Miracle-dz/UniFAR-FTUs huggingface dataset:
Control factors of spectral reflectance in tidal flat: a case study in the Gomso Bay, Korea

The objective of this study is to investigate the control factors of spectral reflectance and microwave scattering in tidal flat. The control factors are generally considered as grain size, soil moisture content, local slope, and creeks. We have carried out field data sampling in the Gomso Bay, Korea, including grain size, soil moisture content and its variation with time, surface roughness, ground levelling, and field spectral reflectance measurement. We have analyzed Landsat TM, EOS-Terra ASTER, RADARSAT, ERS-1/2, and JERS-1 SAR data. L- and C-band radar scattering modelling was also conducted. The results show that tidal condition and remnant surface water are important additional parameters. SWIR is shown to be correlated most with sediment grain size. The grain size of 0.0625 mm has normally been considered as a critical size of mud and sand discrimination. But we propose here that 0.25 mm is more practical grain size criterion in optic remote sensing. The radar scattering modelling show variation of less than 15 dB within the tidal flat, and L-band HV-polarization is turned out to be the best for tidal flat observation. The SAR data is also sensitive to the effective exposed area (EEA) effect.
SGL: A Domain-Specific Language for Large-Scale Analysis of Open-Source Code

Today software is built in fundamentally different ways from how it was a decade ago. It is increasingly common for applications to be assembled out of open-source components, resulting in the use of large amounts of third-party code. This third-party code is a means for vulnerabilities to make their way downstream into applications. Recent vulnerabilities such as Heartbleed, FREAK SSL/TLS, GHOST, and the Equifax data breach (due to a flaw in Apache Struts) were ultimately caused by third-party components. We argue that an automated way to audit the open-source ecosystem, catalog existing vulnerabilities, and discover new flaws is essential to using open-source safely. To this end, we describe the Security Graph Language (SGL), a domain-specific language for analyzing graph-structured datasets of open-source code and cataloguing vulnerabilities. SGL allows users to express complex queries on relations between libraries and vulnerabilities in the style of a program analysis language. SGL queries double as an executable representation for vulnerabilities, allowing vulnerabilities to be automatically checked against a database and deduplicated using a canonical representation. We outline a novel optimization for SGL queries based on regular path query containment, improving query performance by up to 3 orders of magnitude. We also demonstrate the effectiveness of SGL in practice to find zero-day vulnerabilities by identifying several flaws in the open-source version of Oracle GlassFish Server.
Remaining Useful Life Prediction Using a Novel Two-Stage Wiener Process With Stage Correlation

Remaining useful life (RUL) prediction of products is a critical component of prognostics and health management. Recently, the RUL prediction based on a two-stage degradation process has received increasing attention. However, existing works generally assume that the two stages are mutually independent, which is not reasonable in many applications. To address this problem, we propose a novel two-stage Wiener process model with stage correlation and a Bayesian approach for RUL prediction. Different from previous studies, we incorporate the stage correlation into the prior distributions of model parameters to improve the accuracy of predictions. We also derive the RUL distribution through the total probability formula to comprehensively consider the possibilities that the product fails at different stages. Once real-time monitoring data are available, we employ the Gibbs sampling algorithm to update the posterior distributions of model parameters as well as the RUL distribution. The superiority of the proposed method is demonstrated through a simulation study and an application to the bearing degradation data.
A Synthetic Visual Analysis System for Time-Varying Network

In recent years, with the rapid development of computer science and information technology, the world is filled and propelled of time-varying and multivariate data. The enormous growth of data in the last decades led to big data challenge in the network security field. Traditional visual analysis method for the time-varying network is inadequate. Efficient methods for visual clutter reduction, network structure exploration and network behavior detection are needed. In this paper, we propose two methods: A triangular-based algorithm for time-varying behavior presentation and an improved brush PCP aims to assist the visual analysis task in time-varying network pattern detection. A synthetic visual analysis system is designed and implemented on the basis of these two novel methods. Our system is capable of visually analyzing vast amounts of network data. To better describe and demonstrate the usefulness and performance of our system, we utilize the ChinaVis2015 Challenge dataset as a case study.
Efficacy of liraglutide in treating type 2 diabetes mellitus complicated with non-alcoholic fatty liver disease

