Miracle-dz/UniFAR-FTUs huggingface dataset:
Which Investments Do Firms Protect? Liquidity Management and Real Adjustments When Access to Finance Falls Sharply

We study how firms engaged in both R&D and fixed investment manage liquidity and adjust real investment during the recent financial crisis. Among firms with positive R&D expenditures, cuts to fixed investment in the crisis are typically far more severe than cuts to R&D. These firms allocate cash reserves to buffer R&D but do not use cash to protect fixed investment. Some firms appear to go so far as to allow the stock of fixed assets to fall to stabilize R&D. The use of cash holdings and fixed assets to protect R&D is particularly strong among firms most likely to face financing frictions at the start of the crisis. We only find evidence that firms use cash to buffer fixed investment when we expand the sample to include firms with no R&D spending to compete for funds. Our study provides direct evidence on the real effects of liquidity management, highlights a key benefit of precautionary cash reserves, and illustrates the adjustments firms make to navigate a financial crisis.
Impact of Cellular miRNAs on Circulating miRNA Biomarker Signatures

Effective diagnosis and surveillance of complex multi-factorial disorders such as cancer can be improved by screening of easily accessible biomarkers. Highly stable cell free Circulating Nucleic Acids (CNA) present as both RNA and DNA species have been discovered in the blood and plasma of humans. Correlations between tumor-associated genomic/epigenetic/transcriptional changes and alterations in CNA levels are strong predictors of the utility of this biomarker class as promising clinical indicators. Towards this goal microRNAs (miRNAs) representing a class of naturally occurring small non-coding RNAs of 19–25 nt in length have emerged as an important set of markers that can associate their specific expression profiles with cancer development. In this study we investigate some of the pre-analytic considerations for isolating plasma fractions for the study of miRNA biomarkers. We find that measurement of circulating miRNA levels are frequently confounded by varying levels of cellular miRNAs of different hematopoietic origins. In order to assess the relative proportions of this cell-derived class, we have fractionated whole blood into plasma and its ensuing sub-fractions. Cellular miRNA signatures in cohorts of normal individuals are catalogued and the abundance and gender specific expression of bona fide circulating markers explored after calibrating the signal for this interfering class. A map of differentially expressed profiles is presented and the intrinsic variability of circulating miRNA species investigated in subsets of healthy males and females.
Autophagy gene ATG7 regulates ultraviolet radiation-induced inflammation and skin tumorigenesis

ABSTRACT Macroautophagy (hereafter autophagy) is a cellular “self-eating” process that is implicated in many human cancers, where it can act to either promote or suppress tumorigenesis. However, the role of autophagy in regulation of inflammation during tumorigenesis remains unclear. Here we show that autophagy is induced in the epidermis by ultraviolet (UV) irradiation and autophagy gene Atg7 promoted UV-induced inflammation and skin tumorigenesis. Atg7 regulated UV-induced cytokine expression and secretion, and promoted Ptgs2/Cox-2 expression through both a CREB1/CREB-dependent cell autonomous mechanism and an IL1B/IL1β-dependent non-cell autonomous mechanism. Adding PGE2 increased UV-induced skin inflammation and tumorigenesis, reversing the epidermal phenotype in mice with Atg7 deletion in keratinocytes. Similar to ATG7 knockdown in human keratinocytes, ATG5 knockdown inhibited UVB-induced expression of PTGS2 and cytokines. Furthermore, ATG7 loss increased the activation of the AMPK pathway and the phosphorylation of CRTC1, and led to endoplasmic reticulum (ER) accumulation and reduction of ER stress. Inducing ER stress and inhibiting calcium influx into the ER by thapsigargin reverses the inflammation and tumorigenesis phenotype in mice with epidermal Atg7 deletion. Taken together, these findings demonstrate that deleting autophagy gene Atg7 leads to a suppression of carcinogen-induced protumorigenic inflammatory microenvironment and tumorigenesis of the epithelium.
First-Principles Investigation of Phase Stability, Electronic Structure and Optical Properties of MgZnO Monolayer

MgZnO bulk has attracted much attention as candidates for application in optoelectronic devices in the blue and ultraviolet region. However, there has been no reported study regarding two-dimensional MgZnO monolayer in spite of its unique properties due to quantum confinement effect. Here, using density functional theory calculations, we investigated the phase stability, electronic structure and optical properties of MgxZn1−xO monolayer with Mg concentration x range from 0 to 1. Our calculations show that MgZnO monolayer remains the graphene-like structure with various Mg concentrations. The phase segregation occurring in bulk systems has not been observed in the monolayer due to size effect, which is advantageous for application. Moreover, MgZnO monolayer exhibits interesting tuning of electronic structure and optical properties with Mg concentration. The band gap increases with increasing Mg concentration. More interestingly, a direct to indirect band gap transition is observed for MgZnO monolayer when Mg concentration is higher than 75 at %. We also predict that Mg doping leads to a blue shift of the optical absorption peaks. Our results may provide guidance for designing the growth process and potential application of MgZnO monolayer.
High-level multicore programming with XJava

