cometadata/datacite-rtg-text-other-reclassification-records huggingface dataset:
Detection of Time Lags Between Quasar Continuum Emission Bands based on Pan-STARRS Light-curves
Creators: name: Jiang, Yan-Fei
name: Green, Paul J.
name: Greene, Jenny E.
name: Morganson, Eric
name: Shen, Yue
name: Pancoast, Anna
name: MacLeod, Chelsea L.
name: Anderson, Scott F.
name: Brandt, W. N.
name: Grier, C. J.
name: Rix, H. W.
name: Ruan, John J.
name: Protopapas, Pavlos
name: Scott, Caroline
name: Burgett, W. S.
name: Hodapp, K. W.
name: Huber, M. E.
name: Kaiser, N.
name: Kudritzki, R. P.
name: Magnier, E. A.
name: Metcalfe, N.
name: Tonry, J. T.
name: Wainscoat, R. J.
name: Waters, C.
Publisher: name: arXiv
Year: 2016
Subjects: subject: High Energy Astrophysical Phenomena (astro-ph.HE)
subject: FOS: Physical sciences

description: We study the time lags between the continuum emission of quasars at different wavelengths, based on more than four years of multi-band ($g$, $r$, $i$, $z$) light-curves in the Pan-STARRS Medium Deep Fields. As photons from different bands emerge from different radial ranges in the accretion disk, the lags constrain the sizes of the accretion disks. We select 240 quasars with redshifts $z \approx 1$ or $z \approx 0.3$ that are relatively emission line free. The light curves are sampled from day to month timescales, which makes it possible to detect lags on the scale of the light crossing time of the accretion disks. With the code JAVELIN, we detect typical lags of several days in the rest frame between the $g$ band and the $riz$ bands. The detected lags are $\sim 2-3$ times larger than the light crossing time estimated from the standard thin disk model, consistent with the recently measured lag in NGC5548 and micro-lensing measurements of quasars. The lags in our sample are found to increase with increasing luminosity. Furthermore, the increase in lags going from $g-r$ to $g-i$ and then to $g-z$ is slower than predicted in the thin disk model, particularly for high luminosity quasars. The radial temperature profile in the disk must be different from what is assumed. We also find evidence that the lags decrease with increasing line ratios between ultraviolet FeII lines and MgII, which may point to changes in the accretion disk structure at higher metallicity.
description: 18 pages, 17 Figures, 3 Tables, submitted to ApJ
Chiral edge soliton in nonlinear Chern systems
Creators: name: Ezawa, Motohiko
Publisher: name: arXiv
Year: 2022
Subjects: subject: Mesoscale and Nanoscale Physics (cond-mat.mes-hall)
subject: Optics (physics.optics)
subject: FOS: Physical sciences

description: We study the effect on the chiral edge states by including a nonlinearity to a Chern insulator which has two chiral edge states with opposite chiralities. We explore a quench dynamics by giving a pulse to one site on an edge and analyzing the time evolution of a wave packet. Without the nonlinearity, an initial pulse spreads symmetrically and diffuses. On the other hand, with the nonlinearity present, a solitary wave is formed by the self-trapping effect of the nonlinear term and undergoes a unidirectional propagation along the edge, which we identify as a chiral edge soliton. A further increase of the nonlinearity induces a self-trapping transition, where the chiral wave packet stops its motion. It is intriguing that the nonlinearity is controlled only by changing the initial condition without changing a sample.
description: 5 pages, 6 figures
Person Re-Identification by Camera Correlation Aware Feature Augmentation
Creators: name: Chen, Ying-Cong
name: Zhu, Xiatian
name: Zheng, Wei-Shi
name: Lai, Jian-Huang
Publisher: name: arXiv
Year: 2017
Subjects: subject: Computer Vision and Pattern Recognition (cs.CV)
subject: FOS: Computer and information sciences

description: The challenge of person re-identification (re-id) is to match individual images of the same person captured by different non-overlapping camera views against significant and unknown cross-view feature distortion. While a large number of distance metric/subspace learning models have been developed for re-id, the cross-view transformations they learned are view-generic and thus potentially less effective in quantifying the feature distortion inherent to each camera view. Learning view-specific feature transformations for re-id (i.e., view-specific re-id), an under-studied approach, becomes an alternative resort for this problem. In this work, we formulate a novel view-specific person re-identification framework from the feature augmentation point of view, called Camera coRrelation Aware Feature augmenTation (CRAFT). Specifically, CRAFT performs cross-view adaptation by automatically measuring camera correlation from cross-view visual data distribution and adaptively conducting feature augmentation to transform the original features into a new adaptive space. Through our augmentation framework, view-generic learning algorithms can be readily generalized to learn and optimize view-specific sub-models whilst simultaneously modelling view-generic discrimination information. Therefore, our framework not only inherits the strength of view-generic model learning but also provides an effective way to take into account view specific characteristics. Our CRAFT framework can be extended to jointly learn view-specific feature transformations for person re-id across a large network with more than two cameras, a largely under-investigated but realistic re-id setting. Additionally, we present a domain-generic deep person appearance representation which is designed particularly to be towards view invariant for facilitating cross-view adaptation by CRAFT.
description: To Appear in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017
Analysis of Low Excitation HDO Transitions Toward the High-Mass Star-forming Regions G34.26+0.15, W51e$_{1}$/e$_{2}$, and W49N
Creators: name: Kulczak-Jastrzȩbska, Magda
Publisher: name: arXiv
Year: 2017
Subjects: subject: Astrophysics of Galaxies (astro-ph.GA)
subject: FOS: Physical sciences

