Miracle-dz/UniFAR-FTUs huggingface dataset:
On Memory Accelerated Signal Processing within Software Defined Radios

Since J. Mitola's work in 1992, Software Defined Radios (SDRs) have been quite a hot topic in wireless systems research. Though many notable achievements were reported in the field, the scarcity of computational power on general purpose CPUs has always constrained their wide adoption in production environments. If conveniently applied within an SDR context, classical concepts known in computer science as space/time tradeoffs can be extremely helpful when trying to mitigate this problem. Inspired by and building on those concepts, this paper presents a novel SDR implementation technique which we call Memory Acceleration (MA) that makes extensive use of the memory resources available on a general purpose computing system, in order to accelerate signal computation. MA can provide substantial acceleration factors when applied to conventional SDRs without reducing their peculiar flexibility. As a practical proof of this, an example of MA applied in the real world to the ETSI DVB-T Viterbi decoder is provided. Actually MA is shown able to provide, when applied to such Viterbi decoder, an acceleration factor of 10.4x, with no impact on error correction performances of the decoder and by making no use of any other typical performance enhancement techniques such as low level (Assembler) programming or parallel computation, which though remain compatible with MA. Opportunity for extending the MA approach to the entire radio system, thus implementing what we call a Memory-Based Software Defined Radio (MB-SDR) is finally considered and discussed.
Modeling the Interference Generated from Car Base Stations towards Indoor Femto-Cells

In future wireless networks, a significant number of users will be vehicular. One promising solution to improve the capacity for these vehicular users is to employ moving relays or car base stations. The system forms cell inside the vehicle and then uses rooftop antenna for back-hauling to overcome the vehicular penetration loss. In this paper, we develop a model for aggregate interference distribution generated from moving/parked cars to indoor users in order to study whether indoor femto-cells can coexist on the same spectrum with vehicular communications. Since spectrum authorization for vehicular communications is open at moment, we consider two spectrum sharing scenarios (i) communication from mounted antennas on the roof of the vehicles to the infrastructure network utilizes same spectrum with indoor femto-cells (ii) in-vehicle communication utilizes same spectrum with indoor femto-cells while vehicular to infrastructure (V2I) communication is allocated at different spectrum. Based on our findings we suggest that V2I and indoor femto-cells should be allocated at different spectrum. The reason being that mounted roof-top antennas facing the indoor cells generate unacceptable interference levels. On the other hand, in-vehicle communication and indoor cells can share the spectrum thanks to the vehicle body isolation and the lower transmit power levels that can be used inside the vehicle.
Revisiting the 'Dual Welfare State': Sickness, Injury & Unemployment Programs in Two 'Liberal' Regimes

In the 1990s, feminist scholars such as Sainsbury (1993) argued that some countries developed systems of social provision that focused either on social insurance or on social assistance programs, with varying outcomes for men and women. This paper investigates aspects of what Sainsbury called the ‘dual welfare state’, using Canada and New Zealand as case studies. Although both have been labelled as ‘liberal’ or ‘residual’ welfare regimes, the paper focuses on differences in program design for sickness, injury and unemployment that contribute to gendered outcomes. The paper finds that the Canadian programs in these three areas are delivered mainly as social insurance while sickness and employment programs in New Zealand are based on social assistance. Canadian programs exclude many women or pay them less than men, as benefits are based on labour market participation and employment earnings. However, Canadian programs also use the individual as the unit of analysis, providing higher levels of benefits and greater autonomy for partnered women working full-time than do similar programs in New Zealand. This analysis shows that social programs continue to be underpinned by cultural ideas about family and gendered work, about who deserves state assistance, and what role the state should play in promoting health and wellbeing. Social insurance and social assistance not only lead to gendered outcomes but they also generate different consequences for women in varying circumstances, even within similar types of welfare regimes.
KNOWS: Kognitiv Networking Over White Spaces

