May 2023 arXiv papers — page 40

John Panickacheril John, Jörg Schumacher

Buoyancy-driven turbulent convection leads to a fully compressible flow with a strong top-down asymmetry of first- and second-order statistics when the adiabatic equilibrium profiles of temperature, density and pressure decay very strongly across the convection layer. The growth of this asymmetry and the formation of an increasingly thicker stabilized sublay

Duc-Khanh To, Gianfranco Adimari, Monica Chiogna

The empirical likelihood is a powerful nonparametric tool, that emulates its parametric counterpart -- the parametric likelihood -- preserving many of its large-sample properties. This article tackles the problem of assessing the discriminatory power of three-class diagnostic tests from an empirical likelihood perspective. In particular, we concentrate on in

Federico Cassano, Ming-Ho Yee, Noah Shinn, Arjun Guha

TypeScript and Python are two programming languages that support optional type annotations, which are useful but tedious to introduce and maintain. This has motivated automated type prediction: given an untyped program, produce a well-typed output program. Large language models (LLMs) are promising for type prediction, but there are challenges: fill-in-the-m

He Wen

The collaboration between humans and artificial intelligence (AI) is a significant feature in this digital age. However, humans and AI may have observation, interpretation, and action conflicts when working synchronously. This phenomenon is often masked by faults and, unfortunately, overlooked. This paper systematically introduces the human-AI conflict conce

Sima Rastegar, Alireza Rastkar Ebrahimzadeh, Jaber Jahanbin Sardroodi

Using density functional theory (DFT), we investigated the structural, electronic and optical properties of functionalized and doped MXenes such as M$_1$M$_2$CO$_2$ (M$_1$ = Zr,Hf; M$_2$ = Hf,Ti,Sc). This study aimed to find a suitable photocatalyst that would work well in the water splitting process. Among the calculated nanostructures, MXenes ZrHfCO$_2$ an

Roey Magen, Ohad Shamir

We provide several new results on the sample complexity of vector-valued linear predictors (parameterized by a matrix), and more generally neural networks. Focusing on size-independent bounds, where only the Frobenius norm distance of the parameters from some fixed reference matrix $W_0$ is controlled, we show that the sample complexity behavior can be surpr

Khang Tran, Ferdinando Fioretto, Issa Khalil, My T. Thai

This paper introduces FairDP, a novel training mechanism designed to provide group fairness certification for the trained model's decisions, along with a differential privacy (DP) guarantee to protect training data. The key idea of FairDP is to train models for distinct individual groups independently, add noise to each group's gradient for data privacy prot

Muhammad Adil, Houbing Song, Muhammad Khurram Khan, Ahmed Farouk

Internet technology has proven to be a vital contributor to many cutting-edge innovations that have given humans access to interact virtually with objects. Until now, numerous virtual systems had been developed for digital transformation to enable access to thousands of services and applications that range from virtual gaming to social networks. However, the

Eric Cancès, Fabian M. Faulstich, Alfred Kirsch, Eloïse Letournel

This article provides the first mathematical analysis of the Density Matrix Embedding Theory (DMET) method. We prove that, under certain assumptions, (i) the exact ground-state density matrix is a fixed-point of the DMET map for non-interacting systems, (ii) there exists a unique physical solution in the weakly-interacting regime, and (iii) DMET is exact at

Vyoma Raman, Catherine Vera, CJ Manna

In this study, we introduce a novel two-pronged scoring system to measure individual and systemic bias in immigration courts under the U.S. Executive Office of Immigration Review (EOIR). We analyze nearly 6 million immigration court proceedings and 228 case features to build on prior research showing that U.S. asylum decisions vary dramatically based on fact

Chenkai Sun, Jinning Li, Hou Pong Chan, ChengXiang Zhai

Predicting how a user responds to news events enables important applications such as allowing intelligent agents or content producers to estimate the effect on different communities and revise unreleased messages to prevent unexpected bad outcomes such as social conflict and moral injury. We present a new task, Response Forecasting on Personas for News Media

Abhijeet Sahu, Katherine Davis

Voltage control is crucial to large-scale power system reliable operation, as timely reactive power support can help prevent widespread outages. However, there is currently no built in mechanism for power systems to ensure that the voltage control objective to maintain reliable operation will survive or sustain the uncertainty caused under adversary presence

Anton Wahrstätter, Liyi Zhou, Kaihua Qin, Davor Svetinovic

With the emergence of Miner Extractable Value (MEV), block construction markets on blockchains have evolved into a competitive arena. Following Ethereum's transition from Proof of Work (PoW) to Proof of Stake (PoS), the Proposer Builder Separation (PBS) mechanism has emerged as the dominant force in the Ethereum block construction market. This paper presents

