Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Scale Volatility and External Event Analysis of Crude Oil Price Prediction

To study the characteristics of crude oil price fluctuations and analyze the impacts of external events, this paper first employs the CEEMD method to decompose the crude oil historical prices into different components and extracts a market fluctuation, a shock from extreme events, and a long-term trend. And we find that when determining the crude oil prices, the shock from extreme events has become the most important factor. Then we combine the ICSS test with the Chow test to get the structural breaks and analyze extreme event impacts. Finally, considering the interaction between external event impacts and crude oil prices, we establish the models based on VAR, SVM, and structural breaks to predict the crude oil prices, finding that the CEEMD- VAR-SVM model with structural breaks performs best compared to other models we establish.
High-Order Mode Mode-Locked Fiber Laser Based on Few-Mode Saturable Absorber

We presented two kinds of high-order mode (HOM) mode-locked fiber lasers based on few-mode (FM) saturable absorbers for direct HOM oscillation. One is a ring fiber laser based on a carbon nanotube (CNT) saturable absorber (SA), in which the combination of a long period fiber grating (LPFG) and a mode selective coupler (MSC) is used for the cycle of mode conversion between LP01 and LP11 modes. The other is a linear-cavity laser using a FM-based SESAM with the HOM oscillation, in which a MSC is used for mode conversion inside the cavity. The presented mode-locked fiber lasers can achieve the saturable absorption of the HOM and the cylindrical vector beams (CVBs). And we also compared the nonlinear saturation absorption characteristics of two SAs operating at LP01 and LP11 modes.
Thermal and Stress Analysis of Linear Hybrid Excited Flux Switching Machine with Modular Stator

Performance of electrical machine greatly varies with its operating temperature and amount of stress it is experiencing. To evaluate the performance and working of Linear Hybrid Excited Flux Switching Machine (LHEFSM) thermal analysis is performed. JMAG (FEA package) is used to analyze thermal behaviour of the machine which is then validated by equivalent magnetic circuit using Lumped Parametric Model (LPMC). Developed model is simulated using SIMULINK and is then compared with the FEA analysis. A good agreement between the two is observed with a percentage error of less than 3%. In the end stress analysis of the mover is performed and the resulting stress is evaluated at different mover speed.
Boreholes Data Analysis Architecture Based on Clustering and Prediction Models for Enhancing Underground Safety Verification

During the last decade, substantial resources have been invested to exploit massive amounts of boreholes data collected through groundwater extraction. Furthermore, boreholes depth can be considered one of the crucial factors in digging borehole efficiency. Therefore, a new solution is needed to process and analyze boreholes data to monitor digging operations and identify the boreholes shortcomings. This research study presents a boreholes data analysis architecture based on data and predictive analysis models to improve borehole efficiency, underground safety verification, and risk evaluation. The proposed architecture aims to process and analyze borehole data based on different hydrogeological characteristics using data and predictive analytics to enhance underground safety verification and planning of borehole resources. The proposed architecture is developed based on two modules; descriptive data analysis and predictive analysis modules. The descriptive analysis aims to utilize data and clustering analysis techniques to process and extract hidden hydrogeological characteristics from borehole history data. The predictive analysis aims to develop a bi-directional long short-term memory (BD-LSTM) to predict the boreholes depth to minimize the cost and time of the digging operations. Furthermore, different performance measures are utilized to evaluate the performance of the proposed clustering and regression models. Moreover, our proposed BD-LSTM model is evaluated and compared with conventional machine learning (ML) regression models. The $R^{2}$ score of the proposed BD-LSTM is 0.989, which indicates that the proposed model accurately and precisely predicts boreholes depth compared to the conventional regression models. The experimental and comparative analysis results reveal the significance and effectiveness of the proposed borehole data analysis architecture. The experimental results will improve underground safety management and the efficiency of boreholes for future wells.
The Matthews Correlation Coefficient (MCC) is More Informative Than Cohen’s Kappa and Brier Score in Binary Classification Assessment

