March 2023 arXiv papers — page 102

Radja Boughezal, Yingsheng Huang, Frank Petriello

We study the impact of LHC forward-backward asymmetry (AFB) measurements at high invariant-mass in the Drell-Yan process on probes of semileptonic four-fermion operators in the Standard Model effective field theory (SMEFT). In particular, we study whether AFB measurements can resolve degeneracies in the Wilson coefficient parameter space that appear when con

Nan Wu, Yingjie Li, Cong Hao, Steve Dai

Reasoning high-level abstractions from bit-blasted Boolean networks (BNs) such as gate-level netlists can significantly benefit functional verification, logic minimization, datapath synthesis, malicious logic identification, etc. Mostly, conventional reasoning approaches leverage structural hashing and functional propagation, suffering from limited scalabili

Giorgos Armeniakos, Georgios Zervakis, Dimitrios Soudris, Mehdi B. Tahoori

Printed electronics (PE) promises on-demand fabrication, low non-recurring engineering costs, and sub-cent fabrication costs. It also allows for high customization that would be infeasible in silicon, and bespoke architectures prevail to improve the efficiency of emerging PE machine learning (ML) applications. Nevertheless, large feature sizes in PE prohibit

Tomasz Winiarski

The complexity of today's robot control systems implies difficulty in developing them efficiently and reliably. Systems engineering (SE) and frameworks come to help. The framework metamodels are needed to support the standardisation and correctness of the created application models. Although the use of frameworks is widespread nowadays, for the most popular

Arnav Kundu, Chungkuk Yoo, Srijan Mishra, Minsik Cho

Model quantization and compression is widely used techniques to reduce usage of computing resource at inference time. While state-of-the-art works have been achieved reasonable accuracy with higher bit such as 4bit or 8bit, but still it is challenging to quantize/compress a model further, e.g., 1bit or 2bit. To overcome the challenge, we focus on outliers in

Luis C. Garcia-Peraza-Herrera, Conor Horgan, Sebastien Ourselin, Michael Ebner

Visual discrimination of clinical tissue types remains challenging, with traditional RGB imaging providing limited contrast for such tasks. Hyperspectral imaging (HSI) is a promising technology providing rich spectral information that can extend far beyond three-channel RGB imaging. Moreover, recently developed snapshot HSI cameras enable real-time imaging w

Omer Ben-Neria, Philipp Lücke

Definable stationary sets, and specifically, ordinal definable ones, play a significant role in the study of canonical inner models of set theory and the class HOD of hereditarily ordinal definable sets. Fixing a certain notion of definability and an uncountable cardinal, one can consider the associated family of definable closed unbounded sets. In this pape

Chinmay Savadikar, Michelle Dai, Tianfu Wu

To effectively manage the complexities of real-world dynamic environments, continual learning must incrementally acquire, update, and accumulate knowledge from a stream of tasks of different nature without suffering from catastrophic forgetting of prior knowledge. While this capability is innate to human cognition, it remains a significant challenge for mode

Avinash Kumar, Anish Kumar, Sumit Sharma, Surjeet Singh

Current practice in parameter space exploration in euclidean space is dominated by randomized sampling or design of experiment methods. The biggest issue with these methods is not keeping track of what part of parameter space has been explored and what has not. In this context, we utilize the geometric learning of explored data space using modern machine lea

Mohamad T Sultan, Hesham El Sayed, Manzoor Ahmed Khan

Mobile Ad-hoc Network (MANET) is a distributed, decentralized network of wireless portable nodes connecting directly without any fixed communication base station or centralized administration. Nodes in MANET move continuously in random directions and follow an arbitrary manner, which presents numerous challenges to these networks and make them more susceptib

Robert S. Markiewicz, Bahadur Singh, Christopher Lane, Arun Bansil

Beyond the two-dimensional (2D) saddle-point Van Hove singularities (VHSs) with logarithmic divergences in the density of states (DOS), recent studies have identified higher-order VHSs with faster-than-logarithmic divergences that can amplify electron correlation effects. Here we show that the cuprate high-Tc superconductors harbor high-order VHSs in their e

Dillon Wong, Kevin P. Nuckolls, Myungchul Oh, Ryan L. Lee

At partial fillings of its flat electronic bands, magic-angle twisted bilayer graphene (MATBG) hosts a rich variety of competing correlated phases that show sample to sample variations. Divergent phase diagrams in MATBG are often attributed to the sublattice polarization energy scale, tuned by the degree of alignment of the hexagonal boron nitride (hBN) subs

