Miracle-dz/UniFAR-FTUs huggingface dataset:
Profit efficiency under a directional technology distance function approach

Purpose - The aim of this study is to investigate profit efficiency in the banking industries of 11 Central and Eastern European (CEE) countries for the period 1998-2005. Design/methodology/approach - The authors employ a directional technology distance function approach to measure profit efficiency and decompose it into its technical and allocative components. They use these efficiency measures to investigate potential differences in banking performance across countries and across banks of different size and with different ownership status. Findings - The results indicate that the highest proportion of profit inefficiency in the CEE region is attributed to allocative inefficiency, recognizing that considerable variation and different patterns in inefficiency levels across banking systems can be observed. Small and domestic private banks appear to be the most efficient. A negative relationship between efficiency and bank size, the capitalization ratio and market concentration, and a positive relationship with the European Bank for Reconstruction and Development index of banking reform are also found. Research limitations/implications - Bank performance relative to best practice is measured across the CEE region. While it is found that on average technical inefficiency is relatively small and about one quarter of the banks lie on the technological frontier, the size of technical inefficiencies is likely to be exacerbated if the sample were to include Western European banks. Practical implications - The effects of banking reforms are evident by recent positive trends in profit and allocative efficiencies estimated for CEE banking sectors. These trends suggest that policy makers should intensify efforts to further improve the financial services regulatory and supervisory framework while freeing any remaining explicit or implicit barriers to bank competition. Originality/value - The study departs from the traditional literature of efficiency. It uses a directional distance function approach to model multi input – multi output banking technology and to investigate profit efficiency in CEE countries.
Die Auswirkungen der Geldmenge und des Kreditvolumens auf die Immobilienpreise: Ein ARDL-Ansatz für Deutschland

Die aktuellen Finanzmarktturbulenzen wurden durch Entwicklungen im Immobiliensektor ausgelost. Vor diesem Hintergrund analysiert dieser Beitrag den Zusammenhang zwischen den Immobilienpreisen und der Geldmengen- und Kreditvolumensentwicklung fur den Zeitraum 1992 -2006 (westdeutsche Preisdaten) bzw. 1997 bis 2006 (ostdeutsche Preisdaten). Die Untersuchung konzentriert sich erstmals auf die Bundesrepublik Deutschland, deren Immobilienmarkt von einer moderaten Preisentwicklung gekennzeichnet ist und im Vergleich zu den meisten europaischen Landern damit eine Sonderstellung einnimmt. Auf der Grundlage eines Autoregressiven Distributed Lag (ARDL)-Ansatzes werden Tests auf Kointegration der genannten Variablen durchgefuhrt. Nach Schatzungen der Langfristparameter werden die zugehorigen Fehlerkorrekturmodell-Regressionen fur Immobilienpreise in unterschiedlicher Abgrenzung geschatzt. Die Ergebnisse liefern Evidenz fur eine Mitverantwortung der Geld- und Kreditpolitik fur die Immobilienpreisentwicklung gerade in Westdeutschland. Es folgen einige Robustheitstests. Zuvor unberucksichtigte Variable der Steuerpolitik wie Sonderabschreibungen in den Neuen Bundeslandern uben demnach auf die Immobilienpreise ebenfalls einen wichtigen Einfluss aus, ohne jedoch den Einfluss der Liquiditat zu dominieren. Andere Forderprogramme wie beispielsweise vergunstigte Kredite fur klimapolitische Masnahmen (CO2-Gebaudesanierung) erwiesen sich als nicht signifikant. Schlieslich verandert auch die Anwendung eines alternativen Kointegrationstestverfahrens die Ergebnisse nicht.
Cointegration trends among the NAFTA equity markets

Recent economic and financial liberalization policies have increased the internationalization of emerging markets. A daring strategy undertaken by Mexico constituted the North America Free Trade Agreement (NAFTA); in addition to increased trade and investments, it should have led to stronger links among the equity markets from Canada, United States and Mexico. This study tests a ? first generation? financial integration among those markets, applying Johansen? s cointegration technique. It also examines integration between the NAFTA markets and the world capital market. Results evidence a time-varying integration process among NAFTA equity markets. Integration of the NAFTA capital markets to the world capital market evidences a mild segmentation and a time-varying integration as well.
Contextualized fault-tolerant infrastructure for P2P mobile service composition

