August 2022 arXiv papers — page 13

Nanase Harada, Sergio Martin, Jeff Mangum, Kazushi Sakamoto

Centers of starburst galaxies may be characterized by a specific gas and ice chemistry due to their gas dynamics and the presence of various ice desorption mechanisms. This may result in a peculiar observable composition. We analyze abundances of $CO_2$, a reliable tracer of ice chemistry, from data collected as part of the ALMA large program ALCHEMI, a wide

Bonil Shah, P. M. Jat, Kalyan Sashidhar

The growth of big-data sectors such as the Internet of Things (IoT) generates enormous volumes of data. As IoT devices generate a vast volume of time-series data, the Time Series Database (TSDB) popularity has grown alongside the rise of IoT. Time series databases are developed to manage and analyze huge amounts of time series data. However, it is not easy t

Lekan Molu

In the article$^a$, the authors introduced a time-varying Lyapunov function for the stability analysis of nonlinear systems whose motion is governed by standard Newton-Euler equations. The authors established asymptotic stability with the choice of two symmetric positive definite matrices restricted by certain eigenvalue bounds in the control law. Exponentia

Dilishiya De Silva, Rebecca Fisher, Ben Radford, Helen Thompson

Optimal sampling strategies are critical for surveys of deeper coral reef and shoal systems, due to the significant cost of accessing and field sampling these remote and poorly understood ecosystems. Additionally, well-established standard diver-based sampling techniques used in shallow reef systems cannot be deployed because of water depth. Here we develop

Yi Luo, Yijie Zhang, Tairan Liu, Alan Yu

Exposure to bio-aerosols such as mold spores and pollen can lead to adverse health effects. There is a need for a portable and cost-effective device for long-term monitoring and quantification of various bio-aerosols. To address this need, we present a mobile and cost-effective label-free bio-aerosol sensor that takes holographic images of flowing particulat

James Powell, Martin Klein, Lyudmila Balakireva

Beyond bibliometrics, there is interest in characterizing the evolution of the number of ideas in scientific papers. A common approach for investigating this involves analyzing the titles of publications to detect vocabulary changes over time. With the notion that phrases, or more specifically keyphrases, represent concepts, lexical diversity metrics are app

Haifan zhou, Wenjing Zhou, Junfeng Wu

Since multidrug combination is widely applied, the accurate prediction of drug-drug interaction (DDI) is becoming more and more critical. In our method, we use graph to represent drug-drug interaction: nodes represent drug; edges represent drug-drug interactions. Based on our assumption, we convert the prediction of DDI to link prediction problem, utilizing

Ivan Sekulic, Ji Tong Wang, Jian Wei You, Nicolae C. Panoiu

We derive the $\mathcal{T}$-matrix formalism tailored for numerical analysis of second-harmonic (SH) generation from arbitrarily shaped particles made of centrosymmetric optical materials. First, the transfer matrix of a single particle is computed via the extended boundary condition method, in which the electromagnetic fields both at fundamental frequency a

Sahil Gopalkrishna Naik, Govind Lal Sidhardh, Samrat Sen, Arup Roy

Nonlocality, as established by seminal Bell's theorem, is considered to be the most striking feature of correlations present in space like separated events. Its practical application in device independent protocols, such as secure key distribution, randomness certification, {\it etc.}, demands identification and amplification of such correlations observed in

Shlok Mohta, Hisahiro Suganuma, Yoshiki Tanaka

In this paper, we present a new neural architectural block for the vision domain, named Mixing Regionally and Locally (MRL), developed with the aim of effectively and efficiently mixing the provided input features. We bifurcate the input feature mixing task as mixing at a regional and local scale. To achieve an efficient mix, we exploit the domain-wide recep

Ran Wang, Jinming Liu, Heming Sun, Jiro Katto

Lossless image compression is an essential research field in image compression. Recently, learning-based image compression methods achieved impressive performance compared with traditional lossless methods, such as WebP, JPEG2000, and FLIF. However, there are still many impressive lossy compression methods that can be applied to lossless compression. Therefo

Miaomiao Ren, Marcel Jackson, Xianzhong Zhao, Donglin Lei

The present paper is a continuation of \cite{jrz} and is devoted to the study of limit varieties of additively idempotent semirings. A limit variety is a nonfinitely based variety whose proper subvarieties are all finitely based. We present concrete constructions for one infinite family of limit additively idempotent semiring varieties, and one further ad ho

Di Xiao, Zi-Gao Dai

The total available sample of fast radio bursts (FRBs) has been growing steadily in recent years, facilitating the study of FRBs from a statistical point of view. At the same time, the classification of FRBs is currently an imperative issue. We propose that the brightness temperature of bursts can serve as an ideal criterion for classification. In this work,

