Miracle-dz/UniFAR-FTUs huggingface dataset:
Delivering Scientific Influence Analysis as a Service on Research Grants Repository

Research grants have played an important role in seeding and promoting fundamental research projects worldwide. There is a growing demand for developing and delivering scientific influence analysis as a service on research grant repositories. Such analysis can provide insight on how research grants help foster new research collaborations, encourage cross-organization collaborations, influence new research trends, and identify technical leadership. This article presents the design and development of a grant-based scientific influence analysis service, coined as <sc>GImpact</sc>. It takes a graph-theoretic approach to design and develop the scientific influence analysis algorithms over a real research-grant repository with three original contributions. First, we model the scientific influence analysis problem as a graph-based analysis problem by constructing heterogeneous graphs from the grants dataset, including mining the dataset to identify and extract important features and represent such features as a research grants information network. Second, we develop the scientific influence analysis algorithms over the research grants information network, which compute the overall scientific influence score by integrating self-influence score and multiple co-influence scores. The self-influence score reflects the grant-based research collaborations among institutions, and the co-influence scores reflect various types of cross-institution collaborations in terms of disciplines and keywords (subject areas). Third, we leverage the cluster analysis on the institution graph as an example application of scientific influence analysis service. By partitioning the institution graph into <inline-formula><tex-math notation="LaTeX">$K$</tex-math><alternatives><mml:math><mml:mi>K</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq1-3025318.gif"/></alternatives></inline-formula> clusters, with <inline-formula><tex-math notation="LaTeX">$K$</tex-math><alternatives><mml:math><mml:mi>K</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq2-3025318.gif"/></alternatives></inline-formula> as one of the service interface parameters, we show how different disciplines and different keywords are co-related through the grant-based influence analysis. We evaluate <sc>GImpact</sc> using a real grants dataset, consisting of 2512 institutions and their grants received over a period of 14 years. Our experimental results show that the <sc>GImpact</sc> influence analysis approach can effectively identify the grant-based research collaboration groups and provide valuable insight on an in-depth understanding of the scientific influence of research grants on research programs, institution leadership, and future collaboration opportunities.
Accelerating proximal Markov chain Monte Carlo by using explicit stabilised methods

We present a highly efficient proximal Markov chain Monte Carlo methodology to perform Bayesian computation in imaging problems. Similarly to previous proximal Monte Carlo approaches, the proposed method is derived from an approximation of the Langevin diffusion. However, instead of the conventional Euler-Maruyama approximation that underpins existing proximal Monte Carlo methods, here we use a state-of-the-art orthogonal Runge-Kutta-Chebyshev stochastic approximation that combines several gradient evaluations to significantly accelerate its convergence speed, similarly to accelerated gradient optimisation methods. The proposed methodology is demonstrated via a range of numerical experiments, including non-blind image deconvolution, hyperspectral unmixing, and tomographic reconstruction, with total-variation and $\ell_1$-type priors. Comparisons with Euler-type proximal Monte Carlo methods confirm that the Markov chains generated with our method exhibit significantly faster convergence speeds, achieve larger effective sample sizes, and produce lower mean square estimation errors at equal computational budget.
Reinforcement Learning in Healthcare: A Survey

As a subfield of machine learning, reinforcement learning (RL) aims at optimizing decision making by using interaction samples of an agent with its environment and the potentially delayed feedbacks. In contrast to traditional supervised learning that typically relies on one-shot, exhaustive, and supervised reward signals, RL tackles sequential decision-making problems with sampled, evaluative, and delayed feedbacks simultaneously. Such a distinctive feature makes RL techniques a suitable candidate for developing powerful solutions in various healthcare domains, where diagnosing decisions or treatment regimes are usually characterized by a prolonged period with delayed feedbacks. By first briefly examining theoretical foundations and key methods in RL research, this survey provides an extensive overview of RL applications in a variety of healthcare domains, ranging from dynamic treatment regimes in chronic diseases and critical care, automated medical diagnosis, and many other control or scheduling problems that have infiltrated every aspect of the healthcare system. In addition, we discuss the challenges and open issues in the current research and highlight some potential solutions and directions for future research.
A Class D Power Amplifier for Multifrequency Eddy Current Testing Based on Multisimultaneous-Frequency Selective Harmonic Elimination Pulsewidth Modulation

Efficiency and multisimultaneous-frequency (MSF) output capability are two major criteria characterizing the performance of a power amplifier in the application of multifrequency eddy current testing (MECT). Switch-mode power amplifiers are known to have a very high efficiency, yet they have rarely been adopted in the instrumental development of MECT. In addition, switch-mode power amplifiers themselves are lacking in the research literature for MSF capability. In this article, a Class D power amplifier is designed so as to address the two issues. An MSF selective harmonic elimination pulsewidth modulation method is proposed to generate alternating magnetic fields, which are rich in selected harmonics. A field-programmable-gate-array-based experimental system has been developed to verify the design. Results show that the proposed methodology is capable of generating high MSF currents in the transmitting coil with a low distortion of signal.
A Review of Point Cloud Semantic Segmentation

