Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial interpolation of point velocities in stream cross-section

Abstract The most frequently used instrument for measuring velocity distribution in the cross-section of small rivers is the propeller-type current meter. Output of measuring using this instrument is point data of a tiny bulk. Spatial interpolation of measured data should produce a dense velocity profile, which is not available from the measuring itself. This paper describes the preparation of interpolation models. Measuring campaign was realized to obtain operational data. It took place on real streams with different velocity distributions. Seven data sets were obtained from four cross-sections varying in the number of measuring points, 24-82. Following methods of interpolation of the data were used in the same context: methods of geometric interpolation arithmetic mean and inverse distance weighted, the method of fitting the trend to the data thin-plate spline and the geostatistical method of ordinary kriging. Calibration of interpolation models carried out in the computational program Scilab is presented. The models were tested with error criteria by cross-validation. Ordinary kriging was proposed to be the most suitable interpolation method, giving the lowest values of used error criteria among the rest of the interpolation methods.
Video Game Spaces: Image, Play, and Structure in 3D Worlds

The move to 3D graphics represents a dramatic artistic and technical development in the history of video games that suggests an overall transformation of games as media. The experience of space has become a key element of how we understand games and how we play them. In Video Game Spaces, Michael Nitsche investigates what this shift means for video game design and analysis. Navigable 3D spaces allow us to crawl, jump, fly, or even teleport through fictional worlds that come to life in our imagination. We encounter these spaces through a combination of perception and interaction. Drawing on concepts from literary studies, architecture, and cinema, Nitsche argues that game spaces can evoke narratives because the player is interpreting them in order to engage with them. Consequently, Nitsche approaches game spaces not as pure visual spectacles but as meaningful virtual locations. His argument investigates what structures are at work in these locations, proceeds to an in-depth analysis of the audiovisual presentation of gameworlds, and ultimately explores how we use and comprehend their functionality. Nitsche introduces five analytical layersrule-based space, mediated space, fictional space, play space, and social spaceand uses them in the analyses of games that range from early classics to recent titles. He revisits current topics in game research, including narrative, rules, and play, from this new perspective. Video Game Spaces provides a range of necessary arguments and tools for media scholars, designers, and game researchers with an interest in 3D game worlds and the new challenges they pose.
Time-series methods and applications

Volume 27 of "Advances in Econometrics", entitled "Missing Data Methods", contains 16 chapters authored by specialists in the field, covering topics such as: Missing-Data Imputation in Nonstationary Panel Data Models; Markov Switching Models in Empirical Finance; Bayesian Analysis of Multivariate Sample Selection Models Using Gaussian Copulas; Consistent Estimation and Orthogonality; and Likelihood-Based Estimators for Endogenous or Truncated Samples in Standard Stratified Sampling.
Design of the 2001 and 2011 Census Coverage Surveys for England and Wales

Summary. In many countries, a key component of measuring census coverage is a post‐enumeration survey. In the 2001 censuses of the UK this was called the Census Coverage Survey. The paper reviews the design of the 2001 Census Coverage Survey and develops the design for the survey in 2011, taking advantage of the experience of 2001 and the data that were generated by the 2001 process. This leads to a proposed design that is less clustered than in 2001 and has an allocation that is more skewed towards areas where coverage in the 2011 census is expected to be lowest. The updated design balances optimal allocation against maintaining a sufficient sample across all areas.
Android malware detection method based on new gray image and VGG

Android malware is transformed into gray image and recognized by deep learning model, so as to realize end-to-end detection. This method has the natural advantage of resisting code obfuscation attack. At present, most of the methods used by researchers for gray image recognition of Android malware are roughly processed by a single convolutional neural network, which does not make good use of this advantage. Therefore, this paper proposes a new Android malware detection method, which is different from the traditional detection method XML file and class.dex file is purified, combined and mapped into a gray image, and then the VGG neural network model is used to detect the gray image. At the same time, we take out the models such as mobilenet and alexnet for comparison. The experimental results show that the purification method has good Android malware detection effect, and the VGG model training results have higher detection accuracy than the other two algorithms, and all evaluation indexes have good performance.
Music Deep Learning: A Survey on Deep Learning Methods for Music Processing