Parada T. P. Hutauruk, Hana Gil, Seung-il Nam, Chang Ho Hyun

In the present work, we investigate the neutral-current neutrino-nucleon scattering in the nuclear medium using various energy-density functional (EDF) models such as the KIDS (Korea-IBS-Daegu-SKKU) and SLy4, together with the quark-meson coupling (QMC) model for the nucleon form factors at finite density. The differential cross section (DCS) and neutrino me

Xintong Qin, Zhengyu Song, Tianwei Hou, Wenjuan Yu

In this paper, a novel concept called simultaneously transmitting and reflecting RIS (STAR-RIS) is introduced into the wireless-powered mobile edge computing (MEC) systems to improve the efficiency of energy transfer and task offloading. Compared with traditional reflecting-only RIS, STAR-RIS extends the half-space coverage to full-space coverage by simultan

Hsiang-Chin Chien, Ching-Ping Wang, Jung-Chih Chen, Chia-Yen Lee

The lung airway tree modeling is essential to work for the diagnosis of pulmonary diseases, especially for X-Ray computed tomography (CT). The airway tree modeling on CT images can provide the experts with 3-dimension measurements like wall thickness, etc. This information can tremendously aid the diagnosis of pulmonary diseases like chronic obstructive pulm

Shoma Shimizu, Takayuki Nishio, Shota Saito, Yoichi Hirose

This paper proposes a neural architecture search (NAS) method for split computing. Split computing is an emerging machine-learning inference technique that addresses the privacy and latency challenges of deploying deep learning in IoT systems. In split computing, neural network models are separated and cooperatively processed using edge servers and IoT devic

Adelle J. Goodwin, James Miller-Jones, Sjoert van Velzen, Michael Bietenholz

Tidal disruption events (TDEs) occur when a star passes too close to a supermassive black hole and is destroyed by tidal gravitational forces. Radio observations of TDEs trace synchrotron emission from outflowing material that may be ejected from the inner regions of the accretion flow around the SMBH or by the tidal debris stream. Radio detections of tidal

X. H. Wu, Y. Y. Lu, P. W. Zhao

A multi-task learning (MTL) framework, called gradient kernel ridge regression, for nuclear masses and separation energies is developed by introducing gradient kernel functions to the kernel ridge regression (KRR) approach. By taking the WS4 mass model as an example, the gradient KRR network is trained with the mass model residuals, i.e., deviations between

Li Lyna Zhang, Youkow Homma, Yujing Wang, Min Wu

Ad relevance modeling plays a critical role in online advertising systems including Microsoft Bing. To leverage powerful transformers like BERT in this low-latency setting, many existing approaches perform ad-side computations offline. While efficient, these approaches are unable to serve cold start ads, resulting in poor relevance predictions for such ads.

Zhengyu Song, Xintong Qin, Yuanyuan Hao, Tianwei Hou

Driven by the visions of Internet of Things (IoT), there is an ever-increasing demand for computation resources of IoT users to support diverse applications. Mobile edge computing (MEC) has been deemed a promising solution to settle the conflict between the resource-hungry mobile applications and the resource-constrained IoT users. On the other hand, in orde

Weisheng Zhao, Ying Li, Ruizhi Lin

Let $\gamma(G)$ denote the domination number of a graph $G$. A vertex $v\in V(G)$ is called a \emph{critical vertex} of $G$ if $\gamma(G-v)=\gamma(G)-1$. A graph is called \emph{vertex-critical} if every vertex of it is critical. In this paper, we correspondingly introduce two such definitions: (i) a set $S\subseteq V(G)$ is called a \emph{strong critical ve

Zhenkun Li, Yi Xie, Boyu Zhang

Suppose $\Sigma$ is a compact oriented surface (possibly with boundary) that has genus zero, and L is a link in the interior of $(-1,1)\times\Sigma$. We prove that the Asaeda-Przytycki-Sikora (APS) homology of L has rank 2 if and only if L is isotopic to an embedded knot in $\{0\}\times\Sigma$. As a consequence, the APS homology detects the unknot in $(-1,1)

Xianzhe Dai, Shouhei Honda, Jiayin Pan, Guofang Wei

We establish two surprising types of Weyl's laws for some compact $\mathrm{RCD}(K, N)$/Ricci limit spaces. The first type could have power growth of any order (bigger than one). The other one has an order corrected by logarithm similar to some fractals even though the space is 2-dimensional. Moreover the limits in both types can be written in terms of the si

