Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Data Blocking and Skipping Framework Applying Heuristic Rules

Data blocking has been an effective technique of data skipping to reduce data access and shorten query response time in query engines. By generating fine-grained, balanced blocks and corresponding metadata, a query may skip a block if the metadata indicates that the block does not contain relevant data. Obviously, the deciding factor of a promising blocking strategy depends on how to produce effective data layout in reasonable time that is expected to skip most data. In this paper, we propose several algorithms that drastically reduce the time complexity of existent blocking strategies based on workload analysis, at the cost of relatively small loss of estimated tuples could be skipped. Via theoretical analysis, we prove that the time complexity of our algorithms is apparently lower than that of ward algorithm. Afterwards, we demonstrate the whole blocking and skipping workflow, install it into Spark SQL and obtain experimental evaluation results. Experimental results show that our technique gains significant improvement in aspect of blocking efficiency compared to ward algorithm, while keeping almost the same level of skipping ability.
Building Fuzzy Blocks from Data Cubes

Multidimensional databases have become very popular for decision making frameworks. In this context, huge amounts of data are stored in data warehouses and decision makers try and navigate through this data using OLAP tools in order to visualize and analyze it. Although navigating through the data is one of the key issues, many issues are still open, and users are still not provided with intelligent tools for automatically identifying relevant parts from the data. In this paper, we address this problem and we propose to mine homogeneous areas of the data, which we call blocks. In previous work, we have defined a level-wise method to automatically mine such blocks. However, these blocks are crisp in their definition, although they are described by fuzzy rules. We extend our previous work by proposing ways to mine fuzzy blocks, and we compare the three approaches, showing that fuzzifying blocks leads to more clearly defined areas from the data.
Status of coral reef health in the northern Red Sea, Egypt

This study aimed to provide baseline knowledge of coral diseases prevalence and coral health at nine reef sites in the northern Egyptian Red Sea: I (El-Ain Al-Sukhna), II (Ras Za’farana), III (Hurghada), IV (Ras Ghozlani), V (Old Quay), VI (Yolanda reef), VII (Ras Umm Sid, Sharm El-Sheikh), VIII (WoodHouse reef, Sharm El-Sheikh), and IX (canyon reef, Dahab). Sites IV, V and VI are located in Ras Mohamed, marine protected area. Field surveys of coral diseases and signs of compromised health in the studied reefs revealed a mean prevalence of 1.03%. Macroscopic observations were diagnosed as: black band disease (0.44%), white syndrome (0.13%), pink line syndrome (0.04%), ulcerative white spots (0.02%), skeletal eroding band (0.001%), coral bleaching (2.2%), Drupella cornus snail predation (0.1%), fish predation (0.2%), pigmentation response (0.13%), sediment damage (0.08%), algae overgrowth (0.06%) and sponge overgrowth (0.04%). Prevalence of coral diseases and bleaching were higher in non-MPA sites than in MPA sites. The highest prevalence of coral diseases was recorded on the coral Favia stelligera, followed by Porites lutea, and Goniastrea edwardsi. Enhanced local anthropogenic stresses and increasing sea surface temperature due to global warming are the suggested potential factors responsible for the initiation and the persistence of some coral diseases in the studied reefs.
REGIONAL FEATURES OF THE BIOCLIMATE OF CENTRAL AND SOUTHERN EUROPE AGAINST THE BACKGROUND OF THE KÖPPEN-GEIGER CLIMATE CLASSIFICATION

This paper presents an application of the Universal Thermal Climate Index UTCI to studies of regional variability in human-biometeorological conditions. The variability in question was assessed by reference to selected meteorological stations representing Central and Southern Europe, i.e. Kolobrzeg, Warsaw and Świeradow (in Poland), Prague, Budapest, Ljubljana, Milan, Rome and Athens, with the bioclimatic features characterising these localities being presented against the background of the Koppen-Geiger climate classification. In line with that classification, the first five stations are found to represent the cold climate zones (Dfb, Dfc). The last four stations are in turn located in the temperate climate zones (Cfa, Cfb, Csa). Seasonal changes in UTCI values and the frequency of occurrence of UTCI categories are discussed. Significant regional differences in bioclimatic characteristics were found between the stations representing various
A Transformer-Based 3-dB Differential Coupler

