Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Classification of Radio Sources in the Galactic Plane Using B-CNN By Vivian Adhiambo Supervised By: Prof. Melvin Hoare Submitted in accordance with the requirements for the degree of Master By Research

The ever-increasing number of sky surveys calls for the need for more efficient data processing alternatives. CNN has been widely used in computer vision and is currently being incorporated in classifying astronomical objects. A Branch Convolution Neural Network (B-CNN) is a hierarchicalbased Convolution Neural Network (CNN) that outputs multiple predictions of coarse to fine levels. This thesis involved developing a B-CNN-based classifier for, automatic classification of objects discovered in the Co-Ordinated Radio ’N’ Infrared Survey for High-mass star formation (CORNISH) south survey. First, a 2-level model was developed and trained on similar CORNISH north sources, with additional multi-wavelength data from the IR and the sub-millimeter surveys. From the catalogue, sources used were the ultra-compact HII (UCHII) regions, HII regions, planetary nebulae (PN), radio stars, and extra-galactic background sources. The developed classifier was then tested on a sample of already classified CORNISH south sources with an accuracy of 67% for the coarse level and 33% for the fine level.
Scalable Microshutter Systems for UV, Visible, and Infrared Spectroscopy

The team at GSFC includes scientists Matt Greenhouse/PI/Code 665, Alexander Kutyrev/Deputy PI/Code 665, Rainer Fettig/Deputy PI – Technical/BeaconGov, and engineers Meng-Ping Chang/PDL, Carl Kotecki/Testing Lead, Regis Brekosky/Fabrication Lead, Frederick Wang/Packaging Lead, Beth Paquette/3D-Printing lead, Tim Miller, Ed Aguayo, Ron Hu, Nick Costen, Samelys Rodriguez, Ming Ke, Kyowon Kim, and Knute Ray in Codes 553, 564, and Newton Inc. Rainer Fettig joined the team in March 2020 as the Deputy PI – Technical. Ming Ke, a PhD student from UCLA, was hired as a pathway intern in May 2020. Our collaborators include Prof. Stephan McCandliss/JHU/Co-I, and his team.
Investigating Inconsistency Understanding to Support Interactive Inconsistency Resolution in Declarative Process Models

Handling inconsistencies in business rules is an important part of corporate compliance management. This includes the resolution of inconsistencies, which currently is a fully automated process that might not always be plausible in a real-world scenario. To include human experts and develop interactive resolution approaches, an understanding of inconsistencies is crucial. Thus, we focus on investigating inconsistency understanding in declarative process models by testing the applicability of insights from declarative process model understanding to different inconsistency characteristics. In the future, this will provide the basis for a series of cognitive experiments evaluating the effects of inconsistency characteristics and representation on inconsistency understanding in declarative process models.
Organizational Readiness for Digital Intrapreneurship: towards the Design of an Assessment Tool

In today's hyper-dynamic business environment, the capability to foster innovation is critical. Many organizations recognize their employees as an unresolved source for innovation during digital transformation. Consequently, intrapreneurship has become of strategic importance, and initiatives, such as digital intrapreneurship platforms, arise. However, many initiatives do not provide aspired outcomes due to the lack of organizational readiness. We follow the action design research method to design a multi-dimensional framework that measures organizational readiness for digital intrapreneurship. Hitherto, we identify 27 factors that contribute to an organization's readiness for the successful implementation and usage of digital intrapreneurship platforms. Ultimately, we strive to provide a digital intrapreneurship readiness tool that helps innovation managers to detect and remove hindering factors before implementing solutions.
Do Market-oriented Reforms Explain China’s Economic Growth?

