Miracle-dz/UniFAR-FTUs huggingface dataset:
Decentralized Trajectory Tracking Control for Soft Robots Interacting With the Environment

Despite the classic nature of the problem, trajectory tracking for soft robots, i.e., robots with compliant elements deliberately introduced in their design, still presents several challenges. One of these is to design controllers which can obtain sufficiently high performance while preserving the physical characteristics intrinsic to soft robots. Indeed, classic control schemes using high-gain feedback actions fundamentally alter the natural compliance of soft robots effectively stiffening them, thus de facto defeating their main design purpose. As an alternative approach, we consider here using a low-gain feedback, while exploiting feedforward components. In order to cope with the complexity and uncertainty of the dynamics, we adopt a decentralized, iteratively learned feedforward action, combined with a locally optimal feedback control. The relative authority of the feedback and feedforward control actions adapts with the degree of uncertainty of the learned component. The effectiveness of the method is experimentally verified on several robotic structures and working conditions, including unexpected interactions with the environment, where preservation of softness is critical for safety and robustness.
Neuronal correlates of motion‐defined shape perception in primate dorsal and ventral streams

Human and non‐human primates can readily perceive the shape of objects using visual motion. Classically, shape, and motion are considered to be separately processed via ventral and dorsal cortical pathways, respectively. However, many lines of anatomical and physiological evidence have indicated that these two pathways are likely to be interconnected at some stage. For motion‐defined shape perception, these two pathways should interact with each other because the ventral pathway must utilize motion, which the dorsal pathway processes, to extract shape signal. However, it is unknown how interactions between cortical pathways are involved in neural mechanisms underlying motion‐defined shape perception. We review evidence from psychophysical, lesion, neuroimaging and physiological research on motion‐defined shape perception and then discuss the effects of behavioral demands on neural activity in ventral and dorsal cortical areas. Further, we discuss functions of two candidate sets of levels: early and higher‐order cortical areas. The extrastriate area V4 and middle temporal (MT) area, which are reciprocally connected, at the early level are plausible areas for extracting the shape and/or constituent parts of shape from motion cues because neural dynamics are different from those during luminance‐defined shape perception. On the other hand, among other higher‐order visual areas, the anterior superior temporal sulcus likely contributes to the processing of cue‐invariant shape recognition rather than cue‐dependent shape processing. We suggest that sharing information about motion and shape between the early visual areas in the dorsal and ventral pathways is dependent on visual cues and behavioral requirements, indicating the interplay between the pathways.
Nonlinear Observer for Vehicle Motion Tracking

This paper focuses on the development and use of a nonlinear observer for tracking of vehicle motion trajectories on highways while using a radar or laser sensor. Previous results on vehicle tracking have typically used an interacting multiple model filter that needs different models for different modes of vehicle motion. This paper uses a single nonlinear vehicle model that can be used for all modes of vehicle motion. A corresponding exponentially stable nonlinear observer is needed. Previous nonlinear observer design results do not work for the nonlinear system under consideration. Hence, a new nonlinear observer that utilizes better bounds on the coupled nonlinear functions in the dynamics is developed. The observer design with the developed technique is implemented in both simulations and experiments. Experimental results show that the observer can simultaneously estimate longitudinal position, lateral position, velocity and orientation variables for the vehicle from radar measurements during highway driving.
The mode of action of anticholinergics in asthma

Acetylcholine binds to muscarinic receptors to play a key role in the pathophysiology of asthma, leading to bronchoconstriction, increased mucus secretion, inflammation and airway remodelling. Anticholinergics are muscarinic receptor antagonists that are used in the treatment of chronic obstructive pulmonary disease and asthma. Recent in vivo and in vitro data have increased our understanding of how acetylcholine contributes to the disease manifestations of asthma, as well as elucidating the mechanism of action of anticholinergics. This review assesses the latest literature on acetylcholine in asthma pathophysiology, with a closer look at its role in airway inflammation and remodelling. New insights into the mechanism of action of anticholinergics, their effects on airway remodelling, and a review of the efficacy and safety of long-acting anticholinergics in asthma treatment will also be covered, including a summary of the latest clinical trial data. Pre-clinical data suggest that anticholinergics can reduce acetylcholine-induced airway inflammation and remodelling http://ow.ly/xqAQ30loP8F
Primary Sample Space Path Guiding

Guiding path tracing in light transport simulation has been one of the practical choices for variance reduction in production rendering. For this purpose, typically structures in the spatial-directional domain are built. We present a novel scheme for unbiased path guiding. Different from existing methods, we work in primary sample space. We collect records of primary samples as well as the luminance that the resulting path contributes and build a multiple dimensional structure, from which we derive random numbers that are fed into the path tracer. This scheme is executed completely outside the rendering kernel. We demonstrate that this method is practical and efficient. We manage to reduce variance and zero radiance paths by only working in the primary sample space.
Transform Electric Power Curve into Dynamometer Diagram Image Using Deep Recurrent Neural Network