This paper presents a transformer-based 3-dB differential coupler, which features the suppression of both common-mode and cross-mode signals. Transformer is utilized rather than the quarter-/half-wavelength lines, and the circuit size can be significantly reduced. The bandwidth of the proposed differential coupler can be enhanced by increasing the coupling coefficient of the transformer. For the proof of concept, the proposed transformer-based 3-dB differential coupler is implemented on the substrate integrated suspended line (SISL) platform. Well agreed simulated and measured results verified the feasibility of the proposed differential coupler. From 0.4 to 0.6 GHz, the measured common-mode suppression is better than 10 dB with a maximum 30 dB, and the measured cross-mode suppression is better than 30 dB. Compared with the previous designs, the self-packaged SISL differential coupler has a much smaller size of $0.08 \lambda _{0} \times 0.08\lambda _{0}$ . The proposed design methodology has advantages of compact size, suppression of both common-mode and cross-mode signals, and controllable bandwidth.
Sox 9 Directs Hypertrophic Maturation and Blocks Osteoblast Differentiation of Growth Plate Chondrocytes

Peter Dy,1,3 Weihuan Wang,1,3 Pallavi Bhattaram,1 Qiuqing Wang,1 Lai Wang,2 R. Tracy Ballock,2 and Véronique Lefebvre1,* 1Department of Cell Biology 2Departments of Biomedical Engineering and Orthopaedic Surgery Orthopaedic and Rheumatologic Research Center, Cleveland Clinic Lerner Research Institute, Cleveland, OH 44195, USA 3These authors contributed equally to this work *Correspondence: lefebvv@ccf.org DOI 10.1016/j.devcel. 2011.12. 024
Diagnosis of Diabetes using Artificial Intelligence Techniques by using Bio Medical Signal Data

Data Mining pursuits at discovering know-how out of information and supplying it in a shape that is effortlessly compressible to humans. It is a technique this is evolved to study huge quantities of data automatically gathered. Artificial intelligence approach like fuzzy, ANN and so forth are presently used for fixing a wide variety of issues in distinctive application area for choice based version designing. These systems lets in us to introduce the getting to know and variation talents subsequently such form of framework has been used in numerous unique manner of diagnosis of disease. It enables in developing computational paradigm that gives a mathematical tool for dealing with the uncertainty and the imprecision standard of human reasoning. Relational between symptoms and dangers elements for Diabetic based on the expert’s medical know-how is taken and additionally associated complications or due to a few commonplace metabolic disease it can cause imaginative and prescient loss, coronary heart failure, stroke, foot ulcer, nerves. In this work overview is supplied on various strategies which are is taken into consideration in analysis where symptoms located inside the patient and relation representing the clinical information that relates the signs and symptoms in set S to the diseases in set D to diagnose the set B of the feasible sicknesses of the patients can be inferred by the compositional rule of inference. It has been found that Neural Networks are correctly used for getting to know membership capabilities, fuzzy inference rules and other context dependent patterns; fuzzification of neural networks extends their capabilities in applicability. The classifier is the heart of the automated prognosis system. The reliable classifier have to diagnose the ailment at as high accuracy as feasible even though there are many uncontrolled versions. In literature, special classifiers have been proposed for automatic analysis of PD. The NNs and adaptive neuro fuzzy classifier with linguistic hedges (ANFIS-LH) are investigated for automatic analysis of PD. The performance of probabilistic neural community (PNN) for automated diagnosis of PD is evaluated. SVM classifier is likewise investigated for the identical goal. NNs have some drawbacks consisting of need of long schooling time and uncertainties in activation function to be used in hidden layer, variety of cells in hidden layer, and the range of hidden layer. In case of SVM, type of kernel function and penalty consistent and so forth influences the category performance. If these parameters are not appropriately decided on, the class performance of SVM degrades. Similarly, the performance of ANFIS depends on type and parameters of membership characteristic and output linear parameters.
Assessment of Low-Density Lipoprotein Cholesterol (LDL-C) Goal Achievement Rate among Type 2 Diabetic Patients in Security Forces Hospital Diabetic Center, Riyadh, Saudi Arabia

Background: Diabetes mellitus is a cardiovascular risk equivalent and associated with a higher risk of cardiovascular mortality comparing with general population. Small low-density lipoprotein cholesterol (LDL-C) particle size, which is highly atherogenic, is significantly associated with progression of coronary artery disease. American Diabetes Association (ADA) and National Cholesterol Education Program (NCEP) Adult Treatment Panel (ATP) III recommend aggressive dyslipidemia management in diabetic patients with a primary of LDL-C level < 100 mg/dL (<2.6 mmol/L).
Deep learning for the design of nano-photonic structures

