March 2024 arXiv papers — page 175

Taiki Shiotani, Takeshi Waki, Yoshikazu Tabata, Hiroyuki Nakamura

We have succeeded for the first time in synthesizing single crystals of nanolaminated borides (Fe$_{1-x}$Mn$_{x}$)$_2$AlB$_2$ in the entire Fe-Mn composition range using the Al self-flux method, and have established $T$-$x$, $H$-$T$ and three-dimensional $H$-$T$-$x$ magnetic phase diagrams from the results of magnetization measurements. The ferromagnetic cor

Yuanzhe Huang, Saurab Faruque, Minjie Wu, Akiko Mizuno

Traditional approaches in mental health research apply General Linear Models (GLM) to describe the longitudinal dynamics of observed psycho-behavioral measurements (questionnaire summary scores). Similarly, GLMs are also applied to characterize relationships between neurobiological measurements (regional fMRI signals) and perceptual stimuli or other regional

Yiming Meng, Taha Shafa, Jesse Wei, Melkior Ornik

In this paper, we present a novel method to drive a nonlinear system to a desired state, with limited a priori knowledge of its dynamic model: local dynamics at a single point and the bounds on the rate of change of these dynamics. This method synthesizes control actions by utilizing locally learned dynamics along a trajectory, based on data available up to

Yingrui Ji, Yao Zhu, Zhigang Li, Jiansheng Chen

In the dynamic realms of machine learning and deep learning, the robustness and reliability of models are paramount, especially in critical real-world applications. A fundamental challenge in this sphere is managing Out-of-Distribution (OOD) samples, significantly increasing the risks of model misclassification and uncertainty. Our work addresses this challe

Vahid Ahmadi Kalkhorani, DeLiang Wang

We introduce CrossNet, a complex spectral mapping approach to speaker separation and enhancement in reverberant and noisy conditions. The proposed architecture comprises an encoder layer, a global multi-head self-attention module, a cross-band module, a narrow-band module, and an output layer. CrossNet captures global, cross-band, and narrow-band correlation

Novan Fauzi Al Giffary, Feri Sulianta

The rapid development of information technology, especially the Internet, has facilitated users with a quick and easy way to seek information. With these convenience offered by internet services, many individuals who initially invested in gold and precious metals are now shifting into digital investments in form of cryptocurrencies. However, investments in c

Biswadeep Chakraborty, Beomseok Kang, Harshit Kumar, Saibal Mukhopadhyay

Recurrent Spiking Neural Networks (RSNNs) have emerged as a computationally efficient and brain-inspired learning model. The design of sparse RSNNs with fewer neurons and synapses helps reduce the computational complexity of RSNNs. Traditionally, sparse SNNs are obtained by first training a dense and complex SNN for a target task, and, then, pruning neurons

Sungho Kang, YeongHyeon Park, Hyunkyu Park, Juneho Yi

Scene depth estimation from paintings can streamline the process of 3D sculpture creation so that visually impaired people appreciate the paintings with tactile sense. However, measuring depth of oriental landscape painting images is extremely challenging due to its unique method of depicting depth and poor preservation. To address the problem of scene depth

Max Lamparth, Anthony Corso, Jacob Ganz, Oriana Skylar Mastro

To some, the advent of artificial intelligence (AI) promises better decision-making and increased military effectiveness while reducing the influence of human error and emotions. However, there is still debate about how AI systems, especially large language models (LLMs) that can be applied to many tasks, behave compared to humans in high-stakes military dec

Siqi Zhou, Ling Wang, Jie Liu, Jinshan Tang

Accurate and timely prediction of crop growth is of great significance to ensure crop yields and researchers have developed several crop models for the prediction of crop growth. However, there are large difference between the simulation results obtained by the crop models and the actual results, thus in this paper, we proposed to combine the simulation resu

Liuyi Wang, Zongtao He, Ronghao Dang, Huiyi Chen

Vision-and-Language Navigation (VLN) has gained significant research interest in recent years due to its potential applications in real-world scenarios. However, existing VLN methods struggle with the issue of spurious associations, resulting in poor generalization with a significant performance gap between seen and unseen environments. In this paper, we tac

Yasufumi Aita, Toru Araki

A subset $S$ of vertices in a graph $G$ is a secure total dominating set of $G$ if $S$ is a total dominating set of $G$ and, for each vertex $u \not\in S$, there is a vertex $v \in S$ such that $uv$ is an edge and $(S \setminus \{v\}) \cup \{u\}$ is also a total dominating set of $G$. We show that if $G$ is a maximal outerplanar graph of order $n$, then $G$

