Miracle-dz/UniFAR-FTUs huggingface dataset:
Modularity in NEAT Reinforcement Learning Networks

Modularity is essential to many well-performing structured systems, as it is a useful means of managing complexity [8]. An analysis of modularity in neural networks produced by machine learning algorithms can offer valuable insight into the workings of such algorithms and how modularity can be leveraged to improve performance. However, this property is often overlooked in the neuroevolutionary literature, so the modular nature of many learning algorithms is unknown. This property was assessed on the popular algorithm"NeuroEvolution of Augmenting Topologies"(NEAT) for standard simulation benchmark control problems due to NEAT's ability to optimise network topology. This paper shows that NEAT networks seem to rapidly increase in modularity over time with the rate and convergence dependent on the problem. Interestingly, NEAT tends towards increasingly modular networks even when network fitness converges. It was shown that the ideal level of network modularity in the explored parameter space is highly dependent on other network variables, dispelling theories that modularity has a straightforward relationship to network performance. This is further proven in this paper by demonstrating that rewarding modularity directly did not improve fitness.
A New Hybrid Multi-Objective Scheduling Model for Hierarchical Hub and Flexible Flow Shop Problems

In general, customers are looking to receive their orders in the fastest time possible and to make purchases at a reasonable price. Consequently, the importance of having an optimal delivery time is increasingly evident these days. One of the structures that can meet the demand for large supply chains with numerous orders is the hierarchical integrated hub structure. Such a structure improves efficiency and reduces chain costs. To make logistics more cost-effective, hub-and-spoke networks are necessary as a means to achieve economies of scale. Many hub network design models only consider hub type but do not take into account the hub scale measured using freight volume. This paper proposes a multi-objective scheduling model for hierarchical hub structures (HHS), which is layered from top to bottom. In the third layer, the central hub takes factory products from decentralized hubs and sends them to other decentralized hubs to which customers are connected. In the second layer, non-central hubs are responsible for receiving products from the factory and transferring them to central hubs. These hubs are also responsible for receiving products from central hubs and sending them to customers. Lastly, the first layer contains factories responsible for producing products and providing for their customers. The factory uses the flexible flow-shop platform and structure to produce its products. The model’s objective is to minimize transportation and production costs as well as product arrival times. To validate and evaluate the model, small instances have been solved and analyzed in detail with the weighted sum and ε-constraint method. Consequently, based on the mean ideal distance (MID) metric, two methods were compared for the designed instances.
Design and Implementation of a Quantum Kernel for Natural Language Processing

Natural language processing (NLP) is the field that attempts to make human language accessible to computers, and it relies on applying a mathematical model to express the meaning of symbolic language. One such model, DisCoCat, defines how to express both the meaning of individual words as well as their compositional nature. This model can be naturally implemented on quantum computers, leading to the field quantum NLP (QNLP). Recent experimental work used quantum machine learning techniques to map from text to class label using the expectation value of the quantum encoded sentence. Theoretical work has been done on computing the similarity of sentences but relies on an unrealized quantum memory store. The main goal of this thesis is to leverage the DisCoCat model to design a quantum-based kernel function that can be used by a support vector machine (SVM) for NLP tasks. Two similarity measures were studied: (i) the transition amplitude approach and (ii) the SWAP test. A simple NLP meaning classification task from previous work was used to train the word embeddings and evaluate the performance of both models. The Python module lambeq and its related software stack was used for implementation. The explicit model from previous work was used to train word embeddings and achieved a testing accuracy of $93.09 \pm 0.01$%. It was shown that both the SVM variants achieved a higher testing accuracy of $95.72 \pm 0.01$% for approach (i) and $97.14 \pm 0.01$% for (ii). The SWAP test was then simulated under a noise model defined by the real quantum device, ibmq_guadalupe. The explicit model achieved an accuracy of $91.94 \pm 0.01$% while the SWAP test SVM achieved 96.7% on the testing dataset, suggesting that the kernelized classifiers are resilient to noise. These are encouraging results and motivate further investigations of our proposed kernelized QNLP paradigm.
Detailed Balanced Chemical Reaction Networks as Generalized Boltzmann Machines

Can a micron sized sack of interacting molecules understand, and adapt to a constantly-fluctuating environment? Cellular life provides an existence proof in the affirmative, but the principles that allow for life's existence are far from being proven. One challenge in engineering and understanding biochemical computation is the intrinsic noise due to chemical fluctuations. In this paper, we draw insights from machine learning theory, chemical reaction network theory, and statistical physics to show that the broad and biologically relevant class of detailed balanced chemical reaction networks is capable of representing and conditioning complex distributions. These results illustrate how a biochemical computer can use intrinsic chemical noise to perform complex computations. Furthermore, we use our explicit physical model to derive thermodynamic costs of inference.
Provably Safe Reinforcement Learning: A Theoretical and Experimental Comparison

