Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Iterative Tone Mapping With High Efficiency and Robustness

This paper proposes an efficient approach for tone mapping, which provides a high perceptual image quality for diverse scenes. Most existing methods, optimizing images for the perceptual model, use an iterative process and this process is time consuming. To solve this problem, we proposed a new layer-based non-iterative approach to finding an optimal detail layer for generating a tone-mapped image. The proposed method consists of the following three steps. First, an image is decomposed into a base layer and a detail layer to separate the illumination and detail components. Next, the base layer is globally compressed by applying the statistical naturalness model based on the statistics of the luminance and contrast in the natural scenes. The detail layer is locally optimized based on the structure fidelity measure, representing the degree of local structural detail preservation. Finally, the proposed method constructs the final tone-mapped image by combining the resultant layers. The performance evaluation reveals that the proposed method outperforms the benchmarking methods for almost all the benchmarking test images. Specifically, the proposed method improves an average tone mapping quality index-II (TMQI-II), a feature similarity index for tone-mapped images (FSITM), and a high-dynamic range-visible difference predictor (HDR-VDP)-2.2 by up to 0.651 (223.4%), 0.088 (11.5%), and 10.371 (25.2%), respectively, compared with the benchmarking methods, whereas it improves the processing speed by over 2611 times. Furthermore, the proposed method decreases the standard deviations of TMQI-II, FSITM, and HDR-VDP-2.2, and processing time by up to 81.4%, 18.9%, 12.6%, and 99.9%, respectively, when compared with the benchmarking methods.
De-Mark GAN: Removing Dense Watermark with Generative Adversarial Network

This paper mainly considers the MeshFace verification problem with dense watermarks. A dense watermark often covers the crucial parts of face photo, thus degenerating the performance in the existing face verification system. The key to solving it is to preserve the ID information while removing the dense watermark. In this paper, we propose an improved GAN model, named De-mark GAN, for MeshFace verification. It consists of one generator and one global-internal discriminator. The generator is an encoderdecoder architecture with a pixel reconstruction loss and a feature loss. It maps a MeshFace photo to a representation vector, and then decodes the vector to a RGB ID photo. The succedent global-internal discriminator integrates a global discriminator and an internal discriminator with a global loss and internal loss, respectively. It can ensure the generated image quality and preserve the the ID information of recovered ID photos. Experimental results show that the verification benefits well from the recovered ID photos with high quality and our proposed De-mark GAN can achieve a competitive result in both image quality and verification.
The Effect of an Intelligent Tutor on Math Problem-Solving of Students with Learning Disabilities

Reform-based math instruction calls for students’ construction of conceptual understanding, solving challenging problems and explanation of reasoning. However, existing literature shows that students with learning disabilities (LD) easily get lost in reform-based instruction. As an outcome of collaborative work between math education and special education in instructing students with D, we’ve developed an intelligent tutor (PGBM-COMPS) to nurture multiplicative reasoning of students with LD. The intelligent tutor dynamically models individual student's evolving conceptions and recommends tasks to promote her/his advancement to a higher level in the learning trajectory and solve complex word problems using mathematical model equations. This study evaluated the effect of this intelligent tutor on improving multiplicative reasoning and problem solving of students with LD.
Ratio of Creatine Kinase to Alanine Aminotransferase as a Biomarker of Acute Liver Injury in Dystrophinopathy

Objective To investigate the ratios of creatine kinase (CK) to aminotransferases as biomarkers of acute liver injury in dystrophinopathy. Methods C57 and mdx (dystrophic) mice were treated with a hepatotoxic reagent D-galactosamine (D-GalN). The degrees of liver and muscle injury were assessed using histological examinations. To examine whether serum CK-adjusted aminotransferase levels could indicate liver status in dystrophic mice, the CK/alanine aminotransferase (ALT) and CK/aspartate aminotransferase (AST) ratios were analyzed. Furthermore, we enrolled 658 male patients with dystrophinopathy and 378 male patients without muscle and liver injury as control, whose serum ALT, AST, and CK levels were examined. Results Animal experiments indicated that D-GalN treatment could induce acute liver injury but not muscle injury. Additionally, D-GalN decreased the CK/ALT and CK/AST ratios in both C57 mice and mdx mice (P < 0.001). However, there was an overlap of the CK/AST ratio between dystrophic mice with and without acute liver injury. In patients with dystrophinopathy, CK-adjusted ALT diminished the variability associated with age, genotype, clinical phenotype, and motor function (P > 0.05). Conclusions CK/ALT is a potential biomarker for the differential evaluation of acute liver injury in dystrophic mice, which highlights the value to further evaluate the practice of CK/ALT in dystrophinopathy patients.
Routable and Matched Layout Styles for Analog Module Generation

