Miracle-dz/UniFAR-FTUs huggingface dataset:
Cleansing Databases of Misspelled Proper Nouns

The paper presents a data cleansing technique for string databases. We propose and evaluate an algorithm that identifies a group of strings that consists of (multiple) occurrences of a correctly spelled string plus nearby misspelled strings. All strings in a group are replaced by the most frequent string of this group. Our method targets proper noun databases, including names and addresses, which are not handled by dictionaries. At the technical level we give an efficient solution for computing the center of a group of strings and determine the border of the group. We use inverse strings together with sampling to efficiently identify and cleanse a database. The experimental evaluation shows that for proper nouns the center calculation and border detection algorithms are robust and even very small sample sizes yield good results.
A backup appliance composed of high-capacity disk drives

Disk drives are now available with capacity and price per capacity comparable to nearline tape systems. Because disks have superior performance, density and maintainability characteristics, it seems likely that they will soon overtake tapes as the backup medium of choice. The authors outline the potential advantages of a backup system composed of high-capacity disk drives and describe what implications such a system would have for backup software.
Multi objective Cost sensitive subspace reduction technique for access control system

A Cost sensitive subspace reduction technique using multi objective optimizer has been designed for an access control system having a hierarchy of classes starting from the most important class to the least desirable class. The main aim of this paper is to extract the cost sensitive features from facial images of test subjects and project the faces on to a domain with reduced dimension. The feature vector consisting of the cost sensitive information has been obtained by assigning appropriate cost values for different classes so that the occurrence of false rejection is higher than that of false acceptance. The reduced feature vectors were trained using neural network classifier. The proposed method has been found to be better than many contemporary methods. The percentage value of false acceptance obtained is of the order of 1 to 2 which is very less so far as suppressing the acceptance of less desirable classes is concerned.
Information Asymmetry in the Artificial Financial Market Represented by Scale-Free Network

Financial markets are complex systems in which the market price of the financial instruments reflects the distribution of information and investors’ expectations. If we regard investors as nodes (vertices) and interacting relations between investors as links (edges), than financial markets can be viewed as complex networks, whose structure corresponds to scale-free networks. Since the classic mathematical models have not yielded satisfactory results in market analysis, in recent years, the complex system analysis is carried out using agent-based models. In this paper we analyze the diffusion of information in the financial market presented by means of a scale-free network. We hypothesize that large investors are better informed than the smaller ones and explain reasons for appearance of information asymmetry between the traders. Considering the assumption that financial market is scale-free network, we present a simple agent-based computational model of the financial market, formed in Netlog and R programming environment, and investigate the effects of distribution of information through network. Simulations of artificial stock market (ASM) model with different number of heterogeneous agents and different priority in orders’ realization shows that initial wealth of agents is reallocated in favor of better informed agents. Small and “uninformed” agents can reduce the information gap by imitating the wealthier neighboring agent and increase the wealth. However, favoring realization of large orders can paradoxically improve the position of small agents in the market by reducing the effect of inaccurate anticipation of market movements due to lack of information and efficacy to make precise anticipation.
Changes in soft tissue nasal widths associated with rapid maxillary expansion in prepubertal and postpubertal subjects.

OBJECTIVE To evaluate changes in the soft tissue width of the nose induced by rapid maxillary expansion (RME). Data on greater alar cartilage (GAC) and alar base (AB) widths were compared with a normative sample within the same age range. MATERIALS AND METHODS This prospective study consisted of an RME sample of 79 patients treated with an RME protocol. Mean age at the start of RME treatment was 13.5 years; average duration of treatment was 6.7 months. Patients were grouped into prepubertal and postpubertal groups based on their cervical vertebral maturation (CVM) stage. AB and GAC widths were determined at three separate time points. The normative sample consisted of 437 orthodontically untreated whites, aged 10-16 years. A repeated measures analysis of variance (ANOVA) was used to determine group differences. In addition, independent sample t-tests were used to compare posttreatment nasal width values vs the untreated normative sample. RESULTS Increases in AB and GAC widths of the nose in the RME sample were less than 1.5 mm. No significant differences were noted in width changes between the prepubertal and postpubertal subgroups. Comparisons of T3 values showed that on average nasal width increases were greater in the RME group than in untreated norms by 1.7 mm for the GAC measure (statistically significant), and by less than 1 mm for the AB measure. CONCLUSIONS RME has no significant clinical effects on the widths of the apical base and the greater alar cartilage of the nose; no differences were observed between the two maturational subgroups.
Consensus or Deadlock? Consequences of Simple Behavioral Rules for Coordination in Group Decisions