Our visual perception of our surroundings is ultimately limited by the diffraction-limit, which stipulates that optical information smaller than roughly half the illumination wavelength is not retrievable. Over the past decades, many breakthroughs have led to unprecedented imaging capabilities beyond the diffraction-limit, with applications in biology and nanotechnology. In this context, nano-photonics has had a profound impact on the field of optics by enabling the manipulation of light-matter interaction with subwave-length structures [1, 2, 3]. However, despite the many advances in this field, its impact and penetration in our daily life has been hindered by a convoluted and iterative process, cycling through modeling, nanofabrication and nano-characterization. The fundamental reason is the fact that not only the prediction of the optical response is very time consuming and requires solving Maxwell's equations with dedicated numerical packages [4, 5, 6]. But, more significantly, the inverse problem, i.e. designing a nanostructure with an on-demand optical response, is currently a prohibitive task even with the most advanced numerical tools due to the high non-linearity of the problem [7, 8]. Here, we harness the power of Deep Learning and show its ability to predict the geometry of nanostructures based solely on their far-field response. This approach addresses in a direct way the currently inaccessible inverse problem breaking the ground for on-demand design of optical response with applications such as sensing, imaging and also for Plasmons mediated cancer thermotherapy.
CFCLP-a novel Clustering Framework based on Combinatorial approach and Linear Programming in Wireless Sensor Network

It is widely known that the process and performance of data aggregation in wireless sensor network is dependent on the effectiveness of clustering mechanism. However, existing research work towards clustering mechanism has been found to focus on selection on cluster-head majorly on the basis on energy. This is the prime reason for which the energy effectiveness has not completely been achieved in wireless sensor network. The present paper introduces a novel analytical modeling of CFCLP that leverages data aggregation by a novel combinatorial approach using linear programming for evolving up with a new clustering mechanism. A sophisticated mathematical modeling is used for designing and its algorithm being implemented on MEMSIC nodes to find that CFCLP is capable of maintaining good balance between energy saving performance and data delivery among the sensor nodes for longer iterations.
IBM Research System at TAC 2017: Adverse Drug Reactions Extraction from Drug Labels

Identifying Adverse Drug Reactions (ADRs) is an important concern in clinical medicine. Automatic extraction of ADRs from the corpus enable many clinical decision support applications, and drug labels or ’package inserts’ are among the primary sources of information. The Adverse Drug Reaction extraction from Drug Labels challenge at TAC 2017 deﬁned several NLP tasks leading up to ADR extraction from a subset of drug labels. We participated in the Task 1 (extraction of mentions of severity, drug class, negation, animal, and factor) and the Task 2 (identiﬁcation of relationship types - negated, hypothetical and effect - between ADRs and related assertions). For Task 1, we implemented a joint Bi-directional-LSTM (BiLSTM)-CRF and Attention-BiLSTM neural network for identifying contiguous, discontiguous and overlapping mentions simultaneously. For Task 2, we used another Attention-BiLSTM network. Our system achieved F-measures of 78.21 for untyped exact match and 78.00 for typed exact match, for Task 1. Using the mentions generated by our system, we achieved F-measures of 45.16 and 44.60 for the binary relation detection (Task 2) without and with type identiﬁcation respectively. However, using the gold standard mentions we achieved 91.20 and 87.86 F-measures for the Task 2. Thus, through this work, we demonstrated effective adaptation of BiLSTM networks for a subset of ADR extraction tasks.
A Robust Affine Invariant Point Extraction Algorithm for Image Registration

Affine invariance is a critical property for control points in point based image registration. In this paper, a highly robust affine invariant point extraction algorithm is proposed. For a pair of two shapes, they are handled as the longitudinal segments parallel to the line connecting centroids of two shapes, and then some longitudinal segments with the uniformly sampled interception on y-axis are adopted to calculate a descriptor which reflects the relative attitude between two shapes. In the meantime, the intersection points of sampled longitudinal segments and the contours of the shapes are taken as control points. After the corresponding shapes are found based on minimal distance between descriptors, the affine invariant control points from corresponding shapes are then used to estimate the transformation between images. Experimental results on synthetic and real data show that, our algorithm outperforms SC with higher precision.
Experimental evidence for a seasonal shift in the strength of a female mating preference

The costs and benefits of mate choice can vary both spatially and temporally. Phenotypic plasticity in mate choice, which could be due to changes in choice criteria (e.g., acceptance thresholds) or shifts in underlying mating preferences (i.e., relative values assigned to different males), can therefore increase reproductive success. We examined shifts in female mating preference for male claw size between 2 seasons (winter and summer) in the fiddler crab Uca mjoebergi. We used custom-built robotic crabs in 2-stimulus mate choice experiments conducted in the field. Females showed a consistent preference for larger claw size, but this was significantly weaker during winter. This seasonal difference was observed for 2 successive years. We propose that the change in female preference for claw size is due to a seasonal difference in the value of a larger burrow, which strongly influences burrow temperature. Burrow temperature largely determines larval development rate and larvae release in fiddler crabs is constrained to a narrow time window where survival is optimal. Females appear to alter their preference for burrow width, which is signaled by claw size, between winter and summer, potentially allowing them to optimize developmental time of larvae and/or to reduce mate search costs. Copyright 2010, Oxford University Press.
IECAS Event Detection System at TAC KBP 2017 Event Nugget Track

