A Two-Stage Online Approach for Collaborative Multi-Agent Planning under Uncertainty

In a team of multiple agents, the pursuance of a common goal is a defining characteristic. Since agents may have different capabilities, and effects of actions may be uncertain, a common goal can generally only be achieved through a careful cooperation between the different agents. In this work, we propose a novel two-stage planner that combines online planning at both team level and individual level through a subgoal delegation scheme. The proposal brings the advantages of online planning approaches to the multi-agent setting. A number of modifications are made to a classical UCT approximate algorithm to (i) adapt it to the application domains considered, (ii) reduce the branching factor in the underlying search process, and (iii) effectively manage uncertain information of action effects by using information fusion mechanisms. The proposed online multi-agent planner reduces the cost of planning and decreases the temporal cost of reaching a goal, while significantly increasing the chance of success of achieving the common goal. 

Joint Analysis of Multiple Algorithms and Performance Measures

There has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and time complexity). Once one has developed an approach to a problem of interest, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Standard tests used for this purpose are able to consider jointly neither performance measures nor multiple competitors at once. The aim of this paper is to resolve these issues by developing statistical procedures that are able to account for multiple competing measures at the same time and to compare multiple algorithms altogether. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameters of such models, as usually the number of studied cases is very reduced in such comparisons. Data from a comparison among general purpose classifiers is used to show a practical application of our tests.

Complexity of risk: Mixed methods approach to understanding youth risk and insecurity in post-conflict settings

In settings of intergroup conflict, identifying contextually-relevant risk factors for youth development in an important task. In Vukovar, Croatia, a city devastated during the war in former Yugoslavia, ethno-political tensions remain. The current study utilized a mixed method approach to identify two salient community-level risk factors (ethnic tension and general antisocial behavior) and related emotional insecurity responses (ethnic and non-ethnic insecurity) among youth in Vukovar. In Study 1, focus group discussions (N=66) with mother, fathers, and adolescents 11 to 15-years-old were analyzed using the Constant Comparative Method, revealing two types of risk and insecurity responses. In Study 2, youth (N=227, 58% male, M=15.88 SD=1.12 years old) responded to quantitative scales developed from the focus groups; discriminate validity was demonstrated and path analyses established predictive validity between each type of risk and insecurity. First, community ethnic tension (i.e., threats related to war/ethnic identity) significantly predicted ethnic insecurity for all youth (β=.41, p<.001). Second, experience with community antisocial behavior (i.e., general crime found in any context) predicted non-ethnic community insecurity for girls (β=.32, p<.05), but not for boys. These findings are the first to show multiple forms of emotional insecurity at the community level; implications for future research are discussed.

Multiple face detection at different resolutions for perceptual user interfaces

[EN]This paper describes in detail a real-time multiple face detection system for video streams. The system adds to the good performance provided by a window shift approach, the combination of different cues available in video streams due to temporal coherence. The results achieved by this combined solution outperform the basic face detector obtaining a 98% success rate for around 27000 images, providing additionally eye detection and a relation between the successive detections in time by means of detection threads.

Determining day length and temperature regulation of flowering: a molecular and modelling approach

Thesis (Ph.D.)--University of Washington, 2016-08

Random Access over Multi-Packet Reception Channels: A Mean-Field Approach

Thesis (Ph.D.)--University of Washington, 2016-06

Development of a transitioning approach to reduce surface water volumes in combined sewer systems

