April 2023 arXiv papers — page 87

Pieter Allaart, Derong Kong

Given $\beta>1$, let $T_\beta$ be the $\beta$-transformation on the unit circle $[0,1)$ such that $T_\beta(x)=\beta x\pmod 1$. For each $t\in[0,1)$ let $K_\beta(t)$ be the survivor set consisting of all $x\in[0,1)$ whose orbit $\{T^n_\beta(x): n\ge 0\}$ never enters the interval $[0,t)$. Letting $\mathscr{E}_\beta$ denote the bifurcation set of the set-value

Aqeel Ahmed, Zaheer Ahmad

In this paper, we present the effects of high-energy Ni++ ion irradiation on the structural and optical properties of GaN films. Three different irradiation doses of 1013, 1014, and 1015 ions/cm2 were used while keeping the ion energy at 700keV. The irradiation induced structural and optical changes in GaN films were measured using X-ray diffraction (XRD) an

Hongzhi Guo, Marlin Prince, Javionn Ramsey, Jarvis Turner

Metallic pipes and other containers are widely used to store and transport toxic gases and liquids. Various sensors have been designed to monitor the environment inside metallic pipes and containers, such as pressure, liquid-level, and chemical sensors. Moreover, sensors are also used to inspect and detect pipe leakages. However, sensors are usually placed o

Daoji Huang, Pavlo Pylyavskyy

In our previous work we have introduced an analogue of Robinson-Schensted-Knuth correspondence for Schubert calculus of the complete flag varieties. The objects inserted are certain biwords, the outcomes of insertion are bumpless pipe dreams, and the recording objects are decorated chains in Bruhat order. In this paper we study a class of biwords that have a

R. García-Delgado, G. Salgado, O. A. Sánchez-Valenzuela

Quadratic Hom-Lie algebras with equivariant twist maps are studied. They are completely characterized in terms of a maximal proper ideal that contains the kernel of the twist map and a complementary subspace to it that is either 1-dimensional, or has the structure of a simple Lie algebra. It is shown how the analogue of the double extension construction work

Yabo Guo, Peng Sun, Zhengdao Yuan, Qinghua Guo

Reconfigurable intelligent surface (RIS) has emerged as a promising technology for improving capacity and extending coverage of wireless networks. In this work, we consider RIS-aided millimeter wave (mmWave) multiple-input and multiple-output (MIMO) communications, where acquiring accurate channel state information is challenging due to the high dimensionali

Kai Wang, Yingjie Peng, Yangyao Chen

We investigate the two-halo galactic conformity effect for central galaxies, which is the spatial correlation of the star formation activities for central galaxies to several Mpcs, by studying the dependence of the star formation activities of central galaxies on their large-scale structure in our local Universe using the SDSS data. Here we adopt a novel env

S. C. Mancas, K. R. Acharya, H. C. Rosu

In this paper, Liouville soliton surfaces based on some soliton solutions of the Liouville equation are constructed and displayed graphically, including some of those corresponding to Darboux-transformed counterparts. We find that the Liouville soliton surfaces are centroaffine surfaces of Tzitzeica type and their centroaffine invariant can be expressed in t

Fumio Hirata

Structural transition induced by a local conformational change in biomolecules is formulated based on the generalized Langevin theory for the structural fluctuation of a molecule in solution, and the linear response theory, derived by Kim and Hirata in 2012. A chemical/mechanical change introduced at a moiety of biomolecules, such as an amino acid substituti

Jintao Luo, Sixing Yin

Intelligent reflecting surface (IRS) has been widely studied in recent years, it has emerged as a new technology which can reflect the incident signal by intelligently configuring the reflection elements, thus changing the signal propagation environment, enhancing the signals users desire and suppressing the interference between users. In this paper, we stud

Farshid Saeedi, Seyedeh Nafiseh Akbarossadat

Let L be a finite-dimensional n-Lie algebra with free presentation F/R. Then the concept of c-nilpotent multiplier of L, denoted by M(c)(L), is defined as follows: M(c)(L) =(gamma c+1(F) R)/gamma c+1(R, F, . . . , F). In this paper, we obtain some inequalities and certain bounds for the dimension of M(c)(L) by using the basic commutators. Also, we discuss th

Tyler H. Chang, Stefan M. Wild

Multiobjective simulation optimization (MOSO) problems are optimization problems with multiple conflicting objectives, where evaluation of at least one of the objectives depends on a black-box numerical code or real-world experiment, which we refer to as a simulation. While an extensive body of research is dedicated to developing new algorithms and methods f

Mehrnaz Mofakhami, Ioannis Mitliagkas, Gauthier Gidel

Performative prediction is a framework for learning models that influence the data they intend to predict. We focus on finding classifiers that are performatively stable, i.e. optimal for the data distribution they induce. Standard convergence results for finding a performatively stable classifier with the method of repeated risk minimization assume that the

Daisuke Kazukawa, Hiroki Nakajima, Takashi Shioya

Gromov introduced two distance functions, the box distance and the observable distance, on the space of isomorphism classes of metric measure spaces and developed the convergence theory of metric measure spaces. We investigate several topological properties on the space equipped with these distance functions toward a deep understanding of convergence theory.

