May 2022 arXiv papers — page 58

Kelvin Kwakye, Younho Seong, Sun Yi

The provision of paratransit services is one option to meet the transportation needs of Vulnerable Road Users (VRUs). Like any other means of transportation, paratransit has obstacles such as high operational costs and longer trip times. As a result, customers are dissatisfied, and paratransit operators have a low approval rating. Researchers have undertaken

Xutao Zheng, Huaizhong Gao, Jiaxing Wen, Ming Zeng

Recently, silicon photomultipliers (SiPMs) have been used in several space-borne missions, owing to their solid state, compact size, low operating voltage, and insensitivity to magnetic fields. However, operating SiPMs in space results in radiation damage and degraded performance. In-orbit quantitative studies on these effects are limited. In this study, we

Fuzhao Xue, Jianghai Chen, Aixin Sun, Xiaozhe Ren

Transformer-based models have delivered impressive results on many tasks, particularly vision and language tasks. In many model training situations, conventional configurations are typically adopted. For example, we often set the base model with hidden dimensions (i.e. model width) to be 768 and the number of transformer layers (i.e. model depth) to be 12. I

Rahul Yedida, Hong Jin Kang, Huy Tu, Xueqi Yang

Automatically generated static code warnings suffer from a large number of false alarms. Hence, developers only take action on a small percent of those warnings. To better predict which static code warnings should not be ignored, we suggest that analysts need to look deeper into their algorithms to find choices that better improve the particulars of their sp

Jiayin Liu, Yuan Zhou

We establish a Rademacher type theorem involving Hamiltonians $H(x,p)$ under very weak conditions in both of Euclidean and Carnot-Carath\'eodory spaces. In particular,$H(x,p)$ is assumed to be only measurable in the variable $x$, and to be quasiconvex and lower-semicontinuous in the variable $p$. Without the lower-semicontinuity in the variable $p$, we provi

Raj Kishor Joshi, Sanjit Debnath, Indranil Chattopadhyay

We study time-dependent relativistic jets under the influence of radiation field of the accretion disk. The accretion disk consists of an inner compact corona and an outer sub-Keplerian disk. The thermodynamics of the fluid is governed by a relativistic equation of state (EoS) for multispecies fluid which enables to study the effect of composition on jet-dyn

Mingyao Cui, Zidong Wu, Yuhao Chen, Shenheng Xu

Ultra-massive multiple-input-multiple-output (UM-MIMO) is promising to meet the high rate requirements for future 6G. However, due to the large number of antennas and high path loss, the hardware power consumption and computing power consumption of UM-MIMO will be unaffordable. To address this problem, we implement a low-power communication system based on r

Pavan C. Madhusudana, Neil Birkbeck, Yilin Wang, Balu Adsumilli

We consider the problem of capturing distortions arising from changes in frame rate as part of Video Quality Assessment (VQA). Variable frame rate (VFR) videos have become much more common, and streamed videos commonly range from 30 frames per second (fps) up to 120 fps. VFR-VQA offers unique challenges in terms of distortion types as well as in making non-u

Xiaoyin Hu, Nachuan Xiao, Xin Liu, Kim-Chuan Toh

This paper focus on the minimization of a possibly nonsmooth objective function over the Stiefel manifold. The existing approaches either lack efficiency or can only tackle prox-friendly objective functions. We propose a constraint dissolving function named NCDF and show that it has the same first-order stationary points and local minimizers as the original

Zixin Ye, Tongxin Li, Steven H. Low

We study the problem of phase optimization for electric-vehicle (EV) charging. We formulate our problem as a non-convex mixed-integer programming problem whose objective is to minimize the charging loss. Despite the hardness of directly solving this non-convex problem, we solve a relaxation of the original problem by proposing the PXA algorithm where "P", "X

Zhong Ji, Zhenfei Hu, Yaodong Wang, Shengjia Li

Pedestrian Attribute Recognition (PAR) is a challenging task in intelligent video surveillance. Two key challenges in PAR include complex alignment relations between images and attributes, and imbalanced data distribution. Existing approaches usually formulate PAR as a recognition task. Different from them, this paper addresses it as a decision-making task v

Oleg Vasilyev, Alex Dauenhauer, Vedant Dharnidharka, John Bohannon

We propose a simple and practical method for named entity linking (NEL), based on entity representation by multiple embeddings. To explore this method, and to review its dependency on parameters, we measure its performance on Namesakes, a highly challenging dataset of ambiguously named entities. Our observations suggest that the minimal number of mentions re

