Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Model Predictive Control for Building HVAC systems: A Case Study

Model predictive control (MPC) in building HVAC systems incorporates predictions of weather and occupancy to determine the optimal operating setpoints. However, application of MPC strategies to large buildings might not be feasible in real time due to the large number of degrees of freedom in the underlying optimization problem. Decomposing the problem into several smaller sub-problems to be solved in parallel is one way to circumvent the high computational requirements. Such an approach, termed Distributed MPC, requires certain approximations about the underlying sub-problems to converge to a consistent solution thus leading to a trade-off between computational load and optimality. In this paper, we present a simulation-based evaluation for a Distributed MPC formulation for a case study based on a medium-sized commercial building. Results indicate that distributed MPC can offer near-optimal control at a fraction of the computational time that centralized MPC requires while maintaining occupant comfort.
The 2011 IDN Homograph Attack Mitigation Survey

The advent of internationalized domain names (IDNs) has introduced a new threat, with the non-English character sets allowing for visual mimicry of domain names. Whilst this potential for this form of attack has been well recognized, many applications such as Internet browsers and e-mail clients have been slow to adopt successful mitigation strategies and countermeasures. This research examines those strategies and countermeasures, identifying areas of weakness that allow for homograph attacks. As well as examining the presentation of IDNs in e-mail clients and Internet browser URL bars, this year's study examines the presentation of IDNs in browser-based security certificates and requests for locational data access.
On the M/G/1 queue with heavy-tailed service time distributions

textabstractIn present teletraffic applications of queueing theory service time distributions $B(t)$ with a heavy tail occur, i.e. $1-B(t) sim t^{-nu$ for $t rightarrow infty$ with $nu > 1$. For such service time distributions not much explicit information is available concerning the tail probabilities of the inherent waiting time distribution $W (t)$. In the present study the waiting time distribution is studied for a stable $M/G/1$ model for a class of service time distributions with $1 < nu < 2$. For $nu =1, frac{1{2$ the explicit expression for $Q(t)$ is derived. For rational $nu$ with $1< nu < 2$, an asymptotic series for the tail probabilities of $W(t)$ is derived.
Soil Communities Promote Temporal Stability and Species Asynchrony in Experimental Grassland Communities

Background Over the past two decades many studies have demonstrated that plant species diversity promotes primary productivity and stability in grassland ecosystems. Additionally, soil community characteristics have also been shown to influence the productivity and composition of plant communities, yet little is known about whether soil communities also play a role in stabilizing the productivity of an ecosystem. Methodology/Principal Findings Here we use microcosms to assess the effects of the presence of soil communities on plant community dynamics and stability over a one-year time span. Microcosms were filled with sterilized soil and inoculated with either unaltered field soil or field soil sterilized to eliminate the naturally occurring soil biota. Eliminating the naturally occurring soil biota not only resulted in lower plant productivity, and reduced plant species diversity, and evenness, but also destabilized the net aboveground productivity of the plant communities over time, which was largely driven by changes in abundance of the dominant grass Lolium perenne. In contrast, the grass and legumes contributed more to net aboveground productivity of the plant communities in microcosms where soil biota had been inoculated. Additionally, the forbs exhibited compensatory dynamics with grasses and legumes, thus lowering temporal variation in productivity in microcosms that received the unaltered soil inocula. Overall, asynchrony among plant species was higher in microcosms where an unaltered soil community had been inoculated, which lead to higher temporal stability in community productivity. Conclusions/Significance Our results suggest that soil communities increase plant species asynchrony and stabilize plant community productivity by equalizing the performance among competing plant species through potential antagonistic and facilitative effects on individual plant species.
A heuristic approach to evaluate occurrences of products for the planogram maintenance

Planogram is a detailed visual map of products in a retail store and establishes the position of products in order to increase sales and to supply the best location for suppliers. So, the aims of “correct” planogram are several and are: increasing sales, increasing profits, introducing a new item, supporting an innovative merchandising approach, and better manages the shelves. Deviating from the planogram defeats the purpose of any of those goals. A fundamental aspect in the retail operations is to maintain the integrity of the planogram. This work intends to provide a solution to this problem, proposing a system that individually identifies the presence of a specific product in the image of a shelf. This even though the product is moved, rotated, misplaced, or even in poor lighting conditions. This paper presents a method to find and count multiple instances of the same object that occurs into an image of a shelf in a store without using classifiers. The procedures here described are based on a heuristic algorithm that involves morphological operation, template matching and histogram comparison. Experimental results are presented in order to verify the effectiveness of the proposed approach. They demonstrate that the algorithm provides satisfactory results when the user manually chooses the most significant label of the product to find in the shelf image.
FOREST CLASSIFICATION USING HIGH SPECTRAL AND SPATIAL RESOLUTION DATA