Peer-to-peer applications harness sharing between free resources (storage, contents, services, human presence, etc.). Most existing wireless P2P applications concern merely the sharing of a variety of contents. For magnifying the extent of the sharing in wireless P2P (WP2P) environment, This work presents a P2P mobile service sharing infrastructure that empowers an autonomous peer to propel distributed problem solving (e.g., in the travel domain) through service sharing and execution in an intelligent way. This infrastructure of service composition is not only highly robust to failure but also keenly aware of the surrounding context in wireless environments. We also have implemented this infrastructure into a system platform named UbiSrvInt (that shows promising evaluation results).
RX64DTH -a fully integrated 64-channel ASIC for a digital X-ray imaging system with energy window selection

We report on the multichannel IC (RX64DTH) designed for position sensitive X-ray measurements with silicon strip detectors and dedicated to medical imaging applications. This integrated circuit has a binary readout architecture with a double threshold allowing on selection energy window for measured signals. The design was realized in a 0.8 /spl mu/m CMOS process. The core of the RX64DTH IC consists of 64 readout channels. The single channel is built with four basic blocks: charge sensitive preamplifier, shaper, two independent discriminators, and two independent 20-bit counters. Each readout channel counts pulses which are above the low discriminator threshold and counts pulses independently above the high discriminator threshold. The energy resolution in such architecture is limited by the noise of a single channel and by channel to channel threshold spread. We present the noise and matching performance of a 384-channel module built with a silicon strip detector and six RX64DTH ICs. In the 384-channel module an equivalent noise charge of about 200 el. rms is achieved for the shaper peaking time of 0.8 /spl mu/s and strip capacitance of 3 pF. The deviation of discriminator thresholds for the whole system is only 87 el. rms. The obtained results show that the energy resolution and uniformity of analog parameters (noise, gain, offset) are sufficient for medical diagnostic applications such as dual energy mammography and angiography.
Strategic Foundations of General Equilibrium: Perfect competition

The theory of competition has held a central place in economic analysis since Adam Smith. This book, written by one of the most distinguished of contemporary economic theorists, reports on a major research program to provide strategic foundations for the theory of perfect competition. Beginning with a concise survey of how the theory of competition has evolved, Gale makes extensive and rigorous use of dynamic matching and bargaining models to provide a more complete description of how a competitive equlibrium is achieved. Whereas economists have made use of a macroscopic description of markets in which certain behavioral characteristics, such as price-taking behavior, are taken for granted, Gale uses game theory to re-evaluate this assumption, beginning with individual agents and modelling their strategic interaction. A strategic foundation for competitive equilibrium shows how such interaction leads to competitive, price-taking behavior. Essential reading for graduate courses in game theory and general equilibrium.
Measuring competitive intelligence effectiveness: Insights from the advertising industry

This article investigates the need and ability of competitive intelligence (CI) departments to become accountable for the work they produce. Synthesis of current literature on CI and advertising effectiveness measures resulted in the creation of a Competitive Intelligence Measurement Model (CIMM), which provides concrete generic measures for determining CI effectiveness. Additionally, the model aids in the calculation of the return on competitive intelligence investment (ROCII). CIMM classifies CI output into two categories: short-term tactical CI output and long-term strategic CI output. The model describes measures needed to determine effectiveness of CI output and explains how to calculate ROCII. © 2001 John Wiley & Sons, Inc.
A novel fault recovery scheme for reliability of femtocell networks

Femtocell network is widely used for providing high data rate. However, there are some doubts about the reliability of femto base stations (BSs). When a fault occurs in a femto BS, MSs belonging to the faulted femto BS should handover to other BSs. Handover to inappropriate BS may decrease network efficiency. To make a smooth handover procedure, the usage of a candidate BS list is suggested. The candidate BS list consists of the recommended BSs based on received signal strength and cell load. By using the candidate BS list, MSs in the faulted femto BS can minimize the damage of failure.
Fair's Fair? Principles, Partisanship, and Perceptions of the Fairness of Campaign Rhetoric

