December 2023 arXiv papers — page 115

Issam Serraoui, Eric Granger, Abdenour Hadid, Abdelmalik Taleb-Ahmed

Automatic pain intensity estimation plays a pivotal role in healthcare and medical fields. While many methods have been developed to gauge human pain using behavioral or physiological indicators, facial expressions have emerged as a prominent tool for this purpose. Nevertheless, the dependence on labeled data for these techniques often renders them expensive

Yajun Wei

Resonance is a topic included in most introductory physics courses. Any mechanical system experiences resonance if it is driven by a periodic force with a frequency that matches its natural frequency. There are plenty of simple demonstrations of the resonance phenomena of mechanical systems which can be set up using readily available items . This work presen

Arnav Burudgunte, Arlei Silva

Large infrastructure networks (e.g. for transportation and power distribution) require constant monitoring for failures, congestion, and other adversarial events. However, assigning a sensor to every link in the network is often infeasible due to placement and maintenance costs. Instead, sensors can be placed only on a few key links, and machine learning alg

Zhiqiang Cai, Brooke Hejnal

We propose and study the evolving neural network (ENN) method for solving one-dimensional scalar hyperbolic conservation laws with linear and quadratic spatial fluxes. The ENN method first represents the initial data and the inflow boundary data by neural networks. Then, it evolves the neural network representation of the initial data along the temporal dire

Philipe De Fabritiis, Fillipe M. Guedes, Marcelo S. Guimaraes, Itzhak Roditi

Mermin's inequalities are investigated in a Quantum Field Theory framework by using von Neumann algebras built with Weyl operators. We devise a general construction based on the Tomita-Takesaki modular theory and use it to compute the vacuum expectation value of the Mermin operator, analyzing the parameter space and explicitly exhibiting a violation of Mermi

Chongyang Ren, Sisi Sun, Pengfei Zhang

Here we report an optical quasi-periodic oscillation (QPO) with a period of $\sim$134~day detected in g- and r-band light curves of a narrow-line Seyfert 1 galaxy TXS 1206+549 at redshift of 1.34 with the data from the Zwicky Transient Facility (ZTF) observations. After considering the trial factor, the significance levels in the two bands are 3.1 $\sigma$ a

Bin Chen, Yujin Guo, Shu Zhang

As a continuation of \cite{me}, we consider ground states of the $N$ coupled fermionic nonlinear Schr\"{o}dinger system with a parameter $a $ and the Coulomb potential $V(x)$ in the $L^2$-critical case, where $a>0$ represents the attractive strength of the quantum particles. For any given $N\in\mathbb{N}^+$, we prove that the system admits ground states, if

Jie Zhang, Xinmin Yang

In this paper, we propose a proximal iteratively reweighted algorithm with extrapolation based on block coordinate update aimed at solving a class of optimization problems which is the sum of a smooth possibly nonconvex loss function and a general nonconvex regularizer with a special structure. The proposed algorithm can be used to solve the $\ell_p(0<p<1)$

Soham Bafana

Understanding object recognition patterns in mice is crucial for advancing behavioral neuroscience and has significant implications for human health, particularly in the realm of Alzheimer's research. This study is centered on the development, application, and evaluation of a state-of-the-art computational pipeline designed to analyze such behaviors, specifi

Haoming Liu, Yuanhe Guo, Shengjie Wang, Hongyi Wen

Diffusion models, capable of high-quality image generation, receive unparalleled popularity for their ease of extension. Active users have created a massive collection of domain-specific diffusion models by fine-tuning base models on self-collected datasets. Recent work has focused on improving a single diffusion model by uncovering semantic and visual infor

Jie Zhang, Xinmin Yang

In this paper, we propose a fast proximal gradient algorithm for multiobjective optimization, it is proved that the convergence rate of the accelerated algorithm for multiobjective optimization developed by Tanabe et al. can be improved from $O(1/k^2)$ to $o(1/k^2)$ by introducing different extrapolation term $\frac{k-1}{k+\alpha-1}$ with $\alpha>3$. Further

Chaochao Huang, Chaoqian Wang

When involution affects individuals, their efforts do not augment resources but merely compete for limited resources. From entrance exams to company overtime, such efforts lead to unnecessary costs, undermining group welfare. Meanwhile, the individual advantages or collective disadvantages from this unnecessary effort may accumulate over time, such as the lo

Pan Shi, Jiahao Yuan, Fei Yan, Haifeng Yu

The advancement of scalable quantum information processing relies on the accurate and parallel manipulation of a vast number of qubits, potentially reaching into the millions. Superconducting qubits, traditionally controlled through individual circuitry, currently face a formidable scalability challenge due to the excessive use of wires. This challenge is ne

