Miracle-dz/UniFAR-FTUs huggingface dataset:
Text Embeddings in Riemannian Manifolds

Unsupervised text embedding models are ubiquitous in the field of Natural language Processing. These models embed words, sentences or documents as vectors in an Euclidean space with the principle that textual information that are semantically similar have similar representations i.e they lie close to each other in the semantic space. Word2vec and GLoVe are the most popular examples of such models. Both these models provide efficient training of word embeddings and provide evaluation methods like word similarity and word analogy tasks that proves the effectiveness of these models. However, they have their fair share of drawbacks. For instance, there is little to no explanation on why word vector summation solves word analogy. These models also suffer from meaning conflation deficiency where multiple senses of a word are represented by one single vector and thus lead to inaccurate semantic modelling. This is also an issue with document and sentence embeddings which span multiple words, phrases and topics. Existing unsupervised models which tackle meaning conflation deficiency perform sense representation where each senses of a word are represented by an individual vector. This is done by modifying the Word2vec skip-gram algorithm and adding some extra constraints on the vector embedding space. Instead of adding extra constraints on the Word2vec algorithm, we aim to address this issue of by exploiting the geometry of the embedding space. We propose three unsupervised text embedding models that embed texts in Riemannian manifolds by integrating the linguistic principles of Word2vec and GLoVe with optimization tools from differential geometry. The first and the second model uses the same joint modelling framework but embeds both words and documents in the Grassmannian manifold and a custom product manifold respectively. Both models generate quality document embeddings given by their evaluation results in document clustering and document classification tasks. The third model embed words in Spectahedron manifold where each word is a matrix whose eigenvectors correspond with the multiple senses of that word. Finally, we provide a Lie-group theoretic understanding of linear substructures present in Word2vec and GLoVe that solves word analogy. Lie groups are differentiable manifolds with a group structure. Some basic properties of these groups show that the linear substructures are present due to the tangent space of the Identity element of the group.
A Novel Approach to Outlier Detection using Modified Grey Wolf Optimization and k-Nearest Neighbors Algorithm

Objectives: Detecting dataset anomalies has been an interesting yet challenging area in this front. This work proposes a hybrid model using meta-heuristics to detect dataset anomalies efficiently. Methods/Statistical Analysis: A distance based modified grey wolf optimization algorithm is designed which uses the k- Nearest Neighbor algorithm for better results. The proposed approach works well with supervised datasets and gives anomalies with respect to each attribute of the dataset based on a threshold using a confidence interval. Findings: The proposed approach works well with regression as well as classification datasets in the supervised scenario. The results in terms of number of anomalies and the accuracy using confusion matrix are depicted and have been evaluated for a classification dataset considering a minority class to be anomalous in comparison to the majority class. The results have been evaluated based on varying the threshold and ‘k’ values and depending on the data set domain and data distribution the optimal parameters can be identified and used. Application/Improvements: The proposed approach can be used for anomaly detection of datasets of different domains of supervised scenario. It can also be extended to unsupervised scenario by integrating it with K-means clustering.
Measuring cumulus expansion of mammalian oocytes: comparing the reliability of methods and how arti�cial intelligence could automate the measurement

Cumulus expansion is an important indicator of oocyte maturation, often correlated with greater oocyte developmental capacity. Although multiple methods have been described to assess cumulus expansion, none of them is considered a gold standard. Additionally, these methods are subjective and time-consuming. Here, the reliability of three cumulus expansion measurement methods was evaluated and a deep learning model was created to automatically perform the measurement. Cumulus-oocyte complexes were compared before and after in vitro maturation by three independent observers using three methods: (1) measurement of the cumulus area, (2) measurement of three distances between the zona pellucida and outer cumulus, and (3) scoring cumulus expansion on a 5-point Likert scale. Inter-and intra-observer agreements were calculated using intraclass-correlation coe�cients (ICC). The area method resulted in the best overall agreement with an ICC of 0.89 versus 0.54 and 0.30 for the 3-distance and scoring method, respectively. Therefore, the area method served as the base to create the deep learning model, which outperformed two observers while equivalent to the third. Measuring the area is the most reliable method to manually evaluate cumulus expansion, whilst deep learning automatically performs the calculation with human-level accuracy and could therefore be a valuable prospective tool for embryologists.
Development of a High Throughput, Whole Organism Assay for Studying Metabolic Capacity in Crassostrea virginica

