Bayesian hierarchical reconstruction of protein profiles including a digestion model

Introduction : Mass spectrometry approaches are very attractive to detect protein panels in a sensitive and high speed way. MS can be coupled to many proteomic separation techniques. However, controlling technological variability on these analytical chains is a critical point. Adequate information processing is mandatory for data analysis to take into account the complexity of the analysed mixture, to improve the measurement reliability and to make the technology user friendly. Therefore we develop a hierarchical parametric probabilistic model of the LC-MS analytical chain including the technological variability. We introduce a Bayesian reconstruction methodology to recover the protein biomarkers content in a robust way. We will focus on the digestion step since it brings a major contribution to technological variability. Method : In this communication, we introduce a hierarchical model of the LC-MS analytical chain. Such a chain is a cascade of molecular events depicted by a graph structure, each node being associated to a molecular state such as protein, peptide and ion and each branch to a molecular processing such as digestion, ionisation and LC-MS separation. This molecular graph defines a hierarchical mixture model. We extend the Bayesian statistical framework we have introduced previously [1] to this hierarchical description. As an example, we will consider the digestion step. We describe the digestion process on a pair of peptides within the targeted protein as a Bernoulli random process associated with a cleavage probability controlled by the digestion kinetic law.Comment: pr\'esentation orale; 59th American Society for Mass Spectrometry Conference, Dallas : France (2011

Multi-modal knowledge graph inference via media convergence and logic rule

Media convergence works by processing information from different modalities and applying them to different domains. It is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective. To address the issue, an inference method based on Media Convergence and Rule-guided Joint Inference model (MCRJI) has been proposed. The authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction. First, a multi-headed self-attention approach is used to obtain the attention of different media features of entities during semantic synthesis. Second, logic rules of different lengths are mined from knowledge graph to learn new entity representations. Finally, knowledge graph inference is performed based on representing entities that converge multi-media features. Numerous experimental results show that MCRJI outperforms other advanced baselines in using multi-media features and knowledge graph inference, demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi-media features

On the Analysis of Non-euclidean data: Sparsification, Classification and Generation

In light of the groundbreaking achievements of convolutional neural networks (CNNs) in 2D image processing, there has been a pronounced interest in adapting these methods to non-Euclidean data, such as graphs and 3D geometric data. Point clouds, in particular, present unique challenges as they are sparse, unordered, and locality-sensitive, making the adaptation of CNNs to point cloud processing a non-trivial task. Similar challenges are encountered in the context of graph data. Consequently, the exploration of extending successful neural processing paradigms from 2D images to these non-Euclidean domains has emerged as a vibrant and dynamic research area. This thesis focuses on advancing graph neural networks (GNNs) and analyzing 3D point clouds, emphasizing sparsification, classification and generation. For graph neural networks, a significant contribution is the introduction of Sparse Graph Attention Networks (SGAT), integrating a sparse attention mechanism into graph attention networks (GATs) through $L_0$-norm regularization. SGAT excels in edge removal (50\%-80\% on large graphs), enhancing interpretability without compromising performance on assortative graphs and improving it on disassortative graphs. In 3D point cloud analysis, an autoregressive approach, APSNet, formulates task-oriented point cloud sampling as a sequential generation process, and develops an attention-based point cloud sampling network that optimally samples 8 points out of 1024, tailoring the process for tasks like 3D point cloud classification, reconstruction, and registration. Extending into a non-autoregressive method, PTSNet, a point transformer, utilizes a transformer-based dynamic query generator. This innovation enables PTSNet to capture long-range correlations, mitigating issues like gradient vanishing and reducing duplicate samples compared to LSTM-based methods. Lastly, the thesis proposes GDPNet, first hybrid Generative and Discriminative PointNet, extending the Joint Energy-based Model (JEM) for point cloud generation and classification. GDPNet retains strong discriminative power of modern PointNet classifiers, while generating point cloud samples rivaling state-of-the-art generative approaches

