Miracle-dz/UniFAR-FTUs huggingface dataset:
Binding equilibrium and kinetics of membrane-anchored receptors and ligands in cell adhesion: Insights from computational model systems and theory

ABSTRACT The adhesion of cell membranes is mediated by the binding of membrane-anchored receptor and ligand proteins. In this article, we review recent results from simulations and theory that lead to novel insights on how the binding equilibrium and kinetics of these proteins is affected by the membranes and by the membrane anchoring and molecular properties of the proteins. Simulations and theory both indicate that the binding equilibrium constant and the on- and off-rate constants of anchored receptors and ligands in their 2-dimensional (2D) membrane environment strongly depend on the membrane roughness from thermally excited shape fluctuations on nanoscales. Recent theory corroborated by simulations provides a general relation between and the binding constant of soluble variants of the receptors and ligands that lack the membrane anchors and are free to diffuse in 3 dimensions (3D).
MAISim: mobile agent malware simulator

One of the problems related to the simulation of attacks against critical infrastructures is the lack of adequate tools for the simulation of malicious software (malware). Malware attacks are the most frequent in the Internet and they pose a serious threat against critical networked infrastructures. To address this issue we developed Mobile Agent Malware Simulator (MAISim). The framework uses the technology of mobile agents and it aims at simulation of various types of malicious software (viruses, worms, malicious mobile code). Moreover it can be flexibly deployed over computer network of an arbitrary information system.
Extreme-ultraviolet Phase-shifting Point-diffraction Interferometer: a Wave-front Metrology Tool with Subangstrom Reference-wave Accuracy

The phase-shifting point-diffraction interferometer ͑PS͞PDI͒ was recently developed and implemented at Lawrence Berkeley National Laboratory to characterize extreme-ultraviolet ͑EUV͒ projection optical systems for lithography. Here we quantitatively characterize the accuracy and precision of the PS͞PDI. Experimental measurements are compared with theoretical results. Two major classes of errors affect the accuracy of the interferometer: systematic effects arising from measurement geometry and systematic and random errors due to an imperfect reference wave. To characterize these effects, and hence to calibrate the interferometer, a null test is used. This null test also serves as a measure of the accuracy of the interferometer. We show the EUV PS͞PDI, as currently implemented, to have a systematic error-limited reference-wave accuracy of 0.0028 waves ͑␭͞357 or 0.038 nm at ␭ ϭ 13.5 nm͒ within a numerical aperture of 0.082.
Voice Activity Detection in Presence of Transient Noise Using Spectral Clustering

Voice activity detection has attracted significant research efforts in the last two decades. Despite much progress in designing voice activity detectors, voice activity detection (VAD) in presence of transient noise is a challenging problem. In this paper, we develop a novel VAD algorithm based on spectral clustering methods. We propose a VAD technique which is a supervised learning algorithm. This algorithm divides the input signal into two separate clusters (i.e., speech presence and speech absence frames). We use labeled data in order to adjust the parameters of the kernel used in spectral clustering methods for computing the similarity matrix. The parameters obtained in the training stage together with the eigenvectors of the normalized Laplacian of the similarity matrix and Gaussian mixture model (GMM) are utilized to compute the likelihood ratio needed for voice activity detection. Simulation results demonstrate the advantage of the proposed method compared to conventional statistical model-based VAD algorithms in presence of transient noise.
COMPACT – A Reclamation Soil Compaction Model Part II. Sensitivity Analysis and Applications

COMPACT, a physically based, event-oriented compaction model, was developed as a management or research tool to evaluate the influence of a surface mining system on compaction of soil material during reclamation. Two systems of area mining reclamation operations were simulated by COMPACT. The first system involved scrapers and bulldozers and the second also included trucks. Scrapers or trucks were used to pick up and deposit the soil material. Bulldozers were then used to shape the site for reclamation. The simulated results were compared with measured results and show how equipment patterns and soil parameters can affect overall soil compaction. This simulation model allows equipment, soil material, and operational parameters to be changed easily so managers and researchers can understand the soil compaction processes at surface mine sites.
DMTCP: Scalable User-Level Transparent Checkpointing for Cluster Computations

As the size of clusters increases, failures are becoming increasingly frequent. Applications must become fault tolerant if they are to run for extended periods of time. We present DMTCP (Distributed MultiThreaded CheckPointing), the first user-level distributed checkpointing package not dependent on a specific message passing library. This contrasts with existing approaches either specific to libraries such as MPI or requiring kernel modification. DMTCP provides fault tolerance through checkpointing. DMTCP transparently checkpoints general cluster computations consisting of many nodes, processes, and threads. DMTCP automatically accounts for TCP/IP sockets, UNIX domain sockets, pipes, ptys (pseudo-terminals), signal handlers, ordinary file descriptors, shared file descriptors, and other operating system artifacts. We demonstrate checkpointing and restart of applications communicating through MPICH2, OpenMPI, and sockets directly. These applications were written with a variety of languages including Fortran, C, C++, and Python. Results show that checkpoint time remains nearly constant as the number of nodes increases.
Zero-Brittleness Size-Effect Method for One-Size Fracture Test of Concrete

