2,038 research outputs found
Stochastic compartmental models and CD8+ T cell exhaustion
In this PhD thesis, mathematical models for cell differentiation are presented. Cell differentiation is a widely observed process in cellular biology allowing a small pool of not specialised cells to develop and maintain a bigger population of cells with a specific function. Different mathematical techniques are employed in this thesis, to study cell differentiation process. We propose a time-independent stochastic mathematical model to represent a general differentiation process via a sequence of compartments. Since we are interested in the ultimate fate of the system, we define a discrete-time branching processes and consider the impact, on the final population, of cells passing through only one or multiple compartments. Further, we include time dependency and define a continuous-time Markov chain to analyse cells dynamics along the sequence of compartments over time. Also, we focus on the journey of a single cell over time and compute a number of summary statistics of interest. Moreover, the impact of different types of differentiation events is considered and numerical results inspired by biological applications, mainly related to immunology, are
summarised to illustrate our theoretical approach and methods. In the last Chapter, we focus on a specific cell differentiation process: cells of the immune system have been observed to differentiate towards a dysfunctional state, called exhaustion, during a chronic infection or cancer. One of the aims of this PhD thesis is to shed light into the exhaustion-differentiation process of CD8+
T cells and its reversibility which is a topic of interest for the current and future development of immunotherapies. In particular, based on data collected by the Kaech Lab, several deterministic mathematical models are defined to investigate cells’ trajectory towards the exhausted state as well as the duration of the antigen signal at early time point of stimulation
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
On the Utility of Representation Learning Algorithms for Myoelectric Interfacing
Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden
Synthetic Aperture Radar (SAR) Meets Deep Learning
This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports
A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery
Semantic segmentation (classification) of Earth Observation imagery is a
crucial task in remote sensing. This paper presents a comprehensive review of
technical factors to consider when designing neural networks for this purpose.
The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural
Networks (RNNs), Generative Adversarial Networks (GANs), and transformer
models, discussing prominent design patterns for these ANN families and their
implications for semantic segmentation. Common pre-processing techniques for
ensuring optimal data preparation are also covered. These include methods for
image normalization and chipping, as well as strategies for addressing data
imbalance in training samples, and techniques for overcoming limited data,
including augmentation techniques, transfer learning, and domain adaptation. By
encompassing both the technical aspects of neural network design and the
data-related considerations, this review provides researchers and practitioners
with a comprehensive and up-to-date understanding of the factors involved in
designing effective neural networks for semantic segmentation of Earth
Observation imagery.Comment: 145 pages with 32 figure
Tradition and Innovation in Construction Project Management
This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings
(b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!)
(b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!
Anpassen verteilter eingebetteter Anwendungen im laufenden Betrieb
The availability of third-party apps is among the key success factors for software ecosystems: The users benefit from more features and innovation speed, while third-party solution vendors can leverage the platform to create successful offerings.
However, this requires a certain decoupling of engineering activities of the different parties not achieved for distributed control systems, yet.
While late and dynamic integration of third-party components would be required, resulting control systems must provide high reliability regarding real-time requirements, which leads to integration complexity.
Closing this gap would particularly contribute to the vision of software-defined manufacturing, where an ecosystem of modern IT-based control system components could lead to faster innovations due to their higher abstraction and availability of various frameworks.
Therefore, this thesis addresses the research question:
How we can use modern IT technologies and enable independent evolution and easy third-party integration of software components in distributed control systems, where deterministic end-to-end reactivity is required, and especially, how can we apply distributed changes to such systems consistently and reactively during operation?
This thesis describes the challenges and related approaches in detail and points out that existing approaches do not fully address our research question.
To tackle this gap, a formal specification of a runtime platform concept is presented in conjunction with a model-based engineering approach.
The engineering approach decouples the engineering steps of component definition, integration, and deployment.
The runtime platform supports this approach by isolating the components, while still offering predictable end-to-end real-time behavior.
Independent evolution of software components is supported through a concept for synchronous reconfiguration during full operation, i.e., dynamic orchestration of components.
Time-critical state transfer is supported, too, and can lead to bounded quality degradation, at most.