Shuhao Shi, Kai Qiao, Jie Yang, Baojie Song

The presence of a large number of bots on social media leads to adverse effects. Although Random forest algorithm is widely used in bot detection and can significantly enhance the performance of weak classifiers, it cannot utilize the interaction between accounts. This paper proposes a Random Forest boosted Graph Neural Network for social bot detection, call

Samuel W. Akingbade, Marian Gidea, Matteo Manzi, Vahid Nateghi

We present a heuristic argument for the propensity of Topological Data Analysis (TDA) to detect early warning signals of critical transitions in financial time series. Our argument is based on the Log-Periodic Power Law Singularity (LPPLS) model, which characterizes financial bubbles as super-exponential growth (or decay) of an asset price superimposed with

Qi Heng Ho, Zachary N. Sunberg, Morteza Lahijanian

This paper introduces a sampling-based strategy synthesis algorithm for nondeterministic hybrid systems with complex continuous dynamics under temporal and reachability constraints. We model the evolution of the hybrid system as a two-player game, where the nondeterminism is an adversarial player whose objective is to prevent achieving temporal and reachabil

Yiqun Yao, Siqi fan, Xiusheng Huang, Xuezhi Fang

As language models scale up, it becomes increasingly expensive to verify research ideas because conclusions on small models do not trivially transfer to large ones. A possible solution is to establish a generic system that accurately predicts certain metrics for large models without training them. Existing scaling laws require hyperparameter search on the la

Gunhee Cho, Zetian Yan

We establish a sharp Sobolev trace inequality on the Siegel domain $\Omega_{n+1}$ involving the weighted norm-$W^{2,2}(\Omega_{n+1}, \rho^{1-2[\gamma]})$. The inequality is closely related the realization of fractional powers of the sub-Laplacian on the Heisenberg group $H^n=\partial \Omega_{n+1}$ as generalized Dirichlet-to-Neumann operators associated to t

Isabel M. Rayas Fernández, Christopher E. Denniston, Gaurav S. Sukhatme

We are motivated by quantile estimation of algae concentration in lakes and how decentralized multirobot teams can effectively tackle this problem. We find that multirobot teams improve performance in this task over single robots, and communication-enabled teams further over communication-deprived teams; however, real robots are resource-constrained, and com

Saeed Boorboor, Yoonsang Kim, Ping Hu, Josef M. Moses

We present Submerse, an end-to-end framework for visualizing flooding scenarios on large and immersive display ecologies. Specifically, we reconstruct a surface mesh from input flood simulation data and generate a to-scale 3D virtual scene by incorporating geographical data such as terrain, textures, buildings, and additional scene objects. To optimize compu

Ngoc Long Nguyen, Jérémy Anger, Axel Davy, Pablo Arias

High-resolution satellite imagery is a key element for many Earth monitoring applications. Satellites such as Sentinel-2 feature characteristics that are favorable for super-resolution algorithms such as aliasing and band-misalignment. Unfortunately the lack of reliable high-resolution (HR) ground truth limits the application of deep learning methods to this

Shan Jia, Mingzhen Huang, Zhou Zhou, Yan Ju

Recent advancements in language-image models have led to the development of highly realistic images that can be generated from textual descriptions. However, the increased visual quality of these generated images poses a potential threat to the field of media forensics. This paper aims to investigate the level of challenge that language-image generation mode

Nikolai A. Zarkevich, Timothy M. Smith, John W. Lawson

The secondary phase, such as Ni$_3$Al-based $L1_2$ $\gamma^\prime$, is crucially important for precipitation strengthening of superalloys. Composition-structure-property relations provide useful insights for guided alloy design. Here we use density functional theory combined with the multiple scattering theory to compute dependencies of the structural energi

Giovana Morais, Matthew E. P. Davies, Marcelo Queiroz, Magdalena Fuentes

Self-supervision methods learn representations by solving pretext tasks that do not require human-generated labels, alleviating the need for time-consuming annotations. These methods have been applied in computer vision, natural language processing, environmental sound analysis, and recently in music information retrieval, e.g. for pitch estimation. Particul

