Miracle-dz/UniFAR-FTUs huggingface dataset:
Validation of a Novel Inverse Kinematics for Upper-Limb Rehabilitation Exoskeletons

—In this work, we present and validate a novel inverse kinematics method for rehabilitation upper-limb exoskeletons that require joint coordination constraints. Starting from the conventional differential kinematics algorithm based on the inversion of the Jacobian matrix, we describe and test the improved algorithm based on the Projected-Gradient method, which takes into account inter-joint coordination constraints. The Harmony exoskeleton is used as a platform to demonstrate the method. In detail, we address the joint constraints needed to match anatomical shoulder movement and results show good performances of the proposed algorithm.
The influence of strain on image reconstruction in Bragg coherent X-ray diffraction imaging and ptychography

A quantitative analysis of the effect of strain on image reconstruction in Bragg coherent X-ray diffraction imaging and ptychography reveals reconstruction artifacts caused by the limited spatial resolution. With the modulus homogenization constraint applied, the reconstruction artifacts are efficiently removed and an Fe–Al alloy sample with strong strain that features π phase steps due to anti-phase domain boundaries is successfully reconstructed.
Quasi-Unsupervised Fruit Counting Using Domain Adaptation and Spatial Consistency

—Autonomous robotic platforms can be effectively used to perform automatic fruits yield estimation. To this aim, robots need data-driven models that process image streams and count, even approximately, the number of fruits in an orchard. However, training such models following a supervised paradigm is expensive and unpractical. Extending pre-trained models to perform yield estimation for a completely new type of fruit is even more challenging, but interesting since this situation is typical in practice. In this work, we combine a State-of-the-Art weakly-supervised fruit counting model with an unsupervised style transfer method for addressing the task above. Experiments on datasets collected in four different orchards show that the proposed approach is more accurate than the supervised baseline methods.
UC Merced Proceedings of the Annual Meeting of the Cognitive Science Society

A fundamental goal in cognitive and historical linguistic research on semantic change is to characterize the regularity in how word meanings change over time. We examine a common belief that has not yet been evaluated comprehensively, which asserts that gender of a word inﬂuences its direction of semantic change. By this account, female terms like mistress should undergo pejorative change in meaning systematically more so than male terms like master . We evaluate this claim in gender-marked word pairs in English and French respectively as languages without and with grammatical gender. Our results provide supporting evidence for gender asymmetry in semantic change of English words but not French words. Our study raises questions about the generality of the claim about gender asymmetry in semantic change and provides a scalable computational framework for understanding the social roots of word meaning change.
Spatiotemporal Change Analysis and Future Scenario of LULC Using the CA-ANN Approach: A Case Study of the Greater Bay Area, China

Land use land cover (LULC) transition analysis is a systematic approach that helps in understanding physical and human involvement in the natural environment and sustainable development. The study of the spatiotemporal shifting pattern of LULC, the simulation of future scenarios and the intensity analysis at the interval, category and transition levels provide a comprehensive prospect to determine current and future development scenarios. In this study, we used multitemporal remote sensing data from 1980–2020 with a 10-year interval, explanatory variables (Digital Elevation Model (DEM), slope, population, GDP, distance from roads, distance from the city center and distance from streams) and an integrated CA-ANN approach within the MOLUSCE plugin of QGIS to model the spatiotemporal change transition potential and future LULC simulation in the Greater Bay Area. The results indicate that physical and socioeconomic driving factors have significant impacts on the landscape patterns. Over the last four decades, the study area experienced rapid urban expansion (4.75% to 14.75%), resulting in the loss of forest (53.49% to 50.57%), cropland (21.85% to 16.04%) and grassland (13.89% to 12.05%). The projected results (2030–2050) also endorse the increasing trend in built-up area, forest, and water at the cost of substantial amounts of cropland and grassland.
An Accurate PMU-Based Fault Location Scheme for Shunt-Compensated Transmission Lines

In this paper, an accurate fault location scheme based on phasor measurement unit (PMU) is proposed for shunt-compensated transmission lines. It is assumed that the voltage and current phasors on both sides of the shunt-compensated line have been provided by PMUs. In the proposed method, the faulted section is determined by presenting the absolute difference of positive(or negative-) sequence current angles index, firstly. After determining faulted section, the voltage phasor at the shunt-compensator terminal is estimated via the sound section. The faulted section can be assumed as a perfect transmission line that synchronized voltage and current phasors at one end and voltage phasor at the other end are available. Secondly, a new fault location algorithm is presented to locate the precise fault point in the faulted section. In this algorithm, the location of the fault and the fault resistance are calculated simultaneously by solving an optimization problem, utilizing the heuristic Particle Swarm Optimization (PSO) method. The simulation results in MATLAB/SIMULINK platform demonstrate the high performance of the proposed method in finding the fault location in shunt-compensated transmission lines. The proposed scheme has high accuracy for both symmetrical and asymmetrical fault types and high fault resistance.
Toward Using Reinforcement Learning for Trigger Selection in Network Slice Mobility