Type 2 diabetes mellitus (T2DM) complicated with non-alcoholic fatty liver disease (NAFLD) is difficult to treat. The present study explored the efficacy of (liraglutide) Lira in treating T2DM complicated with NAFLD. A total of 127 patients suffering from T2DM complicated with NAFLD were enrolled in the present study, and randomly assigned to a Lira group (liraglutide injection: 0.6–1.2 mg/day, 12 weeks, n=52) or a Metformin (Met) group (oral metformin: 1000–1500 mg/day, 12 weeks, n=75). During the treatment phase, the values for fasting plasma glucose (FPG), 2 h plasma glucose (2hPG), glycated hemoglobin (HbA1c), aspartate aminotransferase (AST)/alanine aminotransferase (ALT), and adiponectin (APN) decreased in both the Lira and Met groups, and the levels of Δ2hPG, ΔAST/ALT, and ΔAPN in the Lira group were significantly lower than those in the Met group. The values for total cholesterol (TC), triglycerides (TG), low-and high-density lipoproteins (LDL and HDL), ALT, AST, weight, body mass index (BMI), waist to hip ratio (WHR), and C-reactive protein were markedly increased in both groups, and levels of ΔAST, ΔALT, Δweight, ΔBMI, ΔWHR, and ΔCRP (C-reactive protein) in the Lira group were significantly higher than those in the Met group. An analysis of treatment efficacy showed that liraglutide was better than metformin in its ability to significantly decrease the ALT levels in patients with combined T2DM and NAFLD. Furthermore, liraglutide was more effective than metformin at ameliorating the severity of T2DM complicated with NAFLD, and produced its effects by alleviating liver inflammation and improving liver function.
How do palliative care doctors recognise imminently dying patients? A judgement analysis

Objectives To identify a group of palliative care doctors who perform well on a prognostic test and to understand how they make their survival predictions. Design Prospective observational study and two cross-sectional online studies. Setting Phase I: an online prognostic test, developed from a prospective observational study of patients referred to palliative care. Phase II: an online judgement task consisting of 50 hypothetical vignettes. Participants All members of the Association of Palliative Medicine (APM) were eligible (n=~1100). 99 doctors completed the prognostic test and were included in the phase I analysis. The top 20% were invited to participate in phase II; 14/19 doctors completed the judgement task and were included in the phase II analysis. Measures Phase I: participants were asked to give a probability of death within 72 hours (0%–100%) for all 20 cases. Accuracy on the prognostic test was measured with the Brier score which was used to identify the ‘expert’ group (scale range: 0 (expert)–1 (non-expert)). Phase II: participants gave a probability of death within 72 hours (0%–100%). A mixed model regression analysis was completed using the percentage estimate as the outcome and the patient information included in the vignettes as the predictors. Results The mean Brier score of all participants was 0.237 (95% CI 0.235 to 0.239). The mean Brier score of the ‘experts’ was 0.184 (95% CI 0.176 to 0.192). Six of the seven prognostic variables included in the hypothetical vignettes were significantly associated with clinician predictions of death. The Palliative Performance Score was identified as being the most influential in the doctors’ prognostic decision making (β=0.48, p<0.001). Conclusions This study identified six clinical signs and symptoms which influenced the judgement policies of palliative care doctors. These results may be used to teach novice doctors how to improve their prognostic skills.
Geometric stability in force control

Previous implementations of robot force control seldom produced satisfactory results, and researchers in the past have experienced significant instability problems associated with their force controllers. When a manipulator is constrained to an environment (force-controlled), geometric stability due to the manipulator configuration and the force-controlled direction is shown to be a significant factor in overall system stability. This exploratory study points out a rather intuitive, geometrically based stability and analyzes the phenomenon both analytically and graphically. Sequential joint self-motion algorithms for kinematically redundant manipulators are suggested for reduced transitional impact and greater stability in the ensuing force-controlled operation. <<ETX>>
Efficient Data Stream Clustering With Sliding Windows Based on Locality-Sensitive Hashing