Afonso Mota, Stella Koch, Daniel Matthiae, Nuno Santos

Exoplanet habitability remains a challenging field due to the large distances separating Earth from other stars. Using insights from biology and astrophysics, we studied the habitability of M-dwarf exoplanets by modeling their surface temperature and flare UV and X-ray doses using the Martian atmosphere as a shielding model. Analyzing the Proxima Centauri an

Jingping Dong, Miao Gao, Xun-Wang Yan, Fengjie Ma

We propose two novel two-dimensional (2D) topological materials, (PtN$_4$)$_3$C$_{32}$ and (MnN$_4$)$_3$C$_{32}$, with a special geometry that we named as kagome-in-honeycomb (KIH) lattice structure, to illustrate the coexistence of the paradigmatic states of kagome physics, Dirac fermions and flat bands, that are difficult to be simultaneously observed in t

Sean Lamont, Michael Norrish, Amir Dezfouli, Christian Walder

Artificial Intelligence for Theorem Proving has given rise to a plethora of benchmarks and methodologies, particularly in Interactive Theorem Proving (ITP). Research in the area is fragmented, with a diverse set of approaches being spread across several ITP systems. This presents a significant challenge to the comparison of methods, which are often complex a

Francisco Ibarrola, Kazjon Grace

Quality and diversity have been proposed as reasonable heuristics for assessing content generated by co-creative systems, but to date there has been little agreement around what constitutes the latter or how to measure it. Proposed approaches for assessing generative models in terms of diversity have limitations in that they compare the model's outputs to a

Yong Li, Shiguang Shan

Facial action unit (AU) detection, aiming to classify AU present in the facial image, has long suffered from insufficient AU annotations. In this paper, we aim to mitigate this data scarcity issue by learning AU representations from a large number of unlabelled facial videos in a contrastive learning paradigm. We formulate the self-supervised AU representati

Christopher Reyes, Jeremy Sakstein

The secondary component of GW190814 has mass in the range $2.5$--$2.67{\rm M}_\odot$, placing it within the lower mass gap separating neutron stars from black holes. According to the predictions of general relativity and state-of-the-art nuclear equations of state, this object is too heavy to be a neutron star.~In this work, we explore the possibility that t

James Lynch, Ethan S. Wold, Jeff Gau, Simon Sponberg

Flying insects are thought to achieve energy-efficient flapping flight by storing and releasing elastic energy in their muscles, tendons, and thorax. However, flight systems consisting elastic elements coupled to nonlinear, unsteady aerodynamic forces also present possible challenges to generating steady and responsive wing motions. In previous work, we exam

Paula Maddigan, Andrew Lensen, Bing Xue

Genetic programming (GP) has the potential to generate explainable results, especially when used for dimensionality reduction. In this research, we investigate the potential of leveraging eXplainable AI (XAI) and large language models (LLMs) like ChatGPT to improve the interpretability of GP-based non-linear dimensionality reduction. Our study introduces a n

Naoki Miura, Hiroaki Funayama, Seiya Kikuchi, Yuichiroh Matsubayashi

This paper proposes the task of automatic assessment of Sentence Translation Exercises (STEs), that have been used in the early stage of L2 language learning. We formalize the task as grading student responses for each rubric criterion pre-specified by the educators. We then create a dataset for STE between Japanese and English including 21 questions, along

So Hirawata, Noriko Otani

This study proposes a system designed to enumerate the process of collaborative composition among humans, using automatic music composition technology. By integrating multiple Recurrent Neural Network (RNN) models, the system provides an experience akin to collaborating with several composers, thereby fostering diverse creativity. Through dynamic adaptation

Taro Suzuki

This paper presents an efficient method for updating particles in a particle filter (PF) to address the position estimation problem when dealing with sharp-peaked likelihood functions derived from multiple observations. Sharp-peaked likelihood functions commonly arise from millimeter-accurate distance observations of carrier phases in the global navigation s

L. Lotina, K. Nomura

We present an extensive study of hexadecapole correlations in the rare-earth region near $N=90$ and the effects these correlations have on various nuclear properties, such as the low-energy spectra, as well as quadrupole, hexadecapole, and monopole transition strengths. In order to examine hexadecapole correlations, we employ a mapped $sdg$ interacting boson

