Massive Scale Research Articles

The Depiction of Narcissistic Personality Disorder of Elizabeth Halsey as Found in "Bad Teacher" Movie

Narcis Narcisism gained attention and discussion in recent years. This was caused by the rise of social media facilitating self-promotion and self-expression on a massive scale so that narcissistic behaviours can be seen more and more obvious. It could become a problem when they were excessive, inflexible, and maladaptive, leading to harm or distress to individuals or those around them. These extreme traits can be classified as Narcissistic Personality Disorder (NPD). This study aimed to describe the narcissistic's trait of Elizabeth Halsey in Bad Teacher Movie. Elizabeth Halsey worked as a teacher who should demonstrate empathy by understanding her students' perspectives, needs, and emotions, how she can creates a safe and supportive learning environment where students feel valued and heard but her character as a teacher showed narcissistic traits that are the opposite of what a teacher should have. This research used a psychoanalysis approach. The method of this research was qualitative method. In collecting the data, the researchers watched the movie, marking and classifying in what kinds of types of traits that the Elizabeth Halsey had. In analyzing the data, the researchers used descriptive method firstly by identifying all the statements that describe the types of the traits faced by Elizabeth Halsey, and then analyzed the data based on the finding. The results of this study found that there were four of nine traits of narcissistic in Elizabeth Halsey's Character. The first is entitlement trait, she only thought about herself, she did not have any shame on herself, she still asked money from her ex fiance. The second was exploitative traits, in this case, Elizabeth Halsey took advantage on many people, she would do everything without thinking's other feelings and opinion. The third was lack of empathy, she did not showed any empathy for her students who got bullied in front of her, and the last was envy trait, she did not accept the reality that the wealthy man fell in love with her enemy at school, she arranged everything to make her enemy kicked out from school.

Investigating the Impact of the of Technological Developments, Environmental Quality, and Economy on Tourism Receipts in Indonesia

This paper uses a panel technique to investigate the relationship between Technological Developments, Environmental, and GDP Quality on Tourism Receipts in Indonesia (including 33 provinces) for the 2011-2022 period. As a country with rich natural, cultural and ancestral heritage, Indonesia has the potential to attract foreign tourists. Especially with technological advancements that have made tourism promotion possible on a massive scale. On the other hand, an increase in the number of tourists has an impact on the environment as explained in the theory of the Environmental Kuznet Curve (EKC). The research method used is Vector Error Correction Model (VECM) model and Granger causality test. Environmental quality is measured through the Environmental Quality Index indicator. Meanwhile, technology development is measured through the Information and Communication Technology Development Index. The research results explain that Economy, Environmental Quality, and Technological Developments have a positive impact on Tourism Receipts in Indonesia. Therefore, it is important for the government to utilize digital technology to introduce Indonesia's tourism wealth. The increase in tourists must also be accompanied by efforts to protect the environment so that it does not have a negative impact on environmental damage.

Managing the remediation strategy of contaminated megasites using field-scale calibration of geo-electrical imaging with chemical monitoring

Groundwater contamination is a threat to drinking water resources and ecosystems. Remediation by injection of chemical reagents into the aquifer may be preferred to excavation to reduce cost and environmental footprint. Yet, successful remediation requires complete contact between contamination and reagents. Subsurface heterogeneities are often responsible for diffusion into low-permeable zones, which may inhibit this contact. Monitoring the spatial distribution of injected reagents over time is crucial to achieve complete interaction. Source zone contamination at megasites is particularly challenging to remediate and monitor due to the massive scale and mixture of contaminants. Source zone remediation at Kærgård Plantation megasite (Denmark) is monitored here, with a new methodology, using high-resolution cross-borehole electrical resistivity tomography (XB-ERT) imaging calibrated by chemical analyses on groundwater samples. At this site, high levels of toxic non-aqueous phase liquids (NAPL) are targeted by in-situ chemical oxidation using activated persulfate. It may take numerous injection points with extensive injection campaigns to distribute reagents, which requires an understanding of how reagent may transport within the aquifer. A geophysical (XB-ERT) monitoring network of unprecedented size was installed to identify untreated zones and help manage the remediation strategy. The combination of spatially continuous geophysical information with discrete but precise chemical information, allowed detailed monitoring of sulfate distribution, produced during persulfate activation. Untreated zones identified in the first remediation campaign were resolved in the second campaign. The monitoring allowed adjusting the number of injection screens and the injection strategy from one campaign to the next, which resulted in better persulfate distribution and contaminant degradation in the second campaign. Furthermore, geophysical transects repeated over the timespan of a remediation campaign allowed high-resolution time-lapse imaging of reagent transport, which could in the future improve the predictability of transport models, compared to only using on a-priori assumptions of the hydraulic conductivity field.

