Molecular cocrystals of 5-(4-bromophenyl)-1,3,4-thiadiazol-2-amine: hydrogen bonding in the structures of the 1:1 adduct with 2-(naphthalen-2-yloxy)acetic acid and the salt with 3,5-dinitrobenzoic acid

The structures of the anhydrous products from the interaction of 2-amino-5-(4-bromophenyl)-1,3,4-thiadiazole with (2-naphthoxy)acetic acid, the 1:1 adduct C8H6BrN3S . C12H10O3 (I) and 3,5-dinitrobenzoic acid, the salt C8H7BrN3S+ C7H3N2O6- (II) have been determined. In the adduct (I), a heterodimer is formed through a cyclic hydrogen-bonding motif [graph set R2/2(8)], involving carboxylic acid O-H...N(hetero)and amine N-H...O(carboxyl) interactions. The heterodimers are essentially planar with a thiadiazole to naphthyl ring dihedral angle of 15.9(2)deg. and the intramolecular thiadiazole to phenyl ring angle of 4.7(2)deg. An amine N-H...N(hetero) hydrogen bond between the heterodimers generates a one-dimensional chain structure extending down [001]. Also present are weak benzene-benzene and naphthalene-naphthalene pi-pi stacking interactions down the b axis [minimum ring centroid separation, 3.936(3) Ang.]. With the salt (II), the cation-anion association is also through a cyclic R2/2(8) motif but involving duplex N-H...O(carboxyl) hydrogen bonds, giving a heterodimer which is close to planar [dihedral angles between the thiadiazole ring and the two benzene rings, 5.00(16)deg. (intra) and 7.23(15)deg. (inter)]. A secondary centrosymmetric cyclic N-H...O(carboxyl) hydrogen-bonding association involving the second amino H-atom generates a heterotetramer. Also present in the crystal are weak pi-pi i-\p interactions between thiadiazolium rings [minimum ring centroid separation, 3.936(3)Ang.], as well as a short Br...O(nitro) interaction [3.314(4)Ang.]. The two structures reported here now provide a total of three crystallographically characterized examples of co-crystalline products from the interaction of 2-amino-5-(4-bromophenyl)-1,3,4-thiadiazole with carboxylic acids, of which only one involves proton-transfer.

An efficient algorithm for the detection of Eden

In this paper, a polynomial time algorithm is presented for solving the Eden problem for graph cellular automata. The algorithm is based on our neighborhood elimination operation which removes local neighborhood configurations which cannot be used in a pre-image of a given configuration. This paper presents a detailed derivation of our algorithm from first principles, and a detailed complexity and accuracy analysis is also given. In the case of time complexity, it is shown that the average case time complexity of the algorithm is \Theta(n^2), and the best and worst cases are \Omega(n) and O(n^3) respectively. This represents a vast improvement in the upper bound over current methods, without compromising average case performance.

A dynamic approach for evaluating earthwork hauls in construction

A graph theoretic approach is developed for accurately computing haulage costs in earthwork projects. This is vital as haulage is a predominant factor in the real cost of earthworks. A variety of metrics can be used in our approach, but a fuel consumption proxy is recommended. This approach is novel as it considers the constantly changing terrain that results from cutting and filling activities and replaces inaccurate “static” calculations that have been used previously. The approach is also capable of efficiently correcting the violation of top down cutting and bottom up filling conditions that can be found in existing earthwork assignments and sequences. This approach assumes that the project site is partitioned into uniform blocks. A directed graph is then utilised to describe the terrain surface. This digraph is altered after each cut and fill, in order to reflect the true state of the terrain. A shortest path algorithm is successively applied to calculate the cost of each haul and these costs are summed to provide a total cost of haulage

