Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the Hidden Dimension in Graph Processing

Task partitioning of a graph-parallel system is traditionally considered equivalent to the graph partition problem. Such equivalence exists because the properties associated with each vertex/edge are normally considered indivisible. However, this assumption is not true for many Machine Learning and Data Mining (MLDM) problems: instead of a single value, a vector of data elements is defined as the property for each vertex/edge. This feature opens a new dimension for task partitioning because a vertex could be divided and assigned to different nodes. To explore this new opportunity, this paper presents 3D partitioning, a novel category of task partition algorithms that significantly reduces network traffic for certain MLDM applications. Based on 3D partitioning, we build a distributed graph engine CUBE. Our evaluation results show that CUBE outperforms state-of-the-art graph-parallel system PowerLyra by up to 4:7× (up to 7:3× speedup against PowerGraph).
INDOMETHACIN-LOADED MICROSPHERES: PREPARATION, CHARACTERIZATION AND IN-VITRO EVALUATION REGARDING ETHYLCELLULOSE MATRIX MATERIAL

Indomethacin-loaded microspheres of ethylcellulose were prepared by the emulsion solvent evaporation technique. The aim of this work was to investigate the influence of process variation in polymer type via viscosity grades of ethylcellulose N10 and N100, drug to polymer ratio, stirring rate of the propeller and surfactant type on the micromeritic properties of microspheres such as particle size distribution, bulk and tapped density, surface topography, tangent of angle of repose, compressibility index, Hausner ratio and flow rates. All microspheres presented a narrow particle size distribution and good flow characters according to USP 28-NF 23 criteria, besides microspheres were more spherical in shape in their manufacture with ethylcellulose N100 and higher ratio of both polymers. Thus, in the case of ethylcellulos e, the viscosity and ratio of the polymer in dispersion medium were found to be the controlling factors of drug release. Ethylcellulose N10 and N100 membrane materials indicated difference in release patterns of microspheres. Microspheres exhibited lower burst effect with decreased drug release rate, when the drug was incorporated with ethylcellulose N100 and higher ratio of each polymer. Therefore, indomethacin release from ethylcellulose microspheres could not be evaluated by any of the kinetic models.
Dynamic left ventricular obstruction evoked by exercise: importance of outflow tract size.

We have read with interest the article by Zywica et al. 1 recently published in an issue of the European journal of Echocardiography. The authors analysed, in 300 patients referred for exercise echocardiography, the prevalence and predictive factors of dynamic left ventricular outflow tract obstruction on immediate post-exercise echocardiographic examination. This phenomenon, which appears in 5% of patients in the Zywica et al. 1 series, has been previously described in others works, with frequencies ranging from …
Parametric Study of Beams with Externally Bonded FRP Reinforcement

FRP reinforcement may be externally bonded to the soffit of existing flexural members in order to increase their strength and rigidity. A parametric analysis is conducted to investigate the effects of FRP reinforcement on serviceability, strength, and failure mechanisms of repaired RC beams. FRP reinforcement parameters considered in the analysis are: stiffness, bonded length, thickness, and the adhesive stiffness. The choice of the repair material parameters is important in the design phase in order to obtain the desired results of strengthening or stiffening without other unforeseen effects. In this paper, three typical RC beam cross sections are considered with height-to-width ratios of 0.5, 1, and 4. Two characteristic compressive strength levels (20 and 30 MPa), and two shear span-to-reinforcement depth ratios (4.5 and 7) are considered. All other parameters related to material and geometry of the beams are maintained constant. The results of the analysis are shown in terms of repaired-to-un-repaired strength and deflection ratios. They indicate that brittle failure mechanisms can develop at loads much lower than expected when considering only flexural performance controlled by concrete crushing and FRP tensile rupture. The analytical model used for the parametrization accounts for brittle failure mechanisms induced by debonding of the FRP reinforcement or shear-tension failure in concrete in the plane of the main longitudinal steel reinforcing bars. Even when considering the limitation of the RC member due to its un-modifiable shear resistance, it is shown that the application of FRP reinforcement can considerably increase load resistance capacity and limit deflection at service.
Detecting hot topics in technology news streams

