October 2023 arXiv papers — page 78

Ruifeng Ren, Yong Liu

Pre-trained large language models based on Transformers have demonstrated remarkable in-context learning (ICL) abilities. With just a few demonstration examples, the models can implement new tasks without any parameter updates. However, it is still an open question to understand the mechanism of ICL. In this paper, we attempt to explore the ICL process in Tr

Zhizhen Zhang, Xiaohui Xie, Yishuo Zhang, Lanshan Zhang

Information cascade popularity prediction is critical for many applications, including but not limited to identifying fake news and accurate recommendations. Traditional feature-based methods heavily rely on handcrafted features, which are domain-specific and lack generalizability to new domains. To address this problem, researchers have turned to neural net

Ying Zhang, Junbo Zhao, Di Shi, Sungjoo Chung

Distribution system state estimation (DSSE) is paramount for effective state monitoring and control. However, stochastic outputs of renewables and asynchronous streaming of multi-rate measurements in practical systems largely degrade the estimation performance. This paper proposes a deep reinforcement learning (DRL)-enabled adaptive DSSE algorithm in unbalan

Byoung S. Ham

The delayed-choice quantum eraser has been intensively studied for the wave-particle duality of a single photon in an interferometric system over the last decades. Coincidence measurements between quantum erasers have also been applied for the nonlocal quantum feature, satisfying the Bell inequality violation. However, those quantum features have not been cl

Neeraj Baghel, Shiv Ram Dubey, Satish Kumar Singh

Image super-resolution generation aims to generate a high-resolution image from its low-resolution image. However, more complex neural networks bring high computational costs and memory storage. It is still an active area for offering the promise of overcoming resolution limitations in many applications. In recent years, transformers have made significant pr

Zhaohui Zheng, Yuming Chen, Qibin Hou, Xiang Li

A fundamental limitation of object detectors is that they suffer from "spatial bias", and in particular perform less satisfactorily when detecting objects near image borders. For a long time, there has been a lack of effective ways to measure and identify spatial bias, and little is known about where it comes from and what degree it is. To this end, we prese

X. T. Li, S. J. Tu, L. Chaix, C. Fawaz

We investigated the high energy spin excitations in electron-doped $\mathrm{La}_{2-x} \mathrm{Ce}_x \mathrm{CuO}_{4}$, a cuprate superconductor, by resonant inelastic x-ray scattering (RIXS) measurements. Efforts were paid to disentangle the paramagnon signal from non-spin-flip spectral weight mixing in the RIXS spectrum at $\bf{Q_{\|}}$ = $(0.6\pi, 0)$ and

Besnik Fetahu, Zhiyu Chen, Sudipta Kar, Oleg Rokhlenko

We present MULTICONER V2, a dataset for fine-grained Named Entity Recognition covering 33 entity classes across 12 languages, in both monolingual and multilingual settings. This dataset aims to tackle the following practical challenges in NER: (i) effective handling of fine-grained classes that include complex entities like movie titles, and (ii) performance

Andreas Abel, Shrey Sharma, Jan Reineke

Basic-block throughput models such as uiCA, IACA, GRANITE, Ithemal, llvm-mca, OSACA, or CQA guide optimizing compilers and help performance engineers identify and eliminate bottlenecks. For this purpose, basic-block throughput models should ideally be fast, accurate, and interpretable. Recent advances have significantly improved accuracy: uiCA, the state-of-

S. Gardiner, J. Isaacson, L. Pickering

Simulations of neutrino interactions are playing an increasingly important role in the pursuit of high-priority measurements for the field of particle physics. A significant technical barrier for efficient development of these simulations is the lack of a standard data format for representing individual neutrino scattering events. We propose and define such

Zhaoyi Xu, Athina Petropulu

We consider an OFDM transmitter aided by an intelligent reflecting surface (IRS) and propose a novel approach to enhance waveform security by employing time modulation (TM) at the IRS side. By controlling the periodic TM pattern of the IRS elements, the system is designed to preserve communication information towards an authorized recipient and scramble the

Solomon McKiernan

This paper analyses foundational techniques for improving wireless communication systems, including coding methods, modulation schemes, and channel equalization. Using industry-standard simulation tools, the paper evaluates the performance of these techniques under different channel conditions. Convolutional codes, punctured and unpunctured, are assessed for

Junzhe Shi, Ulf Jakob Flø Aarsnes, Shengyu Tao, Ruiting Wang

Fuel cell (FC)/battery hybrid systems have attracted substantial attention for achieving zero-emissions buses, trucks, ships, and planes. An online energy management system (EMS) is essential for these hybrid systems, it controls energy flow and ensures optimal system performance. Key aspects include fuel efficiency and mitigating FC and battery degradation.

