Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal step-size of least mean absolute fourth algorithm in low SNR

There is a need to improve the capability of the adaptive filtering algorithm against Gaussian or multiple types of non-Gaussian noises, time-varying system, and systems with low SNR. In this paper, we propose an optimized least mean absolute fourth (OPLMF) algorithm, especially for a time-varying unknown system with low signal-noise-rate (SNR). The optimal step-size of OPLMF is obtained by minimizing the mean-square deviation (MSD) at any given moment in time. In addition, the mean convergence and steady-state error of OPLMF are derived. Also the theoretical computational complexity of OPLMF is analyzed. Furthermore, the simulation experiment results of system identification are used to illustrate the principle and efficiency of the OPLMF algorithm. The performance of the algorithm is analyzed mathematically and validated experimentally. Simulation results demonstrate that the proposed OPLMF is superior to the normalized LMF (NLMF) and variable step-size of LMF using quotient form (VSSLMFQ) algorithms.
Modeling Time Dependent Water Chemistry Due to Powerful X-Ray Flares from T-Tauri Stars

Young stars emit strong flares of X-ray radiation that penetrate the surface layers of their associated protoplanetary disks. It is still an open question as to whether flares create significant changes in disk chemical composition. We present models of the time-evolving chemistry of gas-phase H2O during X-ray flaring events. The chemistry is modeled at point locations in the disk between 1 and 50 au at vertical heights ranging from the midplane to the surface. We find that strong, rare flares, i.e., those that increase the unattenuated X-ray ionization rate by a factor of 100 every few years, can temporarily increase the gas-phase H2O abundance relative to H by more than a factor of ∼3–5 along the disk surface (Z/R ≥ 0.3). We report that a “typical” flare, i.e., those that increase the unattenuated X-ray ionization rate by a factor of a few every few weeks, will not lead to significant, observable changes. Dissociative recombination of H3O+, H2O adsorption and desorption onto dust grains, and ultraviolet photolysis of H2O and related species are found to be the three dominant processes regulating the gas-phase H2O abundance. While the changes are found to be significant, we find that the effect on gas-phase water abundances throughout the disk is short-lived (days). Even though we do not see a substantial increase in long-term water (gas and ice) production, the flares’ large effects may be detectable as time-varying inner disk water “bursts” at radii between 5 and 30 au with future far-infrared observations.
Low-Area Low-Power Delta-Sigma Column and Pixel Sensors

Delta-sigma analog-to-digital converters (ADCs) are widely used in audio applications, but their use in video applications is emerging. By introducing novel decimator and modulator design flows, this thesis advances the state-of-the-art in delta-sigma data conversion and image sensing. As the approach concerns arrays of many ADCs, it is essential to minimize the layout area and power consumption of each ADC. For maximum scalability, each column or pixel must include a decimator. Conventional decimation, e.g., based on comb filters, is unsuitable for this purpose. Instead, finite-impulse response decimation may be realized efficiently in bit-serial fashion by generating optimal coefficients at the chip-level. As for the modulator, architectural choices and modeling approaches are taken to reduce both area and power while realizing specifications and tolerating mismatch. Very small capacitors suffice to achieve reasonable specifications. These design flows are used with 0. 18μm CMOS technology to fabricate specific designs. In a first chip with several data converters, column and pixellevel ADCs achieve figures of merit (150 and 137dB) comparable to stateof-the-art delta-sigma ADCs, but with smaller modulator areas (1,850 and 627μm2). In a second chip with two image sensors, logarithmic pixels are combined with column and pixel-level ADCs to make digital video cameras. Both image sensors achieve peak signal-to-noise-and-distortion ratios (35 and 46dB) comparable to the human eye and better than state-of-the-art logarithmic cameras. Although the present results are immediately useful, the approach is also suitable for low-voltage nanoscale CMOS processes, which would further reduce the layout area and power consumption.
Automated surface wave dispersion measurements and surface wave tomography of central and northern Europe

