Miracle-dz/UniFAR-FTUs huggingface dataset:
OPT-Min-Min Scheduling Algorithm of Grid Resources

As a newly emerged distributed computing platform, the Gird aims at the implementation of resources sharing and collaborative computing on the internet. Research on grid resource scheduling algorithm is one of the core problems of grid technology, as well as an essential part of grid resource management. Resource scheduling in grid environment refers to the application of tasks scheduling algorithm to assign the works in the grid to the right resources. During the allocation process, the computing performance of grid resource nodes, communication parameters and loading balance, etc., shall be put into consideration. Since they are all dynamic for both the grid resources and the grid resource nodes, requirements on grid resource scheduling algorithm are relatively higher if compared to those existing parallel resource scheduling algorithm. Based on the Min-Min scheduling algorithm, the OPT- Min-Min scheduling algorithm is presented to overcome the drawbacks. Upon the basis of applying Min-Min pre- scheduling into stage one, by adapting the strategy of two- rounds scheduling, the assignment on heavy load resources are rescheduled to balance the load. Actual cases are used to illustrate the superiority of OPT-Min-Min scheduling algorithm to the Min-Min scheduling algorithm. Simulation experiments were carried out to the batch grid resource scheduling algorithms including Min-Min, Max-Min, Min- mean and OPT-Min-Min. According to the ETC generation technique used in experimental benchmarks, ETC matrix was generated. By the comparing and analyzing the Min- Min, Max-Min and Min-mean scheduling algorithm, the validity of OPT-Min-Min scheduling algorithm is proved. Index Terms—Grid; Scheduling Strategy; OPT-Min-Min; resource scheduling algorithm; load balance
The dependence of the dynamo alpha on vorticity

We use data from numerical simulations of dynamo-generated turbulence in the shearing box approximation to determine the dynamo a-effect and its dependence on the rotation law O(r). The data suggest that the dynamo a is not simply proportional to the local angular velocity O(r), as is usually assumed, but rather is proportional to the local vorticity w(r) = r-1d/dr(Or 2 ). We also find tentative evidence to support the proposition that the backreaction of the magnetic field on a sets in when the field reaches equipartition with the energy in the turbulent motions. Furthermore, we propose an explanation as to why the sign of a is found to be opposite to that in the standard picture.
Privacy-Preserving HMM Forward Computation

In many areas such as bioinformatics, pattern recognition, and signal processing, Hidden Markov Models (HMMs) have become an indispensable statistical tool. A fundamental building block for these applications is the Forward algorithm which computes the likelihood to observe a given sequence of emissions for a given HMM. The classical Forward algorithm requires that one party holds both the model and observation sequences. However, we observe for many emerging applications and services that the models and observation sequences are held by different parties who are not able to share their information due to applicable data protection legislation or due to concerns over intellectual property and privacy. This renders the application of HMMs infeasible. In this paper, we show how to resolve this evident conflict of interests using secure two-party computation. Concretely, we propose Priward which enables two mutually untrusting parties to compute the Forward algorithm securely, i.e., without requiring either party to share her sensitive inputs with the other or any third party. The evaluation of our implementation of Priward shows that our solution is efficient, accurate, and outperforms related works by a factor of 4 to 126. To highlight the applicability of our approach in real-world deployments, we combine Priward with the widely used HMMER biosequence analysis framework and show how to analyze real genome sequences in a privacy-preserving manner.
Network streaming of dynamic 3D content with on-line compression of frame data

Real-time 3D content distribution over a network requires facing several challenges, most notably the handling of the large amount of data usually associated with 3D meshes. The scope of the present paper falls within the well-established context of real-time capture and streaming of OpenGL command sequences, focusing in particular on data compression schemes. However, we advance beyond the state-of-the-art improving over previous attempts of “in-frame” geometric compression on 3D structures inferred from generic OpenGL command sequences and adding “inter-frame” redundancy exploitation of the traffic generated by the typical architecture of interactive applications.
Food and interaction design: designing for food in everyday life

Food and interaction design presents an interesting challenge to the HCI community in attending to the pervasive nature of food, the socio-cultural differences in food practices and a changing global foodscape. To design for meaningful and positive interactions it is essential to identify daily food practices and the opportunities for the design of technology to support such practices. This workshop brings together a community of researchers and practitioners in human-food interaction to attend to the practical and theoretical difficulties in designing for human-food interactions in everyday life. Through a practical field study and workshop we explore themes of food experiences, health and wellbeing, sustainability and alternative food cultures.
Electromagnetic performance of single walled carbon nanotube bundles

