April 2024 arXiv papers — page 133

Bill Deng, Mircea Voineagu

Over the real numbers with $\Z/2-$coefficients, we compute the $C_2$-equivariant Borel motivic cohomology ring, the Bredon motivic cohomology groups and prove that the Bredon motivic cohomology ring of the real numbers is a proper subring in the $RO(C_2\times C_2)$-graded Bredon cohomology ring of a point. This generalizes Voevodsky's computation of the moti

Anant A. Joshi, Amirhossein Taghvaei, Prashant G. Mehta, Sean P. Meyn

In this paper, stochastic optimal control problems in continuous time and space are considered. In recent years, such problems have received renewed attention from the lens of reinforcement learning (RL) which is also one of our motivation. The main contribution is a simulation-based algorithm -- dual ensemble Kalman filter (EnKF) -- to numerically approxima

Yutian Zhong, Xiaoming Zhang, Zongxin Mo, Shuangyang Zhang

Multispectral photoacoustic tomography (PAT) is an imaging modality that utilizes the photoacoustic effect to achieve non-invasive and high-contrast imaging of internal tissues. However, the hardware cost and computational demand of a multispectral PAT system consisting of up to thousands of detectors are huge. To address this challenge, we propose an ultra-

Koffka Khan

As the demand for high-quality video content continues to rise, adaptive video streaming plays a pivotal role in delivering an optimal viewing experience. However, traditional content recommendation systems face challenges in dynamically adapting to users' preferences, content features, and contextual information. This review paper explores the integration o

Yifei Wang, Wenhan Ma, Stefanie Jegelka, Yisen Wang

Relying only on unlabeled data, Self-supervised learning (SSL) can learn rich features in an economical and scalable way. As the drive-horse for building foundation models, SSL has received a lot of attention recently with wide applications, which also raises security concerns where backdoor attack is a major type of threat: if the released dataset is malici

M. Samsonyan, A. A. Kocharyan, V. G. Gurzadyan

A mechanism for smearing of the primordial gravitational waves during the radiation-dominated phase of the evolution of the Universe is considered. It is shown that the primordial gravitational waves can possess hyperbolicity features due to their propagation through the matter inhomogeneities. This mechanism of smearing can lead to the flattening of the ori

Samrah Arif, M. Arif Khan, Sabih Ur Rehman

In the expanding field of the Internet of Things (IoT), wireless channel estimation is a significant challenge. This is specifically true for low-power IoT (LP-IoT) communication, where efficiency and accuracy are extremely important. This research establishes two distinct LP-IoT wireless channel estimation models using Artificial Neural Networks (ANN): a Fe

Geyou Zhang, Ce Zhu, Kai Liu

Phase shifting profilometry (PSP) is favored in high-precision 3D scanning due to its high accuracy, robustness, and pixel-wise property. However, a fundamental assumption of PSP that the object should remain static is violated in dynamic measurement, making PSP susceptible to object moving, resulting in ripple-like errors in the point clouds. We propose a p

Tajmilur Rahman, Rahul Singh, Mir Yousuf Sultan

The advent of Large Language Models (LLMs) has revolutionized various domains of artificial intelligence, including the realm of software engineering. In this research, we evaluate the efficacy of pre-trained LLMs in replicating the tasks traditionally performed by developers in response to code review comments. We provide code contexts to five popular LLMs

Serge F. Timashev

Based on the results of F. Wilf on the need to take into account the quantum-mechanical correspondence rules in the Dirac equation for an electron, it was shown that the equation obtained by giving physical meaning to $\alpha$-Dirac operators should be considered as a phenomenological equation for a particle of non-zero size - the EM polaron, previously intr

Guangyang Wu, Xin Tao, Changlin Li, Wenyi Wang

Previous methods for Video Frame Interpolation (VFI) have encountered challenges, notably the manifestation of blur and ghosting effects. These issues can be traced back to two pivotal factors: unavoidable motion errors and misalignment in supervision. In practice, motion estimates often prove to be error-prone, resulting in misaligned features. Furthermore,

Ningfeng Liu, Jie Yu, Siyu Xiu, Xinfang Zhao

Molecular generation, an essential method for identifying new drug structures, has been supported by advancements in machine learning and computational technology. However, challenges remain in multi-objective generation, model adaptability, and practical application in drug discovery. In this study, we developed a versatile 'plug-in' molecular generation mo

