Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-sustained activity in attractor networks using neuromorphic VLSI

We describe and demonstrate the implementation of attractor neural network dynamics in analog VLSI chips [1]. The on-chip network is composed of an excitatory and an inhibitory population of recurrently connected linear integrate-and-fire neurons. Besides the recurrent input these two populations receive external input in the form of spike trains from an Address-Event-Representation (AER) based system. External AER input stimulates the attractor network and provides also an adequate background activity for the on-chip populations. We use the mean-field approximation of a model attractor neural network to identify regions of parameter space allowing for attractor states, matching hardware constraints. Consistency between theoretical predictions and the observed collective behaviour of the network on chip is checked using the ‘effective transfer function’ (ETF) [2]. We demonstrate that the silicon network can support two equilibrium states of sustained firing activity that are attractors of the dynamics, and that external stimulation can provoke a transition from the lower to the higher state.
Kinesis: A new approach to replica placement in distributed storage systems

Kinesis is a novel data placement model for distributed storage systems. It exemplifies three design principles: structure (division of servers into a few failure-isolated segments), freedom of choice (freedom to allocate the best servers to store and retrieve data based on current resource availability), and scattered distribution (independent, pseudo-random spread of replicas in the system). These design principles enable storage systems to achieve balanced utilization of storage and network resources in the presence of incremental system expansions, failures of single and shared components, and skewed distributions of data size and popularity. In turn, this ability leads to significantly reduced resource provisioning costs, good user-perceived response times, and fast, parallelized recovery from independent and correlated failures. This article validates Kinesis through theoretical analysis, simulations, and experiments on a prototype implementation. Evaluations driven by real-world traces show that Kinesis can significantly outperform the widely used Chain replica-placement strategy in terms of resource requirements, end-to-end delay, and failure recovery.
Some proximal methods for CBCT and PET tomography

The reconstruction of the images obtained via the Cone Beam Computerized Tomography (CBCT) and Positron Emission Tomography (PET) Scanners are ill-posed inverse problems. One needs to adress carefully the problem of inversion of the mathematical operators involved. Recent advances in optimization have yielded efficient algorithms to solve very general classes of inverse problems via the minimization of non-differentiable convex functions. We show that such models are well suited to solve the CBCT and PET reconstruction problems. On the one hand, they can incorporate directly the physics of new acquisition devices, free of dark noise; on the other hand, they can take into account the specificity of the pure Poisson noise. We propose various fast numerical schemes to recover the original data and compare them to state-of-the-art algorithms on simulated data. We study more specifically how different contrasts and resolutions may be resolved as the level of noise and/or the number of projections of the acquired sinograms decrease. We conclude that the proposed algorithms compare favorably with respect to well-established methods in tomography.
Recognizing User Preferences Based on Layered Activity Recognition and First-Order Logic

Only few cognitive architectures have been proposed that cover the complete range from recognizers working on the direct sensor input, to logical inference mechanisms of classical artificial intelligence (AI). Logical systems operate on abstract predicates, which are often related to an action-like state transition, especially when compared to the classes recognized by pattern recognition approaches. On the other hand, pattern recognition is often limited to static patterns, and temporal and multi-modal aspects of a class are often not regarded, e.g. by testing only on pre-segmented data. Recent trends in AI aim at developing applications and methods that are motivated by data-driven real world scenarios, while the field of pattern recognition attempts to push forward the boundary of pattern complexity. We propose a new generic architecture to close the gap between AI and pattern recognition approaches. In order to detect abstract complex patterns, we process sequential data in layers. On each layer, a set of elementary classes is recognized and the outcome of the classification is passed to the successive layer such that the time granularity increases. Layers can combine modalities, additional symbolic information or make use of reasoning algorithms. We evaluated our approach in an on-line scenario of activity recognition using three layers. The obtained results show that the combination of concepts from pattern recognition and high-level symbolic information leads to a prosperous and powerful symbiosis.
Evaluation of a Commercial Latex Agglutination Assay for Serological Diagnosis of Leptospirosis

