Miracle-dz/UniFAR-FTUs huggingface dataset:
Movement strategies for intelligent mobile routers

If we give routers the ability to move, they no longer have to sit idly in a non-optimal position. They can make their own decisions to attempt to improve the efficacy of themselves or the network. In our work, we will focus on how a router can use local information to try and better its contribution to the network. Our main contribution is the development of simple movement strategies that a robot router can employ to try and improve the network. Our long term goal is to understand the role that mobility can play in improving wireless networks. There are many applications where network connections are not satisfying and a robotic router could improve them. In a simple case, providing temporary self-improving wireless internet to an event, such as a conference, etc., could be more easily accomplished by deploying a mobile robot router. The research area of distributed robots can be aided by allowing intermediate robots to improve the throughput of other robots in the network that are communicating. A robot may need to send high bandwidth data, such as video, to a base station through a set of intermediate ad-hoc routers. If these intermediate nodes could move to account for the robot’s movement, they could improve the bandwidth of the network. Previous work has focused on determining how to provide connectivity to nodes in the field with mobile intermediate wireless router robots [1]. These schemes treat the robotic router problem as a problem of maintaining the visibility of a single moving target. Our work differs because we focus on improving network throughput, while maintaining connectivity.
A virtual laboratory of a manufacturing plant operated with mobile robots

Mobile robots are used in manufacturing workshops and storage plants to automate internal logistics. A model of a manufacturing plant which is representative of the forthcoming flexible systems is presented in this paper. It has been implemented in physical simulator V-Rep, with the KUKA youBot robot as the unit to perform material transportation and handling. This implementation has been used to create a virtual laboratory for students to learn about embedded robot controllers. Results show that the model is robust and that part of the system complexity must be transparent to users to focus on system design issues. Furthermore, the virtual laboratory has been used to explore some approaches to develop navigation control with minimal infrastructure support and other embedded software, which proves its validity as a research tool, too.
A soft computing based approach to estimate reusability of software components

Component-based development (CBD) is the process of assembling existing software components in an application such that they satisfy a predefined functionality. Reduced development time, effort and cost are few merits of CBD. As components are black-box, therefore the use of conventional metrics in CBD is difficult, because these metrics needs analysis of source codes. To assess the reuse of component, it is important to estimate reusability of these components. Present paper discusses reusability in relation to CBD and proposes a reusability metrics for black box components. It identifies the factors affecting reusability of the components and establishes relationship in between these factors and reusability. Fuzzy logic based approach is used to estimate reusability and results found on real time applications are quite satisfactory. The proposed approach can be used by the application developers to select highly reusable components in the systems which will eventually help in maintaining the system in a better way.
Circadian Rhythm Generation and Entrainment in Astrocytes

In mammals, the master circadian pacemaker is considered the suprachiasmatic nucleus (SCN) of the hypothalamus. The SCN consists of a heterogeneous population of neurons and relatively understudied glia. We investigated whether glia, like neurons, rhythmically express circadian genes. We generated pure cultures of cortical astrocytes from Period2::luciferase (Per2::luc) knock-in mice and Period1::luciferase (Per1::luc) transgenic rats and recorded bioluminescence as a real-time reporter of gene activity. We found that rat Per1::luc and mouse Per2::luc astroglia express circadian rhythms with a genetically determined period. These rhythms damped out after several days but were reinitiated by a variety of treatments, including a full volume exchange of the medium. If cultures were treated before damping out, the phase of Per1::luc rhythmicity was shifted, depending on the time of the pulse relative to the peak of Per1 expression. Glial rhythms entrained to daily 1.5°C temperature cycles and were significantly sustained when cocultured with explants of the adult SCN but not with cortical explants. Thus, multiple signals, including a diffusible factor(s) from the SCN, are sufficient to either entrain or restart circadian oscillations in cortical glia.
Management of anterior dental crossbite with removable appliances