Cónall Kelly, Gabriel Lord, Fandi Sun

We develop adaptive time-stepping strategies for It\^o-type stochastic differential equations (SDEs) with jump perturbations. Our approach builds on adaptive strategies for SDEs. Adaptive methods can ensure strong convergence of nonlinear SDEs with drift and diffusion coefficients that violate global Lipschitz bounds by adjusting the stepsize dynamically on

Tomoe Kishimoto, Masahiro Morinaga, Masahiko Saito, Junichi Tanaka

This study aims to improve the performance of event classification in collider physics by introducing a pre-training strategy. Event classification is a typical problem in collider physics, where the goal is to distinguish the signal events of interest from background events as much as possible to search for new phenomena in nature. A pre-training strategy w

Connor Lawless, Jakob Schoeffer, Lindy Le, Kael Rowan

A critical factor in the success of decision support systems is the accurate modeling of user preferences. Psychology research has demonstrated that users often develop their preferences during the elicitation process, highlighting the pivotal role of system-user interaction in developing personalized systems. This paper introduces a novel approach, combinin

Orlem Lima dos Santos, Karen Rosero, Roberto de Alencar Lotufo

Sound Event Detection and Localization (SELD) constitutes a complex task that depends on extensive multichannel audio recordings with annotated sound events and their respective locations. In this paper, we introduce a self-supervised approach for SELD adapted from the pre-training methodology of wav2vec 2.0, which learns representations directly from raw au

Steve Kirkland, Hermie Monterde

The join $X\vee Y$ of two graphs $X$ and $Y$ is the graph obtained by joining each vertex of $X$ to each vertex of $Y$. We explore the behaviour of a continuous quantum walk on a weighted join graph having the adjacency matrix or Laplacian matrix as its associated Hamiltonian. We characterize strong cospectrality, periodicity and perfect state transfer (PST)

Hafida Massit, Roumaissae Eljazzar, Mohamed Rossafi

In this paper, we present the concept of continuous biframes in a Hilbert space. We examine the essential properties of biframes with an emphasis on the biframe operator. Moreover, we introduce a new type of Riesz bases, referred to as continuous biframe-Riesz bases.

Hongyue Fan, Jingjie Ni, Fangfei Li

In this paper, we investigate the problem of controlling probabilistic Boolean control networks (PBCNs) to achieve reachability with maximum probability in the finite time horizon. We address three questions: 1) finding control policies that achieve reachability with maximum probability under fixed, and particularly, varied finite time horizon, 2) leveraging

A. A. Valverde, F. G. Kondev, B. Liu, D. Ray

We report precision mass measurements of $^{133}$Sb, $^{133g,m}$Te, and $^{133g,m}$I, produced at CARIBU at Argonne National Laboratory's ATLAS facility and measured using the Canadian Penning Trap mass spectrometer. These masses clarify an anomaly in the $^{133}$Te $\beta$-decay. The masses reported in the 2020 Atomic Mass Evaluation (M. Wang et al., 2021)

Jae-Won Chung, Yile Gu, Insu Jang, Luoxi Meng

Training large AI models on numerous GPUs consumes a massive amount of energy, making power delivery one of the largest limiting factors in building and operating datacenters for AI workloads. However, we observe that not all energy consumed during training directly contributes to end-to-end throughput; a significant portion can be removed without slowing do

Renlong Jie, Xiaojun Meng, Xin Jiang, Qun Liu

Unsupervised extractive summarization is an important technique in information extraction and retrieval. Compared with supervised method, it does not require high-quality human-labelled summaries for training and thus can be easily applied for documents with different types, domains or languages. Most of existing unsupervised methods including TextRank and P

Gourav Datta, Zeyu Liu, James Diffenderfer, Bhavya Kailkhura

Bio-inspired Spiking Neural Networks (SNN) are now demonstrating comparable accuracy to intricate convolutional neural networks (CNN), all while delivering remarkable energy and latency efficiency when deployed on neuromorphic hardware. In particular, ANN-to-SNN conversion has recently gained significant traction in developing deep SNNs with close to state-o

Pareesa Ameneh Golnari

Diffusion models, such as Stable Diffusion (SD), offer the ability to generate high-resolution images with diverse features, but they come at a significant computational and memory cost. In classifier-free guided diffusion models, prolonged inference times are attributed to the necessity of computing two separate diffusion models at each denoising step. Rece

