Inductive reasoning about unawareness

We develop a model of games with awareness that allows for differential levels of awareness. We show that, for the standard modal logical interpretations of belief and awareness, a player cannot believe there exist propositions of which he is unaware. Nevertheless, we argue that a boundedly rational individual may regard the possibility that there exist propositions of which she is unaware as being supported by inductive reasoning, based on past experience and consideration of the limited awareness of others. In this paper, we provide a formal representation of inductive reasoning in the context of a dynamic game with awareness. We show that, given differential awareness over time and between players, individuals can derive inductive support for propositions expressing their own unawareness.

Analysis of an epidemic model with awareness decay on regular random networks

The existence of a die-out threshold (different from the classic disease-invasion one) defining a region of slow extinction of an epidemic has been proved elsewhere for susceptible-aware-infectious-susceptible models without awareness decay, through bifurcation analysis. By means of an equivalent mean-field model defined on regular random networks, we interpret the dynamics of the system in this region and prove that the existence of bifurcation for of this second epidemic threshold crucially depends on the absence of awareness decay. We show that the continuum of equilibria that characterizes the slow die-out dynamics collapses into a unique equilibrium when a constant rate of awareness decay is assumed, no matter how small, and that the resulting bifurcation from the disease-free equilibrium is equivalent to that of standard epidemic models. We illustrate these findings with continuous-time stochastic simulations on regular random networks with different degrees. Finally, the behaviour of solutions with and without decay in awareness is compared around the second epidemic threshold for a small rate of awareness decay

Fearless: Yaou Liu

Humbly and passionately serving the campus community as a true “servant leader” for the past three-and-a-half years, actively engaging in dialogues and initiatives to promote awareness about social injustices, and constantly striving to learn more, act more, and teach more, Yaou Liu ’14, is a fearless role model for the campus community, showing in everything she does a restless passion to see the injustices in the world righted, awareness increased, and the future changed for the better. She is an inspiring, courageous student who has enriched the lives of many both on campus and in the greater Gettysburg community, using her leadership skills to express what she believes, and lead others to understanding. Her time here at Gettysburg has changed her, but she, too, has changed Gettysburg. [excerpt

Energy Efficient Adaptive Network Coding Schemes for Satellite Communications

In this paper, we propose novel energy efficient adaptive network coding and modulation schemes for time variant channels. We evaluate such schemes under a realistic channel model for open area environments and Geostationary Earth Orbit (GEO) satellites. Compared to non-adaptive network coding and adaptive rate efficient network-coded schemes for time variant channels, we show that our proposed schemes, through physical layer awareness can be designed to transmit only if a target quality of service (QoS) is achieved. As a result, such schemes can provide remarkable energy savings.Comment: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 24 March 201

Formal certification and compliance for run-time service environments

With the increased awareness of security and safety of services in on-demand distributed service provisioning (such as the recent adoption of Cloud infrastructures), certification and compliance checking of services is becoming a key element for service engineering. Existing certification techniques tend to support mainly design-time checking of service properties and tend not to support the run-time monitoring and progressive certification in the service execution environment. In this paper we discuss an approach which provides both design-time and runtime behavioural compliance checking for a services architecture, through enabling a progressive event-driven model-checking technique. Providing an integrated approach to certification and compliance is a challenge however using analysis and monitoring techniques we present such an approach for on-going compliance checking

A Learning-Based Framework for Two-Dimensional Vehicle Maneuver Prediction over V2V Networks

Situational awareness in vehicular networks could be substantially improved utilizing reliable trajectory prediction methods. More precise situational awareness, in turn, results in notably better performance of critical safety applications, such as Forward Collision Warning (FCW), as well as comfort applications like Cooperative Adaptive Cruise Control (CACC). Therefore, vehicle trajectory prediction problem needs to be deeply investigated in order to come up with an end to end framework with enough precision required by the safety applications' controllers. This problem has been tackled in the literature using different methods. However, machine learning, which is a promising and emerging field with remarkable potential for time series prediction, has not been explored enough for this purpose. In this paper, a two-layer neural network-based system is developed which predicts the future values of vehicle parameters, such as velocity, acceleration, and yaw rate, in the first layer and then predicts the two-dimensional, i.e. longitudinal and lateral, trajectory points based on the first layer's outputs. The performance of the proposed framework has been evaluated in realistic cut-in scenarios from Safety Pilot Model Deployment (SPMD) dataset and the results show a noticeable improvement in the prediction accuracy in comparison with the kinematics model which is the dominant employed model by the automotive industry. Both ideal and nonideal communication circumstances have been investigated for our system evaluation. For non-ideal case, an estimation step is included in the framework before the parameter prediction block to handle the drawbacks of packet drops or sensor failures and reconstruct the time series of vehicle parameters at a desirable frequency

Mobile Video Object Detection with Temporally-Aware Feature Maps

This paper introduces an online model for object detection in videos designed to run in real-time on low-powered mobile and embedded devices. Our approach combines fast single-image object detection with convolutional long short term memory (LSTM) layers to create an interweaved recurrent-convolutional architecture. Additionally, we propose an efficient Bottleneck-LSTM layer that significantly reduces computational cost compared to regular LSTMs. Our network achieves temporal awareness by using Bottleneck-LSTMs to refine and propagate feature maps across frames. This approach is substantially faster than existing detection methods in video, outperforming the fastest single-frame models in model size and computational cost while attaining accuracy comparable to much more expensive single-frame models on the Imagenet VID 2015 dataset. Our model reaches a real-time inference speed of up to 15 FPS on a mobile CPU.Comment: In CVPR 201

Simultaneous Feature and Body-Part Learning for Real-Time Robot Awareness of Human Behaviors

Robot awareness of human actions is an essential research problem in robotics with many important real-world applications, including human-robot collaboration and teaming. Over the past few years, depth sensors have become a standard device widely used by intelligent robots for 3D perception, which can also offer human skeletal data in 3D space. Several methods based on skeletal data were designed to enable robot awareness of human actions with satisfactory accuracy. However, previous methods treated all body parts and features equally important, without the capability to identify discriminative body parts and features. In this paper, we propose a novel simultaneous Feature And Body-part Learning (FABL) approach that simultaneously identifies discriminative body parts and features, and efficiently integrates all available information together to enable real-time robot awareness of human behaviors. We formulate FABL as a regression-like optimization problem with structured sparsity-inducing norms to model interrelationships of body parts and features. We also develop an optimization algorithm to solve the formulated problem, which possesses a theoretical guarantee to find the optimal solution. To evaluate FABL, three experiments were performed using public benchmark datasets, including the MSR Action3D and CAD-60 datasets, as well as a Baxter robot in practical assistive living applications. Experimental results show that our FABL approach obtains a high recognition accuracy with a processing speed of the order-of-magnitude of 10e4 Hz, which makes FABL a promising method to enable real-time robot awareness of human behaviors in practical robotics applications.Comment: 8 pages, 6 figures, accepted by ICRA'1