Rucha Bhalchandra Joshi, Annada Prasad Behera, Subhankar Mishra

Building Information Modeling has been used to analyze as well as increase the energy efficiency of the buildings. It has shown significant promise in existing buildings by deconstruction and retrofitting. Current cities which were built without the knowledge of energy savings are now demanding better ways to become smart in energy utilization. However, the

Nikolay Filonov, Ilya Kachkovskiy

We consider discrete periodic operator on $\mathbb Z^d$ with respect to lattices $\Gamma\subset\mathbb Z^d$ of full rank. We describe the class of lattices $\Gamma$ for which the operator may have a spectral gap for arbitrarily small potentials. We also show that, for a large class of lattices, the dimensions of the level sets of spectral band functions at t

Mengyuan Zhang, Kai Liu

Subspace clustering is to find underlying low-dimensional subspaces and cluster the data points correctly. In this paper, we propose a novel multi-view subspace clustering method. Most existing methods suffer from two critical issues. First, they usually adopt a two-stage framework and isolate the processes of affinity learning, multi-view information fusion

Gheorghe Nenciu, Irina Nenciu

We consider the problem of essential self-adjointness of the drift-diffusion operator $H=-\frac{1}{\rho}\nabla\cdot \rho \mathbb D\nabla +V$ on domains $\Omega \subset \mathbb{R}^d$ with $\mathcal{C}^2$-boundary $\partial \Omega$ and for large classes of coefficients $\rho,\; \mathbb{D}$ and $V$. We give criteria showing how the behavior as $x \rightarrow \p

Chris Nagele, Hideyuki Umeda, Koh Takahashi, Takashi Yoshida

Observed supermassive black holes in the early universe have several proposed formation channels, in part because most of these channels are difficult to probe. One of the more promising channels, the direct collapse of a supermassive star, has several possible probes including the explosion of a helium-core supermassive star triggered by a general relativis

Hao Wang

Regularization is a popular technique to solve the overfitting problem of machine learning algorithms. Most regularization technique relies on parameter selection of the regularization coefficient. Plug-in method and cross-validation approach are two most common parameter selection approaches for regression methods such as Ridge Regression, Lasso Regression

Lokesh Chandra Das, Dipankar Dasgupta, Myounggyu Won

Dynamic wireless charging (DWC) is an emerging technology that allows electric vehicles (EVs) to be wirelessly charged while in motion. It is gaining significant momentum as it can potentially address the range limitation issue for EVs. However, due to significant power loss caused by wireless power transfer, improving charging efficiency remains as a major

Jing Ma, Xiang Xiang, Ke Wang, Yuchuan Wu

Black-Box Knowledge Distillation (B2KD) is a formulated problem for cloud-to-edge model compression with invisible data and models hosted on the server. B2KD faces challenges such as limited Internet exchange and edge-cloud disparity of data distributions. In this paper, we formalize a two-step workflow consisting of deprivatization and distillation, and the

Hiroki Sayama

Social fragmentation transition is a transition of social states between many disconnected communities with distinct opinions and a well-connected single network with homogeneous opinions. This is a timely research topic with high relevance to various current societal issues. We had previously studied this problem using numerical simulations of adaptive soci

Andriyan Bilyk, Javad Doliskani, Zhiyong Gong

We investigate the security assumptions behind three public-key quantum money schemes. Aaronson and Christiano proposed a scheme based on hidden subspaces of the vector space $\mathbb{F}_2^n$ in 2012. It was conjectured by Pena et al in 2015 that the hard problem underlying the scheme can be solved in quasi-polynomial time. We confirm this conjecture by givi

Danny Hernandez, Tom Brown, Tom Conerly, Nova DasSarma

Recent large language models have been trained on vast datasets, but also often on repeated data, either intentionally for the purpose of upweighting higher quality data, or unintentionally because data deduplication is not perfect and the model is exposed to repeated data at the sentence, paragraph, or document level. Some works have reported substantial ne

Guilherme Parreira da Silva, Henrique Aparecido Laureano, Ricardo Rasmussen Petterle, Paulo Justiniano Ribeiro Júnior

Researchers are often interested in understanding the relationship between a set of covariates and a set of response variables. To achieve this goal, the use of regression analysis, either linear or generalized linear models, is largely applied. However, such models only allow users to model one response variable at a time. Moreover, it is not possible to di