Recent 5G trials have demonstrated the usefulness of the Network Slicing concept that delivers customizable services to new and under-serviced industry sectors. However, user mobility’s impact on the optimal resource allocation within and between slices deserves more attention. Slices and their dedicated resources should be offered where the services are to be consumed to minimize network latency and associated overheads and costs. Different mobility patterns lead to different resource re-allocation triggers, leading eventually to slice mobility when enough resources are to be migrated. The selection of the proper triggers for resource re-allocation and related slice mobility patterns is challenging due to triggers’ multiplicity and overlapping nature. In this paper, we investigate the applicability of two Deep Reinforcement Learning based algorithms for allowing a fine-grained selection of mobility triggers that may instantiate slice and resource mobility actions. While the first proposed algorithm relies on a value-based learning method, the second one exploits a hybrid approach to optimize the action selection process. We present an enhanced ETSI Network Function Virtualization edge computing architecture that incorporates the studied mechanisms to implement service and slice migration. We evaluate the proposed methods’ efficiency in a simulated environment and compare their performance in terms of training stability, learning time, and scalability. Finally, we identify and quantify the applicability aspects of the respective approaches.
Deep Reinforcement Learning and Transfer Learning with FlappyBird

Reinforcement learning’s growth in popularity in recent years is partly due to its ability to play some video games with a level of mastery that no human can reach. Transfer learning is popular in the ﬁeld of deep learning, and using pre-trained models on certain tasks speeds up training time and increases performance signiﬁ-cantly. In this project we aim to apply transfer learning to the popular video game FlappyBird and analyze its performance compared to traditional reinforcement learning algorithms.
Interaction between coronene and graphite from temperature-programmed desorption and DFT-vdW calculations:Importance of entropic effects and insights into graphite interlayer binding

The adsorption of polycyclic aromatic hydrocarbon (PAH) molecules on graphitic surfaces provides a model system with which to investigate weak van der Waals (vdW) interactions. There are few experimental investigations of either the interaction between large PAH molecules and graphite or the binding between graphite layers. Determining the adsorption energy in these molecular systems provides a valuable benchmark for validating theoretical methods for implementing van der Waals interactions and, hence, also a means to investigate the interlayer binding in graphite. Here, we investigate the interaction between the coronene molecule and highly oriented pyrolytic graphite by using temperature-programmed desorption. We show how entropic effects play an important role in governing the desorption kinetics for large molecules such as coronene and must be taken into account in order to derive a realistic binding energy. DFT calculations demonstrate that the optB88-vdW functional is able to reproduce the experimen.
New Horizons for Diagnostic Pitfalls of Cerebral Venous Thrombosis: Clinical Utility of a Newly Developed Cerebral Venous Thrombosis Diagnostic Score: A Case Report and Literature Review

Patient: Male, 35-year-old Final Diagnosis: Cerebral venous Symptoms: Bloody diarrhea • throbbing persistent headache • weight loss Medication: — Clinical Procedure: — Specialty: Neurology Objective: Mistake in diagnosis Background: Diagnosing cerebral venous thrombosis (CVT) poses significant challenges owing to a nonspecific clinical presentation, poorly correlated laboratory biomarkers, and low sensitivity of non-contrast head computed tomography (CT). We describe a case of missed CVT diagnosis, due to low clinical suspicion and nonrecognition of anemia as a prothrombotic factor, especially during an ulcerative colitis (UC) flare. A recently proposed CVT clinical probability score can guide clinicians in pursuing further neurovascular imaging. Case Report: A 35-year-old man, with treatment-naive UC, presented to the Emergency Department (ED) with new-onset diffuse headache, 4 weeks of bloody diarrhea, and weight loss. Initial ED laboratory studies revealed severe anemia and unremarkable non-contrast head CT. Two days later, the patient returned to the ED for worsening headache. Non-contrast head CT revealed a left temporal hypodensity. This was later confirmed as acute ischemia on magnetic resonance imaging (MRI). MR venogram revealed thrombosis of the left transverse and sigmoid sinuses, leading to initiation of therapeutic subcutaneous anticoagulation. Repeat MRI, secondary to worsening headache, revealed the development of petechial hemorrhages within the core of venous ischemia in the left temporal lobe. Therapeutic anticoagulation, along with symptomatic management of UC, led to clinical stabilization. Conclusions: CVT should be suspected in patients with UC, especially in the context of anemia, presenting with new-onset or worsening headaches. Recognizing anemia as a thrombogenic factor is crucial. Diagnosis of CVT is challenging due to non-focal symptoms and poorly correlating diagnostic tests. We endorse implementing the CVT clinical probability score into AHA/ASA CVT guidelines to enhance diagnostic accuracy.
Semantic Segmentation for Flexible and Autonomous Manufacturing

