End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners

For human drivers, having rear and side-view mirrors is vital for safe driving. They deliver a more complete view of what is happening around the car. Human drivers also heavily exploit their mental map for navigation. Nonetheless, several methods have been published that learn driving models with only a front-facing camera and without a route planner. This lack of information renders the self-driving task quite intractable. We investigate the problem in a more realistic setting, which consists of a surround-view camera system with eight cameras, a route planner, and a CAN bus reader. In particular, we develop a sensor setup that provides data for a 360-degree view of the area surrounding the vehicle, the driving route to the destination, and low-level driving maneuvers (e.g. steering angle and speed) by human drivers. With such a sensor setup we collect a new driving dataset, covering diverse driving scenarios and varying weather/illumination conditions. Finally, we learn a novel driving model by integrating information from the surround-view cameras and the route planner. Two route planners are exploited: 1) by representing the planned routes on OpenStreetMap as a stack of GPS coordinates, and 2) by rendering the planned routes on TomTom Go Mobile and recording the progression into a video. Our experiments show that: 1) 360-degree surround-view cameras help avoid failures made with a single front-view camera, in particular for city driving and intersection scenarios; and 2) route planners help the driving task significantly, especially for steering angle prediction.Comment: to be published at ECCV 201

Regulation of Apoptotic Effects by Erythrocarpine E, a Cytotoxic Limonoid from Chisocheton erythrocarpus in HSC-4 Human Oral Cancer Cells

The aim of this study was to determine the cytotoxic and apoptotic effects of erythrocarpine E (CEB4), a limonoid extracted from Chisocheton erythrocarpus on human oral squamous cell carcinoma. Based on preliminary dimethyl-2-thiazolyl-2,5-diphenyl-2H-tetrazolium bromide (MTT) assays, CEB4 treated HSC-4 cells demonstrated a cytotoxic effect and inhibited cell proliferation in a time and dose dependent manner with an IC50 value of 4.0±1.9 µM within 24 h of treatment. CEB4 was also found to have minimal cytotoxic effects on the normal cell line, NHBE with cell viability levels maintained above 80% upon treatment. Annexin V-fluorescein isothiocyanate (FITC), poly-ADP ribose polymerase (PARP) cleavage and DNA fragmentation assay results showed that CEB4 induces apoptosis mediated cell death. Western blotting results demonstrated that the induction of apoptosis by CEB4 appeared to be mediated through regulation of the p53 signalling pathway as there was an increase in p53 phosphorylation levels. CEB4 was also found to up-regulate the pro-apoptotic protein, Bax, while down-regulating the anti-apoptotic protein, Bcl-2, suggesting the involvement of the intrinsic mitochondrial pathway. Reduced levels of initiator procaspase-9 and executioner caspase-3 zymogen were also observed following CEB4 exposure, hence indicating the involvement of cytochrome c mediated apoptosis. These results demonstrate the cytotoxic and apoptotic ability of erythrocarpine E, and suggest its potential development as a cancer chemopreventive agent

Seizure episodes detection via smart medical sensing system

Cyber-physical systems (CPS) consist of seamless network of sensors and actuators integrated with physical processes related to human activities. The CPS exploits sensors and actuators to monitor and control different physical process that can affect the computations of the devices. This paper presents the monitoring of physical activities exploiting wireless devices as sensors used in medical cyber-physical systems. Patients undergoing epileptic seizures experience involuntary body movements such as jerking, muscle twitching, falling, and convulsions. The proposed method exploits S-Band sensing used in medical CPS that leverage wireless devices such as omni-directional antenna at the transmitter side, four-beam patch antenna at the receiver side, RF signal generator and vector signal analyzer that perform signal conditioning by providing amplitude and raw phase data. The method uses wireless monitoring and recording system for measurement and classification of a clinical condition (epileptic seizures) versus normal daily routine activities. The data acquired that are perturbations of the radio signal is analyzed as amplitude, phase information, and statistical models. Extracting the statistical features, we leverage various machine learning algorithms such as support vector machine, random forest, and K-nearest neighbor that classify the data to differentiate patient’s various activities such as press-ups, walking, sitting, squatting, and seizure episodes. The performance parameters used in three machine learning algorithms are accuracy, precision, recall, Cohen’s Kappa coefficient, and F-measure. The values obtained using five performance parameters provide the accuracy of more than 90%

A quantitative systems view of the spindle assembly checkpoint

The idle assembly checkpoint acts to delay chromosome segregation until all duplicated sister chromatids are captured by the mitotic spindle. This pathway ensures that each daughter cell receives a complete copy of the genome. The high fidelity and robustness of this process have made it a subject of intense study in both the experimental and computational realms. A significant number of checkpoint proteins have been identified but how they orchestrate the communication between local spindle attachment and global cytoplasmic signalling to delay segregation is not yet understood. Here, we propose a systems view of the spindle assembly checkpoint to focus attention on the key regulators of the dynamics of this pathway. These regulators in turn have been the subject of detailed cellular measurements and computational modelling to connect molecular function to the dynamics of spindle assembly checkpoint signalling. A review of these efforts reveals the insights provided by such approaches and underscores the need for further interdisciplinary studies to reveal in full the quantitative underpinnings of this cellular control pathway

The influence of refuge sharing on social behaviour in the lizard Tiliqua rugosa

Refuge sharing by otherwise solitary individuals during periods of inactivity is an integral part of social behaviour and has been suggested to be the precursor to more complex social behaviour. We compared social association patterns of active versus inactive sheltering individuals in the social Australian sleepy lizard, Tiliqua rugosa, to empirically test the hypothesis that refuge sharing facilitates social associations while individuals are active. We fitted 18 neighbouring lizards with Global Positioning System (GPS) recorders to continuously monitor social associations among all individuals, based on location records taken every 10 min for 3 months. Based on these spatial data, we constructed three weighted, undirected social networks. Two networks were based on empirical association data (one for active and one for inactive lizards in their refuges), and a third null model network was based on hypothetical random refuge sharing. We found patterns opposite to the predictions of our hypothesis. Most importantly, association strength was higher in active than in inactive sheltering lizards. That is, individual lizards were more likely to associate with other lizards while active than while inactive and in shelters. Thus, refuge sharing did not lead to increased frequencies of social associations while lizards were active, and we did not find any evidence that refuge sharing was a precursor to sleepy lizard social behaviour. Our study of an unusually social reptile provides both quantitative data on the relationship between refuge sharing and social associations during periods of activity and further insights into the evolution of social behaviour in vertebrates