P. Kelly, J. Jobel, O. Eiger, A. Abd

We use Dark Energy Survey Year 3 (DES Y3) clusters with archival X-ray data from XMM-Newton and Chandra to assess the centering performance of the redMaPPer cluster finder and to measure key richness observable scaling relations. In terms of centering, we find that 10-20% of redMaPPer clusters are miscentered with no significant difference in bins of low ver

Yiwei Wang, Yujun Cai, Muhao Chen, Yuxuan Liang

Instruction-tuned large language models (LLMs), such as ChatGPT, have led to promising zero-shot performance in discriminative natural language understanding (NLU) tasks. This involves querying the LLM using a prompt containing the question, and the candidate labels to choose from. The question-answering capabilities of ChatGPT arise from its pre-training on

Zachary Yang, Nicolas Grenan-Godbout, Reihaneh Rabbany

Real-time toxicity detection in online environments poses a significant challenge, due to the increasing prevalence of social media and gaming platforms. We introduce ToxBuster, a simple and scalable model that reliably detects toxic content in real-time for a line of chat by including chat history and metadata. ToxBuster consistently outperforms conventiona

Chengyan Xie, Ilaria Pascucci, Feng Long, Klaus M. Pontoppidan

We present an analysis of the JDISC JWST/MIRI-MRS spectrum of Sz~114, an accreting M5 star surrounded by a large dust disk with a shallow gap at $\sim 39$ au. The spectrum is molecular-rich: we report the detection of water, CO, CO$_2$, HCN, C$_2$H$_2$, and H$_2$. The only identified atomic/ionic transition is from [NeII] at 12.81 micron. A distinct feature

Yanbo Zhang, Yaojun Chen

Given two graphs $G_1$ and $G_2$, the Ramsey number $r(G_1,G_2)$ refers to the smallest positive integer $N$ such that any graph $G$ with $N$ vertices contains $G_1$ as a subgraph, or the complement of $G$ contains $G_2$ as a subgraph. A connected graph $H$ is said to be $p$-good if $r(K_p,H)=(p-1)(|H|-1)+1$. A generalized fan, denoted as $K_1+nH$, is formed

Gabor Zoltai, Yue Xie, Frank Neumann

Among the wide variety of evolutionary computing models, Finite State Machines (FSMs) have several attractions for fundamental research. They are easy to understand in concept and can be visualised clearly in simple cases. They have a ready fitness criterion through their relationship with Regular Languages. They have also been shown to be tractably evolvabl

Joseph Dongho, Joel Fotso Tachago, Franck Tchinda

In this paper, we study the stochastic homogenization for a family of integral functionals with convex and nonstandard growth integrands defined on Orlicz-Sobolev's spaces. One fundamental in this topic is to extend the classical compactness results of the two-scale convergence in the mean method to this type of spaces. Moreover, it is shown by the two-scale

Aminul Huq, Dimitris Zermas, George Bebis

Early identification of abnormalities in plants is an important task for ensuring proper growth and achieving high yields from crops. Precision agriculture can significantly benefit from modern computer vision tools to make farming strategies addressing these issues efficient and effective. As farming lands are typically quite large, farmers have to manually

Kirpa Garg, Sartaj Ul Hasan, Constanza Riera, Pantelimon Stanica

The Feistel Boomerang Connectivity Table and the related notion of $F$-Boomerang uniformity (also known as the second-order zero differential uniformity) has been recently introduced by Boukerrou et al.~\cite{Bouk}. These tools shall provide a major impetus in the analysis of the security of the Feistel network-based ciphers. In the same paper, a characteriz

Idil Ismail, Shayantan Chaudhuri, Dylan Morgan, Christopher D. Woodgate

This article is intended as a guide for new graduate students in the field of computational science. With the increasing influx of students from diverse backgrounds joining the ever-popular field, this short guide aims to help students navigate through the various computational techniques that they are likely to encounter during their studies. These techniqu

Gufang Zhao, Changlong Zhong

Associated to any root datum, there is an elliptic affine Hecke algebra defined by Ginzburg, Kapranov, and Vasserot. In this study, we establish a Fourier-Mukai functor from the representation category of the elliptic affine Hecke algebra to the corresponding category associated with the Langlands dual root datum. To achieve this connection, we employ the el

