March 2023 arXiv papers — page 42

Michael K. Brown, Daniel Erman

We give a short, new proof of a recent result of Hanlon-Hicks-Lazarev about toric varieties. As in their work, this leads to a proof of a conjecture of Berkesch-Erman-Smith on virtual resolutions and to a resolution of the diagonal in the simplicial case.

Takayuki Myo, Kiyoshi Kato

We investigate the soft dipole resonance in the proton-rich nucleus $^8_6$C$_2$, which is a collective dipole oscillation of four valence protons against the $\alpha$ core,and discuss the isospin symmetry with the mirror nucleus $^8_2$He$_6$. We use the $\alpha+N+N+N+N$ five-body cluster model and many-body resonances are obtained using the complex-scaling m

Jiashu Wu, Yang Wang, Hao Dai, Chengzhong Xu

As Internet of Things devices become prevalent, using intrusion detection to protect IoT from malicious intrusions is of vital importance. However, the data scarcity of IoT hinders the effectiveness of traditional intrusion detection methods. To tackle this issue, in this paper, we propose the Adaptive Bi-Recommendation and Self-Improving Network (ABRSI) bas

Sharon Zilberhertz

We prove that the sequence of the sums of two squares do not have metric Poissonian pair correlation.

Stephanie Tsuei, Wenjie Mo, Stefano Soatto

In state estimation algorithms that use feature tracks as input, it is customary to assume that the errors in feature track positions are zero-mean Gaussian. Using a combination of calibrated camera intrinsics, ground-truth camera pose, and depth images, it is possible to compute ground-truth positions for feature tracks extracted using an image processing a

Ramana Nagasamudram, Anindya Banerjee, David A. Naumann

Verifying relations between programs arises as a task in various verification contexts such as optimizing transformations, relating new versions of programs with older versions (regression verification), and noninterference. However, relational verification for programs acting on dynamically allocated mutable state is not well supported by existing tools, wh

Vladimir Batagelj

The notion of a core is generalized to multiway networks. To determine the multiway cores, we adapted already-known algorithms for determining the generalized cores in one-mode and two-mode networks. A new node property, node diversity has been introduced. The newly introduced notions are illustrated with application on the multiway networks of European airp

Tianyi Zhao, Shamik Sarkar, Enes Krijestorac, Danijela Cabric

Radio frequency fingerprinting has been proposed for device identification. However, experimental studies also demonstrated its sensitivity to deployment changes. Recent works have addressed channel impacts by developing robust algorithms accounting for time and location variability, but the impacts of receiver impairments on transmitter fingerprints are yet

Anurag Ghosh, N. Dinesh Reddy, Christoph Mertz, Srinivasa G. Narasimhan

Real-time efficient perception is critical for autonomous navigation and city scale sensing. Orthogonal to architectural improvements, streaming perception approaches have exploited adaptive sampling improving real-time detection performance. In this work, we propose a learnable geometry-guided prior that incorporates rough geometry of the 3D scene (a ground

Ana Jojic, Zhen Wang, Nebojsa Jojic

We demonstrate that, through appropriate prompting, GPT-3 family of models can be triggered to perform iterative behaviours necessary to execute (rather than just write or recall) programs that involve loops, including several popular algorithms found in computer science curricula or software developer interviews. We trigger execution and description of Iter

Sukrit Venkatagiri, Joseph S. Schafer, Stephen Prochaska

Mis- and disinformation can spread rapidly on video-sharing platforms (VSPs). Despite the growing use of VSPs, there has not been a proportional increase in our ability to understand this medium and the messages conveyed through it. In this work, we draw on our prior experiences to outline three core challenges faced in studying VSPs in high-stakes and fast-

Xiaomeng Hu, Jiawang Nie

This paper studies generalized semi-infinite programs (GSIPs) given by polynomials. We propose a hierarchy of polynomial optimization relaxations to solve them. They are based on Lagrange multiplier expressions and polynomial extensions. Moment-SOS relaxations are applied to solve the polynomial optimization. The convergence of this hierarchy is shown under

Pingchuan Ma, Alexandros Haliassos, Adriana Fernandez-Lopez, Honglie Chen

Audio-visual speech recognition has received a lot of attention due to its robustness against acoustic noise. Recently, the performance of automatic, visual, and audio-visual speech recognition (ASR, VSR, and AV-ASR, respectively) has been substantially improved, mainly due to the use of larger models and training sets. However, accurate labelling of dataset

