temperature halo gas. Using S

Per Sebastian Skardal, Can Xu

We study synchronization in large populations of coupled phase oscillators with time-delays, higher order interactions. With each of these effects individually giving rise to bistabiltiy between incoherence and synchronization via a subcriticality at the onset of synchronization and the development of a saddle node, we find that their combination yields anot

Tassadaq Hussain, Wei-Chien Wang, Mandar Gogate, Kia Dashtipour

In acoustic signal processing, the target signals usually carry semantic information, which is encoded in a hierarchal structure of short and long-term contexts. However, the background noise distorts these structures in a nonuniform way. The existing deep acoustic signal enhancement (ASE) architectures ignore this kind of local and global effect. To address

Masaru Hongo, Dam Thanh Son

We construct an effective field theory of a two-neutron halo nucleus in the limit where the two-neutron separation energy $B$ and the neutron-neutron two-body virtual energy $\epsilon_n$ are smaller than any other energy scale in the problem, but the scattering between the core and a single neutron is not fine-tuned, and the Efimov effect does not operate. T

Hossein Mohammadi, Walaa AlQwider, Talha Faizur Rahman, Vuk Marojevic

Research has shown that communications systems and receivers suffer from high power adjacent channel signals, called blockers, that drive the radio frequency (RF) front end into nonlinear operation. Since simple systems, such as the Internet of Things (IoT), will coexist with sophisticated communications transceivers, radars and other spectrum consumers, the

Yiling Jia, Weitong Zhang, Dongruo Zhou, Quanquan Gu

Thanks to the power of representation learning, neural contextual bandit algorithms demonstrate remarkable performance improvement against their classical counterparts. But because their exploration has to be performed in the entire neural network parameter space to obtain nearly optimal regret, the resulting computational cost is prohibitively high. We pert

Jonathan J. Carter, Pascal Birckigt, Oliver Gerberding, Qingfeng Li

High-purity glasses are used for their low optical and mechanical loss, which makes them an excellent material for oscillators in optical systems, such as inertial sensors. Complex geometries often require the assembly of multiple pieces of glass and their permanent bonding. One common method is hydroxide catalysis bonding, which leaves an enclosed medium la

Ivan Kolář

We solve the non-local equation ${-e^{-\ell^2\square}\square{\phi}=J}$ for i) static scalar fields in static spacetimes and ii) time-dependent scalar fields in ultrastatic spacetimes. Corresponding equations are rewritten as non-local Poisson/inhomogeneous Helmholtz equations in compact and non-compact weighted/Riemannian manifolds using static/frequency-dom