March 2023 arXiv papers — page 135

Aftab Hussain, Md Rafiqul Islam Rabin, Bowen Xu, David Lo

Although deep neural models substantially reduce the overhead of feature engineering, the features readily available in the inputs might significantly impact training cost and the performance of the models. In this paper, we explore the impact of an unsuperivsed feature enrichment approach based on variable roles on the performance of neural models of code.

Sanna Gulati, Debbijoy Bhattacharya, M. C. Ramadevi, C. S. Stalin

A comprehensive study of the nucleus and western hotspot of Pictor A is carried out using AstroSat observations, 13 years of Fermi, and archival Swift observations, along with other published data. We report the first detection of the western hotspot of Pictor A in the far-UV band using observations from AstroSat-UVIT. The broad-band SED of the western hotsp

Junkai Fan, Fei Guo, Jianjun Qian, Xiang Li

Removing haze from real-world images is challenging due to unpredictable weather conditions, resulting in the misalignment of hazy and clear image pairs. In this paper, we propose an innovative dehazing framework that operates under non-aligned supervision. This framework is grounded in the atmospheric scattering model, and consists of three interconnected n

Rukhshanda Hussain, Hritam Basak

Glaucoma is a chronic visual disease that may cause permanent irreversible blindness. Measurement of the cup-to-disc ratio (CDR) plays a pivotal role in the detection of glaucoma in its early stage, preventing visual disparities. Therefore, accurate and automatic segmentation of optic disc (OD) and optic cup (OC) from retinal fundus images is a fundamental r

Elia Bertoldo, Victor Pérez Sánchez, Maria Martínez, Manel Martínez

We propose and demonstrate two practical mitigation methods to attenuate the cosmic muon flux, compatible with experiments involving superconducting qubits: shallow underground sites and specific device orientation. Using a specifically-built cosmic muon detector, we identify underground sites, widely present in urban environments, where significant attenuat

Robert J. McCann, Cale Rankin, Kelvin Shuangjian Zhang

We prove the interior $C^{1,1}$ regularity of the indirect utilities which solve a subclass of principal-agent problems originally considered by Figalli, Kim, and McCann. Our approach is based on construction of a suitable comparison function which, essentially, allows one to pinch the solution between parabolas. The original ideas for this proof arise from

Ziyang Gao, Philipp Habegger

Recent developments on the uniformity of the number of rational points on curves and subvarieties in a moving abelian variety rely on the geometric concept of the degeneracy locus. The first-named author investigated the degeneracy locus in certain mixed Shimura varieties. In this expository note we revisit some of these results while minimizing the use of m

Lu Yu, Wei Xiang

Recently vision transformer models have become prominent models for a range of tasks. These models, however, usually suffer from intensive computational costs and heavy memory requirements, making them impractical for deployment on edge platforms. Recent studies have proposed to prune transformers in an unexplainable manner, which overlook the relationship b

Letong Wang, Xiaojun Dong, Yan Gu, Yihan Sun

Computing strongly connected components (SCC) is a fundamental problems in graph processing. As today's real-world graphs are getting larger and larger, parallel SCC is increasingly important. SCC is challenging in the parallel setting and is particularly hard on large-diameter graphs. Many existing parallel SCC implementations can be even slower than Tarjan

Bradley E. Schaefer

T CrB is one of the most-famous and brightest novae known, and is a recurrent nova with prior eruptions in 1866 and 1946 that peak at $V$=2.0. I have constructed light curves spanning 1842--2022 with 213,730 magnitudes, where the $B$ and $V$ magnitudes are fully corrected to the Johnson system. These light curves first reveal a unique complex high-state (wit

Rui Luo, Chunpeng Wang, Colin Keil, David Nguyen

This paper reports on Team Northeastern's Avatar system for telepresence, and our holistic approach to meet the ANA Avatar XPRIZE Final testing task requirements. The system features a dual-arm configuration with hydraulically actuated glove-gripper pair for haptic force feedback. Our proposed Avatar system was evaluated in the ANA Avatar XPRIZE Finals and c

Amir Mohammad Naseri, Walter Lucia, Amr Youssef

Remotely controlled mobile robots are important examples of Cyber-Physical Systems (CPSs). Recently, these robots are being deployed in many safety critical applications. Therefore, ensuring their cyber-security is of paramount importance. Different control schemes that have been proposed to secure such systems against sophisticated cyber-attacks require the

Behnam Khojasteh, Friedrich Solowjow, Sebastian Trimpe, Katherine J. Kuchenbecker