Jakob Jordan, Noah Jaitner, Tom Meyer, Luca Brahmè

Rapid mapping of the mechanical properties of soft biological tissues from light microscopy to macroscopic imaging could transform fundamental biophysical research by providing clinical biomarkers to complement in vivo elastography. We here introduce superfast optical time-harmonic elastography (OTHE) to remotely encode surface and subsurface shear wave fiel

Kosuke Nogaki, Youichi Yanase

Field-induced superconductivity has long presented a counterintuitive phenomenon and a pivotal challenge in condensed matter physics. In this Letter, we introduce a mechanism for achieving field-induced superconductivity wherein the sublattice degree of freedom and the Coulomb interaction are tightly entwined. Our multipole-resolved analysis elucidates that

Mani L. Bhaumik

The primary ingredients of reality are the universal quantum fields, which fluctuate persistently, spontaneously, and randomly. The general perception of the scientific community is that these quantum fluctuations are due to the uncertainty principle. Here, we present cogent arguments to show that the uncertainty principle is a consequence of the quantum flu

Matija Bucić, James Davies

In 1975 Erd\H{o}s initiated the study of the following very natural question. What can be said about the chromatic number of unit distance graphs in $\mathbb{R}^2$ that have large girth? Over the years this question and its natural extension to $\mathbb{R}^d$ attracted considerable attention with the high-dimensional variant reiterated recently by Alon and K

Syed A. Shah, Michael R. Clark, Joseph Zyss, Maxim Sukharev

With the help of a theoretical model and finite-difference-time-domain simulations based on the hydrodynamic-Maxwell model, we examine the effect of difference frequency generation in an array of L-shaped metal nano-particles characterized by intrinsic plasmonic nonlinearity. The outcomes of the calculations reveal the spectral interplay of the gain and loss

Jian Shi, Peter Wonka

We present \textit{VoxelKP}, a novel fully sparse network architecture tailored for human keypoint estimation in LiDAR data. The key challenge is that objects are distributed sparsely in 3D space, while human keypoint detection requires detailed local information wherever humans are present. We propose four novel ideas in this paper. First, we propose sparse

Tian-Xiang Lu, Yan Wang, Keyu Xia, Xing Xiao

Phonon lasers or coherent amplifications of mechanical oscillations have provided powerful tools for both fundamental studies of coherent acoustics and diverse applications ranging from ultrasensitive force sensing to phononic information processing. Here, we propose how to achieve directional phonon lasing with an optomechanical resonator coupled to a nonli

Jacob Fox, Jonathan Tidor, Shengtong Zhang

A graph is $H$-Ramsey if every two-coloring of its edges contains a monochromatic copy of $H$. Define the $F$-Ramsey number of $H$, denoted by $r_F(H)$, to be the minimum number of copies of $F$ in a graph which is $H$-Ramsey. This generalizes the Ramsey number and size Ramsey number of a graph. Addressing a question of Spiro, we prove that \[r_{K_3}(K_t)=\b

Miloš Arsenović, Jelena Gajić

We obtain Schwarz-Pick lemma for $(\alpha, \beta)$-harmonic functions u in the disc, where $\alpha$ and $\beta$ are complex parameters satisfying $\Re \alpha + \Re \beta > -1$. We prove sharp estimate of derivative at the origin for such functions in terms of $L^p$ norm of the boundary function. Also, we give asymptotically sharp estimate of the norm of the

Kyriakos Psarakis, George Christodoulou, George Siachamis, Marios Fragkoulis

Developing stateful cloud applications, such as low-latency workflows and microservices with strict consistency requirements, remains arduous for programmers. The Stateful Functions-as-a-Service (SFaaS) paradigm aims to serve these use cases. However, existing approaches provide weak transactional guarantees or perform expensive external state accesses requi

Philipp V. Rouast

This report introduces VitalLens, an app that estimates vital signs such as heart rate and respiration rate from selfie video in real time. VitalLens uses a computer vision model trained on a diverse dataset of video and physiological sensor data. We benchmark performance on several diverse datasets, including VV-Medium, which consists of 289 unique particip

Taesik Gong, Si Young Jang, Utku Günay Acer, Fahim Kawsar

The advent of tiny artificial intelligence (AI) accelerators enables AI to run at the extreme edge, offering reduced latency, lower power cost, and improved privacy. When integrated into wearable devices, these accelerators open exciting opportunities, allowing various AI apps to run directly on the body. We present Synergy that provides AI apps with best-ef

