Miracle-dz/UniFAR-FTUs huggingface dataset:
RETRACTED: Visual Darkness Reduces Perceived Risk of Contagious-Disease Transmission From Interpersonal Interaction

We examined the psychological impact of visual darkness on people's perceived risk of contagious-disease transmission. We posited that darkness triggers an abstract construal level and increases perceived social distance from others, rendering threats from others to seem less relevant to the self. We found that participants staying in a dimly lit room (Studies 1 and 3-5) or wearing sunglasses (Study 2) tended to estimate a lower risk of catching contagious diseases from others than did those staying in a brightly lit room or wearing clear glasses. The effect persisted in both laboratory (Studies 1-4) and real-life settings (Study 5). The effect arises because visual darkness elevates perceived social distance from the contagion (Study 3) and is attenuated among abstract (vs. concrete) thinkers (Study 4). These findings delineate a systematic, unconscious influence of visual darkness-a subtle yet pervasive situational factor-on perceived risk of contagion. Theoretical contributions and policy implications are discussed.
Indeterminacy and Business Fluctuations Under Partial Capital Mobility

The paper examines a modified real business cycle model of a small open economy such as the Korean economy. The model economy is assumed to produce output with two types of capital: traded capital and noniⓒtraded capital. Domestic individuals can borrow from the world capital market only by using traded capital as collateral, and investment in noniⓒtraded capital must be financed by domestic savings. It implies that the capital mobility is partial rather than perfect although domestic residents have free access to the world capital market. By incorporating externalities into the production technology, the model economy is able to generate dynamic path of equilibrium time series solely driven by noniⓒfundamental shocks such as animal spirits of investors or selfiⓒfulfilling expectations if the ratio of traded capital to total capital is sufficiently high. The paper shows that the second moment properties of the Korean aggregate data could be reproduced in a modified real business cycle framework where fluctuations are driven only by noniⓒfundamental shocks, not by the disturbances to economic fundamentals such as technology. This requires a high value of traded capital relative to total capital, which means that the domestic capital market must be highly integrated with the world capital market. It implies that there exists some critical level of capital mobility beyond which the business cycles of an open economy becomes highly vulnerable to noniⓒfundamental shocks such as selfiⓒfulfilling expectations.
Great Expectations: the materiality of commensurability in social media

This chapter considers the question of commensuration — the process of comparison according to a common metric — and how it is accomplished on online social media websites. When commensurability is produced through the distributed reviews and ratings of thousands of user-generated postings, and transformed through filtering and weighting algorithms into ratings and rankings, it may be expected that different things will be paid attention to, connected, and compared. The chapter is interested in understanding these differences and the implications of online user-based evaluation mechanisms for how commensurability is organized and achieved.
Attack Detection for Load Frequency Control Systems Using Stochastic Unknown Input Estimators

False data injection attacks (FDIAs) against the automatic generation control (AGC) system can lead to unstable or non-optimal operation of the power grid. This paper introduces a method to detect FDIAs targeting the AGC system by developing a stochastic unknown input estimator (SUIE). The SUIE estimates the states of the load-frequency control system, which contains the AGC as a control loop. An increase in the SUIE’s residual function (RF) beyond a defined threshold signifies an FDIA. The SUIE can be designed such that it works independently from some or all inputs to the system’s state-space model. In addition, the effect of process and measurement noise on the estimated states is minimized through an optimal gain setting technique for the SUIE. Therefore, not only does the SUIE eliminate the need for information about real-time load changes throughout the grid, it also maximizes the state estimation accuracy. The combination of these features distinguishes the proposed method from existing FDIA detection techniques for the AGC system. This paper also develops a number of attack identification SUIEs (AISUIEs) to determine which measurements are compromised by an FDIA, thus facilitating FDIA mitigation strategies. The AISUIEs model FDIAs targeting each AGC measurement by an attack input. These inputs serve as the unknown inputs of different AISUIEs, whose RFs indicate the type of attack. The designed AISUIEs also differentiate between attacks and non-attack abnormalities such as faults. Simulation analysis of a three-area power system corroborates the effectiveness of the proposed method. In addition, the performance of the proposed method is tested using an OPAL real-time simulator, and is compared with another technique from the literature.
MIA: Metric Importance Analysis for Big Data Workload Characterization

