Miracle-dz/UniFAR-FTUs huggingface dataset:
Supervised Training on Synthetic Languages: A Novel Framework for Unsupervised Parsing

This thesis focuses on unsupervised dependency parsing—parsing sentences of a language into dependency trees without accessing the training data of that language. Different from most prior work that uses unsupervised learning to estimate the parsing parameters, we estimate the parameters by supervised training on synthetic languages. Our parsing framework has three major components: Synthetic language generation gives a rich set of training languages by mix-and-match over the real languages; surface-form feature extraction maps an unparsed corpus of a language into a fixed-length vector as the syntactic signature of that language; and, finally, language-agnostic parsing incorporates the syntactic signature during parsing so that the decision on each word token is reliant upon the general syntax of the target language. The fundamental question we are trying to answer is whether some useful information about the syntax of a language could be inferred from its surface-form evidence (unparsed corpus). This is the same question that has been implicitly asked by previous papers on unsupervised parsing, which only assumes an unparsed corpus to be available for the target language. We show that, indeed, useful features of the target language can be extracted automatically from an unparsed corpus, which consists only of gold part-of-speech (POS) sequences. Providing these features to our neural parser
The Readiness Potential: a systematic review about the influencing factors and the development of an algorithm for automatic features extraction

The goals that this thesis work wants to achieve are two: one objective is to redact a schematic review of the Readiness Potential, the components it is made of (early BP, late BP and also LRP) and the factors modulating its waveform; the other is to write a program that performs onsets and amplitudes measurements using methods found in literature and to compare these results to observe the robustness of the different algorithms. These methods include peak and mean amplitude measurements using different parameters, as well as many onsets measurements, divided in criterion-based, baseline-based and regression-based methods. To understand the robustness of these techniques we divided our dataset in three categories: high-quality, medium-quality and low-quality data. For high-quality data, as expected, no problem arises and the measurements are correctly performed, while for lower quality data many measurements are impossible or produce implausible results. For amplitudes methods, the less reliable is the method based on the work from Wright et al., while the other two produce reliable results in medium and and high-quality conditions. For onset measurements, the method that revealed to be less reliable is the criterion-based method, while the most reliable methods are the regression-based methods. The cleanliness of the data on which the proposed algorithms are performed is crucial for the goodness of the measurements. One of the most problematic issue in dirty data is the noise present in the baseline period.
AUTOMATED PNEUMONIA DETECTION IN X-RAY IMAGES VIA DEPTHWISE SEPARABLE CONVOLUTION BASED LEARNING

: In the medical study of lungs' infection, pneumonia may be caused by bacteria, or viruses. When lungs are infected by pneumonia, the air sacs become inflamed and fill up with fluid or pus. Highly-trained radiography expert interpreters are responsible for interpreting possible pneumonia in industry radiography. Consequently, it intensively relies on the aptitude and experience of the interpreter, plus the inadequate X-ray image's quality. Thus, the detection of pneumonia using different medical imaging techniques becomes very challenging. Any wrong detection could lead to serious consequences in medical treatment. Accurate identification is preliminary to any kind of intervention. Therefore, leveraging technology in automatic detection of these radiography has become essential. Unfortunately, constructing and training a complex deep learning model from scratch is mostly infeasible due to the lack of hardware infrastructure. Therefore, this paper exploits the idea of transfer learning which is the improvement of learning in a new prediction task through the transfer of knowledge from a related prediction task that has already been learned. This will improve the current computer vision methods based on the use of deep learning to more effectively diagnose the presence of pneumonia in X-rays images. By utilizing convolutional neural networks re-trained with our obtained data, our experiment shows that the proposed idea performs perfectly and achieves the classification accuracy of 98.0% ± 0.17 with the acceptable deployment time on a normal laptop.
KẾT HỢP KỸ THUẬT GOM NHÓM VÀ PHẢN HỒI TƯƠNG ĐỒNG TRONG TÌM KIẾM ẢNH

