Miracle-dz/UniFAR-FTUs huggingface dataset:
ReLIEF: A Reinforcement-Learning-Based Real-Time Task Assignment Strategy in Emerging Fault-Tolerant Fog Computing

Due to the real-time requirements in several IoT applications, fog computing has emerged to overcome the long latency and other constraints of cloud computing. Due to the high probability of packet loss, energy limitation of IoT devices, and the external disturbances that may frequently occur on the fog infrastructure, the timing constraints of real-time tasks may be compromised. Therefore, the reliability of executing real-time tasks has always been a significant challenge in fog computing. In addition to the correct execution of the tasks, it is also important to execute them before their deadlines according to their real-time classification. State-of-the-art methods generally focus on the delay or functionality of tasks in fog computing systems. However, those methods do not widely focus on the reliability of tasks with real-time constraints in dynamic environments. In this article, a novel primary backup task assignment strategy based on machine learning (ReLIEF) is proposed to improve the reliability of fog-based IoT systems. To identify suitable nodes for the execution of the primary and backup tasks, ReLIEF employs a reinforcement learning (RL) approach, which has an outstanding performance in dynamic environments by establishing a balance between communication delay and workload on each fog device. Based on the simulations, our newly proposed technique has been able to reduce the amount of task dropping rate by up to 84% against the state of the art. Moreover, it is capable of balancing the workload distribution while increasing the reliability of the system by nearly 72% compared with its counterparts.
Fabrication of metallic nanowires with a scanning tunneling microscope

A procedure to pattern thin metal films on a nanometer scale with a scanning tunneling microscope (STM) operating in air is reported. A 30 nm film of hydrogenated amorphous silicon (a‐Si:H) is deposited on a 10 nm film of TaIr. Applying a negative voltage between the STM tip and the a‐Si:H film causes the local oxidation of a‐Si:H. The oxide which is formed is used as a mask to wet etch the not‐oxidized a‐Si:H and subsequently, the remaining pattern is transferred into the metal film by Ar ion milling. Metal wires as narrow as 40 nm have been fabricated. Since a‐Si:H can be deposited in very thin layers on almost any substrate, the presented procedure can be applied to structure all kind of thin films on a nanometer scale.
Embedding filtering criteria into a wrapper marker selection method for brain tumor classification: an application on metabolic peak area ratios

The purpose of this study is to identify reliable sets of metabolic markers that provide accurate classification of complex brain tumors and facilitate the process of clinical diagnosis. Several ratios of metabolites are tested alone or in combination with imaging markers. A wrapper feature selection and classification methodology is studied, employing Fisher's criterion for ranking the markers. The set of extracted markers that express statistical significance is further studied in terms of biological behavior with respect to the brain tumor type and grade. The outcome of this study indicates that the proposed method by exploiting the intrinsic properties of data can actually reveal reliable and biologically relevant sets of metabolic markers, which form an important adjunct toward a more accurate type and grade discrimination of complex brain tumors
Parameter estimation for finite mixtures of uniform distributions

The paper considers the problem of parameter estimation, using the method of moments and the method of maximum likelihood, in the context of mixtures of Uniform distributions. In dealing with the case of mixtures of two Uniform distributions, the paper extends material in Gupta and Miyawaki (1978). This work is then extended to the case of mixtures of three Uniforms. Brief mention is made of a test between two special hypotheses about Uniform mixtures.
DOD-Explainer: Explainable Drug Overdose Deaths Predictor from Crime and Socioeconomic Data

Drug overdose deaths are a dreadful crisis that leads to substantial societal impairments. Its harmful impact directly affects families and communities. To assist policymakers in mitigating this crisis, it is crucial to study the societal, economic, and criminal contributing factors linked to the crisis. Unfortunately, current data-driven works assume a singular factor, such as poverty being the cause and disregarding other realistic causes. Besides, recent works exhibited a lack of explainable models and spatial analysis of the crisis. Thus, DOD-Explainer links the gap by developing a realistic framework that predicts highly impacted counties of drug overdose deaths from crime and socioeconomic data. DOD-Explainer overcomes the challenge of data scarcity by proposing three data augmentation methods. Then, an algorithm is proposed to provide realistic explanations of the leading causes of the crisis. The results show that our application achieves the best predictive accuracy from several models, accurately identifies the most/least impacted counties by the crisis, and reveals the most contributing factors of drug overdoses.
The Emergence of Carbon Nanomaterials as Effective Nano-Avenues to Fight against COVID-19

