Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of parameters of the simple multivariate linear model with student-t errors

This paper considers estimation of the intercept and slope vector parameters of the simple multivariate linear regression model with Student-t errors in the presence of uncertain prior information on the value of the unknown slope vector. The unrestricted,restricted, preliminary test, shrinkage, and positive-rule shrinkage estimators are defined together with the expressions for the bias, quadratic bias, quadratic risk and mean squared errors (mse) functions of the estimators are derived. Comparison of the estimators is made using quadratic risk criterion. Based on the study we conclude that for p> or = 3 shrinkage estimators are recommended, and for p< or = 2, the preliminary test estimators are preferable.
DaTeC: Contextual data flow testing of java classes

Many mature development processes use structural coverage metrics to monitor the quality of testing. Recent studies suggest that commonly used control flow testing criteria poorly address state-based behavior of object oriented software. This paper presents DaTeC, a tool that provides useful coverage information of Java object states by implementing a novel contextual data flow testing approach.
Healing outcomes of root canal treatment for C-shaped mandibular second molars: a retrospective analysis

Objectives This study aimed to evaluate the healing rate of non-surgical endodontic treatment between C-shaped and non-C-shaped mandibular second molars. Materials and Methods Clinical records and radiological images of patients who had undergone endodontic treatment on mandibular second molars between 2007 and 2014 were screened. The periapical index scoring system was applied to compare healing outcomes. Information about preoperative and postoperative factors as well as the demographic data of the patients was acquired and evaluated using chi-square and multinomial logistic regression tests. Results The total healing rate was 68.4%. Healing rates for the mandibular second molar were 70.9% in C-shaped canals (n = 79) and 66.6% in non-C-shaped ones (n = 117). The difference was not statistically significant. Conclusions The presence of a C-shaped canal in the mandibular second molar did not have a significantly negative effect on healing after treatment. Instead, proper pulpal diagnosis and final restoration were indicated as having significantly greater influence on the healing outcomes of C-shaped and non-C-shaped canals, respectively.
Some remarks on subduction zones

The rate of subduction is larger than the convergence rate along W-directed subduction zones, whereas it is smaller along Eor NE-directed subduction zones. Along Wdirected slabs, the subduction rate is the convergence rate plus the slab retreat rate, which tends to equal the backarc extension rate. Along Eor NE-directed slabs, the subduction rate is decreased by the shortening in the upper plate. The two opposite kinematics predict a subduction hinge migrating away from (W-directed subduction), or toward the (E-NE-directed subduction) upper plate. Relative to the mantle, the W-directed slab hinges are fixed, whereas they move west or southwest along Eor NE-directed subduction zones. With a given convergence rate, the amount of shortening in the orogens related to Eor NE-directed subduction zones is inversely proportional to the viscosity of the upper plate, i.e., higher the viscosity, smaller the shortening and faster the subduction rate. The convergence/shortening ratio in this type of orogens can be a useful tool for mapping upper plate viscosity values. In the central Andes the ratio is 2.2. These observations suggest that subduction zones are passive features with respect to the far field velocity of plate motions and the relative “eastward” mantle flow. The slab pull is questioned as not a first order force for plate motions. Mantle convection is also inadequate to explain the Earth’s surface kinematics. The Earth’s rotation seems to be the simplest candidate to explain the asymmetry along subduction zones. Corresponding author: C. Doglioni; Dipartimento di Scienze della Terra, Università La Sapienza, P.le A. Moro 5, 00185 Roma, Italy. Phone +39 0649914549, e-mail: carlo.doglioni@uniroma1.it
Toward a general, scaleable framework for Bayesian teaching with applications to topic models

Machines, not humans, are the world's dominant knowledge accumulators but humans remain the dominant decision makers. Interpreting and disseminating the knowledge accumulated by machines requires expertise, time, and is prone to failure. The problem of how best to convey accumulated knowledge from computers to humans is a critical bottleneck in the broader application of machine learning. We propose an approach based on human teaching where the problem is formalized as selecting a small subset of the data that will, with high probability, lead the human user to the correct inference. This approach, though successful for modeling human learning in simple laboratory experiments, has failed to achieve broader relevance due to challenges in formulating general and scalable algorithms. We propose general-purpose teaching via pseudo-marginal sampling and demonstrate the algorithm by teaching topic models. Simulation results show our sampling-based approach: effectively approximates the probability where ground-truth is possible via enumeration, results in data that are markedly different from those expected by random sampling, and speeds learning especially for small amounts of data. Application to movie synopsis data illustrates differences between teaching and random sampling for teaching distributions and specific topics, and demonstrates gains in scalability and applicability to real-world problems.
Method for Measuring Twitter Content Influence

