Miracle-dz/UniFAR-FTUs huggingface dataset:
Image stitching using simplified SIFT

This paper used the improved simplified SIFT feature to image stitching. In order to enhance the robustness, the gradient direction histogram was smoothed using Gaussian filter. The matching method was also improved to eliminate the error matching and many-to-one match. When computing transformation matrix, we used RANSAC algorithm to improve computational accuracy. Finally, two pictures were integrated by the method of fading out. The experimental results show that this method greatly improved the efficiency of splicing.
Detecting Gene Relations from MEDLINE Abstracts

Research in bioinformatics in the past decade has generated a large volume of textual biological data stored in databases such as MEDLINE. It takes a copious amount of effort and time, even for expert users, to manually extract useful information embedded in such a large volume of retrieved data and automated intelligent text analysis tools are increasingly becoming essential. In this article, we present a simple analysis and knowledge discovery method that can identify related genes as well as their shared functionality (if any) based on a collection of relevant retrieved relevant MEDLINE documents. The relative computational simplicity of the proposed method makes it possible to process and analyze large volumes of data in a short time. Hence, it significantly contributes to and enhances a user's ability to discover such embedded information. Two case studies are presented that indicate the usefulness of the proposed method.
Improving classification accuracy using automatically extracted training data

Classification is a core task in knowledge discovery and data mining, and there has been substantial research effort in developing sophisticated classification models. In a parallel thread, recent work from the NLP community suggests that for tasks such as natural language disambiguation even a simple algorithm can outperform a sophisticated one, if it is provided with large quantities of high quality training data. In those applications, training data occurs naturally in text corpora, and high quality training data sets running into billions of words have been reportedly used. We explore how we can apply the lessons from the NLP community to KDD tasks. Specifically, we investigate how to identify data sources that can yield training data at low cost and study whether the quantity of the automatically extracted training data can compensate for its lower quality. We carry out this investigation for the specific task of inferring whether a search query has commercial intent. We mine toolbar and click logs to extract queries from sites that are predominantly commercial (e.g., Amazon) and non-commercial (e.g., Wikipedia). We compare the accuracy obtained using such training data against manually labeled training data. Our results show that we can have large accuracy gains using automatically extracted training data at much lower cost.
In vivo and in vitro biophysical properties of hair cells from the lateral line and inner ear of developing and adult zebrafish

Sound and balance information is detected and processed by sensory hair cells in the auditory and vestibular organs, respectively. The zebrafish represents a potentially powerful model organism in which to investigate sensory encoding by hair cells because of its accessibility for in vivo studies and its pliable genetics. Our current understanding of hair cell biophysics in the developing zebrafish is very limited. In this study, we used in vivo and near‐physiological in vitro recordings to measure basolateral membrane currents, voltage changes and synaptic activity in hair cells in the developing and mature zebrafish. We found that the biophysical profile of lateral line hair cells in the zebrafish changes from the larval to the juvenile stage, and that juvenile neuromasts contain a much higher proportion of mature cells. These results demonstrate the potential of the zebrafish for investigating the mechanisms of signal encoding and transmission by hair cells.
An efficient local region and clustering-based ensemble system for intrusion detection

The dramatic proliferation of sophisticated cyber attacks, in conjunction with the ever growing use of Internet-based services and applications, is nowadays becoming a great concern in any organization. Among many efficient security solutions proposed in the literature to deal with this evolving threat, ensemble approaches, a particular family of data mining, have proven very successful in designing high performance intrusion detection systems (IDSs) resting on the mutual combination of multiple classifiers. However, the strength of ensemble systems depends heavily on the methods to generate and combine individual classifiers. In this thread, we propose a novel design method to generate a robust ensemble-based IDS. In our approach, individual classifiers are built using both the input feature space and additional features exploited from k-means clustering. In addition, the ensemble combination is calculated based on the classification ability of classifiers on different local data regions defined in form of k-means clustering. Experimental results prove that our solution is superior to several well-known methods.
Macroflows and Microflows: Enabling Rapid Network Innovation through a Split SDN Data Plane

