Miracle-dz/UniFAR-FTUs huggingface dataset:
Federated machine learning for a facilitated implementation of Artificial Intelligence in healthcare – a proof of concept study for the prediction of coronary artery calcification scores

Abstract The implementation of Artificial Intelligence (AI) still faces significant hurdles and one key factor is the access to data. One approach that could support that is federated machine learning (FL) since it allows for privacy preserving data access. For this proof of concept, a prediction model for coronary artery calcification scores (CACS) has been applied. The FL was trained based on the data in the different institutions, while the centralized machine learning model was trained on one allocation of data. Both algorithms predict patients with risk scores ≥5 based on age, biological sex, waist circumference, dyslipidemia and HbA1c. The centralized model yields a sensitivity of c. 66% and a specificity of c. 70%. The FL slightly outperforms that with a sensitivity of 67% while slightly underperforming it with a specificity of 69%. It could be demonstrated that CACS prediction is feasible via both, a centralized and an FL approach, and that both show very comparable accuracy. In order to increase accuracy, additional and a higher volume of patient data is required and for that FL is utterly necessary. The developed “CACulator” serves as proof of concept, is available as research tool and shall support future research to facilitate AI implementation.
Protecting privacy when mining and sharing user data

In today’s digital age, online services, such as search engines and social networks, collect large amounts of data about their users and their users’ online activities. Large-scale mining and sharing of this data has been a key driver of innovation and improvement in the quality of these services, but has also raised major user privacy concerns. This thesis aims to help companies find ways to mine and share user data for the purpose of furthering innovation while all the while protecting their users’ privacy, and to motivate and help them reason about the privacy-utility trade-o↵s using a rigorous quantifiable definition of privacy. To achieve this we explore examples of privacy violations, propose privacy-preserving algorithms, and analyze the trade-o↵s between utility and privacy for several concrete algorithmic problems in search and social network domains. We propose and execute two novel privacy attacks on an advertising system of a social network that lead to breaches of user privacy. The attacks take advantage of the advertising system’s use of users’ private profile data, the powerful microtargeting capabilities provided by the system, and the detailed ad campaign performance reports provided to advertisers, in order to infer private information about users. The proposed attacks build a case for a need to reason about data sharing and mining practices using a rigorous definition of privacy, elucidate the privacy and utility tradeo↵s that may arise in advertising systems that allow fine-grained targeting based on user profile and activity characteristics, and have contributed to changes in the social network’s advertising system aimed at increasing the barriers to practical execution of such attacks in the future. We propose a practical algorithm for sharing a subset of user search data consisting of queries and clicks in a provably privacy-preserving manner. The algorithm protects privacy by limiting the amount of each user’s data used and, non-deterministically, throwing away infrequent elements in the data, with the specific parameters of the algorithm being determined by the privacy guarantees desired. The proposed algorithm, and the insights gained from its analysis o↵er a systematic and
DRUG UTILIZATION PATTERN IN PREDIALYSIS AND DIALYSIS PATIENTS IN A TERTIARY CARE TEACHING HOSPITAL.

Introduction : Chronic kidney disease is increasingly recognized as a global public health programme. Rational drug prescription is difficult in CKD patients due to a higher risk of drug related problems since they need complex therapeutic regimens and they require frequent monitoring and dosage adjustments. Methodology : Objective of the study was to compare the drug utilization pattern of predialysis and dialysis patients. Study design & Settings : Prospective observational study in the Department of Nephrology, Govt. Medical College, Thiruvananthapuram. Study period & size : Six months with 72 participants Results and Discussion : Majority of the study population were in the age range of 52.7 11.3 years in predialysis and 53.1 11.5 years in dialysis group. Male predominance was found in both groups. In our study, most of the patients were in the upper lower socioeconomic class and none of them from upper socioeconomic class. Hypertension and Diabetes Mellitus were the commonly observed comorbidity in both groups. Antihypertensives and Antianaemics were commonly used drug class in dialysis patients but in predialysis it was Antihypertensives and antidiabetics. Conclusion : In India, the common co-morbid conditions of ESRD are hypertension and diabetes mellitus so the early detection and treatment of these diseases plays a vital role in the prevention and progression of renal failure. A clinical pharmacist can help to prevent this progression of ESRD by providing patient counseling about lifestyle modification, rational drug use and educating the risk group for regular checkup and adhering to the treatment thus can reduce the economic burden of the patient.
Visual scene perception in the human brain: connections to memory, categorization, and social cognition