Deep Learning has emerged as a powerful set of computational methods achieving great results in a variety of different tasks. Music signal processing, a field with rich commercial applications, seems to benefit too from this data-driven approach. In this paper a review of the state of the art Deep Learning methods applied on music signal processing is provided. A special focus is given in music information retrieval and music generation. In addition, possible future research directions are discussed.
Coexistence of Myelin Oligodendrocyte Glycoprotein Immunoglobulin G and Neuronal or Glial Antibodies in the Central Nervous System: A Systematic Review

Background: Myelin oligodendrocyte glycoprotein immunoglobulin G (MOG-IgG) has been considered a diagnostic marker for patients with demyelinating disease, termed “MOG-IgG associated disorder” (MOGAD). Recently, the coexistence of MOG-IgG and other neuronal or glial antibodies has attracted extensive attention from clinicians. In this article, we systematically review the characteristics of MOG-IgG-related antibody coexistence syndrome. Methods: Two authors independently searched PubMed for relevant studies published before October 2021. We also manually searched the references of each related article. The appropriateness of the included studies was assessed by reading the titles, abstracts, and full texts if necessary. Results: Thirty-five relevant publications that met our inclusion criteria were finally included, of which fourteen were retrospective studies and twenty-one were case reports. A total of 113 patients were reported to show the coexistence of MOG-IgG and neuronal or glial antibodies. Additionally, 68.14% of patients were double positive for MOG-IgG and N-Methyl-D-Aspartate Receptor-IgG (NMDAR-IgG), followed by 23.01% of patients who were double positive for MOG-IgG and aquaporin4-IgG (AQP4-IgG). Encephalitis was the predominant phenotype when MOG-IgG coexisted with NMDAR-IgG, probably accompanied by imaging features of demyelination. Patients with dual positivity for MOG-IgG and AQP4-IgG experienced more severe disease and more frequent relapses. The coexistence of MOG-IgG and antibodies other than NMDAR-IgG and AQP4-IgG was extremely rare, and the clinical presentations were diverse and atypical. Except for patients who were double positive for MOG-IgG and AQP4-IgG, most patients with multiple antibodies had a good prognosis. Conclusions: MOG-IgG may coexist with neuronal or glial antibodies. Expanded screening for neuronal or glial antibodies should be performed in patients with atypical clinical and radiological features.
Cometary X-rays: Solar wind charge exchange in cometary atmospheres

The interaction of the solar wind with the planets and the interstellar medium is of key importance for the evolution of our solar system. The interaction with Earth's atmosphere is best known for the northern light. In case of Mars, the interaction with the solar wind might have lead to the erosion of its atmosphere. Solar wind-atmosphere interactions can be studied particularly well in cometary atmospheres, because in that case the solar wind flow is not attenuated by a planetary magnetic field and interacts directly with its atmosphere, the coma. The size of the cometary atmosphere (in the order of 10$^4-10^5$~km) allows remote tracking of the ions as they penetrate into the comet's atmosphere, offering a unique window on the cometary atmosphere, the solar wind and the interaction of these two plasmas. When solar wind ions fly through an atmosphere they are neutralized via charge exchange reactions with the neutral gaseous species. These reactions depend strongly on target species and collision velocity. The resulting X-ray and Far-UV emission can therefore be regarded as a fingerprint of the underlying reaction, with many diagnostic qualities. My thesis studies have focussed on all aspects relevant for X-ray emission from comets: experimental studies of state-to-state charge exchange cross sections, observations of X-ray emission from comets with Chandra, XMM, and Swift, and theoretical modelling of the interaction of solar wind ions with cometary gasses and the resulting X-ray emission spectrum. Together, this has greatly improved our understanding of the interaction of the solar wind with solar system objects and in more general, of physical processes in wind-environment collisions. The thorough understanding of cometary charge exchange emission has opened the door to the direct observation of more complex solar wind interactions such as those with Mars and Venus.
Infrared Small Target Detection Based on the Weighted Double Local Contrast Measure Utilizing a Novel Window