The convergence of total-factor energy efficiency across Chinese cities: A distribution dynamics approach

Improving energy efficiency is considered the most direct route to reducing carbon emissions on a massive scale. To examine the long-run trend of China's energy efficiency, this paper employs a distribution dynamics approach to analyze the convergence of total-factor energy efficiency (TFEE) across 286 Chinese cities between 2002 and 2014. The result suggests the existence of convergence, which comes more from the efficiency declines in the high TFEE cities rather than the efficiency gains in the low TFEE cities. Despite the fact that a few cities have performed well in energy efficiency, the majority of cities have converged to a low-efficiency point. Further analyses show that the inefficiency was attributed to inefficient energy use in small- and medium-sized cities. Our analysis provides strong scientific support for China's ongoing energy conservation and environmental protection policies.

De la „Cortina de fier” la „Cortina de nailon”.România și Marea Britanie în anii destinderii, 1960-1970

The article analyses the evolution of Romanian-British relations in the context of the détente. The mirror image of general and special interests, the way in which the diplomatic agenda of the two countries was structured in the light of the dynamics of the Cold War, and the transformations the world went through in the 1960s show that British interests toward Romania must be seen in the broader context of the redefinition of the UK’s foreign policy towards the countries of Central and South-Eastern Europe. If at the beginning of the Cold War, relations between Romania and the UK were completely frozen, limited to minimal diplomatic representation, in the second half of the 1960s, a completely different dynamic can be detected. The opening to the East was dictated by both specific interests and the traditional lines of British diplomacy in the region. On the other hand, access to technology was a priority for the Romanian Government and, as can be seen from the evolution of economic data, Romania bought industrial plant and equipment from the British market on a massive scale. On the other hand, while becoming an important partner in technology transfer, Bucharest pushed for trade liberalisation and allowing Romanian products access to the British market.

Analyzing E-commerce Trends through Big Data

Big data is a new driver of the world economic and societal changes. The world’s data collection is reaching a tipping point for major technological changes that can bring new ways in decision making, managing our health, cities, finance and education. While the data complexities are increasing including data’s volume, variety, velocity and veracity, the real impact hinges on our ability to uncover the `value’ in the data through Big Data Analytics technologies. Big Data Analytics poses a grand challenge on the design of highly scalable algorithms and systems to integrate the data and uncover large hidden values from datasets that are diverse, complex, and of a massive scale.

Smartic: A smart tool for Big Data analytics and IoT

The Internet of Things (IoT) is leading the physical and digital world of technology to converge. Real-time and massive scale connections produce a large amount of versatile data, where Big Data comes into the picture. Big Data refers to large, diverse sets of information with dimensions that go beyond the capabilities of widely used database management systems, or standard data processing software tools to manage within a given limit. Almost every big dataset is dirty and may contain missing data, mistyping, inaccuracies, and many more issues that impact Big Data analytics performances. One of the biggest challenges in Big Data analytics is to discover and repair dirty data; failure to do this can lead to inaccurate analytics results and unpredictable conclusions. Different imputation methods were employed in the experimentation with various missing value imputation techniques, and the performances of machine learning (ML) models were compared. A hybrid model that integrates ML and sample-based statistical techniques for missing value imputation is being proposed. Furthermore, the continuation involved the dataset with the best missing value imputation, chosen based on ML model performance for subsequent feature engineering and hyperparameter tuning. K-means clustering and principal component analysis were applied in our study. Accuracy, the evaluated outcome, improved dramatically and proved that the XGBoost model gives very high accuracy at around 0.125 root mean squared logarithmic error (RMSLE). To overcome overfitting, K-fold cross-validation was implemented.

