Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-linear Visual Knowledge Discovery with Elliptic Paired Coordinates

It is challenging for humans to enable visual knowledge discovery in data with more than 2-3 dimensions with a naked eye. This chapter explores the efficiency of discovering predictive machine learning models interactively using new Elliptic Paired coordinates (EPC) visualizations. It is shown that EPC are capable to visualize multidimensional data and support visual machine learning with preservation of multidimensional information in 2-D. Relative to parallel and radial coordinates, EPC visualization requires only a half of the visual elements for each n-D point. An interactive software system EllipseVis, which is developed in this work, processes high-dimensional datasets, creates EPC visualizations, and produces predictive classification models by discovering dominance rules in EPC. By using interactive and automatic processes it discovers zones in EPC with a high dominance of a single class. The EPC methodology has been successful in discovering non-linear predictive models with high coverage and precision in the computational experiments. This can benefit multiple domains by producing visually appealing dominance rules. This chapter presents results of successful testing the EPC non-linear methodology in experiments using real and simulated data, EPC generalized to the Dynamic Elliptic Paired Coordinates (DEPC), incorporation of the weights of coordinates to optimize the visual discovery, introduction of an alternative EPC design and introduction of the concept of incompact machine learning methodology based on EPC/DEPC.
MOOCRep: A Unified Pre-trained Embedding of MOOC Entities

Many machine learning models have been built to tackle information overload issues on Massive Open Online Courses (MOOC) platforms. These models rely on learning powerful representations of MOOC entities. However, they suffer from the problem of scarce expert label data. To overcome this problem, we propose to learn pre-trained representations of MOOC entities using abundant unlabeled data from the structure of MOOCs which can directly be applied to the downstream tasks. While existing pre-training methods have been successful in NLP areas as they learn powerful textual representation, their models do not leverage the richer information about MOOC entities. This richer information includes the graph relationship between the lectures, concepts, and courses along with the domain knowledge about the complexity of a concept. We develop MOOCRep, a novel method based on Transformer language model trained with two pre-training objectives : 1) graph-based objective to capture the powerful signal of entities and relations that exist in the graph, and 2) domain-oriented objective to effectively incorporate the complexity level of concepts. Our experiments reveal that MOOCRep's embeddings outperform state-of-the-art representation learning methods on two tasks important for education community, concept pre-requisite prediction and lecture recommendation.
Extending Text-to-Speech Synthesis with Articulatory Movement Prediction using Ultrasound Tongue Imaging

In this paper, we present our first experiments in text-to-articulation prediction, using ultrasound tongue image targets. We extend a traditional (vocoder-based) DNN-TTS framework with predicting PCA-compressed ultrasound images, of which the continuous tongue motion can be reconstructed in synchrony with synthesized speech. We use the data of eight speakers, train fully connected and recurrent neural networks, and show that FC-DNNs are more suitable for the prediction of sequential data than LSTMs, in case of limited training data. Objective experiments and visualized predictions show that the proposed solution is feasible and the generated ultrasound videos are close to natural tongue movement. Articulatory movement prediction from text input can be useful for audiovisual speech synthesis or computer-assisted pronunciation training.
The Nature of Hi-absorption-selected Galaxies at z ≈ 4

We report a Karl G. Jansky Very Large Array (JVLA) search for redshifted CO(1–0) or CO(2–1) emission, and a Hubble Space Telescope Wide Field Camera 3 (HST-WFC3) search for rest-frame near-ultraviolet (NUV) stellar emission, from seven Hi-selected galaxies associated with high-metallicity ([M/H] ≥ −1.3) damped Lyα absorbers (DLAs) at z ≈ 4. The galaxies were earlier identified by Atacama Large Millimeter/submillimeter Array imaging of their [Cii] 158 μm emission. We also used the JVLA to search for CO(2–1) emission from the field of a low-metallicity ([M/H] = −2.47) DLA at z ≈ 4.8. No statistically significant CO emission is detected from any of the galaxies, yielding upper limits of M mol < (7.4–17.9) × 1010 × (α CO/4.36) M ⊙ on their molecular gas mass. We detect rest-frame NUV emission from four of the seven [Cii] 158 μm-emitting galaxies, the first detections of the stellar continuum from Hi-selected galaxies at z ≳ 4. The HST-WFC3 images yield typical sizes of the stellar continua of ≈2–4 kpc and inferred dust-unobscured star formation rates (SFRs) of ≈5.0–17.5 M ⊙ yr−1, consistent with, or slightly lower than, the total SFRs estimated from the far-infrared (FIR) luminosity. We further stacked the CO(2–1) emission signals of six [Cii] 158 μm-emitting galaxies in the image plane. Our non-detection of CO(2–1) emission in the stacked image yields the limit M mol < 4.1 × 1010 × (α CO/4.36) M ⊙ on the average molecular gas mass of the six galaxies. Our molecular gas mass estimates and NUV SFR estimates in Hi-selected galaxies at z ≈ 4 are consistent with those of main-sequence galaxies with similar [Cii] 158 μm and FIR luminosities at similar redshifts. However, the NUV emission in the Hi-selected galaxies appears more extended than that in main-sequence galaxies at similar redshifts.
Social Catalysts: Characterizing People Who Spark Conversations Among Others