Detecting hot topics with a fine granularity in technology news streams is an interesting and important problem given the large amount of reports and a relatively narrow range of topics. In this paper, a three-phase method is proposed. In the first phase, the document topic distribution vector is generated and keywords are extracted for each document using topic model pachinko allocation. In the second phase, the documents are clustered based on the document topic distribution vector obtained from the previous phase using affinity propagation. And in the last phase, actual events denoted by combinations of keywords within each cluster are found out using frequent pattern mining algorithms. We evaluate our approach on a collection of technology news reports from various sites in a fixed time period. T he results show that this method is effective.
Compressed FEC Codes With Spatial-Coupling

An error floor problem is observed for a spatially coupled sparse-regression (SCSR) code with limited sparsity in low-to-medium rates. This letter presents a scheme that also involves spatial-coupling and compressed-sensing (SCCS) similar to SCSR. We replace position modulation in SCSR by a general concatenated forward error control code. We introduce a chaining principle that improves the error floor behavior of the underlying code. We show that SCCS based on chained Hadamard codes can offer significantly improved performance.
The mitochondrial and endoplasmic reticulum pathways involved in the apoptosis of bursa of Fabricius cells in broilers exposed to dietary aflatoxin B1

Aflatoxin B1 (AFB1), a toxic metabolite produced by some fungi, exerts well-known hepatocarcinogenic and immunosuppressive effects, the latter can increase the apoptotic immune cells in vitro. However, it is largely unknown that which signaling pathways contribute to excessive apoptosis of immune cells which induced by AFB1. In this study, we investigated the roles of the mitochondria, endoplasmic reticulum (ER) and death receptor activated apoptotic pathways in the bursal of Fabricius (BF) cells in the broilers exposed to AFB1 diet. We found that (1) AFB1 diet induced morphological changes in the BF. (2) FCM and TUNEL methods showed that excessive apoptosis could be resulted from AFB1 intake. (3) AFB1-induced apoptosis of bursal cells involved mitochondrial pathway (increase of Bax, Bak, cytC, caspase-9, Apaf-1, caspase-3 and decrease of Bcl-2 and Bcl-xL) and ER pathway (increase of Grp78/Bip, Grp94 and CaM). (4) Oxidative stress was confirmed in the BF of chicken fed on AFB1 diet. Overall, this work is the first to demonstrate that the activation of mitochondria and ER apoptosis pathways can lead to excessive apoptosis in BF cells, and oxidative stress is a crucial driver during AFB1 exposure.
TAMU at KBP 2017: Event Nugget Detection and Coreference Resolution

In this paper, we describe TAMU's system submitted to the TAC KBP 2017 event nugget detection and coreference resolution task. Our system builds on the statistical and empirical observations made on training and development data. We found that modifiers of event nuggets tend to have unique syntactic distribution. Their parts-of-speech tags and dependency relations provides them essential characteristics that are useful in identifying their span and also defining their types and realis status. We further found that the joint modeling of event span detection and realis status identification performs better than the individual models for both tasks. Our simple system designed using minimal features achieved the micro-average F1 scores of 57.72, 44.27 and 42.47 for event span detection, type identification and realis status classification tasks respectively. Also, our system achieved the CoNLL F1 score of 27.20 in event coreference resolution task.
Trajectory Optimization for Completion Time Minimization in UAV-Enabled Multicasting

