Miracle-dz/UniFAR-FTUs huggingface dataset:
The correlation of carbon source and ammonium accumulation in culture broth by nitrogen-fixing bacterial isolates

Several nitrogen-fixing bacteria were isolated from the agricultural soil samples of Kyaukse District, Mandalay Division, Myanmar. Among the isolated strains K-3 and T-7 were identified as Azotobacter beijerinckii and Azotobacter vinellandii and and M4 was identified as Stenotrophomonas maltophilia. Although other studies have reported that wild types of the nitrogen-fixing bacteria cannot produce excess ammonia, our experiments indicated that these three strains accumulate relatively high amounts of ammonia in medium with a low concentration of sugar. Azotobacter beijerinckii accumulated approximately 0.48 mM of ammonia after incubation for 96 hour in 0.5% nitrogen free glucose mineral medium (NFGMM), while A. vinelandii accumulated approximately 0.36 mM in 0.25% nitrogen free fructose mineral medium (NFFMM) after 96 hour. Stenotrophomonas maltophilia accumulated highest amounts of ammonia in glucose supplemented medium approximately 0.52mM after 96h incubation. A. beijerinckii also accumulated ammonia in fructose, galactose, and sucrose media. Azotobacter vinelandii accumulated ammonia mainly in fructose and Stenotrophomonas maltophilia accumulated ammonia in glucose, sucrose and mannitol. In this research work, it was noted that ammonia began to accumulate when no sugar remained in the medium, therefore we suggested that carbon source plays a key role in ammonia accumulation.
WHERE DO ALPHAS COME FROM ? : A MEASURE OF THE VALUE OF ACTIVE INVESTMENT MANAGEMENT ∗

The value of active investment management is traditionally measured by alpha, beta, volatility, tracking error, and the Sharpe and information ratios. These are essentially static characteristics of the marginal distributions of returns at a single point in time, and do not incorporate dynamic aspects of a manager’s investment process. In this paper, I propose a measure of the value of active investment management that captures both static and dynamic contributions of a portfolio manager’s decisions. The measure is based on a decomposition of a portfolio’s expected return into two distinct components: a static weighted-average of the individual securities’ expected returns, and the sum of covariances between returns and portfolio weights. The former component measures the portion of the manager’s expected return due to static investments in the underlying securities, while the latter component captures the forecast power implicit in the manager’s dynamic investment choices. This measure can be computed for long-only investments, long/short portfolios, and asset allocation rules, and is particularly relevant for hedge-fund strategies where both components are significant contributors to their expected returns, but only one should garner the high fees that hedge funds typically charge. Several analytical and empirical examples are provided to illustrate the practical relevance of this decomposition.
Feature Attention Network: Interpretable Depression Detection from Social Media

Although depression is one of the most common mental disorders, the depressed individuals may not be aware of their symptoms at all so that they sometimes miss the appropriate time for treatment. In order to prevent this problem, many researchers looked into social media to ﬁgure out depressed individuals by analyzing the differences in language use. While they have recently achieved reasonable performance in detecting depression, especially using deep learning methods, such methods still do not provide a clear way to explain why certain individuals have been detected as depressed. To address this issue, we propose Feature Attention Network (FAN), inspired by the process of diagnosing depression by an expert who has background knowledge about depression. We evaluate the performance of our model on a large scale general forum (Reddit Self-reported Depression Diagnosis) dataset. Experimental results demonstrate that FAN shows good performance with high interpretability despite a smaller number of posts in training data. We investigate different aspects of posts by depressed users through four feature networks built upon psychological studies, which will help researchers to investigate social media posts to ﬁnd useful evidence for depressive symptoms.
Compact beamforming in medical ultrasound scanners

This Ph.D. project was carried out at the Center for Fast Ultrasound Imaging, Technical University of Denmark, under the supervision of Prof. Jorgen Arendt Jensen, Assoc. Prof. Jens Sparso and Prof. Erik Bruun. The goal was to investigate methods for efficient beamforming, which make it possible to fit a large number of channels on a single integrated circuit. The use of oversampled analog-to-digital (A/D) converters with the corresponding beamforming was identified as a particularly promising approach, since it provides both inexpensive and compact A/D conversion and allows for much more compact implementation of the beamformer compared to the case where conventional A/D conversion is used. The compact and economic beamforming is a key aspect in the progress of medical ultrasound imaging. Currently, 64 or 128 channels are widely used in scanners, top-of-the-range scanners have 256 channels, and even more channels are necessary for 3-dimensional (3D) diagnostic imaging. On the other hand, there is a demand for inexpensive portable devices for use outside hospitals, in field conditions, where power consumption and compactness are important factors. The thesis starts with an introduction into medical ultrasound, its basic principles, system evolution and its place among medical imaging techniques. Then, ultrasound acoustics is introduced, as a necessary base for understanding the concepts of acoustic focusing and beamforming, which follow. The necessary focusing information for high-quality imaging is large, and compressing it leads to better compactness of the beamformers. The existing methods for compressing and recursive generation of focusing data, along with original work in the area, are presented in Chapter 4. The principles and the performance limitations of the oversampled delta-sigma converters are given in Chapter 5, followed by an overview of the present architectures of oversampled beamformers. Then, a new architecture is introduced, which has the potential of achieving the highest image quality that an oversampling beamformer can provide. That architecture has been implemented using VHDL, and estimates for its performance have been obtained. The results indicate that a 32-channel beamformer reaches the target operation frequency of 140MHz, thereby providing diagnostic image with dynamic range of 60 dB for an excitation central fre-
DEM-FEM model of the outflow of backfilling sand from behind a seawall under wave motion

