Miracle-dz/UniFAR-FTUs huggingface dataset:
Longitudinal Case Study of Regression-Based Hand Prosthesis Control in Daily Life

Hand prostheses are usually controlled by electromyographic (EMG) signals from the remnant muscles of the residual limb. Most prostheses used today are controlled with very simple techniques using only two EMG electrodes that allow to control a single prosthetic function at a time only. Recently, modern prosthesis controllers based on EMG classification, have become clinically available, which allow to directly access more functions, but still in a sequential manner only. We have recently shown in laboratory tests that a regression-based mapping from EMG signals into prosthetic control commands allows for a simultaneous activation of two functions and an independent control of their velocities with high reliability. Here we aimed to study how such regression-based control performs in daily life in a two-month case study. The performance is evaluated in functional tests and with a questionnaire at the beginning and the end of this phase and compared with the participant’s own prosthesis, controlled with a classical approach. Already 1 day after training of the regression model, the participant with transradial amputation outperformed the performance achieved with his own Michelangelo hand in two out of three functional metrics. No retraining of the model was required during the entire study duration. During the use of the system at home, the performance improved further and outperformed the conventional control in all three metrics. This study demonstrates that the high fidelity of linear regression-based prosthesis control is not restricted to a laboratory environment, but can be transferred to daily use.
Exciting Complexity: The Role of Motor Circuit Elements in ALS Pathophysiology

Amyotrophic lateral sclerosis (ALS) is a fatal disease, characterized by the degeneration of both upper and lower motor neurons. Despite decades of research, we still to date lack a cure or disease modifying treatment, emphasizing the need for a much-improved insight into disease mechanisms and cell type vulnerability. Altered neuronal excitability is a common phenomenon reported in ALS patients, as well as in animal models of the disease, but the cellular and circuit processes involved, as well as the causal relevance of those observations to molecular alterations and final cell death, remain poorly understood. Here, we review evidence from clinical studies, cell type-specific electrophysiology, genetic manipulations and molecular characterizations in animal models and culture experiments, which argue for a causal involvement of complex alterations of structure, function and connectivity of different neuronal subtypes within the cortical and spinal cord motor circuitries. We also summarize the current knowledge regarding the detrimental role of astrocytes and reassess the frequently proposed hypothesis of glutamate-mediated excitotoxicity with respect to changes in neuronal excitability. Together, these findings suggest multifaceted cell type-, brain area- and disease stage- specific disturbances of the excitation/inhibition balance as a cardinal aspect of ALS pathophysiology.
Guest Column

Randomness is a valuable resource in computation. Randomness is used to run various Monte Carlo simulations of complex systems such as the stock market or weather prediction systems. Various randomized algorithms have been discovered that often vastly outperform known deterministic counterparts (see [MR10] for examples). Cryptography is another area that crucially relies on access to random bits, and it is known that various basic cryptographic primitives fail to be secure if the quality of the randomness used is poor [DOPS04]. However natural sources of randomness are typically defective. This leads to the following basic question: \Can we efficiently produce truly random bits given access to defective sources of randomness? "
60 Years of Mastering Concurrent Computing through Sequential Thinking

Modern computing systems are highly concurrent. Threads run concurrently in shared-memory multi-core systems, and programs run in different servers communicating by sending messages to each other. Concurrent programming is hard because it requires to cope with many possible, unpredictable behaviors of the processes, and the communication media. The article argues that right from the start in 1960's, the main way of dealing with concurrency has been by reduction to sequential reasoning. It traces this history, and illustrates it through several examples, from early ideas based on mutual exclusion (which was initially introduced to access shared physical resources), passing through consensus and concurrent objects (which are immaterial data), until today distributed ledgers. A discussion is also presented, which addresses the limits that this approach encounters, related to fault-tolerance, performance, and inherently concurrent problems.
Assessing the Open Data Maturity of Government Agencies Having Different Years of Implementation Experience: A Study in Taiwan

In this global open data movement, government agencies of Taiwan have dedicated efforts and resources to implement open data policy and have accumulated years of experience in opening their datasets to the public. Accordingly, this study attempts to explore the open data maturity of the government agencies and to investigate whether the agencies possess different levels of commitments and capabilities in their open data implementations. Empirical data were collected by surveys, and the survey items were developed based on an open data maturity model adopted from the literature. There were three major perspectives utilized to explore the phenomenon of interest, including the establishment and legal perspective, the technological perspective, and the citizen and data reuse perspective. The discussions of the research results are expected to provide insights to practitioners and to enrich the current open data literature.
Policy & Regulation Against Fake News: Case of Russia

