Miracle-dz/UniFAR-FTUs huggingface dataset:
Earnings Management and Ownership Retention for Initial Public Offering Firms in China

This paper implements a comprehensive study on the value of 139 initial public offering (IPO) firms in Shenzhen and Shanghai A-share stock market during January 2006 to December 2008. Empirical results reveal that earnings management and ownership retention jointly affect the equilibrium market valuation of IPO firms in the presence of information asymmetry. Specifically, this study extends the one-signal framework of Leland and Pyle (1977). In other words, we obtain an efficient two-signal equilibrium model in which earnings management and ownership retention go public acting as effective signals, conveying the information of IPO firm value to the market. This would help to transmit the IPO firm value in China stock market to rational investors, which in turn reinforces the equilibrium of the China stork market.
EU regulations on algorithmic decision-making and a "right to explanation"

We summarize the potential impact that the European Union’s new General Data Protection Regulation will have on the routine use of machine learning algorithms. Slated to take effect as law across the EU in 2018, it will restrict automated individual decision-making (that is, algorithms that make decisions based on user-level predictors) which “signiﬁcantly affect” users. The law will also create a “right to explanation,” whereby a user can ask for an explanation of an algorithmic decision that was made about them. We argue that while this law will pose large challenges for industry, it highlights opportunities for machine learning researchers to take the lead in designing algorithms and evaluation frameworks which avoid discrimination.
Epileptic Electroencephalogram Signal Classification based on Sparse Representation

Epilepsy seizure detection in Electroencephalogram (EEG) is a major issue in the diagnosis of epilepsy and it can be considered as a classification problem. According to the particular property of EEG, a novel method based on sparse representation is proposed for epilepsy detection in this paper. Classification accuracy, robustness on noisy data and parameters (the size of dictionary and the number of features) of proposed method are tested and analysed on the public available data. The proposed method can obtain the highest classification accuracy among the discussed methods when the suitable parameters are set, and the proposed method based on sparse representations for classification is robust to noise. This is consistent with the theory that sparse representations can capture the inherent structure of signal. Furthermore, it is shown by experiments that the optimal selection of the parameters is critical to the performance of epilepsy detec-
Increase in periventricular white matter hyperintensities parallels decline in mental processing speed in a non-demented elderly population

Objective: To investigate the influence of deep white matter hyperintensities (DWMH) and periventricular white matter hyperintensities (PVWMH) on progression of cognitive decline in non-demented elderly people. Methods: All data come from the nested MRI sub-study of the PROspective Study of Pravastatin in the Elderly at Risk (PROSPER). We performed a 3 year follow up study on 554 subjects of the PROSPER study using both repeated magnetic resonance imaging and cognitive testing. Cognitive decline and its dependency on WMH severity was assessed using linear regression models adjusted for sex, age, education, treatment group, and test version when applicable. Results: We found that the volume of PVWMH at baseline was longitudinally associated with reduced mental processing speed (p = 0.0075). In addition, we found that the progression in PVWMH volume paralleled the decline in mental processing speed (p = 0.024). In contrast, neither presence nor progression of DWMH was associated with change in performance on any of the cognitive tests. Conclusion: PVWMH should not be considered benign but probably underlie impairment in cognitive processing speed.
Efficient Implementation of Iterative Adaptive Approach Spectral Estimation Techniques

This paper presents computationally efficient implementations for several recent algorithms based on the iterative adaptive approach (IAA) for uniformly sampled one- and two-dimensional data sets, considering both the complete data case, and the cases when the data sets are missing samples, either lacking arbitrary locations, or having gaps or periodically reoccurring gaps. By exploiting the method's inherent low displacement rank, together with the development of suitable Gohberg-Semencul representations, and the use of data dependent trigonometric polynomials, the proposed implementations are shown to offer a reduction of the necessary computational complexity by at least one order of magnitude. Numerical simulations together with theoretical complexity measures illustrate the achieved performance gain.
Cxcl12 evolution – subfunctionalization of a ligand through altered interaction with the chemokine receptor