description: We present observations of the ground state 1$_{0,1}$-0$_{0,0}$ rotational transition of HDO at 464.925 GHz and the 1$_{1,0}$-1$_{0,1}$ transition at 509.292 GHz toward the three high-mass star forming regions: G34.26+0.15, W49N, and W51e$_{1}$/e$_{2}$, carried out with the Caltech Submillimeter Observatory. The latter transition is observed for the first time from the ground. The spectra are modeled, together with observations of higher-energy HDO transitions, as well as submillimeter dust continuum fluxes from the literature, using a spherically symmetric radiative transfer model to derive the radial distribution of the HDO abundance in the target sources. The abundance profile is divided into an inner hot core region, with kinetic temperatures higher than 100~K, and a cold outer envelope with lower kinetic temperatures. The derived HDO abundance with respect to H$_2$ is (0.3-3.7)$\times 10^{-8}$ in the hot inner region ($T > 100 \mathrm{K}$) and (7.0-10.0)$\times 10^{-11}$ in the cold outer envelope. We also used two H$_{2}^{18}$O fundamental transitions to constrain the H$_{2}$O abundances in the outer envelopes. The HDO/H$_{2}$O ratios in these cold regions are found to be (1.8-3.1)$\times 10^{-3}$ and are consequently higher than in the hot inner regions of these sources.
Agile Effort Estimation: Have We Solved the Problem Yet? Insights From A Replication Study
Creators: name: Tawosi, Vali
name: Moussa, Rebecca
name: Sarro, Federica
Publisher: name: arXiv
Year: 2022
Subjects: subject: Software Engineering (cs.SE)
subject: Machine Learning (cs.LG)
subject: Machine Learning (stat.ML)
subject: FOS: Computer and information sciences

description: In the last decade, several studies have explored automated techniques to estimate the effort of agile software development. We perform a close replication and extension of a seminal work proposing the use of Deep Learning for Agile Effort Estimation (namely Deep-SE), which has set the state-of-the-art since. Specifically, we replicate three of the original research questions aiming at investigating the effectiveness of Deep-SE for both within-project and cross-project effort estimation. We benchmark Deep-SE against three baselines (i.e., Random, Mean and Median effort estimators) and a previously proposed method to estimate agile software project development effort (dubbed TF/IDF-SVM), as done in the original study. To this end, we use the data from the original study and an additional dataset of 31,960 issues mined from TAWOS, as using more data allows us to strengthen the confidence in the results, and to further mitigate external validity threats. The results of our replication show that Deep-SE outperforms the Median baseline estimator and TF/IDF-SVM in only very few cases with statistical significance (8/42 and 9/32 cases, respectively), thus confounding previous findings on the efficacy of Deep-SE. The two additional RQs revealed that neither augmenting the training set nor pre-training Deep-SE play lead to an improvement of its accuracy and convergence speed. These results suggest that using semantic similarity is not enough to differentiate user stories with respect to their story points; thus, future work has yet to explore and find new techniques and features that obtain accurate agile software development estimates.
description: Accepted for publication in IEEE Transactions on Software Engineering (TSE, 2022)
A finite element-convolutional neural network model (FE-CNN) for stress field analysis around arbitrary inclusions
Creators: Rezasefat, Mohammad
Hogan, James D.
Publisher: name: University of Alberta Library
Year: 2023

This study presents a data-driven finite element-machine learning surrogate model for predicting the end-to-end full-field stress distribution and stress concentration around an arbitrary-shaped inclusion. This is important because the model’s capacity to handle large datasets, consider variations in size and shape, and accurately replicate stress fields makes it a valuable tool for studying how inclusion characteristics affect material performance. An automatized dataset generation method using finite element simulation is proposed, validated, and used for attaining a dataset with one thousand inclusion shapes motivated by experimental observations and their corresponding spatially-varying stress distributions. A U-Net-based convolutional neural network (CNN) is trained using the dataset, and its performance is evaluated through quantitative and qualitative comparisons. The dataset, consisting of these stress data arrays, is directly fed into the CNN model for training and evaluation. This approach bypasses the need for converting the stress data into image format, allowing for a more direct and efficient input representation for the CNN. The model was evaluated through a series of sensitivity analyses, focusing on the impact of dataset size and model resolution on accuracy and performance. The results demonstrated that increasing the dataset size significantly improved the model’s prediction accuracy, as indicated by the correlation values. Additionally, the investigation into the effect of model resolution revealed that higher resolutions led to better stress field predictions and reduced error. Overall, the surrogate model proved effective in accurately predicting the effective stress concentration in inclusions, showcasing its potential in practical applications requiring stress analysis such as structural engineering, material design, failure analysis, and multi-scale modeling.
Theory of collective topologically-protected Majorana fermion excitations of networks of localized Majorana modes
Creators: name: Damle, Kedar
Publisher: name: arXiv
Year: 2022
Subjects: subject: Strongly Correlated Electrons (cond-mat.str-el)
subject: FOS: Physical sciences

