Multiple Instance Curriculum Learning for Weakly Supervised Object Detection

When supervising an object detector with weakly labeled data, most existing approaches are prone to trapping in the discriminative object parts, e.g., finding the face of a cat instead of the full body, due to lacking the supervision on the extent of full objects. To address this challenge, we incorporate object segmentation into the detector training, which guides the model to correctly localize the full objects. We propose the multiple instance curriculum learning (MICL) method, which injects curriculum learning (CL) into the multiple instance learning (MIL) framework. The MICL method starts by automatically picking the easy training examples, where the extent of the segmentation masks agree with detection bounding boxes. The training set is gradually expanded to include harder examples to train strong detectors that handle complex images. The proposed MICL method with segmentation in the loop outperforms the state-of-the-art weakly supervised object detectors by a substantial margin on the PASCAL VOC datasets.Comment: Published in BMVC 201

Review on Machine Learning-based Defect Detection of Shield Tunnel Lining

At present, machine learning methods are widely used in various industries for their high adaptability, optimization function, and self-learning reserve function. Besides, the world-famous cities have almost built and formed subway networks that promote economic development. This paper presents the art states of Defect detection of Shield Tunnel lining based on Machine learning (DSTM). In addition, the processing method of image data from the shield tunnel is being explored to adapt to its complex environment. Comparison and analysis are used to show the performance of the algorithms in terms of the effects of data set establishment, algorithm selection, and detection devices. Based on the analysis results, Convolutional Neural Network methods show high recognition accuracy and better adaptability to the complexity of the environment in the shield tunnel compared to traditional machine learning methods. The Support Vector Machine algorithms show high recognition performance only for small data sets. To improve detection models and increase detection accuracy, measures such as optimizing features, fusing algorithms, creating a high-quality data set, increasing the sample size, and using devices with high detection accuracy can be recommended. Finally, we analyze the challenges in the field of coupling DSTM, meanwhile, the possible development direction of DSTM is prospected

Synthesis of Fe-MCM-41 Using Iron Ore Tailings as the Silicon and Iron Source

Highly ordered Fe-MCM-41 molecular sieve was successfully synthesized by using n-hexadecyl-trimethyl ammonium bromide (CTAB) as the template and the iron ore tailings (IOTs) as the silicon and iron source. X-ray diffraction (XRD), high-resolution transmission electron microscopy (HRTEM), diffuse reflectance UV-visible spectroscopy, 29Si magic-angle spinning (MAS) nuclear magnetic resonance (NMR), and nitrogen adsorption/desorption were used to characterize the samples. The results showed that the mesoporous materials had highly ordered 2-dimensional hexagonal structure. The synthesized sample had high surface area, and part of iron atoms is retained in the framework with formation of tetrahedron after removal of the template by calcinations. The results obtained in the present work demonstrate the feasibility of employing iron ore tailings as a potential source of silicon and iron to produce Fe-MCM-41 mesoporous materials

Operationalization of the best available techniques and best environmental practices in deep seabed mining regime: a regulatory perspective

Best Practices, including Best Available Techniques (BAT) and Best Environmental Practices (BEP), are typically included to provide for or promote particular practices, methods, measures, or standards in respect of the efficient recovery of a resource and the level of environmental protection. Deep seabed mining (DSM) is an activity to obtain mineral resources from the deep sea, which may have certain adverse impacts on the marine environment. International Seabed Authority (ISA), the regulator of DSM activities in the Area authorized by the United Nations Convention on the Law of the Sea (UNCLOS), has introduced those terms in its Mining Code as critical tools for the reduction in environmental risks arising from DSM. Terms that are not included by the UNCLOS, such as BAT and BEP, are commonly invoked, yet often without specification in the regulatory discourse for DSM. In the absence of precise definitions and operational details, the terms BAT and BEP may not be able to function as anticipated in the DSM domain. Against this backdrop, this paper attempts to explore possible means by which the ISA might enable the contractor to operationalize the BAT and BEP, including providing definitions, their placement in the exploitation regulations, and the criteria for its operationalization in the Standards and Guidelines. This paper cites the existing international instruments that incorporate the terms BAT and BEP and takes particular note of DSM into account to highlight specific considerations for their practical implementation for DSM

