Miracle-dz/UniFAR-FTUs huggingface dataset:
High-resolution Air Quality Modelling And TimeScale Analysis Of Ozone And NOx In Osaka,Japan

The ozone pollution in the Osaka region is mainly influenced by the long-range transport of tropospheric ozone or its precursors from outside in winter but nearly 50% of the total ozone is chemically produced by regional emissions in summertime. The effects of the regional transport and the chemical production of ozone by regional emissions in summertime can be analysed by spectral decomposition of the time series and studying the inherent intra-day, diurnal and longer-term time scales in the time series of ozone and NOx. The relative contributions of these time scale components associated with different physical processes are also used to evaluate the performance of air quality models in simulating ozone and NOx. We used the Japanese national emission data with resolution of 10-km and 1-km resolution in the MM5-CMAQ modelling system for the nested simulation in Osaka with the finest domain having 1-km grid size. Among the different time scales, diurnal time scale has the highest correlation for ozone, and longer time scale has the highest correlation for NO and NO2. Spatial variation in the simulation of NOx is evident from the study of correlation of diurnal and longer-term time scales, with higher correlations in the longer-term time scale. Overall, the time scale analysis of ozone and NOx time series has proved useful for detailed quantitative analysis of the air quality modelling in Osaka.
Tensor Low-Rank Discriminant Embedding for Hyperspectral Image Dimensionality Reduction

Recently, low-rank embedding (LRE) has yielded satisfactory results in dimensionality reduction (DR), for which low-rank representation and projection learning are integrated into one model to generate robust low-dimensional features. However, LRE requires to convert samples into vectors even if the data naturally appear in high-order form. Furthermore, LRE fails to take the label information into consideration. To address these problems, this paper proposes a novel supervised DR method based on multilinear algebra, i.e., the algebra of tensors. By the motivation of extending LRE into tensor space and simultaneously combining the tensor discriminant analysis, we establish tensor low-rank discriminant embedding (TLRDE) model for hyperspectral image (HSI) DR. The model of TLRDE is solved by an alternative iteration algorithm, whose convergence is also mathematically proven. The proposed TLRDE method employs the tensor representation to preserve the intrinsic geometrical structure, uses low-rank reconstruction to uncover the potential relationship among the data points, and combines label information to enhance the discriminability of features. Moreover, the proposed TLRDE does not suffer from the small sample size problem. The experimental results on three real HSI data sets validate the effectiveness of our proposed TLRDE method.
The Application of ZFD Formula to Kinematic Control of Redundant Robot Manipulators With Guaranteed Motion Precision

Zhang et al. recently design a special difference formula called the Zhang finite-difference (ZFD) formula. This paper applies the ZFD formula for the kinematic control of redundant robot manipulators, due to its good performance in approximating first-order derivatives. Specifically, the ZFD formula is employed to discrete the pseudoinverse-based (P-based) kinematic control scheme; a new P-based kinematic control scheme with guaranteed motion precision is developed for redundant robot manipulators. Simulation results based on a five-link planar robot manipulator, which performs different end-effector paths, validate the effectiveness of the new scheme and indicate the application prospects of the ZFD formula.
Simulation of Crowd Evacuation in Laboratory using Social Force Model

This paper presents the simulation of crowd evacuation when a disaster occurs, and an immediate evacuation from the area is needed. The area that is used as an example is a laboratory space with different layouts to determine if the layout has any impacts in evacuating the enclosed space of the laboratory. The simulation is executed by using a combination of agent-based modeling and a simulation technique for crowd evacuation which is known as the Social Force Model (SFM). The simulation has been developed by using OpenGL and Vecmath package on a platform of Microsoft Visual Studio 2017. The simulation is performed to observe if the layout of a space plays a role in reducing the evacuation time. There are 3 layouts have been simulated where the number of agents is 19 and 25 agents depending on the space of the layouts. The simulation results show satisfactory performance, and it shows that the layout arrangements of the selected laboratories do affect the evacuation time.
Towards finding optimal mixture of subspaces for data classification ; Veri sınıflama için en iyi altuzay karışımlarının bulunmasına doğru

