March 2023 arXiv papers — page 88

Matevž Fabjančič, Octavian Machidon, Hashim Sharif, Yifan Zhao

Runtime-tunable context-dependent network compression would make mobile deep learning (DL) adaptable to often varying resource availability, input "difficulty", or user needs. The existing compression techniques significantly reduce the memory, processing, and energy tax of DL, yet, the resulting models tend to be permanently impaired, sacrificing the infere

Rosie Wood, Ondrej Masek, Valentina Erastova

Biochars are black carbonaceous solids produced through biomass pyrolysis under conditions of little or no oxygen. Whilst their properties are well studied, and their applications numerous, the underlying molecular structures within biochars still need to be defined. This raises a substantial barrier to the molecular modelling of biochars and has limited com

Chia-Yu Hsu, Wenwen Li

Improving the interpretability of geospatial artificial intelligence (GeoAI) models has become critically important to open the "black box" of complex AI models, such as deep learning. This paper compares popular saliency map generation techniques and their strengths and weaknesses in interpreting GeoAI and deep learning models' reasoning behaviors, particul

Cédric Pilatte

A set $S\subset \mathbb{N}$ is a Sidon set if all pairwise sums $s_1+s_2$ (for $s_1, s_2\in S$, $s_1\leq s_2$) are distinct. A set $S\subset \mathbb{N}$ is an asymptotic basis of order 3 if every sufficiently large integer $n$ can be written as the sum of three elements of $S$. In 1993, Erd\H{o}s, S\'{a}rk\"{o}zy and S\'{o}s asked whether there exists a set

Min Hua, Cetengfei Zhang, Fanggang Zhang, Zhi Li

The recently emerging multi-mode plug-in hybrid electric vehicle (PHEV) technology is one of the pathways making contributions to decarbonization, and its energy management requires multiple-input and multipleoutput (MIMO) control. At the present, the existing methods usually decouple the MIMO control into singleoutput (MISO) control and can only achieve its

Manas Mehta, Nugzar Chkhaidze, Yizhen Wang

This report details the design, development, and implementation of IncidentUI, an Android tablet application designed to measure user-experienced ride comfort and record disengagement data for autonomous vehicles (AV) during test drives. The goal of our project was to develop an Android application to run on a peripheral tablet and communicate with the Drive

Yongsu Ahn, Yu-Ru Lin, Panpan Xu, Zeng Dai

Classification models learn to generalize the associations between data samples and their target classes. However, researchers have increasingly observed that machine learning practice easily leads to systematic errors in AI applications, a phenomenon referred to as AI blindspots. Such blindspots arise when a model is trained with training samples (e.g., cat

Miikka Kuutila, Carita Kiili, Reijo Kupiainen, Eetu Huusko

The present study investigates the role of source characteristics, evidence quality, crowdsourcing platform, and prior beliefs of the topic in adult readers' credibility evaluations of short healthrelated social media posts. Researchers designed content for the posts concerning five health topics by manipulating source characteristics (source's expertise, ge

Vani Nagarajan, Milind Kulkarni

General Purpose computing on Graphical Processing Units (GPGPU) has resulted in unprecedented levels of speedup over its CPU counterparts, allowing programmers to harness the computational power of GPU shader cores to accelerate other computing applications. But this style of acceleration is best suited for regular computations (e.g., linear algebra). Recent

Slava Rychkov

Numerical evidence suggests that the Random Field Ising Model loses Parisi-Sourlas SUSY and the dimensional reduction property somewhere between 4 and 5 dimensions, while a related model of branched polymers retains these features in any $d$. These notes give a leisurely introduction to a recent theory, developed jointly with A. Kaviraj and E. Trevisani, whi

William Misener, Hilke E. Schlichting, Edward D. Young

Sub-Neptune exoplanets are commonly hypothesized to consist of a silicate-rich magma ocean topped by a hydrogen-rich atmosphere. Previous work studying the outgassing of silicate material has demonstrated that such atmosphere-interior interactions can affect the atmosphere's overall structure and extent. But these models only considered SiO in an atmosphere

Sanjay Bhattacherjee, Palash Sarkar

The first observation of the paper is that methods for determining proportional representation in electoral systems may be suitable as alternatives to the pro-rata order matching algorithm used in stock exchanges. The main part of our work is to comprehensively consider various well known proportional representation methods and analyse in details their suita

Sergey Khrapak

A concise overview of the vibrational model of heat transfer in simple fluids with soft pairwise interactions is presented. The model is applied to evaluate the thermal conductivity coefficient of the strongly coupled Yukawa fluid, which often serves as a simplest model of a real liquid-like dusty (complex) plasma. A reasonable agreement with the available d