This case report describes the treatment of an 8-year-old girl with anterior dental crossbite using a series of removable appliances to bring the teeth into a normal position. Clinical presentation and intervention: A removable acrylic appliance with a bite plate incorporating a screw was used to correct the anterior dental crossbite and align the incisors. The subsequent eruption of the maxillary left lateral incisor on the palatinal side was treated with a second acrylic plate incorporating a labiolingual spring. After an 8-month period, the anterior crossbite involving multiple incisors was corrected.
Scientific Evaluation of VSP Nd:YAG Lasers for Hair Removal

A variety of laser systems with varying wavelengths, pulse durations, spotsizes and energy fluences are currently used for hair removal. In this paper VSP Nd:YAG laser efficacy and safety were evaluated. We found that the modern, high-power, second generation VSP Nd:YAG laser systems with cold air cooling and scanner technology gave the best possibilities to adjust treatment parameters to the individual cases and thus optimize treatment efficacy while preserving the epidermis from unwanted damage.
The application of competitive Hopfield neural network to medical image segmentation

In this paper, a parallel and unsupervised approach using the competitive Hopfield neural network (CHNN) is proposed for medical image segmentation. It is a kind of Hopfield network which incorporates the winner-takes-all (WTA) learning mechanism. The image segmentation is conceptually formulated as a problem of pixel clustering based upon the global information of the gray level distribution. Thus, the energy function for minimization is defined as the mean of the squared distance measures of the gray levels within each class. The proposed network avoids the onerous procedure of determining values for the weighting factors in the energy function. In addition, its training scheme enables the network to learn rapidly and effectively. For an image of n gray levels and c interesting objects, the proposed CHNN would consist of n by c neurons and be independent of the image size. In both simulation studies and practical medical image segmentation, the CHNN method shows promising results in comparison with two well-known methods: the hard and the fuzzy c-means (FCM) methods.
Reduced load Erlang fixed point analysis of optical burst switched networks with deflection routing and wavelength reservation

Network simulations and Markovian queuing models for nodes in isolation have suggested that de∞ection routing (alternate routing) may be a viable method to resolve wavelength contention. However, we show that de∞ection routing may destabilise OBS networks operating at high loads. To prevent the destabilising efiect of de∞ection routing, we propose and analyse a technique called wavelength reservation to intentionally limit the amount of de∞ection at high loads. This paper is the flrst to present a new reduced load Erlang flxed point analysis of OBS networks with de∞ection routing and wavelength reservation. We apply the new analysis to evaluate the beneflt of deploying de∞ection routing and wavelength reservation in a sample OBS network.
Towards a Better Treatment of Parkinson ' s disease

46 Abbreviations . 46 Introduction . 47 Materials and Methods . 50 Partial Lesion Model . 50 Cylinder Test . 51 Results . 51 Model Verification . 51 Sensitivity of Cylinder Test. 53 Consistency of Cylinder Test. 57 Correcting for Different Sets of Model Animals. 59 Dose Response. 62 Therapeutic Effect of Raclopride. 66 Overall Therapeutic Effect of Raclopride . 68 Discussion . 70 4. Discussion . 72 Discussion and Conclusion . 72 Introduction . 72 Hypothesis . 72 Electrophysiological Studies . 72 Behavioral Studies . 73 Conclusion . 74 References . 75
Thermodynamic Tree: The Space of Admissible Paths