In this paper, we empirically quantify inherent limitations of merchant switching silicon, which constrain SDNs' innovation potential. To overcome these limitations, we propose a Split SDN Data Plane (SSDP) architecture -- anew switch architecture which allows rapid network innovation by complementing a cost-effective, yet inflexible, front end merchant silicon switch chip with a deeply programmable co-processor subsystem. We implemented SSDP on a prototype Dell Power Connect platform with a programmable multi-core data plane subsystem. To demonstrate SSDP's potential, we developed diverse real-world cases on the prototype platform. Benchmarking results show that, while delivering on its rapid innovation promise (with significantly shorter turn-around time), a SSDP architecture also provides reasonably high switching rates on deep flow tables.
A weighted ℓ1 minimization algorithm for compressed sensing ECG

Compressive sensing has recently been applied to electrocardiogram (ECG) acquisition and reconstruction with the aim of lowering energy consumption and sampling rates in wireless body area networks for ambulatory ECG monitoring. However, most current methods only adopt a sparse prior on the ECG wavelet representation. In this paper, we propose to further exploit the wavelet representation structure by incorporating two properties in the formulation of the optimization problem: the exponentially decaying magnitude of the detail coefficients across scales and the accumulation of signal energy in the approximation subband. We derive a weighted ℓ1 minimization algorithm, based on a maximum a posteriori (MAP) approach, that leads to a significant reduction in the number of measurements and superior reconstruction performance compared to current CS-based methods with application to wireless ECG systems.
LIGHT MICROSCOPIC IMMUNOCYTOCHEMICAL IDENTIFICATION OF LEUCINE ENKEPHALIN IN HUMAN CLAUSTRUM

Leucine-enkephalin is a potent and naturally-occurring opioid peptide which serves to inhibit other neurotransmitters involved with pain perception, thereby reducing its emotional and physical impact. Nevertheless, there is little data in the literature concerning leucine-enkephalin-immunoreactivity (Leu-enk-ir) in the human claustrum. The objectives of this study were to confirm the existence of leucine-enkephalin immunoreactive neurons and fibers in the human claustrum. Light microscopy was used to describe their morphology and distribution. Samples of claustrum were obtained from the brains of two females (39 and 48 years of age) and two males (27 and 42 years of age). The brains did not show any overt signs of pathology or trauma. Immunoreactivity to Leuenk was assessed via the Avidin-Biotin Complex Method. Light-microscopic analysis confirmed the presence of Leu-enk-ir neurons and fibres in all areas of the human claustrum. The cell bodies varied in shape and size, and were divided into three groups: small, medium and large. The density of immunostaining varied both within and between the cell types, with some neurons, staining more darkly or lightly than others. The large and medium sized cells most likely correspond to claustrocortical projection neurons while the small-sized cells appear to be inhibitory interneurons. It is our hope that these results will be contributed to a better understanding the functions of claustrum, in both health and disease, given its relationship with the development of autism, schizophrenia, Alzheimer disease, Parkinson disease and Huntington disease.
Receiver Coding Gain in DVB-H Terminals Using Application Layer FEC Codes

DVB-H is targeted for broadcasting digital content to handheld devices. The content is generally divided into streaming media or file downloading. In file downloading scenarios there is typically a requirement on a zero error ratio which can be met using either a data carousel or additional forward error correction codes. Both methods however induce a higher energy consumption in the receiver. This paper analyses the energy consumption needed for two different forward error correction codes based on emulator results for typical hardware in a handheld device. The energy used for error correction is compared to the energy used when receiving more carousel rounds in order to meet the zero error ratio requirement. This difference is denoted as receiver coding gain. Additionally, error correction also leads to a reduction in the reception time.
ESAT6-Induced IFNγ and CXCL9 Can Differentiate Severity of Tuberculosis

