Miracle-dz/UniFAR-FTUs huggingface dataset:
MultiMLton: A multicore-aware runtime for standard ML

Abstract MultiMLton is an extension of the MLton compiler and runtime system that targets scalable, multicore architectures. It provides specific support for ACML, a derivative of Concurrent ML that allows for the construction of composable asynchronous events. To effectively manage asynchrony, we require the runtime to efficiently handle potentially large numbers of lightweight, short-lived threads, many of which are created specifically to deal with the implicit concurrency introduced by asynchronous events. Scalability demands also dictate that the runtime minimize global coordination. MultiMLton therefore implements a split-heap memory manager that allows mutators and collectors running on different cores to operate mostly independently. More significantly, MultiMLton exploits the premise that there is a surfeit of available concurrency in ACML programs to realize a new collector design that completely eliminates the need for read barriers, a source of significant overhead in other managed runtimes. These two symbiotic features - a thread design specifically tailored to support asynchronous communication, and a memory manager that exploits lightweight concurrency to greatly reduce barrier overheads - are MultiMLton's key novelties. In this article, we describe the rationale, design, and implementation of these features, and provide experimental results over a range of parallel benchmarks and different multicore architectures including an 864 core Azul Vega 3, and a 48 core non-coherent Intel SCC (Single-Cloud Computer), that justify our design decisions.
Volatility Spillovers in the Spanish Food Marketing Chain: The Case of Tomato

ABSTRACT This research aims to assess volatility spillovers along the Spanish tomato marketing chain. A multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) model is fit to weekly tomato prices at different levels of the marketing chain (producers, wholesalers and consumers). Empirical results suggest significant price transmission and volatility spillovers between different market levels. While causality links flow in all directions, volatility transmission is stronger from the consumer to the producer and wholesale prices rather than the other way around. Consumer price first and second moments are quite rigid, while producer and wholesale prices tend to suffer from higher instability.
18F-FDG Uptake in Lung, Breast, and Colon Cancers: Molecular Biology Correlates and Disease Characterization*

It is hoped that in the not too distant future, noninvasive imaging–based molecular interrogation and characterization of tumors can improve our fundamental understanding of the dynamic biologic behavior of cancer. For example, the new dimension of diagnostic information that is provided by 18F-FDG PET has led to improved clinical decision making and management changes in a substantial number of patients with cancer. In this context, the aim of this review is to bring together and summarize the current data on the correlation between the underlying molecular biology and the clinical observations of tumor 18F-FDG accumulation in 3 major human cancers: lung, breast, and colon.
Performance of Energy Aware Routing Protocol(MECB-AODV): A Modified Energy ConstrainedProtocol for Mobile Ad hoc Networks

Mobile Ad Hoc Networks (MANETs) also called mesh networks are self-configuring networks of mobile devices connected by wireless links. The aim of this paper is to evaluate the performance of an energy aware routing protocol, called MECB-AODV (Modified Energy Constraint protocol Based on AODV) which derives from the AODV protocol and which is based on the local decisions of intermediate stations to maintain the connectivity of the network as long as possible. The results obtained using the Network Simulator NS-2 demonstrates how small changes in the principle of the AODV protocol can efficiently balance the energy consumption between nodes, which increases the network lifetime as well as increases the throughput.
The Commercialization Of The Atlanta Pride Festival: “Somebody's Got To Pay For It”

This thesis is focused on the commercialization of the Atlanta Pride Festival during the years 1992-1997. Through personal interviews, I have concluded that the Atlanta Pride Festival produced complicated experiences for participants who had mixed feelings about the commercialization. INDEX WORDS: Gay, Pride, Lesbian, Atlanta, Commercialization THE COMMERCIALIZATION OF THE ATLANTA PRIDE FESTIVAL: “SOMEBODY’S GOT TO PAY FOR IT”
Generation of Induced Pluripotent Stem Cells from Human Amniotic Fluid Cells by Reprogramming with Two Factors in Feeder-free Conditions

