Miracle-dz/UniFAR-FTUs huggingface dataset:
Heterogeneous CSR Approaches, Corporate Social Performance and Corporate Financial Performance

In this paper, we identify heterogeneous CSR approaches by segregating the promised and realised social performance of firms to better understand how corporate social performance affects financial performance. Empirical CSR literature provides predominantly mixed findings on the social performance to financial performance relation because it often implicitly assumes that social performance is homogenous. We move beyond this homogeneity by observing that 50%, 24%, and 26% of the firms respectively approach strategic CSR, CSR-as-insurance, and corporate greenwashing. Where empirical CSR literature primarily analyses social performance in general, we show that it is precisely the heterogeneity in CSR approaches that shapes the social and financial performance of firms. Specifically, strategic CSR firms outperform CSR-as-insurance and especially corporate greenwashing firms in both realised social performance and financial performance.
2D simulation of chemotactic bacteria aggregation

We start from a mathematical model which describes the collective motion of bacteria taking into account the underlying biochemistry. This model was first introduced by Keller-Segel . A new formulation of the system of partial differential equations is obtained by the introduction of a new variable (this new variable is similar to the quasi-Fermi level in the framework of semiconductor modelling). This new system of P.D. E. is approximat- ed via a mixed finite element technique. The solution algorithm is then described and finally we give some preliminary numerical results.Especially our method is well adapted to compute the concentration of bacteria.
Reputational Assets and Social Media Marketing Activeness: Empirical Insights from China

We explore the linkages between social media marketing activeness and reputational assets on digital platforms with a unique sample of over 8,000 C2C sellers on both Taobao, China’s largest C2C online shopping platform, and Sina Weibo, China’s largest microblogging platform. A unique collaborative effort between the two platforms enables us to examine whether C2C sellers are motivated to engage in marketing activities on a separate platform. Applying machine learning and NLP methods, we first identify whether C2C sellers conduct social media marketing on their microblogs. We then differentiate between earned and owned reputation factors accumulated on both platforms and test their relationships to social media marketing activeness. We find that earned reputation factors on both platforms are significantly associated with social media marketing activeness. However, we identify a conflict of owned reputation factors between the two platforms, which provides a potential explanation for the limited success of the cross-platform collaboration.
Moderating Effect of Senior Management Support on the Relationship Between Schedule Delay Factors and Project Performance

Abstract Schedule delays in construction projects are often a major concern and considered as a global phenomenon in the construction industry. In regards to project delivery, lack of senior management support is one of the main issues that impact project outcomes. This empirical research study aims to investigate the moderating effect of senior management support on the relationship between schedule delay factors and project performance. A questionnaire survey method was adopted to collect data from project directors, project managers, civil and construction engineers, project supervisors, and experts from small, medium, and large construction companies from major cities in Pakistan. A response rate of 84% was obtained based on 310 valid responses from a sample of 368 potential participants that received the survey. The cross-sectional data were used to test direct relationships and moderating effects through regression analysis and “process” method, respectively. The findings indicate that schedule delays in construction projects occur due to lack of commitment, insufficient site management, poor site coordination, lack of clarity in project scope, lack of communication and substandard contracts, in addition to major delays owing to improper planning. Moreover, the relationship between schedule delay factors and project performance is moderated by all dimensions of senior management support, i.e. providing resources, structural arrangements, communication, expertise, and power.
Experimental and Numerical Investigations of Micro-Meso Damage Evolution for a WC/Co-Type Tool Material

Commercial Co/WC/diamond composites with 90vol. % Co also belong to hard metals and, as a kind of tool materials, are very useful. Their deformation behavior can be both ductile and quasi-brittle, determined by the diamond portion and local morphology. Another characteristic is that submicron-sized WC particles, possessing non-negligible strengthening influence due to the size effect, cannot be fully present in a representative microstructure. This work emphasizes the local damage evolutions’ dependence on microstructural features. Rice&Tracey damage and cohesive zone model describe the ductile and quasi-brittle damage behavior. The mechanism-based strain gradient plasticity takes the size effect of submicron-sized WC particles into consideration. Both real and artificial microstructures are used. Besides homogeneous boundary conditions (BCs), the periodic BCs are also applied in a 2D damage simulation. This work proves that FE models with two phases, the homogenized Co-WC matrix and diamond particles, can correctly predict damage evolution. FE results show that theWC phase has a higher mean stress value than the diamond phase, which is proved by the nano-indentation test. From FE simulation results, local hot spots appear in the matrix closed to sharp diamond corners/edges and crossing regions of shear bands. The experimental and numerical results are compared on micro and macro scales. For the local strain distribution and the damage development, numerical predictions match the reality well, even in morphological details. Furthermore, since the published data about WC-Co type tool materials with Co>50vol. % are rare, the obtained knowledge in this work also contributes to the data collection.
The Relation between Non-Executive Employee Stock Options and Corporate Innovation: Evidence from FAS 123R

