Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonparametric Identification of the Surgeon's Hand Vibration in Haptic Devices

Surgical handcontrollers are evolved in the last two decades by adding haptic feedback and vibration sensation. Haptic feedback can provide a realistic simulation of low frequency phenomena, while adding vibration to handcontrollers simulate high frequency vibration phenomena such as bone drilling. Two main methods of adding vibration to haptic devices are back drivable robot arms and adding vibration actuators to the handle (stylus) of the hand controllers. In both of these methods, a high fidelity method to investigate the dynamics at the grasping point of the handcontrollers is required to properly design the vibration generators. Developed dynamic models of the user hand while holding the stylus of the haptic devices includes a combination of experimental setups, and SDOF/MDOF dynamic models. The current dynamic models are limited by the range of excitation frequency where the tool was held with different styles. This grasping style affects the model parameters, which has not been investigated in previous studies. In this study, an experimental setup is developed to investigate a dynamic model of the grasping point of the haptic endeffectors. The system was modeled by its frequency response with a non-parametric identification method. The amplitude and phase of the frequency response of the operator's hand are plotted as a function of frequency. The natural frequency of the hand model is in agreement with the assumed SDOF model. The phase difference and the coherence spectrum is analyzed to investigate the natural frequencies and amplitude ratios at different grasping configurations and finger styles. The results showed changes in the natural frequencies and amplitude ratios for different hand configurations and finger styles. The developed model in this study can be used in conjunction with the vibration generators in haptic handcontrollers to more accurately render the developed vibration at surgical corridors at the end-user hand.
An Asynchronous Pipeline based Implementation of Membrane Potential of IF model

With the development of artificial neural network (ANN), the conflict between performance, power consumption, and volume becomes more obvious. The spiking neuron network(SNN) is the main brain-like ANN model, of which the calculation of membrane potential is the key. Since the efficiency of this type of computation is main consideration of determining the entire neural network, we introduce a design characterized by a relatively lightweight and simple structure in an asynchronous way, which is also more compatible with the SNN. We also verify our design with FPGA successful.
Early Identification of Critical Blocks: Making Replicated Distributed Storage Systems Reliable Against Node Failures

In large-scale replicated distributed storage systems consisting of hundreds to thousands of nodes, node failures are not rare and can cause data blocks to lose their replicas and become faulty. A simple but effective approach to prevent data loss from the node failures, i.e., ensuring reliability, is to shorten the identification time of the node failures and faulty blocks, which is determined by both timeouts and check intervals for node states. However, to maintain low repair network traffic, the identification time is actually relatively long and even dominates repair processes of critical blocks. In this paper, we propose a novel scheme, named RICK, to explore the potential in the identification time, and thus improve data reliability of replicated distributed storage systems while maintaining a low repair cost. First, by introducing an additional replica state, critical blocks (with two or more lost replicas) have individual short timeouts while sick blocks (with only one lost replica) preserve the long timeouts. Second, by replacing the static check intervals for node states with adaptive ones, the check intervals and the identification time of critical blocks are further shortened, which improves data reliability. Meanwhile, due to the low ratio of critical blocks in all faulty blocks, the repair network traffic remains low. The results from our simulation and prototype implementation show that RICK improves data reliability of replicated distributed storage systems by a factor of up to 14 in terms of mean time to data loss. Meanwhile, the extra repair network traffic caused by RICK is less than 1.5 percent of the total network traffic for data repairs.
Exploiting Generalization in the Subspaces for Faster Model-Based Reinforcement Learning

Due to the lack of enough generalization in the state space, common methods of reinforcement learning suffer from slow learning speed, especially in the early learning trials. This paper introduces a model-based method in discrete state spaces for increasing the learning speed in terms of required experiences (but not required computation time) by exploiting generalization in the experiences of the subspaces. A subspace is formed by choosing a subset of features in the original state representation. Generalization and faster learning in a subspace are due to many-to-one mapping of experiences from the state space to each state in the subspace. Nevertheless, due to inherent perceptual aliasing (PA) in the subspaces, the policy suggested by each subspace does not generally converge to the optimal policy. Our approach, called model-based learning with subspaces (MoBLeSs), calculates the confidence intervals of the estimated $Q$ -values in the state space and in the subspaces. These confidence intervals are used in the decision-making, such that the agent benefits the most from the possible generalization while avoiding from the detriment of the PA in the subspaces. The convergence of MoBLeS to the optimal policy is theoretically investigated. In addition, we show through several experiments that MoBLeS improves the learning speed in the early trials.
Key-Invariant Convolutional Neural Network Toward Efficient Cover Song Identification

