Synthetic Patterns Research Articles

Detecting Nontechnical Losses in Smart Meters Using a MLP-GRU Deep Model and Augmenting Data via Theft Attacks

The current study uses a data-driven method for Nontechnical Loss (NTL) detection using smart meter data. Data augmentation is performed using six distinct theft attacks on benign users’ samples to balance the data from honest and theft samples. The theft attacks help to generate synthetic patterns that mimic real-world electricity theft patterns. Moreover, we propose a hybrid model including the Multi-Layer Perceptron and Gated Recurrent Unit (MLP-GRU) networks for detecting electricity theft. In the model, the MLP network examines the auxiliary data to analyze nonmalicious factors in daily consumption data, whereas the GRU network uses smart meter data acquired from the Pakistan Residential Electricity Consumption (PRECON) dataset as the input. Additionally, a random search algorithm is used for tuning the hyperparameters of the proposed deep learning model. In the simulations, the proposed model is compared with the MLP-Long Term Short Memory (LSTM) scheme and other traditional schemes. The results show that the proposed model has scores of 0.93 and 0.96 for the area under the precision–recall curve and the area under the receiver operating characteristic curve, respectively. The precision–recall curve and the area under the receiver operating characteristic curve scores for the MLP-LSTM are 0.93 and 0.89, respectively.

TTQR: A Traffic- and Thermal-Aware Q-Routing for 3D Network-on-Chip.

The die-stacking structure of 3D network-on-chips (3D NoC) leads to high power density and unequal thermal conductance between different layers, which results in low reliability and performance degradation of 3D NoCs. Congestion-aware adaptive routing, which is capable of balancing the network's traffic load, can alleviate congestion and thermal problems so as to improve the performance of the network. In this study, we propose a traffic- and thermal-aware Q-routing algorithm (TTQR) based on Q-learning, a reinforcement learning method. The proposed algorithm saves the local traffic status and the global temperature information to the Q1-table and Q2-table, respectively. The values of two tables are updated by the packet header and saved in a small size, which saves the hardware overhead. Based on the ratio of the Q1-value to the Q2-value corresponding to each direction, the packet's output port is selected. As a result, packets are transferred to the chosen path to alleviate thermal problems and achieve more balanced inter-layer traffic. Through the Access Noxim simulation platform, we compare the proposed routing algorithm with the TAAR routing algorithm. According to experimental results using synthetic traffic patterns, our proposed methods outperform the TAAR routing algorithm by an average of 63.6% and 41.4% in average latency and throughput, respectively.

Anticipative QoS Control: A Self-Reconfigurable On-Chip Communication.

A self-reconfigurable Network-on-Chip (NoC) architecture that supports anticipative Quality of Service (QoS) control with penetrative switch ability is proposed to enhance the performance of bidirectional-channel NoC communication while supporting prioritized packet transmission services. The anticipative QoS control not only allows each communication channel to be dynamically self-configured to transmit flits in either direction for a better channel utilization of on-chip hardware resources, but also enhances the latency performance for QoS services. The proposed anticipative control is based on penetratingly observing channel direction requests of routers that is two hops away from the current one. The added ability enables a router to allocate high-priority packets to a dedicated virtual channel and then rapidly bypass it to the next destination router. The provided flexibility of packet switch promises better channel bandwidth utilization, lower packet delivery latency, and furthermore guarantees the high-priority packets being served with a better QoS. Accordingly, in this paper, an enhanced NoC architecture supporting the hybrid anticipative QoS, penetrative switch, and bidirectional-channel control, namely Anticipative QoS Bidirectional-channel NoC (AQ-BiNoC) is presented. Tested with cycle-accurate synthetic traffic patterns, significant performance enhancement has been observed when the proposed AQ-BiNoC architecture is compared against conventional NoC designs.