Dianhao Zhang, Mien Van, Stephen Mcllvanna, Yuzhu Sun

In advanced manufacturing, strict safety guarantees are required to allow humans and robots to work together in a shared workspace. One of the challenges in this application field is the variety and unpredictability of human behavior, leading to potential dangers for human coworkers. This paper presents a novel control framework by adopting safety-critical c

Ruiqi Xian, Xijun Wang, Divya Kothandaraman, Dinesh Manocha

We present a new algorithm for selection of informative frames in video action recognition. Our approach is designed for aerial videos captured using a moving camera where human actors occupy a small spatial resolution of video frames. Our algorithm utilizes the motion bias within aerial videos, which enables the selection of motion-salient frames. We introd

Swapnil Sharma, Nikita Anand, Kranthi Kiran G.

Large language models pre-trained for code generation can generate high-quality short code but often struggle with generating coherent long code and understanding higher-level or system-level specifications. This issue is also observed in language modeling for long text generation, and one proposed solution is the use of a latent stochastic process. This app

Guillermo Puebla, Jeffrey S. Bowers

Visual reasoning is a long-term goal of vision research. In the last decade, several works have attempted to apply deep neural networks (DNNs) to the task of learning visual relations from images, with modest results in terms of the generalization of the relations learned. In recent years, several innovations in DNNs have been developed in order to enable le

Daisuke Kazukawa, Hiroki Nakajima, Takashi Shioya

We study the topological structure of the space $\mathcal{X}$ of isomorphism classes of metric measure spaces equipped with the box or concentration topologies. We consider the scale-change action of the multiplicative group $\mathbb{R}_+$ of positive real numbers on $\mathcal{X}$, which has a one-point metric measure space, say $*$, as only one fixed-point.

Alexander E. Cohen, Alasdair D. Hastewell, Sreeparna Pradhan, Steven W. Flavell

Spectral mode representations play an essential role in various areas of physics, from quantum mechanics to fluid turbulence, but they are not yet extensively used to characterize and describe the behavioral dynamics of living systems. Here, we show that mode-based linear models inferred from experimental live-imaging data can provide an accurate low-dimensi

Matthew J. Blacker, Sean A. Hartnoll

We solve the Wheeler-DeWitt equation in the 'cosmological interior' (the past causal diamond of future infinity) of four dimensional dS-Schwarzschild spacetimes. Within minisuperspace there is a basis of solutions labelled by a constant $c$, conjugate to the mass of the black hole. We propose that these solutions are in correspondence with partition function

Deqiong Li, Qiongxiang Huang

It is well known that a graph $G$ has a symmetric spectrum if and only if it is bipartite, a signed graph $\Gamma=(G,\sigma)$ has a symmetric spectrum if $G$ is bipartite. However, there exists a spectrally symmetric signed graph $\Gamma=(G,\sigma)$ such that $G$ is not bipartite. In this paper, we focus to characterize the signed graphs with symmetric spect

Peter Webb

We introduce the theory of biset functors defined on finite categories. Previously, biset functors have been defined on groups, and in that context they are closely related to Mackey functors. Standard examples on groups include representation rings, the Burnside ring and group cohomology. The new theory allows these same examples, but defined for arbitrary

Manuel Lafond, Wenfeng Lai, Adiesha Liyanage, Binhai Zhu

Motivated by computing duplication patterns in sequences, a new fundamental problem called the longest subsequence-repeated subsequence (LSRS) is proposed. Given a sequence $S$ of length $n$, a letter-repeated subsequence is a subsequence of $S$ in the form of $x_1^{d_1}x_2^{d_2}\cdots x_k^{d_k}$ with $x_i$ a subsequence of $S$, $x_j\neq x_{j+1}$ and $d_i\ge

Swapnil Sharma, Nikita Anand, Kranthi Kiran G. V., Alind Jain

Large pre-trained language models are widely used in the community. These models are usually trained on unmoderated and unfiltered data from open sources like the Internet. Due to this, biases that we see in platforms online which are a reflection of those in society are in turn captured and learned by these models. These models are deployed in applications

Leonardo Levinas

In the famous thought experiment studied in this article, Galileo attempted to refute the Aristotelian hypothesis that heavier bodies should fall more quickly than lighter ones. After pointing out some inconsistencies in Galileo's approach, we show, through the design of two alternative but equivalent experiments, that from his imaginary experiment, it is no

