February 2024 arXiv papers — page 136

Xinggang Hu, Yanmin Wu, Mingyuan Zhao, Linghao Yang

Visual SLAM (Simultaneous Localization and Mapping) based on planar features has found widespread applications in fields such as environmental structure perception and augmented reality. However, current research faces challenges in accurately localizing and mapping in planar ambiguous scenes, primarily due to the poor accuracy of the employed planar feature

Andrew J. Soto Levins

If a module $M$ has finite projective dimension, then the Ext modules of $M$ against any other module eventually vanish and the projective dimension of $M$ gives a uniform bound for this vanishing. For modules of infinite projective dimension there can still exist a bound. Such a bound is called the Auslander bound. In this paper we define a similar bound fo

Jiahe Yue, Hong-lin Liao, Nan Liu

We provide a new theoretical framework for the variable-step deferred correction (DC) methods based on the well-known BDF2 formula. By using the discrete orthogonal convolution kernels, some high-order BDF2-DC methods are proven to be stable on arbitrary time grids according to the recent definition of stability (SINUM, 60: 2253-2272). It significantly relax

Xunkai Li, Jingyuan Ma, Zhengyu Wu, Daohan Su

Scalable graph neural networks (GNNs) have emerged as a promising technique, which exhibits superior predictive performance and high running efficiency across numerous large-scale graph-based web applications. However, (i) Most scalable GNNs tend to treat all nodes in graphs with the same propagation rules, neglecting their topological uniqueness; (ii) Exist

Longchao Da, Chen Chu, Weinan Zhang, Hua Wei

Traffic simulation is an essential tool for transportation infrastructure planning, intelligent traffic control policy learning, and traffic flow analysis. Its effectiveness relies heavily on the realism of the simulators used. Traditional traffic simulators, such as SUMO and CityFlow, are often limited by their reliance on rule-based models with hyperparame

Haizhong Zheng, Xiaoyan Bai, Xueshen Liu, Z. Morley Mao

Large Language Models (LLMs) have achieved remarkable success with their billion-level parameters, yet they incur high inference overheads. The emergence of activation sparsity in LLMs provides a natural approach to reduce this cost by involving only parts of the parameters for inference. However, existing methods only focus on utilizing this naturally forme

Joshua Zingale, Jugal Kalita

Controlled text generation (CTG) seeks to guide large language model (LLM) output to produce text that conforms to desired criteria. The current study presents a novel CTG algorithm that enforces adherence toward specific rhetorical relations in an LLM sentence-completion context by a parser-driven decoding scheme that requires no model fine-tuning. The meth

Patrick Yung Kang Lee, Paul Hendrik Bucci, Leo Itsuki Foord-Kelcey, Alamjeet Singh

Large text corpora, such as Reddit posts, have become an increasingly prevalent site of qualitative inquiry. However, most large text corpora are intractable for qualitative researchers. Instead, teams rely on statistical subsampling to reduce corpora to a manageable size for qualitative analysis. While previous work for navigating large corpora involves vis

Binbin Huang, Hailiang Zhao, Lingbin Wang, Wenzhuo Qian

In the resource-constrained IoT-edge computing environment, Split Federated (SplitFed) learning is implemented to enhance training efficiency. This method involves each terminal device dividing its full DNN model at a designated layer into a device-side model and a server-side model, then offloading the latter to the edge server. However, existing research o

Brian Cho, Kyra Gan, Nathan Kallus

We propose a novel nonparametric sequential test for composite hypotheses for means of multiple data streams. Our proposed method, \emph{peeking with expectation-based averaged capital} (PEAK), builds upon the testing-by-betting framework and provides a non-asymptotic $\alpha$-level test across any stopping time. Our contributions are two-fold: (1) we propos

Tara Akhound-Sadegh, Jarrid Rector-Brooks, Avishek Joey Bose, Sarthak Mittal

Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-body systems, is a foundational problem in science. In this paper, we propose Iterated Denoising Energy Matching (iDEM), an iterative algorithm that uses a novel stochastic score matching objective leveraging solely the

Shima Imani, Hamid Palangi

Large Language Models (LLMs) have demonstrated impressive performance across a wide range of applications; however, assessing their reasoning capabilities remains a significant challenge. In this paper, we introduce a framework grounded in group and symmetry principles, which have played a crucial role in fields such as physics and mathematics, and offer ano