Quantifying ethnic segregation in cities through random walks

Socioeconomic segregation has an important role in the emergence of large-scale inequalities in urban areas. Most of the available measures of spatial segregation depend on the scale and size of the system under study, or neglect large-scale spatial correlations, or rely on ad-hoc parameters, making it hard to compare different systems on equal grounds. We propose here a family of non-parametric measures for spatial distributions, based on the statistics of the trajectories of random walks on graphs associated to a spatial system. These quantities provide a consistent estimation of segregation in synthetic spatial patterns, and we use them to analyse the ethnic segregation of metropolitan areas in the US and the UK. We show that the spatial diversity of ethnic distributions, as measured through diffusion on graphs, allow us to compare the ethnic segregation of urban areas having different size, shape, or peculiar microscopic characteristics, and exhibits a strong association with socio-economic deprivation.

Integrated Test Pattern Extraction and Generation for Accurate Lithography Modeling

A set of good test patterns is a key for accurate lithography modeling. We address three sub-problems in test pattern extraction: (1) Each pattern is associated with one or more gauges, where CD is measured and modeling is performed. We perform gauge clustering in feature space, while the goal is to minimize the number of chosen patterns. (2) Sample patterns do not usually guarantee enough coverage, so once coverage of chosen patterns is identified, some synthetic patterns are systematically generated to fill the empty region in feature space. (3) Pattern representation defines the feature space. We propose to use some Gaussian convolutions (or a few of their principal components) together with image parameter set (IPS) to reflect exposure for optical model and both PEB and development for resist model. Experiments with 1xnm DRAM design demonstrate that lithography modeling through our test pattern extraction and generation is performed with 35% smaller number of test patterns while CD error is reduced by 55%, with pattern extraction in feature space of IPS parameters as a reference.

A Segmented Adaptive Router for Near Energy-Proportional Networks-on-Chip

A Network-on-Chip (NoC) is an essential component of a chip multiprocessor (CMP) which however contributes to a large fraction of system energy. The unpredictability of traffic across a NoC frequently involves an expensive over-sizing of NoC resources which in turn leads to a significant contribution to the CMP power consumption. There exists a body of work addressing this issue, however so far solutions fall short when aiming for power reduction whilst maintaining high NoC performance. This paper proposes to combine router architecture optimizations with smart resource management to overcome this limitation. Based on a fully segmented architecture, we present an online adaptive router adjusting its active routing resources to meet the current traffic demand. This enhanced power-gating strategy significantly decreases both static and dynamic power consumption of the NoC, up to 70% for synthetic traffic patterns and up to 58% for real traffic workloads, while preserving NoC latency and throughput. Thanks to these adaptive power-saving mechanisms the proposed segmented NoC router provides near energy-proportional operation across the range of used benchmarks.

FRDS: An efficient unique on-Chip interconnection network architecture

Networks-on-Chip (NoC) is an impending research area for designing future System-on-Chip (SoC). NoC futures are allied with intellectual property (IP) reusability, parallel communication within IPs and scalability against standard interconnection NoC topology. In this paper, we introduce a new hybrid topology namely Four Regular Dense Spidergon (FRDS) based deterministic NoC architecture with 64 cores. We propose cluster and generic heuristic based approach For mapping of applications onto FRDS topology. Genetic algorithm (GA) ＋ simulated annealing (SA) have been considered under generic heuristic approach for successful mapping of core into topology and fair comparison of the performance of the proposed FRDS. Under normalized condition with respect to Mesh, experimental findings of proposed FRDS against real benchmark traces provide an average improvement on execution time with 1.20% and 4.81% over DMesh and ZMesh. Further, an average improvement on energy consumption was obtained 8.13% and 5.35% over DMesh and ZMesh. We adopt constant geometric traffic and the synthetic traffic pattern to measure the performance of proposed FRDS. The experimental results clearly indicates an improvement in power latency product under geometric traffic by 16.27% and 12.23% over DMesh and ZMesh. Similarly under synthetic traffic pattern, the proposed FRDS offers an improvement up to 22.51% and 45.53% over DMesh and ZMesh respectively. To realize the effectiveness of the link usage, brief evaluation of link utilization has been carried out with balanced load.

