Miracle-dz/UniFAR-FTUs huggingface dataset:
Instruction precomputation with memoization for fault detection

Fault tolerance (FT) has become a major concern in computing systems. Instruction duplication has been proposed to verify application execution at run time. Two techniques, instruction memoization and precomputation, have been shown to improve the performance and fault coverage of duplication. This work shows that the combination of these two techniques is much more powerful than either one in isolation. In addition to performance, it improves the long-lasting transient and permanent fault coverage upon the memoization scheme. Compared to the precomputation scheme, it reduces the long-lasting transient and permanent fault coverage of 10.6% of the instructions, but covers 2.6 times as many instructions against shorter transient faults. On a system with 2 integer ALUs, the combined scheme reduces the performance degradation due to duplication by on average 27.3% and 22.2% compared to the precomputation and memoization-based techniques, respectively, with similar hardware requirements.
Struggling for a normal life: work as an individual self-care management strategy among persons living with non-malignant chronic pain.

BACKGROUND A significant part of the population suffers from non-malignant chronic pain that is not treated by pain specialists. No successful long-term treatment exists. The patients have to deal with their condition in collaboration with health personnel establishing treatment programmes under uncertain circumstances with few guidelines. Thus, there is a strong need for knowledge on how patients with chronic non-malignant pain manage their condition. OBJECTIVES The aim of the study was to explore how patients with chronic non-malignant pain deal with their condition. METHODS Twenty patients with chronic non-malignant pain (aged 26-63 in year 2006) told in an open-ended interview situation, how they lived with and dealt with their condition. The interviews were analysed within a phenomenological meaning condensation framework. RESULTS For all patients the pain was as an integrated part of their life that required huge efforts to cope with. Typically, the patients experienced loneliness, fear of stigmatization and despair because of their unpredictable condition, and they wanted to come back to a normalized daily life, first and foremost by getting back to paid work. CONCLUSIONS In general, the patients developed individual strategies that were influenced by their local contexts and life situation as well as the pain itself. This may be interpreted in line with Bourdieu's notions of habitus, strategies and social capital.
Unpacking and Transforming Feature Functions: New Ways to Smooth Phrase Tables

State of the art phrase-based statistical machine translation systems typically contain two features which estimate the “forward” and “backward” conditional translation probabilities for a given pair of source and target phrase. These two “relative frequency” (RF) features are derived from three counts: the joint count of the source and target phrase and their marginal counts. We propose to “unpack” these three statistics, making them independent “3-count” features instead of two RF features. In our experiments, the 3-count features perform better than the RF ones in three of four systems we tested. By transforming and generalizing these 3-count features slightly, further improvements are obtained. Furthermore, under several different experimental conditions, we compare 3-count and generalized 3-count features to new features derived from Kneser-Ney smoothing, to a new low-frequency penalty feature, and to several known smoothing/discounting schemes. Generalized 3-count performs similarly to or better than all of the smoothing methods except modified Kneser-Ney. In our experiments, the best phrase table (not language model) smoothing yields +0.6-1.4 BLEU.
Perceptual hashing for SAR image segmentation

ABSTRACT Image segmentation is an important step in the implementation of the interpretation of synthetic aperture radar (SAR) image due to speckle. This article proposes a SAR image segmentation method based on perceptual hashing. The new algorithm is divided into two phases. The first phase is to obtain initial regions with multi-thresholding based on histogram after reducing the speckle noise. The initial regions are used as input data. And the next phase is to merge regions according to the similarity between regions. In this phase, to segment SAR image effectively, the proposed hashing algorithm is used to obtain hash value and similarity between regions, which preserve the texture features of SAR images. In addition, we can obtain a smooth segmentation result by reducing the redundant information with principal component analysis. Furthermore, morphological methods are used to eliminate the uneven background in the segmentation results. These improvements make our algorithm more effective to segment the images with high speed. The experimental results of four real and one synthetic SAR images verify the efficiency of our algorithm.
KURT KONOLIGE : SPARSE SPARSE BUNDLE ADJUSTMENT 1 Sparse Sparse Bundle Adjustment

Sparse Bundle Adjustment (SBA) is a method for simultaneously optimizing a set of camera poses and visible points. It exploits the sparse primary structure of the problem, where connections exist just between points and cameras. In this paper, we implement an efficient version of SBA for systems where the secondary structure (relations among cameras) is also sparse. The method, which we call Sparse SBA (sSBA), integrates an efficient method for setting up the linear subproblem with recent advances in direct sparse Cholesky solvers. sSBA outperforms the current SBA standard implementation on datasets with sparse secondary structure by at least an order of magnitude, while also being more efficient on dense datasets.
Mechanics of ultrasound elastography

