A Depth-Based Hybrid Approach for Safe Flight Corridor Generation in Memoryless Planning.

This paper presents a depth-based hybrid method to generate safe flight corridors for a memoryless local navigation planner. It is first proposed to use raw depth images as inputs in the learning-based object-detection engine with no requirement for map fusion. We then employ an object-detection network to directly predict the base of polyhedral safe corridors in a new raw depth image. Furthermore, we apply a verification procedure to eliminate any false predictions so that the resulting collision-free corridors are guaranteed. More importantly, the proposed mechanism helps produce separate safe corridors with minimal overlap that are suitable to be used as space boundaries for path planning. The average intersection of union (IoU) of corridors obtained by the proposed algorithm is less than 2%. To evaluate the effectiveness of our method, we incorporated it into a memoryless planner with a straight-line path-planning algorithm. We then tested the entire system in both synthetic and real-world obstacle-dense environments. The obtained results with very high success rates demonstrate that the proposed approach is highly capable of producing safe corridors for memoryless local planning.

A Spatio-Temporal Attention-Based Model for Infant Movement Assessment From Videos.

The absence or abnormality of fidgety movements of joints or limbs is strongly indicative of cerebral palsy in infants. Developing computer-based methods for assessing infant movements in videos is pivotal for improved cerebral palsy screening. Most existing methods use appearance-based features and are thus sensitive to strong but irrelevant signals caused by background clutter or a moving camera. Moreover, these features are computed over the whole frame, thus they measure gross whole body movements rather than specific joint/limb motion. Addressing these challenges, we develop and validate a new method for fidgety movement assessment from consumer-grade videos using human poses extracted from short clips. Human poses capture only relevant motion profiles of joints and limbs and are thus free from irrelevant appearance artifacts. The dynamics and coordination between joints are modeled using spatio-temporal graph convolutional networks. Frames and body parts that contain discriminative information about fidgety movements are selected through a spatio-temporal attention mechanism. We validate the proposed model on the cerebral palsy screening task using a real-life consumer-grade video dataset collected at an Australian hospital through the Cerebral Palsy Alliance, Australia. Our experiments show that the proposed method achieves the ROC-AUC score of 81.87%, significantly outperforming existing competing methods with better interpretability.

One-step synthesis of carbon-supported electrocatalysts.

Cost-efficiency, durability, and reliability of catalysts, as well as their operational lifetime, are the main challenges in chemical energy conversion. Here, we present a novel, one-step approach for the synthesis of Pt/C hybrid material by plasma-enhanced chemical vapor deposition (PE-CVD). The platinum loading, degree of oxidation, and the very narrow particle size distribution are precisely adjusted in the Pt/C hybrid material due to the simultaneous deposition of platinum and carbon during the process. The as-synthesized Pt/C hybrid materials are promising electrocatalysts for use in fuel cell applications as they show significantly improved electrochemical long-term stability compared to the industrial standard HiSPEC 4000. The PE-CVD process is furthermore expected to be extendable to the general deposition of metal-containing carbon materials from other commercially available metal acetylacetonate precursors.

Plastic flow and localization in an amorphous material: Experimental interpretation of the fluidity.

We present a thorough study of the plastic response of a granular material progressively loaded. We study experimentally the evolution of the plastic field from a homogeneous one to a heterogeneous one and its fluctuations in terms of incremental strain. We show that the plastic field can be decomposed in two components evolving on two decoupled strain increment scales. We argue that the slowly varying part of the field can be identified with the so-called fluidity field introduced recently to interpret the rheological behavior of amorphous materials. This fluidity field progressively concentrates along a macroscopic direction corresponding to the Mohr-Coulomb angle.

Magnetic resonance-based treatment planning for prostate intensity-modulated radiotherapy: creation of digitally reconstructed radiographs.

PURPOSE: To develop a technique to create magnetic resonance (MR)-based digitally reconstructed radiographs (DRR) for initial patient setup for routine clinical applications of MR-based treatment planning for prostate intensity-modulated radiotherapy. METHODS AND MATERIALS: Twenty prostate cancer patients' computed tomography (CT) and MR images were used for the study. Computed tomography and MR images were fused. The pelvic bony structures, including femoral heads, pubic rami, ischium, and ischial tuberosity, that are relevant for routine clinical patient setup were manually contoured on axial MR images. The contoured bony structures were then assigned a bulk density of 2.0 g/cm(3). The MR-based DRRs were generated. The accuracy of the MR-based DDRs was quantitatively evaluated by comparing MR-based DRRs with CT-based DRRs for these patients. For each patient, eight measuring points on both coronal and sagittal DRRs were used for quantitative evaluation. RESULTS: The maximum difference in the mean values of these measurement points was 1.3 +/- 1.6 mm, and the maximum difference in absolute positions was within 3 mm for the 20 patients investigated. CONCLUSIONS: Magnetic resonance-based DRRs are comparable to CT-based DRRs for prostate intensity-modulated radiotherapy and can be used for patient treatment setup when MR-based treatment planning is applied clinically.

Classification of suicide attempters in schizophrenia using sociocultural and clinical features: A machine learning approach.

OBJECTIVE: Suicide is a major concern for those afflicted by schizophrenia. Identifying patients at the highest risk for future suicide attempts remains a complex problem for psychiatric interventions. Machine learning models allow for the integration of many risk factors in order to build an algorithm that predicts which patients are likely to attempt suicide. Currently it is unclear how to integrate previously identified risk factors into a clinically relevant predictive tool to estimate the probability of a patient with schizophrenia for attempting suicide. METHODS: We conducted a cross-sectional assessment on a sample of 345 participants diagnosed with schizophrenia spectrum disorders. Suicide attempters and non-attempters were clearly identified using the Columbia Suicide Severity Rating Scale (C-SSRS) and the Beck Suicide Ideation Scale (BSS). We developed four classification algorithms using a regularized regression, random forest, elastic net and support vector machine models with sociocultural and clinical variables as features to train the models. RESULTS: All classification models performed similarly in identifying suicide attempters and non-attempters. Our regularized logistic regression model demonstrated an accuracy of 67% and an area under the curve (AUC) of 0.71, while the random forest model demonstrated 66% accuracy and an AUC of 0.67. Support vector classifier (SVC) model demonstrated an accuracy of 67% and an AUC of 0.70, and the elastic net model demonstrated and accuracy of 65% and an AUC of 0.71. CONCLUSION: Machine learning algorithms offer a relatively successful method for incorporating many clinical features to predict individuals at risk for future suicide attempts. Increased performance of these models using clinically relevant variables offers the potential to facilitate early treatment and intervention to prevent future suicide attempts.

Diagnosis and treatment for abdominal injury through emergency laparoscopy

Abdominal injury include trauma on belly and abdominal injury is a common surgical emergency in our country, occupy from 10-13%. The death rate is still high:10%. In the world, the view of using laparoscopy in diagnosis and treatment for abdominal injury cases are performed effectively on almost of surgical centres. In Viet Nam, this method was applied successful at Cho Ray hospital, Viet Duc hospital and Army hospital No 108 since the year of 1990. However, in our country, the research on applying laparoscopy on diagnosis and treatment abdominal injury is limited