C. Wu, E. B. Gunnarsson, E. M. Myklebust, A. Köhn-Luque

Tumor heterogeneity is a complex and widely recognized trait that poses significant challenges in developing effective cancer therapies. In particular, many tumors harbor a variety of subpopulations with distinct therapeutic response characteristics. Characterizing this heterogeneity by determining the subpopulation structure within a tumor enables more prec

Quindel Jones, Andrés R. Vindas Meléndez, Ariana Mendible, Manuchehr Aminian

Data science for social justice (DS4SJ) is data-scientific work that supports the liberation of oppressed and marginalized people. By nature, this work lies at the intersection of technical scholarship and activist practice. We discuss this growing efforts in DS4SJ within the broad mathematics community. We begin by defining terms and offering a series of gu

Ender Minyard

There has been substantial research undertaken on the role of computational systems that encourage autotelic creativity. Previous studies on the role of software in autotelic creativity have not explored code editing tools in much detail. This study sets out to examine the role of code editing tools in autotelic creativity. The principal findings of this res

Shiyu Feng, Ahmad Abuaish, Patricio A. Vela

This paper extends the gap-based navigation technique in Potential Gap by guaranteeing safety for nonholonomic robots for all tiers of the local planner hierarchy, so called Safer Gap. The first tier generates a Bezier-based collision-free path through gaps. A subset of navigable free-space from the robot through a gap, called the keyhole, is defined to be t

Rui Xie, Shuyang Bai, Ping Ma

The Internet of Things (IoT) system generates massive high-speed temporally correlated streaming data and is often connected with online inference tasks under computational or energy constraints. Online analysis of these streaming time series data often faces a trade-off between statistical efficiency and computational cost. One important approach to balance

Shyam Venkatasubramanian, Sandeep Gogineni, Bosung Kang, Ali Pezeshki

Recent works exploring data-driven approaches to classical problems in adaptive radar have demonstrated promising results pertaining to the task of radar target localization. Via the use of space-time adaptive processing (STAP) techniques and convolutional neural networks, these data-driven approaches to target localization have helped benchmark the performa

Pingping Cai, Zhenyao Wu, Xinyi Wu, Song Wang

Designing a point cloud upsampler, which aims to generate a clean and dense point cloud given a sparse point representation, is a fundamental and challenging problem in computer vision. A line of attempts achieves this goal by establishing a point-to-point mapping function via deep neural networks. However, these approaches are prone to produce outlier point

Victor Dachet, Amina Benzerga, Raphaël Fonteneau, Damien Ernst

In this paper, we propose a multi-RREH (Remote Renewable Energy Hub) based optimization framework. This framework allows a valorization of CO2 using carbon capture technologies. This valorization is grounded on the idea that CO2 gathered from the atmosphere or post combustion can be combined with hydrogen to produce synthetic methane. The hydrogen is obtaine

Tomas Kulvicius, Sigrun Lang, Claudius AA Widmann, Nina Hansmann

Theoretical background: early verbal development is not yet fully understood, especially in its formative phase. Research question: can a reliable, easy-to-use coding scheme for the classification of early infant vocalizations be defined that is applicable as a basis for further analysis of language development? Methods: in a longitudinal study of 45 neuroty

Aimo Hinkkanen, Matti Vuorinen

We prove that if $E$ is a compact subset of the unit disk ${\mathbb D}$ in the complex plane, if $E$ contains a sequence of distinct points $a_n\not= 0$ for $n\geq 1$ such that $\lim_{n\to\infty} a_n=0$ and for all $n$ we have $ |a_{n+1}| \geq \frac{1}{2} |a_n| $, and if $G={\mathbb D} \setminus E$ is connected and $0\in \partial G$, then there is a constant

John M. Davis, Gustavo S. Orozco-Galvan, Salvador Barraza-Lopez

Low-buckled silicene, germanene, and stanene are group$-IV$ graphene allotropes. They form a honeycomb lattice out of two interpenetrating ($A$ and $B$) triangular sublattices that are vertically separated by a small distance $\Delta_z$. The atomic numbers $Z$ of silicon, germanium, and tin are larger to carbon's ($Z_C=6$), making them the first experimental

Dvij Kalaria, Qin Lin, John M. Dolan

Autonomous racing is a challenging problem, as the vehicle needs to operate at the friction or handling limits in order to achieve minimum lap times. Autonomous race cars require highly accurate perception, state estimation, planning and precise application of controls. What makes it even more challenging is the accurate identification of vehicle model param

Lixing Zhang, Lu Wang, Maxim F. Gelin, Yang Zhao

We investigate the dynamics of Landau-Zener transitions in an anisotropic, dissipative three-level model (3-LZM) using the numerically accurate multiple Davydov D2 Ansatz in the framework of time-dependent variation. It is demonstrated that a non-monotonic relationship exists between the Landau-Zener transition probability and the phonon coupling strength wh

