Resolving the black hole information paradox

The recent progress in string theory strongly suggests that formation and evaporation of black holes is a unitary process. This fact makes it imperative that we find a flaw in the semiclassical reasoning that implies a loss of information. We propose a new criterion that limits the domain of classical gravity: the hypersurfaces of a foliation cannot be stretched too much. This conjectured criterion may have important consequences for the early Universe.Comment: harvmac, 11 pages (1 figure) (This essay received an ``honorable mention'' in the Annual Essay Competition of the Gravity Research Foundation for the year 2000.

Probabilistic Search for Object Segmentation and Recognition

The problem of searching for a model-based scene interpretation is analyzed within a probabilistic framework. Object models are formulated as generative models for range data of the scene. A new statistical criterion, the truncated object probability, is introduced to infer an optimal sequence of object hypotheses to be evaluated for their match to the data. The truncated probability is partly determined by prior knowledge of the objects and partly learned from data. Some experiments on sequence quality and object segmentation and recognition from stereo data are presented. The article recovers classic concepts from object recognition (grouping, geometric hashing, alignment) from the probabilistic perspective and adds insight into the optimal ordering of object hypotheses for evaluation. Moreover, it introduces point-relation densities, a key component of the truncated probability, as statistical models of local surface shape.Comment: 18 pages, 5 figure

RPNet: an End-to-End Network for Relative Camera Pose Estimation

This paper addresses the task of relative camera pose estimation from raw image pixels, by means of deep neural networks. The proposed RPNet network takes pairs of images as input and directly infers the relative poses, without the need of camera intrinsic/extrinsic. While state-of-the-art systems based on SIFT + RANSAC, are able to recover the translation vector only up to scale, RPNet is trained to produce the full translation vector, in an end-to-end way. Experimental results on the Cambridge Landmark dataset show very promising results regarding the recovery of the full translation vector. They also show that RPNet produces more accurate and more stable results than traditional approaches, especially for hard images (repetitive textures, textureless images, etc). To the best of our knowledge, RPNet is the first attempt to recover full translation vectors in relative pose estimation

Instrumentation for hydrogen slush storage containers

Hydrogen liquid and slush tank continuous inventory during ground storag

Hydrogen slush density reference system

A hydrogen slush density reference system was designed for calibration of field-type instruments and/or transfer standards. The device is based on the buoyancy principle of Archimedes. The solids are weighed in a low-mass container so arranged that solids and container are buoyed by triple-point liquid hydrogen during the weighing process. Several types of hydrogen slush density transducers were developed and tested for possible use as transfer standards. The most successful transducers found were those which depend on change in dielectric constant, after which the Clausius-Mossotti function is used to relate dielectric constant and density

Remote sensing in Michigan for land resource management

The Environmental Research Institute of Michigan is conducting a program whose goal is the large-scale adoption, by both public agencies and private interests in Michigan, of NASA earth-resource survey technology as an important aid in the solution of current problems in resource management and environmental protection. During the period from June 1975 to June 1976, remote sensing techniques to aid Michigan government agencies were used to achieve the following major results: (1) supply justification for public acquisition of land to establish the St. John's Marshland Recreation Area; (2) recommend economical and effective methods for performing a statewide wetlands survey; (3) assist in the enforcement of state laws relating to sand and gravel mining, soil erosion and sedimentation, and shorelands protection; (4) accomplish a variety of regional resource management actions in the East Central Michigan Planning and Development Region. Other tasks on which remote sensing technology was used include industrial and school site selection, ice detachment in the Soo Harbor, grave detection, and data presentation for wastewater management programs

Remote sensing in Michigan for land resource management

An extensive program was conducted to establish practical uses of NASA earth resource survey technology in meeting resource management problems throughout Michigan. As a result, a broad interest in and understanding of the usefulness of remote sensing methods was developed and a wide variety of applications was undertaken to provide information needed for informed decision making and effective action

Re-identification of c. 15 700 cal yr BP tephra bed at Kaipo Bog, eastern North Island: implications for dispersal of Rotorua and Puketarata tephra beds.

A 10 mm thick, c. 15 700 calendar yr BP (c. 13 100 14C yr BP) rhyolitic tephra bed in the well-studied montane Kaipo Bog sequence of eastern North Island was previously correlated with Maroa-derived Puketarata Tephra. We revise this correlation to Okataina-derived Rotorua Tephra based on new compositional data from biotite phenocrysts and glass. The new correlation limits the known dispersal of Puketarata Tephra (sensu stricto, c. 16 800 cal yr BP) and eliminates requirements to either reassess its age or to invoke dual Puketarata eruptive events. Our data show that Rotorua Tephra comprises two glass-shard types: an early-erupted low-K2O type that was dispersed mostly to the northwest, and a high-K2O type dispersed mostly to the south and southeast, contemporary with late-stage lava extrusion. Late-stage Rotorua eruptives contain biotite that is enriched in FeO compared with biotite from Puketarata pyroclastics. The occurrence of Rotorua Tephra in Kaipo Bog (100 km from the source) substantially extends its known distribution to the southeast. Our analyses demonstrate that unrecognised syn-eruption compositional and dispersal changes can cause errors in fingerprinting tephra deposits. However, the compositional complexity, once recognised, provides additional fingerprinting criteria, and also documents magmatic and dispersal processes

AdS/CFT and the Information Paradox

The information paradox in the quantum evolution of black holes is studied within the framework of the AdS/CFT correspondence. The unitarity of the CFT strongly suggests that all information about an initial state that forms a black hole is returned in the Hawking radiation. The CFT dynamics implies an information retention time of order the black hole lifetime. This fact determines many qualitative properties of the non-local effects that must show up in a semi-classical effective theory in the bulk. We argue that no violations of causality are apparent to local observers, but the semi-classical theory in the bulk duplicates degrees of freedom inside and outside the event horizon. Non-local quantum effects are required to eliminate this redundancy. This leads to a breakdown of the usual classical-quantum correspondence principle in Lorentzian black hole spacetimes.Comment: 16 pages, harvmac, reference added, minor correction

Neural NILM: Deep Neural Networks Applied to Energy Disaggregation

Energy disaggregation estimates appliance-by-appliance electricity consumption from a single meter that measures the whole home's electricity demand. Recently, deep neural networks have driven remarkable improvements in classification performance in neighbouring machine learning fields such as image classification and automatic speech recognition. In this paper, we adapt three deep neural network architectures to energy disaggregation: 1) a form of recurrent neural network called `long short-term memory' (LSTM); 2) denoising autoencoders; and 3) a network which regresses the start time, end time and average power demand of each appliance activation. We use seven metrics to test the performance of these algorithms on real aggregate power data from five appliances. Tests are performed against a house not seen during training and against houses seen during training. We find that all three neural nets achieve better F1 scores (averaged over all five appliances) than either combinatorial optimisation or factorial hidden Markov models and that our neural net algorithms generalise well to an unseen house.Comment: To appear in ACM BuildSys'15, November 4--5, 2015, Seou