Miracle-dz/UniFAR-FTUs huggingface dataset:
STUDY OF TOTAL PRODUCTIVE MAINTENANCE AND IMPROVING THE PRODUCTION

The purpose of this research is to investigate the contributions of successful total productive maintenance (TPM) initiatives to competitive manufacturing. It also seeks to critically examine the implications of strategic TPM implementation initiatives in an Indian manufacturing organisation. Total Productive Maintenance (TPM) is a maintenance program which involves a newly defined concept for maintaining plants and equipment. This research is focusing in implementing Keikaku-Hozen (KH) Pillar activities for solving the breakdown problem in an industrial sector. The study is carried out in a Manufacturing industry which is now facing problem in establishing processing certain machining in their Plant, and also maintaining it to reduce downtime due to maintenance issue. The approach is directed in the direction for finding the root cause of the problem due to which maintenance problem rise in a manufacturing sector in Indian industries. The work includes solving the maintenance issue by doing root cause analysis (RCA) of the problem due to which downtime increases. Customization of TPM principles to project based industry is an important part of this research. The study highlights the contributions of strategic TPM initiatives to organisational performance and also the need for the successful management of TPM programmes for establishing sustained maintenance improvement initiatives. By applying TPM and doing root cause analysis the maintenance issue is reduced by 50%.
Longevity Associated Geometry Identified in Satellite Images: Sidewalks, Driveways and Hiking Trails

Importance: Following a century of increase, life expectancy in the United States has stagnated and begun to decline in recent decades. Using satellite images and street view images prior work has demonstrated associations of the built environment with income, education, access to care and health factors such as obesity. However, assessment of learned image feature relationships with variation in crude mortality rate across the United States has been lacking. Objective: Investigate prediction of county-level mortality rates in the U.S. using satellite images. Design: Satellite images were extracted with the Google Static Maps application programming interface for 430 counties representing approximately 68.9% of the US population. A convolutional neural network was trained using crude mortality rates for each county in 2015 to predict mortality. Learned image features were interpreted using Shapley Additive Feature Explanations, clustered, and compared to mortality and its associated covariate predictors. Main Outcomes and Measures: County mortality was predicted using satellite images. Results: Predicted mortality from satellite images in a held-out test set of counties was strongly correlated to the true crude mortality rate (Pearson r=0.72). Learned image features were clustered, and we identified 10 clusters that were associated with education, income, geographical region, race and age. Conclusion and Relevance: The application of deep learning techniques to remotely-sensed features of the built environment can serve as a useful predictor of mortality in the United States. Tools that are able to identify image features associated with health-related outcomes can inform targeted public health interventions.
A multicentre study of the spectrum of histopathological changes in renal allograft biopsies over a period of nine years from South India

Abstract Introduction: Following renal transplant, renal dysfunction is not uncommon. Assessment of histopathological changes in renal dysfunction is done using the Banff 07 Update to the Banff 97 Diagnostic Categories for Renal Allograft Biopsies which is the accepted formulation worldwide. Objectives: To evaluate the spectrum of histopathological changes seen in renal allograft dysfunction. Materials and Methods: A retrospective study was conducted in a tertiary care center in South India to analyze all the renal allograft biopsies received by the Department of Pathology, Madras Medical Mission, Chennai, from multiple tertiary centers across India, over a period of 9 years. A total of 1151 renal allograft biopsies from 1120 patients were studied. The biopsies were grouped into six categories according to the Banff 07 update to the Banff 97 diagnostic categories for renal allograft biopsies. Univariate analysis was done and the continuous variables were expressed as percentages. Results: The study population was predominantly males (75%) with mean age of 37Â±12.47 years. The median serum creatinine was 2.3 mg/dL. The time of allograft biopsy ranged from 0 hours to 5 years. The maximum number of biopsies obtained for each recipient was three (range 1 to 3). The biopsies were broadly categorized using the Banff criteria (2007 update) into normal (4.95%), antibody-mediated rejection (7.38%), borderline rejection (5.29%), T-cell mediated rejection (31.36%), interstitial fibrosis and tubular atrophy (5.03%) and non-rejection category (41.96%). 46 (3.99%) biopsies were insufficient for categorization. Conclusion: The appropriate histopathological diagnosis for allograft biopsies aids in tailoring the immunosuppression therapy, prognosticating and appropriate management. Key point Allograft biopsy is an invaluable diagnostic tool in detecting the causes for renal allograft dysfunction, for diagnosing both rejection and the non-rejection causes to provide an insight even into the rarer causes of graft dysfunction. The appropriate histopathological diagnosis aids in tailoring the immunosuppression therapy, prognosticating and appropriate management. Citation: Koshy PJ, Tripathy A, Vijayan M, Nair S, Yuvaraj A, Natarajan G, et al. A multicentre study of the spectrum of histopathological changes in renal allograft biopsies over a period of nine years from South India. Immunopathol Persa. 2017;3(1):e05.
Robust Deep Reinforcement Learning against Adversarial Perturbations on Observations