This paper studies an unmanned aerial vehicle (UAV)-enabled multicasting system, where a UAV is dispatched to disseminate a common file to a number of geographically distributed ground terminals (GTs). Our objective is to design the UAV trajectory to minimize its mission completion time, while ensuring that each GT is able to successfully recover the file with a high probability required. We consider the use of practical random linear network coding (RLNC) for UAV multicasting, so that each GT is able to recover the file as long as it receives a sufficiently large number of coded packets. However, the formulated UAV trajectory optimization problem is non-convex and difficult to be directly solved. To tackle this issue, we first derive an analytical lower bound for the success probability of each GT's file recovery. Based on this result, we then reformulate the problem into a more tractable form, where the UAV trajectory only needs to be designed to meet a set of constraints each on the minimum connection time with a GT, during which their distance is below a designed threshold. We show that the optimal UAV trajectory only needs to constitute connected line segments, thus it can be obtained by determining first the optimal set of waypoints and then UAV speed along the lines connecting the waypoints. We propose practical schemes for the waypoints design based on a novel concept of virtual base station (VBS) placement and by applying convex optimization techniques. Furthermore, for given set of waypoints, we obtain the optimal UAV speed over the resulting path efficiently by solving a linear programming (LP) problem. Numerical results show that the proposed UAV-enabled multicasting with optimized trajectory design achieves significant performance gains as compared to benchmark schemes.
Investigating Surface and Near-Surface Bushfire Fuel Attributes: A Comparison between Visual Assessments and Image-Based Point Clouds

Visual assessment, following guides such as the Overall Fuel Hazard Assessment Guide (OFHAG), is a common approach for assessing the structure and hazard of varying bushfire fuel layers. Visual assessments can be vulnerable to imprecision due to subjectivity between assessors, while emerging techniques such as image-based point clouds can offer land managers potentially more repeatable descriptions of fuel structure. This study compared the variability of estimates of surface and near-surface fuel attributes generated by eight assessment teams using the OFHAG and Fuels3D, a smartphone method utilising image-based point clouds, within three assessment plots in an Australian lowland forest. Surface fuel hazard scores derived from underpinning attributes were also assessed. Overall, this study found considerable variability between teams on most visually assessed variables, resulting in inconsistent hazard scores. Variability was observed within point cloud estimates but was, however, on average two to eight times less than that seen in visual estimates, indicating greater consistency and repeatability of this method. It is proposed that while variability within the Fuels3D method may be overcome through improved methods and equipment, inconsistencies in the OFHAG are likely due to the inherent subjectivity between assessors, which may be more difficult to overcome. This study demonstrates the capability of the Fuels3D method to efficiently and consistently collect data on fuel hazard and structure, and, as such, this method shows potential for use in fire management practices where accurate and reliable data is essential.
Development of the duct system during exocrine pancreas differentiation in the grass snake Natrix natrix (Lepidosauria, Serpentes)

We analyzed the development of the pancreatic ducts in grass snake Natrix natrix L. embryos with special focus on the three‐dimensional (3D)‐structure of the duct network, ultrastructural differentiation of ducts with attention to cell types and lumen formation. Our results indicated that the system of ducts in the embryonic pancreas of the grass snake can be divided into extralobular, intralobular, and intercalated ducts, similarly as in other vertebrate species. However, the pattern of branching was different from that in other vertebrates, which was related to the specific topography of the snake's internal organs. The process of duct remodeling in Natrix embryos began when the duct walls started to change from multilayered to single‐layered and ended together with tube formation. It began in the dorsal pancreatic bud and proceeded toward the caudal direction. The lumen of pancreatic ducts differentiated by cavitation because a population of centrally located cells was cleared through cell death resembling anoikis. During embryonic development in the pancreatic duct walls of the grass snake four types of cells were present, that is, principal, endocrine, goblet, and basal cells, which is different from other vertebrate species. The principal cells were electron‐dense, contained indented nuclei with abundant heterochromatin, microvilli and cilia, and were connected by interdigitations of lateral membranes and junctional complexes. The endocrine cells were electron‐translucent and some of them included endocrine granules. The goblet cells were filled with large granules with nonhomogeneous, moderately electron‐dense material. The basal cells were small, electron‐dense, and did not reach the duct lumen.
PCA and KPCA integrated Support Vector Machine for multi-fault classification

