Miracle-dz/UniFAR-FTUs huggingface dataset:
GPOP: A scalable cache- and memory-efficient framework for Graph Processing Over Partitions-2mm

The past decade has seen the development of many sharedmemory graph processing frameworks intended to reduce the effort of developing high performance parallel applications. However many of these frameworks, based on Vertexcentric or Edge-centric paradigms suffer from several issues, such as poor cache utilization, irregular memory accesses, heavy use of synchronization primitives or theoretical inefficiency, that deteriorate over-all performance and scalability. Recently, a cache and memory efficient partition-centric paradigm was proposed for computing PageRank [17]. In this paper, we generalize this approach to develop a novel Graph Processing Over Partitions (GPOP) framework that is cache-efficient, scalable and work-efficient. GPOP exploits the locality of partitioning to dramatically improve the cache performance of a variety of graph algorithms. It achieves high scalability by enabling completely lock and atomic free computation. GPOP’s built-in analytical performance model enables it to use a hybrid of source and partition centric communication modes in a way that ensures workefficiency each iteration while simultaneously boosting high bandwidth sequential memory accesses. Finally, the GPOP framework is designed with programmability in mind. It completely abstracts away underlying parallelism and programming model details from the user, and provides an easy to program set of APIs with the ability to selectively continue the active vertex set across iterations. Such functionality is useful for many graph algorithms but not intrinsically supported by the current frameworks. We extensively evaluate the performance of GPOP for a variety of graph algorithms, using several large datasets. We observe that GPOP incurs upto 8.6× and 5.2× less L2 cache misses compared to Ligra and GraphMat, respectively. In terms of execution time, GPOP is upto 19× and 6.1× faster than Ligra and GraphMat, respectively.
On the Application of Meta-Design Techniques in Hardware Design Domain

is becoming increasingly important in hardware and embedded system design. This paper analyzes meta-design techniques oriented at developing meta-programs and meta-models for well-understood domains. Meta-design techniques include meta-programming and meta-modeling. At the programming level of design process, meta-design means developing generic components that are usable in a wider context of application than original domain components. At the modeling level, meta-design means developing design patterns that describe general solutions to the common recurring design problems, and meta-models that describe the relationship between different types of design models and abstractions. The paper describes and evaluates the implementation of meta-design in hardware design domain using object-oriented and meta-programming techniques. The presented ideas are illustrated with a case study.
Global Water Shortages and the Challenge Facing Mexico

Numerous countries are facing a severe water shortage for food production, drinking water and industry, with profound impacts on the world's poor and the potential for global conflicts. This article traces the past 30 years' trends in irrigation and water productivity and projects global water supply and demand for the next 30 years, including estimates for Mexico. Options to address water shortages are discussed, including developing additional water resources, improving the management and productivity of already developed water resources and increasing food imports. In the short run, problems associated with water scarcity in many regions will become more severe. But with appropriate policies and institutions, the word's water resources can be managed to meet the needs of a much larger population.
Lateral, Vertical, and Longitudinal Source Area Connectivity Drive Runoff and Carbon Export Across Watershed Scales

Watersheds are three‐dimensional hydrologic systems where the longitudinal expansion/contraction of stream networks, vertical connection/disconnection between shallow and deep groundwater systems, and lateral connectivity of these water sources to streams mediate runoff production and nutrient export. The connectivity of runoff source areas during both baseflow and stormflow conditions and their combined influence on biogeochemical fluxes remain poorly understood. Here we focused on a set of 3.3 and 48.4 ha nested watersheds (North Carolina, USA). These watersheds comprise ephemeral and intermittent runoff‐producing headwaters and perennial runoff‐producing lowlands. Within these landscape elements, we characterized the timing and magnitude of precipitation, runoff, and runoff‐generating flow paths. The active surface drainage network (ASDN) reflected connectivity to, and contributions from, source areas that differed under baseflow and stormflow conditions. The baseflow‐associated ASDN expanded and contracted seasonally, driven by the rise and fall of the seasonal water table. Superimposed on this were event‐activated source area contributions driven by connectivity to surficial and shallow subsurface flow paths. Frequently activated shallow flow paths also caused increased in‐stream dissolved organic carbon (DOC) concentrations with increases in runoff across both watershed scales. The spread and variability within this DOC‐runoff relationship was driven by a seasonal depletion of DOC from continual shallow subsurface flow path activation and subsequent replenishment from autumn litterfall. Our findings suggest that hydrobiogeochemical signals at larger watershed outlets can be driven by the expansion, contraction, and connection of lateral, longitudinal, and vertical source areas with distinct runoff generation processes.
TR 13-013 Energy-Optimal Trajectory Planning for Car-Like Robots

