Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient and Featureless Approaches to Bathymetric Simultaneous Localisation and Mapping

Stephen Alexander Barkby Doctor of Philosophy The University of Sydney March 2011 Efficient and Featureless Approaches to Bathymetric Simultaneous Localisation and Mapping This thesis investigates efficient forms of Simultaneous Localization and Mapping (SLAM) that do not require explicit identification, tracking or association of map features. The specific application considered here is subsea robotic bathymetric mapping. In this context, SLAM allows a GPS-denied robot operating near the sea floor to create a self-consistent bathymetric map. This is accomplished using a Rao-Blackwellized Particle Filter (RBPF) whereby each particle maintains a hypothesis of the current vehicle state and map that is efficiently maintained using Distributed Particle Mapping. Through particle weighting and resampling, successive observations of the seafloor structure are used to improve the estimated trajectory and resulting map by enforcing map self consistency. The main contributions of this thesis are two novel map representations, either of which can be paired with the RBPF to perform SLAM. The first is a grid-based 2D depth map that is efficiently stored by exploiting redundancies between different maps. The second is a trajectory map representation that, instead of directly storing estimates of seabed depth, records the trajectory of each particle and synchronises it to a common log of bathymetric observations. Upon detecting a loop closure each particle is weighted by matching new observations to the current predictions. For the grid map approach this is done by extracting the predictions stored in the observed cells. For the trajectory map approach predictions are instead generated from a local reconstruction of their map using Gaussian Process Regression. While the former allows for faster map access the latter requires less memory and fully exploits the spatial correlation in the environment, allowing predictions of seabed depth to be generated in areas that were not directly observed previously. In this case particle resampling therefore not only enforces self-consistency in overlapping sections of the map but additionally enforces self-consistency between neighboring map borders. Both approaches are validated using multibeam sonar data collected from several missions of varying scale by a variety of different Unmanned Underwater Vehicles. These trials demonstrate how the corrections provided by both approaches improve the trajectory and map when compared to dead reckoning fused with Ultra Short Baseline or Long Baseline observations. Furthermore, results are compared with a pre-existing state of the art bathymetric SLAM technique, confirming that similar results can be achieved at a fraction of the computation cost. Lastly the added capabilities of the trajectory map are validated using two different bathymetric datasets. These demonstrate how navigation and mapping corrections can still be
C T ] 2 9 M ay 2 01 7 Categories with norms Wies

We study a metric-like structure on categories, showing that the concept of the limit of a sequence in a metric space and the concept of the colimit of a sequence in a category have a common generalization. The main concept is a norm on a category, generalizing pseudo-metrics and group valuations. In this new context, we discuss topics like Cauchy completion and the Banach Contraction Principle.
Wireless Underground Sensor Networks: A Comprehensive Survey and Tutorial

The Internet of Things has developed greatly over the past decade to cater to many diverse applications across almost all fields of life. Many of these applications can either profit or even explicitly require deployment underground, such as precision agriculture, but also land, pipeline, or mine monitoring. Underground deployment offers many advantages, such as concealment of the devices for their protection. However, the underground environment is also very challenging, especially for wireless communications and energy harvesting. In this survey and tutorial, we offer a comprehensive view of the complete topic, from theoretical foundations of wireless communications underground, through system architectures and applications, to energy harvesting options. These topics cannot be viewed separately from each other, as they are deeply intertwined and all of them need to be considered before a possible deployment. We will show that wireless underground sensor networks have a great potential for a variety of applications and are an intriguing alternative to overground deployments. We will describe the state of the art in a tutorial style, so that beginners can also profit. Last but not least, we will identify remaining challenges to guide researchers in this area.
The thermoelectric module