The Chinese economy has been growing at a rate of 10% on average since the early 1980s. Many researchers attribute China’s stellar economic performance to its government’s active pro-market reforms. In this paper, we apply state-of-the-art textual language processing techniques to establish several stylized facts about the evolution of China’s institutional landscape using 1.4 million legal documents issued by its government over the past four decades. We find that (1) the share of of laws and regulations issued by local governments in our corpus has been rising; (2) the share of "informal" policies (those that lack a formal legal status) has been increasing; (3) the share of laws on economic issues has been gradually declining, and (4) the Chinese government has been constantly nullifying existing laws, especially after 2000. We then quantify the degree of market orientation of China’s policies within and between legal documents using word embedding techniques, and relate them to provincial economic growth and other macroeconomic outcomes. Preliminary empirical results show that only a small part of provincial economic and foreign direct investment growth can be explained by the focus on neoliberal ideas and reforms in the country’s laws and regulations.
Anti-mullerian hormone and Inhibin B after haemopoietic stem cell transplantation in childhood: a comparison of myeloablative, reduced intensity and treosulfan-based chemotherapy conditioning regimens.

Serum concentrations of Anti-Mullerian Hormone (AMH) and Inhibin B, along with pubertal status, hormone replacement and pregnancy data were used to assess potential fertility in survivors of childhood HSCT after three chemotherapy-conditioning regimens of differing intensity. Of 428 patients transplanted at GOSH between 1990-2012 for leukaemia and immunodeficiency, 121 survived >1 year after a single HSCT conditioned with one of 3 regimens: Group A had a Treosulfan-based regimen (low-toxicity); Group B had Fludarabine/Melphalan (Flu-Mel) (reduced-intensity) and Group C had Busulphan/Cyclophosphamide (Bu-Cy) (myelo-ablative). Mean age at HSCT and follow-up were 3.6 and 11.8 years. Mean length of follow-up was 9.9 years. The mean AMH SD scores were significantly higher in Group A (-1.047) and Group B (-1.255) than Group C (-1.543), suggesting that ovarian reserve was less impaired after Treosulfan and Flu-Mel than after Bu-Cy. The mean serum AMH concentration was significantly better with Treosulfan than with Flu-Mel or Bu-Cy and >1.0 microg/l. In males, mean Inhibin B SD scores were significantly higher in Group A (-0.506) than in Group B (-2.53) and Group C (-1.23) with the Flu-Mel group suffering the greatest impairment. In conclusion, a Treosulfan-based regimen confers a more favourable outlook for gonadal reserve than Flu-Mel or Bu-Cy in both sexes. Significantly higher values of Inhibin B after Bu-Cy than after Flu-Mel may reflect recovery over time.
Experience Value as a Mediator Variable for Co-Creation Relationships on Alumni Loyalty: Using the SEM-PLS Approach

Article Info Abstract Purpose – This marketing research aimed to analyze the effect of co-creation and experience value on students loyalty as the customers of universities. Methodology – The survey was conducted on 278 alumni who came from five private universities in Palembang. The data were collected through online form from each university's alumni unit. The validity and reliability had been met. Data analysis was carried out by partial least square in structural equation model approach (SEM-PLS). Findings – The results showed that co-creation positively and significantly affected on experience value and alumni loyalty. Students who gain experience value from lectures could interact and could active on campus. High interaction among students, lectures, and campus showed a strong co-creation level and better experience value, impacting alumni loyalty to recommend and care about their alma mater. There should be provided space to build the campus through a strong alumni engagement. The more experience students have on campus, the more they care about their alma mater. Originality – This study observed the experience of alumni who were more objective in providing their perceptions because they were no problem of interest. Some researchers used students loyalty measure that was less objective because there still an interest as a student.
Movements Induced by "Riloid" Patterns in Migraine and Control using a Reality Induced

Title: Investigating Head Groups Virtual Display Short title: Head Movements by "Riloid" Patterns Certain striped patterns can induce illusory motion, such as those used in op-art. The visual system and the vestibular system work together closely, and so it is possible that illusory motion from a visual stimulus can result in uncertainty in the vestibular system. This increased uncertainty may be measureable in terms of the magnitude of head movements. Head movements were measured using a head-mounted visual display. Results showed that stimuli associated with illusory motion also seems to induce greater head movements when compared to similar stimuli. Individuals with migraine are more susceptible to visual discomfort, and this includes illusory motion from striped stimuli. However, there was no evidence of increased effect of illusory motion on those with migraine compared to those without, suggesting that while motion illusions may affect discomfort judgements, this is not limited to only those with migraine.
Analytical Predictor-Corrector Guidance Algorithm Based on Drag Modulation Flight Control System for Mars Aerocapture

