Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge Base Population via Relational One-Class Graph Convolutional Networks

We consider the problem of knowledge base population (KBP) of a coauthor network. Speciﬁcally, we consider the link prediction problem as a relational modeling task. We develop a relational extension of graph convolutional networks (GCNs) that exploits recent success inside relational probabilistic models. As a result of this extension, we present a novel knowledge base consisting of knowledge for four major machine learning conferences extracted from the Microsoft Academic Graph. Our empirical evaluation for link prediction on a subset of the extracted knowledge base demonstrates the superiority of relational GCNs over various relational embedding models.
In-situ tuning of Sn doped In2O3 (ITO) films properties by controlling deposition Argon/Oxygen flow

In this work, we report that the properties of Sn-doped In2O3 (ITO) can be properly tuned by varying Argon/ Oxygen (Ar/O2) percentage during the sputtering process from 7% O2 to 93% O2. The characteristics of the ITO grown in oxygen deficient to the oxygen-rich condition are properly studied. It is found that there is a strong correlation between the concentration incorporation of oxygen with the properties of ITO films. ITO films were grown in oxygen deficient condition (7% O2) resulted in a rougher surface, wider band gap, and lower resistivity compared to the other films grown with 33%, 67%, and 93%. Blue shifts in absorbance edge and band gap widening indicate that the number of carrier concentration was also changed linearly with the presence of oxygen in the film. These findings provide a simple way to effectively tune the properties of ITO films for ITO to be applied in optoelectronics, power device as well as sensor applications.
Single camera photogrammetry utilizing non-DMD/LCoS based projection systems

Triangulation based optical photogrammetry devices for 3-D surface measurement relying on active illumination methods, such as fringe projection metrology, typically utilize Digital Mirror Device (DMD) or Liquid Crystal on Silicon (LCoS) based projection systems. Measurement performance of these metrology devices is reliant on accurate geometric calibration of the camera and projector. Geometric camera calibration is typically achieved using the Zhang method [1] under the assumption that the camera operates under perspective projection. DMD or LCoS based projectors can be considered as operating under the same perspective projection assumption, but in reverse [2]. As a result, numerous studies have been done geometrically calibrating DMD/LCoS projectors using the same parameterizations such as objective focal length, pixel skewness, and principal point of the projection chip.
Digital core repository coupled with machine learning as a tool to classify and assess petrophysical rock properties

To make efficient use of image-based rock physics workflow, it is necessary to optimize different criteria, among which: quantity, representativeness, size and resolution. Advances in artificial intelligence give insights of databases potential. Deep learning methods not only enable to classify rock images, but could also help to estimate their petrophysical properties. In this study we prepare a set of thousands high-resolution 3D images captured in a set of four reservoir rock samples as a base for learning and training. The Voxilon software computes numerical petrophysical analysis. We identify different descriptors directly from 3D images used as inputs. We use convolutional neural network modelling with supervised training using TensorFlow framework. Using approximately fifteen thousand 2D images to drive the classification network, the test on thousand unseen images shows any error of rock type misclassification. The porosity trend provides good fit between digital benchmark datasets and machine learning tests. In a few minutes, database screening classifies carbonates and sandstones images and associates the porosity values and distribution. This work aims at conveying the potential of deep learning method in reservoir characterization to petroleum research, to illustrate how a smart image-based rock physics database at industrial scale can swiftly give access to rock properties.
Towards Time Inference for Timeline Generation of Historical Wikipedia Articles

—In this paper, we combine several temporal information extraction techniques to generate timelines from historical Wikipedia articles. We extend HeidelTime to signiﬁcantly increase precision and recall of temporal expression extraction in our domain, while modestly increasing performance in other domains. We then use a Markov Logic Network acting on time points, and a simple heuristic to assign explicit time intervals to every event in an article, improving performance signiﬁcantly from prior work.
Time-varying Limit Order Book Networks