We automated the inter-station method. The method automatically selects smooth parts of phase velocity dispersion curves for all the events in one inter-station path, an average dispersion curve is obtained for each inter-station path after statistical analysis of individual measurements. We applied the method to a huge data-set covering almost entire Europe and obtained phase velocity measurements in broad period range from 8s to more than 250 s for both Love and Rayleigh waves. We used phase velocity measurements in tomographic inversion and obtained high quality very broad band phase velocity maps for central and northern Europe. Synthetic reconstruction tests reveal that the regions with dense path coverage possess a lateral resolution of about 100 km to 150 km. The low period phase velocity maps clearly image the central European basin system (CEBS). At intermediate periods (e.g. around 60 s) Trans - European Suture zone marks a clear and distinct boundary between old and cold East European Craton with that of younger Phanerozoic central Europe. The boundary is sharp beneath the TTZ (Tornquist- Teisseyre Zone) on the south-estern part of the TESZ, where as gradual transition towards the north western part of the TESZ i.e. STZ (Sorgenfrei-Tornquist Zone). European slab is imaged clearly at western Alps and is visible at the periods from 40s to 120s.
Strong warming of subarctic forest soil deteriorated soil structure via carbon loss – Indications from organic matter fractionation

Abstract. Net loss of soil organic carbon (SOC) from terrestrial ecosystems is a likely consequence of global warming and this may affect key soil functions. Strongest changes in temperature are expected to occur at high northern latitudes, with boreal forest and tundra as prevailing land-cover types. However, specific ecosystem responses to warming are understudied. We used a natural geothermal soil warming gradient in an Icelandic spruce forest (0–17.5 °C warming intensity) to assess changes in SOC content in 0–10 cm (topsoil) and 20–30 cm (subsoil) after 10 years of soil warming. Five different SOC fractions were isolated and the amount of stable aggregates (63–2000 µm) was assessed to link SOC to soil structure changes. Results were compared to an adjacent, previously investigated warmed grassland. Soil warming had depleted SOC in the forest soil by −2.7 g kg−1 °C−1 (−3.6 % °C−1) in the topsoil and −1.6 g kg−1 °C−1 (−4.5 % °C−1) in the subsoil. Distribution of SOC in different fractions was significantly altered, with particulate organic matter and SOC in sand and stable aggregates being relatively depleted and SOC attached to silt and clay being relatively enriched in warmed soils. The major reason for this shift was aggregate break-down: topsoil aggregate mass proportion was reduced from 60.7 ± 2.2 % in the unwarmed reference to 28.9 ± 4.6 % in the most warmed soil. Across both depths, loss of one unit SOC caused a depletion of 4.5 units aggregated soil, which strongly affected bulk density (R2 = 0.91 when correlated to SOC and R2 = 0.51 when correlated to soil mass in stable aggregates). The proportion of water extractable carbon increased with decreasing aggregation, indicating an indirect SOC protective effect of aggregates > 63 µm. Topsoil changes in total SOC and fraction distribution were more pronounced in the forest than in the adjacent warmed grassland soils, due to higher and more labile initial SOC. However, no ecosystem effect was observed in the response of subsoil SOC and fraction distribution. Whole profile differences across ecosystems might thus be small. Changes in soil structure upon warming should be studied more deeply and taken into consideration when interpreting or modelling biotic responses to warming.
Avian distribution along a gradient of urbanization in northeastern Puerto Rico