This work aims to study the fault classification problem in complicated industrial processes. Two modified multi-classification methods of Support Vector Machine (SVM), i.e., Principal Component Analysis based Support Vector Machine (PCA-SVM) as well as Kernel Principal Component Analysis based Support Vector Machine (KPCA-SVM), are respectively proposed to classify multi-fault for the underlying process. The continuous stirred tank heater (CSTH) benchmark is adopted in simulation to validate the effectiveness of the proposed approaches. Simulation results indicate that compared with the original PCA-SVM, KPCA-SVM generates a higher classification rate for the underlying process at the cost of larger computation loads.
An Effective Illustrative Visualization Framework Based on Photic Extremum Lines (PELs)

Conveying shape using feature lines is an important visualization tool in visual computing. The existing feature lines (e.g., ridges, valleys, silhouettes, suggestive contours, etc.) are solely determined by local geometry properties (e.g., normals and curvatures) as well as the view position. This paper is strongly inspired by the observation in human vision and perception that a sudden change in the luminance plays a critical role to faithfully represent and recover the 3D information. In particular, we adopt the edge detection techniques in image processing for 3D shape visualization and present photic extremum lines (PELs) which emphasize significant variations of illumination over 3D surfaces. Comparing with the existing feature lines, PELs are more flexible and offer users more freedom to achieve desirable visualization effects. In addition, the user can easily control the shape visualization by changing the light position, the number of light sources, and choosing various light models. We compare PELs with the existing approaches and demonstrate that PEL is a flexible and effective tool to illustrate 3D surface and volume for visual computing.
Evaluation of DC voltage ripple in single-phase H-bridge PWM inverters

In this paper the complete analysis of the dc-link voltage ripple is given for single-phase H-bridge PWM inverters. The dc voltage ripple amplitude is analytically determined as a function of the modulation index and the load phase angle, considering both the switching frequency component and the double fundamental frequency component (i.e. 100 Hz). Peak-to-peak distribution, maximum amplitude, and rms values of the voltage switching ripple over the fundamental period are analytically determined and numerically verified by numerical simulations carried out by Matlab/Simulink. Simple and effective guidelines to design the dc-link capacitor are given on the basis of the dc-voltage ripple requirements.
Surrogate Scoring Rules and a Dominant Truth Serum for Information Elicitation

Strictly proper scoring rules (SPSR) are widely used when designing incentive mechanisms to elicit private information from strategic agents using realized ground truth signals, and they can help quantify the value of elicited information. In this paper, we extend such scoring rules to settings where a mechanism designer does not have access to ground truth. We consider two such settings: (i) a setting when the mechanism designer has access to a noisy proxy version of the ground truth, with known biases; and (ii) the standard peer prediction setting where agents' reports are the only source of information that the mechanism designer has. We introduce surrogate scoring rules (SSR) for the first setting, which use the noisy ground truth to evaluate quality of elicited information. We show that SSR preserves the strict properness of SPSR. Using SSR, we then develop a multi-task scoring mechanism -- called dominant truth serum (DTS) -- to achieve strict properness when the mechanism designer only has access to agents' reports. In comparison to standard equilibrium concepts in peer prediction, we show that DTS can achieve truthfulness in a multi-task dominant strategy. A salient feature of SSR and DTS is that they quantify the quality of information despite lack of ground truth, just as proper scoring rules do for the with verification setting. Our method is verified both theoretically and empirically using data collected from real human participants.
An Overview of PEER's Performance-Based Earthquake Engineering Methodology

