Miracle-dz/UniFAR-FTUs huggingface dataset:
A Scalable Context-Aware Objective Function (SCAOF) of Routing Protocol for Agricultural Low-Power and Lossy Networks (RPAL)

In recent years, IoT (Internet of Things) technologies have seen great advances, particularly, the IPv6 Routing Protocol for Low-power and Lossy Networks (RPL), which provides a powerful and flexible routing framework that can be applied in a variety of application scenarios. In this context, as an important role of IoT, Wireless Sensor Networks (WSNs) can utilize RPL to design efficient routing protocols for a specific application to increase the ubiquity of networks with resource-constrained WSN nodes that are low-cost and easy to deploy. In this article, our work starts with the description of Agricultural Low-power and Lossy Networks (A-LLNs) complying with the LLN framework, and to clarify the requirements of this application-oriented routing solution. After a brief review of existing optimization techniques for RPL, our contribution is dedicated to a Scalable Context-Aware Objective Function (SCAOF) that can adapt RPL to the environmental monitoring of A-LLNs, through combining energy-aware, reliability-aware, robustness-aware and resource-aware contexts according to the composite routing metrics approach. The correct behavior of this enhanced RPL version (RPAL) was verified by performance evaluations on both simulation and field tests. The obtained experimental results confirm that SCAOF can deliver the desired advantages on network lifetime extension, and high reliability and efficiency in different simulation scenarios and hardware testbeds.
Managing alternative sports: new organisational spaces for the diffusion of Italian parkour

This article explores the encounter between parkour as an unstructured and culturally innovative practice, challenging both physical as well as organisational spaces, and UISP (Unione Italiana Sport per Tutti/Italian Union of Sport for All) as a sport-promotion body open to organisational and cultural experimentation. Drawing on a multi-method qualitative approach (analysis of documentary material, interviews and focus groups), it looks at the role of UISP in the diffusion and legitimisation of parkour within the Italian context, investigating the interplay between the cultural and organisational logics of both this new practice itself on the one hand, and the organisations that are trying to accommodate it on the other. The incorporation in a sport-for-all organisation like UISP provides traceurs with a safe and legitimised space, which is, however, ‘loose' enough to maintain the fluidity of the practice. Nonetheless, by enabling the coexistence of different and competing definitions and uses of parkour, this fluid organisational space reproduces tensions among traceurs and weakens their voice in UISP's decision-making processes.
Mining Shoppers Data Streams to Predict Customers Loyalty

For a consumer brand, the most valuable customers are those who return after this purchase. Therefore, we want to know if it is possible to predict which shoppers will buy a new item with enough purchase history. Fortunately, while dealing with Big Data and with data streams in particular, it is a common practice to summarize or aggregate customers' transaction history to the periods of few months. As an outcome, we compress the given huge volume of data, and transfer the data stream to the standard rectangular format. Consequently, we can explore a variety of practically or theoretically motivated tasks. For example, we can rank the given field of customers in accordance to their loyalty or intension to repurchase in the near future. This objective has very important practical application. It leads to preferential treatment of the right customers. We tested our model (with competitive results) online during Kaggle-based Acquire Valued Shoppers Challenge in 2014.
Lightweight Specialized 3-Valued Logic Shape Analyzer

We describe the design and implementation of a specialized shape analysis tool based on 3-valued logic. Our analyzer can reason about programs manipulating recursive data structures, such as singlyand doubly-linked lists, yielding precise results that are comparable to those of the wellknown reference implementation, in only a fraction of the time. In particular, (a) we apply a new and effective technique for performing structurebased abstraction refinement, and (b) introduce a new definition for ordering of abstract heap descriptors which leads to a significant deflation in the sets of abstract states explored by the analysis. Although currently restricted by design in its applicability for different flavors of analyses, we argue that—thanks to a modular and extendable architecture—generalizing the capabilities of our tool can be achieved by means of further mostly straightforward (yet non-trivial) software engineering effort.
Improved Language Modeling for English-Persian Statistical Machine Translation

As interaction between speakers of different languages continues to increase, the everpresent problem of language barriers must be overcome. For the same reason, automatic language translation (Machine Translation) has become an attractive area of research and development. Statistical Machine Translation (SMT) has been used for translation between many language pairs, the results of which have shown considerable success. The focus of this research is on the English/Persian language pair. This paper investigates the development and evaluation of the performance of a statistical machine translation system by building a baseline system using subtitles from Persian films. We present an overview of previous related work in English/Persian machine translation, and examine the available corpora for this language pair. We finally show the results of the experiments of our system using an in-house corpus and compare the results we obtained when building a language model with different sized monolingual corpora. Different automatic evaluation metrics like BLEU, NIST and IBM-BLEU were used to evaluate the performance of the system on half of the corpus built. Finally, we look at future work by outlining ways of getting highly accurate translations as fast as possible.
PCR-based methods for the enrichment of minority alleles and mutations.

