Miracle-dz/UniFAR-FTUs huggingface dataset:
Ju l 2 01 9 Network-Assisted D 2 D Relay Selection Under the Presence of Dynamic Obstacles *

Millimeter wave (mmWave) channels in device to device (D2D) communication are susceptible to blockages in spite of using directional beams from multi-input multi-output (MIMO) antennas to compensate for high propagation loss. This motivates one to look for the presence of obstacles while forming D2D links among user equipments (UEs) which are in motion. In D2D communication, moving UEs also act as relays to forward data from one UE to another which introduces the problem of relay selection. The problem becomes more challenging when the obstacles are also in motion (dynamic obstacles) along with the moving UEs. First we have developed a probabilistic model for relay selection which considers both moving UEs and dynamic obstacles. Then we have analyzed the probability of dynamic obstacles blocking a link in 3D Euclidean space by exploiting the information from MIMO radar connected to the base station. Finally, using this information, we have developed unique strategies based on simple geometry to find the best relay which maximizes the expected data rate. Through simulations we have shown that our proposed strategy gives a significant improvement in packet loss due to mobility of nodes and dynamic obstacles in a mmWave channel over traditional approaches which do not consider dynamic obstacle’s presence.
AE2-Nets: Autoencoder in Autoencoder Networks

Learning on data represented with multiple views (e.g., multiple types of descriptors or modalities) is a rapidly growing direction in machine learning and computer vision. Although effectiveness achieved, most existing algorithms usually focus on classification or clustering tasks. Differently, in this paper, we focus on unsupervised representation learning and propose a novel framework termed Autoencoder in Autoencoder Networks (AE^2-Nets), which integrates information from heterogeneous sources into an intact representation by the nested autoencoder framework. The proposed method has the following merits: (1) our model jointly performs view-specific representation learning (with the inner autoencoder networks) and multi-view information encoding (with the outer autoencoder networks) in a unified framework; (2) due to the degradation process from the latent representation to each single view, our model flexibly balances the complementarity and consistence among multiple views. The proposed model is efficiently solved by the alternating direction method (ADM), and demonstrates the effectiveness compared with state-of-the-art algorithms.
Verification of Task Parallel Programs Using Predictive Analysis

Verification of Task Parallel Programs Using Predictive Analysis Radha Vi Jay Nakade Department of Computer Science, BYU Master of Science Task parallel programming languages provide a way for creating asynchronous tasks that can run concurrently. The advantage of using task parallelism is that the programmer can write code that is independent of the underlying hardware. The runtime determines the number of processor cores that are available and the most efficient way to execute the tasks. When two or more concurrently executing tasks access a shared memory location and if at least one of the accesses is for writing, data race is observed in the program. Data races can introduce non-determinism in the program output making it important to have data race detection tools. To detect data races in task parallel programs, a new Sound and Complete technique based on computation graphs is presented in this work. The data race detection algorithm runs in O(N2) time where N is number of nodes in the graph. A computation graph is a directed acyclic graph that represents the execution of the program. For detecting data races, the computation graph stores shared heap locations accessed by the tasks. An algorithm for creating computation graphs augmented with memory locations accessed by the tasks is also described here. This algorithm runs in O(N) time where N is the number of operations performed in the tasks. This work also presents an implementation of this technique for the Java implementation of the Habanero programming model. The results of this data race detector are compared to Java Pathfinder’s precise race detector extension and permission regions based race detector extension. The results show a significant reduction in the time required for data race detection using this technique.
INTRASEASONAL MODULATION OF PRECIPITATION OVER THE NORTH AMERICAN MONSOON REGION

This study investigates summer precipitation variability over the North American Monsoon System (NAMS) on a variety of timescales for the period 1948-present. Our study area extends from tropical Mexico northward to the desert southwest and Colorado Plateau/southern Rockies region of the United States. The main focus concerns the time and space scales of variability of precipitation over the NAMS, and their relationship to large scale atmospheric circulation. While we are primarily emphasizing intraseasonal time scales, interannual variability is also of interest, especially as it contrasts to the patterns isolated on the intraseasonal time scale, and also as it relates to the modulation of higher frequency disturbances from year-to-year and within monsoon rainy seasons. Ultimately we plan to compare results from historical data to events observed during the field phase of the North American Monsoon Experiment (NAME), planned for the summer of 2004.
HYBRID BACKUP MODEL WITH GENETIC BRANCH POINT OPTIMIZATION FOR SINGLE ELEMENT FAILURE IN MPLS NETWORKS