Nikolaos Skouloudis, Alexandre Megretski

We design a controller for an agent whose mission is to reach a stationary target while avoiding a family of obstacles which are not known a-priori. The agent moves in the two dimensional plane with non-trivial double integrator dynamics and receives only local information from its surroundings. Under mild assumptions on the family of obstacles (smoothness,

Yuji Saikai

While sample efficiency is the main motive for use of Bayesian optimisation when black-box functions are expensive to evaluate, the standard approach based on type II maximum likelihood (ML-II) may fail and result in disappointing performance in small-sample trials. The paper provides three compelling reasons to adopt fully Bayesian optimisation (FBO) as an

Hang Chen

Let $M$ be an $m$-dimensional compact Riemannian manifold with boundary. We obtain the upper bound of the harmonic mean of the first $m$ nonzero Neumann eigenvalues and Steklov eigenvalues involving the conformal volume and relative conformal volume, respectively. We also give an optimal sharp extrinsic upper bound for closed submanifolds in space forms. The

Xintong Qin, Zhengyu Song, Tianwei Hou, Wenjuan Yu

The unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) has been deemed a promising paradigm to provide ubiquitous communication and computing services for the Internet of Things (IoT). Besides, by intelligently reflecting the received signals, the reconfigurable intelligent surface (RIS) can significantly improve the propagation environment an

Xiangzhong Luo, Di Liu, Hao Kong, Shuo Huai

Benefiting from the search efficiency, differentiable neural architecture search (NAS) has evolved as the most dominant alternative to automatically design competitive deep neural networks (DNNs). We note that DNNs must be executed under strictly hard performance constraints in real-world scenarios, for example, the runtime latency on autonomous vehicles. Ho

Oliver Russell, Wei Sun

We prove the three-dimensional Gaussian product inequality (GPI) $E[X_1^{2}X_2^{2m_2}X_3^{2m_3}]\ge E[X_1^{2}]E[X_2^{2m_2}]E[X_3^{2m_3}]$ for any centered Gaussian random vector $(X_1,X_2,X_3)$ and $m_2,m_3\in\mathbb{N}$. We discover a novel inequality for the moment ratio $\frac{|E[ X_2^{2m_2+1}X_3^{2m_3+1}]|}{E[ X_2^{2m_2}X_3^{2m_3}]}$, which implies the 3

Richard Ngo, Lawrence Chan, Sören Mindermann

In coming years or decades, artificial general intelligence (AGI) may surpass human capabilities across many critical domains. We argue that, without substantial effort to prevent it, AGIs could learn to pursue goals that are in conflict (i.e. misaligned) with human interests. If trained like today's most capable models, AGIs could learn to act deceptively t

A. M. Al Zahrani

We study the circular orbits of charged particles around a weakly charged Schwarzschild black hole immersed in a weak, axisymmetric magnetic field. We start by reviewing the circular orbits of neutral particles and charged particles around only weakly charged and only weakly magnetized black holes. The case of a weakly magnetized and charged black hole is in

Mingwei Jin, Neil MacFarlane, Zhaohui Ma, Yongmeng Sua

Integrated Silicon photonics has played an important role in advancing the applications of quantum information and quantum science. However, due to different material properties, it is challenging to integrate all components with excellent performance based on homogeneous material. Here, by combining high nonlinearity and low losses in a heterogeneous silico

Rongfei Chen, Wenju Zhou, Yang Li, Huiyu Zhou

Multimodal sentiment analysis has a wide range of applications due to its information complementarity in multimodal interactions. Previous works focus more on investigating efficient joint representations, but they rarely consider the insufficient unimodal features extraction and data redundancy of multimodal fusion. In this paper, a Video-based Cross-modal

Yuandan Wang, Junhao Yang, Yu Dang, Haohao Wang

We propose a mechanism to achieve the group velocity control of bifurcation light via an imaginary coupling effect in the non-reciprocal lattice. The physical model is composed of two-layer photonic lattices with non-reciprocal coupling in each unit cell, which can support a real energy spectrum with a pair of Dirac points in the first Brillouin zone due to

Shuang Liu, Chenjin Deng, Chaoran Wang, Zunwang Bo

Micro-vibration, a ubiquitous nature phenomenon, can be seen as a characteristic feature on the objects, these vibrations always have tiny amplitudes which are much less than the wavelengths of the sensing systems, thus these motions information can only be reflected in the phase item of echo. Normally the conventional radar system can detect these micro vib