Yujin Lee, Jinyoung Kim, Byoung-cheol Koh, Young Soo Yoon

We developed machine learning algorithms for distinguishing scintillation signals from a plastic-liquid coupled detector known as a phoswich. The challenge lies in discriminating signals from organic scintillators with similar shapes and short decay times. Using a single-readout phoswich detector, we successfully identified $\gamma$ radiation signals from tw

Jiajia Li, Dong Chen, Xunyuan Yin, Zhaojian Li

Effective weed control plays a crucial role in optimizing crop yield and enhancing agricultural product quality. However, the reliance on herbicide application not only poses a critical threat to the environment but also promotes the emergence of resistant weeds. Fortunately, recent advances in precision weed management enabled by ML and DL provide a sustain

Sooran Kim, Mark S. Kaiser, Xiongtao Dai

Implementation of spatial generalized linear models with a functional covariate can be accomplished through the use of a truncated basis expansion of the covariate process. In practice, one must select a truncation level for use. We compare five criteria for the selection of an appropriate truncation level, including AIC and BIC based on a log composite like

Claudie Beaulieu, Colin Gallagher, Rebecca Killick, Robert Lund

The global mean surface temperature is widely studied to monitor climate change. A current debate centers around whether there has been a recent (post-1970s) surge/acceleration in the warming rate. This paper addresses whether an acceleration in the warming rate is detectable from a statistical perspective. We use changepoint models, which are statistical te

Mine Dogucu, Sinem Demirci, Harry Bendekgey, Federica Zoe Ricci

The presence of data science has been profound in the scientific community in almost every discipline. An important part of the data science education expansion has been at the undergraduate level. We conducted a systematic literature review to (1) portray current evidence and knowledge gaps in self-proclaimed undergraduate data science education research an

Marlon Brenes, Brett Min, Nicholas Anto-Sztrikacs, Nir Bar-Gill

Understanding the dynamics of dissipative quantum systems, particularly beyond the weak coupling approximation, is central to various quantum applications. While numerically exact methods provide accurate solutions, they often lack the analytical insight provided by theoretical approaches. In this study, we employ the recently-developed method dubbed the eff

Chen Yuhua

This article investigates deep learning methodologies for single-modality clinical data analysis, as a crucial precursor to multi-modal medical research. Building on Guo JingYuan's work, the study refines clinical data processing through Compact Convolutional Transformer (CCT), Patch Up, and the innovative CamCenterLoss technique, establishing a foundation f

Luisa Watkins, Pietro Stinco, Alessandra Tesei, Florian Meyer

We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-di

Shao-Hsuan Wu, Yiyao Zhang, Ke Chen, Shang Hua Yang

Sub-terahertz (Sub-THz) waves possess exceptional attributes, capable of penetrating non-metallic and non-polarized materials while ensuring bio-safety. However, their practicality in imaging is marred by the emergence of troublesome speckle artifacts, primarily due to diffraction effects caused by wavelengths comparable to object dimensions. In addressing t

Guangyao Chen, Peixi Peng, Yangru Huang, Mengyue Geng

One important desideratum of lifelong learning aims to discover novel classes from unlabelled data in a continuous manner. The central challenge is twofold: discovering and learning novel classes while mitigating the issue of catastrophic forgetting of established knowledge. To this end, we introduce a new paradigm called Adaptive Discovering and Merging (AD

Sanja Rukavina, Vladimir D. Tonchev

The parameters 2-(36,15,6) are the smallest parameters of symmetric designs for which a complete classification up to isomorphism is yet unknown. Bouyukliev, Fack and Winne classified all 2-$(36,15,6)$ designs that admit an automorphism of odd prime order, and gave a partial classification of such designs that admit an automorphism of order 2. In this paper,

MD Kamran Chowdhury Shisher, Yin Sun

In this paper, we analyze the monotonicity of information aging in a remote estimation system, where historical observations of a Gaussian autoregressive AR(p) process are used to predict its future values. We consider two widely used loss functions in estimation: (i) logarithmic loss function for maximum likelihood estimation and (ii) quadratic loss functio

A. Posses, M. Aravena, J. González-López, N. M. Förster Schreiber

The ALMA [CII] Resolved Ism in STar-forming gALaxies (CRISTAL) survey is a Cycle 8 ALMA Large Programme that studies the cold gas component of high-redshift galaxies. Its sub-arcsecond resolution observations are key to disentangling physical mechanisms that shape galaxies during cosmic dawn. In this paper, we explore the morphology and kinematics of the col