This paper proposes a new and less laborious version of the size-effect method for measuring the fracture energy or fracture toughness of concrete as well as other nonlinear fracture characteristics, such as the effective length of fracture process zone or critical crack-tip opening displacement. The size-effect method, based on the size-effect law, is the simplest to carry out because only the maximum loads of specimens need to be measured. No measurements of postpeak deflection and unloading stiffness, nor observations of crack-tip location, are needed; the testing machine need not be very stiff, and there is no need for closed-loop displacement control. The simplicity of the method makes it suitable not only for the laboratory but also for field quality control. In the original version of the size-effect method, notched specimens of different sizes are tested. The proposed new version further simplifies testing by allowing the use of notched specimens of only one size. The idea is to supplement the one-size notched-beam tests with an evaluation of the maximum load value for specimens with a zero-brittleness number. There are two types of methods that will achieve this. In one type of method, zero-brittleness data are obtained by using plastic limit analysis based on the modulus of rupture or compression strength of concrete to calculate the limiting nominal strength for zero specimen size. In the second type of method, zero-brittleness data are obtained by testing the maximum loads of notchless specimens of the same size. The former type leads to simpler calculations but has the drawback that the material strength to calculate the maximum load for zero size depends on specimen geometry. Both types of the zero-brittleness version of the size-effect method are validated by previously reported test data. The proposed method should also be applicable to other quasibrittle materials such as rock, ice, tough ceramics, and brittle composites.
Pseudo-Example Based Iterative SVM Learning Approach for Gender Classification

In order to increase the detection accuracy in gender classification, a pseudo-example based iterative learning approach combining support vector machine (SVM) and active appearance model (AAM) was proposed. AAM was applied to model the original training examples before constructing the SVM classifier. During the current iteration, some pairs of support vectors with different gender were selected randomly and then their AAM parameters were interpolated properly to generate new pseudo face images as candidate examples with new gender feature pattern. Only the candidates that would be classified by the current classifier incorrectly or correctly but with low confidence were selected for the following iterations. The pseudo-examples created in this way complemented the original training examples effectively, and the proposed pseudo-example selecting scheme outperformed the conventional Bootstrap method. Experimental results show that, this iterative learning approach can upgrade the gender detection accuracy stepwise
LIGERO: A light but efficient router conceived for cache-coherent chip multiprocessors

Although abstraction is the best approach to deal with computing system complexity, sometimes implementation details should be considered. Considering on-chip interconnection networks in particular, underestimating the underlying system specificity could have nonnegligible impact on performance, cost, or correctness. This article presents a very efficient router that has been devised to deal with cache-coherent chip multiprocessor particularities in a balanced way. Employing the same principles of packet rotation structures as in the rotary router, we present a router configuration with the following novel features: (1) reduced buffering requirements, (2) optimized pipeline under contentionless conditions, (3) more efficient deadlock avoidance mechanism, and (4) optimized in-order delivery guarantee. Putting it all together, our proposal provides a set of features that no other router, to the best of our knowledge, has achieved previously. These are: (1') low implementation cost, (2') low pass-through latency under low load, (3') improved resource utilization through adaptive routing and a buffering scheme free of head-of-line blocking, (4') guarantee of coherence protocol correctness via end-to-end deadlock avoidance and in-order delivery, and (5') improvement of coherence protocol responsiveness through adaptive in-network multicast support. We conduct a thorough evaluation that includes hardware cost estimation and performance evaluation under a wide spectrum of realistic workloads and coherence protocols. Comparing our proposal with VCTM, an optimized state-of-the-art wormhole router, it requires 50% less area, reduces on-chip cache hierarchy energy delay product on average by 20%, and improves the cache-coherency chip multiprocessor performance under realistic working conditions by up to 20%.
Wind-driven inner-shelf circulation off central Oregon during summer