Machine learning and deep learning have been used extensively to classify physical surfaces through images and time-series contact data. However, these methods rely on human expertise and entail the time-consuming processes of data and parameter tuning. To overcome these challenges, we propose an easily implemented framework that can directly handle heteroge

Seita Nojiri, Toshihiro Nishimura, Kenjiro Tadakuma, Tetsuyou Watanabe

This study proposes a soft robotic device with a slim and flexible body that switches between air blowing and suction with a single airflow control. Suction is achieved by jet flow entraining surrounding air, and blowing is achieved by blocking and reversing jet flow. The thin and flexible flap gate enables the switching. Air flow is blocked while the gate i

Alisa Allaire, Christopher G. Atkeson

Tasks involving locally unstable or discontinuous dynamics (such as bifurcations and collisions) remain challenging in robotics, because small variations in the environment can have a significant impact on task outcomes. For such tasks, learning a robust deterministic policy is difficult. We focus on structuring exploration with multiple stochastic policies

Toshihiro Nishimura, Tsubasa Muryoe, Tetsuyou Watanabe

This study proposes a novel robotic hand that can achieve self-adaptive grasping and a large payload (over 20 kg) with a single actuator. Accordingly, two novel mechanisms, an actuation system with self-motion switching and a self-adaptive finger with a self-locking mechanism, are installed in a 1-degree-of-freedom robotic hand. The actuation system switches

Sanyam Vyas, John Hannay, Andrew Bolton, Professor Pete Burnap

Within recent times, cybercriminals have curated a variety of organised and resolute cyber attacks within a range of cyber systems, leading to consequential ramifications to private and governmental institutions. Current security-based automation and orchestrations focus on automating fixed purpose and hard-coded solutions, which are easily surpassed by mode

Zhiyuan Liu, Fei Gao, Guo P. Chen, Thomas M. Henderson

The antisymmetrized geminal power (AGP), a wave function equivalent to number-projected Hartree--Fock--Bogoliubov (HFB), and number-projected Bardeen--Cooper--Schrieffer (BCS) when working in the paired (natural orbitals) basis, has proven to be an excellent reference for strong pairing interactions. Several correlation methods have also been applied on top

Hengxin Tan, Yongkang Li, Yizhou Liu, Daniel Kaplan

The recently discovered kagome materials $A$V$_3$Sb$_5$ ($A$ = K, Rb, Cs) attract intense research interest in intertwined topology, superconductivity, and charge density waves (CDW). Although the in-plane $2\times2$ CDW is well studied, its out-of-plane structural correlation with the Fermi surface properties is less understood. In this work, we advance the

Karthik Shetty, Annette Birkhold, Srikrishna Jaganathan, Norbert Strobel

Objective: A digital twin of a patient can be a valuable tool for enhancing clinical tasks such as workflow automation, patient-specific X-ray dose optimization, markerless tracking, positioning, and navigation assistance in image-guided interventions. However, it is crucial that the patient's surface and internal organs are of high quality for any pose and

Jiajun Du, Xuecheng Tao, Tomislav Begušić, Lu Wei

Recently developed electronic pre-resonance stimulated Raman scattering (epr-SRS) microscopy, in which the Raman signal of a dye is significantly boosted by setting the incident laser frequency near the electronic excitation energy, has pushed the sensitivity of SRS microscopy close to that offered by confocal fluorescence microscopy. Prominently, the mainta

J. O. Gonzalez-Hernandez, T. Rainaldi, T. C. Rogers

Parametrizing TMD parton densities and fragmentation functions in ways that consistently match their large transverse momentum behavior in standard collinear factorization has remained notoriously difficult. We show how the problem is solved in a recently introduced set of steps for combining perturbative and nonperturbative transverse momentum in TMD factor

Hossein Ebrahimi, Milos Krsmanovic, Hessein Ali, Ranajay Ghosh

Biomimetic scale-covered substrates are architected meta-structures exhibiting fascinating emergent nonlinearities via the geometry of collective scales contacts. In spite of much progress in understanding their elastic nonlinearity, their dissipative behavior arising from scales sliding is relatively uninvestigated in the dynamic regime. Recently discovered

Shangming Chen, Fengde Chen, Vaibhava Srivastava, Rana D. Parshad

Population ecology theory is replete with density dependent processes. However trait-mediated or behavioral indirect interactions can both reinforce or oppose density-dependent effects. This paper presents the first two species competitive ODE and PDE systems where an Allee effect, which is a density dependent process and the fear effect, which is non-consum

