Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Integration of a Dexterous Interface with Hybrid Haptic Feedback

: Haptic interfaces allow natural physical interactions with virtual environments. By measuring the user’s movements and providing force feedback, they recreate a physical sense of presence in the virtual world, thus improving the user’s immersion. These characteristics led to their adoption in various VR applications, e.g. fitting, training or ergonomic studies. Until recently however, most of the commercially available systems were equipped with a handle which constraints the simulated movements to the manipulation of tools having a shape similar to the handgrip. More dexterous devices which do not constraint the hand’s posture are required to allow for the simulation of more various grasps and fine manipulation. Such interfaces are currently the subject of intense research, with new products arrived recently on the market. Some of these devices allow generic force feedback on the fingers thanks to multidirectional actuation. They remain however complex and cumbersome. To overcome this limitation, some other devices limit the number of actuators. More compact solutions can be obtained this way, but force feedback is limited to only few directions. In this paper, we present a different approach. By combining force and local pseudo-force feedback, we aim at allowing a rich and multidirectional haptic feedback in a light and compact fashion. This paper presents an innovative haptic glove implementing such hybrid haptic feedback developed for interactions with digital mock-ups, with details on its main components and its integration in a VR application.
Spatio-Temporal Variability of Aerosol Optical Depth, Total Ozone and NO2 Over East Asia: Strategy for the Validation to the GEMS Scientific Products

In this study, the spatio-temporal variability of aerosol optical depth (AOD), total column ozone (TCO), and total column NO2 (TCN) was identified over East Asia using long-term datasets from ground-based and satellite observations. Based on the statistical results, optimized spatio-temporal ranges for the validation study were determined with respect to the target materials. To determine both spatial and temporal ranges for the validation study, we confirmed that the observed datasets can be statistically considered as the same quantity within the ranges. Based on the thresholds of R2>0.95 (temporal) and R>0.95 (spatial), the basic ranges for spatial and temporal scales for AOD validation was within 30 km and 30 min, respectively. Furthermore, the spatial scales for AOD validation showed seasonal variation, which expanded the range to 40 km in summer and autumn. Because of the seasonal change of latitudinal gradient of the TCO, the seasonal variation of the north-south range is a considerable point. For the TCO validation, the north-south range is varied from 0.87◦ in spring to 1.05◦ in summer. The spatio-temporal range for TCN validation was 20 min (temporal) and 20–50 km (spatial). However, the nearest value of satellite data was used in the validation because the spatio-temporal variation of TCN is large in summer and autumn. Estimation of the spatio-temporal variability for respective pollutants may contribute to improving the validation of satellite products.
Understanding the Land Surface Phenology and Gross Primary Production of Sugarcane Plantations by Eddy Flux Measurements, MODIS Images, and Data-Driven Models

Sugarcane (complex hybrids of Saccharum spp., C4 plant) croplands provide cane stalk feedstock for sugar and biofuel (ethanol) production. It is critical for us to analyze the phenology and gross primary production (GPP) of sugarcane croplands, which would help us to better understand and monitor the sugarcane growing condition and the carbon cycle. In this study, we combined the data from two sugarcane EC flux tower sites in Brazil and the USA, images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and data-driven models to study the phenology and GPP of sugarcane croplands. The seasonal dynamics of climate, vegetation indices from MODIS images, and GPP from two sugarcane flux tower sites (GPPEC) reveal the temporal consistency in sugarcane phenology (crop calendar: green-up dates and harvesting dates) as estimated by the vegetation indices and GPPEC data. The Land Surface Water Index (LSWI) is shown to be useful to delineate the phenology of sugarcane croplands. The relationship between the sugarcane GPPEC and the Enhanced Vegetation Index (EVI) is stronger than the relationship between the GPPEC and the Normalized Difference Vegetation Index (NDVI). We ran the Vegetation Photosynthesis Model (VPM), which uses the light use efficiency (LUE) concept and is driven by climate data and MODIS images, to estimate the daily GPP at the two sugarcane sites (GPPVPM). The seasonal dynamics of the GPPVPM and GPPEC at the two sites agreed reasonably well with each other, which indicates that VPM is a powerful tool for estimating the GPP of sugarcane croplands in Brazil and the USA. This study clearly highlights the potential of combining eddy covariance technology, satellite-based remote sensing technology, and data-driven models for better understanding and monitoring the phenology and GPP of sugarcane croplands under different climate and management practices.
Deep Learning with Transfer Learning Method for Error Compensation of Cable-driven Robot

