Miracle-dz/UniFAR-FTUs huggingface dataset:
Time warp invariant sparse coding and dictionary learning for time series classification and clustering. (Représentations parcimonieuses et apprentissage de dictionnaires pour la classification et le clustering de séries temporelles)

L'apprentissage de dictionnaires a partir de donnees temporelles est un probleme fondamental pour l’extraction de caracteristiques temporelles latentes, la revelation de primitives saillantes et la representation de donnees temporelles complexes. Cette these porte sur l’apprentissage de dictionnaires pour la representation parcimonieuse de series temporelles. On s’interesse a l’apprentissage de representations pour la reconstruction, la classification et le clustering de series temporelles sous des transformations de distortions temporelles. Nous proposons de nouveaux modeles invariants aux distortions temporelles.La premiere partie du travail porte sur l’apprentissage de dictionnaire pour des tâches de reconstruction et de classification de series temporelles. Nous avons propose un modele TWI-OMP (Time-Warp Invariant Orthogonal Matching Pursuit) invariant aux distorsions temporelles, base sur un operateur de maximisation du cosinus entre des series temporelles. Nous avons ensuite introduit le concept d’atomes jumeles (sibling atomes) et avons propose une approche d’apprentissage de dictionnaires TWI-kSVD etendant la methode kSVD a des series temporelles.Dans la seconde partie du travail, nous nous sommes interesses a l’apprentissage de dictionnaires pour le clustering de series temporelles. Nous avons propose une formalisation du probleme et une solution TWI-DLCLUST par descente de gradient.Les modeles proposes sont evalues au travers plusieurs jeux de donnees publiques et reelles puis compares aux approches majeures de l’etat de l’art. Les experiences conduites et les resultats obtenus montrent l’interet des modeles d’apprentissage de representations proposes pour la classification et le clustering de series temporelles.
Réseaux de neurones profonds appliqués à la compréhension de la parole. (Deep learning applied to spoken langage understanding)

Cette these s'inscrit dans le cadre de l'emergence de l'apprentissage profond et aborde la comprehension de la parole assimilee a l'extraction et a la representation automatique du sens contenu dans les mots d'une phrase parlee. Nous etudions une tâche d'etiquetage en concepts semantiques dans un contexte de dialogue oral evaluee sur le corpus francais MEDIA. Depuis une dizaine d'annees, les modeles neuronaux prennent l'ascendant dans de nombreuses tâches de traitement du langage naturel grâce a des avancees algorithmiques ou a la mise a disposition d'outils de calcul puissants comme les processeurs graphiques. De nombreux obstacles rendent la comprehension complexe, comme l'interpretation difficile des transcriptions automatiques de la parole etant donne que de nombreuses erreurs sont introduites par le processus de reconnaissance automatique en amont du module de comprehension. Nous presentons un etat de l'art decrivant la comprehension de la parole puis les methodes d'apprentissage automatique supervise pour la resoudre en commencant par des systemes classiques pour finir avec des techniques d'apprentissage profond. Les contributions sont ensuite exposees suivant trois axes. Premierement, nous developpons une architecture neuronale efficace consistant en un reseau recurent bidirectionnel encodeur-decodeur avec mecanisme d’attention. Puis nous abordons la gestion des erreurs de reconnaissance automatique et des solutions pour limiter leur impact sur nos performances. Enfin, nous envisageons une desambiguisation de la tâche de comprehension permettant de rendre notre systeme plus performant.
Context-Aware Embeddings for Automatic Art Analysis

Automatic art analysis aims to classify and retrieve artistic representations from a collection of images by using computer vision and machine learning techniques. In this work, we propose to enhance visual representations from neural networks with contextual artistic information. Whereas visual representations are able to capture information about the content and the style of an artwork, our proposed context-aware embeddings additionally encode relationships between different artistic attributes, such as author, school, or historical period. We design two different approaches for using context in automatic art analysis. In the first one, contextual data is obtained through a multi-task learning model, in which several attributes are trained together to find visual relationships between elements. In the second approach, context is obtained through an art-specific knowledge graph, which encodes relationships between artistic attributes. An exhaustive evaluation of both of our models in several art analysis problems, such as author identification, type classification, or cross-modal retrieval, show that performance is improved by up to 7.3% in art classification and 37.24% in retrieval when context-aware embeddings are used.
Algorithmic Contributions to Qualitative Constraint-based Spatial and Temporal Reasoning. (Contributions Algorithmiques au Raisonnement Spatial et Temporel basé sur des Contraintes Qualitatives)