Abstract The ectopic expression of transcription factors for reprogramming human somatic cells to a pluripotent state represents a valuable resource for the development of in vitro-based models for human disease and has great potential in regenerative therapies. However, the majority of studies have used skin fibroblasts to generate induced pluripotent stem cells (iPSCs) that typically require the enforced expression of several transcription factors, thereby posing a mutagenesis risk by the insertion of viral transgenes. To reduce this risk, iPSCs have been generated with OCT4 and KLF4 from human neural stem cells that endogenously express the remaining reprogramming factors. However, human neural stem cells are rare and difficult to obtain. Here, we show that iPSCs can be generated from human amniotic fluid cells (hAFCs) with two transcription factors: OCT4 and KLF4. Furthermore, iPSCs can be readily derived from hAFCs in a feeder-free conditions, thereby eliminating the potential variability caused by using feeder cells. Our results indicate that hAFCs represent an accessible source of cells that can be reprogrammed into pluripotent stem cells with two Yamanaka factors. Therefore, hAFCs may become a preferred cell type in the future for safe reprogramming without any exogenous genetic material.
Design and Implementation of Orthogonal Frequency Division Multiplexing ( OFDM ) Signaling Research Project Description Study

The Electrical Engineering Senior Capstone Project is intended to give each student experience in completing a sophisticated design project that spans most of the senior year. Planning, management of time, allocation of responsibility, documentation, and presentation of the results are integrated with the technical design task. The students work with one or two faculty advisors who have expertise in the project research area. The student is fully responsible for the design project, with the advisor(s) acting as guide and mentor. Each student is expected to work an eight hour lab period each week from October through May.
Financial Interests and Economic Diversity in State Legislatures

Objective I examine how the prevalence of U.S. state legislators economically connected to an industry, and the diversity of economic connections in a legislature, vary in response to the employment share of that industry in the state, the partisan split of the legislature, and professionalization (or its components). Methods I use conflict-of-interest filings for 1999–2000, 2000 Census employment data, and related data to examine the prevalence of connections to several sectors using seemingly unrelated regression. Results The employment share of an industry in the state is a powerful predictor of connections to that industry among legislators, as is the partisan split of the chamber. Professionalization plays a role, dominated by its pay component, but to a smaller degree. Professionalization is the primary predictor of the diversity of connections. Conclusion Legislators’ economic connections are driven in part by underlying conditions such as the economic makeup of the state.
A Survey for Feature Extraction Methods in Handwritten Script Identification

Feature extraction is one of the basic function of handwritten Script Identification. It involves measuring those features of the input pattern are relevant to classification . This paper provides a review of these advances . The aim is to provide an appreciation for the range of techniques that have been developed rather than to simply list sources. Various types of features proposed for handwritten script identification include horizontal and vertical histogram , curvature information and local extreme of curvature, topological features such as loops group of white pixels surrounded by black ones ,dots a cluster of say 1-3 pixels and junction is a point with more than 2 neighbours all in thinned black and white images, Parameters of polynomial, Contour information where contours is the outside boundary of a pattern, PCA is a way of identifying and expressing pattern in data.
Directional Repetitive Control of a Metrological AFM

Atomic force microscopes (AFMs) are used for sample imaging and characterization at nanometer scale. In this work, we consider a metrological AFM, which is used for the calibration of transfer standards for commercial AFMs. The metrological AFM uses a three-degree-of-freedom (DOF) stage to move the sample with respect to the probe of the AFM. The repetitive sample topography introduces repetitive disturbances in the system. To suppress these disturbances, repetitive control (RC) is applied to the imaging axis. A rotated sample orientation with respect to the actuation axes introduces a nonrepetitiveness in the originally fully repetitive errors and yields a deteriorated performance of RC. Directional repetitive control (DRC) is introduced to align the axes of the scanning movement with the sample orientation under the microscope. Experiments show that the proposed directional repetitive controller significantly reduces the tracking error as compared to standard repetitive control.
Scientific Review of the Literature on Estimating the Prevalence of Drug Misuse on the Local Level CT.97.EP.05

Acknowledgements This publication is based on an EMCDDA-commissioned project which examined methods for estimating the prevalence of problem drug use at the local level. During this and a previous project, the authors relied on the support of researchers throughout the European Union and within the EMCDDA. It would not have been possible to conduct local prevalence estimation projects without the support of various agencies and bodies which supplied data on drug use in the participating cities. The authors would like to acknowledge the following individuals who contributed to these projects:
Compact metric measure spaces and Λ-coalescents coming down from infinity

We study topological properties of random metric spaces which arise by Λ-coalescents. These are stochastic processes, which start with an infinite number of lines and evolve through multiple mergers in an exchangeable setting. We show that the resulting Λ-coalescent measure tree is compact iff the Λ-coalescent comes down from infinity, i.e. only consists of finitely many lines at any positive time. If the Λ-coalescent stays infinite, the resulting metric measure space is not even locally compact. Our results are based on general notions of compact and locally compact (isometry classes of) metric measure spaces. In particular, we give characterizations for general (random) metric measure spaces to be (locally) compact using the Gromov-weak topology.
Optimization of object-oriented recursive queries using cost-controlled strategies