Peter B. Lerner

Copulas are widely used in financial economics as well as in other areas of applied mathematics. Yet, there is much arbitrariness in their choice. The author proposes "a natural copula" concept, which minimizes Wasserstein distance between distributions in some space, in which both these distributions are embedded. Transport properties and hydrodynamic inter

Noah Bray-Ali

Protons in the gap between base pairs of the double helix store the code of life by breaking the chiral symmetry that swaps the sense strand with its complementary partner. When these hydrogen bonds break during replication and transcription, pairs of protons switch sides restoring chiral symmetry and destroying genetic information. Using time-independent se

Mohammad Reza Zarei, Michael Christensen, Sarah Everts, Majid Komeili

Vaccine hesitancy continues to be a main challenge for public health officials during the COVID-19 pandemic. As this hesitancy undermines vaccine campaigns, many researchers have sought to identify its root causes, finding that the increasing volume of anti-vaccine misinformation on social media platforms is a key element of this problem. We explored Twitter

Omar A. Castaño-Idarraga, Raul Ramos-Pollán, Freddie Kalaitzis

This work proposes a hybrid unsupervised and supervised learning method to pre-train models applied in Earth observation downstream tasks when only a handful of labels denoting very general semantic concepts are available. We combine a contrastive approach to pre-train models with a pixel-wise regression pre-text task to predict coarse elevation maps, which

Hari Mohan Srivastava, Giriraj Methi, Anil Kumar, Mohammad Izadi

The differential transform method is used to find numerical approximation of solution to a class of certain nonlinear differential algebraic equations. The method is based on Taylor's theorem. Coefficients of the Taylor series are determined by constructing a recurrence relation. To deal with nonlinearity of the problems, the Fa\`{a} di Bruno's formula conta

Timon S. Gutleb, José A. Carrillo

We introduce a method which provides accurate numerical solutions to fractional-in-time partial differential equations posed on $[0,T] \times \Omega$ with $\Omega \subset \mathbb{R}^d$ without the excessive memory requirements associated with the nonlocal fractional derivative operator. Our approach combines recent advances in the development and utilization

Joseph Poata, Fabio Taddei, Michele Governale

Two-dimensional second-order topological insulators are characterized by the presence of topologically protected zero-energy bound states localized at the corners of a flake. In this paper we theoretically study the occurrence and features of such corner states inside flakes in the shape of a convex polygon. We consider two different models, both in Cartan c

Praneeth Kacham, Rasmus Pagh, Mikkel Thorup, David P. Woodruff

We revisit Nisan's classical pseudorandom generator (PRG) for space-bounded computation (STOC 1990) and its applications in streaming algorithms. We describe a new generator, HashPRG, that can be thought of as a symmetric version of Nisan's generator over larger alphabets. Our generator allows a trade-off between seed length and the time needed to compute a

Nikolai A. Zarkevich, Timothy M. Smith, John W. Lawson

NiCoCr middle-entropy alloy is known for its exceptional strength at both low and elevated operating temperatures. Mechanical properties of NiCoCr-based alloys are affected by certain features of the energy landscape, such as the energy difference between the hcp and fcc phases (which is known to correlate with the stacking fault energy in the fcc phase) and

Everett Schlawin, Ryan Challener, Megan Mansfield, Emily Rauscher

High precision lightcurves combined with eclipse mapping techniques can reveal the horizontal and vertical structure of a planet's thermal emission and the dynamics of hot Jupiters. Someday, they even may reveal the surface maps of rocky planets. However, inverting lightcurves into maps requires an understanding of the planet, star and instrumental trends be

Verónica Becher, Agustín Marchionna, Gérald Tenenbaum

Given an integer $b\geqslant 2$ and a set $P$ of prime numbers, the set $T_P $ of Toeplitz numbers comprises all elements of $[0,b[$ whose digits $(a_n)_{n\geqslant 1}$ in the base-$b$ expansion satisfy $a_n=a_{pn}$ for all $p\in P$ and $n\geqslant 1$. Using a completely additive arithmetical function, we construct a number in~$T_P$ that is simply Borel norm

Kelvin Kwakye, Younho Seong, Armstrong Aboah, Sun Yi

In recent years, distracted driving has garnered considerable attention as it continues to pose a significant threat to public safety on the roads. This has increased the need for innovative solutions that can identify and eliminate distracted driving behavior before it results in fatal accidents. In this paper, we propose a Signal-Based anomaly detection al