Multicore chips are becoming mainstream, but programming them is difficult because the prevalent thread-based programming model is error-prone and does not scale well. To address this problem, we designed XJava, an extension of Java that permits the direct expression of producer/consumer, pipeline, master/slave, and data parallelism. The central concept of the extension is the task, a parallel activity similar to a filter in Unix. Tasks can be combined with new operators to create arbitrary nestings of parallel activities. Preliminary experience with XJava and its compiler suggests that the extensions lead to code savings and reduce the potential for synchronization defects, while preserving the advantages of object-orientation and type-safety. The proposed extensions provide intuitive “what-you-see-is-what-you-get” parallelism. They also enable other software tools, such as auto-tuning and accurate static analysis for race detection.
The Child–Adolescent Perfectionism Scale

There is growing interest in perfectionism among children and adolescents as well as growing interest in the measures designed to assess perfectionism in young people. The current article describes the development and psychometric characteristics of the Child–Adolescent Perfectionism Scale (CAPS), a measure that assesses self-oriented perfectionism and socially prescribed perfectionism. The results of three studies involving multiple samples are reported. The psychometric features of this measure are summarized, including extensive data that attest to the reliability and validity of the CAPS subscales. Normative data are also provided in Study 1. The results of Study 2 suggest that the academic behavior of perfectionistic students is motivated by a complex blend of factors that include a strong emphasis on introjected regulation in both self-oriented and socially prescribed perfectionism; however, there are key motivational differences between these perfectionism dimensions. Finally, Study 3 confirmed that self-oriented and socially prescribed perfectionism are associated with various indices of stress, distress, and maladjustment. Collectively, our results support the use of the CAPS and the notion that vulnerable children and adolescents who are perfectionistic are under substantial pressure to meet expectations. The assessment and theoretical implications of these results are discussed.
On Generalized First Fall Degree Assumptions

The first fall degree assumption provides a complexity approximation of Grobner basis algorithms when the degree of regularity of a polynomial system cannot be precisely evaluated. Most importantly, this assumption was recently used by Petit and Quisquater’s to conjecture that the elliptic curve discrete logarithm problem can be solved in subexponential time for binary fields (binary ECDLP). The validity of the assumption may however depend on the systems in play. In this paper, we theoretically and experimentally study the first fall degree assumption for a class of polynomial systems including those considered in Petit and Quisquater’s analysis. In some cases, we show that the first fall degree assumption seems to hold and we deduce complexity improvements on previous binary ECDLP algorithms. On the other hand, we also show that the assumption is unlikely to hold in other cases where it would have very unexpected consequences. Our results shed light on a Grobner basis assumption with major consequences on several cryptanalysis problems, including binary ECDLP.
Causal networks as the backbone for temporal data-to-text

Causal networks have been successfully used to explain the process of narrative comprehension in humans (Trabasso et al., 1989). This motivated their use in the automatic creation of fairy tales (Swartjes and Theune, 2006; Theune et al., 2007). Some have suggested that causal relations also play an important role in improving narrative generation from real-life temporal data (Hunter et al., 2012; Gervás, 2014). Several narrative data-to-text systems already identify and make use of some causal relations (Hallett, 2008; Hunter et al., 2012; Bouayad-Agha et al., 2012; Wanner et al., 2010). Is it possible go one step further in the identification of causal relations and aim at extracting a causal network that could be used to improve the coherence of generated texts? Given that real-life temporal data could be analysed and interpreted automatically so as to produce an appropriate causal network, how could this information be best used to organize a coherent narrative adapted to the communicative needs of a wide range of data-to-text applications? We will here address the second question by proposing a bottom-up document planning method for building the rhetorical structure of coherent narratives from a causal network. The causal network represents the plot of the story to be told by the narrative text. The rhetorical structure represents one way, corresponding to a particular viewpoint, of organizing the text to tell that story. The goal is therefore to translate the semantic information contained in a causal network into a rhetorical structure using parameters that characterize this perspective. The proposed approach uses hierarchical clustering and parameterization by adjacency and ordering preferences. It is robust to partial causal networks and does not require the generated rhetorical structure to be a tree. The document planning stage would be preceded by signal analysis and data interpretation and followed by microplanning and surface realisation in a complete data-to-text pipeline (Reiter, 2007).
Analysis of children: humanoid robot interaction to support social skills development

