Exploiting damped techniques for nonlinear conjugate gradient methods

In this paper we propose the use of damped techniques within Nonlinear Conjugate Gradient (NCG) methods. Damped techniques were introduced by Powell and recently reproposed by Al-Baali and till now, only applied in the framework of quasi–Newton methods. We extend their use to NCG methods in large scale unconstrained optimization, aiming at possibly improving the efficiency and the robustness of the latter methods, especially when solving difficult problems. We consider both unpreconditioned and Preconditioned NCG (PNCG). In the latter case, we embed damped techniques within a class of preconditioners based on quasi–Newton updates. Our purpose is to possibly provide efficient preconditioners which approximate, in some sense, the inverse of the Hessian matrix, while still preserving information provided by the secant equation or some of its modifications. The results of an extensive numerical experience highlights that the proposed approach is quite promising

Applied (Meta)-Heuristic in Intelligent Systems

Engineering and business problems are becoming increasingly difficult to solve due to the new economics triggered by big data, artificial intelligence, and the internet of things. Exact algorithms and heuristics are insufficient for solving such large and unstructured problems; instead, metaheuristic algorithms have emerged as the prevailing methods. A generic metaheuristic framework guides the course of search trajectories beyond local optimality, thus overcoming the limitations of traditional computation methods. The application of modern metaheuristics ranges from unmanned aerial and ground surface vehicles, unmanned factories, resource-constrained production, and humanoids to green logistics, renewable energy, circular economy, agricultural technology, environmental protection, finance technology, and the entertainment industry. This Special Issue presents high-quality papers proposing modern metaheuristics in intelligent systems

Unified Algorithms for RL with Decision-Estimation Coefficients: No-Regret, PAC, and Reward-Free Learning

Finding unified complexity measures and algorithms for sample-efficient learning is a central topic of research in reinforcement learning (RL). The Decision-Estimation Coefficient (DEC) is recently proposed by Foster et al. (2021) as a necessary and sufficient complexity measure for sample-efficient no-regret RL. This paper makes progress towards a unified theory for RL with the DEC framework. First, we propose two new DEC-type complexity measures: Explorative DEC (EDEC), and Reward-Free DEC (RFDEC). We show that they are necessary and sufficient for sample-efficient PAC learning and reward-free learning, thereby extending the original DEC which only captures no-regret learning. Next, we design new unified sample-efficient algorithms for all three learning goals. Our algorithms instantiate variants of the Estimation-To-Decisions (E2D) meta-algorithm with a strong and general model estimation subroutine. Even in the no-regret setting, our algorithm E2D-TA improves upon the algorithms of Foster et al. (2021) which require either bounding a variant of the DEC which may be prohibitively large, or designing problem-specific estimation subroutines. As applications, we recover existing and obtain new sample-efficient learning results for a wide range of tractable RL problems using essentially a single algorithm. We also generalize the DEC to give sample-efficient algorithms for all-policy model estimation, with applications for learning equilibria in Markov Games. Finally, as a connection, we re-analyze two existing optimistic model-based algorithms based on Posterior Sampling or Maximum Likelihood Estimation, showing that they enjoy similar regret bounds as E2D-TA under similar structural conditions as the DEC

Progenitor cells in auricular cartilage demonstrate promising cartilage regenerative potential in 3D hydrogel culture

The reconstruction of auricular deformities is a very challenging surgical procedure that could benefit from a tissue engineering approach. Nevertheless, a major obstacle is presented by the acquisition of sufficient amounts of autologous cells to create a cartilage construct the size of the human ear. Extensively expanded chondrocytes are unable to retain their phenotype, while bone marrow-derived mesenchymal stromal cells (MSC) show endochondral terminal differentiation by formation of a calcified matrix. The identification of tissue-specific progenitor cells in auricular cartilage, which can be expanded to high numbers without loss of cartilage phenotype, has great prospects for cartilage regeneration of larger constructs. This study investigates the largely unexplored potential of auricular progenitor cells for cartilage tissue engineering in 3D hydrogels