Precision Leak Detection in Supermarket Refrigeration Systems Integrating Categorical Gradient Boosting with Advanced Thresholding

Supermarket refrigeration systems are integral to food security and the global economy. Their massive scale, characterized by numerous evaporators, remote condensers, miles of intricate piping, and high working pressure, frequently leads to problematic leaks. Such leaks can have severe consequences, impacting not only the profits of the supermarkets, but also the environment. With the advent of Industry 4.0 and machine learning techniques, data-driven automatic fault detection and diagnosis methods are becoming increasingly popular in managing supermarket refrigeration systems. This paper presents a novel leak-detection framework, explicitly designed for supermarket refrigeration systems. This framework is capable of identifying both slow and catastrophic leaks, each exhibiting unique behaviours. A noteworthy feature of the proposed solution is its independence from the refrigerant level in the receiver, which is a common dependency in many existing solutions for leak detection. Instead, it focuses on parameters that are universally present in supermarket refrigeration systems. The approach utilizes the categorical gradient boosting regression model and a thresholding algorithm, focusing on features that are sensitive to leaks as target features. These include the coefficient of performance, subcooling temperature, superheat temperature, mass flow rate, compression ratio, and energy consumption. In the case of slow leaks, only the coefficient of performance shows a response. However, for catastrophic leaks, all parameters except energy consumption demonstrate responses. This method detects slow leaks with an average F1 score of 0.92 within five days of occurrence. The catastrophic leak detection yields F1 scores of 0.7200 for the coefficient of performance, 1.0000 for the subcooling temperature, 0.4118 for the superheat temperature, 0.6957 for the mass flow rate, and 0.8824 for the compression ratio, respectively.

Reversibility of Greenland ice sheet mass loss under artificial carbon dioxide removal scenarios

With ongoing anthropogenic CO2 emissions, the Greenland ice sheet (GIS) approaches critical thresholds of inevitable, long-term mass loss. Future technologies might be able to efficiently remove CO2 from the atmosphere and thereby cool down our planet. We explore whether and to what extent a realization of this concept could lead to a regrowth of the GIS once it has partly melted. Using the fully coupled Earth system model of intermediate complexity CLIMBER-X, emission pulses between 0 and 4000 GtC are released into the atmosphere, and after 1 kyr, 2 kyr, and 5 kyr, the atmospheric CO2 concentration is reduced back to its pre-industrial value. We find that independent of a specific trajectory, once the southern part of the GIS has partly melted with a total mass loss of more than 0.4 m sea level equivalent, regrowth is inhibited. Uncertainties preclude determination of precise thresholds, but model results indicate that cumulative industrial-era emissions approaching 1000–1500 GtC and beyond increasingly risk irreversible mass loss of the GIS. Once this threshold is passed, artificial atmospheric carbon removal would need to be utilised within the next centuries at massive scale. Beyond that, artificial atmospheric carbon removal has limited abilities to avoid long-term mass loss of the GIS. In conclusion, keeping cumulative anthropogenic emissions below 1000–1500 GtC is the only safe way to avoid irreversible mass loss of the GIS.

PopShift: A Thermodynamically Sound Approach to Estimate Binding Free Energies by Accounting for Ligand-Induced Population Shifts from a Ligand-Free Markov State Model.

Obtaining accurate binding free energies from in silico screens has been a long-standing goal for the computational chemistry community. However, accuracy and computational cost are at odds with one another, limiting the utility of methods that perform this type of calculation. Many methods achieve massive scale by explicitly or implicitly assuming that the target protein adopts a single structure, or undergoes limited fluctuations around that structure, to minimize computational cost. Others simulate each protein-ligand complex of interest, accepting lower throughput in exchange for better predictions of binding affinities. Here, we present the PopShift framework for accounting for the ensemble of structures a protein adopts and their relative probabilities. Protein degrees of freedom are enumerated once, and then arbitrarily many molecules can be screened against this ensemble. Specifically, we use Markov state models (MSMs) as a compressed representation of a protein's thermodynamic ensemble. We start with a ligand-free MSM and then calculate how addition of a ligand shifts the populations of each protein conformational state based on the strength of the interaction between that protein conformation and the ligand. In this work we use docking to estimate the affinity between a given protein structure and ligand, but any estimator of binding affinities could be used in the PopShift framework. We test PopShift on the classic benchmark pocket T4 Lysozyme L99A. We find that PopShift is more accurate than common strategies, such as docking to a single structure and traditional ensemble docking─producing results that compare favorably with alchemical binding free energy calculations in terms of RMSE but not correlation─and may have a more favorable computational cost profile in some applications. In addition to predicting binding free energies and ligand poses, PopShift also provides insight into how the probability of different protein structures is shifted upon addition of various concentrations of ligand, providing a platform for predicting affinities and allosteric effects of ligand binding. Therefore, we expect PopShift will be valuable for hit finding and for providing insight into phenomena like allostery.

Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm

Parallel power loads anomalies are processed by a fast-density peak clustering technique that capitalizes on the hybrid strengths of Canopy and K-means algorithms all within Apache Mahout’s distributed machine-learning environment. The study taps into Apache Hadoop’s robust tools for data storage and processing, including HDFS and MapReduce, to effectively manage and analyze big data challenges. The preprocessing phase utilizes Canopy clustering to expedite the initial partitioning of data points, which are subsequently refined by K-means to enhance clustering performance. Experimental results confirm that incorporating the Canopy as an initial step markedly reduces the computational effort to process the vast quantity of parallel power load abnormalities. The Canopy clustering approach, enabled by distributed machine learning through Apache Mahout, is utilized as a preprocessing step within the K-means clustering technique. The hybrid algorithm was implemented to minimise the length of time needed to address the massive scale of the detected parallel power load abnormalities. Data vectors are generated based on the time needed, sequential and parallel candidate feature data are obtained, and the data rate is combined. After classifying the time set using the canopy with the K-means algorithm and the vector representation weighted by factors, the clustering impact is assessed using purity, precision, recall, and F value. The results showed that using canopy as a preprocessing step cut the time it proceeds to deal with the significant number of power load abnormalities found in parallel using a fast density peak dataset and the time it proceeds for the k-means algorithm to run. Additionally, tests demonstrate that combining canopy and the K-means algorithm to analyze data performs consistently and dependably on the Hadoop platform and has a clustering result that offers a scalable and effective solution for power system monitoring.

The Russian Diaspora in Africa. History and Modernity

The article is devoted to the history of the Russian diaspora on the African continent. Russia is a country with a relatively long emigration tradition. Having taken on a massive scale at the end of the XIX century, emigration from Russia continues to this day, representing a complex and multifaceted phenomenon that has always had a great impact on the life of Russian society, regardless of the position that the state authorities took in relation to it at one time or another in history. The article traces its formation at different stages – from its inception at the beginning of the twentieth century to the middle of the twentieth century, marked by the beginning of the national liberation struggle of African peoples for their independence. An independent section of the article is devoted to the current state of the Russian Diaspora in the countries of the African continent., which highlights the problems of the formation of the Russian Diaspora at the present stage, comprehensively analyzes the civil and socio–cultural characteristics of the female Russian contingent - the main source of replenishment of the Russian Diaspora in modern Africa, the specifics of this group of compatriots and their descendants from Afro- Russian marriages de jure and de facto, its role in the social and cultural life of recipient countries and relations with the historical homeland. the unique potential of the “soft power” of the Russian Federation, concluded in its interaction with Russian-speaking communities on the African continent.

Genomic data integration tutorial, a plant case study

BackgroundThe ongoing evolution of the Next Generation Sequencing (NGS) technologies has led to the production of genomic data on a massive scale. While tools for genomic data integration and analysis are becoming increasingly available, the conceptual and analytical complexities still represent a great challenge in many biological contexts.ResultsTo address this issue, we describe a six-steps tutorial for the best practices in genomic data integration, consisting of (1) designing a data matrix; (2) formulating a specific biological question toward data description, selection and prediction; (3) selecting a tool adapted to the targeted questions; (4) preprocessing of the data; (5) conducting preliminary analysis, and finally (6) executing genomic data integration.ConclusionThe tutorial has been tested and demonstrated on publicly available genomic data generated from poplar (Populus L.), a woody plant model. We also developed a new graphical output for the unsupervised multi-block analysis, cimDiablo_v2, available at https://forgemia.inra.fr/umr-gdec/omics-integration-on-poplar, and allowing the selection of master drivers in genomic data variation and interplay.

Changes in substance use, recovery, and quality of life during the initial phase of the COVID-19 pandemic.

