Miracle-dz/UniFAR-FTUs huggingface dataset:
Multichannel Fast QR-Decomposition Algorithms: Weight Extraction Method and Its Applications

Multichannel fast QR decomposition RLS (MC-FQRD-RLS) algorithms are well known for their good numerical properties and low computational complexity. The main limitation is that they lack an explicit weight vector term, limiting themselves to problems seeking an estimate of the output error signal. This paper presents techniques which allow us to use MC-FQRD-RLS algorithms with applications that previously have required explicit knowledge of the adaptive filter weights. We first consider a multichannel system identification setup and present how to obtain, at any time, the filter weights associated with the MC-FQRD-RLS algorithm. Thereafter, we turn to problems where the filter weights are periodically updated using training data, and then used for fixed filtering of a useful data sequence, e.g., burst-trained equalizers. Finally, we consider a particular control structure, indirect learning, where a copy of the coefficient vector is filtering a different input sequence than that of the adaptive filter. Simulations are carried out for Volterra system identification, decision feedback equalization, and adaptive predistortion of high-power amplifiers. The results verify our claims that the proposed techniques achieve the same performance as the inverse QRD-RLS algorithm at a much lower computational cost.
Parameter synthesis with IC3

Parametric systems arise in different application domains, such as software, cyber-physical systems or tasks scheduling. A key challenge is to estimate the values of parameters that guarantee the desired behaviours of the system. In this paper, we propose a novel approach based on an extension of the IC3 algorithm for infinite-state transition systems. The algorithm finds the feasible region of parameters by complement, incrementally finding and blocking sets of “bad” parameters which lead to system failures. If the algorithm terminates we obtain the precise region of feasible parameters of the system. We describe an implementation for symbolic transition systems with linear constraints and perform an experimental evaluation on benchmarks taken from the domain of hybrid systems. The results demonstrate the potential of the approach.
Effect of pH on optical properties during blood coagulation progress by optical coherence tomography

Optical coherence tomography (OCT) has been used to evaluate blood coagulation process and is proved efficiently by using OCT signal slope and 1/e light penetration depth (D1/e). The purpose of this study is to investigate the effect of pH on the two optical properties of whole blood during in vitro clot formation under static by optical coherence tomography. After the addition of 0.9% (w/v) sodium chloride solution with pH=6, 7, 8, 9, OCT signal slope (OCTSS) have a total decrease of 6.98%, 4.07%, 4.16%, 3.69% after 60-min coagulation, and the blood clotting time are 985, 1041, 1065, 967, respectively. Our results suggest that when blood pH was lower or higher than normal value, the blood clotting time shows a decrease and the process of blood coagulation appears abnormal.
High Performance Texture Mapping Architectures

Logic-embedded memories are part of a rapidly emerging technology and offer enormous transfer rates and computing power. This is shown with the example of the TEXRAM, a result of the MONOGRAPH ESPRIT project. We analyze the benefits of new memory technologies in the context of the current market. We show how their use and integration to high-performance graphics systems meet price and performance requirements and determine future market chances. The application of highly integrated memories and image compression enables high-performance texture mapping. The size of the texture memory is considerably reduced and the bandwidth between the memory and texturing unit is lowered. We present alternative solutions ranging from low-cost hardware macrocells to high-end texture mapping architectures integrating logic-enhanced memories.
Providing a Service for Interactive Online Decision Aids through Estimating Consumers' Incremental Search Benefits

Consumer information search has been a focus of research nowadays, especially in the context of online business environments. One of the research questions is to determine how much information to search (i.e., when to stop searching), since extensive literature on behavior science has revealed that consumers often search either “too little” or “too much”, even with the help of existing interactive online decision aids (IODAs). In order to address this issue, this paper introduces a new approach to IODAs with effective estimation of the incremental search benefits. In doing so, the approach incorporates two important aspects into consideration, namely point estimation and distribution estimation, so as to make use of the relevant information by combining both current and historical facts in reflecting the behavioral patterns of the consumers in search. Moreover, experiments based on data provided by Netflix illustrate that the proposed approach is effective and advantageous over existing ones.
A control method to improve the efficiency of a soft- switching non-isolated bidirectional DC-DC converter for hybrid and plug-in electric vehicle applications

