Miracle-dz/UniFAR-FTUs huggingface dataset:
Control of Smart Building Dynamic Energy Service Preferences for Efficient Regulation Service

We propose and solve a stochastic dynamic programming (DP) formulation of the optimal provision of regulation service reserves (RSR) by controlling dynamic demand preferences in smart buildings. A major contribution over past dynamic pricing work is that we pioneer the relaxation of static, uniformly distributed utility of demand. In this paper we model explicitly the dynamics of energy user preferences leading to a non-uniform and time varying probability distribution of demand utility. More explicitly, we model active and idle duty cycle appliances in a smart building as a closed queuing system with price-controlled arrival rates into the active appliance queue. Focusing on cooling appliances, we model the utility associated with the transition from idle to active as a non-uniform time varying function. We (i) derive an analytic characterization of the optimal policy and the differential cost function, and (ii) prove monotonicity and convexity properties. This enables us to propose and implement a smart assisted value iteration algorithm as well as an approximate DP solution based on the aforementioned functional approximations and properties. Numerical results demonstrate the validity of the solution techniques and the computational advantages of the proposed near optimal approximate-DP solutions for realistic, large-state-space problems.
The NTNU Summarization System at TAC 2009

In this paper, we presents the results obtained by using a probabilistic summarization framework for the TAC 2009 update summarization task, which has the merits of combining the sentence generative probability and the sentence prior probability for sentence ranking systematically. Especially, each sentence of a document to be summarized is treated as a probabilistic generative model for predicting the documents. Nevertheless, the results of our first participation in the TAC evaluation seem to have room for further improvement.
Active learning using a Variational Dirichlet Process model for pre-clustering and classification of underwater stereo imagery

This paper demonstrates an implementation of pool-based active learning through uncertainty sampling using a Variational Dirichlet Process (VDP) model. The VDP is used for both pre-clustering and classification, and is extended to incorporate fixed labels from an oracle (human annotator). Three different uncertainty sampling techniques are explored - least confident sampling, margin sampling and entropy based sampling. Clustering with the VDP is done in a completely unsupervised manner, without the need to specify the number of clusters. This appears particularly useful in improving the results when there are only few labelled samples, or if the cost of labelling is high. Results are shown for a toy dataset and the performance is compared to similar implementations using an Expectation Maximisation model (EM) and a Naive Bayes classifier (NB). The VDP active learning framework is tested on a stereo image dataset obtained by an autonomous underwater vehicle that covers several linear kilometres and consists of thousands of stereo image pairs. Our results show that combining an active learning strategy with the VDP significantly reduces the number of labelled images required to achieve a desired level of accuracy.
Study of characteristics of large data collections to build a high performance system

In Memory Computing is moving data into dynamic memory which is traditionally stored in secondary storage devices. In this paper we try to improve the capability of Java Virtual Machine (JVM) to process large data collections/application by pertinence of In-Memory Computing concepts. It helps JVM/processor to process huge data sets efficiently due to high-speed access and reduction of latency of slow secondary storage devices. It is implemented by providing more dynamic memory to JVM or by the implementation of In Memory Database concepts. In this paper we correlate Actual machine with JVM and try to improve ability of JVM to process large data collections by eliminating disk access and replacing Database with JAVA Collections in the Model Layer hence using ‘in memory’ concepts, Henceforth a high performance system is designed.
Learning, Memory, and Executive Function in New MDMA Users: A 2-Year Follow-Up Study

3,4-Methylenedioxymethamphetamine (MDMA) is associated with changes in neurocognitive performance. Recent studies in laboratory animals have provided additional support for the neurodegeneration hypothesis. However, results from animal research need to be applied to humans with caution. Moreover, several of the studies that examine MDMA users suffer from methodological shortcomings. Therefore, a prospective cohort study was designed in order to overcome these previous methodological shortcomings and to assess the relationship between the continuing use of MDMA and cognitive performance in incipient MDMA users. It was hypothesized that, depending on the amount of MDMA taken, the continued use of MDMA over a 2-year period would lead to further decreases in cognitive performance, especially in visual paired association learning tasks. Ninety-six subjects were assessed, at the second follow-up assessment: 31 of these were non-users, 55 moderate-users, and 10 heavy-users. Separate repeated measures analyses of variance were conducted for each cognitive domain, including attention and information processing speed, episodic memory, and executive functioning. Furthermore, possible confounders including age, general intelligence, cannabis use, alcohol use, use of other concomitant substances, recent medical treatment, participation in sports, level of nutrition, sleep patterns, and subjective well-being were assessed. The Repeated measures analysis of variance (rANOVA) revealed that a marginally significant change in immediate and delayed recall test performances of visual paired associates learning had taken place within the follow-up period of 2 years. No further deterioration in continuing MDMA-users was observed in the second follow-up period. No significant differences with the other neuropsychological tests were noted. It seems that MDMA use can impair visual paired associates learning in new users. However, the groups differed in their use of concomitant use of illicit drugs. Therefore, performance differences between the groups cannot completely ascribed to the use of MDMA.
Personality Estimation from Japanese Text