Ensuring safety of reinforcement learning (RL) algorithms is crucial for many real-world tasks. However, vanilla RL does not guarantee safety for an agent. In recent years, several methods have been proposed to provide safety guarantees for RL. To the best of our knowledge, there is no comprehensive comparison of these provably safe RL methods. We therefore introduce a categorization for existing provably safe RL methods, and present the theoretical foundations for both continuous and discrete action spaces. Additionally, we evaluate provably safe RL on an inverted pendulum. In the experiments, it is shown that indeed only provably safe RL methods guarantee safety.
A Scalable, Parallel Approach for Multi-point, High-fidelity Aerostructural Optimization of Aircraft Configurations

A Scalable, Parallel Approach for Multi-Point, High-Fidelity Aerostructural Optimization of Aircraft Configurations Gaetan K. W. Kenway Doctor of Philosophy Graduate Department of Aerospace Engineering University of Toronto 2013 This thesis presents new tools and techniques developed to address the challenging problem of high-fidelity aerostructural optimization with respect to large numbers of design variables. A new mesh-movement scheme is developed that is both computationally efficient and sufficiently robust to accommodate large geometric design changes and aerostructural deformations. A fully coupled Newton–Krylov method is presented that accelerates the convergence of aerostructural systems and provides a 20% performance improvement over the traditional nonlinear block Gauss–Seidel approach and can handle more flexible structures. A coupled adjoint method is used that efficiently computes derivatives for a gradient-based optimization algorithm. The implementation uses only machine accurate derivative techniques and is verified to yield fully consistent derivatives by comparing against the complex step method. The fully-coupled large-scale coupled adjoint solution method is shown to have 30% better performance than the segregated approach. The parallel scalability of the coupled adjoint technique is demonstrated on an Euler Computational Fluid Dynamics (CFD) model with more than 80 million state variables coupled to a detailed structural finite-element model of the wing with more than 1 million degrees of freedom. Multi-point high-fidelity aerostructural optimizations of a long-range wide-body, transonic transport aircraft configuration are performed using the developed techniques. The aerostructural analysis employs Euler CFD with a 2 million cell mesh and a structural finite element model with 300 000 DOF. Two design optimization problems are solved: one where takeoff gross weight is minimized, and another where fuel burn is minimized. Each
The probability of random trees being isomorphic

We study the fundamental question of how likely it is that two randomly chosen trees are isomorphic to each other for different models of random trees. We show that the probability decays exponentially for rooted labeled trees as well as for Galton--Watson trees with bounded degrees but that this is not true for plane trees, thus providing a counterexample in the general case of Galton--Watson trees without degree restrictions. We also derive limiting distributions for some related parameters: the number of vertices of given degrees in pairs of labeled trees conditioned on being isomorphic, thus showing that they have a different shape than usual labeled trees, as well as the number of labelings and plane representations of P\'olya trees. The results rely on both probabilistic and analytic tools.
DeltaConv

Learning from 3D point-cloud data has rapidly gained momentum, motivated by the success of deep learning on images and the increased availability of 3D data. In this paper, we aim to construct anisotropic convolution layers that work directly on the surface derived from a point cloud. This is challenging because of the lack of a global coordinate system for tangential directions on surfaces. We introduce DeltaConv, a convolution layer that combines geometric operators from vector calculus to enable the construction of anisotropic filters on point clouds. Because these operators are defined on scalar- and vector-fields, we separate the network into a scalar- and a vector-stream, which are connected by the operators. The vector stream enables the network to explicitly represent, evaluate, and process directional information. Our convolutions are robust and simple to implement and match or improve on state-of-the-art approaches on several benchmarks, while also speeding up training and inference.
Actuation of an Inertia-Coupled Rimless Wheel Model across Level Ground

Actuation of an Inertia-Coupled Rimless Wheel Model across Level Ground Seth Caleb Weeks Supervising Professor: Dr. Mario Gomes The inertia-coupled rimless wheel model is a passive dynamic walking device which is theoretically capable of achieving highly efficient motion with no energy losses. Under non-ideal circumstances, energy losses due to air drag require the use of actuation to maintain stable motions. The Actuated Inertia-coupled Rimless Wheel Across Flat Terrain (AIRWAFT) model provides actuation to an inertia-coupled rimless wheel model across level ground to compensate for energy losses by applying hip-torque between the frame and inertia wheel via a motor. Two methods of defining the openloop actuation are presented. Position control defines the relative position of the drum relative to the frame. Torque control specifies the amount of torque between the frame and the drum. The performance of the model was evaluated with respect to changes in various geometrical and control parameters and initial conditions. This parameter study led to the discovery of a stable, periodic motion with a cost of transport of 0.33.
A novel textual track‐data‐based approach for estimating individual infection risk of COVID‐19