One of the integral parts of the infrared search and tracking (IRST) system is the infrared small target detection. To facilitate the detection of infrared small targets in complex backgrounds, infrared small target detection based on the weighted double local contrast measure (WDLCM) utilizing a novel window detection framework is proposed. First, a novel window is designed to measure the weighted double local contrast. Second, a double local contrast measure (DLCM) is proposed to enhance the region of infrared small targets in infrared images. It consists of the local contrast between the target region and surrounding background region and that within the target region. Then, a weighting function ( $W$ ) is established to further enhance targets and suppress surrounding backgrounds by exploiting the variance of the target region, the standard deviation of the surrounding background region, and the difference variance between the target region and surrounding background region. Finally, after obtaining a WDLCM saliency map, adaptive threshold segmentation will be employed in order to capture real targets. The experimental results on different scene datasets show that the proposed method has a high detection rate, a low false alarm rate, and good real-time performance compared with existing methods.
Humanoid Robot Balancing

There have been many researches about humanoid robot motion control, for example, walking pattern generation (Huang et al, 2001) (Kajita et al, 2002, 2003), walking control (Choi et al, 2006) (Grizzle et al, 2003) (Hirai et al, 1998) (Kajita et al, 2001) (Kim & Oh, 2004), (Lohmeier et al, 2004) (Park, 2001) (Takanishi et al, 1990) (Westervelt et al, 2003), running control (Nagasaki et al, 2004, 2005), balancing control (Kajita et al, 2001) and whole body coordination (Choi et al, 2007) (Kajita et al, 2003) (Sentis & Khatib, 2005) (Goswami & Kallem, 2004) (Harada et al, 2003) (Sugihara & Nakamura, 2002). Especially, the whole body coordination algorithm with good performance becomes a core part in the development of humanoid robot because it is able to offer the enhanced stability and flexibility to the humanoid motion planning. In this chapter, we explain the kinematic resolution method of CoM Jacobian with embedded motion which was suggested in (Choi et al, 2007), actually, which offers the ability of balancing to humanoid robot. For example, if humanoid robot stretches two arms forward, then the position of CoM(center of mass) of humanoid robot moves forward and its ZMP(zero moment point) swings back and forth. In this case, the proposed kinematic resolution method of CoM Jacobian with embedded motion offers the joint configurations of supporting limb(s) calculated automatically to maintain the position of CoM fixed at one point. Also, a design of balancing controller with good performance becomes another important part in development of humanoid robot. In balancing control, the ZMP control is the most important factor in implementing stable bipedal robot motions. If the ZMP is located in the region of supporting sole, then the robot will not fall down during motions. In order to compensate the error between the desired and actual ZMP, various ZMP control methods have been suggested; for example, direct/indirect ZMP control methods (Choi et al, 2004) (Kajita et al, 2003) and the impedance control (Park, 2001). Despite many references to bipedal balancing control methods, research on the stability of bipedal balancing controllers is still lacking. The exponential stability of periodic walking motion was partially proved for a planar bipedal robot in (Grizzle et al, 2003) (Westervelt et al, 2003). Also, the ISS (disturbance input-to-state stability) of the indirect ZMP controller was proved for the simplified bipedal robot model in (Choi et al, 2004, 2007). In this chapter, we will explain the balancing control method and its ISS proof which were suggested in (Choi et al, 2007). The O pe n A cc es s D at ab as e w w w . ite ch on lin e. co m
A Fully-Integrated Wideband Digital Polar Transmitter With 11-bit Digital-to-Phase Converter in 40nm CMOS