Aerocapture is one of the essential technologies for future large-scale space exploration missions, as it can significantly reduce the Δv and fuel requirement. The performance and robustness of two different aerocapture control methods are analyzed around Mars exploration, and then an analytic predictor-corrector guidance algorithm for drag modulation flight control system is proposed. A piecewise linear function between velocity and flight path angle is established by appropriate approximations and assumptions, and then the state at atmosphere interface can be predicted by an analytical method; therefore, aerocapture guidance can be realized by feedback control. Numerical simulation is used to evaluate performance and robustness of the algorithm. The simulation results show that the guidance algorithm is accurate and robust, which can effectively overcome the influence of atmospheric density error, aerodynamic parameter error, and initial state uncertainty.
Modeling of Five-Phase, Self-Excited Induction Generator for Wind Mill Application

Abstract Wind turbines with higher power ratings are desired to reduce construction cost and maintenance time and increase energy yields. Three-phase induction generators are widely used in renewable power generation. Self-excited, squirrel cage induction generators are ideally suited for remote, standalone applications of low and medium power generation. The emerging multiphase induction generator (MPIG) with phases more than three is receiving significant attention for high power applications. This paper aspires at the performance investigation of five-phase induction generator (FPIG) in self-excited mode for standalone wind applications. The dynamic model of FPIG is developed using Matlab/Simulink. The performance of wind-driven FPIG under different operating conditions has been analyzed. The process of voltage build-up, current and torque under no load and loaded conditions are presented. The results are compared with three-phase self-excited induction generator (SEIG) to show the effectiveness of FPIG for high-power wind generation.
Complete Genome Sequences of T4-Like

The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters. T4-like bacteriophages have been explored for phage therapy and are model organisms for phage genomics and evolution. Here, we describe the sequencing of 11 T4-like phages. We found a high nucleotide similarity among the T4 T he complete sequences of T4-like myoviruses would enhance studies of phage evolution and genomics, as well as biotechnology applications involving phage cocktails. In this study, we sequenced the genomes of the RB3, RB5, RB6, RB7, RB9, RB10, RB27, RB33, RB55, RB59, and RB68 phages. The RB phages were originally isolated by Rosina Berry in 1964 from six sewage treatment plants in Long Island, NY, for studies on speciation among T-even phages (1). We prepared phage lysates as previously described (2) from host Escherichia coli B (CGSC 5365), extracted DNA with the Phage DNA isolation kit (bioWORLD, Dublin, OH), and se-quenced the samples as paired-end 250-bp reads on the MiSeq instrument (Illumina, San Diego, CA). A total of 789,300 (for RB6) to 3,932,449 (for RB7) paired reads were generated per sample. On average, 82.8% of the pairs survived quality control and trimming with Trimmomatic (3). The insert sizes were ~330 bp; the median coverage of sequenced phages was 2,966ϫ, ranging from 259ϫ (for RB55) to 6,985ϫ (for RB7). We performed de novo assembly using Velvet version 1.2. 08 (4), with k-mer lengths of K51, K57, and K63, and we were able to obtain a single ~168-kbp contig from at least one of the assemblies. We used Geneious version 7.1. 7 for postassembly processing and filled any assembly gaps by iterative mapping of reads to the scaffold. The circularly permuted linear double-stranded DNA ge-nomes of the 11 RB phages have lengths of ~168 kbp. Approximately 270 open reading frames (ORFs) per phage were predicted with Glimmer 3 (5). The annotations were transferred from the published genomes of T4 and T4-like phages with at least 98% similarity. The remaining ORFs were annotated by lowering the similarity cutoff to 70% or performing BLAST searches (6). Eight to 10 tRNAs were predicted in each genome by tRNAscan-SE 1.21 convention in T4-like phages, we oriented completed genomes to start with rIIA. The sequenced phages have a similar genome organization and nucleotide identity. Using progressiveMauve alignment (8), we found that RB7, RB27, RB33, and RB68 are 73 to 86% similar to one another and …
Potent clinical prognostic and systemic adjuvant therapeutic value of plasma fractalkine in PD-L1/PD-1 blockade immunotherapy for lung cancer

