Miracle-dz/UniFAR-FTUs huggingface dataset:
Meandering Subtropical Jet and Precipitation over Summertime East Asia and the Northwestern Pacific

AbstractIt has been revealed that in summertime, precipitation is enhanced to the south of the upper-level tropopausal potential vorticity contours, which are accompanied by instantaneous jets, over the eastern coastal region of China to the northwestern Pacific. It is frequently exhibited as precipitation bands ranging in size from over a thousand to several thousands of kilometers long. In this study, an analysis was conducted to quantify the relationship depending on the phase of upper-level disturbances. With composite analysis, it is shown that the enhancement along the contours occurs at all phases; it occurs not only to the east but also to the west of the upper-level troughs, although it is weaker. The midtropospheric distributions of upwelling and the Q-vector convergence are collocated with the precipitation enhancement, suggesting the importance of dynamical induction by geostrophic flow at all phases. The effects of upper-level disturbances and low-level jets (LLJs) with a southerly component .
Exploring users' sense of safety in public using an Augmented Reality application

Nowadays, Augmented Reality (AR) is available on almost all smartphones creating some exciting interaction opportunities but also challenges. For example, already after the famous AR app Pokemon GO was released in July 2016, numerous accidents related to the use of the app were reported by users. At the same time, the spread of AR can be noticed in the tourism industry, enabling tourists to explore their surroundings in new ways but also exposing them to safety issues. This preliminary study explores users' sense of safety when manipulating the amount and UI elements visualization parameters of Point of Interest (POI) markers in a developed AR application. The results show that the amount of POI markers that are displayed is significant for participants' sense of safety. The influence of manipulating UI elements in terms of “transparency”, “color”, and “size” cannot be proven. Nevertheless, most tested people stated that manipulating transparency and size somehow influences their sense of safety, so a closer look at them should be taken in future studies.
Signal amplification properties of Electrolyte-Gated Organic Field-Effect Transistors

Electrolyte-gated organic field-effect transistors (EGOFETs) are key enabling elements for the development of bio-electronic devices, therefore it is imperative to increase the knowledge on how EGOFETs work, not only in stationary conditions but also in the time and frequency domain. In this framework, the EGOFET small-signal circuit is presented and the recording of action potentials (APs) is simulated by analyzing EGOFETs as trans-conductance amplifiers as well as voltage amplifiers. It is shown that when operating as a trans-conductance amplifier, EGOFETs ideally allow to well preserve the shape of the recorded AP but they amplify high-frequency noise thus heavily worsening the signal-to-noise ratio (SNR). Whereas, when implemented in a voltage amplifier, EGOFETs allow a substantial SNR improvement, but introduce some distortion in the recorded signal.
EMTP Simulation Model for Mutual Grounding Resistance considering Propagation Time

A grounding conductor (GC) is represented by a distributed-parameter line. Therefore, the voltage and current propagating on the GC should be treated as surge phenomena. On the other hand, grounding resistances are noted by constant matrix without propagation time between grounding electrodes (GEs). A GC is often used to connect GEs. Induced voltages are raised on the GEs before the voltage propagating along the GC reaches if the propagation time of mutual grounding resistance is ignored. Thus, accurate surge analysis for large grounding system should consider the propagation time between GEs. The author has proposed a simulation model of mutual grounding resistance considering the propagation time. This paper describes the proposed model to be used in the EMTP.
Dynamic Tensor Linearization and Time Slicing for Efficient Factorization of Infinite Data Streams

Streaming tensor factorization is an effective tool for unsupervised analysis of time-evolving sparse data, which emerge in many critical domains such as cybersecurity and trend analysis. In contrast to traditional tensors, time-evolving tensors demonstrate extreme sparsity and sparsity variation over time, resulting in irregular memory access and inefficient use of parallel computing resources. Additionally, due to the prohibitive cost of dynamically generating compressed sparse tensor formats, the state-of-the-art approaches process streaming tensors in a raw form that fails to capture data locality and suffers from high synchronization cost. To address these challenges, we propose a new dynamic tensor linearization framework that quickly encodes streaming multi-dimensional data on-the-fly in a compact representation, which has substantially lower memory usage and higher data reuse and parallelism than the original raw data. This is achieved by using a spatial sketching algorithm that keeps all incoming nonzero elements but remaps them into a tensor sketch with considerably reduced multi-dimensional image space. Moreover, we present a dynamic time slicing mechanism that uses variable-width time slices (instead of the traditional fixed-width) to balance the frequency of factor updates and the utilization of computing resources. We demonstrate the efficacy of our framework by accelerating two high-performance streaming tensor algorithms, namely, CP-stream and spCP-stream, and significantly improve their performance for a range of real-world streaming tensors. On a modern 56-core CPU, our framework achieves 10.3 − 11× and 6.4 − 7.2× geometric-mean speedup for the CP-stream and spCP-stream algorithms, respectively.
Accelerating Distributed Deep Learning Training with Compression Assisted Allgather and Reduce-Scatter Communication