Achim Zeileis, Paul Murrell

Prior to version 4.0.0 R had a poor default color palette (using highly saturated red, green, blue, etc.) and provided very few alternative palettes, most of which also had poor perceptual properties (like the infamous rainbow palette). Starting with version 4.0.0 R gained a new and much improved default palette and, in addition, a selection of more than 100

Anna Naszodi

In this paper, we identify two different sets of problems. The first covers the problems that the iterative proportional fitting (IPF) algorithm was developed to solve. These concern completing a population table by using a sample. The other set concerns constructing a counterfactual population table with the purpose of comparing two populations. The IPF is

Gerald A. Miller

Nuclear super-allowed $\beta$ decay has been used to obtain tight limits on the value of the CKM matrix element $V_{ud}$ that is important for unitarity tests, and therefore important for tests of the standard model. Current requirements on precision are so intense that effects formerly thought too small to matter are now relevant. This article is a brief re

Debadarshini Mishra, Aaron C. LaForge, Lauren M. Gorman, Sergio Díaz-Tendero

Roaming has been proposed as a possible mechanism contributing to molecular dissociation in which the fragments can move around one another at relatively large intermolecular distances before separating entirely. While roaming, there is sufficient time for other processes to occur, which can lead to the formation of new molecular compounds. Due to the neutra

Maximilian Bardo, Steffen Unkel

In statistical models for the analysis of time-to-event data, individual heterogeneity is usually accounted for by means of one or more random effects, also known as frailties. In the vast majority of the literature, the random effect is assumed to follow a continuous probability distribution. However, in some areas of application, a discrete frailty distrib

Abraham Holleran, Susan E. Martonosi, Michael Veatch

As observed during the COVID-19 pandemic, high-income countries, such as the U.S., may exhibit vaccine nationalism during a pandemic: stockpiling doses of vaccine for their own citizens and being reluctant to distribute doses of the vaccine to lower-income countries. While many cite moral objections to vaccine nationalism, vaccine inequity during a pandemic

Zoltán Balogh, Gréta Mezei, Nóra Tenk, András Magyarkuti

We explore the merits of neural network boosted, principal-component-projection-based, unsupervised data classification in single-molecule break junction measurements, demonstrating that this method identifies highly relevant trace classes according to the well-defined and well-visualized internal correlations of the dataset. To this end, we investigate sing

Takuro Mochizuki, Szilárd Szabó

Let $X$ be a compact Riemann surface. Let $(E,\theta)$ be a stable Higgs bundle of degree $0$ on $X$. Let $h_{\det(E)}$ denote a flat metric of the determinant bundle $\det(E)$. For any $t>0$, there exists a unique harmonic metric $h_t$ of $(E,\theta)$ such that $\det(h_t)=h_{\det(E)}$. We prove that if the Higgs bundle is induced by a line bundle on the nor

Amber Li, Tom Silver

State abstraction is an effective technique for planning in robotics environments with continuous states and actions, long task horizons, and sparse feedback. In object-oriented environments, predicates are a particularly useful form of state abstraction because of their compatibility with symbolic planners and their capacity for relational generalization. H

Nicholas Konz, Maciej A. Mazurowski

The image acquisition parameters (IAPs) used to create MRI scans are central to defining the appearance of the images. Deep learning models trained on data acquired using certain parameters might not generalize well to images acquired with different parameters. Being able to recover such parameters directly from an image could help determine whether a deep l

Emily First, Markus N. Rabe, Talia Ringer, Yuriy Brun

Formally verifying software properties is a highly desirable but labor-intensive task. Recent work has developed methods to automate formal verification using proof assistants, such as Coq and Isabelle/HOL, e.g., by training a model to predict one proof step at a time, and using that model to search through the space of possible proofs. This paper introduces

Yulei Qiu, Jihong Zhu, Cosimo Della Santina, Michael Gienger

Deformable Object Manipulation (DOM) is an important field of research as it contributes to practical tasks such as automatic cloth handling, cable routing, surgical operation, etc. Perception is considered one of the major challenges in DOM due to the complex dynamics and high degree of freedom of deformable objects. In this paper, we develop a novel image-

Terry Rudolph

The potential for artificial intelligence (AI) to take over the work of physicists should be treated with glee. Here I evaluate one of the scientific discoveries in quantum photonics made by a leading AI in the field, in order to try and gain insight into when I will be allowed to go spend my days sipping mezcal margaritas on a warm beach. My analysis leads

Emmanouil Fountoulakis, Nikolaos Pappas, Marios Kountouris

We consider the minimization of the cost of actuation error under resource constraints for real-time tracking in wireless autonomous systems. A transmitter monitors the state of a discrete random process and sends updates to the receiver over an unreliable wireless channel. The receiver takes actions according to the estimated state of the source. For each d