Leying Zhang, Yao Qian, Long Zhou, Shujie Liu

Recent advancements in zero-shot text-to-speech (TTS) modeling have led to significant strides in generating high-fidelity and diverse speech. However, dialogue generation, along with achieving human-like naturalness in speech, continues to be a challenge. In this paper, we introduce CoVoMix: Conversational Voice Mixture Generation, a novel model for zero-sh

Yuying Li, Jinchi Zhu

The widespread use of Generative Artificial Intelligence in the innovation and generation of communication content is mainly due to its exceptional creative ability, operational efficiency, and compatibility with diverse industries. Nevertheless, this has also sparked ethical problems, such as unauthorized access to data, biased decision-making by algorithms

Richard Hepworth, Emily Roff

Two important invariants of directed graphs, namely magnitude homology and path homology, have recently been shown to be intimately connected: there is a 'magnitude-path spectral sequence' or 'MPSS' in which magnitude homology appears as the first page, and in which path homology appears as an axis of the second page. In this paper we study the homological a

X. Xiao, P. Chen, X. Cao, K. Liu

Epidemic outbreaks can cause critical health concerns and severe global economic crises. For countries or regions with new infectious disease outbreaks, it is essential to generate preventive strategies by learning lessons from others with similar risk profiles. A Strategy Transfer and Decision Support Approach (STDSA) is proposed based on the profile simila

S. Alex Rautu, Alexandra Zidovska, Michael J. Shelley

Nucleocytoplasmic transport is essential for cellular function, presenting a canonical example of rapid molecular sorting inside cells. It consists of a coordinated interplay between import/export of molecules in/out the cell nucleus. Here, we investigate the role of spatio-temporal dynamics of the nucleocytoplasmic transport and its regulation. We develop a

Honglu He, Chen-lung Lu, Glenn Saunders, Pinghai Yang

Industrial robotic applications such as spraying, welding, and additive manufacturing frequently require fast, accurate, and uniform motion along a 3D spatial curve. To increase process throughput, some manufacturers propose a dual-robot setup to overcome the speed limitation of a single robot. Industrial robot motion is programmed through waypoints connecte

Sunyanan Choochotkaew, Chen Wang, Huamin Chen, Tatsuhiro Chiba

Estimating power consumption in modern Cloud environments is essential for carbon quantification toward green computing. Specifically, it is important to properly account for the power consumed by each of the running applications, which are packaged as containers. This paper examines multiple challenges associated with this goal. The first challenge is that

Antigoni Polychroniadou, Gabriele Cipriani, Richard Hua, Tucker Balch

Banks publish daily a list of available securities/assets (axe list) to selected clients to help them effectively locate Long (buy) or Short (sell) trades at reduced financing rates. This reduces costs for the bank, as the list aggregates the bank's internal firm inventory per asset for all clients of long as well as short trades. However, this is somewhat p

Dandan Fan, Xiaofeng Gu, Huiqiu Lin

An $(a,b)$-biregular bipartite graph is a bipartite graph with bipartition $(X, Y)$ such that each vertex in $X$ has degree $a$ and each vertex in $Y$ has degree $b$. By the bipartite expander mixing lemma, biregular bipartite graphs have nice pseudorandom and expansion properties when the second largest adjacency eigenvalue is not large. In this paper, we p

Ian Doust, Anthony Weston

Associated to any finite metric space are a large number of objects and quantities which provide some degree of structural or geometric information about the space. In this paper we show that in the setting of subsets of weighted Hamming cubes there are unexpected relationships between many of these quantities. We obtain in particular formulas for the determ

Kyrie Zhixuan Zhou, Abhinav Choudhry, Ece Gumusel, Madelyn Rose Sanfilippo

Text-to-video generative AI models such as Sora OpenAI have the potential to disrupt multiple industries. In this paper, we report a qualitative social media analysis aiming to uncover people's perceived impact of and concerns about Sora's integration. We collected and analyzed comments (N=292) under popular posts about Sora-generated videos, comparison betw

Yexin Liu, Weiming Zhang, Athanasios V. Vasilakos, Lin Wang

Unsupervised visible-infrared person re-identification (UVI-ReID) has recently gained great attention due to its potential for enhancing human detection in diverse environments without labeling. Previous methods utilize intra-modality clustering and cross-modality feature matching to achieve UVI-ReID. However, there exist two challenges: 1) noisy pseudo labe

Yuka Hashizume, Li Li, Atsushi Miyashita, Tomoki Toda

To achieve a flexible recommendation and retrieval system, it is desirable to calculate music similarity by focusing on multiple partial elements of musical pieces and allowing the users to select the element they want to focus on. A previous study proposed using multiple individual networks for calculating music similarity based on each instrumental sound,