Even if measuring the outcome of binary classifications is a pivotal task in machine learning and statistics, no consensus has been reached yet about which statistical rate to employ to this end. In the last century, the computer science and statistics communities have introduced several scores summing up the correctness of the predictions with respect to the ground truth values. Among these scores, the Matthews correlation coefficient (MCC) was shown to have several advantages over confusion entropy, accuracy, F1 score, balanced accuracy, bookmaker informedness, markedness, and diagnostic odds ratio: MCC, in fact, produces a high score only if the majority of the predicted negative data instances and the majority of the positive data instances are correct, and therefore it results being very trustworthy on imbalanced datasets. In this study, we compare MCC with two other popular scores: Cohen’s Kappa, a metric that originated in social sciences, and the Brier score, a strictly proper scoring function which emerged in weather forecasting studies. After explaining the mathematical properties and the relationships between MCC and each of these two rates, we report some use cases where these scores generate different values, which lead to discordant outcomes, where MCC provides a more truthful and informative result. We highlight the reasons why it is more advisable to use MCC rather that Cohen’s Kappa and the Brier score to evaluate binary classifications.
Auditory Evoked Potential Detection During Pure-Tone Audiometry

Modern audiometry is largely a behavioural task, with the pure-tone audiogram (PTA) being the gold standard for evaluating frequency-specific hearing thresholds in adults. The nature of behavioural audiometry makes estimating accurate hearing thresholds difficult in infants and people with disabilities, where following instructions or interacting with the test may be difficult or impossible. We propose a method in which Auditory Evoked Potentials (AEPs) are used as an alternative to behavioural audiometry for detecting frequency-specific thresholds. Specifically, P300 responses elicited by the tones of a PTA are automatically detected from electroencephalogram (EEG) data, to evaluate hearing acuity. To assess the effectiveness of this method, we created a dataset of EEG recordings from participants presented with a series of pure tones at 6 different frequencies with steadily decreasing volumes, during a PTA test. This dataset was used to train a support vector machine (SVM) to identify when a participant was played a tone, whether they perceived it or not using their EEG. Results demonstrate that detecting hearing events can be very accurate for participants for whom the classifier has been trained apriori. However, accuracy drops significantly for unseen participants - when the classifier has not been trained on any prior data from a given participant before classifying their EEG. However, by establishing that AEP response-based audiometry is viable for detecting tones, future work will explore the ability of more powerful deep neural networks to accurately estimate for unseen participants.
Heading for motor imagery brain-computer interfaces (MI-BCIs) usable out-of-the-lab: Impact of dry electrode setup on classification accuracy

A primary challenge to make motor-imagery Brain-Computer Interfaces (MI-BCIs) technologies usable and actually used out-of-the-lab consists of providing EEG systems that are efficient in terms of classification accuracy- and easy to install, e.g., using a minimal number of dry electrodes. We hypothesize that the optimal signal processing method might depend on the number of (dry) electrodes that are used. Therefore, we compared for the first time the classification accuracy associated with different dry electrode setups, i.e., 7 configurations from 8 to 32 channels, and various signal processing methods, namely (1) regularized Common Spatial Pattern (rCSP) + Linear Discriminant Analysis, (2) rCSP + Support Vector Machine (SVM), (3) Minimum Distance to Riemannian Mean and (4) SVM in the Riemannian Tangent Space. This offline comparison was performed on the data of 10 participants (one session each). Our results suggest that for all methods. MI-BCI performance drops significantly for 8 and 12 channels ($p < 0.01$). Moreover, method 3 was associated with the lowest performances ($p < 0.05$). Finally, post-doc analyses suggest that methods 1 and 2 perform best with the highest numbers of electrodes 28 and 32. For method 4 the best performance is obtained using 20 and 24 channels, which seems to be the optimal combination ($p < 0.05$). These results show the importance of selecting the signal processing pipeline as a function of the location and number of the electrodes.
Neural Network Architecture Search and Model Compression for Fast Prediction of UAS Traffic Density

