Miracle-dz/UniFAR-FTUs huggingface dataset:
obust power spectral estimation for EEG data

Background: Typical electroencephalogram (EEG) recordings often contain substantial artifact. These artifacts, often large and intermittent, can interfere with quantification of the EEG via its power spectrum. To reduce the impact of artifact, EEG records are typically cleaned by a preprocessing stage that removes individual segments or components of the recording. However, such preprocessing can introduce bias, discard available signal, and be labor-intensive. With this motivation, we present a method that uses robust statistics to reduce dependence on preprocessing by minimizing the effect of large intermittent outliers on the spectral estimates. New method: Using the multitaper method (Thomson, 1982) as a starting point, we replaced the final step of the standard power spectrum calculation with a quantile-based estimator, and the Jackknife approach to confidence intervals with a Bayesian approach. The method is implemented in provided MATLAB modules, which extend the widely used Chronux toolbox. Results: Using both simulated and human data, we show that in the presence of large intermittent outliers, the robust method produces improved estimates of the power spectrum, and that the Bayesian confidence intervals yield close-to-veridical coverage factors. Comparison to existing method: The robust method, as compared to the standard method, is less affected by artifact: inclusion of outliers produces fewer changes in the shape of the power spectrum as well as in the coverage factor. Conclusion: In the presence of large intermittent outliers, the robust method can reduce dependence on data preprocessing as compared to standard methods of spectral estimation. © 2016 Elsevier B.V. All rights reserved. Abbreviations: EEG, electroencephalogram; PDF, probability density function; DF, cumulative density function. ∗ Corresponding author. E-mail addresses: tam2022@med.cornell. edu (T. Melman), dvicto@med.cornell. edu (J.D. Victor). ttp://dx.doi. org/10.1016/j.jneumeth. 2016.04. 015 165-0270/© 2016 Elsevier B.V. All rights reserved.
Computing a Minimal Set of t-Spanning Motion Primitives for Lattice Planners

In this paper we consider the problem of computing an optimal set of motion primitives for a lattice planner. The objective we consider is to compute a minimal set of motion primitives that t-span a configuration space lattice. A set of motion primitives t-span a lattice if, given a real number t greater or equal to one, any configuration in the lattice can be reached via a sequence of motion primitives whose cost is no more than t times the cost of the optimal path to that configuration. Determining the smallest set of t–spanning motion primitives allows for quick traversal of a state lattice in the context of robotic motion planning, while maintaining a t–factor adherence to the theoretically optimal path. While several heuristics exist to determine a t–spanning set of motion primitives, these are presented without guarantees on the size of the set relative to optimal. This paper provides a proof that the minimal t–spanning control set problem for a lattice defined over an arbitrary robot configuration space is NP-complete, and presents a compact mixed integer linear programming formulation to compute an optimal t-spanner. We show that solutions obtained by the mixed integer linear program have significantly fewer motion primitives than state of the art heuristic algorithms, and out perform a set of standard primitives used in robotic path planning.
Instance and Output Optimal Parallel Algorithms for Acyclic Joins

Massively parallel join algorithms have received much attention in recent years, while most prior work has focused on worst-optimal algorithms. However, the worst-case optimality of these join algorithms relies on hard instances having very large output sizes, which rarely appear in practice. A stronger notion of optimality is \em output-optimal, which requires an algorithm to be optimal within the class of all instances sharing the same input and output size. An even stronger optimality is \em instance-optimal, i.e., the algorithm is optimal on every single instance, but this may not always be achievable. In the traditional RAM model of computation, the classical Yannakakis algorithm is instance-optimal on any acyclic join. But in the massively parallel computation (MPC) model, the situation becomes much more complicated. We first show that for the class of r-hierarchical joins, instance-optimality can still be achieved in the MPC model. Then, we give a new MPC algorithm for an arbitrary acyclic join with load $O (\IN øver p + \sqrt\IN \cdot ØUT øver p )$, where $\IN,ØUT$ are the input and output sizes of the join, and p is the number of servers in the MPC model. This improves the MPC version of the Yannakakis algorithm by an $O (\sqrtØUT øver \IN )$ factor. Furthermore, we show that this is output-optimal when $ØUT = O(p \cdot \IN)$, for every acyclic but non-r-hierarchical join. Finally, we give the first output-sensitive lower bound for the triangle join in the MPC model, showing that it is inherently more difficult than acyclic joins.
An Optimization Model for Balancing Assembly Lines With Stochastic Task Times and Zoning Constraints