Ultrasound elastography enables in vivo measurement of the mechanical properties of living soft tissues in a non-destructive and non-invasive manner and has attracted considerable interest for clinical use in recent years. Continuum mechanics plays an essential role in understanding and improving ultrasound-based elastography methods and is the main focus of this review. In particular, the mechanics theories involved in both static and dynamic elastography methods are surveyed. They may help understand the challenges in and opportunities for the practical applications of various ultrasound elastography methods to characterize the linear elastic, viscoelastic, anisotropic elastic and hyperelastic properties of both bulk and thin-walled soft materials, especially the in vivo characterization of biological soft tissues.
An introduction to the WEKA data mining system

This is a proposal for a half day tutorial on Weka, an open source Data Mining software package written in Java and available from www.cs. waikato.ac. nz/~ml/weka/index.html. The goal of the tutorial is to introduce faculty to the package and to the pedagogical possibilities for its use in the undergraduate computer science and engineering curricula. The Weka system provides a rich set of powerful Machine Learning algorithms for Data Mining tasks, some not found in commercial data mining systems. These include basic statistics and visualization tools, as well as tools for pre-processing, classification, and clustering, all available through an easy to use graphical user interface.
Capturing Global Mood Levels using Blog Posts

The personal, diary-like nature of blogs prompts many bloggers to indicate their mood at the time of posting. Aggregating these indications over a large amount of bloggers gives a “blogosphere state-of-mind” for each point in time: the intensity of different moods among bloggers at that time. In this paper, we address the task of estimating this state-of-mind from the text written by bloggers. To this end, we build models that predict the levels of various moods according to the language used by bloggers at a given time; our models show high correlation with the moods actually measured, and substantially outperform a baseline.
THE CARBON MONOXIDE-BINDING PIGMENT OF LIVER MICROSOMES. I. EVIDENCE FOR ITS HEMOPROTEIN NATURE.

The presence in mammalian liver microsomes of a carbon monoxide-binding pigment has been reported by Klingenberg (1) and by Garfinkel (2). The CO compound of the reduced pigment has an intense absorption band at 450 rnl.c and thus can be readily detected in dithionite-treated microsomes by difference spectrophotometry. The CO difference spectrum of reduced microsomes is, however, unusual in that it shows no peaks other than that at 450 rnp and, therefore, provides no clue to the nature of the pigment. The elucidation of its nature has further been hampered by the reported lability of the microsomal pigment to detergents, low pH, and enzymatic digestion (1,2). In addition, the CO compound has been reported as not photodissociable (1). In preliminary communications (3, 4), we have reported evidence for the hemoprotein nature of the microsomal CObinding pigment, provisionally called “P-450,” and shown that it can be converted into a solubilised form, which we term “P420,” by treatment of microsomes anaerobically with snake venom or deosycholate. The solubiliaation is accompanied by an unusual change in the spectral properties of the pigment. Further, the solubilized pigment has been partly purified, free from cytochrome 55, and shown to possess absorption spectra characteristic of hemoproteins (5). The present paper gives a detailed account of the investigations on rabbit liver microsomes and crude microsomal digests, which have led us to postulate the hemoprotein nature of the pigment. Purification and properties of the solubilized hemoprotein will be reported in the accompanying paper (6).
A Model-Based Anomaly Detection Approach for Analyzing Streaming Aircraft Engine Measurement Data

This paper presents a model-based anomaly detection architecture designed for analyzing streaming transient aircraft engine measurement data. The technique calculates and monitors residuals between sensed engine outputs and model predicted outputs for anomaly detection purposes. Pivotal to the performance of this technique is the ability to construct a model that accurately reflects the nominal operating performance of the engine. The dynamic model applied in the architecture is a piecewise linear design comprising steady-state trim points and dynamic state space matrices. A simple curve-fitting technique for updating the model trim point information based on steadystate information extracted from available nominal engine measurement data is presented. Results from the application of the model-based approach for processing actual engine test data are shown. These include both nominal fault-free test case data and seeded fault test case data. The results indicate that the updates applied to improve the model trim point information also improve anomaly detection performance. Recommendations for follow-on enhancements to the technique are also presented and discussed.
Multi-Staged Quality Assurance for Self-Adaptive Systems

The emerging approach to tackle the increasing complexity of today's software systems is the use of self-adaptation techniques. Most often, self-adaptation is introduced in terms of externalized adaptation rules (e.g. event-condition-action rules). Modeling and implementing adaptation rules introduces an additional amount of complexity that potentially results in erroneous system specification models. As a consequence, quality analysis and assurance must be considered early in the development cycle of self-adaptive systems. In this paper, we propose an efficient quality assurance approach for self-adaptive systems in terms of an integrated modeling and analysis approach, which is capable to identify errors (e.g. conflicting adaptation rules) in the set of adaptation rules early in the design process. Based on Adapt Cases, a high-level design modeling language for self-adaptive systems, we describe a multi-staged model-checking approach and address the problem of state space explosion by considering properties that are specific for adaptation modeling.
Local node selection for target tracking based on underwater wireless sensor networks