Is a spontaneous transition from a state x to a state y allowed by thermodynamics? Such a question arises often in chemical thermodynamics and kinetics. We ask the following more formal question: Is there a continuous path between these states, along which the conservation laws hold, the concentra- tions remain nonnegative, and the relevant thermodynamic potential G (Gibbs energy, for example) monotonically decreases? The obvious necessary condition, G(x) ≥ G(y), is not sufficient, and we construct the necessary and sufficient conditions. For example, it is impossible to overstep the equi- librium in 1-dimensional (1D) systems (with n components and n−1 conservation laws). The system cannot come from a state x to a state y if they are on the opposite sides of the equilibrium even if G(x) >G (y). We find the general multidimensional analogue of this 1D rule and constructively solve the problem of the thermodynamically admissible transitions. We study dynamical systems, which are given in a positively invariant convex polyhedron D and have a convex Lyapunov function G. An admissible path is a continuous curve in D along which G does not increase. For x, y ∈ D, x y (x precedes y) if there exists an admissible path from x to y and x ∼ y if x y and y x. The tree ofG in D is a quotient space D/ ∼. We provide an algorithm for the construction of this tree. In this algorithm, the restriction of G onto the 1-skeleton of D (the union of edges) is used. The problem of existence of admissible paths between states is solved constructively. The regions attainable by the admissible paths are described.
Active Semi-supervised Spectral Clustering

Spectral clustering is widely used in these years. Recently, methods that connect spectral clustering and semi-supervised clustering become popular. These methods improve the result through using constraint information in spectral clustering. Generally, there are two ways to select constrained information, one is random selection method and the other is active learning method. Here we focus on active learning methods. In this paper, we propose an active learning process, which considers the local and global information of dataset, and decide which constraint to choose by studying the change of eigenvectors.
Activity Recognition In Smart Home Using Weighted Dempster - Shafer Theory

Smart homes are equipped with a variety of sensors to monitor the human activities. The information gathered from the heterogeneous sensors may not be always reliable and have different degrees of uncertainly. One of the most important techniques have been proposed to deal with uncertainty is Dempster-Shafer Theory (DST). In this paper, aims to define more precise sensor reliability and decrease uncertainty in sensor data in the activity recognition process within smart home. In the proposed method, in training step some models are built for per activity according extracted features from training samples and then in the prediction step when a new signal sensor is observed, the features extracted from that signal and applied to models and a weight is calculated for that sensor. These weights are considered as sensor reliability and uses in the decision making process.
Coresets, sparse greedy approximation, and the Frank-Wolfe algorithm

The problem of maximizing a concave function <i>f</i>(<i>x</i>) in a simplex <i>S</i> can be solved approximately by a simple greedy algorithm. For given <i>k</i>, the algorithm can find a point <i>x</i>(<i>k</i>) on a <i>k</i>-dimensional face of <i>S</i>, such that <i>f</i>(<i>x</i>(<i>k</i>)) ≥ <i>f</i>(<i>x</i>*) - <i>O</i>(1/<i>k</i>). Here <i>f</i>(<i>x</i>*) is the maximum value of <i>f</i> in <i>S. </i> This algorithm and analysis were known before, and related to problems of statistics and machine learning, such as boosting, regression, and density mixture estimation. In other work, coming from computational geometry, the existence of ε-<i>coresets</i> was shown for the minimum enclosing ball problem, by means of a simple greedy algorithm. Similar greedy algorithms, that are special cases of the Frank-Wolfe algorithm, were described for other enclosure problems. Here these results are tied together, stronger convergence results are reviewed, and several coreset bounds are generalized or strengthened.
A Sequential Algorithm for Solving Nonlinear Optimization Problems with Chance Constraints

An algorithm is presented for solving nonlinear optimization problems with chance constraints, i.e., those in which a constraint involving an uncertain parameter must be satisfied with at least a minimum probability. In particular, the algorithm is designed to solve cardinality-constrained nonlinear optimization problems that arise in sample average approximations of chance-constrained problems, as well as in other applications in which it is only desired to enforce a minimum number of constraints. The algorithm employs a novel exact penalty function which is minimized sequentially by solving quadratic optimization subproblems with linear cardinality constraints. Properties of minimizers of the penalty function in relation to minimizers of the corresponding nonlinear optimization problem are presented, and convergence of the proposed algorithm to stationarity with respect to the penalty function is proved. The effectiveness of the algorithm is demonstrated through numerical experiments with a nonlinear ca.
DSM-Oriented Scales and Statistically Based Syndromes for Ages 18 to 59: Linking Taxonomic Paradigms to Facilitate Multitaxonomic Approaches