Michael Barnett, William Brock, Lars Peter Hansen, Ruimeng Hu

We study the implications of model uncertainty in a climate-economics framework with three types of capital: "dirty" capital that produces carbon emissions when used for production, "clean" capital that generates no emissions but is initially less productive than dirty capital, and knowledge capital that increases with R\&D investment and leads to technologi

Xiaolong Tu, Anik Mallik, Dawei Chen, Kyungtae Han

Today, deep learning optimization is primarily driven by research focused on achieving high inference accuracy and reducing latency. However, the energy efficiency aspect is often overlooked, possibly due to a lack of sustainability mindset in the field and the absence of a holistic energy dataset. In this paper, we conduct a threefold study, including energ

Suhan Zhong, Jianxin Zhou

The main purpose of this project is to develop a new active set method (ASM) called a working set method (WSM) for solving a general nonlinear inequality constrained minimization problem in a Hilber space. Mathematical analysis is carried out to validate the method and to show its merits over other ASMs. Since the method is quite general and new, some result

Said Togru, Marco Moldovan

This project presents an automated solution for the efficient identification of car models and makes from images, aimed at streamlining the vehicle listing process on online car-selling platforms. Through a thorough exploration encompassing various efficient network architectures including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and

Mingyang Li

We investigate the asymptotic geometry of Hermitian non-K\"ahler Ricci-flat metrics with finite $\int|Rm|^2$ at infinity. Specifically, we prove: 1. Any such metric is asymptotic to an ALE, ALF-A, AF, skewed special Kasner, ALH* model at infinity. 2. Any Hermitian non-K\"ahler gravitational instanton with non-Euclidean volume growth is one of the following:

Jiani Zhang, Zhengyuan Shen, Balasubramaniam Srinivasan, Shen Wang

Recent advances in large language models have revolutionized many sectors, including the database industry. One common challenge when dealing with large volumes of tabular data is the pervasive use of abbreviated column names, which can negatively impact performance on various data search, access, and understanding tasks. To address this issue, we introduce

Maozhong Xu, Maoning Tang, Qingxin Meng

In this paper, our primary focus lies in the thorough investigation of a specific category of nonlinear fully coupled forward-backward stochastic differential equations involving time delays and advancements with the incorporation of L\'{e}vy processes, which we shall abbreviate as FBSDELDAs. Drawing inspiration from diverse examples of linear-quadratic (LQ)

Iain A. Bisset, Bhaskar Dutta, Wei-Chih Huang, Louis E. Strigari

Stopped-pion experiments that measure coherent elastic neutrino-nucleus scattering (CE$\nu$NS) are sensitive to sterile neutrinos via disappearance. Using timing and energy spectra to perform flavor decomposition, we show that the delayed electron neutrino component provides an independent test of short-baseline anomalies that hint at $\sim$ eV-mass sterile

Tong Liu, Hadi Meidani

The traffic assignment problem is one of the significant components of traffic flow analysis for which various solution approaches have been proposed. However, deploying these approaches for large-scale networks poses significant challenges. In this paper, we leverage the power of heterogeneous graph neural networks to propose a novel end-to-end surrogate mo

Vincenzo Calderonio

The purpose of this paper is to analyse the opacity of algorithms, contextualized in the open debate on responsibility for artificial intelligence causation; with an experimental approach by which, applying the proposed conversational methodology of the Turing Test, we expect to evaluate the performance of one of the best existing NLP model of generative AI

Jianwei Li, Qi Lei, Wei Cheng, Dongkuan Xu

The pruning objective has recently extended beyond accuracy and sparsity to robustness in language models. Despite this, existing methods struggle to enhance robustness against adversarial attacks when continually increasing model sparsity and require a retraining process. As humans step into the era of large language models, these issues become increasingly

Alexandr Kostochka, Ruth Luo, Grace McCourt

Dirac proved that each $n$-vertex $2$-connected graph with minimum degree $k$ contains a cycle of length at least $\min\{2k, n\}$. We obtain analogous results for Berge cycles in hypergraphs. Recently, the authors proved an exact lower bound on the minimum degree ensuring a Berge cycle of length at least $\min\{2k, n\}$ in $n$-vertex $r$-uniform $2$-connecte

Barrett Martin Lattimer, Patrick Chen, Xinyuan Zhang, Yi Yang

Generative AI models exhibit remarkable potential; however, hallucinations across various tasks present a significant challenge, particularly for longer inputs that current approaches struggle to address effectively. We introduce SCALE (Source Chunking Approach for Large-scale inconsistency Evaluation), a task-agnostic model for detecting factual inconsisten