Object-oriented data models are being extended with recursion to gain expressive power. This complicates the optimization problem which has to deal with recursive queries on complex objects. Because unary operations invoking methods or path expressions on objects may be costly to execute, traditional heuristics for optimizing recursive queries are no longer valid. In this paper we propose a cost-based optimization method which handles object-oriented recursive queries. In particular, it is able to delay the decision of pushing selective operations through recursion until the effect of such a transformation can be measured by a cost model. The approach integrates rewriting and increases the optimization opportunities for recursive queries on objects while allowing for efficient optimization.
Perceived barriers to self-management for people with dementia in the early stages

People with dementia in the early stages currently experience a care gap, which self-management may address. We explore perceived barriers to self-management. Using a systematic approach (logic mapping), 19 participants (people with dementia, carers, health care professionals and charity representatives) described self-management barriers facing people living with dementia. Thematic content analysis revealed six main themes: the lived experience of dementia, diagnosis, role of carer/family, impact of health care professionals, organisation of health services and societal views. People with dementia were seen as passive recipients of care, unable to self-manage owing to the impact of dementia on cognitive abilities. The need for interventions that are largely emotion focused, encourage activity maintenance and improve quality of life are described. Barriers to self-management exist at multiple levels, suggesting a whole-systems approach is required.
Multi-core FPGA Execution for Electromagnetic Simulation by FDTD

Recently, general-purpose processors have made the transition from high-frequency single cores to multiple cores as a consequence of multi-core processors requiring less power. To further reduce power consumption, it is necessary for field-programmable gate arrays (FPGAs) to embed multi-core processors. In this paper, we design a multi core processor which processor is embedded into SAFHC cluster. As an electromagnetic simulation by FDTD, we propose a multi-core FPGA execution using ASIP (Application-Specific Instruction Set Processor). In the performance evaluations, we achieved a speed up with two cores of 41% over compared with a single core. The integral power consumption of two cores is 21% lower than that of a single core.
2016 Ieee International Conference on Big Data (big Data) Bdtune: Hierarchical Correlation-based Performance Analysis and Rule-based Diagnosis for Big Data Systems

—Although big data systems are in widespread use and there have much research efforts for improving big data systems performance, efficiently analysing and diagnosing performance bottlenecks over these massively distributed systems remain a major challenge. In this paper, we propose a hierarchical correlation-based analysis and rule-based diagnostic approach for big data systems. The key approaches lie in identifying performance bottlenecks, classifying root causes, analyzing performance according to multi-level performance metrics, and setting diagnostic rules for performance tuning. Based on this approach, we have implemented BDTune—a lightweight, extensible and transparent tool that can provide valuable insights into performance of big data applications with a very low overhead. We also report our experience on how to use BDTune to conduct performance analysis and performance bottlenecks diagnosis, and demonstrate BDTune can help users find the performance bottlenecks and provide optimization recommendations.
On the Influence of Text Complexity on Discourse-Level Choices

Text complexity can be reduced by making different choices at the lexical and grammatical levels. However, discourse-level choices may also affect a text’s complexity. In a coherent text, explicit discourse relations (e.g. CAUSE , CONDITION ) are expressed using discourse markers (e.g. since, because , etc.) that may be preferred for texts at different readability levels. In this paper, we investigate the differences in discourse properties of texts across readability levels. In particular, we investigate the effect of readability level on (1) the usage of discourse relations, (2) the usage of discourse markers and (3) the distribution of discourse markers signaling explicit discourse relations. Our analysis of the Simple English Wikipedia corpus shows that complex and simple texts seem to have the same distribution of discourse relations; however, these relations are expressed using different discourse markers depending on the readability level of the text
Poster Abstract : Sensor Modeling and Sensor Data for UnderWater Acoustic Sensor Networks ( UW-ASN ) Applications

Wireless sensor networks enabled the development of distributed sensing application in many types of scenes, including application in exploration of natural undersea resources and gathering of scientific data in collaborative monitoring missions. The expected growth in Wireless Sensor Network (WSN) applications requires efficient modeling techniques to sensor data acquisition and their eventual storage and management. This paper proposes a based model for fast and reusable development applications for UnderWater Acoustic Sensor Networks (UW-ASN), converting the primitive data acquisition and raw data in data structure into RDF (Resource Description Framework) files using a process model, providing to developers and users an easy way to access useful information about the vehicles that are deployed to perform collaborative monitoring tasks over a given area. This model characterize and describe clearly the sensor nodes, the vehicle under Water and the sensing application, using vocabularies (RDF Schemas) to enrich the information with meta-data about the context environment. With all information modeling in interoperable format (RDF files), analyzes and presentation of the information and remote processing is enabled.
Capacity bounds for relay-aided wireless multiple multicast with backhaul

