Miracle-dz/UniFAR-FTUs huggingface dataset:
Fine-Grained Energy-Efficient Consolidation in SDN Networks and Devices

The constant evolution and expansion of the Internet and Internet-related technologies has exposed the limitations of the current networking infrastructures, which are represented by the unsustainable power consumption and low level of scalability. In fact, these infrastructures are still based on the typical, ossified architecture of the TCP/IP paradigm. In order to cope with the Future Internet requirements, recent contributions envisage an evolution towards more programmable and efficient paradigms. In this respect, this paper describes the basic issues, the technical approaches, and the methodologies for the implementation of power management primitives in the context of the emerging Software Defined Networking. In detail, we propose to extend one of the most prominent solutions aimed at increasing networking flexibility, the OpenFlow Protocol, to integrate the energy-aware capabilities offered by the Green Abstraction Layer (GAL). However, the mere introduction of node-level solutions would be of little or no use in the absence of a network-wide management scheme to guarantee inter-operability and effectiveness of the proposed architecture. In this respect, this work also proposes an analytical model for the management of a network with these capabilities. The results will show how our solutions are well suited to provide a scalable and efficient network architecture able to manage the orchestration and consolidation of the available resources.
An agenda for social democracy

The aim of this paper is to restate the case for social democracy and to propose a policy agenda in response to the global financial crisis. The crisis is not a temporary aberration, to be followed by a return to the ‘normality’ of the late 20th century, dominated by the ideology of economic liberalism. Rather the economic and social system that emerges from the global financial crisis will be radically transformed. Social democrats face both new opportunities for reform, and new challenges and constraints resulting from the collapse of the economic order of the last three decades.
On the Interaction between Ultra-High Frequency Measures of Volatility

We analyze several measures of volatility (realized variance, bipower variation and squared daily returns) as estimators of integrated variance of a continuous time stochastic process for an asset price. We use a Multiplicative Error Model to describe the evolution of each measure as the product of its conditional expectation and a positive valued iid innovation. By inserting past values of each measure and asymmetric effects based on the sign of the return in the specification of the conditional expectation, one can investigate the information content of each indicator relative to the others. The results show that there is a directed dynamic relationship among measures, with squared returns and bipower variance interdependent with one another, and affecting realized variance without any feed-back from the latter.
Breast Cancer Progression Modeling Using Static Sample Data ( Supplementary Data )

Mathematically, a cancer progression trajectory can be viewed as a complex manifold with a branching structure embedded in a high-dimensional genomic space. Since only a small fraction of genes may involve in tumor growth and spread, the first step is to identify cancer progression related genes supporting a complex manifold. This problem has been extensively studied in both the statistics and oncology communities in different contexts. One of the most commonly used approaches is correlation analysis. A gene with its expression levels highly correlated with patient survival time is likely to play a role in cancer development. However, correlation analysis can only find genes with linear dependency with survival time. Moreover, by analyzing one gene at a time, correlation analysis ignores possible interactions among genes. In molecular classification, a commonly used approach is to first partition patients into a bad or good prognostic group at a predefined end point (usually 5-years survival or time to metastasis) and then perform feature selection for classification analysis [1]. A major drawback of this approach is that patients with survival time slightly larger or smaller than the end point will be put into two different groups. In order to explore the magnitude information of response variables, we propose to address the problem of selecting cancer progression related genes within the regression framework. The idea is not new, and has been widely used in the statistics and oncology community. There are a number of excellent algorithms exemplified by Lasso [2] and its variants [3, 4] that operate under a linear-model assumption. However, the dependency between gene expression changes and disease progression is unlikely to be linear. Recent results have shown that cancer progression processes can be complicated and can be represented by a phylogenetic tree-like structure [5]. We develop a new feature-selection algorithm for nonlinear regression. The basic idea is to decompose a nonlinear regression problem into a set of linear classification problems and learn the feature relevance within a large-margin framework. It is built logically based on our previous work on feature selection for high-dimensional classification problems [6]. We first develop a new approach to estimating sample responses and prediction errors. We then use a feature weighting strategy to find a feature subspace where an error function is minimized. We formulate it as an optimization problem with a well-defined objective function within the SVM framework, and solve it using an iterative approach. In
Filters or Holt Winters Technique to Improve the Forecasts for USA Inflation Rate