description: Predictions of localized Majorana modes, and ideas for manipulating these degrees of freedom, are the two key ingredients in proposals for physical platforms for Majorana quantum computation. Several proposals envisage a scalable network of such Majorana modes coupled bilinearly to each other by quantum-mechanical mixing amplitudes. Here, we develop a theoretical framework for characterizing collective topologically protected zero-energy Majorana fermion excitations of such networks of localized Majorana modes. A key ingredient in our work is the Gallai-Edmonds decomposition of a general graph, which we use to obtain an alternate ``local'' proof of a ``global'' result of Lov{á}sz and Anderson on the dimension of the topologically protected null space of {\em real skew-symmetric} (or pure-imaginary hermitean) adjacency matrices of general graphs. Our approach to Lov{á}sz and Anderson's result constructs a maximally-localized basis for the said null-space from the Gallai-Edmonds decomposition of the graph. Applied to the graph of the Majorana network in question, this gives a method for characterizing basis-independent properties of these collective topologically protected Majorana fermion excitations, and relating these properties to the correlation function of monomers in the ensemble of maximum matchings (maximally-packed dimer covers) of the corresponding network graph. Our approach can also be used to identify signatures of zero-energy excitations in systems modeled by a free-fermion Hamiltonian with a hopping matrix of this type; an interesting example is provided by vacancy-induced Curie tails in generalizations (on non-bipartite lattices) of Kitaev's honeycomb model.
description: published version with two additional figures
Plasmonic complex fluids of nematiclike and helicoidal self-assemblies of gold nanorods with a negative order parameter
Creators: name: Liu, Qingkun
name: Senyuk, Bohdan
name: Tang, Jianwei
name: Lee, Taewoo
name: Qian, Jun
name: He, Sailing
name: Smalyukh, Ivan I.
Publisher: name: arXiv
Year: 2016
Subjects: subject: Soft Condensed Matter (cond-mat.soft)
subject: Materials Science (cond-mat.mtrl-sci)
subject: FOS: Physical sciences

description: We describe a soft matter system of self-organized oblate micelles and plasmonic gold nanorods that exhibit a negative orientational order parameter. Because of anisotropic surface anchoring interactions, colloidal gold nanorods tend to align perpendicular to the director describing the average orientation of normals to the discoidal micelles. Helicoidal structures of highly concentrated nanorods with a negative order parameter are realized by adding a chiral additive and are further controlled by means of confinement and mechanical stress. Polarization-sensitive absorption, scattering, and two-photon luminescence are used to characterize orientations and spatial distributions of nanorods. Self-alignment and effective-medium optical properties of these hybrid inorganic-organic complex fluids match predictions of a simple model based on anisotropic surface anchoring interactions of nanorods with the structured host medium.
Spiker: an FPGA-optimized Hardware acceleration for Spiking Neural Networks
Creators: name: Carpegna, Alessio
name: Savino, Alessandro
name: Di Carlo, Stefano
Publisher: name: arXiv
Year: 2022
Subjects: subject: Neural and Evolutionary Computing (cs.NE)
subject: Artificial Intelligence (cs.AI)
subject: FOS: Computer and information sciences

description: Spiking Neural Networks (SNN) are an emerging type of biologically plausible and efficient Artificial Neural Network (ANN). This work presents the development of a hardware accelerator for a SNN for high-performance inference, targeting a Xilinx Artix-7 Field Programmable Gate Array (FPGA). The model used inside the neuron is the Leaky Integrate and Fire (LIF). The execution is clock-driven, meaning that the internal state of the neuron is updated at every clock cycle, even in absence of spikes. The inference capabilities of the accelerator are evaluated using the MINST dataset. The training is performed offline on a full precision model. The results show a good improvement in performance if compared with the state-of-the-art accelerators, requiring 215μs per image. The energy consumption is slightly higher than the most optimized design, with an average value of 13mJ per image. The test design consists of a single layer of four-hundred neurons and uses around 40% of the available resources on the FPGA. This makes it suitable for a time-constrained application at the edge, leaving space for other acceleration tasks on the FPGA.
description: 6 pages, 3 figures, 4 tables
Data Based Identification and Prediction of Nonlinear and Complex Dynamical Systems
Creators: name: Wang, Wenxu
name: Lai, Ying-Cheng
name: Grebogi, Celso
Publisher: name: arXiv
Year: 2017
Subjects: subject: Data Analysis, Statistics and Probability (physics.data-an)
subject: Chaotic Dynamics (nlin.CD)
subject: Physics and Society (physics.soc-ph)
subject: FOS: Physical sciences

