arXiv papers from January 2022

Bogdan Batko

A recent generalization of the Conley index to discrete multivalued dynamical systems without a continuous selector is motivated by applications to data-driven dynamics. In the present paper we continue the program by studying attractor-repeller pairs and Morse decompositions in this setting. In particular, we prove Morse equation and Morse inequalities.

Amir Iqbal, Sushant Veer, Yan Gu

Legged robot locomotion on a dynamic rigid surface (i.e., a rigid surface moving in the inertial frame) involves complex full-order dynamics that is high-dimensional, nonlinear, and time-varying. Towards deriving an analytically tractable dynamic model, this study theoretically extends the reduced-order linear inverted pendulum (LIP) model from legged locomo

Anthony Ortiz, Dhaval Negandhi, Sagar R Mysorekar, Joseph Kiesecker

Rapid development of renewable energy sources, particularly solar photovoltaics (PV), is critical to mitigate climate change. As a result, India has set ambitious goals to install 500 gigawatts of solar energy capacity by 2030. Given the large footprint projected to meet renewables energy targets, the potential for land use conflicts over environmental value

Liyu Chen, Rahul Jain, Haipeng Luo

We study regret minimization for infinite-horizon average-reward Markov Decision Processes (MDPs) under cost constraints. We start by designing a policy optimization algorithm with carefully designed action-value estimator and bonus term, and show that for ergodic MDPs, our algorithm ensures $\widetilde{O}(\sqrt{T})$ regret and constant constraint violation,

J. Menezes, S. Batista, M. Tenorio, E. A. Triaca

Antipredator behaviour is a self-preservation strategy present in many biological systems, where individuals join the effort in a collective reaction to avoid being caught by an approaching predator. We study a nonhierarchical tritrophic system, whose predator-prey interactions are described by the rock-paper-scissors game rules. We performed a set of spatia

Muhammad Usman, Youcheng Sun, Divya Gopinath, Corina S. Pasareanu

We study backdoor poisoning attacks against image classification networks, whereby an attacker inserts a trigger into a subset of the training data, in such a way that at test time, this trigger causes the classifier to predict some target class. %There are several techniques proposed in the literature that aim to detect the attack but only a few also propos

Xhevat Z. Krasniqi

In this paper, we have proved four theorems on the degree of approximation of continuous functions by matrix means of their Fourier series which is expressed in terms of the modulus of continuity and a non-negative mediate function.

Xin Sun, Feifei He, Daowen Qiu, Piotr Kulicki

We designed two rules of binary quantum computed vote: Quantum Logical Veto (QLV) and Quantum Logical Nomination (QLN). The conjunction and disjunction from quantum computational logic are used to define QLV and QLN, respectively. Compared to classical vote, quantum computed vote is fairer, more democratic and has stronger expressive power. Since the advanta

Aleksey A. Kocherzhenko, Nirmal Sobha Kartha, Tengfei Li, Hsin-Yi

Personalization enables businesses to learn customer preferences from past interactions and thus to target individual customers with more relevant content. We consider the problem of predicting the optimal promotional offer for a given customer out of several options as a contextual bandit problem. Identifying information for the customer and/or the campaign

Ehsan Amid, Rohan Anil, Christopher Fifty, Manfred K. Warmuth

Optimizers like Adam and AdaGrad have been very successful in training large-scale neural networks. Yet, the performance of these methods is heavily dependent on a carefully tuned learning rate schedule. We show that in many large-scale applications, augmenting a given optimizer with an adaptive tuning method of the step-size greatly improves the performance

Ben Adcock, Juan M. Cardenas, Nick Dexter

Many problems in computational science and engineering can be described in terms of approximating a smooth function of $d$ variables, defined over an unknown domain of interest $\Omega\subset \mathbb{R}^d$, from sample data. Here both the curse of dimensionality ($d\gg 1$) and the lack of domain knowledge with $\Omega$ potentially irregular and/or disconnect

Henry Cousins, Taryn Hall, Yinglong Guo, Luke Tso

Gene set analysis methods rely on knowledge-based representations of genetic interactions in the form of both gene set collections and protein-protein interaction (PPI) networks. Explicit representations of genetic interactions often fail to capture complex interdependencies among genes, limiting the analytic power of such methods. Here we propose an extensi

Pedro H. Azevedo de Amorim

Much work has been done to give semantics to probabilistic programming languages. In recent years, most of the semantics used to reason about probabilistic programs fall in two categories: semantics based on Markov kernels and semantics based on linear operators. Both styles of semantics have found numerous applications in reasoning about probabilistic progr

