Evaluating Neural Network Decoder Performance for Quantum Error Correction Using Various Data Generation Models

Neural networks have been shown in the past to perform quantum error correction (QEC) decoding with greater accuracy and efficiency than algorithmic decoders. Because the qubits in a quantum computer are volatile and only usable on the order of milliseconds before they decohere, a means of fast quantum error correction is necessary in order to correct data qubit errors within the time budget of a quantum algorithm. Algorithmic decoders are good at resolving errors on logical qubits with only a few data qubits, but are less efficient in systems containing more data qubits. With neural network decoders, practical quantum computation becomes much more realizable since the error corrective operations are calculated much faster than with the MWPM or partial lookup table implementations. This research is aimed at furthering neural network QEC decoder research by generating exhaustive and randomly sampled data sets using high-performance computing algorithms to evaluate the effect of data set generation methods on the effectiveness of these neural networks compared to similar models. The results of this work show that different data sets affect various performance metrics including accuracy, F1 score, area under the receiver operating characteristic curve, and QEC cycles

Evaluation of the recreational marron fishery against environmental change and human interaction: Final FRDC Report – Project No. 2003/027

The distribution of marron in the southwest of Australia has seen many changes since European settlement. Reconstructions of their range from historical records suggested that marron inhabited the waters between the Harvey River and Denmark River. Due to translocation, their range has expanded as far north as the Hutt River and as far east as Esperance. Although at present marron still exist in all the original rivers within the southwest, their distribution within these rivers has contracted. Poor water quality, salinity, low rainfall and environmental degradation in the upper and lower reaches have restricted marron populations

GPR for large-scale estimation of groundwater recharge distribution

The Gnangara Mound, north of Perth, Western Australia, has been investigated using Ground-Penetrating Radar (GPR). Several hundred line-kilometers of GPR of common offset data have been acquired over an area of approximately 800 km2. The acquisition of these datatasets was performed at two different center frequencies (50 and 250 MHz)in order to better resolve the complexity of the hydrogeological targets of interest which are water retentive layers found above the water table. These layers impede the recharge of the surficial aquifer and may have important impact on local ecosystems but also on the management of the ground water resource. The data presented here-in demonstrate the successful imaging of the regional water table and of these water retentive layers. For thefirst time, these data provide insight into the spatial distribution and the continuity of these water retentive layers and provide important information to be included in the flow modeling of the ground water in this region of the world

Design and application of a flow cell for carbon-film based electrochemical enzyme biosensors

A flow cell has been designed for use with an electrochemical enzyme biosensor, based on low-cost carbon-film electrodes. Three types of mediators were used: cobalt and copper hexacyanoferrates and poly(neutral red) (PNR), covered with glucose oxidase (GOx) immobilised by cross-linking with glutaraldehyde in the presence of bovine serum albumin or inside a oxysilane sol-gel network. Mixtures of sol-gel precursors were made from 3-aminopropyl-triethoxysilane (APTOS) together with methyltrimethoxysilane (MTMOS), methyltriethoxysilane (MTEOS), tetraethyloxysilane (TEOS) or 3-glycidoxypropyl-trimethoxysilane (GOPMOS), and the best chosen for encapsulation. Optimisation in batch mode, using amperometric detection at fixed potential, showed the PNR-GOx modified carbon-film electrodes to be best for flow analysis for both glutaraldehyde and sol-gel enzyme immobilisation. Both types of enzyme electrode were tested under flow conditions and the reproducibility and stability of the biosensors were evaluated. The biosensors were used for fermentation monitoring of glucose in grape must and interference studies were also performed.http://www.sciencedirect.com/science/article/B6THP-4M0BH9Y-2/1/306f5db86217ea276bf808fb05c0288

