Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Local Outlier Factor with Locality-Sensitive Hashing

Outlier detection remains a heated area due to its essential role in a wide range of applications, including intrusion detection, fraud detection in finance, medical diagnosis, etc. Local Outlier Factor (LOF) has been one of the most influential outlier detection techniques over the past decades. LOF has distinctive advantages on skewed datasets with regions of various densities. However, the traditional centralized LOF faces new challenges in the era of big data and no longer satisfies the rigid time constraints required by many modern applications, due to its expensive computation overhead. A few researchers have explored the distributed solution of LOF, but existant methods are limited by their grid-based data partitioning strategy, which falls short when applied to high-dimensional data. In this thesis, we study efficient distributed solutions for LOF. A baseline MapReduce solution for LOF implemented with Apache Spark, named MR-LOF, is introduced. We demonstrate its disadvantages in communication cost and execution time through complexity analysis and experimental evaluation. Then an approximate LOF method is proposed, which relies on locality-sensitive hashing (LSH) for partitioning data and enables fully distributed local computation. We name it MR-LOF-LSH. To further improve the approximate LOF, we introduce a process called cross-partition updating. With cross-partition updating, the actual global k-nearest neighbors (k-NN) of the outlier candidates are found, and the related information of the neighbors is used to update the outlier scores of the candidates. The experimental results show that MR-LOF achieves a speedup of up to 29 times over the centralized LOF. MR-LOF-LSH further reduces the execution time by a factor of up to 9.9 compared to MR-LOF. The results also highlight that MR-LOF-LSH scales well as the cluster size increases. Moreover, with a sufficient candidate size, MRLOF-LSH is able to detect in most scenarios over 90% of the top outliers with the highest LOF scores computed by the centralized LOF algorithm.
Evaluation der Experimentierklausel nach § 6c SGB II - Untersuchungsfeld 4: Makroanalyse und regionale Vergleiche: Zwischenbericht 2007

\"Im Rahmen der Evaluation der Experimentierklausel nach Paragraph 6c SGB II umfasst die Aufgabenstellung des Forschungsfeldes 4 - Makroanalyse und regionale Vergleiche - eine Analyse der regionalen Wirkungen von ARGEn und zkT (zugelassene kommunale TrA¤ger), die Analyse der gesamtwirtschaftlichen Wirkungen und ein Performancevergleich der Regionen. Insbesondere im Rahmen der Makroanalyse gilt es die Frage zu beantworten, ’Was wA¤re geschehen, wenn zum 1. 1. 2005 deutschlandweit entweder das ARGE-Modell oder die kommunale TrA¤gerschaft eingefA¼hrt worden wA¤re? ’ und zwar unter besonderer BerA¼cksichtigung regionaler Besonderheiten und Verflechtungen, sowie Substitutions-, VerdrA¤ngungs- und Mitnahmeeffekten. Bei einem Vergleich der ARGEn mit den zkT muss berA¼cksichtigt werden, ob sich Kreise mit spezifischen Merkmalen fA¼r die eine oder die andere Form der Aufgabenwahrnehmung entschieden haben. Auf Grundlage der Vorarbeiten des Paragraph 6c-Forschungsverbundes dA¼rften folgende Faktoren die Optionsneigung beeinflusst haben: Lokale Arbeitsmarktlage, GrA¶A�e der Kommune, Finanzkraft, Sozio-geographische Merkmale, Politische Merkmale und Vorerfahrungen. Die regionalen Vergleiche dienen der Darstellung und der deskriptiven Beschreibung der Entwicklung zentraler Indikatoren fA¼r die ZielgrA¶A�en ’Integration in ErwerbstA¤tigkeit’, ’Verbesserung der BeschA¤ftigungsfA¤higkeit’ und ’soziale Stabilisierung’ im Vergleich der drei Formen der Aufgabenwahrnehmung sowie der unterschiedlichen Organisationstypen. \" (Autorenreferat) ; VerA¶ffentlichungsversion ; Published Version
A New Approach to Economic Dispatch using Evolutionary Programming