Two1 novel automatic generation methods for analog layout—a symmetrical twin-row method for MOS transistors and a twisted common-centroid method for capacitor arrays—are introduced. Based on the proposed layout styles and the corresponding algorithms, the symmetry and common-centroid placement patterns for analog devices are realized to guarantee matching properties. On this basis, as the most prominent contribution of this article, channel routing-based algorithms for the proposed layout styles are presented and could achieve 100% routability due to well-arranged devices and corresponding low routing complexity. The algorithms benefits include a small layout area that maximizes the diffusion-sharing of MOS transistors and less routing layer usage for common-centroid device arrays. Moreover, we successfully applied our algorithms to the layout designs of two typical analog modules including a two-stage operating amplifier and a Successive Approximation Register Analog-to-Digital Converter (SAR-ADC). The generated layouts and the circuit simulation results demonstrate the effectiveness of our algorithms in terms of their routability and matching properties. Our algorithms can also be extended to apply to a variety of essential MOS analog circuits.
Relative Contributions of the Speed Characteristic and Other Possible Ecological Factors in Synchronization to a Visual Beat Consisting of Periodically Moving Stimuli

Daily music experience involves synchronizing movements in time with a perceived periodic beat. Contrary to the auditory-specific view of beat synchronization, synchronization to a visual beat composed of a periodically bouncing ball has been shown to be not less stable than synchronization to auditory beats. The ecological relevance of periodically moving visual stimuli is considered to be essential for such synchronization improvement. However, multiple factors could be associated with the ecological relevance and the relative contributions of the ecological factors to the synchronization improvement remain unclear. The present study investigated whether ecological factors other than a proposed critical factor, i.e., the speed characteristic, are required to account for the synchronization improvement of the bouncing ball. A periodically contracting ring that had the same speed characteristic as the periodically bouncing ball but lacked other possible ecological factors of the ball was designed. The results showed that synchronization was more stable for the bouncing ball than for the contracting ring, and this stability difference was larger in the difficult 300-ms than in the comfortable 600-ms inter-beat interval tapping condition. The finding suggests that ecological factors other than the speed characteristic are required to explain the synchronization improvement of periodically moving visual stimuli, particularly in difficult tapping conditions.
Reliability Aware Web Accessibility Experience Metric

Web accessibility metrics can measure the accessibility levels of websites. Although many metrics with different motivations have been proposed, current metrics are limited in their applicability when considering user experience. This study proposes Reliability Aware Web Accessibility Experience Metric (RA-WAEM), a novel Web accessibility metric which considers the user experience of people with disabilities and their reliability in objectively assessing the severity of accessibility barriers. We present an optimization algorithm based on Expectation Maximization to derive the parameters of RA-WAEM efficiently. Moreover, we conduct an extensive accessibility study on 46 websites with 323,098 Web pages and collect the user experience of 122 people. An evaluation on this dataset shows that RA-WAEM outperforms state of the art accessibility metrics in reflecting the user experience.
Moderating Effects of Exercise Duration and Intensity in Neuromuscular vs. Endurance Exercise Interventions for the Treatment of Depression: A Meta-Analytical Review

Background: Exercise training is a beneficial treatment strategy for depression. Previous meta-analytical reviews mainly examined the effect of aerobic exercise on depressive symptoms neglecting comparisons with neuromuscular training and meta-regression considering relevant exercise training prescriptors such as exercise duration, intensity, number of exercise sessions (volume) and frequency. Methods: A structured literature search was conducted in biomedical and psychological databases and study selection was conducted following the PICOS approach. (Randomized) controlled trials that compared supervised neuromuscular or endurance exercise interventions with an inactive control group (CON) in clinically depressed in- or out-patients over 18 years were included. Eligibility and study quality were evaluated by two independent researchers. Standardized mean differences (SMD) for the reduction of depressive symptoms, measured with different evaluation scales (e.g., BDI, HAM-D, PHQ-9, HRSD, MADRS, GDS) were calculated with the adjusted Hedges'g equation as main outcome for the comparison of endurance and neuromuscular exercise interventions vs. CON. Statistical analyses were conducted using a random effects inverse-variance model. Multivariate meta-regression analysis was performed in order to examine the modulating effects of exercise training prescriptors. Results: Twenty seven trials with 1,452 clinically depressed adults were included. 20 out of 27 included trials reached a PEDro score of at least 6, representing high-quality. Irrespective of the exercise mode and study quality, large effects in favor of exercise compared to the control condition were found. Compared to CON, sensitivity analyses revealed a moderate to large effect in favor of endurance exercise [SMD: −0.79 (90% CI: −1.10, −0.48); p < 0.00001, I2 = 84%] and a large effect size in favor of neuromuscular exercise [SMD: −1.14 (90 CI: −1.50, −0.78); p < 0.00001, I2 = 80%]. These effects decreased to moderate for endurance and remained large for neuromuscular trials when considering studies of high quality, indicating a significant difference (p = 0.04). Multivariate meta- regression revealed that exercise duration in endurance trials and exercise intensity in neuromuscular trials had a significantly moderating effect. Conclusions: Strong neuromuscular exercise interventions can be slightly more effective than endurance exercise interventions. Interestingly, exercise duration and exercise intensity moderated the effect size meaningfully. This result might be used on exercise in depression to increase efficacy.
Learning Neuron Non-Linearities with Kernel-Based Deep Neural Networks

