Miracle-dz/UniFAR-FTUs huggingface dataset:
Testing the low-volatility anomaly using monotonic relation tests

In this thesis we test the low-volatility anomaly using monotonic relation tests. We use both portfolios based on all-cap and large cap stocks which are sorted on some characteristic, such as the volatility, to test whether there is a significant relation between the expected or risk-adjusted return and the characteristic. We show that top-minus-bottom tests can give the wrong conclusion about the presence of a monotonic relation. Also, we conclude that there is indeed an anomaly present in the risk-return relation, although we generally find that this relation is not monotonic.
AUTOMATIC CLASSIFICATION OF AUDIO DATA USING GRADIENT DESCENT NEURAL NETWORK BASED ALGORITHM 1

Audio mining is a technique by which the core of an audio signal can be automatically searched and analyzed. This research work addresses feature extraction from audio and audio similarity measures and proposes an algorithm to mine any type of audio data. This Hybrid Algorithm for Audio Mining (HAAM) consists of two different phases. The first phase named Training Phase, takes Training Audio data as input and then Sonogram, Spectrum Histogram, Periodicity Histogram and Fluctuation Pattern for the audio data are calculated. Then the features are extracted using Mel Frequency Cepstral Coefficient (MFCC) and the essential features are reduced from these for further processing. Then, classification is done by using a supervised classification technique. Then the features of the audio files are trained using Gradient Descent Adaptive Learning Back propagation Network. The second phase is the Testing Phase, which takes Testing Audio data as input and then preprocessing is done and the features are extracted. Then classification is done using the findings from the trained data by matching the features of the test audio with the equivalent or closest feature audio classes. The working prototype of this algorithm has been implemented and tested. During the experiments, Instrumental music data have been used as input and the system performed well and classified the input music in an accurate manner and presented the results obtained. The system is suitable for classifying the different types of audio data and can be used in many applications including speech recognition, audio classification in scientific research and engineering, audio data comparison and detection in audio surveillance applications.
Exploring age-related changes in creativity

Age-Related Changes in Creativity By Diane Villa Dr. Sean Lane, Examination Committee Chair Assistant Professor o f Psychology University o f Nevada, Las Vegas The effects o f aging on creativity using a laboratory task involving combining assigned parts and categories into novel and practical items was explored. Older adults demonstrated equivalent creativity to that of young adults. Measures o f working memory and processing speed were also administered. Older adults did not perfiDrm as well as younger adults. Results indicate that reducing the demands on processing capacity enables older adults to perform as well as younger adults on creativity tasks.
Stagnation temperature measurements in the USQ hypersonic wind tunnel

A thermocouple probe with a heated shield has been used to measure stagnation temperature at the nozzle exit of the University of Southern Queensland hypersonic wind tunnel. The thermocouple probe consisted of a welded junction T-type thermocouple mounted within a heated tube with a vent hole downstream of the junction. Pressure transducers within the barrel of the wind tunnel have also been used to obtain the pressure history during the free piston compression process. The pressure measurements have been used to provide a theoretical value for the flow stagnation temperature for direct comparison with the thermocouple measurements. Assuming isentropic compression of the test gas, the flow stagnation temperature would be about 571 K for the current operating condition. After applying a response-time correction for the thermocouple signals, a stagnation temperature value of about 495 K was obtained from the measurements. The measured stagnation temperature of the test gas is somewhat lower than the isentropic value because of heat loss from the test gas to the barrel during the test gas compression and discharge process.
Representation Learning in Continuous Entity-Set Associations