Wen-Hao Wu, Cheng-Yong Zhang, Cheng-Gang Shao, Wei-Liang Qian

In the electromagnetic channel, chaotic gravitational lensing is a peculiar phenomenon in strong gravitational lensing. In this work, we analyze the properties and emergence of chaotic gravitational lensing in the Manko-Novikov black hole spacetime. Aiming to understand better the underlying physics, we elaborate on the boundaries of the accessible region in

Gianluca Mittone, Walter Riviera, Iacopo Colonnelli, Robert Birke

Since its debut in 2016, Federated Learning (FL) has been tied to the inner workings of Deep Neural Networks (DNNs). On the one hand, this allowed its development and widespread use as DNNs proliferated. On the other hand, it neglected all those scenarios in which using DNNs is not possible or advantageous. The fact that most current FL frameworks only allow

Anna Naszodi

We illustrate the point with an empirical analysis of assortative mating in the US, namely, that the outcome of comparing two distant groups can be sensitive to whether comparing the groups directly, or indirectly via a series of counterfactual decompositions involving the groups' comparisons to some intermediate groups. We argue that the latter approach is

Eugene A. Eliseev, Anna N. Morozovska, Yulian M. Vysochanskii, Lesya P. Yurchenko

Using the Landau-Ginzburg-Devonshire approach, we study light-induced phase transitions, evolution of polar state and domain morphology in photo-ferroelectric nanoparticles (NPs). Light exposure increases the free carrier density near the NP surface and may in turn induce phase transitions from the nonpolar paraelectric to the polar ferroelectric phase. Usin

Chen Qian, Yue Jiang, Jicheng Jin, Thomas Christensen

Topological photonic crystals, which offer topologically protected and back-scattering-immune transport channels, have recently gained significant attention for both scientific and practical reasons. Although most current studies focus on dielectric materials with weak dispersions, this study focuses on topological phases in dispersive materials and presents

Bill Tomlinson, Rebecca W. Black, Donald J. Patterson, Andrew W. Torrance

As AI systems proliferate, their greenhouse gas emissions are an increasingly important concern for human societies. We analyze the emissions of several AI systems (ChatGPT, BLOOM, DALL-E2, Midjourney) relative to those of humans completing the same tasks. We find that an AI writing a page of text emits 130 to 1500 times less CO2e than a human doing so. Simi

Yamin Li, Saishuang Wu, Jiayang Xu, Haiwa Wang

Mother-child interaction is highly dynamic and reciprocal. Switching roles in these back-and-forth interactions serves as a crucial feature of reciprocal behaviors while the underlying neural entrainment is still not well-studied. Here, we designed a role-controlled cooperative task with dual EEG recording to study how differently two brains interact when mo

Laura Zheng, Julio Poveda, James Mullen, Shreelekha Revankar

Autonomous driving research currently faces data sparsity in representation of risky scenarios. Such data is both difficult to obtain ethically in the real world, and unreliable to obtain via simulation. Recent advances in virtual reality (VR) driving simulators lower barriers to tackling this problem in simulation. We propose the first data collection frame

Maibam Ricky Devi

The flavour problem and the viability of texture zeros of the Majorana mass matrix in the 3+1 active-sterile neutrino mixing scenario are investigated in this work using a novel bottom-up technique where we leverage the pertinent concepts of full modular group, modular invariants, and $A_4$ flavour symmetry as theoretical tools for the explicit construction

Yuqi Li, Lihua Zhang

The study of epidemic models plays an important role in mathematical epidemiology. There are many researches on epidemic models using ordinary differential equations, partial differential equations or stochastic differential equations. In contrast to these researches, our work analyzes the SEIRS (Susceptible-Exposed-Infected-Recovered-Susceptible) model usin

Karl Friston, Daniel Ari Friedman, Axel Constant, V. Bleu Knight

This paper introduces a variational formulation of natural selection, paying special attention to the nature of "things" and the way that different "kinds" of "things" are individuated from - and influence - each other. We use the Bayesian mechanics of particular partitions to understand how slow phylogenetic processes constrain - and are constrained by - fa

Mohammed Salouf

In this paper, we study weak solutions to complex Monge-Amp\`ere equations of the form $(\omega + dd^c \varphi)^n= F(\varphi,.)d\mu$ on a bounded strictly pseudoconvex domain in $\mathbb{C}^n$, where $\omega$ is a smooth $(1,1)$-form, $0\leq F$ is a continuous non-decreasing function, and $\mu$ is a positive non-pluripolar measure. Our results extend previou