A network analysis for limit order books (LOB) across stocks yields a better understanding of market behavior. The network is constructed in the presence of microstructure noise and non-synchronous trading. This paper contributes to directed network estimation through penalized vector autoregressive (VAR) approach. The connectedness table can be constructed for both Gaussian and non-Gaussian models, with a connectedness measure directly derived from generalized impulse response function by way of bootstrapping. The directional connectedness "from" and "to" are associated with the forecast error variation for specific order book across various stocks when the arising shocks transmit from one stock to the others. To balance the sparsity and estimation accuracy, a moderate tuning parameter in penalized VAR is determined by Bayesian information criterion (BIC), followed by a ordinary least squares (OLS) post-Lasso estimator, which can be configured to reduce finite-sample bias and ensure better model performance. Moreover we look for the short-horizon large portfolio allocation decisions, and provide novel empirical evidence using high frequency data trading in NASDAQ market. JEL classification: C02, C13, C22, C45, G12
Production Potential of Finger Millet (Eleusine coracana L. Gaertn) as Affected by Integrated Nutrient Management Practices: A Review

Finger millet (Eleusine coracana) is an important small millet crop grown in India and has the pride of place in having highest productivity among millets. It is grown on an area of 1.19 million hectare in India with a total production of 1.98 million tonnes and productivity of 1661 kg ha -1 (Anonymous, 2013). It is a nutritive food for different age groups. Straw makes valuable fodder for both draught and milch animals. The production and productivity of finger millet is low because of inefficient nutrient management, heavy weed infestation, incidence of blast disease etc.
Classifying Chest Pathology Images Using Deep Learning Techniques

: In this review, the application of in-depth learning for medical diagnosis will be corrected. A thorough analysis of various scientific articles in the domain of deep neural network applications in the medical field has been implemented. Has received more than 300 research articles and after several steps of selection, 46 articles have been presented in more detail The research found that the neural network (CNN) is the most prevalent agent when talking about deep learning and medical image analysis. In addition, from the findings of this article, it can be observed that the application of widespread learning technology is widespread. But most of the applications that focus on bioinformatics, medical diagnostics and other similar fields. In this work, we examine the strength of the deep learning method for pathological examination in chest radiography. Convolutional neural networks (CNN) The method of deep architectural classification is popular due to the ability to learn to represent medium and high level images. We explore CNN's ability to identify different types of diseases in chest X-ray images. Moreover, because of the very large training sets that are not available in the medical domain, we therefore explore the possibility of using deep learning methods based on non-medical learning. We tested our algorithm on 93 datasets. We use CNN that is trained with ImageNet, which is a well-known non-animated large image database. The best performance is due to the use of features pulled from CNN and low-level features.
Imagining hidden supporting objects in tabletop scenes

Understanding of complex disordered piles of objects can require consideration of object support relations contributing to the stability of the scene. Such supporting objects are often partially occluded or completely hidden. We explore how supporting hidden objects may be efﬁciently imagined. We evaluate existing state-of-the-art regression and generative adversarial machine learning frameworks and demonstrate that neither are sufﬁcient to imagine stable supporting hidden objects. We propose a novel framework incorporating an explicit stability learning signal. The addition of this signal biases the imagined object distribution strongly towards objects with a large ﬂat base and low centre of gravity, resulting in maximally supported scenes
Millimeter-Wave Antenna Implementation for 5G Smartphones

—The upcoming 5G wireless technology is sparking a number of paradigm shifts in the mobile phone industry regarding antenna design and implementation. The transition to the mm-Wave band allows for unprecedented data transmission rates but, at the same time, opens the Pandora box of reliable communications using high frequencies. The present work assesses the implementation of mm-Wave antennas in 5G smartphones to minimize impairments caused by high frequencies, modern mobile phone form factor constrictions and dynamic environment characteristics. A general comparison process is established based on two mm-Wave antenna families, patches (PBS) and monopoles (MBS), at 39 GHz, to evaluate their potential and limitations when integrated into 5G smartphones. This process comprises two stages: ﬁrst, a coverage study, to assess how well distributed in space is the power radiated by the beam steering array; and second, an evaluation of the MIMO channel performance depending on the antenna family, polarization, environment scenario, and potential body blockage. The latter is done using a discrete geometrical MIMO channel simulator developed to recreate the mm-Wave propagation conditions in any scenario (from rural to urban). It includes the effects of real antennas, smartphone metallic chassis and user inﬂuence (body blockage and depolarization).
Comparative proteomic analysis of sporulation associated protein SCO4114 in Streptomyces coelicolor