Jiamian Wang, Huan Wang, Yulun Zhang, Yun Fu

Image super-resolution (SR) has witnessed extensive neural network designs from CNN to transformer architectures. However, prevailing SR models suffer from prohibitive memory footprint and intensive computations, which limits further deployment on edge devices. This work investigates the potential of network pruning for super-resolution to take advantage of

Anaelia Ovalle, Arjun Subramonian, Vagrant Gautam, Gilbert Gee

Intersectionality is a critical framework that, through inquiry and praxis, allows us to examine how social inequalities persist through domains of structure and discipline. Given AI fairness' raison d'etre of "fairness", we argue that adopting intersectionality as an analytical framework is pivotal to effectively operationalizing fairness. Through a critica

Thomas L. Athey, Daniel J. Tward, Ulrich Mueller, Laurent Younes

The international neuroscience community is building the first comprehensive atlases of brain cell types to understand how the brain functions from a higher resolution, and more integrated perspective than ever before. In order to build these atlases, subsets of neurons (e.g. serotonergic neurons, prefrontal cortical neurons etc.) are traced in individual br

Jinfan Zhou, William Muirhead, Simon C. Williams, Danail Stoyanov

Purpose: Microsurgical Aneurysm Clipping Surgery (MACS) carries a high risk for intraoperative aneurysm rupture. Automated recognition of instances when the aneurysm is exposed in the surgical video would be a valuable reference point for neuronavigation, indicating phase transitioning and more importantly designating moments of high risk for rupture. This a

Matthew Ubl, Benjamin D. Robinson, Matthew T. Hale

This paper considers the quickest search problem to identify anomalies among large numbers of data streams. These streams can model, for example, disjoint regions monitored by a mobile robot. A particular challenge is a version of the problem in which the experimenter must suffer a cost each time the data stream being sampled changes, such as the time the ro

Aritra Ghosh

Let $f$ and $g$ be two holomorphic or Hecke-Maass primitive cusp forms for $SL(2,\mathbb{Z})$ and $\chi$ be a primitive Dirichlet character of modulus $p$, an odd prime. A subconvex bound for the central values of the Rankin-Selberg $L$-functions is $L(s, f \otimes g \otimes \chi)$ is given by $$L(\frac{1}{2}, f \otimes g \otimes \chi) \ll_{f,g,\epsilon}p^{\

Akkas U. Haque, Humayun Kabir, S. C. Banik, M. T. Islam

This paper describes a robotic arm with 5 degrees-of-freedom (DOF) which is controlled by human voice and has been developed in the Mechatronics Laboratory, CUET. This robotic arm is interfaced with a PC by serial communication (RS-232). Users' voice command is captured by a microphone, and this voice is processed by software which is made by Microsoft visua

Jacob Spertus

Risk-limiting audits (RLAs) can provide routine, affirmative evidence that reported election outcomes are correct by checking a random sample of cast ballots. An efficient RLA requires checking relatively few ballots. Here we construct highly efficient RLAs by optimizing supermartingale tuning parameters--$\textit{bets}$--for ballot-level comparison audits.

Ronaldo Ortez, John B. Rundle

As the variety of systems displaying scale invariant characteristics are matched only by their number, it is becoming increasingly important to understand their fundamental and universal elements. Much work has attempted to apply 2nd order phase transition mechanics due to the emergent scale invariance at the critical point. However for many systems, notions

Matthew A. Chan, Sean I. Young, Christopher A. Metzler

Many imaging inverse problems$\unicode{x2014}$such as image-dependent in-painting and dehazing$\unicode{x2014}$are challenging because their forward models are unknown or depend on unknown latent parameters. While one can solve such problems by training a neural network with vast quantities of paired training data, such paired training data is often unavaila

Hussein Cheikh Ali

In dimension $N\geq 5$, and for $0<s<4$ with $\gamma\in\mathbb{R}$, we study the existence of nontrivial weak solutions for the doubly critical problem $$\Delta^2 u-\frac{\gamma}{|x|^4}u= |u|^{2_{0}^{\star}-2}u+\frac{|u|^{2_{s}^{\star}-2}u}{|x|^s}\hbox{ in }\mathbb{R}_+^N,\; u=\Delta u=0\hbox{ on }\partial \mathbb{R}_+^N,$$ where $2_{s}^{\star}:=\frac{2(N-s)

Nicholas Lohr

We characterize the set of semiclassical measures corresponding to sequences of eigenfunctions of the attractive Coulomb operator $\widehat{H}_{\hbar}:=-\frac{\hbar^2}{2}\Delta_{\mathbb{R}^3}-\frac{1}{|x|}$. In particular, any Radon probability measure on the fixed negative energy hypersurface $\Sigma_E$ of the Kepler Hamiltonian $H$ in classical phase space