People assume different and important roles within social networks. Some roles have received extensive study: that of influencers who are well-connected, and that of brokers who bridge unconnected parts of the network. However, very little work has explored another potentially important role, that of creating opportunities for people to interact and facilitating conversation between them. These individuals bring people together and act as social catalysts. In this paper, we test for the presence of social catalysts on the online social network Facebook. We first identify posts that have spurred conversations between the poster's friends and summarize the characteristics of such posts. We then aggregate the number of catalyzed comments at the poster level, as a measure of the individual's "catalystness. " The top 1% of such individuals account for 31% of catalyzed interactions, although their network characteristics do not differ markedly from others who post as frequently and have a similar number of friends. By collecting survey data, we also validate the behavioral measure of catalystness: a person is more likely to be nominated as a social catalyst by their friends if their posts prompt discussions between other people more frequently. The measure, along with other conversation-related features, is one of the most predictive of a person being nominated as a catalyst. Although influencers and brokers may have gotten more attention for their network positions, our findings provide converging evidence that another important role exists and is recognized in online social networks.
The Experimenters' Dilemma: Inferential Preferences over Populations

We compare three populations commonly used in experiments by economists and other social scientists: undergraduate students at a physical location (lab), Amazon's Mechanical Turk (MTurk), and Prolific. The comparison is made along three dimensions: the noise in the data due to inattention, the cost per observation, and the elasticity of response. We draw samples from each population, examining decisions in four one-shot games with varying tensions between the individual and socially efficient choices. When there is no tension, where individual and pro-social incentives coincide, noisy behavior accounts for 60% of the observations on MTurk, 19% on Prolific, and 14% for the lab. Taking costs into account, if noisy data is the only concern Prolific dominates from an inferential power point of view, combining relatively low noise with a cost per observation one fifth of the lab's. However, because the lab population is more sensitive to treatment, across our main PD game comparison the lab still outperforms both Prolific and MTurk.
The Parkes pulsar timing array second data release: timing analysis

The main goal of pulsar timing array experiments is to detect correlated signals such as nanohertz-frequency gravitational waves. Pulsar timing data collected in dense monitoring campaigns can also be used to study the stars themselves, their binary companions, and the intervening ionized interstellar medium. Timing observations are extraordinarily sensitive to changes in path-length between the pulsar and the Earth, enabling precise measurements of the pulsar positions, distances and velocities, and the shapes of their orbits. Here we present a timing analysis of 25 pulsars observed as part of the Parkes Pulsar Timing Array (PPTA) project over time spans of up to 24 yr. The data are from the second data release of the PPTA, which we have extended by including legacy data. We make the first detection of Shapiro delay in four Southern pulsars (PSRs J1017−7156, J1125−6014, J1545−4550, and J1732−5049), and of parallax in six pulsars. The prominent Shapiro delay of PSR J1125−6014 implies a neutron star mass of Mp = 1.5 ± 0.2 M⊙ (68 per cent credibility interval). Measurements of both Shapiro delay and relativistic periastron advance in PSR J1600−3053 yield a large but uncertain pulsar mass of $M_p = 2.06^{+0.44}_{-0.41}$ M⊙ (68 per cent credibility interval). We measure the distance to PSR J1909−3744 to a precision of 10 lyr, indicating that for gravitational wave periods over a decade, the pulsar provides a coherent baseline for pulsar timing array experiments.
Resilient Edge Service Placement and Workload Allocation under Uncertainty

Abstract—In this paper, we study an optimal service placement and workload allocation problem for a service provider (SP), who can procure resources from numerous edge nodes to serve its users. The SP aims to improve the user experience while minimizing its cost, considering various system uncertainties. To tackle this challenging problem, we propose a novel resilience-aware edge service placement and workload allocation model that jointly captures the uncertainties of resource demand and node failures. The first-stage decisions include the optimal service placement and resource procurement, while the optimal workload allocation is determined in the second stage after the uncertainties are disclosed. The salient feature of the proposed model is that it produces a placement and procurement solution that is robust against any possible realization of the uncertainties. By leveraging the column-and-constraint generation method, we introduce two iterative algorithms that can converge to an exact optimal solution within a finite number of iterations. We further suggest an affine decision rule approximation approach for solving large-scale problem instances in a reasonable time. Extensive numerical results are shown to demonstrate the advantages of the proposed model and solutions.
Personalized Federated Learning via Maximizing Correlation with Sparse and Hierarchical Extensions