Traditional sonar-array-based target tracking algorithms may be unsuitable for on-demand tracking missions, since they assume that the sonar arrays should be towed or mounted by a submarine or a ship. Alternatively, underwater wireless sensor networks can offer a promising solution approach. First, each underwater node is battery-powered, so saving energy expenditure is a critical issue. Instead of keeping all sensor nodes active, this paper provides a local node selection (LNS) scheme which increases energy efficiency by waking up only a small part of nodes at each time. Second, considering node's limited computing ability and the real-time requirement for the tracking algorithm, instead of employing the centralised fusion structure, we utilise the distributed Kalman filtering fusion with feedback in this paper. Finally, instead of assuming one sensor node can uniquely determine target's location, a more practical range-only measurement model is proposed. Then the LNS scheme and distributed fusion with feedback are extended to our range-only measurement model. The simulation results demonstrate the efficiency of our scheme.
Protein–protein docking predictions for the CAPRI experiment

We predicted structures for all seven targets in the CAPRI experiment using a new method in development at the time of the challenge. The technique includes a low‐resolution rigid body Monte Carlo search followed by high‐resolution refinement with side‐chain conformational changes and rigid body minimization. Decoys (∼106 per target) were discriminated using a scoring function including van der Waals and solvation interactions, hydrogen bonding, residue–residue pair statistics, and rotamer probabilities. Decoys were ranked, clustered, manually inspected, and selected. The top ranked model for target 6 predicted the experimental structure to 1.5 Å RMSD and included 48 of 65 correct residue–residue contacts. Target 7 was predicted at 5.3 Å RMSD with 22 of 37 correct residue–residue contacts using a homology model from a known complex structure. Using a preliminary version of the protocol in round 1, target 1 was predicted within 8.8 Å although few contacts were correct. For targets 2 and 3, the interface locations and a small fraction of the contacts were correctly identified. Proteins 2003;52:118–122. © 2003 Wiley‐Liss, Inc.
Cytoprotective Effect of Hydroalcoholic Extract of Pinus eldarica Bark against H2O2-Induced Oxidative Stress in Human Endothelial Cells

Background: Pinus eldarica is a widely growing pine in Iran consisting of biologically active constituents with antioxidant properties. This study investigates the effect of hydroalcoholic extract of P. eldarica bark against oxidative damage induced by hydrogen peroxide (H2O2) in human umbilical vein endothelial cells (HUVECs). Methods: The total phenolic content of P. eldarica extract was determined using Folin-Ciocalteu method. The cytotoxicity of P. eldarica extract (25-1000 µg/ml) on HUVECs was assessed using 3-(4,5- Dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) method. Cytoprotective effect of P. eldarica extract (25-500 µg/ml) on H2O2-induced oxidative stress was also evaluated by MTT assay. The intra- and extra-cellular hydroperoxides concentration and ferric reducing antioxidant power (FRAP) were measured in pretreated cells. Results: The total phenolic content of P. eldarica extract was estimated as 37.04±1.8% gallic acid equivalent. P. eldarica extract (25-1000 µg/ml) had no cytotoxic effect on HUVECs viability. The pretreatment of HUVECs with P. eldarica extract at the concentrations of 50-500 µg/ml significantly reduced the cytotoxicity of H2O2. P. eldarica extract decreased hydroperoxides concentration and increased FRAP value in intra-cellular fluid at the concentration range of 100-500 µg/ml and in extra-cellular fluid at the concentration range of 25-500 µg/ml. Conclusions: This study revealed the antioxidant and cytoprotective effects of P. eldarica extract against H2O2-induced oxidative stress in HUVECs. Concerning the high content of phenolic compounds in P. eldarica, more research is needed to evaluate its clinical value in endothelial dysfunction and in other oxidative conditions.
VLSI module placement based on probability search space smoothing

Search space smoothing (SSS) was originally proposed to deal only with traveling salesman problem (TSP). In this paper, two smoothing effects, local smoothing effect and global smoothing effect are distinguished firstly by analysis and experiments. By making use of local smoothing effect in search process, a local search that can make use of local smoothing effect is designed for search space smoothing method. A search space smoothing method that can take advantage of local smoothing effect is named as probability search space smoothing. A VLSI module placement algorithm based on probability search space smoothing is proposed and is test by using MCNC benchmarks ami33 and ami49, experimental results are better than the published results so far.
Statistical Dynamics of On-line Independent Component Analysis

