April 2024 arXiv papers — page 82

Ian Char, Youngseog Chung, Joseph Abbate, Egemen Kolemen

Although tokamaks are one of the most promising devices for realizing nuclear fusion as an energy source, there are still key obstacles when it comes to understanding the dynamics of the plasma and controlling it. As such, it is crucial that high quality models are developed to assist in overcoming these obstacles. In this work, we take an entirely data driv

Xincan Feng, Zhi Qu, Yuchang Cheng, Taro Watanabe

A Knowledge Graph (KG) is the directed graphical representation of entities and relations in the real world. KG can be applied in diverse Natural Language Processing (NLP) tasks where knowledge is required. The need to scale up and complete KG automatically yields Knowledge Graph Embedding (KGE), a shallow machine learning model that is suffering from memory

Enrico Bozzo, Lorenzo Amati, Wayne Baumgartner, Tzu-Ching Chang

Since their first discovery in the late 1960s, Gamma-ray bursts have attracted an exponentially growing interest from the international community due to their central role in the most highly debated open questions of the modern research of astronomy, astrophysics, cosmology, and fundamental physics. These range from the intimate nuclear composition of high d

Junheng Li, Omar Kolt, Quan Nguyen

Dynamic and continuous jumping remains an open yet challenging problem in bipedal robot control. Real-time planning with full body dynamics over the entire jumping trajectory presents unsolved challenges in computation burden. In this paper, we propose a novel variable-model optimization approach, a unified framework of variable-model trajectory optimization

Muhammed Alaa Morsy, Mohamed Anwar, Abdallah Aboutahoun

In this paper, we introduce two families of planar and self-similar graphs which have small-world properties. The constructed models are based on an iterative process where each step of a certain formulation of modules results in a final graph with a self-similar structure. The number of spanning trees of a graph is one of the most graph-theoretical paramete

Sho Kawano, Paul A. Parker, Zehang Richard Li

Area-level models for small area estimation typically rely on areal random effects to shrink design-based direct estimates towards a model-based predictor. Incorporating the spatial dependence of the random effects into these models can further improve the estimates when there are not enough covariates to fully account for spatial dependence of the areal mea

David Krejcirik, Jiri Lipovsky

A closed formula for the spectral determinant for the wave equation on a bounded interval, subject to Dirichlet boundary conditions and an $α$-multiple of the Dirac $δ$-type damping, is derived. Depending on the choice of the branch cut of the logarithm used in its definition, the spectral determinant diverges either for $α=2$ or $α=-2$.

Zhong-Xia Shang, Han-Sen Zhong, Yu-Kun Zhang, Cheng-Cheng Yu

In this work, we give a hybrid quantum-classical algorithm for solving electronic structure problems of molecules using only linear quantum optical systems. The variational ansatz we proposed is a hybrid of non-interacting Boson dynamics and classical computational chemistry methods, specifically, the Hartree-Fock method and the Configuration Interaction met

G. B. de Gracia, A. A. Nogueira

In this work we extend the Kugo-Ojima-Nakanishi covariant operator formalism to quantize two higher derivative systems, considering their extended phase space structures. More specifically, the one describing spin-$0$ particles by a vector field and the generalized electrodynamics. We investigate the commutator structure of these theories and present the def

Kateryna Kvasova, Evan N. Kirby, Rachael L. Beaton

Andromeda XVIII is an isolated dwarf galaxy 579 kpc away from the nearest large galaxy, M31. It is a candidate "backsplash galaxy" that might have been affected by a close passage to M31. We present new Keck/DEIMOS spectroscopy of Andromeda XVIII to assess the likelihood that it is a backsplash galaxy. We estimated the velocities, metallicities ([Fe/H]), and

Thomas Monninger, Vandana Dokkadi, Md Zafar Anwar, Steffen Staab

Autonomous driving requires an accurate representation of the environment. A strategy toward high accuracy is to fuse data from several sensors. Learned Bird's-Eye View (BEV) encoders can achieve this by mapping data from individual sensors into one joint latent space. For cost-efficient camera-only systems, this provides an effective mechanism to fuse data

Jia Wei, Xingjun Zhang, Witold Pedrycz, Longxiang Wang

For image-related deep learning tasks, the first step often involves reading data from external storage and performing preprocessing on the CPU. As accelerator speed increases and the number of single compute node accelerators increases, the computing and data transfer capabilities gap between accelerators and CPUs gradually increases. Data reading and prepr