Aashka Trivedi, Takuma Udagawa, Michele Merler, Rameswar Panda

Large pretrained language models have achieved state-of-the-art results on a variety of downstream tasks. Knowledge Distillation (KD) into a smaller student model addresses their inefficiency, allowing for deployment in resource-constrained environments. However, KD can be ineffective when the student is manually selected from a set of existing options, sinc

Lu Niu, Jeremy Speth, Nathan Vance, Benjamin Sporrer

Remote photoplethysmography (rPPG) allows for noncontact monitoring of blood volume changes from a camera by detecting minor fluctuations in reflected light. Prior applications of rPPG focused on face videos. In this paper we explored the feasibility of rPPG from non-face body regions such as the arms, legs, and hands. We collected a new dataset titled Multi

K. Iwasawa, C. Norman, R. Gilli, P. Gandhi

We present the first spatially resolved, X-ray spectroscopic study of the 4-8 keV diffuse emission found in the central part of the nearby starburst galaxy M82 on a few arcsecond scales. The new details that we see allow a number of important conclusions to be drawn on the nature of the hot gas and its origin as well as feedback on the ISM. We use archival d

Son Quoc Tran, Phong Nguyen-Thuan Do, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen

Although the curse of multilinguality significantly restricts the language abilities of multilingual models in monolingual settings, researchers now still have to rely on multilingual models to develop state-of-the-art systems in Vietnamese Machine Reading Comprehension. This difficulty in researching is because of the limited number of high-quality works in

Suvechhya Lamichhane, Kayleigh A McElveen, Adam Erickson, Ilja Fescenko

[Fe(Htrz)2(trz)](BF4) (Fe-triazole) spin crossover molecules show thermal, electrical, and optical switching between high spin (HS) and low spin (LS) states, making them promising candidates for molecular spintronics. The LS and HS transitions originate from the electronic configurations of Fe(II), and are considered to be diamagnetic and paramagnetic respec

Michael Chertkov

Motivated by applications in hydrodynamics and networks of thermostatically-control loads in buildings we study control of linear dynamical systems driven by additive and also multiplicative noise of a general position. Utilizing mathematical theory of stochastic multiplicative processes we present a universal way to estimate fat, algebraic tails of the stat

Abigail Langbridge, Fearghal O'Donncha, Amadou Ba, Fabio Lorenzi

Many large-scale applications can be elegantly represented using graph structures. Their scalability, however, is often limited by the domain knowledge required to apply them. To address this problem, we propose a novel Causal Temporal Graph Convolutional Neural Network (CTGCN). Our CTGCN architecture is based on a causal discovery mechanism, and is capable

R. Bastos, G. Ortega

Let $G$ be a finitely generated group. We prove that the $n$-fold tensor product $G^{\otimes n}$ is finite (resp. polycyclic) if and only $G$ is finite (resp. polycyclic). Further, assuming that $G$ is finitely presented, we show that $G^{\otimes n}$ is finitely presented if and only if $\gamma_n(G)$ is finitely presented. We also examine some finiteness con

Loïc Crombez, Guilherme D. da Fonseca, Florian Fontan, Yan Gerard

CG:SHOP is an annual geometric optimization challenge and the 2022 edition proposed the problem of coloring a certain geometric graph defined by line segments. Surprisingly, the top three teams used the same technique, called conflict optimization. This technique has been introduced in the 2021 edition of the challenge, to solve a coordinated motion planning

C. M. Diez, V. Grinberg, F. Fürst, I. El Mellah

High-Mass X-ray Binaries (HMXBs) offer a unique opportunity for the investigation of accretion onto compact objects and of wind structure in massive stars. A key source for such studies is the bright neutron star HMXB Vela X-1 whose convenient physical and orbital parameters facilitate the analysis and in particular enable studies of the wind structure in HM

Jorio Cocola, John Tencer, Francesco Rizzi, Eric Parish

Projection-based model order reduction on nonlinear manifolds has been recently proposed for problems with slowly decaying Kolmogorov n-width such as advection-dominated ones. These methods often use neural networks for manifold learning and showcase improved accuracy over traditional linear subspace-reduced order models. A disadvantage of the previously pro

Ayush Aniket, Arpan Chattopadhyay

We study learning in periodic Markov Decision Process (MDP), a special type of non-stationary MDP where both the state transition probabilities and reward functions vary periodically, under the average reward maximization setting. We formulate the problem as a stationary MDP by augmenting the state space with the period index, and propose a periodic upper co