Federated Learning (FL) is a collaborative machine learning technique to train a global model without obtaining clients’ private data. The main challenges in FL are statistical diversity among clients, limited computing capability among client equipments and the excessive communication overhead and long latency between server and clients. To address these problems, we propose a novel personalized federated learning via maximizing correlation ( pFedMac ), and further extend it to sparse and hierarchical models. By minimizing loss functions including the proper-ties of an approximated (cid:96) 1 -norm and the hierarchical correlation, the performance on statistical diversity data is improved and the communicational and computa-tional loads required in the network are reduced. Theoretical proofs show that pFedMac performs better than the (cid:96) 2 -norm distance based personalization methods. Experimentally, we demonstrate the beneﬁts of this sparse hierarchical personalization architecture compared with the state-of-the-art personalization methods and their extensions (e.g. pFedMac achieves 99.75% accuracy on MNIST and 87.27% accuracy on Synthetic under heterogeneous and non-i.i.d data distributions).
MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal

Real estate appraisal refers to the process of developing an unbiased opinion for real property's market value, which plays a vital role in decision-making for various players in the marketplace (e.g., real estate agents, appraisers, lenders, and buyers). However, it is a non-trivial task for accurate real estate appraisal because of three major challenges: (1) The complicated influencing factors for property value; (2) The asynchronously spatiotemporal dependencies among real estate transactions; (3) The diversified correlations between residential communities. To this end, we propose a Multi-Task Hierarchical Graph Representation Learning (MugRep) framework for accurate real estate appraisal. Specifically, by acquiring and integrating multi-source urban data, we first construct a rich feature set to profile the real estate from multiple perspectives~(e.g., geographical distribution, human mobility distribution, and resident demographics distribution). Then, an evolving real estate transaction graph and a corresponding event graph convolution module are proposed to incorporate asynchronously spatiotemporal dependencies among real estate transactions. Moreover, to further incorporate valuable knowledge from the view of residential communities, we devise a hierarchical heterogeneous community graph convolution module to capture diversified correlations between residential communities. Finally, an urban district partitioned multi-task learning module is introduced to generate differently distributed value opinions for real estate. Extensive experiments on two real-world datasets demonstrate the effectiveness of MugRep and its components and features.
An Adaptive E-Learning System Using Justification Based Truth Maintenance System

In most E-learning systems, educational activities are presented in a static way without bearing in mind the particulars or student levels and skills. Personalization and adaptation of an E-learning management system are dependent on the flexibility of the system in providing different learning and content models to individual students based on their characteristics. In this paper, we suggest an Adaptive E-learning system which is providing adaptability with support of justification-based truth maintenance system. The system is accomplished of signifying students with suitable knowledge fillings and customized learning paths based on the student’s profile, interests, and previous results.
Active Divergence with Generative Deep Learning - A Survey and Taxonomy

Generative deep learning systems offer powerful tools for artefact generation, given their ability to model distributions of data and generate high-fidelity results. In the context of computational creativity, however, a major shortcoming is that they are unable to explicitly diverge from the training data in creative ways and are limited to fitting the target data distribution. To address these limitations, there have been a growing number of approaches for optimising, hacking and rewriting these models in order to actively diverge from the training data. We present a taxonomy and comprehensive survey of the state of the art of active divergence techniques, highlighting the potential for computational creativity researchers to advance these methods and use deep generative models in truly creative systems.
Equivalent Laplacian and adjacency quantum walks on irregular graphs

The continuous-time quantum walk is a particle evolving by Schrödinger’s equation in discrete space. Encoding the space as a graph of vertices and edges, the Hamiltonian is proportional to the discrete Laplacian. In some physical systems, however, the Hamiltonian is proportional to the adjacency matrix instead. It is well-known that these quantum walks are equivalent when the graph is regular, i.e., when each vertex has the same number of neighbors. If the graph is irregular, however, the quantum walks evolve differently. In this paper, we show that for some irregular graphs, if the particle is initially localized at a certain vertex, the probability distributions of the two quantum walks are identical, even though the amplitudes differ. We analytically prove this for a graph with five vertices and a graph with six vertices. By simulating the walks on all 1 018 689 568 simple, connected, irregular graphs with eleven vertices or less, we found sixty-four graphs with this notion of equivalence. We also give eight infinite families of graphs supporting these equivalent walks.
Late-time Evolution and Modeling of the Off-axis Gamma-Ray Burst Candidate FIRST J141918.9+394036

