Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of Cascaded based Reversing Voltage Multilevel Inverter using Multi Carrier Modulation Strategies

Received Nov 5, 2017 Revised Dec 9, 2017 Accepted Jan 3, 2018 In this paper, a cascaded based reversing voltage (CBRV) multilevel inverter structure is proposed inorder to compensate the major drawbacks in the conventional multilevel inverters. The proposed topology requires less number of components, less carrier signals and gate drives when compared to existing multilevel inverters particularly at higher levels. Therefore, the complexity and overall cost are greatly reduced particularly for higher output voltage levels. This paper also presents the most relevant control and modulation methods by a triangular based multi carrier pulse width modulation (PWM) scheme for the proposed CBRV inverter topology. This paper presents a comparison between different modulation strategies for CBRV inverter topology based on sinusoidal and space vector references with multi triangular carrier waves. The work strive hard to present the scrutiny that has been made between various PWM control techniques for 1– Ф seven level CBRV inverter structure. The comparison is made in terms of Total Harmonic Distortion (THD) and fundamental RMS voltage. Finally, the simulation results are included to verify the effectiveness of the proposed CBRV inverter topology and validate the proposed theory. A hardware set up was developed for a 1–Ф seven level CBRV inverter topology using FPGA based pulse generation. Keyword:
Access book

The core of our paper is represented by the development of the HypeRSimRIP applica‐ tion that can be used for various networking purposes, such as designing a network or setting routing processes. Likewise, the application implements didactical functions use‐ ful for teaching networking-related concepts and experimental capabilities that enable its users to recreate famous experiments.
Deep learning light field microscopy for rapid four-dimensional imaging of behaving animals

We propose a novel approach to implement efficient reconstruction for light field microscopy. This approach is based on convolutional neural network (CNN). By taking a two-dimensional light field raw image as the input, our model is able to output its corresponding three-dimensional volume at high resolution. Compared to the traditional light field reconstruction method, our approach dramatically reduces the computation time cost, and significantly improves the image quality (resolution). As furthermore combing this deep learning light field microscopy with selective light-sheet illumination, we can achieve high-contrast, high-resolution (~1.6 μ m) three-dimensional imaging of acting C.elegans at a speed of fifty volumes per second. We furthermore apply this technique to the interrogation of beating zebrafish heart, intoto visualizing the cardiovascular hemodynamics inside zebrafish embryo by rapid volumetric recording of beating myocardium and blood flow. Our method is demonstrated to be promising in a wide range of biomedical applications such as neuroscience and development, in which high-resolution, high-speed volumetric imaging is highly desired.
The Client Assignment Problem for Continuous Distributed Interactive Applications

Interactivity is a primary performance measure for distributed interactive applications (DIAs) that enable participants at different locations to interact with each other in real time. Wide geographical spreads of participants in large-scale DIAs necessitate distributed deployment of servers to improve interactivity. In a distributed server architecture, the interactivity performance depends on not only client-to-server network latencies but also inter-server network latencies as well as synchronization delays to meet the consistency and fairness requirements of DIAs. All of these factors are directly affected by how the clients are assigned to the servers. In this paper, we investigate the problem of effectively assigning clients to servers for maximizing the interactivity of DIAs. We focus on continuous DIAs that change their states not only in response to user operations but also due to the passing of time. We analyze the minimum achievable interaction time for DIAs to preserve consistency and provide fairness among clients, and formulate the client assignment problem as a combinational optimization problem. We prove that this problem is NP-complete. Four heuristic assignment algorithms are proposed and evaluated using real Internet latency data. The experimental results show that our proposed greedy algorithm generally produces near optimal interactivity and significantly reduces the interaction time between clients compared to the intuitive algorithm that assigns each client to its nearest server.
Relationships between microbial communities and groundwater chemistry in two pristine confined groundwater aquifers in central China