This paper presents a simple reliable and fast method based on Evolutionary Programming for solving Economic Dispatch problem. The complexity of the ED problem is simplified by taking system lambda (? ) as decision variable and load power-imbalance as fitness function contrast to unit generations and fuel cost respectively. The speciality of this proposed method is that during every iteration the generation schedule corresponding to ? is obtained by satisfying the co-ordination equations. Hence the final optimal solution exactly matches with the solution of Lambda iteration method. The proposed method was tested on 3, 5 and 20-unit system. The results of the study shows that the method has excellent convergence characteristics and very fast. The number of iterations as well as the computation time are drastically reduced in comparison to the results obtained using power generation as variable and fuel cost as fitness function. Index Terms – Economic dispatch, Evolutionary Programming, Incremental cost, Transmission loss.
Nutritional management of diabetes mellitus

List of Contributors.Introduction. 1. Nutritional Recommendations in Diabetes Management (Norma McGough). 2. Diabetes and Physical Activity (Pamela Dyson). 3. Counselling in Diabetes (Tracey Parkin). 4. Dietician and Diabetic: Thoughts on Working and Living with Diabetes (Jo Sutton). 5. The Nutritional Management of Children's Diabetes (Sheridan Waldron, Peter Swift, Lindsey Oliver and Deborah Foote). 6. An Introduction to Type 2 Diabetes (Gary Frost). 7. The Dietary Management of Diabetic Pregnancies (Anne Dornhorst and Gary Frost). 8. Obesity and Diabetes (Karen Slevin, Jacqueline Cleator and John Wilding). 9. Nutritional Management of Cardiac Risk Factors in Type 2 Diabetes (Audrey Brynes). 10. Nutritional Management of the Elderly Person with Diabetes (Mary Hickson and Lucy Wright). 11. The Role of Carbohydrate in the Management of Diabetes (Janette C Brand-Miller and Susanna H A Holt). 12. Effects of Variations in Amount and Kind of Dietary Fat and Carbohydrate in the Dietary Management of Type 2 Diabetes (Gerald M Reaven). 13. Diabetes and Alcohol (Linda Carter and Joanne Boyle). 14. Inpatient Nutritional Support of Sick Patients with Diabetes (Hilary Peake). 15. Diabetes and Renal Replacement Therapy (Marie Kelly and Thushara Dassanayake). 16. Nutritional Management of Diabetic Renal Transplant Recipients (Barbara Engel). Index.
Beyond Labels and Captions: Contextualizing Grounded Semantics for Explainable Visual Interpretation

One of the long-standing problems in artificial intelligence is the development of intelligent agents with complete visual understanding. Understanding entails recognition of scene attributes such as actors, objects and actions as well as reasoning about the common semantic structure that combines these attributes into a coherent description. While significant milestones have been achieved in the field of computer vision, majority of the work has been concentrated on supervised visual recognition where complex visual representations are learned and a few discrete categories or labels are assigned to these representations. This implies a closed world where the underlying assumption is that all environments contain the same objects and events, which are in one-to-one correspondence with the ground evidence in the image. Hence, the learned knowledge is limited to the annotated training set. An open world, on the other hand, does not assume the distribution of semantics and requires generalization beyond the training annotations. Increasingly complex models require massive amounts of training data and offer little to no explainability due to the lack of transparency in the decision-making process. The strength of artificial intelligence systems to offer explanations for their decisions is central to building user confidence and structuring smart human-machine interactions. In this dissertation, we develop an inherently explainable approach for generating rich interpretations of visual scenes. We move towards an open world open-domain visual understanding by decoupling the ideas of recognition and reasoning. We integrate common sense knowledge from
Development and validation of a high-throughput system for the genotyping of Hepatitis C Virus