Le raisonnement spatial et temporel qualitatif est un domaine principal d’etudes de l’intelligence artificielle et, en particulier, du domaine de la representation des connaissances, qui traite des concepts cognitifs fondamentaux de l’espace et du temps de maniere abstraite. Dans notre these, nous nous focalisons sur les formalismes du domaine du raisonnement spatial et temporel qualitatif representant les informations par des contraintes et apportons des contributions sur plusieurs aspects. En particulier, etant donnee des bases de connaissances d’informations qualitatives sur l’espace ou le temps, nous definissons des nouvelles conditions de consistance locale et des techniques associees afin de resoudre efficacement les problemes fondamentaux se posant. Nous traitons notamment du probleme de la satisfiabilite qui est le probleme de decider s’il existe une interpretation quantitative de toutes les entites satisfaisant l’ensemble des contraintes qualitatives. Nous considerons egalement le probleme de l’etiquetage minimal qui consiste a determiner pour toutes les contraintes qualitatives les relations de base participant a au moins une solution ainsi que le probleme de redondance consistant a determiner les contraintes qualitatives non redondantes. En outre, nous enrichissons le domaine des formalismes spatio-temporels par des contributions concernant une logique spatio-temporelle combinant la logique temporelle propositionnelle (PTL) avec un langage de contraintes qualitatives spatiales et une etude de la problematique consistant a gerer une sequence temporelle de configurations spatiales qualitatives devant satisfaire des contraintes de transition.
Visible . A Service of zbw

In most implementation frameworks, agents care only about the outcome and not at all about the way in which it was obtained. Additionally, typical mechanisms for full implementation involve the complete revelation of all private information to the planner. In this paper I consider the problem of full implementation with agents who may prefer to protect their privacy. I analyze the extent to which privacy-protecting mechanisms can be constructed under various assumptions about agents’ predilection for privacy and the permissible game forms.
UvA-DARE (Digital Academic Repository) Clinical subtyping using community detection: Limited utility?

Objectives: To discover psychiatric subtypes, researchers are adopting a method called community detection. This method was not subjected to the same scrutiny in the psychiatric literature as traditional clustering methods. Furthermore, many community detection algorithms have been developed without psychiatric sample sizes and variable numbers in mind. We aim to provide clarity to researchers on the utility of this method. Methods: We provide an introduction to community detection algorithms, specifically describing the crucial differences between correlation ‐ based and distance ‐ based community detection. We compare community detection results to results of traditional methods in a simulation study representing typical psychiatry settings, using three conceptualizations of how subtypes might differ. Results: We discovered that the number of recovered subgroups was often incorrect with several community detection algorithms. Correlation ‐ based community detection fared better than distance ‐ based community detection, and performed relatively well with smaller sample sizes. Latent profile analysis was more consistent in recovering subtypes. Whether methods were successful depended on how differences were introduced. Conclusions: Traditional methods like latent profile analysis remain reasonable choices. Furthermore, results depend on assumptions and theoretical choices underlying subtyping analyses, which researchers need to consider before drawing conclusions on subtypes. Employing multiple subtyping methods to establish method dependency is recommended.
The Efficiency Threshold for the Offspring Population Size of the (μ, λ) EA

Understanding when evolutionary algorithms are efficient or not, and how they efficiently solve problems, is one of the central research tasks in evolutionary computation. In this work, we make progress in understanding the interplay between parent and offspring population size of the $(\mu,\lambda)$ EA. Previous works, roughly speaking, indicate that for $\lambda \ge (1+\varepsilon) e \mu$, this EA easily optimizes the OneMax function, whereas an offspring population size $\lambda \le (1 -\varepsilon) e \mu$ leads to an exponential runtime. Motivated also by the observation that in the efficient regime the $(\mu,\lambda)$ EA loses its ability to escape local optima, we take a closer look into this phase transition. Among other results, we show that when $\mu \le n^{1/2 - c}$ for any constant $c > 0$, then for any $\lambda \le e \mu$ we have a super-polynomial runtime. However, if $\mu \ge n^{2/3 + c}$, then for any $\lambda \ge e \mu$, the runtime is polynomial. For the latter result we observe that the $(\mu,\lambda)$ EA profits from better individuals also because these, by creating slightly worse offspring, stabilize slightly sub-optimal sub-populations. While these first results close to the phase transition do not yet give a complete picture, they indicate that the boundary between efficient and super-polynomial is not just the line $\lambda = e \mu$, and that the reasons for efficiency or not are more complex than what was known so far.
An Application-Specific VLIW Processor with Vector Instruction Set for CNN Acceleration