In this study, transformations of SPF inflation forecasts were made in order to get more accurate predictions. The filters application and Holt Winters technique were chosen as possible strategies of improving the predictions accuracy. The quarterly inflation rate forecasts (1975 Q1-2012 Q3) of USA made by SPF were transformed using an exponential smoothing technique- Holt Winters- and these new predictions are better than the initial ones for all forecasting horizons of 4 quarters. Some filters were applied to SPF forecasts (Hodrick- Prescott, Band-Pass and Christiano-Fitzegerald filters), but Holt Winters method was superior. Full sample asymmetric (Christiano-Fitzegerald) and Band-Pass filter smoothed values are more accurate than the SPF expectations only for some forecast horizons.
A Data-driven Approach for Human Pose Tracking Based on Spatio-temporal Pictorial Structure

In this paper, we present a data-driven approach for human pose tracking in video data. We formulate the human pose tracking problem as a discrete optimization problem based on spatio-temporal pictorial structure model and solve this problem in a greedy framework very efficiently. We propose the model to track the human pose by combining the human pose estimation from single image and traditional object tracking in a video. Our pose tracking objective function consists of the following terms: likeliness of appearance of a part within a frame, temporal displacement of the part from previous frame to the current frame, and the spatial dependency of a part with its parent in the graph structure. Experimental evaluation on benchmark datasets (VideoPose2, Poses in the Wild and Outdoor Pose) as well as on our newly build ICDPose dataset shows the usefulness of our proposed method.
Parallel Gradient Descent for Multilayer Feedforward Neural Networks

We present a parallel approach to classification using neural networks as the hypothesis class. Neural networks can have millions of parameters and learning the optimum value of all parameters from huge datasets in a serial implementation can be a very time consuming task. In this work, we have implemented parallel gradient descent to train multilayer feedforward neural networks. Specifically, we analyze two kinds of parallelization techniques: (a) parallel processing of multiple training examples across several threads and (b) parallelizing matrix operations for a single training example. We have implemented a serial minibatch gradient descent algorithm, its parallel multithreaded version (using Pthread library in C++), a BLAS parallelized version and a CUDA implementation on a GPU. All implementations have been compared and analyzed for the speedup obtained across various network architectures and increasing problem sizes. We have performed our tests on the benchmark dataset: MNIST, and finally also compared our implementations with the corresponding implementations in the state-ofthe-art deep learning library: Theano.
Supervised audio source localisation using microphone array

In this paper, we present a supervised method for speaker localisation in an echoic environment using a microphone array. An audio source is located among a set of areas centered on each microphone. Each area is characterized by a multi-dimensional Gaussian Mixture of the propagation channel between the source and several pairs of microphones. The distance between the sensors has been chosen in order to keep using both level difference and phase information between the microphones. The evaluation results show that the algorithm reaches very good localisation rate and does not depend on the speaker.
Literacy in Theory and Practice

Acknowledgements Prolegomenon Introduction Part I. Literacy In Theory: 1. The 'autonomous' model: I. literacy and rationality 2. The 'autonomous' model: II. goody 3. Literacy and linguistics 4. The 'ideological model Part II. Literacy In Theory And Practice: 5. 'Maktab' literacy 6. 'Commercial' literacy Part III. Literacy In Practice: 7. Unesco and radical literacy campaigns 8. Adult literacy campaigns in the UK and the USA Bibliography Index.
Construction of a traffic sign detector based on voting type co-training

In this paper, we propose a method to construct an accurate traffic sign detector with a small number of manual interactions. When using a statistical learning approach, a huge number of training samples should be prepared for constructing an accurate detector. However, in a real environment, traffic signs have various appearances, and their backgrounds vary widely, too. Therefore, it is very difficult and expensive to manually collect all possible views. Co-training is one of the semi-supervised learning techniques, that can collect training samples efficiently and automatically by using multiple classifiers. In this paper, we employ this approach for improving the accuracy of a traffic sign detector with low cost. The main contributions of this paper are the extension of the co-training method by introducing a majority voting scheme, and the introduction of this framework for improving the accuracy of traffic sign detection. By using this voting type co-training, the proposed method gathers traffic sign samples automatically and accurately, and improves the performance of the traffic sign detector. Experimental results showed that the proposed method improved the accuracy of the detector with a maximum F-measure of 0.95 from 0.72.
The Evolution of Motivation and Incentive Systems Research: A Literature Review

Theories on incentive systems and motivation have changed dramatically since the turn of the twentieth century. They started out by viewing the worker as inherently lazy and opposing work which gave birth to piece-rate and cash-bonus systems. Around mid-century, scholars adopt a different view, in which the worker is seen as having different and more complex needs than just money. They find that workers want interesting projects, recognition by co-workers and superiors, and to grow, both in the job and as a person. Cash bonus systems are found to be harmful if improperly organized. Intrinsic motivators are shown to be superior to extrinsic ones and more valued and managers are encouraged to adopt incentive systems that take such motivators into account. Incentive systems can have a big impact on the successful adoption of strategy and a link has been found between the two. The future of this type of research lies within exploring non-monetary rewards in various contexts, designing alternative incentive systems and researching their applicability in business as well as linking application of incentive systems to the evolving research agenda in business research.
The Guardian of the Republic : A conceptual system to detect outliers on Public Contracts

