Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-Automatic Generation of Device Drivers for Rapid Embedded Platform Development

IP core integration into an embedded platform implies the implementation of a customized device driver complying with both the IP communication protocol and the CPU organization (single processor, SMP, AMP). Such a close dependence between driver and platform organization makes reuse of already existing device drivers very hard. Designers are forced to manually customize the driver code to any different organization of the target platform. This results in a very time-consuming and error-prone task. In this paper, we propose a methodology to semi-automatically generate customized device drivers, thus allowing a more rapid embedded platform development. The methodology exploits the testbench provided with the RTL IP module for extracting the formal model of the IP communication protocol. Then, a taxonomy of device drivers based on the CPU organization allows the system to determine the characteristics of the target platform and to obtain a template of the device driver code. This requires some manual support to identify the target architecture and to generate the desired device driver functionality. The template is used then to automatically generate drivers compliant with 1) the CPU organization, 2) the use in a simulated or in a real platform, 3) the interrupt support, 4) the operating system, 5) the I/O architecture, and 6) possible parallel execution. The proposed methodology has been successfully tested on a family of embedded platforms with different CPU organizations.
SSVEP frequency detection methods considering background EEG

Detection of the frequency of steady-state visual evoked potentials (SSVEP) is addressed. We propose to use the canonical correlation analysis (CCA) with linear discriminant analysis (LDA), as well as some modifications of the so-called rhythmic component extraction (RCE) that can consider the background EEG spectra Classification accuracy and the information transfer rate (ITR) are examined in classification of six commands.
Information in a Network of Neuronal Cells: Effect of Cell Density and Short-Term Depression

Neurons are specialized, electrically excitable cells which use electrical to chemical signals to transmit and elaborate information. Understanding how the cooperation of a great many of neurons in a grid may modify and perhaps improve the information quality, in contrast to few neurons in isolation, is critical for the rational design of cell-materials interfaces for applications in regenerative medicine, tissue engineering, and personalized lab-on-a-chips. In the present paper, we couple an integrate-and-fire model with information theory variables to analyse the extent of information in a network of nerve cells. We provide an estimate of the information in the network in bits as a function of cell density and short-term depression time. In the model, neurons are connected through a Delaunay triangulation of not-intersecting edges; in doing so, the number of connecting synapses per neuron is approximately constant to reproduce the early time of network development in planar neural cell cultures. In simulations where the number of nodes is varied, we observe an optimal value of cell density for which information in the grid is maximized. In simulations in which the posttransmission latency time is varied, we observe that information increases as the latency time decreases and, for specific configurations of the grid, it is largely enhanced in a resonance effect.
Power efficiency of switch architecture extensions for fault tolerant NoC design

The increasingly parallel landscape of embedded computing platforms is bringing the reliability concern for the on-chip interconnection network (NoC) to the forefront. While very few works in the open literature bring their error recovery mechanisms down to microarchitectural and physical implementation, this paper documents the effort of optimizing a baseline NoC switch architecture for different fault-tolerant strategies against single-event upsets. As key contributions achieved, we not only come up with a new efficient fault-tolerant flow control protocol, but also we contrast correction vs. retransmission oriented switch microarchitectures, each implementing both data and control path protection, with physical implementation awareness. The accuracy of the analysis methodology enables us to report counterintuitive power-reliability trade-offs between the design points, serving as guidelines for implementing fault-tolerant communication in a power-constrained environment.
Ocean Interpolation by Four-Dimensional Weighted Least Squares—Application to the Waters around Australasia

A new four-dimensional ocean interpolation system based on locally weighted least squares fitting is presented. A loess filter is used to interpolate irregularly spaced data onto a uniform grid. This involves projecting the data onto quadratic functions of latitude and longitude while simultaneously fitting annual and semiannual harmonics by weighted least squares. The smoothness scale of the mapping method adapts to match the data density, thus producing gridded estimates with maximum resolution. The filter has a vertical dimension, such that the data on adjacent levels are included in the computation. This greatly reduces the effects of discontinuities in data distributions between adjacent levels, since the estimates at each level are no longer independent. The loess scheme has been further modified so that the weighting of data points is adjusted to allow for the influence of both bathymetry and land barriers. This allows the bathymetry to influence the mapped fields in a natural way, reduces leakage of structure between deep and shallow regions and produces far more realistic coastal gradients. The flexibility of the loess approach has allowed further adjustments to compensate for irregularities in spatial and temporal sampling. The mapping is shown to be statistically consistent with an objective measure of the a priori noise of the dataset. Departures of the mapped fields from independent surface temperature climatologies and mean vertical sections derived from withheld expendable bathythermograph (XBT) data are within error limits. The method is applied to the major seas around Australia, New Zealand, Papua New Guinea, and Indonesia (508S‐108N, 1008E;‐1808) to form a high-resolution seasonal climatology of temperature, salinity, oxygen, nitrate, phosphate, and silicate, referred to as the CSIRO (Commonwealth Scientific and Industrial Research Organisation) Atlas of Regional Seas (CARS). Stringent quality control procedures have been applied to a comprehensive dataset assembled from all known sources. The resulting maps successfully resolve both the large-scale structure and narrow coastal features and illustrate how the bathymetry influences the property distributions.
Directional Field Computation for Fingerprints Based on the Principal Component Analysis of Local Gradients