3D Point Cloud Semantic Segmentation (PCSS) is attracting increasing interest, due to its applicability in remote sensing, computer vision and robotics, and due to the new possibilities offered by deep learning techniques. In order to provide a needed up-to-date review of recent developments in PCSS, this article summarizes existing studies on this topic. Firstly, we outline the acquisition and evolution of the 3D point cloud from the perspective of remote sensing and computer vision, as well as the published benchmarks for PCSS studies. Then, traditional and advanced techniques used for Point Cloud Segmentation (PCS) and PCSS are reviewed and compared. Finally, important issues and open questions in PCSS studies are discussed.
Multiple D2D Multicasts in Underlay Cellular Networks

Multicasting for disseminating popular data is an interesting solution for improving the energy and spectral efficiencies of cellular networks. To improve the achievable performance of such networks, underlay device-to-device (D2D) multicast communication offers a practical solution. However, despite significant potential for providing higher throughput and lower delay, implementing underlay D2D multicast communication poses several challenges, such as mutual interference among cellular users (CUs) and D2D multicast groups (MGs), and overhead signaling to provide channel state information, that may limit potential gains. We study a scenario where multiple D2D multicast groups may share a CU's uplink channel. We formulate an optimization problem to maximize the achievable system throughput while fulfilling quality of service (QoS) requirements of every CU and D2D MGs, subject to their corresponding maximum transmit power constraints. The formulated optimization problem is an instance of mixed integer non-linear programming (MINLP) problem, which is computationally intractable, in general. Therefore, to find a feasible solution, we propose a pragmatic two-step process of channel allocation and power allocation. In the first-step, we propose a channel allocation algorithm, which determines the subset of MGs that may share a channel subject to criteria based on two different parameters: interference and outage probabilities. Then, we propose an algorithm to allocate power to these MG subsets that maximizes the system throughput, while satisfying transmit power constraint. Numerical results show the efficacy of proposed approach in terms of higher achievable sum throughput and better spectrum efficiency with respect to various existing schemes.
Reduction of the uncertainty in the atmospheric neutrino flux prediction below 1 GeV using accurately measured atmospheric muon flux

We examine the uncertainty of the calculation of the atmospheric neutrino flux and present a way to reduce it using accurately measured atmospheric muon flux. Considering the difference of the hadronic interaction model and the real one as a variation of hadronic interaction, we find a quantitative estimation method for the error of the atmospheric neutrino flux calculation from the residual of the reconstruction of the atmospheric muon flux observed in a precision experiment, by the study of atmospheric neutrino and muon fluxes response to the variation of hadronic interaction. However, the efficiencty of this method is largely dependent on the observation site of the atmospheric muon flux, as the relation of the error of the atmospheric neutrino flux calculation and the residual of the reconstruction of the atmospheric muon flux is also largely dependent on the muon observation site, especially for the low energy neutrinos. We calculate several observation sites, near Kamioka at sea level, same but 2770m a.s. l. , Hanle India (4500m a.s.l.), and at Balloon altitude ($\sim$ 32km). Then we estimate how stringently can the atmospheric muon reduce the error in the calculation of the atmospheric neutrino flux. We also discuss on the source of error which is difficult to reduce by only the observation of atmospheric muon.
Unsupervised Text Summarization via Mixed Model Back-Translation

Back-translation based approaches have recently lead to significant progress in unsupervised sequence-to-sequence tasks such as machine translation or style transfer. In this work, we extend the paradigm to the problem of learning a sentence summarization system from unaligned data. We present several initial models which rely on the asymmetrical nature of the task to perform the first back-translation step, and demonstrate the value of combining the data created by these diverse initialization methods. Our system outperforms the current state-of-the-art for unsupervised sentence summarization from fully unaligned data by over 2 ROUGE, and matches the performance of recent semi-supervised approaches.
Visually constructing the chemical structure of a single molecule by scanning Raman picoscopy

Abstract The strong spatial confinement of a nanocavity plasmonic field has made it possible to visualize the inner structure of a single molecule and even to distinguish its vibrational modes in real space. With such ever-improved spatial resolution, it is anticipated that full vibrational imaging of a molecule could be achieved to reveal molecular structural details. Here we demonstrate full Raman images of individual vibrational modes at the ångström level for a single Mg-porphine molecule, revealing distinct characteristics of each vibrational mode in real space. Furthermore, by exploiting the underlying interference effect and Raman fingerprint database, we propose a new methodology for structural determination, which we have called ‘scanning Raman picoscopy’, to show how such ultrahigh-resolution spectromicroscopic vibrational images can be used to visually assemble the chemical structure of a single molecule through a simple Lego-like building process.
The VMC survey – XXXIV. Morphology of stellar populations in the Magellanic Clouds