Rindranirina Ramamonjison, Timothy T. Yu, Raymond Li, Haley Li

The Natural Language for Optimization (NL4Opt) Competition was created to investigate methods of extracting the meaning and formulation of an optimization problem based on its text description. Specifically, the goal of the competition is to increase the accessibility and usability of optimization solvers by allowing non-experts to interface with them using

Stephen McCrory, Sylvain Bertrand, Achintya Mohan, Duncan Calvert

We present a feasibility-driven teleoperation framework designed to generate humanoid multi-contact maneuvers for use in unstructured environments. Our framework is designed for motions with arbitrary contact modes and postures. The operator configures a pre-execution preview robot through contact points and kinematic tasks. A fast estimation of the preview

Hao Yu, Zheng Qin, Ji Hou, Mahdi Saleh

The intrinsic rotation invariance lies at the core of matching point clouds with handcrafted descriptors. However, it is widely despised by recent deep matchers that obtain the rotation invariance extrinsically via data augmentation. As the finite number of augmented rotations can never span the continuous SO(3) space, these methods usually show instability

Arunesh Mittal, Kai Yang, Paul Sajda, John Paisley

Several approximate inference methods have been proposed for deep discrete latent variable models. However, non-parametric methods which have previously been successfully employed for classical sparse coding models have largely been unexplored in the context of deep models. We propose a non-parametric iterative algorithm for learning discrete latent represen

Siyu Liu, Xunyuan Yin, Jinfeng Liu

State estimation is crucial for the monitoring and control of post-combustion CO2 capture plants (PCCPs). The performance of state estimation is highly reliant on the configuration of sensors. In this work, we consider the problem of sensor selection for PCCPs and propose a computationally efficient method to determine an appropriate number of sensors and th

Paul D. Grannis, Hugh E. Montgomery

It is generally accepted that it is preferable to build two general purpose detectors at any given collider facility. We reinforce this point by discussing a number of aspects and particular instances in which this has been important. The examples are taken mainly, but not exclusively, from experience at the Tevatron collider.

Konstantin Korolev

Hall effect thrusters are one of the most versatile and popular electric propulsion systems for space use. Industry trends towards interplanetary missions arise advances in design development of such propulsion systems. It is understood that correct sizing of discharge channel in Hall effect thruster impact performance greatly. Since the complete physics mod

Mahdi Taheri, Mohammad Riazati, Mohammad Hasan Ahmadilivani, Maksim Jenihhin

While the role of Deep Neural Networks (DNNs) in a wide range of safety-critical applications is expanding, emerging DNNs experience massive growth in terms of computation power. It raises the necessity of improving the reliability of DNN accelerators yet reducing the computational burden on the hardware platforms, i.e. reducing the energy consumption and ex

Hao Tang, Zhenyu Zhang, Humphrey Shi, Bo Li

We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations in an end-to-end fashion for the challenging graph-constrained house generation task. The proposed graph-Transformer-based generator includes a novel graph Transformer encoder that combines graph convolutions and self-attentions in a Transforme

Nikhil J. Dhinagar, Vignesh Santhalingam, Katherine E. Lawrence, Emily Laltoo

For machine learning applications in medical imaging, the availability of training data is often limited, which hampers the design of radiological classifiers for subtle conditions such as autism spectrum disorder (ASD). Transfer learning is one method to counter this problem of low training data regimes. Here we explore the use of meta-learning for very low

Shengkai Li, Trung V. Phan, Gao Wang, Ramzi R. Khuri

We present here an inertial active spinning swarm consisting of mixtures of opposite handedness torque driven spinners floating on an air bed with low damping. Depending on the relative spin sign, spinners can act as their own anti-particles and annihilate their spins. Rotational energy can become highly focused, with minority fraction spinners pumped to ver

Ariane Fazeny

This thesis generalizes the differential operators on standard oriented graphs and oriented hypergraphs introduced in 10.1137/15M1022793 and arXiv:2007.00325. The extended concepts of gradients, adjoints and $p$-Laplacians for vertices and (hyper)arcs include novel parametrization possibilities, while simultaneously fulfilling expected properties of the cont

Mark Quinlan

Commercially-driven metaverse development has been driven by philosophical and science fiction concepts. Through translating these concepts into products, the developers may have inadvertently excluded individuals with disabilities from this new expanded reality. This ideologically-driven development is presented in this paper through a brief background of w