Background Protective responses against Mycobacterium tuberculosis are dependent on appropriate T cell and macrophage activation. Mycobacterial antigen six kDa early secreted antigenic target (ESAT6) and culture filtrate protein 10 (CFP10) can detect M. tuberculosis specific IFNγ responses. However, most studies have been performed in non-endemic regions and to study pulmonary tuberculosis (PTB). We have studied ESAT6 and CFP10 induced cytokine and chemokines responses in PTB and extrapulmonary (EPul) TB. Methodology IFNγ, IL10, CXCL9 and CCL2 responses were determined using an ex vivo whole blood assay system in PTB (n = 30) and EPulTB patients with limited (LNTB, n = 24) or severe (SevTB, n = 22) disease, and in healthy endemic controls (ECs). Responses to bacterial LPS were also determined. Principal Findings ESAT6- and CFP10-induced IFNγ was comparable between ECs and TB patients. Both ESAT6- and CFP10-induced IFNγ secretion was greater in LNTB than PTB. ESAT6-induced CXCL9 was greater in EPulTB as compared with PTB, with an increase in SevTB as compared with LNTB. CFP10-induced CCL2 was higher in PTB than LNTB patients. LPS-stimulated CXCL9 was greatest in SevTB and LPS-induced CCL2 was increased in PTB as compared with LNTB patients. A positive correlation between ESAT6-induced IFNγ and CXCL9 was present in all TB patients, but IFNγ and CCL2 was only correlated in LNTB. ESAT-induced CCL2 and CXCL9 were significantly associated in LNTB while correlation in response to LPS was only present in SevTB. Conclusions ESAT6 induced IFNγ and CXCL9 can differentiate between limited and severe TB infections.
Description of three new caddisfly species from Mayotte Island, Comoros Archipelago (Insecta: Trichoptera)

We report five new species records from the Comoros Archipelago. Two of the species are known from outside the Archipelago, Hydroptila cruciata Ulmer (Hydroptilidae) and Anisocentropus voeltzkowi Ulmer (Calamoceratidae), and three species are described as new to science: Pisulia stoltzei ,new species (Pisuliidae), and: Chimarra mayottensis, new species and Chimarra koulaeensis, new species (Philopotamidae). Five species have been previously recorded from the Comoros Islands: Cheumatopsyche comorina (Navas), Macrostemum capense (Walker), Cheumatopsyche vala Malicky (Hydropsychidae), Hydroptila voticia Malicky (Hydroptilidae), and Oecetis atpomarus Malicky (Leptoceridae). With this report the number of species from the Comoros is doubled. These findings also represent the first records of Trichoptera from Mayotte.
Entropies and Scaling Exponents of Street and Fracture Networks

Many natural and man-made lineaments form networks that can be analysed through entropy and energy considerations. Here we report the results of a detailed study of the variations in trends and lengths of 1554 named streets and 6004 street segments, forming a part of the evolving street network of the city of Dundee in East Scotland. Based on changes in the scaling exponents (ranging from 0.24 to 3.89), the streets can be divided into 21 populations. For comparison, we analysed 221 active crustal fractures in Iceland that (a) are of similar lengths as the streets of Dundee; (b) are composed of segments; and (c) form evolving networks. The streets and fractures follow power-law size distributions (validated through various statistical tests) that can be partly explained in terms of the energies needed for their formation. The entropies of the 21 street populations and 9 fracture populations show strong linear correlations with (1) the scaling exponents (R 2 = 0.845-0.947 for streets, R 2 = 0.859 for fractures) and with (2) the length ranges, that is, the differences between the longest and shortest streets/fractures, (R 2 = 0.845-0.906 for streets, R 2 = 0.927 for fractures).
WHITTLE ESTIMATION OF EXPONENTIAL VOLATILITY MODELS