A Critical Study on the Synthetic Medication Pattern in the Management of Rheumatoid Arthritis

An inflammatory condition known as arthritis affects one or more joints in the body. The pathophysiology of rheumatoid arthritis (RA), a diverse ailment with a complicated and still poorly known pathogenesis, involves many different cell types in the destruction of the joints. Due to this complexity, clinical course and severity vary widely amongst patients and genetic and/or environmental factors may also impact them. In addition to the conventional synthetic disease-modifying anti-rheumatic drugs, biologics have demonstrated efficacy and safety in symptomatic relief, reducing bone loss, and maintaining the function, which has considerably improved the treatment of rheumatoid arthritis. Since current drugs periodically fail, only substantially treat the ailment or have unfavorable side effects. There are still unmet treatment requirements in rheumatoid arthritis despite the availability of a wide range of therapeutic choices and their combinations. Unfortunately, the ‘nonresponse’ pathways are still not fully understood, although they most definitely have to do with the covert variability of the pathophysiology of the disease. In this research, we will narrate the current and upcoming biological treatments and their cellular aims.

Determining crack kinematics from imaged crack patterns

Determining the relationship between the cause of damage and the subsequent structural behavior of infrastructure systems requires an accurate characterization of the propagation of cracks, which represents the evolution of the damage state. When no information about the cause of damage is available, kinematic approaches can be used to describe the motion of crack contours. Current image-based approaches to derive crack kinematics use digital image correlation (DIC) on a set of sequential images as the crack propagates. However, DIC is invasive in that the structure surfaces must be painted with random speckle patterns, limiting its use primarily to controlled experiments. In this paper, we propose a novel image-based methodology for computing crack opening in Mode I or Mode II. As an input, this method takes a binary image from a semantic segmentation of an image of a crack pattern. This binary image is used to detect the opposite edges along the crack, which are then registered using an optimization algorithm based on the Euclidean transformation model and non-linear least squares. As a final output, this method produces displacement maps in the tangential and normal directions to the crack skeleton. To demonstrate its performance, we validate our methodology first with synthetic crack patterns and then with real crack patterns. Because this methodology for determining crack openings requires only simple data (just a binary crack pattern image), it is straightforward, robust, and adaptable, thus contributing to the development of structural image-based damage assessments. The computational codes and datasets are available to the public for future research and benchmarking on https://github.com/eesd-epfl/crack_kinematics and https://doi.org/10.5281/zenodo.6632071.

SyNPL: Synthetic Notch pluripotent cell lines to monitor and manipulate cell interactions in vitro and in vivo.

ABSTRACTCell-cell interactions govern differentiation and cell competition in pluripotent cells during early development, but the investigation of such processes is hindered by a lack of efficient analysis tools. Here, we introduce SyNPL: clonal pluripotent stem cell lines that employ optimised Synthetic Notch (SynNotch) technology to report cell-cell interactions between engineered ‘sender’ and ‘receiver’ cells in cultured pluripotent cells and chimaeric mouse embryos. A modular design makes it straightforward to adapt the system for programming differentiation decisions non-cell-autonomously in receiver cells in response to direct contact with sender cells. We demonstrate the utility of this system by enforcing neuronal differentiation at the boundary between two cell populations. In summary, we provide a new adaptation of SynNotch technology that could be used to identify cell interactions and to profile changes in gene or protein expression that result from direct cell-cell contact with defined cell populations in culture and in early embryos, and that can be customised to generate synthetic patterning of cell fate decisions.

Analysis of Scaling Policies for NFV Providing 5G/6G Reliability Levels With Fallible Servers

The softwarization of mobile networks enables an efficient use of resources, by dynamically scaling and re-assigning them following variations in demand. Given that the activation of additional servers is not immediate, scaling up resources should anticipate traffic demands to prevent service disruption. At the same time, the activation of more servers than strictly necessary results in a waste of resources, and thus should be avoided. Given the stringent reliability requirements of 5G applications (up to 6 nines) and the fallible nature of servers, finding the right trade-off between efficiency and service disruption is particularly critical. In this paper, we analyze a generic auto-scaling mechanism for communication services, used to de(activate) servers in a cluster, based on occupation thresholds. We model the impact of the activation delay and the finite lifetime of the servers on performance, in terms of power consumption and failure probability. Based on this model, we derive an algorithm to optimally configure the thresholds. Simulation results confirm the accuracy of the model both under synthetic and realistic traffic patterns as well as the effectiveness of the configuration algorithm. We also provide some insights on the best strategy to support an energy-efficient highly-reliable service: deploying a few powerful and reliable machines versus deploying many machines, but less powerful and reliable.