To learn the working situation of rod-pumped wells under ground, we always need to analyze dynamometer diagrams, which are generated by the load sensor and displacement sensor. Rod-pumped wells are usually located in the places with extreme weather, and these sensors are installed on some special oil equipments in the open air. As time goes by, sensors are prone to generating unstable and incorrect data. Unfortunately, load sensors are too expensive to frequently reinstall. Therefore, the resulting dynamometer diagrams sometimes cannot make an accurate diagnosis. Instead, as an absolutely necessary equipment of the rod-pumped well, the electric motor has much longer life and cannot be easily impacted by the weather. The electric power curve during a swabbing period can also reflect the working situation under ground, but is much harder to explain than the dynamometer diagram. This letter presented a novel deep learning architecture, which can transform the electric power curve into the dimensionless dynamometer diagram image. We conduct our experiments on a real-world dataset, and the results show that our method can get an impressive transformation accuracy. key words: deep learning, embedding, recurrent neural network, computer vision
Virtual Footprints Can Improve Walking Performance in People With Parkinson's Disease

In Parkinson's disease (PD) self-directed movement, such as walking, is often found to be impaired while goal directed movement, such as catching a ball, stays relatively unaltered. This dichotomy is most clearly observed when sensory cueing techniques are used to deliver patterns of sound and/or light which in turn act as an external guide that improves gait performance. In this study we developed visual cues that could be presented in an immersive, interactive virtual reality (VR) environment. By controlling how the visual cues (black footprints) were presented, we created different forms of spatial and temporal information. By presenting the black footprints at a pre-specified distance apart we could recreate different step lengths (spatial cues) and by controlling when the black footprints changed color to red, we could convey information about the timing of the foot placement (temporal cues). A group of healthy controls (HC; N = 10) and a group of idiopathic PD patients (PD, N = 12) were asked to walk using visual cues that were tailored to their own gait performance [two spatial conditions (115% [N] and 130% [L] of an individual's baseline step length) and three different temporal conditions (spatial only condition [NT], 100 and 125% baseline step cadence)]. Both groups were found to be able to match their gait performance (step length and step cadence) to the information presented in all the visual cue conditions apart from the 125% step cadence conditions. In all conditions the PD group showed reduced levels of gait variability (p < 0.05) while the HC group did not decrease. For step velocity there was a significant increase in the temporal conditions, the spatial conditions and of the interaction between the two for both groups of participants (p < 0.05). The coefficient of variation of step length, cadence, and velocity were all significantly reduced for the PD group compared to the HC group. In conclusion, our results show how virtual footsteps presented in an immersive, interactive VR environment can significantly improve gait performance in participants with Parkinson's disease.
Optimal treatment for obsessive compulsive disorder: a randomized controlled feasibility study of the clinical-effectiveness and cost-effectiveness of cognitive-behavioural therapy, selective serotonin reuptake inhibitors and their combination in the management of obsessive compulsive disorder

Established treatments for obsessive compulsive disorder (OCD) include cognitive behaviour therapy (CBT) and selective serotonin reuptake inhibitor (SSRI) medication. Combined treatment may outperform monotherapy, but few studies have investigated this. A total of 49 community-based adults with OCD were randomly assigned to CBT, SSRI, or SSRI+CBT. Sertraline (50–200 mg/day) was given as the SSRI for 52 weeks. A 16-h-manualized individual CBT was delivered over 8 weeks with four follow-up sessions. Assessors were ‘blinded’ to treatment allocation. A preliminary health economic evaluation was conducted. At week 16, combined treatment (n=13) was associated with the largest improvement, sertraline (n=7) the next largest and CBT (n=9) the smallest on the observed case analysis. The effect size (Cohen’s d) comparing the improvement in Yale Brown Obsessive Compulsive Scale on CBT versus combined treatment was −0.39 and versus sertraline was −0.27. Between 16 and 52 weeks, the greatest clinical improvement was seen with sertraline, but participant discontinuation prevented reliable analysis. Compared with sertraline, the mean costs were higher for CBT and for combined treatment. The mean Quality Adjusted Life Year scores for sertraline were 0.1823 (95% confidence interval: 0.0447–0.3199) greater than for CBT and 0.1135 (95% confidence interval: ‐0.0290–0.2560), greater than for combined treatment. Combined treatment appeared the most clinically effective option, especially over CBT, but the advantages over SSRI monotherapy were not sustained beyond 16 weeks. SSRI monotherapy was the most cost-effective. A definitive study can and should be conducted.
Learning Invariances using the Marginal Likelihood