Jorge E. Horvath, Rodrigo Rosas Fernandes

Quantum Mechanics (QM) stands alone as a (very) successful physical theory, but the meaning of its variables and the status of many quantities in the mathematical formalism is obscure. This unique situation prompted the need for attribution of a physical meaning to the latter, a procedure known as interpretation. On the other hand, the study of QM is usually

Ignacio Bono Parisi, Inés Pacharoni

The Matrix Bochner Problem aims to classify which weight matrices have their sequence of orthogonal polynomials as eigenfunctions of a second-order differential operator. Casper and Yakimov, in [4], demonstrated that, under certain hypotheses, all solutions to the Matrix Bochner Problem are noncommutative bispectral Darboux transformations of a direct sum of

Zikui Cai, Yaoteng Tan, M. Salman Asif

We propose an approach for adversarial attacks on dense prediction models (such as object detectors and segmentation). It is well known that the attacks generated by a single surrogate model do not transfer to arbitrary (blackbox) victim models. Furthermore, targeted attacks are often more challenging than the untargeted attacks. In this paper, we show that

Peyman Ghasemi, Joon Lee

The use of International Classification of Diseases (ICD) codes in healthcare presents a challenge in selecting relevant codes as features for machine learning models due to this system's large number of codes. In this study, we compared several unsupervised feature selection methods for an ICD code database of 49,075 coronary artery disease patients in Albe

Annika Stein

In the field of high-energy physics, deep learning algorithms continue to gain in relevance and provide performance improvements over traditional methods, for example when identifying rare signals or finding complex patterns. From an analyst's perspective, obtaining highest possible performance is desirable, but recently, some attention has been shifted

Hongjin He, Changjian Liu, Dongmei Xiao

In the paper, we first give the least upper bound formula on the number of centers of planar real polynomial Hamiltonian vector fields. This formula reveals that the greater the number of invariant straight lines of the vector field and the less the number of its centers. Then we obtain some rules on the configurations of centers of planar real polynomial Ha

Junjie Ke, Keren Ye, Jiahui Yu, Yonghui Wu

Assessing the aesthetics of an image is challenging, as it is influenced by multiple factors including composition, color, style, and high-level semantics. Existing image aesthetic assessment (IAA) methods primarily rely on human-labeled rating scores, which oversimplify the visual aesthetic information that humans perceive. Conversely, user comments offer m

Michael J. Zellinger, Peter Bühlmann

Cluster analysis relies on effective benchmarks for evaluating and comparing different algorithms. Simulation studies on synthetic data are popular because important features of the data sets, such as the overlap between clusters, or the variation in cluster shapes, can be effectively varied. Unfortunately, creating evaluation scenarios is often laborious, a

Amelia C. Warden, Christopher D. Wickens, Daniel Rehberg, Francisco R. Ortega

While visual search for targets within a complex scene might benefit from using augmented-reality (AR) head-mounted display (HMD) technologies helping to efficiently direct human attention, imperfectly reliable automation support could manifest in occasional errors. The current study examined the effectiveness of different HMD cues that might support visual

Thomas A. Trainor

Small collision systems, e.g. $p$-$p$ and $p$-Pb collisions, comprise a potential reference for more-central A-A collisions with regard to production (or not) of a thermalized quark-gluon plasma (QGP). Small systems with low particle densities should evolve according to simple QCD mechanisms including projectile-nucleon dissociation and dijet production. But

Victor Grand, Baptiste Flipon, Alexis Gaillac, Marc Bernacki

A recently developed full field level-set model of continuous dynamic recrystallization is applied to simulate zircaloy-4 recrystallization during hot compression and subsequent heat treatment. The influence of strain rate, final strain and initial microstructure is investigated, by experimental and simulation tools. The recrystallization heterogeneity is qu

Julian Martinez-Iriarte

This paper proposes a framework to analyze the effects of counterfactual policies on the unconditional quantiles of an outcome variable. For a given counterfactual policy, we obtain identified sets for the effect of both marginal and global changes in the proportion of treated individuals. To conduct a sensitivity analysis, we introduce the quantile breakdow

