nils-herrmann/clean_scirepeval huggingface dataset:
GPU BASED GLOBAL RENDERING VIA IMPORTANCE DRIVEN ENVIRONMENT MAP SAMPLING
Year: 2018

In some real time applications such as 3D games and virtual reality, it has to produce interactive rendering of global illumination for different objects. Global illumination (GI) has very complex lighting condition such as diffuse light, inter reflection and soft shadows, which is difficult to simulate GI. In this paper, we propose the GPU (Graphics Processing Unit) based global rendering via importance driven environment map sampling. We use HDR (High Dynamic Range) environment map to efficiently represent complex global illumination, which is usually capable of GPU implementation. Based on the importance driven sampling points around bright areas, we obtain fewer but more important illumination points to simulate global illumination. Global rendering equation is deduced and the discretization is acquired using the two pass algorithm of photon mapping via CUDA (Compute Unified Device Architecture) acceleration. Finally, the results show that we acquire realistic effects of rendering such as inter reflections, luster, caustics and soft shadows, while keeping an interactive rendering speed.
Dynamic Cooling of a Micromechanical Membrane in a Double cavity Optomechanical System
Year: 2019

We theoretically present an optomechanical cooling scheme of a double optical modes which are coupled with the same mechanical resonator by the radiation pressure to analyze the dynamical cooling of a micromechanical membrane. The mean value of the total second order moments are obtained by solving linearized quantum Langevin equations. We discuss the effect of the significant parameters of the system on the instantaneous state mean phonon number of the oscillator cooled to the ground state under the resolved sideband regime. Furthermore, the steady state cooling limit is also studied and the results show that the final mean phonon number splits into two minimum values when the detunings of the two cavity modes are different. Moreover, the minimal value of the final mean phonon number is much less than the case of equal driving detuning. By regulating the detunings of the optical modes from the corresponding driving fields and increasing the coupling strengths, the cooling effect will be optimized obviously.
Implications of Green Logistics Management on Sustainable Business and Supply Chain Performance: Evidence from a Survey in the Greek Agri Food Sector
Year: 2020

Green agri food supply chains are increasingly attracting research interest, owing to their potential capacity for resilience against recent crises (e.g. financial, COVID 19) as well as end consumers' concerns on sustainability issues. In this context, this paper aims to explore the relationship between green supply chain management practices and three different performance aspects, namely, supply chain, green (environmental) and business performance, and controlling for environmental dynamism. Field research was conducted through a structured questionnaire contacted to 134 executives of firms in the agri food sector in Greece. The results reveal that information sharing, logistics networking and transportation are the most powerful factors that impact sustainable, business and supply chain performance. In addition, green packaging is related to financial and social performance aspects. Interestingly, green warehousing and logistics emissions failed to establish any relationship with performance outcomes. The conclusions and recommendations drawn in the present study are expected to provide meaningful guidance for the supply chain decision making process, as logistics managers are becoming increasingly aware of exploiting all available resources, knowhow and competitive advantages for attaining sustainable performance.
Listen to Look: Action Recognition by Previewing Audio
Venue: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2020

In the face of the video data deluge, today's expensive clip level classifiers are increasingly impractical. We propose a framework for efficient action recognition in untrimmed video that uses audio as a preview mechanism to eliminate both short term and long term visual redundancies. First, we devise an ImgAud2Vid framework that hallucinates clip level features by distilling from lighter modalities a single frame and its accompanying audio reducing short term temporal redundancy for efficient clip level recognition. Second, building on ImgAud2Vid, we further propose ImgAud Skimming, an attention based long short term memory network that iteratively selects useful moments in untrimmed videos, reducing long term temporal redundancy for efficient video level recognition. Extensive experiments on four action recognition datasets demonstrate that our method achieves the state of the art in terms of both recognition accuracy and speed.
Chapter 15: Psychological Aspect of Financial Planning
Year: 2017

This chapter discusses personal financial planning, which is an interdisciplinary practice that employs a six step process to develop integrated strategies for individuals and families to efficiently mobilize their human and financial capital to achieve their life goals. Financial planning draws from various disciplines, including counseling, psychology, finance, economics, and law. It includes budgeting and cash flow planning, risk management, insurance planning, investment planning, retirement and employee benefits planning, tax planning, and estate planning. The strategic process whereby financial planners develop integrated strategies that draw from all these fields in pursuit of client goals is the profession's unique domain. Heuristics and mental biases to which clients may be prone overlay the entire financial planning process, however. Financial planners should understand and consider these issues when developing recommendations uniquely suited to each client, maximizing the probability that the client will embrace and implement the recommended strategies.
Optimizing Player and Viewer Amusement in Suspense Video Games
Venue: IEEE Access
Year: 2019