Children robot interaction1 has shown benefits in academic or medical applications. We started a long-term project with the goal of modifying some social behaviors on disabled children using a humanoid robot that implements tasks that a therapist does frequently; this paper reports the initial stage of our project, it focuses on the analysis, design and test of children - NAO robot interactions by taking into account a repertoire of basic social behaviors: rapport establishment, attention acquisition, imitation behavior and follow-up of instructions. Preliminary results show that interactions with regular children (aged from 5 to 12 years old) are validated and ready to be used as a promising alternative to support social skills development in disabled children.
On Parameter Identifiability of MIMO Radar

A multi-input multi-output (MIMO) radar system, unlike a standard phased-array radar, can transmit multiple linearly independent probing signals via its antennas. We show herein that this waveform diversity enables the MIMO radar to significantly improve its parameter identifiability. Specifically, we show that the maximum number of targets that can be uniquely identified by the MIMO radar is up to Mt times that of its phased-array counterpart, where Mt is the number of transmit antennas.
Compaction uniformity and environmental adaptability of printed RFID antenna

The manufacture of RFID antenna tags (RFIDs) using silk-screen printing technology with conductive silver paste is one of economic methods to reduce the cost of RFIDs. The distance between conductive particles is minified via hot compaction to conductive silver paste, resulting in lower resistance and higher Q value. The compaction conditions, including temperature, pressure and time, were systematically studied based on the analysis method of uniform design, with regression equation by nonlinear regression analysis fitted by Minitab Statistical Analysis Software. The effect of environmental factors to resistance of RFID antenna was also studied through the high and low temperature impact test and the constant temperature and humidity test. The results indicate that resistance of RFID antenna printed with conductive silver paste can be reduced more than 50%, the resistance variation of RFID antenna can be predicted by regression equation with correlation coefficient 99.8% in certain range and antenna printed with conductive silver paste have good environmental adaptability.
Optimal Bandwidth Allocation With Delayed State Observation and Batch Assignment

In this paper we consider the problem of allocating bandwidth/server to multiple user trans-mitters/queues with identical but arbitrary arrival processes, to minimize the total expected holding cost of backlogged packets in the system over a ﬁnite horizon. The special features of this problem are that (1) packets continuously arrive at these queues regardless of the allocation decision, (2) there is a large delay between when the allocation decision is made and when the allocation is used, thus decisions can be viewed as based on delayed or obsolete state information/observations; and (3) the bandwidth allocation is in batches of time slots so that each queue can be assigned any number of slots not exceeding the total number in a batch. This problem is motivated by channel allocation in a communication system involving large propagation delay, e.g., a typical satellite data communication scenario. This problem can be cast as a special case of the restless bandit problem with multiple plays with the additional properties that the state observation is imperfect and that each queue can be served multiple times in between two decision epochs. We identify two sufﬁcient conditions which jointly deﬁne a class of policies that are optimal. We further show that under very mild assumptions on the arrival process these two conditions are also necessary for the optimality of a policy. Without these assumptions on the arrival process the two conditions are not in general necessary, which we show via two examples.
A Novel Green Service Level Agreement for Cloud Computing using Fuzzy Logic

Cloud computing has several features including elasticity and economy of scale that have allowed it to find several uses in scientific, economic and industrial fields. However, the Cloud model relies heavily on the architecture of the data centers that host the Cloud services. To date, these data centers consume huge quantities of electrical energy in order to ensure the operation of both computer equipment and auxiliary equipment such as cooling systems. In order to reduce the environmental and economic impact of this energy consumption, several initiatives have emerged especially in the context of Green computing. Through this article, we propose a Cloud architecture that includes a service level agreement negotiation module based on the concept of fuzzy logic. The proposed solution aims to introduce a virtual machine consolidation policy in order to complete a global three-tier architecture. The final solution includes different modules of load balancing and scheduling based on fuzzy logic, metaheuristic and Map reduce algorithms in order to optimize both energy efficiency, response time and cost of Cloud services.
Runtime Adaptive Hybrid Query Engine based on FPGAs