The DEM and FEM techniques are combined to numerically model the collapse of a structure consisting of a fixed vertical revetment and backfilling sand under wave impacts in a vertical domain. The course of events inside the granular material in the vicinity of the wall during its washing out is the object of this research. The study is based on the methods of computational mechanics of sediment transport; hence the sediment and pore water oscillation interaction and the inter-particle collisions are considered. Small-scale laboratory tests were performed to provide verification of the numerical model.
Appendix: List of publications

Despite of many criticisms, agile software development (Beck et al., Manifesto for Agile Software Development, http://agilemanifesto.org, [1]) philosophy has been proved to be quite successful to increase the project success rate up to a significant extent. The primary reasons behind the criticism of agile are the strong violation of the traditional disciplined software engineering theories, principles and practices. The goal of this research paper is to establish the fact that agility may also be achieved by following traditional process models especially with BRIDGE (Mandal, 2009 IEEE International Advance Computing Conference (IACC 2009) Patiala, India, [2]) process model. Hence, the aim of this paper is to integrate the agile and traditional disciplined software development process models and establish the compatibility between both the approaches. At the beginning, we have explored the objectives and principles of agile software development. Next, we briefly discussed the BRIDGE process model and further justified that—following BRIDGE process model, the philosophy of agile may also be achieved and conclude that BRIDGE model has both the capability of traditional software development process model as well as of the agile process.
Searching for cost-effective subgroups : application of an adaptive search algorithm to individual patient data from a trial of physical therapies for low back pain

Low Back Pain Repository Study Team Sally Brown Warwick University Melina Dritsaki Warwick Medical School Tim Friede University Medical Center Göttingen Siew Wan Hee Warwick Medical School Jake Jordan Brunel University Sallie Lamb University of Oxford Joanne Lord Brunel University Dipesh Mistry Warwick Medical School Shilpa Patel Warwick Medical School Nigel Stallard Warwick Medical School Colin Tysall Warwick University Martin Underwood Warwick Medical School
LensXPlain: Visualizing and Explaining Contributing Subsets for Aggregate Query Answers

In this demonstration, we will present LensXPlain , an interactive system to help users understand answers of aggregate queries by providing meaningful explanations. Given a SQL group-by query and a question from a user “ why output o is high /low ”, or “ why output o 1 is higher/lower than o 2 ”, LensXPlain helps users explore the results and ﬁnd sub-sets of tuples captured by predicates that contributed the most toward such observations. The contributions are measured either by intervention (if the contributing tuples are removed, the values or the ratios in the user question change in the opposite direction), or by aggravation (if the query is restricted to the contributing tuples, the observations change more in the same direction). LensXPlain uses ensemble learning for recommending useful attributes in explanations, and employs a suite of optimizations to enable explanation generation and reﬁnement at an interactive speed. In the demonstration, the audience can run aggregation queries over real world datasets, browse the answers using a graphical user interface, ask questions on unexpected/interesting query results with simple visualizations, and explore and reﬁne explanations returned by LensXPlain
Comparing ORB and AKAZE for visual odometry of unmanned aerial vehicles

To navigate autonomously, an Unmanned Aerial Vehicle, also known as drone, must be able to estimate its position during flight. This is usually achieved with the use of the global positioning system combined with an inertial measurement unit. Although widely used for this purpose, the GPS has some disadvantages, like the presence of GPS-denied cluttered environments. A way to circumvent this issue is to employ visual odometry techniques with feature-based methods to compute the aircraft motion and thereby allow the position estimation. We employ two recent features detectors and descriptors ORB and AKAZE and compare their accuracy and processing time. The objective is to evaluate their suitability for visual odometry task in real-time embedded systems of UAVs. We have concluded that, although ORB is faster to compute, AKAZE shows a better compromise between speed and performance than ORB for images with low resolution.
Assessment of priorities in the Analytical Hierarchy Process by Evolutionary Computing