Varsha Suresh, Desmond C. Ong

Machine learning models automatically learn discriminative features from the data, and are therefore susceptible to learn strongly-correlated biases, such as using protected attributes like gender and race. Most existing bias mitigation approaches aim to explicitly reduce the model's focus on these protected features. In this work, we propose to mitigate bia

Marc Aiguier, Isabelle Bloch, Salim Nibouche, Ramon Pino Perez

Modal logics have proved useful for many reasoning tasks in symbolic artificial intelligence (AI), such as belief revision, spatial reasoning, among others. On the other hand, mathematical morphology (MM) is a theory for non-linear analysis of structures, that was widely developed and applied in image analysis. Its mathematical bases rely on algebra, complet

Peihan Li, Lifeng Zhou

We study the problem of assigning robots with actions to track targets. The objective is to optimize the robot team's tracking quality which can be defined as the reduction in the uncertainty of the targets' states. Specifically, we consider two assignment problems given the different sensing capabilities of the robots. In the first assignment problem, a sin

Wassilij Gnedin, Srikanth B. Iyengar, Henning Krause

In the recent paper "The Nakayama functor and its completion for Gorenstein algebras", a class of Gorenstein algebras over commutative noetherian rings was introduced, and duality theorems for various categories of representations were established. The manuscript on hand provides more context to the results presented in the aforementioned work, identifies ne

Alan Edelman, John Urschel

This paper combines modern numerical computation with theoretical results to improve our understanding of the growth factor problem for Gaussian elimination. On the computational side we obtain lower bounds for the maximum growth for complete pivoting for $n=1:75$ and $n=100$ using the Julia JuMP optimization package. At $n=100$ we obtain a growth factor big

Timothy Chase, Chris Gnam, John Crassidis, Karthik Dantu

The detection of hazardous terrain during the planetary landing of spacecraft plays a critical role in assuring vehicle safety and mission success. A cheap and effective way of detecting hazardous terrain is through the use of visual cameras, which ensure operational ability from atmospheric entry through touchdown. Plagued by resource constraints and limite

Mehdi Lejmi, Nicoletta Tardini

A hypercomplex structure $(I,J,K)$ on a manifold $M$ is said to be $C^\infty$-pure-and-full if the Dolbeault cohomology $H^{2,0}_{\partial}(M,I)$ is the direct sum of two natural subgroups called the $\bar{J}$-invariant and the $\bar{J}$-anti-invariant subgroups. We prove that a compact hypercomplex manifold that satisfies the quaternionic version of the $dd

Yihan Wang, Saavik Ford, Rosalba Perna, Barry McKernan

Two-body scatterings under the potential of a massive object are very common in astrophysics. If the massive body is far enough away that the two small bodies are in their own gravitational sphere of influence, the gravity of the massive body can be temporarily ignored. However, this requires the scattering process to be fast enough that the small objects do

Eli Newby, Jorge Gómez Tejeda Zañudo, Réka Albert

Because the attractors of biological networks reflect stable behaviors (e.g., cell phenotypes), identifying control interventions that can drive a system towards its attractors (attractor control) is of particular relevance when controlling biological systems. Driving a network's feedback vertex set (FVS) by node-state override into a state consistent with a

Kai Zhang, Yutong Dai, Hongyi Wang, Eric Xing

Federated learning is a promising paradigm that allows multiple clients to collaboratively train a model without sharing the local data. However, the presence of heterogeneous devices in federated learning, such as mobile phones and IoT devices with varying memory capabilities, would limit the scale and hence the performance of the model could be trained. Th

Mihai-Silviu Lazorec

Let $o(G)$ be the average of the element orders of a finite group $G$. A research topic concerning this quantity is understanding the relation between $o(G)$ and $o(H)$, where $H$ is a subgroup of $G$. Let $\mathcal{N}$ be the class of finite nilpotent groups and let $L(G)$ be the subgroup lattice of $G$. In this paper, we show that the set $\lbrace \frac{o(

Callie E. Hood, Jonathan J. Fortney, Michael R. Line, Jacqueline K. Faherty

Brown dwarf spectra offer vital testbeds for our understanding of the chemical and physical processes that sculpt substellar atmospheres. Recently, atmospheric retrieval approaches have been applied to a number of low-resolution (R~100) spectra of brown dwarfs, yielding constraints on the abundances of chemical species and temperature structures of these atm