Zhicheng Zheng, Xin Yan, Zhenfang Chen, Jingzhou Wang

We introduce the Continuum Physical Dataset (ContPhy), a novel benchmark for assessing machine physical commonsense. ContPhy complements existing physical reasoning benchmarks by encompassing the inference of diverse physical properties, such as mass and density, across various scenarios and predicting corresponding dynamics. We evaluated a range of AI model

Siming Yan, Min Bai, Weifeng Chen, Xiong Zhou

By combining natural language understanding, generation capabilities, and breadth of knowledge of large language models with image perception, recent large vision language models (LVLMs) have shown unprecedented visual reasoning capabilities. However, the generated text often suffers from inaccurate grounding in the visual input, resulting in errors such as

Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan

This paper tackles the problem of motion deblurring of dynamic scenes. Although end-to-end fully convolutional designs have recently advanced the state-of-the-art in non-uniform motion deblurring, their performance-complexity trade-off is still sub-optimal. Most existing approaches achieve a large receptive field by increasing the number of generic convoluti

Peng Shu, Huaqin Zhao, Hanqi Jiang, Yiwei Li

Large language models and multimodal large language models have revolutionized artificial intelligence recently. An increasing number of regions are now embracing these advanced technologies. Within this context, robot coding education is garnering increasing attention. To teach young children how to code and compete in robot challenges, large language model

Itziar Baragaña, Alicia Roca

We solve the problem of determining the Weierstrass structure of a regular matrix pencil obtained by a low rank perturbation of another regular matrix pencil. We apply the result to find necessary and sufficient conditions for the existence of a low rank perturbation such that the perturbed pencil has prescribed eigenvalues and algebraic multiplicities. The

Ruiyang Qin, Yuting Hu, Zheyu Yan, Jinjun Xiong

Neural Architecture Search (NAS) has become the de fecto tools in the industry in automating the design of deep neural networks for various applications, especially those driven by mobile and edge devices with limited computing resources. The emerging large language models (LLMs), due to their prowess, have also been incorporated into NAS recently and show s

Alexis Wartelle, Marisel Di Pietro Martínez, Olivier Fruchart, Philippe David

X-ray magnetic circular dichroism is an efficient contrast mechanism allowing for a direct sensitivity to magnetization. Combined with an imaging technique such as photoemission electron microscopy, it has been successfully applied to high-resolution investigations of ferromagnetic thin films but also of three-dimensional systems thanks to the transmission-t

Lehel Csillag, Tiberiu Harko

In the present paper we introduce a geometric generalization of standard general relativity, based on a geometry initially introduced by Friedmann and Schouten in 1924, through the notion of a semi-symmetric connection. The semi-symmetric connection is a particular connection that extends the Levi-Civita one, by allowing for the presence of torsion. While th

Hui-Min Zhao, Di-Di Zheng, Xiao-Jun Zhang, Jin-Hui Wu

We propose a scheme for realizing broadband and tunable transmission non-reciprocity by utilizing two-photon near-resonant transitions in thermal atoms as single-photon far-detuned transitions can be eliminated. Our basic idea is to largely reduce the Doppler broadenings on a pair of two-photon, probe and coupling, transitions and meanwhile make the only fou

Richard Clare

In this work, we undertake a comprehensive reformulation, modification, and extension of Smith and Spiegelhalter's (1980) and (1982) Bayes Factor work within the evolving subject of Objective Bayes Factors. Our primary focus centers on defining and computing empirical and theoretical bounds for the Intrinsic Bayes Factor (IBF) across various models, includin

Nadia Alshahwan, Mark Harman, Alexandru Marginean, Rotem Tal

TestGen automatically generates unit tests, carved from serialized observations of complex objects, observed during app execution. We describe the development and deployment of TestGen at Meta. In particular, we focus on the scalability challenges overcome during development in order to deploy observation-based test carving at scale in industry. So far, Test

G. S. Seabra, N. T. Mücke, V. L. S. Silva, D. Voskov

This study investigates the integration of machine learning (ML) and data assimilation (DA) techniques, focusing on implementing surrogate models for Geological Carbon Storage (GCS) projects while maintaining high fidelity physical results in posterior states. Initially, we evaluate the surrogate modeling capability of two distinct machine learning models, F

Jixin Chen

The circular orbits and elliptical orbits of moving objects in a gravitational field are essential information in astronomy. There have been many methods developed in the literature and textbooks to describe these orbits. In this report, I propose to use the vis-viva equation to construct a complex function to store the state of a moving object in elliptical