Omar Rodríguez-Tzompantzi

General exotic bi-gravity, obtained in Ozkan et al. (Phys Rev Lett 123(3):031303, 2019), is a unitary parity-preserving model which describes two interacting spin-two fields in three-dimensional spacetime. Adopting a symplectic viewpoint, we investigate the dynamical structure of general exotic bi-gravity theory. In particular, by exploiting the properties o

Enrique Saurez, Joshua Fried, Gohar Irfan Chaudhry, Esha Choukse

This report explores the use of kernel-bypass networking in FaaS runtimes and demonstrates how using Junction, a novel kernel-bypass system, as the backend for executing components in faasd can enhance performance and isolation. Junction achieves this by reducing network and compute overheads and minimizing interactions with the host operating system. Juncti

Aadesh Madnaik, N. Cameron Matson, Karthikeyan Sundaresan

Mobile networks have increased spectral efficiency through advanced multiplexing strategies that are coordinated by base stations (BS) in licensed spectrum. However, external interference on clients leads to significant performance degradation during dynamic (unlicensed) spectrum access (DSA). We introduce the notion of network tomography for DSA, whereby cl

Zewei Tian, Min Sun, Alex Liu, Shawon Sarkar

This paper explores the transformative potential of computer-assisted textual analysis in enhancing instructional quality through in-depth insights from educational artifacts. We integrate Richard Elmore's Instructional Core Framework to examine how artificial intelligence (AI) and machine learning (ML) methods, particularly natural language processing (

Ruichen Ma, Guanchao Qiao, Yian Liu, Liwei Meng

Binary neural networks utilize 1-bit quantized weights and activations to reduce both the model's storage demands and computational burden. However, advanced binary architectures still incorporate millions of inefficient and nonhardware-friendly full-precision multiplication operations. A&B BNN is proposed to directly remove part of the multiplication op

GuanWen Qiu, Da Kuang, Surbhi Goel

Existing research often posits spurious features as easier to learn than core features in neural network optimization, but the impact of their relative simplicity remains under-explored. Moreover, studies mainly focus on end performance rather than the learning dynamics of feature learning. In this paper, we propose a theoretical framework and an associated

Gaston Giribet

The relation between celestial holography and Liouville field theory is investigated. It is shown that duality relations between different Selberg type integrals appearing in the Coulomb gas realization of Liouville correlation functions induce a series of relations between celestial amplitudes with shifted values of the operators dimensions $\Delta $. This

Jay Patrikar, Joao Dantas, Brady Moon, Milad Hamidi

We introduce TartanAviation, an open-source multi-modal dataset focused on terminal-area airspace operations. TartanAviation provides a holistic view of the airport environment by concurrently collecting image, speech, and ADS-B trajectory data using setups installed inside airport boundaries. The datasets were collected at both towered and non-towered airfi

Naoyuki Hirata

The origin and early evolution of the Jovian moon Ganymede, known to have an internal ocean, have garnered considerable interest in the field of origin of satellites and life. Ganymede has an ancient impact structure, called a furrow system. The furrow system is the largest impact structures in the outer solar system and the impact should have significantly

Anand Krishnan, Xingjian Yang, Utsav Seth, Jonathan M. Jeyachandran

Hand-intensive manufacturing processes, such as composite layup and textile draping, require significant human dexterity to accommodate task complexity. These strenuous hand motions often lead to musculoskeletal disorders and rehabilitation surgeries. We develop a data-driven ergonomic risk assessment system with a special focus on hand and finger activity t

Changan Chen, Rui Wang, Christoph Vogel, Marc Pollefeys

In this paper we propose an efficient data-driven solution to self-localization within a floorplan. Floorplan data is readily available, long-term persistent and inherently robust to changes in the visual appearance. Our method does not require retraining per map and location or demand a large database of images of the area of interest. We propose a novel pr

Ziqi Pi, Giovanni Zocchi

Action potential generation underlies some of the most consequential dynamical systems on Earth, from brains to hearts. It is therefore interesting to develop synthetic cell-free systems, based on the same molecular mechanisms, which may allow for the exploration of parameter regions and phenomena not attainable, or not apparent, in the live cell. We previou

Samuel Kim, Min Sang Kim

The effectiveness of clopidogrel, a widely used antiplatelet medication, varies significantly among individuals, necessitating the development of precise predictive models to optimize patient care. In this study, we leverage federated learning strategies to address clopidogrel treatment failure detection. Our research harnesses the collaborative power of mul

