Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating Transfer Learning Methods for Robot Control Policies

Robot control has traditionally been a multidisciplinary problem involving computer vision, planning, kinematics and more. Such systems are comprised of complex stages in a pipeline meant to perceive information via sensors from the outside world, process it in order to extract features and use said features when planning and executing future actions. Each stage presents its own unique challenges and requires different areas of expertise from teams working on the project. An end-to-end deep learning system for robot control promises to remove the need for these multi-stage pipelines and reduce the overall expertise needed to build and maintain them. Reinforcement and supervised learning are two different approaches to building such a system. No matter which approach, deep learning’s need for large datasets makes it impractical to employ for all but the most well-funded teams which can afford to task multiple robots with learning for a an extended period. Transfer learning can help tackle this challenge by using information from a domain where data is plentiful and a small sample from a related domain for which data is scarce. In the case of robot control this is usually a case of transferring between a simulation, where samples are quick and easy to generate, and the real world. This project has two goals. It compares and contrasts reinforcement and supervised learning systems and evaluates three transfer learning techniques in a simple two dimensional environment. The project finds that within the 2D environment a simple reinforcement model performs a better than a more complex supervised one and that Progressive networks are the most powerful of the three transfer learning methods.
TOWARD PERFORMING A FILTER-VACUUMING PROCEDURE USING A HUMANOID ROBOT

As humanoid robotic systems advance, there exist opportunities to further demonstrate their semiautonomous and autonomous capabilities on humandesigned procedural tasks. Robonaut 2 (R2), currently aboard the International Space Station (ISS), provides a testbed for robot execution of tasks designed for crewmembers to perform periodically. R2 is physically capable of dextrous tasks with bimanual tool use, but lacks the sensory, control, and planning algorithms required to handle any of the current maintenance procedures. This paper details a case study extending the library of skills available to R2 by examining the task of vacuuming an ISS bacteria air filter.
Output Robust Feedback Control with Exact Unmatched Uncertainties Compensation Based on HOSM Observation

This manuscript tackles the regulation problem of linear time invariant systems with unmatched perturbations. A hierarchical sliding mode observer is used allowing theoretically exact state and perturbation estimation. A compensation control approach based on the identified perturbation values is proposed ensuring exact regulation of the unmatched states. A simulation example shows the feasibility of this approach.
Servant Leadership: A Taxonomic Approach towards Systematic Literature Review

Connection between inferior leadership and massive business meltdown and inadequacy of seasoned leadership models create an urge to seek leadership equipped with ethical behavior, concern for subordinates, all the stakeholders and the society as a whole. This literature survey examines existing literature addressing servant leadership theory. Servant leadership theory has not yet been legitimized as a mainstream leadership theory therefore, it is worthy to examine its antecedents, outcomes and different mediating and moderating mechanisms to have in-depth knowledge of servant leadership concept and to understand how it differs from other mainstream leadership theories. This study analyzed the literature using descriptive approach as proposed by Turnnidge & Côté (2016). The study has been carried out in three phases. First phase is concerned with screening of abstracts and citations related to variable of interests, in second phase, peer discussion was made to retain valuable literature and exclusion of literature which does not fall in set criterion. In the third phase, full text of articles and relevant literature were reviewed which were potentially related to and are of interest. It was found that servant leadership is a different concept which is more comprehensive and predicts more positive outcomes than its predecessor leadership models. Systematic literature review provided detailed insight with respect to servant leadership theory and its outcomes which led to the development of taxonomy which was further used to develop integrated model of causal fl ow through mediating and moderating mechanisms; and then was further refined to identify servant leadership interaction with different organization and behavioral variables. Findings of this research suggest possible impact of servant leadership in power distant culture which is inconsistent in contemporary research and needs to be investigated for validation of servant leadership’s concept in Third World countries.
Detection of Fluorescence in Transgenic Arabidopsis Lines Expressing Fluorophores in Different Subcellular Localizations

The usage of fluorophores such as YFP and GFP is a widely distributed and established method in plant research. To monitor fluorescent reporter proteins several techniques based on instruments such as fluorescent microscopes or imaging systems are available. Detection of free or fused fluorescent reporters in different subcellular localizations and with different expression levels can be performed with the NightSHADE LB985 imaging system. The obtained results correlated with data received by detection of fluorescent using the Mithras LB940 multimode plate reader.
Unsupervised Recovery of Tree Metrics from Learned Language Representations

Deep contextualized word embedding models likely take advantage of traditional linguistic structure to make predictions, but there are few clear ways to measure what structure these models know. Current supervised approaches might add information that the models don’t have access to while unsupervised approaches deal only with whether the models learn specific linguistic phenomena. In this work we attempt to bridge this gap by designing an unsupervised structural probe to using a variant of the EM algorithm. While our probe was not too successful, its failures point to future directions to further develop the idea of an unsupervised probe.
Carbon Limitation and Drought Sensitivity at Contrasting Elevation and Competition of Abies pinsapo Forests. Does Experimental Thinning Enhance Water Supply and Carbohydrates?