Raman Ebrahimi, Parinaz Naghizadeh

Network games provide a framework to study strategic decision making processes that are governed by structured interdependencies among agents. However, existing models do not account for environments in which agents simultaneously interact over multiple networks, or when agents operate over multiple action dimensions. In this paper, we propose new models of

Yemeng Liu, Jing Ren, Jianshuo Xu, Xiaomei Bai

The spread of the Coronavirus disease-2019 epidemic has caused many courses and exams to be conducted online. The cheating behavior detection model in examination invigilation systems plays a pivotal role in guaranteeing the equality of long-distance examinations. However, cheating behavior is rare, and most researchers do not comprehensively take into accou

Simone Rusconi, Christina Schenk, Arghir Zarnescu, Elena Akhmatskaya

Rational computer-aided design of multiphase polymer materials is vital for rapid progress in many important applications, such as: diagnostic tests, drug delivery, coatings, additives for constructing materials, cosmetics, etc. Several property predictive models, including the prospective Population Balance Model for Latex Particles Morphology Formation (LP

Daniel Engelsman, Itzik Klein

In underwater navigation, accurate heading information is crucial for accurately and continuously tracking trajectories, especially during extended missions beneath the waves. In order to determine the initial heading, a gyrocompassing procedure must be employed. As unmanned underwater vehicles (UUV) are susceptible to ocean currents and other disturbances,

Maitreya Suin, Rama Chellappa

Recent generative-prior-based methods have shown promising blind face restoration performance. They usually project the degraded images to the latent space and then decode high-quality faces either by single-stage latent optimization or directly from the encoding. Generating fine-grained facial details faithful to inputs remains a challenging problem. Most e

Saúl Hernández-Ortiz, Ricardo Martínez von Dossow, Alfredo Raya

We study the chiral phase transition within the Linear Sigma Model with quarks from its thermodynamical potential considering quantum corrections up to ring diagrams in the high-temperature regime. Demanding a second order phase transition as expected in the chiral limit at low baryon chemical potential, that the curvature of the critical line matches the on

Dixian Zhu, Tianbao Yang, Livnat Jerby

Regression is a fundamental task in machine learning that has garnered extensive attention over the past decades. The conventional approach for regression involves employing loss functions that primarily concentrate on aligning model prediction with the ground truth for each individual data sample. Recent research endeavors have introduced novel perspectives

D. Leviatan, I. O. Shevchuk

We give here the final results about the validity of Jackson-type estimates in comonotone approximation of $2\pi$-periodic functions by trigonometric polynomials. For coconvex and the so called co-$q$-monotone, $q>2$, approximations, everything is known by now. Thus, this paper concludes the research on Jackson type estimates of Shape Preserving Approximatio

Mohak Bhardwaj, Thomas Lampe, Michael Neunert, Francesco Romano

Recent advances in real-world applications of reinforcement learning (RL) have relied on the ability to accurately simulate systems at scale. However, domains such as fluid dynamical systems exhibit complex dynamic phenomena that are hard to simulate at high integration rates, limiting the direct application of modern deep RL algorithms to often expensive or

Samiha Rouf, Casey Moore, Debabrata Saha, Dan Nguyen

PULSAR (personalized ultrafractionated stereotactic adaptive radiotherapy) is a form of radiotherapy method where a patient is given a large dose or pulse of radiation a couple of weeks apart rather than daily small doses. The tumor response is then monitored to determine when the subsequent pulse should be given. Pre-clinical trials have shown better tumor

Md Rabiul Hasan, Muztoba Ahmed Khan, Thorsten Wuest

Sustainable supply chain management is a key objective of Industry 5.0, leveraging technologies like real-time data analytics, connectivity, and intelligent automation. At the same time, composite materials present benefits such as lightweight structures, crucial for reducing fuel consumption. This study investigates the intersection between sustainable supp

Gerry Wan, Shinan Liu, Francesco Bronzino, Nick Feamster

Machine learning has shown tremendous potential for improving the capabilities of network traffic analysis applications, often outperforming simpler rule-based heuristics. However, ML-based solutions remain difficult to deploy in practice. Many existing approaches only optimize the predictive performance of their models, overlooking the practical challenges

Jeffrey Sardina, Luca Costabello, Christophe Guéret

Knowledge Graphs (KGs) have become increasingly common for representing large-scale linked data. However, their immense size has required graph learning systems to assist humans in analysis, interpretation, and pattern detection. While there have been promising results for researcher- and clinician- empowerment through a variety of KG learning systems, we id

