December 2023 arXiv papers — page 79

Longchao Da, Porter Jenkins, Trevor Schwantes, Jeffrey Dotson

In practice, it is essential to compare and rank candidate policies offline before real-world deployment for safety and reliability. Prior work seeks to solve this offline policy ranking (OPR) problem through value-based methods, such as Off-policy evaluation (OPE). However, they fail to analyze special cases performance (e.g., worst or best cases), due to t

Ridwan Alam, Aaron Aguirre, Collin Stultz

For a number of antiarrhythmics, drug loading requires a 3 day hospitalization with monitoring for QT prolongation. Automated QT monitoring with wearable ECG monitors would facilitate out-of-hospital care. We develop a deep learning model that infers QT intervals from ECG lead-I - the lead most often acquired from ambulatory ECG monitors - and to use this mo

Xuan Long Do, Mohammad Hassanpour, Ahmed Masry, Parsa Kavehzadeh

A number of tasks have been proposed recently to facilitate easy access to charts such as chart QA and summarization. The dominant paradigm to solve these tasks has been to fine-tune a pretrained model on the task data. However, this approach is not only expensive but also not generalizable to unseen tasks. On the other hand, large language models (LLMs) hav

Kirill Shulga

We examine a two-qubit system influenced by a time-periodic external field while interacting with a Markovian bath. This scenario significantly impacts the temporal coherence characteristics of the system. By solving the evolution equation for the density matrix operator, we determine the characteristic equilibration time and analyze the concurrence paramete

Shreya Ghosh, Abu Shafin Mohammad Mahdee Jameel, Aly El Gamal

In this paper, we explore transferability in learning between different attack classes in a network intrusion detection setup. We evaluate transferability of attack classes by training a deep learning model with a specific attack class and testing it on a separate attack class. We observe the effects of real and synthetically generated data augmentation tech

Li-Chang Hung

We generalize the Li-Yau inequality for second derivatives and we also establish Li-Yau type inequality for fourth derivatives. Our derivation relies on the representation formula for the heat equation.

Qihang Zhou, Shibo He, Haoyu Liu, Jiming Chen

Anomaly detection in multivariate time series (MTS) has been widely studied in one-class classification (OCC) setting. The training samples in OCC are assumed to be normal, which is difficult to guarantee in practical situations. Such a case may degrade the performance of OCC-based anomaly detection methods which fit the training distribution as the normal d

Vikramjit Mitra, Jingping Nie, Erdrin Azemi

Representations derived from models such as BERT (Bidirectional Encoder Representations from Transformers) and HuBERT (Hidden units BERT), have helped to achieve state-of-the-art performance in dimensional speech emotion recognition. Despite their large dimensionality, and even though these representations are not tailored for emotion recognition tasks, they

Jingwen Zhang, Zikun Zhou, Guangming Lu, Jiandong Tian

3D single object tracking remains a challenging problem due to the sparsity and incompleteness of the point clouds. Existing algorithms attempt to address the challenges in two strategies. The first strategy is to learn dense geometric features based on the captured sparse point cloud. Nevertheless, it is quite a formidable task since the learned dense geome

Yangfan Zhang, Yun Yang

This article considers Bayesian model selection via mean-field (MF) variational approximation. Towards this goal, we study the non-asymptotic properties of MF inference under the Bayesian framework that allows latent variables and model mis-specification. Concretely, we show a Bernstein von-Mises (BvM) theorem for the variational distribution from MF under p

Elena Caceres, Rafael Carrasco, Vaishnavi Patil

Recently, an infinite class of holographic generalized complexities was proposed. These gravitational observables display the behavior required to be duals of complexity, in particular, linear growth at late times and switchback effect. In this work, we aim to understand generalized complexities in the framework of Lorentzian threads. We reformulate the prob

Jonah Casebeer, Junkai Wu, Paris Smaragdis

Adaptive filters (AFs) are vital for enhancing the performance of downstream tasks, such as speech recognition, sound event detection, and keyword spotting. However, traditional AF design prioritizes isolated signal-level objectives, often overlooking downstream task performance. This can lead to suboptimal performance. Recent research has leveraged meta-lea

Guang Yang, Jie Li, Xinbo Gao

Multi-exposure image fusion aims to generate a single high-dynamic image by integrating images with different exposures. Existing deep learning-based multi-exposure image fusion methods primarily focus on spatial domain fusion, neglecting the global modeling ability of the frequency domain. To effectively leverage the global illumination modeling ability of