The overarching goal of this research is to establish a successful forum for a transition from the existing paradigm of managing wastewater infrastructure to a more sustainable paradigm that achieves a more efficient utilisation of wastewater assets. A transitioning approach to support a more efficient utilisation of surface water and wastewater assets and infrastructure is proposed and developed. The determined transitioning approach possesses key stages namely developing the arena, developing the agenda, case study, and monitoring. The case study stage investigates a drainage utility identifying their improvement drivers, the removal of surface water through detailed drainage modelling and the financial examination of the costs incurred under the various scenarios conducted. Understanding the implications of removing/attenuating surface water from the network is improved through obtaining data by detailed drainage modelling. Infoworks software is used to investigate and assess the current and future operational scenarios of a wastewater system operating over one calendar year. Modelling scenarios were conducted removing surface water from selected areas focusing on the volumes requiring pumping and durations of pumping station(s) operation prior to treatment during storm conditions. The financial implication of removing surface water in combined sewer systems is examined in three main components. Firstly the costs of electricity incurred at the single sewage pumping station (SPS) investigated during the various scenarios modelled require to be addressed. Secondly the costs to retrofit sustainable urban drainage system (SUDS) solutions needs to be identified. Thirdly the implications of removing surface water for the drainage utility at the national level and the potential saving for householder’s committing to a surface water disconnection rebate scheme. When addressed at the macro level i.e., with over 2,100 pumping stations, some operating in sequence and contained within one drainage utility annually treating 315,360 megalitres the significance of the same multiple quantifiable and intangible benefits becomes amplified. The research aims, objectives and findings are presented to the identified and convened stakeholders. The transitioning approach developed encourages positive discourse between stakeholders. The level of success of the transitioning approach determined is then tested using a quantitative methodology through the completion of questionnaires. From the questionnaires completed the respondents unanimously agreed that surface water flows should be removed as well as reduced from the combined sewer system. The respondents agreed that the removal of surface water from a typical combined sewer system is justified by applying a transitioning approach focusing on the energy consumption required to pump increased volumes during storm events. This response is significant based upon the economic evidence and is contrary to the respondents previous position that finance was their most influencing factor. When provided with other potentially available benefits the respondents were even more supportive of the justification to remove surface water from the combined sewer system. The combined findings of the work presented in this thesis provide further justification that the transitioning approach applied to the removal of surface water from a typical combined sewer system, as determined in this research has been successful.

Optimization of a multielectrode array (MEA)-based approach to study the impact of Aβ on the SH-SY5Y cell line

The human brain stores, integrates, and transmits information recurring to millions of neurons, interconnected by countless synapses. Though neurons communicate through chemical signaling, information is coded and conducted in the form of electrical signals. Neuroelectrophysiology focus on the study of this type of signaling. Both intra and extracellular approaches are used in research, but none holds as much potential in high-throughput screening and drug discovery, as extracellular recordings using multielectrode arrays (MEAs). MEAs measure neuronal activity, both in vitro and in vivo. Their key advantage is the capability to record electrical activity at multiple sites simultaneously. Alzheimer’s disease (AD) is the most common neurodegenerative disease and one of the leading causes of death worldwide. It is characterized by neurofibrillar tangles and aggregates of amyloid-β (Aβ) peptides, which lead to the loss of synapses and ultimately neuronal death. Currently, there is no cure and the drugs available can only delay its progression. In vitro MEA assays enable rapid screening of neuroprotective and neuroharming compounds. Therefore, MEA recordings are of great use in both AD basic and clinical research. The main aim of this thesis was to optimize the formation of SH-SY5Y neuronal networks on MEAs. These can be extremely useful for facilities that do not have access to primary neuronal cultures, but can also save resources and facilitate obtaining faster high-throughput results to those that do. Adhesion-mediating compounds proved to impact cell morphology, viability and exhibition of spontaneous electrical activity. Moreover, SH-SY5Y cells were successfully differentiated and demonstrated acute effects on neuronal function after Aβ addition. This effect on electrical signaling was dependent on Aβ oligomers concentration. The results here presented allow us to conclude that the SH-SY5Y cell line can be successfully differentiated in properly coated MEAs and be used for assessing acute Aβ effects on neuronal signaling.

A network approach to overcoming barriers to market engagement for SMEs in energy efficiency initiatives such as the Green Deal

The Green Deal (GD) was launched in 2013 by the UK Government as a market-led scheme to encourage uptake of energy efficiency measures in the UK and create green sector jobs. The scheme closed in July 2015 after 30 months due to government concerns over low uptake and industry standards but additional factors potentially contributed to its failure such as poor scheme design and lack of understanding of the customer and supply chain journey. We explore the role of key delivery agents of GD services, specifically SMEs, and we use the LoCal-Net project as a case study to examine the use of networks to identify and reduce barriers to SME market engagement. We find that SMEs experienced multiple barriers to interaction with the GD such as lack of access to information, training, and confusion over delivery of the scheme but benefited from interaction with the network to access information, improve understanding of the scheme, increasing networking opportunities and forming new business models and partnerships to reduce risk. The importance of SMEs as delivery agents and their role in the design of market-led schemes such as the GD are discussed with recommendations for improving SME engagement in green sector initiatives.