We employ FAS 123R as an exogenous shock to the use of stock options, to determine if non-executive employee options have a causal effect on innovation output. Employing a difference-in-differences research design, we find no evidence that the decline in non-executive options impacted innovation output, pre- versus post-FAS 123R. Our result is robust to numerous tests, including alternative measures of innovation and different definition for treatment and control firms. Our findings call into question the notion that non-executive stock options are a first-order driver of corporate innovation policies.
Wealth Effect on Portfolio Allocation in Incomplete Markets

We develop a novel five-component decomposition of optimal dynamic portfolio choice, which reveals the simultaneous impacts from market incompleteness and wealth-dependent utilities. Under the HARA utility and a nonrandom interest rate, we can explicitly solve for the optimal policy as a combination of a bond holding scheme and the corresponding simpler CRRA strategy. Under a stochastic volatility model estimated on US equity data, we use closed-form solution to demonstrate the sophisticated impacts from the wealth-dependent utilities, including cycle-dependence and hysteresis effect in optimal portfolio allocation, as well as a risk-return trade-off in investment performance.
Structural, electronic, magnetic and thermodynamic properties of Ni1−xTixO alloys an ab initio calculation and Monte Carlo study

ABSTRACT Structural, electronic, magnetic and thermodynamic main features for Ni1−xTixO ternary alloys in rock-salt structure with Ti content in the range were studied using the full potential Linearized augmented plane wave (FP-LAPW) method within density functional theory. The exchange-correlation potential was calculated by the generalized gradient approximation. The analysis of the electronic density of states curves allowed the computation of the magnetic moments which are considered to lie along (010) axes. The thermodynamic stability of this alloy was investigated by calculating the excess enthalpy of mixing as well as the phase diagram. In addition, the Monte Carlo simulations have been exploited to calculate the transition temperature and magnetic coercive field in the alloy.
Performance analysis of solar assisted multi‐effect absorption cooling systems using nanofluids: A comparative analysis

The performance comparison of multi‐effect absorption refrigeration systems has been conducted in the present study. The absorption cooling cycles are operated on the solar heat in order to improve the utilization of high temperature heat sources for absorption systems. The absorption refrigeration cycles of multi‐effect are modeled and designed for the identical refrigeration capacity along with the similar operating conditions. The engineering equation solver tool is deployed to analyze the coefficient of performance (COP) and exergetic efficiency of the absorption cooling cycles. Performance simulations were carried out over a range of operating conditions, including the effect of heat transfer fluids (nanofluids) used in solar parabolic trough collectors. The COP of the triple effect absorption refrigeration cycle (TEARC) is observed to be 1.752. The COP of the double effect absorption refrigeration cycle (DEARC) is perceived to be 51.9% higher as compared with single effect absorption refrigeration cycle (SEARC) which has a COP of 0.852. The exergetic efficiency of the TEARC is witnessed to be 16% higher than DEARC, and it is 31% higher than SEARC at an evaporator temperature of 7 ̊C. The effect of nanoparticle's (Al2O3) concentration and percentage of weak and strong solutions of LiBr‐H2O is also evaluated at design conditions. A high temperature heat reservoir is required to operate the TEARC, whereas, the SEARC and DEARC operate on lower temperatures than triple effect cycle.
Variability in black carbon mass concentration in surface snow at Svalbard

Abstract. Black carbon (BC) is a significant forcing agent in the Arctic, but substantial uncertainty remains to quantify its climate effects due to the complexity of the different mechanisms involved, in particular related to processes in the snowpack after deposition. In this study, we provide detailed and unique information on the evolution and variability in BC content in the upper surface snow layer during the spring period in Svalbard (Ny-Ålesund). A total of two different snow-sampling strategies were adopted during spring 2014 (from 1 April to 24 June) and during a specific period in 2015 (28 April to 1 May), providing the refractory BC (rBC) mass concentration variability on a seasonal variability with a daily resolution (hereafter seasonal/daily) and daily variability with an hourly sampling resolution (hereafter daily/hourly) timescales. The present work aims to identify which atmospheric variables could interact with and modify the mass concentration of BC in the upper snowpack, which is the snow layer where BC particles affects the snow albedo. Atmospheric, meteorological and snow-related physico-chemical parameters were considered in a multiple linear regression model to identify the factors that could explain the variations in BC mass concentrations during the observation period. Precipitation events were the main drivers of the BC variability during the seasonal experiment; however, in the high-resolution sampling, a negative association has been found. Snow metamorphism and the activation of local sources (Ny-Ålesund was a coal mine settlement) during the snowmelt periods appeared to play a non-negligible role. The statistical analysis suggests that the BC content in the snow is not directly associated to the atmospheric BC load.
Valence Quark Ratio in the Proton

