Miracle-dz/UniFAR-FTUs huggingface dataset:
Constructing Test Instances for Basis Pursuit Denoising

The number of available algorithms for the so-called Basis Pursuit Denoising problem (or the related LASSO-problem) is large and keeps growing. Similarly, the number of experiments to evaluate and compare these algorithms on different instances is growing. In this correspondence, we present a method to produce instances with exact solutions that is based on a simple observation, related to the so-called source condition from sparse regularization.
A Survey of H.264 AVC/SVC Encryption

Video encryption has been heavily researched in the recent years. This survey summarizes the latest research results on video encryption with a special focus on applicability and on the most widely-deployed video format H.264 including its scalable extension SVC. The survey intends to give researchers and practitioners an analytic and critical overview of the state-of-the-art of video encryption narrowed down to its joint application with the H.264 standard suite and associated protocols (packaging/streaming) and processes (transcoding/watermarking).
Another stemmer

In natural language processing, conflation is the process of merging or lumping together nonidentical words which refer to the same principal concept. This can relate both to words which are entirely different in form (e.g., "group" and "collection"), and to words which share some common root (e.g., "group", "grouping", "subgroups"). In the former case the words can only be mapped by referring to a dictionary or thesaurus, but in the latter case use can be made of the orthographic similarities between the forms. One popular approach is to remove affixes from the input words, thus reducing them to a stem; if this could be done correctly, all the variant forms of a word would be converted to the same standard form. Since the process is aimed at mapping for retrieval purposes, the stem need not be a linguistically correct lemma or root (see also Frakes 1982).
Pemphigus Foliaceus.

A case of pemphigus foliaceus clinically resembling dermatitis - herpetiformis is being reported. Patient did not respond to dapsone. Later on the basis of histology a diagnosis of pemphigus foliaceus was established. Cases having over lapping features of pemphigus foliaceus and dermatitis herpetiformis have been reported earlier by other workers.
Tumor-Penetrating Peptides

Tumor-homing peptides can be used to deliver drugs into tumors. Phage library screening in live mice has recently identified homing peptides that specifically recognize the endothelium of tumor vessels, extravasate, and penetrate deep into the extravascular tumor tissue. The prototypic peptide of this class, iRGD (CRGDKGPDC), contains the integrin-binding RGD motif. RGD mediates tumor-homing through binding to αv integrins, which are selectively expressed on various cells in tumors, including tumor endothelial cells. The tumor-penetrating properties of iRGD are mediated by a second sequence motif, R/KXXR/K. This C-end Rule (or CendR) motif is active only when the second basic residue is exposed at the C-terminus of the peptide. Proteolytic processing of iRGD in tumors activates the cryptic CendR motif, which then binds to neuropilin-1 activating an endocytic bulk transport pathway through tumor tissue. Phage screening has also yielded tumor-penetrating peptides that function like iRGD in activating the CendR pathway, but bind to a different primary receptor. Moreover, novel tumor-homing peptides can be constructed from tumor-homing motifs, CendR elements and protease cleavage sites. Pathologies other than tumors can be targeted with tissue-penetrating peptides, and the primary receptor can also be a vascular “zip code” of a normal tissue. The CendR technology provides a solution to a major problem in tumor therapy, poor penetration of drugs into tumors. The tumor-penetrating peptides are capable of taking a payload deep into tumor tissue in mice, and they also penetrate into human tumors ex vivo. Targeting with these peptides specifically increases the accumulation in tumors of a variety of drugs and contrast agents, such as doxorubicin, antibodies, and nanoparticle-based compounds. Remarkably the drug to be targeted does not have to be coupled to the peptide; the bulk transport system activated by the peptide sweeps along any compound that is present in the blood.
Node discovery scheme of DDS using dynamic bloom filters

This paper proposes SDP-Dynamic Bloom, which is an extension of the node discovery scheme in data distribution service (DDS). A node discovery scheme is required to establish communication through discovery of matched endpoints. DDS uses simple discovery protocol (SDP) as its node discovery scheme. SDP works by sending each endpoint information to all endpoints that produce high network traffic in large-scale networks. A node discovery scheme based on dynamic Bloom filters (DBF) is proposed to improve DDS scalability by reducing the number of sent and stored messages in the network. Furthermore, the proposed scheme reduces unnecessary computing time spent restructuring Bloom filters, especially in unstable large-scale network environments. The simulation results show that the proposed scheme improves network latency.
Dynamic Post-Earthquake Image Segmentation with an Adaptive Spectral-Spatial Descriptor

