Miracle-dz/UniFAR-FTUs huggingface dataset:
Voice conversion applied to text-to-speech systems

Aquesta tesi planteja l'estudi i el disseny de sistemes de Conversio de Veu (CV). Els sistemes de CV modifiquen la veu d'un locutor (locutor origen) de tal forma que sembli la veu d'un altre locutor determinat (locutor desti). Aquesta tecnologia s'ha ideat sobretot com a tecnica per a la creacio de nous locutor en sistemes de conversio text a veu (CTV). L'objectiu de la tesi es el desenvolupament d'un sistema de CV per ser aplicat a la sortida d'un sistema de CTV. Els sistemes de CV aplicats a CTV tenen dues caracteristiques principals: la quantitat de dades del locutor origen no es limitada, ja que qualsevol locucio pot ser generada pel CTV, i es disposa d'informacio fonetica. S'han explorat ambdues caracteristiques per tal de millorar el funcionament del sistemes de CV actuals. La personalitat de la veu d'un locutor ve determinada per diversos factors acustics i linguistics. En aquest treball, la personalitat de la veu s'ha descrit a partir de nomes caracteristiques acustiques segmentals. En concret, s'han utilitzat dues estrategies diferents per a convertir els parametres de tracte vocal i la senyal residual estimats amb tecniques de prediccio lineal. Els sistemes actuals de conversio de tracte vocal es basen en Models de Mescles de Gaussianes (GMM) per modelar l'espai acustic dels locutors i transformar els parametres de tracte vocal. Una limitacio d'aquests sistemes es que no poden tractar amb dades del locutor origen que no tenen correspondencia amb el locutor desti. S'han proposat dues aproximacions per utilitzar aquestes dades: una versio modificada de l'algoritme EM amb les matrius de covarianca fixes, i una estrategia que completa les dades sense correspondencia amb els vector d'origen transformats. Per tal de millorar els resultats dels sistemes GMM, s'han proposat dues tecniques noves en la conversio de tracte vocal. La primera tecnica ha consistit en introduir informacio dinamica al model acustic per mitja d'
Which Subject Lines and Messages Improve Response to E-mail Invitations to Web Surveys?

What subject lines and messages in an e-mailed invitation maximize cooperation for a web survey? Answering this question is ever more important as survey researchers increasingly turn to web surveys to reduce costs. We test respondents’ preferences for subject lines and messages in an e-mailed invitation to participate in a Consumer Assessment of Healthcare Providers and Systems survey. Findings suggest that respondents prefer informative and straightforward subject lines that reference the purpose and the sponsor of the survey. Brief and uninformative subject lines should be avoided. In the body of the e-mail, respondents prefer messages that expand on the study’s purpose and usefulness and extend a guarantee of confidentiality. Messages that encourage participation for the sake of the study’s representativeness or accuracy are unpersuasive to potential respondents. Few differences emerged in preferences among demographic subgroups.
Testing 3D Building Modelling Framework In Building Renovation

The paper describes a process where digital measuring survey data is transferred into 3D building model to be used as a foundation for renovation design. The process and method is tested in a case study of an office building of 8 floors. Measuring survey data is more often documented to 2D plan drawings, whereas 3D-modelling was more preferable in the case project. The final aim of the case project is to further test building product model or building information model (BIM) based design methods in building renovation. Product modelling is one emerging framework to manage building related information in contemporary design & construction. Model based methods are more commonly used in new buildings, whereas renovation is usually done with more traditional techniques. Case project results underline the importance of measuring and modelling definition phase. Measuring and documenting objectives for 3D-model based design work are different than for traditional design work. Measuring survey has to be done under the coordination of the designer participants. Selecting and informing the proper and capable surveying partners is also important.
Precocious breeding in female common voles and its relevance to rodent fluctuations

Age at first reproduction has been recognized as the most significant life-history parameter in determining population growth rates. Females in fluctuating rodent populations show great phenotypic variation in this parameter with precocious breeding, defined as pre-weaning fertile mating, being among the most extreme cases of early reproduction in mammals. Here we present the results of experiments in which 14-d-old female common voles (Microtus arvalis) were paired with adult males. Precocious breeding was common and coupled with unexpectedly large litters. We consider this reproductive feature to be adaptive in strictly seasonal environments with greater variation in length of the breeding season, with high resource availability, and a low level of intraspecific competition. Under these conditions, linkage between precocious breeding and high fertility has evolved as part of the most effective life-history strategy which produces the high intrinsic rates of population increase typical of small rodent populations. Seasonality of the environment was suggested to be the main factor behind the geographic pattern in small rodent cyclicity
Social Capital: A Key Ingredient for Clusters in Post-Communist Societies