Shizhao Lu, Arthi Jayaraman

In material research, structural characterization often requires multiple complementary techniques to obtain a holistic morphological view of the synthesized material. Depending on the availability of and accessibility of the different characterization techniques (e.g., scattering, microscopy, spectroscopy), each research facility or academic research lab ma

Yuri Sarkisov, Andre Coelho, Maihara Santos, Min Jun Kim

During operation, aerial manipulation systems are affected by various disturbances. Among them is a gravitational torque caused by the weight of the robotic arm. Common propeller-based actuation is ineffective against such disturbances because of possible overheating and high power consumption. To overcome this issue, in this paper we propose a winchbased ac

Parmida Ghahremani, Joseph Marino, Juan Hernandez-Prera, Janis V. de la Iglesia

We introduce a new AI-ready computational pathology dataset containing restained and co-registered digitized images from eight head-and-neck squamous cell carcinoma patients. Specifically, the same tumor sections were stained with the expensive multiplex immunofluorescence (mIF) assay first and then restained with cheaper multiplex immunohistochemistry (mIHC

Mackenzie R. Neal, Paul D. McNicholas

The importance of variable selection for clustering has been recognized for some time, and mixture models are well-established as a statistical approach to clustering. Yet, the literature on variable selection in model-based clustering remains largely rooted in the assumption of Gaussian clusters. Unsurprisingly, variable selection algorithms based on this a

Iván Álvarez-Rios, Francisco S. Guzmán

We present the construction of stationary boson-fermion spherically symmetric configurations governed by Newtonian gravity. Bosons are described in the Gross-Pitaevskii regime and fermions are assumed to obey Euler equations for an inviscid fluid with polytropic equation of state. The two components are coupled through the gravitational potential. The famili

Andrea Cordaro, Ralph Müller, Stefan Tabernig, Nico Tucher

Multi-junction solar cells provide a path to overcome the efficiency limits of standard silicon solar cells by harvesting more efficiently a broader range of the solar spectrum. However, Si-based multi-junction architectures are hindered by incomplete harvesting in the near-infrared (near-IR) spectral range, as Si sub-cells have weak absorption close to the

Quang-Tuan Dang

We study the nature of finite-time singularities for the Chern-Ricci flow, partially answering a question of Tosatti-Weinkove. We show that a solution of degenerate parabolic complex Monge-Amp\`ere equations starting from arbitrarily positive (1,1)-currents are smooth outside some analytic subset, generalizing works by Di Nezza-Lu. We extend Guedj-Lu's recen

Jaskaran Singh Walia, Karthik Seemakurthy

Accurately quantifying and removing submerged underwater waste plays a crucial role in safeguarding marine life and preserving the environment. While detecting floating and surface debris is relatively straightforward, quantifying submerged waste presents significant challenges due to factors like light refraction, absorption, suspended particles, and color

Jing Zhou, Jiannan Lu, Anas Shallah

While there exists a large amount of literature on the general challenges of and best practices for trustworthy online A/B testing, there are limited studies on sample size estimation, which plays a crucial role in trustworthy and efficient A/B testing that ensures the resulting inference has a sufficient power and type I error control. For example, when sam

Sourin Chatterjee, Ahad N. Zehmakan

Controlling and understanding epidemic outbreaks has recently drawn great interest in a large spectrum of research communities. Vaccination is one of the most well-established and effective strategies in order to contain an epidemic. In the present study, we investigate a network-based virus-spreading model building on the popular SIR model. Furthermore, we

Aric Wheeler, Kevin Zumbrun

Generalizing results of \cite{MC,S} and \cite{HSZ} for certain model reaction-diffusion and reaction-convection-diffusion equations, we derive and rigorously justify weakly nonlinear amplitude equations governing general Turing bifurcation in the presence of conservation laws. In the nonconvective, reaction-diffusion case, this is seen similarly as in \cite{

Benedikt Stufler

We determine the Gromov--Hausdorff--Prokhorov scaling limits and local limits of Kemp's $d$-dimensional binary trees and other models of supertrees. The limits exhibit a root vertex with infinite degree and are constructed by rescaling infinitely many independent stable trees or other spaces according to a function of a two-parameter Poisson--Dirichlet proce

Sinéad A. Ryan, Peter C. Johnsen, Mohamed F. Elhanoty, Anya Grafov

The direct manipulation of spins via light may provide a path toward ultrafast energy-efficient devices. However, distinguishing the microscopic processes that can occur during ultrafast laser excitation in magnetic alloys is challenging. Here, we study the Heusler compound Co2MnGa, a material that exhibits very strong light-induced spin transfers across the