Pengyu Chu, Zhaojian Li, Kaixiang Zhang, Dong Chen

Automated apple harvesting has attracted significant research interest in recent years due to its potential to revolutionize the apple industry, addressing the issues of shortage and high costs in labor. One key technology to fully enable efficient automated harvesting is accurate and robust apple detection, which is challenging due to complex orchard enviro

Viani S. Morales-Guzman, Jorge G. Hirsch

Chore\~no et al. [J. Math. Phys. 59, 073506 (2018)] reported analytic solutions to the resonant case of the Tavis-Cummings model, obtained by mapping it to a Hamiltonian with three bosons and applying a Bogoliubov transformation. This comment points out that the Bogoliubov transformation employed is not unitary, cannot be inverted, and cannot enforce the sym

Deema Alnuhait, Qingyang Wu, Zhou Yu

While current dialogue systems like ChatGPT have made significant advancements in text-based interactions, they often overlook the potential of other modalities in enhancing the overall user experience. We present FaceChat, a web-based dialogue framework that enables emotionally-sensitive and face-to-face conversations. By seamlessly integrating cutting-edge

Shengxin Tu, Chun Li, Donglin Zeng, Bryan E. Shepherd

Clustered data are common in biomedical research. Observations in the same cluster are often more similar to each other than to observations from other clusters. The intraclass correlation coefficient (ICC), first introduced by R. A. Fisher, is frequently used to measure this degree of similarity. However, the ICC is sensitive to extreme values and skewed di

H. Takagi, R. Takagi, S. Minami, T. Nomoto

In ferromagnets, electric current generally induces transverse Hall voltage in proportion to magnetization (anomalous Hall effect), and it is frequently used for electrical readout of the up and down spin states. While these properties are usually not expected in antiferromagnets, recent theoretical studies predicted that non-coplanar antiferromagnetic order

Zohreh Aghababaeyan, Manel Abdellatif, Mahboubeh Dadkhah, Lionel Briand

Deep neural networks (DNNs) are widely used in various application domains such as image processing, speech recognition, and natural language processing. However, testing DNN models may be challenging due to the complexity and size of their input domain. Particularly, testing DNN models often requires generating or exploring large unlabeled datasets. In prac

Stefano Almi, Marco Morandotti, Francesco Solombrino

Motivated by the applications, a class of optimal control problems is investigated, where the goal is to influence the behavior of a given population through another controlled one interacting with the first. Diffusive terms accounting for randomness in the evolution are taken into account. A well-posedness theory under very low regularity of the control vec

Dylan Ebert, Chen Sun, Ellie Pavlick

Three-dimensional trajectories, or the 3D position and rotation of objects over time, have been shown to encode key aspects of verb semantics (e.g., the meanings of roll vs. slide). However, most multimodal models in NLP use 2D images as representations of the world. Given the importance of 3D space in formal models of verb semantics, we expect that these 2D

Daniel Sousa, Christopher Small

NASA's Earth Surface Mineral Dust Source Investigation (EMIT) mission seeks to use spaceborne imaging spectroscopy (hyperspectral imaging) to map the mineralogy of arid dust source regions. Here we apply recent developments in Joint Characterization (JC) and the spectral Mixture Residual (MR) to explore the information content of data from this novel mission

K. El Bourakadi, Z. Sakhi, M. Bennai

The post-inflationary epochs are critical for comprehending the early evolution of our Universe. This article delves into the cosmological signatures that shed light on these early epochs, particularly focusing on the generation of various phenomena such as matter production via inflaton oscillation and parametric resonance, primordial black holes, and gravi

Nathan McJames, Andrew Parnell, Yong Chen Goh, Ann O'Shea

Bayesian Causal Forests (BCF) is a causal inference machine learning model based on a highly flexible non-parametric regression and classification tool called Bayesian Additive Regression Trees (BART). Motivated by data from the Trends in International Mathematics and Science Study (TIMSS), which includes data on student achievement in both mathematics and s

Georgios Andreadis, Peter A. N. Bosman, Tanja Alderliesten

Finding a realistic deformation that transforms one image into another, in case large deformations are required, is considered a key challenge in medical image analysis. Having a proper image registration approach to achieve this could unleash a number of applications requiring information to be transferred between images. Clinical adoption is currently hamp

Muhammad Rizwan Khan, Harshan Reddy Gopidi, Hamid Reza Darabian, Dorota A. Pawlak

Using the first-principles calculations and La3Te4 as an example of an n-type gapped metal, we demonstrate that gapped metals can develop spontaneous defect formation resulting in off-stoichiometric compounds. Importantly, these compounds have different free carrier concentrations and can be realized by optimizing synthesis conditions. The ability to manipul

