Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Learning of Tactile Force Estimation through Robot Interaction

Current methods for estimating force from tactile sensor signals are either inaccurate analytic models or task-specific learned models. In this paper, we explore learning a robust model that maps tactile sensor signals to force. We specifically explore learning a mapping for the SynTouch BioTac sensor via neural networks. We propose a voxelized input feature layer for spatial signals and leverage information about the sensor surface to regularize the loss function. To learn a robust tactile force model that transfers across tasks, we generate ground truth data from three different sources: (1) the BioTac rigidly mounted to a force torque (FT) sensor, (2) a robot interacting with a ball rigidly attached to the same FT sensor, and (3) through force inference on a planar pushing task by formalizing the mechanics as a system of particles and optimizing over the object motion. A total of 140k samples were collected from the three sources. We achieve a median angular accuracy of 3.5 degrees in predicting force direction (66% improvement over the current state of the art) and a median magnitude accuracy of 0.06 N (93% improvement) on a test dataset. Additionally, we evaluate the learned force model in a force feedback grasp controller performing object lifting and gentle placement. Our results can be found on https: //sites.google. com/view/tactile-force.
ComPaSS‐GWAS: A method to reduce type I error in genome‐wide association studies when replication data are not available

Results from association studies are traditionally corroborated by replicating the findings in an independent data set. Although replication studies may be comparable for the main trait or phenotype of interest, it is unlikely that secondary phenotypes will be comparable across studies, making replication problematic. Alternatively, there may simply not be a replication sample available because of the nature or frequency of the phenotype. In these situations, an approach based on complementary pairs stability selection for genome‐wide association study (ComPaSS‐GWAS), is proposed as an ad‐hoc alternative to replication. In this method, the sample is randomly split into two conditionally independent halves multiple times (resamples) and a GWAS is performed on each half in each resample. Similar in spirit to testing for association with independent discovery and replication samples, a marker is corroborated if its p‐value is significant in both halves of the resample. Simulation experiments were performed for both nongenetic and genetic models. The type I error rate and power of ComPaSS‐GWAS were determined and compared to the statistical properties of a traditional GWAS. Simulation results show that the type I error rate decreased as the number of resamples increased with only a small reduction in power and that these results were comparable with those from a traditional GWAS. Blood levels of vitamin pyridoxal 5′‐phosphate from the Trinity Student Study (TSS) were used to validate this approach. The results from the validation study were compared to, and were consistent with, those obtained from previously published independent replication data and functional studies.
Decentralized Adaptive Output Feedback Fault Detection and Control for Uncertain Nonlinear Interconnected Systems

This paper studies the problem of decentralized adaptive output feedback fault detection and control for a class of uncertain nonlinear interconnected systems. The K-filters are designed to estimate the unmeasured state variables of the system. Moreover, the built-in noise dampening filters are introduced to attenuate the influence caused by the measurement noises. Then the fault detection scheme is proposed by designing the residual and threshold signals. Subsequently, by using the backstepping design method, the decentralized switched control strategies are proposed with the help of the neural network approximation technique. Based on the Lyapunov stability theory, it is proved strictly that all signals of the resulting closed-loop system are bounded. Finally, a simulation example is presented to verify the effectiveness of the theoretical result.
End-to-End Visuomotor Learning of Drawing Sequences using Recurrent Neural Networks

Drawing is one of the complex cognitive abilities of humans. Cognitive neuropsychological studies have attempted to develop models that can explain the observations of the drawing behavior. These models exhibit limitations to reproduce the drawing behaviors because of individual factors that are related to the drawing style or non-reproducibility of motions. A constructive approach provides another methodology to investigate the complex systems by constructing models that can reproducibly replicate the behaviors. In this study, we focus on an ability to reuse the integrated visuomotor memory of drawing to associate the drawing motion from an image. Existing computational models of drawing have not considered the visual information in hand-drawn pictures. Therefore, we propose a dynamical model of the visuomotor process of drawing. The proposed model does not require any prior knowledge of the process such as the pre-designed shape primitives or the image processing algorithms. The proposed model is implemented by utilizing a recurrent neural network that learns the visuomotor transition of the drawing process. The association of the model’s drawing motion by reusing the obtained memory can be obtained by minimizing the prediction error of the image. By performing simulator experiments, the proposed model demonstrates its association ability in case of pictures that comprise multiple lines.
Developing an In Vitro Model to Screen Drugs for Nerve Regeneration

