Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on the Natural Language Querying for Remote Sensing Databases

Natural language querying for remote sensing databases means that the users describe the query object directly in natural language when they query remote sensing (RS) databases. This method is not limited by the options on the query interface, and allows non-professional users to access to the databases easily. After making a study on the status of natural language querying for databases, the paper sums up three methods used in natural query language processing, which are Pattern Matching, Dependency Extraction and Knowledge Extension. Then it proposes a new method for Natural Language Processing (NLP) based on the features of the RS query text, which establishes a prototype system that contains three main steps-keywords extraction, keywords extension and SQL generation. The system implements the conversion from natural query language to database manipulation language (e.g. SQL), which greatly facilitates the users' query.
Conformational substates in enzyme mechanism: The 120 K structure of α‐lytic protease at 1.5 Å resolution

Insight into the dynamic properties of α‐lytic protease (αP) has been obtained through the use of low‐temperature X‐ray crystallography and multiple‐conformation refinement. Previous studies of αLP have shown that the residues around the active site are able to move significantly to accommodate substrates of different sizes. Here we show a link between the ability to accommodate ligands and the dynamics of the binding pocket. Although the structure of αLP at 120 K has B‐factors with a uniformly low value of 4.8 Å2 for the main chain, four regions stand out as having significantly higher B‐factors. Because thermal motion should be suppressed at cryogenic temperatures, the high B‐factors are interpreted as the result of trapped conformational substates. The active site residues that are perturbed during accommodation of different substrates are precisely those showing conformational substates, implying that substrate binding selects a subset of conformations from the ensemble of accessible states. To better characterize the precise nature of these substates, a protein model consisting of 16 structures has been refined and evaluated. The model reveals a number of features that could not be well‐described by conventional B‐factors: for example, 40% of the main‐chain residue conformations are distributed asymmetrically or in discrete clusters. Furthermore, these data demonstrate an unexpected correlation between motions on either side of the binding pocket that we suggest is a consequence of “dynamic close packing. ” These results provide strong evidence for the role of protein dynamics in substrate binding and are consistent with the results of dynamic studies of ligand binding in myoglobin and ribonuclease A.
Initial Adoption vs. Institutionalization of E-Procurement in Construction Firms: The Role of Government in Developing Countries

This study explores the role of government in fostering construction firms move from initial adoption to institutionalization of e-procurement in developing countries' context. It proposes the research model that consists of five external environmental constructs that are considered as factors influencing the different levels of e-procurement adoption. It uses PLS-SEM to analyze the data collected from 112 construction businesses in Vietnam in 2012. It finds that the role of government has an extremely significant influence on a decision of initial adoption of e-procurement in construction enterprises through government leadership, legal and regulatory infrastructure, information and technology infrastructure (ITI), and socio-economic and knowledge infrastructure. However, the role of government is less important to a decision of institutionalization of e-procurement when only ITI significantly influences on the decision-making. As a result, useful theoretical and practical implications are proposed.
Automatic extraction of region of interest from images with low depth of field

In this paper, we propose a new segmentation algorithm for separate focused foreground region from defocused background region with low depth of field image. This algorithm first computes the blur parameter each of pixel. Then, these parameter values are clustered to blurry regions and sharp regions using belief propagation algorithm. Experimental results show that this approach also can separate the focused image from defocused image.
Robotic juggling by iterative learning control using optimization

This paper proposes the control strategy for throwing as one of the dynamic manipulation. In dynamic manipulation, the robot's motion is dynamic and quick and there is no constant contact state. The dynamic manipulation has strong nonlinearity between control input and output, which often makes the manipulation unstable. And modeling errors affect the success of its manipulation task seriously. The dynamic manipulation requires more powerful actuator than the static manipulation.We propose the control method using the learning control that can consider the modeling error problem, the performance limits of the actuator and the stability of the learning control.
CTR9, a Component of PAF Complex, Controls Elongation Block at the c-Fos Locus via Signal-Dependent Regulation of Chromatin-Bound NELF Dissociation

PAF complex (PAFc) is an RNA polymerase II associated factor that controls diverse steps of transcription. Although it is generally associated with actively transcribed genes, a repressive PAFc has also been suggested. Here, we report that PAFc regulates the transition from transcription initiation to transcription elongation. PAFc repressed IL-6-induced, but not TNF-α-induced, immediate early gene expression. PAFc constitutively associated with the 5′-coding region of the c-Fos locus, then transiently dissociated upon IL-6 stimulation. When CTR9, a component of PAFc, was depleted, higher levels of serine 5-phosphorylated or serine 2-phosphorylated forms of RNA Polymerase II were associated with the unstimulated c-Fos locus. We also observed an increased association of CDK9, a kinase component of the pTEF-b elongation factor, with the c-Fos locus in the CTR9-depleted condition. Furthermore, association of negative elongation factor, NELF, which is required to proceed to the elongation phase, was significantly reduced by CTR9 depletion, whereas elongation factor SPT5 recruitment was enhanced by CTR9 depletion. Finally, the chromatin association of CTR9 was specifically controlled by IL-6-induced kinase activity, because a JAK2 kinase inhibitor, AG-490, blocked its association. In conclusion, our data suggest that PAFc controls the recruitment of NELF and SPT5 to target loci in a signal- and locus-specific manner.
Modeling, simulation and control architecture for lower limb active exoskeleton