Edgardo Franzin, Stefano Liberati, Jacopo Mazza

While non-rotating black-hole solutions are well known in Einstein--\ae{}ther gravity, no axisymmetric solutions endowed with Killing horizons have been so far found outside of the slowly rotating limit. Here we show that the Kerr spacetime is also an exact vacuum solution of Einstein--\ae{}ther gravity in a phenomenologically viable corner of the parameter

Haozhe Wang, Xianghan Xu, Danrui Ni, David Walker

We report the high-pressure synthesis (6 GPa, 1200 $^{\circ}$C) and ambient pressure characterization of hexagonal HP-BaCoO$_3$. The material (with the 2H crystal structure) has a short intrachain Co-Co distance of about 2.07 $\text{\r{A}}$. Our magnetization investigation revealed robust diamagnetic behavior below approximately 130 K when exposed to weak ap

Julio David Melon Fuksman, Mario Flock, Hubert Klahr

The vertical shear instability (VSI) is a hydrodynamical instability likely to produce turbulence in the dead zones of protoplanetary disks. Various aspects of this instability remain to be understood, including the disk regions where it can operate and the physical phenomena leading to its saturation. In this work, we studied the growth and evolution of sec

Tomohiro Fujita, Kai Murai, Kazunori Nakayama, Wen Yin

We present a new mechanism to generate a coherently oscillating dark vector field from axion-SU(2) gauge field dynamics during inflation. The SU(2) gauge field acquires a nonzero background sourced by an axion during inflation, and it acquires a mass through spontaneous symmetry breaking after inflation. We find that the coherent oscillation of the dark vect

Shayan Zargari, Diluka Galappaththige, Chintha Tellambura

In an era driven by the Internet of Things (IoT) and rapid wireless communication advances, the synergy between sensing and backscatter communication (BackCom) has emerged as a frontier of research. This paper delves deep into the integration of sensing technologies with BackCom, a burgeoning field with significant implications for energy-efficient wireless

Johannes Schneider, Mohit Prabhushankar

The learning dynamics of deep neural networks are not well understood. The information bottleneck (IB) theory proclaimed separate fitting and compression phases. But they have since been heavily debated. We comprehensively analyze the learning dynamics by investigating a layer's reconstruction ability of the input and prediction performance based on the evol

Mengwei Ren, Wei Xiong, Jae Shin Yoon, Zhixin Shu

Portrait harmonization aims to composite a subject into a new background, adjusting its lighting and color to ensure harmony with the background scene. Existing harmonization techniques often only focus on adjusting the global color and brightness of the foreground and ignore crucial illumination cues from the background such as apparent lighting direction,

Bhagyashree Umathe, Umesh Vaidya

The paper is about characterizing the stability boundary of an autonomous dynamical system using the Koopman spectrum. For a dynamical system with an asymptotically stable equilibrium point, the domain of attraction constitutes a region consisting of all initial conditions attracted to the equilibrium point. The stability boundary is a separatrix region that

Johannes J Brust, Philip E Gill

For quasi-Newton methods in unconstrained minimization, it is valuable to develop methods that are robust, i.e., methods that converge on a large number of problems. Trust-region algorithms are often regarded to be more robust than line-search methods, however, because trust-region methods are computationally more expensive, the most popular quasi-Newton imp

Waverly Wei, Xinwei Ma, Jingshen Wang

Understanding treatment effect heterogeneity has become an increasingly popular task in various fields, as it helps design personalized advertisements in e-commerce or targeted treatment in biomedical studies. However, most of the existing work in this research area focused on either analyzing observational data based on strong causal assumptions or conducti

Julio David Melon Fuksman, Mario Flock, Hubert Klahr

We studied the linear and nonlinear evolution of the Vertical Shear Instability (VSI) in axisymmetric models of protoplanetary disks, focusing on the transport of angular momentum, the produced temperature perturbations, and the applicability of local stability conditions. We modeled the gas-dust mixture via high-resolution two-moment (M1) radiation-hydrodyn

Sarin Chandy, Varun Gangal, Yi Yang, Gabriel Maggiotti

We devise, implement and performance-asses DYAD, a layer which can serve as a faster and more memory-efficient approximate replacement for linear layers, (nn.Linear() in Pytorch). These layers appear in common subcomponents, such as in the ff module of Transformers. DYAD is based on a bespoke near-sparse matrix structure which approximates the dense "weight"

Sanskriti Das, Mary Rickel, Adam Leroy, Nickolas M. Pingel

We probe the neutral circumgalactic medium (CGM) along the major axes of NGC891 and NGC4565 in 21-cm emission out to $\gtrsim 100$kpc using the Green Bank Telescope (GBT), extending our previous minor axes observations. We achieve an unprecedented $5\sigma$ sensitivity of $6.1\times 10^{16}$ cm$^{-2}$ per 20 km s$^{-1}$ velocity channel. We detect HI with di