Christis Katsouris

We revisit classical asymptotics when testing for a structural break in linear regression models by obtaining the limit theory of residual-based and Wald-type processes. First, we establish the Brownian bridge limiting distribution of these test statistics. Second, we study the asymptotic behaviour of the partial-sum processes in nonstationary (linear) time

Omar Amer, Walter O. Krawec

Quantum Conference Key Agreement (QCKA) protocols are designed to allow multiple parties to agree on a shared secret key, secure against computationally unbounded adversaries. In this paper, we consider a high-dimensional QCKA protocol and prove its information theoretic security against arbitrary, general, attacks in the finite-key scenario. Our proof techn

Wojciech Górny

We study the set of possible traces of anisotropic least gradient functions. We show that even on the unit disk it changes with the anisotropic norm: for two sufficiently regular strictly convex norms the trace spaces coincide if and only if the norms coincide. The example of a function in exactly one of the trace spaces is given by a characteristic function

Yaren Bilge Kaya, Kayse Lee Maass, Geri L. Dimas, Renata Konrad

Recent estimates indicate that there are over 1 million runaway and homeless youth and young adults (RHY) in the United States (US). Exposure to trauma, violence, and substance abuse, coupled with a lack of community support services, puts homeless youth at high risk of being exploited and trafficked. Although access to safe housing and supportive services s

Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdrán, Timo Schenk, Adrian Lars Benjamin Iten

Device fingerprinting combined with Machine and Deep Learning (ML/DL) report promising performance when detecting cyberattacks targeting data managed by resource-constrained spectrum sensors. However, the amount of data needed to train models and the privacy concerns of such scenarios limit the applicability of centralized ML/DL-based approaches. Federated l

Alexey Lebedev, Ilya Vorobyev, Vladimir Lebedev, Christian Deppe

In this paper, we developed new coding strategies for the Z-channel. In particular, we look at the case with two-stage encoding. In this case, the encoder uses noiseless feedback once and adjusts the further encoding strategy based on the previous partial output of the channel. Nevertheless, the developed codes improve the known results with full feedback fo

William Zuluaga

In this paper, we show that in every coextensive variety V, the assignment that maps each algebra to its set of central elements is both functorial and representable. Furthermore, we prove that the full subcategory of finitely presented algebras in V is coextensive. Finally, we establish that if V is additionally (0, 1)-dense, the Gaeta topos classifies cent

Steven Platt, Berkay Demirel, Miquel Oliver

Traditionally, resource management and capacity allocation has been controlled network-side in cellular deployment. As autonomicity has been added to network design, machine learning technologies have largely followed this paradigm, benefiting from the higher compute capacity and informational context available at the network core. However, when these networ

Antony J. Speranza

Recently, Ciambelli, Leigh, and Pai (CLP) [arXiv:2111.13181] have shown that nonzero charges integrating Hamilton's equation can be defined for all diffeomorphisms acting near the boundary of a subregion in a gravitational theory. This is done by extending the phase space to include a set of embedding fields that parameterize the location of the boundary. Be

Jeff Bilmes

In this manuscript, we offer a gentle review of submodularity and supermodularity and their properties. We offer a plethora of submodular definitions; a full description of a number of example submodular functions and their generalizations; example discrete constraints; a discussion of basic algorithms for maximization, minimization, and other operations; a

Hiroshi Kihara

In previous papers, we used the standard simplices $\Delta^p$ $(p\ge 0)$ endowed with diffeologies having several good properties to introduce the singular complex $S^\dcal(X)$ of a diffeological space $X$. On the other hand, Hector and Christensen-Wu used the standard simplices $\Delta^p_{\rm sub}$ $(p\ge 0)$ endowed with the sub-diffeology of $\rbb^{p+1}$

Blaine McLaughlin, David P. Lake, Matthew Mitchell, Paul E. Barclay

We demonstrate the simultaneous generation of second and third harmonic signals from a telecom wavelength pump in a gallium phosphide (GaP) microdisk. Using analysis of the power scaling of both the second and third harmonic outputs and calculations of nonlinear cavity mode coupling factors, we study contributions to the third harmonic signal from direct and

Anirudha Majumdar, Zhiting Mei, Vincent Pacelli

Our goal is to develop theory and algorithms for establishing fundamental limits on performance imposed by a robot's sensors for a given task. In order to achieve this, we define a quantity that captures the amount of task-relevant information provided by a sensor. Using a novel version of the generalized Fano inequality from information theory, we demonstra

