Влияние климата на состояние северной части елово-пихтовой подзоны темнохвойных бореальных лесов острова

В результате исследования определен характер изменения состояния северной части подзоны бореальных елово-пихтовых лесов Сахалина под влиянием современных климатических изменений на основе данных наблюдений на метеостанциях и космических съемо

Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p

Immersive analytics for oncology patient cohorts

This thesis proposes a novel interactive immersive analytics tool and methods to interrogate the cancer patient cohort in an immersive virtual environment, namely Virtual Reality to Observe Oncology data Models (VROOM). The overall objective is to develop an immersive analytics platform, which includes a data analytics pipeline from raw gene expression data to immersive visualisation on virtual and augmented reality platforms utilising a game engine. Unity3D has been used to implement the visualisation. Work in this thesis could provide oncologists and clinicians with an interactive visualisation and visual analytics platform that helps them to drive their analysis in treatment efficacy and achieve the goal of evidence-based personalised medicine. The thesis integrates the latest discovery and development in cancer patients’ prognoses, immersive technologies, machine learning, decision support system and interactive visualisation to form an immersive analytics platform of complex genomic data. For this thesis, the experimental paradigm that will be followed is in understanding transcriptomics in cancer samples. This thesis specifically investigates gene expression data to determine the biological similarity revealed by the patient's tumour samples' transcriptomic profiles revealing the active genes in different patients. In summary, the thesis contributes to i) a novel immersive analytics platform for patient cohort data interrogation in similarity space where the similarity space is based on the patient's biological and genomic similarity; ii) an effective immersive environment optimisation design based on the usability study of exocentric and egocentric visualisation, audio and sound design optimisation; iii) an integration of trusted and familiar 2D biomedical visual analytics methods into the immersive environment; iv) novel use of the game theory as the decision-making system engine to help the analytics process, and application of the optimal transport theory in missing data imputation to ensure the preservation of data distribution; and v) case studies to showcase the real-world application of the visualisation and its effectiveness

Remote Sensing and Texture Image Classification Network Based on Deep Learning Integrated with Binary Coding and Sinkhorn Distance

In the past two decades, traditional hand-crafted feature based methods and deep feature based methods have successively played the most important role in image classification. In some cases, hand-crafted features still provide better performance than deep features. This paper proposes an innovative network based on deep learning integrated with binary coding and Sinkhorn distance (DBSNet) for remote sensing and texture image classification. The statistical texture features of the image extracted by uniform local binary pattern (ULBP) are introduced as a supplement for deep features extracted by ResNet-50 to enhance the discriminability of features. After the feature fusion, both diversity and redundancy of the features have increased, thus we propose the Sinkhorn loss where an entropy regularization term plays a key role in removing redundant information and training the model quickly and efficiently. Image classification experiments are performed on two texture datasets and five remote sensing datasets. The results show that the statistical texture features of the image extracted by ULBP complement the deep features, and the new Sinkhorn loss performs better than the commonly used softmax loss. The performance of the proposed algorithm DBSNet ranks in the top three on the remote sensing datasets compared with other state-of-the-art algorithms

Visual Pretraining on Large-Scale Image Datasets

This thesis focuses on large-scale visual pretraining in computer vision and addresses various limitations of previous approaches. It introduces a novel technique called Relative Contrastive Loss (RCL) to learn feature representations that encompass real-world semantic variations while respecting positive-negative relativeness. The thesis also presents UniVCL, a unified framework for unsupervised visual contrastive learning methods, leveraging a graph convolutional network (GCN) layer for improved object recognition accuracy. Additionally, the thesis explores the transferability gap between unsupervised and supervised pretraining, emphasizing the role of the multilayer perceptron (MLP) projector in enhancing transfer performance. HumanBench, a comprehensive benchmark for human-centric downstream tasks, is proposed, and a pretraining method called PATH is introduced to learn knowledge in human bodies. The findings confirm the effectiveness of the proposed methods in enhancing the practicality and performance of large-scale visual pretraining

Visual Pretraining on Large-Scale Image Datasets

This thesis focuses on large-scale visual pretraining in computer vision and addresses various limitations of previous approaches. It introduces a novel technique called Relative Contrastive Loss (RCL) to learn feature representations that encompass real-world semantic variations while respecting positive-negative relativeness. The thesis also presents UniVCL, a unified framework for unsupervised visual contrastive learning methods, leveraging a graph convolutional network (GCN) layer for improved object recognition accuracy. Additionally, the thesis explores the transferability gap between unsupervised and supervised pretraining, emphasizing the role of the multilayer perceptron (MLP) projector in enhancing transfer performance. HumanBench, a comprehensive benchmark for human-centric downstream tasks, is proposed, and a pretraining method called PATH is introduced to learn knowledge in human bodies. The findings confirm the effectiveness of the proposed methods in enhancing the practicality and performance of large-scale visual pretraining

Principled methods for mixtures processing

This document is my thesis for getting the habilitation à diriger des recherches, which is the french diploma that is required to fully supervise Ph.D. students. It summarizes the research I did in the last 15 years and also provides the short­term research directions and applications I want to investigate. Regarding my past research, I first describe the work I did on probabilistic audio modeling, including the separation of Gaussian and α­stable stochastic processes. Then, I mention my work on deep learning applied to audio, which rapidly turned into a large effort for community service. Finally, I present my contributions in machine learning, with some works on hardware compressed sensing and probabilistic generative models.My research programme involves a theoretical part that revolves around probabilistic machine learning, and an applied part that concerns the processing of time series arising in both audio and life sciences

Feedback Coding for Efficient Interactive Machine Learning

When training machine learning systems, the most basic scenario consists of the learning algorithm operating on a fixed batch of data, provided in its entirety before training. However, there are a large number of applications where there lies a choice in which data points are selected for labeling, and where this choice can be made “on the fly” after each selected data point is labeled. In such interactive machine learning (IML) systems, it is possible to train a model with far fewer labels than would be required with random sampling. In this thesis, we identify and model query structures in IML to develop direct information maximization solutions as well as approximations that allow for computationally efficient query selection. To do so, we frame IML as a feedback communications problem and directly apply principles and tools from coding theory to design and analyze new interaction selection algorithms. First, we directly apply a recently developed feedback coding scheme to sequential human-computer interaction systems. We then identify simplifying query structures to develop approximate methods for efficient, informative query selection in interactive ordinal embedding construction and preference learning systems. Finally, we combine the direct application of feedback coding with approximate information maximization to design and analyze a general active learning algorithm, which we study in detail for logistic regression.Ph.D