: This paper proposes the application of Deep Learning methods for kinematic error compensation. Particular attention is paid to simulation-based error estimation and the use of the Transfer Learning method for error compensation to reduce physical experiments with a real robot. The obtained results were applied and vali-dated for 4-dof (degrees of freedom) cable-driven parallel robot. The problem of error compensation for the cable-driven parallel robot is highly non-linear. Nevertheless, deep learning-based methods for a considerable training dataset provides better accuracy than simple linear error compensators. To overcome this drawback, we applied the transfer learning method and used the knowledge of robot kinematics simulated in Unity. Unity cable-driven robot simulation was implemented, and the central hypothesis was veriﬁed ﬁrst in the simulated environment. The proposed Transfer Learning method allowed to speed up the process of robotics system integration and recalibration due to the signiﬁcant sample efﬁciency improvement.
Interseismic Coupling beneath the Sikkim-Bhutan Himalaya Constrained by GPS Measurements and Its Implication for Strain Segmentation and Seismic Activity

The Sikkim–Bhutan seismic gap has witnessed a long earthquake quiescence since the 1714 M7.5~8.5 earthquake. The state of stress accumulation beneath the Sikkim–Bhutan Himalaya and its spatial correlation with seismicity remains unclear due to the lack of geodetic measurements and the low levels of seismic activity. We compile Global Positioning System (GPS) measurements in southern Tibet with the available velocities in the Sikkim–Bhutan Himalaya to reveal the characteristics of strain buildup on the Main Himalayan Thrust (MHT). We correct non-tectonic hydrological loading effects in a GPS time series to accurately determine the Three-Dimensional (3D) velocities of each continuous station. Extensive GPS measurements yield convergence rates of 16.2~18.5 mm/y across the Sikkim–Bhutan Himalaya, which is quite consistent with that observed elsewhere in the Himalaya. Based on a double-ramp structure of the MHT, a refined 3D coupling image is inverted using a dense network of GPS velocities. The result indicates significant along-strike variations of fault coupling beneath the Sikkim–Bhutan Himalaya. The locking width (coupling > 0.5) of western Bhutan reaches ~100 km, which is 30~40% wider than Sikkim and eastern Bhutan. An obvious embayment of decoupling zone near the border between Sikkim and western Bhutan is recognized, and coincides spatially with the rupture terminates of the 1934 Mw8.2 and the 1714 M7.5~8.5 earthquakes, indicating that the large megathrust earthquakes along the Sikkim–Bhutan Himalaya are largely segmented by the spatial variation of frictional properties on the MHT. Using a new compilation of seismic records in the Sikkim–Bhutan Himalaya, we analyze the spatial correlation between fault coupling and seismic activity. The result suggests that the seismicity in the Bhutan Himalaya is broadly distributed, instead of restricted in the lower edge of the interseismic locking zone. This implies that the seismic activity in the Bhutan Himalaya is not uniquely controlled by the stress accumulation at the downdip end of the locked portion of the MHT.
Common cytokine receptor γ chain (γc)‐deficient B cells persist in T cell‐deficient γc– mice and respond to a T‐independent antigen

Defects in the common cytokine receptor γ chain (γc) in man result in X‐linked severe combined immunodeficiency disease (SCIDX1) characterized by an absence of α β T cells, γ δ T cells and NK cells, with the presence of circulating B cells. Mice made deficient for γc lack γ δ T cells and NK cells, but in contrast to SCIDX1 patients have appreciable numbers of α β T cells, while B cells are reduced about tenfold in numbers and disappear with age. Here we show that when γc– mice are rendered T cell deficient, B cell numbers are still reduced but the age‐dependent loss of B cells does not occur. The peripheral B cells which persisted in γc– / nude and γc– / TCRβ– / – mice were able to respond to mitogen stimulation in vitro and to mount antigen‐specific T‐independent Ig responses in vivo. These results demonstrate that γc– B cells are functionally competent and suggest that residual α β T cells are implicated in the B cell loss in γc mice. The γc– / nude and γc– / TCRβ– / – mice provide new models to dissect the role of γc‐dependent receptors during murine B cell differentiation.
A Global Gravity Reconstruction Method for Mercury Employing Deep Convolutional Neural Network