ABSTRACT Leptospirosis is a febrile zoonosis of worldwide distribution. A latex agglutination assay was evaluated in two studies, the first using a panel of well-characterized sera from patients with leptospirosis and from patients with other disease states and the second, a prospective hospital-based study, evaluating sera from 186 consecutive patients admitted to hospital with acute febrile illness. The confirmed leptospirosis serum panel included paired acute- and convalescent-phase specimens from 40 cases, of which 34 gave positive latex tests (case sensitivity, 85%; 95% confidence interval [95% CI], 70 to 94%). The other diseases represented in the panel of 112 specimens from nonleptospirosis patients included autoimmune diseases, brucellosis, dengue, melioidosis, malaria, syphilis, toxoplasmosis, viral hepatitis, and a number of other viral infections. The specificity of latex agglutination using this panel was 81% (95% CI, 73 to 87%). Among the patients with acute febrile illness, there were 25 cases of leptospirosis and 161 patients with other diagnoses. The sensitivity and specificity of latex agglutination in this group were 88% (95% CI, 72 to 97%) and 98% (95% CI, 95 to 100%), respectively. In this evaluation, the two distinct groups of specimens gave similar results for sensitivity, but specificity was different in each study. The sensitivity and specificity observed for the hospital study were similar to those obtained in evaluations of other rapid tests in the same population. The results of this study suggest that multiple evaluations of new diagnostic assays should be performed, because performance characteristics may vary in different populations.
Gas phase thermal denaturation of an oligonucleotide duplex and its complexes with minor groove binders.

Electrospray ionization with in-source collisionally induced dissociation has been used to probe the gas phase stability of an oligonucleotide duplex and its complexes with some minor groove binding drugs. On the basis of the arguments developed in detail by Drahos et al. (J. Mass Spectrom. 1999; 34:1373), this type of experiment can also be described as 'thermal denaturation in the gas phase'. We found that the gas phase denaturation curves were very similar to the solution phase denaturation curves determined by the traditional UV spectrophotometric method and, by analogy with the melting temperature T(m) which characterizes the stability in solution, we define a melting voltage V(m) to characterize the stability in the gas phase. A comparison of the T(m) and V(m) relative values suggests that the structure of the complexes is conserved during the electrospray process which transfers the ions from the solution to the gas phase.
Evaluating MPI Implementations Using HPL on an Infiniband Nehalem Linux Cluster

In conjunction with Moore’s Law, computer speeds are expected to double approximately every two years, but with the current challenges that computer manufacturers are facing to double speeds of individual processors, due to various reasons, such as processor temperatures, multiprocessor architectures have become more popular nowadays. Eventually, this has led to an increased interest in standards for writing parallel applications. The Message Passing Interface (MPI) has become the de facto standard for writing parallel applications. Our growing needs for the latest high performance computing solutions in Saudi Aramco, the world’s largest oil producing company, has given us the opportunity to evaluate three of the most commonly used MPI implementations, MVAPICH, Open MPI, and Intel MPI on Intel’s latest Nehalem processor. In this paper, we describe our test bed environment along with the evaluations that we did using the High Performance Linpack (HPL) benchmark, which is the standard benchmark for ranking the world’s Top 500 supercomputers. We also discuss our own developed Web tool that is used to suggest tuned input values for the HPL benchmark. We show the performance numbers in GFLOPS along with the run times and system efficiencies when running on 32, 64 and 128 Infiniband Nehalem Linux cluster nodes using the three implementations of MPI. We finally discuss our results in terms of performance and scalability and we share our interpretations and future work.
Optical wireless ground- link attenuation statistics of fog and snow conditions

Optical wireless technology is a well known technology that found renewed interest for its numerous applications in ground-space and terrestrial communication scenarios. A well known disadvantage of optical wireless communication (involving ground-link) is its sensitivity on local weather conditions - primarily to fog, snow and clouds. This results in substantial loss of optical signal power over the communication channel (i.e., the troposphere). We provide here the optical signal attenuation statistics of the fog and snow conditions from an optical wireless ground-link installed at Graz, Austria and provide some useful analysis that can be handful and beneficial in order to evaluate this technology for ground-space future applications.
Hybrid many-objective particle swarm optimization set-evolution

Many-objective optimization problems (MaOPs) are difficult to be solved by those traditional evolutionary multi-objective (EMO) algorithms due to the loss of enough selection pressure. The indicator-based EMO developed for MaOPs has been proved to be effective, however, it has not been well combined with the framework of particle swarm optimization (PSO). Therefore, we here propose a hybrid indicator-based PSO for MaOPs, in which the sets of solutions are evolved as an “individual”. First, the sets-oriented PSO is designed to perform the evolution on the sets. The global and local best particles are well explored by considering the performance of the evolution and the computational cost. Then, the solutions in some selected sets are further evolved by a modified mutation to approximate to the true Pareto set in the original MaOP space. The proposed algorithm is experimentally validated on some benchmark MaOPs and its merit is empirically demonstrated by comparing to indicator-based evolutionary genetic algorithms and NSGAII.
EFFECT OF RANDIA DUMETORUM LAM ON CLONIDINE AND HALOPERIDOL-INDUCED CATALEPSY IN MICE