The number of daily small Unmanned Aircraft Systems (sUAS) operations in uncontrolled low altitude airspace is expected to reach into the millions in the future. This makes UAS traffic density prediction a critical and challenging problem. In an Unmanned Aircraft System Traffic Management (UTM) framework, an accurate traffic prediction model is the key to manage congestion in very dense areas and allow us to evaluate different mission and resource provision plans in search for the best solution. In our previous work, a deep neural network (DNN) model has been proposed that predicts the instantaneous traffic density based on mission schedule information. However, one of the main drawbacks of the DNN is the high computational cost, which prevents us from applying the model to search for the best mission plan or best locations of launching and landing zones, because it requires exponentially large numbers of predictions based on different input combinations. In this paper, we aim to reduce the complexity of the neural network model. A neural architecture optimization framework that searches for the best compression ratio for each layer is developed. Overall, we are able to reduce the size of the traffic prediction model by 50%. Furthermore, because the pruning adds more regularization on the model and reduces the potential of overfitting, the compressed model also achieves small improvements in the prediction accuracy.
Accessing Healthcare Services During the COVID-19 Pandemic: The Plight of Sexual Assault Survivors

ABSTRACT Introduction Despite a global focus on intimate partner violence during the COVID-19 pandemic, there has been little exploration into how the pandemic and its associated restrictions have impacted sexual assault survivors and their ability to access specialized care and resources. The purpose of this research brief is to use longitudinal data to compare the number of medical forensic examinations done seasonally prepandemic and during the COVID-19 pandemic shelter-in-place order. Methods This analysis uses retrospective data on medical forensic examinations from January 2010 through November 2020 from one large academic Midwestern hospital. Results Results show that monthly medical forensic examinations have increased over time, from a mean of 4.5 cases per month (range: 1–9) in 2010 to a mean of 9 cases per month (range: 7–11) in 2019. In April 2020, when COVID-19-related shutdowns were at their first peak, cases dropped to a historic low of 0 examinations (the lowest number of cases in the past 10 years). Conclusions The data show an initial drop in the number of survivors seeking postassault care after the COVID shutdown; however, cases rebounded through the second full month of shutdown orders. Programs that provide medical forensic examinations need to be prepared for subsequent waves of survivors, who may be COVID-19 positive. We must be better prepared for many of the adverse consequences impacting individuals around the country related to COVID-19 responses.
Theory of fluid slip in charged capillary nanopores

Based on the capillary pore model (space-charge theory) for combined fluid and ion flow through cylindrical nanopores or nanotubes, we derive the continuum equations modified to include wall slip. We focus on the ionic conductance and streaming conductance, cross-coefficients of relevance for electrokinetic energy conversion and electro-osmotic pumping. We combine the theory with a Langmuir-Stern 1-pK charge regulation boundary condition resulting in a non-monotonic dependence of the cross-coefficients on salt concentration.
Fuel Material Selection Strategy for Marine Propulsion LFR