The COVID-19 pandemic disrupted lives on a massive scale. While the pandemic appeared to worsen mental health outcomes broadly, its effects on alcohol/substance use and recovery are unclear. Many studies convolved the sociopolitical unrest beginning in May 2020 with the pandemic. We assessed pandemic-related changes in substance use, recovery involvement, and quality of life among US adults at two specified time periods that isolated pandemic effects from potentially confounding sociopolitical factors. We tested the following hypotheses: the pandemic and consequent policies (1) increased use of alcohol and illicit substances in active users; (2) increased use of alcohol/substances among people in early recovery; (3) reduced participation in recovery activities among those in early recovery, and that (4) use amount and use events correlated with impulsivity in both groups and that (5) substance use and abstinence correlated with resilience. We recruited 1,685 participants through Amazon's Mechanical Turk (MTurk). We assessed demographics, quality of life, alcohol/substance use, recovery activities, and measures of impulsivity and resilience at two time points, pre-pandemic and (early) during-pandemic. Only n = 45 (Active Users; males n = 32) and n = 34 (Recovery; males n = 20) passed data quality checks and were included in the primary analyses. Among Active Users, weekly alcohol consumption and days spent using alcohol and illicit substances decreased during the pandemic. Resilience negatively correlated with pandemic-related substance use in early recovering participants. Significant reduction in the quality of life was coincident with a trend of lower recovery activity participation (31% decline) during the pandemic. The reduced alcohol/substance use and participation in recovery activities might be expected from conditions that promote social isolation. The high prevalence of low-quality data from MTurk cautions for careful use of online data sourcing.

A multi-million-year natural experiment: Comparative genomics on a massive scale and its implications for human health.

Improving the diversity and quality of genome assemblies for non-human mammals has been a long-standing goal of comparative genomics. The last year saw substantial progress towards this goal, including the release of genome alignments for 240 mammals and nearly half the primate order. These resources have increased our ability to identify evolutionarily constrained regions of the genome, and together strongly support the importance of these regions to biomedically relevant trait variation in humans. They also provide new strategies for identifying the genetic basis of changes unique to individual lineages, illustrating the value of evolutionary comparative approaches for understanding human health.

Reassessing the need for carbon dioxide removal: moral implications of alternative climate target pathways

Abstract Non-technical summary Scenarios compatible with the Paris agreement's temperature goal of 1.5 °C involve carbon dioxide removal measures – measures that actively remove CO2 from the atmosphere – on a massive scale. Such large-scale implementations raise significant ethical problems. Van Vuuren et al. (2018), as well as the current IPCC scenarios, show that reduction in energy and or food demand could reduce the need for such activities. There is some reluctance to discuss such societal changes. However, we argue that policy measures enabling societal changes are not necessarily ethically problematic. Therefore, they should be discussed alongside techno-optimistic approaches in any kind of discussions about how to respond to climate change. Technical summary The 1.5 °C goal has given impetus to carbon dioxide removal (CDR) measures, such as bioenergy combined with carbon capture and storage, or afforestation. However, land-based CDR options compete with food production and biodiversity protection. Van Vuuren et al. (2018) looked at alternative pathways including lifestyle changes, low-population projections, or non-CO2 greenhouse gas mitigation, to reach the 1.5 °C temperature objective. Underlined by the recently published IPCC AR6 WGIII report, they show that demand-side management measures are likely to reduce the need for CDR. Yet, policy measures entailed in these scenarios could be associated with ethical problems themselves. In this paper, we therefore investigate ethical implications of four alternative pathways as proposed by Van Vuuren et al. (2018). We find that emission reduction options such as lifestyle changes and reducing population, which are typically perceived as ethically problematic, might be less so on further inspection. In contrast, options associated with less societal transformation and more techno-optimistic approaches turn out to be in need of further scrutiny. The vast majority of emission reduction options considered are not intrinsically ethically problematic; rather everything rests on the precise implementation. Explicitly addressing ethical considerations when developing, advancing, and using integrated assessment scenarios could reignite debates about previously overlooked topics and thereby support necessary societal discourse. Social media summary Policy measures enabling societal changes are not necessarily as ethically problematic as commonly presumed and reduce the need for large-scale CDR.