We created a model to estimate personality trait from authors’ text written in Japanese and measured its performance by conducting surveys and analyzing the Twitter data of 1,630 users. We used the Big Five personality traits for personality trait estimation. Our approach is a combination of category- and Word2Vec-based approaches. For the category-based element, we added several unique Japanese categories along with the ones regularly used in the English model, and for the Word2Vec-based element, we used a model called GloVe. We found that some of the newly added categories have a stronger correlation with personality traits than other categories do and that the combination of the category- and Word2Vec-based approaches improves the accuracy of the personality trait estimation compared with the case of using just one of them.
Automatic Kurdish Sorani text categorization using N-gram based model

N-gram Based Model for text categorization is applied for many languages, in particularly the Indo-European languages family. Regrettably, there is limit study found on applying the mentioned model for Kurdish Sorani Language. This paper presents the results of investigating N-gram frequency statistics technique to classify the Kurdish Sorani Unicode documents of online newspapers into their classes. The investigated technique generates the frequency profiles for the training and the test documents using N-gram word level 1 gram and character level (2, 3, 4, 5, 6, 7, and 8) grams as a text representation. Then, a similarity algorithm called “Dice measure of similarity” is employed in order to classify the documents. Results show that the character level (5 grams) gives better text representation which is led to achieve better text classification.
Fast mining and forecasting of complex time-stamped events

Given huge collections of time-evolving events such as web-click logs, which consist of multiple attributes (e.g., URL, userID, times- tamp), how do we find patterns and trends? How do we go about capturing daily patterns and forecasting future events? We need two properties: (a) effectiveness, that is, the patterns should help us understand the data, discover groups, and enable forecasting, and (b) scalability, that is, the method should be linear with the data size. We introduce TriMine, which performs three-way mining for all three attributes, namely, URLs, users, and time. Specifically TriMine discovers hidden topics, groups of URLs, and groups of users, simultaneously. Thanks to its concise but effective summarization, it makes it possible to accomplish the most challenging and important task, namely, to forecast future events. Extensive experiments on real datasets demonstrate that TriMine discovers meaningful topics and makes long-range forecasts, which are notoriously difficult to achieve. In fact, TriMine consistently outperforms the best state-of-the-art existing methods in terms of accuracy and execution speed (up to 74x faster).
Design Insights for MapReduce from Diverse Production Workloads

Abstract : In this paper, we analyze seven MapReduce workload traces from production clusters at Facebook and at Cloudera customers in e-commerce, telecommunications media, and retail. Cumulatively, these traces comprise over a year's worth of data logged from over 5000 machines, and contain over two million jobs that perform 1.6 exabytes of I/O. Key observations include input data forms up to 77% of all bytes, 90% of jobs access KB to GB sized files that make up less than 16% of stored bytes, up to 60% of jobs re-access data that has been touched within the past 6 hours, peak-to-median job submission rates are 9:1 or greater, an average of 68% of all compute time is spent in map, task-seconds-per-byte is a key metric for balancing compute and data bandwidth task durations range from seconds to hours, and five out of seven workloads contain map-only jobs. We have also deployed a public workload repository with workload replay tools so that the researchers can systematically assess design priorities and compare performance across diverse MapReduce workloads.
Selective Transmission of R5 HIV-1 over X4 HIV-1 at the Dendritic Cell–T Cell Infectious Synapse Is Determined by the T Cell Activation State

Dendritic cells (DCs) are essential antigen-presenting cells for the induction of T cell immunity against HIV. On the other hand, due to the susceptibility of DCs to HIV infection, virus replication is strongly enhanced in DC–T cell interaction via an immunological synapse formed during the antigen presentation process. When HIV-1 is isolated from individuals newly infected with the mixture of R5 and X4 variants, R5 is predominant, irrespective of the route of infection. Because the early massive HIV-1 replication occurs in activated T cells and such T-cell activation is induced by antigen presentation, we postulated that the selective expansion of R5 may largely occur at the level of DC–T cell interaction. Thus, the immunological synapse serves as an infectious synapse through which the virus can be disseminated in vivo. We used fluorescent recombinant X4 and R5 HIV-1 consisting of a common HIV-1 genome structure with distinct envelopes, which allowed us to discriminate the HIV-1 transmitted from DCs infected with the two virus mixtures to antigen-specific CD4+ T cells by flow cytometry. We clearly show that the selective expansion of R5 over X4 HIV-1 did occur, which was determined at an early entry step by the activation status of the CD4+ T cells receiving virus from DCs, but not by virus entry efficiency or productivity in DCs. Our results imply a promising strategy for the efficient control of HIV infection.
Strategies for Neutralising Sexually Explicit Language