Austin Adams, Ciamac C. Moallemi, Sara Reynolds, Dan Robinson

Automated market makers (AMMs) have emerged as the dominant market mechanism for trading on decentralized exchanges implemented on blockchains. This paper presents a single mechanism that targets two important unsolved problems for AMMs: reducing losses to informed orderflow, and maximizing revenue from uninformed orderflow. The ``auction-managed AMM'' works

Humberto Híjar

Liquid crystals are the prototype of the so-called soft condensed matter. In simple terms they are "structured liquids" that historically have received a lot of interest because they help to generate new concepts and knowledge in Physics, and possess important electro--optical applications, e. g., in displays of mobile computers and telephones. More recently

Kevin Purbhoo

We consider three forms of composition of matroids, each of which extends the category of bimatroids to a rigid monoidal category. Many well-known constructions are functorial or defined by morphisms in these categories. Motivating examples include: deletion and contraction, 2-sum, series and parallel connections, the Tutte polynomial, gammoids, positroids,

Steven Crisostomo, E. K. U. Gross, Kieron Burke

DFT calculations yield useful ground-state energies and densities, while Green's function techniques (such as $GW$) are mostly used to produce spectral functions. From the Galitskii-Migdal formula, we extract the exchange-correlation of DFT directly from a Green's function. This spectral representation provides an alternative to the fluctuation-dissipation t

Len Bos, Michael A. Slawinski, Raphaël A. Slawinski, Theodore Stanoev

We prove that, given an average power, the ascent time is minimized if a cyclist maintains a constant ground speed regardless of the slope. Herein, minimizing the time is equivalent to maximizing -- for a given uphill -- the corresponding mean ascent velocity (VAM: velocit\`a ascensionale media), which is a common training metric. We illustrate the proof wit

Aaron Mishkin, Alberto Bietti, Robert M. Gower

We study level set teleportation, an optimization routine which tries to accelerate gradient descent (GD) by maximizing the gradient norm over a level set of the objective. While teleportation intuitively speeds-up GD via bigger steps, current work lacks convergence theory for convex functions, guarantees for solving the teleportation operator, and even clea

Baran Hashemi

Simulating ultra-high-granularity detector responses in Particle Physics represents a critical yet computationally demanding task. This thesis aims to overcome this challenge for the Pixel Vertex Detector (PXD) at the Belle II experiment, which features over 7.5M pixel channels-the highest spatial resolution detector simulation dataset ever analysed with gen

Amaury Gouverneur, Borja Rodríguez-Gálvez, Tobias J. Oechtering, Mikael Skoglund

This paper studies the Bayesian regret of a variant of the Thompson-Sampling algorithm for bandit problems. It builds upon the information-theoretic framework of [Russo and Van Roy, 2015] and, more specifically, on the rate-distortion analysis from [Dong and Van Roy, 2020], where they proved a bound with regret rate of $O(d\sqrt{T \log(T)})$ for the $d$-dime

Mengyuan Li, Shashvat Srivastava, Mengjia Yan

Trusted I/O (TIO) is an appealing solution to improve I/O performance for confidential VMs (CVMs), with the potential to eliminate broad sources of I/O overhead. However, this paper emphasizes that not all types of I/O can derive substantial benefits from TIO, particularly network I/O. Given the obligatory use of encryption protocols for network traffic in C

Jordan Poots

Autonomous parallel-style on-ramp merging in human controlled traffic continues to be an existing issue for autonomous vehicle control. Existing non-learning based solutions for vehicle control rely on rules and optimization primarily. These methods have been seen to present significant challenges. Recent advancements in Deep Reinforcement Learning have show

Intekhab Hossain, Jonas Fischer, Rebekka Burkholz, John Quackenbush

The practical utility of machine learning models in the sciences often hinges on their interpretability. It is common to assess a model's merit for scientific discovery, and thus novel insights, by how well it aligns with already available domain knowledge--a dimension that is currently largely disregarded in the comparison of neural network models. While pr

Eric Kleinherbers, Yaroslav Tserkovnyak

Motivated by the recently discovered magnonic Hanle effect in an insulating antiferromagnet [Wimmer et al., Phys. Rev. Lett. 125, 247204 (2020)], we develop a spin transport theory based on low-energy waves of antiferromagnetic N\'eel order. These waves have two polarizations, which we describe in analogy to optics using the Stokes vector on the Poincar\'e s