Jeffrey Sardina, John D. Kelleher, Declan O'Sullivan

In this paper we introduce TWIG (Topologically-Weighted Intelligence Generation), a novel, embedding-free paradigm for simulating the output of KGEs that uses a tiny fraction of the parameters. TWIG learns weights from inputs that consist of topological features of the graph data, with no coding for latent representations of entities or edges. Our experiment

Xiaoming Bi, Zhongkai Guo, Xiaobo Zou, Yong Huang

Two way ranging data for spacecraft tracking of China's first Martian mission Tianwen-I is analysed. Shortly before the spacecraft entered the Mars parking orbit, the two way coherent microwave link between the spacecraft and the Earth resembles a long arm gravitational wave interferometer, with both the spacecraft and the Earth regarded as in an approximate

Yang Yu, Peng Cheng, Hui-Yu Xing, Fei Gao

Using a symmetry-preserving formulation of a vector$\,\times\,$vector contact interaction (SCI) and treating the proton as a quark + interacting-diquark bound state, whose structure is obtained by solving a Poincar\'e-covariant Faddeev equation, we provide a comprehensive, coherent set of predictions for unpolarised and polarised proton parton distribution f

Ming Shen

Although supervised finetuning (SFT) has emerged as an essential technique to align large language models with humans, it is considered superficial, with style learning being its nature. At the same time, recent works indicate the importance of data selection for SFT, showing that finetuning with high-quality and diverse subsets of the original dataset leads

Azucena Garvía Bosshard, Jonathan Bootle, Christoph Sprenger

The sumcheck protocol, introduced in 1992, is an interactive proof which is a key component of many probabilistic proof systems in computational complexity theory and cryptography, some of which have been deployed. However, none of these proof systems based on the sumcheck protocol enjoy a formally-verified security analysis. In this paper, we make progress

Shigemichi Matsuzaki, Takuma Sugino, Kazuhito Tanaka, Zijun Sha

This paper describes a multi-modal data association method for global localization using object-based maps and camera images. In global localization, or relocalization, using object-based maps, existing methods typically resort to matching all possible combinations of detected objects and landmarks with the same object category, followed by inlier extraction

Anupam Gupta, Ashok Krishnamurthy, Lisa Singh

This paper introduces a novel segmentation framework that integrates a classifier network with a reverse HRNet architecture for efficient image segmentation. Our approach utilizes a ResNet-50 backbone, pretrained in a semi-supervised manner, to generate feature maps at various scales. These maps are then processed by a reverse HRNet, which is adapted to hand

Shelby Cox, Pratik Misra, Pardis Semnani

We study the Gaussian statistical models whose log-likelihood function has a unique complex critical point, i.e., has maximum likelihood degree one. We exploit the connection developed by Am\'endola et. al. between the models having maximum likelihood degree one and homaloidal polynomials. We study the spanning tree generating function of a graph and show th

Yuwen Lu, Yuewen Yang, Qinyi Zhao, Chengzhi Zhang

Recent advancements in HCI and AI research attempt to support user experience (UX) practitioners with AI-enabled tools. Despite the potential of emerging models and new interaction mechanisms, mainstream adoption of such tools remains limited. We took the lens of Human-Centered AI and presented a systematic literature review of 359 papers, aiming to synthesi

David Yan, Winnie Zhang, Luxin Zhang, Anmol Kalia

We introduce animated stickers, a video diffusion model which generates an animation conditioned on a text prompt and static sticker image. Our model is built on top of the state-of-the-art Emu text-to-image model, with the addition of temporal layers to model motion. Due to the domain gap, i.e. differences in visual and motion style, a model which performed

Meng-Chieh Lee, Lingxiao Zhao, Leman Akoglu

Graph kernels used to be the dominant approach to feature engineering for structured data, which are superseded by modern GNNs as the former lacks learnability. Recently, a suite of Kernel Convolution Networks (KCNs) successfully revitalized graph kernels by introducing learnability, which convolves input with learnable hidden graphs using a certain graph ke

Bibrak Qamar Chandio, Prateek Srivastava, Maciej Brodowicz, Martin Swany

The paper provides a unified co-design of 1) a programming and execution model that allows spawning tasks from within the vertex data at runtime, 2) language constructs for \textit{actions} that send work to where the data resides, combining parallel expressiveness of local control objects (LCOs) to implement asynchronous graph processing primitives, 3) and