When a battery-powered robot needs to operate for along period of time, optimizingitsenergyconsumption becomes critical. Driving motors are a major source of power consumption for mobile robots. In this paper, we study the problem of finding optimal paths and velocity profiles for car-like robots so as to minimize the energy consumed duringmotion. We start with an established model for energy consumption of DC motors. Wefirst study theproblem of findingthe energy optimal velocity profiles, given a path for the robot. Wepresent closed form solutionsfor theunconstrained case and for the case where there isa bound onmaximum velocity. We then study a general problem of finding an energy optimal path along with a velocity profile, given a starting and goal position and orientation for the robot. Along the path, theinstantaneousvelocity of therobot may bebounded as a function of its turning radius, which in turn affects the energy consumption. Unlike minimum length paths, minimum energy pathsmay contain circular segmentsof varying radii . We show how to efficiently construct a graph which generalizes Dubins’ paths by including segments with arbitrary radii . Our algorithm uses the closed-form solution for the optimal velocity profiles as a subroutine to find the minimum energy trajectories, up to afinediscretization. We investigate the structure of energy-optimal paths and highlight instanceswhere thesepathsdeviatefrom theminimum length Dubins’ curves. In addition, we present a calibration methodto findenergymodel parameters. Finally, wepresent A preliminary version of this paper without the path planning section appeared in [19]. P. Tokekar · N. Karnad · V. Isler Dept. of Computer Science & Engineering, University of Minnesota. 200UnionStreet SE, Minneapolis, MN 55414, U.S.A. E-mail: {tokekar, karnad, isler}@cs.umn. edu results from experimentsconducted ona custom-built robot for following optimal velocity profiles.
Theoretical Linear-time superbubble identiﬁcation algorithm for genome assembly

DNA sequencing is the process of determining the exact order of the nucleotide bases of an individual’s genome in order to catalogue sequence variation and understand its biological implications. Whole-genome sequencing techniques produce masses of data in the form of short sequences known as reads. Assembling these reads into a whole genome constitutes a major algorithmic challenge. Most assembly algorithms utilise de Bruijn graphs constructed from reads for this purpose. A critical step of these algorithms is to detect typical motif structures in the graph caused by sequencing errors and genome repeats, and ﬁlter them out; one such complex subgraph class is a so-called superbubble . In this paper, we propose an O ( n + m ) -time algorithm to detect all superbubbles in a directed acyclic graph with n vertices and m (directed) edges, improving the best-known O ( m log m ) -time algorithm by Sung et al. © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Survey : Clustering Ensembles Techniques

generated by different clustering algorithms into a single clustering solution. Clustering ensembles have emerged as a prominent method for improving robustness, stability and accuracy of unsupervised classification solutions. So far, many contributions have been done to find consensus clustering. One of the major problems in clustering ensembles is the consensus function. In this paper, firstly, we introduce clustering ensembles, representation of multiple partitions, its challenges and present taxonomy of combination algorithms. Secondly, we describe consensus functions in clustering ensembles including Hypergraph partitioning, Voting approach, Mutual information, Co-association based functions and Finite mixture model, and next explain their advantages, disadvantages and computational complexity. Finally, we compare the characteristics of clustering ensembles algorithms such as computational complexity, robustness, simplicity and accuracy on different datasets in previous techniques.
Time series prediction by Kalman smoother with cross-validated noise density