Mercy Asiedu, Awa Dieng, Iskandar Haykel, Negar Rostamzadeh

With growing application of machine learning (ML) technologies in healthcare, there have been calls for developing techniques to understand and mitigate biases these systems may exhibit. Fair-ness considerations in the development of ML-based solutions for health have particular implications for Africa, which already faces inequitable power imbalances betwee

Mary Alberg, Gerald A. Miller

The relation between the pion's quark distribution function, $q(x)$, its light-front wave function, and the elastic charge form factor, $F(\Delta^2)$ is explored. The square of the leading-twist pion wave function at a special probe scale, $\zeta_H$, is determined using models and Poincare covariance from realistic results for $q(x)$. This wave function is t

David Wittwer, Felix Tamke

Many production processes require the cooperation of various resources. Especially when using expensive machines, their utilization plays a decisive role in efficient production. In agricultural production or civil construction processes, e.g., harvesting or road building, the machines are typically mobile, and synchronization of different machine types is r

Víctor A. Vicente-Benítez

We develop the theory for the Bergman spaces of generalized $L_p$-solutions of the bicomplex-Vekua equation $\overline{\boldsymbol{\partial}}W=aW+b\overline{W}$ on bounded domains, where the coefficients $a$ and $b$ are bounded bicomplex-valued functions. We study the completeness of the Bergman space, the regularity of the solutions, and the boundedness of

Rui Wang, Yuesheng Xu, Mingsong Yan

This paper introduces a hypothesis space for deep learning based on deep neural networks (DNNs). By treating a DNN as a function of two variables - the input variable and the parameter variable - we consider the set of DNNs where the parameter variable belongs to a space of weight matrices and biases determined by a prescribed depth and layer widths. To cons

J. W. Burby, N. Kallinikos, R. S. MacKay, D. Perrella

We solve "half" the problem of finding three-dimensional quasisymmetric magnetic fields that do not necessarily satisfy force balance. This involves determining which hidden symmetries are admissible as quasisymmetries, and then showing explicitly how to construct quasisymmetric magnetic fields given an admissible symmetry. The admissibility conditions take

J. Krause, G. Marchegiani, L. M. Janssen, G. Catelani

Recent research shows that quasiparticle-induced decoherence of superconducting qubits depends on the superconducting-gap asymmetry originating from the different thicknesses of the top and bottom films in Al/AlO$_x$/Al junctions. Magnetic field is a key tuning knob to investigate this dependence as it can change the superconducting gaps in situ. We present

Jacob M. Leamer, Alicia B. Magann, Denys I. Bondar, Gerard McCaul

We introduce a constructive method for mapping non-unitary dynamics to a weighted set of unitary operations. We utilize this construction to derive a new correspondence between real and imaginary time, which we term Imaginary Time Quantum Dynamical Emulation (ITQDE). This correspondence enables an imaginary time evolution to be constructed from the overlaps

Ganesh Babu, Aoife Gowen, Michael Fop, Isobel Claire Gormley

The use of hyperspectral imaging to investigate food samples has grown due to the improved performance and lower cost of instrumentation. Food engineers use hyperspectral images to classify the type and quality of a food sample, typically using classification methods. In order to train these methods, every pixel in each training image needs to be labelled. T

Xin Chen, Hanxian Huang, Yanjun Gao, Yi Wang

Knowledge distillation, the technique of transferring knowledge from large, complex models to smaller ones, marks a pivotal step towards efficient AI deployment. Distilling Step-by-Step~(DSS), a novel method utilizing chain-of-thought~(CoT) distillation, has demonstrated promise by imbuing smaller models with the superior reasoning capabilities of their larg

G. Oliveira-Neto, T. M. Abreu

In the present work, we study the dynamical evolution of an homogeneous and anisotropic, noncommutative (NC) Bianchi I (BI) model coupled to a radiation perfect fluid. Our first motivation is determining if the present model tends to an homogeneous and isotropic NC Friedmann-Robertson-Walker (FRW) model, during its evolution. In order to simplify our task, w

Alex Glinsky, Alexey Sokolsky

It is evident that, currently, generative models are surpassed in quality by human professionals. However, with the advancements in Artificial Intelligence, this gap will narrow, leading to scenarios where individuals who have dedicated years of their lives to mastering a skill become obsolete due to their high costs, which are inherently linked to the time

Yuan Gao, Kunyu Shi, Pengkai Zhu, Edouard Belval

We propose Strongly Supervised pre-training with ScreenShots (S4) - a novel pre-training paradigm for Vision-Language Models using data from large-scale web screenshot rendering. Using web screenshots unlocks a treasure trove of visual and textual cues that are not present in using image-text pairs. In S4, we leverage the inherent tree-structured hierarchy o