Modeling a continuous sequence of associations between entities and sets of entities is crucial for domains such as recommender systems, healthcare, education, and communications. However, learning with a sequence of entity-set associations is challenging because of complex entity-entity dependencies due to the associations and the co-occurrence of entities in the sets. Here we propose DES, a model that extends DeepSets to generate dynamic embeddings of entities using a mutually-recursive recurrent model. We create an algorithm Set-AR to effectively predict sets using DES. We show that our model outperforms four state of the art algorithms in predicting the next entity-set interaction by 15.5%. ACM Reference Format: Srijan Kumar, Sagar Honnungar, and Jure Leskovec. 2018. Representation Learning in Continuous Entity-Set Associations. In Proceedings of ACM Conference (Conference’17). ACM, New York, NY, USA, 5 pages. https://doi. org/10.1145/nnnnnnn.nnnnnnn
Isolation and Characterization of a Thermotoga sp . Strain PD 524 , a Hyperthermophile Isolated from a Hot Spring in Thailand

A hyperthermophilic Thermotoga sp. strain PD524 was isolated from a hot spring in Northern Thailand. Cells were slender rod shaped (0.5-0.6x2.5-10 μm) surrounded by a typical outer membranous toga. Strain PD524 is aero-tolerant at 4C but aero-sensitive at 80C. A heat resistant subpopulation was observed in late stationary growth phase. Cells from late stationary growth phase were revealed substantially more resistant to 0.001% SDS treatment than cells from exponential growth phase. The temperature range for growth was 70-85 C (opt. temp. 80C), pH range was 6-8.5 (opt. pH 7.5-8.0) and NaCl conc. range of 0-<10 g/L (opt. conc. 0.5 g/L). Glucose, sucrose, maltose, D-fructose, xylose, D-mannose, arabinose, trehalose, starch and cellobiose were utilized as growth substrates. Growth was inhibited by S. Growth yield was stimulated by SO4 but not by S2O3 and NO3. Analysis of 16S rRNA gene sequence (KF164213) of strain PD524 revealed the closest similarity (96%) to Thermotoga maritima MSB8, T. neapolitana NES, T. petrophila RKU-1 and T. naphthophila RKU-10.
Multilevel Arnoldi-Tikhonov Regularization Methods for Large-Scale Linear Ill-Posed Systems

—This paper is devoted to the numerical solution of large-scale linear ill-posed systems. A multilevel regularization method is proposed. This method is based on a synthesis of the Arnoldi-Tikhonov regularization technique and the multilevel technique. We show that if the Arnoldi-Tikhonov method is a regularization method, then the multilevel method is also a regularization one. Numerical experiments presented in this paper illustrate the effectiveness of the proposed method.
Repeat Focus Group Discussions as a Tool for Strengthening of Management Capacity in a Community Development Project in Western China

This qualitative study was conducted in a community development structured project, in Ganzi Autonomous Prefecture, Sichuan, China. The paper is the third in a series of three based on the same community intervention. This paper focuses on management capacity building during the intervention. Our interest was to understand how decisions were made in a management team; how they interpreted their own roles; how they interacted and became involved and more spon-taneous; and how they pursued new ideas. Three components were combined in order to build capacity, to use repeat FGDs for data collection, monitoring and empowerment, to organize training for the managers and to conduct core group meetings once a year. Content analysis was chosen as the method for examination of the material. Some key issues emerged out of this study. Firstly, the managers from different levels agreed that it helped generate communication, friendship, transparency, trust and improved skills. A second issue, during the process of implementation, was that the managers described change towards empowerment. Ultimately, for the stakeholders the repeat FGDs became a practical way for regular monitoring. The combination of repeat FGDs, training and core group meetings was successful. We understood from the participants that: train- ing had given them improved knowledge; core group meetings had truly made them part of decision making and the repeat FGDs had equipped them with awareness of their own potentials and capacity.
How Customer Support for Corporate Social Responsibility Influences the Image of Companies: Evidence from the Banking Industry

The authors of this paper carry out two studies to determine whether customer support for corporate social responsibility (CSR support) influences the way customers form their perceptions of CSR practices in the banking industry. Study 1 consists of a cluster analysis which provides information about four customer groups classified according to their support for CSR practices. These groups are labelled as the ‘low support’, ‘social orientation’, ‘individual benefit’, and ‘high support’ clusters. In Study 2, the authors test whether differences exists in the way the four clusters process their CSR perceptions. The results confirm the relevance of motivational attribution when socially oriented and highly involved customers evaluate CSR. Based on this information, the authors provide several recommendations for managers to effectively design and communicate their CSR strategies. Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment
Breast Fibroadenoma and Uterine Fibroid in a Female with HACEK Endocarditis: Is there any Possible Correlation?

