Miracle-dz/UniFAR-FTUs huggingface dataset:
A neural field approach for robot motion control

We introduce a biologically inspired approach for robot motion control. It is based on a so-called neural field which can be described by a nonlinear competitive dynamical system. Movement directions are assigned to the field's artificial neurons by employing codebook vectors. Due to the field's intrinsic dynamical properties the problem of reaching a goal under constraints can be solved efficiently. Our approach is validated by applications to local navigation and manipulator control.
A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation

Temporal difference learning (TD) is a simple iterative algorithm widely used for policy evaluation in Markov reward processes. Bhandari et al. prove finite time convergence rates for TD learning with linear function approximation. The analysis follows using a key insight that establishes rigorous connections between TD updates and those of online gradient descent. In a model where observations are corrupted by i.i. d. noise, convergence results for TD follow by essentially mirroring the analysis for online gradient descent. Using an information-theoretic technique, the authors also provide results for the case when TD is applied to a single Markovian data stream where the algorithm’s updates can be severely biased. Their analysis seamlessly extends to the study of TD learning with eligibility traces and Q-learning for high-dimensional optimal stopping problems.
PeMapNet: Action Recognition from Depth Videos Using Pyramid Energy Maps on Neural Networks

We propose an integrated approach to human action recognition from a depth video. The two major contributions of this approach are a novel feature descriptor for depth videos and the corresponding deep learning neural network structures. In this paper, we first present pyramid energy Maps (PeMaps) as the feature descriptor for a sequence of frames in a depth video. The pyramid structure is able to present the history of an action. Furthermore, PeMaps uses the levels of energy to carry the spatial dynamics of actions in a depth video. We then design PeMapNet that applies convolution neural networks and bidirectional long-short term memory (BLSTM) recurrent neural networks to PeMaps for action recognition. We evaluate our approach on three challenging datasets including MSR-Action3D, UTKinect-Action3D and MSR-Gesture3D. The experimental results demonstrate that our approach obtained higher accuracy than most of the existing methods and advance in efficiency.
Asymmetric Ternary Networks

Deep Neural Networks (DNNs) are widely used in a variety of machine learning tasks currently, especially in speech recognition and image classification. However, the huge demand for memory and computational power makes DNNs cannot be deployed on embedded devices efficiently. In this paper, we propose asymmetric ternary networks (ATNs) – neural networks with weights constrained to ternary values (-α1,0,+α2), which can reduce the DNN models size by about 16 × compared with 32-bits full precision models. Scaling factors {α1,α2} are used to reduce the quantization loss between ternary weights and full precision weights. We compare ATNs with recently proposed ternary weight networks (TWNs) and full precision networks on CIFAR-10 and ImageNet datasets. The results show that our ATN models outperform full precision models of VGG13, VGG16 by 0.11%, 0.33% respectively on CIFAR-10. On ImageNet, our model outperforms TWN AlexNet model by 2.25% of Top-1 accuracy and has only 0.63% accuracy degradation over the fullprecision counterpart.
Same, same but different: Perceptions of patients’ online access to electronic health records among healthcare professionals

In this study, we explore how healthcare professionals in primary care and outpatient clinics perceive the outcomes of giving patients online access to their electronic health records. The study was carried out as a case study and included a workshop, six interviews and a survey that was answered by 146 healthcare professionals. The results indicate that professionals working in primary care perceive that an increase in information-sharing with patients can increase adherence, clarify important information to the patient and allow the patient to quality-control documented information. Professionals at outpatient clinics seem less convinced about the benefits of patient accessible electronic health records and have concerns about how patients manage the information that they are given access to. However, the patient accessible electronic health record has not led to a change in documentation procedures among the majority of the professionals. While the findings can be connected to the context of outpatient clinics and primary care units, other contextual factors might influence the results and more in-depth studies are therefore needed to clarify the concerns.
An oracle based on the general regression neural network