Tong-Tong Wang, Shu-Min Zhao, Jian-Fei Zhang, Xing-Xing Dong

Three singlet new Higgs superfields and right-handed neutrinos are added to MSSM to obtain $U(1)_X$SSM model. Its local gauge group is $SU(3)_C\times SU(2)_L \times U(1)_Y \times U(1)_X$. In the framework of $U(1)_X$SSM, we study muon anomalous magnetic moment and lepton flavor violating decays $l_j\rightarrow{l_i\gamma}(j=2,3;i=1,2)$ within the Mass Inserti

Chao Chen, Zijian Gao, Kele Xu, Sen Yang

To handle the sparsity of the extrinsic rewards in reinforcement learning, researchers have proposed intrinsic reward which enables the agent to learn the skills that might come in handy for pursuing the rewards in the future, such as encouraging the agent to visit novel states. However, the intrinsic reward can be noisy due to the undesirable environment's

Xutao Zhou, Xiangfei Zhang, Chen Chen, Yong Niu

As more and more people choose high-speed rail (HSR) as a means of transportation for short trips, there is ever growing demand of high quality of multimedia services. With its rich spectrum resources, millimeter wave (mm-wave) communications can satisfy the high network capacity requirements for HSR. Also, it is possible for receivers (RXs) to be equipped w

Hao-Guang Li, Chao-Jiang Xu

In this work, we study the nonlinear spatially homogeneous Landau equation with hard potential in a close-to-equilibrium framework, we show that the solution to the Cauchy problem with $L^2$ initial datum enjoys a analytic Gelfand-Shilov regularizing effect in the class $S^1_1(\mathbb{R}^3)$, meaning that the solution of the Cauchy problem and its Fourier tr

Yuheng Jia, Guanxing Lu, Hui Liu, Junhui Hou

In this letter, we propose a novel semi-supervised subspace clustering method, which is able to simultaneously augment the initial supervisory information and construct a discriminative affinity matrix. By representing the limited amount of supervisory information as a pairwise constraint matrix, we observe that the ideal affinity matrix for clustering share

Junichiro Kawamura, Stuart Raby

We study the effects of vector-like leptons on the $W$ boson mass in a model with a vector-like $U(1)^\prime$ gauge symmetry. This model provides simultaneous explanations for the recent anomalies in the muon anomalous magnetic moment and the semi-leptonic decays of $B$ mesons. We found that the recent result of the $W$ boson mass precise measurement at CDF

Jie Huang, Kerui Zhu, Kevin Chen-Chuan Chang, Jinjun Xiong

We propose DEER (Descriptive Knowledge Graph for Explaining Entity Relationships) - an open and informative form of modeling entity relationships. In DEER, relationships between entities are represented by free-text relation descriptions. For instance, the relationship between entities of machine learning and algorithm can be represented as ``Machine learnin

Clara Bicalho, Adam Bouyamourn, Thad Dunning

Scholars frequently use covariate balance tests to test the validity of natural experiments and related designs. Unfortunately, when measured covariates are unrelated to potential outcomes, balance is uninformative about key identification conditions. We show that balance tests can then lead to erroneous conclusions. To build stronger tests, researchers shou

Yi-Cai Zhang

In this work, we investigate the bound states in a one-dimensional spin-1 flat band system with a Coulomb-like potential of type III, which has a unique non-vanishing matrix element in basis $|1\rangle$. It is found that, for such a kind of potential, there exists infinite bound states. Near the threshold of continuous spectrum, the bound state energy is con

G. G. Blesio, R. Žitko, L. O. Manuel, A. A. Aligia

Local quantum phase transitions driven by Kondo correlations have been theoretically proposed in several magnetic nanosystems; however, clear experimental signatures are scant. Modeling a nickelocene molecule on a Cu(100) substrate as a two-orbital Anderson impurity with single-ion easy-plane anisotropy coupled to two conduction bands, we find that recent sc

Chenguang Wang, Xiao Liu, Zui Chen, Haoyun Hong

We introduce a method for improving the structural understanding abilities of language models. Unlike previous approaches that finetune the models with task-specific augmentation, we pretrain language models on a collection of task-agnostic corpora to generate structures from text. Our structure pretraining enables zero-shot transfer of the learned knowledge

Yi-Cai Zhang, Guo-Bao Guo

In this work, we investigate the bound state problem in one dimensional spin-1 Dirac Hamiltonian with a flat band. It is found that, the flat band has significant effects on the bound states. For example, for Dirac delta potential $g\delta(x)$, there exists one bound state for both positive and negative potential strength $g$. Furthermore, when the potential

Andrew D. McNaughton, Mridula S. Bontha, Carter R. Knutson, Jenna A. Pope

Efficient design and discovery of target-driven molecules is a critical step in facilitating lead optimization in drug discovery. Current approaches to develop molecules for a target protein are intuition-driven, hampered by slow iterative design-test cycles due to computational challenges in utilizing 3D structural data, and ultimately limited by the expert

Alexey Kushnir, Vinod Krishnamoorthy

We prove that supply correspondences are characterized by two properties: the law of supply and being homogeneous of degree zero.