Fully Sharded Data Parallel (FSDP) technology achieves higher performance by scaling out data-parallel training of Deep Learning (DL) models. It shards the model parameters, gradients, and optimizer states of the model among multiple GPUs. Consequently, this requires data-intensive Allgather and Reduce-Scatter communication to share the model parameters, which becomes a bottleneck. Existing schemes that use GPU-aware MPI libraries are highly prone to saturating the interconnect bandwidth. Therefore, integrating GPU-based compression into MPI libraries has proven efficient to achieve faster training time. In this paper, we propose an optimized Ring algorithm of Allgather and Reduce-Scatter collectives that encompass an efficient collective-level online compression scheme. At the microbenchmark level, Allgather achieves benefits of up to 83.6% and 30.3% compared to the baseline and existing point-to-point-based compression in a state-of-the-art MPI library on modern GPU clusters. Reduce-Scatter achieves 88.1% and 40.6% compared to baseline and point-to-point compression, respectively. For distributed DL training with PyTorch-FSDP, our approach yields 31.7% faster training than the baseline, and up to 12.5% compared to the existing point-to-point-based compression while maintaining similar accuracy.
Sliding Mode and Super-Twisting Sliding Mode Control Structures for SMA Actuators*

This paper aims to design the optimal sliding mode and super twisting sliding mode controllers for the nonlinear Shape Memory Alloy (SMA) wire actuators. The parameters of the proposed controllers are optimally tuned using the metaheuristic Grey Wolf optimizer algorithm and a comparative analysis is carried out. All control structures are validated by simulations using an accurate evolved Takagi-Sugeno-Kang fuzzy model of SMA wire actuators and their control performance is evaluated.
Essays on Robust Model Selection and Model Averaging for Linear Models

Model selection is central to all applied statistical work. Selecting the variables for use in a regression model is one important example of model selection. This thesis is a collection of essays on robust model selection procedures and model averaging for linear regression models. In the first essay, we propose robust Akaike information criteria (AIC) for MMestimation and an adjusted robust scale based AIC for M and MM-estimation. Our proposed model selection criteria can maintain their robust properties in the presence of a high proportion of outliers and the outliers in the covariates. We compare our proposed criteria with other robust model selection criteria discussed in previous literature. Our simulation studies demonstrate a significant outperformance of robust AIC based on MM-estimation in the presence of outliers in the covariates. The real data example also shows a better performance of robust AIC based on MM-estimation. The second essay focuses on robust versions of the “Least Absolute Shrinkage and Selection Operator” (lasso). The adaptive lasso is a method for performing simultaneous parameter estimation and variable selection. The adaptive weights used in its penalty term mean that the adaptive lasso achieves the oracle property. In this essay, we propose an extension of the adaptive lasso named the Tukeylasso. By using Tukey’s biweight criterion, instead of squared loss, the Tukeylasso is resistant to outliers in both the response and covariates. Importantly, we demonstrate that the Tukey-lasso also enjoys the oracle property. A fast accelerated proximal gradient (APG) algorithm is proposed and implemented for
A Review of Colonial Pipeline Ransomware Attack

In April, 2021 a ransomware attack occurred on Colonial Pipeline. The details of this attack point to the hacking group DarkSide taking advantage of the design flaws in the Colonial Pipeline network. After extensive research, the specificity of this attack was related to VPN access through an unused account. In order to regain control of their systems, Colonial Pipeline paid the attackers. This act has only created an incentive for similar attacks across the nation. The events of this attack have impacted both the United States, where the company is located and the world in a very negative way. This paper analyzes the attack with published data and provides a detailed attack methodology. From the attack methodology the focus then shifts into the impacts that an attack of this caliber had, on both the company, the United States, and the world. We then outline possible defense strategies against this type of ransomware attack, analyzing what could have been done to prevent this attack from happening. In addition, we also detail how companies can prevent future attacks of this caliber. Finally we wrap up or findings and detail the key takeaways of the entire attack.
SW-LCM: A Scalable and Weakly-supervised Land Cover Mapping Method on a New Sunway Supercomputer