Nicolas L. Guidotti, Juan Acebrón, José Monteiro

We present a stochastic method for efficiently computing the solution of time-fractional partial differential equations (fPDEs) that model anomalous diffusion problems of the subdiffusive type. After discretizing the fPDE in space, the ensuing system of fractional linear equations is solved resorting to a Monte Carlo evaluation of the corresponding Mittag-Le

Núria Armengol Urpí, Marco Bagatella, Otmar Hilliges, Georg Martius

Real-world robotic manipulation tasks remain an elusive challenge, since they involve both fine-grained environment interaction, as well as the ability to plan for long-horizon goals. Although deep reinforcement learning (RL) methods have shown encouraging results when planning end-to-end in high-dimensional environments, they remain fundamentally limited by

Savvas Panagiotou, Anna S. Bosman

Low-light image enhancement (LLIE) techniques attempt to increase the visibility of images captured in low-light scenarios. However, as a result of enhancement, a variety of image degradations such as noise and color bias are revealed. Furthermore, each particular LLIE approach may introduce a different form of flaw within its enhanced results. To combat the

Alexander Cerjan, Lars Koekenbier, Hermann Schulz-Baldes

Short-ranged and line-gapped non-hermitian Hamiltonians have strong topological invariants given by an index of an associated Fredholm operator. It is shown how these invariants can be accessed via the signature of a suitable spectral localizer. This numerical technique is implemented in an example with relevance to the design of topological photonic systems

Felice Iandoli, Jingrui Niu

We prove exact controllability for quasi-linear Hamiltonian Schr\"odinger equations on tori of dimension greater or equal then two. The result holds true for sufficiently small initial conditions satisfying natural minimal regularity assumptions, provided that the region of control satisfies the geometric control condition.

Simon Spelthann, Dan Huy Chau, Lars F. Klepzig, Dominik A. Rudolph

Colloidal 2D semiconductor nanoplatelets are highly efficient light emitters, which exhibit large absorption and emission cross sections, and constitute promising laser gain media. However, if dispersed in solutions, such nanoplatelets lack a suitable optical platform for scalable and application-oriented integration into optical setups such as lasers. Here,

Alexander Nicholson, Quentin Stiévenart, Arash Mazidi, Mohammad Ghafari

WebAssembly has attracted great attention as a portable compilation target for programming languages. To facilitate in-depth studies about this technology, we have deployed Wasmizer, a tool that regularly mines GitHub projects and makes an up-to-date dataset of WebAssembly sources and their binaries publicly available. Presently, we have collected 2 540 C an

Alex E. Bernardini, Orfeu Bertolami

Non-equilibrium and instability features of prey-predator-like systems associated to topological quantum domains emerging from a quantum phase-space description are investigated in the framework of the Weyl-Wigner quantum mechanics. Reporting about the generalized Wigner flow for one-dimensional Hamiltonian systems, $\mathcal{H}(x,\,k)$, constrained by $\par

Weichao Yao, Yicheng Wang, Shahwar Ahmed, Martin Hoffmann

We report on an in-band pumped soft-aperture Kerr-lens mode-locked Ho:CALGO bulk laser at 2.1 um, generating 2 W of average power with 112 fs pulses at 91-MHz repetition rate. To the best of our knowledge, this is the highest average power from a 100-fs class mode-locked laser based on a Tm3+ or Ho3+ doped bulk material. We show that the laser has excellent

Tomasz Cieślak, Mario Fuest, Karol Hajduk, Mikołaj Sierżęga

In this paper, we give sufficient conditions for global-in-time existence of classical solutions for the fully parabolic chemorepulsion system posed on a convex, bounded three-dimensional domain. Our main result establishes global-in-time existence of regular nonnegative solutions provided that $\nabla\sqrt{u} \in L^4(0, T; L^2(\Omega))$. Our method is relat

Fragiskos Fourlas, Vignesh Kottayam Viswanathan, Sumeet Satpute, George Nikolakopoulos

With the ever growing number of space debris in orbit, the need to prevent further space population is becoming more and more apparent. Refueling, servicing, inspection and deorbiting of spacecraft are some example missions that require precise navigation and docking in space. Having multiple, collaborating robots handling these tasks can greatly increase th

Shu Zhang, Xinyi Yang, Yihao Feng, Can Qin

Incorporating human feedback has been shown to be crucial to align text generated by large language models to human preferences. We hypothesize that state-of-the-art instructional image editing models, where outputs are generated based on an input image and an editing instruction, could similarly benefit from human feedback, as their outputs may not adhere t

William Aiken, Paul K. Mvula, Paula Branco, Guy-Vincent Jourdan

Artificial Intelligence and Machine Learning have witnessed rapid, significant improvements in Natural Language Processing (NLP) tasks. Utilizing Deep Learning, researchers have taken advantage of repository comments in Software Engineering to produce accurate methods for detecting Self-Admitted Technical Debt (SATD) from 20 open-source Java projects' code.

