Miracle-dz/UniFAR-FTUs huggingface dataset:
Panel Threshold Effect Analysis between Capital Structure and Operating Efficiency of Chinese Listed Companies

This paper, aiming to test whether there exists an optimal debt ratio, employs panel threshold regression model to examine threshold effects of financial leverage on operating efficiency (and firm value) of Chinese listed firms. The findings are that there exists triple threshold effect and the coefficients are all positive and significant for four regimes. These results identify that the operating efficiency is positively related to debt level. However, the positive effect decreases when debt ratio increases since the results show that the lower the debt to asset ratio, the higher the impact coefficient. Our result is consistent with ‘Trade-off Theory of Capital Structure’ and ‘Agency Theory’ and provides an implication that financial managers should use financial leverage wisely to improve the operating efficiency in order to maximize the firm value and investors should refer to the debt ratio to make investment decisions. As corporate bond market hasn’t been well developed in China and firm’s debt financing mainly depends on commercial banks, our result is helpful for loan managers in banks.
THE EFFECT OF ACID PRETREATMENT AND PEROXIDE REINFORCEMENT IN ALKALINE EXTRACTION ON OPTICAL AND STRENGTH PROPERTIES OF EUCALPYTUS CAMALDULENSIS KRAFT PULP DURING DED BLEACHING SEQUENCE

Unbleached kraft pulp from Eucalyptus camaldulensis with an initial kappa number of about 31 and brightness of 13% was used to perform bleaching experiments. The results of the first part of this research (using D0ED1 bleaching sequence at different kappa factors of 0.2, 0.3, and 0.4 in D0 stage) showed that the final brightness, even at a kappa factor of 0.4, was relatively low (about 75%). Thus, the effects of acid pretreatment (A stage) and peroxide reinforcement in alkaline extraction (EP versus E stage) in D0ED1 bleaching sequence on optical and strength properties of paper were investigated in this study. By applying these treatments, especially peroxide reinforcement, final kappa number, yield, yellowness, opacity, and air resistance were reduced, while brightness and lightness were increased at similar or superior strength properties in comparison with the reference bleaching sequence.
Are some risk-adjusted measures better able to predict future fund performance ? June 20 , 2018

This paper investigates whether investment strategies using rankings based on different portfolio performance measures lead to different future abnormal returns. A set of 14 well known risk-adjusted performance measures is applied to a dataset of US equity mutual funds over the period July 1970 to March 2014. The results show some evidence of short-term performance persistence, suggesting that portfolios formed on different performance measures ex ante can generate abnormal returns ex post. The results also show that overall there is little difference arising from the use of different performance measures, with one notable exception, the Rachev ratio. Adjusting for the momentum factor, abnormal returns do not survive even low transactions costs.
Deep Elastic Networks with Model Selection for MultiTask Learning : Supplementary Material

The estimator of the proposed method can produce multiple network models of different sizes based on the hierarchical structure in a block. To control the actual speed-up for inference, each hierarchy accesses a different number of channels in each convolution layer. The ratio of the required number of channels for each level can be adjusted. As shown in Figure 1, the lowest level of hierarchy is represented and it accesses only a few channels. The highest level of hierarchy contains all channels in the figure. If the block is based on a residual block [2], the lowest level does not include any channels.
Image Feature Extractor Image Feature Class Feature Output Softmax Weight Factor Branch Feature Branch Softmax

Zero-shot learning (ZSL) is a recently emerging research topic which aims to build classification models for unseen classes with knowledge transferred from auxiliary seen classes. Though many ZSL works have shown promising results on small-scale datasets by utilizing a bilinear compatibility function, the ZSL performance on large-scale datasets with many classes (say, ImageNet) is still unsatisfactory. We argue that the bilinear compatibility function is a low-rank approximation of the true compatibility function such that it is not expressive enough especially when there are a large number of classes because of the rank limitation. To address this issue, we propose a novel approach, termed as High-rank Deep Embedding Networks (GREEN), for ZSL with many classes. In particular, we propose a feature-dependent mixture of softmaxes as the image-class compatibility function, which is a simple extension of the bilinear compatibility function, but yields much better results. It utilizes a mixture of non-linear transformations with featuredependent latent variables to approximate the true function in a high-rank way, thus making GREEN more expressive. Experiments on several datasets including ImageNet demonstrate GREEN significantly outperforms the state-of-the-art approaches.
[3+2]-Cycloaddition of α-Diazocarbonyl Compounds with Arenediazonium Salts Catalyzed by Silver Nitrate Delivers 2,5-Disubstituted Tetrazoles

Abstract [3+2]-Cycloaddition of arenediazonium salts with diazo compounds (earlier exemplified only for trimethylsilyldiazomethane and 2,2,2-trifluorodiazoethane) has been developed to include a wide range of readily available α-diazocarbonyl compounds. The resulting 2-aryl-5-acyl-2H-tetrazoles are of high value in medicinal chemistry.
Creating Competitive Advantage through Source Basic Capital Strategic Humanity in the Industrial Age 4.0

