Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal operation of a grid-connected fuel cell based combined heat and power systems using particle swarm optimisation for residential sector

ABSTRACT Combined heat and power (CHP) system holds great potential for reducing greenhouse gas emissions and energy cost in the residential sector. Implementing CHP systems in the residential sector may solve energy shortages, climate change and energy conservation issues. In this paper, an approach based evolutionary programming to evaluate the effect of a grid-connected fuel cell based CHP systems on the operational and performance cost of the system is developed. An economic model is developed and an economic analysis carried out for these systems. The optimisation seek to achieve the minimum cost of the system with relevant constraints for residential applications along with variable tariffs for purchasing and selling electricity from the local grid. A well-known meta-heuristic optimisation technique, particle swarm optimisation, is applied to solve the problem. The results show that the grid-connected fuel cell-based CHP system causes lower operational cost in the near future and indicate the feasibility of the proposed technique.
THE 25 mm GARFORD BULB ANCHOR FOR CABLE BOLT REINFORCEMENT PART 1 : LABORATORY RESULTS

The Garford bulb anchor is a modified cable geometry developed and patented by Garford Pty of Australia, and which has recently become widely available in Canada. It is produced by simply deforming the 7 wires (including the central kingwire) of a standard cable into a bulb1 (see Figure 1). Laboratory cable pull tests indicate that the bond capacity of the 25mm Garford bulb anchor is comparable to the nutcase cable, in excess of the birdcage cable, and significantly higher than standard 7-wire strand. Its comparative advantage over the latter is most significant for tests at low radial confinement and high water:cement ratio grout, which corresponds to operational conditions of low quality rock (ie: weak, highly fractured rock masses, foliated hanging walls, failed ground etc.) and poor installation quality control (perhaps the most common explanation for cable bolt failures). It is under such conditions that cable bolting is often ineffective. The most impressive result is that for a reduction of the effective rock mass modulus by a factor of five, the Garford bulb cable capacity remains virtually unchanged. For tests conducted at a 0.4 w:c ratio grout the dominant failure mechanism involved rupture of the strand (tensile strength of the strand = 250kN) and as such the capacity of Garford Bulb Anchor was independent of the effective rock mass confinement. Field test results will be presented in a companion paper. 1 As a matter of terminology, this ungrouted configuration is called a bulb, and the grouted configuration a bulb structure.
Robust Beam Tracking and Data Communication in Millimeter Wave Mobile Networks

Millimeter-wave (mmWave) bands have shown the potential to enable high data rates for next generation mobile networks. In order to cope with high path loss and severe shadowing in mmWave frequencies, it is essential to employ massive antenna arrays and generate narrow transmission patterns (beams). When narrow beams are used, mobile user tracking is indispensable for reliable communication. In this paper, a joint beam tracking and data communication strategy is proposed in which, the base station (BS) increases the beamwidth during data transmission to compensate for location uncertainty caused by user mobility. In order to evade low beamforming gains due to widening the beam pattern, a probing scheme is proposed in which the BS transmits a number of probing packets to refine the estimation of angle of arrival based on the user feedback, which enables reliable data transmission through narrow beams again. In the proposed scheme, time is divided into similar frames each consisting of a probing phase followed by a data communication phase. A steady state analysis is provided based on which, the duration of data transmission and probing phases are optimized. Furthermore, the results are generalized to consider practical constraints such as minimum feasible beamwidth. Simulation results reveal that the proposed method outperforms well-known approaches such as optimized beam sweeping.
Aalborg Universitet Lifetime Benchmarking of Two DC-link Passive Filtering Configurations in Adjustable Speed

Electrolytic capacitors with a DC-side inductor, and the slim DC-link capacitor are two typical filtering configurations in Adjustable Speed Drives (ASDs). The reliability performance of these capacitive DC-link solutions is an essential aspect to be considered, which depends on both component inherent capability and the operational conditions (e.g., electro-thermal stresses) in the field operation. This paper studies the reliability performance of the LC filter and slim capacitor based filter in a standard ASD system. Considering the variations of the capacitor parameters, environment stresses, and lifetime model, the lifetime of the two configurations are compared. Moreover, scalability analysis is presented in terms of power rating, amplitude and phase angle unbalance levels at the grid side. The results serve as a guideline for reliability aspect performance benchmarking of different DC-link solutions in ASD applications.
Hybrid Global Search for Sample Efficient Controller Optimization