Haiying Huang, Adnan Darwiche

The unit selection problem aims to find objects, called units, that optimize a causal objective function which describes the objects' behavior in a causal context (e.g., selecting customers who are about to churn but would most likely change their mind if encouraged). While early studies focused mainly on bounding a specific class of counterfactual objective

Shashi Kant Gupta, Aditya Basu, Bradley Taylor, Anai Kothari

Retrieving information from EHR systems is essential for answering specific questions about patient journeys and improving the delivery of clinical care. Despite this fact, most EHR systems still rely on keyword-based searches. With the advent of generative large language models (LLMs), retrieving information can lead to better search and summarization capab

Brandon Morgan, Dean Hougen

A major contributor to the quality of a deep learning model is the selection of the optimizer. We propose a new dual-joint search space in the realm of neural optimizer search (NOS), along with an integrity check, to automate the process of finding deep learning optimizers. Our dual-joint search space simultaneously allows for the optimization of not only th

Simon Čopar, Žiga Kos

From incompressible flows to electrostatics, harmonic functions can provide solutions to many two-dimensional problems and, similarly, the director field of a planar nematic can be determined using complex analysis. We derive a closed-form solution for a quasi-steady state director field induced by an arbitrarily large set of point defects and circular inclu

Zhengtang Tan, Shouchuan Zhang

All quasi-affine connected Generalized Dynkin Diagram with rank $= 3$ and $2$ are found. All quasi-affine Nichols (Lie braided) algebras with rank $ 3$ and $2$ are also found.

Tsukasa Goto, Masayuki Sugeta, Takeshi Mizushima, Satoshi Fujimoto

We investigate teleportation interference associated with the non-local character of Majorana zero modes (MZMs) as a probe of MZMs focusing on the tolerance of teleportation against disturbances, such as inhomogeneous potentials at junctions and disorder. We develop a method for calculating non-local conductance in mesoscopic topological superconductors with

Tianming Cai, Guoying Zhao, Junbin Zang, Chen Zong

In recent years, the preliminary diagnosis of ADHD using EEG has attracted the attention from researchers. EEG, known for its expediency and efficiency, plays a pivotal role in the diagnosis and treatment of ADHD. However, the non-stationarity of EEG signals and inter-subject variability pose challenges to the diagnostic and classification processes. Topolog

Jae-Won Chung, Nishil Talati, Mosharaf Chowdhury

The "AI for Science, Energy, and Security" report from DOE outlines a significant focus on developing and optimizing artificial intelligence workflows for a foundational impact on a broad range of DOE missions. With the pervasive usage of artificial intelligence (AI) and machine learning (ML) tools and techniques, their energy efficiency is likely to become

Philip Anastassiou, Zhenyu Tang, Kainan Peng, Dongya Jia

We present VoiceShop, a novel speech-to-speech framework that can modify multiple attributes of speech, such as age, gender, accent, and speech style, in a single forward pass while preserving the input speaker's timbre. Previous works have been constrained to specialized models that can only edit these attributes individually and suffer from the following p

Abdel Magied Diab

The influence of non-extensive Tsallis statistics on the hadron phase structure has been investigated using the Polyakov-quark-meson (PQM) model. The analysis examines the non-extensive effects on the temperature dependence of PQM order parameters, thermodynamic quantities related to the QCD equation of state, and fluctuations of conserved charges at varying

Kehua Feng, Keyan Ding, Hongzhi Tan, Kede Ma

Reliable evaluation of large language models (LLMs) is impeded by two key challenges: objective metrics often fail to reflect human perception of natural language, and exhaustive human labeling is prohibitively expensive. Here, we propose a sample-efficient human evaluation method for LLMs based on the principle of MAximum Discrepancy (MAD) Competition. Our

Eva Maxfield Brown, Stephan Druskat, Laurent Hébert-Dufresne, James Howison

Despite the importance of scientific software for research, it is often not formally recognized and rewarded. This is especially true for foundational libraries, which are hidden below packages visible to the users (and thus doubly hidden, since even the packages directly used in research are frequently not visible in the paper). Research stakeholders like f

Namjung Kim, Dongseok Lee, Chanyoung Kim, Dosung Lee

Recent advancements in artificial intelligence (AI)-based design strategies for metamaterials have revolutionized the creation of customizable architectures spanning nano- to macro-scale dimensions, achieving unprecedented mechanical behaviors that surpass the inherent properties of the constituent materials. However, the growing complexity of these methods