Stand-level competition and local climate influence tree responses to increased drought at the regional scale. To evaluate stand density and elevation effects on tree carbon and water balances, we monitored seasonal changes in sap-flow density (SFD), gas exchange, xylem water potential, secondary growth, and non-structural carbohydrates (NSCs) in Abies pinsapo. Trees were subjected to experimental thinning within a low-elevation stand (1200 m), and carbon and water balances were compared to control plots at low and high elevation (1700 m). The hydraulic conductivity and the resistance to cavitation were also characterized, showing relatively high values and no significant differences among treatments. Trees growing at higher elevations presented the highest SFD, photosynthetic rates, and secondary growth, mainly because their growing season was extended until summer. Trees growing at low elevation reduced SFD during late spring and summer while SFD and secondary growth were significantly higher in the thinned stands. Declining NSC concentrations in needles, branches, and sapwood suggest drought-induced control of the carbon supply status. Our results might indicate potential altitudinal shifts, as better performance occurs at higher elevations, while thinning may be suitable as adaptive management to mitigate drought effects in endangered Mediterranean trees.
YouTube Videos Prediction: Will this video be popular?

Being a new type of job, YouTubers earn money through the advertisement and bonus from videos. Hence, the popularity of videos is the top priority for Youtuber. This project attempts to predict the performance of the videos that are going to be uploaded to YouTube. An equation is developed to manually classify all the videos into four classes: non-popular, overwhelming praises, overwhelming bad views, and neutral videos. Title, time gap, category, tags, description, and duration are selected as the features for the algorithm. Several machine learning algorithms are used to predict the performance and backward search is used on features to select the most relevant features. As a result, extreme gradient boosting with three features {time gap, category, description} is chosen due to its optimal balance between cost and performance.
Detection of Web API Content Scraping

Scraping is known to be difficult to detect and prevent, especially in the context of web APIs. It is in the interest of organisations that rely heavily on the content they provide through their web APIs to protect their content from scrapers. In this thesis, a machine learning approach towards detecting web API content scrapers is proposed. Three supervised machine learning algorithms were evaluated to see which would perform better on data from Spotify’s web API. Data used to evaluate the classifiers consisted of aggregated HTTP request data that describes each application having sent HTTP requests to the web API over a span of two weeks. Two separate experiments were performed for each classifier, where the second experiment consisted of synthetic data for scrapers (the minority class) in addition to the original dataset. SMOTE was the algorithm used to perform oversampling in experiment two. The results show that Random Forest was the better classifier, with an MCC value of 0.692, without the use of synthetic data. For this particular problem, it is crucial that the classifier does not have a high false positive rate as legitimate usage of the web API should not be blocked. The Random Forest classifier has a low false positive rate and is therefore more favourable, and considered the strongest classifier out of the three examined.
HeteroHalide: From Image Processing DSL to Efficient FPGA Acceleration

The domain-specific language (DSL) for image processing, Halide, has generated a lot of interest because of its capability of decoupling algorithms from schedules that allow programmers to search for optimized mappings targeting CPU and GPU. Unfortunately, while the Halide community has been growing rapidly, there is currently no way to easily map the vast number of Halide programs to efficient FPGA accelerators. To tackle this challenge, we propose HeteroHalide, an end-to-end system for compiling Halide programs to FPGA accelerators. This system makes use of both algorithm and scheduling information specified in a Halide program. Compared to the existing approaches, flow provided by HeteroHalide is significantly simplified, as it only requires moderate modifications for Halide programs on the scheduling part to be applicable to FPGAs. For part of the compilation flow, and to act as the intermediate representation (IR) of HeteroHalide, we choose HeteroCL, a heterogeneous programming infrastructure which supports multiple implementation backends (such as systolic arrays and stencil implementations). By using HeteroCL, HeteroHalide can generate efficient accelerators by choosing different backends according to the application. The performance evaluation compares the accelerator generated by HeteroHalide with multi-core CPU and an existing Halide-HLS compiler. As a result, HeteroHalide achieves 4.15\texttimes speedup on average over 28 CPU cores, and 2 \textasciitilde 4\texttimes throughput improvement compared with the existing Halide-HLS compiler.
CS 229 Final Report: Recipe Rating Prediction (Natural Language)