The active migration of primordial germ cells (PGCs) from their site of specification towards their target is a valuable model for investigating directed cell migration within the complex environment of the developing embryo. In several vertebrates, PGC migration is guided by Cxcl12, a member of the chemokine superfamily. Interestingly, two distinct Cxcl12 paralogs are expressed in zebrafish embryos and contribute to the chemotattractive landscape. Although this offers versatility in the use of chemokine signals, it also requires a mechanism through which migrating cells prioritize the relevant cues that they encounter. Here, we show that PGCs respond preferentially to one of the paralogs and define the molecular basis for this biased behavior. We find that a single amino acid exchange switches the relative affinity of the Cxcl12 ligands for one of the duplicated Cxcr4 receptors, thereby determining the functional specialization of each chemokine that elicits a distinct function in a distinct process. This scenario represents an example of protein subfunctionalization – the specialization of two gene copies to perform complementary functions following gene duplication – which in this case is based on receptor-ligand interaction. Such specialization increases the complexity and flexibility of chemokine signaling in controlling concurrent developmental processes.
Is the Swedish Stock Market Becoming more Integrated with those of Germany and France

This paper tests for equity market integration between Sweden and EU countries represented by Germany and France. A new causality test method developed by Hacker and Hatemi-J (2006) is applied. This method performs better than the other methods because it is robust to non-normality and the presence of ARCH effects in the financial data. The results show that Sweden did not have a significant causal relationship with Germany and France during the period before Swedish membership in the EU. However, for the period after Sweden joined the EU (in 1995), we find that Sweden became significantly linked with both Germany and France. We interpret these empirical findings as supporting the proposition that the Swedish financial market has become more integrated with the EMU area.
Mediators of Inflammation-Induced Bone Damage in Arthritis and Their Control by Herbal Products

Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammation of the synovial joints leading to bone and cartilage damage. Untreated inflammatory arthritis can result in severe deformities and disability. The use of anti-inflammatory agents and biologics has been the mainstay of treatment of RA. However, the prolonged use of such agents may lead to severe adverse reactions. In addition, many of these drugs are quite expensive. These limitations have necessitated the search for newer therapeutic agents for RA. Natural plant products offer a promising resource for potential antiarthritic agents. We describe here the cellular and soluble mediators of inflammation-induced bone damage (osteoimmunology) in arthritis. We also elaborate upon various herbal products that possess antiarthritic activity, particularly mentioning the specific target molecules. As the use of natural product supplements by RA patients is increasing, this paper presents timely and useful information about the mechanism of action of promising herbal products that can inhibit the progression of inflammation and bone damage in the course of arthritis.
Global Transcriptome Changes Underlying Colony Growth in the Opportunistic Human Pathogen Aspergillus fumigatus

ABSTRACT Aspergillus fumigatus is the most common and deadly pulmonary fungal infection worldwide. In the lung, the fungus usually forms a dense colony of filaments embedded in a polymeric extracellular matrix. To identify candidate genes involved in this biofilm (BF) growth, we used RNA-Seq to compare the transcriptomes of BF and liquid plankton (PL) growth. Sequencing and mapping of tens of millions sequence reads against the A. fumigatus transcriptome identified 3,728 differentially regulated genes in the two conditions. Although many of these genes, including the ones coding for transcription factors, stress response, the ribosome, and the translation machinery, likely reflect the different growth demands in the two conditions, our experiment also identified hundreds of candidate genes for the observed differences in morphology and pathobiology between BF and PL. We found an overrepresentation of upregulated genes in transport, secondary metabolism, and cell wall and surface functions. Furthermore, upregulated genes showed significant spatial structure across the A. fumigatus genome; they were more likely to occur in subtelomeric regions and colocalized in 27 genomic neighborhoods, many of which overlapped with known or candidate secondary metabolism gene clusters. We also identified 1,164 genes that were downregulated. This gene set was not spatially structured across the genome and was overrepresented in genes participating in primary metabolic functions, including carbon and amino acid metabolism. These results add valuable insight into the genetics of biofilm formation in A. fumigatus and other filamentous fungi and identify many relevant, in the context of biofilm biology, candidate genes for downstream functional experiments.
Interspecific Functional Convergence and Divergence and Intraspecific Negative Density Dependence Underlie the Seed-to-Seedling Transition in Tropical Trees

The seed-to-seedling transition constitutes a critical bottleneck in the life history of plants and represents a major determinant of species composition and abundance. However, we have surprisingly little knowledge regarding the forces driving this ontogenetic transition. Here we utilize information regarding organismal function to investigate the strength of intra- and interspecific negative density dependence during the seed-to-seedling transition in Puerto Rican tree species. Our analyses were implemented at individual sites and across an entire 16-ha forest plot, spanning 6 years. The functional richness of seedling assemblages was significantly lower than expected given the seed assemblages, but the functional evenness was significantly higher than expected, indicating the simultaneous importance of constraints on the overall phenotypic space and trait differences for successful transitions from seed to seedling. The results were consistent across years. Within species, we also found evidence for strong intraspecific negative density dependence, where the probability of transition was proportionally lower when in a site with high conspecific density. These results suggest that filtering of similar phenotypes across species and strong negative density dependence within and among species are simultaneously driving the structure and dynamics of tropical tree assemblages during this critical life-history transition.
Cremers Newton Methods for Total Variation Minimization