Anna Wegmann, Tijs van den Broek, Dong Nguyen

Best practices for high conflict conversations like counseling or customer support almost always include recommendations to paraphrase the previous speaker. Although paraphrase classification has received widespread attention in NLP, paraphrases are usually considered independent from context, and common models and datasets are not applicable to dialog setti

Bowen Li, Brandon Van Over, Edwin K. P. Chong, Ali Pezeshki

We consider the celebrated bound introduced by Conforti and Cornu\'ejols (1984) for greedy schemes in submodular optimization. The bound assumes a submodular function defined on a collection of sets forming a matroid and is based on greedy curvature. We show that the bound holds for a very general class of string problems that includes maximizing submodular

Hailong Shu, Yue Wang, Weiwei Song, Huichuang Guo

The field of meteorological forecasting has undergone a significant transformation with the integration of large models, especially those employing deep learning techniques. This paper reviews the advancements and applications of these models in weather prediction, emphasizing their role in transforming traditional forecasting methods. Models like FourCastNe

Souradeep Ghosh, Sourav Bhattacharjee, Souvik Bandyopadhyay

We study the emergence and stability of a prethermal phase in an integrable many-body system subjected to a Fibonacci drive. Despite not being periodic, Fibonacci drives have been shown to introduce dynamical constraints due to their self-similar structure, unlike random driving protocols. From perturbative analysis, this has been argued to result in an expo

Xinfeng Li, Yuchen Yang, Jiangyi Deng, Chen Yan

Text-to-image (T2I) models, such as Stable Diffusion, have exhibited remarkable performance in generating high-quality images from text descriptions in recent years. However, text-to-image models may be tricked into generating not-safe-for-work (NSFW) content, particularly in sexually explicit scenarios. Existing countermeasures mostly focus on filtering ina

Yongquan Qu, Juan Nathaniel, Shuolin Li, Pierre Gentine

Robust integration of physical knowledge and data is key to improve computational simulations, such as Earth system models. Data assimilation is crucial for achieving this goal because it provides a systematic framework to calibrate model outputs with observations, which can include remote sensing imagery and ground station measurements, with uncertainty qua

Yu Ying Chiu, Liwei Jiang, Maria Antoniak, Chan Young Park

Frontier large language models (LLMs) are developed by researchers and practitioners with skewed cultural backgrounds and on datasets with skewed sources. However, LLMs' (lack of) multicultural knowledge cannot be effectively assessed with current methods for developing benchmarks. Existing multicultural evaluations primarily rely on expensive and restricted

Changsheng Chen, Yongyi Deng, Liangwei Lin, Zitong Yu

Document Presentation Attack Detection (DPAD) is an important measure in protecting the authenticity of a document image. However, recent DPAD methods demand additional resources, such as manual effort in collecting additional data or knowing the parameters of acquisition devices. This work proposes a DPAD method based on multi-modal disentangled traces (MMD

Walter Páez Gaviria

We consider a moduli space of lattice polarized K3 surfaces with the additional information of a frame of the trascendental cohomology with respect to the lattice polarization. This moduli space is proved to be quasi-affine, and the existence of vector fields on it, called modular vector fields, is proved. A purely algebraic version of the algebra of Siegel

Andrew S. Na, William Gao, Justin W. L. Wan

It is well known that training a denoising score-based diffusion models requires tens of thousands of epochs and a substantial number of image data to train the model. In this paper, we propose to increase the efficiency in training score-based diffusion models. Our method allows us to decrease the number of epochs needed to train the diffusion model. We acc

Matthew Belyakov, M. Ryleigh Davis, Zachariah Milby, Ian Wong

We use 1.4-4.6 micron multi-band photometry of the small inner Uranian and Neptunian satellites obtained with the James Webb Space Telescope's near-infrared imager NIRCam to characterize their surface compositions. We find that the satellites of the ice giants have, to first-order, similar compositions to one another, with a 3.0 micron absorption feature pos

M. J. Gerard, M. J. Pueschel, B. Geiger, R. J. J. Mackenbach

Using a novel optimization procedure it has been shown that the Helically Symmetric eXperiment (HSX) stellarator can be optimized for reduced trapped-electron-mode (TEM) instability [M.J.~Gerard et al., \textit{Nucl.~Fusion} \textbf{63} (2023) 056004]. Presently, with a set of 563 experimental candidate configurations, gyrokinetic simulations are performed t