Women health care is stereotypically epitomized and confined to women’s reproductive and maternal capacities which tackle only a tiny portion of women’s general health. Studying diseases with salient female predilections and/ or provoke feminine manifestations should be fitted snugly into a coherent database system toward finding effective policies in treating such afflicted population. Autoimmune diseases and microbial pathoses are blatant examples of risky diseases which are encountered strikingly in females. Linking the dots, this paper reports a breast fibro adenoma and concomitant two uterine fibroids of the anterior wall in a 22-year-old female with HACEK endocarditis. This report is ushered to capture the clinician’s rapt attention to such a plausible association.
Implementing product focused Process improvement through The Post Iteration and Process Improvement Workshop

SPI plays a significant role in improving software development performance in a software developing companies. Developing SPI approaches which are contextually easier to integrate with different development environments has been an issue for the research in the area. The SPI framework developed define, the detailed implementation procedures of SPI activities in line with the product development process through the post iteration and process improvement workshop (PIPIW) process steps and SPI tracking model. The framework consists of integrated capability improvement process flow which is developed through the integration of CMMI continuous representation and process components of institutionalization. The developed process steps developed in the PIPIW is evaluated in a case study sited in a software development setting to examine its applicability for SPI implementation at small development settings. Results of the case study prevailed that, the framework is implementable in line with the development activities. In addition the advantages implementing the framework proven to organize and continually improve the practices used for the development activity. The case study in general prevailed practical evaluation of the framework to demonstrate how it address the perceived requirements.
An Improved K - Mode Algorithm for Facial Expression Image Clustering

When recognizing facial expression sequences by discrete Hidden Markov Model, it is necessary to cluster the image frames into several observation states. Considering the complexity of face images in emotion expressing, we partition the whole face into several sub-regions to do clustering separately. Then the categorical values from clustering results of each subregion are combined to do the further clustering. In the paper, we propose an improve K-mode clustering algorithm called Kfrequency to do the category data clustering. Instead of using a simple 0-1 method to determine the similarity between different samples, the K-frequency statistics the frequency of each categorical values of an image in a given cluster and sums them as the similarity between a sample with this cluster. Experiment results on CK+ database show that the bi-level clustering method with K-frequency algorithm outperforms the Single-layer clustering using K-means and bi-level clustering with K-mode. The improved k-modes algorithm K-frequency is more robust than the original k-mode algorithm in dealing with isolated points. Keywordscluster; categorical values; expression recognition
Vers une détection automatique des applications malveillantes dans les environnements Android

Dans ce papier, nous presentons l'etat de l'art sur les attaques et les menaces dans les environnements Android ainsi que les approches de detection associees. La plupart de ces approches utilisent des informations obtenues par instrumentation de la machine virtuelle ou par retro-ingenierie du bytecode des applications. Nous proposons ainsi une nouvelle methode moins couteuse qui repose sur l'analyse des journaux des evenements applicatifs et systemes generes par la plate-forme Android. Cette analyse nous permettra d'etablir des signatures des applications Android associant leurs structures et leurs comportements dynamiques.
The International Finance Multiplier in Business Cycle Fluctuations