The subject of this thesis is Newton’s method applied to Total Variation (TV) minimization. We particularly consider the minimization of the Rudin-Osher-Fatemi (ROF) functional introduced in [57] which is one of the simplest variational models in computer vision. The straightforward application of the ROF model solves the perhaps most fundamental image restoration problem, namely image denoising, which is a preliminary step in many machine vision tasks e.g. object recognition. Additionally, the ROF model can be used to solve a two-phased segmentation model, as we will show in this work. The most important aspect of the ROF model is that the TV regularization term allows for edges in the solution while disfavoring noise. Additionally, due to the convexity of the TV regularizing term, many tools from convex optimization can be used to tackle the ROF model. The challenge in solving the ROF model lies in the non-differentiability of the TV term as well as in its highly non-linear underlying PDE. This difficulty has pushed researchers to investigate alternative formulations [10, 13, 19] more well behaved and to devise new efficient optimization techniques for solving these formulations. The most common method for minimizing the ROF model is the gradient descent method. Being a first-order method the gradient descent algorithm exhibits slow convergence, especially near the minimizer of the ROF model. However, since the advent of the GPU (graphics processing unit) programmability, it is known that first-order methods are well suited to be implemented on the highly parallel GPU architecture. This is due to their low complexity and their low memory requirement. This makes an acceleration, up to real time performance, of many variational problems possible. Another promising approach to solve variational problems efficiently, is to utilize secondorder methods and in particular Newton’s method. Newton’s method provides better convergence results and was considered in many works [19,20,35,41,51,63]. However, its application to the different formulations of the ROF model is not straightforward as many challenges have to be faced. The objective of this thesis is to study these methods. Moreover, we provide a novel method that solves the dual formulation of a Huber regularized ROF model. This regularization avoids the so called staircasing effect which is a major drawback when using TV as a regularizing energy.
Term Limits: Do They Really Affect Fiscal Policy Choices?

According to reputational models of political economy, a term limit may change the behavior of a chief executive because he does not have to stand for election. We test this hypothesis in a sample of 52 countries over the period 1977-2000, using government spending, social and welfare spending and deficit as policy choice variables using panel data estimation techniques. We are unable to find significant differences in the behavior of term-limited and non term-limited chief executives. This is in contrast with some previous empirical results based on U.S. states and international data.
Neuro-fuzzy modelling based on a deterministic annealing approach

This paper introduces a new learning algorithm for artificial neural networks, based on a fuzzy inference system ANBLIR. It is a computationally effective neuro-fuzzy system with parametrized fuzzy sets in the consequent parts of fuzzy if-then rules, which uses a conjunctive as well as a logical interpretation of those rules. In the original approach, the estimation of unknown system parameters was made by means of a combination of both gradient and least-squares methods. The novelty of the learning algorithm consists in the application of a deterministic annealing optimization method. It leads to an improvement in the neuro-fuzzy modelling performance. To show the validity of the introduced method, two examples of application concerning chaotic time series prediction and system identification problems are provided.
Whole grains and health: attitudes to whole grains against a prevailing background of increased marketing and promotion

Abstract Objective To explore current awareness and perceptions of whole grain foods and perceived barriers and facilitators of whole grain consumption. Design Focus groups were conducted to investigate consumer attitudes to whole grains. Discussions were transcribed verbatim and analysed thematically. Setting Discussions were held throughout Northern Ireland with adults who were at least partly responsible for food shopping. Subjects Seven focus groups were held (n 43; thirty-three females, ten males). Results All participants were aware of the term ‘whole grain’ and had a basic level of awareness of their health benefits. Prominent barriers and facilitators of whole grain intake were related to perceptions of the sensory properties (most dominant factor) of whole grains; knowledge of how to locate, identify and use whole grains; and awareness of the health benefits, perceived cost and family influences. Parents of young children appeared to be altruistically motivated with many stating they wanted to ensure their children consumed whole grains in order to establish good eating habits. Conclusions Participants were generally aware of the term ‘whole grain’; however, even against a background of increased availability and promotion of whole grain foods, many key barriers to whole grain consumption were still evident. Alongside general education efforts, opportunities and challenges exist for the food industry to develop novel, but affordable, food products that are able to deliver whole grains in a wide variety of forms, including whole grains ‘in disguise’ for those who are most resistant to change.
Order and disorder in gauge systems and magnets