In recent years, neural networks have surpassed classical algorithms in areas such as object recognition, e.g. in the well-known ImageNet challenge. As a result, great effort is being put into developing fast and efficient accelerators, especially for Convolutional Neural Networks (CNNs). In this work we present ConvAix, a fully C-programmable processor, which — contrary to many existing architectures — does not rely on a hard-wired array of multiply-and-accumulate (MAC) units. Instead it maps computations onto independent vector lanes making use of a carefully designed vector instruction set. The presented processor is targeted towards latency-sensitive applications and is capable of executing up to 192 MAC operations per cycle. ConvAix operates at a target clock frequency of 400 MHz in 28nm CMOS, thereby offering state-of-the-art performance with proper flexibility within its target domain. Simulation results for several 2D convolutional layers from well known CNNs (AlexNet, VGG-16) show an average ALU utilization of 72.5% using vector instructions with 16 bit fixed-point arithmetic. Compared to other well-known designs which are less flexible, ConvAix offers competitive energy efficiency of up to 497 GOP/s/W while even surpassing them in terms of area efficiency and processing speed.
The synergistic effect of concatenation in phylogenomics: the case in Pantoea

With the increased availability of genome sequences for bacteria, it has become routine practice to construct genome-based phylogenies. These phylogenies have formed the basis for various taxonomic decisions, especially for resolving problematic relationships between taxa. Despite the popularity of concatenating shared genes to obtain well-supported phylogenies, various issues regarding this combined-evidence approach have been raised. These include the introduction of phylogenetic error into datasets, as well as incongruence due to organism-level evolutionary processes, particularly horizontal gene transfer and incomplete lineage sorting. Because of the huge effect that this could have on phylogenies, we evaluated the impact of phylogenetic conflict caused by organism-level evolutionary processes on the established species phylogeny for Pantoea, a member of the Enterobacterales. We explored the presence and distribution of phylogenetic conflict at the gene partition and nucleotide levels, by identifying putative inter-lineage recombination events that might have contributed to such conflict. Furthermore, we determined whether smaller, randomly constructed datasets had sufficient signal to reconstruct the current species tree hypothesis or if they would be overshadowed by phylogenetic incongruence. We found that no individual gene tree was fully congruent with the species phylogeny of Pantoea, although many of the expected nodes were supported by various individual genes across the genome. Evidence of recombination was found across all lineages within Pantoea, and provides support for organism-level evolutionary processes as a potential source of phylogenetic conflict. The phylogenetic signal from at least 70 random genes recovered robust, well-supported phylogenies for the backbone and most species relationships of Pantoea, and was unaffected by phylogenetic conflict within the dataset. Furthermore, despite providing limited resolution among taxa at the level of single gene trees, concatenated analyses of genes that were identified as having no signal resulted in a phylogeny that resembled the species phylogeny of Pantoea. This distribution of signal and noise across the genome presents the ideal situation for phylogenetic inference, as the topology from a ≥70-gene concatenated species phylogeny is not driven by single genes, and our data suggests that this finding may also hold true for smaller datasets. We thus argue that, by using a concatenation-based approach in phylogenomics, one can obtain robust phylogenies due to the synergistic effect of the combined signal obtained from multiple genes.
Compare Contact Model-based Control and Contact Model-free Learning: A Survey of Robotic Peg-in-hole Assembly Strategies

In this paper, we present an overview of robotic peg-in-hole assembly and analyze two main strategies: contact model-based and contact model-free strategies. More specifically, we first introduce the contact model control approaches, including contact state recognition and compliant control two steps. Additionally, we focus on a comprehensive analysis of the whole robotic assembly system. Second, without the contact state recognition process, we decompose the contact model-free learning algorithms into two main subfields: learning from demonstrations and learning from environments (mainly based on reinforcement learning). For each subfield, we survey the landmark studies and ongoing research to compare the different categories. We hope to strengthen the relation between these two research communities by revealing the underlying links. Ultimately, the remaining challenges and open questions in the field of robotic peg-in-hole assembly community is discussed. The promising directions and potential future work are also considered.
From Semi-supervised to Almost-unsupervised Speech Recognition with Very-low Resource by Jointly Learning Phonetic Structures from Audio and Text Embeddings

Producing a large amount of annotated speech data for training ASR systems remains difficult for more than 95% of languages all over the world which are low-resourced. However, we note human babies start to learn the language by the sounds (or phonetic structures) of a small number of exemplar words, and "generalize" such knowledge to other words without hearing a large amount of data. We initiate some preliminary work in this direction. Audio Word2Vec is used to learn the phonetic structures from spoken words (signal segments), while another autoencoder is used to learn the phonetic structures from text words. The relationships among the above two can be learned jointly, or separately after the above two are well trained. This relationship can be used in speech recognition with very low resource. In the initial experiments on the TIMIT dataset, only 2.1 hours of speech data (in which 2500 spoken words were annotated and the rest unlabeled) gave a word error rate of 44.6%, and this number can be reduced to 34.2% if 4.1 hr of speech data (in which 20000 spoken words were annotated) were given. These results are not satisfactory, but a good starting point.
Qualitative Evaluation of a Community-Based Physical Activity and Yoga Program for Men Living With Prostate Cancer: Survivor Perspectives

