Miracle-dz/UniFAR-FTUs huggingface dataset:
Canonical correlation analysis when the data are curves.

SUMMARY It is not immediately straightforward to extend canonical correlation analysis to the context of functional data analysis, where the data are themselves curves or functions. The obvious approach breaks down, and it is necessary to use a method involving smoothing in some way. Such a method is introduced and discussed with reference to a data set on human gait. The breakdown of the unsmoothed method is illustrated in a practical context and is demonstrated theoretically. A consistency theorem for the smoothed method is proved. In an increasing number of problems in a wide range of fields, the data observed are not the univariate or multivariate observations of classical statistics, but are func- tions observed continuously. Ramsay and Dalzell (1991) give the name functional data analysis to the analysis of data of this kind. In most cases, the observations will be functions of time, or a closely related variable, but there are clearly applications where the functions are surfaces observed over two- or three-dimensional space. The motivating example for the present paper is a set of data on children's gait collected by the Motion Analysis Laboratory at Children's Hospital, San Diego, California; see Olshen et al. (1989) for full details. For each of a number of children, several angles made by the child's joints (knee, hip, etc.) are observed during the child's gait cycle. One aim of the study is to gain understanding of the gait cycle of a 'normal' child to make comparisons with children suffering from walking diffi- culties. These data motivated Rice and Silverman (1991) to discuss the extension of principal component analysis to the functional setting, and to explain how smoothing can be incorporated into the analysis in a natural way. In a substantial complemen- tary paper, Ramsay and Dalzell (1991) discuss different approaches to regression and principal component analysis in functional data analysis, illustrating their work by a meteorological example. The insights of functional data analysis may be helpful in chemometrics; for instance a standard chemometric problem is the analysis of spectra observed in chromatography, and these are to all intents and purposes functional observations. Another obvious area of potential relevance is the analysis of growth curve data. The focus in the present paper will be on canonical correlation analysis (CCA). In the gait example, one might ask how variability in the knee angle cycle is related to
BAW Filter for space applications at 4.2 GHz

This paper presents a C-band Bulk Acoustic Wave (BAW) filter for space applications. The Solidly Mounted Resonators (SMR) of the filter are based on an Aluminum Nitride (AIN) technology above a Bragg mirror composed of tungsten (W) and silicon dioxide (SiO2). The resonators used to design the filter have a 5.4% electromechanical coupling coefficient at ≈4.2 GHz and their quality factor at the anti-resonance is close to 700. The filter is centered at 4.2 GHz, it has an 83 MHz bandwidth and the minimum insertion loss in the bandwidth is 3.2 dB. Its volume, smaller than 1 mm3, and its mass, less than 1 gram, are drastically reduced compared to technologies commonly used on-board satellites.
Reflector-Based Power Output Maximization and Near-Field Detuning-Mitigation in Miniaturized Tightly-Coupled Flexible Rectenna Arrays

While rectenna arrays can improve the net DC output from low incident power densities, the effects of tight coupling and detuning are not typically explored. Here, the scalability of 915 MHz wire-type compact rectennas is investigated in terms of mutual coupling, DC output, and its immunity to near-field detuning. First, the mutual coupling between tightly-coupled dipole rectennas is investigated using s-parameters, showing a maximum coupling of 6 dB. The rectennas, achieving a 36% Power Conversion Efficiency (PCE) at −20 dBm, have been closely spaced forming a six-element array integrated with a radius of 7 cm. The effect of placing the omnidirectional rectennas on different dielectrics including lossy objects is investigated and a lightweight reflector-backing is proposed, demonstrating a $2.3\times$ higher power output at a low profile of 2 cm $(0.06\lambda)$. The array is connected to a BQ25504 power management integrated circuits (PMIC), charging a $100\mu \mathrm{F}$ electrolytic capacitor in $382\mathbf{s}$ before powering a Bluetooth WSN for one successful advertisement at 0 dBm transmitted power, from a power density of O. $22\ \mu \mathbf{W}/\mathbf{cm}^{2}$, with the potential for a three-fold improvement using an optimally-placed reflector.
Urban Air Mobility (UAM) and Total Mobility Innovation Framework and Analysis Case Study: Boston Area Digital Twin and Economic Analysis