Beginning with precise data on the ratio of structure functions in deep inelastic scattering (DIS) from 3He and 3H, collected on the domain 0.19 ≤ x B ≤ 0.83, where x B is the Bjorken scaling variable, we employ a robust method for extrapolating such data to arrive at a model-independent result for the x B = 1 value of the ratio of neutron and proton structure functions. Combining this with information obtained in analyses of DIS from nuclei, corrected for target-structure dependence, we arrive at a prediction for the proton valence-quark ratio: dν /uνq | x B → 1 = 0.230 (57). Requiring consistency with this result presents a challenge to many descriptions of proton structure.
Understanding Money Trails of Suspicious Activities in a cryptocurrency-based Blockchain

The decentralization, redundancy, and pseudo-anonymity features have made permission-less public blockchain platforms attractive for adoption as technology platforms for cryptocurrencies. However, such adoption has enabled cybercriminals to exploit vulnerabilities in blockchain platforms and target the users through social engineering to carry out malicious activities. Most of the state-of-the-art techniques for detecting malicious actors depend on the transactional behavior of individual wallet addresses but do not analyze the money trails. We propose a heuristics-based approach that adds new features associated with money trails to analyze and find suspicious activities in cryptocurrency blockchains. Here, we focus only on the cyclic behavior and identify hidden patterns present in the temporal transactions graphs in a blockchain. We demonstrate our methods on the transaction data of the Ethereum blockchain. We find that malicious activities (such as Gambling, Phishing, and Money Laundering) have different cyclic patterns in Ethereum. We also identify two suspicious temporal cyclic path-based transfers in Ethereum. Our techniques may apply to other cryptocurrency blockchains with appropriate modifications adapted to the nature of the crypto-currency under investigation.
PIVODL: Privacy-Preserving Vertical Federated Learning Over Distributed Labels

Federated learning (FL) is an emerging privacy preserving machine learning protocol that allows multiple devices to collaboratively train a shared global model without revealing their private local data. Nonparametric models like gradient boosting decision trees (GBDTs) have been commonly used in FL for vertically partitioned data. However, all these studies assume that all the data labels are stored on only one client, which may be unrealistic for real-world applications. Therefore, in this article, we propose a secure vertical FL framework, named privacy-preserving vertical federated learning system over distributed labels (PIVODL), to train GBDTs with data labels distributed on multiple devices. Both homomorphic encryption and differential privacy are adopted to prevent label information from being leaked through transmitted gradients and leaf values. Our experimental results show that both information leakage and model performance degradation of the proposed PIVODL are negligible. Impact Statement—Federated learning is a distributed machine learning framework proposed for privacy preservation. Most federated learning algorithms work on horizontally partitioned data, with only a few exceptions considering vertically partitioned data that is widely seen in the real world. However, existing vertical federated learning makes an unrealistic assumption that data labels are distributed on only one device and no research has been reported so far that considers data labels distributed on multiple client devices. The PIVODL framework reported in this article allows us to build a secure vertical federated XGBoost system, in which the labels may distributed either on one device or on multiple devices, making it possible to apply federated learning to a wider range of real-world problems.
Understanding and Accelerating Neural Architecture Search With Training-Free and Theory-Grounded Metrics

This work targets designing a principled and unified training-free framework for Neural Architecture Search (NAS), with high performance, low cost, and in-depth interpretation. NAS has been explosively studied to automate the discovery of top-performer neural networks, but suffers from heavy resource consumption and often incurs search bias due to truncated training or approximations. Recent NAS works Mellor et al. 2021, Chen et al. 2021, Abdelfattah et al. 2021 start to explore indicators that can predict a network's performance without training. However, they either leveraged limited properties of deep networks, or the benefits of their training-free indicators were not applied to more extensive search methods. By rigorous correlation analysis, we present a unified framework to understand and accelerate NAS, by disentangling “TEG” characteristics of searched networks – Trainability, Expressivity, Generalization – all assessed in a training-free manner. The TEG indicators could be scaled up and integrated with various NAS search methods, including both supernet and single-path NAS approaches. Extensive studies validate the effective and efficient guidance from our TEG-NAS framework, leading to both improved search accuracy and over 56% reduction in search time cost. Moreover, we visualize search trajectories on three landscapes of “TEG” characteristics, observing that a good local minimum is easier to find on NAS-Bench-201 given its simple topology, whereas balancing “TEG” characteristics is much harder on the DARTS space due to its complex landscape geometry.
Factorizations in evaluation monoids of Laurent semirings