: Image retrieval is a field that is receiving a lot of attention from the computer science community. The current research trend is to combine algorithms and feature extraction methods to increase query efficiency. One of the most well-used methods is the relevant feedback technique, which people determine whether a resulting image is appropriate or inconsistent with the query image, then to modify the original query to increase relevant results. In addition, clustering is usually used to increase the efficiency of feedback, instead of each response the user only selects one image, with this method, the user will select a group of images, all images in that group will be considered relevant or not irrelevant. In image retrieval, local feature is frequently used because of suitable to identify objects within the image, and is not affected by the size, rotation, and brightness of the objects in images. In addition, images on the Internet are often accompanied by short notes or descriptions (collectively referred to as descriptions), which are of great value in expressing the content within the image. Simultaneous use of image feature and text feature is a new approach and has been proven effective in machine learning studies. From that fact, the study proposes to use a combination of clustering techniques to get feedback, the feature is local image feature and text feature. The study was evaluated on the data set of "Intangible Cultural Heritage in the Mekong River Delta", which was collected automatically from the Internet using the Web crawler tool, the experimental results showed that the method was The proposal has brought good results in improving the feedback efficiency as well as query results.
Effects of Plant Density on Frequency Distributions of Plant Height in Chenopodium album Stands: Analysis Based on Continuous Monitoring of Height-growth of Individual Plants

Height-growth of individual plants of Chenopodium album in even-aged monospecific stands with initial densities of 400, 800, and 3600 plants m-2 was followed from emergence to fruit maturation to elucidate the factors responsible for size hierarchy formation. Many individuals stopped growing rather abruptly at relatively early stages in the vegetative phase and some of them eventually died. The other plants grew until fruit maturation and attained large final heights. Depending on whether they terminated or continued height-growth during the vegetative phase, the plants were called 'lower' and 'upper' plants. The numbers of the 'upper' plants per unit area were about 100 plants m-2 irrespective of the initial plant densities, and thus the number of 'lower' plants increased with the increase in the plant density. However, there was an upper limit to the number of the surviving 'lower' plants (400-700 plants m-2). The shapes of the final frequency distributions of plant height were largely determined by the ratio of the number of 'upper' plants to that of surviving 'lower' plants, and were bimodal for the 400 plants m-2 plot and L-shaped for the 800 and 3600 plants m-2 plots. From these, it is concluded that the segregation of the plants into 'upper' and 'lower' plants is an essential factor responsible for the size hierarchy formation. The present results also indicate that both bimodal and L-shaped distributions can result from one-sided competition for light, depending on the initial plant densities.
CHARACTERISTIC OF THE PM10 IN URBAN ENVIRONMENT OF MAKASSAR

PM10 concentrations were measured on a weekly basis for a period of one year from February 2012 to January 2013 at one site of Makassar, Province of South Sulawesi, Indonesia. A total of nineteen elemental components have analyzed (i.e Ag, Al, B, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, Si, Ti, and Zn). The elemental components were found in descending order; Ca > Si > Na > Al > Fe > K > Mg > Zn > Ti > Pb > Ni >Mn> Ba > Cu > Cr > B > Ag > Cd > Co. Four ionic species (i.e Cl-, NO3-, SO42-, NH4+) have analyzed, The ionic species were found in descending order; SO42- > NO3- > Cl- > NH4+ as well as black carbon were founded in the sample was 2.01 µg/m3. The elemental enrichment factors indicated that most of the elements were enriched relative to soil origin illustrating of their possible associations with other sources such as marine and anthropogenic derived aerosols.
The taxonomy of the Caloplaca citrina group (Teloschistaceae) in the Black Sea region; with contributions to the cryptic species concept in lichenology