Kara E. Rudolph, Nicholas T. Williams, Ivan Diaz, Sarah Forrest

Introduction: Chronic pain patients are at increased risk of opioid-misuse. Less is known about the unique risk conferred by each pain-management treatment, as treatments are typically implemented together, confounding their independent effects. We estimated the extent to which pain-management strategies were associated with risk of incident opioid use disor

João P. B. Brito, Rafael P. Bernar, Atsushi Higuchi, Luís C. B. Crispino

A semiclassical investigation of the electromagnetic radiation emitted by a charged particle in a radially freely falling motion in Schwarzschild spacetime is carried out. We use quantum field theory at tree level to obtain the one-particle-emission amplitudes. We analyze and compare the energy spectrum and total energy released, which are calculated from th

Robert Kaufman, David Kirsh, Nadir Weibel

Unanswered questions about how human-AV interaction designers can support rider's informational needs hinders Autonomous Vehicles (AV) adoption. To achieve joint human-AV action goals - such as safe transportation, trust, or learning from an AV - sufficient situational awareness must be held by the human, AV, and human-AV system collectively. We present a sy

Jian Liu, Dong Chen, Guo-Wei Wei

Topological data analysis, as a tool for extracting topological features and characterizing geometric shapes, has experienced significant development across diverse fields. Its key mathematical techniques include persistent homology and the recently developed persistent Laplacians. However, classic mathematical models like simplicial complexes often struggle

Simon Tam, Shriram Tallam Puranam Raghu, Étienne Buteau, Erik Scheme

Current electromyography (EMG) pattern recognition (PR) models have been shown to generalize poorly in unconstrained environments, setting back their adoption in applications such as hand gesture control. This problem is often due to limited training data, exacerbated by the use of supervised classification frameworks that are known to be suboptimal in such

Dillon Lohr, Michael J. Proulx, Oleg Komogortsev

This paper performs the crucial work of establishing a baseline for gaze-driven authentication performance to begin answering fundamental research questions using a very large dataset of gaze recordings from 9202 people with a level of eye tracking (ET) signal quality equivalent to modern consumer-facing virtual reality (VR) platforms. The size of the employ

Yiqun Xie, Zhihao Wang, Weiye Chen, Zhili Li

Foundation models, i.e., very large deep learning models, have demonstrated impressive performances in various language and vision tasks that are otherwise difficult to reach using smaller-size models. The major success of GPT-type of language models is particularly exciting and raises expectations on the potential of foundation models in other domains inclu

Matthew Carr, Niall Taggart

We provide two candidates for symplectic Weiss calculus based on two different, but closely related, collections of groups. In the case of the non-compact symplectic groups, i.e., automorphism groups of vector spaces with symplectic forms, we show that the calculus deformation retracts onto unitary calculus as a corollary of the fact that Weiss calculus only

Marzi Heidari, Hanping Zhang, Yuhong Guo

In this paper, we present a novel approach termed Prompt-Driven Feature Diffusion (PDFD) within a semi-supervised learning framework for Open World Semi-Supervised Learning (OW-SSL). At its core, PDFD deploys an efficient feature-level diffusion model with the guidance of class-specific prompts to support discriminative feature representation learning and fe

Benjamin S. Manning, Kehang Zhu, John J. Horton

We present an approach for automatically generating and testing, in silico, social scientific hypotheses. This automation is made possible by recent advances in large language models (LLM), but the key feature of the approach is the use of structural causal models. Structural causal models provide a language to state hypotheses, a blueprint for constructing

Mohammad Khosravani, Chenyang Huang, Amine Trabelsi

The proliferation of social media platforms has given rise to the amount of online debates and arguments. Consequently, the need for automatic summarization methods for such debates is imperative, however this area of summarization is rather understudied. The Key Point Analysis (KPA) task formulates argument summarization as representing the summary of a lar

Zooey Nguyen, Anthony Annunziata, Vinh Luong, Sang Dinh

This paper investigates the impact of domain-specific model fine-tuning and of reasoning mechanisms on the performance of question-answering (Q&A) systems powered by large language models (LLMs) and Retrieval-Augmented Generation (RAG). Using the FinanceBench SEC financial filings dataset, we observe that, for RAG, combining a fine-tuned embedding model with

Le Yan, Zhen Qin, Honglei Zhuang, Rolf Jagerman

The powerful generative abilities of large language models (LLMs) show potential in generating relevance labels for search applications. Previous work has found that directly asking about relevancy, such as ``How relevant is document A to query Q?", results in sub-optimal ranking. Instead, the pairwise ranking prompting (PRP) approach produces promising rank

