Estimating the width of a uniform distribution when data are measured with additive normal errors with known variance

The problem of estimating the width of the symmetric uniform distribution on the line when data are measured with normal additive error is considered. The main purpose is to discuss the efficiency of the maximum likelihood estimator and the moment method estimator. It is shown that the model is regular and that the maximum likelihood estimator is more efficient than the moment method estimator. A sufficient condition is also given for the existence of both estimators.     

Differential graphical games for H∞ control of linear heterogeneous multiagent systems

Differential graphical games have been introduced in the literature to solve state synchronization problem for linear homogeneous agents. When the agents are heterogeneous, the previous notion of graphical games cannot be used anymore and a new definition is required. In this paper, we define a novel concept of differential graphical games for linear heterogeneous agents subject to external unmodeled disturbances, which contain the previously introduced graphical game for homogeneous agents as a special case. Using our new formulation, we can solve both the output regulation and H_∞ output regulation problems. Our graphical game framework yields coupled Hamilton‐Jacobi‐Bellman equations, which are, in general, impossible to solve analytically. Therefore, we propose a new actor‐critic algorithm to solve these coupled equations numerically in real time. Moreover, we find an explicit upper bound for the overall ‐gain of the output synchronization error with respect to disturbance. We demonstrate our developments by a simulation example.     

Formalizing process-based risk with Value-Focused Process Engineering

Following calls to advance the integration of risk and business process modeling paradigms, this paper formalizes the process of incorporating risk into business process models through the principles of Value-Focused Process Engineering (VFPE). In doing so, the paper aims to extend the existing VFPE modeling notation to reflect a set of necessary constructs required to adequately represent risk in goal-oriented business-process models. The extended set of constructs is proposed to support a formal systems view of process-based risk. Process-based risk is formalized on the one hand, as a product of complex interactions between activity-based elements, and on the other hand, as a natural component of the value creation mechanism of an elementary function or a complex process. The proposed risk-aware VFPE formalism also formulates rules for decomposing risk in process models according to the organizational values, thereby enabling better risk visibility, reducing process complexity, and ensuring continuity of business processes.     

Decision-theoretic planning with generalized first-order decision diagrams

Many tasks in AI require representation and manipulation of complex functions. First-Order Decision Diagrams (FODD) are a compact knowledge representation expressing functions over relational structures. They represent numerical functions that, when constrained to the Boolean range, use only existential quantification. Previous work has developed a set of operations for composition and for removing redundancies in FODDs, thus keeping them compact, and showed how to successfully employ FODDs for solving large-scale stochastic planning problems through the formalism of relational Markov decision processes (RMDP). In this paper, we introduce several new ideas enhancing the applicability of FODDs. More specifically, we first introduce Generalized FODDs (GFODD) and composition operations for them, generalizing FODDs to arbitrary quantification. Second, we develop a novel approach for reducing (G)FODDs using model checking. This yields – for the first time – a reduction that maximally reduces the diagram for the FODD case and provides a sound reduction procedure for GFODDs. Finally we show how GFODDs can be used in principle to solve RMDPs with arbitrary quantification, and develop a complete solution for the case where the reward function is specified using an arbitrary number of existential quantifiers followed by an arbitrary number of universal quantifiers.     

Convex Relaxation of a Class of Vertex Penalizing Functionals

We investigate a class of variational problems that incorporate in some sense curvature information of the level lines. The functionals we consider incorporate metrics defined on the orientations of pairs of line segments that meet in the vertices of the level lines. We discuss two particular instances: One instance that minimizes the total number of vertices of the level lines and another instance that minimizes the total sum of the absolute exterior angles between the line segments. In case of smooth level lines, the latter corresponds to the total absolute curvature. We show that these problems can be solved approximately by means of a tractable convex relaxation in higher dimensions. In our numerical experiments we present preliminary results for image segmentation, image denoising and image inpainting.     

Systematic Investigation of Metabolic Oligosaccharide Engineering Efficiency in Intestinal Cells Using a Dibenzocyclooctyne-Monosaccharide Conjugate

Metabolic oligosaccharide engineering (MOE) of cells with synthetic monosaccharides can introduce functionality to the glycans of cell membranes. Unnatural sugars (e. g., peracetylated mannose-azide) can be expressed on the cell surface with the azide group in place. After MOE, the azide group can participate in a copper-free click reaction with an alkyne (e. g., dibenzocyclooctyne, DBCO) probe. This allows the metabolic fate of monosaccharides in cells to be understood. However, in a drug delivery context it is desirable to have azide groups on the probe (e. g. a drug delivery particle) and the alkyne (e. g. DBCO) on the cell surface. Consequently, the labelling efficiency of intestinal cell lines (Caco-2 and HT29-MTX-E12) treated with N-dibenzocyclooctyne-tetra-acetylmannosamine, and the concentration- and time-dependent labelling were determined. Furthermore, the labelling of mucus in HT29-MTX-E12 cells with DBCO was shown. This study highlights the potential for using MOE to target azide-functionalised probes to intestinal tissues for drug delivery applications.     

New Insights to Clathrin and Adaptor Protein 2 for the Design and Development of Therapeutic Strategies

The Amyloid Precursor Protein (APP) has been extensively studied for its role as the precursor of the β-amyloid protein (Aβ) in Alzheimer’s disease (AD). However, our understanding of the normal function of APP is still patchy. Emerging evidence indicates that a dysfunction in APP trafficking and degradation can be responsible for neuronal deficits and progressive degeneration in humans. We recently reported that the Y₆₈₂ mutation in the ₆₈₂YENPTY₆₈₇ domain of APP affects its binding to specific adaptor proteins and leads to its anomalous trafficking, to defects in the autophagy machinery and to neuronal degeneration. In order to identify adaptors that influence APP function, we performed pull-down experiments followed by quantitative mass spectrometry (MS) on hippocampal tissue extracts of three month-old mice incubated with either the ₆₈₂YENPTY₆₈₇ peptide, its mutated form, ₆₈₂GENPTY₆₈₇ or its phosphorylated form, ₆₈₂pYENPTY₆₈₇. Our experiments resulted in the identification of two proteins involved in APP internalization and trafficking: Clathrin heavy chain (hc) and its Adaptor Protein 2 (AP-2). Overall our results consolidate and refine the importance of Y₆₈₂ in APP normal functions from an animal model of premature aging and dementia. Additionally, they open the perspective to consider Clathrin hc and AP-2 as potential targets for the design and development of new therapeutic strategies.