Tianyi Chen, Youngser Park, Ali Saad-Eldin, Zachary Lubberts

Recent advancements have been made in the development of cell-based in-vitro neuronal networks, or organoids. In order to better understand the network structure of these organoids, a super-selective algorithm has been proposed for inferring the effective connectivity networks from multi-electrode array data. In this paper, we apply a novel statistical metho

Sushovan Dhara, Nidhin Kurian Kalarickal, Ashok Dheenan, Sheikh Ifatur Rahman

$\beta$-Ga$_2$O$_3$ trench Schottky barrier diodes fabricated through a Gallium atomic beam etching technique, with excellent field strength and power device figure of merit, are demonstrated. Trench formation was accomplished by a low-damage Ga flux etch that enables near-ideal forward operating characteristics that are independent of fin orientation. The r

Arthur Moreau, Nathan Piasco, Moussab Bennehar, Dzmitry Tsishkou

Beyond novel view synthesis, Neural Radiance Fields are useful for applications that interact with the real world. In this paper, we use them as an implicit map of a given scene and propose a camera relocalization algorithm tailored for this representation. The proposed method enables to compute in real-time the precise position of a device using a single RG

S. Braeck

In axistationary asymptotically flat spacetimes zero angular momentum observers (ZAMOs) define an absolute standard of non--rotation locally, as can be verified by the absence of any Sagnac effect for these observers. Nevertheless, we argue that on a global scale the only physically meaningful concept is that of relative rotation. The argument is substantiat

Fatma Tambag, Koray Koksal, Fikret Yildiz, Mohamed Babiker

A novel experimental technique for the realisation of the optical Faraday effect using Laguerre- Gaussian (LG) light is described. The experiment employs a zero-order vortex half-wave retarder to generate a radially or azimuthally-polarised LG doughnut beam. The light emerging from the retarder then passes through a linear polariser, which gives rise to two

Heikki Mäntysaari, Björn Schenke, Chun Shen, Wenbin Zhao

We show within the Color Glass Condensate framework that exclusive vector meson production at high energy is sensitive to the geometric deformation of the target nucleus at multiple length scales. Studying $e+$U collisions and varying the deformation of the uranium target, we demonstrate that larger deformations result in enhanced incoherent vector meson pro

Zhaoyi Zhou, Zaiwei Chen, Yiheng Lin, Adam Wierman

We introduce a class of networked Markov potential games in which agents are associated with nodes in a network. Each agent has its own local potential function, and the reward of each agent depends only on the states and actions of the agents within a neighborhood. In this context, we propose a localized actor-critic algorithm. The algorithm is scalable sin

Matthias Cosler, Christopher Hahn, Daniel Mendoza, Frederik Schmitt

A rigorous formalization of desired system requirements is indispensable when performing any verification task. This often limits the application of verification techniques, as writing formal specifications is an error-prone and time-consuming manual task. To facilitate this, we present nl2spec, a framework for applying Large Language Models (LLMs) to derive

Fernando E. Rosas, Diego Candia-Rivera, Andrea I Luppi, Yike Guo

Recent research is revealing how cognitive processes are supported by a complex interplay between the brain and the rest of the body, which can be investigated by the analysis of physiological features such as breathing rhythms, heart rate, and skin conductance. Heart rate dynamics are of particular interest as they provide a way to track the sympathetic and

Lisa M. Koch, Christian M. Schürch, Christian F. Baumgartner, Arthur Gretton

Distribution shifts remain a fundamental problem for the safe application of machine learning systems. If undetected, they may impact the real-world performance of such systems or will at least render original performance claims invalid. In this paper, we focus on the detection of subgroup shifts, a type of distribution shift that can occur when subgroups ha

Yasser G. Alqaham, Jing Cheng, Zhenyu Gan

It is often overlooked by roboticists when designing locomotion controllers for their legged machines, that energy consumption plays an important role in selecting the best gaits for locomotion at high speeds or over long distances. The purpose of this study is to examine four similar asymmetrical quadrupedal gaits that are frequently observed in legged anim

Mohsen Heidari, Wojciech Szpankowski

Many conventional learning algorithms rely on loss functions other than the natural 0-1 loss for computational efficiency and theoretical tractability. Among them are approaches based on absolute loss (L1 regression) and square loss (L2 regression). The first is proved to be an \textit{agnostic} PAC learner for various important concept classes such as \text