Broadcast video games need to provide amusement to both players and audience. To achieve this, one of the most consumed genres is suspense, due to the psychological effects it has on both roles. Suspense is typically achieved in video games by controlling the amount of delivered information about the location of the threat. However, previous research suggests that players need more frequent information to reach similar amusement than viewers, even at the cost of jeopardizing viewers' engagement. In order to obtain models that maximize amusement for both interactive and passive audiences, we conducted an experiment in which a group of subjects played a suspenseful video game while another group watched it remotely. The subjects were asked to report their perceived suspense and amusement, and the data were used to obtain regression models for two common strategies to evoke suspense in video games: by alerting when the threat is approaching and by random circumstantial indications about the location of the threat. The results suggest that the optimal level is reached through randomly providing the minimal amount of information that still allows players to counteract the threat. We reckon that these results can be applied to a broad narrative media, beyond interactive games.
Type 2 diabetes data classification using stacked autoencoders in deep neural networks
Year: 2019

This paper aims to classify the Pima Indians diabetes dataset with better accuracy and other evaluation metrics. The Deep Neural Network (DNN) framework will help to diagnose the patient in an effective way with higher accuracy.  In this approach, we proposed a Deep Neural Network framework for diabetes data classification using stacked autoencoders. Features are extracted from the dataset using stacked autoencoders and the dataset is classified using softmax layer. Also, fine tuning of the network is done using backpropagation in supervised fashion with the training dataset. However, the medical diagnosis involves the risk factors of wrong prediction; hence we have used evaluation metrics such as precision, recall, specificity and F1 score for the evaluation of our model and have achieved better results.  The proposed framework is experimented on Pima Indians Diabetes data which has 768 patient records with 8 attributes for each record. We achieved classification accuracy of 86.26%  A stacked autoencoders based Deep Learning framework for classification of Type 2 Diabetes data is proposed in this paper. This approach is experimented on UCI machine learning data and proved the outperformance over various existing classification methods.
Pharmacokinetics and tissue distribution of monotropein and deacetyl asperulosidic acid after oral administration of extracts from Morinda officinalis root in rats
Venue: BMC Complementary and Alternative Medicine
Year: 2018

BackgroundIridoid glycosides (IGs) including monotropein (MON) and deacetyl asperulosidic acid (DA) as the main ingredients, are the major chemical components in Morinda officinalis How. (MO) root, possessing various pharmacological properties including anti osteoporosis, anti inflammation and anti rheumatism activities.The aim of the present study was to further elucidate the pharmacological actions of MO by investigating the pharmacokinetics and tissue distribution of IGs in MO.MethodsAn ultra high performance liquid chromatography tandem mass spectrometry (UHPLC MS) method was developed and validated for simultaneous determination of MON and DA levels in plasma and various tissues of Wistar rats. MON, DA and acetaminophen (ACE) as the internal standard (IS) were extracted from rat plasma and tissue samples by direct deproteinization with methanol. The rats were administered orally at 1650 mg/kg MO and 25, 50 and 100 mg/kg MO iridoid glycosides (MOIGs) or intravenously at MOIG 25 mg/kg for pharmacokinetic study of MON and DA. In addition, 100 mg/kg MOIG was administered orally for tissue distribution study of MON and DA. Non compartmental pharmacokinetic profiles were constructed. Tissue distributions were calculated according to the validated methods.ResultsSignificant differences in the pharmacokinetic parameters were observed in male and female rats. The AUC0 t, Cmax and bioavailability of MON and DA in female rats were higher than those in male rats. MON and DA mainly distributed in the intestine and stomach after oral administration, and noteworthily high concentrations of MON and DA were detected in the rat hypothalamus.ConclusionThe results of the present study may shed new lights on the biological behavior of MOIGs in vivo, help explain their pharmacological actions, and provide experimental clues for rational clinical use of these IGs extracted from the MO root.
A Comparative Analysis of Aerosol Microphysical, Optical and Radiative Properties during the Spring Festival Holiday over Beijing and Surrounding Regions
Year: 2018