Reliability Mechanisms for Controllers in Real-Time Cyber-Physical Systems

Cyber-physical systems (CPSs) are real-world processes that are controlled by computer algorithms. We consider CPSs where a centralized, software-based controller maintains the process in a desired state by exchanging measurements and setpoints with process agents (PAs). As CPSs control processes with low-inertia, e.g., electric grids and autonomous cars, the controller needs to satisfy stringent real-time constraints. However, the controllers are susceptible to delay and crash faults, and the communication network might drop, delay or reorder messages. This degrades the quality of control of the physical process, failure of which can result in damage to life or property. Existing reliability solutions are either not well-suited for real-time CPSs or impose serious restrictions on the controllers. In this thesis, we design, implement and evaluate reliability mechanisms for real-time CPS controllers that require minimal modifications to the controller itself. We begin by abstracting the execution of a CPS using events in the CPS, and the two inherent relations among those events, namely network and computation relations. We use these relations to introduce the intentionality relation that uses these events to capture the state of the physical process. Based on the intentionality relation, we define three correctness properties namely, state safety, optimal selection and consistency, that together provide linearizability (one-copy equivalence) for CPS controllers. We propose intentionality clocks and Quarts, and prove that they provide linearizability. To provide consistency, Quarts ensures agreement among controller replicas, which is typically achieved using consensus. Consensus can add an unbounded-latency overhead. Quarts leverages the properties specific to CPSs to perform agreement using pre-computed priorities among sets of received measurements, resulting in a bounded-latency overhead with high availability. Using simulation, we show that availability of Quarts, with two replicas, is more than an order of magnitude higher than consensus. We also propose Axo, a fault-tolerance protocol that uses active replication to detect and recover faulty replicas, and provide timeliness that requires delayed setpoints be masked from the PAs. We study the effect of delay faults and the impact of fault-tolerance with Axo, by deploying Axo in two real-world CPSs. Then, we realize that the proposed reliability mechanisms also apply to unconventional CPSs such as software defined networking (SDN), where the controlled process is the routing fabric of the network. We show that, in SDN, violating consistency can cause implementation of incorrect routing policies. Thus, we use Quarts and intentionality clocks, to design and implement QCL, a coordination layer for SDN controllers that guarantees control-plane consistency. QCL also drastically reduces the response time of SDN controllers when compared to consensus-based techniques. In the last part of the thesis, we address the problem of reliable communication between the software agents, in a wide-area network that can drop, delay or reorder messages. For this, we propose iPRP, an IP-friendly parallel redundancy protocol for 0 ms repair of packet losses. iPRP requires fail-independent paths for high-reliability. So, we study the fail-independence of Wi-Fi links using real-life measurements, as a first step towards using Wi-Fi for real-time communication in CPSs

Theoretical Foundations of Adversarially Robust Learning

Despite extraordinary progress, current machine learning systems have been shown to be brittle against adversarial examples: seemingly innocuous but carefully crafted perturbations of test examples that cause machine learning predictors to misclassify. Can we learn predictors robust to adversarial examples? and how? There has been much empirical interest in this contemporary challenge in machine learning, and in this thesis, we address it from a theoretical perspective. In this thesis, we explore what robustness properties can we hope to guarantee against adversarial examples and develop an understanding of how to algorithmically guarantee them. We illustrate the need to go beyond traditional approaches and principles such as empirical risk minimization and uniform convergence, and make contributions that can be categorized as follows: (1) introducing problem formulations capturing aspects of emerging practical challenges in robust learning, (2) designing new learning algorithms with provable robustness guarantees, and (3) characterizing the complexity of robust learning and fundamental limitations on the performance of any algorithm.Comment: PhD Thesi