COVID-19 (Coronavirus Disease 2019), a viral respiratory ailment that was first identified in Wuhan, China, in 2019, and then expanded globally, was caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The severity of the illness necessitated quick action to cease the virus’s spread. The best practices to avert the infection include early detection, the use of protective clothing, the consumption of antiviral medicines, and finally the immunization of the patients through vaccination. The family of carbon nanomaterials, which includes graphene, fullerene, carbon nanotube (CNT), and carbon dot (CD), has a great deal of potential to effectively contribute to each of the main trails in the battle against the coronavirus. Consequently, the recent advances in the application of carbon nanomaterials for containing and combating the SARS-CoV-2 virus are discussed herein, along with their associated challenges and futuristic applicability.
ACCURACY IN ESTIMATION WITH NONRESPONSE: A FUNCTION OF DEGREE OF IMBALANCE AND DEGREE OF EXPLANATION

Responsive Design is a trend in recent survey literature concerned notably with managing data collection, through planning and appropriate intervention, so as to realize a well-balanced final set of respondents. In this effort, auxiliary variables, including paradata, are central. But regardless of what is done in the data collection, accurate estimation despite nonresponse is the ultimate goal. The auxiliary variables are important at the estimation stage as well, as when calibrated weights are used in the nonresponse adjustment. For accuracy, two factors intervene: (1) in the data collection, the level of imbalance achieved with the auxiliary information; and (2) in the estimation, the degree to which the auxiliaries explain the study variable. In practice, both objectives are less than completely satisfied. Reduced imbalance in data collection does not by itself guarantee low bias in the estimates. We ask: Is balancing worth a perhaps costly and demanding effort in data collection? Could one have done equally well by saving the use of the auxiliary information until the estimation stage? Complete bias elimination is not achieved at either stage. We outline a theory for a two-factor explanation of accuracy, and apply it to two important surveys at Statistics Sweden. The factors— the degree of imbalance and the degree of explanation—are systematically varied, and their joint effect on the accuracy of the estimates is evaluated empirically. The results show that reduced imbalance makes the adjustment of the simple estimate lose some of its importance. More importantly, the calibration-adjusted estimate realizes some accuracy improvement by having been preceded in data collection by a reduced imbalance. The explanation of why this happens is not simple, but a theoretical justification is outlined.
A Power-Proportional, Dual-Bandwidth, and Constant-Delay Receiver Front-End for Energy-Efficient Dual-Rate Optical Links

This article presents a dual-bandwidth front-end (FE) for a rapidly reconfigurable dual data rate and power-proportional optical receiver. The proof-of-concept receiver FE is capable of operation at 8- and 4-Gb/s data rates. Implemented in 65-nm CMOS technology, the proposed FE consists of a shunt-feedback transimpedance amplifier (TIA), a configurable one-stage-to-three-stage postamplifier (PA), and an offset compensation (OC) loop. By reconfiguring the number of stages in the PA, the FE maintains a near-constant delay when its bandwidth is changed. This allows synchronization to be maintained, with limited bit errors when the target data rate is switched. The prototype receiver was measured with an optical input at 8 and 4 Gb/s. The overall FE dissipates 6.12 mW at 8 Gb/s (0.76 pJ/bit) and 2.86 mW at 4 Gb/s (0.72 pJ/bit). The measured receiver optical sensitivities for 8- and 4-Gb/s inputs at high-bandwidth (HBW) and low-bandwidth (LBW) modes are −7.7 and −9.8 dBm, respectively. The measurement results confirm the matched delay through the FE with delay variations within 8 ps.
Monaural Speech Enhancement Algorithm Based on Gated Convolutional Recurrent Network

