Miracle-dz/UniFAR-FTUs huggingface dataset:
Geographical Feature Extraction for Entities in Location-based Social Networks

Location-based embedding is a fundamental problem to solve in location-based social networks (LBSN). In this paper, we propose a geographical convolutional neural tensor network (GeoCNTN) as a generic embedding model. GeoCNTN first takes the raw location data and extracts from it a more well-conditioned representation by our proposed Geo-CMeans algorithm. We then use a convolutional neural network (CNN) and an embedding structure to extract individual latent structural patterns from the preprocessed data. Finally, we apply a neural tensor network (NTN) to craft the implicitly related features we have obtained into a unified geographical feature. The advantages of our GeoCNTN mainly come from its novel neural network structure, which intrinsically offers a mechanism to extract latent structural features from the geographical data, as well as its wide applicability in various LBSN-related tasks. From two case studies, i.e. link prediction and entity classification in user-group LBSN, we evaluate the embedding efficacy of our model. Results show that GeoCNTN significantly performs better on at least two tasks, with improvement by 9% w.r. t. NDCG and 11% w.r. t. F1 score respectively, using the Meetup-USA dataset.
Contributions of monocular and binocular cues to distance discrimination in natural scenes

Little is known about distance discrimination in real scenes, especially at long distances. This is not surprising given the logistical difficulties of making such measurements. To circumvent these difficulties, we collected 81 stereo images of outdoor scenes, together with precisely registered range images that provided the ground-truth distance at each pixel location. We then presented the stereo images in the correct viewing geometry and measured the ability of human subjects to discriminate the distance between locations in the scene, as a function of absolute distance (3 m to 30 m) and the angular spacing between the locations being compared (2°, 5°, and 10°). Measurements were made for binocular and monocular viewing. Thresholds for binocular viewing were quite small at all distances (Weber fractions less than 1% at 2° spacing and less than 4% at 10° spacing). Thresholds for monocular viewing were higher than those for binocular viewing out to distances of 15–20 m, beyond which they were the same. Using standard cue-combination analysis, we also estimated what the thresholds would be based on binocular-stereo cues alone. With two exceptions, we show that the entire pattern of results is consistent with what one would expect from classical studies of binocular disparity thresholds and separation/size discrimination thresholds measured with simple laboratory stimuli. The first exception is some deviation from the expected pattern at close distances (especially for monocular viewing). The second exception is that thresholds in natural scenes are lower, presumably because of the rich figural cues contained in natural images.
Characterization of Carboxylic Acid Reductases for Biocatalytic Synthesis of Industrial Chemicals

Carboxylic acid reductases (CARs) catalyze the reduction of a broad range of carboxylic acids into aldehydes, which can serve as common biosynthetic precursors to many industrial chemicals. This work presents the systematic biochemical characterization of five carboxylic acid reductases from different microorganisms, including two known and three new ones, by using a panel of short‐chain dicarboxylic acids and hydroxy acids, which are common cellular metabolites. All enzymes displayed broad substrate specificities. Higher catalytic efficiencies were observed when the carbon chain length, either of the dicarboxylates or of the terminal hydroxy acids, was increased from C2 to C6. In addition, when substrates of the same carbon chain length are compared, carboxylic acid reductases favor hydroxy acids over dicarboxylates as their substrates. Whole‐cell bioconversions of eleven carboxylic acid substrates into the corresponding alcohols were investigated by coupling the CAR activity with that of an aldehyde reductase in Escherichia coli hosts. Alcohol products were obtained in yields ranging from 0.5 % to 71 %. The de novo stereospecific biosynthesis of propane‐1,2‐diol enantiomer was successfully demonstrated with use of CARs as the key pathway enzymes. E. coli strains accumulated 7.0 mm (R)‐1,2‐PDO (1.0 % yield) or 9.6 mm (S)‐1,2‐PDO (1.4 % yield) from glucose. This study consolidates carboxylic acid reductases as promising enzymes for sustainable synthesis of industrial chemicals.
Guest Editorial: A Roadmap for Mobile and Cloud Services for Digital Health