David Noever, Matt Ciolino

Using a novel professional certification survey, the study focuses on assessing the vocational skills of two highly cited AI models, GPT-3 and Turbo-GPT3.5. The approach emphasizes the importance of practical readiness over academic performance by examining the models' performances on a benchmark dataset consisting of 1149 professional certifications. This s

Srikumar Ramalingam, Pranjal Awasthi, Sanjiv Kumar

The success of deep learning hinges on enormous data and large models, which require labor-intensive annotations and heavy computation costs. Subset selection is a fundamental problem that can play a key role in identifying smaller portions of the training data, which can then be used to produce similar models as the ones trained with full data. Two prior me

Vidya Prasad, Chen Zhu-Tian, Anna Vilanova, Hanspeter Pfister

Diffusion models have garnered significant attention since they can effectively learn complex multivariate Gaussian distributions, resulting in diverse, high-quality outcomes. They introduce Gaussian noise into training data and reconstruct the original data iteratively. Central to this iterative process is a single Unet, adapting across time steps to facili

Hee Sok Chung

We determine color-octet nonrelativistic QCD matrix elements for quarkonium decays from moments of the two-point correlation function of the QCD field-strength tensor computed in the refined Gribov-Zwanziger theory. We find that a tree-level calculation in the refined Gribov-Zwanziger theory can give a suitable description of the QCD field-strength correlati

Yixin Zhang, Shen Zhao, Hanxue Gu, Maciej A. Mazurowski

Deep neural networks (DNNs) have demonstrated exceptional performance across various image segmentation tasks. However, the process of preparing datasets for training segmentation DNNs is both labor-intensive and costly, as it typically requires pixel-level annotations for each object of interest. To mitigate this challenge, alternative approaches such as us

Saurav Sagar, Mohammed Javed, David S Doermann

The agricultural sector plays an essential role in the economic growth of a country. Specifically, in an Indian context, it is the critical source of livelihood for millions of people living in rural areas. Plant Disease is one of the significant factors affecting the agricultural sector. Plants get infected with diseases for various reasons, including synth

Takashi Horiyama, Yasuaki Kobayashi, Hirotaka Ono, Kazuhisa Seto

The uniqueness of an optimal solution to a combinatorial optimization problem attracts many fields of researchers' attention because it has a wide range of applications, it is related to important classes in computational complexity, and an instance with only one solution is often critical for algorithm designs in theory. However, as the authors know, there

Charles Fefferman, Sergei Ivanov, Matti Lassas, Hariharan Narayanan

We assume that $M_0$ is a $d$-dimensional $C^{2,1}$-smooth submanifold of $R^n$. Let $K_0$ be the convex hull of $M_0,$ and $B^n_1(0)$ be the unit ball. We assume that $ M_0 \subseteq \partial K_0 \subseteq B^n_1(0).$ We also suppose that $M_0$ has volume ($d$-dimensional Hausdorff measure) less or equal to $V$, reach (i.e., normal injectivity radius) greate

Jinxing Zheng, Yuan Cheng, Lei Wang, Fei Liu

We propose a new type of high temperature superconducting (HTS) conductor concept: modularized conductors (MC) connected by Chinese traditional tenon mortise (TM) connection structure, reffered as TMMC. The conductor consists of multiple concentric round sub conductors with slots for stacking REBCO tapes. Innovatively, the REBCO stacks in the adjacent sub co

Ronald Mahler

The paper [12] discussed two approaches for multitarget tracking (MTT): the generalized labeled multi-Bernoulli (GLMB) filter and three Poisson multi-Bernoulli mixture (PMBM) filters. The paper [13] discussed two frameworks for multitarget trajectory representation--labeled random finite set (LRFS) and set of trajectories (SoT)--and the merging of SoT and PM

Ruoyu Wang, Qihua Wang, Wang Miao

Data collection costs can vary widely across variables in data science tasks. Two-phase designs can be employed to save data collection costs. This paper considers the two-phase studies where inexpensive variables are collected for all subjects in the first phase, and expensive variables are measured for a subsample of subjects in the second phase based on a

Qinying Gu, Sebastian Gorgon, Alexander S. Romanov, Feng Li

Spin triplet exciton formation sets limits on technologies using organic semiconductors that are confined to singlet-triplet photophysics. In contrast, excitations in the spin doublet manifold in organic radical semiconductors can show efficient luminescence. Here we explore the dynamics of the spin allowed process of intermolecular energy transfer from trip

