Miracle-dz/UniFAR-FTUs huggingface dataset:
Une strategie pour l'interpretation en analyse spectrale. Detection et caracterisation des composantes d'un spectre. - these realisee dans le cadre de l'operation aspect du gdr-prc isis -

L'objectif de cette these est de proposer une demarche originale d'analyse spectrale de signaux stationnaires. L'originalite reside dans le concept de decision fonde, non pas sur le choix d'une methode pour un signal analyse, mais sur une confrontation de methodes. Cette confrontation a pour but d'estimer les structures spectrales du signal. Nous utilisons une interpretation iterative des spectres fondee sur les proprietes des methodes d'analyse. Une etude theorique complete et approfondie de chaque methode de fourier retenue est presentee. Dans certains cas complexes, des techniques d'optimisation des parametres par une procedure iterative sont developpees. Les avantages et inconvenients de chaque methode sont mis en evidence, ainsi que leur comparaison. Des criteres d'interpretations ont ete developpes. D'un part des criteres de pre-analyse cherchent a emettre des hypotheses sur la nature du signal et a emettre des alarmes : un test d'echantillonnage shannon, deux mesures de la periodicite du signal et une detection de non-stationnarites. D'autre part, des criteres d'interpretation des spectres ont pour but de caracteriser chaque frequence du spectre : une extraction de la ligne de fond du spectre, une detection de pics et un critere de validation de l'hypothese frequence pure. Enfin, une demarche generale d'analyse et d'interpretation spectrale a ete mise au point. Cette demarche et les criteres ont ete valides sur les signaux reels de la base aspect. Les signaux ont ete fournis par nos partenaires industriels, edf et le centre technique des systemes navals et issus des domaines vibratoire et acoustique.
Bayesian Integration of Multi-resolutional Grid Codes for Spatial Cognition

Fourier-like summation of several grid cell modules with different spatial frequencies in the medial entorhinal cortex (MEC) has long been proposed to form the contours of place firing fields. Recent experiments largely, but not completely, support this theory. Place fields are obviously expanded by inactivation of dorsal MEC, which fits the hypothesis. However, contrary to the prediction, inactivation of ventral MEC is also weakly broaden the spatial place firing patterns. In this study, we derive the model from grid spatial frequencies represented by Gaussian profiles to a 1D place field by Bayesian inference, and further provide completely theoretical explanations for expansion of place fields and predictions for alignments of grid components. To understand the information transform across between neocortex, entorhinal cortex, and hippocampus, we propose spatial memory indexing theory from hippocampal indexing theory to investigate how neural dynamics work in the entorhinal-hippocampal circuit. The inputs of place cells in CA3 are converged from three grid modules with different grid spacings layer II of MEC by Bayesian mechanism. We resort to the robot system to test Fourier hypothesis and spatial memory indexing theory, and validate our proposed entorhinal-hippocampal model. And then we demonstrate its cognitive mapping capability on the KITTI odometry benchmark dataset. Results suggest that our model provides a rational theoretical explanation for the biological experimental results. Results also show that the proposed model is robust for simultaneous localization and mapping (SLAM) in the large-scale environment. Our proposed model theoretically supports for Fourier hypothesis in a general Bayesian mechanism, which may pertain to other neural systems in addition to spatial cognition.
Learning Nearly Decomposable Value Functions Via Communication Minimization

Reinforcement learning encounters major challenges in multi-agent settings, such as scalability and non-stationarity. Recently, value function factorization learning emerges as a promising way to address these challenges in collaborative multi-agent systems. However, existing methods have been focusing on learning fully decentralized value functions, which are not efficient for tasks requiring communication. To address this limitation, this paper presents a novel framework for learning nearly decomposable Q-functions (NDQ) via communication minimization, with which agents act on their own most of the time but occasionally send messages to other agents in order for effective coordination. This framework hybridizes value function factorization learning and communication learning by introducing two information-theoretic regularizers. These regularizers are maximizing mutual information between agents' action selection and communication messages while minimizing the entropy of messages between agents. We show how to optimize these regularizers in a way that is easily integrated with existing value function factorization methods such as QMIX. Finally, we demonstrate that, on the StarCraft unit micromanagement benchmark, our framework significantly outperforms baseline methods and allows us to cut off more than $80\%$ of communication without sacrificing the performance. The videos of our experiments are available at this https URL.
A Framework to Assist in the Selection of Appropriate Techniques for the Personalization of Software Systems Based on Characteristics of Available Data