A new carbon nanotube-based antenna for terahertz application is proposed. The wave velocity and propagation constant of single-walled carbon nanotube bundle dipole antenna is analyzed based on the compact equivalent circuit model of single walled carbon nanotube bundle. The results show that the slower wave velocity of a bundle antenna means that a nanotube bundle is shorter than a nanotube dipole antenna having the same operating frequency, and a nanotube bundle dipole antenna shows significant improvement in impedance matching as compared to a nanotube dipole antenna. The results could be useful for applications of the generation in terahertz wave range.
Tctex1d2 associates with short-rib polydactyly syndrome proteins and is required for ciliogenesis

Short-rib polydactyly syndromes (SRPS) arise from mutations in genes involved in retrograde intraflagellar transport (IFT) and basal body homeostasis, which are critical for cilia assembly and function. Recently, mutations in WDR34 or WDR60 (candidate dynein intermediate chains) were identified in SRPS. We have identified and characterized Tctex1d2, which associates with Wdr34, Wdr60 and other dynein complex 1 and 2 subunits. Tctex1d2 and Wdr60 localize to the base of the cilium and their depletion causes defects in ciliogenesis. We propose that Tctex1d2 is a novel dynein light chain important for trafficking to the cilium and potentially retrograde IFT and is a new molecular link to understanding SRPS pathology.
Landau-Pomeranchuk-Migdal effect and transition radiation in structured targets

The radiation from high-energy electrons is investigated for the case when a target consists of several separated plates. The spectrum of radiation is considered in the region in which the bremsstrahlung is under the influence of the multiple scattering of a projectile (the LPM effect); the polarization of a medium and the hard part of the boundary radiation contribute. In this region the general expression for the radiation spectrum is obtained for the N-plate target. A qualitative description of the arising interference pattern is given.
Individual Incentives as Drivers of Innovative Processes and Performance - Executive Summary

Drawing on research in economics and social psychology and using survey data from over 10,000 scientists and engineers (SESTAT 2003), I examine the relationships between individuals' pecuniary and nonpecuniary motives and incentives, innovative effort, and innovative performance in firms. I find that individuals' motives, in particular intrinsic motives such as intellectual challenge, are important drivers of innovative outcomes. I also investigate differences in motives, effort and performance between startups and established firms. While extrinsic motives differ across firm types, intrinsic motives are surprisingly similar. The existing differences in motives explain only a limited amount of the observed significant effort and performance differences in startups versus established firms.
Mapping between ultrasound and vowel speech using DNN framework

Building up the mapping between articulatory movements and corresponding speech could great facility the speech training and speech aid for voiceless patients. In this paper, we propose a deep learning framework for building up a mapping between articulatory information and corresponding speech, which were recorded by ultrasound system. The dataset includes six Chinese vowels. We use Bimodal Deep Autoencoder algorithm based on RBM to learn the relationship between speech and articulation, the weights matrix of representation of them. Speech and ultrasound images have been reconstructed using the extracted features. The reconstruction error of articulation by our method is less than that of PCA based approach. The reconstructed speech is similar to the original one. We propose a mapping from ultrasound tongue image to acoustic signal with a revised Denoising Autoencoder, the results show that it is a promising approach. In contrast, another experiment is conducted to synthesize the ultrasound tongue image from the speech, but the result should be improved.
Multiscale Representation of Agents Heterogeneous Beliefs in Analysis of CAC40 Prices with Frequency Decomposition

This paper focuses on the time seriesâ€™ decomposition and economic representation of its constituent parts. Wavelet transforms are used for adaptive analysis of local behaviour of heterogeneous agents. Unlike fully revealing equilibrium of homogeneous beliefs, in the environment with heterogeneous beliefs prices are driven by prevailing expectations of market participants. Thus, forecasting future prices, one must form expectations of others forecasts. Evolution of agents' expectations largely governs the adaptive nature of market prices. Overlapping beliefs of heterogeneous agents prevent the effective examination of expectation formation and price forecasting by traditional methods. In the approach proposed in this paper, a time series is decomposed into a combination of underlying series, representing beliefs of major clusters of agents. The analysis of individual parts improves statistical inference, isolating effectively nonstationary and nonlinearly features. Emergent local behaviour is also more receptive to prediction. The overall forecast (weighted combination of individual forecasts) is found to be determined and evolved depending on specific market conditions. On the statistical level, the data generating mechanism is considered as complex multi-structured system, with individual layers corresponding to particular frequencies. Reflecting the time preferences of agents, trading strategies being homogeneous intra-type are heterogeneous inter-type for agents with distinct time preferences. Overall market activity at each moment, providing the dynamic feedback across agents' types, generates market prices. The frequency decomposition of a time series identifies the local and global structures and separates short and long time dynamics. The Genetic Algorithm is applied to determine the optimal decomposition of the signal and representation of heterogeneous traders. The Artificial Neural Network is trained to learn information at the scale level that is hidden in the aggregate. The resulting models seek to enhance the understanding of the underlying data generating mechanisms of financial time series and to develop new approaches for financial forecasting.
Activity models for use in low power, high-level synthesis