Ellise Parnoff, A. Raghuram

Ramanujan showed that $\tau(p) \equiv p^{11}+1 \pmod{691}$, where $\tau(n)$ is the $n$-th Fourier coefficient of the unique normalized cusp form of weight $12$ and full level, and the prime $691$ appears in the numerator of $\zeta(12)/\pi^{12}$ for the Riemann zeta function $\zeta(s)$. Searching for such congruences, it is shown that the prime $67$ appears i

Tina Vartziotis, Ippolyti Dellatolas, George Dasoulas, Maximilian Schmidt

The increasing use of information technology has led to a significant share of energy consumption and carbon emissions from data centers. These contributions are expected to rise with the growing demand for big data analytics, increasing digitization, and the development of large artificial intelligence (AI) models. The need to address the environmental impa

Antonios Alexos, Pierre Baldi

The generation of natural and high-quality speech from text is a challenging problem in the field of natural language processing. In addition to speech generation, speech editing is also a crucial task, which requires the seamless and unnoticeable integration of edited speech into synthesized speech. We propose a novel approach to speech editing by leveragin

Willem Veys

The monodromy conjecture is a mysterious open problem in singularity theory. Its original version relates arithmetic and topological/geometric properties of a multivariate polynomial $f$ over the integers, more precisely, poles of the $p$-adic Igusa zeta function of $f$ should induce monodromy eigenvalues of $f$. The case of interest is when the zero set of

Pierre Deligne, A. Raghuram

Given a pure motive $M$ over $\mathbb{Q}$ with a multilinear algebraic structure $\mathsf{s}$ on $M$, and given a representation $V$ of the group respecting $\mathsf{s}$, we describe a functorial transfer $M^V$. We formulate a criterion that guarantees when the two periods of $M^V$ are equal. This has an implication for the critical values of the $L$-functio

Alexander Zuyev, Victoria Grushkovskaya

We consider a kinematic model of a controlled car with two trailers by assuming that each trailer is attached at some distance from the preceding axle ("off-hooked trailers"). For this model, we derive the transformation towards privileged coordinates and present the corresponding nilpotent quasihomogeneous approximate system. The components of this nilpoten

Nathan Goldman, Tomoki Ozawa

This pedagogical piece provides a surprisingly simple demonstration that the quantized Hall conductivity of correlated insulators is given by the many-body Chern number, a topological invariant defined in the space of twisted boundary conditions. In contrast to conventional proofs, generally based on the Kubo formula, our approach entirely relies on combinin

Jose Paolo Talusan, Chaeeun Han, Ayan Mukhopadhyay, Aron Laszka

Public bus transit systems provide critical transportation services for large sections of modern communities. On-time performance and maintaining the reliable quality of service is therefore very important. Unfortunately, disruptions caused by overcrowding, vehicular failures, and road accidents often lead to service performance degradation. Though transit a

Sofia P. Corbà, Lorenzo Sorbo

The scalar and tensor fluctuations generated during inflation can be correlated, if arising from the same underlying mechanism. In this paper we investigate such correlation in the model of axion inflation, where the rolling inflaton produces quanta of a $U(1)$ gauge field which, in turn, source scalar and tensor fluctuations. We compute the primordial corre

Laxman Dhulipala, George Z. Li

We introduce a new notion of neighboring databases for coverage problems such as Max Cover and Set Cover under differential privacy. In contrast to the standard privacy notion for these problems, which is analogous to node-privacy in graphs, our new definition gives a more fine-grained privacy guarantee, which is analogous to edge-privacy. We illustrate seve

Joseph Gatto, Madhusudan Basak, Yash Srivastava, Philip Bohlman

In this paper, we develop an LLM-powered framework for the curation and evaluation of emerging opinion mining in online health communities. We formulate emerging opinion mining as a pairwise stance detection problem between (title, comment) pairs sourced from Reddit, where post titles contain emerging health-related claims on a topic that is not predefined.