High-resolution land cover mapping (LCM) is an important application for studying and understanding the change of the earth surface. While deep learning (DL) methods demonstrate great potential in analyzing satellite images, they largely depend on massive high-quality labels. This paper proposes SW-LCM, a Scalable and Weakly-supervised two-stage Land Cover Mapping method on a new Sunway Supercomputer. Our method consists of a k-means clustering module as a first stage, and an iterative deep learning module as a second stage. With the k-means module providing a good enough starting point (taking inaccurate results as noisy labels), the deep learning module improves the classification results in an iterative way, without any labelling efforts required for processing large scenarios. To achieve efficiency for country-level land cover mapping, we design a customized data partition scheme and an on-the-fly assembly for k-means. Through careful parallelization and optimization, our k-means module scales to 98,304 computing nodes (over 38 million cores), and provides a sustained performance of 437.56 PFLOPS, in a real LCM task of the entire region of China; the iterative updating part scales to 24,576 nodes, with a performance of 11 PFLOPS. We produce a 10-m resolution land cover map of China, with an accuracy of 83.5% (10-class) or 73.2% (25-class), 7% to 8% higher than best existing products, paving ways for finer land surveys to support sustainability-related applications.
Cyber-Attack Detection by Using Event-Based Control in Multi-Agent Cyber-Physical Systems

In this paper, we investigate event-based multi-agent cyber-physical systems against an intelligent attacker. While event-based control has been used as a more secure approach against cyber-attacks in the literature, we will first discuss how a well-informed attacker with knowledge of the system dynamics can get around the benefits of event-based control. We will then provide a covert event-based attack against multi-agent cyber-physical system. A cyber-attack detection mechanism is then provided to detect these cyber-attacks by using a self-triggered control approach in the system
Switching Overvoltages and Polarity Reversal in Presence of HVDC Circuit Breakers

The recent development of High Voltage Direct Current circuit breakers (HVDC CBs) prototypes may introduce many potential advantages for HVDC links in terms of flexibility and possible meshing. However, introduction of circuit breaker in HVDC links also prompts the need for a detailed assessment of switching overvoltages and polarity reversal, which were not typically of concern for modern voltage source converters-based links, using half bridge modules. The paper deals with the evaluation of switching overvoltages and polarity reversal stresses on overhead and submarine cable HVDC links equipped with dc circuit breakers; the consideration are obviously extendable to all the equipment on the DC side. A simplified theoretical approach is presented, and a comparison is made with transient simulations on the foreseen 500 kV HVDC Italian “Hypergrid”. Results evidence that switching overvoltages and polarity reversals become a significant design factor with the introduction of dc circuit breaker in HVDC links.
An RFID Cartesian Portal Enables SAR-Based Localization for Worker Safety

This paper presents a 3-axis Cartesian Portal moving a UHF-RFID antenna which enables SAR-based localization for worker safety applications. It is part of the SENERGY Project founded by the National Institute for Insurance against Accidents at Work (INAIL). The smart system improves the safety of workers during maintenance activities on machinery assembly by checking allowed/not allowed positions. The experimental setup prepared at the University of Pisa is firstly presented. Then tracking performance is shown in indoor environment, when an arrangement of multiple UHF-RFID tags are simultaneously localized.
Model predictive control strategies for industrial demand response of beverage production lines

Utilizing demand response (DR) in industries reduces the need for expensive utilities like storage or backup plants and renders the electricity market more flexible for industrial sites. This paper proposes an MPC-based approach for such sites to respond online to ancillary service requests and participate in DR by controlling optimally the machine speeds of a given number of production lines. We define centralized and distributed optimization programs to cope with a large number of machines within each line and with ancillary services that require a response within seconds (primary services) to several minutes (secondary services). We demonstrate the proof-of-concept by designing controllers for three beverage production lines consisting of filling machines, labelers, shrink packers, and several conveying belts. The efficiency of the algorithmic solution is illustrated by numerical simulations.
ML based D3 R: Detecting DDoS using Random Forest

DDoS attacks are a major security risk to cloud servers and websites. To defend against these attacks, techniques such as reducing server vulnerabilities can be employed. In this study, the Random Forest algorithm is used to detect and prevent DDoS attacks, enhancing cloud security and minimizing attack damage by collecting network traffic data as input, where the performance of RF is analyzed. Results demonstrate the effectiveness of Random Forest in mitigating DDoS attacks in cloud environments.
2D Fluid-Structure Model of Aortic Valve Using a Derived Muscular Model Equation