The spectrogram of speech has characteristics such as time dependence and local correlation, and classical neural network models cannot be fully adapted to the time-frequency characteristics of speech. Convolutional recurrent network (CRN) can not only capture the local patterns of the spectrogram, but also model the dependencies between consecutive frames. In this paper, we introduce gated linear unit (GLU) into the convolutional encoder-decoder (CED) structure of CRN and design a gated convolutional recurrent network (GCRN) for real-time monaural speech enhancement. We replace the long short-term memory (LSTM) in CRN with gated recurrent unit (GRU) and introduce a grouping strategy. The grouped GRU module captures the temporal dependence between consecutive frames while reducing the model complexity. Experimental results show that the GCRN model outperforms the baseline model in terms of objective speech quality and intelligibility under all test conditions. In addition, GCRN greatly reduces the number of trainable parameters.
Deep Learning For Time Series Classification Using New Hand-Crafted Convolution Filters

In recent years, there has been an increasing interest in Deep Learning models for time series classification. In this field, state-of-the-art architectures rely on convolution neural networks that learn one dimensional filters in order to capture patterns allowing to discriminate between the different classes. These filters are randomly initialized and modified throughout model training. In this paper, we explore the creation of handcrafted (non learned) filters in order to capture specific patterns in a time series. We propose a set of filters whose values are fixed and not modified during the training step. Our goal with these filters is to captures pecific patterns in a time series (increase, decrease, peaks) and study the relevance of adding such filters to existing architectures ranging from simple architecture (Fully Convolutional Network (FNC)) to state-of-the-art architecture (InceptionTime). Experiments reveal that adding our manually created filters increase the prediction accuracy on a majority of the 128 datasets of the UCR Archive. They also show that handcrafted filters and learned filters are complementary to obtain the best preforming models. This work is the first step in proposing a catalog of generic and fixed filters that could be useful in a large range of applications to improve deep models accuracy for time series classification.
Dynamic Stop-Loss Approach for Short Term Trades using Deep Learning

Stop-Loss strategies are often used by investors to combat negative returns by predetermining thresholds at which they should exit trades. Though existing traditional Stop-Loss mechanisms such as Fixed Stop-Loss and Trailing Stop-Loss are empirically proven to have the ability to minimize risks associated with trades, they still face serious challenges when it comes to achieving a balance between risk and return. In this study, we develop a Deep Learning model that combines the concept of Stop-Loss with the capabilities offered by Deep Neural Networks. The architecture is composed of three components where trend detection and price prediction components provide inputs to the stop price prediction component which predicts the variation of stop price. The study focuses on short term trades of minute frequency which involves analysing massive chunks of data that fluctuates rapidly within extremely short time intervals. Despite the advancements that has taken place in the context of Big Data analytics, intraday financial time series analysis has not received much academic attention. The model utilizes convolutional layers to capture spatial features along with Long Short Term Memory networks to capture temporal dependencies in price sequences. The proposed solution gives outstanding results for diverse market conditions and it works specifically well for trades that are downtrending. We evaluate our model for a portfolio that consists of five stock symbols from NASDAQ that are adequately liquid and two cryptocurrencies which are highly circulated. The model delivers a stable outcome across all symbols indicating its ability to be generalized over a range of symbols and its tolerance to diverse market conditions. Results also indicate that Stop-Loss mechanisms possess the potential to work well even in speculative markets such as the cryptocurrency market. The ability to reduce losses without compromising on opportunities to realize profits in dynamic and unstable market conditions is an important property of our Stop-Loss solution.
Exploiting Machine Learning Models for Approximate Query Processing

Approximate query processing can help reduce response time for aggregate queries in exploratory data analysis. In this study, we describe basic query transformation rules for processing approximate queries using synthetic data tables or inferential models. Based on the preliminary experimental results, we confirm that ML models can be used to provide approximate query results in response times acceptable for applications.
A numerical scheme for the optimal control of groundwater pollution