The reconfiguration planning is supported by analysis concepts, including simulation of a formally specified system and reconfiguration, and analyzing potential quality degradation with the evolving dataflow graph (EDFG) method.
A platform-specific realization of the concepts, the real-time container architecture, is described as a reference implementation.
The model and the prototype are evaluated regarding their feasibility and applicability of the concepts by two case studies.
The first case study is a minimalistic distributed control system used in different setups with different component variants and reconfiguration plans to compare the model and the prototype and to gather runtime statistics.
The second case study is a smart factory showcase system with more challenging application components and interface technologies.
The conclusion is that the concepts are feasible and applicable, even though the concepts and the prototype still need to be worked on in future -- for example, to reach shorter cycle times.Eine große Auswahl von Drittanbieter-Lösungen ist einer der Schlüsselfaktoren für Software Ecosystems:
Nutzer profitieren vom breiten Angebot und schnellen Innovationen, während Drittanbieter über die Plattform erfolgreiche Lösungen anbieten können.
Das jedoch setzt eine gewisse Entkopplung von Entwicklungsschritten der Beteiligten voraus, welche für verteilte Steuerungssysteme noch nicht erreicht wurde.
Während Drittanbieter-Komponenten möglichst spät -- sogar Laufzeit -- integriert werden müssten, müssen Steuerungssysteme jedoch eine hohe Zuverlässigkeit gegenüber Echtzeitanforderungen aufweisen, was zu Integrationskomplexität führt.
Dies zu lösen würde insbesondere zur Vision von Software-definierter Produktion beitragen, da ein Ecosystem für moderne IT-basierte Steuerungskomponenten wegen deren höherem Abstraktionsgrad und der Vielzahl verfügbarer Frameworks zu schnellerer Innovation führen würde.
Daher behandelt diese Dissertation folgende Forschungsfrage:
Wie können wir moderne IT-Technologien verwenden und unabhängige Entwicklung und einfache Integration von Software-Komponenten in verteilten Steuerungssystemen ermöglichen, wo Ende-zu-Ende-Echtzeitverhalten gefordert ist, und wie können wir insbesondere verteilte Änderungen an solchen Systemen konsistent und im Vollbetrieb vornehmen?
Diese Dissertation beschreibt Herausforderungen und verwandte Ansätze im Detail und zeigt auf, dass existierende Ansätze diese Frage nicht vollständig behandeln.
Um diese Lücke zu schließen, beschreiben wir eine formale Spezifikation einer Laufzeit-Plattform und einen zugehörigen Modell-basierten Engineering-Ansatz.
Dieser Ansatz entkoppelt die Design-Schritte der Entwicklung, Integration und des Deployments von Komponenten.
Die Laufzeit-Plattform unterstützt den Ansatz durch Isolation von Komponenten und zugleich Zeit-deterministischem Ende-zu-Ende-Verhalten.
Unabhängige Entwicklung und Integration werden durch Konzepte für synchrone Rekonfiguration im Vollbetrieb unterstützt, also durch dynamische Orchestrierung.
Dies beinhaltet auch Zeit-kritische Zustands-Transfers mit höchstens begrenzter Qualitätsminderung, wenn überhaupt.
Rekonfigurationsplanung wird durch Analysekonzepte unterstützt, einschließlich der Simulation formal spezifizierter Systeme und Rekonfigurationen und der Analyse der etwaigen Qualitätsminderung mit dem Evolving Dataflow Graph (EDFG).
Die Real-Time Container Architecture wird als Referenzimplementierung und Evaluationsplattform beschrieben.
Zwei Fallstudien untersuchen Machbarkeit und Nützlichkeit der Konzepte.
Die erste verwendet verschiedene Varianten und Rekonfigurationen eines minimalistischen verteilten Steuerungssystems, um Modell und Prototyp zu vergleichen sowie Laufzeitstatistiken zu erheben.
Die zweite Fallstudie ist ein Smart-Factory-Demonstrator, welcher herausforderndere Applikationskomponenten und Schnittstellentechnologien verwendet.