Ajay Narasimha Mopidevi, Kyle Harlow, Christoffer Heckman

Millimeter Wave Radar is being adopted as a viable alternative to lidar and radar in adverse visually degraded conditions, such as the presence of fog and dust. However, this sensor modality suffers from severe sparsity and noise under nominal conditions, which makes it difficult to use in precise applications such as mapping. This work presents a novel solu

Kevin M. Roccapriore, Riccardo Torsi, Joshua Robinson, Sergei V. Kalinin

On- and off-axis electron energy loss spectroscopy (EELS) is a powerful method for probing local electronic structure on single atom level. However, many materials undergo electron-beam induced transformation during the scanning transmission electron microscopy (STEM) and spectroscopy, the problem particularly acute for off-axis EELS signals. Here, we propos

Ting Huang, Qiang Zhang, Witold Pedrycz, Shanlin Yang

This study proposes a new constraint handling technique for assisting metaheuristic optimization algorithms to solve constrained optimization problems more effectively and efficiently. Given any two solutions of any constrained optimization problems, they are first mapped into a two-dimensional Cartesian coordinate system with their objective function value

Ran J. Tessler, Yizhen Zhao

The papers [3,1,4,10] constructed an intersection theory on the moduli space of $r$-spin disks, and proved it satisfies mirror symmetry and relations with integrable hierarchies. That theory considered only disks with a single boundary state. In this work, we initiate the study of more general $r$-spin surfaces. We define graded $r$-spin surfaces with multip

Aditya Rauniyar, Jiaoyang Li, Sebastian Scherer

Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics and AI, with numerous applications in real-world scenarios. One such scenario is filming scenes with multiple actors, where the goal is to capture the scene from multiple angles simultaneously. Here, we present a formation-based filming directive of task assignment followed by a Conflict-Ba

Jianwei Li, Weizhi Gao, Qi Lei, Dongkuan Xu

It is widely acknowledged that large and sparse models have higher accuracy than small and dense models under the same model size constraints. This motivates us to train a large model and then remove its redundant neurons or weights by pruning. Most existing works pruned the networks in a deterministic way, the performance of which solely depends on a single

Jinrui Wang, Mashael AlKadi, Benjamin Bach

This paper describes the design of a dashboard and analysis pipeline to monitor users of visualization tools in the wild. Our pipeline describes how to extract analytical KPIs from extensive log event data involving a mix of user types. The resulting three-page dashboard displays live KPIs, helping analysts understand users, detect exploratory behaviors, pla

Mo Sodwatana, Saif R. Kazi, Kaarthik Sundar, Adam Brandt

We propose an optimization formulation for locational pricing of energy transported through a pipeline network that carries mixtures of natural gas and hydrogen from distributed sources to consumers. The objective includes the economic value provided by the pipeline to consumers of energy and suppliers of natural gas and green hydrogen, as well as incentives

Jordan François

The group of vertical diffeomorphisms of a principal bundle forms the generalised action Lie groupoid associated to the bundle. The former is generated by the group of maps with value in the structure group, which is also the group of bisections of the groupoid. The corresponding Lie algebra of general vertical vector fields is generated by maps with value i

Jennifer Johnson-Leung, Joshua Parker, Brooks Roberts

We give a presentation via generators and relations of the local graded paramodular Hecke algebra of prime level. In particular, we prove that the paramodular Hecke algebra is isomorphic to the quotient of the free $\mathbb{Z}$-algebra generated by four non-commuting variables by an ideal generated by seven relations. Using this description, we derive ration

Xiaolin Chen, Jerry Q Cheng, Lu Tian, Minge Xie

Stemming from the high profile publication of Nissen and Wolski (2007) and subsequent discussions with divergent views on how to handle observed zero-total-event studies, defined to be studies which observe zero events in both treatment and control arms, the research topic concerning the common odds ratio model with zero-total-event studies remains to be an

Min Gyung Yu, Xu Ma, Bowen Huang, Karthik Devaprasad

Energy storage and electrification of buildings hold great potential for future decarbonized energy systems. However, there are several technical and economic barriers that prevent large-scale adoption and integration of energy storage in buildings. These barriers include integration with building control systems, high capital costs, and the necessity to ide

Charlson. C. Kim, B. C. Lyons, Y. Q. Liu, J. T. McClenaghan

This report is in partial fulfillment of deliverable D1.1 Optimization of the SPI model and summarizes axisymmetric ITER SPI parameter scans performed by the NIMROD code for several ITER equilibria. These axisymmetric parameter scans are to assess the sensitivity of various injection parameters in preparation for 3D MHD SPI simulations. The scans are compris