This paper aims to bridge the gap between theory and practice by addressing a real-world assembly line balancing problem (ALBP), where task times are stochastic and there are zoning constraints in addition to the commonly known ALBP constraints. A mixed integer programming (MIP) model is proposed for each of the straight and U-shaped assembly line configurations. The primary objective in both cases is to minimize the number of stations; minimizing the maximum of stations’ meantime; and the stations’ time variance is considered as secondary objectives. Four different scenarios are discussed for each model, with differences in the objective function. The models are validated by solving a real case taken from an automobile manufacturing company and some standard test problems available in this paper. The results indicate that both models are able to provide optimum solutions for problems of different sizes. The technique for order preference by similarity to ideal solution (TOPSIS) is used to create reliable comparisons of the different scenarios and valid analysis of the results. Finally, some insights regarding the selection of straight and U-shaped layouts are provided.
A Predictive Data Feature Exploration-Based Air Quality Prediction Approach

In recent years, people have been paying more and more attention to air quality because it directly affects people’s health and daily life. Effective air quality prediction has become one of the hot research issues. However, this paper is suffering many challenges, such as the instability of data sources and the variation of pollutant concentration along time series. Aiming at this problem, we propose an improved air quality prediction method based on the LightGBM model to predict the PM2.5 concentration at the 35 air quality monitoring stations in Beijing over the next 24 h. In this paper, we resolve the issue of processing the high-dimensional large-scale data by employing the LightGBM model and innovatively take the forecasting data as one of the data sources for predicting the air quality. With exploring the forecasting data feature, we could improve the prediction accuracy with making full use of the available spatial data. Given the lack of data, we employ the sliding window mechanism to deeply mine the high-dimensional temporal features for increasing the training dimensions to millions. We compare the predicted data with the actual data collected at the 35 air quality monitoring stations in Beijing. The experimental results show that the proposed method is superior to other schemes and prove the advantage of integrating the forecasting data and building up the high-dimensional statistical analysis.
An Energy-Efficient Algorithm for Classification of Fall Types Using a Wearable Sensor

Objective: To mitigate damage from falls, it is essential to provide medical attention expeditiously. Many previous studies have focused on detecting falls and have shown that falls can be accurately detected at least in a laboratory setting. However, a very few studies have classified the different types of falls. To this end, in this paper, a novel energy-efficient algorithm that can discriminate the five most common fall types was developed for wearable systems. Methods: A wearable system with an inertial measurement unit sensor was first developed. Then, our novel algorithm, temporal signal angle measurement (TSAM), was used to classify the different types of falls at various sampling frequencies, and the results were compared with those from three different machine learning algorithms. Results: The overall performance of the TSAM and that of the machine learning algorithms were similar. However, the TSAM outperformed the machine learning algorithms at frequencies in the range of 10–20 Hz. As the sampling frequency dropped from 200 to 10Hz, the accuracy of the TSAM ranged from 93.3% to 91.8%. The sensitivity and specificity ranges from 93.3% to 91.8%, and 98.3% to 97.9%, respectively for the same frequency range. Conclusion: Our algorithm can be utilized with energy-efficient wearable devices at low sampling frequencies to classify different types of falls. Significance: Our system can expedite medical assistance in emergency situations caused by falls by providing the necessary information to medical doctors or clinicians.
A Flush-mounted Ultra-wideband Scanning Phased Array with End-fire Radiation Pattern