This article presents a classical type of solution to the time series prediction competition, the CATS benchmark, which is organized as a special session of the IJCNN 2004 conference. The solution is based on sequential application of the Kalman smoother, which is a classical statistical tool for estimation and prediction of time series. The Kalman smoother belongs to the class of linear methods, because the underlying filtering model is linear and the distributions are assumed as Gaussian. Since the time series model of the Kalman smoother assumes that the densities of noise terms are known, these are determined by cross-validation.
An Overview of Steganography for the Computer Forensics Examiner

Steganography is the art of covered or hidden writing. The purpose of steganography is covert communication-to hide the existence of a message from a third party. This paper is intended as a high-level technical introduction to steganography for those unfamiliar with the field. It is directed at forensic computer examiners who need a practical understanding of steganography without delving into the mathematics, although references are provided to some of the ongoing research for the person who needs or wants additional detail. Although this paper provides a historical context for steganography, the emphasis is on digital applications, focusing on hiding information in online image or audio files. Examples of software tools that employ steganography to hide data inside of other files as well as software to detect such hidden files will also be presented.
Dynamic Obstacle Avoidance by Distributed Algorithm based on Reinforcement Learning

In this paper, we focus on the application of reinforcement learning to obstacle avoidance in dynamic environments in wireless sensor networks. A distributed algorithm based on reinforcement learning is developed for sensor networks to guide mobile robot through the dynamic obstacles. The sensor network models the danger of the area under coverage as obstacles, and has the property of adoption of itself against possible changes. The proposed protocol can integrate the reward computation of the sensors with information of the intended place of robot so that it guides the robot step by step through the sensor network by choosing the safest path in dangerous zones. Simulation results show that the mobile robot can get to the target point without colliding with any obstacle after a period of learning. Also, we discussed about time propagation between obstacle, goal, and mobile robot information. Experimental results show that our proposed method has the ability of fast adoption in real applications in wireless sensor networks. doi: 10.5829/idosi.ije. 2015.28. 02b.05
Future Generation Computer Systems

The advent of Cloud computing as a new model of service provisioning in distributed systems encourages researchers to investigate its benefits and drawbacks on executing scientific applications such as workflows. One of the most challenging problems in Clouds is workflow scheduling, i.e., the problem of satisfying the QoS requirements of the user as well as minimizing the cost of workflow execution. We have previously designed and analyzed a two-phase scheduling algorithm for utility Grids, called Partial Critical Paths (PCP), which aims to minimize the cost of workflow execution while meeting a user-defined deadline. However, we believe Clouds are different from utility Grids in three ways: on-demand resource provisioning, homogeneous networks, and the pay-as-you-go pricing model. In this paper, we adapt the PCP algorithm for the Cloud environment and propose two workflow scheduling algorithms: a one-phase algorithm which is called IaaS Cloud Partial Critical Paths (IC-PCP), and a two-phase algorithm which is called IaaS Cloud Partial Critical Paths with Deadline Distribution (IC-PCPD2). Both algorithms have a polynomial time complexity which make them suitable options for scheduling large workflows. The simulation results show that both algorithms have a promising performance, with IC-PCP performing better than IC-PCPD2 in most cases.
Development of a new Framework for Enhancing Security in Fingerprint and Finger-Vein Multimodal Biometric Authentication Systems

A biometric is a unique feature, a measurable trait or characteristic which is utilized in automatically identifying or verifying the identity of a human being. An assortment of inevitable shortcomings has been faced by unimodal biometric recognition like Limited discriminability, noisy biometric data, Upper bound in performance and Lack of permanence, consequence dilapidation of exactness and performance of the system. Multimodal biometrics consolidates the two or more biometric features into a single detection. Problems transpired in unimodal recognition can be alleviated by using multimodal biometric systems that fuse evidence from scores of multiple biometric systems and characteristically provide better recognition as compared to unimodal biometric systems. Biometric authentications exploit inimitable combination of measurable physical Characteristicsfingerprint, finger vein features, voice print, iris of the eye, and so onthat cannot be willingly imitated or forged by others. This paper proffers the match score level of fusion with feature extraction that can be espoused to consolidate the scores attained by fingerprint and finger-vein and new technique fusion with alignment that are credible and the integration strategic that can be espoused to overlapped the features attained by fingerprint and vein. Fusion techniques include processing biometric modalities successively until an adequate match is obtained.
in Computer Science and Software Engineering