This work presents a wideband digital polar transmitter (DPTX) with an integrated capacitor-digital-to-analog converter (C-DAC)-based digital-to-phase converter (DPC). A normalized CORDIC algorithm is introduced to convert the $I/Q$ baseband signals to a constant envelope to overcome the PM bandwidth expansion issue and avoid the amplitude modulation (AM)–phase modulation (PM) nonlinearity. In the DPC, the switched-capacitor DAC topology is employed for good linearity, and the eight-phase cell-reused technique is proposed to reduce the power consumption and increase the phase amplitude. Besides, the harmonic rejection technique is introduced to remove the third-/fifth-order and higher order local oscillator (LO) harmonics to maintain phase linearity. A broadband switched-transformer digital power amplifier (DPA) is adopted in this proposed DPTX for 0–18-dB deep power back-off efficiency enhancement. Implemented in 40nm CMOS, the DPTX is powered by only one 1.1-V supply and occupies a $1.4 \times 1.4$ mm2 die area. The DPC achieves the maximum integral nonlinearity (INL)/differential nonlinearity (DNL) of ±1°/±0.8° and 12.7-mW power consumption at 1.5 GHz. The DPTX realizes wideband frequency coverage of 1.2–2.5 GHz with only 0.7-dB power variations. The DPTX achieves 20.1-dBm peak output power with 23.7% system efficiency at 1.5 GHz. With a 10-MHz 64-quadrature amplitude modulation (QAM) LTE signal, the DPTX achieves 15.0 dBm average output power, 15.2% system efficiency, and an error vector magnitude (EVM) of −28.6 dB at 1.5 GHz.
Sparse feedback stabilisation of linear delay systems by proximal gradient method

ABSTRACT In this paper, the sparse feedback stabilisation in which the gain matrix has as many zero components as possible in linear delay systems is investigated. The necessary and sufficient condition for the asymptotic stability of the delay systems under sparse state feedback is given. By means of a special matrix norm and the transition matrix (fundamental matrix) of delay systems, the sparse gain matrix design problem is transformed into an optimisation problem. We further derive the proximal mapping of the special matrix norm, and then based on the gradient descent of the smooth part of the objective function, the proximal gradient method is introduced to develop an algorithm for solving the non-smooth optimisation problem. Numerical examples are given to illustrate the effectiveness of the proposed method.
An Efficient Analog Convolutional Neural Network Hardware Accelerator Enabled by a Novel Memoryless Architecture for Insect-Sized Robots

For decades, miniaturization of robots has gained considerable attention due to the exciting applications of insect-sized robots, such as ambient monitoring. However, scaling down the robots’ dimensions reduces energy availability drastically for sensors and controllers. It has prohibited many successful technologies tested in larger-scale robots from application in insect-sized ones. As a result, insect-sized robots’ power and sensor/control autonomy is an open field of research. One of these technologies is Convolutional Neural Networks (CNN). This paper presents novelty in different levels of abstraction from architectural to transistor-level that drastically reduces the CNN power to comply with the low power budget of insect-sized robots. Analog computation is utilized for its compactness, and an architecture is devised to simplify the analog circuitry. Proposed convolutional filters, showing four orders of magnitude higher efficiency with respect to the state-of-the-art, consume merely 1.5 nW/image with 92% accuracy and promise application of CNN-based controllers in insect-sized robots.
Do students learn better with immersive virtual reality videos than conventional videos? A comparison of media effects with middle school girls.