International Collaborative Responses to Transnational Cybercrime: how China Strengthen its Capacity to Combat Transnational Cybercrime

The borderless nature of the Internet has greatly facilitated the growth of crime in cyberspace. Cybercrime today has caused huge economic losses worldwide and is violating the right to privacy on a massive scale. The consequences of cybercrime are serious and often occur in different countries and regions. The priority of territorial jurisdiction has been affected by transnational cybercrime., and its convenient application has been lost when the results of the crime are generalized. The basis of subjective territoriality and objective territoriality would be unclear. The abstract cross-border nature of cybercrime has led to frequent conflicts of jurisdiction among States and may even lead to the legal hegemony of cyber-technology powerhouses. The traditional extradition mechanism has gradually failed to respond to the interstate demand to combat transnational cybercrime. In recent years, China has not only introduced strategic policies at the national level to safeguard cybersecurity and combat cybercrime, but has also made progress in criminal legislation, administrative legislation and international cooperation legislation. China has also accumulated experience in police cooperation to combat transnational cybercrime. In order to better cooperate with other countries in international cybercrime , China should actively participate in the formulation of international conventions and regional multilateral agreements on the punishment of cybercrime. It should also further improve its domestic legislation.

CURRENT APPLICATIONS FOR TEACHING MATHEMATICS AT A SECONDARY SPECIALIZED EDUCATIONAL INSTITUTION

Today, the development of digital technologies has received a massive scale, and we are faced with this in allaspects of our lives. More than 4 billion people around the globe have access to the world Wide Web and according to estimates, almost every inhabitant of our planet has some kind of mobile device and half of them are smartphones. This development has influenced the way people interact with other people, learn the news and see the world around them. This is especially true of the learning process, which today has transformed from traditional to digital.

Massive Scale Data Analytics at LCLS-II

The increasing volumes of data produced at light sources such as the Linac Coherent Light Source (LCLS) enable the direct observation of materials and molecular assemblies at the length and timescales of molecular and atomic motion. This exponential increase in the scale and speed of data production is prohibitive to traditional analysis workflows that rely on scientists tuning parameters during live experiments to adapt data collection and analysis. User facilities will increasingly rely on the automated delivery of actionable information in real time for rapid experiment adaptation which presents a considerable challenge for data acquisition, data processing, data management, and workflow orchestration. In addition, the desire from researchers to accelerate science requires rapid analysis, dynamic integration of experiment and theory, the ability to visualize results in near real-time, and the introduction of ML and AI techniques. We present the LCLS-II Data System architecture which is designed to address these challenges via an adaptable data reduction pipeline (DRP) to reduce data volume on-thefly, online monitoring analysis software for real-time data visualization and experiment feedback, and the ability to scale to computing needs by utilizing local and remote compute resources, such as the ASCR Leadership Class Facilities, to enable quasi-real-time data analysis in minutes. We discuss the overall challenges facing LCLS, our ongoing work to develop a system responsive to these challenges, and our vision for future developments.

MCMC: Multi-Constrained Model Compression via One-Stage Envelope Reinforcement Learning.

Model compression methods are being developed to bridge the gap between the massive scale of neural networks and the limited hardware resources on edge devices. Since most real-world applications deployed on resource-limited hardware platforms typically have multiple hardware constraints simultaneously, most existing model compression approaches that only consider optimizing one single hardware objective are ineffective. In this article, we propose an automated pruning method called multi-constrained model compression (MCMC) that allows for the optimization of multiple hardware targets, such as latency, floating point operations (FLOPs), and memory usage, while minimizing the impact on accuracy. Specifically, we propose an improved multi-objective reinforcement learning (MORL) algorithm, the one-stage envelope deep deterministic policy gradient (DDPG) algorithm, to determine the pruning strategy for neural networks. Our improved one-stage envelope DDPG algorithm reduces exploration time and offers greater flexibility in adjusting target priorities, enhancing its suitability for pruning tasks. For instance, on the visual geometry group (VGG)-16 network, our method achieved an 80% reduction in FLOPs, a 2.31× reduction in memory usage, and a 1.92× acceleration, with an accuracy improvement of 0.09% compared with the baseline. For larger datasets, such as ImageNet, we reduced FLOPs by 50% for MobileNet-V1, resulting in a 4.7× faster speed and 1.48× memory compression, while maintaining the same accuracy. When applied to edge devices, such as JETSON XAVIER NX, our method resulted in a 71% reduction in FLOPs for MobileNet-V1, leading to a 1.63× faster speed, 1.64× memory compression, and an accuracy improvement.