Nikolaos Galatos, Isis A. Gallardo

We show that every distributive lattice-ordered pregroup can be embedded into a functional algebra over an integral chain, thus improving the existing Cayley/Holland-style embedding theorem. We use this to show that the variety of all distributive lattice-ordered pregroups is generated by the single functional algebra on the integers. Finally, we show that t

Timothy M. Chan, Ce Jin, Virginia Vassilevska Williams, Yinzhan Xu

We study the classic Text-to-Pattern Hamming Distances problem: given a pattern $P$ of length $m$ and a text $T$ of length $n$, both over a polynomial-size alphabet, compute the Hamming distance between $P$ and $T[i\, .\, . \, i+m-1]$ for every shift $i$, under the standard Word-RAM model with $\Theta(\log n)$-bit words. - We provide an $O(n\sqrt{m})$ time L

Guozhen Lu, Qiaohua Yang

The main results of this paper concern sharp constant of the Trudinger-Moser inequality in $\mathbb{R}^{2}$ for Aharonov-Bohm magnetic fields. This is a borderline case of the Hardy type inequalities for Aharonov-Bohm magnetic fields in $\mathbb{R}^2$ studied by A. Laptev and T. Weidl. As an application, we obtain the exact asymptotic estimates on best const

Charlson. C. Kim, T. Bechtel, J. L. Herfindal, B. C. Lyons

This report is in partial fulfillment of deliverable D1.3 Code Validation (DIII-D). These simulations focus on thermal quench phase of the SPI mitigation and are not typically carried beyond it to the current spike and subsequent current quench. NIMROD SPI simulations[1] are validated against DIII-D experiments. The target plasma for these simulations is DII

Jorge Fernández Fernández, Peter Wheatley, George King, James Jenkins

The Neptunian desert is a region in period-radius parameter space with very few Neptune-sized planets at short orbital periods. Amongst these, LTT 9779 b is the only known Neptune with a period shorter than one day to retain a significant H-He atmosphere. If the Neptune desert is the result of X-ray/EUV-driven photoevaporation, it is surprising that the atmo

M. Horio, F. Forte, D. Sutter, M. Kim

Doped Mott insulators are the starting point for interesting physics such as high temperature superconductivity and quantum spin liquids. For multi-band Mott insulators, orbital selective ground states have been envisioned. However, orbital selective metals and Mott insulators have been difficult to realize experimentally. Here we demonstrate by photoemissio

Felipe Lepe, Jesus Vellojin

In two and three dimensions, we design and analyze a posteriori error estimators for the mixed Stokes eigenvalue problem. The unknowns on this mixed formulation are the pseudotress, velocity and pressure. With a lowest order mixed finite element scheme, together with a postprocressing technique, we prove that the proposed estimator is reliable and efficient.

Wu-Te Yang, Burak Kurkcu, Masayoshi Tomizuka

This paper introduces a novel design method that enhances the force/torque, bendability, and controllability of soft pneumatic actuators (SPAs). The complex structure of the soft actuator is simplified by approximating it as a cantilever beam. This allows us to derive approximated nonlinear kinematic models and a dynamical model, which is explored to underst

Rahul Jain, Jingyu Shi, Andrew Benton, Moiz Rasheed

Mixed Reality (MR) is gaining prominence in manual task skill learning due to its in-situ, embodied, and immersive experience. To teach manual tasks, current methodologies break the task into hierarchies (tasks into subtasks) and visualize the current subtask and future in terms of causality. Existing psychology literature also shows that humans learn tasks

Stefan Giller

The problem of the quantizations of the $L$-shaped billiards and the like ones, i.e. each angle of which is equal to $\pi/2$ or $3\pi/2$, is considered using as a tool the Fourier series expansion method. The respective wave functions and the quantization conditions are written and discussed looking for and discussing about the superscars effects in such mul

Sihan Xu, Ziqiao Ma, Yidong Huang, Honglak Lee

Diffusion models (DMs) have enabled breakthroughs in image synthesis tasks but lack an intuitive interface for consistent image-to-image (I2I) translation. Various methods have been explored to address this issue, including mask-based methods, attention-based methods, and image-conditioning. However, it remains a critical challenge to enable unpaired I2I tra

Daniel McNeela

Recently, the equivariance of models with respect to a group action has become an important topic of research in machine learning. Analysis of the built-in equivariance of existing neural network architectures, as well as the study of building models that explicitly "bake in" equivariance, have become significant research areas in their own right. However, i

