Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital Image Semantic Segmentation Algorithms: A Survey

In the ﬁeld of computer vision, image semantic segmentation is an important research branch and it is also a challenging task. Applications such as autonomous driving, Unmanned Aerial Vehicle System (UAVS), and even virtual or augmented reality systems require accurate and eﬃcient segmentation mechanisms. With the rise of deep learning methods, image semantic segmentation is more and more concerned by relevant researchers. In order to understand the research status, existing problems and development prospects of image semantic segmentation, this paper introduces the mainstream image semantic segmentation methods on the basis of extensive survey. First of all, we introduce the background concept of image semantic segmentation, generalize the commonly used image semantic segmentation methods, and compare the segmentation results of each method. After that, the commonly used image semantic segmentation datasets are summarized. At the same time, several commonly evaluation standards are introduced. Finally, the future development trend of image semantic segmentation is prospected, with a view to providing some ideas for researchers who wish to engage in this ﬁeld.
An improved event-driven model of presynaptic dynamics for large-scale simulations of biophysically realistic and diverse synapses

Author(s): Garcia, Jonathan | Advisor(s): Sejnowski, Terrence J | Abstract: Chemical synapses exhibit a diverse array of internal mechanisms that affect the dynamics of transmission efficacy. Many of these processes, such as release of neurotransmitter and vesicle recycling, depend strongly on activity-dependent influx and accumulation of Ca2+. To model how each of these processes may affect the processing of information in neural circuits, and how their dysfunction may lead to disease states, requires a computationally efficient modelling framework, capable of generating accurate phenomenology without incurring a heavy computational cost per synapse. In this dissertation, I derive physically grounded mathematical models of the instantaneous rate of Ca2+-triggered neurotransmitter release. The Ca2+ traces that drive these dynamics come from simulations in MCell of spike-evoked Ca2+ influx and buffered diffusion through the presynaptic space, an approach that overcomes observational limitations of physiological experiments. With these Ca2+ traces, I drive a validated kinetic model of the SNARE complex, which mediates spike-triggered vesicle fusion for both synchronous and asynchronous release. The profiles of the resulting release rate histograms inform the parameters of the phenomenological release models, including the time scales and the facilitation of release probability. Based on these results, I construct an event-driven model of presynaptic dynamics, treating all Ca2+-sensitive processes, not just vesicle release, as Poisson processes with decaying rate parameters that may undergo activity-dependent facilitation. This approach provides a unified framework for modelling both spontaneous and spike-evoked presynaptic vesicle dynamics, for an arbitrary number of processes that define interaction between an arbitrary number of vesicle pools and recycling pathways. I validate the model against MCell and demonstrate a runtime complexity that bridges the gap between full molecular simulations and abstract synaptic models. Furthermore, I verify that Ca2+-dependent recycling mechanisms are essential for maintaining transmission fidelity during high-frequency stimuli. Finally, the versatility of the framework enables one both to model diverse types of synapses and to perform test and control modelling experiments by including different sets of features and controlling their rates and responsiveness. I present this model as a highly extensible tool for future investigations into the functional impact of different synaptic mechanisms on information processing and dysfunction in model networks.
Internalisation of biophotonic techniques : transfection, injection and thermometry

Single cell manipulation can offer great insights into the whole of an organism, the rapidly growing -omics fields are illustrating the heterogeneity that can be found within cell populations and where these subtle differences may be exploited, from fundamental knowledge to diagnostics and therapeutics. The cutting edge of this single cell work requires the application of interdisciplinary research to fully exploit the boundaries being pushed. Biophotonics is one such body of interdisciplinary research, employing light to manipulate biological samples. This work seeks to make use biophotonic techniques as analogues for conventional biological methods. High throughput raster scan photoporation is utilised for attempted transfection, multiple trap optical tweezers are used in an attempt to optically drive mechanical injection of cells and the thermal impact of these optical tweezers, which require high energy densities to confine particles, is tested, via the exploitation of the temperature sensitive emission of quantum
Microwave Characterization of Low-Loss FDM 3-D Printed ABS With Dielectric-Filled Metal-Pipe Rectangular Waveguide Spectroscopy