Using ground based data, meteorological observations, and atmospheric environmental monitoring data, a comparative analysis of the microphysical and optical properties, and radiative forcing of aerosols was conducted between three stations in different developed environments during a severe air pollution episode during the Spring Festival over Beijing. During the most polluted period, the daily peak values of the aerosol optical depth were ~1.62, ~1.73, and ~0.74, which were about 2.6, 2.9, and 2.1 times higher than the background levels at the CAMS, Xianghe, and Shangdianzi sites, respectively. The daily peak values of the single scattering albedo were ~0.95, ~0.96, and ~0.87. The volume of fine mode particles varied from 0.04 to 0.21 um3 um 2, 0.06 to 0.17 um3 um 2, and 0.01 to 0.10 um3 um 2, which were about 0.3 to 5.8, 1.1 to 4.7, and 1.2 to 8.9 times greater than the background values, respectively. The daily absorption aerosol optical depth was ~0.01 to ~0.13 at CAMS, ~0.03 to ~0.14 at Xianghe, and ~0.01 to ~0.09 at Shangdianzi, and the absorption Angstrom exponents reflected a significant increase in organic aerosols over CAMS and Xianghe and in black carbon over Shangdianzi. Aerosol radiative forcing at the bottom of the atmosphere varied from 20 to 130, 40 to 150, and 10 to 110 W m 2 for the whole holiday period, indicating the cooling effect. The potential source contribution function and concentration weighted trajectory analysis showed that Beijing, the southern parts of Hebei and Shanxi, and the central northern part of Shandong contributed greatly to the pollution.
Anomaly detection in Bitcoin market via price return analysis
Venue: PloS one
Year: 2019

The Bitcoin market becomes the focus of the economic market since its birth, and it has attracted wide attention from both academia and industry. Due to the absence of regulations in the Bitcoin market, it may be easier to bring some kinds of illegal behaviors. Thus, it raises an interesting question: Is there abnormity or illegal behavior in Bitcoin platforms? To answer this question, we investigate the abnormity in five leading Bitcoin platforms. By analyzing the financial index, i.e. the normalized logarithmic price return, we find that the properties of price return in bitFlyer are completely different from others. To find the possible reasons, we find that the abnormal ask price and bid price appear simultaneously in bitFlyer, which may be potentially linked to either price manipulation or money laundering. It verifies our conjecture that there may be abnormity or price manipulation in Bitcoin platforms. Furthermore, our findings in price return could also provide an innovative and effective method to detect the abnormity in Bitcoin platforms.
Overview of mechanical, microstructural, oxidation properties and high temperature applications of superalloys
Year: 2021

This review paper examined the mechanical, microstructural, corrosion properties, high temperature applications (HTAs) and protective measures that have been applied for optimal performance of superalloys. The quest for materials with excellent characteristics necessitated the development of superalloys over the years. Superalloys are class of nickel (Ni) cobalt (Co) and iron (Fe) based alloys used in jet and marine turbine engines due to their outstanding dimensional stability at a much higher temperature compared to most structural and high temperature materials. Superalloys can use a high fraction of their melting point, and this positioned them in the category of high temperature application materials. They are also reported to exhibit reasonable corrosion resistance and good mechanical properties even at elevated temperature, which facilitates their suitability for high stress manufacturing and advance applications, such as in the production of turbine engines for the marine and aerospace industries. The surface of superalloys can be protected using diffusion, overlaying and thermal barrier coatings (TBCs) which act as a barrier against oxidation, corrosion, depletion in microstructure and thermal deformation.
Table to text Generation by Structure aware Seq2seq Learning
Venue: AAAI
Year: 2018

Table to text generation aims to generate a description for a factual table which can be viewed as a set of field value records. To encode both the content and the structure of a table, we propose a novel structure aware seq2seq architecture which consists of field gating encoder and description generator with dual attention. In the encoding phase, we update the cell memory of the LSTM unit by a field gate and its corresponding field value in order to incorporate field information into table representation. In the decoding phase, dual attention mechanism which contains word level attention and field level attention is proposed to model the semantic relevance between the generated description and the table. We conduct experiments on the \texttt{WIKIBIO} dataset which contains over 700k biographies and corresponding infoboxes from Wikipedia. The attention visualizations and case studies show that our model is capable of generating coherent and informative descriptions based on the comprehensive understanding of both the content and the structure of a table. Automatic evaluations also show our model outperforms the baselines by a great margin. Code for this work is available on this https URL
Continuous and Diverse Image to Image Translation via Signed Attribute Vectors
Venue: ArXiv
Year: 2020