Coordinated collective behaviors often emerge from simple rules governing the interactions of individuals in groups. We model mechanisms of coordination among ants during cooperative transport, a challenging task that requires a consensus on travel direction. Decisions required for cooperative transport differ from other, well-studied consensus decisions because groups often deadlock, with individuals trying to move in opposing directions, and cooperative transport groups are often relatively small. Small groups may be more affected by individual nonconformity. Using deterministic and stochastic models, we investigate behavioral factors that affect deadlock duration. Our goal is to determine whether groups following simple behavioral rules can reach a consensus using minimal information. We define and investigate multiple types of behavioral rules that govern individual behavior and also differ in the information available. We find that if individuals more readily give up when they are going against the majority, groups rapidly break deadlocks. This occurs through positive and negative feedbacks that are implemented in our model via a single mechanism. We also find that to quickly reach a consensus, groups must have either a shared bias, high sensitivity to group behavior, or finely tuned persistence. While inspired by ants, our results are generalizable to other collective decisions with deadlocks, and demonstrate that groups of behaviorally simple individuals with no memory and extremely limited information can break symmetry and reach a consensus in a decision between two equal options
On Growing Self - Organizing Neural Networks without Fixed Dimensionality

Kohonen's self-organizing maps (KSOM) can generate mappings from high-dimensional signal spaces to lower dimensional topological structures. The main features of this kind of mappings are formation of topology preserving, feature mappings and probability distribution approximation of input patterns. However, KSOM have some limitations, e.g., a fixed number of neural units and a topology of fixed dimensionality, which makes KSOM impractical for applications where the optimal number of units is not known in advance and resulting in problems if this predefined dimensionality does not match the dimensionality of the feature manifold. Growing Self-organizing neural networks (GSONN) can change their topological structures during learning. GSONN without fixed dimensionality has no topology of a fixed dimensionality imposed on the network. This paper first gives an introduction to neural gas network, a non-grid KSOM. Then, we discuss some GSONN without fixed dimensionality such as growing neural gas and the author's model: twin growing neural gas. It is ended with some testing results comparison and conclusions.
Dominance-Based Rough Set Approach in Selection of Portfolio of Sustainable Development Projects

In our study, the Dominance-based Rough Set Approach (DRSA) has been proposed to assist the Board of Directors of the Community Futures Development Corporations (CFDC), the sub-region of Abitibi-West (Quebec). The CFDC needs a tool for decision support to select the projects that are proposed by the contractors and partners of its territory. In decision making, a balanced set of 22 indicators is considered. These indicators derive from five perspectives: economic, social, demographic, health and wellness. The DRSA proposal is suitable for the data processing with multiple indicators providing on many examples to infer decision rules related to the preference model. In this paper we show that decision rules developed with the use of rough set theory allow us to simplify the process of selecting a portfolio for sustainable development by reducing a number of redundant indicators and identifying the critical values of selected indicators.
A Simple Simulation Approach for the Estimation of Convergence and Performance of Fully Adaptive Equalization in High-Speed Serial Interfaces

We propose a simple approach to simulate the convergence and the performance of high-speed serial interfaces with adaptive equalization. Such a method uses as input the pulse response of the channel (printed circuit board and cables, plus package/socket) that is then modified by adaptive techniques implementing feed-forward, decision-feedback, and linear-continuous time equalization. Probabilistic considerations are used in this novel method to estimate the evolution during time of the LMS loop and the resulting evolution of the eye diagram. Sample results are reported for a realistic link operating at 20 Gb/s.
Design and analysis of a ranking approach to private location-based services

Users of mobile services wish to retrieve nearby points of interest without disclosing their locations to the services. This article addresses the challenge of optimizing the query performance while satisfying given location privacy and query accuracy requirements. The article's proposal, SpaceTwist, aims to offer location privacy for k nearest neighbor (kNN) queries at low communication cost without requiring a trusted anonymizer. The solution can be used with a conventional DBMS as well as with a server optimized for location-based services. In particular, we believe that this is the first solution that expresses the server-side functionality in a single SQL statement. In its basic form, SpaceTwist utilizes well-known incremental NN query processing on the server. When augmented with a server-side granular search technique, SpaceTwist is capable of exploiting relaxed query accuracy guarantees for obtaining better performance. We extend SpaceTwist with so-called ring ranking, which improves the communication cost, delayed termination, which improves the privacy afforded the user, and the ability to function in spatial networks in addition to Euclidean space. We report on analytical and empirical studies that offer insight into the properties of SpaceTwist and suggest that our proposal is indeed capable of offering privacy with very good performance in realistic settings.
Avionics Applications on a Time-Predictable Chip-Multiprocessor