Michał Wójcik, Miłosz Kadziński, Krzysztof Ciomek

We consider preference disaggregation in the context of multiple criteria sorting. The value function parameters and thresholds separating the classes are inferred from the Decision Maker's (DM's) assignment examples. Given the multiplicity of sorting models compatible with indirect preferences, selecting a single, representative one can be conducted differe

Dawei Wang, Meng Qiao, Kunjian Lian, Zhaohui Li

Timing recovery is critical for synchronizing the clocks at the transmitting and receiving ends of a digital coherent communication system. The core of timing recovery is to determine reliably the current sampling error of the local digitizer so that the timing circuit may lock to a stable operation point. Conventional timing phase detectors need to adapt to

Chow-Choong Ngeow, Anupam Bhardwaj, Matthew J. Graham, Steven L. Groom

We present the first gri-band period-luminosity (PL) and period-Wesenheit (PW) relations for the fundamental mode anomalous Cepheids. These PL and PW relations were derived from a combined sample of five anomalous Cepheids in globular cluster M92 and the Large Magellanic Cloud, both of which have distance accurate to ~1% available from literature. Our g-band

Namrata Nath, Sang-Heon Lee, Ivan Lee

Named Entity Recognition (NER) or the extraction of concepts from clinical text is the task of identifying entities in text and slotting them into categories such as problems, treatments, tests, clinical departments, occurrences (such as admission and discharge) and others. NER forms a critical component of processing and leveraging unstructured data from El

Hiromasa Suzuki, Satoru Katsuda, Takaaki Tanaka, Nobuaki Sasaki

Particle acceleration physics at supernova remnant (SNR) shocks is one of the most intriguing problems in astrophysics. SNR RCW~86 provides a suitable environment for understanding the particle acceleration physics because one can extract the information of both accelerated particles and acceleration environment at the same regions through the bright X-ray e

Tomás Alves Salgueiro, Emilio Recart Zapata, Damián Furman, Juan Manuel Pérez

Subjective texts have been studied by several works as they can induce certain behaviours in their users. Most work focuses on user-generated texts in social networks, but some other texts also comprise opinions on certain topics and could influence judgement criteria during political decisions. In this work, we address the task of Targeted Sentiment Analysi

Junxiang Huang, Alexander Huang, Beatriz C. Guerra, Yen-Yun Yu

While much of recent study in semi-supervised learning (SSL) has achieved strong performance on single-label classification problems, an equally important yet underexplored problem is how to leverage the advantage of unlabeled data in multi-label classification tasks. To extend the success of SSL to multi-label classification, we first analyze with illustrat

Nikolas Eptaminitakis, Plamen Stefanov

We study the non-diffusive Westervelt equation in the weakly nonlinear regime. We show that the leading profile equation is of Burgers' type. We show that a compactly supported nonlinearity $\alpha$ can be reconstructed from the tilt of the transmitted high frequency wave packets sent from different directions since those tilts are proportional to the X-ray

Le Jiang, Shuangjun Liu, Xiangyu Bai, Sarah Ostadabbas

Accurately annotated image datasets are essential components for studying animal behaviors from their poses. Compared to the number of species we know and may exist, the existing labeled pose datasets cover only a small portion of them, while building comprehensive large-scale datasets is prohibitively expensive. Here, we present a very data efficient strate

Zizhao Chen, Hongliang Wang, Chia-Hui Yeh, Xilin Liu

Recognizing patterns in lung sounds is crucial to detecting and monitoring respiratory diseases. Current techniques for analyzing respiratory sounds demand domain experts and are subject to interpretation. Hence an accurate and automatic respiratory sound classification system is desired. In this work, we took a data-driven approach to classify abnormal lung

Champ Davis

Let $I: L' \to L$ be a morphism of $L_\infty$-algebras. The goal of this paper is to describe restriction of scalars in the setting of $L_\infty$-modules and prove that it defines a functor $I^*: L\text{-mod} \to L'\text{-mod}$. A more abstract approach to this problem was recently given by Kraft-Schnitzer. In a subsequent paper, this result is applied to sh

Irene Wang

The widespread adoption of handheld devices have fueled rapid growth in new applications. Several of these new applications employ machine learning models to train on user data that is typically private and sensitive. Federated Learning enables machine learning models to train locally on each handheld device while only synchronizing their neuron updates with

Shourya Bose

In this note we prove that the optimum value of a second-order cone program (SOCP) is convex in the right hand side (RHS) parameter.

