Miracle-dz/UniFAR-FTUs huggingface dataset:
Supplementary Frequency Regulation with Multiple Virtual Energy Storage System Aggregators

Abstract High intermittency renewable generation reduces the system inertial and increase system uncertainties introducing new challenges for frequency regulation of future grid, including primary, secondary, and tertiary ancillary services. Residential air-conditioning contributes to electric utility critical peak load where the frequency regulation reserves are low. This article proposes a construction to turn such thermostatic load into a virtual energy storage system (VESS) and provides an algorithm to enable ancillary frequency regulation services by coordinating multiple groups of aggregated VESS. Based on the thermal modeling of individual VESS, the response behavior can be predicated and scheduled by an aggregator control. In primary frequency regulation scheme, the VESS aggregators communicate to share the frequency deviation signals and response according to a droop strategy under constraints of room temperatures and switching time. In secondary frequency regulation scheme, the VESS aggregators communicate and compete with each other to provide supplementary spinning reserve and reduce the generation cost. The ability of VESS is quantified to share the automatic generation control (AGC) responsibility optimally. Case studies are conducted to validate the effect and feasibility of the proposed frequency regulation schemes. The results show that it can provide technical and economic benefits to both participating residences and power system operators.
Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning

Subgraph similarity search, one of the core problems in graph search, concerns whether a target graph approximately contains a query graph. The problem is recently touched by neural methods. However, current neural methods do not consider pruning the target graph, though pruning is critically important in traditional calculations of subgraph similarities. One obstacle to applying pruning in neural methods is {the discrete property of pruning}. In this work, we convert graph pruning to a problem of node relabeling and then relax it to a differentiable problem. Based on this idea, we further design a novel neural network to approximate a type of subgraph distance: the subgraph edit distance (SED). {In particular, we construct the pruning component using a neural structure, and the entire model can be optimized end-to-end. } In the design of the model, we propose an attention mechanism to leverage the information about the query graph and guide the pruning of the target graph. Moreover, we develop a multi-head pruning strategy such that the model can better explore multiple ways of pruning the target graph. The proposed model establishes new state-of-the-art results across seven benchmark datasets. Extensive analysis of the model indicates that the proposed model can reasonably prune the target graph for SED computation. The implementation of our algorithm is released at our Github repo: https://github.com/tufts-ml/Prune4SED.
IMPROVEMENT OF DYNAMIC CHARACTERISTICS FOR POWER SYSTEM WITH WIND FARM USING COMPENSATION DEVICES

The main purpose of this paper is to study the applications of energy storage devices based on Flexible AC Transmission System (FACTS) techniques, namely the Static Var Compensator (SVC), the Static Synchronous Compensator (STATCOM) and the Battery Energy Storage System (BESS), for the improvement of dynamic characteristics of the power systems with wind generation. The system dynamic characteristics are investigated under occurrence of a three-phase short circuit fault at the point of interconnection of the system and the wind farm, without and with the installation of SVC, STATCOM and BESS. The system dynamic behaviors are also examined when the wind power output is decreased due to the wind speed variation. The results show that with a three-phase short circuit fault occurring at the point of interconnection of the system and the wind farm, the compensation equipment will provide reactive power in order to improve the voltage profile, and when the wind power output is decreased, the BESS improves the frequency transient because it can provide real power during system transient.
Influência e de lactação nas concentrações séricas de proteínas, metabólitos, minerais e enzimas de vacas da raça Bonsmara Influence of management and lactation order in the serum concentrations of proteins, metabolites, minerals, and enzymes of Bonsmara breed cows

bioquímicos Abstract The aim of this study was to evaluate the influence of pregnancy and lactation order on the serum concentrations of proteins, metabolites, minerals and enzymes of first, second and third lactation order Bonsmara cows. The Bonsmara breed, originated in SouthAfrica, stands out for its tolerance to heat, and productive characteristics, such as meat quality, high fertility, sexual precocity, ease of calving and good maternal ability. These characteristics make the breed a genetic alternative for crossbreeding with zebu breeds. Blood samples were collected from 93 cows, from which 34, 29 and 30 were first, second and third lactation order cows, respectively. Samples were processed in a multichannel automated analyzer using Labtest Diagnostics® kits. To compare the values between lactation orders, the Kruskall-Wallis test was chosen. To compare between pregnant and nonpregnant cows within lactation orders, the Mann-Whitney test was used. Amongst the constituents analyzed, the order of lactation significantly influenced only the serum albumin concentration (ALB) (p<0.0001). Pregnancy significantly influenced theA:G ratio (p=0.034) in third lactation cows; cholesterol (Chol) (p=0.004), triglycerides (TRI) (p<0.0001), inorganic phosphorus (iP) (p=0.033), iron (Fe) (p=0.001), aspartate aminotransferase (AST) (p=0.018) and alkaline phosphatase (ALP) (p=0.039) in second lactation order cows and the value of the overall group. Creatinine (Crea) (p<0.0001) values were influenced by pregnancy only in the overall group. It was concluded that pregnancy and lactation order significantly influenced the concentration of several serum biochemical constituents of Bonsmara cows, especially in second order lactation cows.
Self Supervised Learning for Lower Limb Muscle Segmentation on MRI