Data stream clustering over sliding windows generates clusters as the window moves. However, iterative clustering using all data in a window is highly inefficient in terms of memory use and computational load. In this paper, we improve data stream clustering over sliding windows using sliding window aggregation and nearest neighbor search techniques. Our algorithm constructs and maintains temporal group features as a summary of the window using the sliding window aggregation technique. In order to maintain a constant size for the summary, the algorithm reduces the size of the summary by joining the nearest neighbor. We exploit locality-sensitive hashing for rapid nearest neighbor searching. In addition, we also suggest a re-clustering policy that determines whether to append a new summary to pre-existing clusters or to perform clustering on the whole summary. We conduct experiments on real-world and synthetic datasets in order to demonstrate that our algorithm can significantly improve continuous clustering on data streams with sliding windows.
Region Based Extensive Response Index Pattern for Facial Expression Recognition

This paper presents a novel descriptor named Region based Extensive Response Index Pattern (RETRaIN) for facial expression recognition. The RETRaIN encodes the relation among the reference and neighboring pixels of facial active regions. These relations are computed by using directional compass mask on an input image and extract the high edge responses in foremost directions. Further extreme edge index positions are selected and encoded into six-bit compact code to reduce feature dimensionality and distinguish between the uniform and non-uniform patterns in the facial features. The performance of the proposed descriptor is tested and evaluated on three benchmark datasets Extended Cohn Kanade, JAFFE, and MUG. The RETRaIN achieves superior recognition accuracy in comparison to state-of-the-art techniques.
Prevención del suicidio

El presente trabajo pretende ofrecer elementos para ampliar la reflexión sobre el fenómeno del suicidio en los adolescentes y los jóvenes visto como uno de los trastornos «asociados a la vida contemporánea». Se busca sensibilizar sobre la responsabilidad que en materia de diagnóstico, prevención y atención tenemos los diferentes actores de la sociedad. En esta ocasión, nos centramos en el ámbito educativo por ser el espacio privilegiado para el aprendizaje, cuya intervención se ha dirigido al desarrollo de habilidades cognitivas de los estudiantes, pero ahora con el reto de ampliar su espectro de acción hacia la formación socio-afectiva de los educandos a través de programas que intencionalmente atiendan al desarrollo de la inteligencia emocional, y de habilidades sociales, lo que constituye el eje central de la propuesta que se expone para la prevención del suicidio.
Tasman-PCR: a genetic diagnostic assay for Tasmanian devil facial tumour diseases

Tasmanian devils have spawned two transmissible cancer clones, known as devil facial tumour 1 (DFT1) and devil facial tumour 2 (DFT2). DFT1 and DFT2 are transmitted between animals by the transfer of allogeneic contagious cancer cells by biting, and both cause facial tumours. DFT1 and DFT2 tumours are grossly indistinguishable, but can be differentiated using histopathology, cytogenetics or genotyping of polymorphic markers. However, standard diagnostic methods require specialist skills and equipment and entail long processing times. Here, we describe Tasman-PCR: a simple PCR-based diagnostic assay that distinguishes DFT1 and DFT2 by amplification of DNA spanning tumour-specific interchromosomal translocations. We demonstrate the high sensitivity and specificity of this assay by testing DNA from 557 tumours and 818 normal devils. A temporal-spatial screen confirmed the reported geographic ranges of DFT1 and DFT2 and did not provide evidence of additional DFT clones. DFT2 affects disproportionately more males than females, and devils can be co-infected with DFT1 and DFT2. Overall, we present a PCR-based assay that delivers rapid, accurate and high-throughput diagnosis of DFT1 and DFT2. This tool provides an additional resource for devil disease management and may assist with ongoing conservation efforts.
Becoming an Educational Researcher: Exploring the Earliest Experiences