In the wake of the gGreat Recession h of 2007-09, recent studies have emphasized the importance of the ginternational finance multiplier (IFM) h mechanism for inter- national business cycles, using calibrated two-country models. This paper develops and estimates a two- country model with the IFM mechanism using 21 time series from the Euro Area (EA) and the US. The estimation results show that during the past quarter-century, EA shocks to the external finance premium and net worth not only had a considerable effect on the EA economy together with an EA neutral technology shock, but also were transmitted to the US through the IFM mechanism and had a great impact on the US economy together with a US marginal efficiency of investment (MEI) shock. The rate of EA neutral technological change and the US MEI shock then have strong correlations with lending attitudes of banks in the EA and the US, and thus the EA neutral technology shock and the US MEI shock are likely to represent disturbances to the banking sectors in the EA and the US. These findings therefore demonstrate that financial factors are important sources of EA and US business cycle fluctuations over the past quarter-century.
Kernel Collaborative Representation Classification Based on Adaptive Dictionary Learning

In recent years, with the progress of technology, face recognition is used more and more widely in various fields. The classification algorithm based on sparse representation has made a great breakthrough in face recognition. However, face images are often affected by different poses, lighting, and expression changes, so test samples are often difficult to represent with limited original training samples. Due to the conventional dictionary learning methods lacking adaptability, we propose a kernel collaborative representation classification based on adaptive dictionary learning. In this paper, the coarse to fine sparse representation is related to the adaptive dictionary learning problem. First, the labeled atom dictionary is learned from each kind of training samples by sparse approximation. Based on this assumption, we use an efficient algorithm to generate an adaptive dictionary that is related with the test sample. Then, based on the adaptive class dictionary, the kernel collaborative representation is used to realize the inter class competition classification. The kernel function is combined with the coarse to fine sparse representation to extract the non-linear factors such as facial expression change, posture, illumination, occlusion and so on. The kernel collaborative representation is used to realize the inter class competition classification. The main advantage of this approach is to combine coarse to fine kernel collaborative representation with dictionary learning to generate adaptive dictionaries that approximate to the test image. Experimental results demonstrate that the proposed appraoch outperforms some previous state-of-the-art dictionary learning methods and sparse coding methods in face recognition.
Cultivated Land Area Change in Shenzhen and Its Socio-Economic Driving Forces Based on STIRPAT Model

The purpose of this paper is to analyze the relationship between prosperous level and cultivated land area were analyzed in Shenzhen city, Method of the STIRPAT model. The results showed that there was no main cause for cultivated land reduction in Guangzhou city (Zhang, 1999). However, population change, changes of the urbanization rate and proportion of the tertiary-industry added value to regional GDP of the area all play important role in the cultivated land reduction. In the scope of observational data, the relationship between the prosperous level and the cultivated land area was not similar to the environmental kuznets curve (EKC). Accordingly, several suggestions were proposed in the study to mitigate the pressure of cultivated land reduction, including population control, urbanization level improvement, industrial structure adjustment, and economic growth mode transition, etc (Cai & Zhang, 2005).
Discovering a Research Agenda for Using Design Rationale in Software Maintenance

Design Rationale consists of the reasons behind decisions made while designing. This information would be particularly useful during software maintenance. In this paper, we describe a study performed to investigate the content, structure, and use of design rationale during maintenance. The major goal of this study was to discover an agenda for further research into the use of design rationale for software maintenance.
Crossing the Tasman: Determinants of price discovery for Australia-New Zealand cross-listed shares

Purpose – The purpose of this paper is to investigate price discovery for cross-listed stocks on the New Zealand Exchange (NZX) and the Australian Stock Exchange (ASX) and find out the determinants of price discovery between the two markets. Design/methodology/approach – Gonzalo Granger Component Shares and Hasbrouck Information Shares were estimated annually for a sample of 19 cross-listed stocks between 1998 and 2012. Then dynamic panel regressions were used to investigate the driving factors behind price discovery between the NZX and ASX. Findings – Strong downward trends were observed in the contribution to price discovery of the NZX, both for New Zealand firms cross-listing on the ASX and Australian firms cross-listing on the NZX. While in the early years in our sample period, price discovery is dominated by the home market, by 2012, 50 per cent of price discovery for New Zealand firms takes place on the ASX, and the NZX acts as a satellite market for Australian firms. It was also observed that the N.
Dynamic HDR environment capture for mixed reality