This paper presents the fully integrated hardware-accelerated query engine for large-scale datasets in the context of Semantic Web databases. As queries are typically unknown at design time, a static approach is not feasible and not flexible to cover a wide range of queries at system runtime. Therefore, we introduce a runtime reconfigurable accelerator based on a Field Programmable Gate Array (FPGA), which transparently incorporates with the freely available Semantic Web database LUPOSDATE. At system runtime, the proposed approach dynamically generates an optimized hardware accelerator in terms of an FPGA configuration for each individual query and transparently retrieves the query result to be displayed to the user. During hardware-accelerated execution the host supplies triple data to the FPGA and retrieves the results from the FPGA via PCIe interface. The benefits and limitations are evaluated on large-scale synthetic datasets with up to 260 million triples as well as the widely known Billion Triples Challenge.
A fluorescent paramagnetic Mn metal–organic framework based on semi-rigid pyrene tetracarboxylic acid: sensing of solvent polarity and explosive nitroaromatics

Fluorescence quenching by paramagnetic metal ions is attenuated in an Mn metal–organic framework (Mn-MOF) based on a pyrene linker. Based on solvent-dependent emission, the Mn-MOF is shown to serve as a solvent polarity probe. Further, sensing of nitroaromatics is demonstrated with a detection limit of ∼125 p.p.m. for TNT.
Blind Deconvolution of Sources in Fourier Space Based on Generalized Laplace Distribution

An approach to multi-channel blind de-convolution is developed, which uses an adaptive filter that performs blind source separation in the Fourier space. The approach keeps during the learning process the same permutation and provides appropriate scaling of components for all frequency bins in the frequency space. Experiments indicate that Generalized Laplace Distribution can be used effectively to blind de-convolution of convolution mixtures of sources in Fourier space compared to the conventional Laplacian and Gaussian function.
UML state machine diagram driven runtime verification of Java programs for message interaction consistency

In object-oriented programs, we often need to set some restrictions on the temporal orders of the message receiving for objects, which forms a class of safety requirements. In this paper, we use UML state machine diagrams as design specifications, and present an approach to runtime verification of Java programs, which is focused on the temporal order of message receiving based consistency verification between the behavior of state machine diagrams and the program execution traces. In the approach, we first instrument the program under verification so as to gather the program execution traces related to a given state machine diagram. Then we drive the instrumented program by random test cases so as to generate the program execution traces. Finally we check if the collected program execution traces are consistent with the behavior of the state machine diagram, which means that the temporal orders of the message receiving occurring in the program traces are consistent with the ones occurring in the state machine diagram. Our approach can be used to detect not only the program bugs resulting from the wrong temporal orders of message receiving, but also the imperfect state machine models constructed in reverse engineering for legacy systems, and leads to a testing tool which may proceed in a fully automatic fashion.
One Class Classification Methods Based Non-Relevance Feedback Document Retrieval

We applied active learning techniques based on support vector machine for evaluating documents each iteration, which is called relevance feedback. Our proposed approach has been very useful for document retrieval with relevance feedback experimentally. However, the initial retrieved documents, which are displayed to a user, sometimes don't include relevant documents. In order to solve this problem, we propose a new feedback method using information of non-relevant documents only. We named this method non-relevance feedback document retrieval. The non-relevance feedback document retrievals are based on one class support vector machine and support vector data description. Our experimental results show that one class support vector machine based method can retrieve relevant documents efficiently using information of non-relevant documents only
Social Media Data Aggregation and Mining for Internet-Scale Customer Relationship Management

Industry and government organisations across the world increasingly use social media for Customer Relationship Management (CRM) activities that include communicating with customers about their services/products and distilling customer opinions for assessing their effectiveness. In this paper, we explore the exploitation of social media for CRM, and describe the development and functionality of VIZIE - a social media monitoring system designed to help media monitors to distill the perception of potential and actual customers of the value of existing products and services, identify and correct customer misinformation, discover how to better meeting customer expectations, and (if needed) communicate directly with the customers by using the customers' preferred social media forum. VIZIE monitors the most popular social media networks and mines their content in real time independently of topical domain and social media source/type. VIZIE achieves these by integrating information from multiple social media sources to create a broader, more complete context compared individual social media posting about specific topics of interest to a VIZIE user. VIZIE also provides the ability to automatically create overviews and summaries of any such topic, permits user queries, and supports drill down to individual social media posting. VIZIE accomplishes these by using a variety of natural language processing and information retrieval techniques that are specifically selected for (or tailored to deliver) real-time responsiveness. We provide an overview of the real-world requirements that informed the design of this system that has been used by more than twenty organisations and describe an overview of its architecture, the data analysis methods employed in its implementation, and their orchestration for achieving real-time response at an internet-wide big social data analysis scope.