Guoxian Song, Hongyi Xu, Jing Liu, Tiancheng Zhi

While substantial progresses have been made in automated 2D portrait stylization, admirable 3D portrait stylization from a single user photo remains to be an unresolved challenge. One primary obstacle here is the lack of high quality stylized 3D training data. In this paper, we propose a novel framework \emph{AgileGAN3D} that can produce 3D artistically appe

Raghavendra N. Bhat

In the paper "An Abelian Loop for Non-Composites" (arXiv:110.14716), we introduced a group-like structure consisting of odd prime numbers and 1, with properties that allowed us to prove analogous results to well known theorems in Number Theory. In this paper, we explore some theorems and conjectures in the SP space.

Richard A. Davis, Leon Fernandes, Konstantinos Fokianos

A novel methodology is proposed for clustering multivariate time series data using energy distance defined in Sz\'ekely and Rizzo (2013). Specifically, a dissimilarity matrix is formed using the energy distance statistic to measure separation between the finite dimensional distributions for the component time series. Once the pairwise dissimilarity matrix is

Caleb K. Harada, Courtney D. Dressing, Munazza K. Alam, James Kirk

Moons orbiting exoplanets ("exomoons") may hold clues about planet formation, migration, and habitability. In this work, we investigate the plausibility of exomoons orbiting the temperate ($T_\text{eq}=294$ K) giant ($R = 9.2$ R$_\oplus$) planet HIP 41378 f, which has been shown to have a low apparent bulk density of $0.09\,\text{g}\,\text{cm}^{-3}$ and a fl

Kaan Gokcesu, Hakan Gokcesu

This study presents an effective global optimization technique designed for multivariate functions that are H\"older continuous. Unlike traditional methods that construct lower bounding proxy functions, this algorithm employs a predetermined query creation rule that makes it computationally superior. The algorithm's performance is assessed using the average

Paula Maddigan, Teo Susnjak

The explosion of data in recent years is driving individuals to leverage technology to generate insights. Traditional tools bring heavy learning overheads and the requirement for understanding complex charting techniques. Such barriers can hinder those who may benefit from harnessing data for informed decision making. The emerging field of generating data vi

Ryan-Rhys Griffiths

In many areas of the observational and experimental sciences data is scarce. Data observation in high-energy astrophysics is disrupted by celestial occlusions and limited telescope time while data derived from laboratory experiments in synthetic chemistry and materials science is time and cost-intensive to collect. On the other hand, knowledge about the data

Hong Yi Huang

Let $G$ be a permutation group on a finite set $\Omega$. The base size of $G$ is the minimal size of a subset of $\Omega$ with trivial pointwise stabiliser in $G$. In this paper, we extend earlier work of Fawcett by determining the precise base size of every finite primitive permutation group of diagonal type. In particular, this is the first family of primi

Albert S. Berahas, Raghu Bollapragada, Shagun Gupta

Gradient tracking methods have emerged as one of the most popular approaches for solving decentralized optimization problems over networks. In this setting, each node in the network has a portion of the global objective function, and the goal is to collectively optimize this function. At every iteration, gradient tracking methods perform two operations (step

Björn Bokelmann, Stefan Lessmann

Many forecasting applications have a limited distributed target variable, which is zero for most observations and positive for the remaining observations. In the econometrics literature, there is much research about statistical model building for limited distributed target variables. Especially, there are two component model approaches, where one model is bu

S. Lotfimarangloo, V. A. Chirayath, S. Mukherjee, H. Akafzade

Magnetic bottle Time-of-Flight (ToF) spectrometers can measure the energy spectra of all electrons emitted into a 2$\pi$ sr solid angle simultaneously, greatly reducing data collection time. When the detection of the annihilation gamma ($\gamma$) and the detection of the electron (e) are used as timing signals for ToF spectrometers, the e-$\gamma$ time diffe

Phillip Schneider, Nils Rehtanz, Kristiina Jokinen, Florian Matthes

As the healthcare sector is facing major challenges, such as aging populations, staff shortages, and common chronic diseases, delivering high-quality care to individuals has become very difficult. Conversational agents have shown to be a promising technology to alleviate some of these issues. In the form of digital health assistants, they have the potential

Di Fu, Fares Abawi, Stefan Wermter

Robot facial expressions and gaze are important factors for enhancing human-robot interaction (HRI), but their effects on human collaboration and perception are not well understood, for instance, in collaborative game scenarios. In this study, we designed a collaborative triadic HRI game scenario, where two participants worked together to insert objects into

Gregory Dexter, Rajiv Khanna, Jawad Raheel, Petros Drineas

We present novel bounds for coreset construction, feature selection, and dimensionality reduction for logistic regression. All three approaches can be thought of as sketching the logistic regression inputs. On the coreset construction front, we resolve open problems from prior work and present novel bounds for the complexity of coreset construction methods.