description: The problem of reconstructing nonlinear and complex dynamical systems from measured data or time series is central to many scientific disciplines including physical, biological, computer, and social sciences, as well as engineering and economics. In this paper, we review the recent advances in this forefront and rapidly evolving field, aiming to cover topics such as compressive sensing (a novel optimization paradigm for sparse-signal reconstruction), noised-induced dynamical mapping, perturbations, reverse engineering, synchronization, inner composition alignment, global silencing, Granger Causality and alternative optimization algorithms. Often, these rely on various concepts from statistical and nonlinear physics such as phase transitions, bifurcation, stabilities, and robustness. The methodologies have the potential to significantly improve our ability to understand a variety of complex dynamical systems ranging from gene regulatory systems to social networks towards the ultimate goal of controlling such systems. Despite recent progress, many challenges remain. A purpose of this Review is then to point out the specific difficulties as they arise from different contexts, so as to stimulate further efforts in this interdisciplinary field.
description: 138 pages, 37 figures, Review Article
Bounding Entanglement Entropy Using Zeros of Local Correlation Matrices
Creators: name: Yao, Zhiyuan
name: Pan, Lei
name: Liu, Shang
name: Zhang, Pengfei
Publisher: name: arXiv
Year: 2022
Subjects: subject: Strongly Correlated Electrons (cond-mat.str-el)
subject: Quantum Gases (cond-mat.quant-gas)
subject: Quantum Physics (quant-ph)
subject: FOS: Physical sciences

description: Correlation functions and entanglement are two different aspects to characterize quantum many-body states. While many correlation functions are experimentally accessible, entanglement entropy (EE), the simplest characterization of quantum entanglement, is usually difficult to measure. In this Letter, we propose a protocol to bound EE by local measurements. This protocol utilizes local correlation matrices and focuses on their (approximate) zero eigenvalues. Given a quantum state, each (approximate) zero eigenvalue can be used to define a set of local projection operators. An auxiliary Hamiltonian can then be constructed by summing these projectors. When the construction only involves projectors of zero eigenvalues, we prove the EE of a subsystem is bounded by the ground-state degeneracy of the auxiliary Hamiltonian on this subsystem. When projectors from nonzero eigenvalues are included, we show the EE can be bounded by a thermal entropy of the subsystem. Our protocol can be applied experimentally to investigate exotic quantum many-body states prepared in quantum simulators.
description: Published version, 4 pages + appendices & references, 4 figures in the main text
The Warm Circum-Galactic Medium: 10^5-6 K Gas Associated with a Single Galaxy Halo or with an Entire Group of Galaxies?
Creators: name: Stocke, John
name: Keeney, Brian
name: Danforth, Charles
name: Oppenheimer, Ben
name: Pratt, Cameron
name: Berlind, Andreas
Publisher: name: arXiv
Year: 2017
Subjects: subject: Astrophysics of Galaxies (astro-ph.GA)
subject: FOS: Physical sciences

description: In preparation for a Hubble Space Telescope (HST) observing project using the Cosmic Origins Spectrograph (COS), the positions of all AGN targets having high-S/N far-UV G130M spectra were cross-correlated with a large catalog of low-redshift galaxy groups homogeneously selected from the spectroscopic sample of the Sloan Digital Sky Survey (SDSS). Searching for targets behind only those groups at z = 0.1-0.2 (which places the OVI doublet in the wavelength region of peak COS sensitivity) we identified only one potential S/N = 15-20 target, FBQS 1010+3003. An OVI-only absorber was found in its G130M spectrum at z = 0.11326, close to the redshift of a foreground small group of luminous galaxies at z = 0.11685. Because there is no associated Lyalpha absorption, any characterization of this absorber is necessarily minimal; however, the OVI detection likely traces "warm" gas in collisional ionization equilibrium at T ~ 300,000 K. While this discovery is consistent with being interface gas between cooler, photoionized clouds and a hotter intra-group medium, it could also be warm, interface gas associated with the circum-galactic medium (CGM) of the single closest galaxy. In this case a detailed analysis of the galaxy distribution (complete to 0.2 L*) strongly favors the individual galaxy association. This analysis highlights the necessity of both high-S/N > 20 COS data and a deep galaxy redshift survey of the region in order to test more rigorously the association of OVI-absorbing gas with a galaxy group. A Cycle 23 HST/COS program currently is targeting 10 UV-bright AGN behind 12 low-redshift galaxy groups to test the warm, group gas hypothesis.
description: 11 pages, 3 figures, submitted to ApJ
Ordinal Causal Discovery
Creators: name: Ni, Yang
name: Mallick, Bani
Publisher: name: arXiv
Year: 2022
Subjects: subject: Methodology (stat.ME)
subject: Machine Learning (stat.ML)
subject: FOS: Computer and information sciences

description: Causal discovery for purely observational, categorical data is a long-standing challenging problem. Unlike continuous data, the vast majority of existing methods for categorical data focus on inferring the Markov equivalence class only, which leaves the direction of some causal relationships undetermined. This paper proposes an identifiable ordinal causal discovery method that exploits the ordinal information contained in many real-world applications to uniquely identify the causal structure. The proposed method is applicable beyond ordinal data via data discretization. Through real-world and synthetic experiments, we demonstrate that the proposed ordinal causal discovery method combined with simple score-and-search algorithms has favorable and robust performance compared to state-of-the-art alternative methods in both ordinal categorical and non-categorical data. An accompanied R package OrdCD is freely available on CRAN and at https://web.stat.tamu.edu/~yni/files/OrdCD_1.0.0.tar.gz.
Audio-based performance evaluation of squash players
Creators: name: Hajdu-Szucs, Katalin
name: Fenyvesi, Nora
name: Steger, Jozsef
name: Vattay, Gabor
Publisher: name: arXiv
Year: 2017
Subjects: subject: Audio and Speech Processing (eess.AS)
subject: Sound (cs.SD)
subject: FOS: Electrical engineering, electronic engineering, information engineering
subject: FOS: Computer and information sciences