The human visual system faces a monumental data processing challenge: using about a pound of slow, inexact biological processors, it must analyze the barrage of constantly-shifting light patterns hitting the eye and quickly extract a stable, highlevel model of the environment around us. Almost every piece of this process is mysterious: exactly what information is being gleaned from the visual signal, how this information is represented, and how this processing is implemented in neural circuits. Despite the superiority of silicon computers for most big-data processing, our emulations of the human visual system are still rudimentary, and can capture only basic information from visual images such as which objects are present. In this work, I describe a number of projects toward understanding higher-level processing of visual scenes. The first examines the neural basis of understanding human-object interactions, showing how an emergent property of a scene (created by the interaction of two scene parts) can activate representations in social cognition regions. The second investigates how scenes are categorized, arguing that one of the fundamental features encoded about a scene is the type of actions which could be performed in that environment. Finally, I present a large body of work on how scene processing interacts with long-term memory systems. These chapters describe several novel types of mathematical models for measuring connections between brain regions, and end with a new organizing proposal for scene perception regions.
EcoPhyloMapper: An r package for integrating geographical ranges, phylogeny and morphology

Spatial patterns of species richness, phylogenetic and morphological diversity are key to answering many questions in ecology and evolution. Across spatial scales, geographical and environmental features, as well as evolutionary history and phenotypic traits, are thought to play roles in shaping both local species communities and regional assemblages. By examining these geographical patterns, it is possible to infer how different axes of biodiversity influence one another, and how their interaction with abiotic factors has led to the spatial distribution of species assemblages—and their attributes—that we observe in the present. Although there has been interest in this area of research for some time, it has recently become more tractable to include multivariate shape data in such analyses. Shape information has the potential to provide a more direct measure of the functional morphology of species as compared to individual trait measurements and might be more relevant to understanding community composition. However, few tools currently exist to explore geographical patterns of both phylogenetic and shape diversity. We present the EcoPhyloMapper r package (epm) that aims to streamline the handling of geographical range polygons or point occurrences and integration of resulting species metacommunities with phylogenetic trees and morphological shape. Geographical maps can be generated that demonstrate spatial patterns in diversity metrics pertaining to phylogenetic similarity, multivariate shape similarity and disparity, and combinations of the two. Patterns of taxonomic, phylogenetic and shape disparity turnover can also be visualized. Biodiversity indices summarized across grid cells can easily be exported to GIS software as well as to other r packages that specialize in community assembly or geospatial statistics. This r package will facilitate the geographical exploration of multivariate shape data in concert with phylogenetic diversity, which will, in turn, support macroecological research exploring how species assemblages are structured. Furthermore, this r package should prove useful across a wide range of macroecological applications that extend beyond the study of morphology.
Automated identification of diverse Neotropical pollen samples using convolutional neural networks

Pollen is used to investigate a diverse range of ecological problems, from identifying plant–pollinator relationships to tracking flowering phenology. Pollen types are identified according to a set of distinctive morphological characters which are understood to capture taxonomic differences and phylogenetic relationships among taxa. However, categorizing morphological variation among hyperdiverse pollen samples represents a challenge even for an expert analyst. We present an automated workflow for pollen analysis, from the automated scanning of pollen sample slides to the automated detection and identification of pollen taxa using convolutional neural networks (CNNs). We analysed aerial pollen samples from lowland Panama and used a microscope slide scanner to capture three‐dimensional representations of 150 sample slides. These pollen sample images were annotated by an expert using a virtual microscope. Metadata were digitally recorded for ~100 pollen grains per slide, including location, identification and the analyst's confidence of the given identification. We used these annotated images to train and test our detection and classification CNN models. Our approach is two‐part. We first compared three methods for training CNN models to detect pollen grains on a palynological slide. We next investigated approaches to training CNN models for pollen identification. Because the diversity of pollen taxa in environmental and palaeontological samples follows a long‐tailed distribution, we experimented with methods for addressing imbalanced representation using our most abundant 46 taxa. We found that properly weighting pollen taxa in our training objective functions yielded improved accuracy for individual taxa. Our average accuracy for the 46‐way classification problem was 82.3%. We achieved 89.5% accuracy for our 25 most abundant taxa. Pollen represents a challenging visual classification problem that can serve as a model for other areas of biology that rely on visual identification. Our results add to the body of research demonstrating the potential for a fully automated pollen classification system for environmental and palaeontological samples. Slide imaging, pollen detection and specimen identification can be automated to produce a streamlined workflow.
Analysis of Clinical Trials Using SAS®: A Practical Guide

In my view, introductory courses in multivariate data analysis that incorporate either R or S–PLUS programming would be very well served having this book listed as a supplemental text, both for its helpful programming tips and succinct, insightful discussion of the statistical methods. Instructors of multivariate courses that do not use R or S–PLUS will also find this text a useful source of additional exercises and examples for their students.
EVALUATION OF SOME SUGAR BEET VARIETIES UNDER THREE HARVESTING DATES