Yifan Gao, Qingyu Yin, Zheng Li, Rui Meng

Keyphrase generation is the task of automatically predicting keyphrases given a piece of long text. Despite its recent flourishing, keyphrase generation on non-English languages haven't been vastly investigated. In this paper, we call attention to a new setting named multilingual keyphrase generation and we contribute two new datasets, EcommerceMKP and Acade

Luca Moriconi, Rodrigo M. Pereira, Victor J. Valadão

The small-scale statistical properties of velocity circulation in classical homogeneous and isotropic turbulent flows are assessed through a modeling framework that brings together the multiplicative cascade and the structural descriptions of turbulence. We find that vortex structures exhibit short-distance repulsive correlations, which is evidenced when the

Samuel Wookey, Yaoshiang Ho, Tom Rikert, Juan David Gil Lopez

Masterful is a software platform to train deep learning computer vision models. Data and model architecture are inputs to the platform, and the output is a trained model. The platform's primary goal is to maximize a trained model's accuracy, which it achieves through its regularization and semi-supervised learning implementations. The platform's secondary go

Hao Ai, Zidong Cao, Jinjing Zhu, Haotian Bai

Omnidirectional image (ODI) data is captured with a 360x180 field-of-view, which is much wider than the pinhole cameras and contains richer spatial information than the conventional planar images. Accordingly, omnidirectional vision has attracted booming attention due to its more advantageous performance in numerous applications, such as autonomous driving a

Michael Oberst, Alexander D'Amour, Minmin Chen, Yuyan Wang

Several problems in statistics involve the combination of high-variance unbiased estimators with low-variance estimators that are only unbiased under strong assumptions. A notable example is the estimation of causal effects while combining small experimental datasets with larger observational datasets. There exist a series of recent proposals on how to perfo

Zeeshan Ahmad, Naimul Khan

Physiological Signals are the most reliable form of signals for emotion recognition, as they cannot be controlled deliberately by the subject. Existing review papers on emotion recognition based on physiological signals surveyed only the regular steps involved in the workflow of emotion recognition such as preprocessing, feature extraction, and classificatio

Nguyen Huu Phong, Bernardete Ribeiro

In this research, we present our findings to recognize American Sign Language from series of hand gestures. While most researches in literature focus only on static handshapes, our work target dynamic hand gestures. Since dynamic signs dataset are very few, we collect an initial dataset of 150 videos for 10 signs and an extension of 225 videos for 15 signs.

Reuben R. W. Wang, John L. Bohn

We analytically derive the transport tensor of thermal conductivity in an ultracold, but not yet quantum degenerate, gas of Bosonic lanthanide atoms using the Chapman-Enskog procedure. The tensor coefficients inherit an anisotropy from the anisotropic collision cross section for these dipolar species, manifest in their dependence on the dipole moment, dipole

Suguman Bansal, Lydia Kavraki, Moshe Y. Vardi, Andrew Wells

Reactive synthesis from high-level specifications that combine hard constraints expressed in Linear Temporal Logic LTL with soft constraints expressed by discounted-sum (DS) rewards has applications in planning and reinforcement learning. An existing approach combines techniques from LTL synthesis with optimization for the DS rewards but has failed to yield

Q. Tyrell Davis, Josh Bongard

Recent work with Lenia, a continuously-valued cellular automata (CA) framework, has yielded $\sim$100s of compelling, bioreminiscent and mobile patterns. Lenia can be viewed as a continuously-valued generalization of the Game of Life, a seminal cellular automaton developed by John Conway that exhibits complex and universal behavior based on simple birth and

Q. Tyrell Davis, Josh Bongard

Step size in continuous cellular automata (CA) plays an important role in the stability and behavior of self-organizing patterns. Continous CA dynamics are defined by formula very similar to numerical estimation of physics-based ordinary differential equations, specifically Euler's method, for which a large step size is often inaccurate and unstable. Rather

A. R. Genreith-Schriever, H. Banerjee, A. S. Menon, E. N. Bassey

Ni-rich cathode materials achieve both high voltages and capacities in Li-ion batteries but are prone to structural instabilities and oxygen loss via the formation of singlet oxygen. Using ab initio molecular dynamics simulations, we observe spontaneous O$_2$ loss from the (012) surface of delithiated LiNiO$_2$, singlet oxygen forming in the process. We find