Learning Set Representations for LWIR In-Scene Atmospheric Compensation

Atmospheric compensation of long-wave infrared (LWIR) hyperspectral imagery is investigated in this article using set representations learned by a neural network. This approach relies on synthetic at-sensor radiance data derived from collected radiosondes and a diverse database of measured emissivity spectra sampled at a range of surface temperatures. The network loss function relies on LWIR radiative transfer equations to update model parameters. Atmospheric predictions are made on a set of diverse pixels extracted from the scene, without knowledge of blackbody pixels or pixel temperatures. The network architecture utilizes permutation-invariant layers to predict a set representation, similar to the work performed in point cloud classification. When applied to collected hyperspectral image data, this method shows comparable performance to Fast Line-of-Sight Atmospheric Analysis of Hypercubes-Infrared (FLAASH-IR), using an auto- mated pixel selection approach. Additionally, inference time is significantly reduced compared to FLAASH-IR with predictions made on average in 0.24 s on a 128 pixel by 5000 pixel data cube using a mobile graphics card. This computational speed-up on a low-power platform results in an autonomous atmospheric compensation method effective for real-time, onboard use, while only requiring a diversity of materials in the scene

Multimodal Representation Learning and Set Attention for LWIR In-Scene Atmospheric Compensation

A multimodal generative modeling approach combined with permutation-invariant set attention is investigated in this paper to support long-wave infrared (LWIR) in-scene atmospheric compensation. The generative model can produce realistic atmospheric state vectors (T;H2O;O3) and their corresponding transmittance, upwelling radiance, and downwelling radiance (TUD) vectors by sampling a low-dimensional space. Variational loss, LWIR radiative transfer loss and atmospheric state loss constrain the low-dimensional space, resulting in lower reconstruction error compared to standard mean-squared error approaches. A permutation-invariant network predicts the generative model low-dimensional components from in-scene data, allowing for simultaneous estimates of the atmospheric state and TUD vector. Forward modeling the predicted atmospheric state vector results in a second atmospheric compensation estimate. Results are reported for collected LWIR data and compared to Fast Line-of-Sight Atmospheric Analysis of Hypercubes - Infrared (FLAASH-IR), demonstrating commensurate performance when applied to a target detection scenario. Additionally, an approximate 8 times reduction in detection time is realized using this neural network-based algorithm compared to FLAASH-IR. Accelerating the target detection pipeline while providing multiple atmospheric estimates is necessary for many real-world, time sensitive tasks

Brown Dwarfs in the Pleiades Cluster Confirmed by the Lithium Test

We present 10 m Keck spectra of the two Pleiades brown dwarfs Teide 1 and Calar 3 showing a clear detection of the 670.8 nm Li resonance line. In Teide 1, we have also obtained evidence for the presence of the subordinate line at 812.6 nm. A high Li abundance (log N(Li) >= 2.5), consistent with little if any depletion, is inferred from the observed lines. Since Pleiades brown dwarfs are unable to burn Li the significant preservation of this fragile element confirms the substellar nature of our two objects. Regardless of their age, their low luminosities and Li content place Teide 1 and Calar 3 comfortably in the genuine brown dwarf realm. Given the probable age of the Pleiades cluster, their masses are estimated at 55 +- 15 Jupiter masses.Comment: 14 pages gzipped and uuencoded. Figures are included. Also available at http://www.iac.es/. Accepted for publication in ApJ Letter

The global distribution of Bacillus anthracis and associated anthrax risk to humans, livestock and wildlife.

Bacillus anthracis is a spore-forming, Gram-positive bacterium responsible for anthrax, an acute infection that most significantly affects grazing livestock and wild ungulates, but also poses a threat to human health. The geographic extent of B. anthracis is poorly understood, despite multi-decade research on anthrax epizootic and epidemic dynamics; many countries have limited or inadequate surveillance systems, even within known endemic regions. Here, we compile a global occurrence dataset of human, livestock and wildlife anthrax outbreaks. With these records, we use boosted regression trees to produce a map of the global distribution of B. anthracis as a proxy for anthrax risk. We estimate that 1.83 billion people (95% credible interval (CI): 0.59-4.16 billion) live within regions of anthrax risk, but most of that population faces little occupational exposure. More informatively, a global total of 63.8 million poor livestock keepers (95% CI: 17.5-168.6 million) and 1.1 billion livestock (95% CI: 0.4-2.3 billion) live within vulnerable regions. Human and livestock vulnerability are both concentrated in rural rainfed systems throughout arid and temperate land across Eurasia, Africa and North America. We conclude by mapping where anthrax risk could disrupt sensitive conservation efforts for wild ungulates that coincide with anthrax-prone landscapes