Two field experiments were carried out at Tamiya, El-Fayoum Governorate during 2006/2007 and 2007/2008 seasons to evaluate yield, yield components and quality of six sugar beet varieties namely LP 11, Demapoly, LP 13 , Pleno , Kawemira and LP 12 under three harvesting dates (180, 195 and 210 days after sowing). Results revealed the superiority of Demaploy in root weight, root yield, sugar yield, sucrose % and purity % when it was harvested after 210 days from sowing in the two growing seasons. Harvesting dates and varieties had significant effects on all studied traits except root diameter. Interaction between harvest dates x varieties had insignificant effect on all studied traits in both seasons except for sucrose % and purity % in the 1 st season only. Under the conditions of this study, Demapoly is the proper variety for El-Fayoum Governorate environmental. In addition, use the characteristics of yield component which affected yields (root length, root diameter and root fresh weight/plant) as a morphological characteristics which affected root yield, besides high sucrose % and low reducing sugar % as tools can be used to evaluate and select sugar beet varieties for highly sugar production.
Unsupervised feature learning via sparse hierarchical representations

Machine learning has proved a powerful tool for artificial intelligence and data mining problems. However, its success has usually relied on having a good feature representation of the data, and having a poor representation can severely limit the performance of learning algorithms. These feature representations are often hand-designed, require significant amounts of domain knowledge and human labor, and do not generalize well to new domains. To address these issues, I will present machine learning algorithms that can automatically learn good feature representations from unlabeled data in various domains, such as images, audio, text, and robotic sensors. Specifically, I will first describe how efficient sparse coding algorithms — which represent each input example using a small number of basis vectors — can be used to learn good low-level representations from unlabeled data. I also show that this gives feature representations that yield improved performance in many machine learning tasks. In addition, building on the deep learning framework, I will present two new algorithms, sparse deep belief networks and convolutional deep belief networks, for building more complex, hierarchical representations, in which more complex features are automatically learned as a composition of simpler ones. When applied to images, this method automatically learns features that correspond to objects and decompositions of objects into object-parts. These features often lead to performance competitive with or better than highly hand-engineered computer vision algorithms in object recognition and segmentation tasks. Further, the same algorithm can be used to learn feature representations from audio data. In particular, the learned features yield improved performance over state-ofthe-art methods in several speech recognition tasks.
Metabolism of Dietary and Microbial Vitamin B Family in the Regulation of Host Immunity

Vitamins are micronutrients that have physiological effects on various biological responses, including host immunity. Therefore, vitamin deficiency leads to increased risk of developing infectious, allergic, and inflammatory diseases. Since B vitamins are synthesized by plants, yeasts, and bacteria, but not by mammals, mammals must acquire B vitamins from dietary or microbial sources, such as the intestinal microbiota. Similarly, some intestinal bacteria are unable to synthesize B vitamins and must acquire them from the host diet or from other intestinal bacteria for their growth and survival. This suggests that the composition and function of the intestinal microbiota may affect host B vitamin usage and, by extension, host immunity. Here, we review the immunological functions of B vitamins and their metabolism by intestinal bacteria with respect to the control of host immunity.
$L^p$ continuity of projectors of weighted harmonic Bergman spaces

In this paper we study spaces $A^p(w)$ consisting of harmonic functions in $B^n$ the unit ball in $\mathbb{R}^n$ and belonging to $L^p(w)$, where $dw(x)=w(1-\vert x\vert)dx$ and $w:(0,1]\rightarrow\mathbb{R}^+$ will denote a continuous integrable function. For weights satisfying certain Dini type conditions we construct families of projections of $L^p(w)$ onto $A^p(w)$. We use this to get for $1<p<\infty$ and $\frac{1}{p} + \frac{1}{p'} =1$, a duality $A^p(w)^\ast=A^{p'}(w')$, where $w'$ depends on $p$ and $w$.
Accelerating machine learning with training data management