We used behavioral and emotional problem items to construct (a) nosologically based Diagnostic and Statistical Manual of Mental Disorders (DSM) oriented scales from experts' ratings of the items' consistency with DSM-IV (4th ed.; American Psychiatric Association, 1994) diagnostic categories, and (b) statistically based syndromes from factor analyses of adults' self-ratings and ratings of adults by people who knew them (N = 4,628). Quantified, operationally defined, and normed DSM-oriented scales and statistically based syndromes facilitate multitaxonomic approaches to the assessment of adult psychopathology. Psychometric properties and cross-informant correlations were similar for DSM-oriented scales and statistically derived syndromes. Statistical associations between phenotypically similar DSM-oriented scales and statistically based syndromes were moderate to strong. Multitaxonomic approaches can avoid reification of provisional taxa that may result from excessive reliance on a single taxonomic paradigm.
A Framework for Universally Composable Diffie-Hellman Key Exchange

The analysis of real-world protocols, in particular key exchange protocols and protocols building on these protocols, is a very complex, error-prone, and tedious task. Besides the complexity of the protocols itself, one important reason for this is that the security of the protocols has to be reduced to the security of the underlying cryptographic primitives for every protocol time and again. We would therefore like to get rid of reduction proofs for real-world key exchange protocols as much as possible and in many cases altogether, also for higher-level protocols which use the exchanged keys. So far some first steps have been taken in this direction. But existing work is still quite limited, and, for example, does not support Diffie-Hellman (DH) key exchange, a prevalent cryptographic primitive for real-world protocols. In this paper, building on work by Küsters and Tuengerthal, we provide an ideal functionality in the universal composability setting which supports several common cryptographic primitives, including DH key exchange. This functionality helps to avoid reduction proofs in the analysis of real-world protocols and often eliminates them completely. We also propose a new general ideal key exchange functionality which allows higher-level protocols to use exchanged keys in an ideal way. As a proof of concept, we apply our framework to three practical DH key exchange protocols, namely ISO 9798-3, SIGMA, and OPTLS.
Metric and Kernel Learning Using a Linear Transformation

Metric and kernel learning arise in several machine learning applications. However, most existing metric learning algorithms are limited to learning metrics over low-dimensional data, while existing kernel learning algorithms are often limited to the transductive setting and do not generalize to new data points. In this paper, we study the connections between metric learning and kernel learning that arise when studying metric learning as a linear transformation learning problem. In particular, we propose a general optimization framework for learning metrics via linear transformations, and analyze in detail a special case of our framework-that of minimizing the LogDet divergence subject to linear constraints. We then propose a general regularized framework for learning a kernel matrix, and show it to be equivalent to our metric learning framework. Our theoretical connections between metric and kernel learning have two main consequences: 1) the learned kernel matrix parameterizes a linear transformation kernel function and can be applied inductively to new data points, 2) our result yields a constructive method for kernelizing most existing Mahalanobis metric learning formulations. We demonstrate our learning approach by applying it to large-scale real world problems in computer vision, text mining and semi-supervised kernel dimensionality reduction.
SpRay: A visual analytics approach for gene expression data

We present a new application, SpRay, designed for the visual exploration of gene expression data. It is based on an extension and adaption of parallel coordinates to support the visual exploration of large and high-dimensional datasets. In particular, we investigate the visual analysis of gene expression data as generated by micro-array experiments; We combine refined visual exploration with statistical methods to a visual analytics approach that proved to be particularly successful in this application domain. We will demonstrate the usefulness on several multidimensional gene expression datasets from different bioinformatics applications.
Effect of heliox breathing on flow limitation in chronic heart failure patients