BACKGROUND The ability to identify low-level somatic DNA mutations and minority alleles within an excess wild-type sample is becoming essential for characterizing early and posttreatment tumor status in cancer patients. Over the past 2 decades, much research has focused on improving the selectivity of PCR-based technologies for enhancing the detection of minority (mutant) alleles in clinical samples. Routine application in clinical and diagnostic settings requires that these techniques be accurate and cost-effective and require little effort to optimize, perform, and analyze. CONTENT Enrichment methods typically segregate by their ability to enrich for, and detect, either known or unknown mutations. Although there are several robust approaches for detecting known mutations within a high background of wild-type DNA, there are few techniques capable of enriching and detecting low-level unknown mutations. One promising development is COLD-PCR (coamplification at lower denaturation temperature), which enables enrichment of PCR amplicons containing unknown mutations at any position, such that they can be subsequently sequenced to identify the exact nucleotide change. SUMMARY This review summarizes technologies available for detecting minority DNA mutations, placing an emphasis on newer methods that facilitate the enrichment of unknown low-level DNA variants such that the mutation can subsequently be sequenced. The enrichment of minority alleles is imperative in clinical and diagnostic applications, especially in those related to cancer detection, and continued technology development is warranted.
Place cell discharge is extremely variable during individual passes of the rat through the firing field.

The idea that the rat hippocampus stores a map of space is based on the existence of "place cells" that show "location-specific" firing. The discharge of place cells is confined with remarkable precision to a cell-specific part of the environment called the cell's "firing field. " We demonstrate here that firing is not nearly as reliable in the time domain as in the positional domain. Discharge during passes through the firing field was compared with a model with Poisson variance of the location-specific firing determined by the time-averaged positional firing rate distribution. Place cells characteristically fire too little or too much compared with expectations from the random model. This fundamental property of place cells is referred to as "excess firing variance" and has three main implications: (i) Place cell discharge is not only driven by the summation of many small, asynchronous excitatory synaptic inputs. (ii) Place cell discharge may encode a signal in addition to the current head location. (iii) The excess firing variance helps explain why the errors in computing the rat's position from the simultaneous activity of many place cells are large.
Proportional fair spatial scheduling for wireless access point with multiple antenna - reverse link with scalar feedback

We propose a novel proportional fair space time scheduling algorithm to enhance the reverse link capacity of a generic wireless access architecture. The access point is assumed to have n/sub R/ antenna while the client device has single transmit antenna. We also assume slow fading channel where clients are moving with pedestrian speed. System capacity is enhanced by exploiting the burstiness of data source, selection diversity of different client users as well as the distributed MIMO configuration when we have n/sub R/ antenna at the access point. We shall illustrate that the proposed algorithms could be applied to IEEE 802.11a wireless LAN system with slight modification of the MAC layer of client device. Significant gain in system capacity is found which is attributed to the distributed MIMO configuration between the access point and multiple client users as well as the selection diversity of different client users.
On the Geometry and Deformation of Switching Manifolds for Aut onomous Hybrid Systems

This paper provides a geometrical analysis of autonomous hybrid optimal control systems (HOCS) by study- ing the properties of switching manifolds in Euclidean space and their associated optimal hybrid trajectories. Motivational examples are to be found in the speed dependent operation of automatic gear shift systems of heavy trucks (1). In this paper the mathematical formulation of a hybrid system is presented and then the Hybrid Maximum Principle (HMP) necessary conditions for the optimality of a hybrid system trajectory are given, (see (6),(7)). Second order optimality conditions are given in terms of the Hessian matrix of the value function and geometrical data involving the curvature of the switching manifold at its intersection with an optimal trajectory. At the end, the energy of a switching manifold deformation mapping is defined and the hybrid cost optimization is performed with respect to such deformation mappings.
Email-Spam Filtering with the Hashing-Trick

This paper delves into a recently proposed technique for collaborative spam filtering [7] that facilitates personalization with finite-sized memory guarantees. In large scale open membership email systems most users do not label enough messages for an individual local classifier to be effective, while the data is too noisy to be used for a global filter across all users. Our hybrid global/individual classifier is particularly effective at absorbing the influence of users who label emails very differently from the general public – because of strange taste or malicious intent. We can accomplish this while still providing sufficient classifier quality to users with few labeled instances. Our proposed technique can be used with a variety of classifiers and can be implemented in a few lines of code. We verify the efficacy of our proposed technique on a popular web spam benchmark data set.
Method for the serological typing of the capsular polysaccharides of Staphylococcus aureus