TOWARDS FINDING OPTIMAL MIXTURE OF SUBSPACES FOR DATA CLASSIFICATION MOHAMED ELHAFIZ MUSTAFA MUSA Ph.D., Department of Computer Engineering Supervisor: Assoc.Prof. Dr. Volkan Atalay October 2003, 85 pages In pattern recognition, when data has different structures in different parts of the input space, fitting one global model can be slow and inaccurate. Learning methods can quickly learn the structure of the data in local regions, consequently, offering faster and more accurate model fitting. Breaking training data set into smaller subsets may lead to curse of dimensionality problem, as a training sample subset may not be enough for estimating the required set of parameters for the submodels. Increasing the size of training data may not be at hand in many situations. Interestingly, the data in local regions becomes more correlated. Therefore, by decorrelation methods we can reduce data dimensions and hence the number of parameters. In other words, we can find uncorrelated low dimensional subspaces that capture most of the data variability. The current subspace modelling methods have proved better performance than the global modelling methods for the given type of training data structure. Nevertheless these methods still need more research work as they are suffering from two limitations • There is no standard method to specify the optimal number of subspaces. • There is no standard method to specify the optimal dimensionality for each iii subspace. In the current models these two parameters are determined beforehand. In this dissertation we propose and test algorithms that try to find a suboptimal number of principal subspaces and a suboptimal dimensionality for each principal subspaces automatically.
STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
Learning to discover and localize visual objects with open vocabulary

To alleviate the cost of obtaining accurate bounding boxes for training today's state-of-the-art object detection models, recent weakly supervised detection work has proposed techniques to learn from image-level labels. However, requiring discrete image-level labels is both restrictive and suboptimal. Real-world "supervision" usually consists of more unstructured text, such as captions. In this work we learn association maps between images and captions. We then use a novel objectness criterion to rank the resulting candidate boxes, such that high-ranking boxes have strong gradients along all edges. Thus, we can detect objects beyond a fixed object category vocabulary, if those objects are frequent and distinctive enough. We show that our objectness criterion improves the proposed bounding boxes in relation to prior weakly supervised detection methods. Further, we show encouraging results on object detection from image-level captions only.
T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling

In this paper we propose a data augmentation method for time series with irregular sampling, Time-Conditional Generative Adversarial Network (T-CGAN). Our approach is based on Conditional Generative Adversarial Networks (CGAN), where the generative step is implemented by a deconvolutional NN and the discriminative step by a convolutional NN. Both the generator and the discriminator are conditioned on the sampling timestamps, to learn the hidden relationship between data and timestamps, and consequently to generate new time series. We evaluate our model with synthetic and real-world datasets. For the synthetic data, we compare the performance of a classifier trained with T-CGAN-generated data, against the performance of the same classifier trained on the original data. Results show that classifiers trained on T-CGAN-generated data perform the same as classifiers trained on real data, even with very short time series and small training sets. For the real world datasets, we compare our method with other techniques of data augmentation for time series, such as time slicing and time warping, over a classification problem with unbalanced datasets. Results show that our method always outperforms the other approaches, both in case of regularly sampled and irregularly sampled time series. We achieve particularly good performance in case with a small training set and short, noisy, irregularly-sampled time series.
Visual Analytics Ontology-Guided I-DE System: A Case Study of Head and Neck Cancer in Australia

Visual analytics is inherently multi-disciplinary in nature encompassing diverse concepts from a wide range of scientific disciplines. It is a cutting-edge technology for multi-dimensional data analysis. In this paper, we discuss the development of a Visual Analytic Ontology (VAO) that provides a standard, unified, machine-understandable representation of expert knowledge that can be used to create rules to aid in the visual analysis of data. Further, we describe the development of Intelligent Data Ecosystem (I-DE) that uses the VAO to visualize the analytic information. We exemplify the efficacy of I-DE using the head and neck cancer case study data from Australia.
Color image segmentation using modified HSI system for road following

A novel method for outdoor natural color image segmentation for road following is presented. From the results of experiments it can be seen that the hue, saturation, and intensity (HSI) system should be used rather than the red, green, blue (RGB) system, and segmentation can be done in S-I space with good performance. An automatic adaptive threshold selection method is developed. The preliminary results indicate the effectiveness and efficiency of this method. <<ETX>>
Development, Testing and Maintenance of Android Apps: Challenges, Approaches, Tools, and Future Directions