Joshua Mann, James Rosenzweig

The virtual detector is a commonly utilized technique to measure the properties of a wavefunction in simulation. One type of virtual detector measures the probability density and current at a set position over time, permitting an instantaneous measurement of momentum at a boundary. This may be used as the boundary condition between a quantum and a classical

Bruno Nachtergaele

I review the role of Lieb-Robinson bounds in characterizing and utilizing the locality properties of the Heisenberg dynamics of quantum lattice systems. In particular, I discuss two definitions of gapped ground state phases and show that they are essentially equivalent.

Nariman L. Dehghani, Abdollah Shafieezadeh

Significant outages from weather and climate extremes have highlighted the critical need for resilience-centered risk management of the grid. This paper proposes a multi-stage stochastic robust optimization (SRO) model that advances the existing planning frameworks on two main fronts. First, it captures interactions of operational measures with hardening dec

Chris Sachs, Ajay Govindarajan, Simon Crosby

Swim extends the actor model to support applications composed of linked distributed actors that continuously analyze boundless streams of events from millions of sources, to respond in-sync with the real-world. Swim builds a running application from streaming events, creating a distributed dataflow graph of linked, stateful, concurrent streaming actors that

Jungeum Kim, Xiao Wang

The idea of robustness is central and critical to modern statistical analysis. However, despite the recent advances of deep neural networks (DNNs), many studies have shown that DNNs are vulnerable to adversarial attacks. Making imperceptible changes to an image can cause DNN models to make the wrong classification with high confidence, such as classifying a

Nguyen Huu Phong, Augusto Santos, Bernardete Ribeiro

Convolutional Neural Networks (ConvNets or CNNs) have been candidly deployed in the scope of computer vision and related fields. Nevertheless, the dynamics of training of these neural networks lie still elusive: it is hard and computationally expensive to train them. A myriad of architectures and training strategies have been proposed to overcome this challe

Luca Di Liello, Siddhant Garg, Luca Soldaini, Alessandro Moschitti

An important task for designing QA systems is answer sentence selection (AS2): selecting the sentence containing (or constituting) the answer to a question from a set of retrieved relevant documents. In this paper, we propose three novel sentence-level transformer pre-training objectives that incorporate paragraph-level semantics within and across documents,

Hamid Mozaffari, Amir Houmansadr

Federated Learning (FL) enables data owners to train a shared global model without sharing their private data. Unfortunately, FL is susceptible to an intrinsic fairness issue: due to heterogeneity in clients' data distributions, the final trained model can give disproportionate advantages across the participating clients. In this work, we present Equal and E

Quimey Pears Stefano, Ignacio Perito, Lorena Rebón

Alternatively to the full reconstruction of an unknown quantum process, the so-called selective and efficient quantum process tomography (SEQPT) allows estimating, individually and up to the required accuracy, a given element of the matrix that describes such an operation with a polynomial amount of resources. The implementation of this protocol has been car

Gustavo de Paula Ramos, Gaetano Siciliano

Consider the following Schr\"odinger-Bopp-Podolsky system in $\mathbb{R}^3$ under an $L^2$-norm constraint, \[ \begin{cases} -\Delta u + \omega u + \phi u = u|u|^{p-2},\newline -\Delta \phi + a^2\Delta^2\phi=4\pi u^2,\newline \|u\|_{L^2}=\rho, \end{cases} \] where $a,\rho>0$ and our unknowns are $u,\phi\colon\mathbb{R}^3\to\mathbb{R}^3$ and $\omega\in\mathbb

Patrick Lee, Martha Gavidia, Anna Feldman, Jing Peng

This paper presents a linguistically driven proof of concept for finding potentially euphemistic terms, or PETs. Acknowledging that PETs tend to be commonly used expressions for a certain range of sensitive topics, we make use of distributional similarities to select and filter phrase candidates from a sentence and rank them using a set of simple sentiment-b

João V. B. Soares, Avijit Shah, Topojoy Biswas

We present a model for temporally precise action spotting in videos, which uses a dense set of detection anchors, predicting a detection confidence and corresponding fine-grained temporal displacement for each anchor. We experiment with two trunk architectures, both of which are able to incorporate large temporal contexts while preserving the smaller-scale f