This chapter explores the role of social capital in shaping inter-firms relations within local clusters and identifies whether a lack of social capital can be considered an impediment to cluster formation and development in post-communist countries. It is important to note that despite offering a definition, this chapter does not provide one model of social capital, nor define one type of impact on cluster performance. Social capital is one element among many other determinants and studying the link between social capital and cluster performance does not mean asserting that social capital is a positive value per se for clusters. However, attention is focused on some major features that characterise social capital and that positively impact on business clusters development: a sound base of trust among economic and institutional actors, together with valued and acknowledged co-operation. The chapter is structured as follows: First, parallel definitions of social capital and clusters are provided; second, the links between the two concepts are analysed (in particular the impact of social capital on cluster building and performance); third, specific issues to postcommunist countries are raised; and lastly, a policy debate is initiated.
UNE NOUVELLE ESPÈCE DE JUNGORNIS (AVES, APODIFORMES) ET DE NOUVELLES FORMES DE CORACIIFORMES S. S. DANS L'ÉOCÈNE SUPÉRIEUR DU QUERCY

Cette etude concerne des restes d'oiseaux de tres petite taille, provenant du gisement de Perriere, date de l'Eocene superieur. Ces restes comportent un humerus qui peut etre attribue a une nouvelle espece de Jungornis. La famille des Jungornithidae est le groupe-frere des Trochilidae, ou Colibris, dont la repartition actuelle est uniquement americaine. La forme de Perriere est plus ancienne que l'unique autre espece de ce genre, decrite dans l'Oligocene inferieur du Caucase. Cela montre que, des l'Eocene superieur, certaines formes avaient deja entrepris une diversification qui a abouti a l'emergence de cette famille actuelle extremement specialisee. Cela confirme aussi que les ancetres de cette famille etaient presents dans l'Ancien Monde (Mayr, 2004). Les autres restes appartiennent a une autre famille d'Apodiformes, a des Caprimulgiformes, et a des Coraciiformes sensu stricto, qui peuvent etre attribues a la famille eteinte des Geranopteridae. La presence de ces formes montre que ce dernier ordre etait egalement tres diversifie a l'Eocene superieur.
Geometric theory of elasticity and shape optimization for solids

On the basis of the equations of the general relativity (GR) supplemented by the relations for the stress-strain state of a continuum, the equations of the geometric theory of elasticity are derived. These equations relate the stress-strain state of the body to the Riemann space geometry. The geometric properties of the space which models the deformed continuum are proposed to be utilized for solving structural optimization problems. Applications of this approach are demonstrated on the symmetric problems for a spherical body and a disk.
From Image to Code: Executable Adversarial Examples of Android Applications

Recent years, Machine Learning has been widely used in malware analysis and achieved unprecedented success. However, deep learning models are found to be highly vulnerable to adversarial examples, which leads to the machine learning-based malware analysis methods vulnerable to malware makers. Exploring the attack algorithm can not only promote the generation of more effective malware analysis methods, but also can promote the development of the defense algorithm. Different machine learning models use different malware features as their classification basis, and accordingly there will be different attack methods against them. For malware visualization method, corresponding effective adversarial attack has not yet appeared. Most existing malware adversarial examples for malware visualization are generated at the feature level, and do not consider whether the generated adversarial examples can be executed and complete their original functions. In this paper, we explored how to modify an Android executable file without affecting its original functions and made it become an adversarial example. We proposed an executable adversarial examples attack strategy for machine learning-based malware visualization analysis. Experimental result shows that the executable adversarial examples we generated can be normally run on Android devices without affecting its original functions, and can confuse the malware family classifier with 93% success rate. We explored possible defense methods and hope to contribute to building a more robust malware classification method.
Two Sides of the Same Coin: White-box and Black-box Attacks for Transfer Learning

Transfer learning has become a common practice for training deep learning models with limited labeled data in a target domain. On the other hand, deep models are vulnerable to adversarial attacks. Though transfer learning has been widely applied, its effect on model robustness is unclear. To figure out this problem, we conduct extensive empirical evaluations to show that fine-tuning effectively enhances model robustness under white-box FGSM attacks. We also propose a black-box attack method for transfer learning models which attacks the target model with the adversarial examples produced by its source model. To systematically measure the effect of both white-box and black-box attacks, we propose a new metric to evaluate how transferable are the adversarial examples produced by a source model to a target model. Empirical results show that the adversarial examples are more transferable when fine-tuning is used than they are when the two networks are trained independently.
Can icefish (Salangidae) production be predicted