Zhuoyuan Wang, Reece Keller, Xiyu Deng, Kenta Hoshino

Optimal and safety-critical control are fundamental problems for stochastic systems, and are widely considered in real-world scenarios such as robotic manipulation and autonomous driving. In this paper, we consider the problem of efficiently finding optimal and safe control for high-dimensional systems. Specifically, we propose to use dimensionality reductio

Yan Wang, Ruiqi Liu, Tong Gao, Feng Shu

In this paper, authentication for mobile radio frequency identification (RFID) systems with low-cost RFID sensor tags is studied. Firstly, an adaptive modulus (AM) encryption algorithm is proposed. Subsequently, in order to enhance the security without additional storage of new key matrices, a self-updating encryption order (SUEO) algorithm is designed. Furt

Raúl O. Chametla, Ondrej Chrenko, Mauricio Reyes-Ruiz, F. J. Sánchez-Salcedo

By means of three dimensional resistive-magnetohydrodynamical models, we study the evolution of the so-called dead zones focused on the magnitude of the Reynolds and Maxwell stresses. We consider two different types of static resistivity radial profiles which give rise to an intermediate dead zone or an intermediate active zone. As we are interested in analy

Jinho Choi, Bohai Li, Bassel Al Homssi, Jihong Park

Non-terrestrial networks (NTNs), including low Earth orbit (LEO) satellites, are expected to play a pivotal role in achieving global coverage for Internet-of-Things (IoT) applications in sixth-generation (6G) systems. Although specific frequency bands have been identified for satellite use in NTNs, persistent challenges arise due to the limited availability

James Farrell, Jure Dobnikar, Rudolf Podgornik, Tine Curk

The ability of double-stranded DNA or RNA to locally melt and form kinks leads to strong non-linear elasticity effects that qualitatively affect their packing in confined spaces. Using analytical theory and numerical simulation we show that kink formation entails a mixed spool-nematic ordering of double-stranded DNA or RNA in spherical capsids, consisting of

Xuefeng Feng, Ruodi Yan, Sijie Gao, Yun-Kau Lau

In an effort to understand the Penrose inequality for black holes with angular momentum, an axisymmetric, vacuum, asymptotically Euclidean initial data set subject to certain quasi-stationary conditions is considered for a case study. A new geometric definition of angular velocity of a rotating black hole is defined in terms of the momentum constraint, witho

Andrew B. Kahng, Robert R. Nerem, Yusu Wang, Chien-Yi Yang

Recent years have witnessed rapid advances in the use of neural networks to solve combinatorial optimization problems. Nevertheless, designing the "right" neural model that can effectively handle a given optimization problem can be challenging, and often there is no theoretical understanding or justification of the resulting neural model. In this paper, we f

Dawei Yang, Ning He, Xing Hu, Zhihang Yuan

Although neural networks have made remarkable advancements in various applications, they require substantial computational and memory resources. Network quantization is a powerful technique to compress neural networks, allowing for more efficient and scalable AI deployments. Recently, Re-parameterization has emerged as a promising technique to enhance model

Wangkun Xu, Jianhong Wang, Fei Teng

Successful machine learning involves a complete pipeline of data, model, and downstream applications. Instead of treating them separately, there has been a prominent increase of attention within the constrained optimization (CO) and machine learning (ML) communities towards combining prediction and optimization models. The so-called end-to-end (E2E) learning

Qinqian Lei, Bo Wang, Robby T. Tan

Detecting human-object interactions (HOI) in a few-shot setting remains a challenge. Existing meta-learning methods struggle to extract representative features for classification due to the limited data, while existing few-shot HOI models rely on HOI text labels for classification. Moreover, some query images may display visual similarity to those outside th

Tariq M Khan, Syed S. Naqvi, Erik Meijering

Segmentation is an important task in a wide range of computer vision applications, including medical image analysis. Recent years have seen an increase in the complexity of medical image segmentation approaches based on sophisticated convolutional neural network architectures. This progress has led to incremental enhancements in performance on widely recogni

Ziniu Li, Tian Xu, Yang Yu

Aligning intelligent agents with human preferences and values is important. This paper examines two popular alignment methods: Direct Preference Optimization (DPO) and Reward-Model-Based Policy Optimization (RMB-PO). A variant of RMB-PO, referred to as RMB-PO+ is also considered. These methods, either explicitly or implicitly, learn a reward model from prefe