23‐3: Distinguished Paper: Utilizing Advanced Spatio‐Temporal Backgrounds with Dynamic Test Signals for HDR Display Metrology

Test targets are indispensable for display metrology and in combination with a light measurement device, they can be used to assess a display's performance. Simple synthetic patterns with static backgrounds are currently the target of choice. However, modern display devices have significantly increased in complexity. With that, using static test sequences is likely not painting the full picture of a display's performance, especially as spatial and temporal complexity can only be addressed rudimentarily. In this paper, we discuss novel assessment approaches using dynamic test targets with spatio‐temporal backgrounds, and how they can be compiled in meaningful test sequences e.g. for automated measurements.

LBF-NoC: Learning-Based Framework to Predict Performance, Power and Area for Network-On-Chip Architectures

Extensive large-scale data and applications have increasing requests for high-performance computations which is fulfilled by Chip Multiprocessors (CMP) and System-on-Chips (SoCs). Network-on-Chips (NoCs) emerged as the reliable on-chip communication framework for CMPs and SoCs. NoC architectures are evaluated based on design parameters such as latency, area, and power. Cycle-accurate simulators are used to perform the design space exploration of NoC architectures. Cycle-accurate simulators become slow for interactive usage as the NoC topology size increases. To overcome these limitations, we employ a Machine Learning (ML) approach to predict the NoC simulation results within a short span of time. LBF-NoC: Learning-based framework is proposed to predict performance, power and area for Direct and Indirect NoC architectures. This provides chip designers with an efficient way to analyze various NoC features. LBF-NoC is modeled using distinct ML regression algorithms to predict overall performance of NoCs considering different synthetic traffic patterns. The performance metrics of five different (Mesh, Torus, Cmesh, Fat-Tree and Flattened Butterfly) NoC architectures can be analyzed using the proposed LBF-NoC framework. BookSim simulator is employed to validate the results. Various architecture sizes from [Formula: see text] to [Formula: see text] are used in the experiments considering various virtual channels, traffic patterns, and injection rates. The prediction error of LBF-NoC is 6% to 8%, and the overall speedup is [Formula: see text] to [Formula: see text] with respect to BookSim simulator.

Utilizing advanced spatio‐temporal backgrounds with dynamic test signals for high dynamic range display metrology

AbstractTest targets are indispensable for display metrology and in combination with a light measurement device, and they can be used to assess a display's performance. Simple synthetic patterns with static backgrounds are currently the target of choice. However, modern display devices have significantly increased in complexity. With that, using static test sequences is likely not painting the full picture of a display's performance, especially as spatial and temporal complexity can only be addressed rudimentarily. In this paper, we discuss novel assessment approaches using dynamic test targets with spatio‐temporal backgrounds, and how they can be compiled in meaningful test sequences e.g. for automated measurements.

Objective crystallographic symmetry classifications of a noisy crystal pattern with strong Fedorov-type pseudosymmetries and its optimal image-quality enhancement.

Statistically sound crystallographic symmetry classifications are obtained with information-theory-based methods in the presence of approximately Gaussian distributed noise. A set of three synthetic patterns with strong Fedorov-type pseudosymmetries and varying amounts of noise serve as examples. Contrary to traditional crystallographic symmetry classifications with an image processing program such as CRISP, the classification process does not need to be supervised by a human being and is free of any subjectively set thresholds in the geometric model selection process. This enables crystallographic symmetry classification of digital images that are more or less periodic in two dimensions (2D), also known as crystal patterns, as recorded with sufficient structural resolution from a wide range of crystalline samples with different types of scanning probe and transmission electron microscopes. Correct symmetry classifications enable the optimal crystallographic processing of such images. That processing consists of the averaging over all asymmetric units in all unit cells in the selected image area and significantly enhances both the signal-to-noise ratio and the structural resolution of a microscopic study of a crystal. For sufficiently complex crystal patterns, the information-theoretic symmetry classification methods are more accurate than both visual classifications by human experts and the recommendations of one of the popular crystallographic image processing programs of electron crystallography.