Various analytical approaches to performance-based earthquake engineering are in devel- opment. This paper summarizes the approach being pursued by the Pacific Earthquake Engineering Re- search (PEER) Center. It works in four stages: hazard analysis, structural analysis, damage analysis, and loss analysis. In the hazard analysis, one evaluates the seismic hazard at the facility site, producing sample ground-motion time histories whose intensity measure (IM) is appropriate to varying hazard lev- els. In the structural-analysis phase, a nonlinear time-history structural analysis is performed to calcu- late the response of the facility to a ground motion of given IM in terms of drifts, accelerations, ground failure, or other engineering demand parameters (EDP). In the third, damage-analysis, phase, these EDPs are used with component fragility functions to determine measures of damage (DM) to the facility components. Finally, given DM, one evaluates repair efforts to determine repair costs, operability, and repair duration, and the potential for casualties. These measures of performance are referred to as deci- sion variables (DV), since they can be used to inform stakeholder decisions about future performance. Each relationship, from location and design to IM, IM to EDP, EDP to DM, and DM to DV, involves uncertainty and is treated probabilistically. PEER is currently exercising and illustrating its methodol- ogy on six real facilities, called testbeds, each of which explores a different aspect of PBEE.
Frequency Domain Trinicon-Based Blind Source Separation Method with Multi-Source Activity Detection for Sparsely Mixed Signals

The TRINICON (‘Triple-N ICA for convolutive mixtures') framework is an effective blind signal separation (BSS) method for separating sound sources from convolutive mixtures. It makes full use of the non-whiteness, non-stationarity and non-Gaussianity properties of the source signals and can be implemented either in time domain or in frequency domain, avoiding the notorious internal permutation problem. It usually has best performance when the sources are continuously mixed. In this paper, the offline dual-channel frequency domain TRINICON implementation for sparsely mixed signals is investigated, and a multi-source activity detection is proposed to locate the active period of each source, based on which the filter updating strategy is regularized to improve the separation performance. The objective metric provided by the BSSEVAL toolkit is utilized to evaluate the performance of the proposed scheme.
Trade-off analysis and design of a high power density PM machine for flooded industrial pump

This paper presents the trade-off analysis and design of a high power-density machine for industrial pump applications. The developed permanent magnet synchronous machine drives an electric, oil flooded pump. Different slot/pole combination and winding configuration have been investigated in order to identify the optimal combination that satisfies the electromagnetic and thermal constraint while keeping the losses as small as possible. Several strategies such as the use of the Cobalt iron material for the stator core lamination and the adoption of Halbach array have been investigated in this work to improve the performance capabilities of the designed machine. The electromagnetic performances have been evaluated by using a finite element method. Thermal behaviour has been determined using a lumped parameter network. The outcome of the thermal analysis helped to identify the optimal cooling configurations. The final results are presented highlighting the achieved design targets.
A Two-Phase Time Synchronization-Free Localization Algorithm for Underwater Sensor Networks

Underwater Sensor Networks (UWSNs) can enable a broad range of applications such as resource monitoring, disaster prevention, and navigation-assistance. Sensor nodes location in UWSNs is an especially relevant topic. Global Positioning System (GPS) information is not suitable for use in UWSNs because of the underwater propagation problems. Hence, some localization algorithms based on the precise time synchronization between sensor nodes that have been proposed for UWSNs are not feasible. In this paper, we propose a localization algorithm called Two-Phase Time Synchronization-Free Localization Algorithm (TP-TSFLA). TP-TSFLA contains two phases, namely, range-based estimation phase and range-free evaluation phase. In the first phase, we address a time synchronization-free localization scheme based on the Particle Swarm Optimization (PSO) algorithm to obtain the coordinates of the unknown sensor nodes. In the second phase, we propose a Circle-based Range-Free Localization Algorithm (CRFLA) to locate the unlocalized sensor nodes which cannot obtain the location information through the first phase. In the second phase, sensor nodes which are localized in the first phase act as the new anchor nodes to help realize localization. Hence, in this algorithm, we use a small number of mobile beacons to help obtain the location information without any other anchor nodes. Besides, to improve the precision of the range-free method, an extension of CRFLA achieved by designing a coordinate adjustment scheme is updated. The simulation results show that TP-TSFLA can achieve a relative high localization ratio without time synchronization.