Cover song identification has long been a challenging task due to key, timbre and structure variations in different renditions of a song. Previous research mostly involves handcrafted features and sequence alignment methods, where further breakthroughs can hardly be achieved. In this paper, we utilize a supervised deep learning method to learn an effective feature extractor for cover song identification. Regarding it as a classification problem, we propose a key-invariant convolutional neural network robust against key transposition for classification. Having been trained, the network is used to extract representations of music, which could be used to measure the similarity between songs. Besides, the representations are highly sparse; effective algorithms can be devised to accelerate the computation. Experimental results show that our model achieves high precision with low time cost on several datasets. Especially, our method outperforms the state-of-the-art approach on one of the datasets.
Fast Genetic Algorithm Path Planner for Fixed-Wing Military UAV Using GPU

Military unmanned aerial vehicles (UAVs) are employed in highly dynamic environments and must often adjust their trajectories based on the evolving situation. To operate autonomously and safely, a UAV must be equipped with a path planning module capable of quickly recalculating a feasible and quasi-optimal path in flight while in the event a new obstacle or threat has been detected or simply if the destination point is changed during the mission. To allow for a fast path planning, this paper proposes a parallel implementation of the genetic algorithm on graphics processing unit (GPU). The trajectories are built as series of line segments connected by circular arcs resulting in smooth paths suitable for fixed-wing UAVs. The fitness function we defined takes into account the dynamic constraints of the UAVs and aims to minimize fuel consumption and average flying altitude in order to improve range and avoid detection by enemy radars. This fitness function is also implemented on the GPU and different parallelization strategies were developed and tested for each step of the fitness evaluation. By exploiting the massively parallel architecture of GPUs, the execution time of the proposed path planner was reduced by a factor of 290x compared to a sequential execution on CPU. The path planning module developed was tested using 18 scenarios on six realistic three-dimensional terrains with multiple no-fly zones. We found that the proposed GPU-based path planner was able to find quasi-optimal solutions in a timely fashion allowing in-flight planning.
Classification of physical exercise intensity by using facial expression analysis

Facial expression analysis has a wide area of applications including health, psychology, sports etc. In this study, we explored different methods of automatic classification of exercise intensities using facial image processing of a subject performing exercise on a cycloergometer during an incremental standardized protocol. The method can be implemented in real time using facial video analysis. The experiments were done with images extracted from a 12 min HD video collected in laboratorial normalized settings (TechSport from the University of Trás-os-Montes e Alto Douro) with a static camera (90° angle with face and camera). The time slot for video to extract images for a particular class of exercise intensity is correspondence to the incremental heart rate. The facial expression recognition has been performed mainly in two steps: facial landmark detection and classification using the facial landmarks. Luxand application was used to detect 70 landmarks were detect using the adaptation of code available in Luxand application and we applied machine learning classification algorithms including discriminant analysis, KNN and SVM to classify the exercise intensities from the facial images. KNN algorithms presents up to 100% accuracy in classification into 2 and 3 classes. The distances between a lowermost landmark of the faces, which is indicated in landmark number 11 in the Luxand application, and the 26 landmarks around mouth were calculated and considered as features vector to train and test the classifier. Separate experiments were done for classification into two, three, and four classes and the accuracy of each algorithm was analyzed. From the overall results, classification into two and three classes was easy and resulted in very good classification performance whereas the classification with four classes had poor classification performance in each algorithm. Preliminary results suggest that distinguishing more levels of exertion, might require additional feature variables.
Self-Commissioning Identification of Permanent Magnet Flux-Linkage Magnitude in Sensorless Drives for PMSM at Quasi Stand-Still

The expanding market interest on Permanent Magnet Synchronous Motors (PMSM) invoked the emphasis on their efficient control and design optimization. The highperformance control of a motor demands the precise identification of several machine parameters, namely the phase resistance (and dead-time effect curve), d - and q -axis inductances or flux vs. current characteristic and the Permanent Magnet flux-linkage magnitude (PM-flux). In this paper a novel technique for the identification of PM-flux, which does not require the use of a mechanical position or speed sensor is presented. The motor is supposed to be free to move or subject to light load. DC current is imposed (close to nominal amplitude) and the current vector is then rotated by a limited amount, forcing rotor to rotate through the same angle. A mathematical model is developed to estimate the PM-flux using measured voltages and currents within this limited rotor movement. The stator resistance estimation and voltage offset error compensation are also included in this implementation. The method requires the knowledge of angular transition of the rotor to estimate the flux rather than its position. For the Surface Mount-PMSM (SM-PMSM), the initial and final rotor position are determined by the direction of DC current vector, while for the Interior-PMSM (IPMSM), HFI methods could be applied, thus eliminating the need of position sensor in either case. Simulation and experimental results provided on a SM-PMSM confirms the validity of the proposed method. The technique can estimate PM-flux with an error lower than 10%.
PP2A-B55 promotes nuclear envelope reformation after mitosis in Drosophila