Mobile devices have become a permanent fixture in modern society. As such, it is of critical importance that the mobile development process is made as frictionless as possible to facilitate the creation of high-quality apps for end users. This keynote offers a brief introduction to mobile development paradigms, surveys the major categories of research conducted to date towards improving mobile software engineering, examines open challenges, and outlines a roadmap of future work aimed to support mobile developers.
Three‐dimensional printing biotechnology for the regeneration of the tooth and tooth‐supporting tissues

The tooth and its supporting tissues are organized with complex three‐dimensional (3D) architecture, including the dental pulp with a blood supply and nerve tissues, complex multilayer periodontium, and highly aligned periodontal ligament (PDL). Mimicking such 3D complexity and the multicellular interactions naturally existing in dental structures represents great challenges in dental regeneration. Attempts to construct the complex system of the tooth and tooth‐supporting apparatus (i.e., the PDL, alveolar bone, and cementum) have made certain progress owing to 3D printing biotechnology. Recent advances have enabled the 3D printing of biocompatible materials, seed cells, and supporting components into complex 3D functional living tissue. Furthermore, 3D bioprinting is driving major innovations in regenerative medicine, giving the field of regenerative dentistry a boost. The fabrication of scaffolds via 3D printing is already being performed extensively at the laboratory bench and in clinical trials; however, printing living cells and matrix materials together to produce tissue constructs by 3D bioprinting remains limited to the regeneration of dental pulp and the tooth germ. This review summarizes the application of scaffolds for cell seeding and biofabricated tissues via 3D printing and bioprinting, respectively, in the tooth and its supporting tissues. Additionally, the key advantages and prospects of 3D bioprinting in regenerative dentistry are highlighted, providing new ideas for dental regeneration.
A Baseline for Multi-Label Image Classification Using Ensemble Deep CNN

Recent studies on multi-label image classification have been focusing on designing more complex architectures of deep neural networks such as the use of attention mechanism and region proposal networks. Although performance gains have been reported in literature, the backbone deep models of the proposed approaches and the evaluation metrics employed in different works vary, making it difficult to compare with each other fairly. Moreover, due to the lack of properly investigated baselines, the advantage introduced by the proposed techniques in literature are vague. To address these issues, we make a thorough investigation of the mainstream deep convolutional neural network architectures for multi-label image classification and present a strong baseline. With only data augmentation and model ensemble, we achieve better performance than those previously reported on three benchmark datasets. We hope the work presented in this paper will provide insights to the future studies on multi-label image classification.
Patient and public involvement in the design, administration and evaluation of patient feedback tools, an example in psychiatry: a systematic review and critical interpretative synthesis

Background Patient feedback is considered integral to healthcare design, delivery and reform. However, while there is a strong policy commitment to evidencing patient and public involvement (PPI) in the design of patient feedback tools, it remains unclear whether this happens in practice. Methods A systematic review using thematic analysis and critical interpretative synthesis of peer-reviewed and grey literature published between 2007 and 2017 exploring the presence of PPI in the design, administration and evaluation of patient feedback tools for practising psychiatrists. The research process was carried out in collaboration with a volunteer mental health patient research partner. Results Fourteen articles (10 peer-reviewed, four grey literature) discussing the development of nine patient feedback tools were included. Six of the nine tools reviewed were designed from a professional perspective only. Tool content and its categorization primarily remained at the professional’s discretion. Patient participation rates, presence of missing data and psychometric validation were used to determine validity and patient acceptability. In most instances, patients remained passive recipients with limited opportunity to actively influence change at any stage. No article reviewed reported PPI in all aspects of tool design, administration or evaluation. Conclusions The majority of patient feedback tools are designed, administered and evaluated from the professional perspective only. Existing tools appear to assume that: professional and patient agendas are synonymous; psychometric validation is indicative of patient acceptability; and psychiatric patients do not have the capacity or desire to be involved. Future patient feedback tools should be co-produced from the outset to ensure they are valued by all those involved. A reconsideration of the purpose of patient feedback, and what constitutes valid patient feedback, is also required.
Design and Characterisation of a Fast Steering Mirror Compensation System Based on Double Porro Prisms by a Screw-Ray Tracing Method