Avionics applications need to be certified for the highest criticality standard. This certification includes schedulability analysis and worst-case execution time (WCET) analysis. WCET analysis is only possible when the software is written to be WCET analyzable and when the platform is time-predictable. In this paper we present prototype avionics applications that have been ported to the time-predictable T-CREST platform. The applications are WCET analyzable, and T-CREST is supported by the aiT WCET analyzer. This combination allows us to provide WCET bounds of avionic tasks, even when executing on a multicore processor.
Hierarchical triangular patches for terrain rendering with their matching blocks

To efficiently represent large artificial terrains on today's graphic hardwares, we propose a new terrain rendering scheme with hierarchical triangular patches and pre-constructed match ing blocks. It renders terrain patches at different LODs(levels-of-details) according to the current visual requirement. The hierarchical structure can reduce the total number of 'draw primitive(DirectX)' calls significantly by replacing a large set of triangles with a single patch where appropriate while other conventional methods try to reduce the total number of primitives to be rendered. After the initial construction of patches, the new scheme does not re-triangulate during runtime, further enhancing its performance. We also propose a specially designed matching method to eliminate cracks on the boundaries between neighboring patches. It constructs a few matching blocks at initialization with which it replaces original patches on boundaries where cracks may occur.
Permutation decoding: Towards an approach using algebraic properties of the -subcode

In this paper, we show a manner to use properties of the permutation group on some binary linear codes to improve the decoding algorithms. We search especially for articular permutations and we prove bounds on dimension of a special subcode of the idempotent sub-code. This -subcode can have very lower dimension in practice than the original code. We give several examples at the end and explain what can be the gain with this way of decoding.
In-situ adhesion studies of a single living bovine endothelial cell using MEMS sensor

Although previous studies of the adhesion properties of single living cells have been conducted a full understanding has still not been attained. In this article a MEMS sensor has been employed to study, quantitatively and qualitatively, the adhesion properties of a single living bovine endothelial cell. The strength of a single focal adhesion site to an extracellular matrix coated substrate is found to be 37 nN. Focal adhesion sites have been observed, in-situ, to be spaced on the order of 1 /spl mu/m intervals. A model is also proposed to describe the mechanism of detachment of a single living cell from a substrate.
ARAP++: an extension of the local/global approach to mesh parameterization

Mesh parameterization is one of the fundamental operations in computer graphics (CG) and computeraided design (CAD). In this paper, we propose a novel local/global parameterization approach, ARAP++, for singleand multi-boundary triangular meshes. It is an extension of the as-rigid-as-possible (ARAP) approach, which stitches together 1-ring patches instead of individual triangles. To optimize the spring energy, we introduce a linear iterative scheme which employs convex combination weights and a fitting Jacobian matrix corresponding to a prescribed family of transformations. Our algorithm is simple, efficient, and robust. The geometric properties (angle and area) of the original model can also be preserved by appropriately prescribing the singular values of the fitting matrix. To reduce the area and stretch distortions for high-curvature models, a stretch operator is introduced. Numerical results demonstrate that ARAP++ outperforms several state-of-the-art methods in terms of controlling the distortions of angle, area, and stretch. Furthermore, it achieves a better visualization performance for several applications, such as texture mapping and surface remeshing.
Circulating Prolactin Associates With Diabetes and Impaired Glucose Regulation

OBJECTIVE Prolactin is a major stimulus for the β-cell adaptation during gestation and guards postpartum women against gestational diabetes. Most studies of the role of prolactin on glucose metabolism have been conducted in humans and animals during pregnancy. However, little is known concerning the association between circulating prolactin and glucose metabolism outside pregnancy in epidemiological studies. We aimed to determine whether the variation of circulating prolactin concentration associates with diabetes and impaired glucose regulation (IGR) in a cross-sectional study. RESEARCH DESIGN AND METHODS We recruited 2,377 participants (1,034 men and 1,343 postmenopausal women) without hyperprolactinemia, aged 40 years and older, in Shanghai, China. Diabetes and IGR were determined by an oral glucose tolerance test. Multinomial logit analyses were performed to evaluate the relationship of prolactin with diabetes and IGR. RESULTS Prolactin levels decreased from normal glucose regulation to IGR to diabetes. Multinomial logit analyses, adjusted for potential confounding factors, showed that high circulating prolactin was associated with lower prevalence of diabetes and IGR. The adjusted odds ratios (95% CI) for IGR and diabetes for the highest compared with the lowest quartile of prolactin were 0.54 (95% CI 0.33–0.89) and 0.38 (0.24–0.59) in men and 0.54 (0.36–0.81) and 0.47 (0.32–0.70) in women. CONCLUSIONS High circulating prolactin associates with lower prevalence of diabetes and IGR in the current study. Further studies are warranted to confirm this association.
Multi-Way Partitioning Via Geometric Embeddings, Orderings, and Dynamic Programming