Keshav Agrawal, Susan Athey, Ayush Kanodia, Emil Palikot

We study the impact of personalized content recommendations on the usage of an educational app for children. In a randomized controlled trial, we show that the introduction of personalized recommendations increases the consumption of content in the personalized section of the app by approximately 60%. We further show that the overall app usage increases by 1

Jingru Zhang, Mathias Basner, Christopher W. Jones, David F. Dinges

Physical activity has long been shown to be associated with biological and physiological performance and risk of diseases. It is of great interest to assess whether the effect of an exposure or intervention on an outcome is mediated through physical activity measured by modern wearable devices such as actigraphy. However, existing methods for mediation analy

F. Minato, H. Sagawa, S. Yoshida

Neutron and proton densities of doubly-closed shell nuclei 40Ca, 48Ca and 208Pb are studied based on a Hartree-Fock model with SAMi, SAMi-J27 and SAMi-T energy density functionals (EDFs). The ground state correlations (GSC) induced by isoscalar and isovector phonons are also evaluated by the second order perturbation theory with a self-consistent random phas

Haruhisa Enomoto, Arashi Sakai

We study IE-closed subcategories of a module category, subcategories which are closed under taking Images and Extensions. We investigate the relation between IE-closed subcategories and torsion pairs, and characterize $\tau$-tilting finite algebras using IE-closed subcategories. For the hereditary case, we show that IE-closed subcategories can be classified

J. Zhang, W. Guo, J. S. Carpenter, Andrew Leroux

Linear mixed-effects models are widely used in analyzing repeated measures data, including clustered and longitudinal data, where inferences of both fixed effects and variance components are of importance. Unlike the fixed effect inference that has been well studied, inference on the variance components is more challenging due to null value being on the boun

Yingfu Xu, Guido C. H. E. de Croon

Learning-based visual ego-motion estimation is promising yet not ready for navigating agile mobile robots in the real world. In this article, we propose CUAHN-VIO, a robust and efficient monocular visual-inertial odometry (VIO) designed for micro aerial vehicles (MAVs) equipped with a downward-facing camera. The vision frontend is a content-and-uncertainty-a

Kouhei Nakaji, Suguru Endo, Yuichiro Matsuzaki, Hideaki Hakoshima

Simulating large quantum systems is the ultimate goal of quantum computing. Variational quantum simulation (VQS) gives us a tool to achieve the goal in near-term devices by distributing the computation load to both classical and quantum computers. However, as the size of the quantum system becomes large, the execution of VQS becomes more and more challenging

Zikai Xiong, Robert M. Freund

The Frank-Wolfe method has become increasingly useful in statistical and machine learning applications, due to the structure-inducing properties of the iterates, and especially in settings where linear minimization over the feasible set is more computationally efficient than projection. In the setting of Empirical Risk Minimization -- one of the fundamental

Ryan Alvarado, Piotr Hajłasz, Lukáš Malý

We present an elementary proof of a well-known theorem of Cheeger which states that if a metric-measure space $X$ supports a $p$-Poincar\'e inequality, then the $N^{1,p}(X)$ Sobolev space is reflexive and separable whenever $p\in (1,\infty)$. We also prove separability of the space when $p=1$. Our proof is based on a straightforward construction of an equiva

Botond Tyukodi, Armand Barbot, Reinaldo Garciá-Garciá, Matthias Lerbinger

The coarse-graining of amorphous plasticity from the atomistic to the mesoscopic scale is studied in the framework of a simple scalar elasto-plastic model. Building on recent results obtained on the atomistic scale, we discuss the interest in a disordered landscape-informed threshold disorder to reproduce the physics of amorphous plasticity. We show that acc

Daniel A. Goldston, Apoorva Panidapu, Jordan Schettler

We derive explicit formulas for integrals of certain symmetric polynomials used in Keiju Sono's multidimensional sieve of $E_2$-numbers, i.e., integers which are products of two distinct primes. We use these computations to produce the currently best-known bounds for gaps between multiple $E_2$-numbers. For example, we show there are infinitely many occurren

Samuel Wilson, Tobias Fischer, Feras Dayoub, Dimity Miller

We address the problem of out-of-distribution (OOD) detection for the task of object detection. We show that residual convolutional layers with batch normalisation produce Sensitivity-Aware FEatures (SAFE) that are consistently powerful for distinguishing in-distribution from out-of-distribution detections. We extract SAFE vectors for every detected object,

Artem Ryabov, Mykola Tasinkevych

Self-propelled nanoparticles moving through liquids offer the possibility of creating advanced applications where such nanoswimmers can operate as artificial molecular-sized motors. Achieving control over the motion of nanoswimmers is a crucial aspect for their reliable functioning. While the directionality of micron-sized swimmers can be controlled with gre