Generalising well in supervised learning tasks relies on correctly extrapolating the training data to a large region of the input space. One way to achieve this is to constrain the predictions to be invariant to transformations on the input that are known to be irrelevant (e.g. translation). Commonly, this is done through data augmentation, where the training set is enlarged by applying hand-crafted transformations to the inputs. We argue that invariances should instead be incorporated in the model structure, and learned using the marginal likelihood, which correctly rewards the reduced complexity of invariant models. We demonstrate this for Gaussian process models, due to the ease with which their marginal likelihood can be estimated. Our main contribution is a variational inference scheme for Gaussian processes containing invariances described by a sampling procedure. We learn the sampling procedure by back-propagating through it to maximise the marginal likelihood.
Technology of Stearine Transfer Using Laser-Heating for Lab-On-Paper Development

In this article, we demonstrate a manufacture of the lab-on-paper device. The paper presents a technology of laser thermal transfer of stearine, into a porous paper often used in chemistry. Such a rapid, one-step technology was developed to obtain a paper-based analytical device with using only paper, stearine disc and CO2 laser plotter. Melted in stearine makes hydrophobic surface which is microfluidic wall for analyzed fluids. The results show that paper-based analytical device is a cheap pH tester for a one-time use. Moreover, the whole device can be integrated with much more advanced chemicals or simple electronic for image analysis.
R3-Net: A Deep Network for Multioriented Vehicle Detection in Aerial Images and Videos

Vehicle detection is a significant and challenging task in aerial remote sensing applications. Most existing methods detect vehicles with regular rectangle boxes and fail to offer the orientation of vehicles. However, the orientation information is crucial for several practical applications, such as the trajectory and motion estimation of vehicles. In this paper, we propose a novel deep network, called a rotatable region-based residual network (R3-Net), to detect multioriented vehicles in aerial images and videos. More specially, R3-Net is utilized to generate rotatable rectangular target boxes in a half coordinate system. First, we use a rotatable region proposal network (R-RPN) to generate rotatable region of interests (R-RoIs) from feature maps produced by a deep convolutional neural network. Here, a proposed batch averaging rotatable anchor strategy is applied to initialize the shape of vehicle candidates. Next, we propose a rotatable detection network (R-DN) for the final classification and regression of the R-RoIs. In R-DN, a novel rotatable position-sensitive pooling is designed to keep the position and orientation information simultaneously while downsampling the feature maps of R-RoIs. In our model, R-RPN and R-DN can be trained jointly. We test our network on two open vehicle detection image data sets, namely, DLR 3K Munich Data set and VEDAI Data set, demonstrating the high precision and robustness of our method. In addition, further experiments on aerial videos show the good generalization capability of the proposed method and its potential for vehicle tracking in aerial videos. The demo video is available at https://youtu.be/xCYD-tYudN0.
Modeling of Multi-Dimension Load Balancing Problem

The German initiative “Industrie 4.0” has been the focal point of industrial research in Germany in the last decade. The initiative main objectives are to establish a production with a higher degree of flexibility and cooperation that has not been reached so far. Resource sharing and load reallocation play a crucial role in reinforcing the aforementioned objectives. Previously, we introduced a list of KPIs, a benchmark that can simulate the different load algorithms, and a stability analysis that listed all the boundary conditions needed in the automation domain. In this paper, we present a multi-dimensional mathematical model that describes the trade-off between balancing interdependent loads simultaneously reducing the reality gap between the introduced models and realistic situations.
Development of Visual Inspection System for Assembly Machine

In a factory that assembles various devices, functional inspection and visual inspection are carried out before the product is shipped out. Most functional tests are done automatically through the system, whereas visual inspection is done by the human eyes. Human remember what they have just seen before, and it is difficult to detect the failure of a similar assembly device. Even if the function of the assembling device is satisfied, there is a problem in the reliability of the quality control due to the appearance error. Various image processing techniques have been developed to solve this problem. In this paper, we proposed a method to improve the accuracy of error detection by applying the CNN model to the external inspection of the assembling device.
Soft input decoder for high-rate generalised concatenated codes

Generalised concatenated (GC) codes are well suited for error correction in flash memories for high-reliability data storage. The GC codes are constructed from inner extended binary Bose–Chaudhuri–Hocquenghem (BCH) codes and outer Reed–Solomon codes. The extended BCH codes enable high-rate GC codes and low-complexity soft input decoding. This work proposes a decoder architecture for high-rate GC codes. For such codes, outer error and erasure decoding are mandatory. A pipelined decoder architecture is proposed that achieves a high data throughput with hard input decoding. In addition, a low-complexity soft input decoder is proposed. This soft decoding approach combines a bit-flipping strategy with algebraic decoding. The decoder components for the hard input decoding can be utilised which reduces the overhead for the soft input decoding. Nevertheless, the soft input decoding achieves a significant coding gain compared with hard input decoding.
Privacy, censorship, data breaches and Internet freedom: The drivers of support and opposition to Dark Web technologies