A method is described for typing Staphylococcus aureus capsular polysaccharides that is based on direct bacterial cell agglutination and immunoprecipitation of cell extracts with monospecific antisera. Encapsulated strains were identified by their inagglutinability with teichoic acid antisera. The typing sera reacted specifically with extracts of eight prototype strains.
Filtered mining in program code repositories

An approach to automatically mine program code repositories in order to support software reuse is proposed in this paper. Code attributes and structural relationships are used to find and rank the potential elements based on a given query. Static program analysis is used to allow a filtered mining through three well-established metrics that have a direct impact on code quality. The approach has been empirically evaluated and the results obtained were promising.
Cognitive Radio Enhancements for Legacy Networks Using Cooperative Diversity

Two driving goals for cognitive radio (CR) techniques are maximizing spectrum utilization and avoiding interference to primary users. In this paper, we deal with the CR concept for legacy primary links optimized for noninterference (singe user) environments. In this type of network, primary destinations are not able to deal with possible interference and a missed cognitive detection significantly reduces the system performance. The enhancement of the primary network with cooperative diversity in addition to the well-known diversity gain for the primary link improves the sensing ability of the system and protects the primary user from possible interference. Both Amplify-Forward and Decode-Forward (DF) cooperative schemes are studied for the problem under consideration and a new DF policy which introduces a cognitive relay behavior is investigated. The proposed technique provides CR benefits without complicated network modifications and seems to be an attractive solution for future legacy networks with flexibility limitations.
Web Semantics : Science , Services and Agents on the World Wide Web

In this paper, we address the problem of fast processing of SPARQL queries on a large RDF dataset, where the RDF statements are quadruples (or quads). Quads can capture provenance or other relevant information about facts. This is especially powerful in modeling knowledge graphs, which are becoming increasingly important on the Web to provide high quality search results to users. We propose a new approach called RIQ that employs a decrease-and-conquer strategy for fast SPARQL query processing. Rather than indexing the entire RDF dataset, RIQ identifies groups of similar RDF graphs and creates indexes on each group separately. It employs a new vector representation for RDF graphs and locality sensitive hashing to construct the groups efficiently. It constructs a novel filtering index on the groups and compactly represents the index as a combination of Bloom and Counting Bloom Filters. During query processing,RIQ employs a streamlined approach. It constructs a query plan for a SPARQLquery (containing one or more graph patterns), searches the filtering index to quickly identify candidate groups that may contain matches for the query, and rewrites the original query to produce an optimized query for each candidate. The optimized queries are then executed using an existing SPARQL processor that supports quads to produce the final results. We conducted a comprehensive evaluation of RIQ using a real and synthetic dataset, each containing about 1.4 billion quads. Our results show that RIQ can outperform its competitors designed to support named graph queries on RDF quads (e.g., Jena TDB and Virtuoso) for a variety of queries. © 2016 Elsevier B.V. All rights reserved.
Text Line Segmentation of Historical Arabic Documents

This paper presents a text line segmentation method for printed or handwritten historical Arabic documents. Documents are first classified into 2 classes using a K-means scheme. These classes correspond to document complexity (easy or not easy to segment). Then, a document which includes overlapping and touching characters, is divided into vertical strips. The extracted text blocks obtained by horizontal projection are classified into three categories: small, average and large text blocks. After segmenting the large text blocks, the lines are obtained by matching adjacent blocks within two successive strips using spatial relationship. The document without overlapping or touching characters is segmented by making abstraction on the segmentation module of the large text blocks. The text line segmentation method has a 96% accuracy on a collection of 100 historical documents
How to Use Economic Theory to Improve Estimators: Shrinking Toward Theoretical Restrictions

Abstract We propose to use economic theories to construct shrinkage estimators that perform well when the theories' empirical implications are approximately correct but perform no worse than unrestricted estimators when the theories' implications do not hold. We implement this construction in various settings, including labor demand and wage inequality, and estimation of consumer demand. We provide asymptotic and finite sample characterizations of the behavior of the proposed estimators. Our approach is an alternative to the use of theory as something to be tested or to be imposed on estimates. Our approach complements uses of theory for identification and extrapolation.
Semantics of Parametric Polymorphism in Imperative Programming Languages

Programming languages such as CLU, Ada and Modula-3 have included facilities for parametric polymorphism and, more recently, C++ and Java have also added similar facilities. In this paper, we examine the issues of defining denotational semantics for imperative programming languages with polymorphism. We use the framework of reflexive graphs of categories previously developed for a general axiomatization of relational parametricity constraints implicit in polymorphic functions. We specialize it to the context of imperative programming languages, which in turn involve parametricity constraints implicit in local variables. The two levels of parametricity inherent in such languages can be captured in a pleasing way in ”higher-order” reflexive graphs.
AN ELEMENTARY PROOF OF THE GENERAL Q-PARAMETRIZATION OF ALL STABILIZING CONTROLLERS