Alexei M. Frolov

Two different Hamiltonian formulations of the metric gravity are discussed and applied to describe a free gravitational field in the $d$ dimensional Riemann space-time. Theory of canonical transformations, which relate equivalent Hamiltonian formulations of the metric gravity, is investigated in details. In particular, we have formulated the conditions of ca

Andrei Zlotchevski, Dawn Drain, Alexey Svyatkovskiy, Colin Clement

Large Transformer models achieved the state-of-the-art status for Natural Language Understanding tasks and are increasingly becoming the baseline model architecture for modeling source code. Transformers are usually pre-trained on large unsupervised corpora, learning token representations and transformations relevant to modeling generally available text, and

Florian Besau, Steven Hoehner

A new intrinsic volume metric is introduced for the class of convex bodies in $\mathbb{R}^n$. As an application, an inequality is proved for the asymptotic best approximation of the Euclidean unit ball by arbitrarily positioned polytopes with a restricted number of vertices under this metric. This result improves the best known estimate, and shows that dropp

Andrea Alba, Santino Ramirez, Gelasio Salazar

Given a link projection $P$ and a link $L$, it is natural to ask whether it is possible that $P$ is a projection of $L$. Taniyama answered this question for the cases in which $L$ is a prime knot or link with crossing number at most five. Recently, Takimura settled the issue for the knot $6_2$. We answer this question for the case in which $L$ is the link $L

L. Simonson, S. K. Ozdemir, A. Eisfeld, A. Metelmann

The concept of exceptional points-based optical amplifiers (EPOAs) has been recently proposed as a new paradigm for miniaturizing optical amplifiers while simultaneously enhancing their gain-bandwidth product. While the operation of this new family of amplifiers in the classical domain provides a clear advantage, their performance in the quantum domain has n

Victoria Quijano

We present relations in the mapping class monoid of $S_{0,0}^n$ between products of boundary parallel twists and those involving only non boundary parallel twists. These are of particular interest because each element gives an open book decomposition of a contact 3-manifold, and different factorizations of the same mapping class give potentially distinct sym

Najma Taimoor, Semeen Rehman

Recent technological and economic developments have transformed the healthcare sector towards more personalized and IoT-based healthcare services. These services are realized through control and monitoring applications that are typically developed using artificial intelligence/machine learning-based algorithms, which play a significant role in highlighting t

Sara Atito, Syed Muhammad Anwar, Muhammad Awais, Josef Kitler

The availability of large scale data with high quality ground truth labels is a challenge when developing supervised machine learning solutions for healthcare domain. Although, the amount of digital data in clinical workflows is increasing, most of this data is distributed on clinical sites and protected to ensure patient privacy. Radiological readings and d

Christian Gorski

In 1993 van den Berg and Kesten proved a strict monotonicity theorem for first passage percolation on $\mathbb{Z}^d$, $d \ge 2$: given two probability measures $\nu$ and $\tilde{\nu}$ with finite mean, if $\tilde{\nu}$ is strictly more variable than $\nu$ and $\nu$ is subcritical in an appropriate sense, the time constant associated to $\tilde{\nu}$ is stric

Cong Mu, Youngser Park, Carey E. Priebe

We propose a dynamic network sampling scheme to optimize block recovery for stochastic blockmodel (SBM) in the case where it is prohibitively expensive to observe the entire graph. Theoretically, we provide justification of our proposed Chernoff-optimal dynamic sampling scheme via the Chernoff information. Practically, we evaluate the performance, in terms o

Vincent Cohen-Addad, Chenglin Fan, Euiwoong Lee, Arnaud de Mesmay

Given $x \in (\mathbb{R}_{\geq 0})^{\binom{[n]}{2}}$ recording pairwise distances, the METRIC VIOLATION DISTANCE (MVD) problem asks to compute the $\ell_0$ distance between $x$ and the metric cone; i.e., modify the minimum number of entries of $x$ to make it a metric. Due to its large number of applications in various data analysis and optimization tasks, th

Martin Lotz, Joel A. Tropp

The intrinsic volumes of a convex body are fundamental invariants that capture information about the average volume of the projection of the convex body onto a random subspace of fixed dimension. The intrinsic volumes also play a central role in integral geometry formulas that describe how moving convex bodies interact. Recent work has demonstrated that the

R. Arpaia, L. Martinelli, M. Moretti Sala, S. Caprara

The universality of the strange metal phase in many quantum materials is often attributed to the presence of a quantum critical point (QCP), a zero-temperature phase transition ruled by quantum fluctuations. In cuprates, where superconductivity hinders direct QCP observation, indirect evidence comes from the identification of fluctuations compatible with the