Dushyant M. Narayan, Peipei Hao, Rafał Kurleto, Bryan S. Berggren

BaNi$_2$As$_2$ is a structural analog of the pnictide superconductor BaFe$_2$As$_2$, which, like the iron-based superconductors, hosts a variety of ordered phases including charge density waves (CDWs), electronic nematicity, and superconductivity. Upon isovalent Sr substitution on the Ba site, the charge and nematic orders are suppressed, followed by a sixfo

Kaiyuan Hua, Xiaofei Wang, Hwanhee Hong

Restricted mean survival time (RMST) models have gained popularity when analyzing time-to-event outcomes because RMST models offer more straightforward interpretations of treatment effects with fewer assumptions than hazard ratios commonly estimated from Cox models. However, few network meta-analysis (NMA) methods have been developed using RMST. In this pape

Elaheh Jafarigol, Theodore Trafalis

The classification of weather data involves categorizing meteorological phenomena into classes, thereby facilitating nuanced analyses and precise predictions for various sectors such as agriculture, aviation, and disaster management. This involves utilizing machine learning models to analyze large, multidimensional weather datasets for patterns and trends. T

Zhongze Zhang, Tao Jiang, Wei Yu

This paper addresses an uplink localization problem in which the base station (BS) aims to locate a remote user with the aid of reconfigurable intelligent surface (RIS). This paper proposes a strategy in which the user transmits pilots over multiple time frames, and the BS adaptively adjusts the RIS reflection coefficients based on the observations already r

Sergei Khlebnikov

A recent experiment has demonstrated formation of a supersonic region in a convergent two-dimensional flow of a condensate of cesium atoms. Theoretical description of this effect has made use of stationary solutions to the Gross-Pitaevskii equation with a 3-body dissipative term. Here, we further develop that description, focusing on a new stationary solutio

Felix Gerken, Ingo Runkel, Christoph Schweigert, Thore Posske

Recently, large degeneracy based on product eigenstates has been found in spin ladders, kagome-like lattices, and motif magnetism, connected to spin liquids, anyonic phases, and quantum scars. We unify these systems by a complete classification of product eigenstates of Heisenberg XXZ Hamiltonians with Dzyaloshinskii-Moriya interaction on general graphs in t

Vikram Voleti

This dissertation attempts to drive innovation in the field of generative modeling for computer vision, by exploring novel formulations of conditional generative models, and innovative applications in images, 3D animations, and video. Our research focuses on architectures that offer reversible transformations of noise and visual data, and the application of

Fernanda Duplancic, Sol Alonso, Georgina Coldwell, Daniela Galdeano

Context. The location of the Solar System constrains the detection of extragalactic sources beyond the Milky Way(MW) plane. The optical observations are hampered in the so--called Zone of Avoidance (ZOA) where stellar crowding and Galactic absorption are severe. Observations at longer wavelengths are needed to discover new background galaxies and complete th

Ali Goodarzi, Maryam Rahimi, MohammadJavad Valizadeh, Fakhteh Ghanbarnejad

In dealing with nonlinear systems, it is common to use numerical solutions. Unlike the careful behavior towards the numerical results in chaotic regions, the validity of numerical results in regions of transient chaos might not always be taken into consideration. This article demonstrates that using numerical methods to solve systems undergoing transient cha

Yuxin Zhang, Haidong Tian, Huaixuan Li, Chiho Yoon

Two-dimensional (2D) materials have drawn immense interest in scientific and technological communities, owing to their extraordinary properties that are profoundly altered from their bulk counterparts and their enriched tunability by gating, proximity, strain, and external fields. For digital applications, an ideal 2D material would have high mobility, air s

Jiaqi Yang, Panayotis Manganaris, Arun Mannodi-Kanakkithodi

Expanding the pool of stable halide perovskites with attractive optoelectronic properties is crucial to addressing current limitations in their performance as photovoltaic (PV) absorbers. In this article, we demonstrate how a high-throughput density functional theory (DFT) dataset of halide perovskite alloys can be used to train accurate surrogate models for

G. R. Santos, M. J. Neves

The effects of the Ohmic and magnetic density currents are investigated in the linearized Euler-Heisenberg electrodynamics. The linearization is introduced through an external magnetic field, in which the vector potential of the Euler-Heisenberg electrodynamics is expanded around of a magnetic background field, that we consider uniform and constant in this p