Advances in discriminative dependency parsing

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 167-176).Achieving a greater understanding of natural language syntax and parsing is a critical step in producing useful natural language processing systems. In this thesis, we focus on the formalism of dependency grammar as it allows one to model important head modifier relationships with a minimum of extraneous structure. Recent research in dependency parsing has highlighted the discriminative structured prediction framework (McDonald et al., 2005a; Carreras, 2007; Suzuki et al., 2009), which is characterized by two advantages: first, the availability of powerful discriminative learning algorithms like log-linear and max-margin models (Lafferty et al., 2001; Taskar et al., 2003), and second, the ability to use arbitrarily-defined feature representations. This thesis explores three advances in the field of discriminative dependency parsing. First, we show that the classic Matrix-Tree Theorem (Kirchhoff, 1847; Tutte, 1984) can be applied to the problem of non-projective dependency parsing, enabling both log-linear and max-margin parameter estimation in this setting. Second, we present novel third-order dependency parsing algorithms that extend the amount of context available to discriminative parsers while retaining computational complexity equivalent to existing second-order parsers. Finally, we describe a simple but effective method for augmenting the features of a dependency parser with information derived from standard clustering algorithms; our semi-supervised approach is able to deliver consistent benefits regardless of the amount of available training data.by Terry Koo.Ph.D

Efficiency and Robustness in Individualized Decision Making

Recent development in data-driven decision science has seen great advances in individualized decision making. Given data with covariates, treatment assignments and outcomes, one common goal is to find individualized decision rules that map the individual characteristics or contextual information to the treatment assignment, such that the overall expected outcome can be optimized. In this dissertation, we propose several new approaches to learn efficient and robust individualized decision rules. In the first project, we consider the robust learning problem when training and testing distributions can be different. A novel framework of the Distributionally Robust Individualized Treatment Rule (DR-ITR) is proposed to maximize the worst-case value function under distributional changes. The testing performance among a set of distributions close to training can be guaranteed reasonably well. For the second project, we consider the problem of treatment-free effect misspecification and heteroscedasticity. We propose an Efficient Learning (E-Learning) framework for finding an optimal ITR with improved efficiency in the multiple treatment setting. The proposed E-Learning is optimal among a regular class of semiparametric estimates that can allow treatment-free effect misspecification and heteroscedasticity. We demonstrate its effectiveness when one of or both misspecified treatment-free effect and heteroscedasticity exist. For the third project, we study the multi-stage multi-treatment decision problem. A new Backward Change Point Structural Nested Mean Model (BCP-SNMM) is developed to allow an unknown backward change point of the SNMM. We further propose the Dynamic Efficient Learning (DE-Learning) framework that is optimal under the BCP-SNMM and enjoys more robustness. Compared with the existing G-Estimation, DE-Learning is a tractable procedure for rigorous semiparametric efficient estimation, with much fewer nuisance functions to estimate and can be implemented in a backward stagewise manner.Doctor of Philosoph

Molecular modelling of platelet endothelial cell adhesion molecule 1 and its interaction with glycosaminoglycans

The Platelet Endothelial Cell Adhesion Molecule 1 (PECAM-1) has many functions including its roles in leukocyte extravasation as part of the inflammatory response, and in the maintenance of vascular integrity through its contribution to endothelial cell-cell adhesion. Various heterophilic ligands of PECAM-1 have been proposed. The possible interaction of PECAM-1 with glycosaminoglycans (GAGs) is the focus of this thesis. The three dimensional structure of the extracellular immunoglobulin (Ig)-domains of PECAM-1 was constructed using homology modelling and threading methods. Potential heparin/heparan sulfate binding sites were predicted on the basis of their amino acid consensus sequences and a comparison with known structures of sulfate binding proteins. Heparin and other GAG fragments have been docked to investigate the structural determinants of their protein binding specificity and selectivity. It is predicted that two regions in PECAM-1 appear to bind heparin oligosaccharides. A high affinity binding region was located in Ig-domains 2 and 3 and a low affinity region was located in Ig-domains 5 and 6.These GAG binding regions are distinct from regions involved in PECAM-1 homophilic interactions. Docking of heparin fragments of different size revealed that fragments as small as a pentasaccharide appear to be able to bind to domains 2 and 3 with high affinity. Binding of longer heparin fragments suggests that key interactions can occur between six sulfates in a hexasaccharide with no further increase in binding affinity for longer fragments. Molecular dynamics simulations were also used to characterise and quantify the interactions of heparin fragments with PECAM-1. These simulations confirmed the existence of regions of high and low affinity for GAG binding and revealed that both electrostatic and van der Waals interactions determine the specificity and binding affinity of GAG fragments to PECAM-1. The simulations also suggested the existence of ‘open’ and ‘closed’ conformations of PECAM-1 around domains 2 and 3