With the recurrence of infectious diseases caused by coronaviruses, which pose a significant threat to human health, there is an unprecedented urgency to devise an effective method to identify and assess who is most at risk of contracting these diseases. China has successfully controlled the spread of COVID‐19 through the disclosure of track data belonging to diagnosed patients. This paper proposes a novel textual track‐data‐based approach for individual infection risk measurement. The proposed approach is divided into three steps. First, track features are extracted from track data to build a general portrait of COVID‐19 patients. Then, based on the extracted track features, we construct an infection risk indicator system to calculate the infection risk index (IRI). Finally, individuals are divided into different infection risk categories based on the IRI values. By doing so, the proposed approach can determine the risk of an individual contracting COVID‐19, which facilitates the identification of high‐risk populations. Thus, the proposed approach can be used for risk prevention and control of COVID‐19. In the empirical analysis, we comprehensively collected 9455 pieces of track data from 20 January 2020 to 30 July 2020, covering 32 provinces/provincial municipalities in China. The empirical results show that the Chinese COVID‐19 patients have six key features that indicate infection risk: place, region, close‐contact person, contact manner, travel mode, and symptom. The IRI values for all 9455 patients vary from 0 to 43.19. Individuals are classified into the following five infection risk categories: low, moderate‐low, moderate, moderate‐high, and high risk.
Generation of a chromosome‐level genome assembly for Pacific halibut (Hippoglossus stenolepis) and characterization of its sex‐determining genomic region

The Pacific halibut (Hippoglossus stenolepis) is a key species in the North Pacific Ocean and Bering Sea ecosystems, where it also supports important fisheries. However, the lack of genomic resources limits our understanding of evolutionary, environmental and anthropogenic forces affecting key life history characteristics of Pacific halibut and prevents the application of genomic tools in fisheries management and conservation efforts. In the present study, we report on the first generation of a high‐quality chromosome‐level assembly of the Pacific halibut genome, with an estimated size of 602 Mb, 24 chromosome‐length scaffolds that contain 99.8% of the assembly and a N50 scaffold length of 27.3 Mb. In the first application of this important resource, we conducted genome‐wide analyses of sex‐specific genetic variation by pool sequencing and characterized a potential sex‐determining region in chromosome 9 with a high density of female‐specific SNPs. Within this region, we identified the bmpr1ba gene as a potential candidate for master sex‐determining (MSD) gene. bmpr1ba is a member of the TGF‐β family that in teleosts has provided the largest number of MSD genes, including a paralogue of this gene in Atlantic herring. The genome assembly constitutes an essential resource for future studies on Pacific halibut population structure and dynamics, evolutionary history and responses to environmental and anthropogenic influences. Furthermore, the genomic location of the sex‐determining region in Pacific halibut has been identified and a putative candidate MSD gene has been proposed, providing further support for the rapid evolution of sex‐determining mechanisms in teleost fish.
The role of leadership in times of systems disruption: a qualitative study of health and social care integration

Objectives To understand whether and how effective integration of health and social care might occur in the context of major system disruption (the COVID-19 pandemic), with a focus on how the initiative may overcome past barriers to integration. Design Rapid, descriptive case study approach with deviant case sampling to gather and analyse key informant interviews and relevant archival documents. Setting The innovation (‘COVID-19 Protect’) took place in Norfolk and Waveney, UK, and aimed to foster integration across highly diverse organisations, capitalising on existing digital technology to proactively identify and support individuals most at risk of severe illness from COVID-19. Participants Twenty-six key informants directly involved with project conceptualisation and early implementation. Participants included clinicians, executives, digital/information technology leads, and others. Final sample size was determined by theoretical saturation. Results Four primary recurrent themes characterised the experiences of diverse team members in the project: (1) ways of working that supported rapid collaboration, (2) leveraging diversity and clinician input for systems change, (3) allowing for both central control and local adaptation and (4) balancing risk taking and accountability. Conclusions This rapid case study underscores the role of leadership in large systems change efforts, particularly in times of major disruption. Project leadership overcame barriers to integration highlighted by prior studies, including engaging with aversion to clinical/safety risk, fostering distributed leadership and developing shared organisational practices for data sharing and service delivery. These insights offer considerations for future efforts to support strategic integration of health and social care.
Multidrug‐resistant Escherichia coli from free‐living pigeons (Columba livia): Insights into antibiotic environmental contamination and detection of resistance genes