A low-profile, ultra-wideband (UWB), end-fire scanning phased array is proposed in this paper. The array consists of four log-periodic monopole array antennas (LPMAs) and is flush-mounted in a shallow cavity to further reduce the array height. Simulated results show an operating frequency band from 1.0 to 3.0 GHz and end-fire radiation patterns with ±45° scanning performance in horizontal planes, demonstrating that the proposed end-fire array is suitable for low-profile, conformal applications with satisfactory scanning performance.
A Novel Spatio-Temporal Model for City-Scale Traffic Speed Prediction

City-scale traffic speed prediction provides significant data foundation for the intelligent transportation system, which enriches commuters with up-to-date information about traffic condition. However, predicting on-road vehicle speed accurately is challenging, as the speed of the vehicle on the urban road is affected by various types of factors. These factors can be categorized into three main aspects, which are temporal, spatial, and other latent information. In this paper, we propose a novel spatio-temporal model named L-U-Net based on U-Net as well as long short-term memory architecture and develop an effective speed prediction model, which is capable of forecasting city-scale traffic conditions. It is worth noting that our model can avoid the high complexity and uncertainty of subjective features extraction and can be easily extended to solve other spatio-temporal prediction problems such as flow prediction. The experimental results demonstrate that the prediction model we proposed can forecast urban traffic speed effectively.
Surface Deformation Monitoring in Coal Mine Area Based on PSI

It is difficult to get robust deformation results using conventional differential interferometric synthetic aperture radar (D-InSAR) method, due to the limitation of the phase measurement error, baseline error, and atmospheric delay in synthetic aperture radar (SAR) interferometry. Persistent scatterer interferometry (PSI) represents a powerful tool to measure mining subsidence, as it offers a synoptic view that can be repeated at different time intervals and at various scales. In many cases, PSI data are integrated with in situ monitoring instrumentation, since the joint use of satellite and ground-based data facilitates the geological interpretation of mining subsidence and allows a better understanding of mining subsidence geometry and kinematics. In this paper, PSI interferometry and conventional ground-based monitoring techniques have been used to characterize and to monitor Ge-Ting mining located in Shandong, China. The PSI technique used in this paper overcomes most of the limitations of conventional interferometric approaches by identifying, within the area of interest, a set of “radar benchmarks” (PS), where very precise displacement measurements can be carried out. More than 78000 PS were identified by processing SAR images acquired from 2004 to 2009 by the ENVISAT. The PSInSAR application at a sub-regional scale detected slow ground deformations ranging from 0 to −58 cm/year and resulting from various processes (swelling/shrinkage of clay soils and water pumping). The results reveal the evolution process of “bowl” in mining subsidence and are helpful to the early warning of the mine disaster.
Analogous Study of Security Threats in Cognitive Radio

Utilization of Wireless sensor network is growing with the development in modern technologies. On other side electromagnetic spectrum is limited resources. Application of wireless communication is expanding day by day which directly threaten electromagnetic spectrum band to become congested. Cognitive Radio solves this issue by implementation of unused frequency bands as "White Space". There is another important factor that gets attention in cognitive model i.e: Wireless Security. One of the famous causes of security threat is malicious node in cognitive radio wireless sensor networks (CRWSN). The goal of this paper is to focus on security issues which are related to CRWSN as Fusion techniques, Co-operative Spectrum sensing along with two dangerous attacks in CR: Primary User Emulation (PUE) and Spectrum Sensing Data Falsification (SSDF).
A Fingerprint Database Reconstruction Method Based on Ordinary Kriging Algorithm for Indoor Localization