Tingxuan Wu, Cindy Feng, Longhai Li

Residual diagnostic methods play a critical role in assessing model assumptions and detecting outliers in statistical modelling. In the context of survival models with censored observations, Li et al. (2021) introduced the Z-residual, which follows an approximately normal distribution under the true model. This property makes it possible to use Z-residuals f

Shashwat C. Sardesai, Sarah J. Vigeland, Kyle A. Gersbach, Stephen R. Taylor

The optimal statistic (OS) is a frequentist estimator for the amplitude and significance of a spatially-correlated signal in pulsar timing array (PTA) data, and it is widely used to search for the gravitational wave background (GWB). However, the OS cannot perfectly distinguish between different spatial correlations. In this paper, we introduce the multiple

Esme Bajo, Robert Davis, Jesús A. De Loera, Alexey Garber

We generalize R. P. Stanley's celebrated theorem that the $h^\ast$-polynomial of the Ehrhart series of a rational polytope has nonnegative coefficients and is monotone under containment of polytopes. We show that these results continue to hold for weighted Ehrhart series where lattice points are counted with polynomial weights, as long as the weights are hom

J. S. C. Prentice

We report on a novel algorithm for controlling global error in a step-by-step (stepwise) sense, in the numerical solution of a scalar, autonomous, nonstiff or weakly stiff problem. The algorithm exploits the remainder term of a Taylor expansion of the solution. It requires the use of the DP853 triple to solve an auxiliary problem which, in turn, enables the

Anthony T. Chieco, Douglas J. Durian

Aqueous foams and a wide range of related systems are believed to coarsen by gas diffusion between neighboring domains into a statistically self-similar scaling state, after the decay of initial transients, such that dimensionless size and shape distributions become time independent and the average grows as a power law. Partial integrodifferential equations

Jiang Hu, Kangkang Deng, Na Li, Quanzheng Li

With a computationally efficient approximation of the second-order information, natural gradient methods have been successful in solving large-scale structured optimization problems. We study the natural gradient methods for the large-scale decentralized optimization problems on Riemannian manifolds, where the local objective function defined by the local da

Ali Nawaz Babar, Thor Weis, Konstantinos Tsoukalas, Shima Kadkhodazadeh

Despite tremendous progress in the research on self-assembled nanotechnological building blocks such as macromolecules, nanowires, and two-dimensional materials, synthetic self-assembly methods bridging nanoscopic to macroscopic dimensions remain unscalable and inferior to biological self-assembly. In contrast, planar semiconductor technology has had an imme

Chongbin Zhao, Qirong Jiang, Yixin Guo

Multiple types of Nyquist-like impedance-based criteria are utilized for the small-signal stability analysis of converter-based AC systems. It is usually considered that the determinant-based criterion can determine the overall stability of a system while the eigenvalue-based criterion can give more insights into the mechanism of the instability. This paper

Arushi Rai, Adriana Kovashka

The use of large-scale vision-language datasets is limited for object detection due to the negative impact of label noise on localization. Prior methods have shown how such large-scale datasets can be used for pretraining, which can provide initial signal for localization, but is insufficient without clean bounding-box data for at least some categories. We p

Herschel Herring, Md Selim Habib

In this letter, to the best of our knowledge, a new type of hollow-core anti-resonant fiber (HC-ARF) design using hybrid silica/chalcogenide cladding is presented for single-polarization, high-birefringence, and endlessly single-mode operation at 2 $\mu$m wavelength. We show that the inclusion of a chalcogenide layer in the cladding allows strong suppression

Yitong Hu, Zixuan Zhu, Yizhe Wang, Junxiang Wang

Interdisciplinary collaboration has become a driving force for scientific breakthroughs, and evaluating scholars' performance in interdisciplinary researches is essential for promoting such collaborations. However, traditional scholar evaluation methods based solely on individual achievements do not consider interdisciplinary cooperation, creating a challeng

Mugdha Khedkar

Many Android applications collect data from users. When they do, they must protect this collected data according to the current legal frameworks. Such data protection has become even more important since the European Union rolled out the General Data Protection Regulation (GDPR). App developers have limited tool support to reason about data protection throug

Graeme Henrickson, Anna Stokke, Max Wiebe

We give a cyclic sieving phenomenon for symplectic $\lambda$-tableaux $SP(\lambda,2m)$, where $\lambda$ is a partition of an odd integer $n$ and $gcd(m,p)=1$ for any odd prime $p\leq n$. We use the crystal structure on Kashiwara-Nakashima symplectic tableaux to get a cyclic sieving action as the product $\sigma$ of simple reflections in the Weyl group. The c