Connor Devitt, Renyuan Wang, Sudhanshu Tiwari, Sunil A. Bhave

This paper reports on the design, fabrication, and characterization of an edge-coupled magnetostatic forward volume wave bandpass filter. Using micromachining techniques, the filter is fabricated from a yttrium iron garnet (YIG) film grown on a gadolinium gallium garnet (GGG) substrate with inductive transducers. By adjusting an out-of-plane magnetic field,

Roman Bezrukavnikov, Ivan Karpov, Vasily Krylov

A key tool for the study of an affine Hecke algebra $\mathcal{H}$ is provided by Springer theory of the Langlands dual group via the realization of $\mathcal{H}$ as equivariant $K$-theory of the Steinberg variety. We prove a similar geometric description for Lusztig's asymptotic affine Hecke algebra $J$ identifying it with the sum of equivariant $K$-groups o

Haitian Yang, Wen-An Yong

This technical note is concerned with boundary stabilization of multi-dimensional discrete-velocity kinetic models. By exploiting a certain stability structure of the models and adapting an appropriate Lyapunov functional, we derive feasible control laws so that the corresponding solutions decay exponentially in time. The result is illustrated with an applic

Nirmalya Thakur

Mining and analysis of the big data of Twitter conversations have been of significant interest to the scientific community in the fields of healthcare, epidemiology, big data, data science, computer science, and their related areas, as can be seen from several works in the last few years that focused on sentiment analysis and other forms of text analysis of

Wei Ai, Yuntao Shou, Tao Meng, Nan Yin

With the continuous development of deep learning (DL), the task of multimodal dialogue emotion recognition (MDER) has recently received extensive research attention, which is also an essential branch of DL. The MDER aims to identify the emotional information contained in different modalities, e.g., text, video, and audio, in different dialogue scenes. Howeve

Yi Xie, Jie Zhang, Shiqian Zhao, Tianwei Zhang

While deep learning models have shown significant performance across various domains, their deployment needs extensive resources and advanced computing infrastructure. As a solution, Machine Learning as a Service (MLaaS) has emerged, lowering the barriers for users to release or productize their deep learning models. However, previous studies have highlighte

Meijie Kong, Hongfei Fu

In this paper, a two-sided variable-coefficient space-fractional diffusion equation with fractional Neumann boundary condition is considered. To conquer the weak singularity caused by nonlocal space-fractional differential operators, a fractional block-centered finite difference (BCFD) method on general nonuniform grids is proposed. However, this discretizat

Lei Zhang, Emanuel Gull

Analytical continuation is a central step in the simulation of finite-temperature field theories in which numerically obtained Matsubara data is continued to the real frequency axis for physical interpretation. Numerical analytic continuation is considered to be an ill-posed problem where uncertainties on the Matsubara axis are aplified exponentially. Here,

C. Cai, S. Cai, D. L. Cooke

Improvements of investigations on the Tethered Satellite System (TSS)-1R electron current enhancement due to magnetic limited collections are reported. New analytical expressions are obtained for the potential and temperature changes across the pre-sheath. The mathematical treatments in this work are more rigorous than one past approach. More experimental me

Chase Reuter

We prove that, in a neighborhood of the Euclidean ball, there are no other fixed points of the $p$-centroid body operator, using spherical harmonic techniques. We also show that the Euclidean ball is locally the only body whose centroid body is a dilate of its polar intersection body.

Yunbi Nam, Sunwoo Han

Random Forest is a machine learning method that offers many advantages, including the ability to easily measure variable importance. Class balancing technique is a well-known solution to deal with class imbalance problem. However, it has not been actively studied on RF variable importance. In this paper, we study the effect of class balancing on RF variable

Ibrahim Emirahmetoglu, Jeffrey Hajewski, Suely Oliveira, David E. Stewart

A smoothing algorithm is presented for solving the soft-margin Support Vector Machine (SVM) optimization problem with an $\ell^{1}$ penalty. This algorithm is designed to require a modest number of passes over the data, which is an important measure of its cost for very large datasets. The algorithm uses smoothing for the hinge-loss function, and an active s

Zain Alabedeen Ali, Konstantin Yakovlev

We consider an Anonymous Multi-Agent Path-Finding (AMAPF) problem where the set of agents is confined to a graph, a set of goal vertices is given and each of these vertices has to be reached by some agent. The problem is to find an assignment of the goals to the agents as well as the collision-free paths, and we are interested in finding the solution with th