Studies conducted as a part of the Advanced Air Mobility (AAM) mission of the National Aeronautics and Space Administration (NASA) estimate that by 2035 significant numbers of new air vehicles will be operating to support Urban Air Mobility (UAM). These vehicles will differ in size, speed, and configuration depending on the purpose, which may include final-mile delivery, new forms of public transportation, cargo delivery and air taxi services. UAMs are planned to operate in high density populated urban areas, and the community integration of these new vehicles into the existing transportation system remains a primary concern for local and state governments. UAM is coming and cities are unprepared. UAM has the potential to transform a city’s economy, create jobs, reduce emissions compared to conventional road vehicles and aircraft, and reduce infrastructure costs. It is the start of a new multimodal transportation era. This paper addresses the potential to move UAM work forward by providing a case study and framework for cities when addressing UAM.A multimodal team of U.S.DOT researchers at the Volpe National Transportation Systems Center developed a solution to provide stakeholders (such as city and state planners) with a set of tools to meet this challenge. These tools will enable stakeholders who are currently seeking to move ahead of this fast-paced industry, such as the Federal Aviation Administration (FAA), NASA, and states or cities looking to attract UAM investment. The team set out to conduct a Boston-based case study that is transferable to other cities, analyzing the integration of UAM into the Greater Boston transportation network. This work provides a site-specific UAM conceptual design framework and economic analysis that can be customized to any metropolitan area. The study provides a novel, government-focused perspective on the multimodal integration of UAM.
Utilization of GIS Machine Learning for Mapping of Building Shapes in Tibang Village, Banda Aceh City, Aceh, Indonesia

Tibang is one of the villages in Banda Aceh which is located in a high tsunami-hazard zone. Today, many initial aid houses in the area have grown larger than the original, and new housing complex has begun to emerge. Therefore, it is necessary to find an efficient method to map the buildings in the village, so that an up-to-date buildings map of the village can be prepared quickly. Machine learning GIS is the use of machine learning algorithms in processing and analyzing spatially-based GIS data. This study aimed at applying the Support Vector Machines (SVM) method of GIS Machine learning for classifying buildings in a very-high-resolution image of Tibang Village, SVM has been used with Object-Based Image analysis (OBIA) to classify UAV-based images for various Land-Use / Land Cover (LULC) applications. The stages in classifying buildings using SVM are starting with processing high-resolution data from UAV photos to create an ortho-mosaic image. The image was then segmented using OBIA techniques and then was classified using the SVM method. The result of the SVM classification was 230 house objects identified in the village of Tibang, Syiah Kuala District with the number of training data samples used for the building class being 29 with various variations of the shape of the building objects identified in the UAV images.
Gaming With OOP Learn: A Mobile Serious Game to Learn Object-Oriented Programming

Information Technology has proved to be important in various sectors of the economy, one of them being the education sector. Sophisticated equipment and advanced software applications have contributed to enhancing learning techniques across many universities around the world. Nowadays, youngsters’ interest in playing games has been on the rise whether it is for entertainment or non-entertainment purposes. Serious games take this advantage and prove to be effective in improving the learning environment and efficiency of the learning process. Hence, learners’ ability to learn new skills and the capacity for retention through game-based learning has found great success as opposed to the traditional learning method that tends to be boring. Object-Oriented programming has always been quite challenging for IT students. This work focuses on the development of a mobile serious game ‘Gaming With OOP Learn’ to teach Object-Oriented Programming concepts to undergraduate students and to have their teachers assess their performance and provide feedback as a means of improving their performance. Thorough information was gathered through a review of existing similar systems and the ACM guidelines for Object-Oriented Programming concepts have been taken into account when implementing the different games proposed in Gaming With OOP Learn.
Navigating a road network using Reinforcement Learning

Agent based models are increasingly utilised in game development and film making because they allow the creation of large scale content with minimal effort. We use Reinforcement Learning and a curriculum learning approach to train a single model for agents navigating a road network encompassing different types of intersections, to respect traffic regulations. We train agents to navigate two game scenes: a two-way crossroad intersection governed by traffic lights and a four-way roundabout intersection, and we discuss the performance of the trained model with respect to robustness and success rates.
The chemical evolution of r-process elements from neutron star mergers: the role of a 2-phase interstellar medium

Neutron star mergers (NM) are a plausible source of heavy r-process elements such as Europium, but previous chemical evolution models have either failed to reproduce the observed Europium trends for Milky Way thick disc stars (with [Fe/H] ~ -1) or have done so only by adopting unrealistically short merger timescales. Using analytic arguments and numerical simulations, we demonstrate that models with a single-phase interstellar medium (ISM) and metallicity-independent yields cannot reproduce observations showing [Eu/alpha] > 0 or [Eu/Fe] > [alpha/Fe] for alpha-elements such as Mg and Si. However, this problem is easily resolved if we allow for a 2-phase ISM, with hot-phase cooling times \tau_{cool} of order 1 Gyr and a larger fraction of NM yields injected directly into the cold star-forming phase relative to alpha-element yields from core collapse supernovae (ccSNe). We find good agreement with observations in models with a cold phase injection ratio f_{c,NM}/f_{c,ccSN} of order 2, and a characteristic merger timescale \tau_NM=150 Myr. We show that the observed super-solar [Eu/alpha] at intermediate metallicities implies that a significant fraction of Eu originates from NM or another source besides ccSNe, and that these non-ccSN yields are preferentially deposited in the star-forming phase of the ISM at early times.
Identifying User Attention for Car Sharing Service via Text Comments Data