The subject of this dissertation is the work I carried out at the Universita degli Studi di Padova in collaboration with the CRS (Centro Ricerche Scientifiche) Dott. Dino Paladin, where I conducted part of the project. The CRS Dott. Dino Paladin is a center specialized in biotechnology research and that collaborates with AB ANALITICA srl for the commercialization of the developed diagnostic CE IVD devices. My PhD project is focused on the development of a standardized and performing procedure for the Hepatitis C Virus (HCV) genotyping. HCV is a small, enveloped, positive sense single-strand RNA virus belonging to Flaviviridae family and it is the etiologic agent of the so called post-transfusion non-A, non-B hepatitis. The World Health Organization (WHO) evaluates more than 130-150 million of persons worldwide that are chronically HCV infected, and the morbidity and mortality attributable to HCV infection continues to increase. Although the research aims to formulate a pangenotypic anti-viral regimen, nowadays the HCV genotype information is still required to choose the best anti-viral treatment for the patient. Because of the high demand for this type of analysis, we decided to design and develop a high-throughput process for the HCV genotyping, starting from the clinical sample up to the genotype result. In the first part of my PhD work, I dealt with the development of an automated process for the extraction of pathogen nucleic acids. In particular, we developed the GENEQUALITY X120 Pathogen kit to use with the automated platform GENEQUALITY X120 (AB ANALITICA srl). This kit revealed extraction efficiency for both pathogen DNA and RNA and therefore it has been chosen for the HCV RNA extractions. In the second part of my study, I focused on the performance evaluation of the AMPLIQUALITY HCV TYPE PLUS device, which is a kit based on a Reverse Line Blot (RLB) technique intended for the genotyping of HCV. The results revealed genotyping and subtyping efficiency. The developed kits are currently CE IVD marked and commercialized.
3D-Printing with Incorporated Iron Particles for Magnetic Actuation and MPI

Magnetic particle imaging (MPI) scanners cannot only be used to image the distribution of magnetic nanoparticles, but the magnetic fields also facilitate to actuate magnetic devices. This enables the dual use of MPI scanners for simultaneous actuation and visualization of magnetic objects. It is of great interest for a variety of medical applications to magnetically steer devices, such as catheters or capsule endoscopes. Endoscopic capsules, which are driven by the natural peristaltic movements, are already in clinical routine. However, they cannot be used for the investigation of the stomach, since the capsules cannot be steered. Hence, steerable magnetic capsules can be used to endoscopically investigate the whole gastrointestinal tract, to take a biopsy or to deliver drugs locally. The 3D tomographic localization of such steerable capsules is an open task, but it is essential to localize detected abnormalities for subsequent treatments. Since MPI provides tomographic real-time images of magnetic material, it seems to be beneficial to visualize the actuation process with MPI. In this work, a material for additive manufacturing is investigated, which consists of polylactide with incorporated iron powder of µm-sized particles. The material is analyzed by light microscopy, vibrating sample magnetometry and magnetic particle spectrometry. Then the feasibility to actuate and visualize macroscopic devices made of this material inside an MPI scanner is shown. The fabricated object has a length of about 2 cm and can be rotated when applying sinusoidal currents to the focus field coils of an MPI scanner. In addition, the objects' shape led to a forward velocity in water. The suitability of the 3D-printing material for MPI is shown and static 3D images are presented. Int. J. Mag. Part. Imag. 6(1), 2020, Article ID: 2003001, DOI: 10.18416/IJMPI.2020. 2003001
High genetic differentiation within the Hemidactylus turcicus complex (Reptilia: Gekkonidae) in the Levant, with comments on the phylogeny and systematics of the genus

The molecular phylogeny of Hemidactylus turcicus (sensu lato) and related Levantine taxa of Hemidactylus geckos were studied using mitochondrial DNA sequence data. Five main phylogenetic lineages were detected within the distribution area of H. turcicus: (1) H. turcicus (sensu stricto) from the Mediterranean region comprising two widely distributed haplotype groups divergent by 2.1%; (2) H. cf. turcicus from north-eastern Israel forming a divergent (7.2%) sister lineage to H. turcicus s.s. ; (3) H. turcicus lavadeserticus from the black Syrian basalt desert; (4) H. mindiae from southern Jordan; and (5) a highly supported lineage representing an unnamed species of Hemidactylus distributed in southern Syria and Jordan. On the basis of the obtained phylogenies, genetic divergences and morphological comparisons, the subspecies H. turcicus lavadeserticus is elevated to full species level and the unnamed Hemidactylus clade is described as a new species, H. dawudazraqi sp. n. In addition, an unnamed lineage of Hemidactylus from southern Sinai and exceptional genetic differentiation within “H. turcicus-like” forms from Yemen are reported, the type locality of H. turcicus is discussed and also comments are provided on the phylogeny and systematics of the genus Hemidactylus.
Design Techniques for High-Resolution Continuous-Time Delta–Sigma Converters With Low In-Band Noise Spectral Density