Basil M. Idrees, Lavish Arora, Ketan Rajawat

We consider stochastic optimization problems with non-convex functional constraints, such as those arising in trajectory generation, sparse approximation, and robust classification. To this end, we put forth a recursive momentum-based accelerated successive convex approximation (SCA) algorithm. At each iteration, the proposed algorithm entails constructing c

Laura Victoria Rolandi, Jean Hélder Marques Ribeiro, Chi-An Yeh, Kunihiko Taira

Resolvent analysis is a powerful tool that can reveal the linear amplification mechanisms between the forcing inputs and the response outputs about a base flow. These mechanisms can be revealed in terms of a pair of forcing and response modes and the associated gains (amplification magnitude) in the order of energy contents at a given frequency. The linear r

Rachid Karami, Sheng-Chun Kao, Hyoukjun Kwon

Among ML operators today, GEneralMatrix Multiplication (GEMM)-based operators are known to be key operators that build the main backbone of ML models. As their computational overhead dominates the overall execution time (e.g., 42.8% - 96.6% in our results), GEMM operators have been the prime optimization targets for fast ML inference. This led to advanced GP

José Ruiz-Franco, Andrea Giuntoli

Glassy polymer melts such as the plastics used in pipes, structural materials, and medical devices are ubiquitous in daily life. They accumulate damage over time due to their use, which limits their functionalities and demands periodic replacement. The resulting economic and social burden could be mitigated by the design of self-healing mechanisms that expan

Andrea Ghezzi, Wim Van Roy, Sebastian Sager, Moritz Diehl

Sequential quadratic programming and sequential convex programming efficiently solve nonlinear programs (NLPs) by linearizing inner nonlinearities while preserving the outer convex structure. This paper introduces a sequential mixed-integer quadratic programming (MIQP) algorithm to extend this methodology to mixed-integer nonlinear problems (MINLPs), leverag

Jiaji Chen, Xuanbo Miao, Hongbin Ma, Jonathan B. Hopkins

The exploration of intelligent machines has recently spurred the development of physical neural networks, a class of intelligent metamaterials capable of learning, whether in silico or in situ, from observed data. In this study, we introduce a back-propagation framework for lattice-based mechanical neural networks (MNNs) to achieve prescribed static and dyna

Jonathan Gadea Harder, Aneta Neumann, Frank Neumann

This paper explores the enhancement of solution diversity in evolutionary algorithms (EAs) for the maximum matching problem, concentrating on complete bipartite graphs and paths. We adopt binary string encoding for matchings and use Hamming distance to measure diversity, aiming for its maximization. Our study centers on the $(\mu+1)$-EA and $2P-EA_D$, which

Rong-Xin Miao

This paper investigates the Casimir effect of a wedge and its holographic dual. We prove that the displacement operator universally determines the wedge Casimir effect in the smooth limit. Besides, we argue that the wedge Casimir energy increases with the opening angle and test it with several examples. Furthermore, we construct the holographic dual of wedge

Sana Ebrahimi, Nima Shahbazi, Abolfazl Asudeh

The extensive scope of large language models (LLMs) across various domains underscores the critical importance of responsibility in their application, beyond natural language processing. In particular, the randomized nature of LLMs, coupled with inherent biases and historical stereotypes in data, raises critical concerns regarding reliability and equity. Add

James Buenfil, Eardi Lila

In this paper, we introduce a novel statistical model for the integrative analysis of Riemannian-valued functional data and high-dimensional data. We apply this model to explore the dependence structure between each subject's dynamic functional connectivity -- represented by a temporally indexed collection of positive definite covariance matrices -- and high

Jeonghun Sohn, Jongjun M. Lee, Hyun-Woo Lee

The Dyakonov-Perel (DP) mechanism of spin relaxation has long been considered irrelevant in centrosymmetric systems since it was developed originally for non-centrosymmetric ones. We investigate whether this conventional understanding extends to the realm of orbital relaxation, which has recently attracted significant attention. Surprisingly, we find that or

John A. Scott, James Bishop, Garrett Budnik, Milos Toth

Focused ion beam (FIB) techniques are employed widely for nanofabrication, and processing of materials and devices. However, ion irradiation often gives rise to severe damage due to atomic displacements that cause defect formation, migration and clustering within the ion-solid interaction volume. The resulting restructuring degrades the functionality of mate