Multiple endocrine neoplasia, the old and the new: a mini review

Multiple endocrine neoplasia syndromes have since been classified as types 1 and 2, each with specific phenotypic patterns. MEN1 is usually associated with pituitary, parathyroid and paraneoplastic neuroendocrine tumours. The hallmark of MEN2 is a very high lifetime risk of developing medullary thyroid carcinoma (MTC) more than 95% in untreated patients. Three clinical subtypesdMEN2A, MEN2B, and familial MTC (FMTC) have been defined based on the risk of pheochromocytoma, hyperparathyroidism, and the presence or absence of characteristic physical features). MEN2 occurs as a result of germline activating missense mutations of the RET (REarranged during Transfection) proto-oncogene. MEN2-associated mutations are almost always located in exons 10, 11, or 13 through 16. Strong genotype-phenotype correlations exist with respect to clinical subtype, age at onset, and aggressiveness of MTC in MEN2. These are used to determine the age at which prophylactic thyroidectomy should occur and whether screening for pheochromocytoma or hyperparathyroidism is necessary. Specific RET mutations can also impact management in patients presenting with apparently sporadic MTC. Therefore, genetic testing should be performed before surgical intervention in all patients diagnosed with MTC. Recently, Pellegata et al. have reported that germline mutations in CDKN1B can predispose to the development of multiple endocrine tumours in both rats and humans and this new MEN syndrome is named MENX and MEN4, respectively. CDKN1B. A recent report showed that in sporadic MTC, CDKN1B V109G polymorphism correlates with a more favorable disease progression than the wild-type allele and might be considered a new promising prognostic marker. New insights on MEN syndrome pathogenesis and related inherited endocrine disorders are of particular interest for an adequate surgical and therapeutic approach.

Rice-Infecting Pseudomonas Genomes Are Highly Accessorized and Harbor Multiple Putative Virulence Mechanisms to Cause Sheath Brown Rot

Sheath rot complex and seed discoloration in rice involve a number of pathogenic bacteria that cannot be associated with distinctive symptoms. These pathogens can easily travel on asymptomatic seeds and therefore represent a threat to rice cropping systems. Among the rice-infecting Pseudomonas, P. fuscovaginae has been associated with sheath brown rot disease in several rice growing areas around the world. The appearance of a similar Pseudomonas population, which here we named P. fuscovaginae-like, represents a perfect opportunity to understand common genomic features that can explain the infection mechanism in rice. We showed that the novel population is indeed closely related to P. fuscovaginae. A comparative genomics approach on eight rice-infecting Pseudomonas revealed heterogeneous genomes and a high number of strain-specific genes. The genomes of P. fuscovaginae-like harbor four secretion systems (Type I, II, III, and VI) and other important pathogenicity machinery that could probably facilitate rice colonization. We identified 123 core secreted proteins, most of which have strong signatures of positive selection suggesting functional adaptation. Transcript accumulation of putative pathogenicity-related genes during rice colonization revealed a concerted virulence mechanism. The study suggests that rice-infecting Pseudomonas causing sheath brown rot are intrinsically diverse and maintain a variable set of metabolic capabilities as a potential strategy to occupy a range of environments.

High-resolution image reconstruction from multiple low-resolution images.

In this paper, we demonstrate a digital signal processing (DSP) algorithm for improving spatial resolution of images captured by CMOS cameras. The basic approach is to reconstruct a high resolution (HR) image from a shift-related low resolution (LR) image sequence. The aliasing relationship of Fourier transforms between discrete and continuous images in the frequency domain is used for mapping LR images to a HR image. The method of projection onto convex sets (POCS) is applied to trace the best estimate of pixel matching from the LR images to the reconstructed HR image. Computer simulations and preliminary experimental results have shown that the algorithm works effectively on the application of post-image-captured processing for CMOS cameras. It can also be applied to HR digital image reconstruction, where shift information of the LR image sequence is known.