A. Werle, E. Giunchi, B. Poggianti, M. Gullieuszik

Using images collected with the WFC3 camera on board of the Hubble Space Telescope, we detect stellar clumps in continuum-subtracted $H\alpha$ and ultraviolet (F275W filter), such clumps are often embedded in larger regions (star-forming complexes) detected in the optical (F606W filter). We model the photometry of these objects using BAGPIPES to obtain their

Mir Mehedi Faruk, Edward Morvan, Jan Pieter van der Schaar

We analyze null- and spacelike radial geodesics in Schwarzschild-de Sitter spacetime connecting two conjugate static sphere observers, i.e. free-falling observers at a fixed radius in between the two horizons. We explicitly determine the changes in the causal structure with respect to these natural observers as a result of the inward bending of the black hol

Vivek Chaudhary

In this paper, we explore the graph partitioning problem, a pivotal combina-torial optimization challenge with extensive applications in various fields such as science, technology, and business. Recognized as an NP-hard prob-lem, graph partitioning lacks polynomial-time algorithms for its resolution. Recently, there has been a burgeoning interest in leveragi

Lingfeng Sun, Devesh K. Jha, Chiori Hori, Siddarth Jain

Designing robotic agents to perform open vocabulary tasks has been the long-standing goal in robotics and AI. Recently, Large Language Models (LLMs) have achieved impressive results in creating robotic agents for performing open vocabulary tasks. However, planning for these tasks in the presence of uncertainties is challenging as it requires \enquote{chain-o

Rajdeep Mondal, Rathin Singha, Todd Millstein, George Varghese

Model-based testing (MBT), whereby a model of the system under test is analyzed to generate high-coverage test cases, has been used to test protocol implementations. A key barrier to the use of MBT is the need for users to understand protocol RFCs in detail to create a compliant model. Our new approach to MBT uses LLMs to automatically build rich models of i

Yifan Zhang, Rui Wu, Sergiu M. Dascalu, Frederick C. Harris

Transformers have achieved remarkable performance in multivariate time series(MTS) forecasting due to their capability to capture long-term dependencies. However, the canonical attention mechanism has two key limitations: (1) its quadratic time complexity limits the sequence length, and (2) it generates future values from the entire historical sequence. To a

Yutong Gao, Charles A. Ellis, Vince D. Calhoun, Robyn L. Miller

The high dimensionality and complexity of neuroimaging data necessitate large datasets to develop robust and high-performing deep learning models. However, the neuroimaging field is notably hampered by the scarcity of such datasets. In this work, we proposed a data augmentation and validation framework that utilizes dynamic forecasting with Long Short-Term M

Ioanna Stamou, Sebastien Clesse

We investigate the viability of primordial black hole (PBH) formation in the Standard Model (SM) in a scenario that does not rely on specific inflationary features or any exotic physics such as phase transitions or non-minimal coupling to gravity. If the Brout-Englert-Higgs (BEH) field lies exactly at the transition between metastability and stability, its p

Paniz Halvachi, Alexandra Peste, Dan Alistarh, Christoph H. Lampert

We present ELSA, a practical solution for creating deep networks that can easily be deployed at different levels of sparsity. The core idea is to embed one or more sparse networks within a single dense network as a proper subset of the weights. At prediction time, any sparse model can be extracted effortlessly simply be zeroing out weights according to a pre

John Kos, Kenneth Eaton, Sareen Zhang, Rahul Dass

Conceptual and simulation models can function as useful pedagogical tools, however it is important to categorize different outcomes when evaluating them in order to more meaningfully interpret results. VERA is a ecology-based conceptual modeling software that enables users to simulate interactions between biotics and abiotics in an ecosystem, allowing users

Milad Rogha

The unprecedented performance of machine learning models in recent years, particularly Deep Learning and transformer models, has resulted in their application in various domains such as finance, healthcare, and education. However, the models are error-prone and cannot be used autonomously, especially in decision-making scenarios where, technically or ethical

Margaret Hawton

We revisit the quantum oscillator model of the electromagnetic field and conclude that, while the nonlocal positive and negative frequency ladder operators generate a photon Fock basis, the Hermitian field operators obtained by second quantization of real Maxwell fields describe photon-antiphoton pairs that couple locally to Fermionic matter and can be model