Die Erfindung betrifft ein thermoelektrisches Modul, – mit einem Modulgehause (12), das einen Modulinnenraum (14) umschliest, – mit mehreren im Modulinnenraum (14) angeordneten thermoelektrischen Elementen (16), – mit mehreren im Modulinnenraum (14) angeordneten Leiterbrucken (18) zum elektrischen Verschalten der thermoelektrischen Elemente (16), – wobei das Modulgehause (12) an einer ersten Seite (20) eine erste Seitenwand (22) aufweist, die mit mehreren ersten Leiterbrucken (24) warmeleitend verbunden ist, – wobei das Modulgehause (12) an einer zweiten Seite (26) eine zweite Seitenwand (28) aufweist, die mit mehreren zweiten Leiterbrucken (30) warmeleitend verbunden ist, – wobei sich die thermoelektrischen Elemente (16) zwischen den ersten und zweiten Leiterbrucken (24, 30) erstrecken. Erfindungswesentlich ist dabei, dass das thermoelektrische Modul (10) eine Flussigmetallschicht (36) aufweist, die zwischen den ersten Leiterbrucken (24) und der ersten Seitenwand (22) angeordnet ist.
AutoPrep: An Automatic Preprocessing Framework for In-the-Wild Speech Data

Recently, the utilization of extensive open-sourced text data has significantly advanced the performance of text-based large language models (LLMs). However, the use of in-the-wild large-scale speech data in the speech technology community remains constrained. One reason for this limitation is that a considerable amount of the publicly available speech data is compromised by background noise, speech overlapping, lack of speech segmentation information, missing speaker labels, and incomplete transcriptions, which can largely hinder their usefulness. On the other hand, human annotation of speech data is both time-consuming and costly. To address this issue, we introduce an automatic in-the-wild speech data preprocessing framework (AutoPrep) in this paper, which is designed to enhance speech quality, generate speaker labels, and produce transcriptions automatically. The proposed AutoPrep framework comprises six components: speech enhancement, speech segmentation, speaker clustering, target speech extraction, quality filtering and automatic speech recognition. Experiments conducted on the open-sourced WenetSpeech and our self-collected AutoPrepWild corpora demonstrate that the proposed AutoPrep framework can generate preprocessed data with similar DNSMOS and PDNSMOS scores compared to several open-sourced TTS datasets. The corresponding TTS system can achieve up to 0.68 in-domain speaker similarity.
Power system load prediction model based on dual attentionmechanism gate recurrent unit neural network

This paper proposes a dual attention mechanism gate recurrent unit (DAGRU) neural network model for predicting the overload of the power system load. Based on the GRU neural network, the dual attention mechanism of feature and time is introduced to dig the potential correlation between the output and input characteristics of the power system load. Among which, the feature attention is utilized to analyze the relationship between historical information and input, and then timing attention is utilized to automatically extract the historical information of key points in GRU network to improve the stability of time series prediction. The simulation results show that compared with GRU and LSTM networks, the proposed DAGRU network model improves the prediction accuracy of electrical load.
Testing the predictions of reinforcement: long-term empirical data from a damselfly mosaic hybrid zone

Theoretical work suggests that reinforcement can cause the strengthening of prezygotic isolation in sympatry by mitigating the costs of maladaptive hybridization. However, only a handful of studies have tested all predictions of this theory in natural populations. We investigated reinforcement in a mosaic hybrid zone of the damselflies Ischnura elegans and I. graellsii. Firstly, we tested if the conditions of reinforcement were fulfilled by quantifying whether hybridization was costly, and prezygotic isolation was strengthening in sympatry compared with in allopatry. Secondly, we investigated three specific predictions of reinforcement: rarer female effect, presence of concordant prezygotic and postzygotic isolation asymmetries in sympatry, and greater premating asymmetries associated with weaker postzygotic isolation in sympatry. We found that reinforcement has strengthened mechanical isolation in one cross direction between species in sympatry. Our study details a case of reinforcement and heterospecific gene flow causing opposite effects between reciprocal heterospecific crosses and describes a natural model in which reproductive isolation is built by the simultaneous effects of reinforcement, the lock-and-key model, Bateson-Dobzhansky-Müller incompatibilities and Haldane’s rule.
Evaluation of renewable energy prediction information for cyber-physical power system