Shirin Panahi, Nelson Amaya, Isaac Klickstein, Galen Novello

We respond briefly to a comment [1, arXiv:2110.15493] recently posted online on our paper [2, arXiv:2108.07893]. Complete and cluster synchronization of random networks is undoubtedly a topic of interest in the Physics, Engineering, and Nonlinear Dynamics literature. In [3] we study both complete and cluster synchronization of networks and introduce indices

Kun Zhang, Kwangmin Yu, Vladimir Korepin

Quantum search algorithm (also known as Grover's algorithm) lays the foundation for many other quantum algorithms. Although it is very simple, its implementation is limited on noisy intermediate-scale quantum (NISQ) processors. Grover's algorithm was designed without considering the physical resources, such as depth, in the real implementations. Therefore, G

Zhaobidan Feng, Eric C. Rowell, Shuang Ming

Ocneanu rigidity implies that there are finitely many (braided) fusion categories with a given set of fusion rules. While there is no method for determining all such categories up to equivalence, there are a few cases for which can. For example, Kazhdan and Wenzl described all fusion categories with fusion rules isomorphic to those of $SU(N)_k$. In this pape

Aleksandr Perevalov, Dennis Diefenbach, Ricardo Usbeck, Andreas Both

The ability to have the same experience for different user groups (i.e., accessibility) is one of the most important characteristics of Web-based systems. The same is true for Knowledge Graph Question Answering (KGQA) systems that provide the access to Semantic Web data via natural language interface. While following our research agenda on the multilingual a

Omar Fawzi, Alexander Müller-Hermes, Ala Shayeghi

The threshold theorem is a fundamental result in the theory of fault-tolerant quantum computation stating that arbitrarily long quantum computations can be performed with a polylogarithmic overhead provided the noise level is below a constant level. A recent work by Fawzi, Grospellier and Leverrier (FOCS 2018) building on a result by Gottesman (QIC 2013) has

Vrinda Mehta, Fengping Jin, Hans De Raedt, Kristel Michielsen

In recent years, quantum annealing has gained the status of being a promising candidate for solving various optimization problems. Using a set of hard 2-satisfiabilty (2-SAT) problems, consisting of upto 18-variables problems, we analyze the scaling complexity of the quantum annealing algorithm and study the distributions of the minimum energy gap and the su

Stav Belogolovsky, Ido Greenberg, Danny Eitan, Shie Mannor

Neural differential equations predict the derivative of a stochastic process. This allows irregular forecasting with arbitrary time-steps. However, the expressive temporal flexibility often comes with a high sensitivity to noise. In addition, current methods model measurements and control together, limiting generalization to different control policies. These

Tao Ge, Maria Medrano, Rui Liao, Jeffrey F. Williamson

CT images have been used to generate radiation therapy treatment plans for more than two decades. Dual-energy CT (DECT) has shown high accuracy in estimating electronic density or proton stopping-power maps used in treatment planning. However, the presence of metal implants introduces severe streaking artifacts in the reconstructed images, affecting the diag

Adolfo G. Ramirez-Aristizabal, Mohammad K. Ebrahimpour, Christopher T. Kello

Classifying EEG responses to naturalistic acoustic stimuli is of theoretical and practical importance, but standard approaches are limited by processing individual channels separately on very short sound segments (a few seconds or less). Recent developments have shown classification for music stimuli (~2 mins) by extracting spectral components from EEG and u

J. Hood, P. Barry, T. Cecil, C. Chang

Future measurements of the millimeter-wavelength sky require a low-loss superconducting microstrip, typically made from niobium and silicon-nitride, coupling the antenna to detectors. We propose a simple device for characterizing these low-loss microstrips at 150 GHz. In our device we illuminate an antenna with a thermal source and compare the measured power

Victor Montenegro, Gareth Siôn Jones, Sougato Bose, Abolfazl Bayat

Quantum sensors outperform their classical counterparts in their estimation precision, given the same amount of resources. So far, quantum-enhanced sensitivity has been achieved by exploiting the superposition principle. This enhancement has been obtained for particular forms of entangled states, adaptive measurement basis change, critical many-body systems,

Andrew Corbett, Dmitry Kangin

Continuous-depth neural networks, such as Neural ODEs, have refashioned the understanding of residual neural networks in terms of non-linear vector-valued optimal control problems. The common solution is to use the adjoint sensitivity method to replicate a forward-backward pass optimisation problem. We propose a new approach which explicates the network's `d

Bilal Khalid, Shree Hari Sureshbabu, Arnab Banerjee, Sabre Kais

The critical point and the critical exponents for a phase transition can be determined using the Finite-Size Scaling (FSS) analysis. This method assumes that the phase transition occurs only in the infinite size limit. However, there has been a lot of interest recently in quantum phase transitions occuring in finite size systems such as a single two-level sy