The strong consistency and asymptotic normality of the Whittle estimate of the parameters in a class of exponential volatility processes are established. Among many models of interest, this class includes one-shock models, such as the EGARCH model of Nelson (1991), and two-shock models, such as the SV model of Taylor (1986). The variable of interest might not have finite fractional moment of any order and so, in particular, finite variance is not imposed. We allow for a wide range of degrees of persistence of shocks to conditional variance, allowing for both short and long memory. A detailed Monte-Carlo exercise shows the small-sample properties of the estimator. We present an empirical application using the Standard & Poor’s 500 composite stock index.
Phantm: PHP analyzer for type mismatch

We present Phantm, a static analyzer that uses a flow-sensitive analysis to detect type errors in PHP applications. Phantm can infer types for nested arrays, and can leverage runtime information and procedure summaries for more precise results. Phantm found over 200 true problems when applied to three applications with over 50'000 lines of code, including the popular DokuWiki code base.
A study of speech coding parameters in speech recognition

Speech recognition over different transmission channels will set demands to the parametric encoded/decoded speech. The effects of different types of noise have been studied a lot and the effects of the parameterization process in speech has been known to cause degradation in decoded speech when compared to the original speech. But does the encoding/decoding process modify the speech so much that it will cause degradation in the speech recognition result? If it does what may cause the speech recognition degradation? We have studied the effect of the parameterization and the causes of the nine different codec configurations to isolated word recognition.
Precolouring in compressive spectrum estimation for cognitive radio

One of the major challenges in cognitive radio (CR) networks is the need to sample signals as efficiently as possible without incurring the loss of vital information. Compressive Sensing (CS) is a new sampling paradigm which provides a theoretical framework for sub-sampling signals which are characterized as being sparse in the frequency domain. The random demodulator (RD) is a CS-based architecture which has been employed to acquire frequency sparse, bandlimited signals which typify the signals which often occur in many CR-related applications. This paper investigates the impact of precolouring upon CS performance by combining the RD with an autoregressive (AR) filter model to enhance compressive spectral estimation. Quantitative results with quadrature phased shift keying (QPSK) modulated multiband signals, corroborate that adopting a precolouring strategy both reduces the spectral leakage in the power spectrum, and concomitantly improves the overall signal-to-noise ratio (SNR) performance of the compressive spectrum estimator.
Deployment Strategies for Distributed Applications on Cloud Computing Infrastructures

Cloud computing enables on-demand access to a shared pool of IT resources. In the case of Infrastructure as a Service (IaaS), the cloud user typically acquires Virtual Machines (VMs) from the provider. It is up to the user to decide at what time and for how long they want to use these VMs. Because of the pay-per-use nature of most clouds, there is a strong incentive to use as few resources as possible and release them quickly when they are no longer needed. Every step of the deployment process, i.e., acquiring VMs, creating network links, and installing, configuring and starting software components on them, should therefore be as fast as possible. The amount of time the deployment process takes can be influenced by the user by performing some steps in parallel or using timing knowledge of previous deployments. This paper presents four different strategies for deploying applications on cloud computing infrastructures. Performance measurements of application deployments on three public IaaS clouds are used to show the speed differences between these strategies.
Password Rehearsal Memory Games

As increasingly personal and sensitive information gets stored electronically, there is a growing need for secure mechanisms to protect access to this information. Today, the most common userfacing technique is text passwords. Unfortunately, analysis of user behaviour suggests that most people engage in poor security practices with respect to their passwords. For instance, it is estimated that the average password is reused between approximately six websites [1]. Additionally, research has shown that the task of creating passwords that are both secure and memorable is challenging for users [2]. In spite of these findings, Bonneau and Schechter found that with enough practice “most users can memorize strong cryptographic secrets” [3].
Priming Immunization with DNA Augments Immunogenicity of Recombinant Adenoviral Vectors for Both HIV-1 Specific Antibody and T-Cell Responses