Rui Deng, Tianpei Gu

Reconstructing degraded images is a critical task in image processing. Although CNN and Transformer-based models are prevalent in this field, they exhibit inherent limitations, such as inadequate long-range dependency modeling and high computational costs. To overcome these issues, we introduce the Channel-Aware U-Shaped Mamba (CU-Mamba) model, which incorpo

Omri Shaltiel, Alon Salhov, Omri Gat, Eran Sharon

A short, abrupt increase in energy injection rate into steady strongly-driven rotating turbulent flow is used as a probe for energy transfer in the system. The injected excessive energy is localized in time and space and its spectra differ from those of the steady turbulent flow. This allows measuring energy transfer rates, in three different domains: In rea

Rachel, Chen, Wenjia Zheng, Sandeep Jalui

With the advancements in 3D printing technologies, it is extremely important that the quality of 3D printed objects, and dimensional accuracies should meet the customer's specifications. Various factors during metal printing affect the printed parts' quality, including the power quality, the printing stage parameters, the print part's location inside the pri

Evan Habbershaw, Cory D. Hauck, Jingwei Hu, Jeffrey R. Haack

A multi-species Fokker-Planck model for simulating particle collisions in a plasma is presented. The model includes various parameters that must be tuned. Under reasonable assumptions on these parameters, the model satisfies appropriate conservation laws, dissipates an entropy, and satisfies an $\mathcal{H}$-Theorem. In addition, the model parameters provide

Olga Dergachyova, Zidan Yu, Shota Hodono, Martijn Cloos

The nuclear magnetic resonance signal from sodium (23Na) nuclei demonstrates a fast bi-exponential T2 decay in biological tissues (T2,short = 0.5-5 ms and T2,long = 10-30 ms). Hence, blurring observed in sodium images acquired with center-out sequences is generally assumed to be dominated by signal attenuation at higher k-space frequencies. Most of the studi

Dayu Yang, Fumian Chen, Hui Fang

Large Language Models (LLMs) have demonstrated great potential in Conversational Recommender Systems (CRS). However, the application of LLMs to CRS has exposed a notable discrepancy in behavior between LLM-based CRS and human recommenders: LLMs often appear inflexible and passive, frequently rushing to complete the recommendation task without sufficient inqu

Mihai Mariş, Anthony Mur

We consider the nonlinear Schr\"odinger equation with nonzero conditions at infinity in $\R^2$. We investigate the existence of traveling waves that are periodic in the direction transverse to the direction of propagation and minimize the energy when the momentum is kept fixed. We show that for any given value of the momentum, there is a critical value of th

Nitol Saha, Md Masruk Aulia, Md. Mostafizur Rahman, Mohammed Shafiul Alam Khan

This research focused on the development of a cost-effective IoT solution for energy and environment monitoring geared towards manufacturing industries. The proposed system is developed using open-source software that can be easily deployed in any manufacturing environment. The system collects real-time temperature, humidity, and energy data from different d

Zuowen Wang, Chang Gao, Zongwei Wu, Marcos V. Conde

This survey reviews the AIS 2024 Event-Based Eye Tracking (EET) Challenge. The task of the challenge focuses on processing eye movement recorded with event cameras and predicting the pupil center of the eye. The challenge emphasizes efficient eye tracking with event cameras to achieve good task accuracy and efficiency trade-off. During the challenge period,

MohammadHossein AskariHemmat, Ahmadreza Jeddi, Reyhane Askari Hemmat, Ivan Lazarevich

Quantization lowers memory usage, computational requirements, and latency by utilizing fewer bits to represent model weights and activations. In this work, we investigate the generalization properties of quantized neural networks, a characteristic that has received little attention despite its implications on model performance. In particular, first, we devel

Heitor P. Casagrande, Bo Xing, William J. Munro, Chu Guo

Algorithms developed to solve many-body quantum problems, like tensor networks, can turn into powerful quantum-inspired tools to tackle problems in the classical domain. In this work, we focus on matrix product operators, a prominent numerical technique to study many-body quantum systems, especially in one dimension. It has been previously shown that such a

Federico Crippa

Threshold policies are decision rules that assign treatments based on whether an observable characteristic exceeds a certain threshold. They are widespread across multiple domains, including welfare programs, taxation, and clinical medicine. This paper examines the problem of designing threshold policies using experimental data, when the goal is to maximize