Alfredo Rial, Ania M. Piotrowska

Decentralized, offline, and privacy-preserving e-cash could fulfil the need for both scalable and byzantine fault-resistant payment systems. Existing offline anonymous e-cash schemes are unsuitable for distributed environments due to a central bank. We construct a distributed offline anonymous e-cash scheme, in which the role of the bank is performed by a qu

S. A. Franchino-Viñas, S. Mignemi, J. J. Relancio

In this manuscript, we will discuss the notion of curved momentum space, as it arises in the discussion of noncommutative or doubly special relativity theories. We will illustrate it with two simple examples, the Casimir effect in anti-Snyder space and the introduction of fermions in doubly special relativity. We will point out the existence of intriguing re

Kaan Gokcesu, Hakan Gokcesu

Our research deals with the optimization version of the set partition problem, where the objective is to minimize the absolute difference between the sums of the two disjoint partitions. Although this problem is known to be NP-hard and requires exponential time to solve, we propose a less demanding version of this problem where the goal is to find a locally

Georgia Papadogeorgou, Srijata Samanta

This manuscript unites causal inference and spatial statistics, presenting novel insights for causal inference in spatial data analysis, and drawing from tools in spatial statistics to estimate causal effects. We introduce spatial causal graphs to highlight that spatial confounding and interference can be entangled, in that investigating the presence of one

Max Nendel, Jan Streicher

In this paper, we deal with an axiomatic approach to default risk. We introduce the notion of a default risk measure, which generalizes the classical probability of default (PD), and allows to incorporate model risk in various forms. We discuss different properties and representations of default risk measures via monetary risk measures, families of related t

Nikhil J. Dhinagar, Sophia I. Thomopoulos, Emily Laltoo, Paul M. Thompson

Neuroimaging of large populations is valuable to identify factors that promote or resist brain disease, and to assist diagnosis, subtyping, and prognosis. Data-driven models such as convolutional neural networks (CNNs) have increasingly been applied to brain images to perform diagnostic and prognostic tasks by learning robust features. Vision transformers (V

Nafiul Rashid, Trier Mortlock, Mohammad Abdullah Al Faruque

Wearable medical technology has become increasingly popular in recent years. One function of wearable health devices is stress detection, which relies on sensor inputs to determine the mental state of patients. This continuous, real-time monitoring can provide healthcare professionals with vital physiological data and enhance the quality of patient care. Cur

Benson Farb, Eduard Looijenga

In this paper we construct various moduli spaces of K3 surfaces $M$ equipped with a surjective holomorphic map $\pi:M\to\Pb^1$ with generic fiber a complex torus (e.g., an elliptic fibration). Examples include moduli spaces of such maps with primitive fibers; with reduced, irreducible fibers; equipped with a section; etc. Such spaces are closely related to t

Rishabh Khandelwal, Asmit Nayak, Paul Chung, Kassem Fawaz

Privacy nutrition labels provide a way to understand an app's key data practices without reading the long and hard-to-read privacy policies. Recently, the app distribution platforms for iOS(Apple) and Android(Google) have implemented mandates requiring app developers to fill privacy nutrition labels highlighting their privacy practices such as data collectio

Dušan Popov

We use the discrete approach to solve the Schr\"odinger as well as the Bloch equations for a free particle and the quantum gas of free particles embedded in an infinite quantum well with the finite width. We obtain the expressions of energy eigenvalues, the eigenfunctions as well as the density matrix and partition function for the discrete case. By applying

Jeannette Janssen, Kyle MacKeigan

In this paper, we study orthogonal colourings of random geometric graphs. Two colourings of a graph are orthogonal if they have the property that when two vertices receive the same colour in one colouring, then those vertices receive distinct colours in the other colouring. A random geometric graph $RG(n,r)$ is a graph constructed by randomly placing $n$ ver

Alessandro Pini, Paolo Vallarino

We study the correlation function between one single-trace scalar operator and a circular Wilson loop in the $4d$ $\mathcal{N}=2$ superconformal field theory with gauge group $SU(N)$ and matter transforming in the symmetric and anti-symmetric representations. By exploiting supersymmetric localization, we resum the perturbative expansion of this correlator in

Faseeh Ahmad, Matthias Mayr, Volker Krueger

The ability to learn new tasks and quickly adapt to different variations or dimensions is an important attribute in agile robotics. In our previous work, we have explored Behavior Trees and Motion Generators (BTMGs) as a robot arm policy representation to facilitate the learning and execution of assembly tasks. The current implementation of the BTMGs for a s

Joonas Ilmavirta, Antti Kykkänen

We prove solenoidal injectivity for the geodesic X-ray transform of tensor fields on simple Riemannian manifolds with $C^{1,1}$ metrics and non-positive sectional curvature. The proof of the result rests on Pestov energy estimates for a transport equation on the non-smooth unit sphere bundle of the manifold. Our low regularity setting requires keeping track