Qiangqiang Huang, John J. Leonard

Inferring the posterior distribution in SLAM is critical for evaluating the uncertainty in localization and mapping, as well as supporting subsequent planning tasks aiming to reduce uncertainty for safe navigation. However, real-time full posterior inference techniques, such as Gaussian approximation and particle filters, either lack expressiveness for repre

Connor Mooney, Ovidiu Savin

We construct viscosity solutions to the special Lagrangian equation that are Lipschitz but not $C^1$.

Tao Ma, Jin Zhu, Hengrui Cai, Zhengling Qi

In real-world applications of reinforcement learning, it is often challenging to obtain a state representation that is parsimonious and satisfies the Markov property without prior knowledge. Consequently, it is common practice to construct a state larger than necessary, e.g., by concatenating measurements over contiguous time points. However, needlessly incr

David B. Williams-Young, Andrey Asadchev, Doru Thom Popovici, David Clark

With the growing reliance of modern supercomputers on accelerator-based architectures such a GPUs, the development and optimization of electronic structure methods to exploit these massively parallel resources has become a recent priority. While significant strides have been made in the development of GPU accelerated, distributed memory algorithms for many-b

Ilias Triantafyllopoulos, Georgios Paraskevopoulos, Alexandros Potamianos

We propose a deep architecture for depression detection from social media posts. The proposed architecture builds upon BERT to extract language representations from social media posts and combines these representations using an attentive bidirectional GRU network. We incorporate affective information, by augmenting the text representations with features extr

Hongyi Chen, Shiyu Feng, Ye Zhao, Changliu Liu

This paper describes a hierarchical solution consisting of a multi-phase planner and a low-level safe controller to jointly solve the safe navigation problem in crowded, dynamic, and uncertain environments. The planner employs dynamic gap analysis and trajectory optimization to achieve collision avoidance with respect to the predicted trajectories of dynamic

Manisha Shrestha, Mattia Bulla, Lorenzo Nativi, Ivan Markin

A merger of binary neutron stars creates heavy unstable elements whose radioactive decay produces a thermal emission known as a kilonova. In this paper, we predict the photometric and polarimetric behaviour of this emission by performing 3-D Monte Carlo radiative transfer simulations. In particular, we choose three hydrodynamical models for merger ejecta, tw

Alexander Mozeika, Mohammad M. Jalalzai, Marcin P. Pawlowski

Blockchains facilitate decentralization, security, identity, and data management in cyber-physical systems. However, consensus protocols used in blockchains are prone to high message and computational complexity costs and are not suitable to be used in IoT. One way to reduce message complexity is to randomly assign network nodes into committees or shards. Ke

Veselin B. Kostov, Tamas Borkovits, Saul A. Rappaport, Brian P. Powell

We present the discovery of a new highly compact quadruple star system, TIC 219006972, consisting of two eclipsing binary stars with orbital periods of 8.3 days and 13.7 days, and an outer orbital period of only 168 days. This period is a full factor of 2 shorter than the quadruple with the shortest outer period reported previously, VW LMi, where the two bin

Bokai Yao

This dissertation aims to provide a comprehensive account of set theory with urelements. In Chapter 1, I present mathematical and philosophical motivations for studying urelement set theory and lay out the necessary technical preliminaries. Chapter 2 is devoted to the axiomatization of urelement set theory, where I introduce a hierarchy of axioms and discuss

J. R. Myra, J. Cheng, S. E. Parker

Blob-filaments (or simply 'blobs') are coherent structures formed by turbulence and sustained by nonlinear processes in the edge and scrape-off layer (SOL) of tokamaks and other magnetically confined plasmas. The dynamics of these blob-filaments, in particular their radial motion, can influence the scrape-off layer width and plasma interactions with both the

Wiktor Piotrowski, Roni Stern, Yoni Sher, Jacob Le

Planning agents are ill-equipped to act in novel situations in which their domain model no longer accurately represents the world. We introduce an approach for such agents operating in open worlds that detects the presence of novelties and effectively adapts their domain models and consequent action selection. It uses observations of action execution and mea

Toshiyasu Arai

In this note we show through infinitary derivations that each provably well-founded strict partial order in ${\rm ACA}_{0}$ admits an embedding to an ordinal$<\varepsilon_{0}$.