Bacterial resistance is a public and one health problem. Free‐living birds can be reservoirs of multidrug‐resistant bacteria and resistance genes. This study aimed to characterize the antimicrobial resistance of Escherichia coli isolated from free‐living urban pigeons (Columba livia) in South Brazil. Ninety‐two animals were sampled, and one isolate was obtained from each one. The isolates were characterized, and the antimicrobial resistance profile and beta‐lactam and colistin resistance genes were investigated. The isolates were classified as phylogroups B1 (35%), B2 (33%), A (16%) and D (16%), and 14% of the strains had the eae virulence gene. All isolates were resistant to at least one antimicrobial, and 63% of them were multidrug‐resistant. Geographical location where the pigeons were captured and presence of the eae gene were associated with multidrug resistance. blaVIM and mcr‐1 genes were detected in one and two isolates, respectively. This is the first report of these genes in E. coli of pigeons. The blaVIM‐positive isolate was classified as Shiga toxin‐producing E. coli, and the isolates with mcr‐1 were classified as Enterohaemorrhagic E. coli and Enteropathogenic E. coli, which raise additional concerns related to public health since these are zoonotic pathotypes. The results reveal that pigeons carry multidrug‐resistant pathogenic E. coli, which may interest public health. Nonetheless, further studies on whether these animals are sources of contamination for humans must be performed to understand their role in spreading antimicrobial resistance.
Novel Control of a Modular Multilevel Converter for Photovoltaic Applications

The number of applications of solar photovoltaic (PV) systems in power generation grids has increased in the last decade because of their ability to generate efficient and reliable power in a variety of low installation in domestic applications. Various PV converter topologies have therefore emerged, among which the modular multilevel converter (MMC) is very attractive due to its modularity and transformerless features. The modeling and control of the MMC has become an interesting issue due to the extremely large expansion of PV power plants at the residential scale and due to the power quality requirement of this application. This paper proposes a novel control method of MMC which is used to directly integrate the photovoltaic arrays with the power grid. Traditionally, a closed loop control has been used, although circulating current control and capacitors voltage balancing in each individual leg have remained unsolved problem. In this paper, the integration of model predictive control (MPC) and traditional closed loop control is proposed to control the MMC structure in a PV grid tied mode. Simulation results demonstrate the efficiency and effectiveness of the proposed control model.
C9ORF72-derived poly-GA DPRs undergo endocytic uptake in iAstrocytes and spread to motor neurons

Extracellularly delivered poly-GAs are internalised into astrocytes via an endocytosis-dependent pathway and then spread to motor neurons, implying non-cell autonomous mechanisms in C9ORF72-ALS/FTD. Dipeptide repeat (DPR) proteins are aggregation-prone polypeptides encoded by the pathogenic GGGGCC repeat expansion in the C9ORF72 gene, the most common genetic cause of amyotrophic lateral sclerosis and frontotemporal dementia. In this study, we focus on the role of poly-GA DPRs in disease spread. We demonstrate that recombinant poly-GA oligomers can directly convert into solid-like aggregates and form characteristic β-sheet fibrils in vitro. To dissect the process of cell-to-cell DPR transmission, we closely follow the fate of poly-GA DPRs in either their oligomeric or fibrillized form after administration in the cell culture medium. We observe that poly-GA DPRs are taken up via dynamin-dependent and -independent endocytosis, eventually converging at the lysosomal compartment and leading to axonal swellings in neurons. We then use a co-culture system to demonstrate astrocyte-to-motor neuron DPR propagation, showing that astrocytes may internalise and release aberrant peptides in disease pathogenesis. Overall, our results shed light on the mechanisms of poly-GA cellular uptake and propagation, suggesting lysosomal impairment as a possible feature underlying the cellular pathogenicity of these DPR species.
Towards a mechanistic understanding of microalgae-bacteria interactions: integration of metabolomic analysis and computational models.

Interactions amongst marine microalgae and heterotrophic bacteria drive processes underlying major biogeochemical cycles and are important for many artificial systems. These dynamic and complex interactions span the range from cooperative to competitive and it is the diverse and intricate networks of metabolites and chemical mediators that are predicted to principally dictate the nature of the relationship at any point in time. Recent advances in technologies to identify, analyze, and quantify metabolites have allowed for a comprehensive view of the molecules available for exchange and/or reflective of organismal interactions, setting the stage for development of mechanistic understanding of these systems. Here, we (i) review the current knowledge landscape of microalgal-bacterial interactions by focusing on metabolomic studies of selected, simplified model systems; (ii) describe the state of the field of metabolomics, with specific focus on techniques and approaches developed for microalga-bacterial interaction studies; (iii) outline the main approaches for development of mathematical models of these interacting systems, which collectively have the power to enhance interpretation of experimental data and generate novel testable hypotheses. We share the viewpoint that a comprehensive and integrated series of -omics approaches that include theoretical formulations are necessary to develop predictive and mechanistic understanding of these biological entities.