Urbanization has complex direct and indirect effects on native biota (Marzluff 2001). For birds, urbanization can affect species abundance, diversity, richness, distribution, biomass and community composition (Blair 1996, 2004, Clergeau et al. 1998, Crooks et al. 2004, White et al. 2005). In addition, urbanization also increases local rates of extinction and loss of native species, and is a major cause of biotic homogenization (McKinney and Lockwood 1999, McKinney 2002, 2006). However, urbanization provides research opportunities to study the effects of habitat modification on ecological communities. A useful research approach has been to study avian community composition along a gradient of urbanization (Blair 1996, Clergeau et al. 1998, Crooks et al. 2004). Studies across urban-rural gradients suggest that species richness and diversity peak at intermediate levels of urbanization (Lancaster and Rees 1979, Blair 1996, 2004, Crooks et al. 2004, van Rensburg et al. 2009) and that avian biomass increases with urbanization (Chace and Walsh 2006). The Caribbean island of Puerto Rico is attractive for urban studies because it is densely populated, has a high degree of urban sprawl (Martinuzzi et al. 2007), and the landscape is in rapid transformation due to urban expansion and forest recovery (Thomlinson et al. 1996, Thomlinson and Rivera 2000, López et al. 2001, Grau et al. 2003), as typical of other tropical regions (Aide and Grau 2004). In recent decades Puerto Rico has become predominantly urban with about 84% of its 3.9 million inhabitants living in cities (USBC 2000). At present about 11% of Puerto Rico is covered by urban/built-up surfaces (Martinuzzi et al. 2007). Within this setting, the island’s terrestrial avifauna provides ample diversity in which the effects of urbanization can be compared among endemic, resident and exotic species. The island is notable in having 36 naturalized exotic bird species (12.7% of the recorded bird species; Wege and Anadón-Irizarry 2008). Together with 16 endemic species and 142 breeding species the island provides an ideal situation in which the effects of Avian distribution along a gradient of urbanization in northeastern Puerto Rico
Mesh Generation and Flow Simulation in Large Tridimensional Fracture Networks

Fractures in the Earth's subsurface have a strong impact in many physical and chemical phenomena, as their properties are very different from those of the surrounding rocks. They are generally organized as multi-scale structures, which can be modeled by Discrete Fracture Networks (DFNs) that may contain hundreds of thousands of ellipses in the tridi-mensional space. This paper presents our approach to generate meshes of such large DFNs and to simulate single-phase flow problems using these meshes.
Using a Plasma FIB Equipped with Xe, N2, O2 and Ar for Atom Probe Sample Preparation – Ion Implantation and Success Rates

Focused ion beam (FIB) is a common tool for the preparation of site-specific atom probe specimens. However, for conventional Ga ion beam instruments, the removal rates are relatively slow for large sections of material (> 50 μm), as the Ga source has a low angular intensity, causing spherical aberrations that affect the milling rate [1]. Furthermore, using Ga ions for the removal of material leads to damage in the form of amorphisation, defect generation in the topmost layer, and implantation of Ga ions into the microstructure, which can lead to artefacts or even specimen failure during the atom probe analysis [2]. The development of the Xe plasma FIB (PFIB) has enabled much faster removal rates, as the milling rate is governed by the column rather than the ion source [1]. Xe ions also have a much lower penetration depth and radial range, causing less damage.
Characterization, testing, and optimization of load aggregation methods for ground heat exchanger response-factor models

Ground heat exchangers (GHEs) are often simulated using response factor-type models that rely on the GHE load history. As the simulation progresses, the number of GHE load history terms that need to be included in the temperature response computation grows. For extended, multiyear simulations the required computation time due to these history terms can be excessive, which limits the usefulness of response factor-type models. To cope with this, load aggregation procedures have been developed that preserve the net effect the individual loads but reduce the overall number of computations. The previously developed load aggregation methods can be characterized as two different methods, but with different parameters. The parameters each affect the accuracy and computational efficiency of the methods, yet there has been no systematic investigation of the parameter space. In this article, both methods are implemented and compared for a wide range of parameters. A final recommendation is made that results in a 73-fold reduction in simulation time when compared to nonaggregated simulations. For a worst-case situation with an imbalanced load profile on a densely packed borehole field, the MBE is less than 0.13 °C for 5- and 10-year simulations. The resulting predicted heat pump energy consumption error is less than 0.3%.
Online and Classroom Simulations: Does Video Use Inspire Interest, Comprehensibility, or Achieve Learning Outcomes?

