4,409 research outputs found
SOURCE, DRAIN, AND GATE SERIES RESISTANCES AND ELECTRON SATURATION VELOCITY IN ION-IMPLANTED GAAS-FETS
No full textWe would like to thank R.Jiracek of SRC, Honeywell, for his help in the wafer testing. We also acknowledge useful and fruitful discussions of this work Dr.W.Frensley
Monitoring agriculture areas with satellite images and deep learning
No full textAgriculture applications rely on accurate land monitoring, especially paddy areas, for timely food security control and support actions. However, traditional monitoring requires field works or surveys performed by experts, which is costly, slow, and sparse. Agriculture monitoring systems are looking for sustainable land use monitoring solutions, starting with remote sensing on satellite data for cheap and timely paddy mapping. The aim of this study is to develop an autonomous and intelligent system built on top of imagery data streams, which is available from low-Earth orbiting satellites, to differentiate crop areas from non-crop areas. However, such agriculture mapping framework poses unique challenges for satellite image processing, including the seasonal nature of crop, the complexity of spectral channels, and adversarial conditions such as cloud and solar radiance. In this paper, we propose a novel multi-temporal high-spatial resolution classification method with an advanced spatio-temporal–spectral deep neural network to locate paddy fields at the pixel level for a whole year long and for each temporal instance. Our method is built and tested on the case study of Landsat 8 data due to its high spatial resolution. Empirical evaluations on real imagery datasets of different landscapes from 2016 to 2018 show the superior of our mapping model against the baselines with over 0.93 F1-score, the importance of each model design, the robustness against seasonal effects, and the visual mapping results.No Full Tex
Enhancing Electron Transfer and Stability of Screen-Printed Carbon Electrodes Modified with AgNP-Reduced Graphene Oxide Nanocomposite
No full textThis paper presents a reliable solution to enhance the electron transfer and stability of screen-printed carbon electrodes (SPCEs) for the direct detection of pathogenic bacteria. A nanocomposite of silver nanoparticles (AgNPs) and reduced graphene oxide (rGO) was used to modify the SPCEs. Herein, the nanocomposite was synthesized via a hydrothermal method and then characterized by physicochemical methods. The electron transfer rate and electrochemical properties of the AgNP-rGO nanocomposite-modified SPCEs were investigated using cyclic voltammetry (CV) and electrochemical impedance spectroscopy. Measurements were performed for the detection of Salmonella bacteria without any labels. Results showed that the nanocomposite firmly adhered to the surfaces of the SPCEs, led to an increase of approximately 160% in the peak current, and decreased the charge transfer resistance to 0.45 kΩ. Electrochemical stability was found in 30 CV cycles. The modified SPCEs could detect Salmonella bacteria directly at concentrations of 10–105 CFU/mL, with a limit of detection (LoD) of as low as 22 CFU/mL. A possible mechanism was proposed to explain the enhanced electron transfer on the surface and the stability of the AgNP-rGO nanocomposite-modified SPCEs. The biosensor showed high stability, cost-effectiveness, and simplicity for the direct detection of pathogenic bacteria. Graphical Abstract: [Figure not available: see fulltext.
