Rich Model Research Articles

Data-driven model for improving wall-modeled large-eddy simulation of supersonic turbulent flows with separation

A machine learning algorithm is presented, serving as a data-driven modeling tool for wall-modeled large eddy simulations (WMLESs). The proposed model is formulated to address the problems of log layer mismatch and inaccurate prediction of skin friction, particularly for supersonic separated and reattached flows. This machine learning algorithm uses random forest regression to map the local mean flow fields to the discrepancies in the skin friction (heat flux) while complying with Galilean invariance as the flow features input is provided using relative velocities. The model is tested on two different supersonic flows, namely, flow over a flat plate and flow around an expansion-compression corner. The performance is evaluated by comparing the skin friction (heat flux) and flow properties with exact values. The ultimate goal is to build a robust and generalizable machine learning model to improve the prediction of WMLES of supersonic flows. To this end, the model is trained by a set of flows containing some essential flow physics to devise a generalizable model. Although the general machine learning model shows some advantages over the baseline WMLES model, it is concluded the data set is far from being representative of the rich flow physics model; therefore, the machine learning model should be trained and tested by a broader set of flows.

Beyond Bauhaus: Modernism in Britain, 1933–1966

Beyond Bauhaus: Modernism in Britain, 1933–1966

E-Readiness Evaluation of High School Student Biology Learning During the Covid-19 Pandemic

E-Readiness is the level of readiness that a person needs to understand and apply information and communication technology. E-Readiness can also be interpreted as readiness to sort, interpret, and process digital knowledge. E-Readiness is very important for students to be able to take part in online learning during the Covid-19 pandemic. The purpose of this study was to describe the level of E-Readiness in learning biology for high school students in West Lombok Regency in participating in online learning of biology subjects. This study uses a quantitative descriptive research method. The instrument used to obtain student E-Readiness data is the E-Readiness questionnaire. Data collection was carried out using a survey technique by distributing questionnaires using the google form application. The number of respondents who filled out the questionnaire was 622 students. Data analysis was carried out using descriptive statistical analysis techniques that refer to the An Integration Information Rich E-Readiness Assessment Tool evaluation model. Data analysis using the evaluation model was carried out on each indicator of the E-readiness segment. The results of data analysis found that: (1) Enterprise E-Readiness segment 79.88% ready; (2) ICT readiness segment 82.96 ready; (3) Human resources readiness 83.88 ready, 4. Information readiness segment, 70.72% ready, and 5. External environment readiness 84.42% is ready. From the data analysis, it was found that the E-Readiness of studying biology for high school students was 3.1% Very Unprepared, 16.4% Unprepared, 54.9% Ready, and 25.6% Very Ready. On average 80.5% of high school students have good skills in using information and communication technology. Thus, it can be concluded that an average of 80.5% of students' E-Readiness in learning biology had a good level of readiness, and 19.5% did not yet have a readiness level to participate in online biology learning in West Lombok Regency

Inequalities in body mass index, diet and physical activity in the UK: Longitudinal evidence across childhood and adolescence

We use longitudinal data across a key developmental period, spanning much of childhood and adolescence (age 5 to 17, years 2006–2018) from the UK Millennium Cohort Study, a nationally representative study with an initial sample of just over 19,000. We first examine the extent to which inequalities in overweight, obesity, BMI and body fat over this period are consistent with the evolution of inequalities in health behaviours, including exercise and healthy diet markers (i.e., skipping breakfast) (n = 7,220). We next study the links between SES, health behaviours and adiposity (BMI, body fat), using rich models that account for the influence of a range of unobserved factors that are fixed over time. In this way, we improve on existing estimates measuring the relationship between SES and health behaviours on the one hand and adiposity on the other. The advantage of the individual fixed effects models is that they exploit within-individual changes over time to help mitigate biases due to unobserved fixed characteristics (n = 6,883).We observe stark income inequalities in BMI and body fat in childhood (age 5), which have further widened by age 17. Inequalities in obesity, physical activity, and skipping breakfast are observed to widen from age 7 onwards. Ordinary Least Square estimates reveal the previously documented SES gradient in adiposity, which is reduced slightly once health behaviours including breakfast consumption and physical activity are accounted for. The main substantive change in estimates comes from the fixed effects specification. Here we observe mixed findings on the SES associations, with a positive association between income and adiposity and a negative association with wealth. The role of health behaviours is attenuated but they remain important, particularly for body fat.