This article considers the role of principles in perceptions of what constitutes acceptable rhetoric in election campaigns. Previous research suggests that citizens use principles of what is an acceptable attack in campaigns when evaluating charges and countercharges, while acknowledging that under some circumstances citizens accept rhetoric that would seem to breach their principles. This research has not adequately considered how partisanship can affect a respondent's proclivity to call upon principles in assessing campaign rhetoric. This article draws on three original surveys to test competing models of the role of partisanship and finds robust results in support of a strong partisan bias. It concludes that it is not that citizens are resilient to violations of their principles, but that nearly the opposite is the case: citizens’ sensitivity to attacks on their preferred candidate leads them to take a very partial view of when to apply their principles faithfully to a judgment and when not to.
zfh-1, the Drosophila Homologue of ZEB, Is a Transcriptional Repressor That Regulates Somatic Myogenesis

ABSTRACT zfh-1 is a member of the zfh family of proteins, which all contain zinc finger and homeodomains. The roles and mechanisms of action of most family members are still unclear. However, we have shown previously that another member of the family, the vertebrate ZEB protein, is a transcriptional repressor that binds E box sequences and inhibits myotube formation in cell culture assays. zfh-1 is downregulated in Drosophila embryos prior to myogenesis. Embryos with zfh-1 loss-of-function mutation show alterations in the number and position of embryonic somatic muscles, suggesting that zfh-1 could have a regulatory role in myogenesis. However, nothing is known about the nature or mechanism of action of zfh-1. Here, we demonstrate that zfh-1 is a transcription factor that binds E box sequences and acts as an active transcriptional repressor. When zfh-1 expression was maintained in the embryo beyond its normal temporal pattern of downregulation, the differentiation of somatic but not visceral muscle was blocked. One potential target of zfh-1 in somatic myogenesis could be the myogenic factor mef2. mef2 is known to be regulated by the transcription factor twist, and we show here that zfh-1 binds to sites in the mef2 upstream regulatory region and inhibits twist transcriptional activation. Even though there is little sequence similarity in the repressor domains of ZEB and zfh-1, we present evidence that zfh-1 is the functional homologue of ZEB and that the role of these proteins in myogenesis is conserved fromDrosophila to mammals.
Combining Ethnopharmacology and Virtual Screening for Lead Structure Discovery: COX-Inhibitors as Application Example

Virtual screening of large libraries of organic compounds combined with pharmacological high throughput screening is widely used for drug discovery in the pharmaceutical industry. Our aim was to explore the efficiency of using a biased 3D database comprising secondary metabolites from antiinflammatory medicinal plants as a source for the virtual screening. For this study pharmacophore models of cyclooxygenase I and II (COX-1, COX-2), key enzymes in the inflammation process, were generated with structure-based as well as common feature based modeling, resulting in three COX hypotheses. Four different multiconfomational 3D databases limited in molecular weight between 300 and 700 Da were applied to the screening in order to compare and analyze the obtained hit rates. Two of them were created in-house (DIOS, NPD). The database DIOS consists of 2752 compounds from phytochemical reports of antiinflammatory medicinal plants described by the ethnopharmacological source 'De material medica' of Pedanius Dioscorides, whereas NPD contains almost 80,000 compounds gathered arbitrarily from natural sources. In addition, two available multiconformational 3D libraries comprising marketed and development drug substances (DWI and NCI), mainly originating from synthesis, were used for comparison. As a test of the pharmacophore models' capability in natural sources, the models were used to search for known COX inhibitory natural products. This was achieved with some exceptions, which are discussed in the paper. Depending on the hypothesis used, DWI and NCI library searches produced hit rates in the range of 6.6% to 13.7%. A slight increase of the number of molecules assessed for binding was achieved with the database of natural products (NPD). Using the biased 3D database DIOS, however, the average increase of efficiency reached 77% to 133% compared to the hit rates resulting from WDI and NCI. The statistical benefit of a combination of an ethnopharmacological approach with the potential of computer aided drug discovery by in silico screening was demonstrated exemplified on the applied targets COX-1 and COX-2.
Antral mucosal Helicobacter pylori infection density as a risk factor of duodenal ulcer.