Constructing a fingerprint database using the received signal strength is a widely used solution for indoor positioning to fit the positioning result online through matching the database with algorithm. Traditional fingerprint database construction methods are time-consuming and difficult to sample in special locations. In this paper, Kriging interpolation algorithm is proposed to interpolate or extrapolate fingerprint databases, in order to solve such problems, such as large workload, long time-consuming and difficult of sampling special features in the construction of fingerprint databases. The experiment is done using the Kriging algorithm and Inverse Distance Weighted algorithm to interpolate the database with 60% sampling points as known parameter. The experimental results show that the interpolation error of the Kriging algorithm is 4.49dBm, which is 5% lower than that by the Inverse Distance Weighted algorithm.
Deriving the Political Affinity of Twitter Users from Their Followers

In this work, we show that Twitter users can reveal valuable political information about particular Nodes of Interest (NOIs) they opt to follow. More precisely, we utilize an interesting graph projection method and a series of algorithmic approaches, such as modularity clustering, a minimum linear arrangement (MinLA) approximation algorithm and the DeGroot opinion update model in order to reveal the political affinity of selected NOIs. Our methods, which are purely structure-based, are applied to a snapshot of the Twitter network based on the user accounts of NOIs, consisting of the members of the current Greek Parliament along with their respective followers. The findings confirm that the information obtained can portray with significant precision the political affinity of the NOIs. We, furthermore, argue that these methods are of general interest for imprinting the political leaning of other NOIs, for example news media, and potentially classifying them in respect to their political bias.
Software Project Management and Its Tools in Practice in IT Industry of Pakistan

This paper analyzes and evaluate the current level of Software Project Management And its tolls in practice in IT Industry of Pakistan. 90% IT project in Pakistan are outsourced and many international companies have built their off-shore IT companies in Pakistan due to cheap employment. As mostly projects are from developed countries and are large and complex IT projects so there is high demand of local Software Project Manager to manage these projects. Due to this high demand there are an increase in institutes which start providing Software Project Management courses and certifications and this is how Software Project Management become an integral part of IT industry of Pakistan but in small companies the roll of Project Manager varies. The introduction of this paper describes the importance and essence of Software Project Management and its tools in practice and their characteristics. Our team has collects data to find out success of project implementation, Software Project Management knowledge, reasons for success and failure, trade-off decisions and capability of Software Project Manager and their journey to become project manager.
Kindergarten Children Attitude Towards Humanoid Robots: What is the Effect of the First Experience?

Possible applications of robots are growing in educational contexts, where they can support and enhance the traditional learning at any level, including kindergarten. However, the acceptance of such novel technology among the kids is not fully understood, especially for the youngest ones. In this abstract, we present an experiment that investigates the attitude of 52 pre-schooler children before and after the interaction with a humanoid robot in kindergarten setting. The main hypothesis is that ideas and prejudices can change after a controlled interaction with a physical robot. The study found that children exposed to the robot decrease their distress and positively change their attitude toward the technological device. The results suggest that an early, controlled exposure may facilitate future acceptance.
We Need More Power to Stand Up: Designing to Combat Stigmatization of the Caregivers of Children with Autism in Urban Bangladesh

Stigma, a critical challenge for social justice, has not received much attention in ICTD literature. Most existing designs that aim to combat stigma draw on an 'information and awareness' approach that is often inadequate to address stigma's deeper roots. To address this gap, we have conducted an interview and design study in five special needs schools1 in Dhaka, Bangladesh, involving twenty-nine parents and nine teachers. Based on our study, we present how the primary caretakers of children with autism face the stigma associated with autism spectrum disorder (ASD) and how misogyny, economic apprehension, and misinformation generate this stigma. Drawing from a range of scholarly work in sociology and psychology, we demonstrate how those factors are rooted in the colonial history and contemporary social hierarchy of Bangladesh. Based on our participatory design sessions, we introduce and analyze potential design directions and connect our findings to the politics of inclusion and social justice in the context of the developing world.
Improving Seasonal Prediction of East Asian Summer Rainfall Using NESM3.0: Preliminary Results