The paper investigates the application of evolutionary algorithms (EA) for assessment of priorities in the Analytical Hierarchy Process by solving a two-objective prioritisation problem. We propose two evolutionary computing approaches, based on single-objective and multi-objective EA. Our preliminary results from a Monte-Carlo simulation show that the multi-objective EA outperforms the single-objective solution approach with respect to accuracy and computational efficiency. Keywords—Evolutionary computing, prioritisation methods, multiobjective optimization.
Artist Identification with Convolutional Neural Networks

Artist identification of fine art paintings is a challenging problem primarily handled by art historians with extensive training and expertise. Many previous works have explored this problem by explicitly defining classification features. We train Convolutional Neural Networks (CNNs) with the goal of identifying the artist of a painting as accurately and precisely as possible. Our dataset consists of 300 paintings per artist from 57 well-known artists. We train a variety of models ranging from a simple CNN designed from scratch to a ResNet-18 network with transfer learning. Our best network achieves significantly higher classification accuracy than prior work. Additionally, we perform multiple experiments to explore and understand the learned representation of our networks. Our results demonstrate that CNNs are a powerful tool for artist identification and that when asked to identify artists, they are able to learn a representation of painting style.
Permanent Magnetic Articulograph (PMA) vs Electromagnetic Articulograph (EMA) in Articulation-to-Speech Synthesis for Silent Speech Interface

Silent speech interfaces (SSIs) are devices that enable speech communication when audible speech is unavailable. Articulation-to-speech (ATS) synthesis is a software design in SSI that directly converts articulatory movement information into audible speech signals. Permanent magnetic articulograph (PMA) is a wireless articulator motion tracking technology that is similar to commercial, wired Electromagnetic Articulograph (EMA). PMA has shown great potential for practical SSI applications, because it is wireless. The ATS performance of PMA, however, is unknown when compared with current EMA. In this study, we compared the performance of ATS using a PMA we recently developed and a commercially available EMA (NDI Wave system). Datasets with same stimuli and size that were collected from tongue tip were used in the comparison. The experimental results indicated the performance of PMA was close to, although not as equally good as that of EMA. Furthermore, in PMA, converting the raw magnetic signals to positional signals did not significantly affect the performance of ATS, which support the future direction in PMA-based ATS can be focused on the use of positional signals to maximize the benefit of spatial analysis.
RECONSTRUCTING DIMENSIONALITY OF CUSTOMER CORPORATE SOCIAL RESPONSIBILITY AND CUSTOMER RESPONSE OUTCOMES BY HOTELS IN KENYA

Study explored customer social responsibility and response outcomes perceptions of tourists of twenty selected classified hotels in Kenya. Using systematic random sampling, a sample of 661 resident tourists was selected from a total of 5440. Principal Component Analysis was used for data reduction. Six dimensions of customer social responsibility were extracted designated as environmental CSR, Customer CSR attitude, Customer CSR Orientation, customer switching potential, value of CSR and price criteria. Similarly, five customer response outcomes were obtained designated as customer competitive potential, customer satisfaction, service quality potential, degree of marketability and substitutability. Significant differences on customer CSR and customer responses outcomes across education levels, customer loyalty, gender and age of the customers existed. Additionally, lack of significant differences in perceptions across customer’s intent to stay, country of origin, income level, hotel and customer’s length of stay was found. Customer’s willingness to pay for CSR was neutral in regard to CSR perceptions.
Carbon Pass-Through Rates on Electricity Spot Prices in Australia WORKING PAPER 1706

This study assesses the impact of the Australian Carbon Pricing Mechanism (CPM) on electricity prices in the National Electricity Market (NEM). An econometric model is designed to examine the influence of short-run marginal costs, electricity demand and emission intensities on carbon pass-through rates on spot electricity prices. The developed model also allows to capture the non-linear relationship between spot electricity prices and demand that has typically been suggested in the literature. Our sample period from July 2009 to June 2016 allows us to analyse three different sub-periods in order to quantify pass-through effects: the period before the tax (July 2009 – June 2012), the period when the carbon tax was effective (July 2012 – June 2014) as well as a post-tax period after the repeal of the CPM (July 2014 – June 2016). Therefore, it allows a comprehensive evaluation of the effect of carbon pricing on the Australian electricity spot market. Our results demonstrate a high degree of carbon costs pass-through across the considered markets with the increase in electricity prices typically exceeding carbon costs in particular for the markets in New South Wales, Queensland and South Australia. The estimated carbon pass-through rates seem to be inversely related to emission intensities for each of the regions: they were relatively low for Victoria that is characterised by electricity generation from fossil fuels and high emission intensities, while they were the highest for South Australia, a market with the lowest emission intensities and a higher share of renewable energy generation. We also find that for most markets, emission intensities were only reduced by a relatively small margin during the tax period and that electricity prices have never reverted back to their pre-tax levels after the repeal of the tax.
E-Lumination-Lighting 3 D Scenes Using Examples