As a dividing cell exits mitosis and daughter cells enter interphase, many proteins must be dephosphorylated. The protein phosphatase 2A (PP2A) with its B55 regulatory subunit plays a crucial role in this transition, but the identity of its substrates and how their dephosphorylation promotes mitotic exit are largely unknown. We conducted a maternal-effect screen in Drosophila melanogaster to identify genes that function with PP2A-B55/Tws in the cell cycle. We found that eggs that receive reduced levels of Tws and of components of the nuclear envelope (NE) often fail development, concomitant with NE defects following meiosis and in syncytial mitoses. Our mechanistic studies using Drosophila cells indicate that PP2A-Tws promotes nuclear envelope reformation (NER) during mitotic exit by dephosphorylating BAF and suggests that PP2A-Tws targets additional NE components, including Lamin and Nup107. This work establishes Drosophila as a powerful model to further dissect the molecular mechanisms of NER and suggests additional roles of PP2A-Tws in the completion of meiosis and mitosis.
Interprofessional conflict and conflict management in an educational setting

Abstract Background: Whenever health professionals work together as a team, conflict is inevitable – some would argue even necessary. However, conflict can have negative effects on patient care, job satisfaction, personal wellness, and professional productivity. Purpose: This study aims to describe interprofessional conflicts in a hospital setting from the perspective of three groups of health science students. Methods: An online questionnaire survey collected data from 225 health science students (medicine, nursing, and social work) in Northern California (USA). Quantitative and qualitative analyses were conducted resulting in seven central themes of conflict. Results: There are differences among health professional students in terms of how conflict is experienced and managed. Nursing students and medical students were more likely to take on the “victim” role when explaining their conflict, and their conflicts were more likely to be intra-professional. The most common cause of conflicts was related to hierarchy and power issues. The majority were dissatisfied with the way the conflict was resolved. Conclusions: Findings from this study can be used to design interprofessional curriculum to improve outcomes from conflicts and improve wellbeing, job satisfaction, and reduce patient turnover.
Advances in the available non-biological pharmacotherapy prevention and treatment of acute mountain sickness and high altitude cerebral and pulmonary oedema

ABSTRACT Introduction: The physiological responses on exposure to high altitude are relatively well known, but new discoveries are still being made, and novel prevention and treatment strategies may arise. Basic information has changed little since our previous review in this journal 10 years ago, but considerable more detail on standard therapies, and promising new approaches are now available. Areas covered: Herein, the authors review the role of pharmacological agents in preventing and treating high-altitude illnesses. The authors have drawn on their own experience and that of international experts in this field. The literature search was concluded in March 2018. Expert opinion: Slow ascent remains the primary prevention strategy, with rapid descent for the management of serious altitude illnesses. Pharmacological agents are particularly helpful when rapid ascent cannot be avoided or when rapid descent is not possible. Acetazolamide remains the drug of choice for prophylaxis of acute mountain sickness. However, evidence indicates that reduced dosage schemes compared to the current recommendations are warranted. Calcium channel blockers and phosphodiesterase inhibitors remain the drugs of choice for the management of high-altitude pulmonary edema. Dexamethasone should be reserved for the treatment of more severe cases of altitude illnesses such as cerebral edema.
Observation and Analysis of Bit-by-Bit Cell Current Variation During Data-Retention of TaOx-based ReRAM

This work analyzes the bit-by-bit memory cell current variation during the data-retention to study the long time data-retention behavior of TaOx-based resistive random access memory (ReRAM). For the first time, the current variation during data-retention is observed and analyzed for each memory cell of ReRAM. Cell current of low resistance state (LRS) cells shows complicated characteristics, that is, decreasing in the long term and fluctuating in the short term due to oxygen vacancy diffusion. On the other hand, high resistance state cell hardly shifts during the data-retention. A physical model for LRS cell current fluctuation is proposed in this study, based on the numerical calculation.
Computational Design Tools for Soft Inductive Tactile Sensors