Twitter is a microblogging website with specific characteristics not found in other social network services. This platform contains a good deal of valuable content, and users can access this content using Twitter search. However, Twitter search returns only time-descending ordered content including keywords. Thus, we propose a linear-time method of measuring the influence of Twitter content considering not only time, but also characteristics of each Twitter account. In analyzing these characteristics, we have found that the number of retweets can measure shareability, while the number of followers held by the content author can measure spreadability. We perform experiments using real Twitter data for proving the effectiveness of the proposed method. We demonstrate that this proposed method is effective at finding up-to-date content. Further, in comparing our method with analysis via PageRank, we demonstrate that our method is more effective at accurately measuring influence. Keywords-Twitter;
Multi-model approach to non-stationary reinforcement learning

This paper proposes a novel alogrithm for a class of nonstationary reinforcement learning problems in which the environmental changes are rare and finite. Through discarding corrupted models and combining similar ones, the proposed algorithm maintains a collection of frequently encountered environment models and enables an effective adaptation when a similar environment recurs. The algorithm has empirically compared with the finite window approach, a widely-used method for non-stationary RL problems. Results have shown that our algorithm consistently outperforms the finite window approach in various empirical setups.
Harnessing neural activity to promote repair of the damaged corticospinal system after spinal cord injury

As most spinal cord injuries (SCIs) are incomplete, an important target for promoting neural repair and recovery of lost motor function is to promote the connections of spared descending spinal pathways with spinal motor circuits. Among the pathways, the corticospinal tract (CST) is most associated with skilled voluntary functions in humans and many animals. CST loss, whether at its origin in the motor cortex or in the white matter tracts subcortically and in the spinal cord, leads to movement impairments and paralysis. To restore motor function after injury will require repair of the damaged CST. In this review, I discuss how knowledge of activity-dependent development of the CST-which establishes connectional specificity through axon pruning, axon outgrowth, and synaptic competition among CST terminals-informed a novel activity-based therapy for promoting sprouting of spared CST axons after injur in mature animals. This therapy, which comprises motor cortex electrical stimulation with and without concurrent trans-spinal direct current stimulation, leads to an increase in the gray matter axon length of spared CST axons in the rat spinal cord and, after a pyramidal tract lesion, restoration of skilled locomotor movements. I discuss how this approach is now being applied to a C 4 contusion rat model.
Viral Escape by Selection of Cytotoxic T Cell–Resistant Variants in Influenza a Virus Pneumonia

Antigenic variation is a strategy exploited by influenza viruses to promote survival in the face of the host adaptive immune response and constitutes a major obstacle to efficient vaccine development. Thus, variation in the surface glycoproteins hemagglutinin and neuraminidase is reflected by changes in susceptibility to antibody neutralization. This has led to the current view that antibody-mediated selection of influenza A viruses constitutes the basis for annual influenza epidemics and periodic pandemics. However, infection with this virus elicits a vigorous protective CD8+ cytotoxic T lymphocyte (CTL) response, suggesting that CD8+ CTLs might exert selection pressure on the virus. Studies with influenza A virus–infected transgenic mice bearing a T cell receptor (TCR) specific for viral nucleoprotein reveal that virus reemergence and persistence occurs weeks after the acute infection has apparently been controlled. The persisting virus is no longer recognized by CTLs, indicating that amino acid changes in the major viral nucleoprotein CTL epitope can be rapidly accumulated in vivo. These mutations lead to a total or partial loss of recognition by polyclonal CTLs by affecting presentation of viral peptide by class I major histocompatibility complex (MHC) molecules, or by interfering with TCR recognition of the mutant peptide–MHC complex. These data illustrate the distinct features of pulmonary immunity in selection of CTL escape variants. The likelihood of emergence and the biological impact of CTL escape variants on the clinical outcome of influenza pneumonia in an immunocompetent host, which is relevant for the design of preventive vaccines against this and other respiratory viral infections, are discussed.
A TC : Large : Collaborative Research : 3 Dsec : Trustworthy System Security through 3-D Integrated Hardware : Project Summary

While hardware resources, in the form of both transistors and full microprocessor cores, are now abundant, economic factors prevent specialized hardware mechanisms, required for secure processing, from being integrated into commodity parts. The Information Assurance community is caught between the need to exploit cheap, fast, commodity microprocessors and the need to ensure that critical security properties hold (isolation, separation, etc.). For example, multi-core processors, due to their wide adoption, impressive performance, and low cost, are very attractive platforms for computation. Unfortunately, highly secure, isolated processing of sensitive information on such platforms could be extremely difficult to achieve due to extensive resource sharing and the lack of strong security primitives.
Generalised Autoregressive Conditional Heteroscedasticity (Garch) Models For Stock Market Volatility