Characteristics of the data being processed can be used to reduce the power consumption in the data path of a VLSI circuit by exploiting their relationship with transition activity during high-level synthesis. Important relationships between fixed-point, two's complement data characteristics and 0/spl rarr/1 transition activity in static CMOS circuits are presented in this paper. Models for computing transition activity in terms of a new set of transition parameters are developed. Propagation of data characteristics through multiplication and addition functional units is discussed. The use of the relationships and models to analyze and significantly reduce 0/spl rarr/1 transition activity with little computational effort is illustrated with examples.
Neurobehavioral implications of habitual snoring in children.

OBJECTIVE Current guidelines for the treatment of children with obstructive sleep apnea (OSA) suggest that primary snoring (PS) in children is benign. However, PS has not been well evaluated, and it is unknown whether PS is associated with serious morbidity. This study investigated whether PS is associated with neurobehavioral deficits in children. METHODS Parents of 5- to 7-year-old snoring children in public schools were surveyed about their child's sleeping habits. Children with a history of snoring and nonsnoring children were invited for overnight polysomnographic assessment and a battery of neurobehavioral tests. Only children who did not have a history of attention-deficit/hyperactivity disorder and were not considered hyperactive by parental report were tested. RESULTS Children with a history of snoring, an obstructive apnea index of <1/hour of total sleep time (hrTST), an apnea/hypopnea index <5/hrTST, and no gas exchange abnormalities were classified as PS (n = 87). Control subjects were defined as children without a history of snoring, an obstructive apnea index <1/hrTST, an apnea/hypopnea index <5/hrTST, and no gas exchange abnormalities (n = 31). Although means for both groups were in the normal range, the PS children were found to perform worse on measures related to attention, social problems, and anxious/depressive symptoms. In addition, although within the normal range, both overall cognitive abilities and certain language and visuospatial functions were significantly lower for the PS group than for the control subjects. CONCLUSIONS PS seems to be associated with significant neurobehavioral deficits in a subset of children, possibly related to increased susceptibility to sleep fragmentation. Larger studies are urgently required because current guidelines for treatment of snoring in children may require reevaluation.
Deciphering the Sox-Oct partner code by quantitative cooperativity measurements

Several Sox-Oct transcription factor (TF) combinations have been shown to cooperate on diverse enhancers to determine cell fates. Here, we developed a method to quantify biochemically the Sox-Oct cooperation and assessed the pairing of the high-mobility group (HMG) domains of 11 Sox TFs with Oct4 on a series of composite DNA elements. This way, we clustered Sox proteins according to their dimerization preferences illustrating that Sox HMG domains evolved different propensities to cooperate with Oct4. Sox2, Sox14, Sox21 and Sox15 strongly cooperate on the canonical element but compete with Oct4 on a recently discovered compressed element. Sry also cooperates on the canonical element but binds additively to the compressed element. In contrast, Sox17 and Sox4 cooperate more strongly on the compressed than on the canonical element. Sox5 and Sox18 show some cooperation on both elements, whereas Sox8 and Sox9 compete on both elements. Testing rationally mutated Sox proteins combined with structural modeling highlights critical amino acids for differential Sox-Oct4 partnerships and demonstrates that the cooperativity correlates with the efficiency in producing induced pluripotent stem cells. Our results suggest selective Sox-Oct partnerships in genome regulation and provide a toolset to study protein cooperation on DNA.
Embedding deterministic patterns in partial pseudo-exhaustive test

The topic of this thesis is related to testing of very large scale integration circuits. The thesis presents the idea of optimizing mixed-mode built-in self-test (BIST) scheme. Mixed-mode BIST consists of two phases. The first phase is pseudo-random testing or partial pseudo-exhaustive testing (P-PET). For the faults not detected by the first phase, deterministic test patterns are generated and applied in the second phase. Hence, the defect coverage of the first phase influences the number of patterns to be generated and stored. The advantages of P-PET in comparison with usual pseudo-random test are in obtaining higher fault coverage and reducing the number of deterministic patterns in the second phase of mixed-mode BIST. Test pattern generation for P-PET is achieved by selecting characteristic polynomials of multiple-polynomial linear feedback shift register (MP-LFSR). In this thesis, the mixed-mode BIST scheme with P-PET in the first phase is further improved in terms of the fault coverage of the first phase. This is achieved by optimization of polynomial selection of P-PET. In usual mixed-mode BIST, the set of undetected by the first phase faults is handled in the second phase by generating deterministic test patterns for them. The method in the thesis is based on consideration of these patterns during polynomial selection. In other words, we are embedding deterministic test patterns in P-PET. In order to solve the problem, the algorithm for the selection of characteristic polynomials covering the pre-generated patterns is developed. The advantages of the proposed approach in terms of the defect coverage and the number of faults left after the first phase are presented using contemporary industrial circuits. A comparison with usual pseudo-random testing is also performed. The results prove the benefits of P-PET with embedded test patterns in terms of the fault coverage, while maintaining comparable test length and time.
Benchmarking Trajectory Data for Trip Recommendation