description: In competitive sports it is often very hard to quantify the performance. A player to score or overtake may depend on only millesimal of seconds or millimeters. In racquet sports like tennis, table tennis and squash many events will occur in a short time duration, whose recording and analysis can help reveal the differences in performance. In this paper we show that it is possible to architect a framework that utilizes the characteristic sound patterns to precisely classify the types of and localize the positions of these events. From these basic information the shot types and the ball speed along the trajectories can be estimated. Comparing these estimates with the optimal speed and target the precision of the shot can be defined. The detailed shot statistics and precision information significantly enriches and improves data available today. Feeding them back to the players and the coaches facilitates to describe playing performance objectively and to improve strategy skills. The framework is implemented, its hardware and software components are installed and tested in a squash court.
Skeleton-Based Action Segmentation with Multi-Stage Spatial-Temporal Graph Convolutional Neural Networks
Creators: name: Filtjens, Benjamin
name: Vanrumste, Bart
name: Slaets, Peter
Publisher: name: arXiv
Year: 2022
Subjects: subject: Computer Vision and Pattern Recognition (cs.CV)
subject: FOS: Computer and information sciences

description: The ability to identify and temporally segment fine-grained actions in motion capture sequences is crucial for applications in human movement analysis. Motion capture is typically performed with optical or inertial measurement systems, which encode human movement as a time series of human joint locations and orientations or their higher-order representations. State-of-the-art action segmentation approaches use multiple stages of temporal convolutions. The main idea is to generate an initial prediction with several layers of temporal convolutions and refine these predictions over multiple stages, also with temporal convolutions. Although these approaches capture long-term temporal patterns, the initial predictions do not adequately consider the spatial hierarchy among the human joints. To address this limitation, we recently introduced multi-stage spatial-temporal graph convolutional neural networks (MS-GCN). Our framework replaces the initial stage of temporal convolutions with spatial graph convolutions and dilated temporal convolutions, which better exploit the spatial configuration of the joints and their long-term temporal dynamics. Our framework was compared to four strong baselines on five tasks. Experimental results demonstrate that our framework is a strong baseline for skeleton-based action segmentation.
The craft and coordination of data curation: complicating "workflow" views of data science
Creators: name: Thomer, Andrea K.
name: Akmon, Dharma
name: York, Jeremy
name: Tyler, Allison R. B.
name: Polasek, Faye
name: Lafia, Sara
name: Hemphill, Libby
name: Yakel, Elizabeth
Publisher: name: arXiv
Year: 2022
Subjects: subject: Digital Libraries (cs.DL)
subject: Databases (cs.DB)
subject: Human-Computer Interaction (cs.HC)
subject: FOS: Computer and information sciences

description: Data curation is the process of making a dataset fit-for-use and archiveable. It is critical to data-intensive science because it makes complex data pipelines possible, makes studies reproducible, and makes data (re)usable. Yet the complexities of the hands-on, technical and intellectual work of data curation is frequently overlooked or downplayed. Obscuring the work of data curation not only renders the labor and contributions of the data curators invisible; it also makes it harder to tease out the impact curators' work has on the later usability, reliability, and reproducibility of data. To better understand the specific work of data curation -- and thereby, explore ways of showing curators' impact -- we conducted a close examination of data curation at a large social science data repository, the Inter-university Consortium of Political and Social Research (ICPSR). We asked, What does curatorial work entail at ICPSR, and what work is more or less visible to different stakeholders and in different contexts? And, how is that curatorial work coordinated across the organization? We triangulate accounts of data curation from interviews and records of curation in Jira tickets to develop a rich and detailed account of curatorial work. We find that curators describe a number of craft practices needed to perform their work, which defies the rote sequence of events implied by many lifecycle or workflow models. Further, we show how best practices and craft practices are deeply intertwined.
description: submitted to CSCW 2022 (Feb revision cycle)
Multi-Modal Knowledge Graph Construction and Application: A Survey
Creators: name: Zhu, Xiangru
name: Li, Zhixu
name: Wang, Xiaodan
name: Jiang, Xueyao
name: Sun, Penglei
name: Wang, Xuwu
name: Xiao, Yanghua
name: Yuan, Nicholas Jing
Publisher: name: arXiv
Year: 2022
Subjects: subject: Artificial Intelligence (cs.AI)
subject: Computation and Language (cs.CL)
subject: Computer Vision and Pattern Recognition (cs.CV)
subject: FOS: Computer and information sciences
subject: I.2.4; E.0; E.2

description: Recent years have witnessed the resurgence of knowledge engineering which is featured by the fast growth of knowledge graphs. However, most of existing knowledge graphs are represented with pure symbols, which hurts the machine's capability to understand the real world. The multi-modalization of knowledge graphs is an inevitable key step towards the realization of human-level machine intelligence. The results of this endeavor are Multi-modal Knowledge Graphs (MMKGs). In this survey on MMKGs constructed by texts and images, we first give definitions of MMKGs, followed with the preliminaries on multi-modal tasks and techniques. We then systematically review the challenges, progresses and opportunities on the construction and application of MMKGs respectively, with detailed analyses of the strength and weakness of different solutions. We finalize this survey with open research problems relevant to MMKGs.
description: 20 pages, 8 figures, 6 tables. Accepted by TKDE 2022
Multimodal Clustering for Community Detection
Creators: name: Ignatov, Dmitry I.
name: Semenov, Alexander
name: Komissarova, Daria
name: Gnatyshak, Dmitry V.
Publisher: name: arXiv
Year: 2017
Subjects: subject: Social and Information Networks (cs.SI)
subject: Discrete Mathematics (cs.DM)
subject: Machine Learning (stat.ML)
subject: FOS: Computer and information sciences
subject: I.5.3; J.4
subject: 62H30, 91C20, 62H30