This study examined whether a video illustration of a complex phenomenon promoted learner interest, perceived comprehensibility, and better learning in online- and classroom-based contexts. In the first study, undergraduate participants (N = 101) viewed learning materials which contained a video only, a video and textual explanation, or a textual explanation alone. Participants rated the interestingness and comprehensibility of the instructional materials and completed a learning outcomes test. The second study (N = 56) included the same learning materials in a classroom context. The video presentation of the material did not improve learning outcomes, in either context. Participants in the computer-delivered context who only viewed the text learned the material better than those who had viewed the video. In the classroom-delivered context, the video neither helped nor hurt the learning outcomes, but it also did not significantly boost learners’ interest. Taken together, findings from the present study indicate potential limited utility of including video material within these instructional contexts.
Two-stage Neural Architecture Search for Microscopy Image Segmentation

Modern microscopy hardware is capable of acquiring gigabytes or terabytes of 2D, 3D, and 4D data in short spans of time [1]. The rapid growth in dataset size necessitates the automation of common image analysis tasks in order to fully leverage the information that the data provides. One fundamental image analysis task is segmentation the grouping of pixels into regions corresponding to image content. In the past decade, automated segmentation algorithms based on deep neural networks have made significant progress on automating difficult microscopy segmentation problems, such as those encountered in largescale biological electron microscopy using imaging platforms such as serial block-face scanning electron microscopy (SBF-SEM) [1]. However, challenges remain for bolstering performance to levels of accuracy required for practical, turn-key solutions for biomedical research. One challenge is identifying neural network architectures best suited for a given segmentation problem. Large neural networks such as those used for image segmentation may require dozens of design choices to fully specify a computational module, and multiple modules may be combined to form a final network architecture. Searching through the combinatorial collection of hyperparameters which quantify these design choices is computationally expensive, even with a cluster of GPU-enabled compute nodes available. Naive search strategies such as grid search or random search at the level of individual network layers may fail to discover architectures that are more useful than those designed by hand via human intuition.
The Case for Deep Query Optimisation

Query Optimisation (QO) is the most important optimisation problem in databases. The goal of QO is to compute the best physical plan under a given cost model. In that process, physical operators are used as building blocks for the planning and optimisation process. In this paper, we propose to deepen that process. We present Deep Query Optimisation (DQO). In DQO, we break up the abstraction of a 'physical' operator to consider more fine-granular subcomponents. These subcomponents are then used to enumerate (sub-)plans both offline and at query time. This idea triggers several exciting research directions: (1) How exactly can DQO help to compute better plans than (shallow) QO and at which costs? (2) DQO can be used to precompute and synthesise database operators and any other database component as Materialised Algorithmic Views (MAVs). (3) We identify the Algorithmic View Selection Problem (AVSP), i.e. which MAVs should be materialised when? This paper presents the high-level idea of DQO using an analogy inspired from biology. Then we proceed to question the terms 'physical' and 'physical operator'. We present experiments with a 'physical operator' formerly known as 'hash-based grouping'. We benchmark that operator both independently as well as in the context of DQO-enabled dynamic programming. We conclude by sketching a DQO research agenda.
On Implementing Usability Evaluation Activities within Agile Environment: A Plan Based-Process

In fast-paced software development cycles, poor governance may cause a loss of focus on the overarching objectives and implementation of usability work. We propose a conceptual framework that would assist software development teams in understanding and implementing usability evaluations in the described agile environments. The framework provides a common language that conveys the “big picture” of software development, details the roles needed for usability, and facilitates the analysis, discussion and communication of responsibilities and decision structures. The framework is supported by a visual mapping guideline that eases discussion and simplifies the process of incorporating usability work in companies.
cSeiz: An Edge-Device for Accurate Seizure Detection and Control for Smart Healthcare