Peripheral nerve injuries (PNI) have a high prevalence and can be debilitating, resulting in life‐long loss or disturbance in end‐organ function, which compromises quality of life for patients. Current therapies use microsurgical approaches but there is the potential for enhancing recovery through other therapeutic modalities such as; cell‐based conduits, gene therapy and small molecules. A number of molecular targets and drugs which have the potential to improve nerve regeneration have been identified, however, there are challenges associated with moving therapies toward clinical translation. Due to the lack of detailed knowledge about the pro‐regenerative effect of potential drug treatments, there is a need for effective in vitro models to screen compounds to inform future pre‐clinical and clinical studies. The interaction between regenerating neurites and supporting Schwann cells is a key feature of the nerve environment, therefore, in vitro models that mimic this cellular association are useful tools. In this study, we have investigated various cell culture models, including simple monolayer systems and more complex 3D‐engineered co‐cultures, as models for use in PNI drug development. Anat Rec, 301:1628–1637, 2018. © 2018 The Authors. The Anatomical Record published by Wiley Periodicals, Inc. on behalf of American Association of Anatomists.
The Relationship Between Lifestyle, Health Promotion Lifestyle Profile II And High Blood Pressure In University Students

BACKGROUND: Identifying and controlling systemic arterial blood pressure is important in young people, and it is possible to reduce the frequency of systemic arterial hypertension by improving the lifestyle. AIM: The aim of the study is to assess the relationship between healthy lifestyle behaviors and systemic blood pressure in university students. MATERIALS: The study sample consisted of 200 university students from a state university in Edirne. Lifestyles and habits were evaluated with Health Promotion Lifestyle Profile II. Students’ blood pressure was measured from both arms twice. RESULTS: The mean HPLP-II score of those who frequently feel good was significantly higher than those who rarely feel good. The mean score of those who frequently wake up between 06:00-09:00 in the morning was statistically significantly higher than those who wake up outside these hours. Those who perform social or artistic activities during their leisure times had a mean scale score higher than those who don’t perform. Although there wasn’t a statistically significant difference according to smoking status, the mean score of non-smokers was higher than smokers. The mean scale scores were higher in frequent salt users than non-frequent users; participants with low saturated fatty acid intake had higher scores than those with high intake, and rare fast food consumers had higher scores than frequent consumers. The statistically significant difference between blood pressure values of females and males was due to higher blood pressure in male students. Those working in a part-time job had higher blood pressure values than those who weren’t working. Among the students whose body mass indexes could be evaluated, there were differences in blood pressure values. CONCLUSION: It has been observed in our study that health-related responsibilities and lifestyle behaviours increase with better leisure time activities, improved eating habits and a positive outlook on life. Turning youngs’ tendencies towards healthy lifestyle behaviours to habits can make them healthier, more collective and more productive regarding physical, social and psychological well-being.
Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review

Background State-of-the-art classifiers based on convolutional neural networks (CNNs) were shown to classify images of skin cancer on par with dermatologists and could enable lifesaving and fast diagnoses, even outside the hospital via installation of apps on mobile devices. To our knowledge, at present there is no review of the current work in this research area. Objective This study presents the first systematic review of the state-of-the-art research on classifying skin lesions with CNNs. We limit our review to skin lesion classifiers. In particular, methods that apply a CNN only for segmentation or for the classification of dermoscopic patterns are not considered here. Furthermore, this study discusses why the comparability of the presented procedures is very difficult and which challenges must be addressed in the future. Methods We searched the Google Scholar, PubMed, Medline, ScienceDirect, and Web of Science databases for systematic reviews and original research articles published in English. Only papers that reported sufficient scientific proceedings are included in this review. Results We found 13 papers that classified skin lesions using CNNs. In principle, classification methods can be differentiated according to three principles. Approaches that use a CNN already trained by means of another large dataset and then optimize its parameters to the classification of skin lesions are the most common ones used and they display the best performance with the currently available limited datasets. Conclusions CNNs display a high performance as state-of-the-art skin lesion classifiers. Unfortunately, it is difficult to compare different classification methods because some approaches use nonpublic datasets for training and/or testing, thereby making reproducibility difficult. Future publications should use publicly available benchmarks and fully disclose methods used for training to allow comparability.
Unsupervised Learning of Depth and Pose Estimation based on Continuous Frame Window

We present an unsupervised learning framework for the task of monocular depth and camera motion estimation from video sequences. In common with recent work, we use an unsupervised end-to-end learning method, requiring monocular video sequences for training. What makes the difference is, our approach not only uses image reconstruction as the supervisory signal but also exploits the pose estimation method which was used in traditional SLAM approach to enhance the supervisory signal and add training constraints. In pose estimation, a continuous frame window is set to construct the pose graph. Our method uses single-view depth and multi-view pose networks, with a loss based on reconstructing nearby images to the target using the predicted depth and pose. During training, the networks are thus coupled by the loss but can be applied independently at test time. Our evaluation of experiments on the KITTI dataset proves the effectiveness of our method: 1) monocular depth performs superior to the supervised methods that use ground-truth depth data for training and the existing unsupervised learning method. Our method performs comparably with the supervised methods that use ground-truth pose data for training. 2) pose estimation performs almost the same compared to established SLAM systems under comparable input settings.
Performance Analysis of Small Cell Networks Using Accurate Path Loss Model at 39 GHz