Segmentation is one of the most important tasks in medical image analysis. Unavailability of large and high quality labeled data set poses a challenge to implement Deep learning models. Self-supervised learning (SSL) approaches with various pretext tasks overcome these limitations. Towards this, we implemented a U-Net architecture, with context prediction as the pretext task followed by instance segmentation as the downstream task. Our dataset consisted of 2D MRI images of lower human limbs, with ground truth segmentation labels of six muscles. We consider three different masking percentages and kernel sizes for inpainting along with four strategies for transfer learning (freezing either encoder or decoder layers in U-Net, neither, or both layers). It was found that 16×16 sized kenels with 10% image masking had the largest Dice score on the validation set when the encoder layers were frozen and the decoder layers were fine-tuned. We compared the performance of this model to the baseline, a fully supervised segmentation model using U-Net. The performance of the SSL model was about 4.60% better than the baseline model.
Lane-level Path Guiding Method for Unmanned Ground Vehicle

A lane-level digital map is constructed with multi-sensors to meet the requirements of unmanned ground vehicle for high-precision digital map and the needs to navigate in structured road environment. Based on this lane-level digital map, a lane-level path guiding algorithm is also proposed. First, a lane-level digital map is constructed with the multi-sensor data collected by data collecting platform and the lanes and segments are built according to the traffic rules. Next, different property tables are designed for different geographic elements to represent their characteristics. Then, a global path planning method is proposed according to the characteristic of structured road environment. Finally, a local path planning method is proposed to avoid the obstacles when UGV meets the obstacles. According to the results of on-vehicle experiments and contests for unmanned vehicles, the proposed digital map and path guiding method prove to meet the requirements of unmanned ground vehicles in structured road environment and offer reliable guidance and guarantee for fast and safe driving of unmanned ground vehicles.
Image Matching Challenge 2022 - An ensembling approach

The image-matching task in computer vision presents two images of the same landmark, captured by two different cameras in two different positions and asks for a 3 × 3 matrix describing the relative change in pose from the first camera to the second. We investigate several avenues for improvement over existing methods on the task of finding this ’fundamental matrix. ’ Despite the attention this problem has received in recent years, we are able to provide some new insights into improving performance. As a contribution to quantity of data and quality of training, we rig-orously formulate a versatile and non-trivial data augmentation strategy and show empirically that it is effective. As for our contributions to models for this problem, we show one model, LoFTR to be extensively trained enough that fine-tuning on a large new dataset offers no significant improvements. With this shown, we direct our attention to exploring the aggregated predictions of an ensemble of models for the task. This is well-motivated since all the most successful approaches to image-matching arrive at a list of candidate pairs of points (one in each image) which should correspond to the same point in 3D space and thus impose a conditional on the fundamental matrix. We explore several methods of aggregating models’ lists of candidate matching point pairs and report on their efficacy.
Multi-Modal Image Captioning

Image captioning is a popular benchmark task in the field of computer vision with important real world applications. Our project explores image captioning on Flickr8k, a small image captioning dataset, and applies traditional and state-of-the-art approaches for the task. We experiment with various model architectures including the multi-modal paradigm, pretrained encoders and transformer decoders with multi-headed attention. Our best model, a transformer encoder-decoder network with a pretrained CNN ResNet-18 encoder achieved a BLEU-1 score of 0.879 and a BLEU-4 score of 0.543 on the dataset.
Improving Feature Selection on Heart Disease Dataset with Boruta Approach

-- Coronary artery disease (CAD) is one of the deadliest diseases globally, including in Indonesia. CAD occurs due to the narrowing or blockage of coronary arteries, which is usually caused by atherosclerosis. Various studies have been conducted to predict the nature and characteristics of this disease. Some researchers use the Z-Alizadeh Sani dataset, which consists of 54 attributes with two classification results, CAD and Normal, to classify its data. Feature selection is one way to reduce the number of attributes that exist by leaving the attributes that have a high effect on the dataset. In this study, the Boruta method used as a feature selection to minimize the attributes and leave the attributes with a high relative with the dataset. By reducing the attributes in the dataset through the feature selection process, sets of 17 and 18 attributes selected as attributes with a high relative with the dataset. These attributes then used to calculate the accurate value of the dataset using the several classification methods, and 90,3% accuracy is obtained from this study.
Analysis of Volatility Surfaces with Computer Vision