Data analytics is at the foundation of both high-quality products and services in modern economies and societies. Big data workloads run on complex large-scale computing clusters, which implies significant challenges for deeply understanding and characterizing overall system performance. In general, performance is affected by many factors at multiple layers in the system stack, hence it is challenging to identify the key metrics when understanding big data workload performance. In this paper, we propose a novel workload characterization methodology using ensemble learning, called Metric Importance Analysis (MIA), to quantify the respective importance of workload metrics. By focusing on the most important metrics, MIA reduces the complexity of the analysis without losing information. Moreover, we develop the MIA-based Kiviat Plot (MKP) and Benchmark Similarity Matrix (BSM) which provide more insightful information than the traditional linkage clustering based dendrogram to visualize program behavior (dis)similarity. To demonstrate the applicability of MIA, we use it to characterize three big data benchmark suites: HiBench, CloudRank-D and SZTS. The results show that MIA is able to characterize complex big data workloads in a simple, intuitive manner, and reveal interesting insights. Moreover, through a case study, we demonstrate that tuning the configuration parameters related to the important metrics found by MIA results in higher performance improvements than through tuning the parameters related to the less important ones.
Deep Neural Machine Translation with Weakly-Recurrent Units

Recurrent neural networks (RNNs) have represented for years the state of the art in neural machine translation. Recently, new architectures have been proposed, which can leverage parallel computation on GPUs better than classical RNNs. Faster training and inference combined with different sequence-to-sequence modeling also lead to performance improvements. While the new models completely depart from the original recurrent architecture, we decided to investigate how to make RNNs more efficient. In this work, we propose a new recurrent NMT architecture, called Simple Recurrent NMT, built on a class of fast and weakly-recurrent units that use layer normalization and multiple attentions. Our experiments on the WMT14 English-to-German and WMT16 English-Romanian benchmarks show that our model represents a valid alternative to LSTMs, as it can achieve better results at a significantly lower computational cost.
Growth Hormone and Aging: Updated Review

Role of growth hormone (GH) in mammalian aging is actively explored in clinical, epidemiological, and experimental studies. The age-related decline in GH levels is variously interpreted as a symptom of neuroendocrine aging, as one of causes of altered body composition and other unwelcome symptoms of aging, or as a mechanism of natural protection from cancer and other chronic diseases. Absence of GH signals due to mutations affecting anterior pituitary development, GH secretion, or GH receptors produces an impressive extension of longevity in laboratory mice. Extension of healthspan in these animals and analysis of survival curves suggest that in the absence of GH, aging is slowed down or delayed. The corresponding endocrine syndromes in the human have no consistent impact on longevity, but are associated with remarkable protection from age-related disease. Moreover, survival to extremely old age has been associated with reduced somatotropic (GH and insulin-like growth factor-1) signaling in women and men. In both humans and mice, elevation of GH levels into the supranormal (pathological) range is associated with increased disease risks and reduced life expectancy likely representing acceleration of aging. The widely advertised potential of GH as an anti-aging agent attracted much interest. However, results obtained thus far have been disappointing with few documented benefits and many troublesome side effects. Possible utility of GH in the treatment of sarcopenia and frailty remains to be explored.
Coupling-Assisted Renormalization of Excitons and Vibrations in Compressed MoSe2-WSe2 Heterostructure

Vertical heterostructures (HSs) constructed with two-dimensional (2D) materials is expected to generate fascinating properties due to interlayer coupling between neighboring layers. However, interlayer coupling can be easily obscured by cross-contamination during transfer processes, rendering their experimental demonstration challenging. Here, we explore the coupling-assisted renormalization of excitons and vibrations in a mechanically fabricated MoSe2–WSe2 HS through high-pressure photoluminescence, Raman spectra, and density functional theory calculations. Accompanied by the interlayer coupling enhancement, the excitonic and vibrational renormalizations involving dimensionality and composition variations were achieved. A cycle of 2D–3D–2D excitonic evolution was disclosed and pressure-induced emergence of X– exciton of MoSe2 in HS was found reflecting the band structure transition in the MoSe2–WSe2 HS. The Raman spectra reveals that the coupled A2″ vibrations of WSe2 and MoSe2 in HS was stiffened and ou.
Habitat constraints on carotenoid‐based coloration in a small euryhaline teleost

Abstract Display of bright and striking color patterns is a widespread way of communication in many animal species. Carotenoid‐based coloration accounts for most of the bright yellow, orange, and red displays in invertebrates, fish, amphibians, reptiles, and birds, being widely considered a signal of individual health. This type of coloration is under the influence of several factors, such as sexual selection, predator pressure, pigment availability, and light transmission. Fish offer numerous examples of visual communication by means of color patterns. We used a small cyprinodontid fish, Aphanius fasciatus (Valenciennes, 1821), as a model species to assess habitat constraints on the color display in male caudal fin. Populations from natural and open/closed artificial habitats were tested for differences in the pigmentation of caudal fins. The most important factors explaining the intensity of coloration were the habitat type and the chlorophyll concentration in the sediment, followed by water turbidity; yellow fins were observed in natural habitats with low chlorophyll concentration and high water turbidity, while orange fins occurred in artificial habitats with high chlorophyll concentration and low turbidity. Furthermore, A. fasciatus in artificial habitats showed a higher somatic and a lower reproductive allotment with respect to natural habitats, according to the existing literature on the species. Furthermore, in closed artificial habitats, where the most intense reddish coloration of caudal fins was observed, a trade‐off between somatic growth and the coloration intensity of a carotenoid‐based sexual ornament has been observed; in these populations, intensity of caudal fin coloration was negatively related to the somatic allotment. Results of this study suggested how both the pigmentation of male's caudal fin and the life history strategies of the species are constrained by habitat characteristics.
Myogenic stem cells for the therapy of primary myopathies: wishful thinking or therapeutic perspective?

