Bag-Level Aggregation for Multiple Instance Active Learning in Instance Classification Problems

A growing number of applications, e.g. video surveillance and medical image analysis, require training recognition systems from large amounts of weakly annotated data while some targeted interactions with a domain expert are allowed to improve the training process. In such cases, active learning (AL) can reduce labeling costs for training a classifier by querying the expert to provide the labels of most informative instances. This paper focuses on AL methods for instance classification problems in multiple instance learning (MIL), where data is arranged into sets, called bags, that are weakly labeled. Most AL methods focus on single instance learning problems. These methods are not suitable for MIL problems because they cannot account for the bag structure of data. In this paper, new methods for bag-level aggregation of instance informativeness are proposed for multiple instance active learning (MIAL). The \textit{aggregated informativeness} method identifies the most informative instances based on classifier uncertainty, and queries bags incorporating the most information. The other proposed method, called \textit{cluster-based aggregative sampling}, clusters data hierarchically in the instance space. The informativeness of instances is assessed by considering bag labels, inferred instance labels, and the proportion of labels that remain to be discovered in clusters. Both proposed methods significantly outperform reference methods in extensive experiments using benchmark data from several application domains. Results indicate that using an appropriate strategy to address MIAL problems yields a significant reduction in the number of queries needed to achieve the same level of performance as single instance AL methods

Feature Learning from Spectrograms for Assessment of Personality Traits

Several methods have recently been proposed to analyze speech and automatically infer the personality of the speaker. These methods often rely on prosodic and other hand crafted speech processing features extracted with off-the-shelf toolboxes. To achieve high accuracy, numerous features are typically extracted using complex and highly parameterized algorithms. In this paper, a new method based on feature learning and spectrogram analysis is proposed to simplify the feature extraction process while maintaining a high level of accuracy. The proposed method learns a dictionary of discriminant features from patches extracted in the spectrogram representations of training speech segments. Each speech segment is then encoded using the dictionary, and the resulting feature set is used to perform classification of personality traits. Experiments indicate that the proposed method achieves state-of-the-art results with a significant reduction in complexity when compared to the most recent reference methods. The number of features, and difficulties linked to the feature extraction process are greatly reduced as only one type of descriptors is used, for which the 6 parameters can be tuned automatically. In contrast, the simplest reference method uses 4 types of descriptors to which 6 functionals are applied, resulting in over 20 parameters to be tuned.Comment: 12 pages, 3 figure

Multiple Instance Learning: A Survey of Problem Characteristics and Applications

Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag. This formulation is gaining interest because it naturally fits various problems and allows to leverage weakly labeled data. Consequently, it has been used in diverse application fields such as computer vision and document classification. However, learning from bags raises important challenges that are unique to MIL. This paper provides a comprehensive survey of the characteristics which define and differentiate the types of MIL problems. Until now, these problem characteristics have not been formally identified and described. As a result, the variations in performance of MIL algorithms from one data set to another are difficult to explain. In this paper, MIL problem characteristics are grouped into four broad categories: the composition of the bags, the types of data distribution, the ambiguity of instance labels, and the task to be performed. Methods specialized to address each category are reviewed. Then, the extent to which these characteristics manifest themselves in key MIL application areas are described. Finally, experiments are conducted to compare the performance of 16 state-of-the-art MIL methods on selected problem characteristics. This paper provides insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research

Génération et synchronisation des horloges pour un système micro-ondes 1024 QAM

Un récepteur QAM doit échantillonner le signal modulé reçu une fois par symbole. L'échantillonnage doit se faire au moment idéal, au centre de "l'oeil". Lorsque du bruit de phase corrompt les horloges à l'émetteur et au récepteur, l'oeil apparaît plus fermé, dégradant les performances théoriques du système de communication. Les systèmes QAM à haut niveau sont très sensibles au bruit de phase sur les horloges. Ce mémoire propose un système de génération et synchronisation des horloges adapté aux communications QAM à haut niveau, minimisant le bruit de phase. Le système est basé sur deux boucles à verrouillage de phase numériques. La boucle à l'émetteur contient un diviseur de fréquence fractionnaire par conversion sigma-delta afin d'obtenir une bonne résolution de la fréquence en sortie sans sacrifier la largeur de bande de la boucle. La boucle au récepteur comporte un préfiltre numérique de mise en forme du signal minimisant le bruit de phase généré par la boucle. Le circuit de génération d'horloge a été entièrement réalisé sur une plateforme de développement avec FPGA. Le spectre théorique du bruit de phase de ce circuit est d'abord calculé en prenant en considération chaque source de bruit. Les calculs théoriques sont comparés aux spectres relevés en pratique, confirmant la validité des équations développées dans ce travail

Early Detection for Optimal-Latency Communications in Multi-Hop Links

Modern wireless machine-to-machine-type communications aim to provide both ultra reliability and low latency, stringent requirements that appear to be mutually exclusive. From the noisy channel coding theorem, we know that reliable communications mandate transmission rates that are lower than the channel capacity. To guarantee arbitrarily-low error probability, this implies the use of messages whose lengths tend to infinity. However, long messages are not suitable for low-latency communications. In this paper, we propose an early-detection scheme for wireless communications under a finite-blocklength regime that employs a sequential-test technique to reduce latency while maintaining reliability. We prove that our scheme leads to an average detection time smaller than the symbol duration. Furthermore, in multi-hop low-traffic or continuous-transmission links, we show that our scheme can reliably detect symbols before the end of their transmission, significantly reducing the latency, while keeping the error probability below a predefined threshold.Comment: 6 pages, to be presented at the International Symposium on Wireless Communication Systems (ISWCS) 2019; Fixed some reference

Measuring Disentanglement: A Review of Metrics

Learning to disentangle and represent factors of variation in data is an important problem in AI. While many advances are made to learn these representations, it is still unclear how to quantify disentanglement. Several metrics exist, however little is known on their implicit assumptions, what they truly measure and their limits. As a result, it is difficult to interpret results when comparing different representations. In this work, we survey supervised disentanglement metrics and thoroughly analyze them. We propose a new taxonomy in which all metrics fall into one of three families: intervention-based, predictor-based and information-based. We conduct extensive experiments, where we isolate representation properties to compare all metrics on many aspects. From experiment results and analysis, we provide insights on relations between disentangled representation properties. Finally, we provide guidelines on how to measure disentanglement and report the results

Continuous compensation of binary-weighted DAC nonlinearities in bandpass delta-sigma modulators

We present a novel calibration technique to compensate for DAC element mismatches in bandpass multibit deltasigma (Δ∑) modulators. The proposed technique is purely digital and requires only a minor modification to the modulator loop. It is compatible with binary weighted element DACs and the storage requirements for the calibrated coefficients increases only linearly with the number of quantizer bits. The calibration is performed without breaking the loop, which allows continuous tracking of environmental drifts. Simulation results show a peak signal to noise and distortion ratio (SNDR) of 68 dB after calibration for a DAC with ±1% mismatches, a sinusoid input signal near 1/4 of the sampling frequency and an oversampling ratio of only 10. Those results represent a 26 dB improvement over the non-calibrated case while being within 2 dB of an ideal-DAC case