Streptomyces coelicolor are Gram-positive bacteria with a complex life cycle and produce a variety of secondary metabolites, occupying about 70% of the currently known antibiotics. Its morphological differentiation is closely related to secondary metabolism, so the research on morphological differentiation related regulation mechanism has always been a hot spot. SCO4114 was proposed to be a sporulation associated protein of the S. coelicolor A3(2), although the regulation on the growth of aerial mycelium is not clear. In order to better understand the functions of SCO4114 and find out the effects of SCO4114 on aerial mycelium formation at the protein level, the proteomic analysis of S. coelicolor A3(2) wild-type (WT) and sco4114 deletion (Δsco4114) strains were conducted by combining iTRAQ labeling and LC-MS analysis. Total of 2,996 proteins were identified, of which 163 were significantly differentially expressed. Due to the iTRAQ-based quantitative proteomics approach, it was indicated that important metabolic pathways such as carbohydrate metabolism, energy metabolism, fatty acid metabolism, amino acid metabolic pathways, purine and pyrimidine metabolism were involved in regulation and expression of proteins. The metabolic pathways also included replication, transcription, translation, and cell walling synthesis, membrane transport, and signal transduction. Based on the analysis of proteomic data, we speculated that knockout of sco4114 gene affect the formation of aerial mycelium by affecting the primary metabolic process of Streptomyces and the stable expression of bld system. The present study is comprehensive proteomic analysis of S. coelicolor strain and it provides some clues to understand the sporulation mechanism of S. coelicolor.
Parallel SAT Solver

Many practical problems in multiple ﬁelds can be converted to a SAT problem, or a sequence of SAT problems, such that their solution immediately implies a solution to the original problem. Despite the enourmous progress achieved over the last decade in the development of SAT solvers, there is strong demand for higher algorithm efﬁciency to solve harder and larger problems. The widespread availability of multi-core, shared memory parallel environments provides an opportunity for such improvements. In this paper we present our results on improving the effectiveness of standard SAT solvers on such architectures, through a two different parallel approaches: In the ﬁrst, multiple instances of the same basic solver using different heuristic strategies, for search-space exploration and problem analysis, share information and cooperate towards the solution of a given problem. In the second, multiple instances cooperatively solve smaller sub-sets of the original problem to ﬁnd a solution for the whole problem. Results from the application of our methodology to known problems from SAT competitions show relevant improvements over the state of the art and yield the promise of further advances.
You Shouldn't Trust Me: Learning Models Which Conceal Unfairness From Multiple Explanation Methods

Transparency of algorithmic systems is an important area of research, which has been discussed as a way for end-users and regulators to develop appropriate trust in machine learning models. One popular approach, LIME [23], even suggests that model expla- nations can answer the question “Why should I trust you? ”. Here we show a straightforward method for modifying a pre-trained model to manipulate the output of many popular feature importance explana- tion methods with little change in accuracy, thus demonstrating the danger of trusting such explanation methods. We show how this ex- planation attack can mask a model’s discriminatory use of a sensitive feature, raising strong concerns about using such explanation meth- ods to check fairness of a model.
Remote Sensing of Environment

a Environmental and Climate Sciences Department, Brookhaven National Laboratory, Bldg. 490A, Upton, NY 11973, USA b Environmental Protection Division, Brookhaven National Laboratory, Bldg. 860, Upton, NY 11973, USA c Department of Geographical Sciences, University of Maryland, 1165 Lefrak Hall, College Park, MD 20742, USA d Department of Geography, University of Utah, 332 S 1400 E, Rm. 217, Salt Lake City, UT 84112, USA e Code 618, Biospheric Sciences Branch, NASA/Goddard Space Flight Center, Greenbelt, MD 20742, USA f Department of Environmental Science and Biology, State University of New York College at Brockport, Brockport, NY 14420, USA
Machine Learning in Network Analysis and Software Testing

—This is a review of some recent works in applying machine learning in network analysis and software testing. Originally done to support work in building more advanced use cases for domain experts and partners to give some ideas on how ML might be applied in different contexts. Not to copy from, but the given an idea of the potential, properties of application domains, data, and other related aspects when looking at what beneﬁts ML could bring.
An Unsupervised Method for Generating Parallel Corpora for Medium and High Density Languages

—Parallel corpora are an extremely useful tool in many natural language processing tasks. However, it is often difﬁcult, impossible, or expensive to obtain parallel corpora for certain language pairs. We propose an unsupervised method that is capable of automatically creating high quality parallel corpora, as well as bilingual lexicons. The outlined method is also capable of automatically augmenting the size of the corpora. The resulting parallel corpora are made freely available for download.
Realtime Suite: a step-by-step introduction to the world of real-time signal acquisition and conditioning.

