Search

Oblivious RAM (ORAM) conceals memory access pattern by translating a single read/write operation into the accesses to a set of randomized locations. The obliviousness is achieved by adding redundancy to the memory system, which comes at the expense of increased performance overhead. In memory systems, locality has always been a critical factor, as accessing the data with temporal or spatial locality would result in a performance gain. Although the two design considerations of obliviousness and locality may seem contradictory at first glance, combining them in a unified design can potentially hide long memory access latency in ORAM without sacrificing provable data security.This paper presents LolliRAM, a cross-layer design to exploit data locality in Oblivious RAM. LolliRAM optimizes ORAM system through two different layers: (i) the data structure in ORAM, and (ii) the fast and secure cache in ORAM controller. The reuse of redundant memory accesses at the layer of data structure in ORAM can effectively reduce memory footprints. Both temporal and spatial locality can be exploited through the elastic grouping of blocks and the optimization at the layer of ORAM controller. We conduct a set of experiments using realistic workloads that are generated from standard benchmarks. Experimental results show that LolliRAM can reduce access latency by 71.57% on average (up to 81.50%) with negligible space overhead in comparison with representative schemes.

There is a nonlinear coupling relationship between the vehicle in the longitudinal, lateral, and vertical directions, which brings great difficulties to the design of the controller. To tackle the chattering problem, a hierarchical integrated controller for distributed electric vehicle is proposed to improve driving safety, handling stability, ride comfort, and road tracking capabilities. The proposed algorithm has been developed to overcome a major challenge of the conventional method, which can improve only partial dynamic performance of the vehicle. The optimal pre-pointing lateral acceleration model is used to simulate the operator's expected reaction to the vehicle. The body control layer decouples complex problems to achieve multi-target independent tracking through a nonlinear sliding-mode control algorithm, and calculates the expected total body force to meet the upper level instructions. The tire force distribution layer is designed to optimize the function by reducing the tire load ratio and balancing the vertical dynamic load coefficient to improve the vehicle's driving stability and ride comfort. The lower actuator control layer controls the corresponding actuator to achieve the optimal tire force output from the middle layer. Finally, the effectiveness of the chassis integrated control system is verified in CarSim and MATLAB co-simulation.

How can we effectively utilise the 2D monocular image information for recovering the 6D pose (6-DoF) of the visual objects? Deep learning has shown to be effective for robust and real-time monocular pose estimation. Oftentimes, the network learns to regress the 6-DoF pose using a naive loss function. However, due to a lack of geometrical scene understanding from the directly regressed pose estimation, there are misalignments between the rendered mesh from the 3D object and the 2D instance segmentation result, e.g., bounding boxes and masks prediction. This paper bridges the gap between 2D mask generation and 3D location prediction via a differentiable neural mesh renderer. We utilise the overlay between the accurate mask prediction and less accurate mesh prediction to iteratively optimise the direct regressed 6D pose information with a focus on translation estimation. By leveraging geometry, we demonstrate that our technique significantly improves direct regression performance on the difficult task of translation estimation and achieve the state of the art results on Peking University/Baidu - Autonomous Driving dataset and the ApolloScape 3D Car Instance dataset. The code can be found at \url{https://bit.ly/2IRihfU}.

This paper proposes a longitudinal-vertical integrated controller for distributed electric vehicles. At present, most of the electronic control systems of the chassis adopt the division of working conditions and the simple assignment of the vertical force according to experience. The coupling relationship between the longitudinal force and the vertical force of the tire is not considered, it is difficult to improve the dynamic performance. This paper therefore proposes an integrated controller. A reference model is introduced to output vehicle kinematics requirements. The body motion controller uses the PID control algorithm to transform the kinematics requirements into the desired resultant force and resultant torque of the vehicle. In the tire force distributor, the optimization objective function is selected, and in consideration of the coupling relationship between the longitudinal and vertical forces and the constraint conditions, the desired resultant force and torque are optimally distributed to each tire. Finally, the MATLAB/Simulink and CarSim co-simulation experiments are carried out, and the results show that the vehicle is able to accurately achieve the control target and the validity is verified.

Non-contact video-based physiological measurement has many applications in health care and human-computer interaction. Practical applications require measurements to be accurate even in the presence of large head rotations. We propose the first end-to-end system for video-based measurement of heart and breathing rate using a deep convolutional network. The system features a new motion representation based on a skin reflection model and a new attention mechanism using appearance information to guide motion estimation, both of which enable robust measurement under heterogeneous lighting and major motions. Our approach significantly outperforms all current state-of-the-art methods on both RGB and infrared video datasets. Furthermore, it allows spatial-temporal distributions of physiological signals to be visualized via the attention mechanism., Comment: Accepted paper at ECCV 2018. 16 pages, 3 figures, supplementary materials in the ancillary files

Many important physical phenomena involve subtle signals that are difficult to observe with the unaided eye, yet visualizing them can be very informative. Current motion magnification techniques can reveal these small temporal variations in video, but require precise prior knowledge about the target signal, and cannot deal with interference motions at a similar frequency. We present DeepMag an end-to-end deep neural video-processing framework based on gradient ascent that enables automated magnification of subtle color and motion signals from a specific source, even in the presence of large motions of various velocities. While the approach is generalizable, the advantages of DeepMag are highlighted via the task of video-based physiological visualization. Through systematic quantitative and qualitative evaluation of the approach on videos with different levels of head motion, we compare the magnification of pulse and respiration to existing state-of-the-art methods. Our method produces magnified videos with substantially fewer artifacts and blurring whilst magnifying the physiological changes by a similar degree., Comment: 24 pages, 13 figures

SecA ATPase is a critical member of the Sec system, which is important in the translocation of membrane and secreted polypeptides/proteins in bacteria. Small molecule inhibitors can be very useful research tools as well as leads for future antimicrobial agent development. Based on previous virtual screening work, we optimized the structures of two hit compounds and obtained SecA ATPase inhibitors with IC50 in the single digit micromolar range. These represent the first low micromolar inhibitors of bacterial SecA and will be very useful for mechanistic studies. Post synthetic modification is an important and efficient way of DNA functionalization especially in DNA aptamer selection. In this research, the feasibility of norbornene (Neo) modified thymidine triphosphate incorporation was described. Besides, substituted tetrazines have been found to undergo facile inversed electron demand Diels-Alder reactions with "tunable" reaction rates. This finding paves the way to utilize tetrazine conjugation reactions for not only DNA but also other labeling work.

Recently, wearable devices have allowed for long term, ambulatory measurement of electrodermal activity (EDA). Despite the fact that ambulatory recording can be noisy, and recording artifacts can easily be mistaken for a physiological response during analysis, to date there is no automatic method for detecting artifacts. This paper describes the development of a machine learning algorithm for automatically detecting EDA artifacts, and provides an empirical evaluation of classification performance. We have encoded our results into a freely available web-based tool for artifact and peak detection., MIT Media Lab Consortium, Samsung (Firm), National Institutes of Health (U.S.) (NIH grant R01GM105018), Natural Sciences and Engineering Research Council of Canada, Seventh Framework Programme (European Commission) (People Programme (Marie Curie Actions), FP7/2007-2013/ under REA grant agreement #327702)