Jiefeng Chen, Timothy Nguyen, Dilan Gorur, Arslan Chaudhry

One of the main motivations of studying continual learning is that the problem setting allows a model to accrue knowledge from past tasks to learn new tasks more efficiently. However, recent studies suggest that the key metric that continual learning algorithms optimize, reduction in catastrophic forgetting, does not correlate well with the forward transfer

Plamen Iliev

The goal of the paper is to analyze a Gaudin model for a polynomial representation of the Kohno-Drinfeld Lie algebra associated with the multinomial distribution. The main result is the construction of an explicit basis of the space of polynomials consisting of common eigenfunctions of Gaudin operators in terms of Aomoto-Gelfand hypergeometric series. The co

Cortelyou C. Kenney

Classical law and economics is foundational to the American legal system. Centered at the University of Chicago, its assumptions, most especially that humans act both rationally and selfishly, informs the thinking of legislatures, judges, and government lawyers, and has shaped nearly every aspect of the way commercial transactions are conducted. But what if

Carol Scarlett, Ephraim Fischbach, Belvin Freeman, Jennifer Coy

The Electron, Proton and Alpha Monitor, EPAM, located at the L1 Position approximately 1-million miles from the earth in the direction of the sun, was designed to detect fluctuations in solar output through counting the numbers of various particles hitting the detector. The EPAM detector is part of an early warning system that can alert the earth to coronal

M. Ellwarth, S. Schäfer, A. Reiners, M. Zechmeister

Solar surface magneto-convection appears as granulation pattern that impacts spectral lines in terms of both shape and wavelength. Such induced effects also tend to vary over the observed solar disc because of the changing observation angle and, thus, the changing observation height as well. Centre-to-limb observations of the resolved Sun offer an insight in

Miguel Á. González-Santamarta, Francisco J. Rodríguez-Lera, Vicente Matellán Olivera, Virginia Riego Del Castillo

Symbolic anchoring is a crucial problem in the field of robotics, as it enables robots to obtain symbolic knowledge from the perceptual information acquired through their sensors. In cognitive-based robots, this process of processing sub-symbolic data from real-world sensors to obtain symbolic knowledge is still an open problem. To address this issue, this p

Gonzalo Alvarez, Ryan Bennink, Stephan Irle, Jacek Jakowski

We introduce QuantumGEP, a scientific computer program that uses gene expression programming (GEP) to find a quantum circuit that either (i) maps a given set of input states to a given set of output states, or (ii) transforms a fixed initial state to minimize a given physical quantity of the output state. QuantumGEP is a driver program that uses evendim, a g

Efe A. Ok, Gerelt Tserenjigmid

Our goal is to develop a partial ordering method for comparing stochastic choice functions on the basis of their individual rationality. To this end, we assign to any stochastic choice function a one-parameter class of deterministic choice correspondences, and then check for the rationality (in the sense of revealed preference) of these correspondences for e

Yuejia Zhai, William Giarè, Carsten van de Bruck, Eleonora Di Valentino

We analyze a cosmological model featuring an interaction between dark energy and dark matter in light of the measurements of the Cosmic Microwave Background released by three independent experiments: the most recent data by the Planck satellite and the Atacama Cosmology Telescope, and WMAP (9-year data). We show that different combinations of the datasets pr

Jacek Miȩkisz, Javad Mohamadichamgavi, Raffi Vardanyan

We present a microscopic model of replicator dynamics with strategy-dependent time delays. In such a model, new players are born from parents who interacted and received payoffs in the past. In the case of small delays, we use Taylor expansion to get ordinary differential equations for frequencies of strategies with time delays as parameters. We apply our te

Oleksandr I. Malyi, Alex Zunger

The YNiO$_3$ nickelate is a paradigm d-electron oxide that manifests the intriguing temperature-mediated sequence of three phases transitions from (i) magnetically ordered insulator to (ii) paramagnetic (PM) insulator and then to (iii) PM metal. Such phenomena raised the question of the nature of the association of magnetism and structural symmetry breaking

Hengyue Zhang, Timothy D. Brandt, Rocio Kiman, Alexander Venner

We measure precise orbits and dynamical masses and derive age constraints for six confirmed and one candidate Sirius-like systems, including the Hyades member HD 27483. Our orbital analysis incorporates radial velocities, relative astrometry, and Hipparcos-Gaia astrometric accelerations. We constrain the main-sequence lifetime of a white dwarf's progenitor f