Hybrid energy storage system (HESS) can be adopted in hybrid, plug-in hybrid, and pure electric vehicles (HEV, PHEV, and EV), where a bidirectional DC-DC converter (BDC) is used to connect batteries and ultracapacitors. The efficiency improvement of the BDC is beneficial to increase the efficiency viability of HESS. Due to ZVS, high efficiency can be obtained at heavy load operations while the efficiency is low at light load operations mainly because of the conduction losses of the auxiliary circuits. These losses can be reduced by optimising the switching frequency. The relationship of efficiency and switching frequency are presented and discussed. A scaleddown 1 kW BDC prototype is built to verify the feasibility of the efficiency improvement. With the aim of achieving ZVS conditions and variable frequency control, the implementing method is proposed. The simulated results are also presented, which can validate the feasibility of the proposed control method.
Conflict Resolution for On-the-fly Change Propagation in Business Processes

Process models are widely used in organisations and can easily become large and complex. In the context of business process management, views are a useful technique to reduce complexity by providing only those process fragments that are relevant for a particular stakeholder. A key challenge in view management is the handling of changes that are performed concurrently by different stakeholders. Since the views may refer to the same process, the performed changes may affect the same region of a business process and cause a conflict. Many approaches have been proposed for resolving conflicts in a post-analysis phase after all changes have been applied. They can be become costly when dealing with multiple changes that lead to multiple conflicts which cannot be resolved automatically and require an additional negotiation phase between stakeholders. In this paper we propose a framework for the on-the-fly conflict resolution of changes that have been performed on views their underlying reference process. Different to existing approaches this framework applies behaviour consistency rules for business processes which consider the execution semantics and can be checked efficiently on the structure of processes without generating all possible execution traces or keeping track of change operations.
Learning image manifold using neighboring similarity integration

The perspective of image manifold and associated manifold learning methods have demonstrated promising results in finding the underlying structure from images in the high dimensional space. Conventional manifold learning methods construct the similarity relationship of image set only based on the pairwise Euclidean distance of images, so they may obtain deceptive similarity and suffer performance degradation. In this paper, we present an Neighboring Similarity Integration(NSI) algorithm to explore image manifold under probability preserving principle. NSI is based on the neighboring similarity of image samples and the local structures of image manifold, and can increases the estimation accuracy of similarity and enhance the learning ability for image manifold. The experimental results of image visualization problem on Yale and MNIST databases are presented to demonstrate the effectiveness of the proposed method.
Algebraic and Geometric Methods in Statistics: Contingency tables

This up-to-date account of algebraic statistics and information geometry explores the emerging connections between the two disciplines, demonstrating how they can be used in design of experiments and how they benefit our understanding of statistical models, in particular, exponential models. This book presents a new way of approaching classical statistical problems and raises scientific questions that would never have been considered without the interaction of these two disciplines. Beginning with a brief introduction to each area, using simple illustrative examples, the book then proceeds with a collection of reviews and some new results written by leading researchers in their respective fields. Part III dwells in both classical and quantum information geometry, containing surveys of key results and new material. Finally, Part IV provides examples of the interplay between algebraic statistics and information geometry. Computer code and proofs are also available online, where key examples are developed in further detail.
QUASID : Quantitative Spatial Interaction Design 1 Project

2a Summary Although the experimental validation of spatial interaction techniques is gaining interest, many virtual reality researchers still consider it an 'art' rather than a 'science'. In the design cycle of spatial interaction techniques, which contains phases of concept formulation, implementation and evaluation, the evaluation part is most often addressed in a qualitative way, rather than being based on firm quantitative evidence, [61]. In those cases where evaluation of spatial interaction techniques is performed, the experimental setup is usually derived from ad-hoc assumptions about the relevance of the task, the environment and other conditions that have an effect on the outcome. This multidisciplinary project has two main objectives. First, to develop a more quantitative approach to the design of spatial interaction techniques. Second, to apply and test this quantitative approach in the design of new spatial interaction techniques for mixed-reality desktop environments. The proposed project will therefore combine research at three complementary levels: i) at the methodological level, by developing classifications and metrics that can be used in the evaluation of spatial interaction techniques for desktop mixed-reality environments, ii) at the device level, by developing robust and effective methods for creating tangible interfaces, and by evaluating the effect of the physical shape of interaction devices on interaction efficiency and effectiveness, iii) at the system level, by studying the usability of tangible interfaces that are based on both (fully) manual and semi-automated spatial interactions. The project is multidisciplinary , in the sense that both a technology-oriented (computer science) and a user-oriented (human factors) perspective are represented. The project is feasible because the applicants introduce complementary perspectives and expertise in visualization, image processing and user-centered design.
Programmed cell death 4 (PDCD4) expression during multistep Barrett's carcinogenesis