[1] Velocity measurements from 17 deployments of moored acoustic Doppler current profilers obtained during four summer upwelling seasons are used to describe the cross-shelf divergence of Ekman transport in the inner shelf off Oregon. For each deployment the measured surface and bottom cross-shelf transports were compared with estimates of the theoretical Ekman transports to find the fraction of full theoretical Ekman transport present. In general, in 15 m of water at 1–2 km offshore, measured transport was 25% of the full Ekman transport. Measured transports reached full Ekman transport 5–6 km offshore in 50 m of water. This result indicates that the region of active upwelling marked by the divergence of Ekman transport was limited to a narrow region along the coast. With small wind stress curl and no major headlands in the region, no along-shelf trends in the transport fractions were observed. Average transport fractions at each station were similar from year to year with one exception. The interannual variability seen at this particular site was most likely a result of local along-shelf bathymetric features. In addition, a weak linear relationship was found between the ambient stratification and the fraction of full Ekman transport. Reduced cross-shelf transport occurred at times of decreased stratification. This type of “shutdown” of the inner-shelf cross-shelf circulation has significant biological implications, sequestering production in the nearshore and reducing larval cross-shelf transport.
Context Comparison as a Minimum Cost Flow Problem

Comparing word contexts is a key component of many NLP tasks, but rarely is it used in conjunction with additional ontological knowledge. One problem is that the amount of overhead required can be high. In this paper, we provide a graphical method which easily combines an ontology with contextual information. We take advantage of the intrinsic graphical structure of an ontology for representing a context. In addition, we turn the ontology into a metric space, such that subgraphs within it, which represent contexts, can be compared. We develop two variants of our graphical method for comparing contexts. Our analysis indicates that our method performs the comparison efficiently and offers a competitive alternative to non-graphical methods.
An augmented Lagrangian dual approach for the H-weighted nearest correlation matrix problem

Higham (2002, IMA J. Numer. Anal., 22, 329–343) considered two types of nearest correlation matrix problems, namely the W-weighted case and the H-weighted case. While the W-weighted case has since been well studied to make several Lagrangian dual-based efficient numerical methods available, the H-weighted case remains numerically challenging. The difficulty of extending those methods from the W-weighted case to the H-weighted case lies in the fact that an analytic formula for the metric projection onto the positive semidefinite cone under the H-weight, unlike the case under the W-weight, is not available. In this paper we introduce an augmented Lagrangian dual-based approach that avoids the explicit computation of the metric projection under the H-weight. This method solves a sequence of unconstrained convex optimization problems, each of which can be efficiently solved by an inexact semismooth Newton method combined with the conjugate gradient method. Numerical experiments demonstrate that the augmented Lagrangian dual approach is not only fast but also robust.
Prediction of filtering efficiency for DCT-based image denoising

The task of prediction practical efficiency of filtering on the basis of the discrete cosine transform (DCT) methods is considered. It is shown that it is possible to estimate the MSE values of images to be processed by means of calculation rather simple statistics of DCT coefficients. Moreover, the quasi-optimal value of threshold parameter for DCT filtering methods can be easy evaluated as well. The results are presented for different additive Gaussian noise levels and a set of gray-scale test images. Developed method provides an opportunity to decide is it worth applying filtering or not.
A Comparative Study of Blocking Storage Methods for Sparse Matrices on Multicore Architectures

Sparse Matrix-Vector multiplication (SpMV) is a very challenging computationalkernel, since its performance depends greatly on both the input matrix and theunderlying architecture. The main problem of SpMV is its high demands on memorybandwidth, which cannot yet be abudantly offered from modern commodityarchitectures. One of the most promising optimization techniques for SpMV isblocking, which can reduce the indexing structures for storing a sparse matrix,and therefore alleviate the pressure to the memory subsystem. In this paper, westudy and evaluate a number of representative blocking storage formats on a setof modern microarchitectures that can provide up to 64 hardware contexts. Thepurpose of this paper is to present the merits and drawbacks of each method inrelation to the underlying microarchitecture and to provide a consistentoverview of the most promising blocking storage methods for sparse matrices thathave been presented in the literature.
Privacy preserving high utility mining based on genetic algorithms

In this paper, a GA-based privacy-preserving utility mining method is proposed to delete appropriate transactions for hiding sensitive high utility itemsets from a database. The downward closure property and the pre-large concepts are adopted in the proposed algorithm to reduce the cost of rescanning databases. Experiments are also conducted to evaluate the performance of the proposed approach in execution time and the amount of side-effects.
Cluster analysis of simulated gravitational wave triggers using S-means and constrained validation clustering

The fifth science run of LIGO (S5) has recently been concluded. The data collected over 2 years of the run call for a thorough analysis of the glitches seen in the gravitational wave channels, as well as in the auxiliary and environmental channels. The study presents two new techniques for cluster analysis of gravitational wave burst triggers. Traditional approaches to clustering treat the problem as an optimization problem in an ‘open’ search space of clustering models. However, this can lead to problems by producing models that over-fit or under-fit the data as the search is stuck on local minima. The new algorithms tackle local minima by putting constraints in the search process. S-means looks at similarity statistics of burst triggers and builds up clusters that have the advantage of avoiding local minima. Constrained validation clustering tackles the problem by constraining the search in the space of clustering models that are ‘non-splittable’ models in which the centroids of the left and right child of a cluster (after splitting) are nearest to each other; the region of models that either over-fit or under-fit data (i.e. ‘splittable’ models) can therefore be effectively avoided when assumptions about data are satisfied. These methods are demonstrated by using simulated data. The results on simulated data are promising and the methods are expected to be useful for LIGO S5 data analysis.
Identification of Potential New Protein Vaccine Candidates through Pan-Surfomic Analysis of Pneumococcal Clinical Isolates from Adults