We present design considerations for CT<inline-formula> <tex-math notation="LaTeX">$\! \Delta \! \Sigma $ </tex-math></inline-formula>Ms that attempt to achieve high resolution (16+ bits) over a wide bandwidth (>200 kHz), resulting in a low in-band noise spectral density. The main challenges in such designs are parasitic resistance in the reference path, inter-symbol interference (ISI) in the feedback-digital-to-analog converter (DAC) waveform, and flicker noise of the input operational transconductance amplifier (OTA). We introduce the virtual-ground-switched resistor DAC as a way to achieve low distortion by addressing parasitic resistance in the reference path and reducing the effects of ISI. Flicker noise is reduced by chopping the first stage of the input OTA. Chopping artifacts and clock jitter sensitivity are reduced by using a three-stage OTA and finite impulse response (FIR) feedback. These techniques are applied to the design of a 250 kHz bandwidth CT<inline-formula> <tex-math notation="LaTeX">$\! \Delta \! \Sigma \text{M}$ </tex-math></inline-formula> targeting 108 dB signal-to-noise-and-distortion-ratio (SNDR) in a 180 nm CMOS process. The fabricated prototype, which operates at 32 MS/s, achieves 105.3/108.1 dB SNDR/signal-to-noise-ratio (SNR) and consumes 24 mW. The Schreier SNDR figure of merit (FoM) is 175.5 dB.
The Past, Present and Future of Induced Pluripotent Stem Cells

Our body is derived from only one cell, a fertilized egg. At the birth, the body consists of 220 kinds of somatic cells. The fertilized egg divides many times during development. The resulting cells differentiate into many kinds of somatic cells, and a fertilized egg can differentiate into all of the different types of cells, including intraembryonic and extraembryonic tissues. This ability is called totipotency. Fertilized eggs differentiate into various kinds of somatic cells. However, somatic cells do not divide and differentiate into other types of somatic cells after differentiation in a disorderly manner. There are two possibilities to explain this. First, somatic cells can completely lose their potential to differentiate into other kinds of cells during development, or second, they may retain their potential, but such potential may be suppressed after development. The studies to elucidate how these processes occur were the origin of reprogramming science and regenerative medicine. In 1938, Spemann was the first person to carry out nuclear transplantation, but the experiment failed (Spemann, 1938). In 1952, Briggs and King transplanted the nucleus of a frog blastula into enucleated unfertilized eggs. The eggs developed into tadpoles. This was the first cloned animal with nuclear transplantation, and the origin of the cloning technique. An interesting discovery was that the later the nucleuses were taken during the developmental stage, the lower the efficiency of generating clone frogs. It was impossible to produce a cloned frog using the nucleus from a stage later than the development of a tailbud. At that time, they thought the information in the nucleus changed during development (Brigge & King, 1952). However, Gurdon arrived at a different conclusion from Briggs and King. He transplanted the nuclei of small intestinal epithelial cells into enucleated unfertilized eggs and obtained tadpoles (Gurdon, 1962). His data suggested that the nucleus of a somatic cell could be reprogrammed, and thereby regain the ability to differentiate into many kinds of cells. In 1997, the cloning of a sheep demonstrated that mammalian somatic cells could also be reprogrammed (Wilmut, 1997). These data suggested that the information in the nucleus did not change irreversibly during development, and indicated that somatic cells have the potentially ability to differentiate into other kinds of cells after development.
Freshness assessment of stored meat by specific activity of glutathione transferase