We investigate the capacity bounds for relay-aided two-source two-destination wireless networks with backhaul support between source nodes. Each source multicasts its own message to all destinations with the help of an intermediate relay node, which is full-duplex and shared by both sources. We are aiming to characterize the capacity region of this model given discrete memoryless Gaussian channels. We establish three capacity upper bounds by relaxing the cut-set bound, and by extending two capacity bounds originally derived for MIMO relay channels. We also present one lower bound by using decoding-and-forward relaying combined with network beam-forming.
Multiple adaptive notch filters based a time-varying frequency tracking method for Coriolis mass flowmeter

A method based on multiple adaptive notch filters for tracking the frequency variations of the sensor output signal of Coriolis mass flowmeter is presented. For the simulated signal whose frequency, amplitude and phase are time-varying based on the random walk model, a lattice-based adaptive IIR notch filter (LANF) with fast convergence rate is applied to detect the signal frequency and track the time-varying frequencies initially. As the LANF algorithm converges, a simplified lattice-based adaptive IIR notch filter (SLANF) sets its initial notch frequency which equals to the value detected by LANF, and then starts to work. After the SLANF algorithm converges, the time-varying frequencies are tracked by SLANF instead of LANF. Computer simulations show that the presented method can attain a favorable compromise between convergence rate and tracking accuracy, and its calculation complexity is lower than the LANF method merely.
Proposing an evolutionary method based on maximization precision of group recommender systems

In this paper we proposed an evolutionary algorithm to maximize precision of group recommender systems and reducing the online calculations. In this method we try to build a transition matrix that's made by an evolutionary algorithm and then we multiply this transition matrix with user-item rate matrix. By this action we go to a reduced dimension space. The characteristic of this space is that the users that are really more similar, would be closer to each other. Also because the dimension of the user-item matrix has been reduced, the online calculations are hugely reduced and if we have a new user, we can easily multiply his rates on the transition matrix and find out that he has to be in which group. We used the average real rate of group users to an item as a metric to evaluate how much this item is suitable for this specific group. At end we compare this method with other methods that also reduce the dimension on the various datasets. Then we show that our method works better. Finally we have a discussion about weakness and strength of our method.
Predicting the Stability of Homologous Gene Duplications in a Plant RNA Virus

One of the striking features of many eukaryotes is the apparent amount of redundancy in coding and non-coding elements of their genomes. Despite the possible evolutionary advantages, there are fewer examples of redundant sequences in viral genomes, particularly those with RNA genomes. The low prevalence of gene duplication in RNA viruses most likely reflects the strong selective constraints against increasing genome size. Here we investigated the stability of genetically redundant sequences and how adaptive evolution proceeds to remove them. We generated plant RNA viruses with potentially beneficial gene duplications, measured their fitness and performed experimental evolution, hereby exploring their genomic stability and evolutionary potential. We found that all gene duplication events resulted in a loss of viability or significant reductions in fitness. Moreover, upon evolving the viable viruses and analyzing their genomes, we always observed the deletion of the duplicated gene copy and maintenance of the ancestral copy. Interestingly, there were clear differences in the deletion dynamics of the duplicated gene associated with the passage duration, the size of the gene and the position for duplication. Based on the experimental data, we developed a mathematical model to characterize the stability of genetically redundant sequences, and showed that the fitness of viruses with duplications is not enough information to predict genomic stability as a recombination rate dependent on the genetic context – the duplicated gene and its position – is also required. Our results therefore demonstrate experimentally the deleterious nature of gene duplications in RNA viruses, and we identify factors that constrain the maintenance of duplicated genes.
Competitive Balance vs. Incentives to Win: A Theoretical Analysis of Revenue Sharing

We analyze a dynamic model of strategic interaction between the league organizing a professional sport, the teams playing the tournament organized by this league, and broadcasters competing for the rights to televise their matches. Teams and broadcasters maximize expected profits, while the league's objective may be either to maximize the demand for the sport or to maximize the teams' joint profits. Demand depends positively on competitive balance among teams and how intensively they compete to win the tournament. Revenue sharing increases competitive balance but decreases incentives to win. Under demand maximization, a performance-based reward scheme (as used by European top soccer leagues for national TV deals) may be optimal. Under joint profit maximization, full revenue sharing (as used by US team sport leagues for national TV deals) is always optimal.