Counterfactual inference for consumer choice across many product categories

This paper proposes a method for estimating consumer preferences among discrete choices, where the consumer chooses at most one product in a category, but selects from multiple categories in parallel. The consumer’s utility is additive in the different categories. Her preferences about product attributes as well as her price sensitivity vary across products and may be correlated across products. We build on techniques from the machine learning literature on probabilistic models of matrix factorization, extending the methods to account for time-varying product attributes and products going out-of-stock. We evaluate the performance of the model using held-out data from weeks with price changes or out of stock products. We show that our model improves over traditional modeling approaches that consider each category in isolation. One source of the improvement is the ability of the model to accurately estimate heterogeneity in preferences (by pooling information across categories); another source of improvement is its ability to estimate the preferences of consumers who have rarely or never made a purchase in a given category in the training data. Using held-out data, we show that our model can accurately distinguish which consumers are most price sensitive to a given product. We consider counterfactuals such as personally targeted price discounts, showing that using a richer model such as the one we propose substantially increases the benefits of personalization in discounts.

Universal steganalysis of spatial content-independent and content-adaptive steganographic algorithms using normalized feature derived from empirical mode decomposed components

Steganography is a practice of covert communication by hiding secret or payload information inside a cover source medium. Fraudulent entities may make use of it to communicate secretly and pose a great threat to law enforcement agencies. Universal steganalysis is a counter attack designed to identify steganography regardless of the steganographic process involved. As minute payloads that ride over covers, evade detection, steganalysts need to resort to high dimensional feature sets that extract residues in every possible way and a powerful classifier with higher computational power. In this paper, a novel universal steganalyzer driven by Empirical Mode Decomposition technique which is an adaptive and highly efficient method to analyze non-linear and non-stationary signals is proposed. Using this technique, the suspicious digital images are decomposed into intrinsic mode functions and residues to gradually remove the image contents to reveal the feeble artifacts caused by steganographic algorithms. Frobenius norm computed from these components further highlight the distortions. A low dimensional feature with a simple thresholding function assisting the steganalytic community in a blind and generic way can definitely be viewed as a boon to the domain. The proposed method is tested upon spatial content-adaptive steganographic algorithms (HUGO, WOW, S-UNIWARD) and content-independent least significant bit steganographic algorithms (LSBR, LSBM, LSBMR, LSBR2). The experimental results show that the proposed method is able to achieve improved accuracies when compared with Spatial Rich Model, Wu Net, Ye Net and Hybrid CNN. The execution time for the proposed method is also far less compared to state-of-the-art steganalyzers.

JPEG steganalysis based on denoising network and attention module

The core objective of image steganalysis is to explore the presence of weak image steganographic signals. Extracting effective steganographic signal features will play an essential role in digital image steganalysis. However, existing networks rely more on spatial rich model kernels or random learnable kernels to obtain noise residuals during the stage of steganographic signal features extraction. In this paper, we proposed a JPEG steganalysis network which based on denoising network and attention module, mainly including a noise extract block, a noise analysis block, and a judgment block. Specifically, a professional denoising convolutional neural network is first introduced in noise extract block to obtain better steganalysis features. The noise analysis block is integrated with the attention module to finely extract the steganographic signals hidden in the complex texture regions, which is quite effective in improving the signal-to-noise ratio of the stego signal. The judgment block is primarily a classifier to distinguish between cover images and stego images. Comprehensive experiments show a significant improvement in performance over the state-of-the-art steganalysis scheme. Moreover, the proposed network has better generalization capability than the compared steganalysis network for the case of cover-source and quality factor mismatch, which is critical for future steganalysis systems.

LWMPI: An MPI library for NoC‐based lightweight manycore processors with on‐chip memory constraints

AbstractLightweight manycore processors deliver high performance and energy efficiency by bundling hundreds of low‐power cores, a distributed memory architecture with small local memories and Networks‐on‐Chip in a single die. However, the lack of rich and portable programming models for these processors makes software development a challenging task. Currently, two approaches are employed to address programmability in lightweight manycores: Operating Systems (OSes) and baremetal runtime libraries. The former provides portability but exposes complex Operating System (OS)‐level programming interfaces to developers. The latter focuses on providing rich and high performance interfaces, which are vendor‐specific and yield to non‐portable software. In this work, we address these programmability and portability challenges by combining a rich OS with a well‐known standard for parallel programming. We propose a portable and lightweight Message Passing Interface (MPI) library (LWMPI) designed from scratch to cope with restrictions and intricacies of lightweight manycores. We integrated LWMPI into Nanvix, an open‐source distributed OS that runs on silicon lightweight manycores. The results obtained with a synthetic benchmark and a subset of the CAP Bench applications running on Kalray MPPA‐256 unveil that LWMPI not only delivers a lightweight and richer programming interface but also presents good performance and scalability results.