The Magellanic Clouds are nearby dwarf irregular galaxies whose morphologies show different properties when traced by different stellar populations, making them an important laboratory for studying galaxy morphologies. We study the morphology of the Magellanic Clouds using data from the Visible and Infrared Survey Telescope for Astronomy survey of the Magellanic Clouds system. We used about 10 and 2.5 million sources across an area of ∼105 and ∼42 deg2 towards the Large and Small Magellanic Cloud (LMC and SMC), respectively. We estimated median ages of stellar populations occupying different regions of the near-infrared (J − Ks, Ks) colour–magnitude diagram. Morphological maps were produced and detailed features in the central regions were characterized for the first time with bins corresponding to a spatial resolution of 0.13 kpc (LMC) and 0.16 kpc (SMC). In the LMC, we find that main-sequence stars show coherent structures that grow with age and trace the multiple spiral arms of the galaxy, star-forming regions become dimmer as we progress in age, while supergiant stars are centrally concentrated. Intermediate-age stars, despite tracing a regular and symmetrical morphology, show central clumps and hints of spiral arms. In the SMC, young main-sequence stars depict a broken bar. Intermediate-age populations show signatures of elongation towards the Magellanic Bridge that can be attributed to the LMC–SMC interaction ∼200 Myr ago. They also show irregular central features suggesting that the inner SMC has also been influenced by tidal interactions.
Online Inference for Advertising Auctions

Advertisers that engage in real-time bidding (RTB) to display their ads commonly have two goals: learning their optimal bidding policy and estimating the expected effect of exposing users to their ads. Typical strategies to accomplish one of these goals tend to ignore the other, creating an apparent tension between the two. This paper exploits the economic structure of the bid optimization problem faced by advertisers to show that these two objectives can actually be perfectly aligned. By framing the advertiser's problem as a multi-armed bandit (MAB) problem, we propose a modified Thompson Sampling (TS) algorithm that concurrently learns the optimal bidding policy and estimates the expected effect of displaying the ad while minimizing economic losses from potential sub-optimal bidding. Simulations show that not only the proposed method successfully accomplishes the advertiser's goals, but also does so at a much lower cost than more conventional experimentation policies aimed at performing causal inference.
Characterization and Computation of Matrices of Maximal Trace Over Rotations

The constrained orthogonal Procrustes problem is the least-squares problem that calls for a rotation matrix that optimally aligns two corresponding sets of points in d-dimensional Euclidean space. This problem generalizes to the so-called Wahba's problem which is the same problem with nonnegative weights. Given a dxd matrix M, solutions to these problems are intimately related to the problem of finding a dxd rotation matrix U that maximizes the trace of UM, i.e., that makes UM a matrix of maximal trace over rotations, and it is well known this can be achieved with a method based on the computation of the singular value decomposition (SVD) of M. As the main goal of this paper, we characterize dxd matrices of maximal trace over rotation matrices in terms of their eigenvalues, and for d = 2, 3, we show how this characterization can be used to determine whether a matrix is of maximal trace over rotation matrices. Finally, although depending only slightly on the characterization, as a secondary goal of the paper, for d = 2, 3, we identify alternative ways, other than the SVD, of obtaining solutions to the aforementioned problems.
Automated Generation of Test Models from Semi-Structured Requirements

[Context:] Model-based testing is an instrument for automated generation of test cases. It requires identifying requirements in documents, understanding them syntactically and semantically, and then translating them into a test model. One light-weight language for these test models are Cause-Effect-Graphs (CEG) that can be used to derive test cases. [Problem:] The creation of test models is laborious and we lack an automated solution that covers the entire process from requirement detection to test model creation. In addition, the majority of requirements is expressed in natural language (NL), which is hard to translate to test models automatically. [Principal Idea:] We build on the fact that not all NL requirements are equally unstructured. We found that 14% of the lines in requirements documents of our industry partner contain "pseudo-code"-like descriptions of business rules. We apply Machine Learning to identify such semi-structured requirements descriptions and propose a rule-based approach for their translation into CEGs. [Contribution:] We make three contributions: (1) an algorithm for the automatic detection of semi-structured requirements descriptions in documents, (2) an algorithm for the automatic translation of the identified requirements into a CEG and (3) a study demonstrating that our proposed solution leads to 86% time savings for test model creation without loss of quality.
A Contextual Bandit Algorithm for Ad Creative under Ad Fatigue

Selecting ad creative is one of the most important task for DSPs (Demand-Side Platform) in online advertising. DSPs should not only consider the effectiveness of the ad creative but also the user's psychological status when selecting ad creative. In this study, we propose an efficient and easy-to-implement ad creative selection algorithm that explicitly considers wear-in and wear-out effects of ad creative due to the repetitive ad exposures. The proposed system was deployed in a real-world production environment and tested against the baseline. It out-performed the existing system in most of the KPIs.
Analysis of Micro Strip Patch Antenna