Xinghao Zhu, Devesh K. Jha, Diego Romeres, Lingfeng Sun

Automating the assembly of objects from their parts is a complex problem with innumerable applications in manufacturing, maintenance, and recycling. Unlike existing research, which is limited to target segmentation, pose regression, or using fixed target blueprints, our work presents a holistic multi-level framework for part assembly planning consisting of p

Amirreza Kazemi, Martin Ester

Individual treatment effect (ITE) estimation requires adjusting for the covariate shift between populations with different treatments, and deep representation learning has shown great promise in learning a balanced representation of covariates. However the existing methods mostly consider the scenario of binary treatments. In this paper, we consider the more

Srikar Katta, Harsh Parikh, Cynthia Rudin, Alexander Volfovsky

Many modern causal questions ask how treatments affect complex outcomes that are measured using wearable devices and sensors. Current analysis approaches require summarizing these data into scalar statistics (e.g., the mean), but these summaries can be misleading. For example, disparate distributions can have the same means, variances, and other statistics.

Juan Romero, Wolfgang Heidrich, Nick Luiken, Matteo Ravasi

Seismic imaging is the numerical process of creating a volumetric representation of the subsurface geological structures from elastic waves recorded at the surface of the Earth. As such, it is widely utilized in the energy and construction sectors for applications ranging from oil and gas prospection, to geothermal production and carbon capture and storage m

Yiqun Liu, Mohsen Abdoli, Thomas Guionnet, Christine Guillemot

The Versatile Video Coding (VVC) standard has been finalized by Joint Video Exploration Team (JVET) in 2020. Compared to the High Efficiency Video Coding (HEVC) standard, VVC offers about 50% compression efficiency gain, in terms of Bjontegaard Delta-Rate (BD-rate), at the cost of about 10x more encoder complexity. In this paper, we propose a Convolutional N

V. A. Dzuba, V. V. Flambaum, I. B. Samsonov

We propose a new mechanism of absorption of dark matter particles in atoms which resembles the Migdal effect of inelastic dark matter scattering. In this process, atom may be ionized upon absorption of a scalar particle through the scalar-nucleon Yukawa-type interaction. The crucial difference from the inelastic dark matter scattering on atoms is that the to

Luis Fernando García-Mora, Hugo Alberto Rincón-Mejía

We explore some concepts of module theory that derive from the notion of primeness, such as first modules, and extend them to more general environments. We also provide descriptions of simple left semiartinian rings, left local rings, semisimple rings, and simple rings in terms of their $\mathscr A$-first modules with respect to a preradical class.

Lia Doubrawa, Eduardo S. Cypriano, Alexis Finoguenov, Paulo A. A. Lopes

Robust galaxy cluster mass estimates are fundamental for constraining cosmological parameters from counts. For this reason, it is essential to search for tracers that, independent of the cluster's dynamical state, have a small intrinsic scatter and can be easily inferred from observations. This work uses a simulated data set to focus on photometric propertie

Rita Qiuran Lyu, Chong Wu, Xinwei Ma, Jingshen Wang

Mediation analysis is a powerful tool for studying causal pathways between exposure, mediator, and outcome variables of interest. While classical mediation analysis using observational data often requires strong and sometimes unrealistic assumptions, such as unconfoundedness, Mendelian Randomization (MR) avoids unmeasured confounding bias by employing geneti

Sagar Chaudhary, Sachin S. Velankar, Charles M. Schroeder

Capillary suspensions are three-phase mixtures containing a solid particulate phase, a continuous liquid phase, and a second immiscible liquid forming capillary bridges between particles. Capillary suspensions are encountered in a wide array of applications including 3D printing, porous materials, and food formulations, but despite recent progress, the micro

Kaustubh Shivdikar

The advent of Graph Neural Networks (GNNs) has revolutionized the field of machine learning, offering a novel paradigm for learning on graph-structured data. Unlike traditional neural networks, GNNs are capable of capturing complex relationships and dependencies inherent in graph data, making them particularly suited for a wide range of applications includin

Luis Balderas, Miguel Lastra, José M. Benítez

Deep learning models have been widely used during the last decade due to their outstanding learning and abstraction capacities. However, one of the main challenges any scientist has to face using deep learning models is to establish the network's architecture. Due to this difficulty, data scientists usually build over complex models and, as a result, most of

Zane Ozzello, Yannick Meurice

General positivity constraints linking various powers of observables in energy eigenstates can be used to sharply locate acceptable regions for the energy eigenvalues, provided that efficient recursive methods are available to calculate the matrix elements. These recursive methods are derived by looking at the commutation relations of the observables with th

Jinyong Hahn, Zhipeng Liao, Nan Liu, Shuyang Sheng

This paper shows that the endogeneity test using the control function approach in linear instrumental variable models is a variant of the Hausman test. Moreover, we find that the test statistics used in these tests can be numerically ordered, indicating their relative power properties in finite samples.