This paper presents modeling, simulation and control architecture for implementing biped gait in an active lower limb exoskeleton. A lower body biped structure has been modeled in MATLAB Sim Mechanics according to anthropomorphic data. The model is used to derive kinetic and kinematic biped behavior parameters which are required to set control logic and design specifications. The model also includes ground contact forces during impacts. Two separate control modes namely intention based joint trajectory tracking and model based joint torque mode for replicating and controlling walking has been proposed. This paper highlights the preliminary modeling and simulation to control an active lower body exoskeleton for mobility regeneration and enhancement.
Euclidean space of city space syntax

We demonstrate that random walks embed connected undirected graphs into Euclidean space, in which distances and angles acquire the clear statistical interpretation. Being defined on the spatial network of Venetian canals, random walks afford a portrait of the city space syntax and measure its intelligibility. In the present Letter, we investigate the spatial network of 96 canals in Venice (that stretches across 122 small islands between which the canals serve the function of roads) by means of random walks converting city space syntax into Euclidean space. Studies of urban networks have a long history. In most of researches devoted to the improving of transport routes, the optimization of power grids, and the pedestrian movement surveys the relationships between certain components of the urban texture are often measured along streets and routes considered as edges of a planar graph, while the traffic end points and street junctions are treated as nodes. Such a primary graph representation of urban networks is grounded on relations between junctions through the segments of streets. The usual city map based on Euclidean geometry can be considered as an example of primary city graphs.
FETCH REORDERING AND PARTITIONING OF EXECUTION RESOURCES FOR ATOMIC INSTRUCTION BLOCKS WITH CONTROL FLOW ASSERTIONS

The rePLay framework provides a mechanism upon which a variety of code optimizations can be deployed as an application executes. In this thesis, two optimizations are explored. First, the order in which instructions are fetched is optimized. Performance with an optimized schedule is shown to improve by 2.63%. The second optimization is to partition instructions for a clustered microarchitecture. This thesis demonstrates that when such a microarchitecture is implemented along with an optimized fetch schedule, performance still improves by 0.61%.
The Biased Nucleotide Composition of HIV-1 Triggers Type I Interferon Response and Correlates with Subtype D Increased Pathogenicity

The genome of human immunodeficiency virus (HIV) has an average nucleotide composition strongly biased as compared to the human genome. The consequence of such nucleotide composition on HIV pathogenicity has not been investigated yet. To address this question, we analyzed the role of nucleotide bias of HIV-derived nucleic acids in stimulating type-I interferon response in vitro. We found that the biased nucleotide composition of HIV is detected in human cells as compared to humanized sequences, and triggers a strong innate immune response, suggesting the existence of cellular immune mechanisms able to discriminate RNA sequences according to their nucleotide composition or to detect specific secondary structures or linear motifs within biased RNA sequences. We then extended our analysis to the entire genome scale by testing more than 1300 HIV-1 complete genomes to look for an association between nucleotide composition of HIV-1 group M subtypes and their pathogenicity. We found that subtype D, which has an increased pathogenicity compared to the other subtypes, has the most divergent nucleotide composition relative to the human genome. These data support the hypothesis that the biased nucleotide composition of HIV-1 may be related to its pathogenicity.
A hybrid algorithm combining weighted and hashT apriori algorithms in Map Reduce model using Eucalyptus cloud platform

Data mining performs a major role in data analysis which benefits enhancement of business. In modern era, distributed data mining becomes a major research area due to distributed computing. In distributed data mining data can be physically or virtually distributed that helps greatly in finding out interesting facts. Virtual distribution and distributed data mining gets its major impact on big data analysis with the help of cloud and grid computing. This research work performs data mining in a cloud environment using Eucalyptus platform with VMWare workstation and Hadoop platform. This work mines distributed data in a cloud environment using hybrid apriori association rule algorithm which combines the benefits of both hash-t and weighted apriori algorithms. Finally the work compares the performance with the existing implementation of weighted and hash-t algorithms.
Expressions of matrix metalloproteinase-9 (MMP-9), dentin sialophosphoprotein (DSPP), and osteopontin (OPN) at histologically negative surgical margins may predict recurrence of oral squamous cell carcinoma