Genetic improvement of commercial lines has been a key component in the increased production of eastern oysters (Crassostrea virginica). One form of genetic improvement, induced triploidy, is commonly used to produce sterile oysters with faster growth and higher meat quality. However, the details of the physiological advantage provided by triploidy are poorly understood. Whole animal respiration is an important indicator of an individual’s capacity to meet increased energetic demands for growth and stress response. I have developed an assay for the Agilent’s XFe96 Extracellular Flux Analyzer to measure metabolic capacity (i.e., rates of basal, maximal, mitochondrial, and non-mitochondrial oxygen consumption) in intact, recently settled eastern oysters (spat). In developing this assay, I optimized the concentration of two pharmacological agents, carbonyl cyanide 4-(trifluoromethoxy) phenylhydrazone (FCCP) and sodium azide (NaAz), required for estimating maximal and mitochondrial oxygen consumption rates (OCR) in spat samples. I found that 2 μM FCCP and 50 mM NaAz resulted in the best resolution of these partitions. A goal of my research is to use this assay to evaluate the metabolic capacity of oyster lines during the hatchery phase of their production. Thus, I utilized my assay to compare the metabolic capacity of diploid spat from the Rutgers Haskin NEH line to triploid spat from a cross of DEBY diploids and VIMS tetraploids. The triploid spat had increased levels of basal, maximal, and mitochondrial rates. The results of my study suggest that the XFe96 Analyzer provides a valuable platform for measuring metabolic variations among genetically improved oyster lines.
Detection of fast-(cid:13)ux botnets through DNS tra(cid:14)c analysis

Botnets are networks built up of a large number of bot computers, which provide the attacker with massive resources, such as bandwidth, storage, and processing power, in turn, allowing the attacker to launch massive attacks, such as Distributed Denial of Service (DDoS) attacks, or undertake spamming or phishing campaigns. One of the main approaches for botnet detection is based on monitoring and analyzing DNS query/responses in the network, where botnets make their detection more di(cid:14)cult by using techniques such as fast-(cid:13)uxing. Moreover, the main challenge in detecting fast-(cid:13)ux botnets arises from their similar behavior with that of legitimate networks, such as CDNs, which employ a round-robin DNS technique. In this paper, we propose a new system to detect fast-(cid:13)ux botnets by passive DNS monitoring. The proposed system (cid:12)rst (cid:12)lters out domains seen in historical DNS traces assuming that they are benign. We believe this assumption to be valid as benign domains usually have long lifetime as compared to botnet domains, which are usually short-lived. Hence, CDN domains, which are the main cause of misclassi(cid:12)cation when looking for malicious fast-(cid:13)ux domains, are removed. Afterwards, a few simple features are calculated to help in properly categorizing the domains in question as either benign or botnet related. The proposed system is evaluated by employing DNS traces from our campus network and encouraging evaluation results are obtained.
Detection of surface waves in the ground using an acoustic method

Date Approved _________________ 3 ACKNOWLEDGEMENT This work was accomplished in an environment very new to me. I had to adapt to the language, the culture, and discover the campus, the city and the people. Many people helped me go through this experience. Their knowledge, their advice and their support made my research easier. I would like to thank them all. First, my advisor P. Rogers for giving me the opportunity to contribute to this research. W. Scott for managing the landmine detection project at Georgia Tech, and also for his very useful debugging visits, and advice on electronics Y. Berthelot for teaching me the basis on acoustic transducers and supporting the exchange with Georgia Tech Lorraine. J. Martin for his availability, his everyday advice and teaching. His contribution to the good running of the acoustical laboratory was essential. I would also like to thank the graduate students of the acousto-dynamic group for their availability. They maintained a nice work environment.
A Rede na Estratégia da Aranha: “Pós-verdade”, Política e Regressão