While scales for the 'strength' of pornographic images have been developed and used in research and for judicial purposes, the potential emotive impact of text-based pornography has received little attention. In this paper we describe our approach to characterising dimensions of sexually explicit language and outline their use in strategies for neutralising such language. Our intended application of this work is contexts where we may wish to affect the content to which a student, trainee or other professional may be exposed. By controlling the quantity and degree of such content, we aim to minimise any detrimental effects (observer impact) that such content may have on ill-prepared individuals.
Bayesian Regression and Classification

In recent years Bayesian methods have become widespread in many domains including computer vision, signal processing, information retrieval and genome data analysis. The availability of fast computers allows the required computations to be performed in reasonable time, and thereby makes the benefits of a Bayesian treatment accessible to an ever broadening range of applications. In this tutorial we give an overview of the Bayesian approach to pattern recognition in the context of simple regression and classification problems. We then describe in detail a specific Bayesian model for regression and classification called the Relevance Vector Machine. This overcomes many of the limitations of the widely used Support Vector Machine, while retaining the highly desirable property of sparseness.
Ams 274 -generalized Linear Models Winter 2008 Exponential Dispersion Family of Distributions

Central to the development of generalized linear models (glms) is the exponential dispersion family of distributions as it defines the random component for a glm, and extends (normal) linear models, since it includes the normal distribution as a special case. Consider a (univariate) random variable Y that takes values in a set S that can be countable or uncountable (discrete or continuous random variable, respectively). We say that the distribution of Y belongs to the exponential dispersion family of distributions if its density function (continuous case) or mass function (discrete case) has the form f (y; θ, φ) = exp yθ − b(θ) a(φ) + c(y, φ) , (1) where the parameter θ takes values in a subset of R, the dispersion parameter φ takes values in a subset of R + , the support S of the distribution does not depend on the parameters θ and φ, and a(·), b(·), and c(·, ·) are specified functions of φ, θ, and (y, φ), respectively. For specific choices of a(φ) (> 0) and b(θ), c(y, φ) is a normalizing function that ensures that (1) defines a valid density or mass function. Note that for φ known, (1) is a special case of the one-parameter exponential family of distributions (with natural parameter θ). It may or may not be a two-parameter exponential family when both θ and φ are unknown (it depends on the actual form of c(y, φ)). The term exponential dispersion family reflects this partly exponential form of (1) as well as the important role played by the dispersion parameter φ. The exponential dispersion family allows more flexibility than the one-parameter exponential family through the addition of the dispersion parameter φ. At the same time, its form is more convenient for estimation than the one of the two-parameter exponential family. The exponential dispersion family includes the Binomial (for known number of trials) and Poisson distributions (a(φ) = 1 for both) as well as the normal, gamma and inverse Gaussian distributions. Using standard results for expectations of derivatives of log-likelihood functions (see the Appendix), we obtain E(Y) = b (θ) and Var(Y) = a(φ)b (θ). (2) (Here, b (θ) = db(θ)/dθ and b (θ) = d 2 b(θ)/dθ 2 . ) Note the specific mean-variance relationship, Var(Y) = a(φ)dE(Y)/dθ, implied by the exponential dispersion family.
International Conference on Information Fusion ( FUSION ) A Unifying Framework for Multi-Target Tracking and Existence

Most target tracking approaches either assume that the number of targets is constant throughout the time horizon of interest, or that information about target existence (birth and death) is provided by some external source. Here we show how target existence can be integrated within the tracking framework in a rigorous way. The notion ofexistence is not new, and has been considered before in e.g. [1, 2]. We provide here a general probabilistic treatment that impacts as little as possible on existing tracking algorithms so that legacy tracking software (and more generally target tracking architectures) can be reused. We first show how the notion of existence can be incorporated into a single target tracking framework (retaining algorithmic invariance). To place the probabilistic recursions into context we relate this single target tracking architecture to the Probabilistic Data Association [3, 4]filter We then extend the single target results to incorporate existence for multi-target tracking and relate this to an importance sampling implementation of the Joint Probabilistic Data Association (JPDA) [5, 3, 6]framework. The treatmentpresented is entirely general and so facilitates implementation with Kalman Filters, Extended/Unscented Kalman Filters, Particle Filters, etc, i.e. the approach developed is invariant to the filtering and data association mechanisms used, and therein lies the novelty. We apply the proposedframework to the difficult problem of tracking football players in video sequences, where we adopt a mixture Kalman filter implementation.
Dynamic schemes for speculative execution of code