A discrete Logistic model was applied to icefish ( Salangidae ) populations analyzing and simulating the variation of the annual catchment production of icefish populations for four typical lakes/reservoirs such as Dianchi lake in China, it was found as evidence of chaos in a natural eco system that the parameters of icefish populations for four lakes/reservoirs fall into chaotic region under normal circumstance. The results show that: (1) The long term prediction for the production of field icefish population is unreliable because of chaotic behavior. Only the short term prediction is of necessary accuracy. (2) The strict management for the catchment of icefish is necessary in order to remain a proper quantity of parent fishes, on the other hand, controlling the growth rate by changing the catchment death rate would prevent the great fluctuation of icefish production and then obtain a rather stable yield. (3) The water pollution and other factors affecting the structures of prey, will change the intrinsic natural growth rate and maximum population capacity, furthermore affecting the icefish population.
A Comparative Study of Speculative Retrieval for Multi-Modal Data Trails: Towards User-Friendly Human-Vehicle Interactions

In the era of growing developments in Autonomous Vehicles, the importance of Human-Vehicle Interaction has become apparent. However, the requirements of retrieving in-vehicle drivers' multi-modal data trails, by utilizing embedded sensors, have been consid- ered user unfriendly and impractical. Hence, speculative designs, for in-vehicle multi-modal data retrieval, has been demanded for future personalized and intelligent Human-Vehicle Interaction. In this paper, we explore the feasibility to utilize facial recog- nition techniques to build in-vehicle multi-modal data retrieval. We first perform a comprehensive user study to collect relevant data and extra trails through sensors, cameras and questionnaire. Then, we build the whole pipeline through Convolution Neural Net- works to predict multi-model values of three particular categories of data, which are Heart Rate, Skin Conductance and Vehicle Speed, by solely taking facial expressions as input. We further evaluate and validate its effectiveness within the data set, which suggest the promising future of Speculative Designs for Multi-modal Data Retrieval through this approach.
Geodemographic Influence Maximization

Given a set of locations in a city, on which ones should we place ads on so as to reach as many people as possible within a limited budget? Past research has addressed this question under the assumption that dense trajectory data are available to determine the reach of each ad. However, the data that are available in most industrial settings do not consist of dense, long-range trajectories; instead, they consist of statistics on people's short-range point-to-point movements. In this paper, we address the natural problem that arises such data: given a distribution of population and point-to-point movement statistics over a network, find a set of locations within a budget that achieves maximum expected reach. We call this problem geodemographic influence maximization (GIM). We show that the problem is NP-hard, but its objective function is monotone and submodular, thus admits a greedy algorithm with a 1 over 2 (1-1 over e) approximation ratio. Still, this algorithm is inapplicable on large-scale data for high-frequency digital signage ads. We develop an efficient deterministic algorithm, Lazy-Sower, exploiting a novel, tight double-bounding scheme of marginal influence gain as well as the locality proprieties of the problem; a learning-based variant, NN-Sower, utilizes randomization and deep learning to further improve efficiency, with a slight loss of quality. Our exhaustive experimental study on two real-world urban datasets demonstrates the efficacy and efficiency of our solutions compared to baselines.
Experimental parasite community perturbation reveals associations between Sin Nombre virus and gastrointestinal nematodes in a rodent reservoir host

Individuals are often co-infected with several parasite species, yet measuring within-host interactions remains difficult in the wild. Consequently, such interactions’ impacts on host fitness and epidemiology are often unknown. We used anthelmintic drugs to experimentally reduce nematode infection and measured the effects on both nematodes and the important zoonosis Sin Nombre virus (SNV) in its primary wild reservoir (Peromyscus spp.). Treatment significantly reduced nematode infection, but increased SNV seroprevalence. Furthermore, mice that were co-infected with both nematodes and SNV were in better condition and survived as much as four times longer than mice infected with either parasite alone. These results highlight the importance of investigating multiple parasites for understanding interindividual variation and epidemiological dynamics in reservoir populations with zoonotic transmission potential.
Bandit based Optimization of Multiple Objectives on a Music Streaming Platform

Recommender systems powering online multi-stakeholder platforms often face the challenge of jointly optimizing multiple objectives, in an attempt to efficiently match suppliers and consumers. Examples of such objectives include user behavioral metrics (e.g. clicks, streams, dwell time, etc), supplier exposure objectives (e.g. diversity) and platform centric objectives (e.g. promotions). Jointly optimizing multiple metrics in online recommender systems remains a challenging task. Recent work has demonstrated the prowess of contextual bandits in powering recommendation systems to serve recommendation of interest to users. This paper aims at extending contextual bandits to multi-objective setting so as to power recommendations in a multi-stakeholder platforms. Specifically, in a contextual bandit setting, we learn a recommendation policy that can optimize multiple objectives simultaneously in a fair way. This multi-objective online optimization problem is formalized by using the Generalized Gini index (GGI) aggregation function, which combines and balances multiple objectives together. We propose an online gradient ascent learning algorithm to maximise the long-term vectorial rewards for different objectives scalarised using the GGI function. Through extensive experiments on simulated data and large scale music recommendation data from Spotify, a streaming platform, we show that the proposed algorithm learns a superior policy among the disparate objectives compared with other state-of-the-art approaches.
CrowdQuake: A Networked System of Low-Cost Sensors for Earthquake Detection via Deep Learning