Siddharth Singh, He Liu, Rajat Arora, Robert M. Suter

A rigorous methodology is developed for computing elastic fields generated by experimentally observed defect structures within grains in a polycrystal that has undergone tensile extension. An example application is made using a near-field High Energy X-ray Diffraction Microscope measurement of a zirconium sample that underwent $13.6\%$ tensile extension from

Benedikt Stufler

We present a new probabilistic proof of Otter's asymptotic formula for the number of unlabelled trees with a given number of vertices. We additionally prove a new approximation result, showing that the total variation distance between random P\'olya trees and random unlabelled trees tends to zero when the number of vertices tends to infinity. In order to dem

Sungjin Im, Benjamin Moseley, Chenyang Xu, Ruilong Zhang

We revisit the online dynamic acknowledgment problem. In the problem, a sequence of requests arrive over time to be acknowledged, and all outstanding requests can be satisfied simultaneously by one acknowledgement. The goal of the problem is to minimize the total request delay plus acknowledgement cost. This elegant model studies the trade-off between acknow

Thomas Beck, Yaiza Canzani, Jeremy L. Marzuola

We obtain upper bounds on the number of nodal domains of Laplace eigenfunctions on chain domains with Neumann boundary conditions. The chain domains consist of a family of planar domains, with piecewise smooth boundary, that are joined by thin necks. Our work does not assume a lower bound on the width of the necks in the chain domain. As a consequence, we pr

Mrittunjoy Guha Majumdar

Quantum learning paradigms address the question of how best to harness conceptual elements of quantum mechanics and information processing to improve operability and functionality of a computing system for specific tasks through experience. It is one of the fastest evolving framework, which lies at the intersection of physics, statistics and information proc

Mohan Ramu, Joseph Varghese Kureethara

For a given graph $G$, let $f:V(G)\to \{1,2,\ldots,n\}$ be a bijective mapping. For a given edge $uv \in E(G)$, $\sigma(uv)=+$, if $f(u)$ and $f(v)$ have the same parity and $\sigma(uv)=-$, if $f(u)$ and $f(v)$ have opposite parity. The resultant signed graph is called a parity signed graph and the mapping $\sigma$ is called a parity signature of $G$. Let us

Adnan Munir, Abdul Jabbar Siddiqui, Saeed Anwar

To detect unmanned aerial vehicles (UAVs) in real-time, computer vision and deep learning approaches are evolving research areas. Interest in this problem has grown due to concerns regarding the possible hazards and misuse of employing UAVs in many applications. These include potential privacy violations. To address the concerns, vision-based object detectio

Haixin Liu, Grant M. Brodnik, Jizhao Zang, David R. Carlson

We explore optical parametric oscillation (OPO) in nanophotonic resonators, enabling arbitrary, nonlinear phase-matching and nearly lossless control of energy conversion. Such pristine OPO laser converters are determined by nonlinear light-matter interactions, making them both technologically flexible and broadly reconfigurable. We utilize a nanostructured i

Daniel F. Lima, Frankbelson dos S. Azevedo, Luís Fernando C. Pereira, Cleverson Filgueiras

This work presents a study on the nonrelativistic quantum motion of a charged particle in a rotating frame, considering the Aharonov-Bohm effect and a uniform magnetic field. We derive the equation of motion and the corresponding radial equation to describe the system. The Schr\"odinger equation with minimal coupling incorporates rotation effects by substitu

C. A. Ortiz-Rodríguez, P. J. Käpylä, F. H. Navarrete, D. R. G Schleicher

The aim of this study is to explore the magnetic and flow properties of fully convective M dwarfs as a function of rotation period Prot and magnetic Reynolds ReM and Prandlt numbers PrM. We performed three-dimensional simulations of fully convective stars using a star-in-a-box setup. This setup allows global dynamo simulations in a sphere embedded in a Carte

Jhoan K. Hoyos-Osorio, Luis G. Sanchez-Giraldo

Quantifying the difference between probability distributions is crucial in machine learning. However, estimating statistical divergences from empirical samples is challenging due to unknown underlying distributions. This work proposes the representation Jensen-Shannon divergence (RJSD), a novel measure inspired by the traditional Jensen-Shannon divergence. O

Mahathir Monjur, Yubo Luo, Zhenyu Wang, Shahriar Nirjon

A fundamental problem of every intermittently-powered sensing system is that signals acquired by these systems over a longer period in time are also intermittent. As a consequence, these systems fail to capture parts of a longer-duration event that spans over multiple charge-discharge cycles of the capacitor that stores the harvested energy. From an applicat