Today's Internet technology is changing rapidly, the value of information in power systems is increasing. In the renewable energy-rich power grid, relatively accurate renewable energy prediction can reduce uncertainty caused by the random output of renewable energy, thus effectively improve the efficiency of renewable energy use, but also for power companies and power grid companies to provide a reference for economic benefits. In this paper, the value of photovoltaic (PV) predictive information to power systems is evaluated using an innovative evaluation system including Shannon entropy and non-noise ratio. Finally, taking IEEE 30-bus system as an example, the design and implementation of this paper are verified.
An Effective Deep Learning Model for Route Travel Time Estimation on A Road Network

We studied the ETA problem on a road network. We proposed a comprehensive and novel neural network based approach that is able to fully exploit spatio-temporal features extracted from four significant aspects: heterogeneity, proximity, periodicity and dynamicity. We built a link-connection graph to capture each route’s static contexts (i.e., spatial proximity and temporal periodicity), and we collect historical traffic conditions as its dynamic contexts.
Analysis of integrated circuit radiation emission measurement: TEM cell and wideband TEM cell method

With the rapid development of integrated circuits, its electromagnetic compatibility becomes more and more prominent. This paper gives the most commonly used test standards and methods of electromagnetic emission, electromagnetic immunity and pulse immunity at home and abroad. In addition, from the aspects of test equipment, test conditions, test arrangement and test procedures, the TEM cell method and wideband TEM cell method of integrated circuit radiation emission measurement are introduced in detail. Suggestions on the use of two testing methods are given. The actual test results measured by this test method are analyzed. According to the actual test experience, this paper briefly introduces the differences between TEM cell method and wideband TEM cell method and stripline method, and gives the test points for attention.
Searching magnetic states using an unsupervised machine learning algorithm with the Heisenberg model

Magnetism is a canonical example of a spontaneous symmetry breaking. The symmetry of a magnetic state below the Curie temperature is spontaneously broken even though the Hamiltonian is invariant under symmetry. Recently, machine learning algorithms have been successfully utilized to study topics in physics. We applied unsupervised machine learning algorithms to find the magnetic ground states of the Heisenberg model. A fully connected neural network was used to generate the spin configuration from randomly coded features, and the magnetic energy was selected as the cost to be minimized during the machine learning process. We found that ground states solved by the unsupervised learning process are consistent with the theoretical solution. Also, we compared the results with those from traditional computational methods and found that the machine learning algorithm provides an efficient method to solve the magnetic state numerically.
Uncertainty and Leveraged Lucas Trees : The Cross Section of Equilibrium Volatility Risk Premia JOBMARKET PAPER

Volatility risk premia compensate agents for holding assets whose payoffs correlate with times of high return variation. This paper takes a structural approach to explain the cross-section of volatility risk premia of stocks using a Lucas orchard with heterogeneous beliefs, stochastic macro-economic uncertainty, and default risk. I study two manifestations of uncertainty, namely (i) agents’ disagreement and (ii) time-varying volatility of fundamental growth rates. The paper shows that while the former source of risk accounts for the level of the risk premia, the latter mainly affects the higher order moments of the risk premium distribution. Together with uncertainty, default risk associated with levered trees implies a non-monotonic equilibrium link between stock returns and volatility which allows for positive or negative risk premia. Calibrating the economy, I show that the model accounts for predictability of excess stock returns and corporate credit spreads. I construct volatility risk premia from option and stock prices and document that in the time-series, volatility risk premia of individual stocks can be positive or negative, and switch sign rather often. In the cross-section, they are only weakly related to traditional risk factors. I then test the model predictions and find that empirical proxies for investors’ uncertainty about expected growth rates and macro-economic uncertainty are priced risk factors that convey information over and above those contained in other standard factors to explain these risk premia. In line with the model predictions, I present predictability evidence of individual volatility risk premia for stock excess returns and corporate credit spreads. JEL Classification Codes: D80, G12, G13.
Evaluation ultrasonore de l'os cortical par transmission axiale : modélisation et expérimentation in vitro et in vivo