Sabine N. Neal, Sobhit Singh, Xiaochen Fang, Choongjae Won

In order to explore the properties of a two-sublattice ferroelectric, we measured the infrared and Raman scattering response of CuInP2S6 across the ferroelectric and glassy transitions and compared our findings to a symmetry analysis, calculations of phase stability, and lattice dynamics. In addition to uncovering displacive character and a large hysteresis

Raphael Loewy

We consider polynomials in R[x] which map the set of nonnegative (element-wise) matrices of a given order into itself. Let n be a positive integer and define P(n)= {p in R[x] : p(A) is nonnegative (element-wise), for all A, A an n-by-n nonnegative (element-wise) matrix}. This set plays a role in the Nonnegative Inverse Eigenvalue Problem. Clark and Paparella

Pujan Biswas, Sudarshan Kumar, Neeraj Kumbhakarna

The primary target of this paper is to present novel compounds in view of their possible use as oxidizers in propulsion applications using molecular modeling calculations and supersonic flow simulations. Carbon-based heterocyclic compounds tend to have strained molecular structures leading to high heats of formation and energetic behavior. In the present wor

Jiří Benedikt, Petr Girg, Lukáš Kotrla, Peter Takáč

The weak and strong comparison principles, respectively, are investigated for quasi-linear elliptic boundary value problems with the $p$-Laplacian in one space dimension. We treat the degenerate case of $2 < p < \infty$ and allow also for the nontrivial convection velocity $b\colon [-1,1]\to \mathbb{R}$ in the underlying domain $(-1,1)$. We establish the wea

Anuj Mahajan, Mikayel Samvelyan, Tarun Gupta, Benjamin Ellis

Collective intelligence is a fundamental trait shared by several species of living organisms. It has allowed them to thrive in the diverse environmental conditions that exist on our planet. From simple organisations in an ant colony to complex systems in human groups, collective intelligence is vital for solving complex survival tasks. As is commonly observe

Carolina Queiroz, L. Raul Abramo, Natália V. N. Rodrigues, Ignasi Pérez-Ràfols

In this series of papers, we employ several machine learning (ML) methods to classify the point-like sources from the miniJPAS catalogue, and identify quasar candidates. Since no representative sample of spectroscopically confirmed sources exists at present to train these ML algorithms, we rely on mock catalogues. In this first paper we develop a pipeline to

Mohammad Amin Haghpanah, Ehsan Saeedizade, Mehdi Tale Masouleh, Ahmad Kalhor

This paper presents a lightweight algorithm for feature extraction, classification of seven different emotions, and facial expression recognition in a real-time manner based on static images of the human face. In this regard, a Multi-Layer Perceptron (MLP) neural network is trained based on the foregoing algorithm. In order to classify human faces, first, so

Mark Tygert

The author's recent research papers, "Cumulative deviation of a subpopulation from the full population" and "A graphical method of cumulative differences between two subpopulations" (both published in volume 8 of Springer's open-access "Journal of Big Data" during 2021), propose graphical methods and summary statistics, without extensively calibrating formal

Nicklas Sindlev Andersen, Marco Chiarandini, Kristian Debrabant

Given the counters of vehicles that traverse the roads of a traffic network, we reconstruct the travel demand that generated them expressed in terms of the number of origin-destination trips made by users. We model the problem as a bi-level optimization problem. At the inner-level, given a tentative demand, we solve a Dynamic Traffic Assignment (DTA) problem

Martin Lazar, Enrique Zuazua

In this paper we develop a procedure to deal with a family of parameter-dependent ill-posed problems, for which the exact solution in general does not exist. The original problems are relaxed by considering corresponding approximate ones, whose optimal solutions are well dfined, where the optimality is determined by the minimal norm requirement. The procedur

Pujan Biswas, Parmanand Ahirwar, S. Nandagopal, Arvind Kumar

Carbon-based caged and heterocyclic compounds tend to have strained molecular structures leading to high heats of formation and energetic behavior. In the current paper, molecular modelling calculations for 10 caged compounds of this type along with 2 strained aliphatic compounds and 4 simple cyclic chains are presented in view of their possible use as oxidi

Amir Shirian, Krishna Somandepalli, Tanaya Guha

Large scale databases with high-quality manual annotations are scarce in audio domain. We thus explore a self-supervised graph approach to learning audio representations from highly limited labelled data. Considering each audio sample as a graph node, we propose a subgraph-based framework with novel self-supervision tasks that can learn effective audio repre