Learning from models can be useful in domains where data samples are expensive, especially in fields like robotics, where running simulations can be much faster than collecting actual robot data. It is surprising, however, how poorly policies learned in simulation transfer to hardware, partly due to simulation inaccuracies and partly due to controller sensitivity to these inaccuracies. This challenge suggests that we should develop learning methods that utilize other forms of information that transfer more effectively between simulation and hardware. To this end, Bayesian optimization with kernels learned from simulation has emerged as a successful approach. This hybrid model-free global search with embedded model-based information has been shown to yield highly sample-efficient search on hardware. We modify this approach by updating information from simulation based on hardware samples, resulting in a novel way of updating information from dynamics models. This allows us to use inaccurate simulations, and still learn complex controllers for a bipedal robot. Our experiments show that this approach is robust to simulation inaccuracies, including systematic under-modelling and significant errors in dynamics parameters.
Vocal attractiveness in Cantonese: A production study

This paper presents a production study investigating the vocal strategies of Cantonese women when addressing an attractive vs. unattractive male. We recruited 19 young female native speakers of Hong Kong Cantonese who completed an attractiveness rating task, followed by a production task where they were presented a subset of the same faces. By comparing the rating results and corresponding acoustic data of the facial stimuli, we found that when young Cantonese women spoke to an attractive male, they were less breathy, lower in pitch, and with denser formants. Participants who were more satisfied with their own height used these vocal strategies more actively. These results are discussed in terms of the body size projection principle.
Essays on Prediction and Betting Markets

This thesis undertakes an investigation into the applicability of particular types of prediction and betting markets as information aggregation mechanisms (IAMs). Chapter 1 of the thesis provides an extended introduction of the definition and key characteristics of each market. It also provides an overview of related literature. Chapter 2 examines the Plott-Wit-Yang (2003) (PWY) paradox by focusing on individual-level, rather than aggregate-level, data to show that the observed prices are indeed consistent with individuals learning from observed market odds and not simply reflective of myopic betting. Finally, Chapter 3 considers a (call) prediction market, similar in design to Chen and Plott (2002), to investigate the predictability of PhD enrollments at CERG-EI over the 2009/10 calendar year. Subtle differences between Chen and Plott’s design and the one considered in this study provide useful guidance for future design and implementation in real world settings. Overall this thesis is well written and interesting. It is my overall assessment that the thesis is acceptable and warrants a defence leading to the award of doctoral degree. I will now comment further on my impressions of the dissertation.
Single Failure Recovery Method for Erasure Coded Storage System with Heterogeneous Devices

SUMMARY As the demand of data reliability becomes more and more larger, most of today’s storage systems adopt erasure codes to assure the data could be reconstructed when su ﬀ ering from physical device failures. In order to fast recover the lost data from a single failure, recovery optimization methods have attracted a lot of attention in recent years. However, most of the existing optimization methods focus on homogeneous devices, ignoring the fact that the storage devices are usually heterogeneous. In this paper, we propose a new recovery optimization method named HSR (Heterogeneous Storage Recovery) method, which uses both loads and speed rate among physical devices as the optimization target, in order to further improve the recovery performance for heterogeneous devices. The experiment results show that, compared to existing popular recovery optimization methods, HSR method gains much higher recovery speed over heterogeneous storage devices.
Risk Aggregation: A General Approach via the Class of Generalized Gamma Convolutions

Risk aggregation is virtually everywhere in insurance applications. Indeed, in the vast majority of situations, insurers are interested in the properties of the sums of the risks they are exposed to, rather than in the stand-alone risks per se. Unfortunately, the problem of formulating the probability distributions of the aforementioned sums is rather involved, and as a rule does not have an explicit solution. As a result, numerous methods to approximate the distributions of the sums have been proposed, with the moment-matching approximations (MMAs) being arguably the most popular. The arsenal of existing MMAs is quite impressive and contains such very simple methods as the normal and shifted-gamma approximations that, respectively, match the first two and three moments only, as well as such much more intricate methods as the one based on the mixed Erlang distributions (Cossette et al. 2016). Note, however, that in practice the sums of insurance risks can have numerous and just a few summands; in the latter case the normal approximation is very questionable. Also, in practice the distributions of the stand alone risks can be light-tailed or heavy-tailed; in the latter case moments of higher orders (e.g., ≥ 2) may not exist, and so the approximation based on mixed Erlang distributions is of limited usefulness. In this paper we put forward a refined MMA method for approximating the distributions of the sums of insurance risks. Our method approximates the distributions of interest to any desired precision, works equally well for light and heavy-tailed distributions, and is reasonably fast irrespective of the number of the involved summands.
3 RETRIEVAL OF BOUNDARY LAYER CLOUD PROPERTIES USING INFRARED SATELLITE DATA DURING THE DYCOMS-II FIELD EXPERIMENT