Grégoire François, Celestino Angeli, Gian Luigi Bendazzoli, Véronique Brumas

The electric polarizability and the spread of the total position tensors are used to characterize the metallic vs insulator nature of large (finite) systems. Finite clusters are usually treated within the open boundary condition formalism. This introduces border effects, which prevents a fast convergence to the thermodynamic limit and which can be eliminated

Ricardo Cannizzaro, Lars Kunze

Robots operating in real-world environments must reason about possible outcomes of stochastic actions and make decisions based on partial observations of the true world state. A major challenge for making accurate and robust action predictions is the problem of confounding, which if left untreated can lead to prediction errors. The partially observable Marko

Elizabeth Collins-Woodfin, Elliot Paquette

We give a description of the high-dimensional limit of one-pass single-batch stochastic gradient descent (SGD) on a least squares problem. This limit is taken with non-vanishing step-size, and with proportionally related number of samples to problem-dimensionality. The limit is described in terms of a stochastic differential equation in high dimensions, whic

Kazuma Matsumura, Takaya Abe, Takahito Kitada, Takeshi Kumasaka

Quantum dots can be formed in simple GaN/AlGaN field-effect-transistors (FETs) by disordered potential induced by impurities and defects. Here, we investigate the channel length dependence of the formation of quantum dots. We observe decrease of the number of formed quantum dots with decrease of the FET channel length. A few quantum dots are formed in the ca

Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Seid Muhie Yimam, David Ifeoluwa Adelani

We present the first Africentric SemEval Shared task, Sentiment Analysis for African Languages (AfriSenti-SemEval) - The dataset is available at https://github.com/afrisenti-semeval/afrisent-semeval-2023. AfriSenti-SemEval is a sentiment classification challenge in 14 African languages: Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Moz

Khushhall Chandra Mahajan, Amey Porobo Dharwadker, Romil Shah, Simeng Qu

Recommender systems are increasingly successful in recommending personalized content to users. However, these systems often capitalize on popular content. There is also a continuous evolution of user interests that need to be captured, but there is no direct way to systematically explore users' interests. This also tends to affect the overall quality of the

Abhishek Purohit, Maninder Kaur, Zeki Can Seskir, Matthew T. Posner

The emergence of quantum technologies has led to groundbreaking advancements in computing, sensing, secure communications, and simulation of advanced materials with practical applications in every industry sector. The rapid advancement of the quantum technologies ecosystem has made it imperative to assess the maturity of these technologies and their imminent

Tao Zhang, Quanyan Zhu

In dynamic mechanism design literature, one critical aspect has been typically ignored-the agents' periodic participation, which they can adapt and plan strategically. We propose a framework for dynamic principal-multiagent problems, augmenting the classic model by incorporating agents' periodic coupled decisions on participation and regular action selection

Niloufar Fakhfour, Mohammad ShahverdiKondori, Sajjad Hashembeiki, Mohammadjavad Norouzi

In this paper, we tackle the problem of video alignment, the process of matching the frames of a pair of videos containing similar actions. The main challenge in video alignment is that accurate correspondence should be established despite the differences in the execution processes and appearances between the two videos. We introduce an unsupervised method f

Siddhant Garg, Lijun Zhang, Hui Guan

Although multi-task deep neural network (DNN) models have computation and storage benefits over individual single-task DNN models, they can be further optimized via model compression. Numerous structured pruning methods are already developed that can readily achieve speedups in single-task models, but the pruning of multi-task networks has not yet been exten

Harshvardhan Uppaluru, Hossein Rastgoftar

This paper studies the problem of safe and optimal continuum deformation of a large-scale multi-agent system (MAS). We present a novel approach for MAS continuum deformation coordination that aims to achieve safe and efficient agent movement using a leader-follower multi-layer hierarchical optimization framework with a single input layer, multiple hidden lay

Heli Elorreaga, Adrian Gomez

In this work we give a criterion to have an exponential dichotomy over all $\mathbb{R}$ for delayed systems $x'(t)=L(t)x_t$, where $L_{\pm}=\lim_{t\to\pm\infty}L(t)$, and the systems $x'(t)=L_{\pm}x_t$ are autonomous and hyperbolic. The proof is based in a suitable choice of weighted Sobolev spaces $L^p(\mathbb{R},\mu)$ and $W^{1,p}(\mathbb{R},\mu)$, where t

Elham Soltanikazemi, Ashwin Dhakal, Bijaya Kumar Hatuwal, Imad Eddine Toubal

This research focuses on real-time surveillance systems as a means for tackling the issue of non-compliance with helmet regulations, a practice that considerably amplifies the risk for motorcycle drivers or riders. Despite the well-established advantages of helmet usage, achieving widespread compliance remains challenging due to diverse contributing factors.