Ruo-Qian Wang, Yangmin Ding

Urban flooding is becoming a common and devastating hazard to cause life loss and economic damage. Monitoring and understanding urban flooding in the local scale is a challenging task due to the complicated urban landscape, intricate hydraulic process, and the lack of high-quality and resolution data. The emerging smart city technology such as monitoring cam

Tianyu Cui, Yogesh Kumar, Pekka Marttinen, Samuel Kaski

Similarity metrics such as representational similarity analysis (RSA) and centered kernel alignment (CKA) have been used to compare layer-wise representations between neural networks. However, these metrics are confounded by the population structure of data items in the input space, leading to spuriously high similarity for even completely random neural netw

Bao Tran Truong, Oliver Melbourne Allen, Filippo Menczer

The spread of misinformation poses a threat to the social media ecosystem. Effective countermeasures to mitigate this threat require that social media platforms be able to accurately detect low-credibility accounts even before the content they share can be classified as misinformation. Here we present methods to infer account credibility from information dif

Yu Qiu, Xiaoting Zhang

We construct a geometric model for the root category $\mathcal{D}^b(Q)/[2]$ of any Dynkin diagram $Q$, which is an $h_Q$-gon $\mathbf{V}_Q$ with cores, where $h_Q$ is the Coxeter number and $\mathcal{D}^b(Q)$ is the bounded derived category associated to $Q$. As an application, we classify all spaces $\mathrm{ToSt}\mathcal{D}$ of total stability conditions o

Viet Quoc Vo, Ehsan Abbasnejad, Damith C. Ranasinghe

Despite our best efforts, deep learning models remain highly vulnerable to even tiny adversarial perturbations applied to the inputs. The ability to extract information from solely the output of a machine learning model to craft adversarial perturbations to black-box models is a practical threat against real-world systems, such as autonomous cars or machine

Henry Lam

The bootstrap is a versatile inference method that has proven powerful in many statistical problems. However, when applied to modern large-scale models, it could face substantial computation demand from repeated data resampling and model fitting. We present a bootstrap methodology that uses minimal computation, namely with a resample effort as low as one Mon

Zhenxun Zhuang, Mingrui Liu, Ashok Cutkosky, Francesco Orabona

Adam has been widely adopted for training deep neural networks due to less hyperparameter tuning and remarkable performance. To improve generalization, Adam is typically used in tandem with a squared $\ell_2$ regularizer (referred to as Adam-$\ell_2$). However, even better performance can be obtained with AdamW, which decouples the gradient of the regularize

Elynn Y. Chen, Rui Song, Michael I. Jordan

Reinforcement Learning (RL) has the promise of providing data-driven support for decision-making in a wide range of problems in healthcare, education, business, and other domains. Classical RL methods focus on the mean of the total return and, thus, may provide misleading results in the setting of the heterogeneous populations that commonly underlie large-sc

Yuzhe Lu, Haichun Yang, Zuhayr Asad, Zheyu Zhu

Recent studies have demonstrated the diagnostic and prognostic values of global glomerulosclerosis (GGS) in IgA nephropathy, aging, and end-stage renal disease. However, the fine-grained quantitative analysis of multiple GGS subtypes (e.g., obsolescent, solidified, and disappearing glomerulosclerosis) is typically a resource extensive manual process. Very fe

Velásquez-Cadavid J. M., Arrieta-Villamizar J. A., F. D. Lora-Clavijo, O. M. Pimentel

The radiation observed in quasars and active galactic nuclei is mainly produced by a relativistic plasma orbiting close to the black hole event horizon, where strong gravitational effects are relevant. The observational data of such systems can be compared with theoretical models to infer the black hole and plasma properties. In the comparison process, ray t

Agata Smoktunowicz

Let p be a prime number. We show that there is a one-to-one correspondence between the set of strongly nilpotent braces and the set of nilpotent pre-Lie rings of cardinality $p^{n}$, for sufficiently large p. Moreover, there is an injective mapping from the set of left nilpotent pre-Lie rings into the set of left nilpotent braces of cardinality $p^{n}$ for n

Clemens Berger, Mai Gehrke

We establish a duality between global sheaves on spectral spaces and right distributive bands. This is a sheaf-theoretical extension of classical Stone duality between spectral spaces and bounded distributive lattices. The topology of a spectral space admits a refinement, the so-called patch topology, giving rise to a patch monad on sheaves over a fixed spec

Alejandra Ramirez-Luna

We provide a classification theorem for compact stable minimal immersions (CSMI) of codimension $1$ or dimension $1$ (codimension $1$ and $2$ or dimension $1$ and $2$) in the product of a complex (quaternionic) projective space with any other Riemannian manifold. We characterize the complex minimal immersions of codimension $2$ or dimension $2$ as the only C

Mingfei Sun, Sam Devlin, Jacob Beck, Katja Hofmann

We present trust region bounds for optimizing decentralized policies in cooperative Multi-Agent Reinforcement Learning (MARL), which holds even when the transition dynamics are non-stationary. This new analysis provides a theoretical understanding of the strong performance of two recent actor-critic methods for MARL, which both rely on independent ratios, i.