Can Yaylali

We define derived versions of $F$-zips and associate a derived $F$-zip to any proper, smooth morphism of schemes in positive characteristic. We analyze the stack of derived $F$-zips and certain substacks. We make a connection to the classical theory and look at problems that arise when trying to generalize the theory to derived $G$-zips and derived $F$-zips

Nikhita Vedula, Giuseppe Castellucci, Eugene Agichtein, Oleg Rokhlenko

Conversational Task Assistants (CTAs) guide users in performing a multitude of activities, such as making recipes. However, ensuring that interactions remain engaging, interesting, and enjoyable for CTA users is not trivial, especially for time-consuming or challenging tasks. Grounded in psychological theories of human interest, we propose to engage users wi

Carlos Osorio Quero, Daniel Leykam, Irving Rondon Ojeda

Conventional deep learning-based image reconstruction methods require a large amount of training data which can be hard to obtain in practice. Untrained deep learning methods overcome this limitation by training a network to invert a physical model of the image formation process. Here we present a novel untrained Res-U2Net model for phase retrieval. We use t

Eudes Antonio Costa, Ronaldo Antônio Santos

The study examines the relationship between Ball's magic numbers and reverses divisors. These numbers are the source of beautiful and curious properties. Activities related to numbers can be a fun way to motivate mathematics students, while also enabling surprising analysis and connections.

Arnab Kundu

Gersten's injectivity conjecture for a functor $F$ of ``motivic type'', predicts that given a semilocal, ``non-singular'', integral domain $R$ with a fraction field $K$, the restriction morphism induces an injection of $F(R)$ inside $F(K)$. We prove two new cases of this conjecture for smooth algebras over valuation rings. Namely, we show that the higher alg

Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, Shantanu Acharya

The needle-in-a-haystack (NIAH) test, which examines the ability to retrieve a piece of information (the "needle") from long distractor texts (the "haystack"), has been widely adopted to evaluate long-context language models (LMs). However, this simple retrieval-based test is indicative of only a superficial form of long-context understanding. To provide a m

Hossein Rajoli, Sahand Khoshdel, Fatemeh Afghah, Xiaolong Ma

FlameFinder is a deep metric learning (DML) framework designed to accurately detect flames, even when obscured by smoke, using thermal images from firefighter drones during wildfire monitoring. Traditional RGB cameras struggle in such conditions, but thermal cameras can capture smoke-obscured flame features. However, they lack absolute thermal reference poin

Ekaterina Ponizovskaya Devine, Toshishige Yamada, Shih-Yuan Wang, M Saif Islam

The study proposes an ultra-thin back side illuminated (BSI) and top-illuminated, Ge on Si photodetector (PD), for 1 to 1.4 microns wavelength range. The Ge thickness of 350 nm allows us to achieve high-speed performance at >60 GHz, while the nanostructure at the bottom of the Ge layer helps to increase the optical absorption efficiency to above 80%. The BSI

Justin J. Burau, Kameron Mehling, Matthew D. Frye, Mengjie Chen

Efficient sub-Doppler laser cooling and optical trapping of YO molecules offer new opportunities to study collisional dynamics in the quantum regime. Confined in a crossed optical dipole trap, we achieve the highest phase-space density of $2.5 \times 10^{-5}$ for a bulk laser-cooled molecular sample. This sets the stage to study YO--YO collisions in the micr

Ashwin Murali, Tapomoy Guha Sarkar, Jayendra N. Bandyopadhyay

We consider a periodically driven system where the high-frequency driving protocol consists of a sequence of potentials switched on and off at different instants within a period. We explore the possibility of introducing an adiabatic modulation of the driving protocol by considering a slow evolution of the instants when the sequence of potentials is switched

Aser Abbas, Mauro Aimar, Brady R. Cox, Sebastiano Foti

The small-strain damping ratio plays a crucial role in assessing the response of soil deposits to earthquake-induced ground motions and general dynamic loading. The damping ratio can theoretically be inverted for after extracting frequency-dependent Rayleigh wave attenuation coefficients from wavefields collected during surface wave testing. However, determi

Philip Taranto, Patryk Lipka-Bartosik, Nayeli A. Rodríguez-Briones, Martí Perarnau-Llobet

Landauer's limit on heat dissipation during information erasure is critical as devices shrink, requiring optimal pure-state preparation to minimise errors. However, Nernst's third law states this demands infinite resources in energy, time, or control complexity. We address the challenge of cooling quantum systems with finite resources. Using Markovian collis