Salman Khan

Dynamic scene understanding is the ability of a computer system to interpret and make sense of the visual information present in a video of a real-world scene. In this thesis, we present a series of frameworks for dynamic scene understanding starting from road event detection from an autonomous driving perspective to complex video activity detection, followe

Eric Yeats, Cameron Darwin, Frank Liu, Hai Li

Quantification of the number of variables needed to locally explain complex data is often the first step to better understanding it. Existing techniques from intrinsic dimension estimation leverage statistical models to glean this information from samples within a neighborhood. However, existing methods often rely on well-picked hyperparameters and ample dat

Hangfei Lin, Li Miao, Amir Ziai

Few-shot image classification is the task of classifying unseen images to one of N mutually exclusive classes, using only a small number of training examples for each class. The limited availability of these examples (denoted as K) presents a significant challenge to classification accuracy in some cases. To address this, we have developed a method for augme

Anton Levonian

A translation surface is a surface formed by identifying edges of a collection of polygons in the complex plane that are parallel and of equal length using only translations. We determined that the same circle packing can be realized on varying translation surfaces in a certain stratum. We also determined possible complexities of contacts graphs and provide

Zhenyu Wu, Meng Jiang, Chao Shen

Chain-of-Thought (CoT) prompting methods have enabled large language models (LLMs) to generate reasoning paths and solve math word problems (MWPs). However, they are sensitive to mistakes in the paths, as any mistake can result in an incorrect answer. We propose a novel method named Progressive Rectification Prompting (PRP) to improve average accuracy on eig

Jay K. Kueny, Alycia J. Weinberger, Jared R. Males, Katie M. Morzinski

We present resolved images of the inner disk component around HD 141569 using the Magellan adaptive optics system with the Clio2 1 - 5 $\mu$m camera, offering a glimpse of a complex system thought to be in a short evolutionary phase between protoplanetary and debris disk stages. We use a reference star along with the KLIP algorithm for PSF subtraction to det

Travis Driver, Andrew Vaughan, Yang Cheng, Adnan Ansar

This paper proposes the incorporation of techniques from stereophotoclinometry (SPC) into a keypoint-based structure-from-motion (SfM) system to estimate the surface normal and albedo at detected landmarks to improve autonomous surface and shape characterization of small celestial bodies from in-situ imagery. In contrast to the current state-of-the-practice

Julian Gamboa, Xi Shen, Tabassom Hamidfar, Selim M. Shahriar

Space situational awareness demands efficient monitoring of terrestrial sites and celestial bodies, necessitating advanced target recognition systems. Current target recognition systems exhibit limited operational speed due to challenges in handling substantial image data. While machine learning has improved this scenario, highresolution images remain a conc

Daniel Wrench, Tulasi N. Parashar, Sean Oughton, Kevin de Lange

The Reynolds number, Re, is an important quantity for describing a turbulent flow. It tells us about the bandwidth over which energy can cascade from large scales to smaller ones, prior to the onset of dissipation. However, calculating it for nearly collisionless plasmas like the solar wind is challenging. Previous studies have used "effective" Reynolds numb

Anthony D'Addario, Johnathan Kuan, Noah F. Opondo, Ozan Erturk

Bulk-mode acoustic waves in a crystalline material exert lattice strain through the thickness of the sample, which couples to the spin Hamiltonian of defect-based qubits such as the nitrogen-vacancy (NV) center defect in diamond. This mechanism has been previously harnessed for unconventional quantum spin control, spin decoherence protection, and quantum sen

Aida Davani, Mark Díaz, Dylan Baker, Vinodkumar Prabhakaran

Perception of offensiveness is inherently subjective, shaped by the lived experiences and socio-cultural values of the perceivers. Recent years have seen substantial efforts to build AI-based tools that can detect offensive language at scale, as a means to moderate social media platforms, and to ensure safety of conversational AI technologies such as ChatGPT

Vladimir Gurvich, Mariya Naumova

Given integers $n,k,\ell$ such that $0<k<n, \; 1<\ell$ and an integer vector $x = (x_1,\ldots,x_n)$; denote by $m = m(x)$ the number of entries of $x$ that are multiple of $\ell$. Choose $n-k$ entries of $x$ as follows: if $n-k \leq m(x)$, take $n-k$ smallest entries of $x$ multiple of $\ell$; if $n-k > m(x)$, take all $m$ such entries, if any, and add remai

Po-Hao Chang, Igor I. Mazin

The classical Ising model on the triangular lattice (we will call it I-3 model below), while simple in the nearest-neighbors (NN) only approximation, becomes increasingly richer and more complex when further interactions are incorporated. However, the studies so far have not been exhaustive, nor have any attempts been made to estimate how realistic are the p