Maria Eleni Athanasopoulou, Justina Deveikyte, Alan Mosca, Ilaria Peri

Currently the UK Electric market is guided by load (demand) forecasts published every thirty minutes by the regulator. A key factor in predicting demand is weather conditions, with forecasts published every hour. We present HYENA: a hybrid predictive model that combines feature engineering (selection of the candidate predictor features), mobile-window predic

Justin Dumouchelle, Rahul Patel, Elias B. Khalil, Merve Bodur

Stochastic Programming is a powerful modeling framework for decision-making under uncertainty. In this work, we tackle two-stage stochastic programs (2SPs), the most widely used class of stochastic programming models. Solving 2SPs exactly requires optimizing over an expected value function that is computationally intractable. Having a mixed-integer linear pr

Shuai Huang, Deqiang Qiu, Trac D. Tran

Designing efficient sparse recovery algorithms that could handle noisy quantized measurements is important in a variety of applications -- from radar to source localization, spectrum sensing and wireless networking. We take advantage of the approximate message passing (AMP) framework to achieve this goal given its high computational efficiency and state-of-t

Dawei Shen

This is a follow-up of \cite{KS:Kerr1} on the general covariant modulated (GCM) procedure in perturbations of Kerr. In this paper, we construct GCM hypersurfaces, which play a central role in extending GCM admissible spacetimes in \cite{KS:main} where decay estimates are derived in the context of nonlinear stability of Kerr family for $|a|\ll m$. As in \cite

Yujie Zhao, Xiaoming Huo, Yajun Mei

Count data occur widely in many bio-surveillance and healthcare applications, e.g., the numbers of new patients of different types of infectious diseases from different cities/counties/states repeatedly over time, say, daily/weekly/monthly. For this type of count data, one important task is the quick detection and localization of hot-spots in terms of unusua

Junyu Liu, Changchun Zhong, Matthew Otten, Anirban Chandra

Quantum machine learning is a rapidly evolving field of research that could facilitate important applications for quantum computing and also significantly impact data-driven sciences. In our work, based on various arguments from complexity theory and physics, we demonstrate that a single Kerr mode can provide some "quantum enhancements" when dealing with ker

Sławomir Solecki

We present a new, category theoretic point of view on finite Ramsey theory. Our aims are as follows: -- to define the category theoretic notions needed for the development of finite Ramsey Theory, -- to state, in terms of these notions, the general fundamental Ramsey results (of which various concrete Ramsey results are special cases), and -- to give self-co

Sahid Bernabe Catalan, Jimmy Petean

We study positive solutions of the equation $-\Delta_g u + \lambda u = \lambda u^q$, with $\lambda >0$, $q>1$ on the round sphere $\mathbb{S}^n$ . We reduce the equation to an ordinary differential equation by considering isoparametric functions and apply bifurcation theory. We study when the corresponding bifurcation points are transcritical. We apply this

Aurore Courtoy, Joey Huston, Pavel Nadolsky, Keping Xie

In global QCD fits of parton distribution functions (PDFs), a large part of the estimated uncertainty on the PDFs originates from the choices of parametric functional forms and fitting methodology. We argue that these types of uncertainties can be underestimated with common PDF ensembles in high-stake measurements at the Large Hadron Collider and Tevatron. A

Antoni Machowski

We characterize continuum as the smallest cardinality of a family of compact sets needed to cover a locally compact group for which the Open Mapping Theorem does not hold.

Saurabh Kulshreshtha, Olga Kovaleva, Namrata Shivagunde, Anna Rumshisky

Solving crossword puzzles requires diverse reasoning capabilities, access to a vast amount of knowledge about language and the world, and the ability to satisfy the constraints imposed by the structure of the puzzle. In this work, we introduce solving crossword puzzles as a new natural language understanding task. We release the specification of a corpus of

Paschalis Lagias, George D. Magoulas, Ylli Prifti, Alessandro Provetti

The paper introduces a new dataset to assess the performance of machine learning algorithms in the prediction of the seriousness of injury in a traffic accident. The dataset is created by aggregating publicly available datasets from the UK Department for Transport, which are drastically imbalanced with missing attributes sometimes approaching 50\% of the ove

Vincent Maurice, Clément Carlé, Shervin Keshavarzi, Ravinder Chutani

Atomic devices such as atomic clocks and optically-pumped magnetometers rely on the interrogation of atoms contained in a cell whose inner content has to meet high standards of purity and accuracy. Glass-blowing techniques and craftsmanship have evolved over many decades to achieve such standards in macroscopic vapor cells. With the emergence of chip-scale a