Knowledge-based support in Non-Destructive Testing for health monitoring of aircraft structures

Maintenance manuals include general methods and procedures for industrial maintenance and they contain information about principles of maintenance methods. Particularly, Non-Destructive Testing (NDT) methods are important for the detection of aeronautical defects and they can be used for various kinds of material and in different environments. Conventional non-destructive evaluation inspections are done at periodic maintenance checks. Usually, the list of tools used in a maintenance program is simply located in the introduction of manuals, without any precision as regards to their characteristics, except for a short description of the manufacturer and tasks in which they are employed. Improving the identification concepts of the maintenance tools is needed to manage the set of equipments and establish a system of equivalence: it is necessary to have a consistent maintenance conceptualization, flexible enough to fit all current equipment, but also all those likely to be added/used in the future. Our contribution is related to the formal specification of the system of functional equivalences that can facilitate the maintenance activities with means to determine whether a tool can be substituted for another by observing their key parameters in the identified characteristics. Reasoning mechanisms of conceptual graphs constitute the baseline elements to measure the fit or unfit between an equipment model and a maintenance activity model. Graph operations are used for processing answers to a query and this graph-based approach to the search method is in-line with the logical view of information retrieval. The methodology described supports knowledge formalization and capitalization of experienced NDT practitioners. As a result, it enables the selection of a NDT technique and outlines its capabilities with acceptable alternatives

An Attribution Method for Siamese Encoders

Despite the success of Siamese encoder models such as sentence transformers (ST), little is known about the aspects of inputs they pay attention to. A barrier is that their predictions cannot be attributed to individual features, as they compare two inputs rather than processing a single one. This paper derives a local attribution method for Siamese encoders by generalizing the principle of integrated gradients to models with multiple inputs. The solution takes the form of feature-pair attributions, and can be reduced to a token-token matrix for STs. Our method involves the introduction of integrated Jacobians and inherits the advantageous formal properties of integrated gradients: it accounts for the model's full computation graph and is guaranteed to converge to the actual prediction. A pilot study shows that in an ST few token-pairs can often explain large fractions of predictions, and it focuses on nouns and verbs. For accurate predictions, it however needs to attend to the majority of tokens and parts of speech.Comment: Accepted to EMNLP'2

Design and Optimization of Graph Transform for Image and Video Compression

The main contribution of this thesis is the introduction of new methods for designing adaptive transforms for image and video compression. Exploiting graph signal processing techniques, we develop new graph construction methods targeted for image and video compression applications. In this way, we obtain a graph that is, at the same time, a good representation of the image and easy to transmit to the decoder. To do so, we investigate different research directions. First, we propose a new method for graph construction that employs innovative edge metrics, quantization and edge prediction techniques. Then, we propose to use a graph learning approach and we introduce a new graph learning algorithm targeted for image compression that defines the connectivities between pixels by taking into consideration the coding of the image signal and the graph topology in rate-distortion term. Moreover, we also present a new superpixel-driven graph transform that uses clusters of superpixel as coding blocks and then computes the graph transform inside each region. In the second part of this work, we exploit graphs to design directional transforms. In fact, an efficient representation of the image directional information is extremely important in order to obtain high performance image and video coding. In this thesis, we present a new directional transform, called Steerable Discrete Cosine Transform (SDCT). This new transform can be obtained by steering the 2D-DCT basis in any chosen direction. Moreover, we can also use more complex steering patterns than a single pure rotation. In order to show the advantages of the SDCT, we present a few image and video compression methods based on this new directional transform. The obtained results show that the SDCT can be efficiently applied to image and video compression and it outperforms the classical DCT and other directional transforms. Along the same lines, we present also a new generalization of the DFT, called Steerable DFT (SDFT). Differently from the SDCT, the SDFT can be defined in one or two dimensions. The 1D-SDFT represents a rotation in the complex plane, instead the 2D-SDFT performs a rotation in the 2D Euclidean space