Rohan Banerjee, Prishita Ray, Mark Campbell

Deep reinforcement learning (RL) approaches have been broadly applied to a large number of robotics tasks, such as robot manipulation and autonomous driving. However, an open problem in deep RL is learning policies that are robust to variations in the environment, which is an important condition for such systems to be deployed into real-world, unstructured s

Damian Horna, Lango Mateusz, Jerzy Stefanowski

Improving the classification of multi-class imbalanced data is more difficult than its two-class counterpart. In this paper, we use deep neural networks to train new representations of tabular multi-class data. Unlike the typically developed re-sampling pre-processing methods, our proposal modifies the distribution of features, i.e. the positions of examples

Solmaz Sorkhi Asbaghi, Mahmood Mohassel Feghhi, Javad Musevi niya

The Internet of Things (IoT) has a significant demand in society due to its features, and it is constantly improving. In the context of wireless technology, Ultra-reliable and low-latency communication (URLLC) is one of the essential and challenging services in fifth-generation (5G) networks and beyond. The research on URLLC is still in its early stages due

Vladimir Yu. Protasov

It is well-known that every isosceles tetrahedron (disphenoid) admits infinitely many simple closed geodesics on its surface. They can be naturally enumerated by pairs of co-prime integers $n > m > 1$ with two additional cases $(1,0)$ and $(1,1)$. The (n,m)-geodesic is a broken line with $4(n+m)$ vertices, its length tends to infinity as $m\to \infty$. Are t

Antonios Alexos, Junze Liu, Akash Tiwari, Kshitij Bhardwaj

In Inertial Confinement Fusion (ICF) process, roughly a 2mm spherical shell made of high density carbon is used as target for laser beams, which compress and heat it to energy levels needed for high fusion yield. These shells are polished meticulously to meet the standards for a fusion shot. However, the polishing of these shells involves multiple stages, wi

Parker J. Roberts, Benjamin A. Jorns

Non-classical electron diffusion in crossed-field, low-temperature plasmas is measured experimentally. Laser-induced fluorescence and Thomson scattering are used to determine the inverse Hall parameter, a metric for cross-field transport, in a Hall ion source. The measured diffusion is found to depart from fluid model results at the peak electric field, rema

Adam Horsler, Jake Baker, Pedro M. Baiz.

This paper presents a novel two-part pipeline for monitoring progress towards the UN Sustainable Development Goals (SDG's) related to Climate Action and Sustainable Cities and Communities. The pipeline consists of two main parts: the first part takes a raw satellite image of a motorway section and produces traffic count predictions for count sites within the

Kevin Course, Prasanth B. Nair

We consider the problem of inferring latent stochastic differential equations (SDEs) with a time and memory cost that scales independently with the amount of data, the total length of the time series, and the stiffness of the approximate differential equations. This is in stark contrast to typical methods for inferring latent differential equations which, de

Everton L. Aleixo, Juan G. Colonna, Marco Cristo, Everlandio Fernandes

Deep Learning models have achieved remarkable performance in tasks such as image classification or generation, often surpassing human accuracy. However, they can struggle to learn new tasks and update their knowledge without access to previous data, leading to a significant loss of accuracy known as Catastrophic Forgetting (CF). This phenomenon was first obs

David Firth, Fiona Sammut

In current applied research the most-used route to an analysis of composition is through log-ratios -- that is, contrasts among log-transformed measurements. Here we argue instead for a more direct approach, using a statistical model for the arithmetic mean on the original scale of measurement. Central to the approach is a general variance-covariance functio

Kun Yang, Shu-ping Yeh, Menglei Zhang, Jerry Sydir

Dynamic radio resource management (RRM) in wireless networks presents significant challenges, particularly in the context of Radio Access Network (RAN) slicing. This technology, crucial for catering to varying user requirements, often grapples with complex optimization scenarios. Existing Reinforcement Learning (RL) approaches, while achieving good performan

Anthony Conway, Patrick Orson

The fundamental group of the complement of a locally flat surface in a $4$-manifold is called the knot group of the surface. In this article we prove that two locally flat $2$-spheres in $\mathbb{C} P^2$ with knot group $\mathbb{Z}_2$ are ambiently isotopic if they are homologous. This combines with work of Tristram and Lee-Wilczy\'{n}ski, as well as the cla