Ashim Gupta, Carter Wood Blum, Temma Choji, Yingjie Fei

Can language models transform inputs to protect text classifiers against adversarial attacks? In this work, we present ATINTER, a model that intercepts and learns to rewrite adversarial inputs to make them non-adversarial for a downstream text classifier. Our experiments on four datasets and five attack mechanisms reveal that ATINTER is effective at providin

Tianyi Chen, Luming Liang, Tianyu Ding, Ilya Zharkov

To search an optimal sub-network within a general deep neural network (DNN), existing neural architecture search (NAS) methods typically rely on handcrafting a search space beforehand. Such requirements make it challenging to extend them onto general scenarios without significant human expertise and manual intervention. To overcome the limitations, we propos

Geraldine Jeckeln, Selin Yavuzcan, Kate A. Marquis, Prajay Sandipkumar Mehta

Face recognition algorithms perform more accurately than humans in some cases, though humans and machines both show race-based accuracy differences. As algorithms continue to improve, it is important to continually assess their race bias relative to humans. We constructed a challenging test of 'cross-race' face verification and used it to compare humans and

M. Stryjczyk, B. Andel, J. G. Cubiss, K. Rezynkina

Background: The mercury isotopes around $N=104$ are a well-known example of nuclei exhibiting shape coexistence. Mixing of configurations can be studied by measuring the monopole strength $\rho^2(E0)$, however, currently the experimental information is scarce and lacks precision, especially for the $I^\pi \rightarrow I^\pi$ ($I \neq 0$) transitions. Purpose:

Jeongseok Oh, Richard P. Thomas

We develop a theory of complex Kuranishi structures on projective schemes. These are sufficiently rigid to be equivalent to weak perfect obstruction theories, but sufficiently flexible to admit global complex Kuranishi charts. We apply the theory to projective moduli spaces M of stable sheaves on Calabi-Yau 4-folds. Using real derived differential geometry,

Navjot Singh, Suhas Diggavi

In this paper, we consider the problem of learning a linear regression model on a data domain of interest (target) given few samples. To aid learning, we are provided with a set of pre-trained regression models that are trained on potentially different data domains (sources). Assuming a representation structure for the data generating linear models at the so

Shi Li, Chenyang Xu, Ruilong Zhang

The paper revisits the robust $s$-$t$ path problem, one of the most fundamental problems in robust optimization. In the problem, we are given a directed graph with $n$ vertices and $k$ distinct cost functions (scenarios) defined over edges, and aim to choose an $s$-$t$ path such that the total cost of the path is always provable no matter which scenario is r

Rados Bakic

We generalize Coincidence theorem due to Walsh for symmetric and linear polynomial in n complex variables, that is linear in each of them having total degre n. We discuss case when total degree is smaller then n. This case has been already studied by some authors. We obtained extension of known results that led us also to more elegant formulation of the theo

Xu Bao, Zhi-Qi Cheng, Jun-Yan He, Chenyang Li

Accurate facial landmark detection is critical for facial analysis tasks, yet prevailing heatmap and coordinate regression methods grapple with prohibitive computational costs and quantization errors. Through comprehensive theoretical analysis and experimentation, we identify and elucidate the limitations of existing techniques. To overcome these challenges,

Sergey Khrapak

A simple and popular Bridgman's model predicts a linear correlation between the thermal conductivity coefficient and the sound velocity of dense liquids. A proportionality coefficient proposed originally is fixed and independent of the liquid molecular structure. This work reports a systematic analysis of correlations between thermal conductivity and sound v

Mugurel Barcau, Vicenţiu Paşol, George C. Ţurcaş

The present work builds on previous investigations of the authors (and their collaborators) regarding bridges, a certain type of morphisms between encryption schemes, making a step forward in developing a (category theory) language for studying relations between encryption schemes. Here we analyse the conditions under which bridges can be performed sequentia

Akash Awasthi, Son Ly, Jaer Nizam, Samira Zare

The definition of anomaly detection is the identification of an unexpected event. Real-time detection of extreme events such as wildfires, cyclones, or floods using satellite data has become crucial for disaster management. Although several earth-observing satellites provide information about disasters, satellites in the geostationary orbit provide data at i

Irena Barjašić, Stefano Battiston, Vinko Zlatić

Credit Valuation Adjustment captures the difference in the value of derivative contracts when the counterparty default probability is taken into account. However, in the context of a network of contracts, the default probability of a direct counterparty can depend substantially on the default probabilities of indirect counterparties. We develop a model to cl