Up to 50% of oral squamous cell carcinomas (OSCCs) recur following surgical resections with conventional “histologically-negative” margins. Three members of the SIBLING (Small Integrin Binding LIgand N-linked Gylcoprotein) family of proteins: dentin sialophophoprotein (DSPP); bone sialoprotein (BSP); and osteopontin OPN are upregulated in OSCCs. In this study, we aimed to correlate the expression of DSPP, OPN and BSP as well as three SIBLING-partners, matrix metalloproteinase-2 (MMP-2), matrix metalloproteinase-3 (MMP-3), and matrix metalloproteinase-9 (MMP-9), at histologically-negative margins of OSCCs with tumor recurrence. Immunohistochemical analyses of the SIBLINGs and MMP expressions at histologically-negative margins of OSCC was carried out in a retrospective study of 20 patients, and the results correlated with tumor recurrence. Each protein was dichotomized as “present” (≥10% staining) or “absent” (< 10% staining). The Sensitivity, Specificity, Positive Predictive Value(PV+) and Negative Predictive Value (PV−) for recurrence was calculated for each protein, along with their overall diagnostic accuracy, calculated as: (number of true positives + number of true negatives)/ number of patients. OSCC recurred in 9 of 20 patients (45%), a ratio not significantly different from the estimated population recurrence rate of 50% (p = 0.664). Among the SIBLINGs, DSPP and OPN showed the greatest Accuracy with DSPP being more Sensitive (89%) and OPN more Specific (64%). MMP-9 showed the greatest overall Accuracy (80%), slightly less Sensitivity (67%) and more Specificity (100%), than either DSPP or OPN. MMP-9 showed a superior positive PV than either DSPP or OPN. The negative PVs of OPN and MMP-9 were almost identical, and inferior to DSPP. We conclude that DSPP, OPN, or MMP-9 expressions at histologically-negative surgical margins predict OSCC recurrence with MMP-9 being the preferred predictor. These proteins may identify patients who could benefit from more extensive resection, or from adjunct treatments such as radiotherapy.
Self-organized fuzzy system generation from training examples

In the synthesis of a fuzzy system two steps are generally employed: the identification of a structure and the optimization of the parameters defining it. The paper presents a methodology to automatically perform these two steps in conjunction using a three-phase approach to construct a fuzzy system from numerical data. Phase 1 outlines the membership functions and system rules for a specific structure, starting from a very simple initial topology. Phase 2 decides a new and more suitable topology with the information received from the previous step; it determines for which variable the number of fuzzy sets used to discretize the domain must be increased and where these new fuzzy sets should be located. This, in turn, decides in a dynamic way in which part of the input space the number of fuzzy rules should be increased. Phase 3 selects from the different structures obtained to construct a fuzzy system the one providing the best compromise between the accuracy of the approximation and the complexity of the rule set. The accuracy and complexity of the fuzzy system derived by the proposed self-organized fuzzy rule generation procedure (SOFRG) are studied for the problem of function approximation. Simulation results are compared with other methodologies such as artificial neural networks, neuro-fuzzy systems, and genetic algorithms.
Intrusion detection using fuzzy classification and feature selection based on genetic algorithm

Nowadays, different methods are applied for the detection of abnormal attacks on computer networks. In the present study, attempts have been made to minimize errors in intrusion detection and to find its effective optimal features. Generally, an Intrusion Detection System is comprised of three parts: the first part includes sensors that collect information; the second are data processors that detect network or system status; and finally the third part presents types of responses to the attack. In this study, one part of intrusion detection system has been simulated that processes data. In the tests, use has been made of a set of intrusion detection standard data, named KDD99, serving as the evaluated data resource. Also, neural networks have been used for intrusion detection. Recommended procedure include data processing steps through analysis algorithm of main variables, selection of optimal variables and selection of optimal features through Genetic Algorithm by minimizing error of intrusion detection using Adaptive NeuroFuzzy Inference Systems.
Factors Influencing Usability of a Smartphone App to Reduce Excessive Alcohol Consumption: Think Aloud and Interview Studies