This paper discusses a new method, based on the principal component analysis, to estimate the directional eld of ngerprints. The method not only computes the di- rection in any pixel location, but its coherence as well. It will be proven that this method provides exactly the same results as the method that is known from literature. Un- doubtedly, the existence of a completely dierent solution method increases the insight into the problem's nature. Seg- mentation of ngerprints is discussed to illustrate the appli- cation of the coherence of the directional eld.
Leveraging web 2.0 data for scalable semi-supervised learning of domain-specific sentiment lexicons

Since manually constructing domain-specific sentiment lexicons is extremely time consuming and it may not even be feasible for domains where linguistic expertise is not available, research on automatic construction of domain-specific sentiment lexicons has become a hot topic in recent years. The main contribution of this paper is the illustration of a novel semi-supervised learning method which exploits both term-to-term and document-to-term relations hidden in a corpus for the construction of domain-specific sentiment lexicons. More specifically, the proposed two-pass pseudo labeling method combines shallow linguistic parsing and corpus-base statistical learning to make domain-specific sentiment extraction scalable with respect to the sheer volume of opinionated documents archived on the Internet these days. Our experiments show that the proposed method can generate high quality domain-specific sentiment lexicons according to users' evaluation.
Fast Non-local Stereo Matching based on Hierarchical Disparity Prediction

Stereo matching is the key step in estimating depth from two or more images. Recently, some tree-based non-local stereo matching methods have been proposed, which achieved state-of-the-art performance. The algorithms employed some tree structures to aggregate cost and thus improved the performance and reduced the coputation load of the stereo matching. However, the computational complexity of these tree-based algorithms is still high because they search over the entire disparity range. In addition, the extreme greediness of the minimum spanning tree (MST) causes the poor performance in large areas with similar colors but varying disparities. In this paper, we propose an efficient stereo matching method using a hierarchical disparity prediction (HDP) framework to dramatically reduce the disparity search range so as to speed up the tree-based non-local stereo methods. Our disparity prediction scheme works on a graph pyramid derived from an image whose disparity to be estimated. We utilize the disparity of a upper graph to predict a small disparity range for the lower graph. Some independent disparity trees (DT) are generated to form a disparity prediction forest (HDPF) over which the cost aggregation is made. When combined with the state-of-the-art tree-based methods, our scheme not only dramatically speeds up the original methods but also improves their performance by alleviating the second drawback of the tree-based methods. This is partially because our DTs overcome the extreme greediness of the MST. Extensive experimental results on some benchmark datasets demonstrate the effectiveness and efficiency of our framework. For example, the segment-tree based stereo matching becomes about 25.57 times faster and 2.2% more accurate over the Middlebury 2006 full-size dataset.
A profile driven approach for low power synthesis

A profile driven approach to behavioral synthesis is presented. For a given design and a set of input vectors, the switching activity in the design yields a measure of the power consumption. Every module in a parameterized module library is characterized by its average switching activity per input vector. For a given behavioral specification, simulation using user specified inputs is carried out to collect the profile data of various operations and carriers in the specification. In the performance estimation phase, the profile data with the switching activity data in the precharacterized module library is used to estimate the average switching activity of all the module sets meeting other user specified constraints such as area and delay. The module set with the least estimated switching activity is further synthesized. Experimental results show that the switching activity estimated during synthesis deviates by less than 10% on the average from the actual switching activity measured after completing synthesis.
A Bayesian Learning Based Approach for Clustering of Satellite Images

This paper presents a technique for performing unsupervised clustering of satellite images using a unique 'sampling-resampling' based Bayesian learning method. The multi-band pixel values of the satellite image are expected to form a certain number of clusters. The parameters of these clusters are learnt using a Bayesian approach. This technique is unsupervised in the sense that no separate training images are required to initialize the model parameters. Learning of cluster parameters and classification of pixels are done simulaneously. Parameter values obained using Bayesian techniques are expected to be more accurate, hence leading to better classification results, as compared to classical frequentist techniques. Also, the presented 'sampling-resampling' based approach of performing Bayesian learning suggests computational simplicity and ease of implementation.
Shape Animation with Combined Captured and Simulated Dynamics