Shaocong Ma, James Diffenderfer, Bhavya Kailkhura, Yi Zhou

Deep learning has been widely applied to solve partial differential equations (PDEs) in computational fluid dynamics. Recent research proposed a PDE correction framework that leverages deep learning to correct the solution obtained by a PDE solver on a coarse mesh. However, end-to-end training of such a PDE correction model over both solver-dependent paramet

Jessica K. Niblo, Jacob R. Swartley, Zhongmin Zhang, Kateri H. DuBay

Multiple dissipative self-assembly protocols designed to create novel structures or to reduce kinetic traps have recently emerged. Specifically, temporal oscillations of particle interactions have been shown effective at both aims, but investigations thus far have focused on systems of simple colloids or their binary mixtures. In this work, we expand our und

Deepti Hegde, Suhas Lohit, Kuan-Chuan Peng, Michael J. Jones

LiDAR datasets for autonomous driving exhibit biases in properties such as point cloud density, range, and object dimensions. As a result, object detection networks trained and evaluated in different environments often experience performance degradation. Domain adaptation approaches assume access to unannotated samples from the test distribution to address t

Cadence Patrick, Kimberly Ruth, Zakir Durumeric

Open-source software (OSS) supply chain security has become a topic of concern for organizations. Patching an OSS vulnerability can require updating other dependent software products in addition to the original package. However, the landscape of OSS dependencies is not well explored: we do not know what packages are most critical to patch, hindering efforts

Oishee Bintey Hoque, Samarth Swarup, Abhijin Adiga, Sayjro Kossi Nouwakpo

Irrigation mapping plays a crucial role in effective water management, essential for preserving both water quality and quantity, and is key to mitigating the global issue of water scarcity. The complexity of agricultural fields, adorned with diverse irrigation practices, especially when multiple systems coexist in close quarters, poses a unique challenge. Th

Yakun Li, Xiyin Liang, Jinzhi Lei

Stem cell regeneration is a vital biological process in self-renewing tissues, governing development and tissue homeostasis. Gene regulatory network dynamics are pivotal in controlling stem cell regeneration and cell type transitions. However, integrating the quantitative dynamics of gene regulatory networks at the single-cell level with stem cell regenerati

Cedric Donié, Marie K. Reumann, Tony Hartung, Benedikt J. Braun

Bone non-union is among the most severe complications associated with trauma surgery, occurring in 10-30% of cases after long bone fractures. Treating non-unions requires a high level of surgical expertise and often involves multiple revision surgeries, sometimes even leading to amputation. Thus, more accurate prognosis is crucial for patient well-being. Rec

Xue Liu, Yue Deng, Jingying Huang, Yuhong Zhang

The global public health landscape is perpetually challenged by the looming threat of infectious diseases. Central to addressing this concern is the imperative to prevent and manage disease transmission during pandemics, particularly in unique settings. This study addresses the transmission dynamics of infectious diseases within conference venues, presenting

Ashutosh Singh, Maria Sebastian, Mikhail Tokman, Alexey Belyanin

Current carrying chiral edge states in quantum Hall systems have fascinating properties that are usually studied by electron spectroscopy and interferometry. Here we demonstrate that electron occupation, current, and electron coherence in chiral edge states can be selectively probed and controlled by low-energy electromagnetic radiation in the microwave to i

Mike A. Merrill, Mingtian Tan, Vinayak Gupta, Tom Hartvigsen

Time series are critical for decision-making in fields like finance and healthcare. Their importance has driven a recent influx of works passing time series into language models, leading to non-trivial forecasting on some datasets. But it remains unknown whether non-trivial forecasting implies that language models can reason about time series. To address thi

Motoya Shinozaki, Takaya Abe, Kazuma Matsumura, Takumi Aizawa

Defects in semiconductors, traditionally seen as detrimental to electronic device performance, have emerged as potential assets in quantum technologies due to their unique quantum properties. This study investigates the interaction between defects and quantum electron transport in GaN/AlGaN field-effect transistors, highlighting the observation of Fano reson

Peyman Gholami, Hulya Seferoglu

This paper focuses on reducing the communication cost of federated learning by exploring generalization bounds and representation learning. We first characterize a tighter generalization bound for one-round federated learning based on local clients' generalizations and heterogeneity of data distribution (non-iid scenario). We also characterize a generali

Rachel, Chen, Juheon Lee, Chuang Gan

Metal Sintering is a necessary step for Metal Injection Molded parts and binder jet such as HP's metal 3D printer. The metal sintering process introduces large deformation varying from 25 to 50% depending on the green part porosity. In this paper, we use a graph-based deep learning approach to predict the part deformation, which can speed up the deformation