Dmitriy Bespalov, Sourav Bhabesh, Yi Xiang, Liutong Zhou

Recent NLP literature pays little attention to the robustness of toxicity language predictors, while these systems are most likely to be used in adversarial contexts. This paper presents a novel adversarial attack, \texttt{ToxicTrap}, introducing small word-level perturbations to fool SOTA text classifiers to predict toxic text samples as benign. ToxicTrap e

Spandan Dash, Matteo Brogi, Siddharth Gandhi, Marina Lafarga

Observations of cooler atmospheres of super-Earths and Neptune sized objects often show flat transmission spectra. The most likely cause of this trend is the presence of aerosols (i.e. clouds and hazes) in the atmospheres of such objects. High-resolution spectroscopy provides an opportunity to test this hypothesis by targeting molecular species whose spectra

Bernard J. Koch, David Peterson

Over the past decade, AI research has focused heavily on building ever-larger deep learning models. This approach has simultaneously unlocked incredible achievements in science and technology, and hindered AI from overcoming long-standing limitations with respect to explainability, ethical harms, and environmental efficiency. Drawing on qualitative interview

Samson Abramsky, Radha Jagadeesan

We develop a symmetric monoidal closed category of games, incorporating sums and products, to model quantum computation at higher types. This model is expressive, capable of representing all unitary operators at base types. It is compatible with base types and realizable by unitary operators.

Kaylee Burns, Ajinkya Jain, Keegan Go, Fei Xia

Large Language Models (LLMs) have been successful at generating robot policy code, but so far these results have been limited to high-level tasks that do not require precise movement. It is an open question how well such approaches work for tasks that require reasoning over contact forces and working within tight success tolerances. We find that, with the ri

Omid Ghahroodi, Marzia Nouri, Mohammad Vali Sanian, Alireza Sahebi

Evaluating Large Language Models (LLMs) is challenging due to their generative nature, necessitating precise evaluation methodologies. Additionally, non-English LLM evaluation lags behind English, resulting in the absence or weakness of LLMs for many languages. In response to this necessity, we introduce Khayyam Challenge (also known as PersianMMLU), a metic

Agustina Czenky, Julia Plavnik, Andrew Schopieray

Here we study bounds on the Frobenius-Schur exponent of spherical fusion categories based on their global dimension generalizing bounds from the representation theory of finite-dimensional quasi-Hopf algebras. Our main result is that if the Frobenius-Schur exponent of a modular fusion category is a prime power for some prime integer $p$, then it is bounded b

Wei Sun

We use the Jacobi theta function to give a representation of the modulus of the Riemann $\xi$ function. Based on this modulus representation, we show that the Riemann hypothesis is equivalent to the validity of a family of polynomial inequalities. We also present some preliminary results on the polynomial inequalities.

Yonggi Park, Yuanfang Ren, Benjamin Shickel, Ziyuan Guan

Background: The accurate prediction of postoperative complication risk using Electronic Health Records (EHR) and artificial intelligence shows great potential. Training a robust artificial intelligence model typically requires large-scale and diverse datasets. In reality, collecting medical data often encounters challenges surrounding privacy protection. Met

Michael P. Hennessey, Finnegan Wilson, Grace I. Rabinowitz, Max J. Sevcik

In this work, the design and execution of an experiment with the goal of demonstrating control over the evolution of a copper jet is described. Simulations show that when using simple multi-material buffers placed between a copper target with a conical defect and a cylinder of high-explosive, a variety of jetting behaviors occur based on material placement,

Arturo Martínez-Celis, Tomasz Żuchowski

Given a function $f \in \omega^\omega$, a set $A \in [\omega]^\omega$ is free for $f$ if $f[A] \cap A$ is finite. For a class of functions $\Gamma \subseteq \omega^{\omega}$, we define $\mathfrak{ros}_\Gamma$ as the smallest size of a family $\mathcal{A}\subseteq [\omega]^\omega$ such that for every $f\in\Gamma$ there is a set $A \in \mathcal{A}$ which is fr

Waqwoya Abebe, Jan Strube, Luanzheng Guo, Nathan R. Tallent

Image segmentation is a critical enabler for tasks ranging from medical diagnostics to autonomous driving. However, the correct segmentation semantics - where are boundaries located? what segments are logically similar? - change depending on the domain, such that state-of-the-art foundation models can generate meaningless and incorrect results. Moreover, in

Srikumar Sastry, Subash Khanal, Aayush Dhakal, Nathan Jacobs

We present GeoSynth, a model for synthesizing satellite images with global style and image-driven layout control. The global style control is via textual prompts or geographic location. These enable the specification of scene semantics or regional appearance respectively, and can be used together. We train our model on a large dataset of paired satellite ima