La technique dite de transmission axiale permet une evaluation ultrasonore de l'os cortical, a partir d'une mesure de vitesse. Cette technique est etudiee dans la presente these d'une part en modelisant par simulation numerique les phenomenes de propagation mis en jeu, d'autre part a travers une approche experimentale. Un code de simulation de la propagation tridimensionnelle dans les milieux elastiques anisotropes et heterogenes, reposant sur un schema aux differences finies, a ete implemente pour repondre aux contraintes specifiques de la propagation dans l'os cortical. A l'aide d'un tel code, la nature de l'onde se propageant le long de la corticale des os long est etudiee, en fonction de parametres osseux tels que la geometrie de l'os (epaisseur corticale, courbure) ou la microporosite intracorticale. Parallelement, un dispositif experimental a ete developpe et adapte au contexte de mesures cliniques. En particulier, un nouveau de type de sonde permettant une mesure reproductible sur l'os (in vivo et in vitro) a ete concu et valide experimentalement. L'originalite du dispositif tient a la possibilite d'une transmission bidirectionnelle implementee pour s'affranchir de l'effet des tissus mous. Une etude in vitro a ete realisee sur une cinquantaine de radius humain, combinant les mesures ultrasonores de vitesse en transmission axiale a des mesures par rayons X (scanner medical conventionnel et microtomographie Synchrotron). Pour la premiere fois, nous avons evalue l'effet respectif de la densite minerale materielle osseuse et de la microporosite sur la mesure de vitesse ultrasonore. Une campagne de mesure clinique est actuellement en cours, 150 patients et temoins ayant ete mesures a ce jour. La reproductibilite des mesures s'est averee satisfaisante in vivo, grâce au concept de transmission bidirectionnelle. Nous presentons des resultats preliminaires concernant l'effet de l'âge.
Antimicrobial Activities of Endophytic Fungi Secondary Metabolites from Moringa oleifera (Lam)

This study presents the antimicrobial activities of secondary metabolites of endophytic fungi isolated from a medicinal plant Moringa oleifera growing at the University of Dar es Salaam, Tanzania. Two endophytic fungi were identified, Nigrospora sp. from leaves, and Fusarium sp. from roots and seeds. The disc method was used in testing the antimicrobial activities of the crude extracts of endophytic fungi secondary metabolites against four human pathogens, namely, Bacillus substilis, Staphylococcus aureus, Escherichia coli, and Candida albicans. The results revealed that the crude extracts of endophytes secondary metabolites from leaves showed strong antimicrobial activities against two Gram positive bacteria and fungi with Minimum Inhibitory Concentrations (MICs) ranging from 0.016 mg/mL to 0.5 mg/mL, while from roots and seeds showed moderate antimicrobial activities with MIC ranging from 0.6 mg/mL to 1 mg/mL. Qualitative phytochemical analysis of the same extracts also revealed the presence of vital constituents potential for medicinal applications such as alkaloids, phenols, flavonoids, tannins, terpenoids, and saponins. This study demonstrated that secondary metabolites of endophytic fungi from Moringa oleifera possess important phytochemical and exhibit antimicrobial potential against the tested human pathogens which could contribute to the endeavours for new therapeutic
Counting processes and copulas : applications in insurance