Nazia Tasnim, Sujan Sen Gupta, Md. Istiak Hossain Shihab, Fatiha Islam Juee

Communal violence in online forums has become extremely prevalent in South Asia, where many communities of different cultures coexist and share resources. These societies exhibit a phenomenon characterized by strong bonds within their own groups and animosity towards others, leading to conflicts that frequently escalate into violent confrontations. To addres

R. Tartaglia, F. R. Arantes, C. W. Galdino, U. F. Kaneko

HoNiSi$_{3}$ is an intermetallic compound characterized by two successive antiferromagnetic transitions at $T_{N1} = 6.3$ K and $T_{N2} = 10.4$ K. Here, its zero-field microscopic magnetic structure is inferred from resonant x-ray magnetic diffraction experiments on a single crystalline sample that complement previous bulk magnetic susceptibility data. For $

Hicham Mangach, Abdelhaq Belkacem, Younes Achaoui, Abdenbi Bouzid

Recently, man-made dielectric materials composed of finite-sized dielectric constituents have emerged as a promising platform for quasi-bound states in the continuum (QBICs). These states allow for an extraordinary confinement of light within regions smaller than the wavelength scale. Known for their exceptional quality factors, they have become crucial asse

Keyu Wang

We define categories $\mathcal{O}^w$ of representations of Borel subalgebras $\mathcal{U}_q\mathfrak{b}$ of quantum affine algebras $\mathcal{U}_q\hat{\mathfrak{g}}$, which come from the category $\mathcal{O}$ twisted by Weyl group elements $w$. We construct inductive systems of finite-dimensional $\mathcal{U}_q\mathfrak{b}$-modules twisted by $w$, which pro

Georgiy Karev

I consider a family of games with corresponding payoff matrices, parametrized by the parameter The informal question of interest is: what game is the best? Instead of attempting to suggest a more or less arbitrary definition of the best game, I allow natural selection to find it through honest competition between games. Specifically, I consider a model of a

Madhuchhanda Bhattacharjee, Arup Bose

We consider the spatio-temporal gridded daily diurnal temperature range (DTR) data across India during the 72-year period 1951--2022. We augment this data with information on the El Nino-Southern Oscillation (ENSO) and on the climatic regions (Stamp's and Koeppen's classification) and four seasons of India. We use various matrix theory approaches to trim out

Guilherme Duarte, Nelma Moreira, Luca Prigioniero, Rogério Reis

In this paper, we consider block languages, namely sets of words having the same length, and we propose a new representation for these languages. In particular, given an alphabet of size $k$ and a length $\ell$, a block language can be represented by a bitmap of length $k^\ell$, where each bit indicates whether the corresponding word, according to the lexico

Yichen Luo, Yebo Feng, Jiahua Xu, Paolo Tasca

Total value locked (TVL) is widely used to measure the size and popularity of decentralized finance (DeFi). However, TVL can be easily manipulated and inflated through "double counting" activities such as wrapping and leveraging. As existing methodologies addressing double counting are inconsistent and flawed, we propose a new framework, termed "total value

Luca Buoncompagni, Fulvio Mastrogiovanni

We foresee robots that bootstrap knowledge representations and use them for classifying relevant situations and making decisions based on future observations. Particularly for assistive robots, the bootstrapping mechanism might be supervised by humans who should not repeat a training phase several times and should be able to refine the taught representation.

Bryce Cyr, Sandeep Kumar Acharya, Jens Chluba

The presence of an abundant population of low frequency photons at high redshifts (such as a radio background) can source leading order effects on the evolution of the matter and spin temperatures through rapid free-free absorptions. This effect, known as soft photon heating, can have a dramatic impact on the differential brightness temperature, $\Delta T_{\

Seyed M. R. Modaresi, Aomar Osmani, Mohammadreza Razzazi, Abdelghani Chibani

Internet of Things (IoT) devices generate heterogeneous data over time; and relying solely on individual data points is inadequate for accurate analysis. Segmentation is a common preprocessing step in many IoT applications, including IoT-based activity recognition, aiming to address the limitations of individual events and streamline the process. However, th

Muheng Li, Xia Li, Sairos Safai, Damien Weber

In recent advancements in proton therapy, MR-based treatment planning is gaining momentum to minimize additional radiation exposure compared to traditional CT-based methods. This transition highlights the critical need for accurate MR-to-CT image synthesis, which is essential for precise proton dose calculations. Our research introduces the Diffusion Schr\"o