Improving the Robustness of Electromyogram-Pattern Recognition for Prosthetic Control by a Postprocessing Strategy

Electromyogram (EMG) contains rich information for motion decoding. As one of its major applications, EMG-pattern recognition (PR)-based control of prostheses has been proposed and investigated in the field of rehabilitation robotics for decades. These prostheses can offer a higher level of dexterity compared to the commercially available ones. However, limited progress has been made toward clinical application of EMG-PR-based prostheses, due to their unsatisfactory robustness against various interferences during daily use. These interferences may lead to misclassifications of motion intentions, which damage the control performance of EMG-PR-based prostheses. A number of studies have applied methods that undergo a postprocessing stage to determine the current motion outputs, based on previous outputs or other information, which have proved effective in reducing erroneous outputs. In this study, we proposed a postprocessing strategy that locks the outputs during the constant contraction to block out occasional misclassifications, upon detecting the motion onset using a threshold. The strategy was investigated using three different motion onset detectors, namely mean absolute value, Teager–Kaiser energy operator, or mechanomyogram (MMG). Our results indicate that the proposed strategy could suppress erroneous outputs, during rest and constant contractions in particular. In addition, with MMG as the motion onset detector, the strategy was found to produce the most significant improvement in the performance, reducing the total errors up to around 50% (from 22.9 to 11.5%) in comparison to the original classification output in the online test, and it is the most robust against threshold value changes. We speculate that motion onset detectors that are both smooth and responsive would further enhance the efficacy of the proposed postprocessing strategy, which would facilitate the clinical application of EMG-PR-based prosthetic control

Mouse Embryonic Fibroblasts-Derived Extracellular Matrix Facilitates Expansion of Inner Ear-Derived Cells

Objective: Previous reports showed that mouse embryonic fibroblasts (MEFs) could support pluripotent stem cell selfrenewaland maintain their pluripotency. The goal of this study was to reveal whether the decellularized extracellularmatrix derived from MEFs (MEF-ECM) is beneficial to promote the proliferation of inner ear-derived cells.Materials and Methods: In this experimental study, we prepared a cell-free MEF-ECM through decellularization.Scanning electron microscope (SEM) and immunofluorescent staining were conducted for phenotype characterization.Organs of Corti were dissected from postnatal day 2 and the inner ear-derived cells were obtained. The identificationof inner ear-derived cells was conducted by using reverse transcription-polymerase chain reaction (RT-PCR). Cellcounting kit-8 (CCK-8) was used to evaluate the proliferation capability of inner ear-derived cells cultured on the MEFECMand tissue culture plate (TCP).Results: The MEF-ECM was clearly observed after decellularization via SEM, and the immunofluorescence stainingresults revealed that MEF-ECM was composed of three proteins, including collagen I, fibronectin and laminin. Mostimportantly, the results of CCK-8 showed that compared with TCP, MEF-ECM could effectively facilitate the proliferationof inner ear-derived cells.Conclusion: The discovery of the potential of MEF-ECM in promoting inner ear-derived cell proliferation indicatesthat the decellularized matrix microenvironment may play a vital role in keeping proliferation ability of these cells. Ourfindings indicate that the use of MEF-ECM may serve as a novel approach for expanding inner ear-derived cells andpotentially facilitating the clinical application of inner ear-derived cells for hearing loss in the future

Toward improving control performance of myoelectric arm prosthesis by adding wrist position feedback