A Quaternion Two‐Stream R‐CNN Network for Pixel‐Level Color Image Splicing Localization

Recently, Zhou et al. designed a two-stream faster Region-Convolutional neural networks (R-CNN) model RGB-N for color image splicing localization in CVPR2018. However, the RGB-N locates spliced regions only at block-level and ignores the entirety and inherent correlation of three channels. Therefore, an improved quaternion two-stream R-CNN model is proposed to solve these drawbacks: a mask branch combining fully convolutional network and condition random field is added for locating spliced regions at pixel-level; quaternion representation of color images is used to process color spliced images in a holistical way. In addition, feature pyramid network based on quaternion residual network is considered to extract multi-scale features for color spliced images; attention region proposal network is combined with attention mechanism and is designed to pay more attention to the spliced regions; a high-pass filter designed for image splicing detection specifically is adopted to replace steganalysis rich model filter in the RGB-N to obtain noise input for the noise stream. Experimental results on a new synthetic dataset and three standard forgery datasets demonstrate that the proposed method is superior to the existing methods in the abilities of localization, generalization, and robustness.

IMPACT OF THE MATHEMATICAL MODELING ON CONCEPTUAL UNDERSTANDING AMONG STUDENT-TEACHERS

The present study investigates the effect of mathematical modeling on conceptual understanding among student-teachers. Also, the study proposes relevant materials and activities in mathematical modeling. Two classes of 140 student-teachers participated in the study. Mathematical modeling instruction was used in the treatment group, while the comparison group was taught by a teacher-centered method. T-Test results showed a difference between the treatment and comparison groups in terms of conceptual understanding. Moreover, the mathematical modeling group showed improvement in the knowledge, comprehension, and application of geometry and measurement concepts. The results of this study are novel as they are introduced to student-teachers to facilitate teaching mathematics among children. The researchers suggested engaging student-teachers in rich modeling activities, which would deepen their students' mathematics learning.

Effectiveness of stego images pre-noising with fractional noise for digital image steganalysis

Counteraction to sensitive data leakage in cyber-physical systems is topical task today. Solving of the task is complicated to widely usage by attackers of novel steganographic methods for sensitive data embeddinginto innocuous (cover)files, such as digital images. Feature of these embedding methods isminimization of cover image’s parameters alterations during message hiding. This negatively affectsdetection accuracy of formed stego images bystate-of-the-art statistical stegdetectors. Therefore, advanced methods for detection and amplification of cover image’s parameters abnormal changes caused by data embedding are needed.The novel approach for solving of mentioned task is applying of image pre-processing (calibration) methods. These methods are aimed at estimation parameters either of cover, or stego images from current analysed image.The majority of known calibration methods are based on cover image content suppression by utilization of extensive set of high-pass filters. This makes possible close to state-of-the-art detection accuracy by the costof time consuming preselectionof appropriate filters. Therefore, this approach may be inappropriate in real cases, when fast re-train stegdetector for revealing of stego images formed by unknown embedding methodsis required.For overcoming this limitation, we proposed to calibrate an image by amplification of alterations caused by message hiding. This can be realized by data re-embedding into images or their pre-noising.The effectiveness of such approach was proved for wide range of modern embedding methods in the case of message re-embedding. The paper is aimed at performance analysis of image calibration by pre-noising, namely by using of non-stationary fraction noise.The performance analysis of proposed solution was performed for novel HUGO and MG adaptive embedding methods on standard VISION dataset. According to obtained results, we may conclude that applying of proposed solution allows achieving close to state-of-the-art detection accuracy for HUGO embedding method and low (less than 10%) cover image payload.Also, low computation complexity of proposed solution makes it an attractive alternative to novel cover rich models based stegdetectors. Nevertheless, solution’s performance concedes effectiveness of novel stegdetectorsfor medium(less than 20%) and high (more 25%) cover image payloadfor MG embedding method

A preliminary description of mood in Welsh

Abstract In this paper we propose a functional account of the Welsh mood system, focussing on responsives in particular. The discourse functions of responsives are interpreted through the concept of negotiation within the systemic functional linguistic framework, which offers a rich model for accounting for both initiations and responses, including possible tracking and challenging moves. By examining the interaction of mood together with specific features of Welsh, e.g. a dominant VSO clause ordering, mood particles, Subject ellipsis and a complex system of negation, we are able to show that Welsh tends to highlight interpersonal meanings in clause initial position. As the first functional description of Welsh, we also set out important directions for future research, based on the findings presented in this paper.