The Federal Communications Commission (FCC) has announced that it is willing to consider unlicensed operation in the TV broadcast bands. Compared to the ISM bands, this portion of the spectrum has several desirable properties for robust data communications. However, to make efficient use of this spectrum in a way that is non-disruptive to incumbents, there are a number of challenges that must be handled. For example, an unused portion of the spectrum must be found, and it is likely that its availability will vary over time. To address such challenges, we present KNOWS, a cognitive wireless networking system. KNOWS is a hardware-software platform that includes a spectrum-aware Medium Access Control (MAC) protocol and algorithms to deal with spectrum fragmentation. We describe our prototype and present evaluation results obtained from simulating our MAC protocol. We show that in common scenarios KNOWS accomplishes a remarkable 200% throughput improvement over systems that use a IEEE 802.11 based MAC protocol.
A Priority-Based Dynamic Web Requests Scheduling for Web Servers over Content-Centric Networking

Content-Centric Networking (CCN) facilitates the content distribution for static Web applications due to its in-network caching. However, when it comes to dynamic Web request, the original fair scheduling of Interest packets on the CCN Web server (i.e. without differentiating a new service request and the service requests being processed) increases the mean response time, resulting in a decrease of service performance. To address this problem, we present a Priority-based Dynamic Web Requests Scheduling (called PBDRS) for CCN Web server. We first increase the priority of the subsequent Interest packet requests belonging to dynamic Web requests being processed, based on the first-come-first-served strategy. Then, services with small-size response data are also scheduled to a higher priority, leveraging the shortest remaining processing time (SRPT) scheduling policy. To validate the proposed approach, we implemented the proposed PBDRS mechanism in our existing CCN Web server and conducted the performance evaluation experiments based on a real dataset crawled from three popular dynamic Web sites of China in a trace-driven way. Experimental results indicate that PBDRS outperforms existing fair scheduling approach of Interest packets in terms of the mean response time.
Real-time framework for multimodal human-robot interaction

This paper presents a new framework for multimodal data processing in real-time. This framework comprises modules for different input and output signals and was designed for human-human or human-robot interaction scenarios. Single modules for the recording of selected channels like speech, gestures or mimics can be combined with different output options (i.e. robot reactions) in a highly flexible manner. Depending on the included modules, online as well as offline data processing is possible. This framework was used to analyze human-human interaction to gain insights on important factors and their dynamics. Recorded data comprises speech, facial expressions, gestures and physiological data. This naturally produced data was annotated and labeled in order to train recognition modules which will be integrated into the existing framework. The overall aim is to create a system that is able to recognize and react to those parameters that humans take into account during interaction. In this paper, the technical implementation and application in a human-human and a human-robot interaction scenario is presented.
Improved focused crawling approach for retrieving relevant pages based on block partitioning

Crawlers are software which can traverse the internet and retrieve web pages by hyperlinks. In the face of the large number of websites, traditional web crawlers cannot function well to get the relevant pages effectively. To solve these problems, focused crawlers utilize semantic web technologies to analyze the semantics of hyperlinks and web documents. The focused crawler is a special-purpose search engine which aims to selectively seek out pages that are relevant to a predefined set of topics, rather than to exploit all regions of the web. The main characteristic of focused crawling is that the crawler does not need to collect all web pages, but selects and retrieves only the relevant pages. So the major problem is how to retrieve the maximal set of relevant and quality pages. To address this problem, we have designed a focused crawler which calculates the relevancy of block in web page. The Block is partitioned by VIPS algorithm. Page relevancy is calculated by sum of all block relevancy scores in one page. It also calculates the URL score for identifying whether a URL is relevant or not for a specific topic.
Metabolism of hyperpolarized [1‐13C]pyruvate through alternate pathways in rat liver