The three papers in this special section were presented at the 14th International Conference on Smart Homes and Health Telematics (ICOST 2016) that was hosted by Huazhong University of Science and Technology in China. Addresses the emergence of Digital Healthcare where there has been a sharp increase of activities in research, design, development, real-world deployment and evaluation of smart environments, assistive technologies, medical robotics and health telematics systems by employing computing and communication technologies in the health domain. Digital Health can be described as convergence of digital and genomic technologies with health, healthcare, living, and society to enhance the efficiency of healthcare delivery and make medicine prescription and medical treatments more personalized and precise.
Quantifying Reversible Surface Binding via Surface-Integrated Fluorescence Correlation Spectroscopy

We present a simple and versatile single-molecule-based method for the accurate determination of binding rates to surfaces or surface bound receptors. To quantify the reversible surface attachment of fluorescently labeled molecules, we have modified previous schemes for fluorescence correlation spectroscopy with total internal reflection illumination (TIR-FCS) and camera-based detection. In contrast to most modern applications of TIR-FCS, we completely disregard spatial information in the lateral direction. Instead, we perform correlation analysis on a spatially integrated signal, effectively converting the illuminated surface area into the measurement volume. In addition to providing a high surface selectivity, our new approach resolves association and dissociation rates in equilibrium over a wide range of time scales. We chose the transient hybridization of fluorescently labeled single-stranded DNA to the complementary handles of surface-immobilized DNA origami structures as a reliable and well-characterized test system. We varied the number of base pairs in the duplex, yielding different binding times in the range of hundreds of milliseconds to tens of seconds, allowing us to quantify the respective surface affinities and binding rates.
Generative Adversarial Network based Autoencoder: Application to Fault Detection Problem for Closed Loop Dynamical Systems

Fault detection problem for closed loop uncertain dynamical systems, is investigated in this paper, using different deep learning based methods. Traditional classifier based method does not perform well, because of the inherent difficulty of detecting system level faults for closed loop dynamical system. Specifically, acting controller in any closed loop dynamical system, works to reduce the effect of system level faults. A novel Generative Adversarial based deep Autoencoder is designed to classify datasets under normal and faulty operating conditions. This proposed network performs significantly well when compared to any available classifier based methods, and moreover, does not require labeled fault incorporated datasets for training purpose. Finally, this aforementioned network's performance is tested on a high complexity building energy system dataset.
Augmenting WordNet-based Inference with Argument Mapping

WordNet is a useful resource for lexical inference in applications. Inference over predicates, however, often requires a change in argument positions, which is not specified in WordNet. We propose a novel framework for augmenting WordNet-based inferences over predicates with corresponding argument mappings. We further present a concrete implementation of this framework, which yields substantial improvement to WordNet-based inference.
Causality and Counterfactuals in the Situation Calculus

Structural causal models offer a popular framework for exploring causal concepts. However, due to their limited expressiveness, structural models have difficulties coping with such concepts as actual (event-to-event) causation. In this article, we propose a new type of causal model, based on embedding structural considerations in the language of situation calculus. By using situation calculus as a basic language, we leverage its power to express complex, dynamically changing situations and, by relying on structural considerations, we can formulate an effective theory of counterfactuals within the situation-calculus.
Molecular biomarkers for uterine leiomyosarcoma and endometrial stromal sarcoma