Arctic ice loss caused by rapid climate change is predicted to open the Arctic route along the Russian coast in the next decade [1]. By developing the Arctic route, the transportation distance to Europe will be reduced by about 40%, and at the time of return, transportation of low-cost Russian oil and gas is possible, which is expected to greatly contribute to international trade and energy supply stability. The average ice thickness of the Arctic route is about 5m, and it is difficult for diesel icebreakers to break the ice exceeding 2m in thickness. At the same time, diesel fuel produces fine dust and causes air pollution. Therefore, the development of a nuclear-propulsion icebreaker is essential for the exploration and development of the Arctic route. In the case of pressurized water reactor (PWR) type marine reactors that utilize nuclear fuel compling with IAEA standard for commercial ships: 20% or less of low enriched uranium (LEU), short refueling period of about 7 years is inevitable. This short fuel life requires the private port facility for nuclear ships which significantly increases the total life cycle cost of marine reactors. Accordingly, the development of non-refueling ultra-long cycle lead cooled microreactor for economic icebreaker is ongoing in Korea. SNU has developed URANUS, a micro-modular reactor based on lead cooled fast reactors used in Russian nuclear submarine since 1996 [2], and the innovation of the URANUS design is expected to enable 30+ year ultra-long cycle operation with the initial loading of LEU fuel [3]. Since an ultra-long cycle lead cooled micro-reactor for marine propulsion is unprecedented; especially, fast reactor design has never been applied to icebreakers; constructing the optimized reactor design is challenging. And selecting the fuel material is the first part of the reactor design. The majority of conventional fast reactor projects adopted the oxide or metallic fuel design, and there have been accumulated operation experience and data. In this study, the prioritization ranking method was introduced as a fuel material selection strategy for marine propulsion LFR, and a comparative evaluation was performed. Criteria and priority were first set, and then evaluation for each criterion was performed, and finally, a comprehensive comparison was made for oxide and metallic fuel.
QFuzz: quantitative fuzzing for side channels

Side channels pose a significant threat to the confidentiality of software systems. Such vulnerabilities are challenging to detect and evaluate because they arise from non-functional properties of software such as execution times and require reasoning on multiple execution traces. Recently, noninterference notions have been adapted in static analysis, symbolic execution, and greybox fuzzing techniques. However, noninterference is a strict notion and may reject security even if the strength of information leaks are weak. A quantitative notion of security allows for the relaxation of noninterference and tolerates small (unavoidable) leaks. Despite progress in recent years, the existing quantitative approaches have scalability limitations in practice. In this work, we present QFuzz, a greybox fuzzing technique to quantitatively evaluate the strength of side channels with a focus on min entropy. Min entropy is a measure based on the number of distinguishable observations (partitions) to assess the resulting threat from an attacker who tries to compromise secrets in one try. We develop a novel greybox fuzzing equipped with two partitioning algorithms that try to maximize the number of distinguishable observations and the cost differences between them. We evaluate QFuzz on a large set of benchmarks from existing work and real-world libraries (with a total of 70 subjects). QFuzz compares favorably to three state-of-the-art detection techniques. QFuzz provides quantitative information about leaks beyond the capabilities of all three techniques. Crucially, we compare QFuzz to a state-of-the-art quantification tool and find that QFuzz significantly outperforms the tool in scalability while maintaining similar precision. Overall, we find that our approach scales well for real-world applications and provides useful information to evaluate resulting threats. Additionally, QFuzz identifies a zero-day side-channel vulnerability in a security critical Java library that has since been confirmed and fixed by the developers.
Data Augmentation Technique for Process Extraction in Chemistry Publications

In the field of materials science and chemistry, deep understanding of the relation between structure and properties is required for creating new materials. Academists and scientists often seek for that information in scientific publications, since experimental observations have been the primary means to get the various chemical and physical properties of materials. Another important information from experimental observations includes the process of conducting the experiment or manufacturing procedure, because they can refer to that to conduct similar experiments, or to understand how some materials are made. Thus, access to a structured information of experiment process or manufacturing procedure is needed to improve the efficiency of their work. The process extraction task aims to extract information such as materials, conditions, apparatus and other process-specific information from text describing some process. This task suffers from insufficient number of labeled resource, and data annotation is much harder for domain-specific tasks such as in chemistry or material science. One of the popular techniques for increasing the size of labeled training sets is data augmentation. In this paper, we explore simple data augmentation technique focused on process extraction task in chemistry domain. Previous work has proposed some techniques for data augmentation in NLP. One popular study generated new data by backtranslation [7]. Other work has used data noising as smoothing [6] and predictive language models for synonym replacement [3]. Easy Data Augmentation, or EDA [5], comprehensively explores simple text editing-based data augmentation techniques for text classification task and showed a strong performance gain despite the simplicity of the technique. Inspired by their work, in this paper we explore simple data augmentation technique for process extraction in chemistry domain without using any dictionary. We also explore the possibility to create meaningful new data from present data by preserving the meaning of the original information of the present data. 2 Process Extraction
Evaluation of balance, fall risk, and related factors in patients with fibromyalgia syndrome