S. Furkan Ozturk, Deb Kumar Bhowmick, Yael Kapon, Yutao Sang

Life is homochiral and homochirality is a fundamental feature of living systems on Earth. While the exact mechanism that led to homochirality is still not fully understood, any realistic scenario on the origins of life needs to address the emergence of homochirality. In order to impose and maintain chirality in a prebiotic network, an environmental factor fu

Todd Bichoupan

A new characterization is given to describe implication bases of a closure system in terms of the system's quasi-closed sets. Using this characterization, it is possible to show that groups of implications corresponding to distinct essential sets are interchangeable across different bases. It follows from this result that the sum of cardinalities of right si

Ali Bakhshali, Hossein Najafi, Behnam Behinaein Hamgini, Zhuhong Zhang

In this work, we propose to use various artificial neural network (ANN) structures for modeling and compensation of intra- and inter-subcarrier fiber nonlinear interference in digital subcarrier multiplexing (DSCM) optical transmission systems. We perform nonlinear channel equalization by employing different ANN cores including convolutional neural networks

Utkarsh Utkarsh, Valentin Churavy, Yingbo Ma, Tim Besard

We demonstrate a high-performance vendor-agnostic method for massively parallel solving of ensembles of ordinary differential equations (ODEs) and stochastic differential equations (SDEs) on GPUs. The method is integrated with a widely used differential equation solver library in a high-level language (Julia's DifferentialEquations.jl) and enables GPU accele

John F. Nagle

The rich thermotropic behavior of lipid bilayers is addressed using phenomenological theory informed by many experiments. The most recent experiment not yet addressed by theory has shown that the tilt modulus in DMPC lipid bilayers decreases dramatically as the temperature is lowered toward the main transition temperature TM . It is shown that this behavior

Adam N. Elmachtoub, Henry Lam, Haofeng Zhang, Yunfan Zhao

In data-driven stochastic optimization, model parameters of the underlying distribution need to be estimated from data in addition to the optimization task. Recent literature considers integrating the estimation and optimization processes by selecting model parameters that lead to the best empirical objective performance. This integrated approach, which we c

Imtiaz Masud Ziko, Freddy Lecue, Ismail Ben Ayed

We introduce a simple non-linear embedding adaptation layer, which is fine-tuned on top of fixed pre-trained features for one-shot tasks, improving significantly transductive entropy-based inference for low-shot regimes. Our norm-induced transformation could be understood as a re-parametrization of the feature space to disentangle the representations of diff

Hanqiu Chen, Cong Hao

Dynamic Graph Neural Networks (DGNNs) are becoming increasingly popular due to their effectiveness in analyzing and predicting the evolution of complex interconnected graph-based systems. However, hardware deployment of DGNNs still remains a challenge. First, DGNNs do not fully utilize hardware resources because temporal data dependencies cause low hardware

Subhodeep Sarkar, Mostafizur Rahman, Sumanta Chakraborty

In this work, we wish to address the question -- whether the quasi-normal modes, the characteristic frequencies associated with perturbed black hole spacetimes, central to the stability of these black holes, are themselves stable. Though the differential operator governing the perturbation of black hole spacetimes is self-adjoint, the boundary conditions are

Brett R. Gordon, Robert Moakler, Florian Zettelmeyer

Randomized controlled trials (RCTs) provide the most credible estimates of advertising incrementality but are difficult to scale. We propose Predicted Incrementality by Experimentation (PIE), which reframes ad measurement as a campaign-level prediction problem. PIE uses a sample of RCTs to learn a mapping from campaign features to causal effects, then applie

Jacob A. Siefert, Trevor J. Bird, Andrew F. Thompson, Jonah J. Glunt

This paper proposes methods for reachability analysis of nonlinear systems in both open loop and closed loop with advanced controllers. The methods combine hybrid zonotopes, a construct called a state-update set, functional decomposition, and special ordered set approximations to enable linear growth in reachable set memory complexity with time and linear sc

Sara Venturini, Satyaki Sikdar, Francesco Rinaldi, Francesco Tudisco

Collaboration is a key driver of science and innovation. Mainly motivated by the need to leverage different capacities and expertise to solve a scientific problem, collaboration is also an excellent source of information about the future behavior of scholars. In particular, it allows us to infer the likelihood that scientists choose future research direction

Rebekah Rousi

In an ever more connected world, awareness has grown towards the hazards and vulnerabilities that the networking on sensitive digitized information pose for all parties involved. This vulnerability rests in a number of factors, both human and technical.From an ethical perspective, this means people seeking to maximise their own gain, and accomplish their goa