We described a procedure for the determination of freshness of frozen meat or fishsamples during storage periods. Correlations between decline in specific activities of glutathionetransferase (GST) in fresh or frozen tissues of cow, chicken and fish with storage time at ambienttemperature were examined. When fresh tissue is kept at ambient temperature, or if stored frozenand the sample is defrosted, leading to short or prolonged stay at ambient temperatures (20-30oC),specific activity (? ) of GST in the tissue begins to decline gradually with time (? ). We report thatthe relationship between the two parameters is of the form ? = -a? 2 -b? + c; where a, b and c areconstants and are dependent on the organ or tissue being examined. This relationship asexemplified by the quadratic equation is valid for all positive values of ? and ? or when either isequal to zero, and permits the extrapolation of one parameter (? , time of storage), if the other one(? , specific activity) is known. The results obtained also permit us to suggest that measurement ofresidual specific activity of GST in frozen meat or fish samples could provide information aboutstorage history and, by extension, the freshness of the items.
R-Estimation for Asymmetric Independent Component Analysis

Independent component analysis (ICA) recently has attracted much attention in the statistical literature as an appealing alternative to elliptical models. Whereas k-dimensional elliptical densities depend on one single unspecified radial density, however, k-dimensional independent component distributions involve k unspecified component densities. In practice, for given sample size n and dimension k, this makes the statistical analysis much harder. We focus here on the estimation, from an independent sample, of the mixing/demixing matrix of the model. Traditional methods (FOBI, Kernel-ICA, FastICA) mainly originate from the engineering literature. Their consistency requires moment conditions, they are poorly robust, and do not achieve any type of asymptotic efficiency. When based on robust scatter matrices, the two-scatter methods developed by Oja, Sirkia, and Eriksson in 2006 and Nordhausen, Oja, and Ollila in 2008 enjoy better robustness features, but their optimality properties remain unclear. The “classical semiparametric” approach by Chen and Bickel in 2006, quite on the contrary, achieves semiparametric efficiency, but requires the estimation of the densities of the k unobserved independent components. As a reaction, an efficient (signed-)rank-based approach was proposed by Ilmonen and Paindaveine in 2011 for the case of symmetric component densities. The performance of their estimators is quite good, but they unfortunately fail to be root-n consistent as soon as one of the component densities violates the symmetry assumption. In this article, using ranks rather than signed ranks, we extend their approach to the asymmetric case and propose a one-step R-estimator for ICA mixing matrices. The finite-sample performances of those estimators are investigated and compared to those of existing methods under moderately large sample sizes. Particularly good performances are obtained from a version involving data-driven scores taking into account the skewness and kurtosis of residuals. Finally, we show, by an empirical exercise, that our methods also may provide excellent results in a context such as image analysis, where the basic assumptions of ICA are quite unlikely to hold. Supplementary materials for this article are available online.
Privacy Protection and Energy Optimization for 5G-Aided Industrial Internet of Things

The 5G is expected to revolutionize every sector of life by providing interconnectivity of everything everywhere at high speed. However, massively interconnected devices and fast data transmission will bring the challenge of privacy as well as energy deficiency. In today’s fast-paced economy, almost every sector of the economy is dependent on energy resources. On the other hand, the energy sector is mainly dependent on fossil fuels and is constituting about 80% of energy globally. This massive extraction and combustion of fossil fuels lead to a lot of adverse impacts on health, environment, and economy. The newly emerging 5G technology has changed the existing phenomenon of life by connecting everything everywhere using IoT devices. 5G enabled IIoT devices has transformed everything from traditional to smart, e.g. smart city, smart healthcare, smart industry, smart manufacturing etc. However, massive I/O technologies for providing D2D connection has also created the issue of privacy that need to be addressed. Privacy is the fundamental right of every individual. 5G industries and organizations need to preserve it for their stability and competency. Therefore, privacy at all three levels (data, identity and location) need to be maintained. Further, energy optimization is a big challenge that needs to be addressed for leveraging the potential benefits of 5G and 5G aided IIoT. Billions of IIoT devices that are expected to communicate using the 5G network will consume a considerable amount of energy while energy resources are limited. Therefore, energy optimization is a future challenge faced by 5G industries that need to be addressed. To fill these gaps, we have provided a comprehensive framework that will help energy researchers and practitioners in better understanding of 5G aided industry 4.0 infrastructure and energy resource optimization by improving privacy. The proposed framework is evaluated using case studies and mathematical modelling.