The modern information society, which is characterized by many information flows from where the society can get information, is now increasingly concerned about the quality of information. A huge abundance of social media where people discuss a variety of issues from quite everyday topics to those that are crucial for society and the state. Moreover, people are highly inclined to trust information obtained online and through social media rather than from traditional mass media, such as radio and television. We can also say that public opinion is now being formed considerably through social media, which have quickly become an instrument of political influence on modern society. Frequent cases of mass disinformation of people through social networks, which led to serious consequences, forced modern states to seek for legal instruments to counter this phenomenon. Russia, following the global trend, also adopted several legislative decisions in 2019 to prevent the spread of fake news online, which caused a mixed reaction in the Russian society. In this paper, the authors attempt to evaluate these legislative measures in the context of international and foreign practice and to carry out their critical analysis, as well as to make their assumptions about the possible development of this practice and regulation on fake news.
Comparative Evaluation of Topical Corticosteroid and Moisturizer in the Prevention of Radiodermatitis in Breast Cancer Radiotherapy

Background: Radiodermatitis is a frequent side effect of breast cancer radiotherapy (RT). Treating radiation oncologist should know the prevention and treatment of every grade of radiodermatitis. Aims: The aim of this study was to compare the topical corticosteroid and moisturizer usage in breast cancer RT. Materials and Methods: Fifty patients with early-stage breast cancer undergoing breast-conserving surgery referred to our department for adjuvant RT between October 2009 and October 2016 were compared with regard to topical steroid or moisturizer usage. Patients were followed up weekly after the start of treatment. Results: Mean age was 46 years. Twenty-four patients had stage 1 breast cancer and 26 patients had stage 2 disease. KPS (Karnofsky performance score) was 100 for all patients. Five patients (20.8%) had grade 2 and seven (29.1%) patients had grade 1 acute radiodermatitis in the first group. Eleven (42.3%) patients had grade 2 and 12 (46.1%) patients had grade 1 acute radiodermatitis in the second group. Thirteen (54.1%) patients in the first group had no acute radiodermatitis and three (11.5%) patients in the second group had no acute radiodermatitis. No patient in either group experienced grade 3 radiodermatitis. Conclusions: Daily use of topical betamethasone for breast cancer RT improves dermal sparing, reduces acute radiodermatitis, and may be recommended for patients receiving RT to the breast.
Underwater Use of a Hyperspectral Camera to Estimate Optically Active Substances in the Water Column of Freshwater Lakes

Freshwater lakes provide many important ecosystem functions and services to support biodiversity and human well-being. Proximal and remote sensing methods represent an efficient approach to derive water quality indicators such as optically active substances (OAS). Measurements of above-ground remote and in situ proximal sensors, however, are limited to observations of the uppermost water layer. We tested a hyperspectral imaging system, customized for underwater applications, with the aim to assess concentrations of chlorophyll a (CHLa) and colored dissolved organic matter (CDOM) in the water columns of four freshwater lakes with different trophic conditions in Central Germany. We established a measurement protocol that allowed consistent reflectance retrievals at multiple depths within the water column independent of ambient illumination conditions. Imaging information from the camera proved beneficial for an optimized extraction of spectral information since low signal areas in the sensor’s field of view, e.g., due to non-uniform illumination, and other interfering elements, could be removed from the measured reflectance signal for each layer. Predictive hyperspectral models, based on the 470 nm–850 nm reflectance signal, yielded estimates of both water quality parameters (R² = 0.94, RMSE = 8.9 µg L−1 for CHLa; R² = 0.75, RMSE = 0.22 m−1 for CDOM) that were more accurate than commonly applied waveband indices (R² = 0.83, RMSE = 13.2 µg L−1 for CHLa; R² = 0.66, RMSE = 0.25 m−1 for CDOM). Underwater hyperspectral imaging could thus facilitate future water monitoring efforts through the acquisition of consistent spectral reflectance measurements or derived water quality parameters along the water column, which has the potential to improve the link between above-surface proximal and remote sensing observations and in situ point-based water probe measurements for ground truthing or to resolve the vertical distribution of OAS.
A Research on Low Latency Motion Control System using Real-time Scheduling in Edge Server

Reducing end-to-end delay in safety-critical systems, such as controlling remote robots with motion controllers in industrial sites, is a major challenge. 5G mobile communication has greatly contributed to reducing transmission delay, but realtimeness in an edge server system responsible for packet processing and delivery in the middle of an industrial site can contribute even more. Although the Linux community released a kernel patch to enhance the real-time tasks, it occasionally violates the demand of bounded (worst case) latency. In this paper, we propose a real-time scheduling approach in the edge computing server to handle a latency violation and applies it to the two-way motion control system. As a result, our proposed approach shows that data packet processing is more effective than other comparison groups.
Winter Wheat Yield Prediction at County Level and Uncertainty Analysis in Main Wheat-Producing Regions of China with Deep Learning Approaches