We present a novel volumetric animation generation framework to create new types of animations from raw 3D surface or point cloud sequence of captured real performances. The framework considers as input time incoherent 3D observations of a moving shape, and is thus particularly suitable for the output of performance capture platforms. In our system, a suitable virtual representation of the actor is built from real captures that allows seamless combination and simulation with virtual external forces and objects, in which the original captured actor can be reshaped, disassembled or reassembled from user-specified virtual physics. Instead of using the dominant surface-based geometric representation of the capture, which is less suitable for volumetric effects, our pipeline exploits Centroidal Voronoi tessellation decompositions as unified volumetric representation of the real captured actor, which we show can be used seamlessly as a building block for all processing stages, from capture and tracking to virtual physic simulation. The representation makes no human specific assumption and can be used to capture and re-simulate the actor with props or other moving scenery elements. We demonstrate the potential of this pipeline for virtual reanimation of a real captured event with various unprecedented volumetric visual effects, such as volumetric distortion, erosion, morphing, gravity pull, or collisions.
Sequence-Oriented Problem Identification Within Service Encounters

Sequence-oriented problem identification (SOPI) within service encounters include "blueprinting" the sequence of steps that makeup a service encounter, and asking customers to provide evaluations for each step they may experience in the service encounter process. In part, SOPI is a strategic tool that combines and extends Shostack's (1984; 1987) blueprinting of services with the critical incident technique (CIT) (Flanagan 1954; Bitner, Booms & Tetreault 1990). SOPI is a method for learning customers' perspectives of the little things that go right and wrong, as well as the critical incidents, in each of the locations and sequence of steps customers experience related to a service encounter. Such information may be useful in designing customer-inspired improvements in service offerings and in eliminating both minor and major incidents that annoy customers. Here the SOPI technique is illustrated with comparisons of findings from the research method with findings from the critical incident method in an empir.
Mechanisms for High Throughput Computing

For many experimental scientists, scienti c progress and quality of research are strongly linked to computing throughput. In other words, most scientists are concerned with how many oating point operations per week or per month they can extract from their computing environment rather than the number of such operations the environment can provide them per second or minute. Floating point operations per second (FLOPS) has been the yardstick used by most High Performance Computing (HPC) e orts to rank their systems. Little attention has been devoted by the computing community to environments that can deliver large amounts of processing capacity over very long periods of time. We refer to such environments as High Throughput Computing (HTC) environments.
Neocortical Cell Migration: GABAergic Neurons and Cells in Layers I and VI Move in a Cyclin-Dependent Kinase 5-Independent Manner

The adult mammalian cerebral cortex arises from a complex series of neuronal migrations. The primitive layer known as the preplate is split into an outer marginal zone and an inner subplate by invading cortical plate neurons in an “inside-out” pattern of layering with respect to time of neuronal origin. In cyclin-dependent kinase 5 (Cdk5)-deficient mice (cdk5−/−), the earliest born cortical neurons split the preplate, but later born neurons arrest below the subplate, resulting in an ectopic “outside-in” layer of neurons normally destined for layers II–V. We have pursued this analysis incdk5−/− ↔ wild-type chimeric mice coupled with experiments in cell culture. In vitromigration assays show no difference in migrational ability between embryonic cdk5−/− and wild-type neurons. In cdk5−/− chimeras, layers I and VI are made up of both mutant and wild-type genotype neurons, whereas layers II–V contain predominantly wild-type cells. In addition, a thin layer of neurons is found below layer VI, made up ofcdk5−/− cells; bromodeoxyuridine labeling suggests that these neurons were destined for layers II–V. Scattered cdk5−/− cells are found throughout layers II–V, but these neurons are always found to be GABAergic. The findings suggest that Cdk5 is not required for migration of either the deepest cortical plate neurons or the GABAergic neurons from the ganglionic eminences. The migration of layer II–V pyramidal neurons, however, is intrinsically blocked by Cdk5 deficiency, thus suggesting that different neuronal cell types use distinct mechanisms of migration.
An Improved EMD Analysis Method for GPR Images

The EMD method can be adopted to analyze the GPR echo image because it is adaptive for non-stationary and non-linear signals. However, the direct wave in GPR signal contains considerable energy which will generate false object, even aggravate the end effect. And the noise in the signal can influence EMD analysis for further characteristics extraction. Aiming at the problems above, an improved EMD analysis method for GPR images is put forward. Above all, the direct wave is suppressed in mean method, and then reducing the noise based on LWT. Finally it should carry on the EMD analysis on the new image. Through contractive experiment, the result shows the improved method can avoid the false information, furthermore, the IMF components contain more precise object information.
Towards a Phylogeny for Coffea (Rubiaceae): identifying well-supported lineages based on nuclear and plastid DNA sequences.