The availability of massive trajectory data collected from GPS devices has received significant attentions in recent years. A hot topic is trip recommendation, which focuses on searching trajectories that connect (or are close to) a set of query locations, e.g., several sightseeing places specified by a traveller, from a collection of historic trajectories made by other travellers. However, if we know little about the sample coverage of trajectory data when developing an application of trip recommendation, it is difficult for us to answer many practical questions, such as 1) how many (future) queries can be supported with a given set of raw trajectories? 2) how many trajectories are required to achieve a good-enough result? 3) how frequent the update operations need to be performed on trajectory data to keep it long-term effective? In this paper, we focus on studying the overall quality of trajectory data from both spatial and temporal domains and evaluate proposed methods with a real big trajectory dataset. Our results should be useful for both the development of trip recommendation systems and the improvement of trajectory-searching algorithms.
Supporting the Online Community of Computer Graphics Educators

Computer Graphics (CG), Digital Arts and Media fields have evolved considerably over the past few decades. This growth makes it difficult for educators and professionals to keep up with the pace of change, since they must achieve deeper understanding and master new content of computers and imagery, from the latest technology developments. Thus, providing with the necessary means for educators work to be appraised and to be available to others for use is an important resource for CG and for the Community of CG educators and professionals, worldwide. This paper describes and discusses the current status of CGEMS, an online server for educational refereed content that aims at supporting the community of CG by allowing educators to contribute with, and access, high-quality curricular resources.
A novel hybrid approach of Evolutionary Algorithm based on Imperialist Competitive Algorithm

Evolutionary Algorithms are representative as a research field in continuous development and a permanent source of new ideas. In order to minimize a function, some algorithms such as Genetic Algorithms, the Imperialist Competitive Algorithm and Evolutionary Hybrid Algorithms were investigated in this paper. Also, in order to accelerate the search of an optimal solution a new hybrid algorithm based on the Imperialist Competitive Algorithm is proposed. The tests showed that the proposed algorithm tends to reach an optimal solution faster than other algorithms.
Integrative Technology for Bioinformatics

The production of larger and larger databases in molecular biology, particularly those containing genomic data, have lead to a strong interest in Bioinformatics and Computational Biology due to the obvious need to analyze and understand such large collections of data. In particular, microarray technology, with its ability to measure the expression patterns of thousands of genes simultaneously, presents researchers with formidable data analysis difficulties.
A potpourri of authentication mechanisms the mobile device way

Nowadays the use of mobile devices, such as smartphones and tablets, are rapidly increasing in network services, proliferating to almost every environment. This massive appearance of mobile devices creates significant opportunities to leverage these mobile devices to establish novel types of services. However there are also significant concerns about the privacy and security of sensitive data exchanged and stored on these devices. Since these devices are usually embodied with numerous characteristics like camera devices, 3G and NFC connection that can be used to create new alternative authentication schemes in order to guarantee users identity. This paper performs a survey on the current state of the art in alternative authentication mechanisms regarding access and authentication against the traditional login and password scheme by the usage of the mobile devices and their properties.
A Survey on Multihop Ad Hoc Networks for Disaster Response Scenarios

Disastrous events are one of the most challenging applications of multihop ad hoc networks due to possible damages of existing telecommunication infrastructure. The deployed cellular communication infrastructure might be partially or completely destroyed after a natural disaster. Multihop ad hoc communication is an interesting alternative to deal with the lack of communications in disaster scenarios. They have evolved since their origin, leading to different ad hoc paradigms such as MANETs, VANETs, DTNs, or WSNs. This paper presents a survey on multihop ad hoc network paradigms for disaster scenarios. It highlights their applicability to important tasks in disaster relief operations. More specifically, the paper reviews the main work found in the literature, which employed ad hoc networks in disaster scenarios. In addition, it discusses the open challenges and the future research directions for each different ad hoc paradigm.