This study explores linkages between the microbial composition and hydrochemical variables of pristine groundwater to identify active redox conditions and processes. Two confined aquifers underlying the city of Qianjiang in the Jianghan Plain in China were selected for this study, having different recharge sources and strong hydrochemical gradients. Typical methods for establishing redox processes according to threshold concentration criteria for geochemical parameters suggest iron or sulphate reduction processes. High‐throughput 16S rRNA sequencing was used to obtain diversity and taxonomic information on microbial communities. Instead of revealing iron‐ and sulphate‐reducing bacteria, salt‐ and alkali‐tolerant bacteria, such as the phylum Firmicutes and the class Gammaproteobacteria, and in particular, the family Bacillaceae, were dominant in the downstream groundwater of the first aquifer that had high ion concentrations caused by the dissolution of calcite and dolomite; meanwhile, the heterotrophic microaerophilic families Comamonadaceae and Rhodocyclaceae prevailed in the upstream groundwater of the first aquifer. Sulphate‐reducing bacteria were extremely abundant in the upstream groundwater of the second aquifer, as the SO42− concentration was especially high. Methanogens and methanotrophs were predominant in the downstream groundwater of the second aquifer even though the concentration of SO42− was much higher than 0.5 mg L−1. The microbial communities, together with the geochemical parameters, indicated that the upstream region of the first aquifer was suboxic, that Fe(III) and Mn(IV) reductions were not the main redox processes in the downstream groundwater of the first aquifer with high Fe and Mn concentrations, and that the redox processes in the upstream and downstream regions of the second confined aquifer were SO42− reduction and methanogenesis, respectively. This study expands understanding of the linkages between microbial communities and hydrogeochemistry in pristine groundwaters and provides more evidence for identifying active redox conditions and processes.
Analysis of Throughput and Energy Efficiency in the IEEE 802 . 11 Wireless Local Area Networks using Constant backoff Window Algorithm

The IEEE has standardized the 802.11 protocol for Wireless Local Area Networks. The primary medium access control (MAC) technique of 802.11 is called distributed coordination function (DCF). DCF is a carrier sense multiple access with collision avoidance (CSMA/CA) scheme with binary exponential backoff algorithm (BEB). DCF describes two techniques to employ for packet transmission: the two-way handshaking technique called basic access mechanism and an optional four way handshaking technique, known as request-tosend/clear-to-send (RTS/CTS) mechanism. In wireless networks, the energy consumed to transmit bits across a wireless link, is a critical design parameter. The Constant backoff Window Algorithm (CWA) is the modification of the IEEE 802.11 BEB algorithm, which is used to control the contention window in the case of collisions, in order to provide a better Throughput and Energy efficiency. The new algorithm has been tested against the legacy IEEE 802.11 through matlab simulation. The tests have shown significant improvements in performance in throughput and energy efficiency using CWA compared to the original BEB algorithm.
Bulletin of Electrical Engineering and Informatics

Farrukh Hafeez, Usman Ullah Sheikh, Saud Al-Shammari, Muhammad Hamid, Abdul Baqi Khan Khakwani, Zeeshan Ahmad Arfeen School of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia Department of Electrical and Electronic Engineering Technology, Jubail Industrial College, Al Jubail, Saudi Arabia Department of Engineering Technology, Community College of Qatar, Doha, Qatar Department of Electrical Engineering, The Islamia University of Bahawalpur (IUB), Bahawalpur, Pakistan
RESEARCH OUTPUTS / RÉSULTATS DE RECHERCHE

—The challenges of adopting agile software development methods vary from one context to another. This work investigates how the European and Asian cultural backgrounds may impact agile practices adoption. The focus is on three countries: Belgium, Malaysia and Singapore. We gathered data about practices, challenges and impediments encountered by software development teams from interviews of 19 practitioners and two agile software development events (discussion groups). The results of the analysis are prioritized and discussed us-ing the Hofstede Model for national cultures comparison. The identiﬁed hypotheses can serve as a guidance for contextual agile practices improvement. The results need to be considered carefully since more empirical data are required to conﬁrm the practitioners’ opinion. In fact, studying socio-cultural differences is sensitive. Yet such information may be valuable for onshore teams (culturally homogeneous) as well as teams working on globally distributed projects.
Interaction of microphysical and dynamical timescales in orographic precipitation