description: Multimodal clustering is an unsupervised technique for mining interesting patterns in $n$-adic binary relations or $n$-mode networks. Among different types of such generalized patterns one can find biclusters and formal concepts (maximal bicliques) for 2-mode case, triclusters and triconcepts for 3-mode case, closed $n$-sets for $n$-mode case, etc. Object-attribute biclustering (OA-biclustering) for mining large binary datatables (formal contexts or 2-mode networks) arose by the end of the last decade due to intractability of computation problems related to formal concepts; this type of patterns was proposed as a meaningful and scalable approximation of formal concepts. In this paper, our aim is to present recent advance in OA-biclustering and its extensions to mining multi-mode communities in SNA setting. We also discuss connection between clustering coefficients known in SNA community for 1-mode and 2-mode networks and OA-bicluster density, the main quality measure of an OA-bicluster. Our experiments with 2-, 3-, and 4-mode large real-world networks show that this type of patterns is suitable for community detection in multi-mode cases within reasonable time even though the number of corresponding $n$-cliques is still unknown due to computation difficulties. An interpretation of OA-biclusters for 1-mode networks is provided as well.
Artificial Intelligence-Based Analytics for Impacts of COVID-19 and Online Learning on College Students' Mental Health
Creators: name: Rezapour, Mostafa
name: Elmshaeuser, Scott K.
Publisher: name: arXiv
Year: 2022
Subjects: subject: Computers and Society (cs.CY)
subject: Machine Learning (cs.LG)
subject: FOS: Computer and information sciences

description: COVID-19, the disease caused by the novel coronavirus (SARS-CoV-2), first emerged in Wuhan, China late in December 2019. Not long after, the virus spread worldwide and was declared a pandemic by the World Health Organization in March 2020. This caused many changes around the world and in the United States, including an educational shift towards online learning. In this paper, we seek to understand how the COVID-19 pandemic and increase in online learning impact college students' emotional wellbeing. We use several machine learning and statistical models to analyze data collected by the Faculty of Public Administration at the University of Ljubljana, Slovenia in conjunction with an international consortium of universities, other higher education institutions, and students' associations. Our results indicate that features related to students' academic life have the largest impact on their emotional wellbeing. Other important factors include students' satisfaction with their university's and government's handling of the pandemic as well as students' financial security.
description: 41 pages, 31 Figures
Stellar modeling via the Tolman IV solution: The cases of the massive pulsar J0740+6620 and the HESS J1731-347 compact object
Creators: name: Panotopoulos, Grigoris
Publisher: name: arXiv
Year: 2024
Subjects: subject: General Relativity and Quantum Cosmology (gr-qc)
subject: High Energy Astrophysical Phenomena (astro-ph.HE)
subject: Solar and Stellar Astrophysics (astro-ph.SR)
subject: FOS: Physical sciences

description: We model compact objects of known stellar mass and radius made of isotropic matter within Einstein's gravity. The interior solution describing hydrostatic equilibrium we are using throughout the manuscript corresponds to the Tolman IV exact analytic solution obtained long time ago. The three free parameters of the solutions are determined imposing the matching conditions for objects of known stellar mass and radius. Finally, using well established criteria it is shown that contrary to the Kohler Chao solution, the Tolman IV solution is compatible with all requirements for well behaved and realistic solutions. except for the relativistic adiabatic index that diverges at the surface of the stars. The divergence of the index $Γ$ may be resolved including a thin crust assuming a polytropic equation-of-state, which is precisely the case seen in studies of neutron stars. To the best of our knowledge, we model here for the first time the recently discovered massive pulsar PSR J0740+6620 and the strangely light HESS compact object via the Tolman IV solution. The present work may be of interest to model builders as well as a useful reference for future research.
description: 12 pages, 5 figures
Cohen-Macaulayness of trivial extensions
Creators: name: Mahdikhani, A.
name: Sahandi, P.
name: Shirmohammadi, N.
Publisher: name: arXiv
Year: 2017
Subjects: subject: Commutative Algebra (math.AC)
subject: FOS: Mathematics

description: Our goal is to determine when the trivial extensions of commutative rings by modules are Cohen-Macaulay in the sense of Hamilton and Marley. For this purpose, we provide a generalization of the concept of Cohen-Macaulayness of rings to modules.
description: to appear in J. Algebra Appl
Bimodule Connections for Relative Line Modules over the Irreducible Quantum Flag Manifolds
Creators: name: Carotenuto, Alessandro
name: Buachalla, Réamonn Ó
Publisher: name: arXiv
Year: 2022
Subjects: subject: Quantum Algebra (math.QA)
subject: Differential Geometry (math.DG)
subject: FOS: Mathematics