Isomorphisms in co-TT graphs
No full text2019 Spring.Includes bibliographical references.A threshold tolerance graph is a graph where each vertex v is assigned a weight wv and a tolerance tv, and there is an edge between two vertices vx and vy if and only if wx + wy ≥ min(tx,ty). A co-TT graph is the complement of a threshold tolerance graph. Recognition of these graphs can be done in O(n2) time; however no polynomial-time algorithm to identify isomorphisms between pairs of TT or co-TT graphs was previously known. We give an algorithm to identify these isomorphisms, which takes O(n2) time
Un cas d'écriture imagée : l'étrange représentation de nourrice de la tombe de Ken-Amon (TT 93)
No full textAprès avoir montré en quoi, dans la tombe de Ken-Amon (TT 93), la représentation de la nourrice d’Amenhotep II témoigne que cette dame jouissait d’un statut exceptionnel, mais aussi combien cette représentation comporte de détails incongrus, l’auteur se demande si cette nourrice ne s’était pas vu confier un rôle particulier, l’éducation sexuelle du futur roi. L’enquête est étendue à quelques autres représentations de nourrices et de tuteurs royaux et princiers (TT 78, 109, stèle de Méryrê Vienne ÄS 5814). Chacun de ces exemples illustre l’existence, dans l’Égypte ancienne, d’un système d’écriture imagée et codée, parallèle au système hiéroglyphique. After showing how, in the tomb of Ken-Amun (TT 93), the representation of Amenhotep II’s nurse demonstrates that this lady enjoyed a unique status, but also how many incongruous details this representation includes, the author wonders if this nurse was not entrusted a peculiar function, the sexual education of the future king. Other representations of royal and princely tutors (TT 78, 109, Meryrê stela Vienna ÄS 5814) have also been investigated. Each of these examples illustrates the existence, in ancient Egypt, of a writing system, parallel to the hieroglyphic system, coded and full of imagery
tt*-geometry and pluriharmonic maps
No full textInternational audienceIn this paper we use the real differential geometric definition of a metric (an unimodular oriented metric) tt*-bundle of Cortés and the author to define a map from the space of metric (unimodular oriented metric) tt*-bundles of rank r over a complex manifold M to the space of pluriharmonic maps from M to (respectively ), where (p,q) is the signature of the metric. In the sequel the image of the map is characterized. It follows, that in signature (r,0) the image of is the whole space of pluriharmonic maps. This generalizes a result of Dubrovin
Higher-order knowledge-enhanced recommendation with heterogeneous hypergraph multi-attention
No full textRecent advancements in recommender systems have focused on integrating knowledge graphs (KGs) to leverage their auxiliary information. The core idea of KG-enhanced recommenders is to incorporate rich semantic information for more accurate recommendations. However, two main challenges persist: i) Neglecting complex higher-order interactions in the KG-based user-item network, potentially leading to sub-optimal recommendations, and ii) Dealing with the heterogeneous modalities of input sources, such as user-item bipartite graphs and KGs, which may introduce noise and inaccuracies. To address these issues, we present a novel Knowledge-enhanced Heterogeneous Hypergraph Recommender System (KHGRec). KHGRec captures group-wise characteristics of both the interaction network and the KG, modeling complex connections in the KG. Using a collaborative knowledge heterogeneous hypergraph (CKHG), it employs two hypergraph encoders to model group-wise interdependencies and ensure explainability. Additionally, it fuses signals from the input graphs with cross-view self-supervised learning and attention mechanisms. Extensive experiments on four real-world datasets show our model's superiority over various state-of-the-art baselines, with an average 5.18% relative improvement. Additional tests on noise resilience, missing data, and cold-start problems demonstrate the robustness of our KHGRec framework. Our model and evaluation datasets are publicly available at https://github.com/viethungvu1998/KHGRec.Full Tex
performance of the low-rank TT-SVD for large dense tensors on modern multicore CPUs
No full textThere are several factorizations of multidimensional tensors into lower-dimensional components, known as ``tensor networks."" We consider the popular ``tensor-train"" (TT) format and ask, How efficiently can we compute a low-rank approximation from a full tensor on current multicore CPUs? Compared to sparse and dense linear algebra, kernel libraries for multilinear algebra are rare and typically not as well optimized. Linear algebra libraries like BLAS and LAPACK may provide the required operations in principle but often at the cost of additional data movements for rearranging memory layouts. Furthermore, these libraries are typically optimized for the compute-bound case (e.g., square matrix operations), whereas low-rank tensor decompositions lead to memory bandwidth limited operations. We propose a ``TT singular value decomposition"" (TT-SVD) algorithm based on two building blocks: a ``Q-less tall-skinny QR"" factorization and a fused tall-skinny matrix-matrix multiplication and reshape operation. We analyze the performance of the resulting TT-SVD algorithm using the roofline performance model. In addition, we present performance results for different algorithmic variants for shared-memory as well as distributed-memory architectures. Our experiments show that commonly used TT-SVD implementations suffer severe performance penalties. We conclude that a dedicated library for tensor factorization kernels would benefit the community: Computing a low-rank approximation can be as cheap as reading the data twice from main memory. As a consequence, an implementation that achieves realistic performance will move the limit at which one has to resort to randomized methods that only process part of the data.Numerical Analysi
"_Pst-tt!", circa 1962
No full textA dark figure marked "Merchants" opens a door labeled "Back Door Deals". Written on recto: "_Pst-tt!".Crim
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