Justin S. Spilker, Katherine A. Suess, David J. Setton, Rachel Bezanson

Observations and simulations have demonstrated that star formation in galaxies must be actively suppressed to prevent the formation of over-massive galaxies. Galactic outflows driven by stellar feedback or supermassive black hole accretion are often invoked to regulate the amount of cold molecular gas available for future star formation, but may not be the o

Bo Li, Tara N. Sainath, Ruoming Pang, Shuo-yiin Chang

On-device end-to-end (E2E) models have shown improvements over a conventional model on English Voice Search tasks in both quality and latency. E2E models have also shown promising results for multilingual automatic speech recognition (ASR). In this paper, we extend our previous capacity solution to streaming applications and present a streaming multilingual

Yahya Sattar, Samet Oymak, Necmiye Ozay

Bilinear dynamical systems are ubiquitous in many different domains and they can also be used to approximate more general control-affine systems. This motivates the problem of learning bilinear systems from a single trajectory of the system's states and inputs. Under a mild marginal mean-square stability assumption, we identify how much data is needed to est

André M. Sonnet, Epifanio G. Virga

We study the equilibria of a photoresponsive nematic elastomer ribbon within a continuum theory that builds upon the statistical mechanics model put forward by Corbett and Warner [Phys. Rev. E 78, 061701 (2008)]. We prove that the spontaneous deformation induced by illumination is not monotonically dependent on the intensity $I$. The ribbon's deflection firs

Ivo Herzog, Marcos Mazari-Armida

We show that there is a countable universal abelian p-group for purity, i.e., a countable abelian p-group $U$ such that every countable abelian p-group purely embeds in $U$. This is the last result needed to provide a complete solution to Problem 5.1 of [Fuc15] below $\aleph_\omega$. We introduce $\aleph_0$-strongly homogeneous p-groups, show that there is a

Majid Hosseini, Fahad Sohrab, Raju Gottumukkala, Ravi Teja Bhupatiraju

Affective computing has garnered the attention and interest of researchers in recent years, as there is a need for AI systems to better understand and react to human emotions. However, analyzing human emotions, such as mood or stress, is quite complex. While various stress studies use facial expressions and wearables, most existing datasets rely on processin

Avrajit Bandyopadhyay, Thirupathi Sivarani, Timothy C. Beers, A. Susmitha

We present a study on the detailed elemental abundances of newly identified bright very metal-poor stars with the detection of lithium, initially observed as part of the SDSS/MARVELS pre-survey. These stars were selected for high-resolution spectroscopic follow-up as part of the HESP-GOMPA survey. In this work, we discuss the Li abundances detected for sever

Mark Dong, Matthew Zimmermann, David Heim, Hyeongrak Choi

Large-scale generation of quantum entanglement between individually controllable qubits is at the core of quantum computing, communications, and sensing. Modular architectures of remotely-connected quantum technologies have been proposed for a variety of physical qubits, with demonstrations reported in atomic and all-photonic systems. However, an open challe

Aleš Wodecki, Pavel Strachota, Tomáš Oberhuber, Kateřina Škardová

As opposed to the distributed control of parabolic PDE's, very few contributions currently exist pertaining to the Dirichlet boundary condition control for parabolic PDE's. This motivates our interest in the Dirichlet boundary condition control for the phase field model describing the solidification of a pure substance from a supercooled melt. In particular,

Zahra Riahi Samani, Drew Parker, Hamed Akbari, Spyridon Bakas

In malignant primary brain tumors, cancer cells infiltrate into the peritumoral brain structures which results in inevitable recurrence. Quantitative assessment of infiltrative heterogeneity in the peritumoral region, the area where biopsy or resection can be hazardous, is important for clinical decision making. Previous work on characterizing the infiltrati

Ka Yung Cheng, Helmand Shayan, Kai Krycki, Markus Lange-Hegermann

There is a pressing market demand to minimize the test time of Prompt Gamma Neutron Activation Analysis (PGNAA) spectra measurement machine, so that it could function as an instant material analyzer, e.g. to classify waste samples instantaneously and determine the best recycling method based on the detected compositions of the testing sample. This article in

Arsen Khvedelidze, Astghik Torosyan

In the report we analyze the indicator/measure of classicality of quantum states defined as the probability to find a state with a positive Wigner function within a unitary invariant random ensemble. The indicators of classicality of three ensembles associated with the Hilbert-Schmidt, Bures and Bogoliubov-Kubo-Mori metrics on the space of quantum states of