Josef F. Dorfmeister

We discuss how the loop group method for harmonic maps from Riemann manifolds M to inner symmetric spaces S depends on the choice of a base point in M.

Behrooz Tahmasebi, Stefanie Jegelka

In practice, encoding invariances into models improves sample complexity. In this work, we study this phenomenon from a theoretical perspective. In particular, we provide minimax optimal rates for kernel ridge regression on compact manifolds, with a target function that is invariant to a group action on the manifold. Our results hold for any smooth compact L

Rasha Almughrabi

We compute explicitly the Bergman kernels of all two dimensional monomial polyhedra, a class of domains including the Hartogs triangle and some of its generalizations. The kernel is computed from the representation of such domains as quotient of the product of a disc and punctured disc.

Shayan Fazeli, Lionel Levine, Mehrab Beikzadeh, Baharan Mirzasoleiman

Recent advances in remote health monitoring systems have significantly benefited patients and played a crucial role in improving their quality of life. However, while physiological health-focused solutions have demonstrated increasing success and maturity, mental health-focused applications have seen comparatively limited success in spite of the fact that st

Arkadij Bojko, Jiahui Huang

The problem of studying the two seemingly unrelated sets of invariants forming the Segre and the Verlinde series has gone through multiple different adaptations including a version for the virtual geometries of Quot schemes on surfaces and Calabi-Yau fourfolds. Our work is the first one to address the equivariant setting for both $\mathbb{C}^2$ and $\mathbb{

Hongyi Chen, Changliu Liu

This study proposes a safe and sample-efficient reinforcement learning (RL) framework to address two major challenges in developing applicable RL algorithms: satisfying safety constraints and efficiently learning with limited samples. To guarantee safety in real-world complex environments, we use the safe set algorithm (SSA) to monitor and modify the nominal

Xiaojun Yao

We test the eigenstate thermalization hypothesis (ETH) for 2+1 dimensional SU(2) lattice gauge theory. By considering the theory on a chain of plaquettes and truncating basis states for link variables at $j=1/2$, we can map it onto a quantum spin chain with local interactions and numerically exactly diagonalize the Hamiltonian for reasonably large lattice si

Ruiqi Rich Zhu, Cheng He, Yu Jeffrey Hu

Despite the popularity of product recommendations on online investment platforms, few studies have explored their impact on investor behaviors. Using data from a global e-commerce platform, we apply regression discontinuity design to causally examine the effects of product recommendations on online investors' mutual fund investments. Our findings indicate th

Astrid Pechstein, Martin Meindlhumer, Alexander Humer

We propose a new three-dimensional formulation based on the mixed Tangential-Displacement Normal-Normal-Stress (TDNNS) method for elasticity. In elastic TDNNS elements, the tangential component of the displacement field and the normal component of the stress vector are degrees of freedom and continuous across inter-element interfaces. TDNNS finite elements h

Vahid Partovi Nia, Guojun Zhang, Ivan Kobyzev, Michael R. Metel

Deep models are dominating the artificial intelligence (AI) industry since the ImageNet challenge in 2012. The size of deep models is increasing ever since, which brings new challenges to this field with applications in cell phones, personal computers, autonomous cars, and wireless base stations. Here we list a set of problems, ranging from training, inferen

Alexander Dukhovny

The concept of Shannon Entropy for probability distributions and associated Maximum Entropy Principle are extended here to the concepts of Relative Divergence of one Grading Function from another and Maximum Relative Divergence Principle for grading functions on direct products of totally ordered chains (chain bundles). Several Operations Research applicatio

S. Jadach, B. F. L. Ward, Z. A. Was

We extend the YFS IR resummation theory to include all of the attendant collinear contributions which exponentiate. This improves the original YFS formulation in which only a part of these contributions was exponentiated. We show that the new resummed contributions agree with known results from the collinear factorization approach and we argue that they impr