The paper focuses on the processing of the information available in government portals. Nowadays, in an e-Government, common citizens can have access to various contents, such as government decisions, government acts, court decisions, legislation and other information displayed on the official portals. However, the high volume of contents and the way the information is presented raises issues about their utility and validity to the community. The problem is how to process the available information and make it useful for citizens. In our work, we suggest an improvement of the present solutions by proposing a conceptual system to automate the processing of the information, analyze patterns, detect outliers, and send alerts about the deviations found. A preliminary analysis of the problem reveals that the type and the volume of the dataset are important factors to select the more appropriated algorithm in outlier
Right bundle branch block: prevalence, risk factors, and outcome in the general population: results from the Copenhagen City Heart Study.

AIMS To determine the prevalence, predictors of newly acquired, and the prognostic value of right bundle branch block (RBBB) and incomplete RBBB (IRBBB) on a resting 12-lead electrocardiogram in men and women from the general population. METHODS AND RESULTS We followed 18 441 participants included in the Copenhagen City Heart Study examined in 1976-2003 free from previous myocardial infarction (MI), chronic heart failure, and left bundle branch block through registry linkage until 2009 for all-cause mortality and cardiovascular outcomes. The prevalence of RBBB/IRBBB was higher in men (1.4%/4.7% in men vs. 0.5%/2.3% in women, P < 0.001). Significant predictors of newly acquired RBBB were male gender, increasing age, high systolic blood pressure, and presence of IRBBB, whereas predictors of newly acquired IRBBB were male gender, increasing age, and low BMI. Right bundle branch block was associated with significantly increased all-cause and cardiovascular mortality in both genders with age-adjusted hazard ratios (HR) of 1.31 [95% confidence interval (CI), 1.11-1.54] and 1.87 (95% CI, 1.48-2.36) in the gender pooled analysis with little attenuation after multiple adjustment. Right bundle branch block was associated with increased risk of MI with an HR of 1.67 (95% CI, 1.16-2.42) and pacemaker insertion with an HR of 2.17 (95% CI, 1.22-3.86), but not with chronic heart failure (HR 1.37; 95% CI, 0.96-1.94), atrial fibrillation (HR 1.10; 95% CI, 0.73-1.67), or chronic obstructive pulmonary disease (HR 0.99; 95% CI, 0.60-1.62). The presence of IRBBB was not associated with any adverse outcome. CONCLUSION In this cohort study, RBBB and IRBBB were two to three times more common among men than women. Right bundle branch block was associated with increased cardiovascular risk and all-cause mortality, whereas IRBBB was not. Contrary to common perception, RBBB in asymptomatic individuals should alert clinicians to cardiovascular risk.
Binary Weightbook Design for MIMO Beamforming Systems Using Quantized Feedback

We design binary weightbooks for multiple-input multiple-output (MIMO) beamforming systems using quantized feedback based on the Grassmannian beamforming criterion. We show that the Grassmannian criterion for binary weightbook design is to maximize the minimum Hamming distance of the corresponding block code. Thus, a block code that has a large minimum Hamming distance can be used for binary weightbook design for MIMO beamforming systems using quantized feedback. The optimum binary weightbooks when the number of transmit antennas has value from two to five are tabulated.
Design and Performance of the GeneChip ® Human Genome U 133 Plus 2 . 0 and Human Genome U 133 A 2 . 0 Arrays

The GeneChip® Human Genome U133 Plus 2.0 (HG-U133 Plus 2.0) and GeneChip Human Genome U133A 2.0 (HG-U133A 2.0) Arrays represent the latest evolution in the Affymetrix® GeneChip Expression Analysis Platform, offering the most comprehensive tool for the analysis of genome-wide expression on a single array. The HG-U133 Plus 2.0 Array includes all of the probe sets from the previous generation HG-U133A and HG-U133B Arrays, as well as nearly 10,000 additional probe sets. The increased feature density of the HG-U133 Plus 2.0 Array offers researchers the ability to measure transcription over the entire human genome in a single hybridization. Greater than 54,000 probe sets are used to analyze the expression level of more than 47,000 transcripts and variants, including approximately 38,500 well-characterized human genes. The HG-U133A 2.0 Array also leverages this high-density design to include the content of the previous HG-U133A Array on a smaller format for more efficient analysis. More than 22,000 probe sets are employed to analyze the expression level of over 18,400 transcripts and variants, including more than 14,500 well-characterized human genes. Extensive testing on both arrays revealed that data from the HG-U133A Array are similar to data generated on the higher density HG-U133 Plus 2.0 and HG-U133A 2.0 Arrays. In terms of assay sensitivity, control spikes were routinely detected in a complex background at a transcript concentration of one copy per 200,000. Technical Note GeneChip Human Genome U133 Plus 2.0
High-speed and wide-tuning-range LC frequency dividers