Daniel W. Crews, Iman A. M. Datta, Eric T. Meier, Uri Shumlak

The Kadomtsev pinch, namely the Z-pinch profile marginally stable to interchange modes, is revisited in light of observations from axisymmetric MHD modeling of the FuZE sheared-flow-stabilized Z-pinch experiment. We show that Kadomtsev's stability criterion, cleanly derived by the minimum energy principle but of opaque physical significance, has an intuitive

Marcell Szakály, Sebastian Köhler, Ivan Martinovic

Since its introduction in 2012, the Combined Charging System (CCS) has emerged as the leading technology for EV fast charging in Europe, North America and parts of Asia. The charging communication of CCS is defined by the ISO 15118 standards, which have been improved over the years. Most notably, in 2014, important security features such as Transport Layer S

Eckhard Platen

The paper introduces benchmark-neutral pricing and hedging for long-term contingent claims. It employs the growth optimal portfolio of the stocks as numeraire and the new benchmark-neutral pricing measure for pricing. For a realistic parsimonious model, this pricing measure turns out to be an equivalent probability measure, which is not the case for the risk

Goutam Mandal, Sudip Mishra, Abdulla Al Mamon, Sujay Kr. Biswas

This paper presents an investigation of cosmological dynamics of tachyon fluid coupled to varyingmass dark matter particles in the background of spatially flat FLRW universe. The mechanism of varying mass particles scenario assumes the mass of the dark matter depends on time t through the scalar field ${\phi}$ in the sense that the decaying of dark matter re

Nathan Cooper, Torsten Scholak

Large Language Models (LLMs) have become dominant in the Natural Language Processing (NLP) field causing a huge surge in progress in a short amount of time. However, their limitations are still a mystery and have primarily been explored through tailored datasets to analyze a specific human-level skill such as negation, name resolution, etc. In this paper, we

Brandon Morgan, Dean Hougen

Previous work in Neural Loss Function Search (NLFS) has shown a lack of correlation between smaller surrogate functions and large convolutional neural networks with massive regularization. We expand upon this research by revealing another disparity that exists, correlation between different types of image augmentation techniques. We show that different loss

Oliver Johnson, Lampros Gavalakis, Ioannis Kontoyiannis

Sharp, nonasymptotic bounds are obtained for the relative entropy between the distributions of sampling with and without replacement from an urn with balls of $c\geq 2$ colors. Our bounds are asymptotically tight in certain regimes and, unlike previous results, they depend on the number of balls of each colour in the urn. The connection of these results with

Francis Tsow, Tianze Chen, Yu Sun

A robot performing multi-object grasping needs to sense the number of objects in the hand after grasping. The count plays an important role in determining the robot's next move and the outcome and efficiency of the whole pick-place process. This paper presents a data-driven contrastive learning-based counting classifier with a modified loss function as a sim

Christina G. Taylor, Lucas C. Wilcox, Jesse Chan

Cut meshes are a type of mesh that is formed by allowing embedded boundaries to "cut" a simple underlying mesh resulting in a hybrid mesh of cut and standard elements. While cut meshes can allow complex boundaries to be represented well regardless of the mesh resolution, their arbitrarily shaped and sized cut elements can present issues such as the small cel

Lara Mahfouz, Vincent Maurel, Vincent Guipont, Basile Marchand

This study investigates failure mechanisms in a typical thermal barrier coating (TBC) system comprising an EB-PVD columnar top coat, an aluminide bond coat, and a Ni-based single crystal superalloy substrate, simulating gas turbine operating conditions using a burner rig. TBC degradation, initiated by interfacial defects from the LASAT method, was studied du

Alison A. Silva, Fabiano M. Andrade, Francesco Caravelli

The formation of metallic nanofilaments bridging two electrodes across an insulator is a mechanism for resistive switching. Examples of such phenomena include atomic synapses, which constitute a distinct class of memristive devices whose behavior is closely tied to the properties of the filament. Until recently, experimental investigation of the low-temperat

Matthew O. Withers, Chao-Lin Kuo

We propose a new haloscope geometry that can arbitrarily increase the resonator volume for a given target axion mass. This geometry consists of closely packed, overlapping coaxial cavities operating as a single resonator. While the resonant frequency is still determined by the dimensions of the individual "cells," the strong interactions between the cells en

Luis Nunes Vicente, Thaksheel Alleck, Tommaso Giovannelli, Roman Mitchell

In this paper, we examine team ball sports to investigate how the likelihood of weaker teams winning against stronger ones, referred to as underdog achievement, is influenced by inherent randomness factors that affect match outcomes in such sports. To address our research question, we collected data on match scores and computed corresponding team rankings fr

Madhu Gunasingam, Ting-Kam Leonard Wong

We derive explicitly the adapted $2$-Wasserstein distance between non-degenerate Gaussian distributions on $\mathbb{R}^N$ and characterize the optimal bicausal coupling(s). This leads to an adapted version of the Bures-Wasserstein distance on the space of positive definite matrices.