OBJECTIVE Duodenal ulcer is regarded as the end result of alterations in which Helicobacter pylori (HP) plays a major role as a pathogenetic agent. The aim of this prospective study was to evaluate duodenal ulcer risk factors such as alcohol intake, smoking, use of non-steroidal anti-inflammatory drugs, age and sex in relation to HP colonization density. PATIENTS AND METHODS 495 patients consecutively examined by diagnostic upper digestive endoscopy were assessed; none of them had had previous upper digestive endoscopy or peptic ulcer, recent anticulcer-antibiotic treatment, upper gastrointestinal surgery, or cancer. The HP colonization on antral bioptic specimens was graded semi-quantitatively as follows: HP--- (absence of HP); HP+--(focal presence of small amounts of HP); HP++- (intermediate situation between HP+-- and HP+++), and HP+++ (diffuse presence of large amounts of HP). RESULTS Logistic regression analysis identified only male sex (odds ratio 1.88, p < 0.02), smoking more than 10 cigarettes/day (odds ratio 2.53, p < 0.01), and HP grade (HP +-- odds ratio 0.79, p = NS; HP++- odds ratio 2.42, p < 0.02; HP+++ odds ratio 3.66, p < 0.001) as independent risk factors of duodenal ulcer. CONCLUSION The duodenal ulcer risk was found to correlate with HP density, male sex, and smoking, but not with non-steroidal anti-inflammatory drug use.
Video-Based Person Re-Identification via Self Paced Weighting

Person re-identification (re-id) is a fundamental technique to associate various person images, captured by differentsurveillance cameras, to the same person. Compared to the single image based person re-id methods, video-based personre-id has attracted widespread attentions because extra space-time information and more appearance cues that can beused to greatly improve the matching performance. However, most existing video-based person re-id methods equally treatall video frames, ignoring their quality discrepancy caused by object occlusion and motions, which is a common phenomenonin real surveillance scenario. Based on this finding, we propose a novel video-based person re-id method via self paced weighting (SPW). Firstly, we propose a self paced outlier detection method to evaluate the noise degree of video sub sequences. Thereafter, a weighted multi-pair distance metric learning approach is adopted to measure the distance of two person image sequences. Experimental results on two public datasets demonstrate the superiority of the proposed method over current state-of-the-art work.
Do mergers and acquisitions among small and medium-sized enterprises affect the performance of acquiring firms?

This article investigates the post-merger performance effects resulting from mergers and acquisitions (M&As), drawing on a representative sample taken from all Swiss M&As occurring between 2006 and 2008, the majority of which took place between small and medium-sized firms (SMEs). Using this data, the study was able to investigate the impact of M&As on differing measures of economic performance, and also on innovation for the post-merger period of 2008–2010. The study found positive statistically significant performance effects arising from M&As for three of five critical performance indicators utilized. Furthermore, the evidence suggests that growth is a principal motive for external mergers, while efficiency is more influential on internal mergers.
A comparison between longitudinal shedding patterns of Salmonella Typhimurium and Salmonella Dublin on dairy farms

Salmonella in cattle herds may behave as epidemic or endemic infections. An intensive longitudinal sampling study across all management groups and ages on six dairy farms in the UK was used to examine patterns of Salmonella shedding, following the prior identification of either Salmonella Dublin (SD) (three farms) or Salmonella Typhimurium (ST) (three farms) on the premises in the context of clinical salmonellosis. Individual faeces, pooled faeces and environmental samples (total 5711 samples), taken approximately every six weeks for 15–24 weeks, were cultured for Salmonella. SD was detected at low frequency (on any visit, 0.5–18.3 per cent of samples positive) and most consistently in calves. By contrast, ST was isolated at higher frequency (on any visit, 6.8–75 per cent of samples positive), and in higher numbers, up to 107 cfu/g faeces. Significantly more samples from calves were positive for ST than were positive for SD (50.6 per cent v 3.1 per cent; P < 0.001), which was also true for milking cows (46.3 per cent v 4.4 per cent; P < 0.001). The differences could help to explain the different patterns of bovine infection classically associated with these two serovars in the UK. No consistent effect upon shedding was seen among the ST-infected herds following vaccination.
A framework for multiprocessor neural networks systems