A scheme is proposed for the numerical study of optimal control problems related to groundwater quality management in the case of nonpoint‐source pollution. The corresponding state equation is a system of coupled nonlinear partial differential equations. The approximation is given by a finite volume scheme based on a two‐point flux approximation with upwind mobilities embedded in an iterative fixed point approximation. The analysis of the convergence of the scheme allows to establish existence and uniqueness results under reasonable assumptions. Numerical illustrations of the performance of the algorithm are given in realistic situations.
Structure and formation ofH2Ti3O7nanotubes in an alkali environment

The structure and growth of ${\mathrm{H}}_{2}{\mathrm{Ti}}_{3}{\mathrm{O}}_{7}$-type nanotubes have been studied by first-principle calculations. It is shown that the asymmetry in the distribution of hydrogen on the two sides of the surface layer of a ${\mathrm{H}}_{2}{\mathrm{Ti}}_{3}{\mathrm{O}}_{7}$ crystal plate provides a sufficient driving force for the formation of the ${\mathrm{H}}_{2}{\mathrm{Ti}}_{3}{\mathrm{O}}_{7}$ nanotube. Hydrogen deficiency on one side of the surface layer of a ${\mathrm{H}}_{2}{\mathrm{Ti}}_{3}{\mathrm{O}}_{7}$ plate results in a surface tension that increases with increasing hydrogen deficiency and may eventually overcome the coupling from the layers beneath driving the surface layer to peel off from the crystal plate and roll into a tubular structure. While the radius of the resultant nanotube is determined mainly by the layer coupling energy, the thickness of the tube wall is determined by the residual charges on the peeled surface layer. Both the radius and the wall thickness may in principle be controlled via modifying the layer coupling strength and the net charges on the surface.
Holographic motion picture microscopy

Holographic motion pictures provide a means for recording and observing transient, dynamic, small-scale events occurring at unpredictable times and locations in a given volume of space. Using an argon ion laser to generate repetitive pulses of 50 µs duration at a wavelength of 514.5 nm (5145 Å), holographic movies of living marine plankton organisms were produced by recording a series of in-line holograms at 70 frames/s on 35 mm film. The reconstructed holographic images may be viewed with a standard microscope, photographed with a still camera, or copied with a conventional movie camera for subsequent projection. Image resolution of 10 µm was obtained.
A Labeling Task Design for Supporting Recent Algorithmic Needs

Studies on supervised machine learning (ML) recommend involving workers from various backgrounds in training dataset labeling to reduce algorithmic bias. Moreover, sophisticated tasks for categorizing objects in images are necessary to improve ML performance, further complicating micro-tasks. This study aims to develop a task design incorporating the fair participation of people, regardless of their specific backgrounds or task’s difficulty. By collaborating with 75 labelers from diverse backgrounds for 3 months, we analyzed workers’ log-data and relevant narratives to identify the task’s hurdles and helpers. The findings revealed that workers’ decision-making tendencies were affected by the "community" that positively helps workers. Also, the machine’s feedback perceived by workers could make people easily engaged in works. Based on these findings, we suggest an extended human-in-the-loop approach that connects labelers, machines, and communities rather than isolating individual workers.
Output-coupling semiconductor saturable absorber mirror

We present a semiconductor saturable absorber mirror (SESAM), which also acts as an output coupler at the same time. The influence of the output coupler transmission onto the absorber parameters is investigated theoretically, as well as experimentally. A passively Q-switched Nd:YVO4 microchip laser is built using such a nonlinear output coupler, yielding clean pulses of 143 ps duration, 48 nJ energy, and 572 W peak power. This result is compared with the traditional approach, where the SESAM is not used as an output coupler.
Numerical Investigation of on-Chip Multi-Gas Sensing Using a Low-Repetition-Frequency Microcavity Kerr Comb With Backward Interference Structure