Andrei Buciulea, Elvin Isufi, Geert Leus, Antonio G. Marques

Graphs are widely used to represent complex information and signal domains with irregular support. Typically, the underlying graph topology is unknown and must be estimated from the available data. Common approaches assume pairwise node interactions and infer the graph topology based on this premise. In contrast, our novel method not only unveils the graph t

Ying-Ying Li, Muhammad Omer Sajid, Judah Unmuth-Yockey

We explore the potential application of quantum computers to the examination of lattice holography, which extends to the strongly-coupled bulk theory regime. With adiabatic evolution, we compute the ground state of a spin system on a $(2+1)$-dimensional hyperbolic lattice, and measure the spin-spin correlation function on the boundary. Notably, we observe th

Stefan Kolek, Aditya Chattopadhyay, Kwan Ho Ryan Chan, Hector Andrade-Loarca

Information Pursuit (IP) is an explainable prediction algorithm that greedily selects a sequence of interpretable queries about the data in order of information gain, updating its posterior at each step based on observed query-answer pairs. The standard paradigm uses hand-crafted dictionaries of potential data queries curated by a domain expert or a large la

Nhan D. T. Nguyen, Kaare Mikkelsen, Preben Kidmose

Event-related potentials (ERP) have been used to address a wide range of research questions in neuroscience and cognitive psychology including selective auditory attention. The recent progress in auditory attention decoding (AAD) methods is based on algorithms that find a relation between the audio envelope and the neurophysiological response. The most popul

Rodolfo Angeloni, Juan Pablo Uchima Tamayo, Marcelo Jaque Arancibia, Roque Ruiz-Carmona

Light pollution is recognized as a global issue that, like other forms of anthropogenic pollution, has significant impact on ecosystems and adverse effects on living organisms. Multiple evidence suggests that it has been increasing at an unprecedented rate at all spatial scales. Chile, which thanks to its unique environmental conditions has become one of the

Caleb Deen Bastian, Herschel Rabitz, Grzegorz A Rempala

The paper introduces a novel approach to global sensitivity analysis, grounded in the variance-covariance structure of random variables derived from random measures. The proposed methodology facilitates the application of information-theoretic rules for uncertainty quantification, offering several advantages. Specifically, the approach provides valuable insi

Vikas Thamizharasan, Difan Liu, Shantanu Agarwal, Matthew Fisher

We present VecFusion, a new neural architecture that can generate vector fonts with varying topological structures and precise control point positions. Our approach is a cascaded diffusion model which consists of a raster diffusion model followed by a vector diffusion model. The raster model generates low-resolution, rasterized fonts with auxiliary control p

Sai Wang, Ye Zhu, Ruoyu Wang, Amaya Dharmasiri

Generative AI capabilities have grown substantially in recent years, raising renewed concerns about potential malicious use of generated data, or "deep fakes". However, deep fake datasets have not kept up with generative AI advancements sufficiently to enable the development of deep fake detection technology which can meaningfully alert human users in real-w

Antonio Carbone

Recently Paw\l{}ucki showed that compact sets that are definable in some o-minimal structure admit triangulations of class $\mathcal{C}^p$ for each integer $p\geq 1$. In this work, we make use of these new techniques of triangulation to show that all continuous definable maps between compact definable sets can be approximated by differentiable maps without c

Ludovico Bruni Bruno, Giacomo Elefante

In this work we blend interpolation theory with numerical integration, constructing an interpolator based on integrals over $n$-dimensional balls. We show that, under hypotheses on the radius of the $n$-balls, the problem can be treated as an interpolation problem both on a collection of $(n-1)$-spheres $ S^{n-1} $ and multivariate point sets, for which a wi

Mohamed Lichouri, Khaled Lounnas, Aicha Zitouni, Houda Latrache

In this paper, we conduct an in-depth analysis of several key factors influencing the performance of Arabic Dialect Identification NADI'2023, with a specific focus on the first subtask involving country-level dialect identification. Our investigation encompasses the effects of surface preprocessing, morphological preprocessing, FastText vector model, and the

Damir Dzhafarov, Reed Solomon, Manlio Valenti

We study versions of the tree pigeonhole principle, $\mathsf{TT}^1$, in the context of Weihrauch-style computable analysis. The principle has previously been the subject of extensive research in reverse mathematics. Two outstanding questions from the latter investigation are whether $\mathsf{TT}^1$ is $\Pi^1_1$-conservative over the ordinary pigeonhole princ