Uterine leiomyosarcoma (u‐LMS) and endometrial stromal sarcoma (ESS) are among the most frequent soft tissue sarcomas, which, in adults, lead to fatal lung metastases and patients have an extremely poor prognosis. Due to their rarity and heterogeneity, there are no suitable biomarkers for diagnosis and prognosis, although some biomarker candidates have appeared. In 2017, The Cancer Genome Atlas (TCGA) Research Network's work on u‐LMS has confirmed mutations and deletions in RB1, TP53 and PTEN. In addition, whole‐exome sequencing of u‐LMS has confirmed and demonstrated frequent alterations in TP53, RB1, α‐thalassemia/mental retardation syndrome X‐linked (ATRX) and mediator complex subunit 12 (MED12). MED12 is a useful biomarker to diagnose uterine‐derived LMS and tumors arising from (LM) with a relatively favorable prognosis. TP53 and ATRX mutations can be important mechanisms in the pathogenesis of u‐LMS and are correlated with a poor prognosis. In an update based on the 2014 WHO classification, low‐grade ESS is often associated with gene rearrangement bringing about the JAZF 1‐SUZ12 (formerly JAZF1‐JJAZ1) fusion gene, whereas high‐grade ESS is associated with the YWHAE‐NUTM fusion gene. Low‐grade ESS with JAZF1 rearrangement may correlate with metastasis. However, high‐grade ESS with metastasis with YWHAE rearrangement shows a relatively favorable prognosis. The genetic/molecular genetic aberrations in u‐LMS and ESS are reviewed, focusing on molecular biomarkers for these primary and metastatic tumors.
Early academic achievement in children with isolated clefts: a population-based study in England

Objectives We used national data to study differences in academic achievement between 5-year-old children with an isolated oral cleft and the general population. We also assessed differences by cleft type. Methods Children born in England with an oral cleft were identified in a national cleft registry. Their records were linked to databases of hospital admissions (to identify additional anomalies) and educational outcomes. Z-scores (signed number of SD actual score is above national average) were calculated to make outcome scores comparable across school years and across six assessed areas (personal development, communication and language, maths, knowledge of world, physical development andcreative development). Results 2802 children without additional anomalies, 5 years old between 2006 and 2012, were included. Academic achievement was significantly below national average for all six assessed areas with z-scores ranging from −0.24 (95% CI −0.32 to −0.16) for knowledge of world to −0.31 (−0.38 to −0.23) for personal development. Differences were small with only a cleft lip but considerably larger with clefts involving the palate. 29.4% of children were documented as having special education needs (national rate 9.7%), which varied according to cleft type from 13.2% with cleft lip to 47.6% with bilateral cleft lip and palate. Conclusions Compared with national average, 5-year-old children with an isolated oral cleft, especially those involving the palate, have significantly poorer academic achievement across all areas of learning. These outcomes reflect results of modern surgical techniques and multidisciplinary approach. Children with a cleft may benefit from extra academic support when starting school.
Elastic database systems

Distributed on-line transaction processing (OLTP) database management systems (DBMSs) are a critical part of the operation of large enterprises. These systems often serve time-varying workloads due to daily, weekly or seasonal fluctuations in load, or because of rapid growth in demand due to a company’s business success. In addition, many OLTP workloads are heavily skewed to “hot” tuples or ranges of tuples. For example, the majority of NYSE volume involves only 40 stocks. To manage such fluctuations, many companies currently provision database servers for peak demand. This approach is wasteful and not resilient to extreme skew or large workload spikes. To be both efficient and resilient, a distributed OLTP DBMS must be elastic; that is, it must be able to expand and contract its cluster of servers as demand fluctuates, and dynamically balance load as hot tuples vary over time. This thesis presents two elastic OLTP DBMSs, called E-Store and P-Store, which demonstrate the benefits of elasticity for distributed OLTP DBMSs on different types of workloads. E-Store automatically scales the database cluster in response to demand spikes, periodic events, and gradual changes in an application’s workload, but it is particularly well-suited for managing hot spots. In contrast to traditional single-tier hash and range partitioning strategies, E-Store manages hot spots through a two-tier data placement strategy: cold data is distributed in large chunks, while smaller ranges of hot tuples are assigned explicitly to individual nodes. P-Store is an elastic OLTP DBMS that is designed for a subset of OLTP applications in which load varies predictably. For these applications, P-Store performs better than reactive systems like E-Store, because P-Store uses predictive modeling to reconfigure the system in advance of predicted load changes. The experimental evaluation shows the efficacy of the two systems under variations in load across a cluster of machines. Compared to single-tier approaches, E-Store improves throughput by up to 130% while reducing latency by 80%. On a predictable workload, P-Store outperforms a purely reactive system by causing 72% fewer latency violations, and achieves performance comparable to static allocation for peak demand while using 50% fewer servers. Thesis Supervisor: Michael R. Stonebraker Title: Adjunct Professor of Computer Science
Metallic nanolayers: a sub-visible wonderland of optical properties [Invited]

