Predictive-State Decoders: Encoding the Future into Recurrent Networks

Recurrent neural networks (RNNs) are a vital modeling technique that rely on internal states learned indirectly by optimization of a supervised, unsupervised, or reinforcement training loss. RNNs are used to model dynamic processes that are characterized by underlying latent states whose form is often unknown, precluding its analytic representation inside an RNN. In the Predictive-State Representation (PSR) literature, latent state processes are modeled by an internal state representation that directly models the distribution of future observations, and most recent work in this area has relied on explicitly representing and targeting sufficient statistics of this probability distribution. We seek to combine the advantages of RNNs and PSRs by augmenting existing state-of-the-art recurrent neural networks with Predictive-State Decoders (PSDs), which add supervision to the network's internal state representation to target predicting future observations. Predictive-State Decoders are simple to implement and easily incorporated into existing training pipelines via additional loss regularization. We demonstrate the effectiveness of PSDs with experimental results in three different domains: probabilistic filtering, Imitation Learning, and Reinforcement Learning. In each, our method improves statistical performance of state-of-the-art recurrent baselines and does so with fewer iterations and less data.Comment: NIPS 201

Vision and Learning for Deliberative Monocular Cluttered Flight

Cameras provide a rich source of information while being passive, cheap and lightweight for small and medium Unmanned Aerial Vehicles (UAVs). In this work we present the first implementation of receding horizon control, which is widely used in ground vehicles, with monocular vision as the only sensing mode for autonomous UAV flight in dense clutter. We make it feasible on UAVs via a number of contributions: novel coupling of perception and control via relevant and diverse, multiple interpretations of the scene around the robot, leveraging recent advances in machine learning to showcase anytime budgeted cost-sensitive feature selection, and fast non-linear regression for monocular depth prediction. We empirically demonstrate the efficacy of our novel pipeline via real world experiments of more than 2 kms through dense trees with a quadrotor built from off-the-shelf parts. Moreover our pipeline is designed to combine information from other modalities like stereo and lidar as well if available

Dispersion measurements of a 1.3 mu m quantum dot semiconductor optical amplifier over 120 nm of spectral bandwidth

Group delay and higher order dispersion measurements are conducted on a 1.3 mu m quantum dot semiconductor optical amplifier at various injection currents. White-light spectral interferometry is performed, along with a wavelet transform to recover the group delay. The group delay, group velocity dispersion, and higher order dispersion terms are quantified. The measurement spans both ground state and first excited state transitions, ranging from 1200 to 1320 nm. The group velocity dispersion, beta(2), is found to be -6.3x10(3) fs(2) (7.6 fs/nm) at an injection current of 500 mA

Optimization And Learning For Rough Terrain Legged Locomotion

We present a novel approach to legged locomotion over rough terrain that is thoroughly rooted in optimization. This approach relies on a hierarchy of fast, anytime algorithms to plan a set of footholds, along with the dynamic body motions required to execute them. Components within the planning framework coordinate to exchange plans, cost-to-go estimates, and \u27certificates\u27 that ensure the output of an abstract high-level planner can be realized by lower layers of the hierarchy. The burden of careful engineering of cost functions to achieve desired performance is substantially mitigated by a simple inverse optimal control technique. Robustness is achieved by real-time re-planning of the full trajectory, augmented by reflexes and feedback control. We demonstrate the successful application of our approach in guiding the LittleDog quadruped robot over a variety of types of rough terrain. Other novel aspects of our past research efforts include a variety of pioneering inverse optimal control techniques as well as a system for planning using arbitrary pre-recorded robot behavior

Advanced Scanning Electron Microscopy Methods and Applications to Integrated Circuit Failure Analysis

Semiconductor device failure analysis using the scanning electron microscope (SEM) has become a standard component of integrated circuit fabrication. Improvements in SEM capabilities and in digital imaging and processing have advanced standard acquisition modes and have promoted new failure analysis methods. The physical basis of various data acquisition modes, both standard and new, and their implementation on a computer controlled SEM image acquisition/processing system are discussed, emphasizing the advantages of each method. Design considerations for an integrated, online failure analysis system are also described. Recent developments in the integration of the information provided by electron beam analysis, conventional integrated circuit (IC) testing, computer-aided design (CAD), and device parameter testing into a single system promise to provide powerful future tools for failure analysis

Large, Wafer-Thin Optical Apertures Leveraging Photonic Integrated Circuits to Replace Telescopes for Communications

To aid in driving down the size, weight, and power (SWaP) of space-based optical communications terminals, we present a large-aperture telescope-replacement technology that reshapes a beam from a single-mode fiber to ~5 cm and larger apertures on a silicon wafer by using photonic integrated circuit (PIC) components. We achieve multi-centimeter apertures by sacrificing wide-angle steering in favor of good beam quality and manageable controls. Light from a single-mode fiber is coupled to a silicon chip consisting of low-loss silicon nitride waveguides for signal distribution to large phase-controlled emitters. Our demonstrations of beam phasing across a 1.8-cm-diameter, 16-emitter phased array show excellent agreement with simulations. We have designed and simulated a 4.7 cm, 64-emitter array and have begun fabrication as of 2023. This architecture removes the need for beam expansion optics, free-space propagation for beam expansion, and the support structure and housing used in traditional telescope assemblies. Its low size and weight make it compatible with current and future beam steering mechanisms, and its reduced loading provides added potential for size and weight reductions in those subsystems. We believe the architecture can eventually be expanded to larger apertures of 10 cm or more without significantly increasing thickness

Computer-assisted mammographic imaging

Computer-assisted mammography imaging comprises computer-based analysis of digitized images resulting in prompts aiding mammographic interpretation and computerized stereotactic localization devices which improve location accuracy. The commercial prompting systems available are designed to draw attention to mammographic abnormalities detected by algorithms based on symptomatic practise in North America. High sensitivity rates are important commercially but result in increased false prompt rates, which are known to distract radiologists. A national shortage of breast radiologists in the UK necessitates evaluation of such systems in a population breast screening programme to determine effectiveness in increasing cancer detection and feasibility of implementation