— Clustering is a important technique of data mining. Clustering is an unsupervised learning problem that group objects based upon distance or similarity. Each group is known as a cluster. In this paper we make use of a large database ‘Cardiology Dataset’ containing 14 attributes and 303 instances to perform Feature Selection on K-means algorithm. We compared the results of simple clustering technique and clustering (K-means) with feature selection for Cardiology dataset, based upon various parameter using WEKA (Waikato Environment for Knowledge Analysis) and TANAGRA data mining tools. The results of the experiment show that clustering with feature selection give promising results on WEKA with utmost accuracy rate and robustness.
Licensed under Creative Commons Attribution Cc by Ames-cloud: a Framework of Adaptive Mobile Video Streaming and Efficient Social Video Sharing in the Clouds

While demands on video traffic over mobile networks have been souring, the wireless link capacity cannot keep up with the traffic demand. The gap between the traffic demand and the link capacity, along with time-varying link conditions, results in poor service quality of video streaming over mobile networks such as long buffering time and intermittent disruptions. Leveraging the cloud computing technology, we propose a new mobile video streaming framework, dubbed AMES-Cloud, which has two main parts: AMoV (adaptive mobile video streaming) and ESoV (efficient social video sharing). AMoV and ESoV construct a private agent to provide video streaming services efficiently for each mobile user. For a given user, AMoV lets her private agent adaptively adjust her streaming flow with a scalable video coding technique based on the feedback of link quality. Likewise, ESoV monitors the social network interactions among mobile users, and their private agents try to prefetch video content in advance. We implement a prototype of the AMES-Cloud framework to demonstrate its performance. It is shown that the private agents in the clouds can effectively provide the adaptive streaming, and perform video sharing (i.e., prefetching) based on the social network analysis.
PiSQRD: a web server for decomposing proteins into quasi-rigid dynamical domains

SUMMARY The PiSQRD web resource can be used to subdivide protein structures in quasi-rigid dynamical domains. The latter are groups of amino acids behaving as approximately rigid units in the course of protein equilibrium fluctuations. The PiSQRD server takes as input a biomolecular structure and the desired fraction of protein internal fluctuations that must be accounted for by the relative rigid-body motion of the dynamical domains. Next, the lowest energy modes of fluctuation of the protein (optionally provided by the user) are calculated and used to identify the rigid subunits. The resulting optimal subdivision is returned through a web page containing both interactive graphics and detailed data output. AVAILABILITY The PiSQRD web server, which requires Java, is available free of charge for academic users at http://pisqrd.escience-lab.org.
Changing Trace Element Cycles in the 21st Century Ocean

Human activity is altering the ocean. This is happening through climate change, the release of pollutants, and direct exploitation of the marine environment. Recent advances in understanding the chemical cycling of trace elements within the global ocean comes at a critical time. Society is now increasingly viewing the ocean as a resource while also recognising that ocean systems are vulnerable to change.
Hydro-economic Modeling for Optimization of Sustainable Water Management of a River Basin in the Wake of Climate Change: Case Study of the Zarrine River Basin (ZRB), Iran

II Zusammenfasung . III Acknowledgments. IV Table of contents . V List of figures . X List of tables . XIII List of abbreviations . XV 1 General introduction and research framework . 1 1.1 Research background.1 1.1.1 Sustainable management of the water resources . 2 1.1.2 Hydro-economic models . 3 1.2 Research objectives . 4 1.3 General description of the research area . 4 1.4 Research questions and thesis outline . 6 1.5 References . 8 2 Modeling the Impact of Climate Change on Water Availability in the Zarrine River Basin and Inflow to the Boukan Dam, Iran . 9 2.
Efecto de Trichoderma harzianum Rifai en el control de Ustilago maydis (DC.) Cda en maíz (Zea mays L.)