The source of hyperpolarized (HP) [13C]bicarbonate in the liver during metabolism of HP [1‐13C]pyruvate is uncertain and likely changes with physiology. Multiple processes including decarboxylation through pyruvate dehydrogenase or pyruvate carboxylase followed by subsequent decarboxylation via phosphoenolpyruvate carboxykinase (gluconeogenesis) could play a role. Here we tested which metabolic fate of pyruvate contributed to the appearance of HP [13C]bicarbonate during metabolism of HP [1‐13C]pyruvate by the liver in rats after 21 h of fasting compared to rats with free access to food. The 13C NMR of HP [13C]bicarbonate was observed in the liver of fed rats, but not in fasted rats where pyruvate carboxylation and gluconeogenesis was active. To further explore the relative fluxes through pyruvate carboxylase versus pyruvate dehydrogenase in the liver under typical conditions of hyperpolarization studies, separate parallel experiments were performed with rats given non‐hyperpolarized [2,3‐13C]pyruvate. 13C NMR analysis of glutamate isolated from the liver of rats revealed that flux from injected pyruvate through pyruvate dehydrogenase was dominant under fed conditions whereas flux through pyruvate carboxylase dominated under fasted conditions. The NMR signal of HP [13C]bicarbonate does not parallel pyruvate carboxylase activity followed by subsequent decarboxylation reaction leading to glucose production. In the liver of healthy well‐fed rats, the appearance of HP [13C]bicarbonate exclusively reflects decarboxylation of HP [1‐13C]pyruvate via pyruvate dehydrogenase. © 2016 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.
Androgens and Breast Cancer in Premenopausal Women1

We investigated the role of androgens in premenopausal breast cancer by comparing serum testosterone, dihydrotestosterone, androstenedione, dehydroepiandrosterone sulfate, progesterone, sex-hormone-binding globulin-binding capacity, and urinary testosterone and androstanediol in 63 women with breast adenocarcinoma and 70 healthy controls of similar age. With variables dichotomized at the 75th percentile, the age-adjusted relative risk was 3.4 (95% confidence interval, 1.6-7.3) for high versus low levels of serum testosterone, 2.1 (0.9-4.8) for urinary testosterone, and 2.5 (1.1-5.9) for serum dihydrotestosterone. We observed no differ ences in other hormones. The strength of the associations changed markedly with increasing time to the onset of the next menses. The risk for testosterone and dihydrotestosterone, which was negligible in women with onset within 5 days of sampling, increased progressively to nearly 10-fold higher than in unstratified data in women with onset 10 days or more after sampling. This study provides arguments in favor of a role for increased androgenic activity in premenopausal breast cancer. It also suggests that unknown factors related to cycle length may be important in modulating the strength of the association with testosterone. The results are discussed also in reference to possible biases and inadequacies in study design.
Incremental transfer learning for object recognition in streaming video

We present a new incremental learning framework for realtime object recognition in video streams. ImageNet is used to bootstrap a set of one-vs-all incrementally trainable SVMs which are updated by user annotation events during streaming. We adopt an inductive transfer learning (ITL) approach to warp the video feature space to the ImageNet feature space, so enabling the incremental updates. Uniquely, the transformation used for the ITL warp is also learned incrementally using the same update events. We demonstrate a semi-automated video logging (SAVL) system using our incrementally learned ITL approach and show this to outperform existing SAVL which uses non-incremental transfer learning.
Frequency Sensitive Competitive Learning for Balanced Clustering on High-dimensional Hyperspheres

Competitive learning mechanisms for clustering in general suffer from poor performance for very high dimensional ( ) data because of “curse of dimensionality” effects. In applications such as document clustering, it is customary to normalize the high dimensional input vectors to unit length, and it is sometimes also desirable to obtain balanced clusters, i.e., clusters of comparable sizes. The spherical kmeans (spkmeans) algorithm, which normalizes the cluster centers as well as the inputs, has been successfully used to cluster normalized text documents in 2000+ dimensional space. Unfortunately, like regular kmeans and its soft EM based version, spkmeans tends to generate extremely imbalanced clusters in high dimensional spaces when the desired number of clusters is large (tens or more). In this paper, we first show that the spkmeans algorithm can be derived from a certain maximum likelihood formulation using a mixture of von Mises-Fisher distributions as the generative model and in fact it can be considered as a batch mode version of (normalized) competitive learning. The proposed generative model is then adapted in a principled way to yield three frequency sensitive competitive learning variants that are applicable to static data and produced high quality and well balanced clusters for high-dimensional data. Like kmeans, each iteration is linear in the number of data points and in the number of clusters for all the three algorithms. We also propose a frequency sensitive algorithm to cluster streaming data. Experimental results on clustering of high-dimensional text data sets are provided to show the effectiveness and applicability of the proposed techniques.
When Na\"ive Bayes Nearest Neighbours Meet Convolutional Neural Networks