Felix Petersen, Moritz Schubotz, Andre Greiner-Petter, Bela Gipp

We tackle the problem of neural machine translation of mathematical formulae between ambiguous presentation languages and unambiguous content languages. Compared to neural machine translation on natural language, mathematical formulae have a much smaller vocabulary and much longer sequences of symbols, while their translation requires extreme precision to sa

Nourhan Hesham, Mohamed Bouzid, Ahmad Abdel-Qader, Anas Chaaban

The development of delay-sensitive applications that require ultra high reliability created an additional challenge for wireless networks. This led to Ultra-Reliable Low-Latency Communications, as a use case that 5G and beyond 5G systems must support. However, supporting low latency communications requires the use of short codes, while attaining vanishing fr

Yichen Li, Peter Yichen Chen, Tao Du, Wojciech Matusik

Efficient numerical solvers for partial differential equations empower science and engineering. One of the commonly employed numerical solvers is the preconditioned conjugate gradient (PCG) algorithm which can solve large systems to a given precision level. One challenge in PCG solvers is the selection of preconditioners, as different problem-dependent syste

Natsumi Ikeno, Genaro Toledo, Eulogio Oset

We face the inverse problem of obtaining the interaction between coupled channels from the correlation functions of these channels. We apply the method to the interaction of the $D^0 K^+$, $D^+ K^0$, and $D^+_s \eta$ channels, from where the $D^*_{s0}(2317)$ state emerges. We use synthetic data extracted from an interaction model based on the local hidden ga

Agnieszka Ciborowska, Kostadin Damevski

Modern Deep Learning (DL) architectures based on transformers (e.g., BERT, RoBERTa) are exhibiting performance improvements across a number of natural language tasks. While such DL models have shown tremendous potential for use in software engineering applications, they are often hampered by insufficient training data. Particularly constrained are applicatio

Ricardo Bances

In this thesis, an almost complete classification of all graduated twisted tensorial products of $K[x]$ with $K[y]$ is obtained. We obtain a particular example and three main cases: quadratic algebras, classified by Conner and Goetz by a different method, a family called $A(n,d,a)$ with the property of $(n+1)$-extension for $n>1$, and a third case, not fully

I. Dhiman, Sadique Vellamarthodika, Siddharth Gautam

Structural and dynamical behavior of SO$_2$ molecules within ZSM22 is studied using MD simulations, to understand the influence of orientational disorder (OD) and intercrystalline spacing in ZSM22 as a function of adsorbate loading. Addition of inter-crystalline space provides connectivity of isolated pores in ZSM22 and is shown to suppress both translationa

Mariia Seleznova, Dana Weitzner, Raja Giryes, Gitta Kutyniok

This work bridges two important concepts: the Neural Tangent Kernel (NTK), which captures the evolution of deep neural networks (DNNs) during training, and the Neural Collapse (NC) phenomenon, which refers to the emergence of symmetry and structure in the last-layer features of well-trained classification DNNs. We adopt the natural assumption that the empiri

Isabelle Lorge, Janet Pierrehumbert

Vector space models of word meaning all share the assumption that words occurring in similar contexts have similar meanings. In such models, words that are similar in their topical associations but differ in their logical force tend to emerge as semantically close, creating well-known challenges for NLP applications that involve logical reasoning. Modern pre

Quentin Ehret, Abdenacer Makhlouf

The main purpose of this paper is to study restricted formal deformations of restricted Lie-Rinehart algebras in positive characteristic $p$. For $p>2$, we discuss the deformation theory and show that deformations are controlled by the restricted cohomology introduced by Evans and Fuchs. Furthermore, for $p=2$, we introduce a new cohomology complex and show

Youngjae Min, Benjamin Wright, Jeremy Bernstein, Navid Azizan

When machine learning models are trained continually on a sequence of tasks, they are often liable to forget what they learned on previous tasks--a phenomenon known as catastrophic forgetting. Proposed solutions to catastrophic forgetting tend to involve storing information about past tasks, meaning that memory usage is a chief consideration in determining t

Azadeh Sharafi, Andrew S Nencka, Kevin M Koch

Introduction: Wrist instability remains a common health concern. The potential of dynamic Magnetic Resonance Imaging (MRI) in assessing carpal dynamics associated with this condition is a field of ongoing research. This study contributes to this line of inquiry by developing MRI-derived carpal kinematic metrics and investigating their stability. Methods: A p