Chia-Wei Hsu, Chenyue Liu, Zhewei Liu, Ali Mostafavi

Extreme weather poses significant threats to air transportation systems, causing flight rerouting and cancellations, as well as passenger travel delays. With the growing frequency of extreme weather hazards, it is essential to understand the extent to which disruptions in flights and subsequent cancellations impact passenger delays. This study focuses on qua

Junyi Liu, Aleksandar Dragojevic, Shane Flemming, Antonios Katsarakis

In-memory ordered key-value stores are an important building block in modern distributed applications. We present Honeycomb, a hybrid software-hardware system for accelerating read-dominated workloads on ordered key-value stores that provides linearizability for all operations including scans. Honeycomb stores a B-Tree in host memory, and executes SCAN and G

Dmitriy Bilyk, Damir Ferizović, Alexey Glazyrin, Ryan W. Matzke

We study measures and point configurations optimizing energies based on multivariate potentials. The emphasis is put on potentials defined by geometric characteristics of sets of points, which serve as multi-input generalizations of the well-known Riesz potentials for pairwise interaction. One of such potentials is volume squared of the simplex with vertices

Nilanjan Das

We study a "$p$-powered" version $K_n^p(F(R))$ of the well-known Bohr radius problem for the family $F(R)$ of holomorphic functions $f: R\to X$ satisfying $\|f\|<\infty$, where $\|.\|$ is a norm in the function space $F(R)$, $R\subset\mathbb{C}^n$ is a complete Reinhardt domain and $X$ is a complex Banach space. For all $p>0$, we describe in full details the

David Georg Reichelt, Stefan Kühne, Wilhelm Hasselbring

To develop software with optimal performance, even small performance changes need to be identified. Identifying performance changes is challenging since the performance of software is influenced by non-deterministic factors. Therefore, not every performance change is measurable with reasonable effort. In this work, we discuss which performance changes are me

James Mullen, Dinesh Manocha

We present PACE, a novel method for modifying motion-captured virtual agents to interact with and move throughout dense, cluttered 3D scenes. Our approach changes a given motion sequence of a virtual agent as needed to adjust to the obstacles and objects in the environment. We first take the individual frames of the motion sequence most important for modelin

Xiyuan Zhang, Ranak Roy Chowdhury, Jingbo Shang, Rajesh Gupta

Time-series data augmentation mitigates the issue of insufficient training data for deep learning models. Yet, existing augmentation methods are mainly designed for classification, where class labels can be preserved even if augmentation alters the temporal dynamics. We note that augmentation designed for forecasting requires diversity as well as coherence w

Pavel Reich, Walid Maalej

To create unit tests, it may be necessary to refactor the production code, e.g. by widening access to specific methods or by decomposing classes into smaller units that are easier to test independently. We report on an extensive study to understand such composite refactoring procedures for the purpose of improving testability. We collected and studied 346,84

Klaas Pieter Hart

We point out that a construction by Butkovi\v{c}ov\'a of a chain of length $\mathfrak{c}^+$ in the Rudin-Frol\'ik order on $\beta\omega$ can easily be adapted to produce, given an uncountable cardinal $\kappa$, a chain of length $(2^\kappa)^+$ in the Rudin-Frol\'ik order on $\beta\kappa$.

J. Bayron Orjuela-Quintana, Savvas Nesseris

Within the framework of modified gravity (MG), the quasi-static (QS) and sub-horizon (SH) approximations are widely used in analyses aiming to identify departures from the concordance model at late-times. In general, it is assumed that time derivatives are subdominant with respect to spatial derivatives given that the relevant physical modes are those well i

Jolanta Gasiorek, Anna Gasiorek, Bartosz Babiarczuk, Walis Jones

In this paper, we present indirect methods to define the change in the rates of hydrolysis and condensation reactions of a silica oxide network formed of (3-glycidoxypropyl)methyltriethoxysilane (GPTMS) and (3-aminopropyl)triethoxysilane (ApTEOS), which was strongly dependent upon the nature of the solvent used in the reaction environment, such as methanol,

Christopher Turansick

We readdress the problem of nonparametric statistical testing of random utility models proposed in Kitamura and Stoye (2018). Although their test is elegant, it is subject to computational constraints which leaves execution of the test infeasible in many applications. We note that much of the computational burden in Kitamura and Stoye's test is due to their