Car-sharing is an evolving mobility service with great potential for development. Precise identifying the issues that users are concerned about and understanding the heterogeneity of users' preferences are the key tasks for extending the market. The comments data posted by users after experiencing the service provides detailed and individualized service expectations for service improvement, which illustrates the unique value of social media for mobility innovation. Based on the text comments of order and user's characteristics data, users were classified according to frequency and interval of service using to investigate the heterogeneity of users' evaluations or attitudes. The issues that the comments concentrated on were grouped into four categories to examine the improvement and deterioration of the car-sharing service over time. The reasons why users terminate orders are also analyzed. This study proves that the information extracted from language text data has great value and application for enhancing service governing and marketing expansion, which is pivotal to the survival of innovative mobility services.
A High Throughput Hardware Accelerator for FFTW Codelets: A First Look

The Fast Fourier Transform (FFT) is a critical computation for numerous applications in science and engineering. Its implementation has been widely studied and optimized on various computing platforms, with the FFTW library becoming the standard interface in HPC. In this work, we propose hardware acceleration of the FFTW library by putting a software code let into hardware. The hardware is exposed to the user through an FFTW -compatible software library while actual computation takes place behind the scenes on a custom accelerator. To demonstrate a first look at this idea, we design a high throughput accelerator for FFTW twiddle codelets. The FFT hardware is automatically generated using SPIRAL and a test chip is fabricated in a TSMC 28nm process. We provide measured results of the test chip and discuss many opportunities for future work.
Morphology rather than environment drives the SFR–mass relation in the local universe

Exploiting a sample of 680 star-forming galaxies from the Padova-Millennium GalaxyGroup Catalog (PM2GC) (Calvi et al. 2011) in the range 0.038 10.25 and SSFR>10^{-12} yr^{-1}, the SFR-M_ast and SSFR-M_ast relations do not depend on environment, when the global environment is used, while when the halo mass is considered, high mass haloes might have a systematically lower (S)SFR-M_ast relation. Finally, when local densities are exploited, at any given mass galaxies in less dense environments have systematically higher values of SFR. All the relations are characterized by a large scatter ({\sigma}~0.6), which is dueto the coexistence of galaxies of different morphological types. Indeed, at any given mass, late-types are characterized by systematically higher values of SFR and SSFR than S0s and ellipticals. Galaxies of the same morphology show similar trends in all the environments, but their incidence strongly depends on environment and on the environmental parametrization adopted, with late-types generally becoming less common in denser environments, contrasted by the increase of ellipticals and/or S0s. Our results suggest that in the local universe morphology and local interactions, probed by the local density parameterization, have dominant roles in driving the characteristics of the SFR-M_ast relation.
Is Eve nearby? Analysing protocols under the distant-attacker assumption

Various modern protocols tailored to emerging wire-less networks, such as body area networks, rely on the proximity and honesty of devices within the network to achieve their security goals. However, there does not exist a security framework that supports the formal analysis of such protocols, leaving the door open to unexpected flaws. In this article we introduce such a security framework, show how it can be implemented in the protocol verification tool Tamarin, and use it to find previously unknown vulnerabilities on two recent key exchange protocols.
Enhancement of Urban Floodwater Mapping From Aerial Imagery With Dense Shadows via Semisupervised Learning

Timely and accurate mapping of floodwater in urban areas from aerial imagery is critical to support emergency response and rescue work. However, massive shadows cast by buildings and trees over dense built-up urban areas can cause a significant underestimation of flood outcomes, and few studies for flood monitoring explore this in current state-of-the-art approaches. Meanwhile, recent deep learning (DL) algorithms have reported superior performance in flood mapping over conventional machine learning methods. Nevertheless, acquiring a large amount of training data remains challenging in the DL paradigm. In this study, to exploit the potential of the DL algorithm in detecting all visible (including shadowed and nonshadowed) floodwater with limited training samples from aerial imagery, we designed a modified fully convolutional network and combined it with a deep semisupervised learning framework integrating consistency regularization and RandMix strategy into the floodwater detection workflow. Besides, the test-time augmentation technique was leveraged to improve the model performance in the evaluation phase. Extensive experiments on the 2013 Calgary flood demonstrated the effectiveness of our approach on extracting visible floodwater in urban areas with dense shadows. Notably, our method could accurately detect more floodwater with a largely reduced number of labeled training samples, which is a considerable enhancement in the applicability and availability of DL algorithms for flood monitoring in densely shadowed urban areas.
Wideband Sequential Circulator Load Modulated Amplifier with Back-Off Efficiency Enhancement