Over time, the accuracy and speed by which a material can be characterized should improve. Today, the Nicolson–Ross–Weir (NRW) methodology represents a well-established method for extracting complex dielectric properties at microwave frequencies, with the use of a modern vector network analyzer. However, as will be seen, this approach suffers from three fundamental limitations to accuracy. Challenging NRW methods requires a methodical and robust investigation. To this end, using a dielectric-filled metal-pipe rectangular waveguide, five independent approaches are employed to accurately characterize the sample at the Fabry–Pérot resonance frequency (non-frequency dispersive modeling). In addition, manual Graphical and automated Renormalization spectroscopic approaches are introduced for the first time in the waveguide. The results from these various modeling strategies are then compared and contrasted to NRW approaches. As a timely exemplar, 3-D printed acrylonitrile–butadiene–styrene (ABS) samples are characterized and the results are compared with existing data available in the open literature. It is found that the various Fabry–Pérot resonance model results all agree with one another and validate the two new spectroscopic approaches; in doing so, exposing three limitations of the NRW methods. It is also shown that extracted dielectric properties for ABS differ from previously reported results and reasons for this are discussed. From measurement noise resilience analysis, a methodology is presented for determining the upper bound signal-to-noise ratio for the vector network analyzer (not normally associated with such instrumentation). Finally, fused deposition modeling (FDM) 3-D printing can result in a non-homogeneous sample that excites open-box mode resonances. This phenomenon is investigated for the first time analytically and with various modeling strategies.
Efficient Recovery Path Computation for Fast Reroute in Large-Scale Software-Defined Networks

With an increasing demand for resilience in software-defined networks (SDN), it becomes critical to minimize service recovery delay upon route failures. Fast reroute (FRR) mechanisms are widely used in IP and MPLS networks by computing the recovery path before a failure occurs. The centralized control plane in SDN can potentially enhance path computation, so that FRR path computation can better scale in SDN than in traditional networks. However, the traditional FRR path computation algorithms could lead to a poor performance in large-scale SDN. The problem can become more severe for a highly dynamic network, which often sees dozens of failures or configuration changes in any single day. We propose a new algorithm that exploits pruned searching to quickly compute recovery paths for all-pair switches/hosts upon a link failure. For applications requiring stringent path robustness levels, we also extend this algorithm to quickly find the shortest guaranteed-cost path, which ensures that the recovery path used upon on-path link failures has the minimum cost. Compared with traditional solutions, our evaluations show that our algorithm is about 8 ~ 81 times faster than the practical implementation, 1.93 ~ 3.11 times faster than the state-of-the-art solution. Our results also show that the shortest guaranteed-cost path can reduce the cost of the recovery path significantly. Moreover, we design a prototype to show how to deploy our algorithm in an OpenFlow network.
Impact of particle number and mass size distributions of major chemical components on particle mass scattering efficiency in urban Guangzhou in southern China

Abstract. To grasp the key factors affecting particle mass scattering efficiency (MSE), particle mass and number size distribution, PM2.5 and PM10 and their major chemical compositions, and the particle scattering coefficient (bsp) under dry conditions were measured at an urban site in Guangzhou, southern China, during 2015–2016. On an annual average, 10±2 %, 48±7 % and 42±8 % of PM10 mass were in the condensation, droplet and coarse modes, respectively, with mass mean aerodynamic diameters (MMADs) of 0.78±0.07 in the droplet mode and 4.57±0.42 µm in the coarse mode. The identified chemical species mass concentrations can explain 79±3 %, 82±6 % and 57±6 % of the total particle mass in the condensation, droplet and coarse mode, respectively. Organic matter (OM) and elemental carbon (EC) in the condensation mode, OM, (NH4)2SO4, NH4NO3, and crustal element oxides in the droplet mode, and crustal element oxides, OM, and CaSO4 in the coarse mode, were the dominant chemical species in their respective modes. The measured bsp can be reconstructed to the level of 91±10 % using Mie theory with input of the estimated chemically resolved number concentrations of NaCl, NaNO3, Na2SO4, NH4NO3, (NH4)2SO4, K2SO4, CaSO4, Ca(NO3)2, OM, EC, crustal element oxides and unidentified fraction. MSEs of particle and individual chemical species were underestimated by less than 13 % in any season based on the estimated bsp and chemical species mass concentrations. Seasonal average MSEs varied in the range of 3.5±0.1 to 3.9±0.2 m2 g−1 for fine particles (aerodynamic diameter smaller than 2.1 µm), which was mainly caused by seasonal variations in the mass fractions and MSEs of the dominant chemical species (OM, NH4NO3, (NH4)2SO4) in the droplet mode. MSEs of the dominant chemical species were determined by their lognormal size-distribution parameters, including MMADs and standard deviation (σ) in the droplet mode.
Study of Periodic Segments in Analysis of the Skills in Periodic Motions Using Bayesian Networks

In this study, the motions involved in hula hooping were divided into several chronological segments, and a method for determining the skills involved in each segment developed. A significant number of the actions we perform every day, such as walking and running, involves periodic operations. These actions are learned early in childhood and, in later life, we go through the motions unaware of the component actions. However, when it becomes necessary to reacquire these talents through rehabilitation or other means, a considerable amount of effort is subsequently required through repeated trial and error before we can efficiently perform them once more. We have developed an algorithm that can chronologically and quantitatively represent the skills involved in these periodic behaviours. This paper outlines the construction of a Bayesian networks model used for analysis, and presents information on the number of segments, representation of the skills involved in a motion/action when there is variance in the position of a segment, and the reliability of those skills.
VS3‐NET: Neural variational inference model for machine‐reading comprehension