Diogo Landau, Nishant Saurabh, Xavier Andrade, Jorge G Barbosa

Message brokers often mediate communication between data producers and consumers by adding variable-sized messages to ordered distributed queues. Our goal is to determine the number of consumers and consumer-partition assignments needed to ensure that the rate of data consumption keeps up with the rate of data production. We model the problem as a variable i

Luc W. IJspeert, Andrew Tkachenko, Cole Johnston, Andrej Prša

Abstract abridged. Eclipsing binary systems provide the opportunity to measure the fundamental parameters of their component stars in a stellar-model-independent way. This makes them ideal candidates for testing and calibrating theories of stellar structure and (tidal) evolution. Even without spectroscopic follow-up there is often enough information in their

Francois Vigneron, Nicolae Mihalache

This article presents a new algorithm to compute all the roots of two families of polynomials that are of interest for the Mandelbrot set $\mathcal{M}$ : the roots of those polynomials are respectively the parameters $c\in\mathcal{M}$ associated with periodic critical dynamics for $f_c(z)=z^2+c$ (hyperbolic centers) or with pre-periodic dynamics (Misiurewicz

Amir Zandieh, Insu Han, Vahab Mirrokni, Amin Karbasi

Despite the significant success of large language models (LLMs), their extensive memory requirements pose challenges for deploying them in long-context token generation. The substantial memory footprint of LLM decoders arises from the necessity to store all previous tokens in the attention module, a requirement imposed by key-value (KV) caching. In this work

Peter Kazakov, Zilong Liu

This paper aims to search for new optimal and sub-optimal Odd Binary Z-Complimentary Pairs (OBZCPs) for lengths up to 49. As an alternative to the celebrated binary Golay complementary pairs, optimal OBZCPs are the best almost-complementary sequence pairs having odd lengths. We introduce a computer search algorithm with time complexity $O(2^N)$, where $N$ de

Akshay J. Dave, Tat Nghia Nguyen, Richard B. Vilim

This paper introduces an integrated system designed to enhance the explainability of fault diagnostics in complex systems, such as nuclear power plants, where operator understanding is critical for informed decision-making. By combining a physics-based diagnostic tool with a Large Language Model, we offer a novel solution that not only identifies faults but

Sidan A, Tom Banks

Motivated by conjectures about near-horizon dynamics in quantum gravity, we search for lines of perturbatively accessible fixed points emanating from models of $N$ free fermions. Through two loops we find a new class of models, apart from the well-known Abelian Thirring models. Further study is needed to see whether these can lead to true conformal manifolds

Zehui Li, Yuhao Ni, William A V Beardall, Guoxuan Xia

This paper introduces a novel framework for DNA sequence generation, comprising two key components: DiscDiff, a Latent Diffusion Model (LDM) tailored for generating discrete DNA sequences, and Absorb-Escape, a post-training algorithm designed to refine these sequences. Absorb-Escape enhances the realism of the generated sequences by correcting `round errors'

Pedro Osório, Alexandre Bernardino, Ruben Martinez-Cantin, José Santos-Victor

Affordances are fundamental descriptors of relationships between actions, objects and effects. They provide the means whereby a robot can predict effects, recognize actions, select objects and plan its behavior according to desired goals. This paper approaches the problem of an embodied agent exploring the world and learning these affordances autonomously fr

Danila Milanov, Vakhit Shaidulin, Aleksandr Rusakov, Angelina Veselova

Statistical analysis of samples of the orbits of celestial bodies is complicated by the fact that the Keplerian orbit is a multidimensional object, the coordinate representation of which nonlinearly depends on the choice of orbital elements. In this work, using the construction of the Fr\'echet mean, concepts of mean orbit and dispersion of the orbit family

Cristiano M. Garcia, Alceu de Souza Britto, Jean Paul Barddal

Initially supported by Twitter, hashtags are now used on several social media platforms. Hashtags are helpful for tagging, tracking, and grouping posts on similar topics. In this paper, based on a hashtag stream regarding the hashtag #mybodymychoice, we analyze hashtag drifts over time using concepts from graph analysis and textual data streams using the Gir

C. C. Worley, R. Smiljanic, L. Magrini, A. Frasca

The Gaia-ESO Survey is an European Southern Observatory (ESO) public spectroscopic survey that targeted $10^5$ stars in the Milky Way covering the major populations of the disk, bulge and halo. The observations were made using FLAMES on the VLT obtaining both UVES high ($R\sim47,000$) and GIRAFFE medium ($R\sim20,000$) resolution spectra. The analysis of the