Debora Lančová

This dissertation is written as an annotated collection of selected articles. The dissertation focuses on the modelling of accretion disks in X-ray binaries with a black hole or neutron star. The main objective is to use advanced numerical methods to reveal the fundamental processes that influence the observed spectral and temporal features. Among the signif

Mikhail Belolipetsky, Gregory Cosac, Cayo Dória, Gisele Teixeira Paula

Semi-arithmetic Fuchsian groups is a wide class of discrete groups of isometries of the hyperbolic plane which includes arithmetic Fuchsian groups, hyperbolic triangle groups, groups admitting a modular embedding, and others. We introduce a new geometric invariant of a semi-arithmetic group called stretch. Its definition is based on the notion of the Riemann

Nick E. Mavromatos, Pablo Pais, Alfredo Iorio

The concept of torsion in geometry, although known for a long time, has not gained considerable attention by the physics community until relatively recently, due to its diverse and potentially important applications to a plethora of contexts of physical interest. These range from novel materials, such as graphene and graphene-like materials, to advanced theo

Jingyu Shi, Rahul Jain, Runlin Duan, Karthik Ramani

The emergence of Generative Artificial Intelligence (G-AI) has changed the landscape of creative arts with its power to compose novel artwork and thus brought ethical concerns. Despite the efforts by prior works to address these concerns from technical and societal perspectives, there exists little discussion on this topic from an HCI point of view, consider

Jenny S Kim, Kyungmin Kim, Richard Van Weelden

We consider the classic veto bargaining model but allow the agenda setter to engage in persuasion to convince the veto player to approve her proposal. We fully characterize the optimal proposal and experiment when Vetoer has quadratic loss, and show that the proposer-optimal can be achieved either by providing no information or with a simple binary experimen

Aayushman Sharma, Suman Chakravorty

In this paper, we study the use of state-of-the-art nonlinear system identification techniques for the optimal control of nonlinear systems. We show that the nonlinear systems identification problem is equivalent to estimating the generalized moments of an underlying sampling distribution and is bound to suffer from ill-conditioning and variance when approxi

Ankit Pal

This paper introduces a new testbed CLIFT (Clinical Shift) for the clinical domain Question-answering task. The testbed includes 7.5k high-quality question answering samples to provide a diverse and reliable benchmark. We performed a comprehensive experimental study and evaluated several QA deep-learning models under the proposed testbed. Despite impressive

Weiqi Zhang, Youngdae Kim, Kibaek Kim

We consider the unit commitment (UC) problem that employs the alternating current optimal power flow (ACOPF) constraints, which is formulated as a mixed-integer nonlinear programming problem and thus challenging to solve in practice. We develop a new scalable algorithm based on alternating direction method of multiplier (ADMM), which enables quickly finding

John Cyphert, Yotam Feldman, Zachary Kincaid, Thomas Reps

The problem of finding a constant bound on a term given a set of assumptions has wide applications in optimization as well as program analysis. However, in many contexts the objective term may be unbounded. Still, some sort of symbolic bound may be useful. In this paper we introduce the optimal symbolic-bound synthesis problem, and a technique that tackles t

Bowen Li, Michel Schanen, Kibaek Kim

Sequential quadratic programming (SQP) is widely used in solving nonlinear optimization problem, with advantages of warm-starting solutions, as well as finding high-accurate solution and converging quadratically using second-order information, such as the Hessian matrix. In this study we develop a scalable SQP algorithm for solving the alternate current opti

Ebraheem Farag, F. X. Timmes, Morgan T. Chidester, Samalka Anandagoda

We explore neutrino emission from nonrotating, single star models across six initial metallicities and seventy initial masses from the zero-age main sequence to the final fate. Overall, across the mass spectrum, we find metal-poor stellar models tend to have denser, hotter and more massive cores with lower envelope opacities, larger surface luminosities, and

Javier Cembrano, Felix Fischer, Max Klimm

We study functions that produce a ranking of $n$ individuals from $n$ such rankings and are impartial in the sense that the position of an individual in the output ranking does not depend on the input ranking submitted by that individual. When $n \geq 4$, two properties concerning the quality of the output in relation to the input can be achieved in addition

Hunjae "Timothy" Lee, Corey Clark

In the domain of Privacy-Preserving Machine Learning (PPML), Fully Homomorphic Encryption (FHE) is often used for encrypted computation to allow secure and privacy-preserving outsourcing of machine learning modeling. While FHE enables encrypted arithmetic operations, execution of programmatic logic such as control structures or conditional programming have r