The region merging algorithm is a widely used segmentation technique for very high resolution (VHR) remote sensing images. However, the segmentation of post-earthquake VHR images is more difficult due to the complexity of these images, especially high intra-class and low inter-class variability among damage objects. Herein two key issues must be resolved: the first is to find an appropriate descriptor to measure the similarity of two adjacent regions since they exhibit high complexity among the diverse damage objects, such as landslides, debris flow, and collapsed buildings. The other is how to solve over-segmentation and under-segmentation problems, which are commonly encountered with conventional merging strategies due to their strong dependence on local information. To tackle these two issues, an adaptive dynamic region merging approach (ADRM) is introduced, which combines an adaptive spectral-spatial descriptor and a dynamic merging strategy to adapt to the changes of merging regions for successfully detecting objects scattered globally in a post-earthquake image. In the new descriptor, the spectral similarity and spatial similarity of any two adjacent regions are automatically combined to measure their similarity. Accordingly, the new descriptor offers adaptive semantic descriptions for geo-objects and thus is capable of characterizing different damage objects. Besides, in the dynamic region merging strategy, the adaptive spectral-spatial descriptor is embedded in the defined testing order and combined with graph models to construct a dynamic merging strategy. The new strategy can find the global optimal merging order and ensures that the most similar regions are merged at first. With combination of the two strategies, ADRM can identify spatially scattered objects and alleviates the phenomenon of over-segmentation and under-segmentation. The performance of ADRM has been evaluated by comparing with four state-of-the-art segmentation methods, including the fractal net evolution approach (FNEA, as implemented in the eCognition software, Trimble Inc., Westminster, CO, USA), the J-value segmentation (JSEG) method, the graph-based segmentation (GSEG) method, and the statistical region merging (SRM) approach. The experiments were conducted on six VHR subarea images captured by RGB sensors mounted on aerial platforms, which were acquired after the 2008 Wenchuan Ms 8.0 earthquake. Quantitative and qualitative assessments demonstrated that the proposed method offers high feasibility and improved accuracy in the segmentation of post-earthquake VHR aerial images.
Predicting Web Cache Behavior using Stochastic State-Space Models

Accurate analytical models of Web caches are desirable as they can provide inexpensive ways to make resource provisioning decisions at a cache itself as well as at the Web servers it is servicing. Explicitly modeling a Web cache has two major shortcomings: (i) several simplifying assumptions about the operation of the cache for mathematical tractability resulting in loss of accuracy and (ii) measure ments of phenomena internal to the cache that may not always be available without adding monitoring hooks within the cache. Therefore, in this paper, we turn towards statistical techniques to develop a model that is non-intrusiv e (that is, requires no additions to the cache) and treats the Web cache as a black-box (that is, operates solely by observing readily available inputs/outputs and requires no knowledge about the internals of the cache). Relying on the intuition that the internal dynamics of a cache can be captured by a first-order time-dependent process, we develop a model called SMCP, based on the well-studied linear Gaussian state space model, to observe, characterize, and predict the hit rates at a Web cache. A comparison with time-independent models, including one based on Linear Regression (LR), validates our intuition for the need to employ a time-dependent model. A detailed evaluation shows the efficacy of our model with LRU and LFU, two representative cache replacement policies. In our experiments, SMCP predicts hit ratio within 0.1 (absolute value) of their actual value 77.5% and 65% of the times for LRU and LFU, respectively. Secondly, SMCP captures the timevarying behavior more accurately than done by several time-independent models.
Phosphorylation‐dependent and constitutive activation of Rho proteins by wild‐type and oncogenic Vav‐2

We show here that Vav‐2, a member of the Vav family of oncoproteins, acts as a guanosine nucleotide exchange factor (GEF) for RhoG and RhoA‐like GTPases in a phosphotyrosine‐dependent manner. Moreover, we show that Vav‐2 oncogenic activation correlates with the acquisition of phosphorylation‐independent exchange activity. In vivo, wild‐type Vav‐2 is activated oncogenically by tyrosine kinases, an effect enhanced further by co‐expression of RhoA. Likewise, the Vav‐2 oncoprotein synergizes with RhoA and RhoB proteins in cellular transformation. Transient transfection assays in NIH‐3T3 cells show that phosphorylated wild‐type Vav‐2 and the Vav‐2 oncoprotein induce cytoskeletal changes resembling those observed by the activation of the RhoG pathway. In contrast, the constitutive expression of the Vav‐2 oncoprotein in rodent fibroblasts leads to major alterations in cell morphology and to highly enlarged cells in which karyokinesis and cytokinesis frequently are uncoupled. These results identify a regulated GEF for the RhoA subfamily, provide a biochemical explanation for vav family oncogenicity, and establish a new signaling model in which specific Vav‐like proteins couple tyrosine kinase signals with the activation of distinct subsets of the Rho/Rac family of GTPases.
Fast Human Detection Algorithm Based on Subtraction Stereo for Generic Environment