It was predicted long ago that ultra-thin metallic films must exhibit unusual optical properties for radiation frequencies from the radio-frequency domain to the infrared domain. A film would remain highly reflective even when it is orders of magnitude thinner than a skin depth at any frequency. Only when the film is a few nanometers thick (depending on the material but not on the frequency) do its reflectivity and transmittivity become equal, while its absorption peaks at 50%. It has been confirmed experimentally and new directions and applications were proposed. We review the electromagnetic theory of the phenomenon and recent developments in the field and present some new results.
Healthy Plant-Based Diets Are Associated with Lower Risk of All-Cause Mortality in US Adults.

Background Plant-based diets, often referred to as vegetarian diets, are associated with health benefits. However, the association with mortality is less clear. Objective We investigated associations between plant-based diet indexes and all-cause and cardiovascular disease mortality in a nationally representative sample of US adults. Methods Analyses were based on 11,879 participants (20-80 y of age) from NHANES III (1988-1994) linked to data on all-cause and cardiovascular disease mortality through 2011. We constructed an overall plant-based diet index (PDI), which assigns positive scores for plant foods and negative scores for animal foods, on the basis of a food-frequency questionnaire administered at baseline. We also constructed a healthful PDI (hPDI), in which only healthy plant foods received positive scores, and a less-healthful (unhealthy) PDI (uPDI), in which only less-healthful plant foods received positive scores. Cox proportional hazards models were used to estimate the association between plant-based diet consumption in 1988-1994 and subsequent mortality. We tested for effect modification by sex. Results In the overall sample, PDI and uPDI were not associated with all-cause or cardiovascular disease mortality after controlling for demographic characteristics, socioeconomic factors, and health behaviors. However, among those with an hPDI score above the median, a 10-unit increase in hPDI was associated with a 5% lower risk in all-cause mortality in the overall study population (HR: 0.95; 95% CI: 0.91, 0.98) and among women (HR: 0.94; 95% CI: 0.88, 0.99), but not among men (HR: 0.95; 95% CI: 0.90, 1.01). There was no effect modification by sex (P-interaction > 0.10). Conclusions A nonlinear association between hPDI and all-cause mortality was observed. Healthy plant-based diet scores above the median were associated with a lower risk of all-cause mortality in US adults. Future research exploring the impact of quality of plant-based diets on long-term health outcomes is necessary.
Salivary Glands Ultrasonography in Rheumatoid Arthritis Patients. Its Role in Early Detection of Secondary Sjogren's Syndrome

C l i n M e d International Library Citation: El-Barbary AM, Aboelhawa MA, Shahba AA, Shafik NM, Hamouda HE, et al. (2016) Salivary Glands Ultrasonography in Rheumatoid Arthritis Patients. Its Role in Early Detection of Secondary Sjogren’s Syndrome. J Rheum Dis Treat 2:041 Received: July 26, 2016: Accepted: September 24, 2016: Published: October 01, 2016 Copyright: © 2016 El-Barbary AM, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Salivary Glands Ultrasonography in Rheumatoid Arthritis Patients. Its Role in Early Detection of Secondary Sjogren’s Syndrome
A Dual Step Strategy for Retinal Thin Vessel Enhancement/Extraction