This work presents a method to retrieve the optical thickness, effective particle radius and cloud temperature of low-level marine clouds from nighttime satellite imagery. The method uses a direct radiative transfer model to simulate the radiances detected by the MODIS infrared channels and a numerical procedure based on evolutionary techniques to invert the model. The method was applied to MODIS datasets for which almostcoincident airborne observations were conducted during the DYCOMS-II field experiment carried out off the coast of southern California during July 2001. The retrieved parameters were compared to in situ measurements showing that, despite the large vertical variations in measured sizes, the method is able to provide particle sizes representative of the cloud layer.
Association of rs 734312 and rs 10010131 polymorphisms in WFS 1 gene with type 2 diabetes mellitus : a meta-analysis

Recently, many studies have investigated the association of allelic variants of Wolfram syndrome gene (WFS1) with insulin secretion, insulin sensitivity and the risk of hyperglycemia and type 2 diabetes (T2D). Although it is clear that WFS1 mutations cause diabetes, the genetic basis of the association of common WFS1 variants with T2D is still unclear. To date, many case–control studies have been carried out to investigate the role of the WFS1 gene polymorphism in the development of T2D. However, these studies are controversial and individual studies may have insufficient power to reach a comprehensive and reliable conclusion. The aim of this study was to investigate association of WFS1 genes polymorphisms (rs734312 and rs10010131) with T2D by conducting a meta-analysis. Seven studies with 6705 T2D cases and 11144 controls were enrolled in the meta-analysis of the association between rs734312 polymorphism and T2D, and 12 studies with 16304 T2D cases and 22004 controls were enrolled in the meta-analysis of rs10010131 polymorphism. Summary odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using fixed and random-effects models. Heterogeneity among studies was evaluated using the I2 and Begg’s test was used to evaluate publication bias. Sensitivity analysis was also performed. Our results suggested that the G allele of rs734312 polymorphism [dominant: FEM OR 0.873, 95%CI (0.810 – 0.940), recessive: FEM OR 0.876, 95% CI (0.800 – 0.959)] and A allele of rs10010131 polymorphism [dominant:FEM OR 0.853, 95% CI (0.817 – 0.892), recessive:REM OR 0.833, 95% CI (0.756 – 0.917)] in WFS1 gene had significant protective effects on risk of T2D.
Distributional Reinforcement Learning Applied to Robot Soccer Simulation WorkIn-Progress Paper-ALA Workshop at AAMAS-19

Directly approximating the expected value of actions is the most traditional approach to learn in Reinforcement Learning (RL) problems. However, learning the distribution of possible returns for each action (Distributional RL) has been shown to improve learning when allied to function approximation. Although efficient Distributional RL algorithms have been proposed in recent years, its use in multiagent tasks has not been investigated so far. Moreover, Distributional RL has a yet unexplored potential to transfer learning, which makes it a promising topic for research. In this work-in-progress paper, we describe our efforts and results applying Distributional RL in Half Field Offense, a popular and challenging testbed for Multiagent RL. We also describe our long-term plans on how to combine Distributional RL with Multiagent Transfer Learning. To the best of our knowledge, this is the first reported use of Distributional RL in multiagent tasks. Our next step is to build upon this first effort to achieve our long-term objectives.
Non-contact Medium-based Respiratory Analysis through Reinforced Hybrid Model

The ability to monitor natural respiratory behaviors is essential to pulmonology. Existing contact-based respiratory monitoring methods are known for their accuracy, but are generally considered uncomfortable, whereas non-contact methods are comfortable but less accurate. Thin Medium Thermal Imaging (TMTI), a respiration monitoring method that uses a thin medium and thermal imaging to sense breathing activity, was proposed as an alternative non-contact method that monitors respiration directly. However, the accuracy of this method suffered due to lack of information about airflow behaviors occurring between the user’s mouth and the medium. In this paper, we describe a reinforced hybrid model that utilizes a thermal camera with a spectral filter (3-5[µm]) to provide breathing behavior information in addition to a low-cost thermal camera to capture images of the medium. The thermal camera with a spectral filter visualizes CO2, giving us the ability to observe the turbulent behavior of exhaled airflows. By further understanding the relationship between human breathing behaviors and the heat signatures on the medium using this hybrid model, we are able to classify breathing mode with at least 91% accuracy.
Genetic Diversity and Geographic Variation of Chromosomal races of Nannospalax xanthodon ( Nordmann , 1840 ) and Nannospalax ehrenbergi ( Nehring , 1898 ) from Turkey , Revealed by RAPD Analysis

The level of genetic polymorphism in chromosomal races of Nannospalax xanthodon and Nannospalax ehrenbergi was determined by applying random amplified polymorphic DNA (RAPDs) analysis. One hundred and fifty four mole rat samples belonging to two species N. xanthodon (61 populations) and N. ehrenbergi (3 populations) distributed in Anatolia were studied. Remarkable variations of diploid chromosome numbers were identified for N. xanthodon (2n=36 – 60). Eleven RAPD–PCR primers generated 160 polymorphic loci. The mean proportion of polymorphic loci was 92% (147 bands) for all chromosomal race implying high levels of genetic variability in N. xanthodon and N. ehrenbergi. Estimation of genetic diversity based on PCR amplification of RAPDs was high for both species. Results of RAPD– PCR observed within and among species were also connected with the peripatric mode of speciation. We determined that RAPD bands showed high diagnostic value between chromosomal races as they were very distinctive for each chromosomal race and absent from other forms. Genetic distance (Ds) values between chromosomal races suggest that most populations analyzed in this study may be valid biological species.
Determining Causal Effects on Small-Cluster Data