As estrategias algoritmicas da era da “pos-verdade” vieram desconfigurar a politica e estao a abalar os alicerces das velhas democracias. A par disso, as novas dinâmicas clickbait vieram agravar o ja de si fragil posicionamento do fenomeno jornalistico no mundo contemporâneo. Este algorithmic turn, que chegou a politica, as noticias e a interaccao social, esta a evoluir para uma dimensao insondavel, nao somente porque constituiu o algoritmo em gatekeeper, mas tambem porque criou maquinas de propaganda especializadas em engenharia social, em automacao do comportamento politico e em correntes emocionais, manipulando a opiniao e criando novas espirais de silencio. Cultura, politica e comunicacao enfrentam estes novos abismos. Neste texto procuraremos pensar esta vertigem, esta tecnologizacao e distopia da mediacao, do saber e do conhecimento, a nova propaganda, armada agora pela Inteligencia Artificial, em que as plataformas digitais e os seus algoritmos porventura nao dominam ainda completamente os processos de interacao e de comunicacao, embora claramente os (nos) monitorizem e rastreiem, reconduzindo a experiencia democratica para âmbitos mais complexos de regressao historica e politica.
SOLUTION OF STOCHASTIC DIFFERENTIAL EQUATIONS OF THE MODELS OF QUEUEING THEORY BY THE METHOD OF GENERATIONG FUNCTION

The object is mathematical models and their formalization by differential equation systems. The aim is to popularize stochastic models and differential equation systems which solution allows an analytical form. A model formulation and a process of finding a solution to equation systems are of interest. In the queueing theory many models are formalized by systems of linear differential equations with one or more parameters in which distribution of states of queueing systems are unknown functions. In such systems Markov processes are often grounding in the theory of differential equations construction; in a special case postulates of Poisson process are used. Analytical solution of equation systems exists but it is hard to find by traditional methods. In our study we offer a method which allows to find an analytical solution not only for probability distribution but also for moments of any order from one equation system. Description of procedure of differential equation generation for moments of random order varieties is presented. The method is based on the usage of generating (characteristic) functions. This method is effective because it allows to find solutions for moments (here it is expectation and variance) without complex probability calculations. It is especially important in empirical researches of systems that consist of many elements. For example, when we analyze function effectiveness of operating and designed scaling computing systems and supercomputers. Three models and their formalization by differential equation systems that correspond to stochastic processes and analytical solutions of diverse complexity are formulated. Connection between stochastic differential equations systems and their solutions with probability distributions that are classical in probability theory is shown.
Simultaneous acquisition of trajectory and fluorescence lifetime of moving single particles

Fluorescence lifetime imaging (FLIM) has been a powerful tool in life science because it can reveal the interactions of an excited fluorescent molecule and its environment. The combination with two-photon excitation (TPE) and timecorrelated single photon counting (TCSPC) provides it the ability of optical sectioning, high time resolution and detection efficiency. In previous work, we have introduced a two-dimensional acousto-optic deflector (AOD) into TCSPC-based FLIM to achieve fast and flexible FLIM. In this work, we combined the AOD-FLIM system with a single particle tracking (SPT) setup and algorithm and developed an SPT-FLIM system. Using the system, we acquired the trajectory and fluorescence lifetime of a moving particle simultaneously and reconstructed a life-time-marked pseudocolored trajectory, which might reflect dynamic interaction between the moving particle and its local environment along its motion trail. The results indicated the potential of the technique for studying the interaction between specific moving biological macromolecules and the ambient micro-environment in live cells.
Der geriatrische Patient im Rettungsdienst – Besonderheiten der notärztlichen Versorgung