This paper presents two studies comparing the effects of educational immersive virtual reality (IVR) versus traditional videos on conceptual knowledge and self-efficacy. Learning was measured through multiple-choice questions assessing conceptual knowledge, and open-ended questions assessing knowledge understanding, knowledge application, and knowledge creation, based on Bloom’s Taxonomy of learning objectives. In Experiment 1, 53 eighth grade students from an all-girls school learned about humans’ impact on the ocean through either 360-degree videos, using a virtual reality headset, or through traditional videos, using a computer monitor. Measures were taken before and right after treatment. In Experiment 2, 139 sixth-to-eighth grade students from the same school used the same instructional material and equipment from Experiment 1, and measures were taken four times (before treatment, right after two treatment sessions, and five weeks after treatment). Also, we measured learning agency and investigated its mediation role between condition and self-efficacy. The groups did not differ on the multiple-choice scores assessing conceptual knowledge in Experiment 1. In Experiment 2, participants in the IVR group scored higher for knowledge creation than participants in the desktop group, but not knowledge understanding or knowledge application. The IVR group scored higher on self-efficacy than the desktop group in Experiment 1, but not in Experiment 2. Finally, learning agency mediated the relationship between condition and self-efficacy in Experiment 2, indicating a possible mechanism underlying immersion effects on self-efficacy. Results are discussed in light of cognitive sciences and their implication for learning in immersive virtual reality. 90% CI [0, 0.05]; knowledge application: F (1,49) = 0.01, p = 0.915, η 2 p < 0.001, 90% CI [0, 0.02] or knowledge creation: F (1,49) = 0.07, p = 0.797, η 2 p = 0.001, 90% CI [0, 0.05]). This finding suggests that more immersion did not lead to better learning in this experiment and corroborates previous studies that compared conceptual learning outcomes between IVR videos and desktops and found no significant difference in learning between these media (Harrington et al., 2018).
High-Temporal-Resolution Object Detection and Tracking Using Images and Events

Event-based vision is an emerging field of computer vision that offers unique properties, such as asynchronous visual output, high temporal resolutions, and dependence on brightness changes, to generate data. These properties can enable robust high-temporal-resolution object detection and tracking when combined with frame-based vision. In this paper, we present a hybrid, high-temporal-resolution object detection and tracking approach that combines learned and classical methods using synchronized images and event data. Off-the-shelf frame-based object detectors are used for initial object detection and classification. Then, event masks, generated per detection, are used to enable inter-frame tracking at varying temporal resolutions using the event data. Detections are associated across time using a simple, low-cost association metric. Moreover, we collect and label a traffic dataset using the hybrid sensor DAVIS 240c. This dataset is utilized for quantitative evaluation using state-of-the-art detection and tracking metrics. We provide ground truth bounding boxes and object IDs for each vehicle annotation. Further, we generate high-temporal-resolution ground truth data to analyze tracking performance at different temporal rates. Our approach shows promising results, with minimal performance deterioration at higher temporal resolutions (48–384 Hz) when compared with the baseline frame-based performance at 24 Hz.
Alternative parameterization approaches for modelling risk assessment in the presence of imbalance: An example using parotid malignancy

In collaborative statistics, logistic regression models are commonly used with binary outcomes and reference cell coding for categorical predictors. However, despite the usefulness of reference cell coding schemes under many investigative objectives, it is not always appropriate to address research questions of interest. Investigators often consider modifying research questions to align with inference possible using reference cell coding. Alternative coding schemes can offer a more appropriate approach for the investigation. We explored application of deviation from means coding in determining how results from a diagnostic tool provide additional information on a patient's risk of disease with respect to the overall (naïve) risk at clinical presentation. We compared model parameterizations between using reference cell coding and deviation from means coding, by both unweighted and weighted approaches, for assessing risk of parotid malignancy, comparing patients with indeterminate FNAB results with the general (naïve) risk among presenting patients. Unweighted deviation from means coding estimates a 1.2‐fold increase in the odds of malignancy with an indeterminate FNAB result compared to the naïve odds of malignancy at clinical presentation (OR: 1.21 [95% CI: 1.63–2.32], p = 0.5699). The weighted approach takes into account the imbalance in the presenting population and estimates an increased risk (OR: 2.54 [95% CI: 1.52–4.26], p = 0.0004), which is more accurately representing the naïve risk at presentation and to answer the research question of interest. Using standard reference cell coding, an indeterminate result is associated with significantly higher odds than that of a negative result (OR: 5.20 [95% CI: 2.22–12.20], p = 0.0001), but this does not inform us as to the risk with respect to that inherent at clinical presentation and thus may not be useful for clinical decision making.