Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of Hammerstein Model for Solid Oxide Fuel Cells

There is a consensus within the industry that the development of accurate models for solid oxide fuel cells is a key step in understanding the operation of these devices. The Hammerstein model configuration, which includes a nonlinear static block followed by a linear dynamic block, is applied to model the static and dynamic characteristics of the solid oxide fuel cell. The performance of the proposed identification algorithms is illustrated through simulation
Windy with a chance of profit: bid strategy and analysis for wind integration

Integration of wind power with the grid has become an important problem. For integration, a producer needs to bid in a time-ahead market to deliver an amount of energy at a future point in time. Because wind speed and price are both uncertain, a producer needs to place bids on the basis of expected wind power yield and price. To this end, improving the accuracy of the prediction of wind speed has received much attention. However, the trade-off between expected profit and the prediction errors over a multi-period setting has been less studied. We fill this gap by quantifying trade-offs between profits and prediction errors. First, we obtain, under idealized conditions on the price and the yield processes, an optimal bid strategy as a closed-form expression. Next, we evaluate the profit-vs-prediction trade-off using this idealized bidding strategy on synthetic datasets which satisfy all the idealistic assumptions. We also consider two baselines - a naive strategy and an oracle strategy that has perfect knowledge over a limited horizon. Finally, we relax our assumptions and evaluate all strategies under real-world datasets. We identify and work around limitations of the idealized bidding strategy when the underlying assumptions are violated. On synthetic datasets, with no buffering and a (relative) prediction error of 25\% , we find that our bidding approach performs significantly better than a naive approach and compares favourably (86\%) to an oracle with a look-ahead of two time-slots and infinite buffer. On real-world datasets, with buffer equivalent to 20\% of the maximum yield, our approach exceeds the naive approach by 25\%, while remaining within 62\% of a two-step look-ahead oracle that uses infinite buffering.
Real-time building occupancy sensing using neural-network based sensor network

Current occupancy sensing technologies may limit the effectiveness of buildings controls, due to a number of issues ranging from unreliable data, sensor drift, privacy concerns, and insufficient commissioning. More effective control of Heating, Ventilation and Air-conditioning (HVAC) systems may be possible using a smart and adaptive sensing network for occupancy detection, capable of turning off services out of hours, and not over-ventilating, thus enabling energy savings, and not under-ventilating during occupied periods, giving comfort and health benefits. A low-cost and non-intrusive sensor network was deployed in an open-plan office, combining information such as sound level, case temperature, carbon-dioxide (Co2) and motion, to estimate occupancy numbers, while an infrared camera was implemented to establish ground truth occupancy levels. Symmetrical uncertainty analysis was used for feature selection, and a genetic based search to evaluate an optimal sensor combination. Selected multi-sensory features were fused using a neural network. From initial results, estimation accuracy reaching up to 75% for occupied periods was achieved. The proposed system offers promising opportunities for improved comfort control and energy efficiency in buildings.
Improved Appearance-Based 3-D Object Recognition Using Wavelet Features

In this paper we present an improved appearancebased approach for the localization and classification of 3-D objects in 2-D gray level images. Thereby we calculate local feature vectors by the coefficients of the wavelet multiresolution analysis and model them statistically. Since the appearance of the objects, i. e. also the size of the objects in the image, vary due to out-of-image-plane transformations, the features themselves as well as the region of interest are modelled as function of the external transformations. Further, we present and test different measurements for the recognition of objects that have different sizes. The experiments on a large dataset with more than 40000 images show that the approach is well suited for this recognition task.
MINIATURIZATION OF WILKINSON POWER DIVIDERS BY USING DEFECTED GROUND STRUCTURES

It is proposed that the application of defected ground structures (DGS) and electromagnetic bandgap (EBG) reduce the phase velocity, increase effective permittivity of microstrips and reduce the effective the wavelength which eventually lead to the reduction of overall length of Wilkinson power dividers. Furthermore, the adjacent strip lines may approach each other, which cause coupling problems. The undesired coupling maybe cancelled by connecting a capacitance in parallel with the isolation resistor of Wilkinson power divider. Application of DGS and EBG will not only reduce the dimensions of the divider, but also improves its scattering parameters.
Location Constrained Approximate Message Passing ( LCAMP ) Algorithm for Compressed Sensing

Introduction: Fast iterative thresholding methods [1,2] have been extensively studied as alternatives to convex optimization for high-dimensional large-sized problems in compressed sensing (CS) [3]. A common large-sized problem is dynamic contrast enhanced (DCE) MRI, where the dynamic measurements possess data redundancies that can be used to estimate non-zero signal locations. In this work, we present a novel iterative thresholding method called LCAMP (Location Constrained Approximate Message Passing) that combines a non-zero location assumption and an approximate message passing term to previous methods [4]. The method can reduce computational complexity and improve reconstruction accuracy. We demonstrate the proposed reconstruction using 4D breast DCE MRI data, of a size that is often challenging for constrained reconstructions.
DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics

There are many instances in genomics data analyses where measurements are made on a multivariate response. For example, alternative splicing can lead to multiple expressed isoforms from the same primary transcript. There are situations where differences (e.g. between normal and disease state) in the relative ratio of expressed isoforms may have significant phenotypic consequences or lead to prognostic capabilities. Similarly, knowledge of single nucleotide polymorphisms (SNPs) that affect splicing, so-called splicing quantitative trait loci (sQTL) will help to characterize the effects of genetic variation on gene expression. RNA sequencing (RNA-seq) has provided an attractive toolbox to carefully unravel alternative splicing outcomes and recently, fast and accurate methods for transcript quantification have become available. We propose a statistical framework based on the Dirichlet-multinomial distribution that can discover changes in isoform usage between conditions and SNPs that affect relative expression of transcripts using these quantifications. The Dirichlet-multinomial model naturally accounts for the differential gene expression without losing information about overall gene abundance and by joint modeling of isoform expression, it has the capability to account for their correlated nature. The main challenge in this approach is to get robust estimates of model parameters with limited numbers of replicates. We approach this by sharing information and show that our method improves on existing approaches in terms of standard statistical performance metrics. The framework is applicable to other multivariate scenarios, such as Poly-A-seq or where beta-binomial models have been applied (e.g., differential DNA methylation). Our method is available as a Bioconductor R package called DRIMSeq.
A performance-portable generic component for 2D convolution computations on GPU-based systems

In this paper, we describe our work on providing a generic yet optimized GPU (CUDA/OpenCL) implementation for the 2D MapOver- lap skeleton. We explain our implementation with the help of a 2D convo- lution application, implemented using the newly developed skeleton. The memory (constant and shared memory) and adaptive tiling optimizations are applied and their performance implications are evaluated on dier- ent classes of GPUs. We present two dierent metrics to calculate the optimal tiling factor dynamically in an automated way which helps in retaining best performance without manual tuning while moving to new GPU architectures. With our approach, we can achieve average speedups by a factor of 3.6, 2.3, and 2.4 over an otherwise optimized (without tiling) implementation on NVIDIA C2050, GTX280 and 8800 GT GPUs respectively. Above all, the performance portability is achieved without requiring any manual changes in the skeleton program or the skeleton implementation.
XXXXXXXXXX An O ( log n ) Randomized Resource Discovery Algorithm

The problem of a distributed network of computers discovering one another by making network connections is called the resource discovery problem. In this paper, we present the first randomized algorithm, called ‘Absorption’, that can solve the resource discovery problem in O(log n) running time with high probability and with O(n2) expected pointer complexity. In particular, the expected running time of our Absorption algorithm is at most 4 log4/3 n + 1 steps on any strongly-connected network. We also describe a variation of the algorithm, which runs in O(log n) expected time but has O(n) expected message complexity.
Paper Special Section on Knowledge-based Software Engineering Coordination of Local Process Views in Interorganizational Business Process

SUMMARY Collaborative business has been increasingly developing with the environment of globalization and advanced information technologies. In a collaboration environment with multiple organizations, participants from different organizations always have different views about mod-eling the overall business process due to different knowledge and cultural backgrounds. Moreover, flexible support, privacy preservation and process reuse are important issues that should be considered in business process management across organizational boundaries. This paper presents a novel approach of modeling interorganizational business process for collaboration. Our approach allows for modeling loosely coupled interorganizational business process considering different views of organizations. In the proposed model, organizations have their own local process views of modeling business process instead of sharing pre-defined global processes. During process cooperation, local process of an organization can be invisible to other organizations. Further, we propose the coordination mechanisms for different local process views to detect incompatibilities among organizations. We illustrate our proposed approach by a case study of interorgani-zational software development collaboration.
Safe and Timely Dynamic Updates for Multi-threaded Programs

Many dynamic updating systems have been developed that enable a program to be patched while it runs, to fix bugs or add new features. This paper explores techniques for supporting dynamic updates to multi-threaded programs, focusing on the problem of applying an update in a timely fashion while still producing correct behavior. Past work has shown that this tension of safety versus timeliness can be balanced for single-threaded programs. For multi-threaded programs, the task is more difficult because myriad thread interactions complicate understanding the possible program states to which a patch could be applied. Our approach allows the programmer to specify a few program points (e.g., one per thread) at which a patch may be applied, which simplifies reasoning about safety. To improve timeliness, a combination of static analysis and run-time support automatically expands these few points to many more that produce behavior equivalent to the originals. Experiments with thirteen realistic updates to three multi-threaded servers show that we can safely perform a dynamic update within milliseconds when more straightforward alternatives would delay some updates indefinitely.
Adaptive Morphological Filtering of Incomplete Data