Purified polysaccharide and conjugate vaccines are widely used for preventing infections in adults and in children against the Gram-positive bacterium Streptococcus pneumoniae, a pathogen responsible for high morbidity and mortality rates, especially in developing countries. However, these polysaccharide-based vaccines have some important limitations, such as being serotype-dependent, being subjected to losing efficacy because of serotype replacement and high manufacturing complexity and cost. It is expected that protein-based vaccines will overcome these issues by conferring a broad coverage independent of serotype and lowering production costs. In this study, we have applied the “shaving” proteomic approach, consisting of the LC/MS/MS analysis of peptides generated by protease treatment of live cells, to a collection of 16 pneumococcal clinical isolates from adults, representing the most prevalent strains circulating in Spain during the last years. The set of unique proteins identified in all the isolates, called “pan-surfome”, consisted of 254 proteins, which included most of the protective protein antigens reported so far. In search of new candidates with vaccine potential, we identified 32 that were present in at least 50% of the clinical isolates analyzed. We selected four of them (Spr0012, Spr0328, Spr0561 and SP670_2141), whose protection capacity has not yet been tested, for assaying immunogenicity in human sera. All of them induced the production of IgM antibodies in infected patients, thus indicating that they could enter the pipeline for vaccine studies. The pan-surfomic approach shows its utility in the discovery of new proteins that can elicit protection against infectious microorganisms.
Color Image Segmentation Using MDS-Based Multiresolution Nonlinear dimensionality Reduction Model and Fuzzy C-means Clustering

— We present a new approach for color image segmentation based on multiresolution multidimensional scaling and fuzzy c-means clustering. Multiresolution reduction method reduces the dimensionality of high dimensional data and we demonstrate its performance on a large-scale nonlinear dimensionality reduction problem in texture feature extraction step of unsupervised image segmentation problem. The proposed image segmentation algorithm has two steps. First, a multiresolution optimization strategy, which is used to obtain a nonlinear low-dimensional representation of the texture feature set of an image. Then fuzzy c-means clustering performs the segmentation of the image exploiting the low-dimensional texture feature vector. The proposed method has been successfully applied to the Berkeley segmentation database. The obtained results show the potential of this approach compared to the other state of the art image segmentation algorithms.
Removal of the Eye-Blink Artifacts From EEGs via STF-TS Modeling and Robust Minimum Variance Beamforming

A novel scheme for the removal of eye-blink (EB) artifacts from electroencephalogram (EEG) signals based on a novel space--time--frequency (STF) model of EEGs and robust minimum variance beamformer (RMVB) is proposed. In this method, in order to remove the artifact, the RMVB is provided with a priori information, namely, an estimation of the steering vector corresponding to the point source EB artifact. The artifact-removed EEGs are subsequently reconstructed by deflation. The a priori knowledge, the vector corresponding to the spatial distribution of the EB factor, is identified using the STF model of EEGs, provided by the parallel factor analysis (PARAFAC) method. In order to reduce the computational complexity present in the estimation of the STF model using the three-way PARAFAC, the time domain is subdivided into a number of segments, and a four-way array is then set to estimate the STF-time/segment (TS) model of the data using the four-way PARAFAC. The correct number of the factors of the STF model is effectively estimated by using a novel core consistency diagnostic- (CORCONDIA-) based measure. Subsequently, the STF-TS model is shown to closely approximate the classic STF model, with significantly lower computational cost. The results confirm that the proposed algorithm effectively identifies and removes the EB artifact from raw EEG measurements.
Prioritizing Attention in Analytic Monitoring

While data volumes continue to rise, the capacity of human attention remains limited. As a result, users need analytics engines that can assist in prioritizing attention in this fast data that is too large for manual inspection. We present a set of design principles for the design of fast data analytics engines that leverage the relative scarcity of human attention and overabundance of data: return fewer results, prioritize iterative analysis, and ﬁlter fast to compute less. We report on our early experiences employing these principles in the design and deployment of MacroBase, an open source analysis engine for prioritizing attention in fast data. By combining streaming operators for feature transformation, classiﬁcation, and data summarization, MacroBase provides users with interpretable explanations of key behaviors, acting as a search engine for fast data.