Beginning educational researchers can become overwhelmed with the magnitude of the coursework and field experiences related to the research process. This paper explores one novice researcher’s earliest experiences with engaging the methods coursework, designing research studies, entering the field for data collection, and writing manuscripts. Thereafter, the author discusses lessons gleaned from the process and provides suggestions for new educational researchers embarking on the journey.
Atmospheric Artifacts Correction With a Covariance-Weighted Linear Model Over Mountainous Regions

Mitigating the atmospheric phase delay is one of the largest challenges faced by the differential synthetic aperture radar (SAR) interferometry community. Recently, many publications have studied correcting the stratified tropospheric phase delay by assuming a linear model between them and the topography. However, most of these studies have not considered the effect of turbulent atmospheric artifacts when adjusting the linear model to data. In this paper, we present an improved technique that minimizes the influence of the turbulent atmosphere in the model adjustment. In the proposed algorithm, the model is adjusted to the phase differences of pixels instead of using the unwrapped phase of each pixel. In addition, the different phase differences are weighted as a function of its atmospheric phase screen covariance estimated from an empirical variogram to reduce, in the model adjustment, the impact of pixel pairs with a significant turbulent atmosphere. The good performance of the proposed method has been validated with both the simulated and real Sentinel-1A SAR data in the mountainous area of Tenerife island, Spain.
GuacaMol: Benchmarking Models for De Novo Molecular Design

De novo design seeks to generate molecules with required property profiles by virtual design-make-test cycles. With the emergence of deep learning and neural generative models in many application areas, models for molecular design based on neural networks appeared recently and show promising results. However, the new models have not been profiled on consistent tasks, and comparative studies to well-established algorithms have only seldom been performed. To standardize the assessment of both classical and neural models for de novo molecular design, we propose an evaluation framework, GuacaMol, based on a suite of standardized benchmarks. The benchmark tasks encompass measuring the fidelity of the models to reproduce the property distribution of the training sets, the ability to generate novel molecules, the exploration and exploitation of chemical space, and a variety of single and multiobjective optimization tasks. The benchmarking open-source Python code and a leaderboard can be found on https://benevolent.ai/guacamol .
High-Volume Manufacturing Platform for Silicon Photonics

Hybrid integration of advanced CMOS electronics and a high-performing silicon photonics platform, combined with wafer-scale testing and assembly processes have enabled delivery of low-cost, low-power, high-performance, high-volume optical transceivers. In this paper, we will discuss the aspects of each of these factors that have been critical in achieving this success. In particular, we will focus on the silicon photonics process, device library and design kit, the innovative solutions for wafer-level testing and assembly of the optical transceivers, and discuss the benefits and drawbacks of monolithic and hybrid electronics–photonics integration and the impact on the roadmap of future optical transceivers.
A Genome-Wide Study of Allele-Specific Expression in Colorectal Cancer

Accumulating evidence from small-scale studies has suggested that allele-specific expression (ASE) plays an important role in tumor initiation and progression. However, little is known about genome-wide ASE in tumors. In this study, we conducted a comprehensive analysis of ASE in individuals with colorectal cancer (CRC) on a genome-wide scale. We identified 5.4 thousand genome-wide ASEs of single nucleotide variations (SNVs) from tumor and normal tissues of 59 individuals with CRC. We observed an increased ASE level in tumor samples and the ASEs enriched as hotspots on the genome. Around 63% of the genes located there were previously reported to contain complex regulatory elements, e.g., human leukocyte antigen (HLA), or were implicated in tumor progression. Focussing on the allelic expression of somatic mutations, we found that 37.5% of them exhibited ASE, and genes harboring such somatic mutations, were enriched in important pathways implicated in cancers. In addition, by comparing the expected and observed ASE events in tumor samples, we identified 50 tumor specific ASEs which possibly contributed to the somatic events in the regulatory regions of the genes and significantly enriched known cancer driver genes. By analyzing CRC ASEs from several perspectives, we provided a systematic understanding of how ASE is implicated in both tumor and normal tissues and will be of critical value in guiding ASE studies in cancer.
MULINEX: multilingual indexing, navigation and editing extensions for the world-wide web