Utilizing Synthetic Nodules for Improving Nodule Detection in Chest Radiographs.

Algorithms that automatically identify nodular patterns in chest X-ray (CXR) images could benefit radiologists by reducing reading time and improving accuracy. A promising approach is to use deep learning, where a deep neural network (DNN) is trained to classify and localize nodular patterns (including mass) in CXR images. Such algorithms, however, require enough abnormal cases to learn representations of nodular patterns arising in practical clinical settings. Obtaining large amounts of high-quality data is impractical in medical imaging where (1) acquiring labeled images is extremely expensive, (2) annotations are subject to inaccuracies due to the inherent difficulty in interpreting images, and (3) normal cases occur far more frequently than abnormal cases. In this work, we devise a framework to generate realistic nodules and demonstrate how they can be used to train a DNN identify and localize nodular patterns in CXR images. While most previous research applying generative models to medical imaging are limited to generating visually plausible abnormalities and using these patterns for augmentation, we go a step further to show how the training algorithm can be adjusted accordingly to maximally benefit from synthetic abnormal patterns. A high-precision detection model was first developed and tested on internal and external datasets, and the proposed method was shown to enhance the model's recall while retaining the low level of false positives.

Assessment of Sponge City Flood Control Capacity According to Rainfall Pattern Using a Numerical Model after Muti-Source Validation

Urban floods are a common urban disaster that threaten the economy and development of cities. Sponge cities can improve flood resistance ability and reduce floods by setting low-impact development measures (LID). Evaluating flood reduction benefits is the basic link in the construction of sponge cities. Therefore, it is of great significance to evaluate the benefits of sponge cities from the perspective of different rain patterns. In this study, we investigated the urban runoff of various rainfall patterns in Mianyang city using the Strom Water Management Model (SWMM). We employed 2–100-year return periods and three different temporal rainfall downscaling methods to evaluate rain patterns and simulate urban runoff in Mianyang, with and without the implementation of sponge city measures. After calibration, model performance was validated using multi-source data concerning flood peaks and inter-annual variations in flood magnitude. Notably, the effects of peak rainfall patterns on historical floods were generally greater than the effects of synthetic rainfalls generated by temporal downscaling. Compared to the rainfall patterns of historical flood events, the flood protection capacities of sponge cities can be easily overestimated when using the synthetic rainfall patterns generated by temporal downscaling. Overall, an earlier flood peak was associated with better flood sponge city protection capacity. In this context, the results obtained in this study provide useful reference information about the impact of rainfall pattern on urban flood control by LID, and can be used for sponge city design in other part of China.

Individual risk perception and empirical social structures shape the dynamics of infectious disease outbreaks.

The dynamics of a spreading disease and individual behavioral changes are entangled processes that have to be addressed together in order to effectively manage an outbreak. Here, we relate individual risk perception to the adoption of a specific set of control measures, as obtained from an extensive large-scale survey performed via Facebook—involving more than 500,000 respondents from 64 countries—showing that there is a “one-to-one” relationship between perceived epidemic risk and compliance with a set of mitigation rules. We then develop a mathematical model for the spreading of a disease—sharing epidemiological features with COVID-19—that explicitly takes into account non-compliant individual behaviors and evaluates the impact of a population fraction of infectious risk-deniers on the epidemic dynamics. Our modeling study grounds on a wide set of structures, including both synthetic and more than 180 real-world contact patterns, to evaluate, in realistic scenarios, how network features typical of human interaction patterns impact the spread of a disease. In both synthetic and real contact patterns we find that epidemic spreading is hindered for decreasing population fractions of risk-denier individuals. From empirical contact patterns we demonstrate that connectivity heterogeneity and group structure significantly affect the peak of hospitalized population: higher modularity and heterogeneity of social contacts are linked to lower peaks at a fixed fraction of risk-denier individuals while, at the same time, such features increase the relative impact on hospitalizations with respect to the case where everyone correctly perceive the risks.