The development of orographic precipitation depends on the interaction of processes which operate on different timescales (Smith 1979; Jiang and Smith 2003; Smith and Barstad 2004; Kirshbaum and Durran 2004; Roe and Baker 2006). Dynamical timescales are inherent to the flow dynamics (e.g. advection timescale, timescale of instability growth) and can be distinguished from timescales governed by microphysical processes (e.g hydrometeor conversion timescale, timescale of hydrometer fallout). The interaction between these different timescales controls the development of orographic clouds and determines the orographic precipitation distribution. So far, the relevance of dynamical and microphysical timescales for orographic precipitation has only been analyzed within the framework of linear models (Smith 1979; Smith and Barstad 2004) or with numerical models and simplified microphysics (Jiang and Smith 2003; Kirshbaum and Durran 2004). Recent studies indicate that the interaction of these timescales may also be relevant for aerosol-cloud-precipitation interactions in orographic clouds (Muhlbauer and Lohmann 2008).
Shifting More Attention to Video Salient Object Detection Supplemental Material Anonymous CVPR submission

In this document, we include additional materials related to the benchmark, dataset, model, evaluation metrics, and results. • Benchmark. We systematically assess 17 representative VSOD algorithms [3, 4, 7, 9, 11–13, 17–19, 21–25, 27,29] over seven existing VSOD datasets and the proposed DAVSOD with totally 40K frames, making it the largest-scale benchmark. • Dataset. We show a complete statistics of existing datasets and provide some representative sample frames of the proposed DAVSOD dataset. • Model. We describe our implementation details and the training protocol in § 3. • Metrics. In § 4.1, we describe the details of three metrics, e.g., MAE (M) [15], F-measure (F) [1], and Smeasure (S) [5] and the details of metric statistics in Table 4 (manuscript). • Results. We provide more experiment results on existing 7 datasets [7,8,10,12,14,16,23] and the proposed dataset in § 4.3. We refer the reader to the accompanying video attachment (VideoSaliency.mp4 [76MB]) for more details. Due to both limit space and maximum (100MB) file size, the overall dataset will be publicly available in our website. All of the model results (17 state-of-the-art and the proposed SSAV model) on 8 datasets (7 existing datasets and the proposed DAVSOD dataset) will be released in our website to boost the development of video salient objection. 1. Benchmark As shown in Fig. 1, it has witnessed the dramatic development of VSOD modeling, while the community longterm suffered from the lack of a standard representative benchmark. To the best of our knowledge, this work is first one largest-scale VSOD benchmark. 1 1 1 5 6 7 18 0 0 0 0 0 0 1 0 3 6 9 12 15 18 201
Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web

Extracting sentiment from text is a hard semantic problem. We develop a methodology for extracting small investor sentiment from stock message boards. The algorithm comprises different classifier algorithms coupled together by a voting scheme. Accuracy levels are similar to widely used Bayes classifiers, but false positives are lower and sentiment accuracy higher. Time series and cross-sectional aggregation of message information improves the quality of the resultant sentiment index, particularly in the presence of slang and ambiguity. Empirical applications evidence a relationship with stock values---tech-sector postings are related to stock index levels, and to volumes and volatility. The algorithms may be used to assess the impact on investor opinion of management announcements, press releases, third-party news, and regulatory changes.
Characterization of microstrip reflectarray square ring elements by means of an Artificial Neural Network

In this paper an Artificial Neural Network (ANN) is introduced in order to speed up the optimized design procedure of a printed Reflectarray. The ANN is used to model the reflecting behaviour of patch radiator as a function of its geometric parameters, the angle of incidence and the frequency. When used in the RA design process, the reflection coefficient approximated with the ANN produces results that are very close to those obtained with that directly evaluated, proving the accuracy of the ANN model, which, on the other hand, strongly reduces the computational effort and the data memory storage.
Origin of 1.8 Ga zircons in Post Eocene mafic dikes in the Roshtkhar area, NE Iran