의미론적 환경 이해 기반 인간 로봇 협업

학위논문(박사)--서울대학교 대학원 :공과대학 전기·정보공학부,2020. 2. 이범희.Human-robot cooperation is unavoidable in various applications ranging from manufacturing to field robotics owing to the advantages of adaptability and high flexibility. Especially, complex task planning in large, unconstructed, and uncertain environments can employ the complementary capabilities of human and diverse robots. For a team to be effectives, knowledge regarding team goals and current situation needs to be effectively shared as they affect decision making. In this respect, semantic scene understanding in natural language is one of the most fundamental components for information sharing between humans and heterogeneous robots, as robots can perceive the surrounding environment in a form that both humans and other robots can understand. Moreover, natural-language-based scene understanding can reduce network congestion and improve the reliability of acquired data. Especially, in field robotics, transmission of raw sensor data increases network bandwidth and decreases quality of service. We can resolve this problem by transmitting information in the form of natural language that has encoded semantic representations of environments. In this dissertation, I introduce a human and heterogeneous robot cooperation scheme based on semantic scene understanding. I generate sentences and scene graphs, which is a natural language grounded graph over the detected objects and their relationships, with the graph map generated using a robot mapping algorithm. Subsequently, a framework that can utilize the results for cooperative mission planning of humans and robots is proposed. Experiments were performed to verify the effectiveness of the proposed methods. This dissertation comprises two parts: graph-based scene understanding and scene understanding based on the cooperation between human and heterogeneous robots. For the former, I introduce a novel natural language processing method using a semantic graph map. Although semantic graph maps have been widely applied to study the perceptual aspects of the environment, such maps do not find extensive application in natural language processing tasks. Several studies have been conducted on the understanding of workspace images in the field of computer vision; in these studies, the sentences were automatically generated, and therefore, multiple scenes have not yet been utilized for sentence generation. A graph-based convolutional neural network, which comprises spectral graph convolution and graph coarsening, and a recurrent neural network are employed to generate sentences attention over graphs. The proposed method outperforms the conventional methods on a publicly available dataset for single scenes and can be utilized for sequential scenes. Recently, deep learning has demonstrated impressive developments in scene understanding using natural language. However, it has not been extensively applied to high-level processes such as causal reasoning, analogical reasoning, or planning. The symbolic approach that calculates the sequence of appropriate actions by combining the available skills of agents outperforms in reasoning and planning; however, it does not entirely consider semantic knowledge acquisition for human-robot information sharing. An architecture that combines deep learning techniques and symbolic planner for human and heterogeneous robots to achieve a shared goal based on semantic scene understanding is proposed for scene understanding based on human-robot cooperation. In this study, graph-based perception is used for scene understanding. A planning domain definition language (PDDL) planner and JENA-TDB are utilized for mission planning and data acquisition storage, respectively. The effectiveness of the proposed method is verified in two situations: a mission failure, in which the dynamic environment changes, and object detection in a large and unseen environment.