Rossella Della Marca, Alberto d'Onofrio, Mattia Sensi, Sara Sottile

In contemporary society, social networks accelerate decision dynamics causing a rapid switch of opinions in a number of fields, including the prevention of infectious diseases by means of vaccines. This means that opinion dynamics can nowadays be much faster than the spread of epidemics. Hence, we propose a Susceptible-Infectious-Removed epidemic model coupl

Bruno Arcanjo, Bruno Ferrarini, Michael Milford, Klaus D. McDonald-Maier

Visual place recognition (VPR) is an essential component of robot navigation and localization systems that allows them to identify a place using only image data. VPR is challenging due to the significant changes in a place's appearance under different illumination throughout the day, with seasonal weather and when observed from different viewpoints. Currentl

A. A. Semenov, J. Samelin, Ch. Boldt, M. Schünemann

The widely used experimental technique of continuous-wave detection assumes counting pulses of photocurrent from a click-type detector inside a given measurement time window. With such a procedure we miss out the photons detected after each photocurrent pulse during the detector dead time. Additionally, each pulse may initialize so-called afterpulse, which i

Sezer Sorgun, Ali Gökhan Ertaş, İbrahim Gunaltili

A configuration of the triple $(\mathcal{P}, \mathcal{L}, \mathcal{I})$ on the incidence relation which holds the properties of "Any two points are incident with at most one line" and "Any two lines are incident with at most one point". In projective geometry, bipartite graphs can be used as an incidence model between the points and the lines of a configurat

Mahdi Soltanolkotabi, Dominik Stöger, Changzhi Xie

Recently, there has been significant progress in understanding the convergence and generalization properties of gradient-based methods for training overparameterized learning models. However, many aspects including the role of small random initialization and how the various parameters of the model are coupled during gradient-based updates to facilitate good

Heng Yu, Joel Julin, Zoltan A. Milacski, Koichiro Niinuma

Light Field Networks, the re-formulations of radiance fields to oriented rays, are magnitudes faster than their coordinate network counterparts, and provide higher fidelity with respect to representing 3D structures from 2D observations. They would be well suited for generic scene representation and manipulation, but suffer from one problem: they are limited

Ruo Yang, Binghui Wang, Mustafa Bilgic

Integrated Gradients (IG) as well as its variants are well-known techniques for interpreting the decisions of deep neural networks. While IG-based approaches attain state-of-the-art performance, they often integrate noise into their explanation saliency maps, which reduce their interpretability. To minimize the noise, we examine the source of the noise analy

Jason Zhu, Arijit Khan, Cuneyt Gurcan Akcora

Blockchains are significantly easing trade finance, with billions of dollars worth of assets being transacted daily. However, analyzing these networks remains challenging due to the sheer volume and complexity of the data. We introduce a method named InnerCore that detects market manipulators within blockchain-based networks and offers a sentiment indicator

Wenwen Zhang, Xinyu Xiao, Hangguan Shan, Eryun Liu

One-shot object detection (OSOD) aims to detect all object instances towards the given category specified by a query image. Most existing studies in OSOD endeavor to explore effective cross-image correlation and alleviate the semantic feature misalignment, however, ignoring the phenomenon of the model bias towards the base classes and the generalization degr

Pratika Dayal, Sambit K. Giri

Warm Dark Matter (WDM) particles with masses ($\sim$ kilo electronvolt) offer an attractive solution to the small-scale issues faced by the Cold Dark Matter (CDM) paradigm. The delay of structure formation in WDM models and the associated dearth of low-mass systems at high-redshifts makes this an ideal time to revisit WDM constraints in light of the unpreced

Praful Gagrani, Victor Blanco, Eric Smith, David Baum

Developing a mathematical understanding of autocatalysis in reaction networks has both theoretical and practical implications. We review definitions of autocatalytic networks and prove some properties for minimal autocatalytic subnetworks (MASs). We show that it is possible to classify MASs in equivalence classes, and develop mathematical results about their

Lucas Berent, Lukas Burgholzer, Peter-Jan H. S. Derks, Jens Eisert

In classical computing, error-correcting codes are well established and are ubiquitous both in theory and practical applications. For quantum computing, error-correction is essential as well, but harder to realize, coming along with substantial resource overheads and being concomitant with needs for substantial classical computing. Quantum error-correcting c