Julian Gerstenberg, Ralph Neininger, Denis Spiegel

In distributional reinforcement learning not only expected returns but the complete return distributions of a policy are taken into account. The return distribution for a fixed policy is given as the solution of an associated distributional Bellman equation. In this note we consider general distributional Bellman equations and study existence and uniqueness

Nathan D. Bamberger, Dylan Dyer, Keshaba N. Parida, Tarek H. El-Asssad

Structure-function relationships constitute an important tool to investigate the fundamental principles of molecular electronics. Most commonly, this involves identifying a potentially important molecular structural element, followed by designing and synthesizing a set of related organic molecules, and finally interpretation of their experimental and/or comp

Mingfei Sun, Vitaly Kurin, Guoqing Liu, Sam Devlin

Proximal Policy Optimization (PPO) methods learn a policy by iteratively performing multiple mini-batch optimization epochs of a surrogate objective with one set of sampled data. Ratio clipping PPO is a popular variant that clips the probability ratios between the target policy and the policy used to collect samples. Ratio clipping yields a pessimistic estim

Wyatt Bridgman, Xiaoxuan Zhang, Greg Teichert, Mohammad Khalil

In this work we employ an encoder-decoder convolutional neural network to predict the failure locations of porous metal tension specimens based only on their initial porosities. The process we model is complex, with a progression from initial void nucleation, to saturation, and ultimately failure. The objective of predicting failure locations presents an ext

Michael Wissmann, Federico Caglieris, Xiaochen Hong, Saicharan Aswartham

The relationship between unconventional superconductivity, antiferromagnetism and nematic order in iron-based superconductors (FeSCs) is still highly debated. In many FeSCs superconductivity is in proximity of a nematically and magnetically ordered state. LiFeAs is an exceptional stoichiometric FeSC becoming superconducting below 18 K, without undergoing a s

Chengzhuo Ni, Ruiqi Zhang, Xiang Ji, Xuezhou Zhang

Policy gradient (PG) estimation becomes a challenge when we are not allowed to sample with the target policy but only have access to a dataset generated by some unknown behavior policy. Conventional methods for off-policy PG estimation often suffer from either significant bias or exponentially large variance. In this paper, we propose the double Fitted PG es

Zihui Xue, Yuedong Yang, Mengtian Yang, Radu Marculescu

Graph Neural Networks (GNNs) have demonstrated a great potential in a variety of graph-based applications, such as recommender systems, drug discovery, and object recognition. Nevertheless, resource-efficient GNN learning is a rarely explored topic despite its many benefits for edge computing and Internet of Things (IoT) applications. To improve this state o

Tiangang Cui, Xin Tong, Olivier Zahm

Markov chain Monte Carlo (MCMC) methods form one of the algorithmic foundations of Bayesian inverse problems. The recent development of likelihood-informed subspace (LIS) methods offers a viable route to designing efficient MCMC methods for exploring high-dimensional posterior distributions via exploiting the intrinsic low-dimensional structure of the underl

Oleg Gutik, Oksana Prokhorenkova, Diana Sekh

It is proved that the semigroups $\mathrm{\mathbf{End}}(\boldsymbol{B}_{\omega})$ and $\mathrm{\mathbf{End}}(\boldsymbol{B}_{\mathbb{Z}})$ of the endomorphisms of the bicyclic semigroup $\boldsymbol{B}_{\omega}$ and the endomorphisms of the extended bicyclic semigroup $\boldsymbol{B}_{\mathbb{Z}}$ are isomorphic to the semidirect products $(\omega,+)\rtimes_

Shilpa Gupta, Gaurav Dwivedi

This paper aims to establish the existence of a weak solution for the non-local problem: \begin{equation*} \left\{\begin{array}{ll} -a\left(\int_{\Omega}\mathcal{H}(x,|\nabla u|)dx \right) \Delta_{\mathcal{H}}u &=f(x,u) \ \ \hbox{in} \ \ \Omega, \ \ \ \\ \hspace{3.3cm} u &= 0 \ \ \hbox{on} \ \ \partial \Omega, \end{array}\right. \end{equation*} where $\Omega