This paper examines several architectures for predicting future VIX index values using historical equity options surface data. We provide final accuracy and fit metrics, as well as qualitative analysis of the superior or inferior performance of each model given the horizon of desired VIX forecasts. Specifically, this study experiments with CNN, CONV-LSTM, and Transformer based approaches towards volatility prediction, none of which are currently industry standard techniques. We conduct accuracy and residual analysis in the experimental results, and find that CONV-LSTM and Transformer approaches exhibit predictive power above other techniques.
Inferring Movie Genres From Their Poster

Classifying movies by their posters into their correct genres is a problem that lends itself to Convolutional Neural Networks and can prove to be of immense benefit to advertisers, producers and viewers as it can help align the poster’s perceived genre by the audience with its ac-tual genre making it easier for viewers to browse and se-lect movies. Previous work in movie-genre classification has tried extracting low level features from the posters, using standard resnet50 architecture, using features from the movie itself, as well using KNN and Na¨ıve Bayes. In this paper, I explore the application of deeper versions of pre-trained traditional networks like VGG19 and ResNet101 as well as AlexNet to explore the viability of transfer-learning and different architectures for this problem. Using the IMDb movie poster dataset A top accuracy of 42% is achieved using ResNet101. Other networks seem to per-form comparably well suggesting that improvements in performance may depend more on balancing the dataset and feature-engineering than on changing between these 3 network architectures.
OBESITY, HEALTH CARE EXPENDITURE AND EDUCATION IN BAMENDA MUNICIPALITY-CAMEROON: CONSIDERATIONS FOR CURRICULUM INNOVATION

Obesity is a major risk factor to many non-communicable diseases leading to increase health care expenditures and concerns for curriculum innovations in many countries. Thus, the objectives of this study are: to determine the factors influencing health care expenditure, to investigate the effects of obesity on health care expenditure, to examine the relationship between obesity and education and to discuss implications for curriculum innovation on the basis of the findings. The study was conducted in the Bamenda Municipality of Mezam Division in the North West Region of Cameroon. The survey research design was employed. With the use of questionnaire, primary data was collected from 753 obese and non-obese individuals aged 20 and above. The multiple regression and multinomial logit results revealed that, level of monthly expenditure and education had statistically significant effect on health care expenditure in the Bamenda Municipality. There was insignificant effect of obesity on health care expenditures. Gender and age influenced health care expenditure and majority of those who had attained higher levels of education were non obese as opposed to their obese counterparts. The study argued that, curriculum innovation focusing on reducing obesity and health care expenditure via educational attainment remains a long term process. Therefore, educational stakeholders should sensitize the community on the basic rules on weight watching and the Bamenda City Council should create weight loss rehabilitation centers. The adoption of personalized and integrated approaches to innovative curriculum planning and design should be of utmost importance to the government of Cameroon.
HEALTHCARE MANAGEMENT OF TUBERCULOSIS: INTEGRATING A TEACHING HOSPITAL INTO PRIMARY HEALTH CARE

Qualitative, exploratory and interventionist case study at a teaching hospital of Parana, with the objective of proposing a Tuberculosis Healthcare Management model focused on the hospital discharge, with a view to comprehensive care for patients with tuberculosis. Interviews were conducted with key informants (17 subjects), using the Theory of Nursing Praxis Intervention in Collective Health as the theoretical and methodological framework, with its three interdependent dimensions used as analytical categories. Weakness was revealed in the protocols for hospitalized patient care, insufficient integration between levels of care and the need for direct communication between the hospital and primary care nurses. In view of this result, a Healthcare Management Model, expressed as a flow of care with matrix support, nursing consultation and phone contact between nurses. The implementation of this model will lead to greater integration between the levels of care, permitting improvements in patient monitoring with greater probability of treatment compliance.
The Hot Atmospheres of X-Shaped Radio Galaxies