Since Convolutional Neural Networks (CNNs) have become the leading learning paradigm in visual recognition, Naive Bayes Nearest Neighbour (NBNN)-based classifiers have lost momentum in the community. This is because (1) such algorithms cannot use CNN activations as input features; (2) they cannot be used as final layer of CNN architectures for end-to-end training , and (3) they are generally not scalable and hence cannot handle big data. This paper proposes a framework that addresses all these issues, thus bringing back NBNNs on the map. We solve the first by extracting CNN activations from local patches at multiple scale levels, similarly to [1]. We address simultaneously the second and third by proposing a scalable version of Naive Bayes Non-linear Learning (NBNL, [2]). Results obtained using pre-trained CNNs on standard scene and domain adaptation databases show the strength of our approach, opening a new season for NBNNs.
Dialog Systems ” class , Spring 2002-TERM PROJECT-Dialog Act Tagging using Memory-Based Learning

We are applying a memory based learning (MBL) algorithm to the task of automatic dialog act (DA) tagging. This work is along the lines of a recent trend that considers MBL as being more appropriate for natural language processing. We did the experiments on the Switchboard corpus, overcome the problem of feature selection and yield results that seem to be better that previous reported results on the same corpus.
Increased Resistance to the Anticellular Effect of Interferon in an Ultraviolet Light-resistant Human Cell Line , UVr-1

Interferon (~, 13 and 7) susceptibility was tested in a human cell line, UVr-t, a u.v. light-resistant variant of RSa cells; the latter have high sensitivity to both u.v. lethality and the cell profileration inhibition (anticellular) effect of human interferon (HuIFN) preparations. UW-1 cells were less sensitive than the parental RSa cells to the inhibitory effects of HuIFN preparations, as measured by cell proliferation and the incorporation of [3H]deoxythymidine and [3H]deoxyadenosine into acid-insoluble cellular material. Nevertheless, UV ~-1 cells exposed to HuIFN showed almost the same enhanced levels of antiviral activity and pppA(2'p5'A)n synthetase activity as similarly treated RSa cells. Further, UVr-1 cells had much the same binding capacity for 125Ilabelled HuIFN-~A. Thus, it seems likely that the variant has an increased resistance to the anticellular effect but not to the antiviral effect of HuIFN preparations. UV r1 cells showed no significant difference from RSa cells in u.v. -induced DNA repair synthesis. However, when a comparison was made between the susceptibility of normal fibroblasts and fibroblasts from patients with Cockayne's syndrome, characterized by an altered u.v. sensitivity but no alteration of DNA repair replication synthesis, the Cockayne's syndrome fibroblasts, CCK-3 and CCK-4, were more susceptible to HuIFN-/~ as judged by cell proliferation and deoxythymidine incorporation tests.
Network Working Group

This document specifies IPv4 compatibility mechanisms that can be implemented by IPv6 hosts and routers. These mechanisms include providing complete implementations of both versions of the Internet Protocol (IPv4 and IPv6), and tunneling IPv6 packets over IPv4 routing infrastructures. They are designed to allow IPv6 nodes to maintain complete compatibility with IPv4, which should greatly simplify the deployment of IPv6 in the Internet, and facilitate the eventual transition of the entire Internet to IPv6.
Estimated glomerular filtration rate at reinitiation of dialysis and mortality in failed kidney transplant recipients.