Scotty Black, Christian Darken

Remaining competitive in future conflicts with technologically-advanced competitors requires us to accelerate our research and development in artificial intelligence (AI) for wargaming. More importantly, leveraging machine learning for intelligent combat behavior development will be key to one day achieving superhuman performance in this domain--elevating th

Konstantinos Zacharopoulos, Georgios Koutroumpas, Ioannis Arapakis, Konstantinos Georgopoulos

The cellular network plays a pivotal role in providing Internet access, since it is the only global-scale infrastructure with ubiquitous mobility support. To manage and maintain large-scale networks, mobile network operators require timely information, or even accurate performance forecasts. In this paper, we propose LightningNet, a lightweight and distribut

Scotty Black, Christian Darken

In this unprecedented era of technology-driven transformation, it becomes more critical than ever that we aggressively invest in developing robust artificial intelligence (AI) for wargaming in support of decision-making. By advancing AI-enabled systems and pairing these with human judgment, we will be able to enhance all-domain awareness, improve the speed a

Enrique Flores-Montoya, Victor Muntean, Mario Sánchez-Sanz, Daniel Martínez-Ruiz

This paper presents an experimental study of the influence of heat losses on the onset of thermoacoustic instabilities in methane-air premixed flames propagating in a horizontal tube of small diameter, $D = 10$ mm. Flames are ignited at the open end of the tube and propagate towards the closed end undergoing strong oscillations of different features due to t

Di Cao, Xianchen Wang, Junfeng Zhou, Jiakai Zhang

Traditional Time Delay Neural Networks (TDNN) have achieved state-of-the-art performance at the cost of high computational complexity and slower inference speed, making them difficult to implement in an industrial environment. The Densely Connected Time Delay Neural Network (D-TDNN) with Context Aware Masking (CAM) module has proven to be an efficient struct

Noriyuki Otsubo, Takao Yamazaki

We study the Gauss and Jacobi sums from a viewpoint of motives. We exhibit isomorphisms between Chow motives arising from the Artin-Schreier curve and the Fermat varieties over a finite field, that can be regarded as (and yield a new proof of) classically known relations among Gauss and Jacobi sums such as Davenport-Hasse's multiplication formula. As a key s

Tiffany Tseng, Ruijia Cheng, Jeffrey Nichols

Creating 2D animations is a complex, iterative process requiring continuous adjustments to movement, timing, and coordination of multiple elements within a scene. To support designers of varying levels of experience with animation design and implementation, we developed Keyframer, a design tool that generates animation code in response to natural language pr

Mengtao Tang, Daniel P. Stark, Richard S. Ellis, Fengwu Sun

JWST has recently sparked a new era of Ly$\alpha$ spectroscopy, delivering the first measurements of the Ly$\alpha$ escape fraction and velocity profile in typical galaxies at $z\simeq6-10$. These observations offer new prospects for insight into the earliest stages of reionization. But to realize this potential, we need robust models of Ly$\alpha$ propertie

A. Adel, Karim H. Mahmoud, Haitham A. Taha

We have conducted a comprehensive and systematic study of the proton radioactivity and $\alpha$-decay half-lives of neutron-deficient nuclei. This investigation involved the utilization of various Proximity potentials and also considered the incorporation of thermal effects. For the half-life calculations, we employed both temperature-independent and tempera

Diane Castonguay, Elisângela S. Dias, Fernanda N. Mesquita, Julliano R. Nascimento

A $r$-role assignment of a simple graph $G$ is an assignment of $r$ distinct roles to the vertices of $G$, such that two vertices with the same role have the same set of roles assigned to related vertices. Furthermore, a specific $r$-role assignment defines a role graph, in which the vertices are the distinct $r$ roles, and there is an edge between two roles

Ruben Martinez-Cantin, Manuel Lopes, Luis Montesano

We present an active learning algorithm for the problem of body schema learning, i.e. estimating a kinematic model of a serial robot. The learning process is done online using Recursive Least Squares (RLS) estimation, which outperforms gradient methods usually applied in the literature. In addiction, the method provides the required information to apply an a

W. Chango, R. Cerezo, C. Romero

In this paper we applied data fusion approaches for predicting the final academic performance of university students using multiple-source, multimodal data from blended learning environments. We collected and preprocessed data about first-year university students from different sources: theory classes, practical sessions, on-line Moodle sessions, and a final

Christian Acal, Ana M. Aguilera, Annalina Sarra, Adelia Evangelista

Faced with novel coronavirus outbreak, the most hard-hit countries adopted a lockdown strategy to contrast the spread of virus. Many studies have already documented that the COVID-19 control actions have resulted in improved air quality locally and around the world. Following these lines of research, we focus on air quality changes in the urban territory of