Recent image to image (I2I) translation algorithms focus on learning the mapping from a source to a target domain. However, the continuous translation problem that synthesizes intermediate results between the two domains has not been well studied in the literature. Generating a smooth sequence of intermediate results bridges the gap of two different domains, facilitating the morphing effect across domains. Existing I2I approaches are limited to either intra domain or deterministic inter domain continuous translation. In this work, we present an effective signed attribute vector, which enables continuous translation on diverse mapping paths across various domains. In particular, utilizing the sign operation to encode the domain information, we introduce a unified attribute space shared by all domains, thereby allowing the interpolation on attribute vectors of different domains. To enhance the visual quality of continuous translation results, we generate a trajectory between two sign symmetrical attribute vectors and leverage the domain information of the interpolated results along the trajectory for adversarial training. We evaluate the proposed method on a wide range of I2I translation tasks. Both qualitative and quantitative results demonstrate that the proposed framework generates more high quality continuous translation results against the state of the art methods.
Influences on career identity in Vietnamese students at an international university
Year: 2017

This study considers the career identities of Vietnamese students studying at an offshore (Australian) university in Vietnam. The students had completed the first module of a new career development learning program called Career Passport. As part of the program, students were required to write a narrative on their 'career story' to reflect on their experiences and synthesise their learning. The study drew on a social constructionist and narrative psychology approach to explore the major influences on the career identity that emerged from the students' narratives. The strongest influence on the students' career identities and career decision making was family, which is in accordance with the collectivist cultural values of Vietnam. Somewhat less expected was the effect of various media, such as film and television. Despite the influence of family, the narratives reflect a strong desire to meet individual values, needs and interests in what the students would like their future careers to look like. The latter two findings perhaps indicate that Vietnamese students at an international university are more influenced by Western values and expectations of 'career' than their counterparts who study at domestic universities.
Advanced computer modelling technique for finite element analysis of truss structure
Year: 2010

Finite element analysis (FEA) has become common place in recent years, and is now the basis of a multibillion dollar per year industry. Numerical solutions to even very complicated stress problems can now be obtained routinely using FEA, and the method is so important. This study is about modeling of planar truss structure using software Ansys 12.0 (APDL) and determines the maximum deflection, maximum stress and maximum strain when the load is applied to the modeL Ansys is software based on the finite element analysis (FEA) widely used in the computer aided engineering (CAE) field. Four random inputs will be used in this study which is Young Modulus (E) Poison Ratio, Radius (R) and Fy loads. The random output will be the graph maximum of deflection, stress and strain. All of the output values should be passing the eight equations of statistics to make sure the values are valid. This study is going to be basic guidelines on how to implement the civil engineering element using Ansys software.
Application of new quintic polynomial B spline approximation for numerical investigation of Kuramoto Sivashinsky equation
Year: 2020

A spline is a piecewise defined special function that is usually comprised of polynomials of a certain degree. These polynomials are supposed to generate a smooth curve by connecting at given data points. In this work, an application of fifth degree basis spline functions is presented for a numerical investigation of the Kuramoto Sivashinsky equation. The finite forward difference formula is used for temporal integration, whereas the basis splines, together with a new approximation for fourth order spatial derivative, are brought into play for discretization in space direction. In order to corroborate the presented numerical algorithm, some test problems are considered and the computational results are compared with existing methods.
What Drives Mobile Commerce? An Antecedent Model of Mobile Commerce Adoption
Year: 2007

Mobile commerce the consumer's engagement in online transactions with sellers using mobile devices differs from traditional or electronic commerce due to the potential for location specific real time transactions and the unique attributes of mobile devices. This paper aims to understand what drives consumers to engage in mobile transactions by viewing m commerce adoption as a process consisting of three distinct, yet inter related behaviors: (a) 'getting information' (b) 'giving information, and (c) 'purchasing' products and services using mobile devices. First, these three behaviors are integrated, using the theory of implementation intentions. Second, following the theory of planned behavior, each behavioral intention is predicted through its attitude, subjective norm, and perceived behavioral control (self efficacy and controllability) Third, a set of beliefs for each of the three m commerce behaviors is identified, resulting in a comprehensive model of the drivers of m commerce adoption. Finally, an empirical study with consumers in the United States tests and validates the proposed m commerce adoption model. Implications for the adoption of m commerce are proposed.
Enumeration of the hydrogen enhanced localized plasticity mechanism for hydrogen embrittlement in structural materials
Venue: Acta Materialia
Year: 2019

This paper presents a review of recent experimental evidence and simulation results enumerating the development of the hydrogen enhanced localized plasticity (HELP) mechanism as a viable hydrogen embrittlement mechanism for structural materials. A wide range of structural materials, including ferritic, martensitic, and austenitic steels, iron, and nickel are covered by the studies reviewed here, as are a variety of mechanical loading conditions and hydrogen charging conditions, supporting the concept that, despite differences in failure mode, there is a universality to the HELP mechanism.