Epilepsy is one of the most common neurological disorders affecting up to 1% of the world's population and approximately 2.5 million people in the United States. Seizures in more than 30% of epilepsy patients are refractory to anti-epileptic drugs. An important biomedical research effort is focused on the development of an energy efficient implantable device for the real-time control of seizures. In this paper we propose an Internet of Medical Things (IoMT) based automated seizure detection and drug delivery system (DDS) for the control of seizures. The proposed system will detect seizures and inject a fast acting anti-convulsant drug at the onset to suppress seizure progression. The drug injection is performed in two stages. Initially, the seizure detector detects the seizure from the electroencephalography (EEG) signal using a hyper-synchronous signal detection circuit and a signal rejection algorithm (SRA). In the second stage, the drug is released in the seizure onset area upon seizure detection. The design was validated using a system-level simulation and consumer electronics proof of concept. The proposed seizure detector reports a sensitivity of 96.9% and specificity of 97.5%. The use of minimal circuitry leads to a considerable reduction of power consumption compared to previous approaches. The proposed approach can be generalized to other sensor modalities and the use of both wearable and implantable solutions, or a combination of the two.
Breast Cancer Diagnostic System Based on MR images Using KPCA-Wavelet Transform and Support Vector Machine

Automated detection and accurate classification of breast tumors using magnetic resonance image (MRI) are very important for medical analysis and diagnostic fields. Over the last ten years, numbers of methods have been proposed, but only few methods succeed in this field. This paper presents the design and the implementation of CAD system that has the ability to detect and classify the tumor of the breast in the MR images. To achieve this, k-mean clustering methods and morphological operators are applied to segment the tumor. The gray scale, Texture and symmetrical features as well as discrete wavelet transform (DWT) are used in feature extracted stage to obtain the features from MR images. Kernel principle components analysis (K-PCA) are also applied as a feature reduction technique and support vectors machine (SVM) are used as a classifier. Finally, the experiments results have confirmed the robustness and accuracy of proposed system
X-SQL: reinforce schema representation with context

In this work, we present X-SQL, a new network architecture for the problem of parsing natural language to SQL query. X-SQL proposes to enhance the structural schema representation with the contextual output from BERT-style pre-training model, and together with type information to learn a new schema representation for down-stream tasks. We evaluated X-SQL on the WikiSQL dataset and show its new state-of-the-art performance.
LEAP nets for power grid perturbations

We propose a novel neural network embedding approach to model power transmission grids, in which high voltage lines are disconnected and reconnected with one-another from time to time, either accidentally or willfully. We call our architeture LEAP net, for Latent Encoding of Atypical Perturbation. Our method implements a form of transfer learning, permitting to train on a few source domains, then generalize to new target domains, without learning on any example of that domain. We evaluate the viability of this technique to rapidly assess cu-rative actions that human operators take in emergency situations, using real historical data, from the French high voltage power grid.
Estimation of Tropical Cyclone Intensity Using Infrared Data from a Geostationary Satellite

Accurate estimation of tropical cyclone (TC) intensity is of great significance for serious natural disasters. A new method is presented to estimate intensity of TC using satellite infrared data. Firstly, TC region is calculated according to the location of TC center. Secondly, 2D-PCA algorithm is used to extract feature of bright temperature image, and historical data of TC intensity is matched with the k-nearest neighbor algorithm. Thirdly, the matching results are analyzed and the intensity information of TC is estimated. In addition, a TC intensity database, which contains historical data during 2006−2010, is developed for estimation of TC intensity. Experiments show that the proposed method is efficient for real-time estimation of TC intensity, average error of estimation is lower than 15 hPa. ( Citation : Liu, J., X. Xu, and X. Luo, 2019: Estimation of tropical cyclone intensity using infrared data from a geostationary satellite. SOLA , 15 , 189−192, doi:10.2151/sola.2019-034.) aver - age errors of the proposed ETCI method are than 15 hPa.