Li Chen, Ellis Hoag, Kyungwoo Lee, Julian Mestre

Modern compilers leverage block coverage profile data to carry out downstream profile-guided optimizations to improve the runtime performance and the size of a binary. Given a control-flow graph $G=(V, E)$ of a function in the binary, where nodes in $V$ correspond to basic blocks (sequences of instructions that are always executed sequentially) and edges in

Mayari Montes de Oca, Jennifer Hill, Lawrence Aber, Carly Tubbs Dolan

This article estimates, for a sample of 1,777 Syrian refugee children, the impact on basic reading assessments of attending a remedial support program in Lebanon that was infused with social and emotional learning practices. We use flexible methods that capitalize on advantages of both machine learning and Bayesian inferential frameworks to leverage the info

Arsalan Heidarpanah

Since four decades ago, that the transcranial magnetic stimulation (TMS) technique was introduced, the increasing attention of neuroscience researchers and medical engineers has been focused on the development of this technique and its use to manage the treatment of a wide range of neurological conditions, including Alzheimer's disease. The ability of TMS, s

Zayd Hammoudeh, Daniel Lowd

Adversarial training instances can severely distort a model's behavior. This work investigates certified regression defenses, which provide guaranteed limits on how much a regressor's prediction may change under a poisoning attack. Our key insight is that certified regression reduces to voting-based certified classification when using median as a model's pri

Zhen-Ni Xu, Zhao-Qian Yao, Si-Xue Qin, Zhu-Fang Cui

Focusing on the continuum meson bound-state problem, a novel method is used to calculate closed-form Bethe-Salpeter kernels that are symmetry consistent with any reasonable gluon-quark vertex, $\Gamma_\nu$, and therewith deliver a Poincar\'e-invariant treatment of the spectrum and decay constants of the ground- and first-excited states of $u$, $d$, $s$ meson

Jonas Annuscheit, Christian Krumnow

This paper describes our contribution to the MIDOG 2022 challenge for detecting mitotic cells. One of the major problems to be addressed in the MIDOG 2022 challenge is the robustness under the natural variance that appears for real-life data in the histopathology field. To address the problem, we use an adapted YOLOv5s model for object detection in conjuncti

Dmitry Melnikov

Classification of entanglement is an important problem in Quantum Resource Theory. In this paper we discuss an embedding of this problem in the context of Topological Quantum Field Theories (TQFT). This approach allows classifying entanglement patterns in terms of topological equivalence classes. In the bipartite case a classification equivalent to the one b

Erfan Pakdamanian, Erzhen Hu, Shili Sheng, Sarit Kraus

In conditionally automated driving, drivers decoupled from driving while immersed in non-driving-related tasks (NDRTs) could potentially either miss the system-initiated takeover request (TOR) or a sudden TOR may startle them. To better prepare drivers for a safer takeover in an emergency, we propose novel context-aware advisory warnings (CAWA) for automated

Lu Cheng, Nayoung Kim, Huan Liu

Debiasing word embeddings has been largely limited to individual and independent social categories. However, real-world corpora typically present multiple social categories that possibly correlate or intersect with each other. For instance, "hair weaves" is stereotypically associated with African American females, but neither African American nor females alo

Reilly Raab, Luca de Alfaro, Yang Liu

We prove that Fisher-Rao natural gradient descent (FR-NGD) optimally approximates the continuous time replicator equation (an essential model of evolutionary dynamics), and term this correspondence "conjugate natural selection". This correspondence promises alternative approaches for evolutionary computation over continuous or high-dimensional hypothesis spa

George Raptis, Daniel Schäppi

For a small $n$-category $\mathscr{C}$ and an $n$-topos $\mathscr{X}$, we study necessary and sufficient conditions for a functor $f \colon \mathscr{C} \to \mathscr{X}$ to determine a geometric morphism from $\mathscr{X}$ to the $n$-topos $\mathcal{P}(\mathscr{C})_n$ of presheaves on $\mathscr{C}$ for any $n \geq 1$. These results generalize and unify result

Payman Behnam, Uday Kamal, Saibal Mukhopadhyay

In recent years, processing in memory (PIM) based mixedsignal designs have been proposed as energy- and area-efficient solutions with ultra high throughput to accelerate DNN computations. However, PIM designs are sensitive to imperfections such as noise, weight and conductance variations that substantially degrade the DNN accuracy. To address this issue, we

Nathan X. Kodama, Alex Bocharov, Marcus P. da Silva

Several variational quantum circuit approaches to machine learning have been proposed in recent years, with one promising class of variational algorithms involving tensor networks operating on states resulting from local feature maps. In contrast, a random feature approach known as quantum kitchen sinks provides comparable performance, but leverages non-loca