The general regression neural network is well known to be an extremely effective prediction model in a wide variety of problems. It has been established that in many prediction problems, the results obtained by intelligently combining the outputs of several different prediction models are generally superior to the results obtained by using any one of the models. An overseer model that combines predictions from other independently trained prediction models is often called an oracle. The paper describes how the general regression neural network can be modified to serve as a powerful oracle for combining decisions from multiple arbitrary models.
Motion Sensorless IPMSM Control Using Linear Moving Horizon Estimation With Luenberger Observer State Feedback

This paper presents an investigation of motion sensorless control based on the frame work of moving horizon estimation (MHE), validated experimentally with a rear axle electrical drive unit used in automotive applications. Both steady-state performance and dynamic performance, including speed reversal, are investigated. In addition, a well-established active flux-based observer is implemented and referenced to objectively evaluate the proposed MHE. It is shown that the proposed solution is feasible, that the associated dynamic optimization problem can be solved in real time, while exploiting the attractiveness of the approach to obtain highly accurate estimates. Moreover, the absence of ${d}$ -axis flux in the proposed solution is considered a significant strength. Conclusively, it turns the proposed solution into a universal solution for any synchronous motor, regardless of whether a PM, PM-assisted, or reluctance motor is used, nondependent on the presence of magnetic saliency.
Development of a Robust Photogrammetric Metrology System for Monitoring the Healing of Bedsores

A three‐camera close range photogrammetric system for robust and precise measurement of bedsores has been designed and constructed. MEDPHOS (MEDical PHOtogrammetric System) consists of three synchronised cameras with convergent optical axes. A light projector is fixed in the centre of the rig that holds the cameras. A special dot pattern is projected onto the surface to be measured, to compensate for the lack of natural texture on the wound surface. The proposed algorithm consists of the following steps: the cameras and projector are calibrated so that all interior and exterior parameters are known; tailored image segmentation procedures are developed and applied for the detection of the projected pattern dots from the uneven background of the images using morphologic operators; and watershed transformation is used to tackle the problem of overlapping pattern dots. To reduce the effects of non‐uniform illumination and specular reflection of light due to humidity (often the case with wounds), a homomorphic transformation is developed and applied to the images. After segmentation of the images, a connected‐component labelling procedure is used to establish the points for matching. The centroids of these components were precisely calculated. Intensity‐based image matching has been tested without yielding satisfactory results due to the significant deviation from the Lambertian reflection assumption used for solving the correspondence problem. This problem is reliably solved by developing a new algorithm based on geometric constraints that allow feature‐based matching and do not need approximate values of the location of the targets in the images. This robust three‐focal constraint is found to be very effective for matching provided the necessary conditions for the system configuration are met. Auxiliary photometric constraints together with the calibrated projector (which is treated like an active camera) also serve as additional sources of information for reducing the number of remaining ambiguities and checking the consistency of the results. Almost all of the required biometric information can be obtained rapidly, robustly and easily using MEDPHOS. Experimental results showed the effectiveness of the proposed technique.
An automated pick-and-place benchmarking system in robotics

The reproducibility and objective assessment of a robot's manipulation ability is a crucial topic in robotics. The definition of testing protocols and the automation of such test procedures is challenging. Most evaluation protocols in benchmarks and competitions include not completely standardized interactions of a human/referee and are furthermore influenced by partially subjective criteria across different research areas. A promising and more objective solution is to automate benchmarking protocols for a range of reproducible manipulation tasks. We present an approach that is simplified to the essential elements, in which most of the process and evaluation is automated. The proposed system has low price and effort requirements and allows researchers to compare their robots without placing them side by side. To allow a wide range of robots to be benchmarked all parts will be available open source. In our system, an object is positioned in front of the benchmarked robot by a mobile minirobot that localizes itself on a table according to the individual task's predefined positions. By automating most of the process we ensure that the object locations tested as well as the timings are reproducible and well documented. This approach fosters the objective comparability of robot performances.
Estimate-and-Forward Relaying in Diffusion-Based Molecular Communication Networks: Performance Evaluation and Threshold Optimization