Recently, low-cost acceleration sensors have been widely used to detect earthquakes due to the significant development of MEMS technologies. It, however, still requires a high-density network to fully harness the low-cost sensors, especially for real-time earthquake detection. The design of a high-performance and scalable networked system thus becomes essential to be able to process a large amount of sensor data from hundreds to thousands of the sensors. An efficient and accurate earthquake-detection algorithm is also necessary to distinguish earthquake waveforms from various kinds of non-earthquake ones within the huge data in real time. In this paper, we present CrowdQuake, a networked system based on low-cost acceleration sensors, which monitors ground motions and detects earthquakes, by developing a convolutional-recurrent neural network model. This model ensures high detection performance while maintaining false alarms at a negligible level. We also provide detailed case studies on two of a few small earthquakes that have been detected by CrowdQuake during its last one-year operation.
SimClusters: Community-Based Representations for Heterogeneous Recommendations at Twitter

Personalized recommendation products at Twitter target a multitude of heterogeneous items: Tweets, Events, Topics, Hashtags, and users. Each of these targets varies in their cardinality (which affects the scale of the problem) and their "shelf life'' (which constrains the latency of generating the recommendations). Although Twitter has built a variety of recommendation systems before dating back a decade, solutions to the broader problem were mostly tackled piecemeal. In this paper, we present SimClusters, a general-purpose representation layer based on overlapping communities into which users as well as heterogeneous content can be captured as sparse, interpretable vectors to support a multitude of recommendation tasks. We propose a novel algorithm for community discovery based on Metropolis-Hastings sampling, which is both more accurate and significantly faster than off-the-shelf alternatives. SimClusters scales to networks with billions of users and has been effective across a variety of deployed applications at Twitter.
Region-level epimutation rates in Arabidopsis thaliana

Failure to maintain DNA methylation patterns during plant development can occasionally give rise to so-called “spontaneous epimutations”. These stochastic methylation changes are sometimes heritable across generations and thus accumulate in plant genomes over time. Recent evidence indicates that spontaneous epimutations have a major role in shaping patterns of methylation diversity in plant populations. Using single CG dinucleotides as units of analysis, previous work has shown that the epimutation rate is several orders of magnitude higher than the genetic mutation rate. While these large rate differences have obvious implications for understanding genome-methylome co-evolution, the functional relevance of single CG methylation changes remains questionable. In contrast to single CG, solid experimental evidence has linked methylation gains and losses in larger genomic regions with transcriptional variation and heritable phenotypic effects. Here we show that such region-level changes arise stochastically at about the same rate as those at individual CG sites, are only marginal dependent on region size and cytosine density, but strongly dependent on chromosomal location. We also find consistent evidence that region-level epimutations are not restricted to CG contexts but also frequently occur in non-CG regions at the genome-wide scale. Taken together, our results support the view that many differentially methylated regions (DMRs) in natural populations originate from epimutation events and may not be effectively tagged by proximal SNPs. This possibility reinforces the need for epigenome-wide association studies (EWAS) in plants as a way to identify the epigenetic basis of complex traits.
Competitive Analysis for Points of Interest

The competitive relationship of Points of Interest (POIs) refers to the degree of competition between two POIs for business opportunities from third parties in an urban area. Existing studies for competitive analysis usually focus on mining competitive relationships of entities, such as companies or products, from textual data. However, there are few studies which have a focus on competitive analysis for POIs. Indeed, the growing availability of user behavior data about POIs, such as POI reviews and human mobility data, enables a new paradigm for understanding the competitive relationships among POIs. To this end, in this paper, we study how to predict the POI competitive relationship. Along this line, a very first challenge is how to integrate heterogeneous user behavior data with the spatial features of POIs. As a solution, we first build a heterogeneous POI information network (HPIN) from POI reviews and map search data. Then, we develop a graph neural network-based deep learning framework, named DeepR, for POI competitive relationship prediction based on HPIN. Specifically, DeepR contains two components: a spatial adaptive graph neural network (SA-GNN) and a POI pairwise knowledge extraction learning (PKE) model. The SA-GNN is a novel GNN architecture with incorporating POI's spatial information and location distribution by a specially designed spatial oriented aggregation layer and spatial-dependency attentive propagation mechanism. In addition, PKE is devised to distill the POI pairwise knowledge in HPIN being useful for relationship prediction into condensate vectors with relational graph convolution and cross attention. Finally, extensive experiments on two real-world datasets demonstrate the effectiveness of our method.