Recent studies highlight the importance of baseline functional immunity for efficacious immune checkpoint blockade therapies. Extensive systemic immune profiling was performed in a discovery cohort of 112 non-small cell lung cancer patients undergoing PD-L1/PD-1 blockade immunotherapy. Responders showed high baseline phenotypic diversity of myeloid cell types in peripheral blood, in which elevated activated monocytic cells and decreased granulocytic phenotypes were potent prognostic factors. A high-throughput profiling of soluble factors in plasma was carried out that identified fractalkine (FKN), a chemokine involved in adhesion and chemotactic attraction of immune cells, as a biomarker of myeloid cell diversity in human patients and in murine models, which was found significantly increased in objective responders. Secreted FKN inhibited adenocarcinoma and squamous cell carcinoma growth in vivo through a prominent contribution of systemic effector NK cells, enhanced tumor infiltration with immunostimulatory immune cells and inhibition of MDSCs within tumors. A synergy between FKN and PD-L1/PD-1 blockade immunotherapy was found in murine lung cancer models refractory to anti-PD-L1/anti-PD-1 treatment. Transcriptional data from human lung adenocarcinoma samples independently confirmed the data from the discovery cohort. Importantly, recombinant FKN and tumor expressed-FKN were efficacious in delaying tumor growth in vivo with significant abscopal effects, indicating a potential therapeutic use of FKN in combination with immunotherapies.
Effects of surface reflectance and lighting design strategies on energy consumption and visual comfort

Globally, artificial lighting accounts for a considerable part of electrical energy consumption in buildings. Hence, there is a need to design appropriate lighting scenarios for buildings in a way to reduce energy while meeting visual comfort requirements. This research aims to investigate the effects of applying different design strategies on lighting energy use and visual comfort level. Surface finishing reflectance, type, number and mounting height of luminaires are variables in the study. Lighting design software DIALux evo 7.0 provides simulations of a real scale room according to different scenarios whose models were calibrated and validated using experimental field data. The findings indicate that the type of luminaire is the most decisive parameter in quantity and quality of light in an indoor environment. However, increasing the indoor surface reflectance plays a key role in improving energy efficiency and visual comfort. The results show the possibility of electrical energy savings up to 45% by increasing surface reflectance properties. Finally, this paper stresses that there is no single design solution to guarantee both low energy demand and high visual quality, therefore integrating different strategies based on the type of task and the occupant’s activities is highly recommended.
Network-Wide Vehicle Trajectory Prediction in Urban Traffic Networks using Deep Learning

This paper proposes a deep learning approach to learning and predicting network-wide vehicle movement patterns in urban networks. Inspired by recent success in predicting sequence data using recurrent neural networks (RNN), specifically in language modeling that predicts the next words in a sentence given previous words, this research aims to apply RNN to predict the next locations in a vehicle’s trajectory, given previous locations, by viewing a vehicle trajectory as a sentence and a set of locations in a network as vocabulary in human language. To extract a finite set of “locations,” this study partitions the network into “cells,” which represent subregions, and expresses each vehicle trajectory as a sequence of cells. Using large amounts of Bluetooth vehicle trajectory data collected in Brisbane, Australia, this study trains an RNN model to predict cell sequences. It tests the model’s performance by computing the probability of correctly predicting the next k consecutive cells. Compared with a base-case model that relies on a simple transition matrix, the proposed RNN model shows substantially better prediction results. Network-level aggregate measures such as total cell visit count and intercell flow are also tested, and the RNN model is observed to be capable of replicating real-world traffic patterns.
A Real-Time Simulink Interfaced Fast-Charging Methodology of Lithium-Ion Batteries under Temperature Feedback with Fuzzy Logic Control