A FRAMEWORK TO ASSIST IN THE SELECTION OF APPROPRIATE TECHNIQUES FOR THE PERSONALIZATION OF SOFTWARE SYSTEMS BASED ON CHARACTERISTICS OF AVAILABLE DATA Dominic Gagné University of Guelph, 2018 Advisor: Dr. Daniel Gillis The complexity of modern software systems has had the unintended consequence of overwhelming users with large amounts of information, reducing their ability to effectively use these systems for their intended purposes. This phenomenon is known as the information overload problem, and is remedied by personalization. The literature provides several useful algorithms, methods, and techniques that may be used to personalize existing software systems. However, each approach has specific input data requirements. Without extensive prior knowledge of the literature, knowing which approach or procedure to employ for a given system is rarely obvious. A framework that could ease the selection of a suitable method by non-technical individuals for any given software system would be of immense value to those seeking to enable personalization. This thesis will present a framework to assist in the selection of appropriate techniques for the personalization of software systems based on the characteristics of available data.
Gaia 19ajj: A Young Star Brightening Due to Enhanced Accretion and Reduced Extinction

We report on the source Gaia 19ajj, identifying it as a young star associated with a little-studied star-forming region seen along a complex line of sight through the Gum Nebula. The optical lightcurve recently recorded by Gaia exhibits a slow and unsteady 5.5 mag rise over about 3 yr, while the mid-infrared lightcurve from NEOWISE over the same time period shows a 1.2 mag rise having similar structure. Available color information is inconsistent with pure extinction reduction as the cause for the photometric brightening. Optical spectroscopic characteristics in the current bright phase include: little in the way of absorption except for the hallmark Li i 6707 Å signature of youth plus weak, e.g., Ca i and notably Ba ii; strong wind/outflow in Ca ii, Mg i b, Na i D, Hα, K i, and O i; jet signatures in [O i], [S ii], [Ca ii], [Fe ii], and [Ni ii]; and narrow rest-velocity emission in neutral species such as Fe i, Ni i, and Mg i. The infrared spectrum is also characterized by outflow and emission, including: a hot He i wind, jet lines such as [Fe ii] and H2; and weak narrow rest-velocity atomic line emission. The bandheads are weakly in emission, but there is also broad H2O absorption. Gaia 19ajj exhibited a previous bright state in the 2010–2012 time frame. The body of photometric and spectroscopic evidence suggests that the source bears resemblance to V2492 Cyg (PTF 10nvg) and PV Cep, both of which similarly experience bright phases that recur on long timescales, with large-amplitude photometric variations and emission-dominated spectra. We interpret the behavior of Gaia 19ajj as caused by cycles of enhanced disk accretion accompanied by reduced extinction.
On the Opportunistic Topology of Taxi Networks in Urban Mobility Environment

Understanding and characterizing the connectivity of vehicular networks has become increasingly important because of their wide applications and fast development. To address the dynamical links in vehicular networks, time-varying graph (TVG) is one of the most important models. Nowadays, due to the fact that lots of vehicular applications can tolerate a small amount of latency in communication, opportunistic reachability graph (ORG) characterizes the connectivity better by introducing delay tolerance to the model. However, people still do not have a high-level summarization, i.e., the topology, of the vehicular network on how nodes are clustered and isolated. In this paper, based on ORG model, we analyze the opportunistic topology of taxi networks in urban mobility environment by mainly focusing on the number, location and evolution of connected components and the size of the largest components to reveal the unique properties of the taxi networks instead of just links and hops. Our analysis is based on the real taxi traces of big cities and reflects the real urban mobility environment. We find that the opportunistic topology of the networks with delay tolerance is substantially different from the instantaneous topology without considering the delay. Moreover, we unveil the fundamental relationships and trade-offs between the dynamical topology and the key network parameters related to mobility, e.g., delay tolerance, transmission distance, etc. To the best of our knowledge, our study is the first work to reveal the characteristics of opportunistic topology models in the large-scale urban mobility environment with real traces.
Complement Face Forensic Detection and Localization with FacialLandmarks

Recently, Generative Adversarial Networks (GANs) and image manipulating methods are becoming more powerful and can produce highly realistic face images beyond human recognition which have raised significant concerns regarding the authenticity of digital media. Although there have been some prior works that tackle face forensic classification problem, it is not trivial to estimate edited locations from classification predictions. In this paper, we propose, to the best of our knowledge, the first rigorous face forensic localization dataset, which consists of genuine, generated, and manipulated face images. In particular, the pristine parts contain face images from CelebA and FFHQ datasets. The fake images are generated from various GANs methods, namely DCGANs, LSGANs, BEGANs, WGAN-GP, ProGANs, and StyleGANs. Lastly, the edited subset is generated from StarGAN and SEFCGAN based on free-form masks. In total, the dataset contains about 1.3 million facial images labelled with corresponding binary masks. Based on the proposed dataset, we demonstrated that explicit adding facial landmarks information in addition to input images improves the performance. In addition, our proposed method consists of two branches and can coherently predict face forensic detection and localization to outperform the previous state-of-the-art techniques on the newly proposed dataset as well as the faceforecsic++ dataset especially on low-quality videos.
Error analysis of SAV finite element method to phase field crystal model