Automatic segmentation of choroidal thickness in optical coherence tomography

The assessment of choroidal thickness from optical coherence tomography (OCT) images of the human choroid is an important clinical and research task, since it provides valuable information regarding the eye’s normal anatomy and physiology, and changes associated with various eye diseases and the development of refractive error. Due to the time consuming and subjective nature of manual image analysis, there is a need for the development of reliable objective automated methods of image segmentation to derive choroidal thickness measures. However, the detection of the two boundaries which delineate the choroid is a complicated and challenging task, in particular the detection of the outer choroidal boundary, due to a number of issues including: (i) the vascular ocular tissue is non-uniform and rich in non-homogeneous features, and (ii) the boundary can have a low contrast. In this paper, an automatic segmentation technique based on graph-search theory is presented to segment the inner choroidal boundary (ICB) and the outer choroidal boundary (OCB) to obtain the choroid thickness profile from OCT images. Before the segmentation, the B-scan is pre-processed to enhance the two boundaries of interest and to minimize the artifacts produced by surrounding features. The algorithm to detect the ICB is based on a simple edge filter and a directional weighted map penalty, while the algorithm to detect the OCB is based on OCT image enhancement and a dual brightness probability gradient. The method was tested on a large data set of images from a pediatric (1083 B-scans) and an adult (90 B-scans) population, which were previously manually segmented by an experienced observer. The results demonstrate the proposed method provides robust detection of the boundaries of interest and is a useful tool to extract clinical data.

Continuous appearance-based localisation and mapping

This thesis presents a novel approach to mobile robot navigation using visual information towards the goal of long-term autonomy. A novel concept of a continuous appearance-based trajectory is proposed in order to solve the limitations of previous robot navigation systems, and two new algorithms for mobile robots, CAT-SLAM and CAT-Graph, are presented and evaluated. These algorithms yield performance exceeding state-of-the-art methods on public benchmark datasets and large-scale real-world environments, and will help enable widespread use of mobile robots in everyday applications.

Image segmentation and pose estimation of humans in video

This thesis introduces improved techniques towards automatically estimating the pose of humans from video. It examines a complete workflow to estimating pose, from the segmentation of the raw video stream to extract silhouettes, to using the silhouettes in order to determine the relative orientation of parts of the human body. The proposed segmentation algorithms have improved performance and reduced complexity, while the pose estimation shows superior accuracy during difficult cases of self occlusion.

Eight weeks of episodic visual navigation inside a non-stationary environment using adaptive spherical views

This paper presents a long-term experiment where a mobile robot uses adaptive spherical views to localize itself and navigate inside a non-stationary office environment. The office contains seven members of staff and experiences a continuous change in its appearance over time due to their daily activities. The experiment runs as an episodic navigation task in the office over a period of eight weeks. The spherical views are stored in the nodes of a pose graph and they are updated in response to the changes in the environment. The updating mechanism is inspired by the concepts of long- and short-term memories. The experimental evaluation is done using three performance metrics which evaluate the quality of both the adaptive spherical views and the navigation over time.

A short-term production scheduling methodology for open-pit mines

A multi-resource multi-stage scheduling methodology is developed to solve short-term open-pit mine production scheduling problems as a generic multi-resource multi-stage scheduling problem. It is modelled using essential characteristics of short-term mining production operations such as drilling, sampling, blasting and excavating under the capacity constraints of mining equipment at each processing stage. Based on an extended disjunctive graph model, a shifting-bottleneck-procedure algorithm is enhanced and applied to obtain feasible short-term open-pit mine production schedules and near-optimal solutions. The proposed methodology and its solution quality are verified and validated using a real mining case study.

Constructing a clinical decision-making framework for image-guided radiotherapy using a Bayesian Network

A decision-making framework for image-guided radiotherapy (IGRT) is being developed using a Bayesian Network (BN) to graphically describe, and probabilistically quantify, the many interacting factors that are involved in this complex clinical process. Outputs of the BN will provide decision-support for radiation therapists to assist them to make correct inferences relating to the likelihood of treatment delivery accuracy for a given image-guided set-up correction. The framework is being developed as a dynamic object-oriented BN, allowing for complex modelling with specific sub-regions, as well as representation of the sequential decision-making and belief updating associated with IGRT. A prototype graphic structure for the BN was developed by analysing IGRT practices at a local radiotherapy department and incorporating results obtained from a literature review. Clinical stakeholders reviewed the BN to validate its structure. The BN consists of a sub-network for evaluating the accuracy of IGRT practices and technology. The directed acyclic graph (DAG) contains nodes and directional arcs representing the causal relationship between the many interacting factors such as tumour site and its associated critical organs, technology and technique, and inter-user variability. The BN was extended to support on-line and off-line decision-making with respect to treatment plan compliance. Following conceptualisation of the framework, the BN will be quantified. It is anticipated that the finalised decision-making framework will provide a foundation to develop better decision-support strategies and automated correction algorithms for IGRT.