Aim To test the contribution of programmed cell death 4 (PDCD4) tumour suppressor gene in Barrett's carcinogenesis. Methods PDCD4 immunohistochemical expression was assessed in 88 biopsy samples obtained from histologically proven long-segment Barrett's mucosa (BM; 25 non-intestinal columnar metaplasia, 25 intestinal metaplasia (IM), 16 low-grade intraepithelial neoplasia (LG-IEN), 12 high-grade IEN (HG-IEN) and 10 Barrett's adenocarcinoma (BAc)). As controls, 25 additional samples of native oesophageal mucosa (N) were obtained from patients with dyspepsia. To further support the data, the expression levels of miR-21, an important PDCD4 expression regulator, in 14 N, 5 HG-IEN and 11 BAc samples were determined by quantitative real-time PCR analysis. Results PDCD4 immunostaining decreased progressively and significantly with the progression of the phenotypic changes occurring during Barrett's carcinogenesis (p<0.001). Normal basal squamous epithelial layers featured strong PDCD4 nuclear immunoreaction (mostly coexisting with weak–moderate cytoplasmic staining). Non-intestinal columnar metaplasia and intestinal metaplasia preserved a strong nuclear immunostaining; conversely, a significant decrease in PDCD4 nuclear expression was seen in dysplastic (LG-IEN and HG-IEN) and neoplastic lesions. Weak–moderate cytoplasmic immunostaining was evident in cases of LG-IEN, while HG-IEN and BAc samples showed weak cytoplasmic or no protein expression. As expected, miR-21 expression was significantly upregulated in HG-IEN and BAc samples, consistently with PDCD4 dysregulation. Conclusions These data support a significant role for PDCD4 downregulation in the progression of BM to BAc, and confirm miR-21 as a negative regulator of PDCD4 in vivo. Further efforts are needed to validate PDCD4 as a potential prognostic marker in patients with Barrett's oesophagus.
In-band Network Telemetry via Programmable Dataplanes

In-band Network Telemetry (INT) is a new abstraction that allows data packets to query switch-internal state such as queue size, link utilization, and queuing latency. We prototype INT in the recently proposed P4 language using a software switch as the implementation platform. We then show how INT can be used to diagnose performance problems such as intermittent latency spikes. Instructions to replicate our demo are available at http://git.io/sosr15-int-demo
Scalable Rumor Source Detection under Independent Cascade Model in Online Social Networks

This paper studies the problem of identifying rumor source in online social networks in which the spread of information follows the popular Independent Cascade model. In the absence of text information, we develop a monitor based approach to evaluate how likely that a piece of information is actually a rumor. Given the underlying social network structure, a number of monitor nodes are injected into the network whose job is to report the data they receive. Based on observing which of monitors received the information and which did not, we propose a polynomial time algorithm to compute rumor quantifier, a reachability based score for ranking the importance of nodes as the rumor source. Extensive simulation results have shown that, with a reasonable number of monitor nodes and appropriate monitor deployment, our rumor source detection algorithm can recognize rumor source effectively and efficiently.
The Research on Business Incubation Dynamic Network Model Based on Knowledge Transfer

Knowledge transfer rapidly and effectively in the business incubation network, which is significance to improving the survival rate and developing new products of new venture. This paper constructed business incubation network knowledge transfer of dynamic network model, discussed the structural characteristics of business incubation network members constitute a network of groups, it has effect on the knowledge overall level of business incubation and the speed of knowledge transfer, and used system simulation analysis the main parameters of the model.
Using Non-uniform Probability Distribution p* to Improve Identification Performance in Dense RFID Reader Environments

This paper proposes the use of the non-uniform probability distribution function to improve the performance of passive UHF RFID systems with several readers. In a previous work, the authors proposed GDRA, a mechanism based on the NFRA protocol where the readers used a truncated geometric distribution function to select a contention slot, instead of the typical uniform distribution function. Results showed that GDRA increased the RFID system performance but, this improvement could be higher if the optimal non-uniform probability distribution function is used. In This was not possible due to the inability of readers to calculate the number of (interfering) neighboring readers, a necessary parameter to apply it. This work solves this issue, using a mechanism where readers calculate the average number of neighboring readers through a simple formulation extracted from graph theory unit disk applied to wireless sensor networks.
Effect of oxygen plasma etching on graphene studied using Raman spectroscopy and electronic transport measurements

In this paper, we report a study of graphene and graphene field effect devices after their exposure to a series of short pulses of oxygen plasma. Our data from Raman spectroscopy, back-gated field-effect and magnetotransport measurements are presented. The intensity ratio between Raman ‘D’ and ‘G’ peaks, ID/IG (commonly used to characterize disorder in graphene), is observed to initially increase almost linearly with the number (Ne) of plasma-etching pulses, but later decreases at higher Ne values. We also discuss the implications of our data for extracting graphene crystalline domain sizes from ID/IG. At the highest Ne value measured, the ‘2D’ peak is found to be nearly suppressed while the ‘D’ peak is still prominent. Electronic transport measurements in plasma-etched graphene show an up-shifting of the Dirac point, indicating hole doping. We also characterize mobility, quantum Hall states, weak localization and various scattering lengths in a moderately etched sample. Our findings are valuable for understanding the effects of plasma etching on graphene and the physics of disordered graphene through artificially generated defects.
Morphological features for leaf based plant recognition