A multitracer approach to assess the spatial contamination pattern of hake (Merluccius merluccius) in the French Mediterranean

Chemical contamination levels and stable isotope ratios provide integrated information about contaminant exposure, trophic position and also biological and environmental influences on marine organisms. By combining these approaches with otolith shape analyses, the aim of the present study was to document the spatial variability of Hg and PCB contamination of the European hake (Merluccius merluccius) in the French Mediterranean, hypothesizing that local contaminant sources, environmental conditions and biological specificities lead to site-specific contamination patterns. High Hg concentrations discriminated Corsica (average: 1.36 ± 0.80 μg g− 1 dm) from the Gulf of Lions (average values < 0.5 μg g− 1 dm), where Rhône River input caused high PCB burdens. CB 153 average concentrations ranged between 4.00 ± 0.64 and 18.39 ± 12.38 ng g− 1 dm in the Gulf of Lions, whatever the sex of the individuals, whereas the highest values in Corsica were 6.75 ± 4.22 ng g− 1 dm. Otolith shape discriminated juveniles and adults, due to their different habitats. The use of combined ecotracers was revealed as a powerful tool to discriminate between fish populations at large and small spatial scale, and to enable understanding of the environmental and biological influences on contamination patterns.

Leveraging Multiple Features for Image Retrieval and Matching

The goal of image retrieval and matching is to find and locate object instances in images from a large-scale image database. While visual features are abundant, how to combine them to improve performance by individual features remains a challenging task. In this work, we focus on leveraging multiple features for accurate and efficient image retrieval and matching. We first propose two graph-based approaches to rerank initially retrieved images for generic image retrieval. In the graph, vertices are images while edges are similarities between image pairs. Our first approach employs a mixture Markov model based on a random walk model on multiple graphs to fuse graphs. We introduce a probabilistic model to compute the importance of each feature for graph fusion under a naive Bayesian formulation, which requires statistics of similarities from a manually labeled dataset containing irrelevant images. To reduce human labeling, we further propose a fully unsupervised reranking algorithm based on a submodular objective function that can be efficiently optimized by greedy algorithm. By maximizing an information gain term over the graph, our submodular function favors a subset of database images that are similar to query images and resemble each other. The function also exploits the rank relationships of images from multiple ranked lists obtained by different features. We then study a more well-defined application, person re-identification, where the database contains labeled images of human bodies captured by multiple cameras. Re-identifications from multiple cameras are regarded as related tasks to exploit shared information. We apply a novel multi-task learning algorithm using both low level features and attributes. A low rank attribute embedding is joint learned within the multi-task learning formulation to embed original binary attributes to a continuous attribute space, where incorrect and incomplete attributes are rectified and recovered. To locate objects in images, we design an object detector based on object proposals and deep convolutional neural networks (CNN) in view of the emergence of deep networks. We improve a Fast RCNN framework and investigate two new strategies to detect objects accurately and efficiently: scale-dependent pooling (SDP) and cascaded rejection classifiers (CRC). The SDP improves detection accuracy by exploiting appropriate convolutional features depending on the scale of input object proposals. The CRC effectively utilizes convolutional features and greatly eliminates negative proposals in a cascaded manner, while maintaining a high recall for true objects. The two strategies together improve the detection accuracy and reduce the computational cost.

Multiple-Retrieval Case-Based Reasoning for Course Timetabling Problems

The structured representation of cases by attribute graphs in a Case-Based Reasoning (CBR) system for course timetabling has been the subject of previous research by the authors. In that system, the case base is organised as a decision tree and the retrieval process chooses those cases which are sub attribute graph isomorphic to the new case. The drawback of that approach is that it is not suitable for solving large problems. This paper presents a multiple-retrieval approach that partitions a large problem into small solvable sub-problems by recursively inputting the unsolved part of the graph into the decision tree for retrieval. The adaptation combines the retrieved partial solutions of all the partitioned sub-problems and employs a graph heuristic method to construct the whole solution for the new case. We present a methodology which is not dependant upon problem specific information and which, as such, represents an approach which underpins the goal of building more general timetabling systems. We also explore the question of whether this multiple-retrieval CBR could be an effective initialisation method for local search methods such as Hill Climbing, Tabu Search and Simulated Annealing. Significant results are obtained from a wide range of experiments. An evaluation of the CBR system is presented and the impact of the approach on timetabling research is discussed. We see that the approach does indeed represent an effective initialisation method for these approaches.