In this paper, we propose a fast and stable human detection based on subtraction stereo for generic environments. Scanning an input image by detection windows, the numbers and size is controlled by distance obtained from subtraction stereo. A preprocessing phase reduces the effect of noise and shadow, for a better segmentation. This control decrease the computation time, important in real‐time applications, skipping a large number of detection windows. Experimental results show that the proposal is faster and less false detection.
Attitude estimation for small helicopter using extended kalman filter

Accurate attitude estimation is vital for VTOL UAV (vertical take-off and landing unmanned aerial vehicle) systems, including small helicopter. The main focus of present research is to compile the principles of navigation and Kalman filtering, and their application and implementation towards UAV systems. This paper introduces a method based on EKF (Extended Kalman filter) to estimate small helicopter¿s attitude with little drift and low noise, which uses strapdown attitude as state update, and treats the attitude estimated by the bi-vector method as measurement update. By evaluating the EKF method with small helicopter's real ground and flight sensor data, it shows good performance: stable, small error. So it is suitable for small helicopter attitude estimation application.
Reconfigurable UWB Antenna With RF-MEMS for On-Demand WLAN Rejection

A MEMS reconfigurable ultra-wideband (UWB) antenna that rejects on-demand all WLAN signals in the entire 5.15 to 5.825 GHz range (675 MHz bandwidth) is presented. The antenna design, miniaturization procedure, and monolithic integration with the MEMS and biasing network on SiO2 Quartz substrate are described. The integration challenges are addressed and the work is presented in a way that is useful for antenna engineers. A method to vary the rejection bandwidth is also provided. The fabricated prototype is conformal and single-sided. The antenna is measured using a custom-built platform at a university laboratory. Results indicate a successful integration and minimal interference of the MEMS and biasing circuitry with the antenna, paving the road for more integrated reconfigurable antennas on SiO2 using MEMS technology. Such antennas can improve UWB, WLAN and cognitive radio communication links.
Web multimedia information retrieval using improved Bayesian algorithm

The main thrust of this paper is application of a novel data mining approach on the log of user's feedback to improve web multimedia information retrieval performance. A user space model was constructed based on data mining, and then integrated into the original information space model to improve the accuracy of the new information space model. It can remove clutter and irrelevant text information and help to eliminate mismatch between the page author's expression and the user's understanding and expectation. User space model was also utilized to discover the relationship between high-level and low-level features for assigning weight. The authors proposed improved Bayesian algorithm for data mining. Experiment proved that the authors' proposed algorithm was efficient.
Modelling and simulation of reinforced concrete beams Coupled analysis of imperfectly bonded reinforcement in fracturing concrete DIMOSTHENIS

Cracking in reinforced concrete beams is a normal process. It occurs for a small portion of the ultimate load, already in the service state. This kind of local failure in concrete is quantified mainly via crack widths and crack spacing in the tensile zone. Durability, functionality and aesthetics are the main reasons why the aforementioned characteristics should be limited to predefined values. Due to cracking, stiffness redistribution takes place in the flexural member and the reinforced concrete section can no longer be considered to act as a linear elastic, homogeneous material. In addition, the connection between the reinforcement bars and the surrounding concrete is not perfect. A certain slip in the interface between the two materials needs to be considered. Thus, the non-linear constitutive response of concrete and the bond-slip relation need to be incorporated in an accurate model of a reinforced concrete beam under fracture. This thesis aims to develop a finite element model of a beam that is able to describe the interaction between the reinforcement bars and the cracked concrete. Emphasis is given to the parameter of the two predominant length scales in reinforced concrete structures: the local damage in concrete, which comprises the small scale, and the finite length of the reinforcement bars, which accounts for the large scale. For the development of such a 1D model, advanced beam elements were employed, where the effects of the reinforcement bars were homogenized over the concrete cross-section. The implementation and a non-linear FE analysis of the model were performed in a Matlab code. Outputs from that analysis were compared to (i) experimental results from literature, (ii) analytical calculations of a reinforced concrete beam accounting for tension stiffening, (iii) a 1D beam-type model where fracture of concrete was taken into consideration by the stiffness adaptation method, and, (iv) a FE beam-type model considering perfect bond between the constitutive materials developed in the Diana software. The results from the developed model were found to correspond well to the aforementioned methods and the experimental curves. However, further parametric studies are proposed in order to improve the performance of the model.
Modeling of Long Distance Context Dependency in Chinese