Abstract A new taxonomy of the Caloplaca citrina group in the Black Sea region is presented. It is based on the nrDNA ITS molecular data, chemistry (anthraquinone contents) and 20 morphological characters. Six species previously known in the region are accepted: Caloplaca arcis, C. calcitrapa, C. dichroa, C. flavocitrina, C. geleverjae, C. limonia. Five new species are described: Caloplaca arcisproxima, C. austrocitrina, C. communis, C. confusa and C. nigromarina. Seven further species, Caloplaca britannica, C. citrina, C. marina, C. maritima, C. microthallina, C. ora and C. phlogina are also treated briefly. Some maritime species known from the Atlantic coast of Europe are absent from the region, and, surprisingly, Caloplaca citrina s. str. could not be confirmed from the study area. A key to the species present in the region is provided, although morphological characters are of very limited value in this group. The variability and taxonomic importance of particular features are discussed. No significant differences in secondary chemistry were observed among the species. Many examples of convergence and some semi-cryptic species were revealed by molecular data. The term ‘semi-cryptic species’ is introduced here into lichenology for those species which cannot be clearly diagnosed by their morphology, but which are determined by other characters, mainly by their ecology and distribution. We propose to describe formally such species, in spite of difficulties with subsequent morphological identification.
Agamid lizard (Agamidae: Uromastycinae) from the lower Oligocene of Egypt

Agamid lizards are currently found in Africa, Asia, the eastern Mediterranean region and Australia. Together with the Chamaeleonidae and some extinct basal forms, they comprise the Acrodonta, lizards with acrodont dentition. The Acrodonta have been suggested to have a Gondwanan origin, with the oldest members found in the Triassic of India. The first agamids are known from the Late Cretaceous of Asia, and the group is widely distributed in the northern hemisphere throughout much of the Paleogene. However, the fossil history of Acrodonta on the Afro-Arabian plate is extremely limited: a single jaw fragment bearing acrodont dentition, possibly attributable to Agamidae, has been reported from the Paleogene of Morocco, and material identified as agamid was reported from the Oligocene of Oman. We here add to the African record the first clear evidence of Agamidae, represented by several jaws with attached teeth from earliest Oligocene deposits of the Jebel Qatrani Formation in the Fayum Depression, Egypt. Characters of the teeth, including very rounded labial surface, shearing edges concave lingually, shearing tip crescentic and accessory cusps absent, are similar to those of Uromastyx. Species of Uromastyx currently inhabit the Saharan and Somalian regions of Africa and adjacent Arabia.
Using T-PODs to investigate the echolocation of coastal bottlenose dolphins

We investigated the feasibility of using a T-POD, a passive acoustic dolphin detector system, to monitor bottlenose dolphins in the Shannon Estuary, Ireland, from 27 June to 18 August 2005. A v.3 T-POD, logging alternate minutes, was moored in view of an observation site. Land-based theodolite tracking was used to record the position of the closest animal, school size and activity of the closest dolphin school to the T-POD. All cetacean detections on the T-POD synchronous with shore watches in sea states ≤2 were analysed. A total of 94 schools were observed at distances up to 8000 m from the observer. Acoustic detections corresponded well with visual detections, with 82% of the dolphin schools observed within 500 m of the T-POD detected acoustically. The furthest distance dolphins were observed from the T-POD during periods of acoustic detection was 1246 m. Twelve acoustic encounters were logged without corresponding visual detections, four of which may represent false positives. School size did not affect the acoustic detectability of the dolphins, as there was no difference in the size of schools observed with or without corresponding acoustic detections (Kruskal–Wallis, P=0.64). Similarly no relationship was found between acoustic detections and school sizes at different distances to the T-POD (linear regression P=0.5, r2=0.01). Acoustic encounters did not vary in relation to diel patterns (Mann–Whitney, P=0.13) but were related to tidal state (χ2=40.2, P=0.00, df=11) with more encounters logged in the 4 h after high water, probably reflecting prey-related changes in habitat use. T-POD detections correlated well with visual observations and although detection is likely to vary according to T-POD specification, sensitivity and the conditions at the deployment site, the T-POD is a useful tool that has been shown to offer the possibility of continuous monitoring, something that is difficult to achieve with visual methods alone.
Characterization of in Vitro Metabolism of Loganin by Human Intestinal Microflora Using Ultra-High Performance Liquid Chromatography–Quadrupole Time-of-Flight Mass Spectrometry