Sammy Khalife

The expressivity of Graph Neural Networks (GNNs) can be described via appropriate fragments of the first order logic. Any query of the two variable fragment of graded modal logic (GC2) interpreted over labeled graphs can be expressed using a Rectified Linear Unit (ReLU) GNN whose size does not grow with graph input sizes [Barcelo & Al., 2020]. Conversely, a

Breno Orzari, Nadezda Chernyavskaya, Raphael Cobe, Javier Duarte

In high energy physics, one of the most important processes for collider data analysis is the comparison of collected and simulated data. Nowadays the state-of-the-art for data generation is in the form of Monte Carlo (MC) generators. However, because of the upcoming high-luminosity upgrade of the LHC, there will not be enough computational power or time to

Syomantak Chaudhuri, Konstantin Miagkov, Thomas A. Courtade

Differential Privacy (DP) is a well-established framework to quantify privacy loss incurred by any algorithm. Traditional formulations impose a uniform privacy requirement for all users, which is often inconsistent with real-world scenarios in which users dictate their privacy preferences individually. This work considers the problem of mean estimation, wher

Francesca Tavazza, Kamal Choudhary, Brian DeCost

The development of large databases of material properties, together with the availability of powerful computers, has allowed machine learning (ML) modeling to become a widely used tool for predicting material performances. While confidence intervals are commonly reported for such ML models, prediction intervals, i.e., the uncertainty on each prediction, are

Pedram Agand, Mohammad Mahdavian, Manolis Savva, Mo Chen

In end-to-end autonomous driving, the utilization of existing sensor fusion techniques and navigational control methods for imitation learning proves inadequate in challenging situations that involve numerous dynamic agents. To address this issue, we introduce LeTFuser, a lightweight transformer-based algorithm for fusing multiple RGB-D camera representation

Stefan Fasano, James Foster, Sylvain Bertrand, Christian DeBuys

For humans, fast, efficient walking over flat ground represents the vast majority of locomotion that an individual experiences on a daily basis, and for an effective, real-world humanoid robot the same will likely be the case. In this work, we propose a locomotion controller for efficient walking over near-flat ground using a relatively simple, model-based c

Augustin Skopal, Natalia Shagatova

Observational indications of wind-mass transfer from an evolved giant to its distant white dwarf (WD) companion in symbiotic binaries are rare. Here, we present a way to examine the neutral wind from the giant in symbiotic binaries, which is temporarily observable throughout the orbital plane during outbursts. We find that the mass-loss rate from giants in t

Yiqiao Jin, Mohit Chandra, Gaurav Verma, Yibo Hu

Large language models (LLMs) are transforming the ways the general public accesses and consumes information. Their influence is particularly pronounced in pivotal sectors like healthcare, where lay individuals are increasingly appropriating LLMs as conversational agents for everyday queries. While LLMs demonstrate impressive language understanding and genera

Pedro Fortuny Ayuso, Javier Ribón

We provide sharp lower bounds for the multiplicity of a local holomorphic foliation defined in a complex surface in terms of data associated to a germ of invariant curve. Then we apply our methods to invariant curves whose branches are isolated, i.e. they are never contained in non-trivial analytic families of equisingular invariant curves. In this case we s

Alejandro Ayala, Bruno S. Lopes, Ricardo L. S. Farias, Luis C. Parra

The non-monotonic behavior of the speed of sound for isospin imbalanced strongly interacting matter, found by recent lattice QCD simulations, can be reproduced within the Nambu--Jona-Lasinio model and Linear Sigma Model with quarks when the couplings become isospin chemical potential-dependent. The introduction of medium-dependent couplings can potentially a

Pedram Agand, Alexey Iskrov, Mo Chen

Nowadays, transportation networks face the challenge of sub-optimal control policies that can have adverse effects on human health, the environment, and contribute to traffic congestion. Increased levels of air pollution and extended commute times caused by traffic bottlenecks make intersection traffic signal controllers a crucial component of modern transpo

Myeongjae Lee, Guillaume Tahar

In projectivized strata of meromorphic $1$-forms on elliptic curves with only one zero, the locus of residueless differentials is a complex curve endowed with a canonical complex projective structure. Drawing on the multi-scale compactification of strata, we provide formulas to compute the genus of these curves and the degree of their natural forgetful map t

Zhihan Zhang, Shuohang Wang, Wenhao Yu, Yichong Xu

Large language models (LLMs) can perform a wide range of tasks by following natural language instructions, without the necessity of task-specific fine-tuning. Unfortunately, the performance of LLMs is greatly influenced by the quality of these instructions, and manually writing effective instructions for each task is a laborious and subjective process. In th