Conor Igoe, Youngseog Chung, Ian Char, Jeff Schneider

One critical challenge in deploying highly performant machine learning models in real-life applications is out of distribution (OOD) detection. Given a predictive model which is accurate on in distribution (ID) data, an OOD detection system will further equip the model with the option to defer prediction when the input is novel and the model has little confi

Reza Davtalab, Rafael M. O. Cruz, Robert Sabourin

Most dynamic ensemble selection (DES) methods utilize the K-Nearest Neighbors (KNN) algorithm to estimate the competence of classifiers in a small region surrounding the query sample. However, KNN is very sensitive to the local distribution of the data. Moreover, it also has a high computational cost as it requires storing the whole data in memory and perfor

Ahmet Inci, Siri Garudanagiri Virupaksha, Aman Jain, Venkata Vivek Thallam

As the machine learning and systems communities strive to achieve higher energy-efficiency through custom deep neural network (DNN) accelerators, varied bit precision or quantization levels, there is a need for design space exploration frameworks that incorporate quantization-aware processing elements (PE) into the accelerator design space while having accur

Reinoud Jan Slagter

A throughout investigation is made of the exact black hole solution in five-dimensional warped conformal dilaton gravity, found in an earlier investigation. The singularities of the dynamical black hole spacetime are determined by the zeros of a meromorphic quintic polynomial, which has no essential singularities. The solutions of the polynomial are analyzed

Hisay Lama, Masahiro J. Yamamoto, Yujiro Furuta, Takuro Shimaya

Densely packed, motile bacteria can adopt collective states not seen in conventional, passive materials. These states remain in many ways mysterious, and their physical characterization can aid our understanding of natural bacterial colonies and biofilms as well as materials in general. Here, we overcome challenges associated with generating uniformly growin

Sukrut Rao, Moritz Böhle, Bernt Schiele

Deep neural networks are very successful on many vision tasks, but hard to interpret due to their black box nature. To overcome this, various post-hoc attribution methods have been proposed to identify image regions most influential to the models' decisions. Evaluating such methods is challenging since no ground truth attributions exist. We thus propose thre

Elliot Lipnowski, Doron Ravid

An agent acquires a costly flexible signal before making a decision. We explore to what degree knowledge of the agent's information costs helps predict her behavior. We establish an impossibility result: learning costs alone generate no testable restrictions on choice without also imposing constraints on actions' state-dependent utilities. By contrast, choic

Long Wu, Xunyuan Yin, Lei Pan, Jinfeng Liu

In this work, a composite economic model predictive control (CEMPC) is proposed for the optimal operation of a stand-alone integrated energy system (IES). Time-scale multiplicity exists in IESs dynamics is taken into account and addressed using multi-time-scale decomposition. The entire IES is decomposed into three reduced-order subsystems with slow, medium,

Aissa Boukarou, Daniel Oliveira da Silva

We consider the Cauchy problem for an equation of Korteweg-de Vries-Kawahara type with initial data in the analytic Gevrey spaces. By using linear, bilinear and trilinear estimates in analytic Bourgain spaces, we establish the local well-posedness for this problem. By using an approximate conservation law, we extend this to a global result in such a way that

Yuning Wu, Jieliang Luo, Hui Li

Rewards play an essential role in reinforcement learning. In contrast to rule-based game environments with well-defined reward functions, complex real-world robotic applications, such as contact-rich manipulation, lack explicit and informative descriptions that can directly be used as a reward. Previous effort has shown that it is possible to algorithmically

Katrina Yezzi-Woodley, Alexander Terwilliger, Jiafeng Li, Eric Chen

Distinguishing agents of bone modification at paleoanthropological sites is at the root of much of the research directed at understanding early hominin exploitation of large animal resources and the effects those subsistence behaviors had on early hominin evolution. However, current methods, particularly in the area of fracture pattern analysis as a signal o

Francesco Scala, Stefano Mangini, Chiara Macchiavello, Daniele Bajoni

Several proposals have been recently introduced to implement Quantum Machine Learning (QML) algorithms for the analysis of classical data sets employing variational learning means. There has been, however, a limited amount of work on the characterization and analysis of quantum data by means of these techniques, so far. This work focuses on one such ambitiou

Anna Kędzior, Konrad Kułakowski

One of the most widespread multi-criteria decision-making methods is the Analytic Hierarchy Process (AHP). AHP successfully combines the pairwise comparisons method and the hierarchical approach. It allows the decision-maker to set priorities for all ranked alternatives. But what if, for some of them, their ranking value is known (e.g., it can be determined