The prehospital management of geriatric patients involves an understanding of the physiology of aging and necessitates the special acknowledgement of diagnosis and treatment of emergencies in the elderly. It is essential to keep in mind the prevention of an underestimation of the severity of disease and the necessity of an adequate therapy. The prehospital management of moribund patients gains special importance.
Development of a freeze-out surface for 3+1 dimensional hydrodynamics

A review of a macroscopic description of heavy ion collisions is presented, with focus on hydrodynamics. The various models are discussed and motivation is provided for using full 3+ 1 dimensional hydrodynamics. Numerical freeze-out is then discussed, and a numerical development of the freeze-out hypersurface is presented. Possible algorithmic extensions are described. Un ive rsi ty of Ca pe To wn
Performance enhancement of multi-wavelength generations based on SOAs with a microfiber Mach–Zehnder interferometer

Functionality improvement of a non-linear semiconductor optical amplifier for multi-wavelength generation is reported. A microfiber Mach–Zehnder interferometer (MMZI) is incorporated to enhance the multi-wavelength generation performance in terms of optical signal-to-noise ratio (OSNR) and full-width-half-maximum linewidth. The proposed scheme offers better dynamic functionality, with flexible tunable and switchable properties. Incorporating the MMZI causes spatial mode beating interference, resulting in narrow bands and power-efficient modes. Tunable and switchable octuple wavelength is demonstrated using non-linear polarization rotation (NPR), with frequency separation with respect to the free spectral range of the implemented MMZI over a greater than 30 nm span. The NPR effect is induced to suppress mode competition within the homogeneous broadening linewidth of the employed semiconductor optical amplifier.
Stimulated Brillouin Scattering and Longitudinal Strain Performance of BOTDA-Based Nonuniform As<inline-formula><tex-math notation="LaTeX">$_{2}$</tex-math></inline-formula>Se<inline-formula><tex-math notation="LaTeX">$_{3}$</tex-math></inline-formula>-PMMA Tapered Fibers

Stimulated Brillouin scattering and the longitudinal strain performance based on Brillouin Optical Time Domain Analysis (BOTDA) in a 50 cm long nonuniform As2Se3-PMMA tapered fiber is theoretically and experimentally investigated. In the BOTDA measurement a 20 ns pump pulse is used, of which the pulse-width equivalent length is longer than the fiber under test (FUT) of 50 cm nonuniform As2Se3-PMMA tapered fiber. The evolution of Brillouin gain spectrum (BGS) corresponding to different fiber sections is analyzed showing the strain performance of the shift of the BGS frequency and the change of BGS linewidth under the imposed longitudinal strain. A strain-insensitive BGS linewidth is achieved by selecting the fiber gain section under the combined effects of the longitudinal acoustic velocity VA, strain dependence of refractive indexn, the effective fiber lengthLeff and also the pump intensityI along the nonuniform As2Se3-PMMA tapered fiber. The proposed method has the potential for many novel applications such as strain-insensitive sensing device, multiple-parameter sensors and the distributed sensing measurement along the nonuniform As2Se3-PMMA tapered fibers.
Unification Strategies in Cognitive Science

Abstract Cognitive science is an interdisciplinary conglomerate of various research fields and disciplines, which increases the risk of fragmentation of cognitive theories. However, while most previous work has focused on theoretical integration, some kinds of integration may turn out to be monstrous, or result in superficially lumped and unrelated bodies of knowledge. In this paper, I distinguish theoretical integration from theoretical unification, and propose some analyses of theoretical unification dimensions. Moreover, two research strategies that are supposed to lead to unification are analyzed in terms of the mechanistic account of explanation. Finally, I argue that theoretical unification is not an absolute requirement from the mechanistic perspective, and that strategies aiming at unification may be premature in fields where there are multiple conflicting explanatory models.
Quantitative Evaluation of Articular Cartilage in Knee MRI