MULINEX is a "leading-edge application project" which addresses the requirements of two kinds of users: web content providers and service operators who wish to provide multilingual information, and the customers of such multilingual information services (henceforth referred to as end users). The objective of the project is to provide multilingual search, retrieval and navigation functionalities for the WWW.
Optimal placement of robotic manipulators using multiple kinematic criteria

A method to find the optimal placement of manipulators by using multiple kinematic criteria is presented. By adequately locating a manipulator in order to accomplish a specified task, the method allows improvement in several indices of kinematic criteria of the manipulator for different points in the path. In the formulation presented, these indices are incorporated in an objective function which allows them to carry out their global optimization. The method considers the constraints imposed by the reach and by the joint limits of the manipulator. The improvements that can be achieved for the performances of manipulators by applying the method are shown in an example. <<ETX>>
Mapping of snow covered area with Radarsat in Norway

Estimates of snow cover area (SCA) can be obtained from spaceborne SAR sensors such as ERS, Radarsat and Envisat ASAR. A number of algorithms using backscattering images and interferometric coherence have been proposed and documented. It has also been shown that multifrequency and polarization SAR instruments gives enhanced snow mapping capabilities. In this paper we suggest improvements to the Nagler SCA-algorithm and apply them to Radarsat data in the Norwegian mountains. The effect of reduced backscattering due to increased incidence angle is discussed. The effect of mixed pixels (wet snow and other constituents) is addressed, and a sub-pixel method for snow classification is suggested. We also suggest classifying snow on lakes and in forests with different algorithms and separate masks, since the backscattering properties vary from class to class. Finally we try to assess the classification errors by statistical methods.
Self-incompatibility in Papaver Pollen: Programmed Cell Death in an Acidic Environment

Self-Incompatibility (SI) is a genetically controlled mechanism that prevents self-fertilisation and thus encourages outbreeding and genetic diversity. During pollination, most SI systems utilise cell-cell recognition to reject incompatible pollen. Mechanistically, one of the best-studied SI systems is that of Papaver rhoeas (poppy), which involves the interaction between the two S-determinants, a stigma-expressed secreted protein (PrsS) and a pollen-expressed plasma-membrane localised protein (PrpS). This interaction is the critical step in determining acceptance of compatible pollen or rejection of incompatible pollen. Cognate PrpS-PrsS interaction triggers a signalling network causing rapid growth arrest and eventually programmed cell death (PCD) in incompatible pollen. In this review, we provide an overview of recent advances in our understanding of the major components involved in the SI-induced PCD (SI-PCD). In particular, we focus on the importance of SI-induced intracellular acidification and consequences for protein function, and the regulation of soluble inorganic pyrophosphatase (Pr-p26.1) activity by post-translational modification. We also discuss attempts at the identification of protease(s) involved in the SI-PCD process. Finally, we outline future opportunities made possible by the functional transfer of the P. rhoeas SI system to Arabidopsis.
Graph Convolutional Neural Networks via Motif-based Attention

Many real-world problems can be represented as graph-based learning problems. In this paper, we propose a novel framework for learning spatial and attentional convolution neural networks on arbitrary graphs. Different from previous convolutional neural networks on graphs, we first design a motif-matching guided subgraph normalization method to capture neighborhood information. Then we implement subgraph-level self-attentional layers to learn different importances from different subgraphs to solve graph classification problems. Analogous to image-based attentional convolution networks that operate on locally connected and weighted regions of the input, we also extend graph normalization from one-dimensional node sequence to two-dimensional node grid by leveraging motif-matching, and design self-attentional layers without requiring any kinds of cost depending on prior knowledge of the graph structure. Our results on both bioinformatics and social network datasets show that we can significantly improve graph classification benchmarks over traditional graph kernel and existing deep models.