Ahmed Abdelrahman, Omar M. Shehata, Yarah Basyoni, Elsayed I. Morgan

Cooperative autonomous driving plays a pivotal role in improving road capacity and safety within intelligent transportation systems, particularly through the deployment of autonomous vehicles on urban streets. By enabling vehicle-to-vehicle communication, these systems expand the vehicles environmental awareness, allowing them to detect hidden obstacles and

Moslem Moradi, Wenzheng Shi, Ehssan Nazockdast

In studying the transport of particles and inclusions in multi-phase systems we are often interested in integrated quantities such as the total force and the net velocity of the particles. Here, we derive a reciprocal formulation for linear poro-viscoelastic (PVE) materials, which are composed of a linear compressible viscoelastic phase, i.e. the network pha

Dmitrii Lukashevich, Ivan Tyukin, Nikolay Brilliantov

We report a novel approach for the efficient computation of solutions of a broad class of large-scale systems of non-linear ordinary differential equations, describing aggregation kinetics. The method is based on a new take on the dimensionality reduction for this class of equations which can be naturally implemented by a cascade of small feed-forward artifi

Jonas Saqri, Astrid M. Veronig, Andrea Francesco Battaglia, Ewan C. M. Dickson

We investigate two microflares of GOES classes A9 and C1 (after background subtraction) observed by STIX onboard Solar Orbiter with exceptionally strong nonthermal emission. We complement the hard X-ray imaging and spectral analysis by STIX with co-temporal observations in the (E)UV and visual range by AIA and HMI, in order to investigate what makes these mi

Ryan King, Tianbao Yang, Bobak Mortazavi

Within the intensive care unit (ICU), a wealth of patient data, including clinical measurements and clinical notes, is readily available. This data is a valuable resource for comprehending patient health and informing medical decisions, but it also contains many challenges in analysis. Deep learning models show promise in extracting meaningful patterns, but

Emma Stevenson, Victor Rodriguez-Fernandez, Hodei Urrutxua, Vincent Morand

This paper presents a novel methodology for improving the performance of machine learning based space traffic management tasks through the use of a pre-trained orbit model. Taking inspiration from BERT-like self-supervised language models in the field of natural language processing, we introduce ORBERT, and demonstrate the ability of such a model to leverage

Irina Degtiar, Mariel Finucane

Evaluations often inform future program implementation decisions. However, the implementation context may differ, sometimes substantially, from the evaluation study context. This difference leads to uncertainty regarding the relevance of evaluation findings to future decisions. Voluntary interventions pose another challenge to generalizability, as we do not

Ching-An Cheng, Andrey Kolobov, Dipendra Misra, Allen Nie

We introduce a new benchmark, LLF-Bench (Learning from Language Feedback Benchmark; pronounced as "elf-bench"), to evaluate the ability of AI agents to interactively learn from natural language feedback and instructions. Learning from language feedback (LLF) is essential for people, largely because the rich information this feedback provides can help a learn

Christian P. N. Tanner, James K. Utterback, Joshua Portner, Igor Coropceanu

Self-assembly of colloidal nanocrystals (NCs) into superlattices (SLs) is an appealing strategy to design hierarchically organized materials with new functionalities. Mechanistic studies are still needed to uncover the design principles for SL self-assembly, but such studies have been difficult to perform due to the fast time- and short length scales of NC s

J. P. McCandless, C. A. Gorsak, V. Protasenko, D. G. Schlom

Here we report that the source of Si impurities commonly observed on (010) $\beta-Ga_2O_3$ is from exposure of the surface to air. Moreover, we find that a 15 minute HF (49%) treatment reduces the Si density by approximately 1 order of magnitude on (010) $\beta-Ga_2O_3$ surfaces. This reduction in Si is critical for the elimination of the often observed para

Oswaldo Vazquez

Following the recent work of V. Moncrief, A. Marini, R. Maitra and P. Mondal on the geometry of field theoretic configuration spaces, this account examines how the regularized Ricci curvature of the $SU(2)_L \times U(1)_Y$ Yang-Mills orbit space may provide an intrinsic mass to the W boson which contributes to the value obtained from the renormalized Higgs m

Silvano A. Bernabel, Sos S. Agaian

This paper tackles the intricate challenge of improving the quality of nighttime images under hazy and low-light conditions. Overcoming issues including nonuniform illumination glows, texture blurring, glow effects, color distortion, noise disturbance, and overall, low light have proven daunting. Despite the inherent difficulties, this paper introduces a pio