Summary In the present study we evaluated the anticataleptic efficacy of ethanolic extract of Randia dumetorum fruits in clonidine and haloperidol-induced catalepsy in mice. Five groups (n=5) of albino mice were used in the study. Catalepsy was induced by clonidine (1 mg/kg, s.c.) and haloperidol (1 mg/kg, i.p) in clonidine and haloperidol-induced catalepsy in mice respectively. The degree of catalepsy (cataleptic score) was measured as the time the animal maintained an imposed posture. In the clonidine-induced catalepsy model significant (p<0.01) reduction in the cataleptic scores was observed in all the groups treated with Randia dumetorum Lam (180, 360, 720 mg/kg, p.o.) however there was no significant reduction in the cataleptic scores in the haloperidol-induced catalepsy model in mice. The results obtained in the present study indicate that the anticataleptic activity exhibited by ethanolic extract of Randia dumetorum Lam fruits can be attributed to the antihistaminic activity; however Randia dumetorum does not possess antidopaminergic and antiserotonergic activity.
Isocitrate Lyase Is Essential for Pathogenicity of the Fungus Leptosphaeria maculans to Canola (Brassica napus)

ABSTRACT A pathogenicity gene has been identified in Leptosphaeria maculans, the ascomycetous fungus that causes blackleg disease of canola (Brassica napus). This gene encodes isocitrate lyase, a component of the glyoxylate cycle, and is essential for the successful colonization of B. napus. It was identified by a reverse genetics approach whereby a plasmid conferring hygromycin resistance was inserted randomly into the L. maculans genome. Twelve of 516 transformants tested had reduced pathogenicity on cotyledons of B. juncea and B. napus, and 1 of these 12 had a deletion of the isocitrate lyase gene, as well as an insertion of the hygromycin resistance gene. This mutant was unable to grow on fatty acids, including monolaurate, and the isocitrate lyase transcript was not detected. When the wild-type gene was reintroduced into the mutant, growth on monolaurate was restored and pathogenicity was partially restored. L. maculans isocitrate lyase is produced during infection of B. napus cotyledons, while the plant homologue is not. When 2.5% glucose was added to the inoculum of the isocitrate lyase mutant, lesions of sizes similar to those caused by wild-type isolate M1 developed on B. napus cotyledons. These findings suggest that the glyoxylate pathway is essential for disease development by this plant-pathogenic fungus, as has been shown recently for a fungal and bacterial pathogen of animals and a bacterial pathogen of plants. Involvement of the glyoxylate pathway in pathogenesis in animals and plants presents potential drug targets for control of diseases.
Location Name Disambiguation Exploiting Spatial Proximity and Temporal Consistency

As the volume of documents on the Web increases, technologies to extract useful information from them become increasingly essential. For instance, information extracted from social network services such as Twitter and Facebook is useful because it contains a lot of location-specific information. To extract such information, it is necessary to identify the location of each location-relevant expression within a document. Previous studies on location disambiguation have tackled this problem on the basis of word sense disambiguation, and did not make use of location-specific clues. In this paper, we propose a method for location disambiguation that takes advantage of the following two clues: spatial proximity and temporal consistency. We confirm the effectiveness of these clues through experiments on Twitter tweets with GPS information.
2-D Iteratively Reweighted Least Squares Lattice Algorithm and Its Application to Defect Detection in Textured Images

In this paper, a 2-D iteratively reweighted least squares lattice algorithm, which is robust to the outliers, is introduced and is applied to defect detection problem in textured images. First, the philosophy of using different optimization functions that results in weighted least squares solution in the theory of 1-D robust regression is extended to 2-D. Then a new algorithm is derived which combines 2-D robust regression concepts with the 2-D recursive least squares lattice algorithm. With this approach, whatever the probability distribution of the prediction error may be, small weights are assigned to the outliers so that the least squares algorithm will be less sensitive to the outliers. Implementation of the proposed iteratively reweighted least squares lattice algorithm to the problem of defect detection in textured images is then considered. The performance evaluation, in terms of defect detection rate, demonstrates the importance of the proposed algorithm in reducing the effect of the outliers that generally correspond to false alarms in classification of textures as defective or nondefective.
Voluntary Knowledge Sharing in Organizations

Knowledge is a fundamental asset for firms in the contemporary economy. Promoting knowledge sharing within organizations is an increasingly important challenge for managers. Past literature in organizational studies has focused on personal motivations to explain knowledge sharing behaviors. We see an organization as consisting of multiple networks simultaneously, e.g. informational, structural, and relational networks. We propose a model to understand the individual knowledge sharing behavior from informational, structural, and relational perspectives in an organizational context. We find that in the context of interpersonal task related knowledge sharing, perceived value of knowledge, empathy and dependence play a critical role in knowledge sharing while the impacts of competition and relationship building are insignificant.
Region Of Interest Detection For Automotive Applications