Background Interventions delivered by smartphone apps have the potential to help drinkers reduce their consumption of alcohol. To optimize engagement and reduce the high rates of attrition associated with the use of digital interventions, it is necessary to ensure that an app’s design and functionality is appropriate for its intended purposes and target population. Aims To understand the usability of an app to help people reduce their alcohol consumption. Method The app, Drink Less, contains a core module focusing on goal setting, supplemented by five additional modules: self-monitoring and feedback, identity change, cognitive bias re-training, action planning, and social comparison. Two studies were conducted, a “think aloud” study performed with people using the app for the first time and a semistructured interview study performed after users had had access to the app for at least 2 weeks. A thematic analysis of the “think aloud” and interview transcripts was conducted by one coder and verified by a second. Results Twenty-four participants, half of whom were women and half from disadvantaged groups, took part in the two studies. Three main themes identified in the data were “Feeling lost and unsure of what to do next,” “Make the app easy to use,” and “Make the app beneficial and rewarding to use. ” These themes reflected participants’ need for (i) guidance, particularly when first using the app or when entering data; (ii) the data entry process to be simple and the navigation intuitive; (iii) neither the amount of text nor range of options to be overwhelming; (iv) the app to reward them for effort and progress; and (v) it to be clear how the app could help alcohol reduction goals be reached. Conclusion First-time and experienced users want an alcohol reduction app to be easy, rewarding, and beneficial to use. An easy-to-use app would reduce user burden, offer ongoing help, and be esthetically pleasing. A rewarding and beneficial app would provide positive reinforcement, give feedback about progress, and demonstrate credibility. Users need help when first using the app, and they need a compelling reason to continue using it.
Transporter genes expressed by coastal bacterioplankton in response to dissolved organic carbon

Coastal ocean bacterioplankton control the flow of dissolved organic carbon (DOC) from terrestrial and oceanic sources into the marine food web, and regulate the release of inorganic carbon to atmospheric and offshore reservoirs. While the fate of the chemically complex coastal DOC reservoir has long been recognized as a critical feature of the global carbon budget, it has been problematic to identify both the compounds that serve as major conduits for carbon flux and the roles of individual bacterioplankton taxa in mediating that flux. Here we analyse random libraries of expressed genes from a coastal bacterial community to identify sequences representing DOC-transporting proteins. Predicted substrates of expressed transporter genes indicated that carboxylic acids, compatible solutes, polyamines and lipids may be key components of the biologically labile DOC pool in coastal waters, in addition to canonical bacterial substrates such as amino acids, oligopeptides and carbohydrates. Half of the expressed DOC transporter sequences in this coastal ocean appeared to originate from just eight taxa: Roseobacter, SAR11, Flavobacteriales and five orders of γ-Proteobacteria. While all major taxa expressed transporter genes for some DOC components (e.g. amino acids), there were indications of specialization within the bacterioplankton community for others (e.g. carbohydrates, carboxylic acids and polyamines). Experimental manipulations of the natural DOC pool that increased the concentration of phytoplankton- or vascular plant-derived compounds invoked a readily measured response in bacterial transporter gene expression. This highly resolved view of the potential for carbon flux into heterotrophic bacterioplankton cells identifies possible bioreactive components of the coastal DOC pool and highlights differing ecological roles in carbon turnover for the resident bacterial taxa.
Majorization-Minimization Procedures and Convergence of SQP Methods for Semi-Algebraic and Tame Programs

In view of solving nonsmooth and nonconvex problems involving complex constraints (like standard NLP problems), we study general maximization-minimization procedures produced by families of strongly convex subproblems. Using techniques from semi-algebraic geometry and variational analysis—in particular Łojasiewicz inequality—we establish the convergence of sequences generated by these types of schemes to critical points. The broad applicability of this process is illustrated in the context of NLP. In that case, critical points coincide with KKT points. When the data are semi-algebraic or real analytic our method applies (for instance) to the study of various sequential quadratic programming (SQP) schemes: the moving balls method, the penalized SQP method and the extended SQP method. Under standard qualification conditions, this provides—to the best of our knowledge—the first general convergence results for general nonlinear programming problems. We emphasize the fact that, unlike most works on this subject, no second-order conditions and/or convexity assumptions whatsoever are made. Rate of convergence are shown to be of the same form as those commonly encountered with first-order methods.
Cohérences basées sur les valeurs en échec

In constraint satisfaction, basic inferences rely on some properties of constraint networks, called consistencies, that allow the identification of inconsistent instantiations or nogoods. Two main families of consistencies have been introduced so far : those that permit to reason from variables such as (i, j)-consistency and those that permit to reason from constraints such as relational (i, j)-consistency. This paper introduces a new family of consistencies based on the concept of failed value (a value pruned during search). This family is orthogonal to previous ones.
Using an optical mouse sensor to track geophysical field measurements

To position arbitrary outdoor measurements, the most common methods are the use of global positioning system (GPS) technology or distance tracking from reference locations. The system to be introduced here consists of a combination of an optical distance tracking system and a civil, autonomous GPS receiver. Through the application of a recursive filter, both components support and correct each other mutually. The system is intended as an autonomous, real-time and low-cost alternative to expensive differential GPS solutions in geophysical field surveying. The combination approach features a very good relative and decent absolute positioning precision. First measurements using the two-coil geo-electromagnetics instrument Geonics EM31 in combination with the developed positioning system show promising results.