Industry 4.0 was born from the idea of the fourth industrial revolution. Its existence offers many potential benefits. The main objective of this article is to offer a perspective and suggest competitive advantage practices through the best human resources for business in the face of the fourth industrial revolution. This is done by integrating existing literature with logical beliefs. Industry Business Environment 4.0, the main objectives and challenges of Industry 4.0 are discussed here. This article will focus on the business environment in the Industry 4.0 era which covers the main objectives and challenges faced, so that the challenges that exist in Industry 4.0 can be transformed into opportunities in the face of competition through innovation. As a result, we now need to identify the challenges of Industry 4.0 in anticipation of carrying out it, while the challenges are 1) information technology security issues; 2) reliability and stability of production machines; 3) lack of adequate skills; 4) reluctance to change by stakeholders; and 5) loss of a lot of work because it turns into automation. For that we need to anticipate the challenges that exist by creating strategic advantages with human resources who are ready to compete. Keywords—Competitive Advantage: Industry 4.0: Human Resources.
Predicting Network Centralities from Node Attributes

Among the variety of complex network metrics proposed, node importance or centrality has potentially found the most widespread application—from the identification of gene-disease associations to finding relevant pages in web search. In this workshop paper, we present a method that learns mappings from node attributes to latent centralities. We first construct an eigenvector-based Bayesian centrality model, which casts the problem of computing network centrality as one of probabilistic (latent variable) inference. Then, we develop the sparse variational Bayesian centrality Gaussian process (VBC-GP) which simultaneously infers the centralities and learns the mapping. The VBC-GP possesses inherent benefits: it (i) allows a potentially large number of nodes to be represented by the sparse mapping and (ii) permits prediction of centralities on previously unseen nodes. Experiments show that the VBC-GP learns high-quality mappings and compares favorably to a two-step method, i.e., a full-GP trained on the node attributes and network centralities. Finally, we present a case-study using the VBC-GP to distribute a limited number of vaccines to decrease the severity of a viral outbreak.
Kungfu : A Novel Distributed Training System for TensorFlow using Flexible Synchronisation

Modern times are distinguished as the Renaissance of Deep Learning, with remarkable advances across a wide range of domains and abundant research constantly pushing its boundaries. As models and datasets increase in size, people move to distributed clusters, which pose new challenges to training. Distributed training systems usually use the Parallel Stochastic Gradient Descent algorithm to scale out training. This algorithm creates large network traffic and an intensive need for synchronising the entire system. The fundamental limitation is that they inevitably explode the batch size and force the user to use large batches for training, in order to reduce communication traffic and the intensive demands for synchronising the entire system. Existing systems tend to adopt high-end specialised hardware and network such as InfiniBand, which eventually fail because of large network traffic and large clusters reaching hundreds of nodes. The hardware solutions are very expensive, do not scale and fundamentally, the system will suffer from large batch training, so the user may not be able to converge training when scaling out. In this project, I aim to design a system for Deep Learning that enables flexible synchronisation to address communication and large batch training solutions. The system brings three new designs: it has an abstraction that allows the user to declare flexible types of synchronisation, much more complex than Parallel SGD, it has a high-performance communication system where workers exchange gradients and models to collaborate for training and it enables monitoring for network and training statistics, providing support for dynamic adaptation of synchronisation strategies. I develop two advanced synchronisation algorithms based on this new system. One algorithm can exchange partial gradients if the network experiences bottlenecks, while the other enables each worker to selectively synchronise with other peers, reducing the global synchronisation overhead. These algorithms can further benefit from monitoring and adaptation to improve convergence and performance. I run large-scale test-bed experiments in commodity cloud to evaluate the effectiveness and performance of the proposed systems and algorithms. Experimental results show that the new system can converge to state-of-the-art accuracies while improving training performance by up to 22.5 times.
Deterrent Disclosure

We examine how product market competition affects the disclosure of innovation. Theory posits that product market competition can cause firms to increase their disclosure of innovation to deter product market competitors. Consistent with this reasoning, we find that patent applicants in more competitive industries voluntarily accelerate their patent disclosures, which are credibly disclosed via the United States Patent and Trademark Office. Our inferences are robust to using changes in industry-level import tariffs as sources of plausibly exogenous variation in product market competition in differences-in-differences designs. Consistent with patent disclosure deterring product market competitors, we find that timelier patent disclosures are more strongly associated with declines in the similarity of competitors’ products than are less timely patent disclosures. In total, our results suggest that product market competition increases patent disclosure timeliness, which is consistent with firms using the disclosure of innovation to deter product market competition.
Direct reasoning