The effectiveness of deep neural architectures has been widely supported in terms of both experimental and foundational principles. There is also clear evidence that the activation function (e.g. the rectifier and the LSTM units) plays a crucial role in the complexity of learning. Based on this remark, this paper discusses an optimal selection of the neuron non-linearity in a functional framework that is inspired from classic regularization arguments. It is shown that the best activation function is represented by a kernel expansion in the training set, that can be effectively approximated over an opportune set of points modeling 1-D clusters. The idea can be naturally extended to recurrent networks, where the expressiveness of kernel-based activation functions turns out to be a crucial ingredient to capture long-term dependencies. We give experimental evidence of this property by a set of challenging experiments, where we compare the results with neural architectures based on state of the art LSTM cells.
Extreme-Scale High-Order WENO Simulations of 3-D Detonation Wave with 10 Million Cores

High-order stencil computations, frequently found in many applications, pose severe challenges to emerging many-core platforms due to the complexities of hardware architectures as well as the sophisticated computing and data movement patterns. In this article, we tackle the challenges of high-order WENO computations in extreme-scale simulations of 3D gaseous waves on Sunway TaihuLight. We design efficient parallelization algorithms and present effective optimization techniques to fully exploit various parallelisms with reduced memory footprints, enhanced data reuse, and balanced computation load. Test results show the optimized code can scale to 9.98 million cores, solving 12.74 trillion unknowns with 23.12 Pflops double-precision performance.
Mixed Reality for Cross-Cultural Integration: Using Positive Technology to Share Experiences and Promote Communication

In the contemporary society, migrations from other countries or even from other regions of the country are a common phenomenon that rises cultural and psycho-social issues, as well as political and economic challenges. People move from their place of origin for educational or professional purposes or because they are forced to leave due to political, economic and social conditions, and also natural disasters which produce population flows. Whatever the push and pull factors are, when people move permanently or temporarily they tend to maintain close ties with their place of origin (with people, places, culture, practices etc.), while trying to develop attachment with the place of residence (Ehrkamp, 2005). The literature (Appadurai, 1996; Faist, 2000; Smith, 2001) highlighted the dichotomy between transnational and local levels, arguing that transnational ties may prevent assimilation and adaptation to the new place, while other authors (Bhabha, 1994; Kaya, 2002; Ehrkamp, 2005) promoted the concept of “hybridity” between home and host society, or between ethnicity and assimilation, showing that immigrants’ transnational practices create new places of belonging that allow them to engage with the receiving society. According to Berry (2001), four strategies are mainly used by immigrants interacting with the culture of the hosting country:
Comparison of different analysis algorithms to calculate multiple-breath washout outcomes

Lung clearance index (LCI) is the main outcome of the multiple-breath washout (MBW) test. Current recommendations for LCI acquisition are based on low-grade evidence. The aim of this study was to challenge those recommendations using alternative methods for LCI analysis. Nitrogen MBW measurements from school-aged children, 20 healthy controls, 20 with cystic fibrosis (CF) and 17 with primary ciliary dyskinesia (PCD), were analysed using 1) current algorithms (standard), 2) three alternative algorithms to detect with higher precision the end of MBW testing and 3) two alternative algorithms to determine exhaled tracer gas concentrations. LCI values, intra-test repeatability, and ability to discriminate between health and lung disease were compared between these methods. The analysis methods strongly influenced LCI (mean±sd overall differences (%) between standard and alternative analysis methods: −4.9±5.7%; range: −66–19%), but did not improve intra-test variability. Discrimination between health and disease was comparable as areas under the receiver operator curves were not greater than that from standard analysis. This study supports current recommendations for LCI calculation in children. Alternative methods influence LCI estimates and hamper comparability between MBW setups. Alternative algorithms, whenever used, should be carefully reported. Lung clearance index values are strongly affected by the algorithms used for the analysis http://ow.ly/h2Rs30ktPiN
Lightweight Tag-Aware Personalized Recommendation on the Social Web Using Ontological Similarity