Cheng Qian, Kejun Huang, Lucas Glass, Rakshith S. Srinivasa

Tensor completion aims at imputing missing entries from a partially observed tensor. Existing tensor completion methods often assume either multi-linear or nonlinear relationships between latent components. However, real-world tensors have much more complex patterns where both multi-linear and nonlinear relationships may coexist. In such cases, the existing

Ege Berkay Gulcan, Fazli Can

Many real-world applications adopt multi-label data streams as the need for algorithms to deal with rapidly changing data increases. Changes in data distribution, also known as concept drift, cause the existing classification models to rapidly lose their effectiveness. To assist the classifiers, we propose a novel algorithm called Label Dependency Drift Dete

Anders Sundnes Løvlie, Astrid Waagstein, Peter Hyldgård

This article reports on a Research through Design study exploring how to design a tool for helping readers of science journalism understand the strength and uncertainty of scientific evidence in news stories about health science, using both textual and visual information. A central aim has been to teach readers about criteria for assessing scientific evidenc

Dmitry Kuklin

Existing approaches for the field measurements of the frequency-dependent soil properties take a significant amount of time, making it difficult to obtain new experimental data and study the electrical soil properties further. However, a relatively uncomplicated measurement device assembled from accessible electronic components can make the measurements almo

Conrad M Albrecht, Fernando Marianno, Levente J Klein

A key challenge of supervised learning is the availability of human-labeled data. We evaluate a big data processing pipeline to auto-generate labels for remote sensing data. It is based on rasterized statistical features extracted from surveys such as e.g. LiDAR measurements. Using simple combinations of the rasterized statistical layers, it is demonstrated

Goran Muić

This paper is a continuation of our previous works (see Mui\'c in Monatsh. Math. 180, no. 3, 607--629, (2016)) and (Mui\'c, Kodrnja in Ramanujan J. 55, no. 2, 393--420, (2021)) where we have studied maps from $X_0(N)$ into $\mathbb P^2$ (and more general) constructed via modular forms of the same weight. In this short note we study how degrees of the maps an

Moeen Mostafavi, Michael D. Porter, Dawn T. Robinson

Predicting the outcome of a process requires modeling the system dynamic and observing the states. In the context of social behaviors, sentiments characterize the states of the system. Affect Control Theory (ACT) uses sentiments to manifest potential interaction. ACT is a generative theory of culture and behavior based on a three-dimensional sentiment lexico

Eric D. Miller, Catherine E. Grant, Marshall W. Bautz, Silvano Molendi

The Wide Field Imager (WFI) flying on Athena will usher in the next era of studying the hot and energetic Universe. WFI observations of faint, diffuse sources will be limited by uncertainty in the background produced by high-energy particles. These particles produce easily identified "cosmic-ray tracks" along with signals from secondary photons and electrons

Xuezhou Zhang, Yuda Song, Masatoshi Uehara, Mengdi Wang

We present BRIEE (Block-structured Representation learning with Interleaved Explore Exploit), an algorithm for efficient reinforcement learning in Markov Decision Processes with block-structured dynamics (i.e., Block MDPs), where rich observations are generated from a set of unknown latent states. BRIEE interleaves latent states discovery, exploration, and e

Andrea Medaglia, Andrea Tosin, Mattia Zanella

In this paper, we focus on the construction of a hybrid scheme for the approximation of non-Maxwellian kinetic models with uncertainties. In the context of multiagent systems, the introduction of a kernel at the kinetic level is useful to avoid unphysical interactions. The methods here proposed, combine a direct simulation Monte Carlo (DSMC) in the phase spa

Melissa K. Hallum, Svetlana G. Jorstad, Valeri M. Larionov, Alan P. Marscher

We present multi-epoch optical spectra of the $\gamma$-ray bright blazar 1156+295 (4C +29.45, Ton 599) obtained with the 4.3~m Lowell Discovery Telescope. During a multi-wavelength outburst in late 2017, when the $\gamma$-ray flux increased to $2.5\times 10^{-6} \; \rm phot\; cm^{-2}\; s^{-1}$ and the quasar was first detected at energies $\geq100$ GeV, the

Jose Pablo Folch, Shiqiang Zhang, Robert M Lee, Behrang Shafei

Bayesian Optimization is a very effective tool for optimizing expensive black-box functions. Inspired by applications developing and characterizing reaction chemistry using droplet microfluidic reactors, we consider a novel setting where the expense of evaluating the function can increase significantly when making large input changes between iterations. We f