Objectives: In this study, we aimed to evaluate whether fibromyalgia patients had a higher fall risk compared to healthy individuals and to identify its relationship, if there was an increase, with clinical features. Patients and methods: Between March 2018 and September 2018, a total of 50 consecutive female patients with fibromyalgia (median age: 35 years; interquantile range [IQR], 27 to 40 years) and 50 healthy female volunteers (median age: 30 years; IQR, 23 to 40 years) were included in the study. Pain was evaluated with the Visual Analog Scale (VAS), life quality with the Nottingham Health Profile (NHP), balance functions with the Berg Balance Test (BBT), and the risk of falls with a posturography device. Disease activity of fibromyalgia patients was evaluated with the Fibromyalgia Impact Questionnaire (FIQ). Results: The mean fall risk index of the fibromyalgia patients was 45%. The fall risk index was significantly higher (p=0.010) and the BBT scores were significantly lower in the patient group (p<0.001). There was a significant difference in terms of fall risk between the control group and drug-free fibromyalgia patients; however, no significant difference was found between the balance scores of the two groups. In the fibromyalgia group, a weak positive relationship was determined between the fall risk index and the social isolation subscale of the NHP. Conclusion: Our study results showed an increased risk of loss of balance and falls in fibromyalgia patients, compared to healthy individuals. This fall risk increase was also detected in fibromyalgia patients who did not use drugs. These findings suggest a possible relationship between social isolation and an increased risk of falls.
The last CPU

Since the end of Dennard scaling and Moore's Law have been foreseen, specialized hardware has become the focus for continued scaling of application performance. Programmable accelerators such as smart memory, smart disks, and smart NICs are now being integrated into our systems. Many accelerators can be programmed to process their data autonomously and require little or no intervention during normal operation. In this way, entire applications are offloaded, leaving the CPU with the minimal responsibilities of initialization, coordination and error handling. We claim that these responsibilities can also be handled in simple hardware other than the CPU and that it is wasteful to use a CPU for these purposes. We explore the role and the structure of the OS in a system that has no CPU and demonstrate that all necessary functionality can be moved to other hardware. We show that almost all of the pieces for such a system design are already available today. The responsibilities of the operating system must be split between self-managing devices and a system bus that handles privileged operations.
Transactions of the Korean Nuclear Society Virtual Autumn Meeting December 17-18 Group Constants Generation using MCS Monte Carlo Code for Fast Reactor Analysis

Recently, the homogenized group constant (GC) generation using the continuous-energy Monte Carlo (MC) method for fast reactor analysis has achieved a growing attraction among nuclear reactor physics community. Its motivation is based on the advance of MC method on the simulation of complex reactor geometries without introducing any approximation, which is conventional to the deterministic codes. Therefore, this study aims to present a brief methodology for GC generation by the in-house UNIST MC code MCS [1], that can be compatibly utilized in the nodal diffusion code, RAST-K (R2) [2], to analyze the core behavior of fast reactor. The capability of this approach is quantified on the 3600MWth sodium fast reactor (SFR) oxide core (MOX-3600) from the OECD/NEA SFR benchmark [3]. The solutions of the beginning-of-cycle (BOC) steady-state calculation of MCS/R2 for a wholecore problem, including the core multiplication factor, power profiles, control rod worth (CRW), fuel temperature coefficient (FTC) and coolant temperature coefficient (CTC), are generated and verified with those from the MCS MC code.