Luca Buoninfante, Junsei Tokuda, Masahide Yamaguchi

Under reasonable working assumptions including the polynomial boundedness, one proves the well-known Cerulus-Martin lower bound on how fast an elastic scattering amplitude can decrease in the hard-scattering regime. In this paper we consider two non-trivial extensions of the previous bound. (i) We generalize the assumption of polynomial boundedness by allowi

Kazi Zainab Khanam, Aditya Singhal, Vijay Mago

Computing the probability of an edge's existence in a graph network is known as link prediction. While traditional methods calculate the similarity between two given nodes in a static network, recent research has focused on evaluating networks that evolve dynamically. Although deep learning techniques and network representation learning algorithms, such as n

Aydin Shoja, Joost van der Neut, Kees Wapenaar

Recently, the focus of reflection seismologists has shifted to applications where a high-resolution image of the subsurface is required. Least-Squares Reverse-Time Migration (LSRTM) is a common tool used to compute such images. Still, its high computational costs have led seismologists to use target-oriented LSRTM for imaging only a small target of interest

Meng-Han Zhang, Dao-Xin Yao

We study topological magnons on an anisotropic square-hexagon-octagon (SHO) lattice which has been found by a two-dimensional Biphenylene network (BPN). We propose the concepts of type-II Dirac magnonic states where new schemes to achieve topological magnons are unfolded without requiring the Dzyaloshinsky-Moriya interactions (DMIs). In the ferromagnetic sta

Hritom Das, Rocco D. Febbo, Charlie P. Rizzo, Nishith N. Chakraborty

The synapse is a key element of neuromorphic computing in terms of efficiency and accuracy. In this paper, an optimized current-controlled memristive synapse circuit is proposed. Our proposed synapse demonstrates reliability in the face of process variation and the inherent stochastic behavior of memristors. Up to an 82% energy optimization can be seen durin

Youssef Elmougy, Ling Liu

Blockchain provides the unique and accountable channel for financial forensics by mining its open and immutable transaction data. A recent surge has been witnessed by training machine learning models with cryptocurrency transaction data for anomaly detection, such as money laundering and other fraudulent activities. This paper presents a holistic applied dat

Jochem G. Meijer, Pallav Kant, Duco Van Buuren, Detlef Lohse

Freezing of dispersions is omnipresent in science and technology. While the passing of a freezing front over a solid particle is reasonably understood, this is not so for soft particles. Here, using an oil-in-water emulsion as a model system, we show that when engulfed into a growing ice front, a soft particle severely deforms. This deformation strongly depe

Eric Lei, Hamed Hassani, Shirin Saeedi Bidokhti

We discuss a federated learned compression problem, where the goal is to learn a compressor from real-world data which is scattered across clients and may be statistically heterogeneous, yet share a common underlying representation. We propose a distributed source model that encompasses both characteristics, and naturally suggests a compressor architecture t

Bruce D. Lee, Thomas T. C. K. Zhang, Hamed Hassani, Nikolai Matni

While $\mathcal{H}_\infty$ methods can introduce robustness against worst-case perturbations, their nominal performance under conventional stochastic disturbances is often drastically reduced. Though this fundamental tradeoff between nominal performance and robustness is known to exist, it is not well-characterized in quantitative terms. Toward addressing th

Luka Vujeva, Antoine Marchal, Peter G. Martin, Mukesh Taank

We have analyzed the thermal and turbulent properties of the Low-Latitude Intermediate-Velocity Arch 1 (LLIV1). This was accomplished using archival H I emission and absorption data from two 21,cm line surveys: GHIGLS at $9.^\prime$4 resolution and DHIGLS at $1^\prime$ resolution. The spectral decomposition code $\tt{ROHSA}$ was used to model the column dens

Jason Cong, Michalis Romesis, Joseph R. Shinnerl, Kenton Sze

The mixed-size placement benchmarks described in this book chapter directly address several of the shortcomings in previously published suboptimality benchmarks. Two new sets of placement examples are constructed, one targeting the role of nonlocal nets in suboptimality, and another targeting the role of white space and large variations in module sizes. The

Tejas Prasanna, Matthew S. B. Coleman, Matthias J. Raives, Todd A. Thompson

Rapidly rotating magnetars have been associated with gamma-ray bursts (GRBs) and super-luminous supernovae (SLSNe). Using a suite of 2D magnetohydrodynamic simulations at fixed neutrino luminosity and a couple of evolutionary models with evolving neutrino luminosity and magnetar spin period, we show that magnetars are viable central engines for powering GRBs