M. Weiß, J. Stümpfle, F. Dettinger, N. Jazdi

The emergence of connected vehicles is driven by increasing customer and regulatory demands. To meet these, more complex software applications, some of which require service-based cloud and edge backends, are developed. When new software is deployed however, the high complexity and interdependencies between components can lead to unforeseen side effects in o

Soonwoo Kwon, Jonathan Roth

Economists are often interested in the mechanisms by which a treatment affects an outcome. We develop tests for the "sharp null of full mediation" that a treatment $D$ affects an outcome $Y$ only through a particular mechanism (or set of mechanisms) $M$. Our approach exploits connections between mediation analysis and the econometric literature on testing in

Defne E. Ozan, Luca Magri

Adjoint methods have been the pillar of gradient-based optimization for decades. They enable the accurate computation of a gradient (sensitivity) of a quantity of interest with respect to all system's parameters in one calculation. When the gradient is embedded in an optimization routine, the quantity of interest can be optimized for the system to have the d

Deepti Hegde, Suhas Lohit, Kuan-Chuan Peng, Michael J. Jones

Popular representation learning methods encourage feature invariance under transformations applied at the input. However, in 3D perception tasks like object localization and segmentation, outputs are naturally equivariant to some transformations, such as rotation. Using pre-training loss functions that encourage equivariance of features under certain transfo

Yu-Lin Ku, Yao-Gen Shu

Non-contact measurement of the refractive index and thickness of multilayer biological tissues is of great significance for biomedical applications and can greatly improve medical diagnosis and treatment. In this work, we introduce a theoretical method to simultaneously extract the above information using a Fourier domain optical coherence tomography (FD-OCT

A. René Geist, Jonas Frey, Mikel Zhobro, Anna Levina

Many settings in machine learning require the selection of a rotation representation. However, choosing a suitable representation from the many available options is challenging. This paper acts as a survey and guide through rotation representations. We walk through their properties that harm or benefit deep learning with gradient-based optimization. By conso

Teemu Lehtinen, Charles Koutcheme, Arto Hellas

Recent research has explored the creation of questions from code submitted by students. These Questions about Learners' Code (QLCs) are created through program analysis, exploring execution paths, and then creating code comprehension questions from these paths and the broader code structure. Responding to the questions requires reading and tracing the code,

Yan Jin, Erwan Lucas, Jizhao Zang, Travis Briles

Control of nonlinear interactions in microresonators enhances access to classical and quantum field states across nearly limitless bandwidth. A recent innovation has been to leverage coherent scattering of the intraresonator pump as a control of group-velocity dispersion and nonlinear frequency shifts, which are precursors for the dynamical evolution of new

Mir Rayat Imtiaz Hossain, Mennatullah Siam, Leonid Sigal, James J. Little

The emergence of attention-based transformer models has led to their extensive use in various tasks, due to their superior generalization and transfer properties. Recent research has demonstrated that such models, when prompted appropriately, are excellent for few-shot inference. However, such techniques are under-explored for dense prediction tasks like sem

Thomas Vecchiato, Claudio Lucchese, Franco Maria Nardini, Sebastian Bruch

A critical piece of the modern information retrieval puzzle is approximate nearest neighbor search. Its objective is to return a set of $k$ data points that are closest to a query point, with its accuracy measured by the proportion of exact nearest neighbors captured in the returned set. One popular approach to this question is clustering: The indexing algor

Graham Todd, Tim Merino, Sam Earle, Julian Togelius

The Connections puzzle published each day by the New York Times tasks players with dividing a bank of sixteen words into four groups of four words that each relate to a common theme. Solving the puzzle requires both common linguistic knowledge (i.e. definitions and typical usage) as well as, in many cases, lateral or abstract thinking. This is because the fo

Mohammadhossein Firouznia, David Saintillan

We study emergent dynamics in a viscous drop subject to interfacial nematic activity. Using hydrodynamic simulations, we show how the interplay of nematodynamics, activity-driven flows and surface deformations gives rise to a sequence of self-organized behaviors of increasing complexity, from periodic braiding motions of topological defects to chaotic defect

Ricardo Parizotto, Israat Haque, Alberto Schaeffer-Filho

Network programmability allows modification of fine-grain data plane functionality. The performance benefits of data plane programmability have motivated many researchers to offload computation that previously operated only on servers to the network, creating the notion of in-network computing (INC). Because failures can occur in the data plane, fault tolera