30 Acknowledgements We thank Pak K. Chan, Martine Schlag and Jason Zien for past research discussions, for the use of the LASO interface written by Martine Schlag, and for the use of their KP and SB codes. Ken D. Boese supplied the 3-Opt optimization code. Lars Hagen and Jen-Hsin Huang developed the ideas behind the partitioning-speciic net model. The anonymous reviewers provided many comments which substantially improved this work, particularly in the experimental design. Part of this work (ABK) was performed in part during a Spring 1993 sabbatical visit to UC Berkeley; support from NSF MIP-9117328 and the hospitality of Professor Ernest S. Kuh and his research group is gratefully acknowledged. Benz ecri, \Construction d'une Classiication Ascendante Hi erarchique par la Rechereche en Chaine des Voisins R eciproques", Les Cahiers de l'Analyse des Donn ees (VII)2, pp. Table 7: Runtimes in seconds on a Sun Sparc 10, for a single run of DP-RP for each of the ten d-dimensional embeddings (constructed from ~ 2 ; : : :; ~ d+1). Each Embedding entry gives the time required to generate d eigenvectors, and a DP-RP entry gives the time needed to generate 2-through 10-way partitioning solutions.
Using Multi-View Learning to Improve Detection of Investor Sentiments on Twitter

Stocks-related messages on social media have several interesting properties regarding the sentiment analysis (SA) task. On the one hand, the analysis is particularly challenging, because of frequent typos, bad grammar, and idiosyncratic expressions specific to the domain and media. On the other hand, stocks-related messages primarily refer to the state of specific entities – companies and their stocks, at specific times (of sending). This state is an objective property and even has a measurable numeric characteristic, namely the stock price. Given a large dataset of twitter messages, we can create two separate "views" on the dataset by analyzing messages’ text and external properties separately. With this, we can expand the coverage of generic SA tools and learn new sentiment expressions. In this paper, we experiment with this learning method, comparing several types of general SA tools and sets of external properties. The method is shown to produce significant improvement in accuracy.
An introduction to change management theory for veterinary nurses: part one

Abstract: There are many reasons why a change in practice may need to be initiated. While the need for change may be explicit and essential, implementing new ways of working within an established team can be challenging and if mishandled may lead to high levels of stress among staff members. There are robust tools available to support the implementation of change in the workplace. This article does not seek to recommend any particular model to use, but aims to act as an introduction to elements of change management theory to encourage Veterinary Nurses to seek out information to support their practice.
Modelling and Optimal Control of a Docking Maneuver with an Uncontrolled Satellite

Abstract Capturing disused satellites in orbit and their controlled reentry is the aim of the DEOS space mission. Satellites that ran out of fuel or got damaged pose a threat to working projects in orbit. Additionally, the reentry of such objects endangers the population as the place of impact cannot be controlled anymore. This paper demonstrates the modelling of a rendezvous szenario between a controlled service satellite and an uncontrolled target. The situation is modelled via first order ordinary differental equations where a stable target is considered. In order to prevent a collision of the two spacecrafts and to ensure both satellites are docked at the end of the maneuver, additional state constraints, box contraints for the control and a time dependent rendezvous condition for the final time are added. The problem is formulated as an optimal control problem with Bolza type cost functional and solved using a full discretization approach in AMPL/IpOpt. Last, simulation results for capturing a tumbling satellite are given.
Montclair State University Digital Montclair State University Digital Commons Commons Noise-induced switching and extinction in systems with delay Noise-induced switching and extinction in systems with delay

We consider the rates of noise-induced switching between the stable states of dissipative dynamical systems with delay and also the rates of noise-induced extinction, where such systems model population dynamics. We study a class of systems where the evolution depends on the dynamical variables at a preceding time with a ﬁxed time delay, which we call hard delay. For weak noise, the rates of interattractor switching and extinction are exponentially small. Finding these rates to logarithmic accuracy is reduced to variational problems. The solutions of the variational problems give the most probable paths followed in switching or extinction. We show that the equations for the most probable paths are acausal and formulate the appropriate boundary conditions. Explicit results are obtained for small delay compared to the relaxation rate. We also develop a direct variational method to ﬁnd the rates. We ﬁnd that the analytical results agree well with the numerical simulations for both switching and extinction rates.