ABSTRACT The Roshtkhar area is located in the Khaf-Kashmar-Bardaskan volcano-plutonic belt to the northeastern Iran along the regional E–W trending Dorouneh Fault, northeastern of the Lut Block. There are several outcrops of subvolcanic rocks occurring mainly as dikes in the area, which intruded into Cenozoic intrusive rocks. We present U–Pb dating of zircons from a diabase dike and syenite rock using LA-ICP-MS that yielded an age of 1778 ± 10 Ma for the dike, indicating this Cenozoic dike has zircon xenocrysts inherited from deeper sources; and 38.0 ± 0.5 Ma, indicating an Late Eocene crystallization age for the syenite. Geochemically, the dikes typical of high-K calc-alkaline to shoshonitic magmas. Petrographic observations and major and trace element variations suggest that diabase melts underwent variable fractionation of clinopyroxene, olivine, and Fe-Ti oxides and minor crustal contamination during the differentiation process. Primitive mantle-normalized multi-element diagrams display enrichment in LILE, such as Rb, Ba, Th, U, and Sr compared to HFSE, as well as negative anomalies of Nb, Ta, P, and Ti, suggesting derivation from subduction-modified mantle. Chondrite-normalized REE plots show moderately LREE enriched patterns (<3.83 LaN/YbN <8.27), and no significant Eu anomalies. Geochemical modelling using Sm/Yb versus La/Yb and La/Sm ratios suggests a low-degree of batch melting (~1–3%) of a phlogopite-spinel peridotite source to generate the mafic dikes. The geochemical signatures suggest that the Roshtkhar mafic dikes cannot be related directly to subduction and likely resulted from melting of upper mantle in an extensional setting where the heat flow was provided from deeper levels. These dikes presumably derived the zircon xenocrysts from the assimilation of upper crust of Gondwanian basement. Processes responsible for partial melting of metasomatized lithospheric mantle and post-collision magmatism in NE Iran was triggered by heating due to asthenospheric upwelling in an extensional setting. Graphical Abstract
Bayesian Network Structures and Inference Techniques for Automatic Speech Recognition

This paper describes the theory and implementation of Bayesian networks in the context of automatic speech recognition. Bayesian networks provide a succinct and expressive graphical language for factoring joint probability distributions, and we begin by presenting the structures that are appropriate for doing speech recognition training and decoding. This approach is notable because it expresses all the details of a speech recognition system-ranging from pronunciation dictionaries to language models-in a uniform way using only the concepts of random variables and conditional probabilities. A powerful set of computational routines complements the representational utility of Bayesian networks, and the second part of this paper describes these algorithms in detail. We present a novel view of inference in general networks-where inference is done via a change-of-variables that renders the network tree-structured and amenable to a very simple form of inference. We present the technique in terms of straightforward dynamic programming recursions analogous to HMM alpha-beta computation, and then extend it to handle deterministic constraints amongst variables in an extremely efficient manner. The paper concludes with a sequence of experimental results that show the range of effects that can be modeled, and that significant reductions in error-rate can be expected from intelligently factored state representations.
Understanding Low-and High-Level Contributions to Fixation Prediction: Supplementary Material

Our DeepGaze II model uses a similar architecture to DeepGaze I [1], with four primary changes: replacing AlexNet features by VGG features, using a readout network instead of a linear readout, pre-training on the SALICON dataset, and using image-wise crossvalidation over the full MIT1003 dataset rather than subject-wise crossvalidation over only a subset. We quantified the contributions of these changes to achieving our model performance using the full MIT1003 dataset. As seen in Table 1, switching to image-wise crossvalidation on the full dataset (DeepGaze I′) provides a substantial performance boost over the original DeepGaze I model. After considering this change, the largest single improvement over DeepGaze I′ comes from using the pre-trained VGG features in place of AlexNet (though note that we also include more channels from VGG than from AlexNet). Training DeepGaze I′ on the SALICON dataset does not change performance, suggesting that the 258 parameters of this model are already sufficiently constrained by the MIT1003 dataset. Combining SALICON pre-training with the VGG features yields the largest intermediate model performance improvement. Using the readout network without additional pre-training on the SALICON dataset never gives substantially better performance (compare DeepGaze I′ to “readout network”, or “VGG” to “readout net + VGG”), suggesting that SALICON pretraining is required for the readout network to avoid overfitting. Model IG IGE AUC sAUC NSS Centerbias 0.00 0.0 79.6 50.0 1.22 DeepGaze I 0.56 46.1 85.8 73.0 1.92 DeepGaze I′ 0.76 62.3 86.9 75.0 2.16 readout network 0.75 62.0 87.0 75.0 2.16 SALICON 0.76 62.6 86.9 75.0 2.16 VGG 0.84 69.3 87.7 76.4 2.32 Readout net+SALICON 0.82 67.5 87.3 75.6 2.25 Readout net+VGG 0.85 70.0 87.3 76.2 2.34 SALICON+VGG 0.90 74.3 88.0 76.9 2.42 DeepGaze II 0.98 80.3 88.3 77.7 2.48 Gold Standard 1.22 100.0 89.9 81.2 2.82
NMAGIC: Fast Parallel Implementation of a χ2-Made-To-Measure Algorithm for Modeling Observational Data