Zhenzhen Weng, Zeyu Wang, Serena Yeung

Recent advancements in text-to-image generation have enabled significant progress in zero-shot 3D shape generation. This is achieved by score distillation, a methodology that uses pre-trained text-to-image diffusion models to optimize the parameters of a 3D neural presentation, e.g. Neural Radiance Field (NeRF). While showing promising results, existing meth

Zhe Huang, Benjamin S. Wessler, Michael C. Hughes

Aortic stenosis (AS) is a degenerative valve condition that causes substantial morbidity and mortality. This condition is under-diagnosed and under-treated. In clinical practice, AS is diagnosed with expert review of transthoracic echocardiography, which produces dozens of ultrasound images of the heart. Only some of these views show the aortic valve. To aut

David Izquierdo-Villalba, Monica Colpi, Marta Volonteri, Daniele Spinoso

The gravitational wave (GW) antenna LISA will detect the signal from coalescing massive black hole binaries (MBHBs) of $\rm 10^4\,{-}\,10^7\, M_{\odot}$, providing clues on their formation and growth along cosmic history. Some of these events will be localized with a precision of several to less than a deg$^2$, enabling the possible identification of their h

Venkata S Govindarajan, Kyle Mahowald, David I. Beaver, Junyi Jessy Li

While existing work on studying bias in NLP focues on negative or pejorative language use, Govindarajan et al. (2023) offer a revised framing of bias in terms of intergroup social context, and its effects on language behavior. In this paper, we investigate if two pragmatic features (specificity and affect) systematically vary in different intergroup contexts

Adam Czornik, Konrad Kitzing, Stefan Siegmund

Bohl dichotomy is a notion of hyperbolicity for linear nonautonomous difference equations that is weaker than the classical concept of exponential dichotomy. In the class of systems with bounded invertible coefficient matrices which have bounded inverses, we study the relation between the set $\mathrm{BD}$ of systems with Bohl dichotomy and the set $\mathrm{

Sina Ahmadi, Antonios Anastasopoulos

The wide accessibility of social media has provided linguistically under-represented communities with an extraordinary opportunity to create content in their native languages. This, however, comes with certain challenges in script normalization, particularly where the speakers of a language in a bilingual community rely on another script or orthography to wr

Loukas Ilias, Dimitris Askounis

Alzheimer's disease (AD) constitutes a complex neurocognitive disease and is the main cause of dementia. Although many studies have been proposed targeting at diagnosing dementia through spontaneous speech, there are still limitations. Existing state-of-the-art approaches, which propose multimodal methods, train separately language and acoustic models, emplo

Zhuojun Chen, Michel Albonico, Ivano Malavolta

Robotic systems react to different environmental stimuli, potentially resulting in the dynamic reconfiguration of the software controlling such systems. One effect of such dynamism is the reconfiguration of the software architecture reconfiguration of the system at runtime. Such reconfigurations might severely impact the runtime properties of robotic systems

Zihui Zhang, Bo Yang, Bing Wang, Bo Li

We study the problem of 3D semantic segmentation from raw point clouds. Unlike existing methods which primarily rely on a large amount of human annotations for training neural networks, we propose the first purely unsupervised method, called GrowSP, to successfully identify complex semantic classes for every point in 3D scenes, without needing any type of hu

Biswarup Mukhopadhyaya, Sirshendu Samanta, Tousik Samui, Ritesh K. Singh

We consider, in the context of the Large Hadron Collider, the signals of the Type-X two Higgs doublet model (2HDM) in the parameter region answering to the best possible solution to the muon $(g-2)$ data within this framework. The analysis takes into account all theoretical and observational constraints, and is based on the final state comprising a same-sign

Sarang Jokhio, Pierluigi Olleja, Jonas Bärgman, Fei Yan

Using turn signals to convey a driver's intention to change lanes provides a direct and unambiguous way of communicating with nearby drivers. Nonetheless, past research has indicated that drivers may not always use their turn signals prior to starting a lane change. In this study, we analyze realistic driving data to investigate turn signal usage during lane

Itamar Giron, Shmuel Balberg, Menahem Krief

We consider the solutions of the Guderley problem, consisting of a converging and diverging hydrodynamic shock wave in an ideal gas with a power law initial density profile. The self-similar solutions, and specifically the reflected shock coefficient, which determines the path of the reflected shock, are studied in detail, for cylindrical and spherical symme

Ruochen Ma, Jian-Hao Zhang, Zhen Bi, Meng Cheng

Global symmetries greatly enrich the landscape of topological quantum phases, playing an essential role from topological insulators to fractional quantum Hall effect. Topological phases in mixed quantum states, originating from \textit{decoherence} in open quantum systems or \textit{disorders} in imperfect crystalline solids, have recently garnered significa