Primary myopathies: the long road from gene identification to therapy Primary myopathies are characterized by a progressive wasting of skeletal muscle that leads to deterioration of movements and, in the most severe cases, such as in Duchenne’s muscular dystrophy (DMD), to complete paralysis and death. Most myopathies in which the molecular defect has been identified are due to mutations affecting proteins that form a supramolecular link between the cytoskeleton and the extracellular matrix, such as dystrophin, the mutated protein in DMD (1). In the absence of one of these proteins, mechanical stress associated with contraction progressively leads to degeneration of the muscle fiber, although the underlying mechanisms are still poorly understood. In the first phase of the disease, new muscle fibers are formed by fusion of resident myoblasts, called satellite cells (2), which also bear the molecular defect of the fibers that they replace, and hence undergo the same fate. Once the proliferation potential of satellite cells is exhausted, there is no further regeneration and the skeletal muscle is replaced by connective tissue. The current therapeutic approaches to DMD involve pharmacological suppression of the inflammatory and immune responses, which usually provides only modest and temporary beneficial effects. Future approaches depend on cell and gene therapy technology and will require different strategies, none of which are currently ready to enter clinical [. ] Perspective
Positive end-expiratory pressure attenuates hemodynamic effects induced by an overload of inspiratory muscles in patients with COPD

Background Inspiratory muscle training (IMT) using a Threshold® device is commonly used to improve the strength and endurance of inspiratory muscles. However, the effect of IMT, alone or with positive end-expiratory pressure (PEEP), on hemodynamic parameters in patients with chronic obstructive pulmonary disease (COPD) remains unknown. Objective To assess the effects of an overload of inspiratory muscles using IMT fixed at 30% of the maximal inspiratory pressure (MIP), and IMT associated with 5 cmH2O of PEEP (IMT + PEEP), on the echocardiographic parameters in healthy subjects and patients with COPD. Methods Twenty patients with COPD (forced expiratory volume in 1 second 53.19±24.71 pred%) and 15 age-matched healthy volunteers were evaluated using spirometry, MIP, the COPD assessment test (CAT), and the modified Medical Research Council (mMRC) dyspnea scale. The E- (fast-filling phase) and A- (atrial contraction phase) waves were evaluated at the tricuspid and mitral valves during inspiration and expiration in the following sequence: at basal conditions, using IMT, and using IMT + PEEP. Results Patients with COPD had reduced MIPs versus the control group. Ten patients had CAT scores <10 and 12 patients had mMRC scores <2. E-wave values at the mitral valve were significantly decreased with IMT during the inspiratory phase in both groups. These effects were normalized with IMT + PEEP. During the expiratory phase, use of IMT + PEEP normalized the reduction in E-wave values in the COPD group. During inspiration at the tricuspid valve, reduction in E-wave values during IMT was normalized by IMT + PEEP in COPD group. During the expiratory phase, the value of the E-waves was significantly reduced with overload of the inspiratory muscles in both groups, and these effects were normalized with IMT + PEEP. A-waves did not change under any conditions. Conclusion Acute hemodynamic effects induced by overloading of the inspiratory muscles were attenuated and/or reversed by the addition of PEEP in COPD patients.
What makes Human-Robot Dialogues struggle ?

During the last few years, humanoid robots became very popular in the robotic research community and some humanoid robots are already commercially available, such as Asimo from Honda or Qrio from Sony. Comparing the currently possible human-robot communication with the human-human communication we can see that in human-human communication we have efficient strategies to avoid errors and also to recover from them, such as for examplegroundingnew information (Traum, 1999; Traum and Dillenbourg, 1998; Poesio and Traum, 1998). This is still one of the biggest challenges for human-robot communication to develop a system which can cope with real world situations and is error tolerant so that it can react in a reasonable way even when something has been misunderstood or not understood at all. Therefore, in this paper we want to evaluate problematic situations in human-robot-communication and how they can be resolved. Our target scenario is a household situation, in which the user can ask the robot questions related to the kitchen, such as “What’s in the fridge ? ”, ”How do I cook Spaghetti Napoli? ”, ask the robot to set the table, to switch certain lights on or off, to bring some objects, such as cups, dishes, etc. (Gieselmann et al., 2003; Stiefelhagen et al., 2004). In this context which is specifically tailored for unexperienced and older users, it is important that the user can talk to the robot in the same way as to a human servant. This means that the communication should be as natural and as comfortable as possible for the user and therefore, errors should be avoided or at least easy to correct, if they cannot be avoided beforehand.
Learning Graph Representation via Frequent Subgraphs