In this paper, we propose and investigate an estimate-and-forward (EF) relaying scheme with half-duplex transmission protocol in a two-hop diffusion-based molecular communication (DMC) system under the influence of both residual and counting noises. The proposed relaying scheme forwards an estimate of the transmitted number of molecules, which is derived using maximum likelihood principle. Based on this estimate, we assess the performance of EF relaying in terms of molecular throughput and end-to-end error probability. Further, we calculate energy consumption in synthesizing the information molecules at the relay nanomachine. Thereafter, we optimize the detection threshold by solving the convex optimization problem using logarithmic barrier followed by modified Karush–Kuhn–Tucker conditions, Newton Raphson method, and finally rounding the solution to the nearest integer value. Numerical and simulation results show the improvement in molecular throughput when EF relaying scheme is employed in contrast to the baseline direct communication scheme. The systematic comparisons reveal the scenarios where EF scheme can be beneficial over the existing amplify-and-forward and decode-and-forward relaying schemes. Moreover, we examine the effect of various parameters on error performance of the considered DMC system. Eventually, we demonstrate the effectiveness of our optimization solution in terms of accuracy and convergence time.
A combined approach to single-camera-based lane detection in driverless navigation

A combined approach is presented with real-world examples to illustrate how to detect lane lines under several road conditions in driverless navigation. This approach is designed for single-camera perception and is achieved using camera calibration, un-distortion, color thresholding, perspective transformation, pixel identification and ployfitting. Limiting factors such as illumination and radius of curvature of the testing road is considered. The results show that the method is effective in normal conditions but needs further improvement in extremely challenging environments.
Evidence for the Existence of a CXCL17 Receptor Distinct from GPR35

The chemokine CXCL17 is associated with the innate response in mucosal tissues but is poorly characterized. Similarly, the G protein–coupled receptor GPR35, expressed by monocytes and mast cells, has been implicated in the immune response, although its precise role is ill-defined. A recent manuscript reported that GPR35 was able to signal in response to CXCL17, which we set out to confirm in this study. GPR35 was readily expressed using transfection systems but failed to signal in response to CXCL17 in assays of β-arrestin recruitment, inositol phosphate production, calcium flux, and receptor endocytosis. Similarly, in chemotaxis assays, GPR35 did not confirm sensitivity to a range of CXCL17 concentrations above that observed in the parental cell line. We subsequently employed a real time chemotaxis assay (TAXIScan) to investigate the migratory responses of human monocytes and the monocytic cell line THP-1 to a gradient of CXCL17. Freshly isolated human monocytes displayed no obvious migration to CXCL17. Resting THP-1 cells showed a trend toward directional migration along a CXCL17 gradient, which was significantly enhanced by overnight incubation with PGE2. However, pretreatment of PGE2-treated THP-1 cells with the well-characterized GPR35 antagonist ML145 did not significantly impair their migratory responses to CXCL17 gradient. CXCL17 was susceptible to cleavage with chymase, although this had little effect its ability to recruit THP-1 cells. We therefore conclude that GPR35 is unlikely to be a bona fide receptor for CXCL17 and that THP-1 cells express an as yet unidentified receptor for CXCL17.
Power performance analysis of ARM scalable vector extension

Recent CPUs not only have multiple cores but also support wide SIMD (Single Instruction Multiple Data) instructions. This trend is expected to continue in the future. In this paper, we evaluate computing performance and energy consumption for multiple vector lengths using the ARM Scalable Vector Extension. From our evaluations, we confirm that a longer vector length with multi-cycle vector units results in up to approximately 38% better performance and 23% lower energy consumption than a shorter vector length.
CRP-cAMP mediates silencing of Salmonella virulence at the post-transcriptional level