A modern airborne imaging spectrometer provides an exact observation of the object’s spectral properties. Besides, the spatial resolution of imaging spectrometers is good and instruments provide information about object texture. Many nature objects can be identified by their characteristic reflection. However, the spectral properties of certain plants are similar and the identification made only by their reflectance spectra does not work. If the texture of different plant classes is distinguishable, we can identify them by using texture analysis. The purpose of this work is to classify data using both spectral and texture information of hyperspectral image and thereby improve classification results. The goal was to find the best working texture measures and feature sets including both spectral and texture features. Test area located in Southern Finland was imaged by an airborne imaging spectrometer. The area included mostly coniferous and deciduous forests. Separability measures between each class were calculated using different feature sets. Features included original image channels and new texture measures. Finally the data was classified using Bayesian and neural network classification. Separability measures calculated from the training data using individual image channels and texture images were poor. Using more spectral features at the same time predictable improves the results. Average separability increased notably and all the classes were able to distinguish from each other when both spectral and texture features were taken for the classification. Taking texture measures into account in the forest classification was reasonable because forest stands have individual texture and the spatial resolution of the hyperpsectral image was good enough to observe the texture. The methods and results in this study can apply to the satellite case where texture features are calculated from a high resolution satellite sensor and spectral features are calculated from a satellite imaging spectrometer.
LTL types FRP: linear-time temporal logic propositions as types, proofs as functional reactive programs

Functional Reactive Programming (FRP) is a form of reactive programming whose model is pure functions over signals. FRP is often expressed in terms of arrows with loops, which is the type class for a Freyd category (that is a premonoidal category with a cartesian centre) equipped with a premonoidal trace. This type system suffices to define the dataflow structure of a reactive program, but does not express its temporal properties. In this paper, we show that Linear-time Temporal Logic (LTL) is a natural extension of the type system for FRP, which constrains the temporal behaviour of reactive programs. We show that a constructive LTL can be defined in a dependently typed functional language, and that reactive programs form proofs of constructive LTL properties. In particular, implication in LTL gives rise to stateless functions on streams, and the "constrains" modality gives rise to causal functions. We show that reactive programs form a partially traced monoidal category, and hence can be given as a form of arrows with loops, where the type system enforces that only decoupled functions can be looped.
Tree-Structured Template Generation for Web Pages

As the web becomes an increasingly important source of information, tools for modeling, searching, and extracting information from Web pages are indispensable. By modeling the structure of a Web page defined by its markup tags, one can easily extract target information using structural templates. This paper introduces the Tree Template Automatic Generator (TTAG) that learns tree-structured templates from training Web pages. TTAG was applied to both query-based and frequently updated Web sites, and produced effective templates from a small number of examples. The experiments show that TTAG is a powerful extraction tool for semi-structured information sources.
UWIN – UNIX* for Windows

This paper describes an effort of trying to build a UNIX interface layer on top of the Windows NT and Windows 95 operating system. The goal was to build an open environment rich enough to be both a good development environment and a suitable execution environment. The result of this effort is a set of libraries, headers, and utilities that we collectively refer to as UWIN. UWIN contains nearly all the X/Open Release 4 headers, interfaces, and commands. An earlier paper on porting to Windows NT, describes alternative porting strategies and presents some performance results for UWIN. In this paper we discuss some of the design decisions behind UWIN and some of the results so far as well as some of the remaining challenges.
Key to Opening Kidney for In Vitro–In Vivo Extrapolation Entrance in Health and Disease: Part I: In Vitro Systems and Physiological Data

ABSTRACTThe programme for the 2015 AAPS Annual Meeting and Exhibition (Orlando, FL; 25–29 October 2015) included a sunrise session presenting an overview of the state-of-the-art tools for in vitro–in vivo extrapolation (IVIVE) and mechanistic prediction of renal drug disposition. These concepts are based on approaches developed for prediction of hepatic clearance, with consideration of scaling factors physiologically relevant to kidney and the unique and complex structural organisation of this organ. Physiologically relevant kidney models require a number of parameters for mechanistic description of processes, supported by quantitative information on renal physiology (system parameters) and in vitro/in silico drug-related data. This review expands upon the themes raised during the session and highlights the importance of high quality in vitro drug data generated in appropriate experimental setup and robust system-related information for successful IVIVE of renal drug disposition. The different in vitro systems available for studying renal drug metabolism and transport are summarised and recent developments involving state-of-the-art technologies highlighted. Current gaps and uncertainties associated with system parameters related to human kidney for the development of physiologically based pharmacokinetic (PBPK) model and quantitative prediction of renal drug disposition, excretion, and/or metabolism are identified.
TWO-STEP SEMI-SUPERVISED APPROACH FOR MUSIC STRUCTURAL CLASSIFICATION