In this paper, we present an approach for enhancing current MPLS resilience, called Hybrid Backup model (HB), in order to restore a QoS protection path from both node or link failures. At present, there are three main MPLS fault management methods, i.e., global, reverse and local backup models. Firstly, the global backup model is the cheapest model but it suffers from high packet losses and long restoration time. Secondly, the reverse backup model improves the packet loss problem; however, the delay problem still remains. Thirdly, the local backup method is seemed to be the best choice in case of the minimum restoration time and packet losses, nevertheless, it has high cost in term of number of path switch label switching router (PSL), path merge label switching router (PML), label usage, and bandwidth reserved. Therefore, we proposed a new approach based on hybrid of four switching types which are global, global reverse, local and local reverse switching types. To evaluate number and location of branch point, branch point optimization based on Genetic Algorithm is then proposed. The proposed model can be reduce the cost of the local backup model, while it still maintains fast restoration and low packet losses. Furthermore, it can improve some significant network performances such as bandwidth reserved, rejection probability, and total throughput. According to performance comparison between all backup models, numerical and simulation results are presented to support the proposed backup model.
The Effects of the Earnings Quality on Cash Holding in ListedCompanies at Tehran Stock Exchange ( TSE ) 1

The present study undertakes to investigate effects of earnings quality on level of cash holding in companies listed at Tehran Stock Exchange. Based on Ozkan and Opler model, cash is used as dependent variable; operating income and current profit are used as independent variables and firm size, leverage, sales growth, and cash flow from operation are used as control variables. The study covers a period of 8 years from 2003 to 2010. A multivariate regression model is used for testing hypotheses. Results indicate that firm size and cash flow from operation have positive effects on cash holding, while earnings quality based on current profit and sales growth have negative effects on cash holding in companies listed at Tehran Stock Exchange.
‘‘ y it’s all the same no matter how much fruit or vegetables or fresh air we get’’: Traveller women’s perceptions of illness causation and health inequalities

This paper explores the perceptions of illness causation and health inequalities of Travellers, an ethnic minority group who experience considerable social and health disadvantages in Ireland. In order to allow for subjective meanings to emerge, a qualitative methodology with purposive sampling was employed. Participants in the study were invited to respond to a vignette in a focus group setting. Forty-one Traveller women were recruited to the focus groups through community projects or adult education initiatives. The study not only illustrates the complexity of lay perceptions of ill-health and health inequalities, but raises important questions about the prevalence of depression and of domestic violence in the Travelling community. These Traveller women were very willing to discuss the structural factors that contributed to their health status, attributing ill-health to social and environmental factors, such as accommodation, hardship and discrimination. Further, they broadly rejected behavioural explanations of the heart disease described in the vignette. Traveller women’s understandings of health and the factors that determine it are deeply embedded in the social context of their lives and their ethnic identity. These ﬁndings are discussed in the context of social identity and ethnicity, and contribute to theoretical debates about the role of that identity in recognising inequality. The study revealed that Traveller women see many shortcomings in health service provision. They need service provision to be culturally sensitive and responsive to their needs.
0 . 5 V-VIN , 165-mA / mm 2 Fully-Integrated Digital LDO based on Event-Driven Self-Triggering Control

Abstract: In recent digital low-drop-out (LDO) regulator designs, event-driven (ED) control achieved significantly better scaling in the output capacitor and voltage droop over Time-Driven (TD) one. The state-of-the-art ED LDO design, however, still has several drawbacks including long settling time and low current density. This paper presents a new LDO based on novel self-triggering ED control and initialization. The 0.5V digital LDO in a 65 nm achieves the best-inclass performance in the droop voltage, output capacitor size, settling time, and current density over the prior ED and TD LDOs.
Automatic Evaluation of English-to-Korean and Korean-to-English Neural Machine Translation Systems by Linguistic Test Points

BLEU is the most well-known automatic evaluation technology in assessing the performance of machine translation systems. However, BLEU does not know which parts of the NMT translation results are good or bad. This paper describes the automatic evaluation approach of NMT systems by linguistic test points. This approach allows automatic evaluation of each linguistic test point not shown in BLEU and provides intuitive insight into the strengths and flaws of NMT systems in handling various important linguistic test points. The linguistic test points used for automatic evaluation were 58 and consisted of 630 sentences. We conducted the evaluation of two bidirectional English/Korean NMT systems. BLEUs of English-to-Korean NMT systems were 0.0898 and 0.2081 respectively, and their automatic evaluations by linguistic test points were 58.35% and 77.31%, respectively. BLEUs of Korean-to-English NMT systems were 0.3939 and 0.4512 respectively, and their automatic evaluations by linguistic test points were 33.10% and 40.47%, respectively. This means that the automatic evaluation approach by linguistic test points has similar results as BLEU assessment. According to automatic evaluation by linguistic test points, we know that both English-to-Korean NMT systems and Korean-to-English NMT systems have strengths in polysemy translations, but has flaws in style translations and translations of sentences with complex syntactic structures.
Density-Adaptive Sampling for Heterogeneous Point Cloud Object Segmentation in Autonomous Vehicle Applications