In this paper, we construct and analyze an energy stable scheme by combining the latest developed scalar auxiliary variable (SAV) approach and linear finite element method (FEM) for phase field crystal (PFC) model, and show rigorously that the scheme is first-order in time and second-order in space for the L 2 and H -1 gradient flow equations. To reduce efficiently computational cost and capture accurately the phase interface, we give a simple adaptive strategy, equipped with a posteriori gradient estimator, i.e. L 2 norm of the recovered gradient. Extensive numerical experiments are presented to verify our theoretical results and to demonstrate the effectiveness and accuracy of our proposed method.
TruNet: Short Videos Generation from Long Videos via Story-Preserving Truncation

In this work, we introduce a new problem, named as {\em story-preserving long video truncation}, that requires an algorithm to automatically truncate a long-duration video into multiple short and attractive sub-videos with each one containing an unbroken story. This differs from traditional video highlight detection or video summarization problems in that each sub-video is required to maintain a coherent and integral story, which is becoming particularly important for resource-production video sharing platforms such as Youtube, Facebook, TikTok, Kwai, etc. To address the problem, we collect and annotate a new large video truncation dataset, named as TruNet, which contains 1470 videos with on average 11 short stories per video. With the new dataset, we further develop and train a neural architecture for video truncation that consists of two components: a Boundary Aware Network (BAN) and a Fast-Forward Long Short-Term Memory (FF-LSTM). We first use the BAN to generate high quality temporal proposals by jointly considering frame-level attractiveness and boundaryness. We then apply the FF-LSTM, which tends to capture high-order dependencies among a sequence of frames, to decide whether a temporal proposal is a coherent and integral story. We show that our proposed framework outperforms existing approaches for the story-preserving long video truncation problem in both quantitative measures and user-study. The dataset is available for public academic research usage at this https URL.
Extracting Incentives from Black-Box Decisions

An algorithmic decision-maker incentivizes people to act in certain ways to receive better decisions. These incentives can dramatically influence subjects' behaviors and lives, and it is important that both decision-makers and decision-recipients have clarity on which actions are incentivized by the chosen model. While for linear functions, the changes a subject is incentivized to make may be clear, we prove that for many non-linear functions (e.g. neural networks, random forests), classical methods for interpreting the behavior of models (e.g. input gradients) provide poor advice to individuals on which actions they should take. In this work, we propose a mathematical framework for understanding algorithmic incentives as the challenge of solving a Markov Decision Process, where the state includes the set of input features, and the reward is a function of the model's output. We can then leverage the many toolkits for solving MDPs (e.g. tree-based planning, reinforcement learning) to identify the optimal actions each individual is incentivized to take to improve their decision under a given model. We demonstrate the utility of our method by estimating the maximally-incentivized actions in two real-world settings: a recidivism risk predictor we train using ProPublica's COMPAS dataset, and an online credit scoring tool published by the Fair Isaac Corporation (FICO).
Modeling Information Cascades with Self-exciting Processes via Generalized Epidemic Models

Epidemic models and self-exciting processes are two types of models used to describe diffusion phenomena online and offline. These models were originally developed in different scientific communities, and their commonalities are under-explored. This work establishes, for the first time, a general connection between the two model classes via three new mathematical components. The first is a generalized version of stochastic Susceptible-Infected-Recovered (SIR) model with arbitrary recovery time distributions; the second is the relationship between the (latent and arbitrary) recovery time distribution, recovery hazard function, and the infection kernel of self-exciting processes; the third includes methods for simulating, fitting, evaluating and predicting the generalized process. On three large Twitter diffusion datasets, we conduct goodness-of-fit tests and holdout log-likelihood evaluation of self-exciting processes with three infection kernels --- exponential, power-law and Tsallis Q-exponential. We show that the modeling performance of the infection kernels varies with respect to the temporal structures of diffusions, and also with respect to user behavior, such as the likelihood of being bots. We further improve the prediction of popularity by combining two models that are identified as complementary by the goodness-of-fit tests.
Forecasts on the speed of gravitational waves at high z