Blood vessel extraction from retinal images is a challenging and fundamental step in pathological analysis. Most of the vessel extraction algorithms face difficulty in the extraction of thin vessels. In this paper, a dual step strategy for retinal thin vessel enhancement/extraction is proposed. Since thin vessel pixels have intensities closer to the background non-vessel pixels, the first level enhancement algorithms usually suffers in its accurate extraction. This led to explore a novel idea of eliminating the effects of thick vessel pixels in a reference image, via replacing it with neighboring non-vessel pixels. By applying second level enhancement on the vessel subtracted image, thin vessels are projected and improvement in extraction is attained subjectively as well as objectively.
A Survey of Thai Knowledge Extraction for the Semantic Web Research and Tools

As the manual creation of domain models and also of linked data is very costly, the extraction of knowledge from structured and unstructured data has been one of the central research areas in the Semantic Web field in the last two decades. Here, we look specifically at the extraction of formalized knowledge from natural language text, which is the most abundant source of human knowledge available. There are many tools on hand for information and knowledge extraction for English natural language, for written Thai language the situation is different. The goal of this work is to assess the state-of-the-art of research on formal knowledge extraction specifically from Thai language text, and then give suggestions and practical research ideas on how to improve the state-of-the-art. To address the goal, first we distinguish nine knowledge extraction for the Semantic Web tasks defined in literature on knowledge extraction from English text, for example taxonomy extraction, relation extraction, or named entity recognition. For each of the nine tasks, we analyze the publications and tools available for Thai text in the form of a comprehensive literature survey. Additionally to our assessment, we measure the self-assessment by the Thai research community with the help of a questionnaire-based survey on each of the tasks. Furthermore, the structure and size of the Thai community is analyzed using complex literature database queries. Combining all the collected information we finally identify research gaps in knowledge extraction from Thai language. An extensive list of practical research ideas is presented, focusing on concrete suggestions for every knowledge extraction task – which can be implemented and evaluated with reasonable effort. Besides the task-specific hints for improvements of the state-of-the-art, we also include general recommendations on how to raise the efficiency of the respective research community. key words: knowledge extraction, Thai language text, landscape analysis, semantic web
Experimental Analysis of Emergent Dynamics in Complex Networks of Nonlinear Oscillators

The aim of this thesis is to explore and investigate the emergent dynamics of complex networks through a novel and insightful experimental setup realized as a configurable network of chaotic Chua's circuits. In particular part of our work has been devoted to the implementation and characterization of a "2.0 hardware version" of it, where the interconnection network has improved greatly in its main features. In this way the setup has been fully automatized in providing control on network structure and coupling strength. A large set of experiments has been carried out in networks with proportional coupling and arbitrary topology, showing, emergent dynamics encompassing synchronization, patterns and traveling waves, clusters formation. Also, the case of dynamic coupling has been experimentally addressed. The experimental observations have been compared with theoretical results by carrying out a local stability analysis of networks with static and dynamic links. Here we use the Master Stability approach (MSF) and its extensions to the case where the links are of dynamic nature (Proportional Derivative-MSF). Last part of the work has been devoted to the experimental study of cluster synchronization, stimulated by novel theoretical advances based on group theory and network symmetries. A novel network structure referred as "Multiplexed Network" has been experimentally examined, resulting in a great enhancement in synchronization, for which no theoretical models are yet available.
Micro ELISA chip to Detect Multiple Allergic Proteins Simultaneously

The ELISA (Enzyme-Linked Immuno Sorbent Assay) chip to detect multiple allergic proteins simultaneously was developed successfully. This chip consists of multiple nitrocellulose membrane with 1mm diameter that immobilized several kinds of allergic proteins with 50ng amount only is fabricated using the multi-layer lithography. The experimental results demonstrated that 1) the specific allergen protein, and 2) the quantitative change of the antigen-antibody reaction depending on amount of antibody were able to be detected without contamination. The detection procedure that is only to inject antibodies in order into the chip is simple. The time for the whole detection procedure is short with 16 min. This ELISA chip that can specify the allergen protein that causes the patient’s allergic disease is useful for the hyposensitization therapy.