We demonstrate how known convolution techniques for uncertain data can be used to set the shapes of structuring elements in adaptive mathematical morphology, enabling robust morphological processing of partially occluded or otherwise incomplete data. Results are presented for filtering of both gray-scale images containing missing data and 3D profile data where information is missing due to occlusion effects. The latter demonstrates the intended use of the method: enhancement of crack signatures in a surface inspection system for casted steel. The presented method is able to disregard unreliable data in a systematic and robust way, enabling adaptive morphological processing of the available information while avoiding any false edges or other unwanted features introduced by the values of faulty pixels.
Low-Pass Filtering Approach via Empirical Mode Decomposition Improves Short-Scale Entropy-Based Complexity Estimation of QT Interval Variability in Long QT Syndrome Type 1 Patients

Entropy-based complexity of cardiovascular variability at short time scales is largely dependent on the noise and/or action of neural circuits operating at high frequencies. This study proposes a technique for canceling fast variations from cardiovascular variability, thus limiting the effect of these overwhelming influences on entropy-based complexity. The low-pass filtering approach is based on the computation of the fastest intrinsic mode function via empirical mode decomposition (EMD) and its subtraction from the original variability. Sample entropy was exploited to estimate complexity. The procedure was applied to heart period (HP) and QT (interval from Q-wave onset to T-wave end) variability derived from 24-hour Holter recordings in 14 non-mutation carriers (NMCs) and 34 mutation carriers (MCs) subdivided into 11 asymptomatic MCs (AMCs) and 23 symptomatic MCs (SMCs). All individuals belonged to the same family developing long QT syndrome type 1 (LQT1) via
Speed Up Temporal Median Filter for Background Subtraction

Temporal median filter is one of most popular background subtraction methods. However, median operation is very time-consuming which limits its applications. This paper presents a fast algorithm to reduce the computation time of the temporal median operation. By utilizing the characteristics of high correlation of adjacent frames, the fast algorithm designs a simple mechanism to check whether the median of the current frame is equal to that of the previous frame. The proposed algorithm reduces the computing frequency of median operations significantly, and the experimental results indicate it is much faster than the existing algorithms.
Reply to Generalizing the Taylor Principle: A Comment

Farmer, Waggoner, and Zha (2009) show that a new Keynesian model with a regime-switching monetary policy rule can support multiple solutions that depend only on the fundamental shocks in the model. Their note appears to find solutions in regions of the parameter space where there should be no bounded solutions, according to conditions in Davig and Leeper (2007). This puzzling finding is straightforward to explain: Farmer, Waggoner, and Zha (FWZ) derive solutions using a model that differs from the one to which the Davig and Leeper (DL) conditions apply. FWZ's multiple solutions rely on special assumptions about the correlation structure between fundamental shocks and policy regimes, blurring the distinction between "deep" parameters that govern behavior and the parameters that govern the exogenous shock processes, and making it difficult to ascribe any economic interpretation to FWZ's solutions.
Validation of an SRAM Power Model for Architecture Level Power Estimation

As power consumption is becoming a more important issue, several power estimation tools have been proposed. However, effectiveness of a power model is not really known without validating it against a real design. In this work, we validate a recently developed architecture level power estimation tool, PRESTO, against our SRAM design. This tool offers flexibility to adapt to various process technology, through the use of SPICE parameters and metal process parameters to calculate circuit’s parasitic capacitances. Our validation shows that the model gives reasonable accuracy for relative power estimates, but still shows discouraging results for absolute power estimates.
A Prototype for Practical Eye-Gaze Corrected Video Chat on Graphics Hardware

We present a fully functional prototype to convincingly restore eye contact between two video chat participants, with a minimal amount of constraints. The proposed six-fold camera setup is easily integrated into the monitor frame, and is used to interpolate an image as if its virtual camera captured the image through a transparent screen. The peer user has a large freedom of movement, resulting in system specifications that enable genuine practical usage. Our software framework thereby harnesses the powerful computational resources inside graphics hardware, to achieve real-time performance up to 30 frames per second for 800×600 resolution images. Furthermore, an optimal set of finetuned parameters are presented, that optimizes the end-to-end performance of the application, and therefore is still able to achieve high subjective visual quality.
"The Impact of Macroeconomic Conditions on the Instability and Long-Run Inequality of Workers' Earnings in Canada"

This paper examines the variability of workers’ earnings in Canada over the period 1982‑1997. Using a large panel of tax file data, we decompose total variation in earnings across workers and time into a long-run inequality component between workers and an average earnings instability component over time for workers. We find an increase in earnings variability between 1982‑89 and 1990‑97 that is largely confined to men and largely driven by widening long-run earnings inequality. Second, the pattern of unemployment rate and GDP growth rate effects on these variance components is not consistent with conventional explanations and is suggestive of an alternative paradigm of how economic growth over this period widens long-run earnings inequality. Third, when unemployment rate and GDP growth rate effects are considered jointly, macroeconomic improvement is found to reduce the overall variability of earnings as the reduction in earnings instability outweighs the widening of long-run earnings inequality.
Interactive video training for firearms safety.