We describe a made-to-measure algorithm (χ2M2M) for constructing N-particle models of stellar systems from observational data (De Lorenzi Debattista, Gerhard, et al. (2007)), extending earlier ideas by Syer & Tremaine (1996). The algorithm properly accounts for observational errors. We implemented this algorithm in a parallel code NMAGIC and carried out a sequence of tests to illustrate its power and performance: (i) We reconstructed an isotropic Hernquist (1990) model from density moments and projected kinematics including higher-order Gauss-Hermite moments (Gerhard (1993), van der Marel & Franx (1993)). We gave NMAGIC two initial models, made from distribution function (Debattista & Sellwood (2000)), with different density distributions to start with. While both recovered the correct differential energy distribution and intrinsic kinematics, that with density closer to the density of the final model had smaller final deviations from the target observables, and a narrower distribution of weights. (ii) We built a self-consistent oblate three-integral maximum rotator model and compared how the distribution function is recovered from integral field and slit kinematic data. In these experiments we gave the algorithm a difficult problem to solve. Since the target system was maximally rotating, the weights of all counter-rotating particles were zero. Using density observables and either slit or integral field kinematics, NMAGIC was asked to recover this maximally rotating model starting from an isotropic spherical system. A good fit to the kinematic constraint data was achieved. These experiments also showed the advantage of integral field data over slit data for constraining the model. The different applications show that the χ2M2M algorithm is practical, reliable and can be applied to various systems. High quality dynamical models of galaxies can be achieved which match targets to ~1σ for plausible uncertainties in the observables, and without symmetry restrictions. We conclude that χ2M2M holds great promise for unraveling the nature of galaxies.
Improving Optical Character Recognition through Efficient Multiple System Alignment

As large-scale text data become available on the Web, textual errors in a corpus are often inevitable (e.g., digitizing historic documents). Due to the calculation of frequencies of words, however, such textual errors can significantly impact the accuracy of statistical models such as the popular Latent Dirichlet Allocation (LDA) model. To address such an issue, in this paper, we propose two novel extensions to LDA (i.e., TE-LDA and TDE-LDA): (1) The TE-LDA model incorporates textual errors into term generation process; and (2) The TDE-LDA model extends TE-LDA further by taking into account topic dependency to leverage on semantic connections among consecutive words even if parts are typos. Using both real and synthetic data sets with varying degrees of “errors”, our TDE-LDA model outperforms: (1) the traditional LDA model by 16%-39% (real) and 20%-63% (synthetic); and (2) the state-of-the-art N-Grams model by 11%-27% (real) and 16%-54% (synthetic).
Monitoring the Syrian Humanitarian Crisis with the JRC’s Global Human Settlement Layer and Night-Time Satellite Data

3 1. Insights to the humanitarian aspects of the Syrian Crisis . 4 2. Integrating GHSL derived population data with night-light satellite imagery . 5 2.1 Study data . 5 2.1. 1 Night-light satellite images . 5 2.1. 2 Population data (GHS-POP) . 6 2.2 Analysis of night-lights. 6 3. Results . 7 4. Towards a humanitarian crisis now-casting system . 12 5. References . 13 List of figures . 14