Tanner Mengel, Patrick Steffanic, Charles Hughes, Antonio Carlos Oliveira Da Silva

Jet measurements in heavy ion collisions at low jet momentum can provide constraints on the properties of the quark gluon plasma but are overwhelmed by a significant, fluctuating background. We build upon our previous work which demonstrated the ability of the jet multiplicity method to extend jet measurements into the domain of low jet momentum [1, Mengel:2

Alexander V. Khoperskov, Maxim V. Polyakov

The work includes a brief overview of the applications of the powerful and easy-to-perform method of Microwave Radiometry (MWR) for the diagnosis of various diseases. The main goal of this paper is to develop a method for diagnosing breast oncology based on machine learning algorithms using thermometric data, both real medical measurements and simulation res

Daniele Pusceddu, Massimo Bartoletti

Automated Market Makers (AMMs) are an integral component of the decentralized finance (DeFi) ecosystem, as they allow users to exchange crypto-assets without the need for trusted authorities or external price oracles. Although these protocols are based on relatively simple mechanisms, e.g., to algorithmically determine the exchange rate between crypto-assets

Andrew H. Proppe, Guillaume Thekkadath, Duncan England, Philip J. Bustard

In recent years, neural networks have been used to solve phase retrieval problems in imaging with superior accuracy and speed than traditional techniques, especially in the presence of noise. However, in the context of interferometric imaging, phase noise has been largely unaddressed by existing neural network architectures. Such noise arises naturally in an

Yuqing Kong

Recently a growing literature study a new forecast aggregation setting where each forecaster is additionally asked ``what's your expectation for the average of other forecasters' forecasts?''. However, most theoretic results in this setting focus on the scenarios where the additional second-order information helps optimally aggregate the forecasts. Here we a

Kolos Csaba Ágoston, Sándor Bozóki, László Csató

We consider clustering in group decision making where the opinions are given by pairwise comparison matrices. In particular, the k-medoids model is suggested to classify the matrices since it has a linear programming problem formulation that may contain any condition on the properties of the cluster centres. Its objective function depends on the measure of d

Joss Bland-Hawthorn, Thor Tepper-Garcia, Oscar Agertz, Christoph Federrath

Recent observations of high-redshift galaxies ($z \lesssim 7$) reveal that a substantial fraction have turbulent, gas-rich disks with well-ordered rotation and elevated levels of star formation. In some instances, disks show evidence of spiral arms, with bar-like structures. These remarkable observations have encouraged us to explore a new class of dynamical

Miao Zhang, Salman Rahman, Vishwali Mhasawade, Rumi Chunara

New data sources, and artificial intelligence (AI) methods to extract information from them are becoming plentiful, and relevant to decision making in many societal applications. An important example is street view imagery, available in over 100 countries, and considered for applications such as assessing built environment aspects in relation to community he

Javier Conde, Alejandro Pozo, Andrés Munoz-Arcentales, Johnny Choque

The term Data Space, understood as the secure exchange of data in distributed systems, ensuring openness, transparency, decentralization, sovereignty, and interoperability of information, has gained importance during the last years. However, Data Spaces are in an initial phase of definition, and new research is necessary to address their requirements. The Op

Alan R. Vazquez, Weng Kee Wong

Modern randomization methods in clinical trials are invariably adaptive, meaning that the assignment of the next subject to a treatment group uses the accumulated information in the trial. Some of the recent adaptive randomization methods use mathematical programming to construct attractive clinical trials that balance the group features, such as their sizes

Colin Alstad, Michael Burr, Oliver Clarke, Timothy Duff

The main results of this paper establish a partial correspondence between two previously-studied analogues of Groebner bases in the setting of algebras: namely, subalgebra (aka SAGBI) bases for quotients of polynomial rings and Khovanskii bases for valued algebras. We aim to bridge the gap between the concrete, computational aspects of the former and the mor

Naiqing Guan, Nick Koudas

Modern machine learning models require large labelled datasets to achieve good performance, but manually labelling large datasets is expensive and time-consuming. The data programming paradigm enables users to label large datasets efficiently but produces noisy labels, which deteriorates the downstream model's performance. The active learning paradigm, on th