description: It was recently shown (by the second author and Díaz García, Krutov, Somberg, and Strung) that every relative line module over an irreducible quantum flag manifold $\mathcal{O}_q(G/L_S)$ admits a unique $\mathcal{O}_q(G)$-covariant connection with respect to the Heckenberger-Kolb differential calculus $Ω^1_q(G/L_S)$. In this paper we show that these connections are bimodule connections with an invertible associated bimodule map. This is proved by applying general results of Beggs and Majid, on principal connections for quantum principal bundles, to the quantum principal bundle presentation of the Heckenberger-Kolb calculi recently constructed by the authors and Díaz García. Explicit presentations of the associated bimodule maps are given first in terms of generalised quantum determinants, then in terms of the FRT presentation of the algebra $\mathcal{O}_q(G)$, and finally in terms of Takeuchi's categorical equivalence for relative Hopf modules.
A splitting theorem for the Seiberg-Witten invariant of a homology $S^1 \times S^3$
Creators: name: Lin, Jianfeng
name: Ruberman, Daniel
name: Saveliev, Nikolai
Publisher: name: arXiv
Year: 2017
Subjects: subject: Geometric Topology (math.GT)
subject: Analysis of PDEs (math.AP)
subject: Differential Geometry (math.DG)
subject: FOS: Mathematics
subject: 57R57, 57R58, 57M27, 53C21, 58J28, 58J35

description: We study the Seiberg-Witten invariant $λ_{\rm{SW}} (X)$ of smooth spin $4$-manifolds $X$ with integral homology of $S^1\times S^3$ defined by Mrowka, Ruberman, and Saveliev as a signed count of irreducible monopoles amended by an index-theoretic correction term. We prove a splitting formula for this invariant in terms of the Frøyshov invariant $h(X)$ and a certain Lefschetz number in the reduced monopole Floer homology of Kronheimer and Mrowka. We apply this formula to obstruct existence of metrics of positive scalar curvature on certain 4-manifolds, and to exhibit new classes of integral homology $3$-spheres of Rohlin invariant one which have infinite order in the homology cobordism group.
description: 78 pages, 1 figure; added results on Z/2 homology cobordisms and knot concordances
Using analog scrambling circuits for automotive sensor integrity and authenticity
Creators: name: Chenet, Cristiano Pegoraro
name: Savino, Alessandro
name: di Carlo, Stefano
Publisher: name: arXiv
Year: 2022
Subjects: subject: Cryptography and Security (cs.CR)
subject: FOS: Computer and information sciences

description: The automotive domain rapidly increases the embedded amount of complex and interconnected electronics systems. A considerable proportion of them are real-time safety-critical devices and must be protected against cybersecurity attacks. Recent regulations impose carmakers to protect vehicles against replacing trusted electronic hardware and manipulating the information collected by sensors. Analog sensors are critical elements whose security is now strictly regulated by the new UN R155 recommendation but lacks well-developed and established solutions. This work takes a step forward in this direction, adding integrity and authentication to automotive analog sensors proposing a schema to create analog signatures based on a scrambling mechanism implemented with commercial-of-the-shelf (COTS) operational amplifiers. The proposed architecture implements a hardware secret and a hard-to-invert exponential function to generate a signal's signature. A prototype of the circuit was implemented and simulated on LTspice. Preliminary results show the feasibility of the proposed schema and provide interesting hints for further developments to increase the robustness of the approach.
description: 7 pages, 12 figures
Fast and Modular Autonomy Software for Autonomous Racing Vehicles
Creators: name: Saba, Andrew
name: Adetunji, Aderotimi
name: Johnson, Adam
name: Kothari, Aadi
name: Sivaprakasam, Matthew
name: Spisak, Joshua
name: Bharatia, Prem
name: Chauhan, Arjun
name: Duff, Brendan
name: Gasparro, Noah
name: King, Charles
name: Larkin, Ryan
name: Mao, Brian
name: Nye, Micah
name: Parashar, Anjali
name: Attias, Joseph
name: Balciunas, Aurimas
name: Brown, Austin
name: Chang, Chris
name: Gao, Ming
name: Heredia, Cindy
name: Keats, Andrew
name: Lavariega, Jose
name: Muckelroy, William
name: Slavescu, Andre
name: Stathas, Nickolas
name: Suvarna, Nayana
name: Zhang, Chuan Tian
name: Scherer, Sebastian
name: Ramanan, Deva
Publisher: name: arXiv
Year: 2024
Subjects: subject: Robotics (cs.RO)
subject: Artificial Intelligence (cs.AI)
subject: Software Engineering (cs.SE)
subject: FOS: Computer and information sciences