Chi Sun, Arne Brataas, Jacob Linder

Recent works have predicted materials featuring bands with a large spin-splitting distinct from ferromagnetic and relativistically spin-orbit coupled systems. Materials displaying this property are known as altermagnets and feature a spin-polarized band structure reminiscent of a $d$-wave superconducting order parameter. We here consider the contact between

M. Schneider, F. Del Porro, M. Herrero-Valea, S. Liberati

Using a quantum tunneling derivation, we show the resilience of Hawking radiation in Lorentz violating gravity. In particular, we show that the standard derivation of the Hawking effect in relativistic quantum field theory can be extended to Lorentz breaking situations thanks to the presence of universal horizons (causal boundaries for infinite speed signals

Mario J. Pinheiro

The Liouville theorem is a fundamental concept in understanding the properties of systems that adhere to Hamilton's equations. However, the traditional notion of the theorem may not always apply. Specifically, when the entropy gradient in phase space fails to reach equilibrium, the phase-space density may not have a zero time derivative, i.e., $\frac{d\rho}{

Michael Ginn

Language documentation is a critical aspect of language preservation, often including the creation of Interlinear Glossed Text (IGT). Creating IGT is time-consuming and tedious, and automating the process can save valuable annotator effort. This paper describes the baseline system for the SIGMORPHON 2023 Shared Task of Interlinear Glossing. In our system, we

Pierre V. Baudin, Mircea Teodorescu

For a transparent well with a known volume capacity, changes in fluid level result in predictable changes in magnification of an overhead light source. For a given well size and fluid, the relationship between volume and magnification can be calculated if the fluid's index of refraction is known or in a naive fashion with a calibration procedure. Light sourc

Stephenson Strobel

I examine the impacts of extending residency training programs on the supply and quality of physicians practicing primary care. I leverage mandated extended residency lengths for primary care practitioners that were rolled out over 20 years in Canada on a province-by-province basis. I compare these primary care specialties to other specialties that did not c

Priyaa Varshinee Srinivasan

This thesis develops the categorical proof theory for the non-compact multiplicative dagger linear logic, and investigates its applications to Categorical Quantum Mechanics (CQM). The existing frameworks of CQM are categorical proof theories of compact dagger linear logic, and are motivated by the interpretation of quantum systems in the category of finite d

Li An, Jun Shen

Cell pathology in neuropsychiatric disorders has mainly been accessible by analyzing postmortem tissue samples. Although molecular transverse relaxation informs local cellular microenvironment via molecule-environment interactions, precise determination of the transverse relaxation times of molecules with scalar couplings (J), such as glutamate and glutamine

Alberto Espuny Díaz, Lyuben Lichev, Dieter Mitsche, Alexandra Wesolek

A rooted tree is balanced if the degree of a vertex depends only on its distance to the root. In this paper we determine the sharp threshold for the appearance of a large family of balanced spanning trees in the random geometric graph $\mathcal{G}(n,r,d)$. In particular, we find the sharp threshold for balanced binary trees. More generally, we show that all

K. Abe, N. Akhlaq, R. Akutsu, H. Alarakia-Charles

This paper reports the first measurement of muon neutrino charged-current interactions without pions in the final state using multiple detectors with correlated energy spectra at T2K. The data was collected on hydrocarbon targets using the off-axis T2K near detector (ND280) and the on-axis T2K near detector (INGRID) with neutrino energy spectra peaked at 0.6

Rafael Pina, Varuna De Silva, Corentin Artaud

When learning a task as a team, some agents in Multi-Agent Reinforcement Learning (MARL) may fail to understand their true impact in the performance of the team. Such agents end up learning sub-optimal policies, demonstrating undesired lazy behaviours. To investigate this problem, we start by formalising the use of temporal causality applied to MARL problems

Abhineet Agarwal, Anish Agarwal, Suhas Vijaykumar

Consider a setting where there are $N$ heterogeneous units and $p$ interventions. Our goal is to learn unit-specific potential outcomes for any combination of these $p$ interventions, i.e., $N \times 2^p$ causal parameters. Choosing a combination of interventions is a problem that naturally arises in a variety of applications such as factorial design experim