Die Konzepte sind den Studien nach machbar und nützlich, auch wenn sowohl die Konzepte als auch der Prototyp noch weitere Arbeit benötigen -- zum Beispiel, um kürzere Zyklen zu erreichen
On the privacy risks of machine learning models
Machine learning (ML) has made huge progress in the last decade and has been applied to a wide range of critical applications. However, driven by the increasing adoption of machine learning models, the significance of privacy risks has become more crucial than ever. These risks can be classified into two categories depending on the role played by ML models: one in which the models themselves are vulnerable to leaking sensitive information, and the other in which the models are abused to violate privacy. In this dissertation, we investigate the privacy risks of machine learning models from two perspectives, i.e., the vulnerability of ML models and the abuse of ML models. To study the vulnerability of ML models to privacy risks, we conduct two studies on one of the most severe privacy attacks against ML models, namely the membership inference attack (MIA). Firstly, we explore membership leakage in label-only exposure of ML models. We present the first label-only membership inference attack and reveal that membership leakage is more severe than previously shown. Secondly, we perform the first privacy analysis of multi-exit networks through the lens of membership leakage. We leverage existing attack methodologies to quantify the vulnerability of multi-exit networks to membership inference attacks and propose a hybrid attack that exploits the exit information to improve the attack performance. From the perspective of abusing ML models to violate privacy, we focus on deepfake face manipulation that can create visual misinformation. We propose the first defense system \system against GAN-based face manipulation by jeopardizing the process of GAN inversion, which is an essential step for subsequent face manipulation. All findings contribute to the community's insight into the privacy risks of machine learning models. We appeal to the community's consideration of the in-depth investigation of privacy risks, like ours, against the rapidly-evolving machine learning techniques.Das maschinelle Lernen (ML) hat in den letzten zehn Jahren enorme Fortschritte gemacht und wurde für eine breite Palette wichtiger Anwendungen eingesetzt. Durch den zunehmenden Einsatz von Modellen des maschinellen Lernens ist die Bedeutung von Datenschutzrisiken jedoch wichtiger denn je geworden. Diese Risiken können je nach der Rolle, die ML-Modelle spielen, in zwei Kategorien eingeteilt werden: in eine, in der die Modelle selbst anfällig für das Durchsickern sensibler Informationen sind, und in die andere, in der die Modelle zur Verletzung der Privatsphäre missbraucht werden. In dieser Dissertation untersuchen wir die Datenschutzrisiken von Modellen des maschinellen Lernens aus zwei Blickwinkeln, nämlich der Anfälligkeit von ML-Modellen und dem Missbrauch von ML-Modellen. Um die Anfälligkeit von ML-Modellen für Datenschutzrisiken zu untersuchen, führen wir zwei Studien zu einem der schwerwiegendsten Angriffe auf den Datenschutz von ML-Modellen durch, nämlich dem Angriff auf die Mitgliedschaft (membership inference attack, MIA). Erstens erforschen wir das Durchsickern von Mitgliedschaften in ML-Modellen, die sich nur auf Labels beziehen. Wir präsentieren den ersten "label-only membership inference"-Angriff und stellen fest, dass das "membership leakage" schwerwiegender ist als bisher gezeigt. Zweitens führen wir die erste Analyse der Privatsphäre von Netzwerken mit mehreren Ausgängen durch die Linse des Mitgliedschaftsverlustes durch. Wir nutzen bestehende Angriffsmethoden, um die Anfälligkeit von Multi-Exit-Netzwerken für Membership-Inference-Angriffe zu quantifizieren und schlagen einen hybriden Angriff vor, der die Exit-Informationen ausnutzt, um die Angriffsleistung zu verbessern. Unter dem Gesichtspunkt des Missbrauchs von ML-Modellen zur Verletzung der Privatsphäre konzentrieren wir uns auf die Manipulation von Gesichtern, die visuelle Fehlinformationen erzeugen können. Wir schlagen das erste Abwehrsystem \system gegen GAN-basierte Gesichtsmanipulationen vor, indem wir den Prozess der GAN-Inversion gefährden, der ein wesentlicher Schritt für die anschließende Gesichtsmanipulation ist. Alle Ergebnisse tragen dazu bei, dass die Community einen Einblick in die Datenschutzrisiken von maschinellen Lernmodellen erhält. Wir appellieren an die Gemeinschaft, eine eingehende Untersuchung der Risiken für die Privatsphäre, wie die unsere, im Hinblick auf die sich schnell entwickelnden Techniken des maschinellen Lernens in Betracht zu ziehen
Novel neural architectures & algorithms for efficient inference
In the last decade, the machine learning universe embraced deep neural networks (DNNs) wholeheartedly with the advent of neural architectures such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), transformers, etc. These models have empowered many applications, such as ChatGPT, Imagen, etc., and have achieved state-of-the-art (SOTA) performance on many vision, speech, and language modeling tasks. However, SOTA performance comes with various issues, such as large model size, compute-intensive training, increased inference latency, higher working memory, etc. This thesis aims at improving the resource efficiency of neural architectures, i.e., significantly reducing the computational, storage, and energy consumption of a DNN without any significant loss in performance.