BACKGROUND AND AIMS The phylogenetic relationships between species of Coffea and Psilanthus remain poorly understood, owing to low levels of sequence variation recovered in previous studies, coupled with relatively limited species sampling. In this study, the relationships between Coffea and Psilanthus species are assessed based on substantially increased molecular sequence data and greatly improved species sampling. METHODS Phylogenetic relationships are assessed using parsimony, with sequence data from four plastid regions [trnL-F intron, trnL-F intergenic spacer (IGS), rpl16 intron and accD-psa1 IGS], and the internal transcribed spacer (ITS) region of nuclear rDNA (ITS 1/5.8S/ITS 2). Supported lineages in Coffea are discussed within the context of geographical correspondence, biogeography, morphology and systematics. KEY RESULTS Several major lineages with geographical coherence, as identified in previous studies based on smaller data sets, are supported. Other lineages with either geographical or ecological correspondence are recognized for the first time. Coffea subgenus Baracoffea is shown to be monophyletic, but Coffea subgenus Coffea is paraphyletic. Sequence data do not substantiate the monophyly of either Coffea or Psilanthus. Low levels of sequence divergence do not allow detailed resolution of relationships within Coffea, most notably for species of Coffea subgenus Coffea occurring in Madagascar. The origin of C. arabica by recent hybridization between C. canephora and C. eugenioides is supported. Phylogenetic separation resulting from the presence of the Dahomey Gap is inferred based on sequence data from Coffea.
Jealousy of Trade: International Competition and the Nation-State in Historical Perspective

This collection explores eighteenth-century theories of international market competition that continue to be relevant for the twenty-first century. 'Jealousy of Trade' refers to a particular conjunction between politics and the economy that emerged when success in international trade became a matter of the military and political survival of nations. Today, it would be called 'economic nationalism', and in this book Istvan Hont connects the commercial politics of nationalism and globalization in the eighteenth century to theories of commercial society and Enlightenment ideas of the economic limits of politics. The book begins with an analysis of how the notion of 'commerce' was added to Hobbes' 'state of nature' by Samuel Pufendorf. Hont then considers British neo-Machiavellian political economy after the Glorious Revolution. From there he moves to a novel interpretation of the political economy of the Scottish Enlightenment, particularly of David Hume and Adam Smith, concluding with a conceptual history of nation-state and nationalism in the French Revolution. "Jealousy of Trade" combines political theory with intellectual history, illuminating the past but also considering the challenges of today.
Kinematic Analysis Using Neuro-fuzzy Intelligent Technique for Robotic Manipulator

Inverse Kinematics of robotic manipulators is a complex task. This paper involves the forward and inverse kinematic analysis of three degree-of-freedom robotic manipulator. The inverse kinematic solutions have been achieved using a hybrid combination of Neural Networks and Fuzzy Logic Intelligent. The experimental validation has also been attempted on robotic manipulator to trace a desired trajectory. A comparison drawn on the methods applied shows that the results obtained for inverse kinematics are in reasonable agreement with one another.
Annotation and differential analysis of alternative splicing using de novo assembly of RNAseq data

Genome-wide analyses reveal that more than 90% of multi exonic human genes produce at least two transcripts through alternative splicing (AS). Various bioinformatics methods are available to analyze AS from RNAseq data. Most methods start by mapping the reads to an annotated reference genome, but some start by a de novo assembly of the reads. In this paper, we present a systematic comparison of a mapping-first approach (FaRLine) and an assembly-first approach (KisSplice). These two approaches are event-based, as they focus on the regions of the transcripts that vary in their exon content. We applied these methods to an RNAseq dataset from a neuroblastoma SK-N-SH cell line (ENCODE) differentiated or not using retinoic acid. We found that the predictions of the two pipelines overlapped (70% of exon skipping events were common), but with noticeable differences. The assembly-first approach allowed to find more novel variants, including novel unannotated exons and splice sites. It also predicted AS in families of paralog genes. The mapping-first approach allowed to find more lowly expressed splicing variants, and was better in predicting exons overlapping repeated elements. This work demonstrates that annotating AS with a single approach leads to missing a large number of candidates. We further show that these candidates cannot be neglected, since many of them are differentially regulated across conditions, and can be validated experimentally. We therefore advocate for the combine use of both mapping-first and assembly-first approaches for the annotation and differential analysis of AS from RNAseq data.