In this paper, we apply an accurate millimeter wave (mmWave) path loss model, obtained from outdoor measurement operated at 39 GHz in Qingdao, China, into the stochastic geometry analysis to investigate the system performance of small cell networks (SCNs) with non-identical user equipment (UE) - base station (BS) antenna heights. The analytical expressions for coverage probability and outage probability are derived in detail, which are functions of BS density and absolute difference between the antenna heights of BS and UE. The analytical results of coverage probability and outage probability are verified through Monte Carlo simulation. Our study shows that as the SCNs become denser, the impact of antenna heights difference on coverage and outage probability is more significant.
Fine-tuning on Clean Data for End-to-End Speech Translation: FBK @ IWSLT 2018

This paper describes FBK’s submission to the end-to-end English-German speech translation task at IWSLT 2018. Our system relies on a state-of-the-art model based on LSTMs and CNNs, where the CNNs are used to reduce the temporal dimension of the audio input, which is in general much higher than machine translation input. Our model was trained only on the audio-to-text parallel data released for the task, and fine-tuned on cleaned subsets of the original training corpus. The addition of weight normalization and label smoothing improved the baseline system by 1.0 BLEU point on our validation set. The final submission also featured checkpoint averaging within a training run and ensemble decoding of models trained during multiple runs. On test data, our best single model obtained a BLEU score of 9.7, while the ensemble obtained a BLEU score of 10.24.
Aerobic exercise for adult patients with major depressive disorder in mental health services: A systematic review and meta‐analysis

Although exercise is associated with depression relief, the effects of aerobic exercise (AE) interventions on clinically depressed adult patients have not been clearly supported. The purpose of this meta‐analysis was to examine the antidepressant effects of AE versus nonexercise comparators exclusively for depressed adults (18–65 years) recruited through mental health services with a referral or clinical diagnosis of major depression. Eleven e‐databases and bibliographies of 19 systematic reviews were searched for relevant randomized controlled clinical trials. A random effects meta‐analysis (Hedges’ g criterion) was employed for pooling postintervention scores of depression. Heterogeneity and publication bias were examined. Studies were coded considering characteristics of participants and interventions, outcomes and comparisons made, and study design; accordingly, sensitivity and subgroup analyses were calculated. Across 11 eligible trials (13 comparisons) involving 455 patients, AE was delivered on average for 45 min, at moderate intensity, three times/week, for 9.2 weeks and showed a significantly large overall antidepressant effect (g = –0.79, 95% confidence interval = –1.01, –0.57, P < 0.00) with low and nonstatistically significant heterogeneity (I2 = 21%). No publication bias was found. Sensitivity analyses revealed large or moderate to large antidepressant effects for AE (I2 ≤ 30%) among trials with lower risk of bias, trials with short‐term interventions (up to 4 weeks), and trials involving individual preferences for exercise. Subgroup analyses revealed comparable effects for AE across various settings and delivery formats, and in both outpatients and inpatients regardless symptom severity. Notwithstanding the small number of trials reviewed, AE emerged as an effective antidepressant intervention.
Transfer Learning Based Model for Classification of Cocoa Pods

Over time, more sophisticated open source convolutional neural networks are becoming accessible for use. In conjunction with transfer learning, this allows projects with more focused and industry-specific requirements to generate positive results and make innovations within their field. This paper further describes the process of refining a deep learning model trained to identify cocoa pods from their surroundings, to additionally differentiate the ripe cocoa pods that are ready to be harvested. This is an ongoing project aiming to eventually automate the harvesting of cocoa pods on cocoa plantations, using automated robotic workers which can gather and transport products. This refinement is a vital step towards eventually circumnavigating the pitfalls which have, until now, left the cocoa industry working at pre-industrial standards. The obtained results show the satisfactory ability of the model proposed to classify correctly cocoa pods within static images.
A novel gene-pair signature for relapse-free survival prediction in colon cancer