Invasion of epithelial cells by Salmonella enterica requires expression of genes located in the pathogenicity island I (SPI-1). The expression of SPI-1 genes is very tightly regulated and activated only under specific conditions. Most studies have focused on the regulatory pathways that induce SPI-1 expression. Here, we describe a new regulatory circuit involving CRP-cAMP, a widely established metabolic regulator, in silencing of SPI-1 genes under non-permissive conditions. In CRP-cAMP-deficient strains we detected a strong upregulation of SPI-1 genes in the mid-logarithmic growth phase. Genetic analyses revealed that CRP-cAMP modulates the level of HilD, the master regulator of Salmonella invasion. This regulation occurs at the post-transcriptional level and requires the presence of a newly identified regulatory motif within the hilD 3’UTR. We further demonstrate that in Salmonella the Hfq-dependent sRNA Spot 42 is under the transcriptional repression of CRP-cAMP and, when this transcriptional repression is relieved, Spot 42 exerts a positive effect on hilD expression. In vivo and in vitro assays indicate that Spot 42 targets, through its unstructured region III, the 3’UTR of the hilD transcript. Together, our results highlight the biological relevance of the hilD 3’UTR as a hub for post-transcriptional control of Salmonella invasion gene expression.
An Empirical Analysis of the Mutation Operator for Run-Time Adaptive Testing in Self-Adaptive Systems

A self-adaptive system (SAS) can reconfigure at run time in response to uncertainty and/or adversity to continually deliver an acceptable level of service. An SAS can experience uncertainty during execution in terms of environmental conditions for which it was not explicitly designed as well as unanticipated combinations of system parameters that result from a self-reconfiguration or misunderstood requirements. Run-time testing provides assurance that an SAS continually behaves as it was designed even as the system reconfigures and the environment changes. Moreover, introducing adaptive capabilities via lightweight evolutionary algorithms into a run-time testing framework can enable an SAS to effectively update its test cases in response to uncertainty alongside the SAS's adaptation engine while still maintaining assurance that requirements are being satisfied. However, the impact of the evolutionary parameters that configure the search process for run-time testing may have a significant impact on test results. Therefore, this paper provides an empirical study that focuses on the mutation parameter that guides online evolution as applied to a run-time testing framework, in the context of an SAS.
The incidence of anosmia after traumatic brain injury: the SHEFBIT cohort

ABSTRACT Background: While anosmia is common after Traumatic Brain Injury(TBI) (prevalence 4%–68%),studies differ in the associations found with other variables. Aims: To assess the incidence of anosmia within a large, mixed TBI cohort and examine relationships with other injury or demographic features, including depression and global outcome(GOSE). Design, Subjects and Setting: 774 consecutive TBI admissions over 2 years, assessed within a specialist neurorehabilitation clinic. Methods: All patients assessed at 6–8 weeks and 1 year. Tools included the Extended Glasgow Outcome Scale(GOSE), Rivermead Head Injury Follow-up Questionnaire, Rivermead Post-Concussion Symptoms and the Hospital Anxiety and Depression Score. Olfactory function assessed with sensitivity to coffee granules. Results: The overall incidence of anosmia was 19.7%; mild TBI (9.55%), moderate (20.01%), and severe (43.5%). On a logistic regression, features of TBI severity (p < 0.001 (95% CI 0.098–0.438)), medical comorbidities (p = 0.026 (95% CI 0.301–0.927)) and depression (p = 0.006 (95% CI 1.202–2.981)) were significant. Sixty percent of patients with anosmia at 1 year were found to be clinically depressed, compared to 36% of patients without anosmia. Conclusion: In the largest prospective study of post-TBI anosmia, the incidence increased with TBI severity and other medical illness. The presence of anosmia should also raise the clinical suspicion of depression.
Interaction of Graphene Oxide with Proteins and Applications of their Conjugates