Speculative execution of code is becoming a key technique for enhancing the performance of pipeline processors. We study schemes that predict the execution path of a program based on the history of branch executions. Building on previous work, we present a model for analyzing the effective speedup from pipelining using various schemes for speculative execution. We follow this with stochastic analyses of various speculative execution schemes. Finally, we conclude with simulations covering several of the settings we study.
Optimal selection of supply voltages and level conversions during data path scheduling under resource constraints

In this paper we will consider how to select an optimal set of supply voltages and account for level conversion costs when optimizing the schedule of a resource dominated data path for minimum energy dissipation. An integer linear program (ILP) is presented for minimum energy schedules under latency, supply voltage, and resource constraints. The supply voltage assignment for each resource is modeled as fixed for all time. Schedules were generated for a variety of data path structures, resource and latency constraints. Resource constraints tended to limit the use of reduced supply voltages. With latency constraints loosened to 1.5/spl times/ minimum latency, unlimited resources, and two power supplies, energy savings ranged from 53% to 70% compared to 5 V operation. When resource constraints were applied, savings dropped to a range of 46% to 58%. Loosened latency constraints resulted in increased use of lower supply voltages. With resource constraints un-changed and latency constraints of 2/spl times/ minimum latency, energy savings increased to a range of 64% to 75%. In no case did three supplies decrease energy by more than 5% compared to two supplies.
Toward a real time multi-tissue Adaptive Physics-Based Non-Rigid Registration framework for brain tumor resection

This paper presents an adaptive non-rigid registration method for aligning pre-operative MRI with intra-operative MRI (iMRI) to compensate for brain deformation during brain tumor resection. This method extends a successful existing Physics-Based Non-Rigid Registration (PBNRR) technique implemented in ITKv4.5. The new method relies on a parallel adaptive heterogeneous biomechanical Finite Element (FE) model for tissue/tumor removal depicted in the iMRI. In contrast the existing PBNRR in ITK relies on homogeneous static FE model designed for brain shift only (i.e., it is not designed to handle brain tumor resection). As a result, the new method (1) accurately captures the intra-operative deformations associated with the tissue removal due to tumor resection and (2) reduces the end-to-end execution time to within the time constraints imposed by the neurosurgical procedure. The evaluation of the new method is based on 14 clinical cases with: (i) brain shift only (seven cases), (ii) partial tumor resection (two cases), and (iii) complete tumor resection (five cases). The new adaptive method can reduce the alignment error up to seven and five times compared to a rigid and ITK's PBNRR registration methods, respectively. On average, the alignment error of the new method is reduced by 9.23 and 5.63 mm compared to the alignment error from the rigid and PBNRR method implemented in ITK. Moreover, the total execution time for all the case studies is about 1 min or less in a Linux Dell workstation with 12 Intel Xeon 3.47 GHz CPU cores and 96 GB of RAM.
Urinary Incontinence as a Predictor of Death: A Systematic Review and Meta-Analysis

Background The association between urinary incontinence (UI) and increased mortality remains controversial. The objective of our study was to evaluate if this association exists. Methods We performed a systematic review and meta-analysis of observational studies comparing death rates among patients suffering from UI to those without incontinence. We searched in Medline, Embase and the Cochrane library using specific keywords. Studies exploring the post-stroke period were excluded. Hazard ratios (HR) were pooled using models with random effects. We stratified UI by gender and by UI severity and pooled all models with adjustment for confounding variables. Results Thirty-eight studies were retrieved. When compared to non-urinary incontinent participants, UI was associated with an increase in mortality with pooled non adjusted HR of 2.22 (95%CI 1.77–2.78). The risk increased with UI severity: 1.24 (95%CI: 0.79–1.97) for light, 1.71 (95%CI: 1.26–2.31) for moderate, and 2.72 (95%CI: 1.90–3.87) for severe UI respectively. When pooling adjusted measures of association, the resulting HR was 1.27 (95%CI: 1.13–1.42) and increased progressively for light, moderate and severe UI: 1.07 (95%CI: 0.79–1.44), 1.25 (95%CI: 0.99–1.58), and 1.47 (95%CI: 1.03–2.10) respectively. There was no difference between genders. Conclusion UI is a predictor of higher mortality in the general and particularly in the geriatric population. The association increases with the severity of UI and persists when pooling models adjusted for confounders. It is unclear if this association is causative or just reflects an impaired general health condition. As in most meta-analyses of observational studies, methodological issues should be considered when interpreting results.