Soft tactile sensors are a key enabling technology for next generation robotic systems and it is imperative to develop appropriate design tools to inform their design, integration and optimisation. The use of computational models can help speed this process and minimise the need for timely emperical design methods. Here we present the use of computational multi-physics modelling as a design tool for Soft Inductive Tactile Sensors (SITS) which use variation in electromagnetically-induced eddy-current effects as a transducer mechanism. We develop and experimentally validate 2D models which extend existing understanding to provide insight into the configuration of sensing elements for measurement of multi-axis forces and rejection of unwanted environmental disturbances. We analyse the limitations of this approach and discuss opportunities for future improvements to advance this burgeoning area.
Building Sentiment Lexicon with Representation Learning Based on Contrast and Label of Sentiment

Sentiment lexicon is an import part in many sentiment analysis or opinion-mining applications as its rich sentiment information plays an essential role in identifying the sentiment polarity of different text granularity. The latest work tries to automatically construct the sentiment lexicon by using the approaches of representation learning with word embedding. However, some study shows that similar word embedding may have opposite sentiment orientation, such ambiguous word embedding is difficult to distinguish them in sentiment classification model. To solve such problem, we integrate the sentiment label information of text and sentiment contrast information between target words and context word into word vector representation learning model. In this paper, the sentiment contrast information is synonyms and antonyms of the target word, which have positive LMI score with the context word and same sentiment orientation with the target word. We integrate sentiment label information of text and sentiment contrast information into Skip-gram to distinguish such ambiguous word embedding, and build the sentiment lexicon base on the improved word vector representation. The experiments demonstrated the effectiveness of our sentiment lexicon based on the improved word embedding, and its performance is better than the popular sentiment lexicons.
A Study on Multimodal Video Hyperlinking with Visual Aggregation

Video hyperlinking offers a way to explore a video collection, making use of links that connect segments having related content. Hyperlinking systems thus seek to automatically create links by connecting given anchor segments to relevant targets within the collection. In this paper, we further investigate multimodal representations of video segments in a hyper-linking system based on bidirectional deep neural networks, which achieved state-of-the-art results in the TRECVid 2016 evaluation. A systematic study of different input representations is done with a focus on the aggregation of the representation of multiple keyframes. This includes, in particular, the use of memory vectors as a novel aggregation technique, which provides a significant improvement over other aggregation methods on the final hyperlinking task. Additionally, the use of metadata is investigated leading to increased performance and lower computational requirements for the system.
EOD Edge Sampling for Visualizing Dynamic Network via Massive Sequence View

Dynamic network visualization is crucial to understand network evolving behavior. Massive sequence view (MSV) is a classic technique for visualizing dynamic networks and provides users with a fine-grained presentation of time-varying communication trend from both node pair and global network levels. However, MSV is vulnerable to visual clutter caused by overlapping edges, failing to show clear patterns or trends. This paper presents an edge sampling method, using the edge overlapping degree (EOD) concept, to reduce visual clutter in MSV while preserving the time-varying features of network communication. Referring to accept–reject sampling, we use kernel density estimation to characterize the time-varying features between node pairs and generate EOD probability density functions to accomplish sampling in a bottom-up manner. To enhance the sampling effect, we also consider the edge length factor and streaming processing. The case studies on two dynamic network data sets demonstrate that our method can significantly improve the overall readability of MSV and clearly reveal the temporal features of both node pairs and global network. A quantitative evaluation comparing with two other sampling methods using three real-world data sets indicates that our method can well balance visual clutter reduction and temporal feature preservation.
Data hiding using compressed archives

The need to avoid suspicion in a potential observer/investigator, though it can be seen as a weak requirement for the protection of transmitted data, it can be extremely useful. For example, once discovered that the data has been protected by means of certain security tools, a malicious user that is strongly interested in such data could make a great effort in trying to remove the protection, for example, by cracking the password or by exploiting the vulnerabilities of the algorithm that was used to protect the data. Hence, to transfer information in a secure way, it is sometimes better to hide it inside other data, rather than protecting it in a conventional manner. One of the most important techniques in data hiding is steganography. In this work we propose a novel steganographic method which enables to hide information by using a compressed archive as information carrier. The method we propose uses these compressed archives, together with their relative hierarchical structure, to hide bits of information. We remark that by means of our method the hidden information does not have any semantic relation with respect to the content of the compressed archive, whereas such relation will concern how the archive (and the relative sub-archives) was compressed, i.e., what compression algorithms and relative parameters were used, as well as how this operation was performed. Since it is a common practice to send compressed data by means of a communication channel, we believe that our proposal can be a valid tool for steganographic purposes, since the suspect generated in a potential observer could be considered almost negligible.