Graphene oxide (GO) has abundant surface oxygen-containing groups such as epoxide, hydroxyl, and carboxylic groups; it can be prepared through the oxidative intercalation and exfoliation of graphite on a mass scale. Owing to the enriched surface functionalities, the GO is water-soluble and chemically versatile. The surface functional groups can also provide plenty of reaction sites for linking nanoparticles, proteins, enzymes, peptides, bacteria, cells, and nucleic acids through covalent and non-covalent binding. GO has been used as a matrix for protein immobilization in different biotechnological applications such as fluorescence- or electrochemical-based sensors, labeling and imaging, therapy, and targeted delivery. This paper reviews the main strategies for the assembly of proteins onto graphene oxide surface and their applications, especially in the biomedical area.
How Does a Camera Look at One 3D CAD Object?

Camera pose and the camera’s rotation angles and translation vector (RT), are one-to-one relation with a 2D real image when the intrinsic parameter is fixed. In this paper, we propose a novel convolutional neural network (CNN) based framework to intelligently estimate the 6-DOF RTs from images taken on one 3D CAD object directly and indirectly, as well as visually verifying the correctness of the predicted RTs. Such a framework enables us to accurately interpret how a camera looks at the object. The direct way is simple and obtains lower average errors for the predicted RTs experimentally, while the indirect way utilizes the POSIT algorithm via landmarks and is able to avoid the non-Euclidean issue in rotation angles. To our best knowledge, we are the first one to estimate camera’s RTs and effectively interprets how a camera looks at one 3D CAD object from the images taken on it. The experiments on four models quantitatively and qualitatively demonstrate the efficacy of our proposed approach.
Topics may Evolve: Using Complaint Data for Analysis

User complaint data are quite valuable because they can reflect deficiencies of companies. Analyzing these short texts can help companies discover what topics users are complaining about. It is critical to locate and respond to these complaints timely so that companies can improve users’ satisfaction and loyalty. As the data volume is large, topic model can help discover key complaint topics quickly. The complaint data are in the form of short texts and streams, traditional topic models like LDA and BTM are not suitable in this scenario, for the reason that LDA is designed for long texts and BTM can not handle streams. This paper firstly proposes an improved shorttext topic model called PMITI-BTM to generate topics from user complaint data statically, and then further extends this algorithm into a dynamic one to suit the streaming feature of data. To further analyze the topic evolution, we finally propose a clustering algorithm called TDWAP to acquire the evolution process of these topics in different time slices. For each algorithm, we do several experiments to prove its efficiency. Results show that our methods not only can improve the performance of short texts topic discovery, but also can discover the evolution of topics.
A single-model approach for Arabic segmentation, POS tagging, and named entity recognition

This paper presents an entirely new, one-million-word annotated corpus for a comprehensive, machine-learning-based preprocessing of text in Modern Standard Arabic. Contrary to the conventional pipeline architecture, we solve the NLP tasks of word segmentation, POS tagging and named entity recognition as a single sequence labeling task. This single-component configuration results in a faster operation and is able to provide state-of-the-art precision and recall according to our evaluations. The fine-grained output tag set output by our annotator greatly simplifies downstream tasks such as lemmatization. Provided as a trained OpenNLP component, the annotator is free for research purposes.
Human oocyte maturation arrest caused by a novel missense mutation in TUBB8

Objective To explore the etiology of human oocyte maturation arrest in two infertile Chinese sisters. Methods Clinical examination and genetic testing of all available family members were conducted, and the findings were used to create a pedigree. Mutation screening using PCR amplification and DNA Sanger sequencing of the entire tubulin beta 8 class VIII gene (TUBB8) including intron–exon boundaries was performed to identify mutations. Results A novel missense TUBB8 mutation (c.1054G > T, p.A352S) in the patient and her elder sister was detected and shown to be associated with oocyte maturation arrest. Conclusion Our findings expand the known mutation spectrum of TUBB8 and provide insights into the etiology of human oocyte maturation arrest.