Ngram modeling is simple in language modeling and has been widely used in many applications. However, it can only capture the short distance context dependency within an N-word window where the largest practical N for natural language is three. In the meantime, much of context dependency in natural language occurs beyond a three-word window. In order to incorporate this kind of long distance context dependency, this paper proposes a new MI-Ngram modeling approach. The MI-Ngram model consists of two components: an ngram model and an MI model. The ngram model captures the short distance context dependency within an N-word window while the MI model captures the long distance context dependency between the word pairs beyond the N-word window by using the concept of mutual information. It is found that MI-Ngram modeling has much better performance than ngram modeling. Evaluation on the XINHUA new corpus of 29 million words shows that inclusion of the best 1,600,000 word pairs decreases the perplexity of the MI-Trigram model by 20 percent compared with the trigram model. In the meanwhile, evaluation on Chinese word segmentation shows that about 35 percent of errors can be corrected by using the MI-Trigram model compared with the trigram model.
Successive Concave Sparsity Approximation for Compressed Sensing

In this paper, based on a successively accuracy-increasing approximation of the ℓ0 norm, we propose a new algorithm for recovery of sparse vectors from underdetermined measurements. The approximations are realized with a certain class of concave functions that aggressively induce sparsity and their closeness to the ℓ0 norm can be controlled. We prove that the series of the approximations asymptotically coincides with the ℓ1 and ℓ0 norms when the approximation accuracy changes from the worst fitting to the best fitting. When measurements are noise-free, an optimization scheme is proposed that leads to a number of weighted ℓ1 minimization programs, whereas, in the presence of noise, we propose two iterative thresholding methods that are computationally appealing. A convergence guarantee for the iterative thresholding method is provided, and, for a particular function in the class of the approximating functions, we derive the closed-form thresholding operator. We further present some theoretical analyses via the restricted isometry, null space, and spherical section properties. Our extensive numerical simulations indicate that the proposed algorithm closely follows the performance of the oracle estimator for a range of sparsity levels wider than those of the state-of-the-art algorithms.
Measuring PUP Prevalence and PUP Distribution through Pay-Per-Install Services

Potentially unwanted programs (PUP) such as adware and rogueware, while not outright malicious, exhibit intrusive behavior that generates user complaints and makes security vendors flag them as undesirable. PUP has been little studied in the research literature despite recent indications that its prevalence may have surpassed that of malware. In this work we perform the first systematic study of PUP prevalence and its distribution through pay-perinstall (PPI) services, which link advertisers that want to promote their programs with affiliate publishers willing to bundle their programs with offers for other software. Using AV telemetry information comprising of 8 billion events on 3.9 million real hosts during a 19 month period, we discover that over half (54%) of the examined hosts have PUP installed. PUP publishers are highly popular, e.g., the top two PUP publishers rank 15 and 24 amongst all software publishers (benign and PUP). Furthermore, we analyze the who-installs-who relationships, finding that 65% of PUP downloads are performed by other PUP and that 24 PPI services distribute over a quarter of all PUP. We also examine the top advertiser programs distributed by the PPI services, observing that they are dominated by adware running in the browser (e.g., toolbars, extensions) and rogueware. Finally, we investigate the PUP-malware relationships in the form of malware installations by PUP and PUP installations by malware. We conclude that while such events exist, PUP distribution is largely disjoint from malware distribution.
Cellular Automata based Feedback Mechanism in Strengthening biological Sequence Analysis Approach to Robotic Soccer

This paper reports on the application of sequence analysis algorithms for agents in robotic soccer and a suitable representation is proposed to achieve this mapping. The objective of this research is to generate novel better in-game strategies with the aim of faster adaptation to the changing environment. A homogeneous non-communicating multi-agent architecture using the representation is presented. To achieve real-time learning during a game, a bucket brigade algorithm is used to reinforce Cellular Automata Based Classifier. A technique for selecting strategies based on sequence analysis is adopted.
A Current Source Model for CMOS Logic Cells Considering Multiple Input Switching and Stack Effect

This paper presents a current source model (CSM) of a CMOS logic cell, which captures simultaneous switching of multiple inputs while accounting for the effect of internal node voltages of the logic cell. Characterization procedures for various components of the proposed CSM are described and application of the model to output waveform computation is discussed. Experimental results to assess the accuracy and efficiency of the proposed multiple input switching CSM in the context of noise and timing analyses in VLSI circuits are reported.