With the rapid growth of social tagging systems, many research efforts are being put into personalized search and recommendation using social tags (i.e., folksonomies). As users can freely choose their own vocabulary, social tags can be very ambiguous (for instance, due to the use of homonyms or synonyms). Machine learning techniques (such as clustering and deep neural networks) are usually applied to overcome this tag ambiguity problem. However, the machine-learning-based solutions always need very powerful computing facilities to train recommendation models from a large amount of data, so they are inappropriate to be used in lightweight recommender systems. In this paper, we propose an ontological similarity to tackle the tag ambiguity problem without the need of model training by using contextual information. The novelty of this ontological similarity is that it first leverages external domain ontologies to disambiguate tag information, and then semantically quantifies the relevance between user and item profiles according to the semantic similarity of the matching concepts of tags in the respective profiles. Our experiments show that the proposed ontological similarity is semantically more accurate than the state-of-the-art similarity metrics, and can thus be applied to improve the performance of content-based tag-aware personalized recommendation on the social web. Consequently, as a model-training-free solution, ontological similarity is a good disambiguation choice for lightweight recommender systems and a complement to machine-learning-based recommendation solutions.
Sequence-based nomenclature: a reply to Thines et al. and Zamora et al. and provisions for an amended proposal “from the floor” to allow DNA sequences as types of names

We reply to two recently published, multi-authored opinion papers by opponents of sequence-based nomenclature, namely Zamora et al. (IMA Fungus 9: 167–175,2018) and Thines et al. (IMA Fungus 9: 177–183, 2018). While we agree with some of the principal arguments brought forward by these authors, we address misconceptions and demonstrate that some of the presumed evidence presented in these papers has been wrongly interpreted. We disagree that allowing sequences as types would fundamentally alter the nature of types, since a similar nature of abstracted features as type is already allowed in the Code (Art. 40.5), namely an illustration. We also disagree that there is a high risk of introducing artifactual taxa, as this risk can be quantified at well below 5 %, considering the various types of high-throughput sequencing errors. Contrary to apparently widespread misconceptions, sequence-based nomenclature cannot be based on similarity-derived OTUs and their consensus sequences, but must be derived from rigorous, multiple alignment-based phylogenetic methods and quantitative, single-marker species recognition algorithms, using original sequence reads; it is therefore identical in its approach to single-marker studies based on physical types, an approach allowed by the Code. We recognize the limitations of the ITS as a single fungal barcoding marker, but point out that these result in a conservative approach, with “false negatives” surpassing “false positives”; a desirable feature of sequence-based nomenclature. Sequence-based nomenclature does not aim at accurately resolving species, but at naming sequences that represent unknown fungal lineages so that these can serve as a means of communication, so ending the untenable situation of an exponentially growing number of unlabeled fungal sequences that fill online repositories. The risks are outweighed by the gains obtained by a reference library of named sequences spanning the full array of fungal diversity. Finally, we elaborate provisions in addition to our original proposal to amend the Code that would take care of the issues brought forward by opponents to this approach. In particular, taking up the idea of the Candidatus status of invalid, provisional names in prokaryote nomenclature, we propose a compromise that would allow valid publication of voucherless, sequence-based names in a consistent manner, but with the obligate designation as “nom. seq. ” (nomen sequentiae). Such names would not have priority over specimen- or culture-based names unless either epitypified with a physical type or adopted for protection on the recommendation of a committee of the International Commission on the Taxonomy of Fungi following evaluation based on strict quality control of the underlying studies based on established rules or recommendations.
Detecting Malicious Websites in Depth through Analyzing Topics and Web-pages

We are increasingly relying on HTML(Hypertext Markup Language) web-pages, including online shopping, obtaining information and handling official business, whether on a mobile phone or on a computer. But the underground industry or deliberate attacker has also targeted us. They forges misleading URLs(UniformResourceLocators) and web-pages with various tricky skills which are difficult to identify even for the professionals, so as to steal money, manipulate our devices, monitor our lives. Currently, most of the malicious websites detecting technology is based on features of URLs or Web page elements, which make us feel upset, because it is difficult to extract all the features, and its hysteresis quality make it hard to meet the requirement of tracking the rapid changes of malicious web sites, especially the phishing websites. This paper design a thorough associated analysis model of web-pages using the technology of topic tracking, topic abnormal discovery, web-page visual similarity assessment, web-page structure analyzing, URL analyzing, Internet Resources analyzing and so on, to solve the problem of missing detecting malicious websites, especially with unknown features. The detecting model is able to discover the forged payment web-pages, fake web page which damage the reputation of the relevant organization, personal attack web-pages, tampered web page, web-page trojan, etc. In the meantime, it can track the topic of underground industry and sense the topic evolution. According to our experiments, it is effective and has high accuracy.