Purpose Physical activity (PA) programs for prostate cancer survivors have positive effects on many aspects of health-related quality of life. Translating this research into sustainable community-based settings is necessary to ensure access to programs for survivors. This study examines patient perspectives in the community-based TrueNTH Lifestyle Management (TrueNTH LM) program in Calgary, Canada. Methods Eleven men from programs at civic wellness centers participated in 2 small semistructured focus groups (n = 5 and 6) at the University of Calgary. Motivation for program initiation and adherence, benefits and barriers to participation, and individual satisfaction and feedback on program improvement were discussed. Audio recordings were transcribed and analyzed using thematic methodology guided by a pragmatic philosophy on the patient experience in the program. Results Themes identified included perceived benefits of participating (physical, psychological, and social), facilitators for involvement in the PA program (program design, initial free access, tailored to prostate cancer specific needs, psychosocial environment), and opportunities for improvement and sustainability (exercise as a part of standard care, cost structure, home-based options). Conclusions These findings provide valuable insight into patient perspectives on effective characteristics of prostate cancer and exercise programs. TrueNTH LM has implemented findings, and ensuring needs (benefits and barriers) are addressed for prostate cancer survivors when entering community-based PA programs.
Predicting the results of evaluation procedures of academics

Background The 2010 reform of the Italian university system introduced the National Scientific Habilitation (ASN) as a requirement for applying to permanent professor positions. Since the CVs of the 59,149 candidates and the results of their assessments have been made publicly available, the ASN constitutes an opportunity to perform analyses about a nation-wide evaluation process. Objective The main goals of this paper are: (i) predicting the ASN results using the information contained in the candidates’ CVs; (ii) identifying a small set of quantitative indicators that can be used to perform accurate predictions. Approach Semantic technologies are used to extract, systematize and enrich the information contained in the applicants’ CVs, and machine learning methods are used to predict the ASN results and to identify a subset of relevant predictors. Results For predicting the success in the role of associate professor, our best models using all and the top 15 predictors make accurate predictions (F-measure values higher than 0.6) in 88% and 88.6% of the cases, respectively. Similar results have been achieved for the role of full professor. Evaluation The proposed approach outperforms the other models developed to predict the results of researchers’ evaluation procedures. Conclusions Such results allow the development of an automated system for supporting both candidates and committees in the future ASN sessions and other scholars’ evaluation procedures.
An In-Depth Comparison of s-t Reliability Algorithms over Uncertain Graphs

Uncertain, or probabilistic, graphs have been increasingly used to represent noisy linked data in many emerging applications, and have recently attracted the attention of the database research community. A fundamental problem on uncertain graphs is the s-t reliability, which measures the probability that a target node t is reachable from a source node s in a probabilistic (or uncertain) graph, i.e., a graph where every edge is assigned a probability of existence. Due to the inherent complexity of the s-t reliability estimation problem (#P-hard), various sampling and indexing based efficient algorithms were proposed in the literature. However, since they have not been thoroughly compared with each other, it is not clear whether the later algorithm outperforms the earlier ones. More importantly, the comparison framework, datasets, and metrics were often not consistent (e.g., different convergence criteria were employed to find the optimal number of samples) across these works. We address this serious concern by re-implementing six state-of-the-art s-t reliability estimation methods in a common system and code base, using several medium and large-scale, real-world graph datasets, identical evaluation metrics, and query workloads. Through our systematic and in-depth analysis of experimental results, we report surprising findings, such as many follow-up algorithms can actually be several orders of magnitude inefficient, less accurate, and more memory intensive compared to the ones that were proposed earlier. We conclude by discussing our recommendations on the road ahead.
Data Selection with Cluster-Based Language Difference Models and Cynical Selection

We present and apply two methods for addressing the problem of selecting relevant training data out of a general pool for use in tasks such as machine translation. Building on existing work on class-based language difference models [1], we first introduce a cluster-based method that uses Brown clusters to condense the vocabulary of the corpora. Secondly, we implement the cynical data selection method [2], which incrementally constructs a training corpus to efficiently model the task corpus. Both the cluster-based and the cynical data selection approaches are used for the first time within a machine translation system, and we perform a head-to-head comparison. Our intrinsic evaluations show that both new methods outperform the standard Moore-Lewis approach (cross-entropy difference), in terms of better perplexity and OOV rates on in-domain data. The cynical approach converges much quicker, covering nearly all of the in-domain vocabulary with 84% less data than the other methods. Furthermore, the new approaches can be used to select machine translation training data for training better systems. Our results confirm that class-based selection using Brown clusters is a viable alternative to POS-based class-based methods, and removes the reliance on a part-of-speech tagger. Additionally, we are able to validate the recently proposed cynical data selection method, showing that its performance in SMT models surpasses that of traditional cross-entropy difference methods and more closely matches the sentence length of the task corpus.