Background Colon cancer (CC) patients with early relapse usually have a poor prognosis. In this study, we aimed to identify a novel signature to improve the prediction of relapse-free survival (RFS) in CC. Methods Four microarray datasets were merged into a training set (n=1,045), and one RNA-sequencing dataset was used as a validation set (n=384). In the training set, microarray meta-analysis screened out 596 common RFS-related genes across datasets, which were used to construct 177,310 gene pairs. Then, the LASSO penalized generalized linear model identified 16 RFS-related gene pairs, and a risk score was calculated for each sample according to the model coefficients. Results The risk score demonstrated a good ability in predicting RFS (area under the curve [AUC] at 5 years: 0.724; concordance index [C-index]: 0.642, 95% CI: 0.615–0.669). High-risk patients showed a poorer prognosis than low-risk patients (HR: 3.519, 95% CI: 2.870–4.314). Subgroup analysis reached consistent results when considering multiple confounders. In the validation set, the risk score had a similar performance (AUC at 5 years: 0.697; C-index: 0.696, 95% CI: 0.627–0.766; HR: 2.926, 95% CI: 1.892–4.527). When compared with a 13-gene signature, a 15-gene signature, and TNM stage, the score showed a better performance (P<0.0001; P=0.0004; P=0.0125), especially for the patients with a longer follow-up (R2=0.988, P<0.0001). When the follow-up was >5 years (n=314), the score demonstrated an excellent performance (C-index: 0.869, 95% CI: 0.816–0.922; HR: 13.55, 95% CI: 7.409–24.78). Conclusion Our study identified a novel gene-pair signature for prediction of RFS in CC.
A Novel Document Classification Algorithm Based on Statistical Features and Attention Mechanism

Bi-directional Long-Short term Memory in Deep Learning is often used to solve the problem of long-term dependency and gradient explosion. The combination of forward and backward sequences also includes more semantic information. However, the influence of keywords in documents is not clearly addressed. Attention mechanism has been successfully used in several start-of-the-art natural language processing related applications. In the field of text processing, the methods of calculating attention weights are typically at the word level. Although these methods improve the performance of a model, they increase the computational cost significantly. In this paper, we propose to calculate attention weights at the structured event level since 1) events contain richer semantics than words or phrases; and 2) event-based attention mechanism reduces computational cost. Different from the existing deep learning model which does not rely on the text statistical features, we add the statistical features on the basis of attention weight calculation. Compared with the existing models, the semantic information contained in the event structure and the corresponding statistical features improves the quality of the text vector representation and achieves better classification performance. Finally, we evaluate the performance of our model in terms of accuracy, recall and F-Score. The experimental results show that our model achieves better results while reducing the computational cost.
FlashShare: Punching Through Server Storage Stack from Kernel to Firmware for Ultra-Low Latency SSDs

A modern datacenter server aims to achieve high energy efficiency by co-running multiple applications. Some of such applications (e.g., web search) are latency sensitive. Therefore, they require low-latency I/O services to fast respond to requests from clients. However, we observe that simply replacing the storage devices of servers with Ultra-Low-Latency (ULL) SSDs does not notably reduce the latency of I/O services, especially when co-running multiple applications. In this paper, we propose FLASHSHARE to assist ULL SSDs to satisfy different levels of I/O service latency requirements for different co-running applications. Specifically, FLASHSHARE is a holistic cross-stack approach, which can significantly reduce I/O interferences among co-running applications at a server without any change in applications. At the kernel-level, we extend the data structures of the storage stack to pass attributes of (co-running) applications through all the layers of the underlying storage stack spanning from the OS kernel to the SSD firmware. For given attributes, the block layer and NVMe driver of FLASHSHARE differently manage the I/O scheduler and interrupt handler of NVMe. We also enhance the NVMe controller and cache layer at the SSD firmware-level, by dynamically partitioning DRAM in the ULL SSD and adjusting its caching strategies to meet diverse user requirements. The evaluation results demonstrate that FLASHSHARE can shorten the average and 99th-percentile turnaround response times of co-running applications by 22% and 31%, respectively.
Development of resistive memories based on silver doped graphene oxide for neuron simulation

Resistive memory (ReRAM) can be approximately one hundred times faster than current Flash technologies. This device can still be more economical in power consumption. The key element that makes ReRAM memories so attractive is a component called memristor. In this work, device fabrication used doped graphene oxide with silver (GO + 0.1% Ag). ITON (Indium Tin oxynitride) and aluminum were used as contacts. Thin films are obtained by the dip coating technique. The mechanism of the resistive switching effect in doped graphene oxide thin film based devices has been investigated by macroscopic current-voltage (IxV) measurements. Device was tested individually; its electrical characteristics and its behavior were verified in case of voltage variations in order to establish the operating limits for set and reset. From the experimental results, a simulation of neural networks was made using data obtained. The neural network model chosen was the so-called perceptron. This is the simplest form, imitating a single neuron that makes the weighted sum of stimuli in its dendrinos and applies an activation function to decide if it is the case of sending or not a signal in its axon, exciting or inhibiting other neurons. Only two synapses were used, simulating only two characteristics to maintain simplicity both in the example and the presentation of the data. However the example can be expanded to more synapses, inserting more bridges of memristors or even evolving into a multilayer perceptron.