References are indispensable to computing practice. Unlike deep copying, reference copying permits constant-time data assignment and parameter passing for all objects. Unfortunately, reference copying introduces aliasing among mutable objects and complicates software behavior by requiring developers to distinguish between references and values of objects for sound reasoning. The objective of this dissertation is to preserve the benefits of reference copying but simplify reasoning. It introduces a notion of conceptually direct reasoning, in which programmers may ignore references and treat all variables directly as object values. It explains how this notion can be applied without compromising soundness. It proposes a new approach to parameter passing that avoids both aliasing and deep copying. It explains how to segregate the few components that require aliasing from the many that do not. It contains formal specifications of components and appropriate proof rules. As a proof of concept, it applies the principles to the design of DirectJava, a prototype language that is syntactically similar to Java but facilitates conceptually direct reasoning without resorting to copying.
Robo-advice – a true innovation in asset management

In the US, robo-advisor start-ups’ AuM saw an 8-fold increase in recent years on the back of some retirement savings shifting to robo-advisor accounts. European robo-advisors’ AuM is only some 5-6% of that in the US. They might benefit from greater interest from institutional investors, though. The market is becoming a tough place for business-to-consumer robo-advisory start-ups. As a result, there will probably be partnerships and takeovers by established asset management firms or banks in the coming years.
Smarandache Curves in the Euclidean Space

It is safe to report that the many important results in the theory of the curves in E were initiated by G. Monge; and G. Darboux pionnered the moving frame idea. Thereafter, F. Frenet defined his moving frame and his special equations which play important role in mechanics and kinematics as well as in differential geometry (for more details see [1]). At the beginning of the 20th century, A. Einstein’s theory opened a door to new geometries such as Lorentzian Geometry, which is simultaneously the geometry of special relativity, was established. Thereafter, researchers discovered a bridge between modern differential geometry and the mathematical physics of general relativity by giving an invariant treatment of Lorentzian geometry. They adapted the geometrical models to relativistic motion of charged particles. Consequently, the theory of the curves has been one of the most fascinating topic for such modeling process. As it stands, the Frenet-Serret formalism of a relativistic motion describes the dynamics of the charged particles. The mentioned works are treated in Minkowski spacetime. In the light of the existing literature, in [4] authors introduced special curves by FrenetSerret frame vector fields in Minkowski space-time. A regular curve in Minkowski space-time, whose position vector is composed by Frenet frame vectors on another regular curve, is called a Smarandache Curve [4]. In this work, we study special Smarandache Curve in the Euclidean space. We hope these results will be helpful to mathematicians who are specialized on mathematical modeling.
Dataset Paper Videos Hours Classes Description Charades Hollywood in Homes : Crowdsourcing Data Collection for Activity Understanding 9848

The Out the Window (OTW) dataset is a crowdsourced activity dataset containing 5,668 instances of 17 activities from the NIST Activities in Extended Video (ActEV) challenge. These videos are crowdsourced from workers on the Amazon Mechanical Turk using a novel “scenario acting” strategy, which collects multiple instances of natural activities per scenario. Turkers are instructed to lean their mobile device against an upper story window overlooking an outdoor space, walk outside to perform a scenario involving people, vehicles and objects, and finally upload the video to us for annotation. Performance evaluation for activity classification on VIRAT Ground 2.0 [19] shows that the OTW dataset provides an 8.3% improvement in mean classification accuracy, and a 12.5% improvement on the most challenging activities involving people with vehicles.
A SW application system for measuring supply chain operations ' performance using SCOR FAHP technique

This paper proposes a new software (SW) application utilising the SCOR FAHP technique which incorporates the Fuzzy Analytic Hierarchy Process (FAHP) method in the Supply Chain Operations Reference-Model (SCOR) for the purpose of evaluating and improving supply chain operations’ performance. An application in an Egyptian natural bottled water company is illustrated.
Differences and Similarities in Male Body Image in Young, Middle-Aged, and Older Men

The purpose of the present study was to explore differences and similarities in multiple aspects of male body image across age groups, including body dissatisfaction, coping strategies, functional appreciation, and mood. It was first hypothesized that younger men would report greater dissatisfaction with muscularity and body fat than middle-aged and older men. The second hypothesis stated that body dissatisfaction would predict depressive symptoms. The third hypothesis predicted that age and functionality appreciation for the body would be positively correlated. Finally, researchers evaluated the relationship between body image coping strategies and age from an exploratory perspective. Participants included 344 men ages 18-88 recruited through Amazon’s Mechanical Turk and various social media websites. Participants completed a series of questionnaires assessing body dissatisfaction, depressive symptoms, and body image coping strategies. Correlations, analyses of variance, and a hierarchical regression were conducted to test the four hypotheses. The first hypothesis was partially supported, as younger men reported greater dissatisfaction with muscularity. The second hypothesis was supported when a measure of body dissatisfaction predicted depressive symptoms, but not when a figurerating scale was used instead. The third hypothesis received no support. Lastly, researchers found significant differences in body image coping strategies between age groups, such that younger men used more coping strategies than older men.
The borrower orientation of credit unions and earnings management : evidence and real effects