This thesis presents research and development of methods for automatic segmentation and quantitative evaluation of anatomical structures from medical images. An automatic segmentation algorithm is the foundation on which this framework stands, therefore a substantial part of the thesis concerns this subject. The segmentation methods focus on statistical voxel classification, using feature selection to find representative features for the classes, and incorporation of level set methods into the classification is also investigated. The segmentation methods are evaluated on magnetic resonance imaging (MRI) data of the knee and heart. The thesis also contains a quantitative evaluation of articular cartilage in relation to osteoarthritis (OA), with the aim of developing and evaluating imagingbased OA biomarkers suited for clinical trials. Factors such as joint incongruity, cartilage lesions, and fibrillation can all be linked to OA, and they can in turn be related to cartilage surface curvature at different scales. Cartilage curvature is analyzed using mean curvature flow and by a comparison of surface normals. Besides curvature, other biomarkers founded on the automatic segmentation algorithm such as volume, thickness, and homogeneity are also evaluated as potential OA biomarkers. The automatic segmentation methods described in this work perform well compared to gold standard segmentations, and compare favorably to state-of-the-art segmentation methods. The OA biomarkers obtained founded on the automatic cartilage segmentation show potential as disease markers as they are clearly related to radiographic signs of OA.
Alzheimer's Disease Detection from Spontaneous Speech and Text: A review

In the past decade, there has been a surge in research examining the use of voice and speech analysis as a means of detecting neurodegenerative diseases such as Alzheimer's. Many studies have shown that certain acoustic features can be used to differentiate between normal aging and Alzheimer's disease, and speech analysis has been found to be a cost-effective method of detecting Alzheimer's dementia. The aim of this review is to analyze the various algorithms used in speech-based detection and classification of Alzheimer's disease. A literature survey was conducted using databases such as Web of Science, Google Scholar, and Science Direct, and articles published from January 2020 to the present were included based on keywords such as ``Alzheimer's detection'',"speech,"and"natural language processing. "The ADReSS, Pitt corpus, and CCC datasets are commonly used for the analysis of dementia from speech, and this review focuses on the various acoustic and linguistic feature engineering-based classification models drawn from 15 studies. Based on the findings of this study, it appears that a more accurate model for classifying Alzheimer's disease can be developed by considering both linguistic and acoustic data. The review suggests that speech signals can be a useful tool for detecting dementia and may serve as a reliable biomarker for efficiently identifying Alzheimer's disease.
Self-Supervised Acoustic Word Embedding Learning via Correspondence Transformer Encoder

Acoustic word embeddings (AWEs) aims to map a variable-length speech segment into a fixed-dimensional representation. High-quality AWEs should be invariant to variations, such as duration, pitch and speaker. In this paper, we introduce a novel self-supervised method to learn robust AWEs from a large-scale unlabelled speech corpus. Our model, named Correspondence Transformer Encoder (CTE), employs a teacher-student learning framework. We train the model based on the idea that different realisations of the same word should be close in the underlying embedding space. Specifically, we feed the teacher and student encoder with different acoustic instances of the same word and pre-train the model with a word-level loss. Our experiments show that the embeddings extracted from the proposed CTE model are robust to speech variations, e.g. speakers and domains. Additionally, when evaluated on Xitsonga, a low-resource cross-lingual setting, the CTE model achieves new state-of-the-art performance.
Symbolic Semantics for Probabilistic Programs (extended version)

We present a new symbolic execution semantics of probabilistic programs that include observe statements and sampling from continuous distributions. Building on Kozen's seminal work, this symbolic semantics consists of a countable collection of measurable functions, along with a partition of the state space. We use the new semantics to provide a full correctness proof of symbolic execution for probabilistic programs. We also implement this semantics in the tool symProb, and illustrate its use on examples.