By integrating a microcavity Kerr frequency comb with an interferometric arm structure for backward coupling with a slot sensing waveguide, we present a new on-chip multi-gas sensing device. The microcavity used for frequency comb generation has a novel symmetric double-ring structure with a large roundtrip length, which makes it possible to obtain high-resolution frequency-resolved absorption spectra. In the meanwhile, a flat octave-spanning broadband spectrum is obtained by dispersion engineering of the Si3N4-based micro-rings with a horizontal single slot structure. Without the use of an additional gas cell, the large-length sensing waveguide allows for improved integration. A multiple soliton comb with high pump-to-frequency comb conversion efficiency under the optimization of the microcavity structure to improve pump depletion was obtained. The simulation shows its high stability and application feasibility, and the sensing performance of several gas species in different wavelength regions is verified and analyzed.
Automated DNA-preparation system for bacteria out of air sampler liquids

Preventing bacterial contaminations is a significant challenge in applications across a variety of industries, e.g. in food processing, the life sciences or biohazard detection. Here we present a fully automated lab-on-a-chip system wherein a disposable microfluidic chip moulded by polymeric injection is inserted into an operating device. Liquid samples, here obtained from an air sampler, can be processed to extract and lyse bacteria, and subsequently to purify their DNA using a silica matrix. After the washing and elution steps, the DNA solution is dispensed into a reaction vessel for further analysis in a conventional laboratory polymerase chain reaction (PCR) device. We demonstrate the workability and efficiency of our approach with results from a 9 ml liquid sample spiked with E. coli.
Effect of magnetic field on the mechanical properties of magnetostrictive iron-gallium nanowires

This study experimentally investigates the elastic properties of individual iron-gallium nanowires with and without an applied magnetic bias field. The experiments were conducted with a custom manipulator stage designed for use within a scanning electron microscope, where nanowires were mechanically tested both statically and dynamically. Experiments were also performed in the presence of a 20Oe dc magnetic field in order to identify any variation in wire properties. The results suggest that iron-gallium nanowires possess an elastic modulus very similar to the macroscale value, tensile strengths of more than double the bulk material, and minor magnetic field induced stiffening at low stresses.
Electrical and magnetic properties of hot-deformed Nd-Fe-B magnets with different DyF3 additions

The effect of deformation and DyF3 additions on the electrical resistivity and the magnetic performance has been studied in hot-deformed Nd-Fe-B melt-spun ribbons and correlated with respective microstructures. Despite the nanocrystallinity of hot-compacted magnets, the specific electrical resistivity measured by four-point-method was shown to be comparable with that of sintered magnets. Die-upsetting reduces electrical resistivity within the magnetically hard plane because of an enhanced shape anisotropy of the grains. The addition of DyF3 overcompensates this reduction due to the presence of electrically insulating Dy-F rich inclusions and thus reduces eddy current losses within the magnet. Magnetic measurements reveal an increase in coercivity without a change in remanence for die-upset magnets with a total height reduction of 63% and 1.2 wt. % Dy (1.6 wt. %DyF3). Both properties, remanence and coercivity, demonstrate an effective reduction in heavy rare earth Dy for Nd-Fe-B magnets.
Machine Learning for Automated Wheeze Detection in Children

Assessing asthma severity is inherently difficult because it is highly subjective, often overlapping with symptoms of a common cold, and few objective tools currently exist for it. Our long-term goal is to empower parents to initiate timely treatment for acute asthma by supplementing their subjective assessment with an objective asthma severity scoring. Wheeze breath sound detection is an integral part of developing such a scoring system. We have developed a mobile application coupled with an in-house digital stethoscope to record and classify lung sounds. In this paper, we present the results of deep learning algorithms for wheeze detection. Lung sounds collected at the emergency department using our custom stethoscope were labeled as either clear breath sounds or wheeze breath sounds. Two deep learning models, ResNet-18 and Harmonic Networks were trained on the curated dataset. ResNet-18 model identified wheeze from clear breath sounds with an accuracy of 74%, a sensitivity of 77%, and a specificity of 70%. The more data-efficient model, the Harmonic Networks, achieved 84% accuracy, 89% sensitivity, and 78% specificity. In classifying clear and wheeze breath sounds, the Harmonic Networks model outperformed ResNet-18 by 10%.