Impact of climate change on plant species richness across drylands in China: From past to present and into the future

It is commonly believed that climate plays an important role in shaping the patterns of plant species richness. However, it remains unclear how climate may affect plant richness across large spatial scales and also over long-terms, for example across massive arid regions during the Quaternary and into the future. To fill this knowledge gap, we examined the long-term trends in plant species richness across vast drylands in China based on our newly developed climate-species richness models. We first generated optimal random forest models based on the relationships between the current (CUR, 1970–2000) annual, perennial, woody and total species richness and climatic variables. We subsequently hindcasted and forecasted plant species richness in five other periods: last interglacial (LIG, 120,000–140,000 years BP), last glacial maximum (LGM, about 22,000 years BP), mid-Holocene (MH, about 6000 years BP), 2041–2060 (2050), and 2061–2080 (2070). Our results showed that total and herbaceous species richness exhibited similar patterns that were driven by temperature and precipitation. Both groups of species formed a transition zone between arid and semi-arid regions in all periods except LIG. In contrast, woody species richness did not form any transition zone because it was regulated mainly by temperature. Furthermore, the mean richness of herbaceous, woody and all species exhibited gradually increasing trends from LIG to 2070 across drylands in China. On the one hand, herbaceous species were much more sensitive to climate change than woody species. On the other hand, plant species richness in mountain-basin systems and Taklamakan Desert regions were more sensitive to climate change than in meadows and steppes. These results shed new insights into the past and future changes in plant species richness over vast drylands and provide valuable information for future conservation efforts under global warming.

Detecting adversarial examples by additional evidence from noise domain

Deep neural networks are widely adopted powerful tools for perceptual tasks. However, recent research indicated that they are easily fooled by adversarial examples, which are produced by adding imperceptible adversarial perturbations to clean examples. Here the steganalysis rich model (SRM) is utilized to generate noise feature maps, and they are combined with RGB images to discover the difference between adversarial examples and clean examples. In particular, a two-stream pseudo-siamese network that fuses the subtle difference in RGB images with the noise inconsistency in noise features is proposed. The proposed method has strong detection capability and transferability, and can be combined with any model without modifying its architecture or training procedure. The extensive empirical experiments show that, compared with the state-of-the-art detection methods, the proposed approach achieves excellent performance in distinguishing adversarial samples generated by popular attack methods on different real datasets. Moreover, this method has good generalization, it trained by a specific adversary can defend against other adversaries effectively.

Modeling riparian flood plain wetland water richness in pursuance of damming and linking it with a methane emission rate

Modeling water richness in dam-induced riparian wetland and linking methane (CH4) emission rate has immense importance to understand the regime of methanogenesis. Multi-model machine learning approaches have been adopted for developing water richness (WR) models and an equation-based method has been followed for estimating CH4 emission from the wetland. REPTree model is found to be the best representative WR model. About 194.14 km2 wetland areas have been squeezed; very high water richness area has been reduced from 47.37 to 29.59% in the post-dam period. Along with CH4 emission has also been reduced by 52.61%. The high CH4 emission rate is attributed to the higher WR area. Linking CH4 emission with WR demonstrates a positive relation (0.59 for pre-dam and 0.23 for post-dam at >0.001 level of statistical significance) establishing the very vivid effect of WR on CH4 emission.

A Computational Framework for Lithium Ion Cell-Level Model Predictive Control Using a Physics-Based Reduced-Order Model

Most state-of-the art battery-control strategies rely on voltage-based design limits to address performance and lifetime concerns. Such approaches are inherently conservative. However, by exploiting internal electrochemical quantities, it is possible to control battery performance right up to true physical bounds. This letter develops an extensible framework that combines model predictive control (MPC) with computationally efficient realization algorithm (xRA)-generated reduced-order electrochemical models for the advanced control of lithium-ion batteries. The approach is demonstrated on the fast-charge problem where hard constraints are imposed on problem variables to avoid lithium plating induced performance degradation. This letter establishes a general mathematical foundation for the incorporation of electrochemically rich reduced-order models directly into an MPC framework.