J. A. Guerra, E. Lopez-Rodriguez, D. T. Chuss, N. O. Butterfield

Recent high-resolution 53-$\mu$m polarimetric observations from SOFIA/HAWC+ have revealed the inferred plane-of-the-sky magnetic field (B-field) orientation in the Galactic center's Circum-Nuclear Disk (CND). The B-field is mostly aligned with the steamers of ionized material falling onto Sgr A* at large, differential velocities (shear). In such conditions,

Supraja S. Chittari, Zhiyue Lu

Complex and even non-monotonic responses to external control can be found in many thermodynamic systems. In such systems, non-equilibrium shortcuts can rapidly drive the system from an initial state to a desired final state. One example is the Mpemba effect, where pre-heating a system allows a system to cool faster. We present nonequilibrium hasty shortcuts

Yanxi Chen

This technical report studies the problem of ranking from pairwise comparisons in the classical Bradley-Terry-Luce (BTL) model, with a focus on score estimation. For general graphs, we show that, with sufficiently many samples, maximum likelihood estimation (MLE) achieves an entrywise estimation error matching the Cram\'er-Rao lower bound, which can be state

Armstrong Aboah, Bin Wang, Ulas Bagci, Yaw Adu-Gyamfi

Traffic safety is a major global concern. Helmet usage is a key factor in preventing head injuries and fatalities caused by motorcycle accidents. However, helmet usage violations continue to be a significant problem. To identify such violations, automatic helmet detection systems have been proposed and implemented using computer vision techniques. Real-time

Rebekah Rousi

During the 1950s Scandinavian Design caught international attention with its minimalism, simplicity, functionalism and sophistication. Several factors rested at its heart: functionality, democracy and affordability. Aesthetic styles connected to international minimalist, modernist and functionalist movements, which were also symbolically connected to educati

Guillaume Jaume, Anurag Vaidya, Richard Chen, Drew Williamson

Integrating whole-slide images (WSIs) and bulk transcriptomics for predicting patient survival can improve our understanding of patient prognosis. However, this multimodal task is particularly challenging due to the different nature of these data: WSIs represent a very high-dimensional spatial description of a tumor, while bulk transcriptomics represent a gl

Seung Hyun Lee, Sieun Kim, Innfarn Yoo, Feng Yang

We propose a method for adding sound-guided visual effects to specific regions of videos with a zero-shot setting. Animating the appearance of the visual effect is challenging because each frame of the edited video should have visual changes while maintaining temporal consistency. Moreover, existing video editing solutions focus on temporal consistency acros

Shuang Bai, Wurigumula Bao, Kun Qian, Bing Han

Prelithiation as a facile and effective method to compensate the lithium inventory loss in the initial cycle has progressed considerably both on anode and cathode sides. However, much less research has been devoted to the prelithiation effect on the interface stabilization for long-term cycling of Si-based anodes. An in-depth quantitative analysis of the int

Roberto Casadio

We describe the ground state for a gravitationally collapsed ball of dust as the direct product of wavefunctions for dust particles distributed over an arbitrary number of nested layers. This allows us to estimate the expectation value of the global radius as well as the effective energy density and pressures for the dust core of quantum black holes. In part

Toufique Ahmed, Kunal Suresh Pai, Premkumar Devanbu, Earl T. Barr

Large Language Models (LLM) are a new class of computation engines, "programmed" via prompt engineering. We are still learning how to best "program" these LLMs to help developers. We start with the intuition that developers tend to consciously and unconsciously have a collection of semantics facts in mind when working on coding tasks. Mostly these are shallo

Yi Zhou, Paul M. Bellan

The two-stream instability (Buneman instability) is traditionally derived as a collisionless instability with the presumption that collisions inhibit this instability. We show here via a combination of a collisional two-fluid model and associated experimental observations made in the Caltech plasma jet experiment, that in fact, a low frequency mode of the tw

Armstrong Aboah, Ulas Bagci, Abdul Rashid Mussah, Neema Jakisa Owor

Identifying unusual driving behaviors exhibited by drivers during driving is essential for understanding driver behavior and the underlying causes of crashes. Previous studies have primarily approached this problem as a classification task, assuming that naturalistic driving videos come discretized. However, both activity segmentation and classification are

Reza Averly, Wei-Lun Chao

Out-of-distribution (OOD) detection aims to identify test examples that do not belong to the training distribution and are thus unlikely to be predicted reliably. Despite a plethora of existing works, most of them focused only on the scenario where OOD examples come from semantic shift (e.g., unseen categories), ignoring other possible causes (e.g., covariat