Les processus de comptage ont un role majeur et des applications variees dans plusieurs domaines telles que la tarification, la reserve de perte, l'allocation du capital en assurance. Avec toutes ces applications, il y a quelques risques ou des facteurs de risque qui dependent sur un autre ensemble de risque ou des facteurs de risques, et cela constitue precisement un grand interet pour les compagnies d'assurance. Ces compagnies d'assurance veulent construire des modeles specifiques pour capturer quelques, ou toutes les, structures de dependance existantes entre 1es risques connus. Quelques-uns de ces risques connus sont associes avec les processus de comptage. La modelisation de la dependance utilisant la theorie des copules et les processus de comptage a attire l'attention de plusieurs chercheurs ces dernieres annees. Dans ce memoire, nous etudions deux champs d'interet dans la modelisation de la dependance avec applications en assurance et finance. Premierement, nous etudions plusieurs methodes de modelisation, les techniques d'estimation et l'implementation des algorithmes qui sont utilises dans la modelisation des copules autour des processus de comptage. Par exemple, dans le deuxieme chapitre deux de ce memoire, nous allons etudier comment la modelisation de la dependance est utilisee pour un risque bivarie ou pour des facteurs de risque telle que la classe Bonus-Malus et les comptes de reclamations du passe, le compte de reclamations et la taille des reclamations, le compte de reclamations de deux processus de comptage differents qui se sont produits a partir du meme evenement, etc. Dans la deuxieme partie du memoire, nous scrutons et adressons quelques remarques autour du choix de quelques copules de la premiere partie 1 de ce memoire, et nous presentons une discussion au sujet des approches utilisees. Cette deuxieme partie du memoire est motivee par le fait que differentes analyses vont choisir un ensemble different de distributions univariees pour ajuster les memes donnees et choisir differents types de copules pour modeliser les structures de dependance. En fait, on cherche dans la seconde partie a repondre a la question : Devons-nous dependre sur les marginales meme si nous avons un ensemble large de donnees disponibles ? En dernier lieu, nous discutons a propos des estimes des vrais parametres de copule et nous analysons un ensemble de vraies donnees. ______________________________________________________________________________ MOTS-CLES DE L’AUTEUR : Processus de comptage, copule, structure de dependance, parametres de modele, estimation, copule de Clayton, distribution conjointe, processus de Levy.
Detection of Trace Biomarkers in the Atacama Desert with a Novel In Situ Organic Compound Analysis System

We have developed a suite of instruments consisting of subcritical water extraction (SCWE) and a sublimation system called the Mars Organic Detector or MOD for extraction of biomarker compounds from soil, together with a portable microchip capillary electrophoresis (CE) system for high sensitivity analysis of the fluorescentlylabeled biomarker compounds. The CE instrument provides high resolution analysis of amino acid composition and chirality with ppb to part-per-trillion sensitivity (1). The CE instrument can also be used to analyze amines, di-amines, amino sugars and several of the nucleobases, nucleosides and nucleotides (2). Successful in situ Mars exploration requires the development of instrument suites advanced enough to operate in relevant field environments and the demonstration that they have the analytical capability and sensitivity to detect very low levels of biomarker compounds. We describe here the successful field trials of our approach in the Atacama Desert in Chile, an extremely dry, oxidized environment that is an excellent Mars analog site.
Creating future-oriented organizations: Exploring the beneficial effects of future-oriented cognition and behavior

The purpose of this thesis is to develop and empirically test theory on future-oriented cognition and behavior in organizations. Specifically, this thesis investigates dispositional characteristics of organizational members that can positively and negatively affect their engagement in future-oriented behavior at work, and how organizations can create a work environment that supports beneficial effects and buffers detrimental effects of dispositional characteristics on future-oriented behavior in organizations.
Ubiquitous Wireless Sensing - Theory, Technique and Application

Ubiquitous wireless sensing makes use of wireless RF signals in the environment, such as WiFi, 4G/5G, LoRa, UWB, etc. The sensing of information such as the location and state of the sensing target can be achieved by modeling and analyzing the effects of target and environment on the ubiquitous signals. In recent years, a large number of research work has made many advances in wireless sensing theory, technology, and applications. First, the study of generic and dedicated sensing models attempts to answer theoretical questions. e.g., what is the wireless sensing limit? where is the sensing boundary? Second, multi-dimensional performance improvements are achieved to satisfy practical requirements such as long-range and multi-target sensing. Finally, the rich applications provide various sensing opportunities, ranging from static target sensing to sensing with target and device mobility, which provide many promising possibilities to be explored by researchers.