Do heightened privacy perceptions, censorship concerns and exposure to online crime affect people’s level of opposition to dual-use technologies such as the Dark Web? If they do, then how much do these factors actually drive baseline levels of opposition to the Dark Web? Do privacy and censorship concerns get amplified in regimes with significant Internet restrictions? Does Internet freedom itself affect people’s baseline levels of opposition to Tor and other Dark Web technologies? Using new survey data on 17,121 Internet users in 17 different countries, a series of mixed-effect order logit regressions reveal that privacy and censorship concerns are both significant predictors of less opposition to the Dark Web. Past exposure to online crime, in contrast, significantly increases opposition to the Dark Web. Interestingly, restrictions on Internet freedom do not amplify privacy and censorship concerns, but Internet freedom itself is related to baseline levels of opposition to the Dark Web forming an inverted-U-shaped pattern.
Conflict of interest policies and disclosure requirements in neurosurgical journals.

OBJECTIVEAn increasing amount of funding in neurosurgery research comes from industry, which may create a conflict of interest (COI) and the potential to bias results. The reporting and handling of COIs have become difficult, particularly as explicit policies themselves and definitions thereof continue to vary between medical journals. In this study, the authors sought to evaluate the prevalence and comprehensiveness of COI policies among leading neurosurgical journals.METHODSThe authors conducted a cross-sectional study of publicly available online disclosure policies in the 20 highest-ranking neurosurgical journals, as determined by Google Scholar Metrics, in July 2016.RESULTSOverall, 89.5% of the highest-impact neurosurgical journals included COI policy statements. Ten (53%) journals requested declaration of nonfinancial conflicts, while 2 journals specifically set a time period for COIs. Sixteen journals required declaration from the corresponding author, 13 from all authors, 6 from reviewers, and 5 from editors. Four journals were included in the International Committee of Medical Journal Editors (ICMJE) list of publications that follow the Uniform Requirements for Manuscripts Submitted to Biomedical Journals (currently known as Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals). Five journal policies included COI declaration verification, management, or enforcement. The neurosurgery journals with more comprehensive COI policies were significantly more likely to have higher h5-indices (p = 0.003) and higher impact factors (p = 0.01). CONCLUSIONSIn 2016, the majority of, but not all, high-impact neurosurgical journals had publically available COI disclosure policies. Policy inclusiveness and comprehensiveness varied substantially across neurosurgical journals, but COI comprehensiveness was associated with other established markers of individual journals' favorability and influence, such as impact factor and h5-index.
Graphene Quantum Dots Decorated Al-Doped ZnS for Improved Photoelectric Performance

Graphene quantum dots (GQDs) decorated Al-doped ZnS composites were prepared using the solvothermal process, and the hydrothermal method was used to prepare GQDs. Various spectroscopic techniques were used to characterize the products, and the results show that Al-ZnS attached GQD composites present lattice fringes that can be assigned to ZnS and GQDs, respectively. The absorption peaks of Al-ZnS/GQDs are red-shifted because of the doping of aluminum and the incorporation of GQDs. The luminescence intensity of Al-ZnS/GQDs shows a downward trend with the addition of GQDs. As the GQD content changes from 0.6 wt % to 1.8 wt %, the photocurrent density achieves a maximum at the addition of 1.2 wt %. The photocurrent of Al-ZnS/GQDs composites are about 700% and 200% of pure ZnS and Al-ZnS, respectively. The results indicate that Al doping can reduce the energy bandgap of ZnS and produce more photogenerated electrons. The photogenerated electrons from Al-ZnS can be extracted and transferred to GQDs, which act as conducting materials to decrease the recombination rate and improve the photogenerated electron-transfer.
Geomemories a Spatial-Temporal Atlas of the Italian Landscape

The AeroFototeca Nazionale of the Italian Ministry of Cultural Heritage in Rome maintains an extensive set of some million aerial photographs constituting an important memory archive of the Italian territory throughout the 20th century. Together with the Institute of Informatics and Telematics of CNR in Pisa the GeoMemories project was launched with the aim of creating a web platform covering spatial-temporal dimensions and also integrating multimedia data from other archives that displays the evolution of the Italian Landscape. We present some challenges of the project and achievements so far as well as examples of how the tool presented here has a great potential to become a valuable resource for both historians and archaeologists. Categories and Subject Descriptors (according to ACM CCS) : H.5. 1 [Information Interfaces and Representation]: Multimedia Information Systems—Artiﬁcial, augmented and Virtual RealitiesImage Processing and Computer VisionI.4. 3EnhancementI.4. 6Segmentation