The lithium-ion battery has high energy and power density, long life cycle, low toxicity, low discharge rate, more reliability, and better efficiency compared to other batteries. On the other hand, the issue of a reduction in charging time of the lithium-ion battery is still a bottleneck for the commercialization of electric vehicles (EVs). Therefore, an approach to charge lithium-ion batteries at a faster rate is needed. This paper proposes an efficient, real-time, fast-charging methodology of lithium-ion batteries. Fuzzy logic was adopted to drive the charging current trajectory. A temperature control unit was also implemented to evade the effects of fast charging on the aging mechanism. The proposed method of charging also protects the battery from overvoltage and overheating. Extensive testing and comprehensive analysis were conducted to examine the proposed charging technique. The results show that the proposed charging strategy favors a full battery recharging in 9.76% less time than the conventional constant-current–constant-voltage (CC/CV) method. The strategy charges the battery at a 99.26% state of charge (SOC) without significant degradation. The entire scheme was implemented in real time, using Arduino interfaced with MATLABTM Simulink. This decrease in charging time assists in the fast charging of cell phones and notebooks and in the large-scale deployment of EVs.
Comparing Expressiveness of First-Order Modal $\mu$-calculus and First-Order $\mathrm{CTL}^{\ast}$ (Model Theory and It's Application to Algebra)

In this paper we introduce a ﬁrst-order extension of propositional modal $ \mu$ -calculus (ﬁrst-order modal $ \mu$ -calculus) and a ﬁrst-order extension of CTL’ (ﬁrst-order CTL’), and then compare their expressiveness. More precisely we show that ﬁrst-order CTL* is strictly less expressive than ﬁrst-order modal $ \mu$ -calculus. It is known that CTL’ is strictly less expressive than propositional modal $ \mu$ -calculus, hence our result shows that our two ﬁrst-order extensions of CTL’ and PM $ \mu$ preserve an expressiveness relation.
Planning of Partially Accelerated Degradation Test with Two Stress Variables for Practical Lifetime Estimation

The reliability information for novel products and specimens available for various tests is limited during the development stage. In many real cases, the results of general tests under use and the maximum stress levels for checking performance and design are not utilized to obtain reliability information. To solve these problems, this paper proposes a practical partially accelerated degradation test (PADT) plans with two stress variables using a two-phase strategy. In addition, a sample scenario is introduced to demonstrate the feasibility of the proposed procedure. In the first phase, the ratios of the specimens used and the maximum stress levels for each variable are determined to estimate the parameters of an accelerated model based on the D-optimality criteria. To estimate the lifetime information and check the curvature effects of the accelerated model, practical PADT plans are developed in the second phase with three stress levels for each variable, which are based on the compromise concept. In this phase, the ratios for all test points and the middle-stress levels for two variables are determined. This information is used to minimize the asymptotic variance of the maximum likelihood estimator for the q-th quantile of the lifetime distribution under the use conditions. Thus, more accurate lifetime information and model validity can be obtained when using practical PADT plans. Finally, the statistical efficiency of the proposed test plan is demonstrated in a sample scenario.
A real-time social network-based knowledge discovery system for decision making

ABSTRACT The increasing amount of data in social networks has complicated data processing and interpretation. Therefore, intelligent decision-support mechanisms that have the ability to automatically extract meaning from data and interpret the opinions of people in real time have become inevitable. In this study, an intelligent multilingual decision support system was implemented, and a new algorithm that employs text mining and sentiment analysis techniques was developed to automatically interpret the opinions of social network users about the places they plan to visit. The system can be used as a baseline for sentiment analysis in social networks and can be adapted to build new systems. In this study, we set our main focus on Turkish language and show the applicability of our approach for other languages through the experiments for English language. The dataset required for the implementation of text mining techniques was created based on the venue recommendations shared on Foursquare social media platform. As a result, a contribution was made to the way the social network users make decisions without reading thousands of recommendations. Our results show that the developed system achieves classification accuracy of 84.49% for Turkish and 95% for English. Finally, the most liked or disliked foods/beverages are correctly identified for 107 out of 128 venues.