Rendering accurate and convincing virtual content into mixed reality (MR) scenes requires detailed illumination information about the real environment. In existing MR systems, this information is often captured using light probes [1, 8, 9, 17, 19--21], or by reconstructing the real environment as a preprocess [31, 38, 54]. We present a method for capturing and updating a HDR radiance map of the real environment and tracking camera motion in real time using a self-contained camera system, without prior knowledge about the real scene. The method is capable of producing plausible results immediately and improving in quality as more of the scene is reconstructed. We demonstrate how this can be used to render convincing virtual objects whose illumination changes dynamically to reflect the changing real environment around them.
On hybrid model for the valuation of credit risk

This paper presents hybrid model for the valuation of credit risk. Credit risk arises whenever a borrower is expecting to use future cash flows to pay a current debt. It is closely tied to the potential return of investment, the most notable being that the yields on bonds correlate strongly to their perceived credit risk. Hybrid model combines the structural and intensity-based approaches. While avoiding their difficulties, it picks the best features of both approaches; the economic and intuitive appeal of the structural approach and the tractability and empirical fit of the intensity-based approach. In credit derivatives market there are quite a few securities that depend on more than one source of risk, like corporate bonds and convertible bonds, most attractive credit models should involve all these three sources of risk, and interest-rate risk. Our framework brings together these standard block.
A Denoising Method for LiDAR Full-Waveform Data

Decomposition of LiDAR full-waveform data can not only enhance the density and positioning accuracy of a point cloud, but also provide other useful parameters, such as pulse width, peak amplitude, and peak position which are important information for subsequent processing. Full-waveform data usually contain some random noises. Traditional filtering algorithms always cause distortion in the waveform. filtering algorithm is based on Mean Shift method. It can smooth the signal iteratively and will not cause any distortion in the waveform. In this paper, an improved filtering algorithm is proposed, and several experiments on both simulated waveform data and real waveform data are implemented to prove the effectiveness of the proposed algorithm.
Using Chlorophyll Fluorescence and Vegetation Indices to Predict the Timing of Nitrogen Demand in Pentas lanceolata

The objective of this study was to predict the timing of nitrogen (N) demand through analyzing chlorophyll fluorescence (ChlF), soil-plant analysis development (SPAD), and normalized difference vegetation index (NDVI), which are positively correlated with foliar N concentration in star cluster (Pentas lanceolata). The plants were grown in potting soil under optimal conditions for 30 d, followed by weekly irrigation with five concentrations (0, 4, 8, 16, and 24 mM) of N for an additional 30 d. These five N application levels corresponded to leaf N concentrations of 2.62, 3.48, 4.00, 4.23, and 4.69%, respectively. We measured 13 morphological and physiological parameters, as well as the responses of these parameters to various N-fertilizer treatments. The general increases in Dickson’s quality index (DQI), above-ground dry weight (DW), total DW, flowering rate, △F/Fm’, and qP in response to t reatment with 0 to 8 mM N were similar to those of SPAD, NDVI, and Fv/Fm. Consistent and s trong correlations (R² = 0.60 to 0.85) were observed between leaf N concentration (%) and SPAD, NDVI, △F/Fm’, and above-ground DW. Validation of leaf S PAD, NDVI, and △F/Fm’ revealed that these vegetation indices are accurate predictors of leaf N concentration that can be used for non-destructive estimation of the proper timing for N-solution irrigation of P. lanceolata. Moreover, irrigation with 8 mM N-fertilizer i s recommended w hen leaf N concentration, SPAD, NVDI, and △F/Fm’ ratios are reduced from their saturation values of 4.00, 50.68, 0.64, and 0.137%, respectively.