Driven by increasing concerns over automotive safety, the development of on-board automotive driver assistance systems to alert drivers about potential collisions with objects such as pedestrians or vehicles has become a huge area of research. The aim of this paper is to present a novel approach for the first processing stage in many of these types of systems. An algorithm is presented which uses a single visual camera to identify Regions Of Interest (ROIs) in front of a moving vehicle, which may contain objects that represent a “threat”. These ROIs are then categorised into different priorities depending on their position in the image, which in turn gives a measure of their threat level.
Critique graphs for catalogue navigation

Critique-based conversational recommender systems are becoming common place, facilitating richer dialogues with the user than pure content-based or collaborative approaches. Most implementations of these systems combine similarity-based reasoning with constraints to enable users express preferences as critiques of products. Critiques are simple statements like "I like this product, but would prefer one that is less expensive". In this paper we exploit the fact that the repertoire of critiques available to the user is usually known ahead of interaction time to construct a critique graph representation of a catalogue. The critique graph provides a formal basis for reasoning about the set of products that can be reached using critiques from a given product. We introduce the concepts of product cover, support sets of products and catalogue cover. The latter is defined as a set of products from which all products in a catalogue can be reached using a specified best-case maximum number of critiques. We show that for the catalogues we considered, catalogue covers are typically small. We show that the sizes and distributions of product covers and support sets can be used to inform us of the structure of a catalogue and the challenges it would present for interactive navigation. We also propose the notion of a minimum catalogue cover as a set of "entry products" that ensure that all products in the catalogue can be reached by critiquing.
Clustering Files with Extended File Attributes in Metadata

Classification and searching play an important role in modern file systems and file clustering is an effective approach to do this. This paper presents a new labeling system by making use of the Extended File Attributes [1] of file system, and a simple file clustering algorithm based on this labeling system is also introduced. By regarding attributes and attribute-value pairs as labels of files, features of a file can be represented as binary vectors of labels. And some well-known binary vector dissimilarity measures can be performed on this binary vector space, so clustering based on these measures can be done also. This approach is evaluated with several real-life datasets, and results indicate that precise clustering of files is achieved at an acceptable cost
Diffuse Optical Light in Galaxy Clusters. II. Correlations with Cluster Properties

We have measured the flux, profile, color, and substructure in the diffuse intracluster light (ICL) in a sample of 10 galaxy clusters with a range of mass, morphology, redshift, and density. Deep, wide-field observations for this project were made in two bands at the 1 m Swope and 2.5 m du Pont telescopes at Las Campanas Observatory. Careful attention in reduction and analysis was paid to the illumination correction, background subtraction, point-spread function determination, and galaxy subtraction. ICL flux is detected in both bands in all 10 clusters ranging from 7.6 × 1010 to 7.0 × 1011 h L⊙ in r and 1.4 × 1010 to 1.2 × 1011 h L⊙ in the B band. These fluxes account for 6%-22% of the total cluster light within one-quarter of the virial radius in r and 4%-21% in the B band. Average ICL B - r colors range from 1.5 to 2.8 mag when k- and evolution corrected to the present epoch. In several clusters we also detect ICL in group environments near the cluster center and up to 1 h Mpc distant from the cluster center. Our sample, having been selected from the Abell sample, is incomplete in that it does not include high-redshift clusters with low density, low flux, or low mass, and it does not include low-redshift clusters with high flux, high mass, or high density. This bias makes it difficult to interpret correlations between ICL flux and cluster properties. Despite this selection bias, we do find that the presence of a cD galaxy corresponds to both centrally concentrated galaxy profiles and centrally concentrated ICL profiles. This is consistent with ICL either forming from galaxy interactions at the center or forming at earlier times in groups and later combining in the center.
A collaborative filtering algorithm based on Users' Partial Similarity

Collaborative filtering is one of the most successful technologies for building recommender systems, and is extensively used in many personalized systems. However, existing collaborative filtering algorithms have been suffering from data sparsity and scalability problems which lead to inaccuracy of recommendation. In this paper, we focus the collaborative filtering problems on two crucial steps: (1) computing neighbor users for active users and (2) missing data prediction algorithm. Consequently, we propose an effective collaborative filtering algorithm based on Users' Partial Similarity (we call it CFUPS for short). CFUPS's main idea is that we compute the similarity between users rely on partial items with their common interests, not on all common rated items. And we combine items' attributes similarity and their ratings similarity properly for computing missing ratings. Theoretically, our method is effective in improving the recommendation precision and withstanding data sparsity. In the meantime, the experiment result shows that our proposed CFUPS algorithm outperforms other existing collaborative filtering approaches.