Timely and accurate forecasting of crop yields is crucial to food security and sustainable development in the agricultural sector. However, winter wheat yield estimation and forecasting on a regional scale still remains challenging. In this study, we established a two-branch deep learning model to predict winter wheat yield in the main producing regions of China at the county level. The first branch of the model was constructed based on the Long Short-Term Memory (LSTM) networks with inputs from meteorological and remote sensing data. Another branch was constructed using Convolution Neural Networks (CNN) to model static soil features. The model was then trained using the detrended statistical yield data during 1982 to 2015 and evaluated by leave-one-year-out-validation. The evaluation results showed a promising performance of the model with the overall R 2 and RMSE of 0.77 and 721 kg/ha, respectively. We further conducted yield prediction and uncertainty analysis based on the two-branch model and obtained the forecast accuracy in one month prior to harvest of 0.75 and 732 kg/ha. Results also showed that while yield detrending could potentially introduce higher uncertainty, it had the advantage of improving the model performance in yield prediction.
WSNet: Compact and Efficient Networks with Weight Sampling

We present a new approach and a novel architecture, termed WSNet, for learning compact and efficient deep neural networks. Existing approaches conventionally learn full model parameters independently and then compress them via ad hoc processing such as model pruning or filter factorization. Alternatively, WSNet proposes learning model parameters by sampling from a compact set of learnable parameters, which naturally enforces {parameter sharing} throughout the learning process. We demonstrate that such a novel weight sampling approach (and induced WSNet) promotes both weights and computation sharing favorably. By employing this method, we can more efficiently learn much smaller networks with competitive performance compared to baseline networks with equal numbers of convolution filters. Specifically, we consider learning compact and efficient 1D convolutional neural networks for audio classification. Extensive experiments on multiple audio classification datasets verify the effectiveness of WSNet. Combined with weight quantization, the resulted models are up to 180 times smaller and theoretically up to 16 times faster than the well-established baselines, without noticeable performance drop.
Evaluation of GPM-Era Satellite Precipitation Products on the Southern Slopes of the Central Himalayas Against Rain Gauge Data

The Global Precipitation Measurement (GPM) mission provides high-resolution precipitation estimates globally. However, their accuracy needs to be accessed for algorithm enhancement and hydro-meteorological applications. This study applies data from 388 gauges in Nepal to evaluate the spatial-temporal patterns presented in recently-developed GPM-Era satellite-based precipitation (SBP) products, i.e., the Integrated Multi-satellite Retrievals for GPM (IMERG), satellite-only (IMERG-UC), the gauge-calibrated IMERG (IMERG-C), the Global Satellite Mapping of Precipitation (GSMaP), satellite-only (GSMaP-MVK), and the gauge-calibrated GSMaP (GSMaP-Gauge). The main results are as follows: (1) GSMaP-Gauge datasets is more reasonable to represent the observed spatial distribution of precipitation, followed by IMERG-UC, GSMaP-MVK, and IMERG-C. (2) The gauge-calibrated datasets are more consistent (in terms of relative root mean square error (RRMSE) and correlation coefficient (R)) than the satellite-only datasets in representing the seasonal dynamic range of precipitation. However, all four datasets can reproduce the seasonal cycle of precipitation, which is predominately governed by the monsoon system. (3) Although all four SBP products underestimate the monsoonal precipitation, the gauge-calibrated IMERG-C yields smaller mean bias than GSMaP-Gauge, while GSMaP-Gauge shows the smaller RRMSE and higher R-value; indicating IMERG-C is more reliable to estimate precipitation amount than GSMaP-Gauge, whereas GSMaP-Gauge presents more reasonable spatial distribution than IMERG-C. Only IMERG-C moderately reproduces the evident elevation-dependent pattern of precipitation revealed by gauge observations, i.e., gradually increasing with elevation up to 2000 m and then decreasing; while GSMaP-Gauge performs much better in representing the gauge observed spatial pattern than others. (4) The GSMaP-Gauge calibrated based on the daily gauge analysis is more consistent with detecting gauge observed precipitation events among the four datasets. The high-intensity related precipitation extremes (95th percentile) are more intense in regions with an elevation below 2500 m; all four SBP datasets have low accuracy (<30%) and mostly underestimated (by >40%) the frequency of extreme events at most of the stations across the country. This work represents the quantification of the new-generation SBP products on the southern slopes of the central Himalayas in Nepal.
Self-Supervised Representation Learning for Remote Sensing Image Change Detection Based on Temporal Prediction