Although plant recognition has become an increasingly popular research topic, it remains nonetheless a scientific and technical challenge. Besides all the difficulties of classic object recognition, such as illumination, viewpoint and scale variations, plants can additionally exhibit visual changes depending on their age and condition, thus demanding a specialized approach. In this paper, we present two descriptors based on mathematical morphology; the first consists of the computation of morphological covariance on the leaf contour profile and the second is an extension of the recently introduced circular covariance histogram, capturing leaf venation characteristics. The effectiveness of both descriptors has been validated with the ImageClef'12 plant identification dataset.
Epicutaneous Immunotherapy Compared with Sublingual Immunotherapy in Mice Sensitized to Pollen (Phleum pratense)

Background. The aim of this study was to compare the efficacy of epicutaneous immunotherapy (EPIT) to sublingual immunotherapy (SLIT) in a model of mice sensitized to Phleum pratense pollen. Methods. BALB/c mice were sensitized by sub-cutaneous route to pollen protein extract mixed treated for 8 weeks, using sham, EPIT, or SLIT. Measurements involved the serological response and cytokine profile from reactivated splenocytes, plethysmography after aerosol challenge to pollen, cell, and cytokine contents in the bronchoalveolar lavages (BALs). Results. After immunotherapy, sIgE was significantly decreased in the treated groups compared to sham (P < 0.001), whereas sIgG2a increased with EPIT and SLIT (P < 0.001 and P < 0.005 versus sham). Reactivated splenocytes secreted higher levels of Th2 cytokines with sham (P < 0.01). Penh values were higher in sham than EPIT and SLIT. Eosinophil recruitment in BAL was significantly reduced only by EPIT (P < 0.01). Conclusion. In this model of mice sensitized to pollen, EPIT was at least as efficient as SLIT.
Automatic Image Dataset Construction from Click-through Logs Using Deep Neural Network

Labelled image datasets are the backbone for high-level image understanding tasks with wide application scenarios, and continuously drive and evaluate the progress of feature designing and supervised learning models. Recently, the million scale labelled image dataset further contributes to the rebirth of deep convolutional neural network and bypass manual designing handcraft features. However, the construction process of image dataset is mainly manual-based and quite labor intensive, which often take years' efforts to construct a million scale dataset with high quality. In this paper, we propose a deep learning based method to construct large scale image dataset in an automatic way. Specifically, word representation and image representation are learned in a deep neural network from large amount of click-through logs, and further used to define word-word similarity and image-word similarity. These two similarities are used to automatize the two labor intensive steps in manual-based image dataset construction: query formation and noisy image removal. With a new proposed cross convolutional filter regularizer, we can construct a million scale image dataset in one week. Finally, two image datasets are constructed to verify the effectiveness of the method. In addition to scale, the automatically constructed dataset has comparable accuracy, diversity and cross-dataset generalization with manually labelled image datasets.
Genetics of Barley Hooded Suppression

The molecular basis of the barley dominant Hooded (K) mutant is a duplication of 305 bp in intron IV of the homeobox gene Bkn3. A chemical mutagenesis screen was carried out to identify genetical factors that participate in Bkn3 intron-mediated gene regulation. Plants from recurrently mutagenized KK seeds were examined for the suppression of the hooded awn phenotype induced by the K allele and, in total, 41 suK (suppressor of K) recessive mutants were identified. Complementation tests established the existence of five suK loci, and alleles suKB-4, suKC-33, suKD-25, suKE-74, and suKF-76 were studied in detail. All K-suppressed mutants showed a short-awn phenotype. The suK loci have been mapped by bulked segregant analysis nested in a standard mapping procedure based on AFLP markers. K suppressor loci suKB, B, E, and F all map in a short interval of chromosome 7H, while the locus suKD is assigned to chromosome 5H. A complementation test between the four suK mutants mapping on chromosome 7H and the short-awn mutant lks2, located nearby, excluded the allelism between suK loci and lks2. The last experiment made clear that the short-awn phenotype of suK mutants is due to a specific dominant function of the K allele, a function that is independent from the control on hood formation. The suK loci are discussed as candidate participants in the regulation of Bkn3 expression.