Maham Tanveer, Yizhi Wang, Ali Mahdavi-Amiri, Hao Zhang

We introduce a novel method to automatically generate an artistic typography by stylizing one or more letter fonts to visually convey the semantics of an input word, while ensuring that the output remains readable. To address an assortment of challenges with our task at hand including conflicting goals (artistic stylization vs. legibility), lack of ground tr

Benjamin Hackl, Andrew Luo, Stephen Melczer, Jesse Selover

We introduce the new sage_acsv package for the SageMath computer algebra system, allowing users to rigorously compute asymptotics for a large variety of multivariate sequences with rational generating functions. Using Sage's support for exact computations over the algebraic number field, this package provides the first rigorous implementation of algorithms f

Andre Borgato Morelli, André de Carvalho Fiedler, André Luiz Cunha

Currently, transport planning has changed its paradigm from projects oriented to guarantee service levels to projects oriented to guarantee accessibility to opportunities. In this context, a number of studies and tools aimed at calculating accessibility are being made available, however these tools depend on job location data that are not always easily acces

Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf

We introduce a Reinforcement Learning Psychotherapy AI Companion that generates topic recommendations for therapists based on patient responses. The system uses Deep Reinforcement Learning (DRL) to generate multi-objective policies for four different psychiatric conditions: anxiety, depression, schizophrenia, and suicidal cases. We present our experimental r

Ning Fang, Yang Liu, Finn Giuliani, Ben Britton

The slip strength of individual slip systems at different strain rates will control the mechanical response and strongly influence the anisotropy of plastic deformation. In this work, the slip activity and strain rate sensitivity of the <a> basal, <a> prismatic, and <c+a> pyramidal slip systems are explored by testing at variable strain rates (from 10E-4 s^-

Christian Amesoeder, Florian Hartig, Maximilian Pichler

Deep Neural Networks (DNN) have become a central method in ecology. Most current deep learning (DL) applications rely on one of the major deep learning frameworks, in particular Torch or TensorFlow, to build and train DNN. Using these frameworks, however, requires substantially more experience and time than typical regression functions in the R environment.

Chengqian Xian, Camila P. E. de Souza, Wenqing He, Felipe F. Rodrigues

The log-logistic regression model is one of the most commonly used accelerated failure time (AFT) models in survival analysis, for which statistical inference methods are mainly established under the frequentist framework. Recently, Bayesian inference for log-logistic AFT models using Markov chain Monte Carlo (MCMC) techniques has also been widely developed.

Nitish Sontakke, Hosik Chae, Sangjoon Lee, Tianle Huang

The light and soft characteristics of Buoyancy Assisted Lightweight Legged Unit (BALLU) robots have a great potential to provide intrinsically safe interactions in environments involving humans, unlike many heavy and rigid robots. However, their unique and sensitive dynamics impose challenges to obtaining robust control policies in the real world. In this wo

Meridith Joyce, Jamie Tayar

We review the application of the one-dimensional Mixing Length Theory (MLT) model of convection in stellar interiors and low-mass stellar evolution. We summarize the history of MLT, present a derivation of MLT in the context of the 1D stellar structure equations, and discuss the physical regimes in which MLT is relevant. We review attempts to improve and ext

A. Rodrigo, N. Otuka, S. Takács, A. J. Koning

Experimental isomeric ratios of light (A$\le$4) particle-induced nuclear reactions were compiled for the product nuclides having metastable states with half-lives longer than 0.1 sec. The experimental isomeric ratio data were taken from the EXFOR library and reviewed. When an experiment reports isomer production cross sections instead of isomeric ratios, the

Arian Eamaz, Farhang Yeganegi, Deanna Needell, Mojtaba Soltanalian

One-bit quantization with time-varying sampling thresholds has recently found significant utilization potential in statistical signal processing applications due to its relatively low power consumption and low implementation cost. In addition to such advantages, an attractive feature of one-bit analog-to-digital converters (ADCs) is their superior sampling r

A. Alarcón, J. Argillander, D. Spegel-Lexne, G. B. Xavier

Photonic spatial quantum states are a subject of great interest for applications in quantum communication. One important challenge has been how to dynamically generate these states using only fiber-optical components. Here we propose and experimentally demonstrate an all-fiber system that can dynamically switch between any general transverse spatial qubit st

Thomas Eiter, Yoshihiro Shibata

We study an incompressible viscous flow around an obstacle with an oscillating boundary that moves by a translational periodic motion, and we show existence of strong time-periodic solutions for small data in different configurations: If the mean velocity of the body is zero, existence of time-periodic solutions is provided within a framework of Sobolev func