Traditional change detection (CD) methods operate in the simple image domain or hand-crafted features, which has less robustness to the inconsistencies (e.g., brightness and noise distribution, etc.) between bitemporal satellite images. Recently, deep learning techniques have reported compelling performance on robust feature learning. However, generating accurate semantic supervision that reveals real change information in satellite images still remains challenging, especially for manual annotation. To solve this problem, we propose a novel self-supervised representation learning method based on temporal prediction for remote sensing image CD. The main idea of our algorithm is to transform two satellite images into more consistent feature representations through a self-supervised mechanism without semantic supervision and any additional computations. Based on the transformed feature representations, a better difference image (DI) can be obtained, which reduces the propagated error of DI on the final detection result. In the self-supervised mechanism, the network is asked to identify different sample patches between two temporal images, namely, temporal prediction. By designing the network for the temporal prediction task to imitate the discriminator of generative adversarial networks, the distribution-aware feature representations are automatically captured and the result with powerful robustness can be acquired. Experimental results on real remote sensing data sets show the effectiveness and superiority of our method, improving the detection precision up to 0.94–35.49%.
Creating New Near-Surface Air Temperature Datasets to Understand Elevation-Dependent Warming in the Tibetan Plateau

The Tibetan Plateau has been undergoing accelerated warming over recent decades, and is considered an indicator for broader global warming phenomena. However, our understanding of warming rates with elevation in complex mountain regions is incomplete. The most serious concern is the lack of high-quality near-surface air temperature (Tair) datasets in these areas. To address this knowledge gap, we developed an automated mapping framework for the estimation of seamless daily minimum and maximum Land Surface Temperatures (LSTs) for the Tibetan Plateau from the existing MODIS LST products for a long period of time (i.e., 2002–present). Specific machine learning methods were developed and linked with target-oriented validation and then applied to convert LST to Tair. Spatial variables in retrieving Tair, such as solar radiation and vegetation indices, were used in estimation of Tair, whereas MODIS LST products were mainly focused on temporal variation in surface air temperature. We validated our process using independent Tair products, revealing more reliable estimates on Tair; the R2 and RMSE at monthly scales generally fell in the range of 0.9–0.95 and 1–2 °C. Using these continuous and consistent Tair datasets, we found temperature increases in the elevation range between 2000–3000 m and 4000–5000 m, whereas the elevation interval at 6000–7000 m exhibits a cooling trend. The developed datasets, findings and methodology contribute to global studies on accelerated warming.
City Grade Classification Based on Connectivity Analysis by Luojia I Night-Time Light Images in Henan Province, China

City classification can provide important data and technical support for city planning and government decision-making. Traditional city classification mainly relies on the accumulation and analysis of census data, which requires a large time period and relies heavily on historical and statistical data. This paper mainly utilizes Luojia I Night-Time Light (NTL) images to analyze the rank classification of cities in Henan Province, China. Intensity values can be expressed as the mathematical surface of continuous human activities, and the basic characteristics of urban structures are determined by analogy with the topography of the earth. A connectivity analysis method for NTL images is proposed to analyze the connected regions of images at different intensity levels. By constructing a tree structure, different cities can be analyzed “crosswise” and “lengthwise” to generate a series of parametric information from connected regions of NTL images. Based on these parameters, 18 cities in Henan Province were classified and analyzed. The results show that these attribute information can be well used for city center detection and grade classification, and can meet the requirements of application analysis.
Surgical research, staging-guided technical procedures and long-term clinical outcomes for the treatment of peripheral lymphedema: the Genoa Protocol

The Author ’s vast surgical experience in the treatment of primary and secondary peripheral lymphedema is reported. The objective is to describe the techniques and the long-lasting clinical outcomes based on more than 45 years research and clinical applications, with particular reference to staging-guided derivative and reconstructive lymphatic microsurgery at a single site, and to complementary, sequential, minimally invasive procedures of selective liposuction. 5.046 cases of patients (demographic characteristics are at length described) affected by upper and/or lower limb lymphedema, between 1973 and 2020, underwent lymphatic microsurgery and, between 2012 and 2020, Fibro-Lipo-Lymph-Aspiration according to Lymph Vessel Sparing Procedure (FLLA-LVSP), for latest stages of Lymphedema previously treated by Lymphatic Microsurgery with partial improvement of the disease. Derivative Multiple Lymphatic-Venous Anastomoses (MLVA) or, in selected cases of Phlebolymphedema absolutely contraindicating derivative procedures, lymphatic pathway reconstruction, using interpositioned vein grafted shunts (Multiple-Lymphatic-Venous-Lymphatic Anastomose - MLVLA), above all at lower limbs, were performed at a single site, either the brachial-axillary or inguinal-crural region.