Wide tuning-range frequency dividers based on an LC resonator are proposed for high-speed transceivers. The proposed circuit is fabricated using a two-metal and 0.5-/spl mu/m-gate CMOS process and realizes minimum and the maximum frequencies of 7.74 GHz and 8.16 GHz, respectively, with a power consumption of 2.2 mW at a supply voltage of 0.9 V. To enlarge a tuning range without increasing power consumption, the LC divider with MOS varactors is also fabricated using the same process, where the minimum and the maximum frequencies are 1.49 GHz and 1.97 GHz, respectively, with a power consumption of 0.87 mW at a supply voltage of 1.2 V. Finally, application to a phase-locked loop of the proposed circuit is described.
Learning diagnosis profiles through semi-supervised gradient descent of hidden Markov models

In this paper, we consider the problem of adapting the model of a diagnosis-helping module, which interacts with human experts. The approach consists of enforcing strong semantics in the model, so that this interaction may be as intuitive as possible. When learning the model, the problem consists in respecting these semantics while learning with few data. We addressed this problem through a semisupervised gradient descent algorithm applied to partially observable Markov models with fuzzy observations. This method optimizes several criteria at once, guiding the search to a compromise between the expert's directives and objective evaluations. This method has been successfully applied to a tele-medicine application where the system monitors dialyzed patients and alerts nephrologists.
Fast Adaptive Frame Preprocessing for 3D Reconstruction

Abstract: This paper presents a new online preprocessing strategy to detect and discard ongoing bad frames in video sequences. These include frames where an accurate localization between corresponding points is difficult, such as for blurred frames, or which do not provide relevant information with respect to the previous frames in terms of texture, image contrast and non-flat areas. Unlike keyframe selectors and deblurring methods, the proposed approach is a fast preprocessing working on a simple gradient statistic, that does not require to compute complex time-consuming image processing, such as the computation of image feature keypoints, previous poses and 3D structure, or to know a priori the input sequence. The presented method provides a fast and useful frame pre-analysis which can be used to improve further image analysis tasks, including also the keyframe selection or the blur detection, or to directly filter the video sequence as shown in the paper, improving the final 3D reconstruction by discarding noisy frames and decreasing the final computation time by removing some redundant frames. This scheme is adaptive, fast and works at runtime by exploiting the image gradient statistic of the last few frames of the video sequence. Experimental results show that the proposed frame selection strategy is robust and improves the final 3D reconstruction both in terms of number of obtained 3D points and reprojection error, also reducing the computational time.
Network analysis links genome-wide phenotypic and transcriptional stress responses in a bacterial pathogen with a large pan-genome

Background Bacteria modulate subcellular processes to handle stressful environments. Genome-wide profiling of gene expression (RNA-Seq) and fitness (Tn-Seq) allows two views of the same genetic network underlying these responses. However, it remains unclear how they combine, enabling a bacterium to overcome a perturbation. Results Here we generate RNA-Seq and Tn-Seq profiles in three strains of S. pneumoniae in response to stress defined by different levels of nutrient depletion. These profiles show that genes that change their expression and/or become phenotypically important come from a diverse set of functional categories, and genes that are phenotypically important tend to be highly expressed. Surprisingly, we find that expression and fitness changes rarely occur on the same gene, which we confirmed by over 140 validation experiments. To rationalize these unexpected results we built the first genome-scale metabolic model of S. pneumoniae showing that differential expression and phenotypic importance actually correlate between nearest neighbors, although they are distinctly partitioned into small subnetworks. Moreover, a meta-analysis of 234 S. pneumoniae gene expression studies reveals that essential genes and phenotypically important subnetworks rarely change expression, indicating that they are shielded from transcriptional fluctuations and that a clear distinction exists between transcriptional and phenotypic response networks. Conclusions We present a genome-wide computational/experimental approach that contextualizes changes that occur on transcriptomic and phenomic levels in response to stress. Importantly, this highlights the need to connect disparate response networks, for instance in antibiotic target identification, where preferred targets are phenotypically important genes that would be overlooked by transcriptomic analyses alone.