Chaoming Song

Quantum criticality often lies beyond the scope of the conventional Landau paradigm, and a unifying framework has yet to emerge, due in part to the wide variety of quantum orders. We propose a geometric approach to quantum phase transitions (QPTs) that shifts focus from microscopic order to the competition between non-commuting operators. This competition is

Hsiao-Ying Lu, Takanori Fujiwara, Ming-Yi Chang, Yang-chih Fu

Multivariate networks are commonly found in real-world data-driven applications. Uncovering and understanding the relations of interest in multivariate networks is not a trivial task. This paper presents a visual analytics workflow for studying multivariate networks to extract associations between different structural and semantic characteristics of the netw

Edward F. Redish

Learning to use math in physics involves combining (blending) our everyday experiences and the conceptual ideas of physics with symbolic mathematical representations. Graphs are one of the best ways to learn to build the blend. They are a mathematical representation that builds on visual recognition to create a bridge between words and equations. But student

Simon Opie, Daniel Verscharen, Christopher H. K. Chen, Christopher J. Owen

At kinetic scales in the solar wind, instabilities transfer energy from particles to fluctuations in the electromagnetic fields while restoring plasma conditions towards thermodynamic equilibrium. We investigate the interplay between background turbulent fluctuations at the small-scale end of the inertial range and kinetic instabilities acting to reduce prot

Filippo Zimmaro, Pierluigi Contucci, János Kertész

Two of the main factors shaping an individual's opinion are social coordination and personal preferences, or personal biases. To understand the role of those and that of the topology of the network of interactions, we study an extension of the voter model proposed by Masuda and Redner (2011), where the agents are divided into two populations with opposite pr

Sunil Arya, David M. Mount

Approximating convex bodies is a fundamental question in geometry, which has a wide variety of applications. Given a convex body $K$ in $\textbf{R}^d$ for fixed $d$, the objective is to minimize the number of facets of an approximating polytope for a given Hausdorff error $\varepsilon$. It is known that $O((\text{diam}(K)/\varepsilon)^{(d-1)/2})$ facets suff

P. Mathews, S. Zhang, C. Scheu, R. Janisch

The study of defect phases is important for designing nanostructured metals and alloys. Grain boundaries (GBs) form one class of defects that directly influence materials properties, such as deformability and strength. At the same time, alloying can introduce GB phase transformations and can therewith alter mechanical performance. In this work, the defect ph

Kamil Faber, Dominik Zurek, Marcin Pietron, Nathalie Japkowicz

Continual learning (CL) is one of the most promising trends in recent machine learning research. Its goal is to go beyond classical assumptions in machine learning and develop models and learning strategies that present high robustness in dynamic environments. The landscape of CL research is fragmented into several learning evaluation protocols, comprising d

Daniele Guerci, Massimo Capone, Nicola Lanatà

We introduce the time-dependent ghost Gutzwiller approximation (td-gGA), a non-equilibrium extension of the ghost Gutzwiller approximation (gGA), a powerful variational approach which systematically improves on the standard Gutzwiller method by including auxiliary degrees of freedom. We demonstrate the effectiveness of td-gGA by studying the quench dynamics

Aris Tritsis, Shantanu Basu, Christoph Federrath

Given the low ionization fraction of molecular clouds, ambipolar diffusion is thought to be an integral process in star formation. However, chemical and radiative-transfer effects, observational challenges, and the fact that the ion-neutral drift velocity is inherently very small render a definite detection of ambipolar diffusion extremely non-trivial. Here,

Liena Colarte-Gómez, Rosa M. Miró-Roig, Lisa Nicklasson

In this paper, we consider the homogeneous coordinate rings $A(Y_{n,d}) \cong \mathbb{K}[\Omega_{n,d}]$ of monomial projections $Y_{n,d}$ of Veronese varieties parameterized by subsets $\Omega_{n,d}$ of monomials of degree $d$ in $n+1$ variables where: (1) $\Omega_{n,d}$ contains all monomials supported in at most $s$ variables and, (2) $\Omega_{n,d}$ is a s

B. Almeida-Zamora, J. J. Cobos-Martínez, A. Bashir, K. Raya

Inspired by the recent development of an algebraic model which provides an adequate and unified description of the internal structure of the lowest-lying pseudo-scalar mesons, belonging both to the light quarks sector and to the one of heavy quarks, we perform its first extension to the vector-meson case. The algebraic model describes meson's structure in te

Caio Nascimento

A measurement of the neutrino mass scale will be achieved with cosmological probes in the upcoming decade. On one hand, the inclusion of massive neutrinos in the linear perturbation theory of cosmological structure formation is well understood and can be done accurately with state of the art Boltzmann solvers. On the other hand, the numerical implementation