Jonghyun Lee

A Jacobian-free inversion method is presented to accelerate Electrical Resistivity Tomography (ERT) for shallow aquifer characterization. The ERT problem typically implements the adjoint state method to efficiently compute Jacobian during the inversion. However, the adjoint state method needs intrusive forward model code changes and may not be computationall

A. Acosta, R. Gallo, P. García, D. Peluffo-Ordóñez

The dynamical characterization of the heart rate is definitely a problem of vital importance. The selection, construction and adjustment of models that reproduce the dynamic behavior of the cardiac muscle, brings us closer to the solution of the usual classification problem in medicine, i.e. decide whether or not a patient belongs to the healthy class of pat

Md Rajib Hossen, Mohammad A. Islam, Kishwar Ahmed

Cloud applications are increasingly moving away from monolithic services to agile microservices-based deployments. However, efficient resource management for microservices poses a significant hurdle due to the sheer number of loosely coupled and interacting components. The interdependencies between various microservices make existing cloud resource autoscali

Shivam Garg, Alexander Ihler, Sunil Kumar

An autonomous airborne network (AN) consists of multiple unmanned aerial vehicles (UAVs), which can self-configure to provide seamless, low-cost and secure connectivity. AN is preferred for applications in civilian and military sectors because it can improve the network reliability and fault tolerance, reduce mission completion time through collaboration, an

Chong Chen, Yingmin Liu, Orlando P. Simonetti, Matthew Tong

Background: Several studies have shown that both respiratory and cardiac motion can be extracted from the Pilot Tone (PT) signal successfully. However, most of these studies were performed in healthy volunteers. In addition, validating PT using ECG as a reference can be problematic because both PT and ECG tend to be unreliable in patients with arrhythmias. P

Iordanis Kerenidis, Anupam Prakash

We introduce a new approach for quantum linear algebra based on quantum subspace states and present three new quantum machine learning algorithms. The first is a quantum determinant sampling algorithm that samples from the distribution $\Pr[S]= det(X_{S}X_{S}^{T})$ for $|S|=d$ using $O(nd)$ gates and with circuit depth $O(d\log n)$. The state of art classica

Javaria Razzaq, Zahida Ehsan, Arshad M. Mirza

We have investigated linear and nonlinear dynamics of ion-temperature-gradient driven drift mode for Maxwellian and non Maxwellian pair-ion plasma embedded in an inhomogeneous magnetic field having gradients in ion's temperature and number density. Linear dispersion relations are derived and analyzed analytically as well as numerically for different cases. I

Sam Nariman

We investigate a conjecture due to Haefliger and Thurston in the context of foliated manifold bundles. In this context, Haefliger-Thurston's conjecture predicts that every $M$-bundle over a manifold $B$ where $\text{dim}(B)\leq \text{dim}(M)$ is cobordant to a flat $M$-bundle. In particular, we study the bordism class of flat $M$-bundles over low dimensional

Peter K. F. Kuhfittig

The embedding of a curved spacetime in a higher-dimensional flat spacetime has continued to be a topic of interest in the general theory of relativity, as exemplified by the induced-matter theory. This paper deals with spacetimes of embedding class one, i.e., spacetimes that can be embedded in a five-dimensional flat spacetime. Einstein's theory allows the f

Soroor Shekarizadeh, Razieh Rastgoo, Saif Al-Kuwari, Mohammad Sabokrou

Social media plays a significant role in sharing essential information, which helps humanitarian organizations in rescue operations during and after disaster incidents. However, developing an efficient method that can provide rapid analysis of social media images in the early hours of disasters is still largely an open problem, mainly due to the lack of suit

Daozhi Shen, HeeBong Yang, Christian Spudat, Tarun Patel

The ability to perform broadband optical spectroscopy with sub-diffraction-limit resolution is highly sought-after for a wide range of critical applications. However, sophisticated tip-enhanced techniques are currently required to achieve this goal. We bypass this challenge by demonstrating an extremely broadband photodetector based on a two-dimensional (2D)

Mo Zhou, Jianfeng Lu

We propose a single time-scale actor-critic algorithm to solve the linear quadratic regulator (LQR) problem. A least squares temporal difference (LSTD) method is applied to the critic and a natural policy gradient method is used for the actor. We give a proof of convergence with sample complexity $\mathcal{O}(\varepsilon^{-1} \log(\varepsilon^{-1})^2)$. The

William Cai, Ro Encarnacion, Bobbie Chern, Sam Corbett-Davies

In domains ranging from computer vision to natural language processing, machine learning models have been shown to exhibit stark disparities, often performing worse for members of traditionally underserved groups. One factor contributing to these performance gaps is a lack of representation in the data the models are trained on. It is often unclear, however,