Lorenzo Maggi, Alois Herzog, Azra Zejnilagic, Christophe Grangeat

To mitigate Electromagnetic Fields (EMF) human exposure from base stations, international standards bodies define EMF emission requirements that can be translated into limits on the "actual" Equivalent Isotropic Radiated Power (EIRP), i.e., averaged over a sliding time window. We aim to enable base stations to adhere to these constraints while mitigating any

Tom Richmond, Eliza Wajch

A (generalized) topological space is called an iso-dense space if the set of all its isolated points is dense in the space. The main aim of the article is to show in $\mathbf{ZF}$ a new characterization of iso-dense spaces in terms of special quasiorders. For a non-empty family $\mathcal{A}$ of subsets of a set $X$, a quasiorder $\lesssim_{\mathcal{A}}$ on $

Dipam Goswami, Bartłomiej Twardowski, Joost van de Weijer

Few-shot class-incremental learning (FSCIL) aims to adapt the model to new classes from very few data (5 samples) without forgetting the previously learned classes. Recent works in many-shot CIL (MSCIL) (using all available training data) exploited pre-trained models to reduce forgetting and achieve better plasticity. In a similar fashion, we use ViT models

Jeongrok Yu, Seong Ug Kim, Jacob Choi, Jinho D. Choi

Bias is a disproportionate prejudice in favor of one side against another. Due to the success of transformer-based Masked Language Models (MLMs) and their impact on many NLP tasks, a systematic evaluation of bias in these models is needed more than ever. While many studies have evaluated gender bias in English MLMs, only a few works have been conducted for t

Yixu Chen, Zaixi Shang, Hai Wei, Yongjun Wu

In an adaptive bitrate streaming application, the efficiency of video compression and the encoded video quality depend on both the video codec and the quality metric used to perform encoding optimization. The development of such a quality metric need large scale subjective datasets. In this work we merge several datasets into one to support the creation of a

Jane Dwivedi-Yu, Raaz Dwivedi, Timo Schick

The accurate evaluation of differential treatment in language models to specific groups is critical to ensuring a positive and safe user experience. An ideal evaluation should have the properties of being robust, extendable to new groups or attributes, and being able to capture biases that appear in typical usage (rather than just extreme, rare cases). Relat

Dhruva Patel, Harry Desmond

Modern cosmology rests on the cosmological principle, that on large enough scales the Universe is both homogeneous and isotropic. A corollary is that galaxies' spin vectors should be isotropically distributed on the sky. This has been challenged by multiple authors for over a decade, with claims to have detected a statistically significant dipole pattern of

Vartan Choulakian, Jacques Allard, Ron Kenett

This is a case study, where Taxicab Correspondence Analysis reveals that the underlying structure of an extremely sparse binary textual data set can be represented by a binary tree, where the nodes representing clusters of words can be interpreted as topics. The textual data set represents Israel's Declaration of Independence text and 40 diverse Israeli Inte

Alexander P. Antonov, Lorenzo Caprini, Anton Ldov, Christian Scholz

Friction is central to the motion of active (self-propelled) objects such as bacteria, animals, and robots. While in a viscous fluid friction is described by Stokes's law, objects in contact with other solid bodies are often governed by more complex empirical friction laws. Here, we study active particles subject to Coulomb friction using a combination of ac

CMS Collaboration

This paper describes the COMBINE software package used for statistical analyses by the CMS Collaboration. The package, originally designed to perform searches for a Higgs boson and the combined analysis of those searches, has evolved to become the statistical analysis tool presently used in the majority of measurements and searches performed by the CMS Colla

Ayan Acharyya, Molly S. Peeples, Jason Tumlinson, Brian W. O'Shea

Gas-phase metallicity gradients are a crucial element in understanding the chemical evolution of galaxies. We use the FOGGIE simulations to study the metallicity gradients ($\nabla Z$) of six Milky Way-like galaxies throughout their evolution. FOGGIE galaxies generally exhibit steep negative gradients for most of their history, with only a few short-lived in