—Customized mass production of boats and other vehicles requires highly complex manufacturing processes that involve a high amount of automation. Key elements to enhance the efﬁciency of such systems are represented by vision and sensing, which provide robots with detailed information about the working environment. In this paper, we focus on the sanding process of boat molding tools by means of a robot, proposing the use of semantic segmentation to detect the key elements involved in production and increase the automation of the production process. We demonstrate the potential of semantic segmentation in an industrial environment which differs from the domestic scenes typically considered in the literature: it features a lower degree of variability with respect to domestic scenarios, but higher performances are required in the production environment to address challenging manufacturing operations successfully. Our segmentation algorithm has been thoroughly validated on a industrial dataset that was created on purpose, whose acquisition and annotation were speeded up thanks to our optimized pipeline.
Active Biomechanical Human Model for Simulating Loco-Manipulation in Human-Robot Interactions

The growing trend of human-robot collaboration across various industries is motivated by its promising benefits of improving human productivity as well as safety and comfort. For instance, in healthcare, human-robot systems can augment the capabilities of healthcare workers (e.g. power exoskeletons, lift support mechanisms) as well as provide assistance to patients, elder and physically-impaired individuals (e.g. exoskeletons, prosthetics, orthotics) (see Fig. 1). These systems need to be adorned, attached to or held by humans while in operation, resulting in a dynamic coupling between human and robot, which may lead to unanticipated modifications of human motions and forces [1]. Advanced human models provide the benefits of simulating this complex interaction which enables improved system designs capable of providing adequate assistance to humans [2]. For instance, to evaluate human effort when using an assistive exoskeleton for loco-manipulation tasks (e.g. stair climbing while holding rails), a predictive simulation of human model capable of balancing and stably transitioning between various loco-manipulation poses [3] given support provided by the exoskeleton and environment, would be beneficial in the system design. Such simulations would also enable the analysis of kinematic, dynamic and biomechanical responses from the human model, which could be difficult to measure in a user study. Several human models have been proposed to evaluate the usability of assistive robots such as lower-back exoskeleton for lifting [4], lower-limb exoskeleton for running [5], upper-extremity rehabilitation robot [6] and human-robot co-manipulation [7]. However, limited work has been done on a comprehensive active human model for predictive simulation of human motion in loco-manipulation scenarios. Previous studies tended to address the issues of locomotion and environment manipulation in isolation. However, in numerous human-robot interaction scenarios, the human needs to manipulate the environment at the same time as dynamically negotiate for postural stability. Therefore, we propose to develop an active biomechanical human model integrated with a motion planner and controller capable of rendering natural human motion coordination for loco-manipulation tasks. This model will enable us evaluate human performance (kinematics, joint loading, muscle effort) in the interaction with robots, which is crucial to the design
Real-time Localized Photorealistic Video Style Transferr – Supplementary Material

We provide the architectural details of our mask enhancement network, guidance learner, spatiotemporal feature transfer, and grid learner in Table 1. As shown in Figure 1, we extract the VGG-19 features from four scales (CONV1 1, CONV2 1, CONV3 1, and CONV4 1) and match the multi-resolution feature statistics via three splatting blocks [9]. Note we remove the global scene summary path in Xia et al. [9] as we aim to transfer style between local regions, where the global features will not help. In each splatting block, the weights of the first and the last convolutional layers are shared for the content and style feature paths. The output of each ST-AdaIN layer is propagated to the next frame for blending the temporal information. After the feature transferring, we apply two convolutional layers to predict the bilateral grids.
Assessing environmental initiatives through an ecosystem stewardship lens