In this paper we present the Realtime Suite, a project that aims to provide all the tools and guides needed to set up a Linux-RTAI real-time machine within a CACSD environment. This is addressed in particular to researchers and students approaching the world of real-time control applications for the ﬁrst time, or people searching for an open-source alternative to commercial solutions
Ay 190 — Computational Astrophysics: N-Body and SPH Techniques

Frequently in astrophysics we have systems of “particles” which interact only gravitationally. Examples include dark matter and purely stellar systems (in which each star is a particle). Ignoring relativistic effects (which we can in a wide variety of situations) the evolution of such systems is entirely specified by the Newtonian 1/r law of gravity, such that the acceleration of particle i is given by:
Prefrontal Cortical Structural Abnormalities Associated with Hippocampal atrophy following VGKCC-Ab Limbic encephalitis

Limbic encephalitis associated with anti-voltage-gated potassium channel complex antibodies (VGKCC-Ab-LE) is an autoimmune form of encephalitis. VGKCC-Ab-LE patients present welldocumented residual episodic memory impairment and atrophy in the hippocampus (HPC). Nevertheless, it remains unclear whether HPC atrophy per se explains this amnestic syndrome. The present study addresses this gap of knowledge and sought to investigate if the structural brain abnormalities in these patients extend beyond the established HPC atrophy and whether these extra-HPC abnormalities are associated with patients pronounced memory impairment. The analysis comprised volume(VBM) and surface-based morphometry (SBM), the latter being a novel method in the study of this specific disease and a more sensitive one in detecting and localizing abnormalities in the cortex. SBM revealed significant cortical thinning in several areas in the prefrontal cortex (PFC), along with some differences in patients’ gyrification index in the bilateral insula. Furthermore, a correlation between the measures in the PFC and the bilateral insula with the memory performance was found. Moreover, VBM revealed bilateral HPC and also thalamic atrophy, along with a positive correlation between this atrophy and the areas presenting cortical thinning. This study demonstrates that VGKCC-Ab-LE not only results in bilateral HPC and thalamic atrophy, but also that this atrophy may result in the disruption of HPCPFC network or cortico-thalamic-HPC circuits causing significant reductions in the cortical thickness of the PFC. This cortical thinning accompanied by the finding of gyrification reduction in the bilateral insula might be related to the accentuated memory deficits of the patients.
Structural health monitoring network in British Columbia, Canada

This paper presents a structural health monitoring (SHM) network in British Columbia (BC), Canada that involves 14 bridges, 1 tunnel and 12 public schools. The SHM system is part of a province-wide monitoring network, the British Columbia Smart Infrastructure Monitoring System (BCSIMS). The SHM network automatically retrieves structural vibration data from these instrumented structures on a regular basis and makes them available to download on the BCSIMS website (www.bcsims.ca). The collected data is then automatically analysed and permanently stored in a data centre located at the University of British Columbia. The objective of the SHM network is to provide the ministry with information on the performance of instrumented structures following a significant event such as a strong earthquake or powerful wind. Such information is then used to assess the structural safety and health state of these structures to help and support the inspection and maintenance program. The SHM network is designed to automatically issue structural reports within 15 minutes following a significant earthquake. The entire system has been tested and validated by an earthquake which occurred on December 29th, 2015 on Sidney Island, BC. The SHM system responded to the earthquake as it was designed to do, and provided the required information that the engineers needed to make immediate decisions and respond to the emergencies in an efficient manner.
The Development and Validation of an Integrated Learning Method Based on Problem-Based Learning in a Pedagogy, Technology and Assessment Course at Malaysia Public Universities

The study aims to develop and test a Problem-Based Learning (PBL) Learning Module the KPD3016- Teaching, Technology and Assessment course as one of the pedagogical strategies that will assist and become a benchmark for lecturers or educators in promoting and strengthening the abilities of students and graduates of the Bachelor of Education degree programmes in soft skills (such as thinking skills, self-learning, group work, communication skills, leadership, entrepreneur, etc). The module produced is current and innovative and it can be used by Universiti Pendidikan Sultan Idris (UPSI) lecturers in empowering the teaching and learning process in the KPD3016 course. The research findings may contribute relevant information and knowledge in implementing the use of PBL approaches in the teaching and learning process in study programmes at Universiti Pendidikan Sultan Idris and other higher education institutions in Malaysia as well as provide a foundation for the construction of other PBL conceptual training modules.