Yangfan Zhang, Runmin Wang, Xiaofeng Shao

In this article, we propose a class of $L_q$-norm based U-statistics for a family of global testing problems related to high-dimensional data. This includes testing of mean vector and its spatial sign, simultaneous testing of linear model coefficients, and testing of component-wise independence for high-dimensional observations, among others. Under the null

Gavin Ridley, Benoit Forget, Timothy Burke

A new method for directly sampling the neutron resonance upscattering effect is presented. Alternatives have relied on inefficient rejection sampling techniques or large tabular storage of relative velocities. None of these approaches, which require pointwise energy data, are particularly well suited to the windowed multipole cross section representation. Th

P. Swaczyna, M. Bzowski, S. A. Fuselier, A. Galli

The IBEX-Lo instrument on the Interstellar Boundary Explorer (IBEX) mission measures interstellar neutral (ISN) helium atoms. The detection of helium atoms is made through negative hydrogen (H$^-$) ions sputtered by the helium atoms from the IBEX-Lo conversion surface. The energy spectrum of ions sputtered by ISN helium atoms is broad and overlaps the four l

Sebastian Kilde Lofgren, Ricardo Méndez Fragoso, Jonathan Weidow, Jonas Enger

Nobel laureate Wolfgang Paul showed, back in the 1950s, that charged particles can be trapped using alternating electric fields. This technique is commonly referred to as Paul traps or radiofrequency traps (RF-traps) and is used in various areas of modern physics. This paper presents a 3D-printed mechanical Paul trap, a na\"ive simulation of the system in Py

Lara Kuhlmann, Daniel Wilmes, Emmanuel Müller, Markus Pauly

Data cubes are multidimensional databases, often built from several separate databases, that serve as flexible basis for data analysis. Surprisingly, outlier detection on data cubes has not yet been treated extensively. In this work, we provide the first framework to evaluate robust outlier detection methods in data cubes (RODD). We introduce a novel random

Mohammad Bagher Heydari, Morteza Mohammadi Shirkolaei, Majid Karimipour

In this article, tunable surface plasmon polaritons (SPPs) in graphene-based elliptical waveguides containing gyro-electric layers are investigated. The general structure has an elliptical cross-section, where each gyro-electric layer is surrounded by two graphene layers. The DC magnetic bias is applied on the z-axis. As a special case, a new plasma-based el

D. P. de Andrade Deus, J. M. J. Lopes, Roberto H. Miwa

Heterostructures made of stacked 2D materials with different electronic properties are studied for their potential in creating multifunctional devices. Graphene (G) and hexagonal boron nitride (h-BN) van der Waals (vdW) systems have been extensively researched, including recent studies on synthesizing h-BN on graphene/SiC(0001) templates. These studies sugge

R. C. Brewster, C. M. Mynhardt, L. E. Teshima

The independent domination number $i(G)$ of a graph $G$ is the minimum cardinality of a maximal independent set of $G$, also called an $i(G)$-set. The $i$-graph of $G$, denoted $\mathscr{I}(G)$, is the graph whose vertices correspond to the $i(G)$-sets, and where two $i(G)$-sets are adjacent if and only if they differ by two adjacent vertices. Although not a

Alicia Durrer, Julia Wolleb, Florentin Bieder, Tim Sinnecker

Magnetic resonance (MR) images from multiple sources often show differences in image contrast related to acquisition settings or the used scanner type. For long-term studies, longitudinal comparability is essential but can be impaired by these contrast differences, leading to biased results when using automated evaluation tools. This study presents a diffusi

Michele Pugini, Bruno Credidio, Irina Walter, Sebastian Malerz

The recent application of concepts from condensed-matter physics to photoelectron spectroscopy (PES) of volatile, liquid-phase systems has enabled the measurement of electronic energetics of liquids on an absolute scale. Particularly, vertical ionization energies, VIEs, of liquid water and aqueous solutions, both in the bulk and at associated interfaces, can

Akash Fogla, Kanish Kumar, Sunnay Saurav, Bishnu ramanujan

Uncertainty in decision-making is crucial in the machine learning model used for a safety-critical system that operates in the real world. Therefore, it is important to handle uncertainty in a graceful manner for the safe operation of the CPS. In this work, we design a vehicle's lateral controller using a machine-learning model. To this end, we train a rando

Mateusz Dembny, Mikołaj Sierżęga

A sharp double-sided Harnack bound is derived for positive solutions of a fractional order heat equation.