Lighting has much more importance than simply establishing visibility. In cinematography, theatre and videogames, good lighting is expected to create images with emotional content, set atmosphere and mood, direct attention and provide depth to a two-dimensional image. Lighting design is the process of finding light parameters: positions, directions, colours, and intensities to achieve the visual properties wanted for a specific scene. The traditional method of lighting design for a scene is iterative, requiring trial and error until the desired goal is reached. This is a repetitive and tedious work, which requires technical and artistic expertise in the field. Inverse lighting design aims to provide designers with semi-automated approaches and easy to use tools, to configure lighting for scenes, minimizing the time and expertise needed. This work will explore lighting by example. More specifically, the possibility of using videos and images as examples for lighting 3D scenes. The major goal of the proposed system is to emulate in the virtual scenes the same mood present on the short videos or images used as input. Tests were conducted with users, rating the mood of images used as input of the system and the resulting automatically illuminated 3D scenes. The obtained results were satisfactory, presenting some
Short Term Single Station GNSS TEC Prediction Using Radial Basis Function Neural Network

TEC prediction models for 24 hours ahead have been developed from JOG2 GPS TEC data during 2016. Eleven month of TEC data were used as a training model of the radial basis function neural network (RBFNN) and 1 month of last data (December 2016) is used for the RBFNN model testing. The RBFNN inputs are the previous 24 hour TEC data and the minimum of Dst index during the previous 24 hours. Outputs of the model are 24 ahead TEC prediction. Comparison of model prediction show that the RBFNN model is able to predict the next 24 hours TEC is more accurate than the TEC GIM model.
Designing a far-reaching view for highway traffic scenarios with 5G-based intelligent infrastructure

Cooperative vehicle infrastructure systems offer significant potential for improved traffic safety, throughput and improved energy efficiency. Infrastructure sensors along the road can substitute vehicular sensor-sets, providing improved robustness and performance through different mounting positions and orientations, reducing occlusions, stationary locations, facilitating system-wide calibration, optimization for the specific traffic area in view, and vastly increase perception range by combining multiple measurement points. Communication via fifth Generation (5G) networks offers solutions to the corresponding substantial requirements for high bandwidth, low latency and high reliability for data and information communication. We propose a concept, which aims to provide a far-reaching view to (self-driving) vehicles and drivers with infrastructure sensors and 5G communication, as a cognitive system. The system detects and localizes traffic objects and predicts their future movements. The resulting information will be provided to traffic participants allowing for safer, more proactive and comfortable driving.
olecular cloning and functional characterization of caspase 9 in large yellow roaker ( Pseudosciaena crocea )

The genomic and cDNA sequences of a caspase 9 homologue were cloned from large yellow croaker (Pseudosciaena crocea). The large yellow croaker caspase 9 gene (Lyccasp9) consists of 10 exons and 9 introns, spanning 3836 nucleotides. The full-length cDNA of Lyccasp9 is 2595 bp with an open reading frame of 1314 bp encoding a polypeptide of 437 amino acids (aa), which includes a 90-aa caspase recruitment domain (CARD, residues 1–90), a 133-aa p20 domain (residues 171–303) with two putative caspase family histidine and cysteine active sites, as well as an 87-aa p10 domain (residues 348–435). Recombinant Lyccasp9 (rLyccasp9) demonstrated obvious proteolytic activity. However, when histidine in the histidine active site was replaced by aspartic acid (D), or cysteine in the cysteine active site was replaced by glycine (G), the mutated rLyccasp9 (rLyccasp9-Mut-His-D or rLyccasp9Mut-Cys-G) displayed significantly decreased proteolytic activity. Moreover, the proteolytic activity of rLyccasp9-Mut-Cys-G was lower than that of rLyccasp9-Mut-His-D, indicating that both the histidine and cysteine active sites are essential in maintaining the Lyccasp9 proteolytic activity and that the latter is more important. The Lyccasp9 was constitutively expressed in all analyzed tissues, although expression levels varied from tissue to tissue. Real-time PCR analysis revealed that Lyccasp9 transcript in spleen and kidney was quickly increased and then slowly decreased after stimulation with either poly(I:C), a viral mimic, or inactivated trivalent bacterial vaccine. On the other hand, enzyme activities of caspase 9 were also increased in these two tissues post-stimulation, suggesting that the activation of the intrinsic apoptotic pathway may be involved in the immune response induced by poly(I:C) or bacterial vaccine in large yellow croaker. 2009 Elsevier Ltd. All rights reserved.