Mercury, the enigmatic innermost planet in the solar system, is one of the most important targets of space exploration. High-quality gravity field data are significant to refine the physical characterization of Mercury in planetary exploration missions. However, Mercury’s gravity model is limited by relatively low spatial resolution and stripe noises, preventing fine-scale analysis and applications. By analyzing Mercury’s gravity data and topography data in the 2D spatial field, we find they have fairly high spatial structure similarity. Based on this, in this paper, a novel convolution neural network (CNN) approach is proposed to improve the quality of Mercury’s gravity field data. CNN can extract the spatial structure features of gravity data and construct a nonlinear mapping between low- and high-degree data directly. From a low-degree gravity input, the corresponding initial high-degree result can be obtained. Meanwhile, the structure characteristics of the high-resolution digital elevation model (DEM) are extracted and fused to the initial data, to get the final stripe-free result with improved resolution. Given the paucity of Mercury’s data, high-quality lunar datasets are employed as pretraining data after verifying the spatial similarity between gravity and terrain data of the Moon. The HgM007 gravity field obtained by the MErcury Surface, Space ENvironment, GEochemistry and Ranging (MESSENGER) mission at Mercury is selected for experiments to test the ability of the proposed algorithm to remove the stripes caused by quality differences of the highly eccentric orbit data. Experimental results show that our network can directly obtain stripe-free and higher-degree data via inputting low-degree data and implicitly assuming a lunar-like relation between crustal density and porosity. Albeit the CNN-based method cannot be sensitive to subsurface features not present in the initial dataset, it still provides a new perspective for the gravity field refinement.
Signal Timing Optimization Model Based on Bus Priority

This paper focuses on the optimization problem of a signal timing design based on the concept of bus priority. This optimization problem is formulated in the form of a bi-level programming model that minimizes average passenger delay at intersections and vehicle delay in lanes simultaneously. A solution framework that implements the differential evolution (DE) algorithm is developed to efficiently solve the model. A case study based on a real-world intersection in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling and computing methods. The experiment’s result shows that the optimization model can not only significantly improve the priority capacity of the buses at the intersection but also reduce the adverse impact of bus-priority approaches on the private vehicles for the intersections.
Evaluation of the cost-effectiveness of insulin glargine versus NPH insulin for the treatment of type 2 diabetes in the UK

ABSTRACT Background: People with type 2 diabetes typically require insulin treatment following failure on hypoglycaemic agents. The purpose of this study was to evaluate the relative cost-effectiveness of insulin glargine versus NPH insulin for the treatment of type 2 diabetes in the UK. Insulin glargine has been shown to reduce the likelihood of hypoglycaemia and improve HbA1c in type 2 diabetes by comparison with NPH insulin. Methods: The study used a discrete event simulation model designed to forecast the costs and health outcome of a cohort of 1000 subjects over 40 years. The two main scenarios involved a difference in the likelihood of hypoglycaemia or a difference in HbA1c. Prices were in UK£ 2005 costs. Costs and benefits were discounted at 3.5% per year. Effectiveness data were pooled data from randomised clinical trials. Results: The incremental cost–effectiveness ratio of insulin glargine versus NPH insulin was £10 027 per quality adjusted life year under a differential hypoglycaemia-only scenario. The incremental cost–effectiveness ratio of insulin glargine versus NPH insulin was £13 921 per quality adjusted life year under a differential HbA1c-only scenario. In wide-ranging sensitivity analysis, the ICER was consistently below £20 000 per quality adjusted life year gained. Conclusion: This study was the first to evaluate the relative cost-effectiveness of insulin glargine versus NPH insulin. Insulin glargine resulted in significant health benefits and represents excellent value for money for the treatment of type 2 diabetes in the UK.
Development of Geo-KOMPSAT-2A Algorithm for Sea-Ice Detection Using Himawari-8/AHI Data