Yue Ma, Nathan Gemmell, Emma Pearce, Rupert Oulton

Spectroscopy and imaging in the mid-infrared (2.5 $\mu$m $\sim$ $\lambda$ $\sim$ 25 $\mu$m) is bedevilled by the presence of a strong 300 K thermal background at room temperature that makes IR detectors decades noisier than can be readily achieved in the visible. The technique of "imaging with undetected photons" (IUP) exploits the quantum correlations betwe

Tiago Mendes-Santos, Markus Schmitt, Markus Heyl

Spectral functions are central to link experimental probes to theoretical models in condensed matter physics. However, performing exact numerical calculations for interacting quantum matter has remained a key challenge especially beyond one spatial dimension. In this work, we develop a versatile approach using neural quantum states to obtain spectral propert

Enzo Figueiredo, Andrea Maselli, Vitor Cardoso

We develop a numerical approach to find asymptotically flat black hole solutions coupled to anisotropic fluids, described by generic density profiles. Our model allows for a variety of applications in realistic astrophysical scenarios, and is potentially able to describe the geometry of galaxies hosting supermassive black holes, dark matter environments and

Francesco Crocetti, Jeffrey Mao, Alessandro Saviolo, Gabriele Costante

Gaussian Processes (GPs) are expressive models for capturing signal statistics and expressing prediction uncertainty. As a result, the robotics community has gathered interest in leveraging these methods for inference, planning, and control. Unfortunately, despite providing a closed-form inference solution, GPs are non-parametric models that typically scale

Keno K. Bressem, Jens-Michalis Papaioannou, Paul Grundmann, Florian Borchert

This paper presents medBERTde, a pre-trained German BERT model specifically designed for the German medical domain. The model has been trained on a large corpus of 4.7 Million German medical documents and has been shown to achieve new state-of-the-art performance on eight different medical benchmarks covering a wide range of disciplines and medical document

Masatoshi Imanishi, Shunsuke Baba, Kouichiro Nakanishi, Takuma Izumi

We report the results of ALMA 1-2 kpc-resolution, three rotational transition line (J=2-1, J=3-2, and J=4-3) observations of multiple dense molecular gas tracers (HCN, HCO$^{+}$, and HNC) for ten nearby (ultra)luminous infrared galaxies ([U]LIRGs). Following the matching of beam sizes to 1-2 kpc for each (U)LIRG, the high-J to low-J transition-line flux rati

Jamila Smith-Loud, Andrew Smart, Darlene Neal, Amber Ebinama

This paper reports on our initial evaluation of The Equitable AI Research Roundtable -- a coalition of experts in law, education, community engagement, social justice, and technology. EARR was created in collaboration among a large tech firm, nonprofits, NGO research institutions, and universities to provide critical research based perspectives and feedback

Yusen Ye, Jimin Qian, Xiao-Wei Zhang, Chong Wang

Moir\'e superlattices from stacks of van der Waals materials offer an exciting arena in the fields of condensed matter physics and materials science. Typically, these moir\'e superlattices consist of materials with identical or similar structures, and the long moir\'e period arises from a small twist angle or lattice mismatch. In this article, we discuss tha

Ling-Hua Chang, Po-Ning Chen, Fady Alajaji

The error probability of block codes sent under a non-uniform input distribution over the memoryless binary symmetric channel (BSC) and decoded via the maximum a posteriori (MAP) decoding rule is investigated. It is proved that the ratio of the probability of MAP decoder ties to the probability of error when no MAP decoding ties occur grows at most linearly

F. J. Pozuelos, M. Timmermans, B. V. Rackham, L. J. Garcia

Several planetary formation models have been proposed to explain the observed abundance and variety of compositions of super-Earths and mini-Neptunes. In this context, multitransiting systems orbiting low-mass stars whose planets are close to the radius valley are benchmark systems, which help to elucidate which formation model dominates. We report the disco

Yingqing Chen, Christos G. Cassandras

We study the Traffic Light Control (TLC) problem for a single intersection, considering both straight driving vehicle flows and corresponding crossing pedestrian flows with the goal of achieving a fair jointly optimal sharing policy in terms of average waiting times. Using a stochastic hybrid system model, we design a quasi-dynamic policy controlling the tra

Anna Marie Bohmann, Teena Gerhardt, Cary Malkiewich, Mona Merling

Cut-and-paste $K$-theory has recently emerged as an important variant of higher algebraic $K$-theory. However, many of the powerful tools used to study classical higher algebraic $K$-theory do not yet have analogues in the cut-and-paste setting. In particular, there does not yet exist a sensible notion of the Dennis trace for cut-and-paste $K$-theory. In thi