F1uid-structure interaction (FSI) models provide promise for the study of normal and disordered heart function, particularly the effect of fluid dynamics on the motion of aortic valve (AV) leaflet. The objective of this study is to examine the impact of AV normal leaflets and AV with stenosis on blood flow by developing 2D FSI computational model to simulate the AV leaflets when interacting with blood motion. The AV leaflets was modelled using a spring-like derived formulation. Furthermore, the blood flow was modeled as an incompressible Navier-Stokes fluid, and the mesh deformation of the fluid and leaflets due to trans-valvular pressure on AV borders and the ensuing leaflets movement was calculated using the Arbitrary Lagrangian Eulerian (ALE) technique. The 2D model was able to reproduce AV leaflets opening profile as a result of the transvalvular pressure on AV. The maximum velocity simulated at the peak inlet velocity (at 0.2 s) was 1.47 m/s, and this value agreed with the reported range (1.1-1.7m/s) from previous studies. Additionally, the simulated maximum flow velocity of the AV model with calcification was 2.5Sm/s and it classifies as AV with mild stenosis based on the reported range from the literature. The developed FSI model provides insights into the impact fluid dynamics on the AV leaflets movement using a spring-like derived formulation.
The Calculation Model for ZnO Varistor Considering Micro-characteristics

The insulation level of power system is based on the overvoltage protection level of arresters. Zinc oxide(ZnO) varistor is widely used as the core element in power arrester and surge suppressor because of its excellent nonlinear voltage-current(V-I) characteristics and high surge energy absorption capability. Electrical characteristics of ZnO varistor are determined by its complex micro-structure and grain boundary characteristics. In this study, the microstructure model of ZnO varistor is established based on Voronoi model. A large-scale nonlinear resistance network equation for solving the macroscopic electrical properties of ZnO varistors is established by using a grain boundary zoning model considering intergranular bypass effect. Aiming at that it is difficult to solve the large-scale nonlinear resistance network equation by using the conventional Newton iterative method, a fast optimization algorithm based on piecewise linearization and differential reconstruction is proposed. The error index of the calculation result is less than 10$^{-3}$ orders of magnitude. Besides, the effects of microstructure parameters, such as spinel content, nonuniformity of grain size and porosity on the electrical properties of ZnO varistors were obtained by calculation.
Impact of prestigious-STEM Education of corporate board members on innovation effort: Evidence from India

This article studies the innovation effort in India through the education of corporate board members obtained from prestigious STEM higher education institutes known as the Indian Institute of Technology (IITs). Our primary aim is to enquire whether firms with director/s having an IIT-Bachelors’ degree in their corporate boards positively impact the firm’s innovation effort. To answer this question, we build a novel dataset merging two micro-level databases: CMIE-Prowess (firm innovation) and NSE-Infobase (board of directors' characteristics). Based on the sample of 6151 Indian firms for 2006-2015, we find that overall, having board members with IIT-Bachelor’s qualifications do enhance innovation efforts to some extent. However, the positive effect on innovation effort becomes more robust when the director has a research degree over their IIT-Bachelors’ degree. The paper highlights that when it comes to innovation efforts, the dominant narrative of relying solely on IIT-STEM elite undergraduate education (IIT-Bachelor’s) is insufficient and should also focus on and prioritize research education.
I NTRODUCING G EOGRAPHY AND R EADING M AP S KILLS TO K INDERGARTEN C HILDREN BY USING L ARGE -S CALE G IANT M APS

The construction of space in children develops gradually, as they grow up and is related both to their environment and to their spatial and/or geographical experiences. According to previous studies, spatial thinking is malleable, and can be developed with the use of appropriate teaching interventions and educational material. Geospatial thinking and reading map skills required to decode map symbols are a relatively new and very interesting topic in kindergarten’s education. Significance of this study is the creation of two large-scale giant maps, laminated, and appropriate to accompany teaching material that can be used in a teaching intervention based on the Greek kindergarten curriculum. The first map (scale of 1:1000) is a map of the city of Mytilene, and the second one (scale of 1:20000) is of the island of Lesvos; both have dimensions 3X4 meters. The purpose of this study is to present the creation of spatial teaching material, so that map skills in Kindergarten education can be cultivated and developed in order to provide kindergarten teachers with large scale-giant maps and encourage them to introduce them to their teaching, as large scale-giant maps are really powerful educational tools for the development of their pupils’ spatial skills.
Digital Technologies and Productivity: A Firm-Level Investigation for Italy

We use three integrated firm-level databases maintained by the Italian Statistical Institute (Istat). In one of them, based on firms’ responses to a detailed Survey on digital adoption, Istat has identified different groups of firms in terms of their pattern of use of digital technologies. Relying on this statistical work, we divide firms in two groups in our empirical investigation: the one with firms that extensively adopt digital technologies and the other with firms that do not. By considering digital adoption in the first group as a treatment, we employ the propensity score matching (PSM) approach combined with a difference-in-differences (DiD) analysis and establish more directly the causal impact of firm’s use of digital technology on productivity. We find that firms with high digital adoption have a rate of variation of labor productivity, between 2015 and 2018, which is, on average, 2.7 percentage points higher than that of firms with low digital adoption. We also detect heterogeneity in this result as the estimated effect is found to be stronger in manufacturing firms, small firms and young firms.