Structural segmentation involves ﬁnding hoogeneous sections appearing in a song. The name of these sections depends on the genre of interest. The task is particularly challenging as each of the misclassifed labels gives spurious change points. We have proposed a two step method by ﬁnding the boudaries present in the song followed by segment labelling. Novel idea of transforming the features into posteriori space using unsupervised model ﬁtting is elucidated along with interesting behaviour of increasing the mixtures in GMM. Advanced techniques such as hidden markov model is also shown to give reasonable results for the task of boundaries detection. Finally the task of labelling the segments within the detected boundaries are carried out unlike the existing methods which give labels such as A, B, and C. The results are presented for boundary and segment level evaluation.
An Evaluation Model for Degree of Topic Relatedness Based on Dynamic Matching

An evaluation model for degree of topic relatedness based on dynamic matching is proposed in this paper, which is based on web page segmentation and primary content block detection and is aimed at large scale web page collections. The evaluation model can suit automatically with inputted data type and is able to resolve partly the contradiction between time-consuming and accuracy rate. In addition, term weighting formula and nonlinear computing function which are the basis of the proposed evaluation model are improved in this paper after a lot of studies and experiments. The experimental results show that the evaluation model proposed in this paper performed better than present approaches.
HAPGEN2: simulation of multiple disease SNPs

MOTIVATION Performing experiments with simulated data is an inexpensive approach to evaluating competing experimental designs and analysis methods in genome-wide association studies. Simulation based on resampling known haplotypes is fast and efficient and can produce samples with patterns of linkage disequilibrium (LD), which mimic those in real data. However, the inability of current methods to simulate multiple nearby disease SNPs on the same chromosome can limit their application. RESULTS We introduce a new simulation algorithm based on a successful resampling method, HAPGEN, that can simulate multiple nearby disease SNPs on the same chromosome. The new method, HAPGEN2, retains many advantages of resampling methods and expands the range of disease models that current simulators offer. AVAILABILITY HAPGEN2 is freely available from http://www.stats. ox.ac.uk/~marchini/software/gwas/gwas.html. CONTACT zhan@well.ox.ac.uk SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Reordering B-tree files

In this investigation, we address a reordering technique that improves sequential processing to B-tree files dramatically. We show conventional reorganization is not fully helpful, and propose reordering of blocks reflecting logical order. First we obtain all the data in a logical order, and then we put them into pre-order. Here we show some experimental results that says how this technique works well.
Viewpoint Estimation using Triplet Loss with A Novel Viewpoint-based Input Selection Strategy

Viewpoint estimation is a fundamental procedure in vision-based robot tasks. A good viewpoint of the camera relative to objects can help the visual system perform better both in observation and manipulation. Recently, CNN-based algorithms, which can effectively extract discriminative features from images in challenging conditions, are utilized to handle the viewpoint estimation problem. However, most existing algorithms focus on how to leverage the extracted deep features while neglecting the spatial relationship among images that captured from various viewpoints. In this paper, we present a deep metric learning method for solving the viewpoint estimation problem. A triplet loss with a novel viewpoint-based input selection strategy is introduced, which could learn more powerful features after incorporating the spatial relationship between viewpoints. Combined with the traditional classification loss, the presented loss can further enhance the discriminative power of features. To evaluate the performance of our method, a dataset containing a large number of images generated from five different texture-less workpieces is built and the experiment results show the effectiveness of the proposed method.
Robust material classification with a tactile skin using deep learning

Attaching a flexible tactile skin to an existing robotic system is relatively easy compared to integrating most other tactile sensor designs. In this paper we show that material classification purely based on the spatio-temporal signal of a flexible tactile skin can be robustly performed in a real world setting. We compare different classification algorithms and feature sets, including features adopted and extended from previous works in tactile material classification and that are based on the signal's Fourier spectrum. Our convolutional deep learning network architecture, which we also present here, is directly fed with the raw 24000 dimensional sensor signal and performs best by a large margin, reaching a classification accuracy of up to 97.3%.
Reliability Measures for Qualitative Data: Theory and Implications