Sea ice is an important meteorological factor affecting the global climate system, but it is difficult to observe in sea ice ground truth data because of its location mainly at high latitudes and in polar regions. Accordingly, sea-ice detection research has been actively conducted using satellites, since the 1970s. Polar-orbiting and geostationary satellites are used for this purpose; notably, geostationary satellites are capable of real-time monitoring of specific regions. In this paper, we introduce the Geo-KOMPSAT-2A (GK-2A)/Advanced Meteorological Imager (AMI) sea-ice detection algorithm using Japan Meteorological Agency (JMA) Himawari-8/Advanced Himawari Imager (AHI) data as proxy data. The GK-2A/AMI, which is Korea Meteorological Administration (KMA)’s next-generation geostationary satellite launched in December 2018 and Himawari-8/AHI have optically similar channel data, and the observation area includes East Asia and the Western Pacific. The GK-2A/AMI sea-ice detection algorithm produces sea-ice data with a 10-min temporal resolution, a 2-km spatial resolution and sets the Okhotsk Sea and Bohai Sea, where the sea ice is distributed during the winter in the northern hemisphere. It used National Meteorological Satellite Center (NMSC) cloud mask as the preceding data and a dynamic threshold method instead of the static threshold method that is commonly performed in existing sea-ice detection studies. The dynamic threshold methods for sea-ice detection are dynamic wavelength warping (DWW) and IST0 method. The DWW is a method for determining the similarity by comparing the pattern of reflectance change according to the wavelength of two satellite data. The IST0 method detects sea ice by using the correlation between 11.2-μm brightness temperature (BT11.2) and brightness temperature difference (BTD) [BT11.2–BT12.3] according to ice surface temperature (IST). In addition, the GK-2A/AMI sea-ice detection algorithm reclassified the cloud area into sea ice using a simple test. A comparison of the sea-ice data derived the GK-2A/AMI sea-ice detection algorithm with the S-NPP/visible infrared imaging radiometer suite (VIIRS) sea ice characterization product indicates consistency of 99.0% and inconsistency of 0.9%. The overall accuracy (OA) of GK-2A/AMI sea-ice data with the sea ice region of interest (ROI) data, which is constructed by photo-interpretation method from RGB images, is 97.2%.
Multi-Objective Artificial Bee Colony Algorithms and Chaotic-TOPSIS Method for Solving Flowshop Scheduling Problem and Decision Making

Retrieval of optimal solution(s) for a permutation flowshop scheduling problem within a reasonable computational timeframe has been a challenge till yet. The problem includes optimization of various criterions like makespan, total flowtime, earliness, tardiness, etc and obtaining a Pareto solution for final decision making. This paper remodels a discrete artificial bee colony algorithm for permutation flowshop scheduling problem executed through three different scenarios raised the analysis of time complexity measure. To enhance the search procedure, we have explored the alternative and combined use of two local search algorithms named as: iterated greedy search algorithm and iterated local search algorithm in our discrete artificial bee colony algorithm and the results are summarized with respect to completion time, mean weighted tardiness, and mean weighted earliness. The two algorithms are prioritised on insertion and swap of neighbourhood structures which will intensify the local optima in the search space. Further the performance of the algorithm is compared with the test results of multi-objective artificial bee colony algorithm. The result of our optimization process concludes with a set of non-dominated solutions lead to different Pareto fronts. Finally, we propose a chaotic based technique for order of preference by similarity to ideal solution (chaotic-TOPSIS) using a suitable chaotic map for criteria adaptation in order to enhance the decision accuracy in the multi-objective problem domain.
Learning from the 2018 Western Japan Heavy Rains to Detect Floods during the 2019 Hagibis Typhoon