Abstract It is becoming to be well-known that an internally stabilizable transfer matrix does not necessarily admit doubly coprime factorizations. The equivalence between these two concepts is still open for important classes of plants. Hence, we may wonder whether or not it is possible to parametrize all stabilizing controllers of an internally stabilizable plant which does not necessarily admit doubly coprime factorizations. The aim of this paper is to give an elementary proof of the existence of such a general parametrization. This parametrization is obtained by solving the general conditions for internal stabilizability developed within the fractional representation approach to synthesis problems. We show how such ideas can be traced back to the pioneering work of G. Zames and B. Francis on H ∞ -control. Finally if the transfer matrix admits a doubly coprime factorization, then we show that the Q -parametrization becomes the Youla-Kuĉera parametrization.
Identifying metabolite ions of peptide drugs in the presence of an in vivo matrix background.

BACKGROUND Peptides represent a growing class of potential drugs. Information on metabolic clearance can be valuable for peptide drug development but different challenges are encountered with the identification of peptide metabolites in comparison to the process used for small-molecule therapeutics. RESULTS Enfuvirtide was selected as a test compound and dosed intravenously at 2 mg/kg to rats. Plasma samples were collected and analyzed on two different quadrupole-TOF instruments in positive and negative ion modes. Different post-acquisition processing tools were evaluated to identify the metabolites of a peptide drug in the presence of an in vivo matrix. Charge state filtering and ion mobility extraction were applied to reduce the matrix background and combined with more comprehensive software tools generally used for large molecule analyses as well as tools designed for small-molecule metabolite identification work. CONCLUSION Both ion mobility spectrometry and charge state filtration proved to be successful in extracting peptide ions and significantly reducing background signals. Both small- and large-molecule software tools contain specific capabilities that could be usefully combined in a single package for peptide metabolite identification.
Feed-Forward Support Vector Machine Without Multipliers

In this letter, we propose a coordinate rotation digital computer (CORDIC)-like algorithm for computing the feed-forward phase of a support vector machine (SVM) in fixed-point arithmetic, using only shift and add operations and avoiding resource-consuming multiplications. This result is obtained thanks to a hardware-friendly kernel, which greatly simplifies the SVM feed-forward phase computation and, at the same time, maintains good classification performance respect to the conventional Gaussian kernel
Activation of LKB1‐Akt pathway independent of phosphoinositide 3‐kinase plays a critical role in the proliferation of hepatocellular carcinoma from nonalcoholic steatohepatitis

LKB1, originally considered a tumor suppressor, plays an important role in hepatocyte proliferation and liver regeneration. Mice lacking the methionine adenosyltransferase (MAT) gene MAT1A exhibit a chronic reduction in hepatic S‐adenosylmethionine (SAMe) levels, basal activation of LKB1, and spontaneous development of nonalcoholic steatohepatitis (NASH) and hepatocellular carcinoma (HCC). These results are relevant for human health because patients with liver cirrhosis, who are at risk to develop HCC, have a marked reduction in hepatic MAT1A expression and SAMe synthesis. In this study, we isolated a cell line (SAMe‐deficient [SAMe‐D]) from MAT1A knockout (MAT1A‐KO) mouse HCC to examine the role of LKB1 in the development of liver tumors derived from metabolic disorders. We found that LKB1 is required for cell survival in SAMe‐D cells. LKB1 regulates Akt‐mediated survival independent of phosphoinositide 3‐kinase, adenosine monophosphate protein–activated kinase (AMPK), and mammalian target of rapamycin complex (mTORC2). In addition, LKB1 controls the apoptotic response through phosphorylation and retention of p53 in the cytoplasm and the regulation of herpesvirus‐associated ubiquitin‐specific protease (HAUSP) and Hu antigen R (HuR) nucleocytoplasmic shuttling. We identified HAUSP as a target of HuR. Finally, we observed cytoplasmic staining of p53 and p‐LKB1(Ser428) in a NASH‐HCC animal model (from MAT1A‐KO mice) and in liver biopsies obtained from human HCC derived from both alcoholic steatohepatitis and NASH. Conclusion: The SAMe‐D cell line is a relevant model of HCC derived from NASH disease in which LKB1 is the principal conductor of a new regulatory mechanism and could be a practical tool for uncovering new therapeutic strategies. (HEPATOLOGY 2010)