Towards training-free appearance-based localization : probabilistic models for whole-image descriptors

Whole image descriptors have recently been shown to be remarkably robust to perceptual change especially compared to local features. However, whole-image-based localization systems typically rely on heuristic methods for determining appropriate matching thresholds in a particular environment. These environment-specific tuning requirements and the lack of a meaningful interpretation of these arbitrary thresholds limits the general applicability of these systems. In this paper we present a Bayesian model of probability for whole-image descriptors that can be seamlessly integrated into localization systems designed for probabilistic visual input. We demonstrate this method using CAT-Graph, an appearance-based visual localization system originally designed for a FAB-MAP-style probabilistic input. We show that using whole-image descriptors as visual input extends CAT-Graph’s functionality to environments that experience a greater amount of perceptual change. We also present a method of estimating whole-image probability models in an online manner, removing the need for a prior training phase. We show that this online, automated training method can perform comparably to pre-trained, manually tuned local descriptor methods.

A rule-based hybrid method for anomaly detection in online-social-network graphs

Detecting anomalies in the online social network is a significant task as it assists in revealing the useful and interesting information about the user behavior on the network. This paper proposes a rule-based hybrid method using graph theory, Fuzzy clustering and Fuzzy rules for modeling user relationships inherent in online-social-network and for identifying anomalies. Fuzzy C-Means clustering is used to cluster the data and Fuzzy inference engine is used to generate rules based on the cluster behavior. The proposed method is able to achieve improved accuracy for identifying anomalies in comparison to existing methods.

Active security in multiparty computation over black-box groups

Most previous work on unconditionally secure multiparty computation has focused on computing over a finite field (or ring). Multiparty computation over other algebraic structures has not received much attention, but is an interesting topic whose study may provide new and improved tools for certain applications. At CRYPTO 2007, Desmedt et al introduced a construction for a passive-secure multiparty multiplication protocol for black-box groups, reducing it to a certain graph coloring problem, leaving as an open problem to achieve security against active attacks. We present the first n-party protocol for unconditionally secure multiparty computation over a black-box group which is secure under an active attack model, tolerating any adversary structure Δ satisfying the Q 3 property (in which no union of three subsets from Δ covers the whole player set), which is known to be necessary for achieving security in the active setting. Our protocol uses Maurer’s Verifiable Secret Sharing (VSS) but preserves the essential simplicity of the graph-based approach of Desmedt et al, which avoids each shareholder having to rerun the full VSS protocol after each local computation. A corollary of our result is a new active-secure protocol for general multiparty computation of an arbitrary Boolean circuit.

Generating natural language texts from business process models

Process Modeling is a widely used concept for understanding, documenting and also redesigning the operations of organizations. The validation and usage of process models is however affected by the fact that only business analysts fully understand them in detail. This is in particular a problem because they are typically not domain experts. In this paper, we investigate in how far the concept of verbalization can be adapted from object-role modeling to process models. To this end, we define an approach which automatically transforms BPMN process models into natural language texts and combines different techniques from linguistics and graph decomposition in a flexible and accurate manner. The evaluation of the technique is based on a prototypical implementation and involves a test set of 53 BPMN process models showing that natural language texts can be generated in a reliable fashion.

On separation of concurrency and conflicts in acyclic process models

Recently, a new approach for structuring acyclic process models has been introduced. The algorithm is based on a transformation between the Refined Process Structure Tree (RPST) of a control flow graph and the Modular Decomposition Tree (MDT) of ordering relations. In this paper, an extension of the algorithm is presented that allows to partially structure process models in the case when a process model cannot be structured completely. We distinguish four different types of unstructuredness of process models and show that only two are possible in practice. For one of these two types of unstructuredness an algorithm is proposed that returns the maximally structured representation of a process model.

On application of structural decomposition for process model abstraction

Real world business process models may consist of hundreds of elements and have sophisticated structure. Although there are tasks where such models are valuable and appreciated, in general complexity has a negative influence on model comprehension and analysis. Thus, means for managing the complexity of process models are needed. One approach is abstraction of business process models-creation of a process model which preserves the main features of the initial elaborate process model, but leaves out insignificant details. In this paper we study the structural aspects of process model abstraction and introduce an abstraction approach based on process structure trees (PST). The developed approach assures that the abstracted process model preserves the ordering constraints of the initial model. It surpasses pattern-based process model abstraction approaches, allowing to handle graph-structured process models of arbitrary structure. We also provide an evaluation of the proposed approach.