The performance of generalised autoregressive conditional heteroscedasticity (GARCH) model and its modifications in forecasting stock market volatility are evaluated using the rate of returns from the daily stock market indices of Kuala Lumpur Stock Exchange (KLSE). These indices include Composite Index, Tins Index, Plantations Index, Properties Index and Finance Index. The models are stationary GARCH, unconstrained GARCH, non-negative GARCH, GARCH in mean (GARCH-M), exponential GARCH (EGARCH) and integrated GARCH. The parameters of these models and variance processes are estimated jointly using maximum likelihood method. The performance of the within-sample estimation is assessed using several goodness-of-fit statistics and the accuracy of the out-of-sample forecasts is judged using mean squared error.
Cognitive Demand and Reminders Effect on Time-Based Prospective Memory in Amnesic Mild Cognitive Impairment (AMCI) and in Healthy Elderly

Individuals with-Mild Cognitive Impairment (MCI) often complain of difficulty remembering to carry out intended actions. We investigated the relative efficacy of a different reminder in performing a time-based Prospective Memory (PM) task. The PM performance of 24 participants with amnesic Mild Cognitive Impairment (AMCI) has been compared with that of 24 healthy controls. As ongoing task, samples of the Attentive Matrices Test were used. In the PM task subjects were requested to write an “X” every three minutes during a 9 minutes period. Participants received the task consisting either in a low demand condition (checking number “5”) or in a high demand condition (checking numbers “1”, “4”, “9”). In order to be as punctual as possible, participants were asked to simultaneously write the “X” at task time expiration, using a digital clock. Time monitoring was recorded. Reminder occurring was manipulated in that participants could receive critical, accidental or completely absent reminder. As expected, high cognitive demand was negatively correlated with PM performance and time monitoring. Unexpectedly, all the participants did not benefit from the critical reminder. These findings demonstrated, from a behavioral perspective, that Working Memory (WM) and PM processes are not based on the same memory system and PM may require WM resources at high demand.
Constrained Asset Markets

The risk-sharing role of redundant assets is not yet fully understood in constrained asset markets. For example, the well-known notions of arbitrage may fail to explain the viability property of asset prices when redundant assets are involved in generating a nontrivial linear structure of free portfolios in equilibrium in constrained asset markets. This paper establishes the existence of equilibrium in two-period asset markets which are subject to portfolio constraints. First, we provide a full analysis of complicated equilibrium behavior of constrained portfolios with redundant assets by adopting a new notion of arbitrage. To do this, a technique of portfolio decomposition is developed to identify the linear structure of free portfolios embedded in the aggregate set of constrained portfolios. Second, we present a new condition on the aggregate set of portfolios which is indispensable for the existence of equilibrium in constrained asset markets. The literature assumes that the individual portfolio constraint set or the individual marketed set of income transfers is closed to study the presence of optimal portfolios or viability of asset prices in a partial equilibrium framework. As illustrated later, however, this condition alone fails to be sufficient for the existence of equilibrium.
A Clustering Data Fusion Method for Intrusion Detection System

The increasing advance in technological systems has several impacts that affect the security of information systems. The result of such progress leads to an exponential growth in the ability to generate and access to the information. Therefore, there is a need to have both appropriate and specific data. To achieve this goal, data fusion approaches are applied to analyze large scale of heterogeneous data in complex systems. The existing data fusion systems rely generally on human experts but they lack of training dataset for the fusion techniques. Thus, useful autonomous approach should be applied to fuse data automatically and accurately. In this paper, a decision fusion approach based on clustering technique is proposed. This technique enables the generation of composite attack scenarios by selecting events generated by analyzers while considering their efficiency to detect attacks using defined efficiency criteria. The general system architecture is presented to allocate the data fusion component within the network. Then, the core functioning and the characteristics of the data fusion component are presented.
Competitive Balance in Major League Baseball

The purpose of this paper is to test several competing hypotheses of the causes of improving competitive balance in major league baseball. Various researchers have previously attributed this improvement in competitive balance to the introduction of free agency, a narrowing of team market sizes, and a compression of baseball talent. Two models of competitive balance are presented and estimated in this paper, with results varying depending on the measure of competitive balance employed. If the degree of competitive balance is measured by the distribution of team winning percentages within a single season, none of the competing hypotheses is supported. If competitive balance is measured by the correlation of team winning percentages across seasons, however, all three of the competing hypotheses are supported.
A comparison of model view controller and model view presenter