Polysubstance use poisoning deaths in Canada: an analysis of trends from 2014 to 2017 using mortality data

BackgroundOver the past decade, rates of drug poisoning deaths have increased dramatically in Canada. Current evidence suggests that the non-medical use of synthetic opioids, stimulants and patterns of polysubstance use are major factors contributing to this increase.MethodsCounts of substance poisoning deaths involving alcohol, opioids, other central nervous system (CNS) depressants, cocaine, and CNS stimulants excluding cocaine, were acquired from the Canadian Vital Statistics Death Database (CVSD) for the years 2014 to 2017. We used joinpoint regression analysis and the Cochrane-Armitage trend test for proportions to examine changes over time in crude mortality rates and proportions of poisoning deaths involving more than one substance.ResultsBetween 2014 and 2017, the rate of substance poisoning deaths in Canada almost doubled from 6.4 to 11.5 deaths per 100,000 population (Average Annual Percent Change, AAPC: 23%, p < 0.05). Our analysis shows this was due to increased unintentional poisoning deaths (AAPC: 26.6%, p < 0.05) and polysubstance deaths (AAPC: 23.0%, p < 0.05). The proportion of unintentional poisoning deaths involving polysubstance use increased significantly from 38% to 58% among males (p < 0.0001) and 40% to 55% among females (p < 0.0001). Polysubstance use poisonings involving opioids and CNS stimulants (excluding cocaine) increased substantially during the study period (males AAPC: 133.1%, p < 0.01; females AAPC: 118.1%, p < 0.05).ConclusionsIncreases in substance-related poisoning deaths between 2014 and 2017 were associated with polysubstance use. Increased co-use of stimulants with opioids is a key factor contributing to the epidemic of opioid deaths in Canada.

Temporal and spatial variations in benthic nitrogen cycling in a temperate macro-tidal coastal ecosystem: Observation and modeling

We used field observations, laboratory measurements and a reactive transport model (RTM) to investigate temporal and spatial variations in benthic nitrogen (N) cycling in the eutrophic temperate macro-tidal Vilaine Bay (VB), France. A time series of benthic flux measurements and pore-water profiles of dissolved inorganic N (DIN: ammonium, nitrate, nitrite) and dissolved organic N (DON) was conducted at a single station between April and September 2015 (six times). A spatial investigation of the benthic fluxes was performed in July 2016 at this station and three other stations in the VB. All measurements were accompanied by a large panel of physical, chemical and biological descriptors in the water column. In 2015, benthic ammonium fluxes at the monitoring station varied between 75 μmol m−2 h−1 in spring and were less than 10 μmol m−2 h−1 in summer. The benthic DON fluxes co-varied with the ammonium fluxes, ranging from 100 μmol m−2 h−1 in spring to zero in summer. In the summer of 2016, a phytoplankton bloom occurred and as a result the benthic ammonium and DON fluxes reached higher values than in the spring of 2015, accompanied by bottom water hypoxia at one measured station. Benthic nitrate and nitrite fluxes varied between −31 (towards the sediments) and 22 μmol m−2 h−1 and were explained by the bottom water concentration and nitrification rates. After fitting the existing pore-water profiles, the applied RTM correctly simulated the temporal and spatial variations in the benthic DIN fluxes and predicted that a large part of the deposited organic matter (OM) is remineralized aerobically at the sediment-water interface (SWI). The overall results showed a synthetic pattern of benthic N cycling in the VB, based on the occurrence of diatom blooms as the main source of OM in the sediments. The rapid decomposition of this deposited diatom material at the SWI releases large amounts of ammonium and DON to the water column and rapidly consumes oxygen at the sediment surface. When blooms occur in summer, their decomposition can be followed by hypoxia/anoxia in the bottom water. When blooms are absent, benthic N fluxes are weak and mainly fed by the diffusion from the pore-water. By integrating the present results in a 3D ecological model, it should be possible to more accurately predict the development of bottom water hypoxia in the VB.