La investigacion se realizo en areas de la Unidad Basica de Produccion Cooperativa “La Yaya”, perteneciente a la UEB Atencion a Productores Caneros de la Empresa Azucarera Camaguey, en la comunidad “Alfredo Alvarez Mola”, municipio Sibanicu , sobre un suelo Pardo sialitico en el que se evaluo el efecto de la aplicacion de la cepa A - 34 de T. harzianum en el control del U. maydis en el cultivo de maiz . Se utilizo un diseno de bloque al azar con cuatro replicas divididas en nueve unidades experimentales de 30 m 2 . Se realizaron aspersiones del hongo antagonista a los 15, 30, 45 y 60 dias de germinado el cultivo, con dosis de 2,0; 2,5; 3,0; 3,5; 4,0; 4,5; 5,0 y 5,5 kg.ha -1 con un control relativo. Se evaluaron los indicadores incidencia de la enfermedad y eficacia de T. harzianum , los parametros morfologicos altura de la planta, diametro del tallo, longitud de la hoja y los indicadores productivos parametros , masa de la mazorca, diametro de la mazorca, longitud de la mazorca, numero de hileras.mazorca -1 y numero de granos.hilera -1 . Se comprobo que el tratamiento de mejores resultados fue el 8, con dosis de 5,5 kg . ha -1 y diferencias significativas sobre las variantes estudiadas. Effect of Trichoderma harzianum Rifai in the control of Ustilago maydis (DC.) Cda in corn ( Zea mays L.) ABSTRACT The research was conducted in areas of Basic Unit of Cooperative Production "La Yaya", belonging to the UEB Care Caneros Producers Camaguey Sugar Company in the "Alfredo Alvarez Mola" community, municipality Sibanicu on a Brown Sialityc soil in of the application of strain A-34 of T. harzianum was evaluated. Block design with four replications randomly divided into nine experimental units of 30 m 2 was used. Spraying antagonist fungus at 15, 30, 45 and 60 days after germination cultivation at doses of 2.0 were performed; 2.5; 3.0; 3.5; 4.0; 4. 5; 5.0 and 5.5 kg ha -1 with a relative control. Indicators disease incidence and efficiency of T. harzianum , morphological parameters plant height, stem diameter, leaf length and productive indicators parameters, ear mass, ear diameter, ear length b, rows/ear; grain/rows is assessed. It was found that the treatment was better 8, a dose of 5.5 kg ha-1 and significant differences about variants studied.
Role of Bi-Directional Migration in Two Similar Types of Ecosystems

Migration is a key ecological process that enables connections between spatially separated populations. Previous studies have indicated that migration can stabilize chaotic ecosystems. However, the role of migration for two similar types of ecosystems, one chaotic and the other stable, has not yet been studied properly. In the present paper, we investigate the stability of ecological systems that are spatially separated but connected through migration. We consider two similar types of ecosystems that are coupled through migration, where one system shows chaotic dynamics, and other shows stable dynamics. We also note that the direction of the migration is bi-directional and is regulated by the population densities. We propose and analyze the coupled system. We also apply our proposed scheme to three different models. Our results suggest that bi-directional migration makes the coupled system more regular. We have performed numerical simulations to illustrate the dynamics of the coupled systems.
BRIE: transcriptome-wide splicing quantication in single cells

Single-cell RNA-seq (scRNA-seq) provides a comprehensive measurement of stochasticity in transcription, but the limitations of the technology have prevented its application to dissect variability in RNA processing events such as splicing. Here, we present BRIE (Bayesian regression for isoform estimation), a Bayesian hierarchical model that resolves these problems by learning an informative prior distribution from sequence features. We show that BRIE yields reproducible estimates of exon inclusion ratios in single cells and provides an effective tool for differential isoform quantification between scRNA-seq data sets. BRIE, therefore, expands the scope of scRNA-seq experiments to probe the stochasticity of RNA processing.