인간과 이종 로봇 간의 협업은 높은 유연성과 적응력을 보일 수 있다는 점에서 제조업에서 필드 로보틱스까지 다양한 분야에서 필연적이다. 특히, 서로 다른 능력을 지닌 로봇들과 인간으로 구성된 하나의 팀은 넓고 정형화되지 않은 공간에서 서로의 능력을 보완하며 복잡한 임무 수행을 가능하게 한다는 점에서 큰 장점을 갖는다. 효율적인 한 팀이 되기 위해서는, 팀의 공통된 목표 및 각 팀원의 현재 상황에 관한 정보를 실시간으로 공유할 수 있어야 하며 함께 의사 결정을 할 수 있어야 한다. 이러한 관점에서, 자연어를 통한 의미론적 환경 이해는 인간과 서로 다른 로봇들이 모두 이해할 수 있는 형태로 환경을 인지한다는 점에서 가장 필수적인 요소이다. 또한, 우리는 자연어 기반 환경 이해를 통해 네트워크 혼잡을 피함으로써 획득한 정보의 신뢰성을 높일 수 있다. 특히, 대량의 센서 데이터 전송에 의해 네트워크 대역폭이 증가하고 통신 QoS (Quality of Service) 신뢰도가 감소하는 문제가 빈번히 발생하는 필드 로보틱스 영역에서는 의미론적 환경 정보인 자연어를 전송함으로써 통신 대역폭을 감소시키고 통신 QoS 신뢰도를 증가시킬 수 있다. 본 학위 논문에서는 환경의 의미론적 이해 기반 인간 로봇 협동 방법에 대해 소개한다. 먼저, 로봇의 지도 작성 알고리즘을 통해 획득한 그래프 지도를 이용하여 자연어 문장과 검출한 객체 및 각 객체 간의 관계를 자연어 단어로 표현하는 그래프를 생성한다. 그리고 자연어 처리 결과를 이용하여 인간과 다양한 로봇들이 함께 협업하여 임무를 수행할 수 있도록 하는 프레임워크를 제안한다. 본 학위 논문은 크게 그래프를 이용한 의미론적 환경 이해와 의미론적 환경 이해를 통한 인간과 이종 로봇 간의 협업 방법으로 구성된다. 먼저, 그래프를 이용한 의미론적 환경 이해 부분에서는 의미론적 그래프 지도를 이용한 새로운 자연어 처리 방법에 대해 소개한다. 의미론적 그래프 지도 작성 방법은 로봇의 환경 인지 측면에서 많이 연구되었지만 이를 이용한 자연어 처리 방법은 거의 연구되지 않았다. 반면 컴퓨터 비전 분야에서는 이미지를 이용한 환경 이해 연구가 많이 이루어졌지만, 연속적인 장면들은 다루는데는 한계점이 있다. 따라서 우리는 그래프 스펙트럼 이론에 기반한 그래프 컨볼루션과 그래프 축소 레이어로 구성된 그래프 컨볼루션 신경망 및 순환 신경망을 이용하여 그래프를 설명하는 문장을 생성한다. 제안한 방법은 기존의 방법들보다 한 장면에 대해 향상된 성능을 보였으며 연속된 장면들에 대해서도 성공적으로 자연어 문장을 생성한다. 최근 딥러닝은 자연어 기반 환경 인지에 있어 급속도로 큰 발전을 이루었다. 하지만 인과 추론, 유추적 추론, 임무 계획과 같은 높은 수준의 프로세스에는 적용이 힘들다. 반면 임무를 수행하는 데 있어 각 에이전트의 능력에 맞게 행위들의 순서를 계산해주는 상징적 접근법(symbolic approach)은 추론과 임무 계획에 있어 뛰어난 성능을 보이지만 인간과 로봇들 사이의 의미론적 정보 공유 방법에 대해서는 거의 다루지 않는다. 따라서, 인간과 이종 로봇 간의 협업 방법 부분에서는 딥러닝 기법들과 상징적 플래너(symbolic planner)를 연결하는 프레임워크를 제안하여 의미론적 이해를 통한 인간 및 이종 로봇 간의 협업을 가능하게 한다. 우리는 의미론적 주변 환경 이해를 위해 이전 부분에서 제안한 그래프 기반 자연어 문장 생성을 수행한다. PDDL 플래너와 JENA-TDB는 각각 임무 계획 및 정보 획득 저장소로 사용한다. 제안한 방법의 효용성은 시뮬레이션을 통해 두 가지 상황에 대해서 검증한다. 하나는 동적 환경에서 임무 실패 상황이며 다른 하나는 넓은 공간에서 객체를 찾는 상황이다.1 Introduction 1 1.1 Background and Motivation 1 1.2 Literature Review 5 1.2.1 Natural Language-Based Human-Robot Cooperation 5 1.2.2 Artificial Intelligence Planning 5 1.3 The Problem Statement 10 1.4 Contributions 11 1.5 Dissertation Outline 12 2 Natural Language-Based Scene Graph Generation 14 2.1 Introduction 14 2.2 Related Work 16 2.3 Scene Graph Generation 18 2.3.1 Graph Construction 19 2.3.2 Graph Inference 19 2.4 Experiments 22 2.5 Summary 25 3 Language Description with 3D Semantic Graph 26 3.1 Introduction 26 3.2 Related Work 26 3.3 Natural Language Description 29 3.3.1 Preprocess 29 3.3.2 Graph Feature Extraction 33 3.3.3 Natural Language Description with Graph Features 34 3.4 Experiments 35 3.5 Summary 42 4 Natural Question with Semantic Graph 43 4.1 Introduction 43 4.2 Related Work 45 4.3 Natural Question Generation 47 4.3.1 Preprocess 49 4.3.2 Graph Feature Extraction 50 4.3.3 Natural Question with Graph Features 51 4.4 Experiments 52 4.5 Summary 58 5 PDDL Planning with Natural Language 59 5.1 Introduction 59 5.2 Related Work 60 5.3 PDDL Planning with Incomplete World Knowledge 61 5.3.1 Natural Language Process for PDDL Planning 63 5.3.2 PDDL Planning System 64 5.4 Experiments 65 5.5 Summary 69 6 PDDL Planning with Natural Language-Based Scene Understanding 70 6.1 Introduction 70 6.2 Related Work 74 6.3 A Framework for Heterogeneous Multi-Agent Cooperation 77 6.3.1 Natural Language-Based Cognition 78 6.3.2 Knowledge Engine 80 6.3.3 PDDL Planning Agent 81 6.4 Experiments 82 6.4.1 Experiment Setting 82 6.4.2 Scenario 84 6.4.3 Results 87 6.5 Summary 91 7 Conclusion 92Docto