This paper demonstrates a novel power amplifier (PA) architecture; the sequential circulator load modulated amplifier (SCLMA). The sequential configuration resulted in excellent bandwidth and extended back-off efficiency enhancement, making it suitable for modern communication scenarios with large peak-to-average-power-ratio (PAPR) signals. A wideband prototype PA based on GaN transistors and a commercial off-the-shelf circulator is employed to validate the SCLMA concept. The experimental results exhibit a drain efficiency of 55–68 % at the peak output power and 46–53 % at 8-dB output power back-off across 2.0-3.0 GHz. Combined with the measured peak output power of 42.7±0.7 dBm in the same range, make it a promising candidate for use in modern energy-efficient wireless communication transmitters.
Effective Vehicle Tracking in Dynamic Indoor Parking Area based on Hidden Markov Model and Online Learning

As a fundamental requirement for Intelligent Transportation System (ITS), reliable and pervasive vehicular localization has attracted considerable attention. However, it is still challenging to enable such services in dynamic indoor parking environments where Global Navigation Satellite System (GNSS) is not available. In view of this, this work first proposes a Hidden Markov Fusion (HMF) algorithm to fuse WiFi fingerprinting localization with inertial sensors based Dead Reckoning (DR), in which the WiFi fingerprinting localization result is modelled as the emission probability and the displacement inferred by DR is modelled as the transition probability. On this basis, a forward probability is derived to estimate the distribution of a vehicle's position. Moreover, considering that signal features may vary over time in dynamic indoor parking environments, we further propose an online learning framework, which contains an online evaluation method to assess the accuracy of WiFi fingerprinting localization results and an modified Homogeneous Online Transfer Learning (HomOTL) algorithm to continuously update the fingerprinting localization model. Finally, we implement the system prototype and give comprehensive performance evaluation in realistic indoor parking environments, which conclusively demonstrates the effectiveness of the proposed solutions.
Efficient Calibration of Agent-Based Traffic Simulation Using Variational Auto-Encoder

In agent-based traffic simulation, calibration is an essential stage before the models applied to reproduce the individual/group travel behaviors. While traditional methods suffer from a high computational complexity, this paper proposes an improved method to alleviate the computational burden for large-scaled simulations. Specifically, we introduce variational auto-encoder to compress the original agent state vector into a lower dimensional hidden space, where the state transfer probability is calculated fast. Then the probability is mapped into the original space through a decoder, to achieve the agent travel parameters. The dynamic calibration method is tested with other baselines in urban travel demand analysis. Experiment results demonstrate that our method brings about 19% elevation of efficiency with the same accuracy of calibration.
A Hybrid Intrusion Detection Method using Improved Stacking Ensemble Algorithm and False Positive Elimination Strategy for CBTC

With the wide application of information technolo-gies in communication based train control (CB TC) systems, the information security risks it confronts to are ever increasing. It is very difficult to design an intrusion detection system (IDS) which can detect both known and unknown cyber attacks with high true positive rate (TPR) and low false positive rate (FPR), simultaneously. In this paper, we propose a hybrid intrusion detection system (RIDS) which is composed of a signature-based intrusion detection system (SIDS) and an anomaly-based intrusion detection system (AIDS). An improved stacking ensemble algorithm (ISEA) is proposed to enhance the TPR of the SIDS which uses weighted classification probabilities outputted by base-classifiers to generate meta-features for the meta-classifier. The FPR of the AIDS is significantly reduced through adopting the proposed false positive elimination strategy (FPES). In addition, a Bayesian optimization algorithm is used for hyper-parameters adjustment to optimize the overall performance of the RIDS. The proposed RIDS is evaluated using both a KDD99 dataset and a CBTC dataset collected on a hardware-in-the-Ioop simulation platform. For the KDD99 dataset, the TPR and FPR of the proposed RIDS are 98.6% and 1.3%, respectively. For the CBTC dataset, the TPR and FPR are 98.1% and 1.1 %, respectively. Based on the performance comparison among different RIDSs, it is can be concluded that the proposed RIDS is superior to the existing RIDS in both TPR and FPR.
Motor Imagery Approach for BCI Game Development

Brain-computer interface (BCI) is a rapidly growing field with various applications in many domains such as medical, gaming and lifestyle. This paper presents a 3D non-invasive BCI game. Muse 2 headband is used for acquiring electroencephalogram (EEG) data and OpenViBE platform for processing the raw signals and classification. The game is developed in Unity game Engine. Several subjects are included in the study and EEG signals are recorded for three different mental states i.e. left and right Motor Imagery and eye blink, before playing the game for ten times, aiming to collect coins. Average classification result is 94.86% and average coins collected from the users is 30.8 out of 50 coins. Furthermore, longer periods of playing the game leads to increased control over the game.