W. B. De Lima, P. De Fabritiis

In this work we propose a parity-invariant Maxwell-Chern-Simons $U(1) \times U(1)$ model coupled with two charged scalar fields in $2+1$ dimensions, and show that it admits finite-energy topological vortices. We describe the main features of the model and find explicit numerical solutions for the equations of motion, considering different sets of parameters

Juan D. Soler, Marc-Antoine Miville-Deschênes, Sergio Molinari, Ralf S. Klessen

We present a study of the filamentary structure in the atomic hydrogen (HI) emission at the 21 cm wavelength toward the Galactic plane using the observations in the HI4PI survey. Using the Hessian matrix method across radial velocity channels, we identified the filamentary structures and quantified their orientations using circular statistics. We found that

Vaughn Hamilton, Ananta Soneji, Allison McDonald, Elissa Redmiles

With over 1.5 million content creators, OnlyFans is one of the fastest growing subscription-based social media platforms. The platform is primarily associated with sexual content. Thus, OnlyFans creators are uniquely positioned at the intersection of professional social media content creation and sex work. While the experiences and motivations of experienced

Holger Eble

The dual graph $\Gamma(h)$ of a regular triangulation $\Sigma(h)$ carries a natural metric structure. The minimum spanning trees of $\Gamma(h)$ recently proved to be conclusive for detecting significant data signal in the context of population genetics. In this paper we prove that the parameter space of such minimum spanning trees is organized as a polyhedra

N. Joseph Tatro, Payel Das, Pin-Yu Chen, Vijil Chenthamarakshan

Massive molecular simulations of drug-target proteins have been used as a tool to understand disease mechanism and develop therapeutics. This work focuses on learning a generative neural network on a structural ensemble of a drug-target protein, e.g. SARS-CoV-2 Spike protein, obtained from computationally expensive molecular simulations. Model tasks involve

Adriana R. Rodríguez-Kamenetzky, Carlos Carrasco-González, Luis Felipe Rodríguez Jorge, Tom P. Ray

We report new VLA and e-MERLIN high resolution and sensitivity images of the Triple Radio continuum Source in the Serpens star forming region. These observations allowed us to perform a deep multi-frequency, multi-epoch study by exploring the innermost regions (<~100 au) of an intermediate-mass YSO for the first time, with a physical resolution of ~15 au. Th

Jennifer Barnes, Brian D. Metzger

Despite recent progress, the astrophysical channels responsible for rapid neutron capture (r-process) nucleosynthesis remain an unsettled question. Observations of kilonovae following gravitational wave-detected neutron star mergers established mergers as one site of the r-process, but additional sources may be needed to fully explain r-process enrichment in

Enrique Ramirez, Pablo Roig

We carry out an exhaustive analysis of lepton flavor violating processes within the Simplest Little Higgs model. Its discovery could be expected from either $\mu\to e$ conversion in nuclei, $\mu\to e\gamma$ or $\mu\to3e$ decays. Then, the tau sector could help discriminating this model not only via $\tau\to\ell\gamma$ ($\ell=\mu,e$) and $\tau\to3\ell$ decays

Yao Yu, Bai-Cian Ke

We investigate purely leptonic decays of leptons $\l^{\prime-}\to l^-\bar{\nu}_l\nu_{l^{\prime}}$ to distinguish Dirac or Majorana neutrinos. We derive the differences of the decay width (and associated quantities) between the two neutrino hypotheses by the effect of identical particles. Evidence from experimental data makes the hypothesis of Majorana neutri

G. dos Santos, F. Romá, J. Tranchida, S. Castedo

We use spin-lattice dynamics simulations to study the possibility of modeling the magnetic hysteresis behavior of a ferromagnetic material. The temporal evolution of the magnetic and mechanical degrees of freedom is obtained through a set of two coupled Langevin equations. Hysteresis loops are calculated for different angles between the external field and th

Naomi Giertych, Ahmed Shaban, Pragya Haravu, Jonathan P Williams

The aim of our paper is to investigate the properties of the classical phase-dispersion minimization (PDM), analysis of variance (AOV), string-length (SL), and Lomb-Scargle (LS) power statistics from a statistician's perspective. We confirm that when the data are perturbations of a constant function, i.e. under the null hypothesis of no period in the data, a

Marco Mussi, Davide Lombarda, Alberto Maria Metelli, Francesco Trovò

Automated Reinforcement Learning (AutoRL) is a relatively new area of research that is gaining increasing attention. The objective of AutoRL consists in easing the employment of Reinforcement Learning (RL) techniques for the broader public by alleviating some of its main challenges, including data collection, algorithm selection, and hyper-parameter tuning.