Stewardship has been increasingly used in the realm of conservation and sustainable land use as an important pathway for action. Ecosystem stewardship, a specific application of this concept, is an approach for natural resource management, but the lack of empirical examples is a shortcoming to its applicability. With this work, we aimed at investigating whether environmental initiatives taking place in a rural watershed in southeast Brazil can be framed as ecosystem stewardship and, if so, whether they address key socialecological feedbacks that influence the quality of critical local ecosystem services (water, food production, soil, forests). Drawing on data from direct and participant observation at community and technical meetings, nine unstructured interviews, and gray literature, we demonstrated that three initiatives encompass all elements of ecosystem stewardship to some extent (dual goals of ecosystem resilience and human well-being, integration of processes across scales and emphasis on actions that shape the future). Only one initiative, a multi-stakeholder network, fully entails all elements of ecosystem stewardship. The initiatives overlap in space and time and entail pressing and non-urgent issues, therefore they promote, as a group, complementary ecosystem stewardship practices at various levels in the territory. They also address the key feedbacks responsible for the degradation of water, food production, and soil. Knowledge, relational values, and care are salient ingredients that combine in different ways, shaping each initiative. Our findings suggest that ecosystem stewardship arises from local social-ecological challenges combined with stakeholders’ knowledge and understanding of the system dynamics. Collaboration among initiatives can strengthen their effects on undesired feedbacks and enable the design of joint strategies to tackle the erosion of relational values. Actions focusing on reconnecting local communities and forests may safeguard the flux of ecosystem-service bundles on both the short and long term.
Saliva as a source of antibodies for MMR vaccine screening in teenagers

The use of safe and effective vaccines is a well-established public health intervention, with a major impact on the fall in the prevalence of infectious diseases. In the absence of environmental transmission, vaccines do not work for life, as originally detected. This has now generated the occurrence of vaccinated susceptible people, which allows the importation of diseases, since vaccination coverage does not equate to population immunity. The serological control of the vaccination status and the protection of a population is essential, and its execution is not friendly due to the blood collection necessary for the tests. In these assays, the specific IgG for the vaccine agent is quantified, it would be important to detect IgA as well. This is an immunoglobulin secreted for mucous membranes that neutralizes or directs the agent to non-permissive neutrophils, it is very important in vaccine protection, but difficult to detect and quantify. Saliva can be a friendly alternative material as a source of IgA and IgG for use in conventional tests and its obtainment is not invasive, facilitating the acceptance of these studies in protected groups. We standardized assays with detection of antibodies in solid phase, to prospect for effective vaccine coverage in adolescents using saliva as a biological fluid. Once established and standardized, these techniques will allow for eventual vaccine control without the need for aversive blood collection, adequate public health measures, such as revaccination, can be adequately planned.
Towards Adaptive Robot Motor-Babbling

—This work takes inspiration from how human infants gradually acquire knowledge about how their body works by performing suitable exploratory actions. With the aim of replicating this important feature also in robotic systems, in this paper we propose a method that combines adaptive robot control, meant to determine both the kinematic parameters and the type of joint (prismatic or revolute), and trajectory optimization, meant to determine the persistent excitation motions required to acquire the maximum amount of knowledge about the robot itself. The ultimate aim is to enable robots to learn and become aware of their physical-mechanical structure. This would favour the application of powerful and predictable model-based control techniques even when only a minimal knowledge of the robot is available (i.e. the number of joints in our case). A preliminary validation is carried out through simulations on simple 1 DoF and 2 DoF robots, showing promising results and potential improvements to be addressed in the future.
Very High Resolution Images Classification by Fusing Deep Convolutional Neural Networks

Recently, deep convolutional neural networks (CNNs) have made great achievements, whether taken as features extractor or classifier, in particular for very high resolution (VHR) images classification task which is a key point in the remote sensing field. This work aims to improve the VHR image classification accuracy by exploiting the fusion of two pre-trained deep convolutional neural network models. In this paper, we propose to concatenate the features extracted from the last convolutional layer of each pre-trained deep convolutional neural network to get a long features vector which is fed into a fullyconnected layer and then perform a fine-tuning for a VHR image dataset classification. The experimental results are promising since they show that the fusion of two deep CNNs achieves better accuracy for the classification compared to the individual CNN models on the same dataset.
A Study on Switching Intervals in Classic DTC for Induction Motor

In this paper, a conceptual study on switching intervals in the classic direct torque control (DTC) method and a novel modified method have been proposed. In the switching table of classic DTC, the switching vectors have been changed in sectors with 60 degrees intervals and their boundaries are fixed. In this study, these fixed boundaries and length of switching intervals have been challenged and proved that the performance of the classic DTC can be improved with modified intervals with different lengths and boundaries. The final proposed switching table not also benefits simplicity of implementation as classic DTC switching table, but also it offers better performance especially in the aspect of low torque ripples. The proposed final switching table has been derived by a two-stage optimization process and the results have been proved by simulation results.