Abstract For let be the semiring of real numbers with all where is the set of nonnegative integers and is the semiring of Laurent polynomials with coefficients in In this paper, we study various factorization properties of the additive structure of We characterize when is atomic. Then we characterize when satisfies the ascending chain condition on principal ideals in terms of certain well-studied factorization properties. Finally, we characterize when satisfies the unique factorization property and show that, when this is not the case, has infinite elasticity.
TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios

Object detection on drone-captured scenarios is a recent popular task. As drones always navigate in different altitudes, the object scale varies violently, which burdens the optimization of networks. Moreover, high-speed and low-altitude flight bring in the motion blur on the densely packed objects, which leads to great challenge of object distinction. To solve the two issues mentioned above, we propose TPH-YOLOv5. Based on YOLOv5, we add one more prediction head to detect different-scale objects. Then we replace the original prediction heads with Transformer Prediction Heads (TPH) to explore the prediction potential with self-attention mechanism. We also integrate convolutional block attention model (CBAM) to find attention region on scenarios with dense objects. To achieve more improvement of our proposed TPH-YOLOv5, we provide bags of useful strategies such as data augmentation, multi-scale testing, multi-model integration and utilizing extra classifier. Extensive experiments on dataset VisDrone2021 show that TPH-YOLOv5 have good performance with impressive interpretability on drone-captured scenarios. On DET-test-challenge dataset, the AP result of TPH-YOLOv5 are 39.18%, which is better than previous SOTA method (DPNetV3) by 1.81%. On VisDrone Challenge 2021, TPH-YOLOv5 wins 5th place and achieves well-matched results with 1st place model (AP 39.43%). Compared to baseline model (YOLOv5), TPH-YOLOv5 improves about 7%, which is encouraging and competitive.
Green Internet of Vehicles (IoV) in the 6G Era: Toward Sustainable Vehicular Communications and Networking

As one of the most promising applications in future Internet of Things, Internet of Vehicles (IoV) has been acknowledged as a fundamental technology for developing the Intelligent Transportation Systems in smart cities. With the emergence of the sixth generation (6G) communications technologies, massive network infrastructures will be densely deployed and the number of network nodes will increase exponentially, leading to extremely high energy consumption. There has been an upsurge of interest to develop the green IoV towards sustainable vehicular communication and networking in the 6G era. However, as a special mobile ad-hoc network, the energy cost in an IoV system involves the communication and computation energy in addition to the fuel consumption and the electricity cost of moving vehicles. Moreover, the energy harvesting technology, which is likely to be adopted widely in 6G systems, will complicate the optimization of energy efficiency in the entire system. Current studies focus only on part of the energy issues in IoV systems without a comprehensive discussion of the state-of-the-art energy-efficient approaches and the influence of the development of 6G networks on green IoV. In this paper, we present the main considerations for green IoV from five different scenarios, including the communication, computation, traffic, Electric Vehicles (EVs), and energy harvesting management. The literature relevant to each of the scenarios is compared from the perspective of energy optimization (e.g., with respect to resource allocation, workload scheduling, routing design, traffic control, charging management, energy harvesting and sharing) and the related factors affecting energy efficiency (e.g., resource limitation, channel state, network topology, traffic condition). In addition, we introduce the potential challenges and the emerging technologies in 6G for developing green IoV systems. Finally, we discuss the research trends in designing energy-efficient IoV systems.
Multivariate L\'evy Adaptive B-Spline Regression

We develop a fully Bayesian nonparametric regression model based on a L\'evy process prior named MLABS (Multivariate L\'evy Adaptive B-Spline regression) model, a multivariate version of the LARK (L\'evy Adaptive Regression Kernels) models, for estimating unknown functions with either varying degrees of smoothness or high interaction orders. L\'evy process priors have advantages of encouraging sparsity in the expansions and providing automatic selection over the number of basis functions. The unknown regression function is expressed as a weighted sum of tensor product of B-spline basis functions as the elements of an overcomplete system, which can deal with multi-dimensional data. The B-spline basis can express systematically functions with varying degrees of smoothness. By changing a set of degrees of the tensor product basis function, MLABS can adapt the smoothness of target functions due to the nice properties of B-spline bases. The local support of the B-spline basis enables the MLABS to make more delicate predictions than other existing methods in the two-dimensional surface data. Experiments on various simulated and real-world datasets illustrate that the MLABS model has comparable performance on regression and classification problems. We also show that the MLABS model has more stable and accurate predictive abilities than state-of-the-art nonparametric regression models in relatively low-dimensional data.