© 2018 by SIAM. We propose a novel approach to learn distributed representation for graph data. Our idea is to combine a recently introduced neural document embedding model with a traditional pattern mining technique, by treating a graph as a document and frequent subgraphs as atomic units for the embedding process. Compared to the latest graph embedding methods, our proposed method offers three key advantages: fully unsupervised learning, entire-graph embedding, and edge label leveraging. We demonstrate our method on several datasets in comparison with a comprehensive list of up-to-date stateof-the-art baselines where we show its advantages for both classification and clustering tasks.
The relative concentration of bad versus good news flows

This paper examines flows of bad and good news as a feature of the firm’s information environment. We argue that to the extent that managers delay reporting bad news, this leads to bad news being more concentrated. Measuring flows of bad and good news using flows of negative and positive abnormal stock returns, we find that firms with higher volatility of operations and managerial incentives to withhold bad news exhibit relatively more concentrated bad news flows. This relative concentration is also positively associated with lower earnings quality and a higher risk of shareholder litigation. Our results suggest that the relative concentration of bad and good news flows is related to the quality of the firm’s information environment.
Effect of trabeculectomy on lens opacities in an East Asian population.

OBJECTIVE To examine the progression of lens opacity in Asian people after trabeculectomy and determine associated risk factors. METHODS This was an observational case series of 243 people aged 36 to 82 years. Trabeculectomy was performed on 1 eye of each subject. Lens opacity was measured yearly using the Lens Opacification Classification System III. The main outcome measure was worsening of lens opacity defined as an increase of 2 or more Lens Opacification Classification System III units in any of the 3 lens regions, nuclear, cortical, and posterior subcapsular. Risk of progression was evaluated using logistic regression models. RESULTS Data for 177 people were analyzed. One hundred seventeen (66%) of 177 subjects showed progression in opacity in any lens region at 3 years. Seventy-seven (66%) of 117 of those who progressed did so during the first year. Of these, 63 (82%) of 77 had lens opacity in the posterior subcapsular region. Factors associated with progression of posterior-subcapsular lens opacity at 1 year were diabetes (odds ratio, 2.4; 95% confidence interval, 1.0-5.4), use of antiglaucoma medication, dosage of topical steroid postoperatively, and being operated on by a trainee surgeon (odds ratio, 2.3; 95% confidence interval, 1.0-5.2). CONCLUSIONS Trabeculectomy is associated with progression of lens opacity predominantly in the posterior subcapsular region. Modification of risk factors such as postoperative steroid use may delay progression.
Extract reordering rules of sentence structure using neuro-fuzzy machine learning system

Recent development and enhancement in the field of soft computing which is the cutting edge of Neural network and fuzzy logic. Here by we are proposing a solution using Deep Neural Network (DNN) and Fuzzy logics for Natural Language Processing (NLP) with is the sub part of Artificial Intelligence (AI). If we look India we have 22 constitutional human languages and in the world around 6909 living human languages. Building cross Machine Translation (MT) system using these all language which currently not possible because of lack of resources, knowledge and etc. If we have huge corpus of source and target language this propose system will automatically find out the grammatical structure of source sentence matching with the target language using Neural network and fuzzy logic and it is flexible machine learning technique it deals with uncertainly or vagueness existing in a system and formulating fuzzy rules to find grammar structure to a given new sentences.
Improving in-domain data selection for small in-domain sets

Finding sufficient in-domain text data for language modeling is a recurrent challenge. Some methods have already been proposed for selecting parts of out-of-domain text data most closely resembling the in-domain data using a small amount of the latter. Including this new “near-domain” data in training can potentially lead to better language model performance, while reducing training resources relative to incorporating all data. One popular, state-of-the-art selection process based on cross-entropy scores makes use of in-domain and out-ofdomain language models. In order to compensate for the limited availability of the in-domain data required for this method, we introduce enhancements to two of its steps. Firstly, we improve the procedure for drawing the outof-domain sample data used for selection. Secondly, we use word-associations in order to extend the underlying vocabulary of the sample language models used for scoring. These enhancements are applied to selecting text for language modeling of talks given in a technical subject area. Besides comparing perplexity, we judge the resulting language models by their performance in automatic speech recognition and machine translation tasks. We evaluate our method in different contexts. We show that it yields consistent improvements, up to 2% absolute reduction in word error rate and 0.3 Bleu points. We achieve these improvements even given a much smaller in-domain set.