Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparison of Implementation Strategies for Non { UniformData { Parallel Computations

Data{parallel languages allow programmers to easily express parallel computations by means of parallel loop constructs or collective operations. To reduce overheads, the compiler partitions the computations among the processors at compile{time, on the basis of the static data distribution suggested by the programmer. When execution costs are non{uniform and unpredictable, some processors may be assigned more work than others. Workload imbalance can be mitigated by cyclically distributing data and associated computations, or by employing dynamic strategies which build a more balanced schedule at run{time, on the basis of the actual execution costs. This paper discusses static and hybrid (static + dynamic) strategies which can be used to balance the workloads derived from the execution of non{uniform parallel loops. A multi-dimensional ame simulation kernel has been used to evaluate diierent implementation strategies on a Cray T3E. We fed the benchmark code with synthetic input data sets built on the basis of a load imbalance model and we report and compare the results obtained.
Randomized Algorithms

Proof. Consider the following random Galton-Watson process of generating proper witness trees with root labeled Ei. Initially let the only vertex be the root labeled by Ei loop while there is a variable to add Consider each vertex u at depth t− 1. u is labeled by Eiu . Consider each j ∈ Γ+iu Add a child to Eiu labeled Ej with probability xj . Don’t add it with probability (1− xj). end loop Fix a proper witness tree T ∈ TEi . We calculate the probability that the above GaltonWatson process generates the tree T . Let u be a vertex in T . Let Wu = {j ∈ Γ + iu |Ej does not label any child of u}.
A Novel Algorithm for Blurred Image Restoration in the field of Medical Imaging

Medical Image Restoration is a technique which recovers the medical images from the effects of noise and blur. It deals with bringing back the degraded image to its original state i.e. it helps to restore the degraded image into more sharp and clear image. Medical images such as X-ray images play a vital role in dealing with the detection of various diseases in patients. But such images face the problem of Gaussian noise and motion blur. Hence their restoration is must which can be done with the help of image restoration techniques. This research work proposes a novel algorithm for the restoration of X-ray images. The proposed algorithm is then compared with existing algorithms with the help of performance parameters like MSE, PSNR
AIS Electronic Library (AISeL)

The importance of managing quality in developing software systems is well documented. Existing literature presents attributes of good quality management practices in building software systems on time, within budget, and satisfying customer needs. However, most of the current findings on Software Quality Management (SQM) are case-based and company-specific, and may not be generalizable across all software developing organizations. Furthermore, the relationships examined in past research may be incomplete and therefore fail to identify differing dimensions critical for SQM. Against this backdrop, our research provides a synthesis of existing literature by identifying six critical factors of SQM in software developing organizations. An instrument that can be used to measure critical factors of SQM is constructed. Validity and reliability of the instrument is established using a combination of exhaustive literature review, pre-pilot testing a preliminary version of the instrument among a group of academics, and pilot testing revised versions of the instrument among a group of IS professionals who are affected by software quality. An instrument such as the one proposed in this research could be used by senior IS managers to assess the effectiveness of SQM practices in developing quality software. Researchers on the other hand could use the instrument to better understand SQM practices, and build theories and models to manage software quality effectively.
a Chinese interactive feedback system for a virtual Campus

abstraCt Considering the popularity of the Internet, an automatic interactive feedback system for E-learning websites is becoming increasingly desirable. However, computers still have problems understanding natural languages, especially the Chinese language, firstly because the Chinese language has no space to segment lexical entries (its segmentation method is more difficult than that of English) and secondly because of the lack of a complete grammar in the Chinese language, making parsing more difficult and complicated. Building an automated Chinese feedback system for special application domains could solve these problems. This paper proposes an interactive feedback mechanism in a virtual campus that can parse, understand and respond to Chinese sentences. This mechanism utilizes a specific lexical database according to the particular application. In this way, a virtual campus website can implement a special application domain that chooses the proper response in a user friendly, accurate and timely manner.
ODU Digital Commons ODU Digital Commons