Shamik Bhattacharyya, Rachel Kalpana Kalaimani

In this paper, we address the discrete-time dynamic average consensus (DAC) of a multi-agent system in the presence of adversarial attacks. The adversarial attack is considered to be of Byzantine type, which compromises the computation capabilities of the agent and sends arbitrary false data to its neighbours. We assume a few of the agents cannot be compromi

Junhao Liu, Qizhou Zhang, Hauyu Baobab Liu, Keping Qiu

We use molecular line data from ALMA, SMA, JCMT, and NANTEN2 to study the multi-scale ($\sim$15-0.005 pc) velocity statistics in the massive star formation region NGC 6334. We find that the non-thermal motions revealed by the velocity dispersion function (VDF) stay supersonic over scales of several orders of magnitudes. The multi-scale non-thermal motions re

Hikaru Ibayashi, Taufeq Mohammed Razakh, Liqiu Yang, Thomas Linker

Neural-network quantum molecular dynamics (NNQMD) simulations based on machine learning are revolutionizing atomistic simulations of materials by providing quantum-mechanical accuracy but orders-of-magnitude faster, illustrated by ACM Gordon Bell prize (2020) and finalist (2021). State-of-the-art (SOTA) NNQMD model founded on group theory featuring rotationa

M. A. Kurkov

In this paper we overview the Poisson gauge theory focusing on the most recent developments. We discuss the general construction and its symplectic-geometric interpretation. We consider explicit realisations of the formalism for all non-commutativities of the Lie algebraic type. We discuss Seiberg-Witten maps between Poisson gauge field-theoretical models.

Lily Li, Aleksandar Nikolov

The determinant lower bound of Lovasz, Spencer, and Vesztergombi [European Journal of Combinatorics, 1986] is a powerful general way to prove lower bounds on the hereditary discrepancy of a set system. In their paper, Lovasz, Spencer, and Vesztergombi asked if hereditary discrepancy can also be bounded from above by a function of the hereditary discrepancy.

Andrew Killeen, Thibault Bertrand, Chiu Fan Lee

Active nematics is an emerging paradigm for characterising biological systems. One aspect of particularly intense focus is the role active nematic defects play in these systems, as they have been found to mediate a growing number of biological processes. Accurately detecting and classifying these defects in biological systems is, therefore, of vital importan

Sam P. Fisher, Pablo Sánchez-Peralta

Let $k$ be a division ring and let $G$ be either a torsion-free virtually compact special group or a finitely generated torsion-free $3$-manifold group. We embed the group algebra $kG$ in a division ring and prove that the embedding is Hughes-free whenever $G$ is locally indicable. In particular, we prove that Kaplansky's Zero Divisor Conjecture holds for al

Alejandro D. Dominguez-Garcia, Madi Zholbaryssov, Temitope Amuda, Olaoluwapo Ajala

We address the problem of controlling the reactive power setpoints of a set of distributed energy resources (DERs) in a power distribution network so as to mitigate the impact of variability in uncontrolled power injections associated with, e.g., renewable-based generation. We formulate the control design problem as a stochastic optimization problem, which w

S. A. Hosseini, I. V. Karlin

The entropic lattice Boltzmann framework proposed the construction of the discrete equilibrium by taking into consideration minimization of a discrete entropy functional. The effect of this form of the discrete equilibrium on properties of the resulting solver has been the topic of discussions in the literature. Here we present a rigorous analysis of the hyd

Di Luo, Aidan P. Reddy, Trithep Devakul, Liang Fu

Moir\'e engineering in atomically thin van der Waals heterostructures creates artificial quantum materials with designer properties. We solve the many-body problem of interacting electrons confined to a moir\'e superlattice potential minimum (the moir\'e atom) using a 2D fermionic neural network. We show that strong Coulomb interactions in combination with t

Beniamino Accattoli, Adrienne Lancelot, Claudia Faggian

Normal form bisimilarities are a natural form of program equivalence resting on open terms, first introduced by Sangiorgi in call-by-name. The literature contains a normal form bisimilarity for Plotkin's call-by-value $\lambda$-calculus, Lassen's \emph{enf bisimilarity}, which validates all of Moggi's monadic laws and can be extended to validate $\eta$. It d

Aslı Musapaşaoğlu, Mehrdad Nasernejad, Ayesha Asloob Qureshi

Inspired by the definition of $\bf{t}$-spread monomial ideals, in this paper, we introduce $\bf{t}$-spread $d$-partite hypergraph $K^{\bf t}_V$ and study its edge ideal $I(K^{\bf t}_V)$. We prove that $I(K^{\bf t}_V)$ has linear quotients, all powers of $I(K^{\bf t}_V)$ have linear resolution and the Rees algebra of $I(K^{\bf t}_V)$ is a normal Cohen-Macaula