Data based on qualitative judgments are prevalent in both academic research in marketing and applied marketing research. Reliability measurement of qualitative data is important to determine the stability and quality of the data obtained. The authors assume a decision theoretic loss function, formally model the loss to the researcher of using wrong judgments, and show how this produces a new, proportional reduction in loss (PRL) reliability measure that generalizes many existing quantitative and qualitative measures. Because the PRL measure is often cumbersome to compute directly, they provide reference tables that enable the researcher to apply their approach easily. They then use this new approach to explore several important practical issues in conducting marketing research with qualitative judgments. In particular, they address the issues of (1) how reliable qualitative data should be (extending directly from Nunnally's rule of thumb for Cronbach's alpha in quantitative measurement), (2) how many judges are necessary given a known proportion of agreement between judges, and (3) given a fixed number of judges, what proportion of agreement must be obtained to ensure adequate reliability.
Porter: a new, accurate server for protein secondary structure prediction

UNLABELLED Porter is a new system for protein secondary structure prediction in three classes. Porter relies on bidirectional recurrent neural networks with shortcut connections, accurate coding of input profiles obtained from multiple sequence alignments, second stage filtering by recurrent neural networks, incorporation of long range information and large-scale ensembles of predictors. Porter's accuracy, tested by rigorous 5-fold cross-validation on a large set of proteins, exceeds 79%, significantly above a copy of the state-of-the-art SSpro server, better than any system published to date. AVAILABILITY Porter is available as a public web server at http://distill.ucd.ie/porter/ CONTACT gianluca.pollastri@ucd.ie.
A Novel Approach to Video Matting using Optical Flow

The image matting problem refers to foreground object extraction from an image. Similarly, video matting problem refers to extraction of a foreground object from each frame of a video-sequence producing a moving foreground layer. Layer extraction process should deal with the transparency caused by camera point spread function (PSF) and motion blur, thus the natural way is to store extracted layer as a pair of images: color and opacity. The latter is referred to as an alpha-channel. In this paper we propose an algorithm that takes alpha for the first frame (or an arbitrary key-frame) provided by the user (e.g. by using some image matting algorithm) and tracks its motion in time through the video sequence. Unlike the obvious approach that models the whole scene motion from one frame to the next with an optical flow (e.g. to warp the rough input segmentation (trimap) or the alpha channel with this flow), we use it to model the foreground layer motion only. This prevents us from otherwise unavoidable artifacts on the boundaries of foreground objects, which are of the main interest in the matting problem. Instead of matching pixel colors in the pair of frames, we measure how the optical-flow-warped alpha-channel fits the next frame (we use color matching as an additional regularization though). By minimizing this measure with respect to optical flow we fully preserve the structure of the foreground object and prevent temporal incoherence artifacts.
A Modified EPI sequence for high‐resolution imaging at ultra‐short echo time

A robust modification of echo‐planar imaging dubbed double‐shot echo‐planar imaging with center‐out trajectories and intrinsic navigation (DEPICTING) is proposed, which permits imaging at ultra‐short echo time. The k‐space data is sampled by two center‐out trajectories with a minimal delay achieving a temporal efficiency similar to conventional single‐shot echo‐planar imaging. Intersegment phase and intensity imperfections are corrected by exploiting the intrinsic navigator information from both central lines, which are subsequently averaged for image reconstruction. Phase errors induced by inhomogeneities of the main magnetic field are corrected in k‐space, recovering the superior point‐spread function achieved with center‐out trajectories. The minimal echo time (<2 msec) is nearly independent of the acquisition matrix permitting applications, which simultaneously require high spatial and temporal resolution. Examples of demonstrated applications include anatomical imaging, BOLD‐based functional brain mapping, and quantitative perfusion imaging. Magn Reson Med, 2010. © 2010 Wiley‐Liss, Inc.
DARP: Dynamically Adaptable Resilient Pipeline design in microprocessors

In this paper, we demonstrate that the sensitized path delays in various microprocessor pipe stages exhibit intriguing temporal and spatial variations during the execution of real world applications. To effectively exploit these delay variations, we propose Dynamically Adaptable Resilient Pipeline (DARP)-a series of runtime techniques to boost power performance efficiency and fault tolerance in a pipelined microprocessor. DARP employs early error prediction to avoid a major portion of the timing errors. Using a rigorous circuit-architectural infrastructure, we demonstrate substantial improvements in the performance (9.4-20%) and energy efficiency (6.4-27.9%), compared to state-of-the-art techniques.