We propose the VS3‐NET model to solve the task of question answering questions with machine‐reading comprehension that searches for an appropriate answer in a given context. VS3‐NET is a model that trains latent variables for each question using variational inferences based on a model of a simple recurrent unit‐based sentences and self‐matching networks. The types of questions vary, and the answers depend on the type of question. To perform efficient inference and learning, we introduce neural question‐type models to approximate the prior and posterior distributions of the latent variables, and we use these approximated distributions to optimize a reparameterized variational lower bound. The context given in machine‐reading comprehension usually comprises several sentences, leading to performance degradation caused by context length. Therefore, we model a hierarchical structure using sentence encoding, in which as the context becomes longer, the performance degrades. Experimental results show that the proposed VS3‐NET model has an exact‐match score of 76.8% and an F1 score of 84.5% on the SQuAD test set.
Effect of N-Fertilizer Types and Rates Interacted with Water Regime on Micronutrients Uptake by Potato Grown on Sandy Soil

A field experiment was conducted on sand soil to evaluate the impact of interaction between nitrogen sources, rates water regime on micronutrients uptake by potato crop. Potato plants were fertilized with urea and ammonium phosphate applied at rates of 150, and 90 kg ha of both two different N forms in addition to unfertilized treatment. These two rates were splitted into two equal doses; first one was applied at 20 days after seedling initiation and the second at 25 days from the first dose. Plants were grown under two water regimes, i.e. 100% and 60% Etc. Fe, Mn, Cu and Zn were frequently affected by water regimes. Except Cu, other elements were significantly accumulated in tubers under 60% evapotranspiration (Etc) regimes higher than 100% Etc regime. Fe, Mn in tubers was higher in case of urea than ammonium phosphate at rate of 150 kg N ha as compared to 90 kg N ha. Cu values in tubers did not significantly affected by water regimes and N rates but urea form still superior over ammonium phosphate. On the other hand, Zn was more accumulated in tubers of plants fertilized ammonium phosphate applied at 90 kg N ha under 60% Etc water regime comparing to other treatments. Transfer of Fe from soil to potato tubers seems to be very low. Mn was moderately transferred from soil to potato tubers. Copper was moderately to high transferred from soil to plant tubers as affected by tested factors. Potato tubers act as accumulator for Zn since TF was high and nearly to 1. Ammonium phosphate enhanced the transfer of Zn comparing to urea fertilizer. It increases with application of 90 kg N ha as compared to 150 kg N ha. There was no significant difference between 100% and 60% Etc water regimes.
Engineering and evolving helical proteins that improve in vivo stability and inhibit entry of Enfuvirtide-resistant HIV-1

ii ACKNOWLEDGEMENTS . iii LIST OF TABLES . vii LIST OF FIGURES . viii Chapter 1 – Protein Engineering and Evolution Approaches to Evaluate and Inhibit HelixDependent Protein-Protein Interactions . 1 1.
Automatic Detection Approach for Bioresorbable Vascular Scaffolds Using a U-Shaped Convolutional Neural Network

Artificial stent implantation is one of the most effective ways to treat vascular diseases. However, commonly used metal stents have many negative effects, such as being difficult to remove and recover, whereas bio-absorbable stents have become the best way to treat vascular diseases because of their absorbability and harmlessness. It is very important in vascular medical imaging, such as optical coherence tomography (OCT), to be able to effectively track the position of stents in blood vessels. This task is undoubtedly labor-intensive, and it is inefficient to rely on experts to identify various scaffolds from medical images. In this paper, a novel automatic detection method for bioresorbable vascular scaffolds (BVSs) via a U-shaped convolutional neural network is developed. The method is composed of three steps: data preparation, network training, and network testing. First, in the data preparation step, we complete the task of labeling related samples based on expert experience, and then, these labeled OCT images are divided into the original and masked OCT images (corresponding to X and Y in supervised learning, respectively). Next, we train our data on a U-shaped convolutional neural network, which consists of five downsampling modules and four upsampling modules. We can obtain a related training model, which can be used to predict the related samples. In the testing stage, we can easily utilize the trained model to predict the input OCT data so that we can obtain the relevant information about a BVS in an OCT image. Obviously, this method can assist doctors in diagnosing the disease and in making important decisions. Finally, some experiments are performed to validate our proposed method, and the IoU criterion is used to measure the superiority of our proposed method. The results show that our proposed method is completely feasible and superior.
Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization

These days, there is a strong rise in the needs for machine learning applications, requiring an automation of machine learning engineering which is referred to as AutoML. In AutoML the selection, composition and parametrization of machine learning algorithms is automated and tailored to a specific problem, resulting in a machine learning pipeline. Current approaches reduce the AutoML problem to optimization of hyperparameters. Based on recursive task networks, in this paper we present one approach from the field of automated planning and one evolutionary optimization approach. Instead of simply parametrizing a given pipeline, this allows for structure optimization of machine learning pipelines, as well. We evaluate the two approaches in an extensive evaluation, finding both approaches to have their strengths in different areas. Moreover, the two approaches outperform the state-of-the-art tool Auto-WEKA in many settings.
Sensitivity analysis and Bayesian calibration of building energy models

The current sate of the art of Building Energy Simulation (BES) lacks of a rigorous framework for the analysis, calibration and diagnosis of BES models. This research takes this deficiency as an opportunity for proposing a strongly mathematically based methodology serving such purposes, providing: a better consideration of the modelling uncertainties, means to reduce BES model complexity without oversimplification, and methods to test and select different modelling hypotheses depending on field observations. Global Sensitivity Analysis (GSA), Gaussian Process Regression (GPR) in a quasi-Bayesian set up and Markov Chain Monte Carlo (MCMC) methods are the foundations upon which the proposed framework is built. It couples deterministic BES models and stochastic black box models, thus having the physical and probabilistic representation of real phenomena complementing each other. It comprises four phases: Uncertainty Analysis , Sensitivity Analysis , Calibration, Model Selection. The framework was tested on a series of trials having increasing difficulty. Relatively simple preliminary experiments were used to develop the methodology and investigate strengths and weaknesses. They showed its capabilities in treating measurement uncertainties and model deficiencies, but also that these aspects influence the estimation of model parameters. More detailed experiments were used to fully test the efficacy of the method in analysing complex BES models. Novel techniques, based on Bootstrap and Smoothing with Roughness Penalty, for the determination of the uncertainties of multidimensional model inputs, were introduced. The framework was proven effective in adequately simplifying BES models, in precisely identifying parameters, causes of discrepancies and improvements, and in providing clear information about which model was the most suitable in describing the observed processes. This research delivers a powerful tool for the analysis, diagnosis and calibration of BES models, which substantially improves the current practice and that can be already applied to solve many practical problems, such as the investigation of energy conservation measures, model predictive control and fault detection.
Jamming Resources Distribution Optimization Technology Based on the Niche Genetic Algorithm

A new method called niche genetic algorithm for jamming resources distribution optimization,which can search global optimal solution better is proposed. Based on the characteristics of this article,an improved way of coding is given to ensure the effectiveness of the initial population. A measure is proposed to alleviate the dependence of convergence on the initial population,in which two reproduction operations of different types are alternatively used in generating chromosomes. The problem easily- trapped in local optimization and the shortcomings of bad local searching capability existed in standard genetic algorithm are overcome,and the ability of resource scheduling is improved. The computational results show that the success probability is improved obviously on the premise of the convergence speed and global search.
Active checking buffer overflow vulnerability in binaries with symbolic execution

Buffer overflows in C and C++ programs are among the most common and serious classes of software vulnerabilities for two decades. To mitigate and to eradicate this security threat, many detection approaches based on static and dynamic analysis techniques have been proposed. This paper aims to provide an alternative and multipath solution to existing symbolic execution approaches by catching the red-zones in memory, rather than the strict memory object bounds, which are absolute vulnerable to buffer overflows. From the observations of buffer overflow attacks that are commonly overwrite a special position to exploit the vulnerabilities, in this paper, we propose a multipath dynamic buffer overflow detecting approach (symbolic-execution-based), relying on catching the red-zones in the stack and heap memory. We examine every memory store operation both in concrete addresses and symbolic pointers, whose writing size should never overlap or cross a red-zone position. We implement a practical dynamic checking tool called MACBin based on S2E platform. We apply it to test several real world software, the results show that MACBin is useful and effective at detecting buffer overflow vulnerabilities.
A Factorization Approach for Survival Analysis on Diffusion Networks

In this paper, we propose a survival factorization framework that models information cascades by tying together social influence patterns, topical structure, and temporal dynamics. This is achieved through the introduction of a latent space which encodes: (a) the relevance of an information cascade on a topic; (b) the topical authoritativeness and the susceptibility of each individual involved in the information cascade, and (c) temporal topical patterns. By exploiting the cumulative properties of the survival function and of the likelihood of the model on a given adoption log, which records the observed activation times of users and side-information for each cascade, we show that the inference phase is linear in the number of users and in the number of adoptions. The evaluation on both synthetic and real-world data shows the effectiveness of the model in detecting the interplay between topics and social influence patterns, which ultimately provides high accuracy in predicting users activation times.