BACKGROUND Recent observational studies and a controlled trial suggest more favorable outcomes upon later dialysis initiation in chronic kidney disease. The role of estimated glomerular filtration rate (eGFR) in predicting outcome at reinitiation of dialysis in failed kidney transplant recipients is unclear. METHODS Five-year data in a large dialysis organization was linked to the 'Scientific Registry of Transplant Recipients' to identify 747 failed kidney transplant patients with CKD Stage 5, who had restarted dialysis therapy. A propensity score for early (eGFR>10.5 mL/min/1.73 m2) versus late reinitiation of dialysis was fit by logistic regression. The mortality hazard ratio (HR) was estimated across tertiles of the fitted score. RESULTS Patients were 44±14 years old and included 42% women. Male gender {odds ratio (OR), [95% confidence interval (CI)]: 1.82 (1.22-2.73)}, diabetes mellitus [OR: 1.75 (1.14-2.68)] and peripheral vascular disease [OR: 3.55 (1.17-10.77)] were associated with higher odds of early dialysis reinitiation. Each mL/min/1.73 m2 higher eGFR was associated with 6% higher death risk in unadjusted model [HR: 1.06 (1.01-1.11)], and although not significant in fully adjusted models [HR: 1.02 (0.96-1.07)], it was significant in some subgroups including women and younger patients. The death HR of higher eGFR across lowest to highest tertiles of propensity score of early dialysis initiation (corresponding healthiest to sickest patients) were 1.10 (0.98-1.24), 1.00 (0.91-1.10) and 0.99 (0.92-1.07), respectively (P for trend<0.05), indicating a trend toward higher mortality risk with earlier dialysis initiation in the healthiest patients. CONCLUSIONS Earlier return to dialysis therapy in failed kidney transplant patients tends to correlate with worse dialysis survival especially among healthiest and younger patients and women. Additional studies need to verify these findings.
Nonparametric Feature Matching Based Conditional Random Fields for Gesture Recognition from Multi-Modal Video

We present a new gesture recognition method that is based on the conditional random field (CRF) model using multiple feature matching. Our approach solves the labeling problem, determining gesture categories and their temporal ranges at the same time. A generative probabilistic model is formalized and probability densities are nonparametrically estimated by matching input features with a training dataset. In addition to the conventional skeletal joint-based features, the appearance information near the active hand in an RGB image is exploited to capture the detailed motion of fingers. The estimated likelihood function is then used as the unary term for our CRF model. The smoothness term is also incorporated to enforce the temporal coherence of our solution. Frame-wise recognition results can then be obtained by applying an efficient dynamic programming technique. To estimate the parameters of the proposed CRF model, we incorporate the structured support vector machine (SSVM) framework that can perform efficient structured learning by using large-scale datasets. Experimental results demonstrate that our method provides effective gesture recognition results for challenging real gesture datasets. By scoring 0.8563 in the mean Jaccard index, our method has obtained the state-of-the-art results for the gesture recognition track of the 2014 ChaLearn Looking at People (LAP) Challenge.
A Survey on Internet Traffic Classification

Internet traffic classification is remained to be important issue for years due to three major reasons. First, the fact that we cannot limit the number of users connected and obviously there would be continual increase in the number of users connected to the Internet with time. Second, increase in user access speed (due to the development of highly efficient hardware and software technology). Third, there are applications that require more network resources. This survey is done to find out the main problems in the field of Internet traffic classification and to acquire the knowledge about different classification techniques that have significant Impact on the scientific and research community.
Solving Sparse Symmetric Sets of Linear Equations by Preconditioned Conjugate Gradients

An algorithm is described for solving sparse symmetric sets of linear equations whose matrix is positive defimte. An incomplete factonzation is formed and used to precondition the matrix for conjugate gradients and hence greatly reduce the number of iterations needed. The problem of choosing a reasonably accurate factorization without having too many fill-ms is considered. Results of a large number of test runs are presented. Code implementing the algorithm is described briefly.