Deep Reinforcement Learning (DRL) is vulnerable to small adversarial perturbations on state observations. These perturbations do not alter the environment directly, but can mislead the agent into making suboptimal decisions. We analyze the Markov Decision Process (MDP) under this threat model and utilize tools from the neural network veriﬁcation literature to enable robust training for DRL under observational perturbations. Our techniques are general and can be applied to both Deep Q Networks (DQN) and Deep Deterministic Policy Gradient (DDPG) algorithms for discrete and continuous action control problems. We demonstrate that our proposed training procedure signiﬁcantly improves the robustness of DQN and DDPG agents under a suite of strong white box attacks on observations, including a few novel attacks we speciﬁcally craft. Additionally, our training procedure can produce provable certiﬁcates for the robustness of a Deep RL agent.
A Matlab Toolbox for Feature Importance Ranking

More attention is paid for the feature importance ranking (FIR), in particular when high-throughput features can be extracted for intelligent diagnosis and personalized medicine. A large number of FIR methods have been proposed, while few are integrated for comparison and real-life applications. In this study, a matlab toolbox is presented and a total of 30 algorithms are collected. Moreover, the toolbox is evaluated on a database of 163 ultrasound images. To each breast lesion, 15 features are handcrafted. And to Figure out an optimal subset of features for classification, all combinations of features are tested and linear support vector machine is used for the malignancy prediction of lesions annotated in ultrasound images. At last, the effectiveness of FIR is analyzed according to performance comparison. The toolbox is available (https://github.com/NicoYuCN/matFIR). In the future work, more FIR methods, feature selection methods and machine learning classifiers will be integrated.
Decentralized MCTS via Learned Teammate Models

Decentralized online planning can be an attractive paradigm for cooperative multi-agent systems, due to improved scalability and robustness. A key difficulty of such approach lies in making accurate predictions about the decisions of other agents. In this paper, we present a trainable online decentralized planning algorithm based on decentralized Monte Carlo Tree Search, combined with models of teammates learned from previous episodic runs. By only allowing one agent to adapt its models at a time, under the assumption of ideal policy approximation, successive iterations of our method are guaranteed to improve joint policies, and eventually lead to convergence to a Nash equilibrium. We test the efficiency of the algorithm by performing experiments in several scenarios of the spatial task allocation environment introduced in [Claes et al., 2015]. We show that deep learning and convolutional neural networks can be employed to produce accurate policy approximators which exploit the spatial features of the problem, and that the proposed algorithm improves over the baseline planning performance for particularly challenging domain configurations.
Normalized and Geometry-Aware Self-Attention Network for Image Captioning

Self-attention (SA) network has shown profound value in image captioning. In this paper, we improve SA from two aspects to promote the performance of image captioning. First, we propose Normalized Self-Attention (NSA), a reparameterization of SA that brings the benefits of normalization inside SA. While normalization is previously only applied outside SA, we introduce a novel normalization method and demonstrate that it is both possible and beneficial to perform it on the hidden activations inside SA. Second, to compensate for the major limit of Transformer that it fails to model the geometry structure of the input objects, we propose a class of Geometry-aware Self-Attention (GSA) that extends SA to explicitly and efficiently consider the relative geometry relations between the objects in the image. To construct our image captioning model, we combine the two modules and apply it to the vanilla self-attention network. We extensively evaluate our proposals on MS-COCO image captioning dataset and superior results are achieved when comparing to state-of-the-art approaches. Further experiments on three challenging tasks, i.e. video captioning, machine translation, and visual question answering, show the generality of our methods.
USEFULNESS OF A LOCUS LEEF1Aa IN THE GENETIC DIFFERENTIATION OF TOMATO VARIETIES