Sándor Varró, Szabolcs Hack, Gábor Paragi, Péter Földi

We present a general mathematical procedure to handle interactions described by a Morse potential in the presence of a strong harmonic excitation. We account for permanent and field-induced terms and their gradients in the dipole moment function, and we derive analytic formulae for the bond-length change and for the shifted energy eigenvalues of the vibratio

Jiayu Ding, Zhenyu Gan

Symmetry manifests itself in legged locomotion in a variety of ways. No matter where a legged system begins to move periodically, the torso and limbs coordinate with each other's movements in a similar manner. Also, in many gaits observed in nature, the legs on both sides of the torso move in exactly the same way, sometimes they are just half a period out of

Vivek K. Adajania, Siqi Zhou, Arun Kumar Singh, Angela P. Schoellig

This paper presents a scalable online algorithm to generate safe and kinematically feasible trajectories for quadrotor swarms. Existing approaches rely on linearizing Euclidean distance-based collision constraints and on axis-wise decoupling of kinematic constraints to reduce the trajectory optimization problem for each quadrotor to a quadratic program (QP).

Taige Wang, Marc Vila, Michael P. Zaletel, Shubhayu Chatterjee

Electrical control of magnetism has been a major techonogical pursuit of the spintronics community, owing to its far-reaching implications for data storage and transmission. Here, we propose and analyze a new mechanism for electrical switching of isospin, using chiral-stacked graphene multilayers, such as bernal bilayer graphene or rhombohedral trilayer grap

Chenqi Guo, Fabian Benitez-Quiroz, Qianli Feng, Aleix Martinez

Improving the performance on an imbalanced training set is one of the main challenges in nowadays Machine Learning. One way to augment and thus re-balance the image dataset is through existing deep generative models, like class-conditional Generative Adversarial Networks (cGAN) or Diffusion Models by synthesizing images on each of the tail-class. Our experim

Asgar Jamneshan, Or Shalom, Terence Tao

It was conjectured by Bergelson, Tao, and Ziegler \cite{btz} that every Host--Kra $\F_p^\omega$-system of order $k$ is an Abramov system of order $k$. This conjecture has been verified for $k \leq p+1$. In this paper we show that the conjecture fails when $k=5, p=2$. We in fact establish a stronger (combinatorial) statement, in that we produce a bounded func

Samuel T. Badman, Pete Riley, Shaela I. Jones, Tae K. Kim

Drawing connections between heliospheric spacecraft and solar wind sources is a vital step in understanding the evolution of the solar corona into the solar wind and contextualizing \textit{in situ} timeseries. Furthermore, making advanced predictions of this linkage for ongoing heliospheric missions, such as Parker Solar Probe (PSP), is necessary for achiev

Kai North, Marcos Zampieri, Matthew Shardlow

The occurrence of unknown words in texts significantly hinders reading comprehension. To improve accessibility for specific target populations, computational modelling has been applied to identify complex words in texts and substitute them for simpler alternatives. In this paper, we present an overview of computational approaches to lexical complexity predic

Thomas Ayral, Pauline Besserve, Denis Lacroix, Edgar Andres Ruiz Guzman

Quantum computing technologies are making steady progress. This has opened new opportunities for tackling problems whose complexity prevents their description on classical computers. A prototypical example of these complex problems are interacting quantum many-body systems: on the one hand, these systems are known to become rapidly prohibitive to describe us

Jian Wang, Jiarui Xing, Jason Druzgal, William M. Wells

This paper presents a novel predictive model, MetaMorph, for metamorphic registration of images with appearance changes (i.e., caused by brain tumors). In contrast to previous learning-based registration methods that have little or no control over appearance-changes, our model introduces a new regularization that can effectively suppress the negative effects

Jorge Bacca, Emmanuel Martinez, Henry Arguello

Spectral imaging collects and processes information along spatial and spectral coordinates quantified in discrete voxels, which can be treated as a 3D spectral data cube. The spectral images (SIs) allow identifying objects, crops, and materials in the scene through their spectral behavior. Since most spectral optical systems can only employ 1D or maximum 2D

Alisson Tezzin

Recently there has been much interest in deriving the quantum formalism and the set of quantum correlations from simple axioms. In this paper, we provide a step-by-step derivation of the quantum formalism that tackles both these problems and helps us to understand why this formalism is as it is. We begin with a structureless system that only includes real-va

Héctor Bombín, Chris Dawson, Ye-Hua Liu, Naomi Nickerson

Universal fault-tolerant quantum computation will require real-time decoding algorithms capable of quickly extracting logical outcomes from the stream of data generated by noisy quantum hardware. We propose modular decoding, an approach capable of addressing this challenge with minimal additional communication and without sacrificing decoding accuracy. We in