Jean-Paul Ainam, Erim Yanik, Rahul Rahul, Taylor Kunkes

Endotracheal intubation (ETI) is an emergency procedure performed in civilian and combat casualty care settings to establish an airway. Objective and automated assessment of ETI skills is essential for the training and certification of healthcare providers. However, the current approach is based on manual feedback by an expert, which is subjective, time- and

Orhun Mersin Caglidil, Malte Ostendorff, Georg Rehm

Language models are trained mostly on Web data, which often contains social stereotypes and biases that the models can inherit. This has potentially negative consequences, as models can amplify these biases in downstream tasks or applications. However, prior research has primarily focused on the English language, especially in the context of gender bias. In

Santiago Cepeda, Roberto Romero, Daniel Garcia-Perez, Guillermo Blasco

Accurately assessing tumor removal is paramount in the management of glioblastoma. We developed a pipeline using MRI scans and neural networks to segment tumor subregions and the surgical cavity in postoperative images. Our model excels in accurately classifying the extent of resection, offering a valuable tool for clinicians in assessing treatment effective

Gerard t Hooft, William D Phillips, Anton Zeilinger, Roland Allen

Despite its amazing quantitative successes and contributions to revolutionary technologies, physics currently faces many unsolved mysteries ranging from the meaning of quantum mechanics to the nature of the dark energy that will determine the future of the Universe. It is clearly prohibitive for the general reader, and even the best informed physicists, to f

Sourav Roy Chowdhury, Debabrata Deb, Farook Rahaman, Saibal Ray

In this article, we apply the Finsler spacetime to develop the Einstein field equations in the extension of modified geometry. Following Finsler geometry, which is focused on the tangent bundle with a scalar function, a scalar equation should be the field equation that defines this structure. This spacetime maintains the required causality properties on the

Yifan He, Abootaleb Shirvani, Barret Shao, Svetlozar Rachev

This research extends the conventional concepts of the bid--ask spread (BAS) and mid-price to include the total market order book bid--ask spread (TMOBBAS) and the global mid-price (GMP). Using high-frequency trading data, we investigate these new constructs, finding that they have heavy tails and significant deviations from normality in the distributions of

Xiangjun Peng

This work provides new insights and constructs to the field of computer architecture and systems, and these insights are expected to be useful for the broad software stack. First, this work introduces Functionality Locality: this form of Functionality Locality shows that functionalities can be changed with a single piece of information, by solely changing th

Aayush Dhakal, Subash Khanal, Srikumar Sastry, Adeel Ahmad

In remote sensing, we are interested in modeling various modalities for some geographic location. Several works have focused on learning the relationship between a location and type of landscape, habitability, audio, textual descriptions, etc. Recently, a common way to approach these problems is to train a deep-learning model that uses satellite images to in

Toral Gupta, Neil Cornish

We currently lack good waveform models for many gravitational wave sources. Examples where models are lacking include neutron star post merger signals, core collapse supernovae, and signals of unknown origin. Wavelet based techniques have proven effective at detecting and characterizing these signals. Here we introduce a new method that uses collections of e

Lander Besabe, Michele Girfoglio, Annalisa Quaini, Gianluigi Rozza

Although the two-layer quasi-geostrophic equations (2QGE) are a simplified model for the dynamics of a stratified, wind-driven ocean, their numerical simulation is still plagued by the need for high resolution to capture the full spectrum of turbulent scales. Since such high resolution would lead to unreasonable computational times, it is typical to resort t

Neha Srikanth, Marine Carpuat, Rachel Rudinger

Large language models have been shown to behave inconsistently in response to meaning-preserving paraphrastic inputs. At the same time, researchers evaluate the knowledge and reasoning abilities of these models with test evaluations that do not disaggregate the effect of paraphrastic variability on performance. We propose a metric for evaluating the paraphra

Tyrus Berry, Timothy Sauer

We investigate the reconstruction of time series from dynamical networks that are partially observed. In particular, we address the extent to which the time series at a node of the network can be successfully reconstructed when measuring from another node, or subset of nodes, corrupted by observational noise. We will assume the dynamical equations of the net

Daniele S. M. Alves

We propose an adaptation of Entanglement Renormalization for quantum field theories that, through the use of discrete wavelet transforms, strongly parallels the tensor network architecture of the \emph{Multiscale Entanglement Renormalization Ansatz} (a.k.a. MERA). Our approach, called wMERA, has several advantages of over previous attempts to adapt MERA to c