This work develops a model for predicting the star rating of an online recipe from the ingredients used in the recipe. We compare two text embedding strategies for encoding the ingredients, and compare linear regression and kernel methods, using a square kernel and a Gaussian kernel. The linear regression model with one-hot encoding achieves the best performance, with a mean squared error of 0.172, corresponding to the average prediction being within half of a star of the true recipe rating.
PREDICTING WEIGHT LOSS USING THE MYFITNESSPAL DATASET

39% of adults worldwide and over 65% of US adults are clinically overweight or diagnosed with obesity[1][2]. Overweight and obesity increase the risk for health issues including diabetes, hypertension, and osteoarthritis [3]. Half of US adults try to lose weight each year. Most often, they attempt to do so by exercising more and eating less [4], since negative net calorie intake is associated with weight loss. To aid in this process, many individuals use calorie tracking apps. MyFitnessPal, for example, is an online calorie counter used to track and work toward weight loss goals. Users can track calories through diet and workout logs, and they can provide weight information over time. In this project, we develop a model to predict percent weight loss over a month using user data from MyFitnessPal. Understanding the behaviors over time that lead to weight change is important in predicting how successful a new user might be in reaching healthier weights.
Development of genetic and genomic resources for breeding improved sweetpotato

Production of sweetpotato (Ipomoea batatas L. (Lam)), an important staple food in Sub-Saharan Africa, is limited by a number of constraints, such as low adaptability of available varieties and landraces, virus diseases, insect pests and drought. Consequently, yields achieved by resource-poor farmers in SSA are low. Improved and well adapted sweetpotato varieties with increased tolerance to biotic and abiotic stresses could significantly contribute to augment productivity and, once available, would have a large positive impact on food and income security in Sub-Saharan Africa. However, breeding efforts are limited by the crop’s genetic complexity, lack of information about its genetic resources and access to genomics tools for this crop for modern breeding. To mobilize allelic diversity and to facilitate introgression of desirable alleles into breeding populations, we have established genetic and genomics tools including a well defined Composite Genotype Set and a gene index. Furthermore we have designed and tested more than 200 new microsatellite markers and identified 200 SNP markers in stress response genes. A sweetpotato DArT marker system is under development. For establishing the gene index, we applied Next Generation sequencing technologies to characterize the sweetpotato transcriptome. The index comprises 31.165 contigs and 29.080 singletons and was annotated based on sequence comparisons with known proteins. The Composite Genotype Set and the genomics tools will support trait capture efforts on molecular level, will improve the allelic diversity for breeding
The Role of Genetics and Environmental Factors on Autoimmune Disease Incidence With a Focus on Gender Bias in a Family Case Study

Most autoimmune diseases disproportionately affect women more than men, with women comprising about 80% of autoimmune disease incidence. The cause for this discrepancy is not known, although there are both genetic and environmental hypotheses. This thesis project aimed to elucidate whether the increased prevalence in women is due to a genetic or environmental factor by analyzing single nucleotide polymorphism (SNP) data from a family of five individuals with a mix of autoimmune diseases (systemic lupus erythematosus (SLE), psoriasis and Hashimoto’s thyroiditis). The overarching hypothesis was that the increased prevalence of autoimmune diseases in women is caused by differing levels of sex hormones and estrogen receptors, which can in turn affect various pathways that play a role in the development of immunological disease. As such, in the current study, pathways that have been shown to modulate the immune system, such as vitamin D and calcium levels and affect tight junctions and epithelial barrier integrity were examined in the context of the current known literature and SNPs. For each member of the family in the case study, a genetic analysis was carried out to search for the presence of SNPs that are known to increase risk of developing each autoimmune disease. By comparing and contrasting the male father, who has lupus, to the female members of his family, two of which have separate autoimmune diseases and two of which are healthy, differences and similarities in the SNP profile led to the finding of a SNP in the estrogen receptor alpha. Prior studies showed that disease associated with SNPs in the estrogen receptor alpha is correlated with environmental factors, such as smoking (A. Zhou et al., 2017). The male subject, who has lupus, is heterozygous for this polymorphism and so is his female daughter, who does not have lupus. The male subject was a heavy smoker at the time of disease onset and his daughter has never smoked, which may be a reason for the similar SNP profiles but difference in disease onset. In sum, the findings of this study suggest genetic reasons for autoimmune disease incidence in this family. This data also correlates with a previous study that shows that a polymorphism in the estrogen receptor alpha may be influenced by environmental factors, such as smoking, and may have contributed to disease incidence in a member of this family. Further studies will need to be completed in larger cohorts to see which SNPs and environmental factors are the most important factors for disease onset among the population as a whole.
Classification of Insincere Questions on Quora