Human intestinal microbiota comprise a complex biological system with considerable metabolic activity. Various studies have focused on the bioconversion of flavonoids. However, in addition to flavonoids, bioactive components such as iridoids also exist in many natural and traditional Chinese medicines. Little is known about the interactions of the iridoids with bacteria. Loganin, one of the main effective iridoids in the valuable traditional Chinese herbal medicine Cornus officinalis, exhibits various pharmacological activities and biological effects. Human intestinal bacteria were isolated and the conversion capability of loganin was investigated. The metabolites were determined by ultra-high performance liquid chromatography–quadrupole time-of-flight mass spectrometry. Loganin was metabolized to hydrogenated and hydroxylated loganin sulfate, acetylated loganin, loganetin, methylated loganetin, hydrogenated, and hydroxylated loganetin. The metabolic routes and metabolites of loganin were reported for the first time. These metabolites may influence the biological activities of loganin in vivo. Thus, this study provides fundamental information about a traditional Chinese medicine.
Sur le pouvoir expressif des structures applicatives et monadiques indexées. (On the expressive power of indexed applicative and monadic structures)

Il est bien connu que des constructions theoriques tres simples telles que les structures Either (equivalent type theorique de l'operateur logique "ou"), State (representant des transformateurs d'etat composables), Applicative (application des fonctions generalisee) et Monad (composition de programmes sequentielles generalisee), nommes structures en Haskell, couvrent une grande partie de ce qui est habituellement necessaire pour exprimer avec elegance la plupart des idiomes informatiques utilises dans les programmes classiques. Cependant, il est usuellement admis qu'il existe plusieurs classes d'idiomes couramment utilises qui ne s'integrent pas bien a ces structures, les exemples les plus remarquables etant les transformations entre arbres (types de donnees, dont l'utilisation doit s'appuyer soit sur les motifs generalises soit sur une infrastructure de meta programmation lourde) et traitement des exceptions (qui sont d'habitude supposes necessiter un langage special et une prise en charge de l'execution). Ce travail a pour but de montrer que beaucoup de ces idiomes peuvent, en fait, etre exprimes en reutilisant ces structures bien connues avec des modifications mineures (le cas echeant). En d'autres termes, le but de ce travail est d'appliquer les principes du rasoir KISS (Keep It Stupid Simple) et/ou d'Occam aux structures algebriques utilisees pour resoudre des problemes de programmation courants. Techniquement parlant, ce travail a pour but de montrer que les generalisations naturelles de classes de types Applicative et Monad de Haskell, associees a la possibilite d'en faire des produits cartesiens, en produisent un cadre commun tres simple pour exprimer de nombreuses choses pratiques, dont certaines sont des nouvelles methodes tres commodes pour exprimer des idees de programmation communes, tandis que les autres peuvent etre vues comme systemes d'effets. Sur ce dernier point, si l'on veut generaliser des exemples presentes dans une approche de la conception de systemes d'effets en general, on peut alors considerer la structure globale de cette approche comme un cadre quasi syntaxique qui permet d'eriger une structure generale du cadre "mariage" au dessus de differents systemes d'effets adherant aux principes de base. (Bien que ce travail ne soit pas trop approfondi dans la derniere, car il est principalement motive par des exemples qui peuvent etre immediatement appliques a la pratique de Haskell.) Il convient toutefois de noter qu'en fait, ces observations techniques n'ont rien d'etonnant: Applicative et Monad sont respectivement des generalisations de composition fonctionnelle et lineaire des programmes; ainsi, naturellement, les produits cartesiens de ces deux structures doivent couvrir en grande partie ce que les programmes font habituellement.
NOVEL ANTIOXIDANTS FROM SAFFLOWER (CARTHAMUS TINCTORIUS L.) OIL CAKE

Three novel antioxidative serotonin derivatives were isolated from safflower oil cake (Carthamus tinctorius L.). Their structures were established as 4, 4"-bis(N-p-coumaroyl)serotonin (1), 4-[N-(p-coumaroyl)serotonin-4"-yl]-N-feruloyserotonin (2), and 4, 4"-bis(N-feruloyl)serotonin (3). Antioxidative activities of the compounds were measured by ferric thiocyanate method and DPPH method, and the compounds showed strong antioxidative activity.
Deficiencies in the uterine environment and failure to support embryonic development