Mohammad Salman, Ruchi Das

We introduce the concept of multi-sensitivity with respect to a vector for a non-autonomous discrete system. We prove that for a periodic non-autonomous system on the closed unit interval, sensitivity is equivalent to strong multi-sensitivity and justify that the result need not be true if the system is not periodic. In addition, we study strong multi-sensit

Maria Axenovich, Dhruv Mubayi, Lea Weber

The well-known Erd\H{o}s-Hajnal conjecture states that for any graph $F$, there exists $\epsilon>0$ such that every $n$-vertex graph $G$ that contains no induced copy of $F$ has a homogeneous set of size at least $n^{\epsilon}$. We consider a variant of the Erd\H{o}s-Hajnal problem for hypergraphs where we forbid a family of hypergraphs described by their or

Tahir Yaqoob, P. Tzanavaris, S. LaMassa

We present a new analysis of the Suzaku X-ray spectrum of the Compton-thin Seyfert 2 galaxy NGC 4388. The spectrum above $\sim$2 keV can be described by a remarkably simple and rather mundane model, consisting of a uniform, neutral spherical distribution of matter, with a radial column density of $2.58 \pm 0.02 \times 10^{23}$ cm$^{-2}$, and an Fe abundance

A. Tehranchi, M. Lipinska-Chwalek, J. Mayer, J. Neugebauer

Laves phases often form as secondary phases in metallic alloys and have a significant effect on their structural properties. Thus, phase stability studies for these chemically and structurally complex phases in addition to mechanical behavior studies are of great interest. In this work, we use the concept of metastable bulk phase and defect phase diagrams to

Alimu Dayimu, Nikola Simidjievski, Nikolaos Demiris, Jean Abraham

This paper is concerned with sample size determination methodology for prediction models. We propose combining the individual calculations via a learning-type curve. We suggest two distinct ways of doing so, a deterministic skeleton of a learning curve and a Gaussian process centred upon its deterministic counterpart. We employ several learning algorithms fo

Pengfei Li, Yong Tian, Mariana P. Júlio, Marcel S. Pawlowski

We probe the dynamical mass profiles of 10 galaxy clusters from the HIghest X-ray FLUx Galaxy Cluster Sample (HIFLUGCS) using galaxy kinematics. We numerically solve the spherical Jeans equation, and parameterize the dynamical mass profile and the galaxy velocity anisotropy profile using two general functions to ensure that our results are not biased towards

Samantha Scibelli, Yancy Shirley, Anika Schmiedeke, Brian Svoboda

Low-mass stars like our Sun begin their evolution within cold (10 K) and dense ($\sim 10^5$ cm$^{-3}$) cores of gas and dust. The physical structure of starless cores is best probed by thermal emission of dust grains. We present a high resolution dust continuum study of the starless cores in the B10 region of the Taurus Molecular Cloud. New observations at 1

Charles Stahl, Marvin Qi, Paolo Glorioso, Andrew Lucas

We examine the hydrodynamics of systems with spontaneously broken multipolar symmetries using a systematic effective field theory. We focus on the simplest non-trivial setting: a system with charge and dipole symmetry, but without momentum conservation. When no symmetries are broken, our formalism reproduces the quartic subdiffusion ($\omega \sim -i k^4$) ch

Bartomeu Fiol, Ziwen Kong

We compute the planar limit, as all-order power series in the 't Hooft coupling, of various integrated 4-point functions of chiral primary operators of ${\cal N}=4$ SU(N) super Yang-Mills, and of moment map operators of ${\cal N}=2$ SU(N) SQCD. We do so by computing the planar free energy on $S^4$ of the respective massive deformations of these theories, and

Andreas Karch, Carlos Perez-Pardavila, Marcos Riojas, Merna Youssef

We study the growth of entanglement entropy in a doubly holographic model of gravity for a spherical AdS black hole. Compared to previous work, which was limited to the case of planar black holes, this introduces an extra scale to the problem. This allows us to analyze the interplay between the reorganization of entanglement entropy due to island formation a

Raymond C. Simons, Casey Papovich, Ivelina G. Momcheva, Gabriel Brammer

We present an overview of the CANDELS Lyman-a Emission At Reionization (CLEAR) survey. CLEAR is a 130 orbit program of the Hubble Space Telescope using the Wide Field Camera 3 (WFC3) IR G102 grism. CLEAR targets 12 pointings divided between the GOODS-N and GOODS-S fields of the Cosmic Assembly Near-IR Deep Extragalactic Legacy Survey (CANDELS). Combined with