Abstract—Micro strip patch antennas became very popular bec ause of planer profile, ease of analysis and fabric ation, compatibility with integrated circuit technology & their attracti ve radiation characteristics. But also some drawbac ks of low efficiency, narrow bandwidth and surface wave losses. To improve surfa ce wave losses uses Electro Magnetic Band Gap struc t res, for improving efficiency & bandwidth do proper impedance matching. We design and simulate the new EBG structures ope rating at 2.4GHz resonant frequency. Keywords-Gain,Micro srip antenna,improve surface wave losses
Transcritical transitions in swirling quasi-columnar flows

Complete mathematical similarity between nonlinear transcritical transitions in swirled flows and transonic phenomena in compressible gas dynamics is established. It is shown that transition of base supercritical columnar swirling flow to another subcritical columnar swirling flow is governed by an equation identical to that of the shock waves formation in gases. It is proven that under some assumptions transition follows the scenario suggested by Benjamin [1]. The presented study is based on the nonlinear wave theory of swirled flows developed by Leibovich [7]. The results are established through a rigorous mathematical analysis and provide a solid theoretical understanding of the wave dynamics of axisymmetric swirling flows. The unsteady wave analysis suggests a consistent explanation of the mechanism leading to the axisymmetric vortex breakdown phenomenon in high-Reynolds swirling flows.
The Possibility of Deep Learning-Based, Computer-Aided Skin Tumor Classifiers

The incidence of skin tumors has steadily increased. Although most are benign and do not affect survival, some of the more malignant skin tumors present a lethal threat if a delay in diagnosis permits them to become advanced. Ideally, an inspection by an expert dermatologist would accurately detect malignant skin tumors in the early stage; however, it is not practical for every single patient to receive intensive screening by dermatologists. To overcome this issue, many studies are ongoing to develop dermatologist-level, computer-aided diagnostics. Whereas, many systems that can classify dermoscopic images at this dermatologist-equivalent level have been reported, a much fewer number of systems that can classify conventional clinical images have been reported thus far. Recently, the introduction of deep-learning technology, a method that automatically extracts a set of representative features for further classification has dramatically improved classification efficacy. This new technology has the potential to improve the computer classification accuracy of conventional clinical images to the level of skilled dermatologists. In this review, this new technology and present development of computer-aided skin tumor classifiers will be summarized.
Corneal modeling and Keratoconus identification

This study aims to create a diagnostic system for early Keratoconus ( KTC ) identification. Keratoconus is a progressive ocular pathology that may lead to gradual corneal deformation, and might cause decreased quality of vision. KTC is easily identified at advanced stages by means of clinical parameters, measured by non-invasive devices. Meanwhile identification of early KTC is still a big challenge for practitioners. This work proposes a mathematical model of normal ( N ) and KTC eyes. Furthermore, a hybrid machine learning algorithm was implemented as a clinical support tool for ophthalmologists to reach a correct diagnosis. A gaussian-sphere mathematical model was considered to model both N and KTC corneas. 145 N and 312 KTC anterior and posterior corneal maps (layers), were collected at the Antwerp University Hospital (UZA, Belgium). The maps were fitted to the gaussian-spherical model in order to extract potential meaningful parameters to help identify early KTC . Moreover, extracted data were statistically analyzed and used to train a hybrid machine learning algorithm, which applies a probabilistic strategy to support vector machine ( SV M ) and multilayer perceptron ( MLP ) algorithms. Cross-validation techniques were used to validate and evaluate the accuracy of the diagnostic system. The mean squared error ( MSE ) of the gaussian-spherical model amounted to MSE ≤ o (10 - 3 ) mm , MSE 2 [0 : 002 ; 0 : 004] mm in the center, and MSE ~ 0 : 02 mm on corneal borders. The highest accuracy in classifying early KTC versus N eyes from the extracted parameters was 94% during the validation and 99% in the training step. The implemented diagnostic system results in an accurate tool for early KTC detection. Further work is needed to improve this system to model the progression of KTC .
Learning Very Large Graphs and Nonsingular Actions of Discrete Groups

Recently, Goldreich introduced the notion of property testing of bounded-degree graphs with an unknown distribution. We propose a slight modification of his idea: the Radon-Nikodym Oracles. Using these oracles, any reasonable graph property can be tested against any reasonable distribution in the category of planar graphs. We also discuss Randomized Local Distributed Algorithms, which work on very large graphs with unknown distributions. Also, this note is an attempt to explain the intimate relation in between this property testing model and the ergodic theory of nonsingular actions of discrete groups, in particular, hyperfinite actions.