Hanwen Wang, Theinmozhi Arulraj, Alberto Ippolito, Aleksander S. Popel

Virtual patients and digital patients/twins are two similar concepts gaining increasing attention in health care with goals to accelerate drug development and improve patients' survival, but with their own limitations. Although methods have been proposed to generate virtual patient populations using mechanistic models, there are limited number of application

Iain J. Cruickshank, Amir Soofi, Lynnette Hui Xian Ng

Public opinion of military organizations significantly influences their ability to recruit talented individuals. As recruitment efforts increasingly extend into digital spaces like social media, it becomes essential to assess the stance of social media users toward online military content. However, there is a notable lack of data for analyzing opinions on mi

Dennis Grinwald, Philipp Wiesner, Shinichi Nakajima

Statistical heterogeneity in federated learning poses two major challenges: slow global training due to conflicting gradient signals, and the need of personalization for local distributions. In this work, we tackle both challenges by leveraging recent advances in \emph{linear mode connectivity} -- identifying a linearly connected low-loss region in the param

Alessio Amaolo, Pengning Chao, Thomas J. Maldonado, Sean Molesky

We present a method for deriving limits on Raman scattering in structured media and exploit it to constrain the maximum Raman signal resulting from a planewave incident on either a single Raman molecule in the vicinity of a structured medium or a designable Raman medium. Results pertaining to metallic and dielectric structures illustrate the importance of ac

Vinit Ranjan, Bartolomeo Stellato

We introduce a numerical framework to verify the finite step convergence of first-order methods for parametric convex quadratic optimization. We formulate the verification problem as a mathematical optimization problem where we maximize a performance metric (e.g., fixed-point residual at the last iteration) subject to constraints representing proximal algori

Irene Drelichman, Ricardo G. Duran

We prove the stability in weighted $W^{1,1}$ spaces for standard finite element approximations of the Poisson equation in convex polygonal or polyhedral domains, when the weight belongs to Muckenhoupt's class $A_1$ and the family of meshes is quasi-uniform.

Fernanda Gutiérrez, Bernardo Subercaseaux

Conferences such as FUN with Algorithms routinely buy goodies (e.g., t-shirts, coffee mugs, etc) for their attendees. Often, said goodies come in different types, varying by color or design, and organizers need to decide how many goodies of each type to buy. We study the problem of buying optimal amounts of each type under a simple model of preferences by th

Pratiksha Thaker, Yash Maurya, Shengyuan Hu, Zhiwei Steven Wu

Recent work has demonstrated that finetuning is a promising approach to 'unlearn' concepts from large language models. However, finetuning can be expensive, as it requires both generating a set of examples and running iterations of finetuning to update the model. In this work, we show that simple guardrail-based approaches such as prompting and filtering can

Lingbo Liu

Analyzing spatially varying effects is pivotal in geographic analysis. However, accurately capturing and interpreting this variability is challenging due to the increasing complexity and non-linearity of geospatial data. Recent advancements in integrating Geographically Weighted (GW) models with artificial intelligence (AI) methodologies offer novel approach

Jonah Burian

This paper analyzes the Execution Tickets proposal on Ethereum Research, unveiling its potential to revolutionize the Ethereum blockchain's economic model. At the core of this proposal lies a novel ticketing mechanism poised to redefine how the Ethereum protocol distributes the value associated with proposing execution payloads. This innovative approach enab

Anastasia Shlentsova, Freek Roelofs, Sara Issaoun, Jordy Davelaar

The concept of a new space very long baseline interferometry system named the Event Horizon Imager (EHI) has been proposed to dramatically improve black hole imaging and provide precise tests of the theory of general relativity. We investigate the ability to make high-resolution movies of the black hole shadow and jet launching region around the supermassive

Chang Liu, Fuxin Fan, Annette Schwarz, Andreas Maier

Multi-organ segmentation in medical images is a widely researched task and can save much manual efforts of clinicians in daily routines. Automating the organ segmentation process using deep learning (DL) is a promising solution and state-of-the-art segmentation models are achieving promising accuracy. In this work, We proposed a novel data augmentation strat

Björn Benneke, Pierre-Alexis Roy, Louis-Philippe Coulombe, Michael Radica

Even though sub-Neptunes likely represent the most common outcome of planet formation, their natures remain poorly understood. In particular, planets near 1.5-2.5$\,R_\oplus$ often have bulk densities that can be explained equally well with widely different compositions and interior structures, resulting in grossly divergent implications for their formation.

Mengyu Yao, Martin Gutierrez-Amigo, Subhajit Roychowdhury, Ion Errea

The interplay between topology and superconductivity in quantum materials harbors rich physics ripe for discovery. In this study, we investigate the topological properties and superconductivity of the nonsymmorphic chiral superconductor NbGe$_2$ using high-resolution angle-resolved pho-toemission spectroscopy (ARPES), transport measurements, and ab initio ca