The molecular techniques provide new possibilities to characterize advanced genetic materials for registration purposes and for the protection of breeders’ rights. The microsatellites appear as suitable molecular markers due to their highly polymorphic character. Such microsatellites may generate polymorphism useful for the analysis of genetic diversity and relationships within the genus Lycopersicon. The focus of the following study was usefulness of the locus LEEF1Aa in the genetic differentiation among six morphologically different tomato varieties of Lycopersicon esculentum Mill. The fragment analyses were done using Applied Biosystems DNA analyzer (ABI 3130) and GeneMapper®Software program. The obtained data were analyzed using the specific program Power Marker Software. The number of detected alleles for the microsatellites locus LEEF1Aa was six in estimated tomato varieties (219-221-223-225-227-229bp). The allele with the length of 229 bp was noticed only in Lycopersicon esculentum subsp. cultum var. grandifolium, while the alleles (221, 225 and 227 bp) in 4 varieties, the allele of 219 bp in 3 varieties and the allele of 223 bp in 2 varieties. The average PIC value for the locus LEEF1Aa was 0.7552 and it belongs to the group of high informative markers. Obtained results showed that the locus LEEF1Aais good choice for genetic differentiation of tomato varieties in combination with other polymorphic microsatellite loci.
Incorporating tag information to Enhance the Collaborative Filtering Recommendation Algorithm

In traditional collaborative filtering recommendation algorithm, the similarity is calculated based on the common ratings and the accuracy is not well when the data is sparse. The thesis proposes a novel collaborative filtering by incorporating tags(CF-IT). Firstly, it defines the rating similarity by items’ historical rated behaviors using graph theory, and secondly, it defines the tag similarity by items’ tag information. Then, we combine the rating similarity and tag similarity to obtain comprehensive item similarity model. Finally, we conduct the experiment to verify the validity and the accuracy of the algorithm.
Therapeutic interventions in shock: Responses of the (microvascular) endothelium

Shock is associated with a high morbidity and mortality, and prompt recognition and medical management are essential to correct the cause of shock and stabilize the associated physiological disturbances. Effective therapeutic strategies for the treatment of shock patients remain a challenging conundrum in clinical care. In this thesis, we investigated potential therapeutic strategies for the treatment of shock associated changes in microvascular endothelial behavior in hemorrhage and sepsis. Our aims were to understand the role of microvascular flow alterations in regulating endothelial behavior and test the effects of different therapeutic interventions on the inflammatory activation status of microvascular endothelial cells created by flow alterations and cytokines both in vitro and in vivo. Shock is associated with a decrease and loss of intravascular blood flow. In our in vitro study, the abrupt reflow related enhancement of cytokine-induced endothelial pro-inflammatory activation supports the concept that sudden regain of perfusion during resuscitation has an aggravating effect on endothelial pro-inflammatory activation, which may play a significant role in vascular dysfunction and consequent organ injury after recovery from shock. Furthermore, the in vivo pharmacological interventions in hemorrhagic shock and resuscitation model imply the potential of histone deacetylase inhibition and NF-κB inhibition in the treatment of this condition. In addition, using SAINT-O-Somes as carriers of dexamethasone to target to vascular adhesion molecule (VCAM-1) expressed on the surface of endothelial cells, we demonstrated the specific delivery of the carriers in inflamed endothelial cells in sepsis, the therapeutic impact of which needs further investigation.
WHICH POLICIES PROMOTE A DEMOGRAPHIC DIVIDEND? AN EVIDENCE REVIEW

With growing enthusiasm for the potential benefits of the dividend, policymakers in many high-fertility countries are seeking guidance on specific actions that will help their countries achieve a dividend. In response, regional and country-specific roadmaps have been developed, outlining multisectoral approaches to realize a demographic dividend. However, many such roadmaps are broadranging and non-specific, often incorporating dozens of policy recommendations. Countries are increasingly looking for specific, in-depth guidance on policy priorities. Analyses of the contribution of specific policy interventions to enhance good governance to attainment of the demographic dividend are limited. Investments in family planning stimulate the fertility decline required for age structure change and can also contribute to increased female labor force participation and economic growth.
A Survey of Interpretability and Explainability in Human-Agent Systems

This paper presents a taxonomy of interpretability in Human-Agent Systems. We consider four fundamental questions, “Why, what, when, and how” about interpretability. First, we consider why interpretability is needed in the system. Second, once interpretability is established as being needed, we consider what explanations can be generated to meet this need. Third, we consider when the user should be presented this information. Fourth, we consider the level of detail needed within explanations. Last, we consider how objective and subjective measures can be used to evaluate the entire sys-tem including the four fundamental questions regarding interpretability.
HyNNA: Improved Performance for Neuromorphic Vision Sensor Based Surveillance using Hybrid Neural Network Architecture