This study proposes a novel FSM compensation system for four degrees of freedom (DOF) laser errors compensation, which has the advantage of shorter optical path length, fewer elements and an easier set-up process, meaning that it can be used at different locations. A commercial software, Zemax, is used to evaluate the function of the proposed FSM compensation system and the mathematical modelling of the proposed FSM compensation system is established by using a skew-ray tracing method. Finally, the proposed FSM compensation system is then verified experimentally using a laboratory-built prototype and the result shows that the proposed FSM compensation system achieves the ability to compensate the 4 DOF of the laser source.
Energy-Efficient UAV Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach

Unmanned aerial vehicles (UAVs) can be used to serve as aerial base stations to enhance both the coverage and performance of communication networks in various scenarios, such as emergency communications and network access for remote areas. Mobile UAVs can establish communication links for ground users to deliver packets. However, UAVs have limited communication ranges and energy resources. Particularly, for a large region, they cannot cover the entire area all the time or keep flying for a long time. It is thus challenging to control a group of UAVs to achieve certain communication coverage in a long run, while preserving their connectivity and minimizing their energy consumption. Toward this end, we propose to leverage emerging deep reinforcement learning (DRL) for UAV control and present a novel and highly energy-efficient DRL-based method, which we call DRL-based energy-efficient control for coverage and connectivity (DRL-EC3). The proposed method 1) maximizes a novel energy efficiency function with joint consideration for communications coverage, fairness, energy consumption and connectivity; 2) learns the environment and its dynamics; and 3) makes decisions under the guidance of two powerful deep neural networks. We conduct extensive simulations for performance evaluation. Simulation results have shown that DRL-EC3 significantly and consistently outperform two commonly used baseline methods in terms of coverage, fairness, and energy consumption.
Activation of Haa1 and War1 transcription factors by differential binding of weak acid anions in Saccharomyces cerevisiae

Abstract In Saccharomyces cerevisiae, Haa1 and War1 transcription factors are involved in cellular adaptation against hydrophilic weak acids and lipophilic weak acids, respectively. However, it is unclear how these transcription factors are differentially activated depending on the identity of the weak acid. Using a field-effect transistor (FET)-type biosensor based on carbon nanofibers, in the present study we demonstrate that Haa1 and War1 directly bind to various weak acid anions with different affinities. Haa1 is most sensitive to acetate, followed by lactate, whereas War1 is most sensitive to benzoate, followed by sorbate, reflecting their differential activation during weak acid stresses. We show that DNA binding by Haa1 is induced in the presence of acetic acid and that the N-terminal Zn-binding domain is essential for this activity. Acetate binds to the N-terminal 150-residue region, and the transcriptional activation domain is located between amino acid residues 230 and 483. Our data suggest that acetate binding converts an inactive Haa1 to the active form, which is capable of DNA binding and transcriptional activation.
Molecular gas in a z ~ 2.5 triply-imaged, sub-mJy submillimetre galaxy typical of the cosmic far-infrared background

We present the results of observations from the IRAM array of the submm galaxy SMMJ16359+6612 lying at z=2.516 behind the massive cluster A2218. The foreground gravitational lens produces 3 images with a total mag. of 45 of this faint submm galaxy, which has an intrinsic submm flux of f_850mic=0.8mJy placing it below the confusion limit of blank-field surveys. The substantial magnification provides a rare opportunity to probe the nature of a distant sub-mJy submm-selected galaxy, part of the population which produces the bulk of the submm cosmic far-infrared background. Our observations detect the CO(3-2) line in all 3 images, as well as the CO(7-6) line and the dust continuum at 1.3mm for the brightest image. The CO(3-2) velocity profile displays a double-peak profile which is well fit by two Gaussians with FWHM of 220km/s and separated by 280km/s. We estimate the dynamical mass of the system to be ~1.5 10^10 M_sun and an H2 gas mass of 2.6 10^9 M_sun. We identify a spatial offset of ~1\'\' between the two CO(3-2) velocity components, modeling of which indicates that the offset corresponds to just ~3kpc in projection at z=2.5. The spatial and velocity properties of these two components are closely related to features detected in previously published Halpha spectroscopy. We conclude that this source is likely to be a compact merger of 2 fairly typical Ly-break galaxies with a maximal separation between the two nuclei of ~3kpc. This system is much less luminous and massive than other high-z submm galaxies studied to date, but it bears a close similarity to similarly luminous, dusty starburst resulting from lower-mass mergers in the local Universe.