The observation of GW170817 binary neutron star (BNS) merger event has imposed strong bounds on the speed of gravitational waves (GWs) locally, inferring that the speed of GWs propagation is equal to the speed of light. Current GW detectors in operation will not be able to observe BNS merger to long cosmological distance, where possible cosmological corrections on the cosmic expansion history are expected to play an important role, specially for investigating possible deviations from general relativity. Future GW detectors designer projects will be able to detect many coalescences of BNS at high z, such as the third generation of the ground GW detector called Einstein Telescope (ET) and the space-based detector deci-hertz interferometer gravitational wave observatory (DECIGO). In this paper, we relax the condition cT/c = 1 to investigate modified GW propagation where the speed of GWs propagation is not necessarily equal to the speed of light. Also, we consider the possibility for the running of the Planck mass corrections on modified GW propagation. We parametrize both corrections in terms of an effective GW luminosity distance and we perform a forecast analysis using standard siren events from BNS mergers, within the sensitivity predicted for the ET and DECIGO. We find at high z very strong forecast bounds on the running of the Planck mass, namely 𝒪(10−1) and 𝒪(10−2) from ET and DECIGO, respectively. Possible anomalies on GW propagation are bound to |cT/c − 1| ⩽ 10−2 (10−2) from ET (DECIGO), respectively. We finally discuss the consequences of our results on modified gravity phenomenology.
Learning to Navigate from Simulation via Spatial and Semantic Information Synthesis

While training an end-to-end navigation network in the real world is usually of high cost, simulations provide a safe and cheap environment in this training stage. However, training neural network models in simulations brings up the problem of how to effectively transfer the model from simulations to the real world (sim-to-real). In this work, we regard the environment representation as a crucial element in this transfer process and propose a visual information pyramid (VIP) model to systematically investigate a practical environment representation. A novel representation composed of spatial and semantic information synthesis is then established accordingly. To explore the effectiveness of this representation, we compared the performance with representations popularly used in the literature in both simulated and real-world scenarios. Results suggest that our proposed environment representation behaves best. Furthermore, an analysis on the feature map is implemented to investigate the effectiveness through inner reaction, which could be irradiative for future researches on end-to-end navigation.
Interpretable Deep Neural Networks for Dimensional and Categorical Emotion Recognition in-the-wild

Emotions play an important role in people's life. Understanding and recognising is not only important for interpersonal communication, but also has promising applications in Human-Computer Interaction, automobile safety and medical research. This project focuses on extending the emotion recognition database, and training the CNN + RNN emotion recognition neural networks with emotion category representation and valence \& arousal representation. The combined models are constructed by training the two representations simultaneously. The comparison and analysis between the three types of model are discussed. The inner-relationship between two emotion representations and the interpretability of the neural networks are investigated. The findings suggest that categorical emotion recognition performance can benefit from training with a combined model. And the mapping of emotion category and valence \& arousal values can explain this phenomenon.
Actor Critic with Differentially Private Critic

Reinforcement learning algorithms are known to be sample inefficient, and often performance on one task can be substantially improved by leveraging information (e.g., via pre-training) on other related tasks. In this work, we propose a technique to achieve such knowledge transfer in cases where agent trajectories contain sensitive or private information, such as in the healthcare domain. Our approach leverages a differentially private policy evaluation algorithm to initialize an actor-critic model and improve the effectiveness of learning in downstream tasks. We empirically show this technique increases sample efficiency in resource-constrained control problems while preserving the privacy of trajectories collected in an upstream task.
Recent Advances in Imaging Around Corners

Seeing around corners, also known as non-line-of-sight (NLOS) imaging is a computational method to resolve or recover objects hidden around corners. Recent advances in imaging around corners have gained significant interest. This paper reviews different types of existing NLOS imaging techniques and discusses the challenges that need to be addressed, especially for their applications outside of a constrained laboratory environment. Our goal is to introduce this topic to broader research communities as well as provide insights that would lead to further developments in this research area.
Confidence-Calibrated Adversarial Training: Towards Robust Models Generalizing Beyond the Attack Used During Training

Adversarial training is the standard to train models robust against adversarial examples. However, especially for complex datasets, adversarial training incurs a significant loss in accuracy and is known to generalize poorly to stronger attacks, e.g., larger perturbations or other threat models. In this paper, we introduce confidence-calibrated adversarial training (CCAT) where the key idea is to enforce that the confidence on adversarial examples decays with their distance to the attacked examples. We show that CCAT preserves better the accuracy of normal training while robustness against adversarial examples is achieved via confidence thresholding. Most importantly, in strong contrast to adversarial training, the robustness of CCAT generalizes to larger perturbations and other threat models, not encountered during training. We also discuss our extensive work to design strong adaptive attacks against CCAT and standard adversarial training which is of independent interest. We present experimental results on MNIST, SVHN and Cifar10.