Abstract Pregnancy failure in livestock can result from failure to fertilize the oocyte or embryonic loss during gestation. The focus of this review is on cattle and factors affecting, and mechanisms related to, uterine insufficiency for pregnancy. A variety of factors contribute to embryonic loss and it may be exacerbated in certain animals, such as high-producing lactating dairy cows and in some cattle in which estrous synchronization and timed AI was performed, due to reduced concentrations of reproductive steroids. Recent research in beef cows induced to ovulate immature follicles and in lactating dairy cows indicates that deficient uterine function is a major factor responsible for infertility in these animals. Failure to provide adequate concentrations of estradiol before ovulation results in prolonged effects on expression and localization of uterine genes and proteins that participate in regulating uterine functions during early gestation. Furthermore, progesterone concentrations during early gestation affect embryonic growth, interferon-tau production, and uterine function. Therefore, an inadequate uterine environment induced by insufficient steroid concentrations before and after ovulation could cause early embryonic death either by failing to provide an adequate uterine environment for recognition of embryo signaling, adhesion, and implantation, or by failing to support appropriate embryonic growth, which could lead to decreased conceptus size and failed maternal recognition of pregnancy.
Application of Hierarchical Temporal Memory to Anomaly Detection of Vital Signs for Ambient Assisted Living

This thesis presents the development of a framework for anomaly detection of vital signs for an Ambient Assisted Living (AAL) health monitoring scenario. It is driven by spatiotemporal reasoning of vital signs that Cortical Learning Algorithms (CLA) based on Hierarchal Temporal Memory (HTM) theory undertakes in an AAL health monitoring scenario to detect anomalous data points preceding cardiac arrest. This thesis begins with a literature review on the existing Ambient intelligence (AmI) paradigm, AAL technologies and anomaly detection algorithms used in a health monitoring scenario. The research revealed the significance of the temporal and spatial reasoning in the vital signs monitoring as the spatiotemporal patterns of vital signs provide a basis to detect irregularities in the health status of elderly people. The HTM theory is yet to be adequately deployed in an AAL health monitoring scenario. Hence HTM theory, network and core operations of the CLA are explored. Despite the fact that standard implementation of the HTM theory comprises of a single-level hierarchy, multiple vital signs, specifically the correlation between them is not sufficiently considered. This insufficiency is of particular significance considering that vital signs are correlated in time and space, which are used in the health monitoring applications for diagnosis and prognosis tasks. This research proposes a novel framework consisting of multi-level HTM networks. The lower level consists of four models allocated to the four vital signs, Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Heart Rate (HR) and peripheral capillary oxygen saturation (SpO2) in order to learn the spatiotemporal patterns of each vital sign. Additionally, a higher level is introduced to learn spatiotemporal patterns of the anomalous data point detected from the four vital signs. The proposed hierarchical organisation improves the model’s performance by using the semantically improved representation of the sensed data because patterns learned at each level of the hierarchy are reused when combined in novel ways at higher levels. To investigate and evaluate the performance of the proposed framework, several data selection techniques are studied, and accordingly, a total record of 247 elderly patients is extracted from the MIMIC-III clinical database. The performance of the proposed framework is evaluated and compared against several state-of-the-art anomaly detection algorithms using both online and traditional metrics. The proposed framework achieved 83% NAB score which outperforms the HTM and k-NN algorithms by 15%, the HBOS and INFLO SVD by 16% and the k-NN PCA by 21% while the SVM scored 34%. The results prove that multiple HTM networks can achieve better performance when dealing with multi-dimensional data, i.e. data collected from more than one source/sensor.
Elastic properties of the feet of deer (Cervidae)

Dynamic tests have been performed on the feet of deer, and on tendons removed from the feet, to determine their elastic properties. The results have been used to calculate the strain energy stored in each foot while it is on the ground in a fast galloping stride. This is compared with an estimate of the work done by the leg, and the energy-saving role of tendon elasticity is assessed.