Applications in the Internet of Video Things (IoVT) domain have very tight constraints with respect to power and area. While neuromorphic vision sensors (NVS) may offer advantages over traditional imagers in this domain, the existing NVS systems either do not meet the power constraints or have not demonstrated end-to-end system performance. To address this, we improve on a recently proposed hybrid event-frame approach by using morphological image processing algorithms for region proposal and address the low-power requirement for object detection and classification by exploring various convolutional neural network (CNN) architectures. Specifically, we compare the results obtained from our object detection framework against the state-of-the-art low-power NVS surveillance system and show an improved accuracy of 82.16% from 63.1%. Moreover, we show that using multiple bits does not improve accuracy, and thus, system designers can save power and area by using only single bit event polarity information. In addition, we explore the CNN architecture space for object classification and show useful insights to trade-off accuracy for lower power using lesser memory and arithmetic operations.
Analysis of Structural Breaks and Time Series Properties of West African Coffee Production: An Application of the Perron Test

This paper examines the structure of the West African Coffee Agriculture by analyzing three pertinent annual series, namely; acreage, yield and producer price of coffee, in Cote d’Ivoire, Ghana, Nigeria and Togo during the 19612010 period. The Perron (1989) test which examined the null and alternative hypotheses of a unit root or `trend-stationary. ` series respectively, in the presence of possible slope and/or level shifts was particularly adapted to examine the effect of date specific market reform policy among coffeeproducing West African countries. The results for the Perron unit root tests indicates that while coffee producer price,yield and acreage in Nigeria, coffee acreage and producer price in Ghana, coffee producer price in Cote d’Ivoire and coffee yield in Togo were stationary around a linear deterministic trend. The Perron changing growth model also revealed that SAP, otherwise refered to as structural break significantly and positively affected only coffee yield in Nigeria and Cote d’Ivoire, but has no impact whatsoever in Ghana and Togo.
On multiple SLE for the FK–Ising model

We prove convergence of multiple interfaces in the critical planar q = 2 random cluster model, and provide an explicit description of the scaling limit. Remarkably, the expression for the partition function of the resulting multiple SLE(16/3) coincides with the bulk spin correlation in the critical Ising model in the half-plane, after formally replacing a position of each spin and its complex conjugate with a pair of points on the real line. As a corollary, we recover Belavin-Polyakov-Zamolodchikov equations for the spin correlations.
Group Fairness Under Composition

We examine the composition properties of a large class of statistical group fairness deﬁnitions. One corollary of our mostly negative results is that the group intersection techniques proposed by [Kearns, Neel, Roth and Wu 2017] and [Hebert-Johnson, Kim, Reingold and Rothblum 2017] may degrade under composition. We show several cases where group fairness deﬁnitions give mis-leading signals under composition and conclude that additional context is needed to evaluate group fairness under composition.
A study of Physics Problem Solving Practices in Enhancing Real- Life Problem Solving Competencies in Secondary Schools of Hawassa City Administration, Ethiopia: The case of Alamura Secondary School

The purpose of this study was to explore the way general secondary school physics teachers solve physics problems in helping learners develop real-life problem solving competencies. The study used qualitative research methodology as a guiding framework. In order to judge the appropriateness of problem solving strategies relevant criteria were developed by the researcher and validated by experts. Grades nine, ten, eleven and twelve teachers were used as data sources in order to find evidence about the ways physics problems are solved. One class was observed two times and a total of 24 classrooms were selected for classroom observation. It was found from the study that most of the problems were provided without their context and solved without relating the problems with their respective concepts. Most of the teachers insert numerical values into their respective formulas by following certain steps to arrive at solutions that do not require conceptual understanding to arrive at solutions. Generally based on the findings of the study it can be concluded that the problem solving strategies used by teachers were not appropriate in enhancing real-life problem solving competencies in dealing with problem situations. Based on the conclusions made the study suggested the need for providing context rich problems by textbooks and also the need for providing trainings for teachers on their approach of solving problems so that they can help their students develop real-life problem solving competencies.
Short Poem Generation (SPG): A Performance Evaluation of Hidden Markov Model based on Readability Index and Turing Test

We developed a Hidden Markov Model (HMM) that automatically generates short poem. The HMM was trained using the forward-backward algorithm also known as Baum Welch algorithm. The training process was exhausted by a hundreds of iterations through recursion method. Then we used the Viterbi algorithm to decode all the best possible hidden states to predict the next word, and from the previous predicted word, it will generate another word, then another word until it reaches the desire word length that was set in the program. Afterwards, the model was evaluated using several kinds of readability metrics index which measure the reading difficulty and comprehensiveness of the generated poem. Then, we performed a Turing Test, which participated by 75 college students, who are well versed in poetry. They determined if the generated poems was created by a human or a machine. Based from the evaluation results, the highest readability score index of the generated short poem is in the grade 16th level. While 69.2% of the participants in the Turing Test, agreed that most of the machine generated poems were likely created by some well-known poets and writers.