Next-generation communication networks, also known as NextG or 5G and beyond, are the future data transmission systems that aim to connect a large amount of Internet of Things (IoT) devices, systems, applications, and consumers at high-speed data transmission and low latency. Fortunately, NextG networks can achieve these goals with advanced telecommunication, computing, and Artiﬁcial Intelligence (AI) technologies in the last decades and support a wide range of new applications. Among advanced technologies, AI has a signiﬁcant and unique contribution to achieving these goals for beamforming, channel estimation, and Intelligent Reﬂecting Surfaces (IRS) applications of 5G and beyond networks. However, the security threats and mitigation for AI-powered applications in NextG networks have not been investigated deeply in academia and industry due to being new and more complicated. This paper focuses on an AI-powered IRS implementation in NextG networks along with its vulnerability against adversarial machine learning attacks. This paper also proposes the defensive distillation mitigation method to defend and improve the robustness of the AI-powered IRS model, i
Source Images Left Right Target Images Left Right Contour Transfer Disparity Reconstruction Source Disparity Map Target Disparity Map Left Right Iterative Process Gradient-Domain Disparity Estimation Perspective-Aware Warping Iterative Disparity Adaptation EstimatedOriginal Warped

This paper presents a novel technique for seamless stereoscopic image cloning, which performs both shape adjustment and color blending such that the stereoscopic composite is seamless in both the perceived depth and color appearance. The core of the proposed method is an iterative disparity adaptation process which alternates between two steps: disparity estimation, which re-estimates the disparities in the gradient domain so that the disparities are continuous across the boundary of the cloned region; and perspectiveaware warping, which locally re-adjusts the shape and size of the cloned region according to the estimated disparities. This process guarantees not only depth continuity across the boundary but also models local perspective projection in accordance with the disparities, leading to more natural stereoscopic composites. The proposed method allows for easy cloning of objects with intricate silhouettes and vague boundaries because it does not require precise segmentation of the objects. Several challenging cases are demonstrated to show that our method generates more compelling results compared to methods with only global shape adjustment.
Performance Analysis of Ad Hoc Routing Protocols Based on Selective Forwarding Node Algorithms

Network resources are essential in resource critical environments as ad hoc wireless networks (AWNs) due to their dynamic topology and broadcasting of packets over the wireless medium without a central controller. In order to perform a routing algorithm with minimum resource consumption, an ideal ad hoc routing protocol should be designed for low consumed resources such as low power consumption. In this paper focus on ad hoc routing protocols, Optimized Link State Routing Protocol (OLSR) and gossip-based routing protocols, based on selective forwarding node algorithms that reduce the control message overheads. Thus, we present performance analytical results to thoroughly evaluate the routing protocols and demonstrate our Adaptive-Gossiping method advantages over OLSR and Static-Gossiping routing protocols in densely deployed networks.
A Deterministic Edge Detection Using Statistical Approach

A large number of edge detectors are available in image processing literature where the choices of input parameters are to be made by the user and are made on an informal basis. In this paper, an edge detector is proposed, where thresholding is performed using statistical principles. Local thresholding of each individual pixel which depends upon the statistical variability of the gradient vector at that pixel is made. Such a standardization statistic based on the gradient vector at each pixel is used to determine the eligibility of the pixel to be an edge pixel. The results obtained from the proposed method are found to be comparable to those from well-known edge detectors. However, the values of the input parameters providing the appreciable results in the proposed detector are found to be more established than other edge detectors and possess statistical elucidation. The results obtained from the proposed algorithm are compared with Canny's edge detector which is more popular among different edge detectors. The proposed algorithm is implemented using MATLAB-7.1.
Calculation of the Focal Mechanism for Composite Microseismic Events

It is generally difficult to obtain a reliable single-event source mechanism with a sparse surface array, mainly due to the typically low signal/noise ratio and poor azimuthal coverage. In this study, we propose an inversion procedure to estimate the focal mechanism of composite microseismic events - i.e., a set of events interpreted to share a common focal mechanism - recorded using a sparse surface network. Our method uses polarities of P-wave first motion together with Sh/P amplitude ratios. Sensitivity analysis using synthetic data indicates that reliable focal solutions can be obtained if both the amplification factor on Sh/P ratio is within the range of 0.25 ~ 1.0, and > 50% polarities are correctly picked. We apply our approach to a set of 13 microseismic events recorded during hydraulic-fracture stimulation of the Marcellus Shale formation in West Virginia and Pennsylvania, USA. Similar to previous studies of this area, we obtain a focal mechanism comprised of northwest or northeast trending strike-slip faulting accompanied by a minor thrust-faulting component.
Comparative Study of Evolutionary Model and Clustering Methods in Circuit Partitioning Pertaining to VLSI Design