Robust understanding of the driving scene is among the key steps for accurate object detection and reliable autonomous driving. Accomplishing these tasks with a high level of precision, however, is not trivial. One of the challenges come from dealing with the heterogeneous density distribution and massively imbalanced class representation in the point cloud data, making the crude implementation of deep learning architectures for point cloud data from other domains less effective. In this paper, we propose a densityadaptive sampling method that can deal with the point density problem while preserving point-object representation. The method works by balancing the point density of pregridded point cloud data using oversampling, and then empirically sample points from the balanced grid. Using the KITTI Vision 3D Benchmark dataset for point cloud segmentation and PointCNN as the classifier of choice, our proposal provides superior results compared to the original PointCNN implementation, improving the performance from 82.73% using voxel-based sampling to 92.25% using our proposed density-adaptive sampling in terms of per
Position Invariant Spline Curve Based Image Retrieval Using Control Points

: In this paper Cubic Bezier curve-based image retrieval system is proposed. This system evaluates similarity of each image in its database to a query image in terms of shape characteristics. Then, returns those images within a desired range of similarity. The proposed system determines nonlinear relationship between image’s features for more accurate similarity comparison between query image and existing database images. Among existing approaches to shape feature analysis, statistical approach to extract shape features is adopted here. It works in form of control points of spline curves from both given query image and images of available database. These control points are further used to find out the Fourier Descriptors. Control points and Fourier descriptors are used for image retrieval in proposed system. With the vast number of images available on-line, quality CBIR systems are critical. The performance and results obtained by proposed system are compared to other CBIR systems. Comparison reveals proposed system performance is state of the art. In many parameters it outperforms other CBIR systems.
Hardware Realization of Artificial Neural Networks Using Analogue Devices

In this work the analogue neural network has been realized by electronic devices like Operational Amplifiers and Field Effect Transistor (FET) . The FET transistor has been utilized to self adjust weight function for neural network. By use of drain and source resistance as function of applied voltage on the gate in linear characteristic region , this resistance has been connected to the input of Operational Amplifiers which becomes as weight function of neural network. Implementing these mentioned characteristics for both FET transistor and operational amplifier , the analogue neural network structure like neuron , weights function and activation function have been realized individually by using the National Instrument multisim 10 (NI) Software, then the analogue neural network has been trained successfully by using supervised learning rule like single layer perceptron learning rule and delta learning rule. The results, show good fulfillment of a neural network with analogue hardware devices and verifying the learning rules to train network.
Learned Prior Information for Image Compression

We propose a method for image compression by integrating a deep neural network (DNN) with the better portable graphics (BPG) codec. As DNN can learn the prior information from image data, it will reduce the transmission information through BPG codec and achieves a good visual quality for the decompressed image. The proposed method includes three parts: the BPG codec, the artifact reduction network and the colorization network. First, image is converted to the CIE Lab color space. Then, the BPG codec compresses L component and color hint extracted from the a, b components. To satisfy the ﬁle size, the suitable QP values of BPG compression will be found for each image by binary search. Next, the decompressed L will be improved by the artifact reduction network. Finally, the colorization network will predict a and b components from the decompressed L and the color hint. We evaluate the proposed method upon the CLIC validation sets and Kodak image sets by the quantitative metrics (PSNR, MS-SSIM). The comparison with BPG is also presented.
Supplementary Material for Set Transformer

We focus on H in (2). Since feed-forward networks are universal function approximators at the limit of infinite nodes, let the feed-forward layers in front and back of the MAB encode the element-wise functions z → z and z → z 1 p , respectively. We let h = d, so the number of heads is the same as the dimensionality of the inputs, and each head is one-dimensional. Let the projection matrices in multi-head attention (W j ,W K j ,W V j ) represent projections onto the jth dimension and the output matrix (W) the identity matrix. Since the mean operator is a special case of dot-product attention, by simple composition, we see that an MAB can express any dimension-wise function of the form
Nectaries of Melia azedarach ( Meliaceae ) : The First Record for Genus

I found extrafloral nectaries (EFNs) of Melia azedarach (Meliaceae) in Japan. The EFNs occurred at the bases of all petioles only in young plants. The perimeter of the EFN is distinctly depressed below the surface. An EFN secreted an average of 1.6 ± 0.9 μL of nectar per 24 h. Five ant species and Cryptalaus berus (Candeze) (Coleoptera, Elateridae) collected extrafloral nectar from young M. azedarach trees. This is the first record of EFNs in the genus Melia.
Application Of Molecular Genetic Markers To Conservation Of Freshwater Bivalves