Sandesh Kamath, Sankalp Mittal, Amit Deshpande, Vineeth N Balasubramanian

For machine learning models to be reliable and trustworthy, their decisions must be interpretable. As these models find increasing use in safety-critical applications, it is important that not just the model predictions but also their explanations (as feature attributions) be robust to small human-imperceptible input perturbations. Recent works have shown th

Henk Bruin, Silvia Radinger

We study linear recurrence and weak mixing of a two-parameter family of interval translation maps $T_{\alpha,\beta}$ for the subset of parameter space where $T_{\alpha,\beta}$ has a Cantor attractor. For this class, there is a procedure similar to the Rauzy induction which acts as a dynamical system $G$ on parameter space, which was used previously to decide

G. Ramos-Larios, M. A. Guerrero, J. A. Toalá, S. Akras

The acquisition of high-quality deep images of planetary nebulae (PNe) has allowed the detection of a wealth of small-scale features, which highlight the complexity of the formation history and physical processes shaping PNe. Here we present the discovery of three groups of clumps embedded within the nebular shell of the evolved PN NGC3587, the Owl Nebula, t

Samuele Papa, Riccardo Valperga, David Knigge, Miltiadis Kofinas

Neural fields (NeFs) have recently emerged as a versatile method for modeling signals of various modalities, including images, shapes, and scenes. Subsequently, a number of works have explored the use of NeFs as representations for downstream tasks, e.g. classifying an image based on the parameters of a NeF that has been fit to it. However, the impact of the

Masoud Khalkhali, Nathan Pagliaroli

We compute the first twenty moments of three convergent quartic bi-tracial 2-matrix ensembles in the large $N$ limit. These ensembles are toy models for Euclidean quantum gravity originally proposed by John Barrett and collaborators. A perturbative solution is found for the first twenty moments using the Schwinger-Dyson equations and properties of certain bi

Hemang Chawla, Arnav Varma, Elahe Arani, Bahram Zonooz

Transformers have revolutionized deep learning based computer vision with improved performance as well as robustness to natural corruptions and adversarial attacks. Transformers are used predominantly for 2D vision tasks, including image classification, semantic segmentation, and object detection. However, robots and advanced driver assistance systems also r

Jhuma Kabir Mim, Mourad Oussalah, Akash Singhal

In today's age, social media reigns as the paramount communication platform, providing individuals with the avenue to express their conjectures, intellectual propositions, and reflections. Unfortunately, this freedom often comes with a downside as it facilitates the widespread proliferation of hate speech and offensive content, leaving a deleterious impact o

Christian Jacobsen, Yilin Zhuang, Karthik Duraisamy

Recent advances in generative artificial intelligence have had a significant impact on diverse domains spanning computer vision, natural language processing, and drug discovery. This work extends the reach of generative models into physical problem domains, particularly addressing the efficient enforcement of physical laws and conditioning for forward and in

Sundaraparipurnan Narayanan

AI is increasingly playing a pivotal role in businesses and organizations, impacting the outcomes and interests of human users. Automated Machine Learning (AutoML) streamlines the machine learning model development process by automating repetitive tasks and making data-driven decisions, enabling even non-experts to construct high-quality models efficiently.

Rene Carmona, Gokce Dayanikli, Francois Delarue, Mathieu Lauriere

Mean field games (MFG) and mean field control (MFC) problems have been introduced to study large populations of strategic players. They correspond respectively to non-cooperative or cooperative scenarios, where the aim is to find the Nash equilibrium and social optimum. These frameworks provide approximate solutions to situations with a finite number of play

Jeroen M. Zwanepol, Gustavo Rezende Silva, Carlos Hernández Corbato

Autonomous robots need to be able to handle uncertainties when deployed in the real world. For the robot to be able to robustly work in such an environment, it needs to be able to adapt both its architecture as well as its task plan. Architecture adaptation and task plan adaptation are mutually dependent, and therefore require the system to apply runtime arc

Poorna Chander Reddy Puttaparthi, Soham Sanjay Deo, Hakan Gul, Yiming Tang

ChatGPT is currently the most popular large language model (LLM), with over 100 million users, making a significant impact on people's lives. However, due to the presence of jailbreak vulnerabilities, ChatGPT might have negative effects on people's lives, potentially even facilitating criminal activities. Testing whether ChatGPT can cause jailbreak is crucia