The learning dynamics of on-line independent component analysis is analysed in the limit of large data dimension. We study a simple Hebbian learning algorithm that can be used to separate out a small number of non-Gaussian components from a high-dimensional data set. The de-mixing matrix parameters are confined to a Stiefel manifold of tall, orthogonal matrices and we introduce a natural gradient variant of the algorithm which is appropriate to learning on this manifold. For large input dimension the parameter trajectory of both algorithms passes through a sequence of unstable fixed points, each described by a diffusion process in a polynomial potential. Choosing the learning rate too large increases the escape time from each of these fixed points, effectively trapping the learning in a sub-optimal state. In order to avoid these trapping states a very low learning rate must be chosen during the learning transient, resulting in learning time-scales of $O(N^2)$ or $O(N^3)$ iterations where $N$ is the data dimension. Escape from each sub-optimal state results in a sequence of symmetry breaking events as the algorithm learns each source in turn. This is in marked contrast to the learning dynamics displayed by related on-line learning algorithms for multilayer neural networks and principal component analysis. Although the natural gradient variant of the algorithm has nice asymptotic convergence properties, it has an equivalent transient dynamics to the standard Hebbian algorithm.
Use of Continuation or Maintenance Electroconvulsive Therapy in Adolescents With Severe Treatment-Resistant Depression

Retrospective data are presented for 6 adolescents ranging in age from 14 to 17 years, who were diagnosed with severe treatment-resistant major depression (TRD). Subjects were treated with one or more index courses of electroconvulsive therapy (ECT) followed by continuation ECT (C-ECT, up to 6 months of ECT) or maintenance ECT (M-ECT; ECT beyond 6 months) when necessary. Electroconvulsive therapy was continued until remission or until minimal residual symptoms were evident. Pharmacotherapy and psychotherapy were reintroduced during C-ECT or M-ECT. Premorbid functioning was achieved by 5 of 6 cases. Cognitive deficits were not evident. In fact, comparison of pre-ECT and post-ECT neuropsychological functioning revealed a trend toward improved auditory and verbal memory on most of the results. We concluded that C-ECT and M-ECT are useful and safe treatment strategies for selected adolescents with severe treatment-resistant depression, and symptom remission may be achieved without experiencing cognitive impairment.
EEG Frequency Progression during Induction of Anesthesia: from Start of Infusion to Onset of Burst Suppression Pattern

The anesthetic infusion with propofol influences EEG activity rather smoothly by changing the amplitude activity in different frequency bands. This results in a frequency progression pattern (FPP) which can be related to the depth of anesthesia. An iterative algorithm is proposed for the estimation of the shape of this pattern. The presented method is applied to the data recorded from the start of the propofol anesthetic infusion to the onset of the burst suppression pattern (BSP) with nine patients. The results reveal the underlying FPP and how the onset of the BSP is related to it. The proposed method offers potential for the development of automatic assessment systems for the depth of anesthesia.
A Modified PSO Algorithm with the Concept of Intrinsic Repulsive Field

An improved Particle Swarm Optimization Algorithm is proposed in this article. Inspired by the existence of Intermolecular Forces in physics, we bring particles that are influenced by repulsion in this Algorithm to keep the population active at all the stages. Meanwhile, the convergence speed can be assured because of the existence of particles that implement Standard Particle Swarm Optimization. Experimental results show that this improved algorithm can effectively solve the local optimal problems.
Characterization of a cis-Golgi matrix protein, GM130

Antisera raised to a detergent- and salt-resistant matrix fraction from rat liver Golgi stacks were used to screen an expression library from rat liver cDNA. A full-length clone was obtained encoding a protein of 130 kD (termed GM130), the COOH-terminal domain of which was highly homologous to a Golgi human auto-antigen, golgin-95 (Fritzler et al., 1993). Biochemical data showed that GM130 is a peripheral cytoplasmic protein that is tightly bound to Golgi membranes and part of a larger oligomeric complex. Predictions from the protein sequence suggest that GM130 is an extended rod-like protein with coiled-coil domains. Immunofluorescence microscopy showed partial overlap with medial- and trans-Golgi markers but almost complete overlap with the cis-Golgi network (CGN) marker, syntaxin5. Immunoelectron microscopy confirmed this location showing that most of the GM130 was located in the CGN and in one or two cisternae on the cis-side of the Golgi stack. GM130 was not re-distributed to the ER in the presence of brefeldin A but maintained its overlap with syntaxin5 and a partial overlap with the ER- Golgi intermediate compartment marker, p53. Together these results suggest that GM130 is part of a cis-Golgi matrix and has a role in maintaining cis-Golgi structure.
Effect of Culture Medium on the Disk Diffusion Method for Determining Antifungal Susceptibilities of Dermatophytes

ABSTRACT We have evaluated a disk diffusion method to determine the activities of five drugs against 50 strains of dermatophytes and to assess the influence of the culture medium (antibiotic medium 3, high-resolution medium, and RPMI) on the inhibition zone diameters (IZD). There were no differences among the medium/drug combinations, except for itraconazole-RPMI, which showed the narrowest IZD.