Partitioning is a critical area of VLSI CAD. In order to build complex digital logic circuits its often essential to sub-divide multi –million transistor design into manageable Pieces. This paper looks at the various partitioning techniques aspects of VLSI CAD, targeted at various applications. We proposed an evolutionary timeseries model and a statistical glitch prediction system using a neural network with selection of global feature by making use of clustering method model, for partitioning a circuit. For evolutionary time-series model, we made use of genetic, memetic & neuro-memetic techniques. Our work focused in use of clustering methods Kmeans & EM methodology. A comparative study is provided for all techniques to solve the problem of circuit partitioning pertaining to VLSI design. The performance of all approaches is compared using benchmark data provided by MCNC standard cell placement benchmark net lists. Analysis of the investigational results proved that the Neuro-memetic model achieves greater performance then other model in recognizing sub-circuits with minimum amount of interconnections between them. Keywords—VLSI, circuit partitioning, memetic algorithm, genetic algorithm.
Optimal Configuration Planning of Multi-Energy Systems Considering Distributed Renewable Energy

Multi-energy systems (MESs) contribute to increasing energy utilization efficiency and renewable energy accommodation by coupling multiple energy sectors. The preferable characteristic of MESs raises the need for optimizing the configuration of MESs across multiple energy sectors at the planning stage. Based on the energy hub (EH) model, this research presents a two-stage mixed-integer linear programming approach for district level MES planning considering distributed renewable energy integration. The approach models an MES as a directed acyclic graph with multiple layers. The proposed EH configuration planning procedure includes two stages: 1) optimizing what equipment (e.g., energy converters, distributed renewable energy sources and storages) should be invested in for each layer and 2) optimizing the connection relationships between the invested equipment in each two adjacent layers. The proposed approach is able to optimize both the equipment selection and the MES configuration. It can be applied to both expansion planning and initial planning of MESs from scratch. An illustrative example of planning a typical MES is given. A sensitivity analysis is performed to show the impacts of load profiles, energy prices and equipment parameters on the optimal MES configuration. A case study based on the MES in Beijing’s new subsidiary administrative center is conducted using the proposed approach.
Collaboration by

In this document, we provide the additional details and results, which we cannot fit into the manuscript due to the page limit. In the first section, we demonstrate the architecture of the student network. In the second section, we provide details about the quantitative task transferability, which are utilized to decide if and which auxiliary pre-trained model (PTM) shall be introduced. In the third section, we show the results of student network with more target tasks, and comparison with the teahcer PTMs as well as knowledge distillation [2]. Besides, we also provide the qualitative performance of the multi-talent student. In the last section, we compare our multi-talent student model with multiple distilled students each specialized in a distinct task.
Neural network predicts sequence of TP53 gene based on DNA chip

UNLABELLED We have trained an artificial neural network to predict the sequence of the human TP53 tumor suppressor gene based on a p53 GeneChip. The trained neural network uses as input the fluorescence intensities of DNA hybridized to oligonucleotides on the surface of the chip and makes between zero and four errors in the predicted 1300 bp sequence when tested on wild-type TP53 sequence. AVAILABILITY The trained neural network is available for academic use by contacting steen@cbs.dtu. dk
Contextual word similarity and estimation from sparse data

Abstract In recent years there is much interest in word co-occurrence relations, such as n-grams, verb–object combinations, or co-occurrence within a limited context. This paper discusses how to estimate the likelihood of co-occurrences that do not occur in the training data. We present a method that makes local analogies between each specific unobserved co-occurrence and other co-occurrences that contain similar words. These analogies are based on the assumption that similar word co-occurrences have similar values of mutual information. Accordingly, the word similarity metric captures similarities between vectors of mutual information values. Our evaluation suggests that this method performs better than existing, frequency-based, smoothing methods, and may provide an alternative to class-based models. A background survey is included, covering issues of lexical co-occurrence, data sparseness and smoothing, word similarity and clustering, and mutual information.
Standardized Platform for Coregistration of Noncurrent Diffuse Optical and Magnetic Resonance Breast Images Obtained in Different Geometries

We present a novel methodology for combining breast image data obtained at different times, in different geometries, and by different techniques. We combine data based on diffuse optical tomography (DOT) and magnetic resonance imaging (MRI). The software platform integrates advanced multimodal registration and segmentation algorithms, requires minimal user experience, and employs computationally efficient techniques. The resulting superposed 3-D tomographs facilitate tissue analyses based on structural and functional data derived from both modalities, and readily permit enhancement of DOT data reconstruction using MRI-derived a-priori structural information. We demonstrate the multimodal registration method using a simulated phantom, and we present initial patient studies that confirm that tumorous regions in a patient breast found by both imaging modalities exhibit significantly higher total hemoglobin concentration (THC) than surrounding normal tissues. The average THC in the tumorous regions is one to three standard deviations larger than the overall breast average THC for all patients. Disciplines Physical Sciences and Mathematics | Physics Comments Suggested Citation: F.S. Azar et al. (2007)Standardized platform for coregistration of nonconcurrent diffuse optical and magnetic resonance breast images obtained in different geometries. Journal of Biomedical Optics 12(5), 051902. Copyright 2007 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. http://dx.doi. org/10.1117/1.2798630 Author(s) Fred S Azar, Kijoon Lee, Ali Khamene, Regine Choe, Alper Corlu, Soren D Konecky, Frank Sauer, and Arjun G. Yodh This journal article is available at ScholarlyCommons: https://repository.upenn. edu/physics_papers/156 Standardized platform for coregistration of nonconcurrent diffuse optical and magnetic resonance breast images obtained in different geometries
Journal of Network and Computer Applications