The credit union sector has undergone considerable growth in recent years. However, given the relative low sophistication of credit union operations and of their depositor/member base, little attention has been paid to issues of financial transparency and, more specifically, to the use of accounting (earnings) discretion by credit unions. In this paper we provide the first comprehensive evidence of the use of earnings management strategies by credit unions through the use of discretionary charges to the loan loss provision. We show that credit unions carry out strategies of income smoothing, big baths and loss avoidance similar to those of other financial institutions. We then go one step further and show how this earnings discretion may be, at least partly, explained by their particular maximization problem (which leads to their saver/borrower orientation). Specifically, we analyze the effects of earnings discretion on the supply of loans and on dividend payments. We also examine a local exogenous shock and show how those credit unions which managed (increased) their earnings through discretion were able to achieve significantly higher rates of growth of their loan portfolio without losing members or deposits.
NLP Group at ImageCLEFmed Caption 2019

We present the systems that AUEB’s NLP Group used to participate in the ImageCLEFmed 2019 Caption task. The goal of this task is to automatically select medical concepts related to each image, as a first step towards generating image captions, medical reports, or to help in medical diagnosis. We participated with four systems, all using CNN image encoders. The encoder of each system is combined with an image retrieval method or a feed-forward neural network to predict concepts. Our systems were ranked 1st, 2nd, 3rd, and 5th.
Competing concepts of control challenges self-determined behavior-A study of accountability over time in a Beyond Budgeting company -

Accountability can be seen as ‘a chronic feature of daily conducts’ (Giddens, 1979, p. 57) as it in its broadest sense simply refers to ‘the giving and demanding of reasons for conduct’ (Roberts & Scapens, 1985, p. 447). Ahrens (1996) draw on Garfinkel’s ethnomethodological perspective in arguing that accountability appear as a “social process with precisely the function to align wider rationalities and local operational knowledge with social action” (Ahrens, 1996, p. 143). The notion of ‘style of accountability’ is advanced by Ahrens (1996) to explicate this wide, aligned rationality of what is deemed legitimate action, what organizational members argue is ‘good management’ and for which they hold themselves and others accountable to.
Give It a Shot: Few-shot Learning to Normalize ADR Mentions in Social Media Posts

This paper describes the system that team MYTOMORROWS-TU DELFT developed for the 2019 Social Media Mining for Health Applications (SMM4H) Shared Task 3, for the end-to-end normalization of ADR tweet mentions to their corresponding MEDDRA codes. For the first two steps, we reuse a state-of-the art approach, focusing our contribution on the final entity-linking step. For that we propose a simple Few-Shot learning approach, based on pre-trained word embeddings and data from the UMLS, combined with the provided training data. Our system (relaxed F1: 0.337-0.345) outperforms the average (relaxed F1 0.2972) of the participants in this task, demonstrating the potential feasibility of few-shot learning in the context of medical text normalization.
Topic Modeling based on Louvain method in Online Social Networks

Online Social Networks (OSNs) are the most used media nowadays, such as Twitter. The OSNs provide valuable information to marketing and competitiveness based on users posts and opinions stored inside huge volume of data from several themes, topics and subjects. In order to mining the topics discussed on an OSN we present a novel application of Louvain method for Topic Modeling based on communities detection in graphs by modularity. The proposed approach succeeded in finding topics in five different datasets composed of textual content from Twitter and Youtube. Another important contribution achieved was about the presence of texts posted by spammers. In this case, a particular behavior observed by graph architecture (density and degree) allows the classification of a topic as natural or artificial, this last created by the spammers on OSNs.
Investigating the impact of normalizing flows on latent variable machine translation

Natural language processing (NLP) has pervasive applications in everyday life, and has recently witnessed rapid progress. Incorporating latent variables in NLP systems can allow for explicit representations of certain types of information. In neural machine translation systems, for example, latent variables have the potential of enhancing semantic representations. This could help improve general translation quality. Previous work has focused on using variational inference with diagonal covariance Gaussian distributions, which we hypothesize cannot sufficiently encode latent factors of language which could exhibit multi-modal distributive behavior. Normalizing flows are an approach that enables more flexible posterior distribution estimates by introducing a change of variables with invertible functions. They have previously been successfully used in computer vision to enable more flexible posterior distributions of image data. In this work, we investigate the impact of normalizing flows in autoregressive neural machine translation systems. We do so in the context of two currently successful approaches, attention mechanisms, and language models. Our results suggest that normalizing flows can improve translation quality in some scenarios, and require certain modelling assumptions to achieve such improvements.