Processing of Space Surveillance Observations

This thesis covers the processing of different types of space surveillance measurements. The emphasis is on the initial build-up of a catalogue. Most experiments focus on the identification of objects from pairs of observations by testing whether these two observations could originate from the same object. The advantage of this approach is that an orbit from two passes is usually more precise than that from a single pass and thus the chance of associating subsequent measurements with this newly derived orbit is higher. The main contribution is the introduction of a method for perturbed initial orbit determination and observation correlation from two radar tracklets. The perturbed initial orbit determination provides the solutions for all possible numbers of revolutions from two positions under consideration of the secular J2-perturbations, which causes for example the rotation of the orbital plane. The perturbations are considered analytically in an iterative process. From the different numbers of revolutions, the most probable solution can either be selected via the minimum Mahalanobis distance, using the range-rate as a remaining observable, or after an additional post-processing. The robustness and applicability of the method is shown using real radar measurements, which includes a large percentage of successful initial orbit determinations for tracklets which are more than 20 days apart. This method is also extended to the combination of optical and radar measurements for objects in Highly Elliptical Orbits. Concerning the simulation of a cold-start of a catalogue, two different processing strategies are introduced. One is adding a least squares orbit determination using a pair of possibly correlated tracklets, while the other approach collects data over a longer time span and generates a graph network from which clusters of tracklets are derived as new candidate objects. Further analysis focuses on aspects of operational processing of space surveillance measurements. This includes the automated decision making process for the monitoring of the quality of existing catalogue objects and the design of an autonomous processing pipeline for optical measurements at the Zimmerwald observatory. A potential space mission to observe the Geostationary Orbit is also analysed with regard to its performance during the catalogue build-up

Accelerated filtering on graphs using Lanczos method

Signal-processing on graphs has developed into a very active field of research during the last decade. In particular, the number of applications using frames constructed from graphs, like wavelets on graphs, has substantially increased. To attain scalability for large graphs, fast graph-signal filtering techniques are needed. In this contribution, we propose an accelerated algorithm based on the Lanczos method that adapts to the Laplacian spectrum without explicitly computing it. The result is an accurate, robust, scalable and efficient algorithm. Compared to existing methods based on Chebyshev polynomials, our solution achieves higher accuracy without increasing the overall complexity significantly. Furthermore, it is particularly well suited for graphs with large spectral gaps