Towards this goal, we explore novel neural architectures as well as training algorithms that allow low-capacity models to achieve near SOTA performance. We divide this thesis into two dimensions: \textit{Efficient Low Complexity Models}, and \textit{Input Hardness Adaptive Models}.
Along the first dimension, i.e., \textit{Efficient Low Complexity Models}, we improve DNN performance by addressing instabilities in the existing architectures and training methods. We propose novel neural architectures inspired by ordinary differential equations (ODEs) to reinforce input signals and attend to salient feature regions. In addition, we show that carefully designed training schemes improve the performance of existing neural networks. We divide this exploration into two parts:
\textsc{(a) Efficient Low Complexity RNNs.} We improve RNN resource efficiency by addressing poor gradients, noise amplifications, and BPTT training issues. First, we improve RNNs by solving ODEs that eliminate vanishing and exploding gradients during the training. To do so, we present Incremental Recurrent Neural Networks (iRNNs) that keep track of increments in the equilibrium surface. Next, we propose Time Adaptive RNNs that mitigate the noise propagation issue in RNNs by modulating the time constants in the ODE-based transition function. We empirically demonstrate the superiority of ODE-based neural architectures over existing RNNs. Finally, we propose Forward Propagation Through Time (FPTT) algorithm for training RNNs. We show that FPTT yields significant gains compared to the more conventional Backward Propagation Through Time (BPTT) scheme.
\textsc{(b) Efficient Low Complexity CNNs.} Next, we improve CNN architectures by reducing their resource usage. They require greater depth to generate high-level features, resulting in computationally expensive models. We design a novel residual block, the Global layer, that constrains the input and output features by approximately solving partial differential equations (PDEs). It yields better receptive fields than traditional convolutional blocks and thus results in shallower networks. Further, we reduce the model footprint by enforcing a novel inductive bias that formulates the output of a residual block as a spatial interpolation between high-compute anchor pixels and low-compute cheaper pixels. This results in spatially interpolated convolutional blocks (SI-CNNs) that have better compute and performance trade-offs. Finally, we propose an algorithm that enforces various distributional constraints during training in order to achieve better generalization. We refer to this scheme as distributionally constrained learning (DCL).
In the second dimension, i.e., \textit{Input Hardness Adaptive Models}, we introduce the notion of the hardness of any input relative to any architecture. In the first dimension, a neural network allocates the same resources, such as compute, storage, and working memory, for all the inputs. It inherently assumes that all examples are equally hard for a model. In this dimension, we challenge this assumption using input hardness as our reasoning that some inputs are relatively easy for a network to predict compared to others. Input hardness enables us to create selective classifiers wherein a low-capacity network handles simple inputs while abstaining from a prediction on the complex inputs. Next, we create hybrid models that route the hard inputs from the low-capacity abstaining network to a high-capacity expert model. We design various architectures that adhere to this hybrid inference style. Further, input hardness enables us to selectively distill the knowledge of a high-capacity model into a low-capacity model by cleverly discarding hard inputs during the distillation procedure.
Finally, we conclude this thesis by sketching out various interesting future research directions that emerge as an extension of different ideas explored in this work
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