Background Induction of HIV-1-specific T-cell responses relevant to diverse subtypes is a major goal of HIV vaccine development. Prime-boost regimens using heterologous gene-based vaccine vectors have induced potent, polyfunctional T cell responses in preclinical studies. Methods The first opportunity to evaluate the immunogenicity of DNA priming followed by recombinant adenovirus serotype 5 (rAd5) boosting was as open-label rollover trials in subjects who had been enrolled in prior studies of HIV-1 specific DNA vaccines. All subjects underwent apheresis before and after rAd5 boosting to characterize in depth the T cell and antibody response induced by the heterologous DNA/rAd5 prime-boost combination. Results rAd5 boosting was well-tolerated with no serious adverse events. Compared to DNA or rAd5 vaccine alone, sequential DNA/rAd5 administration induced 7-fold higher magnitude Env-biased HIV-1-specific CD8+ T-cell responses and 100-fold greater antibody titers measured by ELISA. There was no significant neutralizing antibody activity against primary isolates. Vaccine-elicited CD4+ and CD8+ T-cells expressed multiple functions and were predominantly long-term (CD127+) central or effector memory T cells and that persisted in blood for >6 months. Epitopes mapped in Gag and Env demonstrated partial cross-clade recognition. Conclusion Heterologous prime-boost using vector-based gene delivery of vaccine antigens is a potent immunization strategy for inducing both antibody and T-cell responses. Trial Registration ClinicalTrails.gov NCT00102089, NCT00108654
Secreted cathepsin L generates endostatin from collagen XVIII

Endostatin, an inhibitor of angiogenesis and tumor growth, was identified originally in conditioned media of murine hemangioendothelioma (EOMA) cells. N‐terminal amino acid sequencing demonstrated that it corresponds to a fragment of basement membrane collagen XVIII. Here we report that cathepsin L is secreted by EOMA cells and is responsible for the generation of endostatin with the predicted N‐terminus, while metalloproteases produce larger fragments in a parallel processing pathway. Efficient endostatin generation requires a moderately acidic pH similar to the pericellular milieu of tumors. The secretion of cathepsin L by a tumor cell line of endothelial origin suggests that this cathepsin may play a role in angiogenesis. We propose that cleavage within collagen XVIII's protease‐sensitive region evolved to regulate excessive proteolysis in conditions of induced angiogenesis.
A systematic approach to multistage detectors in multipath fading channels

We consider linear multistage detectors with universal (large system) weighting for synchronous code-division multiple access (CDMA) in multipath fading channels with many users. A convenient choice of the basis of the projection subspace allows a joint projection of all users. Taking advantage of this property, the complexity per bit of multistage detectors with universal weights scales linearly with the number of users on the uplink CDMA channel, while other known multistage detectors with universal weights and different bases of the projection subspace keep the same quadratic complexity order per bit as the linear minimum mean-square error (LMMSE) detector. We focus on the design of two kinds of detectors with linear complexity. The detector of Type I is obtained as an asymptotic approximation of the polynomial expansion detector proposed by Moshavi et al. The detector of Type II has the same performance as the multistage Wiener filter (MSWF) in large systems. Additionally, general performance expressions for large systems, applicable to any multistage detector with the same basis of the projection subspace (e.g., linear parallel interference canceling detectors), are derived. As a by-product, the performance analysis disproves the widespread belief that the MSWF and the polynomial expansion detector are equivalent. We show that, in general, the MSWF outperforms the latter one and they are equivalent only asymptotically in the case of equal received powers.
Malnutrition in Patients with Chronic Kidney Disease

Malnutrition is a major issue in patients with chronic kidney disease (CKD), adversely affecting morbidity, mortality, functional activity and patients’ quality of life. Our knowledge of the pathogenic mechanisms of malnutrition in patients with CKD, including endstage renal disease, has been improved. This has led to the development of clinical practice guidelines for nutritional care in CKD which provide a framework for the nutritional issues facing patients and physiccians. Extensive research in the field of nutrition in patients with CKD has resulted in the formation of general guidelines, although some uncertainties still exist on some of the best therapeutic or preventive options in uremic malnutrition. It is important to search actively for malnutrition since early diagnosis and treatment can improve the prognosis for CKD patients and reduce the monetary costs connected with treatment.