Introduction: Quora is a popular online platform for people to pose questions and (usually) get well thought out answers to. While for the most part questions are asked in good faith, a small percentage of bad actors post questions that are insincere or problematic (for example, questions founded upon false premises, or ones that just intend to make a statement of some kind). In the interest of improving the overall community experience, a challenge then is to develop a classifier that will take as input a user generated question and automatically classify that question as sincere or insincere. To this end we experimented with a wide variety of models, including Naive Bayes, Logistic Regression, Averaged Perceptron and Recurrent Neural Networks. We also experimented with Google’s pretrained language model, BERT.
Phylogenomic Analysis of Two Co-Circulating Canine Distemper Virus Lineages in Colombia

Canine distemper virus (CDV) is considered a reemerging disease-causing agent in domestic dogs because it presents high divergence among circulating strains worldwide. In Colombia, the South America-3 and South America/North America-4 lineages co-circulate in domestic dogs, both in the Medellin metropolitan area. In this paper, two full CDV genomes from each viral lineage circulating in Medellin were sequenced; we explored the phylogenetic relationship with the available genome sequences; we described the presence of CDV mutations in the South America-3 and South America/North America-4 lineages associated with adaptation to human cells and a crossing of the species barrier and pathogenicity; and we established the evolutionary rates and time of the closest common ancestor for each gene and characterized the presentation of multiple genomic sites by positive selection.
Foliar and Wood Traits Covary along a Vertical Gradient within the Crown of Long-Lived Light-Demanding Species of the Congo Basin Semi-Deciduous Forest

Plant functional traits have shown to be relevant predictors of forest functional responses to climate change. However, the trait-based approach to study plant performances and ecological strategies has mostly been focused on trait comparisons at the interspecific and intraspecific levels. In this study, we analyzed traits variation and association at the individual level. We measured wood and leaf traits at different height locations within the crown of five individuals of Pericopsis elata (Harms) Meeuwen (Fabaceae) from the northern tropical forest of the Democratic Republic of the Congo. All traits varied between and within individuals. The between-individual variation was more important for leaf traits (23%–48%) than for wood traits (~10%) where the within-individual variation showed to be more important (33%–39%). The sample location height within the crown was found to be the driving factor of this within-individual variation. In a gradient from the base to the top of the crown, theoretical specific hydraulic conductivity and specific leaf area decreased while the stomatal density increased. We found significant relationships among traits and between wood and leaf traits. However, these relationships varied with the position within the crown. The relationship between vessel size and vessel density was negative at the bottom part of the crown but positive upward. Also, the negative relationship between stomatal density and stomatal size became stronger with increasing height within the crown. Finally, the positive relationship between specific leaf area and theoretical specific hydraulic conductivity became stronger in higher parts of the crown, suggesting that P. elata constantly adapts its water use with respect to its water supply, more strongly at the top of the crown where the environment is more extreme and less buffered against environmental fluctuations.
Cubic Regularization for Differentiable Games

Due to the non-symmetric nature of the game Jacobian, many ﬁrst-order meth-ods used to ﬁnd Nash equilibria for differentiable games, such as vanilla gradient descent and its variants, often perform poorly and exhibit relatively-slow convergence, oscillation around equilibria, or even divergence. Inspired by the close connection between differentiable games and classical nonlinear optimization, the latter of which has an array of well-established principles and methods in algorithmic design, this paper develops a new algorithm for smooth games based on the cubic-regularization method. The algorithm approximates each player’s cost function as a quadratic function regularized by a cubic term, an idea originally proposed by Nesterov and Polyak as a theoretically guaranteed extension of Newton’s method. The ﬁxed points of the proposed algorithm are shown to coincide with second-order Nash equilibria of the given game. For two-player zero-sum games, these ﬁxed points are also stable under the gradient ﬂow dynamic. The theoretical ﬁndings are supported by numerical experiments.
A Review of Municipal Solid Waste Gasification Technologies for Possible Fixed Bed Hybridization

: The present paper assesses current municipal solid waste ( MSW) gasification process, carryout a critical overview of MSW fixed bed gasifiers. The overview has also focused on the comprehensive evaluation of various gasification process operating parameters and its effects on syngas production for the aforementioned reactors and outline key suggestions for gasifier performance improvement. Thorough evaluation of these gasification process models and evaluation of operating parameters would further assist in the development of gasifiers technology for future MSW gasification. This review discusses gasification technology including its challenges for MSW, propose possible hybrid gasification technology. Results show that fixed bed gasifier design modification by the combination of gasifier features has shown better results in terms of clean producer gas.