One of the biggest bottlenecks in developing machine learning applications today is the need for large hand-labeled training datasets. Even at the world’s most sophisticated technology companies, and especially at other organizations across science, medicine, industry, and government, the time and monetary cost of labeling and managing large training datasets is often the blocking factor in using machine learning. In this thesis, we describe work on training data management systems that enable users to programmatically build and manage training datasets, rather than labeling and managing them by hand, and present algorithms and supporting theory for automatically modeling this noisier process of training set specification in order to improve the resulting training set quality. We then describe extensive empirical results and real-world deployments demonstrating that programmatically building, managing, and modeling training sets in this way can lead to radically faster, more flexible, and more accessible ways of developing machine learning applications. We start by describing data programming, a paradigm for labeling training datasets programmatically rather than by hand, and Snorkel, an open source training data management system built around data programming that has been used by major technology companies, academic labs, and government agencies to build machine learning applications in days or weeks rather than months or years. In Snorkel, rather than hand-labeling training data, users write programmatic operators called labeling functions, which label data using various heuristic or weak supervision strategies such as pattern matching, distant supervision, and other models. These labeling functions can have noisy, conflicting, and correlated outputs, which Snorkel models and combines into clean training labels without requiring any ground truth using theoretically consistent modeling approaches we develop. We then report on extensive empirical validations, user studies, and real-world applications
Predictive Probability Models of Road Traffic Human Deaths with Demographic Factors in Ghana

Road traffic carnages are global concerns and seemingly on the rise in Ghana. Several risk factors have been studied as associated with road traffic fatalities. However, inadequate road traffic fatality (RTF) data and inconsistent probability outcomes for RTF remain major challenges. The objective of this study is to illustrate and estimate probability models that can predict road traffic fatalities. We relied on 66,159 recorded casualties who were involved in road traffic accidents in Ghana from 2015 to 2019. Three generalized linear models, namely logistic regression, probit regression and linear probability model were used for the analysis. We found that gender and age groups have significant effects in predicting the probability of road traffic fatality for all three models. Through a likelihood ratio test, however, it was determined that the logit regression model produced consistent probabilities of traffic fatalities which are very close to the actual probability values across the age groups and gender, compared to the other two models. Thus, we recommend intensified campaign for the use of seat belts in vehicles, targeted at the aged and male users of road transport to reduce the possibility of death in any RTA.
Boundaries to Single-Agent Stability in Additively Separable Hedonic Games

Coalition formation considers the question of how to partition a set of agents into coalitions with respect to their preferences. Additively separable hedonic games (ASHGs) are a dominant model where cardinal single-agent values are aggregated into preferences by taking sums. Output partitions are typically measured by means of stability, and we follow this approach by considering stability based on single-agent movements (to join other coalitions), where a coalition is defined as stable if there exists no beneficial single-agent deviation. Permissible deviations should always lead to an improvement for the deviator, but they may also be constrained by demanding the consent of agents involved in the deviations, i.e., by agents in the abandoned or welcoming coalition. Most of the existing research focuses on the unanimous consent of one or both of these coalitions, but more recent research relaxes this to majority-based consent. Our contribution is twofold. First, we settle the computational complexity of the existence of contractually Nash-stable partitions, where deviations are constrained by the unanimous consent of the abandoned coalition. This resolves the complexity of the last classical stability notion for ASHGs. Second, we identify clear boundaries to the tractability of stable partitions under majority-based stability concepts by proving elaborate hardness results for restricted classes of ASHGs. Slight further restrictions lead to positive results. helpful discussions.
Mu-desynchronization, N400 and corticospinal excitability during observation of natural and anatomically unnatural finger movements

The action observation networks (AON) (or the mirror neuron system) are the neural underpinnings of visuomotor integration and play an important role in motor control. Besides, one of the main functions of the human mirror neuron system is recognition of observed actions and the prediction of its outcome through the comparison with the internal mental motor representation. Previous studies focused on the human mirror neurons (MNs) activation during object-oriented movements observation, therefore intransitive movements observation effects on MNs activity remains relatively little-studied. Moreover, the dependence of MNs activation on the biomechanical characteristics of observed movement and their biological plausibility remained highly underexplored. In this study we proposed that naturalness of observed intransitive movement can modulate the MNs activity. Event-related desynchronization (ERD) of sensorimotor electroencephalography (EEG) rhythms, N400 event-related potentials (ERPs) component and corticospinal excitability were investigated in twenty healthy volunteers during observation of simple non-transitive finger flexion that might be either biomechanically natural or unnatural when finger wriggled out toward the dorsal side of palm. We showed that both natural and unnatural movements caused mu/beta-desynchronization, which gradually increased during the flexion phase and returned to baseline while observation of extension. Desynchronization of the mu-rhythm was significantly higher during observation of the natural movements. At the same time, beta-rhythm was not found to be sensitive to the action naturalness. Also, observation of unnatural movements caused an increased amplitude of the N400 component registered in the centro-parietal regions. We suggest that the sensitivity of N400 to intransitive action observation with no explicit semantic context might imply the broader role of N400 sources within AON. Surprisingly, no changes in corticospinal excitability were found. This lack of excitability modulation by action observation could be related with dependence of the M1 activity on the observed movement phase.