In statistical applications, the assumption of independent and identically distributed data greatly simplifies the process of analysis. However, when data may be ordered into hierarchical clusters in which the clusters are independent, but units within each cluster are correlated, the assumption of independence and identical distribution need not hold. While the statistical literature provides methods of analysis for such situations, analysis is greatly complicated when trying to determine causal effects, which are necessarily much stronger than simple statistical association. In this paper we investigate the methodology of determining causal effects in small-cluster data in a real data application, as well as present a simulation study to investigate the consequences of a misspecified causal model. (Note: This paper is a working draft and results/conclusions may change. The author humbly apologizes for typos, bad notation, and unclear mathematics. All mistakes are entirely his.)
Fault Tolerant P 2 P RIA Crawling

Rich Internet Applications (RIAs) have been widely used in the web over the last decade as they were found to be responsive and user friendly compared to traditional web applications. Distributed RIA crawling has been introduced with the aim of decreasing the crawling time due to the large size of RIAs. However, the current RIA crawling systems do not allow for tolerance to failures that occur in one of their components. In this paper, we address the resilience problem when crawling RIAs in a distributed environment and we introduce an efficient RIA crawling system that is fault tolerant. Our approach is to partition the RIA model that results from the crawling over several storage devices in a peer-to-peer (P2P) network. This makes the distributed data structure invulnerable to the single point of failure. We introduce three data recovery mechanisms for crawling RIAs in an unreliable environment: The Retry, the Redundancy and the Combined mechanisms. We evaluate the performance of the recovery mechanisms and their impact on the crawling performance through analytical reasoning.
Personalizing Smartwatch Based Activity Recognition Using Transfer Learning

Smartwatches are increasingly being used to recognize human daily life activities. These devices may employ different kind of machine learning (ML) solutions. One of such ML models is Gradient Boosting Machine (GBM) which has shown an excellent performance in the literature. The GBM can be trained on available data set before it is deployed on any device. However, this data set may not represent every kind of human behavior in real life. For example, a ML model to detect elder and young persons running activity may give different results because of differences in their activity patterns. This may result in decrease in the accuracy of activity recognition. Therefore, a transfer learning based method is proposed in which user-specific performance can be improved significantly by doing on-device calibration of GBM by just tuning its parameters without retraining its estimators. Results show that this method can significantly improve the user-based accuracy for activity recognition.
Optimization and Learning Algorithms for Stochastic and Adversarial Power Control

Power control in wireless networks is a well-studied problem. However, recently it has been demonstrated that significant throughput gains can be achieved using data-driven online learning algorithms, supported by a cloud computing infrastructure. In this paper, we provide theoretical guarantees for such algorithms. In particular, we consider two variants of the problem: one which emphasizes long-term throughput and the other which emphasizes robust short-term throughput. The first problem reduces to solving a convex optimization problem with noisy, stochastic measurements while the second one is an online optimization problem where an adversary chooses the reward functions. We provide stochastic and online gradient descent methods customized for the power control problem and establish their convergence analysis. We show that in both cases, the total regret over a time horizon $T$ grows sublinearly at rate $O(\sqrt{T})$ for suitable choices of algorithms and algorithm parameters.
Visualization for Finite Element Method Education

In this project, common practices for visualizing scientific data were studied. In addition, the science of cognition and data display was reviewed. The results of this investigation was applied to augment a Civil Engineering introductory course on Finite Element Method at WPI. Software enhancements allowed three dimensional visualization for simulation of engineering structures. The research on cognition and data graphics was used to improve understanding of these visual aids. The plotting function, developed in MATLAB and Julia environments during the course of this project, can help all students visualize the results of their numerical codes.
PROBLEMS IN INTERPOLATION THEORY RELATED TO THE ALMOST EVERYWHERE CONVERGENCE OF FOURIER SERIES

A well-known conjecture in harmonic analysis is that the sequence of partial Fourier sums of a function in L log L(T) converges almost everywhere. The purpose of this expository paper is to discuss connections between this conjecture and recent developments in interpolation theory regarding sublinear translation invariant restricted weak type operators. Open problems in interpolation theory motivated by these results will also be presented. Let f be a measurable function supported on the unit circle T = {z ∈ C : |z| = 1}. The n’th Fourier coefficient of f is defined by