— This paper describes our Event Nugget Detection systems that we submitted to the TAC KBP 2017 Event Track. We implement pipelined event detection systems based on convolutional neural network (CNN) and bidirectional Long Short-Term Memery (BiLSTM) for both event type and realis classiﬁcation. Our systems use only word embeddings as external resources for training so they can be easily adapted to multiple languages. The experimental results show that our approaches can achieve promising performances in the Event Nugget Detection task.
Dynamic mmWave beam tracking for high speed railway communications

With the worldwide deployment of the railway, high-speed railway (HSR) tends to evolve into an intelligent transportation system with efficient and green transportation characteristics. To meet the explosive growth of wireless data traffic in “smart rail”, millimeter-wave (mmWave) communications are used to offer several Gbps data rate via beamforming structures by utilizing huge unlicensed bandwidth. In this paper, the beam tracking strategies for mmWave HSR communications are investigated, and a dynamic beam tracking strategy is proposed by adjusting the beam direction and beam width jointly, where the main idea is to maximize the sum-rate capacity while satisfying the critical quality of service (QoS) requirements. Then it boils down to a demanding non-convex problem, and a near-optimal solution is provided based on the problem decomposition and the genetic algorithm. Finally, along with the moving trains the proposed beam tracking scheme can provide near-optimal capacity performance with a low outage probability, and reduce the implementation complexity.
Performance of RSCC-QPSK-OFDM system with phase error and perfect compensation in Rayleigh fading channel

Orthogonal Frequency Division Multiplexing (OFDM) combines the advantage of high achievable rates and relatively easy implementation. In this paper, the combination of the Recursive Systematic Convolutional Code (RSCC) and the OFDM with pilot-aided data in Raleigh fading channel with compensation the phase error and perfect compensation has been suggested. The system is called RSCC-QPSK-OFDM. The simulation of RSCC-QPSK-OFDM with phase error and perfect compensation for both hard and soft decision of convolutional decoder are given, the effect of code rate and constraint length in the proposed system has been studied. BER performance of rate 1/2, soft decision, RSCC-QPSK-OFDM system with phase error compensation has a gain factor 9 dB at 2·10−4 compared with un-coded system. BER performance of the proposed system with phase error and amplitude compensation has a gain factor 10 dB at 10−4.
Quantification of bank erosion in a drained agricultural lowland catchment

The long‐term and current volumes of sediment exported from stream banks were calculated as potential sources of sediment in a large pond located at the catchment outlet of a small agricultural lowland basin strongly affected by anthropogenic pressure in France. Bank erosion was measured over a short period using a network of erosion pins along a small stream (1400 m long) to quantify the material exported during a single winter (2012–2013). The material exported by this same stream over the last 69 years was quantified using an original approach involving the comparison of a compilation of three‐dimensional historical stream redesign plans that date back to 1944 with the state of the banks in 2013 (differential global positioning system and LiDAR data). The results suggest that a global trend of material loss along the stream banks monitored by erosion pins, with an average erosion rate of 17.7 mm year−1 and an average volume of exported material of 75 t km−1. Over 69 years, this same stream exported an average of 36 t km−1 year−1, and the average loss of material from the banks throughout the whole catchment was estimated to be 14 t km−1 year−1. The contribution of bank material to the filling of the pond over the last 10 years is between 46% and 52% based on an extrapolation of erosion pin dynamics or between 27% and 30% based on the comparison of LiDAR data to the average historical profile extrapolated for the catchment. These results suggest that bank erosion represents a major source of sediment in degraded waters in traditionally understudied agricultural lowland catchments, where anthropogenic pressures are high.
Rough Sets and Cellular Automate Applied to Spatial Load Forecasting

A novel spatial load forecasting (SLF) methodology for distribution network is proposed, cellular automata (CA) theory is used to simulate the process of urban land-use dynamic development, and forecast future land-use types of small-areas. According to fact of urban development, ascertain iterative time and adjustive time of transition rules of CA. In order to eliminate redundancies, overcome faults that land-use rules is always static and suffer influence from subjectivity in traditional methods, rough sets (RS) theory is used to carry out attribute-reduction for potential influencing factors during adjustive time step by step, and obtain dynamic transition rules of CA. Finally, the validity of the proposed method is validated by an actual example.