Freshwater bivalves (Unionacea) are among the most enJangered faunal elements in North America. Molecular genetic studies have much to offer conservation efforts directed to this declining fauna. Molecular genetic data can provide information needed to identify evolutionarily significant units, resolve taxonomic ambiguities, describe population structure, evaluate impacts ot habitat fragmentation and reduced gene flow among populations, reconstruct phylogenetic relationships, clarify fish hostglochidia relationships, and provide evidence in legal actions. Molecular genetic techniques and their application to freshwater bivalves are reviewed.
Semantic Filtering and Event Extraction of Twitter Streams through RDF and SPARQL

For long, there has been a huge demand to develop an efficient mechanism to effectively search and extract much needed information from the social web. Manual annotation is effectively possible in information retrieval for limited number of documents, but it is impractical for large accumulation of document retrieval particularly from social media. The principle objective of this research is to comprehend the emergency news by semantically extracting entities and its relations in the posted user generated content. In order to achieve this, the two most sought process of this enablement is that key event identification of every news items and semantic element extraction from those items. These two processes paves the way for building an effective knowledge base as well as a semantic retrieval engines to augment the event level semantic filtration of news items. It has been well observed and noted at many instances that social media’s prevalence is a major source of collective formation of large public opinions. Nevertheless, deriving much needed information from it is very useful because of the fact that it is fresh and above all, no one has mediated its content. It is not the news that we have pondered for, but the views of millions of people on the particular events. Hence, this research has turned to the new dimension of identifying the events which are deemed important for many levels of processes. We discover the events by clustering similar information from mainstream social media and categorize the events semantically to enrich the whole process to obtain the much sought discovery of hidden information.
Prediction via clusters of CPT codes for improving surgical outcomes

We develop a novel algorithm, Predictive Hierarchical Clustering (PHC), for agglomerative hierarchical clustering of current procedural terminology (CPT) codes, with the goal of finding clusters that improve the performance of a sparse logistic regression model for predicting surgical outcomes. The clustering scheme mimics traditional Hierarchical Clustering; however, our merge criterion is not based on a distance function and does not initialize with $n$ clusters. Our predictive hierarchical clustering aims to cluster subgroups, not individual observations, found within our data, such that the clusters discovered result in an improved performance of a classification model. Therefore, merges are chosen based on which pairings of the subgroups result in the largest improvement in prediction, as measured by the area under an ROC curve. The motivation is to predict patient-specific surgical outcomes using data from ACS NSQIP (American College of Surgeon's National Surgical Quality Improvement Program). An important predictor of surgical outcomes is the actual surgical procedure performed as described by a CPT code. We use PHC to cluster these subgroups together in a way that enables us to better predict patient-specific outcomes, instead of currently used clinically decided clusters. We present two different configurations of our algorithm, one incorporating the clusters of CPT codes as random slopes and the second as random intercepts in the classification model.
Motion Planning for Highly Automated Road Vehicles with a Hybrid Approach Using Nonlinear Optimization and Artificial Neural Networks

*Corr. Author’s Address: Robert Bosch, 2 Stoczek street, 1111 Budapest, Hungary, hegedus.ferenc@mail.bme. hu 148 trojniški vestnik Journal of Mechanical Engineering 63(2017)3, XXX-4 c 2017 Journal of Mechanical Engineering. All rights reserved. Original Scientific Paper — DOI: 10.5545/sv-jme.2017. 4027 Received for review: 2016-11-04 Received revised form: 2017-01-14 Accepted for publication: 2017-02-12
The Restraint Pathway to Binge Eating : Roles of Preoccupation and Appetite Awareness

The Restraint Pathway to Binge Eating: Roles of Preoccupation and Appetite Awareness By Jenna Blewis Previous investigations of the dietary restraint pathway have yielded incongruent findings regarding the association between dieting and binge eating, but few studies have explored the mechanisms involved in this complex pathway. Theory and research implicate preoccupying thoughts about food and poor awareness of hunger and fullness signals as potential mediators of the restraint pathway to binge eating. Accordingly, this study examined whether preoccupation with food and appetite awareness mediated the relationship between dieting and binge eating. Ninety college-aged women completed self-report measures assessing dietary restraint, preoccupation with food, appetite awareness, and binge eating. Results from a series of regression analyses indicated that preoccupation with food and appetite awareness mediated the relationship between dietary restraint and binge eating. In addition, exploratory analyses revealed that preoccupation with food mediated the effects of dietary restraint on appetite awareness, though appetite awareness did not mediate the relationship between preoccupation and restraint. These findings suggest that the dieter’s growing obsession or preoccupation with thoughts about food may result in limited resources to attend to bodily signals, leading to poorer appetite awareness and less mindful eating. Future research should attempt to investigate these relationships longitudinally to gain a better understanding of the development of this pathway. The Restraint Pathway to Binge Eating: Roles of Preoccupation and Appetite Awareness