It has been an outstanding challenge for global climate models to simulate and predict East Asia summer monsoon (EASM) rainfall. This study evaluated the dynamical hindcast skills with the newly developed Nanjing University of Information Science and Technology Earth System Model version 3.0 (NESM3.0). To improve the poor prediction of an earlier version of NESM3.0, we modified convective parameterization schemes to suppress excessive deep convection and enhance insufficient shallow and stratiform clouds. The new version of NESM3.0 with modified parameterizations (MOD hereafter) yields improved rainfall prediction in the northern and southern China but not over the Yangtze River Valley. The improved prediction is primarily attributed to the improvements in the predicted climatological summer mean rainfall and circulations, Nino 3.4 SST anomaly, and the rainfall anomalies associated with the development and decay of El Nino events. However, the MOD still has biases in the predicted leading mode of interannual variability of precipitation. The leading mode captures the dry (wet) anomalies over the South China Sea (northern East Asia) but misplaces precipitation anomalies over the Yangtze River Valley. The model can capture the interannual variation of the circulation indices very well. The results here suggest that, over East Asia land regions, the skillful rainfall prediction relies on not only model’s capability in predicting better summer mean and ENSO teleconnection with EASM, but also accurate prediction of the leading modes of interannual variability.
Plasma densities in the vicinity of Callisto from Galileo plasma wave observations

The Galileo spacecraft has made seven close flybys of Jupiter's moon Callisto. During the closest of these (C22), which approached to within 535 km of the surface, the plasma wave instrument detected a very clear upper hybrid emission as the spacecraft passed near the moon. The peak electron density indicated by the upper hybrid resonance emission was 400 cm−3, almost one‐thousand times the electron density in the magnetosphere of Jupiter at the orbit of Callisto. These observations indicate that Callisto is probably surrounded by a dense ionospheric‐like plasma.
Robust compressed sensing of generative models

The goal of compressed sensing is to estimate a high dimensional vector from an underdetermined system of noisy linear equations. In analogy to classical compressed sensing, here we assume a generative model as a prior, that is, we assume the vector is represented by a deep generative model $G: \mathbb{R}^k \rightarrow \mathbb{R}^n$. Classical recovery approaches such as empirical risk minimization (ERM) are guaranteed to succeed when the measurement matrix is sub-Gaussian. However, when the measurement matrix and measurements are heavy-tailed or have outliers, recovery may fail dramatically. In this paper we propose an algorithm inspired by the Median-of-Means (MOM). Our algorithm guarantees recovery for heavy-tailed data, even in the presence of outliers. Theoretically, our results show our novel MOM-based algorithm enjoys the same sample complexity guarantees as ERM under sub-Gaussian assumptions. Our experiments validate both aspects of our claims: other algorithms are indeed fragile and fail under heavy-tailed and/or corrupted data, while our approach exhibits the predicted robustness.
Digit Stability Inference for Iterative Methods Using Redundant Number Representation

In our recent work on iterative computation in hardware, we showed that arbitrary-precision solvers can perform more favorably than their traditional arithmetic equivalents when the latter’s precisions are either under- or over-budgeted for the solution of the problem at hand. Significant proportions of these performance improvements stem from the ability to infer the existence of identical most-significant digits between iterations. This technique uses properties of algorithms operating on redundantly represented numbers to allow the generation of those digits to be skipped, increasing efficiency. It is unable, however, to guarantee that digits will stabilize, i.e., never change in any future iteration. In this article, we address this shortcoming, using interval and forward error analyses to prove that digits of high significance will become stable when computing the approximants of systems of linear equations using stationary iterative methods. We formalize the relationship between matrix conditioning and the rate of growth in most-significant digit stability, using this information to converge to our desired results more quickly. Versus our previous work, an exemplary hardware realization of this new technique achieves an up-to 2.2× speedup in the solution of a set of variously conditioned systems using the Jacobi method.