Wearing a myoelectric prosthesis is a basic way for limb amputees to restore their lost limb functions in the activities of daily living (ADLs). However, it is estimated that around 40% of amputees refuse the prosthesis. One of the primary reasons would be that the current prostheses lack appropriate sensory feedback. Currently, the amputees only depend on their visual feedback (Vis-FB) when using their arm prostheses. It would be difficult for them to accurately control the wrist position, which is vital for flexible manipulation in ADLs. This manuscript designed a myoelectric arm prosthesis with wrist position feedback (WP-FB). To study the effect level of position feedback on prosthetic control, two tests were performed. The vibrotactile perception range test aims to analyze the perception sensitivity of the vibration in humans and obtain the optimal perception range utilized in the sensory feedback test. The sensory feedback test analyzes the effectiveness of the position feedback by comparing three feedback methods of Vis-FB, WP-FB, and a combination of Vis-FB and WP-FB (VP-FB). These tests were conducted by asking six able-bodied subjects to perform 20 movement combinations of five target positions. The WP-FB was transiently activated with five vibrating motors embedded in an armband to stimulate the arm stump when the prosthetic wrist rotates to the target positions. Our experimental results showed that when WP-FB was added to the prosthetic control, the absolute angular error (AAE) of the prosthetic wrist declined from 4.50° to 1.08° while the success rate 3 (SR3) increased from 0.34 to 0.84, respectively. This study demonstrates the importance of WP-FB to the effective control of the arm prosthesis

Efficient Channel Selection Approach for Motor Imaginary Classification based on Convolutional Neural Network

Brain Computer Interface (BCI) may be the only way to communicate and control for disabled people. Someone's intention can be decoded from their brainwaves during motor imagery action. This can be used to help them control their environment without making any physical movement. To decode someone's intention from brainwaves during motor imagery activities, machine learning models trained on features extracted from the acquired EEG signals have been used. Although the technique has been successful, it has encountered several limitations and difficulties especially during feature extraction. Moreover, many current BCI systems rely on a large number of channels (e.g. 64) to capture spatial information which are necessary during training a machine learning model. In this study, Convolutional Neural Network (CNN) is used to decode five motor imagery intentions from EEG signals obtained from four subjects using 64 channels EEG device. A CNN model trained on raw EEG data managed to achieve a mean classification accuracy of 99.7%. Channel selection based on learned weights extracted from a trained CNN model has been performed with subsequent models trained on only two selected channels with higher weights attained a high accuracy (average of 98%) among three participants out of four

Spasticity Assessment Based on the Maximum Isometrics Voluntary Contraction of Upper Limb Muscles in Post-stroke Hemiplegia

Background: The assessment of muscle properties is an essential prerequisite in the treatment of post-stroke patients with limb spasticity. Most existing spasticity assessment approaches do not consider the muscle activation with voluntary contraction. Including voluntary movements of spastic muscles may provide a new way for the reliable assessment of muscle spasticity.Objective: In this study, we investigated the effectiveness and reliability of maximum isometrics voluntary contraction (MIVC) based method for spasticity assessment in post-stroke hemiplegia.Methods: Fourteen post-stroke hemiplegic patients with arm spasticity were asked to perform two tasks: MIVC and passive isokinetic movements. Three biomechanical signals, torque, position, and time, were recorded from the impaired and non-impaired arms of the patients. Three features, peak torque, keep time of the peak torque, and rise time, were computed from the recorded MIVC signals and used to evaluate the muscle voluntary activation characteristics, respectively. For passive movements, two features, the maximum resistance torque and muscle stiffness, were also obtained to characterize the properties of spastic stretch reflexes. Subsequently, the effectiveness and reliability of the MIVC-based spasticity assessment method were evaluated with spearman correlation analysis and intra class correlation coefficients (ICCs) metrics.Results: The results indicated that the keep time of peak torque and rise time in the impaired arm were higher in comparison to those in the contralateral arm, whereas the peak torque in the impaired side was significantly lower than their contralateral arm. Our results also showed a significant positive correlation (r = 0.503, p = 0.047) between the keep time (tk) and the passive resistant torque. Furthermore, a significantly positive correlation was observed between the keep time (tk) and the muscle stiffness (r = 0.653, p = 0.011). Meanwhile, the ICCs for intra-time measurements of MIVC ranged between 0.815 and 0.988 with one outlier.Conclusion: The findings from this study suggested that the proposed MIVC-based approach would be promising for the reliable and accurate assessment of spasticity in post-stroke patients