Title of Dissertation: The Hot Atmospheres of X-Shaped Radio Galaxies Edmund Hodges-Kluck, Doctor of Philosophy, 2011 Dissertation directed by: Professor Christopher Reynolds Department of Astronomy We present an observational and numerical study of X-shaped radio galaxies, a subset of the double-lobed radio galaxies with a second set of lobes or “wings. ” These sources have been proposed as the “smoking gun” of supermassive black hole mergers, in which case the secondary lobes would be fossil remnants following a black hole spin-flip jet reorientation. However, they may instead originate in the interplay between giant radio lobes and their hot plasma environments, since radio lobes can be considered as bubbles of light fluid in the heavier intracluster medium. Circumstantial evidence from studies of the host galaxies at optical wavelengths indicates that this may indeed be the case, leading to two important questions we attempt to answer in this work: (1) Does it appear that X-shaped radio galaxies are aware of their environments? (2) Can radio galaxies respond to their environments in such a way as to form X-shaped morphology? We use radio, optical, and X-ray imaging data to investigate the first question, finding that, in general, X-shaped sources have jets co-aligned with the major axes of their hot (X-ray emitting) atmospheres and wings co-aligned with their minor axes. However, in at least one case (where the jet clearly does not follow this trend), a deep X-ray observation suggests that rapid reorientation of the jet axis is the best explanation. Moreover, despite the trend we discover, the hydrodynamic models of wing formation have significant theoretical problems. Thus, the second major component of this thesis is concerned with using hydrodynamical simulations to determine whether X-shaped radio galaxies can be produced in response to asymmetries in the atmosphere. We inject jets as light fluids into a model cluster or galactic atmosphere previously in hydrostatic equilibrium, thereby forming bubbles similar to those observed in radio galaxies. Since we inject the jet along the major axis of an asymmetric atmosphere, distortions to the canonical double-lobed radio galaxy result from different responses to the local pressure gradient. With a significantly anisotropic atmosphere and a powerful but decaying jet, we find that X-shaped morphology indeed results for reasonable jet and cluster parameters. However, it is unclear whether our simulated mechanism would be effectual in nature because of the high degree of anisotropy required and the differences between some observed wings and our model wings. We make a number of predictions which we would expect to be observed in the future if the hydrodynamic model is at work. The Hot Atmospheres of X-Shaped Radio Galaxies by Edmund Hodges-Kluck Dissertation submitted to the Faculty of the Graduate School of the University of Maryland at College Park in partial fulfillment of the requirements for the degree of Doctor of Philosophy 2011
Singularities in the unphysical complex plane for deep water waves

Two-dimensional free surface flows may be formulated through boundary integrals which allows analytic continuation in the unphysical complex plane. For two dimensional water waves, Tanveer [41] [42] showed that the only form of singularity in the unphysical plane is of the square-root type. One may wonder what influence these singularities may have on the behavior of water waves. This thesis resolves some of these questions by tracking singularities in the unphysical domain and relating their close approach to the real axis with wave breaking. The main result is the direct verification of Tanveer’s singularity result. A boundary integral technique is used to simulate deep water wave motion. A spectral procedure is used to form-fit the Fourier spectrum of the curvature of the wave profile to a prescribed asymptotic expression. The form-fit provides information on the power and location of the closest singularity to the real axis. The power of the curvature singularities is found to be −3/2 when the curvature is expressed as a function of the Lagrangian variable. This singularity is associated with a pole singularity in the complex arclength plane, and is not an artifact of the parametrization. The singularity approaches the real axis when a plunging breaker occurs. For nonbreaking waves, the singularity wanders above some level in the unphysical plane. It is then established that this curvature singularity is theoretically equivalent to Tanveer’s one-half power
Gestural and Touchscreen Interaction for Human-Robot Collaboration: a Comparative Study

Close human-robot interaction (HRI), especially in industrial scenarios, has been vastly investigated for the advantages of combining human and robot skills. For an effective HRI, the validity of currently available human-machine communication media or tools should be questioned, and new communication modalities should be explored. This article proposes a modular architecture allowing human operators to interact with robots through different modalities. In particular, we implemented the architecture to handle gestural and touchscreen input, respectively, using a smartwatch and a tablet. Finally, we performed a comparative user experience study between these two modalities.
A Multi-tasking Model of Speaker-Keyword Classification for Keeping Human in the Loop of Drone-assisted Inspection

Audio commands are a preferred communication medium to keep inspectors in the loop of civil infrastructure inspection performed by a semi-autonomous drone. To understand job-specific commands from a group of heterogeneous and dynamic inspectors, a model must be developed cost-effectively for the group and easily adapted when the group changes. This paper is motivated to build a multi-tasking deep learning model that possesses a Share-Split-Collaborate architecture. This architecture allows the two classification tasks to share the feature extractor and then split subject-specific and keyword-specific features intertwined in the extracted features through feature projection and collaborative training. A base model for a group of five authorized subjects is trained and tested on the inspection keyword dataset collected by this study. The model achieved a 95.3% or higher mean accuracy in classifying the keywords of any authorized inspectors. Its mean accuracy in speaker classification is 99.2%. Due to the richer keyword representations that the model learns from the pooled training data, adapting the base model to a new inspector requires only a little training data from that inspector, like five utterances per keyword. Using the speaker classification scores for inspector verification can achieve a success rate of at least 93.9% in verifying authorized inspectors and 76.1% in detecting unauthorized ones. Further, the paper demonstrates the applicability of the proposed model to larger-size groups on a public dataset. This paper provides a solution to addressing challenges facing AI-assisted human-robot interaction, including worker heterogeneity, worker dynamics, and job heterogeneity.