Martin J. Savage

Future quantum computers are anticipated to be able to perform simulations of quantum many-body systems and quantum field theories that lie beyond the capabilities of classical computation. This will lead to new insights and predictions for systems ranging from dense non-equilibrium matter, to low-energy nuclear structure and reactions, to high-energy collis

Lucy D'Agostino McGowan, Roger D. Peng, Stephanie C. Hicks

A challenge that data analysts face is building a data analysis that is useful for a given consumer. Previously, we defined a set of principles for describing data analyses that can be used to create a data analysis and to characterize the variation between analyses. Here, we introduce a concept that we call the alignment of a data analysis between the data

Samuel T. King

People living with Type 1 Diabetes (T1D) lose the ability to produce insulin naturally. To compensate, they inject synthetic insulin. One common way to inject insulin is through automated insulin delivery systems, which use sensors to monitor their metabolic state and an insulin pump device to adjust insulin to adapt. In this paper, we present the Metabolic

Lee Youngmin, Ma Xiaomin, Lang S. I. D. Andrew, Valderrama-Araya F. Enrique

In the realm of wireless communication, stochastic modeling of channels is instrumental for the assessment and design of operational systems. Deep learning neural networks (DLNN), including generative adversarial networks (GANs), are being used to approximate wireless Orthogonal frequency-division multiplexing (OFDM) channels with fading and noise, using rea

Thomas Waite, Alexander Robey, Hassani Hamed, George J. Pappas

This paper addresses the problem of data-driven modeling and verification of perception-based autonomous systems. We assume the perception model can be decomposed into a canonical model (obtained from first principles or a simulator) and a noise model that contains the measurement noise introduced by the real environment. We focus on two types of noise, beni

Eva Lilly, Peter Jevčák, Charles Schambeau, Kat Volk

We present a dynamical study of 39 active Centaurs and 17 high-perihelion (q$>$4.5 au) JFCs with a focus on investigating recent orbital changes as potential triggers for comet-like activity. We have identified a common feature in the recent dynamical histories of all active Centaurs and JFC in our sample that is not present in the history of the majority of

Jooyoun Hong, Susan Morey

This paper surveys and summarizes Wolmer Vasconcelos' results surrounding multiplicities, Hilbert coefficients, and their extensions. We particularly focus on Vasconcelos' results regarding multiplicities and Chern coefficients, and other invariants which they bound. The Sally module is an important instrument introduced by Vasconcelos for this study, which

Ian U. Roederer, Nicole Vassh, Erika M. Holmbeck, Matthew R. Mumpower

The heaviest chemical elements are naturally produced by the rapid neutron-capture process (r-process) during neutron star mergers or supernovae. The r-process production of elements heavier than uranium (transuranic nuclei) is poorly understood and inaccessible to experiments, so must be extrapolated using nucleosynthesis models. We examine element abundanc

Husniyah Alzubaidi, Antony Maciocia

We modify the axioms of triangulated categories to include both higher triangles and distinguished maps of higher triangles. The distinguished maps are specializations of Neeman's ``good'' maps of $2$-triangles. The axioms both simplify Neeman's axioms in his 1991 paper and generalize them to higher triangles in the way proposed by Balmer et al. We provide a

Yuri Kabanov, Aleksei Kozhevnikov

We expose a simple solution of the consumption-investment problem pair trading. The proof is based on the remark that the HJB equation can be reduced to a linear parabolic equation solvable explicitly.

Eden Shaveet, Utkarsh Singh, Nicholas Assaderaghi, Maximo Librandi

Memory-based medication non-adherence is an unsolved problem that is responsible for considerable disease burden in the United States. Digital medication intake reminder solutions with minimal onboarding requirements that are usable at the point of medication acquisition may help to alleviate this problem by offering a low barrier way to help people remember

Aarush Pratik Sheth, Jonah Riley Weinbaum, Kevin Javier Zvonarek

As is widely known, the stock market is a complex system in which a multitude of factors influence the performance of individual stocks and the market as a whole. One method for comprehending -- and potentially predicting -- stock market behavior is through network analysis, which can offer insights into the relationships between stocks and the overall marke

Deep Chatterjee, Philip C. Harris, Maanas Goel, Malina Desai

Likelihood-free inference is quickly emerging as a powerful tool to perform fast/effective parameter estimation. We demonstrate a technique of optimizing likelihood-free inference to make it even faster by marginalizing symmetries in a physical problem. In this approach, physical symmetries, for example, time-translation are learned using joint-embedding via