The underlying technology of Bitcoin is blockchain, which was initially designed for ﬁnancial value transfer only. Nonetheless, due to its decentralized architecture, fault tolerance and cryptographic security beneﬁts such as pseudonymous identities, data integrity and authentication, researchers and security analysts around the world are focusing on the blockchain to resolve security and privacy issues of IoT. However, presently, not much work has been done to assess blockchain’s viability for IoT and the associated challenges. Hence, to arrive at intelligible conclusions, this paper carries out a systematic study of the peculiarities of the IoT environment including its security and performance requirements and progression in blockchain technologies. We have identiﬁed the gaps by mapping the security and performance beneﬁts inferred by the blockchain technologies and some of the blockchain-based IoT applications against the IoT requirements. We also discovered some practical issues involved in the integration of IoT devices with the blockchain. In the end, we propose a way forward to resolve some of the signiﬁcant challenges to the blockchain’s adoption in IoT.
Storm Surges, Informational Shocks, and the Price of Urban Real Estate: An Application to the Case of Hurricane Sandy

The impacts of a major hurricane on commercial and residential real estate can be devastating. Recent events in Houston (with Hurricane Harvey), Florida (with Hurricane Irma), and New York City (with Hurricane Sandy) are examples of how flooding damage can unexpectedly extend beyond the FEMA flood zones. Such surprises or shocks can provide property owners—including those that are not flooded—with new information about future flood risks, based on the difference of the property distance from the flood zone and the distance to the actual locations of flooding. We apply a new estimation strategy to quantify the effects of these shocks on property values, using information on repeat property sales to estimate a separate shock effect for each dry property. We demonstrate our approach with an application to non-flooded properties in New York City for Hurricane Sandy. We find that, in general, houses, apartments and commercial properties show the most price volatility within the older, denser urban core, mostly in those neighborhoods that appear to be gentrifying.
LAMP-Seq: Population-scale COVID-19 Diagnostics Using a Compressed Barcode Space-Theory

We want to develop a method to scalably detect SARS-CoV-2-infected patients. Our goal here is to model various approaches for doing so, and characterize the error rates of each approach. There are two stages of barcoding available: patient samples will be individually barcoded at the RT-LAMP stage with a first set of barcodes (barcode 1), and then groups of patient samples will be barcoded with a second set of orthogonal barcodes (barcode 2) in the process of preparing samples for Illumina sequencing. After sequencing, a given barcode pair can be called as positive or negative for SARS-CoV-2 viral RNA. Let there be m total unique barcode 1s and m2 total unique barcode 2s. Suppose that we want to test N total patient samples, which will be done in batches of size b with k pre-assigned barcode 1s per patient sample. Further suppose that a fraction p of the total population is positive for SARS-CoV-2. Then, ≈ n = pb samples in each batch will be positive. There are two types of general approaches considered below, one where k = 1 and one where k > 1 (corresponding to the scenario in the main text). To compute error rates for any approach, we must have a model for how errors arise. We assume that errors from either barcode loss or sample skewing dominate, modeling barcode loss first and then sample skewing.
Image registration using Hough transform and phase correlation

This paper presents image registration using a combination of Hough transform and phase correlation technique in Fourier domain to estimate rotation and translation parameters. These parameters are used to register input images and create a seamless representation of the registered image. The first step is based on 1D phase correlation which calculates the translation of angle in Hough space, obtained form Hough transform. The rotation parameter obtain from this step is used to de-rotated the input images. The second step provides the translation in x-y axis using 2D phase correlation. Finally, the input images are registered using the estimated translation parameter. The experimental results show the accuracy of the proposed method to detect the translation parameters and find the relevant potential angles of rotation for various image details and sizes. The proposed method is ease to operation, less computation complexity, thus requires less amount of process time due to the high efficiency of fast Fourier transform (FFT)