IN 1998, 178 hazardous incident reports were reported to the Federal Corrections and Supervision Division, representing an increase of 52 incidents from 1997. Hazardous incidents are those situations in the office or the field that present an actual danger, risk, peril, or threat to probation or pretrial officers or assistants during the performance of their official responsibilities, or as a result of that performance. Of the 178 reported incidents, 8 percent involved situations with firearms or edged weapons, 28 percent of the incidents occurred in the office, 56 percent of the incidents occurred in the field, and, in 78 percent of the incidents, the perpetrator was the offender under supervision. As these statistics suggest, the possibility of violence by the offender is prevalent. Consequently, the need to provide officers with adequate training and measures to ensure officer safety is critical. Many law enforcement agencies, including many probation and/or pretrial offices, are using interactive video training, or a Firearms Training System (FATS), to enhance their officers’ ability to win violent clashes or hazardous incidents. In early April 2000, two Missouri Department of Corrections Training Officers, the Firearms Instructor for the Eastern District of Missouri and myself, spent three days training 36 probation officers using an interactive video machine. Nine recently hired officers were trained in teams of two, each team completing 1.5 hours on the FATS machine, while 27 veteran officers were trained in teams of two, each team completing one hour on the FATS machine. Immediately after the training, officers completed an exit evaluation/questionnaire. The primary purpose of the training was to help officers increase their mental preparedness when faced with a critical incident by presenting realistic, adrenaline-dumping scenarios. However, the training and subsequent exit evaluation/questionnaire were also used to obtain information about the officers’ perception of the value of carrying a firearm, the extent the training influenced officers’ perception of the probation officer’s role, the extent the training changed officers’ belief about their ability to use lethal force, the extent the training may affect the officers’ performance of field days, and the extent the training had a positive influence on officers’ ability to act decisively in a critical situation.
Issues on Mobile Agent Technology Adoption

The design, development, deployment and use of mobile agent in computing is a relatively new paradigm in distributed systems environment. The deployment of mobile agent in creating distributed systems has been shown to help reduce network load, overcome network latency and encapsulate protocols as well as help the dynamic adaptation, and asynchronous and autonomous execution of distributed applications. Despite these benefits, mobile agent technology adoption is still very low even in the face of high research activities in the area and this is a serious hindrance to tapping the computational benefit of mobile agent technology. This paper explicates the nitty-gritty behind mobile agent technology and exposed the factors militating against its adoption. This will help expose critical areas one need to focus on in mobile agent research to increase the adoption of mobile agent technology.
Thematic Proximity in Content Analysis

This article explains how to calculate thematic proximity within a mixed methods content analysis approach. Thematic proximity of two themes can indicate the presence of meta-themes. Meta-themes are themes which acquire their meaning through the systematic co-occurrence of two or more other themes. By combining qualitative and quantitative techniques of content analysis, the researcher can reveal these latent text patterns. Using a study on Jihadi media as a showcase, the article describes how to detect meta-themes through content analysis. To this end, the article introduces a novel theme-correlation coefficient that adds valuable information to traditional theme relation metrics. It enables researchers to make new empirical observations in text data.
Trp64Arg Polymorphism in Beta3-Adrenergic Receptor Gene Is Associated with Decreased Fat Oxidation Both in Resting and Aerobic Exercise in the Japanese Male

The purpose of our study was to investigate whether the Trp64Arg polymorphism in β3-AR gene and the −3826A/G polymorphism in the UCP1 gene were associated with the reduction in energy expenditure and fat oxidation both in resting and aerobic exercise in Japanese. Eighty-six nonobese young healthy Japanese were recruited. Energy expenditure was measured using indirect calorimetry. The subjects performed an aerobic exercise program at 60% of their maximal heart rate for 30 minutes. The level of fat oxidation at rest and aerobic exercise of the male subjects with Trp/Arg of the β3-AR gene was significantly lower than that of the Trp/Trp genotype. No difference in FO0−30 was observed in the female subjects. There was no association between UCP-1 polymorphism and energy expenditure during aerobic exercise. It was revealed that the Trp64Arg polymorphism in β3-AR gene is associated with reduction of fat oxidation both in resting and aerobic exercise in healthy, young Japanese males.