description: Autonomous motorsports aim to replicate the human racecar driver with software and sensors. As in traditional motorsports, Autonomous Racing Vehicles (ARVs) are pushed to their handling limits in multi-agent scenarios at extremely high ($\geq 150mph$) speeds. This Operational Design Domain (ODD) presents unique challenges across the autonomy stack. The Indy Autonomous Challenge (IAC) is an international competition aiming to advance autonomous vehicle development through ARV competitions. While far from challenging what a human racecar driver can do, the IAC is pushing the state of the art by facilitating full-sized ARV competitions. This paper details the MIT-Pitt-RW Team's approach to autonomous racing in the IAC. In this work, we present our modular and fast approach to agent detection, motion planning and controls to create an autonomy stack. We also provide analysis of the performance of the software stack in single and multi-agent scenarios for rapid deployment in a fast-paced competition environment. We also cover what did and did not work when deployed on a physical system the Dallara AV-21 platform and potential improvements to address these shortcomings. Finally, we convey lessons learned and discuss limitations and future directions for improvement.
description: Published in Journal of Field Robotics
TRILLsson: Distilled Universal Paralinguistic Speech Representations
Creators: name: Shor, Joel
name: Venugopalan, Subhashini
Publisher: name: arXiv
Year: 2022
Subjects: subject: Audio and Speech Processing (eess.AS)
subject: Computation and Language (cs.CL)
subject: Machine Learning (cs.LG)
subject: Sound (cs.SD)
subject: FOS: Electrical engineering, electronic engineering, information engineering
subject: FOS: Computer and information sciences

description: Recent advances in self-supervision have dramatically improved the quality of speech representations. However, deployment of state-of-the-art embedding models on devices has been restricted due to their limited public availability and large resource footprint. Our work addresses these issues by publicly releasing a collection of paralinguistic speech models that are small and near state-of-the-art performance. Our approach is based on knowledge distillation, and our models are distilled on public data only. We explore different architectures and thoroughly evaluate our models on the Non-Semantic Speech (NOSS) benchmark. Our largest distilled model is less than 15% the size of the original model (314MB vs 2.2GB), achieves over 96% the accuracy on 6 of 7 tasks, and is trained on 6.5% the data. The smallest model is 1% in size (22MB) and achieves over 90% the accuracy on 6 of 7 tasks. Our models outperform the open source Wav2Vec 2.0 model on 6 of 7 tasks, and our smallest model outperforms the open source Wav2Vec 2.0 on both emotion recognition tasks despite being 7% the size.
description: Submitted to Interspeech 2022
Was it diffusion? Exploring the spread of daily physical activity policies in Canada
Creators: name: Campbell, Elizabeth J.
name: Olstad, Dana Lee
name: Spence, John C.
name: Storey, Kate E.
name: Nykiforuk, Candace I.J.
Publisher: name: The Health & Fitness Journal of Canada
Year: 2023

description: Background: Between 2005 and 2010, five Canadian provinces adopted daily physical activity (DPA) policies. This study investigated the adoption and spread of those DPA policies in the 5-year period. Purpose: The purpose of this study was to investigate the role, if any, of diffusion in the adoption and spread of DPA policies across provinces in Canada over a 5-year period. Methods: Semi-structured interviews were conducted with 15 DPA policy influencers. Transcripts were analyzed using directed content analysis to examine alignment with an established diffusion framework. Findings were also examined for consistency with mechanisms of policy diffusion and alternative explanations of policy spread. Results: Participant responses aligned most closely with diffusion framework components of attributes of the innovation, system antecedents for innovation, implementation and routinization, receptive context for change, assimilation by the system, system readiness for innovation, interorganizational networks and collaboration, and communication and influence. Findings also revealed evidence of policy learning, imitation, and competition across jurisdictions as the dominant mechanisms of policy diffusion. There was limited evidence that common shock and independent causation contributed to policy spread. Conclusions: The spread of DPA policies across Canada between 2005 and 2010 was consistent with theoretical concepts and mechanisms of policy diffusion.
description: The Health & Fitness Journal of Canada, Vol. 16 No. 1 (2023)
Topological colloids
Creators: name: Senyuk, Bohdan
name: Liu, Qingkun
name: He, Sailing
name: Kamien, Randall D.
name: Kusner, Robert B.
name: Lubensky, Tom C.
name: Smalyukh, Ivan I.
Publisher: name: arXiv
Year: 2016
Subjects: subject: Soft Condensed Matter (cond-mat.soft)
subject: FOS: Physical sciences

description: Abundant in nature, colloids also find increasingly important applications in science and technology, ranging from direct probing of kinetics in crystals and glasses to fabrication of third-generation quantum-dot solar cells. Because naturally occurring colloids have a shape that is typically determined by minimization of interfacial tension (for example, during phase separation) or faceted crystal growth, their surfaces tend to have minimum-area spherical or topologically equivalent shapes such as prisms and irregular grains (all continuously deformable - homeomorphic - to spheres). Although toroidal DNA condensates and vesicles with different numbers of handles can exist and soft matter defects can be shaped as rings and knots, the role of particle topology in colloidal systems remains unexplored. Here we fabricate and study colloidal particles with different numbers of handles and genus g ranging from 1 to 5. When introduced into a nematic liquid crystal - a fluid made of rod-like molecules that spontaneously align along the so-called "director" - these particles induce three-dimensional director fields and topological defects dictated by colloidal topology. Whereas electric fields, photothermal melting and laser tweezing cause transformations between configurations of particle-induced structures, three-dimensional nonlinear optical imaging reveals that topological charge is conserved and that the total charge of particle-induced defects always obeys predictions of the Gauss-Bonnet and Poincare-Hopf index theorems. This allows us to establish and experimentally test the procedure for assignment and summation of topological charges in three-dimensional director fields. Our findings lay the groundwork for new applications of colloids and liquid crystals that range from topological memory devices, through new types of self-assembly, to the experimental study of low-dimensional topology.