Patients with chronic heart failure (CHF) exhibit orthopnoea and tidal expiratory flow limitation in the supine position. It is not known whether the flow-limiting segment occurs in the peripheral or central part of the tracheobronchial tree. The location of the flow-limiting segment can be inferred from the effects of heliox (80% helium/20% oxygen) administration. If maximal expiratory flow increases with this low-density mixture, the choke point should be located in the central airways, where the wave-speed mechanism dominates. If the choke point were located in the peripheral airways, where maximal flow is limited by a viscous mechanism, heliox should have no effect on flow limitation and dynamic hyperinflation. Tidal expiratory flow limitation, dynamic hyperinflation and breathing pattern were assessed in 14 stable CHF patients during air and heliox breathing at rest in the sitting and supine position. No patient was flow-limited in the sitting position. In the supine posture, eight patients exhibited tidal expiratory flow limitation on air. Heliox had no effect on flow limitation and dynamic hyperinflation and only minor effects on the breathing pattern. The lack of density dependence of maximal expiratory flow implies that, in CHF patients, the choke point is located in the peripheral airways.
Increasing resistance of Streptococcus pneumoniae to fluoroquinolones: results of a Hong Kong multicentre study in 2000.

The MICs of 13 antimicrobial agents including seven fluoroquinolones (ciprofloxacin, levofloxacin, sparfloxacin, grepafloxacin, gatifloxacin, moxifloxacin and clinafloxacin) for Streptococcus pneumoniae isolates obtained from all regions of Hong Kong in the year 2000 were determined by the Etest. Overall, 39.4% of 180 isolates were susceptible to penicillin, 11.7% were intermediate and 48.9% were resistant. The overall prevalence of fluoroquinolone non-susceptibility (levofloxacin MIC > or = 4 mg/L) was 13.3% but increased to 27.3% among the penicillin-resistant isolates. For the fluoroquinolone non-susceptible isolates, within-class cross-resistance was common. For the fluoroquinolone non-susceptible isolates, the median MICs of clinafloxacin, gatifloxacin, moxifloxacin, sparfloxacin and grepafloxacin were, respectively, six-, 24-, 32- 84- and 128-fold higher than those for the susceptible isolates. All fluoroquinolone non-susceptible strains were derived from adults. The prevalence of fluoroquinolone resistance was higher in isolates from older patients (17.1% among those > or = 65 years of age versus 9.1% among those 18-64 years of age, P < 0.001) and from adults with chronic obstructive pulmonary disease (24.6% versus 9.3%, P = 0.01). All fluoroquinolone non-susceptible strains were non-susceptible to penicillin (MIC range 2-4 mg/L), cefotaxime (MIC range 1-4 mg/L) and erythromycin (MIC range 4- > or = 256 mg/L). The fluoroquinolone non-susceptible isolates were genetically related to the Spain(23F)-1 clone when analysed by pulse-field gel electrophoresis and multilocus sequence typing. In conclusion, a rapid increase in the prevalence of fluoroquinolone resistance among S. pneumoniae was found in Hong Kong. Typing analysis suggests that this is due to the pan-regional dissemination of a fluoroquinolone-resistant variant (designated Hong Kong(23F)-1) of the globally distributed Spain(23)F-1 clone.
Approximate Sensor Interpretation

Sensor interpretation (SI) involves determining abstract explanations for sensor data. SI differs in several significant ways from the kind of "diagnosis problems" that have been heavily studied within the belief network community. These differences lead to the need for approximate, satisficing problem-solving techniques in most real-world SI problems. Currently, there are no AI techniques with well understood properties that can apply a wide range of approximate SI strategies. In this paper we will examine the differences between SI and diagnosis that lead to the need for approximation, and discuss several approximation techniques. We will then consider the two main AI approaches to SI, blackboard systems and dynamic belief networks, and explore their deficiencies for SI. As a point of comparison, we will also consider techniques used by the target tracking community.