Hanzhi Yang, Nina Mahmoudian

This paper describes the modeling of a custom-made underwater glider capable of flexible maneuvers in constrained areas and proposes a control system. Due to the lack of external actuators, underwater gliders can be greatly influenced by environmental disturbance. In addition, the nonlinearity of the system affects the motions during the transition between e

Robin van Haastrecht

Lieb and Solovej \cite{liebsolBloch} studied traces of quantum channels, defined by the leading component in the decomposition of the tensor product of two irreducible representations of $SU(2)$, to establish a Wehrl-type inequality for integrals of convex functions of matrix coefficients. It is proved that the integral is the limit of the trace of the funct

Thomas Sandholm, Sayandev Mukherjee, Bernardo A. Huberman

We present a novel approach to exploring innovation problem and solution domains using LLM fine-tuning with a custom idea database. By semantically traversing the bi-directional problem and solution tree at different temperature levels we achieve high diversity in solution edit distance while still remaining close to the original problem statement semantical

Alexander Kanitz, Matthew H. McLoughlin, Liam Beckman, Venkat S. Malladi

The Global Alliance for Genomics and Health (GA4GH) Task Execution Service (TES) API is a standardized schema and API for describing and executing batch execution tasks. It provides a common way to submit and manage tasks to a variety of compute environments, including on premise High Performance Compute and High Throughput Computing (HPC/HTC) systems, Cloud

Ming-Yang Zhuang, Qian Yang, Yue Shen, Monika Adamow

High-quality Extragalactic Legacy-field Monitoring (HELM) is a long-term observing program that photometrically monitors several well-studied extragalactic legacy fields with the Dark Energy Camera (DECam) imager on the CTIO 4m Blanco telescope. Since Feb 2019, HELM has been monitoring regions within COSMOS, XMM-LSS, CDF-S, S-CVZ, ELAIS-S1, and SDSS Stripe 8

Lisa Jeffrey, Yukai Zhang

The purpose of this note is to find explicit representatives in deRham cohomology for the generators of the cohomology of the moduli space of parabolic bundles, analogous to the results of \cite{groupcoho} for the moduli space of vector bundles. Further we use the explicit generators to compute the intersection pairing of its cohomology.

César Díaz, Antonio Fernández, Fernando Sacristán, Narciso García

We present a straightforward, non-intrusive adaptive bit rate streaming segment quality selection policy which aims at extending battery lifetime during playback while limiting the impact on the user's quality of experience, thus benefiting consumers of video streaming services. This policy relies on the relationship between the available channel bandwidth a

Javad Parsa, Cristian R. Rojas, Håkan Hjalmarsson

The maximum absolute correlation between regressors, which is called mutual coherence, plays an essential role in sparse estimation. A regressor matrix whose columns are highly correlated may result from optimal input design, since there is no constraint on the mutual coherence, making it difficult to handle sparse estimation. This paper aims to tackle this

Burak Kizilkaya, Changyang She, Guodong Zhao, Muhammad Ali Imran

Teleoperation can be very difficult due to limited perception, high communication latency, and limited degrees of freedom (DoFs) at the operator side. Autonomous teleoperation is proposed to overcome this difficulty by predicting user intentions and performing some parts of the task autonomously to decrease the demand on the operator and increase the task co

Philip Koopman

An October 2023 crash between a GM Cruise robotaxi and a pedestrian in San Francisco resulted not only in a severe injury, but also dramatic upheaval at that company that will likely have lasting effects throughout the industry. Is-sues stem not just from the loss events themselves, but also from how Cruise mishandled dealing with their robotaxi dragging a p

Zhuokai Zhao, Yibo Jiang, Yuxin Chen

Active Learning (AL) has gained prominence in integrating data-intensive machine learning (ML) models into domains with limited labeled data. However, its effectiveness diminishes significantly when the labeling budget is low. In this paper, we first empirically observe the performance degradation of existing AL algorithms in the low-budget settings, and the

Austin Xu, Will Monroe, Klinton Bicknell

We study the problem of zero-shot exercise retrieval in the context of online language learning, to give learners the ability to explicitly request personalized exercises via natural language. Using real-world data collected from language learners, we observe that vector similarity approaches poorly capture the relationship between exercise content and the l

Hainiu Xu, Runcong Zhao, Lixing Zhu, Jinhua Du

Neural Theory-of-Mind (N-ToM), machine's ability to understand and keep track of the mental states of others, is pivotal in developing socially intelligent agents. However, prevalent N-ToM benchmarks have several shortcomings, including the presence of ambiguous and artificial narratives, absence of personality traits and preferences, a lack of questions add