We show how phase transitions in Abelian two-dimensional spin and four-dimensional gauge systems can be understood in terms of condensation of topological objects. In the spin systems these objects are kinks and in the gauge systems either magnetic monopoles or fluxoids (quantized lines of magnetic flux). Four models are studied: two-dimensional Ising and $\mathrm{XY}$ models and four-dimensional ${Z}_{2}$ and U(1) gauge systems.
Development and evaluation of perceptually adapted color gradients for color edge detection

Although several color edge detectors have been proposed, most of them utilized Euclidean color distances in different color spaces to measure color differences. In this paper a set of color gradients based on color visual perception is proposed. These color gradients are designed in uniform color spaces and use perceptual color difference equations. In order to be able to study the gradients performance, they have been applied in a variational level set formulation. A comparative study has been performed. A set of synthetic images, generated for this purpose, has allowed carrying out an extensive evaluation. Both quantitative and qualitative measures are used. It can be conclude that detectors based on CIE94 color difference equation are the best regarding to the correlation with color visual perception. Our main contributions are: the derivation of perceptual color gradients, the design of a perceptual evaluation with a specific synthetic dataset and the comparison between detectors based on different color differences.
A Novel QoS Admission Control for Ad Hoc Networks

For real-time traffic requirements in mobile ad hoc networks a novel and effective QoS admission control routing protocol (QACRP) is proposed in this paper. With a limit amount of extra overhead, QACRP can avoid network congestion by a simple and precise admission control to block most of overloading flow-requests in route discovery process. System simulations show that this proposed scheme can increase the system throughput and reduce both the dropping rate and the end-to-end delay when compared with previous QoS routing schemes like QoS-aware routing protocol and CACP protocol. Hence, QACRP is surely an effective routing protocol to provide QoS guarantee for real-time traffic.
Developing Multi-User Applications For iPhone Designing Engaging Applications for Multiple Users on Next-Generation Mobile Platforms

This project explores Apple iPhone’s capacity as an engaging, multi-user application environment. Taking advantage of iPhone’s unique operating system, powerful hardware, and various modes of interaction, two multiplayer games for Apple’s iPhone were developed, WapIt! and Turret Wars . Next generation mobile devices like iPhone exhibit great potential for diverse new mobile applications, but, until recently, developers have given little consideration to the differences in the way users interact with and perform tasks on mobile and desktop computing devices. As technology advances and mobile computing becomes increasingly popular, focused effort, as advocated by Gorlenko and Merrick [4], must be applied in addressing usability and functionality issues associated with fully mobile wirelessly connected computing devices like iPhone. The games developed in this project demonstrate the process used to develop multi-user applications, including the design of the networking structure, the visual user-interface, and customizable multitouch and accelerometer gestures.
Exporting and non‐exporting small firms in Great Britain

Makes a contribution to the debate surrounding the characteristics of “non‐exporting” (n = 203 firms) and “exporting” (n = 64 firms) new manufacturing and “producer services” small firms in Great Britain. It was appreciated that sample differences may overwhelm the exploratory analysis. Consequently, in order to overcome this potentially distorting influence a “matched pairs” methodology was also utilized. Forty‐two matched pairs of non‐exporting and exporting firms were identified (by age of the business, industry and location type). In total, data were collected on 146 variables covering the firm, the founder and the environment. Dichotomizing between the two types of firms the univariate analysis of the “matched samples” identified statistically significant differences with regard to only 14 variables (10 per cent). Additional multivariate analysis was conducted. Results from a logit regression model of the “matched samples” suggests new firms are “pushed” into “exporting” their sales abroad due to per.
Context Tricks for Cheap Semantic Segmentation

Accurate semantic labeling of image pixels is difficult because intra-class variability is often greater than inter-class variability. In turn, fast semantic segmentation is hard because accurate models are usually too complicated to also run quickly at test-time. Our experience with building and running semantic segmentation systems has also shown a reasonably obvious bottleneck on model complexity, imposed by small training datasets. We therefore propose two simple complementary strategies that leverage context to give better semantic segmentation, while scaling up or down to train on different-sized datasets. As easy modifications for existing semantic segmentation algorithms, we introduce Decorrelated Semantic Texton Forests, and the Context Sensitive Image Level Prior. The proposed modifications are tested using a Semantic Texton Forest (STF) system, and the modifications are validated on two standard benchmark datasets, MSRC-21 and PascalVOC-2010. In Python based comparisons, our system is insignificantly slower than STF at test-time, yet produces superior semantic segmentations overall, with just push-button training.