Web application frameworks are managed by using different design strategies. Design strategies are applied by using different design processes. In each design process, requirement specifications are changed in to different design model that describe the detail of different data structure, system architecture, interface and components. Web application frame work is implemented by using Model View Controller (MVC) and Model View Presenter (MVP). These web application models are used to provide standardized view for web applications. This paper mainly focuses on different design aspect of MVC and MVP. Generally we present different methodologies that are related to the implementation of MVC and MVP and implementation of appropriate platform and suitable environment for MVC and MVP.
Broad Twitter Corpus: A Diverse Named Entity Recognition Resource

One of the main obstacles, hampering method development and comparative evaluation of named entity recognition in social media, is the lack of a sizeable, diverse, high quality annotated corpus, analogous to the CoNLL’2003 news dataset. For instance, the biggest Ritter tweet corpus is only 45,000 tokens – a mere 15% the size of CoNLL’2003. Another major shortcoming is the lack of temporal, geographic, and author diversity. This paper introduces the Broad Twitter Corpus (BTC), which is not only significantly bigger, but sampled across different regions, temporal periods, and types of Twitter users. The gold-standard named entity annotations are made by a combination of NLP experts and crowd workers, which enables us to harness crowd recall while maintaining high quality. We also measure the entity drift observed in our dataset (i.e. how entity representation varies over time), and compare to newswire. The corpus is released openly, including source text and intermediate annotations.
Artistic Illumination Transfer for Portraits

Relighting a portrait in a single image is still a challenging problem, particularly when only a single artistic reference photograph or painting is provided. In this paper, we propose an artistic illumination transfer system for portraits based on a database of portrait images (photographs and paintings) associated with hand‐drawn illumination templates (276) by artists. Users can select a reference portrait image in the database, and the corresponding illumination template is transferred to an input portrait using image warping. Users can also provide reference portrait images those are not in the database. Based on the Face Illumination Descriptor (FID), the system selects from the database the reference image with the closest illumination to that of the user‐provided reference image and adjusts the corresponding illumination template to match the contrast of the user‐provided reference image. Experiments on not only paintings but also photographs, paper‐cuts and sketches demonstrate that convincing illumination transferred results can be rendered by our system.
Value-Event Path Based Policy Management Model

Value based management (VBM) aims to promote value of enterprise. In the management process, value of enterprise is changed dynamically and the value change corresponds to an event which happens to the enterprise, such as success of new product development, fall of share price and so on. In other words, an event indicates a change of enterprise value. In this paper, the concepts of enterprise event, value-event node, and value-event path are proposed and defined formally. A process of VBM can be looked upon as a value-event path. A good management process is a value-event path along which value increases with the event sequence, otherwise value decreases with the event sequence. Furthermore, Value-Event Path Based Policy Management Model is also proposed in this paper. In the model, a policy is a management activity which is driven by an enterprise event, and the execution result of the policy is a new event. Finally, two typical examples are given to show that Value-Event Path Based Policy Management Model is a novel policy-based tool of VBM planning and decision support.
Aggregate Skyline: Analysis for Online Users

Aggregation is among the core functionalities of OLAP systems. Frequently, such queries are issued in decision support systems to identify interesting groups of data. In conventional settings, the queries take a long time to compute (hours! ) and produce massive result-sets at varying degrees of aggregation. Providing real time analysis results to web users can enhance the utility of sites dealing with large amounts of data. However, to do so, needs succinct ways of capturing interesting analysis results rather than complex offline analysis. The result set should be presentable in a few web pages. Furthermore, such results should be computed quickly and updated in the background whenever possible. We propose skyline queries over aggregated data as a means of providing succinct but interesting analysis results. We support aggregation functions from a large class of monotonous functions that can be specified at runtime, thereby allowing user customization of the analysis. We explore a family of algorithms which try to consume only as many data records as are necessary to compute the skyline and identify an optimal algorithm within the family. We further refine the algorithm by taking into account system issues such as disk behavior which are often ignored but have strong impact on real system performance. Experimental results provide strong validation for the performance and progressive nature of the algorithm.
A Field Programmable Gate Array-Based Digital Temperature Sensor with Improved Immunity to Static Supply Shift

A digital temperature sensor is an integrated system that can sense and convert heat measurement from an analog domain to its digital equivalent. However, digital temperature sensing suffers inaccuracy as a result of shift in supply voltage. In this paper, a digital temperature sensor with an improved noise resistant technique for the reliability of digital systems is presented. The proposed method uses a dual delay-line instead of the typical approach that employs conversion and calibration technique to mitigate the effect of noise. The digital temperature sensor is realized using Very High-Speed Integrated Circuit Hardware Description Language and synthesized in Quartus II. The result is a resource efficient digital temperature sensor with a measurement error of - 1.297°C ~ 1.799°C over a temperature range of 40°C ~ 120°C. A temperature sensor resistant to static supply shift of 1.8 ~ 1.5 supply voltage range, suitable for quick deployment within a Field Programmable Gate Array is achieved.