Applications of machine learning on remote sensing data appear to be endless. Its use in damage identification for early response in the aftermath of a large-scale disaster has a specific issue. The collection of training data right after a disaster is costly, time-consuming, and many times impossible. This study analyzes a possible solution to the referred issue: the collection of training data from past disaster events to calibrate a discriminant function. Then the identification of affected areas in a current disaster can be performed in near real-time. The performance of a supervised machine learning classifier to learn from training data collected from the 2018 heavy rainfall at Okayama Prefecture, Japan, and to identify floods due to the typhoon Hagibis on 12 October 2019 at eastern Japan is reported in this paper. The results show a moderate agreement with flood maps provided by local governments and public institutions, and support the assumption that previous disaster information can be used to identify a current disaster in near-real time.
Open Problem: Information Complexity of VC Learning

A recent line of work has studied the information complexity of learning (Russo and Zou, 2016; Raginsky et al., 2016; Alabdulmohsin, 2016; Feldman and Steinke, 2018; Bassily et al., 2018; Dwork et al., 2015a; Rogers et al., 2016; Smith, 2017; Xu and Raginsky, 2017; Nachum and Yehudayoff, 2018; Nachum et al., 2018; Esposito et al., 2019; Bu et al., 2019, etc.). That is, we ask: How much information does the output of a learner reveal about its input? A natural example of a quantity that can be used to quantify the revealed information is the mutual information I(A(Z);Z) between the output of an algorithm A and its input Z (consisting of i.i.d. samples from an unknown distribution). Measuring the information complexity of a learning algorithm can be very informative, as it is related to several properties or guarantees that we might wish to establish. In particular, low information complexity entails generalization guarantees. That is, it implies that that the loss of the output hypothesis on the input dataset is close to its loss on the distribution (Russo and Zou, 2016; Xu and Raginsky, 2017). Conversely, overfitting entails high information complexity. Moreover, these generalization guarantees are robust to postprocessing, thanks to the data processing inequality. The information complexity of an algorithm is also closely related to the study of privacy. Differential privacy (Dwork et al., 2006) has been widely-accepted as a robust guarantee that a hypothesis or model produced by a machine learning algorithm does not reveal sensitive personal information contained in the training data. Differential privacy is known to imply low mutual information (McGregor et al., 2010; Bun and Steinke, 2016), as well as strong generalization guarantees (Dwork et al., 2015b; Bassily et al., 2016; Jung et al., 2019). The celebrated theory of uniform convergence (Vapnik and Chervonenkis, 1971) approaches learning from a different perspective by studying the complexity of hypothesis classes. A hypothesis class has the uniform convergence property if, with high probability over the drawing of an i.i. d. dataset, all hypotheses simultaneously generalize – i.e., their loss on the dataset and on the distribution are similar. In particular, hypothesis classes with bounded Vapnik-Chervonenkis (VC) dimension exhibit strong uniform convergence, which implies sample-efficient PAC learning and agnostic learning (Vapnik and Chervonenkis, 1971; Talagrand, 1994; Alon et al., 1997). Uniform convergence (and VC dimension) make no reference to the learning algorithm itself, instead they only depend on its range. On the other hand, information complexity is a property of a particular algorithm, which also implies generalization. So it is natural to ask whether there is
A Sensorless Hand Guiding Scheme Based on Model Identification and Control for Industrial Robot

Most industrial robots are not capable of teaching by hand and require path points to be specified by teaching pendants. To enable the teaching of industrial robots by hand without any force sensors, this paper proposes a scheme to minimize the external force estimation error and reduce disturbance in the guiding task by using the virtual mass and virtual friction model. In this case, the maximum velocity and acceleration of the robot end effector shall be limited to ensure safety. Thus, the operator is allowed to guide the robot by hand. The joint torque is obtained from the motor current. The inertial force and friction of the links and driving systems are analyzed. The nonlinear dynamic model of the industrial robot is built and its parameters are calibrated by a nonlinear method. The force estimation is referenced to set the virtual friction and to design the force-following controller. Hence the end effector can follow the direction of external force compliantly and suppress jitters. Finally, several experiments on a six degrees of freedom industrial robot demonstrate the validity of the proposed control scheme.