Hadas Baer-Erenfeld, Orit E. Raz

Let $\gamma_1,\gamma_2$ be a pair of constant-degree irreducible algebraic curves in $\mathbb{R}^d$. Assume that $\gamma_i$ is neither contained in a hyperplane nor in a quadric surface in $\mathbb{R}^d$, for each $i=1,2$. We show that for every pair of $n$-point sets $P_1\subset\gamma_1$ and $P_2\subset\gamma_2$, the number of distinct distances spanned by

Timotheus Kampik, Andre Lücke, Jörn Horstmann, Mark Wheeler

This paper provides an introduction to and discussion of SIGNAL, an industry-scale process data querying language and engine for large-scale cloud-based systems that is developed by SAP Signavio. SIGNAL is optimized for fast read access to process data in event log format and utilizes an in-memory columnar store to this end. To facilitate usability, SIGNAL u

Eyal Rozenberg, Aviv Karnieli, Ofir Yesharim, Joshua Foley-Comer

We propose a novel, physically-constrained and differentiable approach for the generation of D-dimensional qudit states via spontaneous parametric down-conversion (SPDC) in quantum optics. We circumvent any limitations imposed by the inherently stochastic nature of the physical process and incorporate a set of stochastic dynamical equations governing its evo

Mariia Rizhko

Ragnarock is a virtual reality (VR) rhythm game in which you play a Viking captain competing in a longship race. With two hammers, the task is to crush the incoming runes in sync with epic Viking music. The runes are defined by a beat map which the player can manually create. The creation of beat maps takes hours. This work aims to automate the process of be

Ting Cai, Kirthevasan Kandasamy

We study actively labeling streaming data, where an active learner is faced with a stream of data points and must carefully choose which of these points to label via an expensive experiment. Such problems frequently arise in applications such as healthcare and astronomy. We first study a setting when the data's inputs belong to one of $K$ discrete distributi

Ori Somech, Yoav Shimshi, Ephraim Shahmoon

We present an analytical approach for the study of driven Dicke superradiance based on a Heisenberg-Langevin formulation. We calculate the steady-state fluctuations of both the atomic-spin and the light-field operators. While the atoms become entangled below a critical drive, exhibiting spin squeezing, we show that the radiated light is in a classical-like c

A. Bazzani, M. Giovannozzi, C. E. Montanari, G. Turchetti

The efficient detection of chaotic behavior in orbits of a complex dynamical system is an active domain of research. Several indicators have been proposed in the past, and new ones have recently been developed in view of improving the performance of chaos detection by means of numerical simulations. The challenge is to predict chaotic behavior based on the a

Janak Tiwari, Tianli Feng

Current phonon transport theory based on ground-state calculations has been successful in predicting thermal conductivity at room and medium temperatures but may misrepresent behavior at high temperatures. In this work, we predict the thermal conductivity ($\kappa$) of ZrC including electronic and phonon contributions from 300 K to 3500 K, by including high-

Paulo E. Stecchini, Marcos P. Diaz, Flavio D'Amico, Francisco Jablonski

In this study we revisit public data on the supersoft X-ray source CAL 83 in the Large Magellanic Cloud. A significant part of our analysis is focused on XMM-Newton X-ray observations, in which updated data reduction procedures and quality assessment were applied. We report on the capability of publicly available hot atmosphere models in describing the sourc

Javier Burroni, Justin Domke, Daniel Sheldon

We present a novel approach for black-box VI that bypasses the difficulties of stochastic gradient ascent, including the task of selecting step-sizes. Our approach involves using a sequence of sample average approximation (SAA) problems. SAA approximates the solution of stochastic optimization problems by transforming them into deterministic ones. We use qua

Lukas Anzeletti, Khoa Lê, Chengcheng Ling

We show that any stochastic differential equation (SDE) driven by Brownian motion with drift satisfying the Krylov-R\"ockner condition has exactly one solution in an ordinary sense for almost every trajectory of the Brownian motion. Consequentially, such SDE is strongly complete and forms a random dynamical system. Also, a further application to a boundary v

Kristen Schumacher, Scott Ellis Perkins, Ashley Shaw, Kent Yagi

Lorentz symmetry is a fundamental property of Einstein's theory of general relativity that one may wish to test with gravitational wave observations. Einstein-aether theory is a model that introduces Lorentz-symmetry breaking in the gravitational sector through an aether vector field, while still leading to second-order field equations. This well-posed theor