We present new radio and optical data, including very-long-baseline interferometry, as well as archival data analysis, for the luminous, decades-long radio transient FIRST J141918.9+394036. The radio data reveal a synchrotron self-absorption peak around 0.3 GHz and a radius of around 1.3 mas (0.5 pc) 26 yr post-discovery, indicating a blastwave energy ∼5 × 1050 erg. The optical spectrum shows a broad [O iii]λ4959,5007 emission line that may indicate collisional excitation in the host galaxy, but its association with the transient cannot be ruled out. The properties of the host galaxy are suggestive of a massive stellar progenitor that formed at low metallicity. Based on the radio light curve, blastwave velocity, energetics, nature of the host galaxy and transient rates, we find that the properties of J1419+3940 are most consistent with long gamma-ray burst (LGRB) afterglows. Other classes of (optically discovered) stellar explosions as well as neutron star mergers are disfavored, and invoking any exotic scenario may not be necessary. It is therefore likely that J1419+3940 is an off-axis LGRB afterglow (as suggested by Law et al. and Marcote et al.), and under this premise the inverse beaming fraction is found to be fb−1≃280−200+700 , corresponding to an average jet half-opening angle ≃5−2+4 degrees (68% confidence), consistent with previous estimates. From the volumetric rate we predict that surveys with the Very Large Array, Australian Square Kilometre Array Pathfinder, and MeerKAT will find a handful of J1419+3940-like events over the coming years.
LexSubCon: Integrating Knowledge from Lexical Resources into Contextual Embeddings for Lexical Substitution

Lexical substitution is the task of generating meaningful substitutes for a word in a given textual context. Contextual word embedding models have achieved state-of-the-art results in the lexical substitution task by relying on contextual information extracted from the replaced word within the sentence. However, such models do not take into account structured knowledge that exists in external lexical databases.We introduce LexSubCon, an end-to-end lexical substitution framework based on contextual embedding models that can identify highly-accurate substitute candidates. This is achieved by combining contextual information with knowledge from structured lexical resources. Our approach involves: (i) introducing a novel mix-up embedding strategy to the target word’s embedding through linearly interpolating the pair of the target input embedding and the average embedding of its probable synonyms; (ii) considering the similarity of the sentence-definition embeddings of the target word and its proposed candidates; and, (iii) calculating the effect of each substitution on the semantics of the sentence through a fine-tuned sentence similarity model. Our experiments show that LexSubCon outperforms previous state-of-the-art methods by at least 2% over all the official lexical substitution metrics on LS07 and CoInCo benchmark datasets that are widely used for lexical substitution tasks.
Electronic structure of $A$Co$_2$As$_2$ ($A=$ Ca, Sr, Ba, Eu) studied using angle-resolved photoemission spectroscopy and theoretical calculations

We present a comprehensive study of the low-energy band structure and Fermi surface (FS) topology of ACo2As2 (A = Ca, Sr, Ba, Eu) using high-resolution angle-resolved photoemission spectroscopy. The experimental FS topology and band dispersion data are compared with theoretical full-potential linearized augmented-plane-wave (FP-LAPW) calculations, which yielded reasonably good agreement. We demonstrate that the FS maps of ACo2As2 are significantly different from those of the parent compounds of Fe-based high-temperature superconductors. Further, the FSs of CaCo2As2 do not show significant changes across its antiferromagnetic transition temperature. The band dispersions extracted in different momentum (kx , ky) directions show a small electron pocket at the center and a large electron pocket at the corner of the Brillouin zone (BZ). The absence of the hole FS in these compounds does not allow nesting between pockets at the Fermi energy (EF), which is in contrast to AFe2As2-type parent compounds of the iron-based superconductors. Interestingly, we find that the hole bands are moved 300–400 meV below EF depending on the A element. Moreover, the existence of nearly flat bands in the vicinity of EF are consistent with the large density of states at EF. These results are important to understand the physical properties as well as the possibility of the emergence of superconductivity in related materials.
Inference of Shape Expression Schemas Typed RDF Graphs

We consider the problem of constructing a Shape Expression Schema (ShEx) that describes the structure of a given input RDF graph. We employ the framework of grammatical inference, where the objective is to find an inference algorithm that is both sound i.e., always producing a schema that validates the input RDF graph, and complete i.e., able to produce any schema, within a given class of schemas, provided that a sufficiently informative input graph is presented. We study the case where the input graph is typed i.e., every node is given with its types. We limit our attention to a practical fragment ShEx0 of Shape Expressions Schemas that has an equivalent graphical representation in the form of shape graphs. We investigate the problem of constructing a canonical representative of a given shape graph. Finally, we present a sound and complete algorithm for shape graphs thus showing that ShEx0 is learnable from typed graphs.