K. S. Babu, Borut Bajc, Vasja Susič

In the context of $\mathrm{E}_{6}$ Grand Unified Theories (GUTs), an intriguing possibility for symmetry breaking to the Standard Model (SM) group involves an intermediate stage characterized by either $\mathrm{SU}(3)\times\mathrm{SU}(3)\times\mathrm{SU}(3)$ (trinification) or $\mathrm{SU}(6)\times\mathrm{SU}(2)$. The more common choices of $\mathrm{SU(5)}$

Benno Krojer, Elinor Poole-Dayan, Vikram Voleti, Christopher Pal

Text-conditioned image generation models have recently shown immense qualitative success using denoising diffusion processes. However, unlike discriminative vision-and-language models, it is a non-trivial task to subject these diffusion-based generative models to automatic fine-grained quantitative evaluation of high-level phenomena such as compositionality.

Dario Balboni, Davide Bacciu

We derive a sound positive semi-definite approximation of the Hessian of deep models for which Hessian-vector products are easily computable. This enables us to provide an adaptive SGD learning rate strategy based on the minimization of the local quadratic approximation, which requires just twice the computation of a single SGD run, but performs comparably w

Megan Giron, Georgios Korpas, Waqas Parvaiz, Prashant Malik

Collateral optimization refers to the systematic allocation of financial assets to satisfy obligations or secure transactions, while simultaneously minimizing costs and optimizing the usage of available resources. {This involves assessing number of characteristics, such as cost of funding and quality of the underlying assets to ascertain the optimal collater

Andrew Weldon, Naveen A. Reddy, Michael W. Topping, Alice E. Shapley

We report on the discovery of cool gas inflows towards three star-forming galaxies at $\left<z\right>\sim$ 2.30. Analysis of Keck Low-Resolution Imaging Spectrometer spectroscopy reveals redshifted low-ionisation interstellar (LIS) metal absorption lines with centroid velocities of 60 - 130 km $\rm{s}^{-1}$. These inflows represent some of the most robust de

Iain Stewart, Varun Vaidya

We present a description of saturation in small $x$ deep inelastic scattering from power counting in a top-down effective theory derived from QCD. A factorization formula isolates the universal physics of the nucleus at leading power in $x$. The onset of saturation is then understood as a breakdown in the expansion in an emergent power counting parameter, wh

Zachary Murray

Asteroid diameters are traditionally difficult to estimate. When a direct measurement of the diameter cannot be made through either occultation or direct radar observation, the most common method is to approximate the diameter from infrared observations. Once the diameter is known, a comparison with visible light observations can be used to find the visible

Xiaohui Chen, Jiankai Sun, Taiqing Wang, Ruocheng Guo

Data subsampling is widely used to speed up the training of large-scale recommendation systems. Most subsampling methods are model-based and often require a pre-trained pilot model to measure data importance via e.g. sample hardness. However, when the pilot model is misspecified, model-based subsampling methods deteriorate. Since model misspecification is pe

Margaret Lazzarini, Kyros Hinton, Cheyanne Shariat, Benjamin F. Williams

We present multi-wavelength characterization of 65 high mass X-ray binary (HMXB) candidates in M33. We use the Chandra ACIS survey of M33 (ChASeM33) catalog to select hard X-ray point sources that are spatially coincident with UV-bright point source optical counterparts in the Panchromatic Hubble Andromeda Treasury: Triangulum Extended Region (PHATTER) catal

Peyman Khordadpour, Saeed Ahmadi

In recent times, I've encountered a principle known as cloud computing, a model that simplifies user access to data and computing power on a demand basis. The main objective of cloud computing is to accommodate users' growing needs by decreasing dependence on human resources, minimizing expenses, and enhancing the speed of data access. Nevertheless, preservi

Basabendu Barman, Nicolás Bernal, Yong Xu, Óscar Zapata

We discuss the production of primordial gravitational waves (GW) from radiative inflaton decay during the period of reheating, assuming perturbative decay of the inflaton either into a pair of bosons or fermions, leading to successful reheating satisfying constraint from Big Bang nucleosynthesis. Assuming that the inflaton $\phi$ oscillates in a general mono

E. Tomasetti, M. Moresco, N. Borghi, K. Jiao

We derive a new constraint on the expansion history of the Universe by applying the cosmic chronometers method, studying the age evolution of high-redshift galaxies with a full-spectral-fitting approach. We select a sample of 39 massive ($log(M/M_\odot)>10.8$) and passive ($log(sSFR/yr^{-1})<-11$) galaxies from the data release 4 of the VANDELS survey at $1<