Artificial neural networks (ANN) are able to simplify classification tasks and have been steadily improving both in accuracy and efficiency. However, there are several issues that need to be addressed when constructing an ANN for handling different scales of data, especially those with a low accuracy score. Parallelism is considered as a practical solution to solve a large workload. However, a comprehensive understanding is needed to generate a scalable neural network that is able to achieve the optimal training time for a large network. Therefore, this paper proposes several strategies, including neural ensemble techniques and parallel architecture, for distributing data to several network processor structures to reduce the time required for recognition tasks without compromising the achieved accuracy. The initial results indicate that the proposed strategies are able to improve the speed up performance for large scale neural networks while maintaining an acceptable accuracy.
Photoprotective Strategies of Overwintering Evergreens

Abstract High levels of solar radiation (direct and reflected off snow) can be absorbed by green leaves in the winter. However, the energy that is absorbed by these leaves often cannot be used through photosynthesis under winter conditions. Most colored objects would simply bleach if faced with the same conditions, because light-absorbing pigments pass energy on to oxygen, forming reactive oxygen species that destroy the pigments. Two key means by which overwintering leaves prevent reactive oxygen-mediated damage have been identified. One, used by all leaves in the winter, involves the employment of xanthophyll carotenoids in a photoprotective process whereby the light energy absorbed by chlorophyll is converted to heat and dissipated harmlessly. The other, found in some evergreen species but not in overwintering annual or biennial mesophytes, involves the degradation of proteins responsible for generating high-energy electrons, thereby suppressing transfer of these electrons to oxygen.
DarkEmbed: Exploit Prediction With Neural Language Models

Software vulnerabilities can expose computer systems to attacks by malicious actors. With the number of vulnerabilities discovered in the recent years surging, creating timely patches for every vulnerability is not always feasible. At the same time, not every vulnerability will be exploited by attackers; hence, prioritizing vulnerabilities by assessing the likelihood they will be exploited has become an important research problem. Recent works used machine learning techniques to predict exploited vulnerabilities by analyzing discussions about vulnerabilities on social media. These methods relied on traditional text processing techniques, which represent statistical features of words, but fail to capture their context. To address this challenge, we propose DarkEmbed, a neural language modeling approach that learns low dimensional distributed representations, i.e., embeddings, of darkweb/deepweb discussions to predict whether vulnerabilities will be exploited. By capturing linguistic regularities of human language, such as syntactic, semantic similarity and logic analogy, the learned embeddings are better able to classify discussions about exploited vulnerabilities than traditional text analysis methods. Evaluations demonstrate the efficacy of learned embeddings on both structured text (such as security blog posts) and unstructured text (darkweb/deepweb posts). DarkEmbed outperforms state-of-the-art approaches on the exploit prediction task with an F1-score of 0.74.
Soft-Cascade Learning with Explicit Computation Time Considerations

This paper presents a novel framework for learning a soft-cascade detector with explicit computation time considerations. Classically, training techniques for softcascade detectors select a set of weak classifiers and their respective thresholds, solely to achieve the desired detection performance without any regard to the detector response time. Nevertheless, since computation time performance is of utmost importance in many time-constrained applications, this work divulges an optimization approach that aims to minimize the mean cascade response time, given a desired detection performance, fixed beforehand. The resulting problem is NP-Hard, therefore finding an optimal threshold vector can be very time-consuming, especially when building a soft-cascade detector of long length. An efficient local search procedure is presented that deals with long-length detectors. Our evaluations on two challenging public datasets confirm that a faster cascade detector can be learned while maintaining similar detection performances.