Midpoint attractor models resolve the mid‐elevation peak in Himalayan plant species richness

The midpoint attractor (MPA) models of species richness integrate a unimodal environmental favourability gradient and neutral effects forced by geometric constraints and thus extend the ecologically neutral mid‐domain model. However, both alternative MPA algorithms assume that underlying environmental favourability peaks within the modelling domain. Here, we used elevational distribution data for 1054 plant species occurring in northwest Himalaya to explore species richness gradients and MPA performance in species groups defined by biogeography, taxonomy and life‐form. MPA models achieved an excellent fit, but the two MPA algorithms produced contrasting estimates of MPA location, especially for species groups with richness originating in lowlands. Therefore, we propose a modification of the MPA model accounting for the environmental favourability peak outside the study domain to reflect these situations. Biogeographic origin was more decisive for MPA location than taxonomic or life‐form classification, indicating relatively low climatic niche conservatism in plants.

Reported communication challenges for adult witnesses with intellectual disabilities giving evidence in court

Communication plays a key role in a witness's ability to give evidence and participate in the court process. Adults with intellectual disabilities (ID) can be negatively impacted by communication difficulties such as: limitations in recall abilities; suggestibility to leading questions; difficult question styles used by advocates; and unfamiliar language used within the court setting. Most research carried out on communication challenges for adults with ID, when giving evidence, has involved participants in psychology-based experimental methodology. In this study 19 court reports assessing actual witnesses (complainants and defendants) with ID, written by Registered Intermediaries in Northern Ireland, were analysed. A wide range of communication difficulties were identified for the adult witnesses. Difficulties resulting from communication used by their communication partner (typically the advocate in a court setting) were also described. A rich model of the challenges for both partners, in giving evidence and in cross-examination, is presented, extending previous research. This study highlights the need for research within UK courts to assess: how witnesses with ID are being questioned; the effectiveness of changes made to the court process to enhance communication; the impact of the court process and environment on communication and alternative question styles for advocates to use.

FNet: A Two-Stream Model for Detecting Adversarial Attacks against 5G-Based Deep Learning Services

With the extensive application of artificial intelligence technology in 5G and Beyond Fifth Generation (B5G) networks, it has become a common trend for artificial intelligence to integrate into modern communication networks. Deep learning is a subset of machine learning and has recently led to significant improvements in many fields. In particular, many 5G-based services use deep learning technology to provide better services. Although deep learning is powerful, it is still vulnerable when faced with 5G-based deep learning services. Because of the nonlinearity of deep learning algorithms, slight perturbation input by the attacker will result in big changes in the output. Although many researchers have proposed methods against adversarial attacks, these methods are not always effective against powerful attacks such as CW. In this paper, we propose a new two-stream network which includes RGB stream and spatial rich model (SRM) noise stream to discover the difference between adversarial examples and clean examples. The RGB stream uses raw data to capture subtle differences in adversarial samples. The SRM noise stream uses the SRM filters to get noise features. We regard the noise features as additional evidence for adversarial detection. Then, we adopt bilinear pooling to fuse the RGB features and the SRM features. Finally, the final features are input into the decision network to decide whether the image is adversarial or not. Experimental results show that our proposed method can accurately detect adversarial examples. Even with powerful attacks, we can still achieve a detection rate of 91.3%. Moreover, our method has good transferability to generalize to other adversaries.

Investigating elevational gradients of species richness in a Mediterranean plant hotspot using a published flora

The Apuan Alps are one of the most peculiar mountain chain in the Mediterranean, being very close to the coastline and reaching an elevation of almost 2000 m. Based on published flora, we investigated the distribution of plant species richness along the whole elevational gradient of this chain considering: (i) all species, (ii) endemic versus alien species; and (iii) functional groups of species based on Raunkiær life forms (RLF). Generalized Linear Models (GLMs) were used to analyse richness patterns along the elevational gradient, and elevational richness models versus the area of the elevational belts were fitted to test the effect of surface area. Our results showed decreasing species richness with increasing elevation. In contrast, endemic species richness increased along the elevational gradient. Alien species were mainly distributed at low elevations, but this result should be taken with caution since we used historical data. Species life forms were not equally distributed along the elevation gradient: chamaephytes and hemicryptophytes were the richest groups at high elevations, while therophytes showed highest species richness at low elevations. Our findings suggest that in the Apuan Alps there is a major elevational gradient in species composition that could reflect plant evolutionary history. Furthermore, we highlight the key role of published floras as a relevant source of biodiversity data.