Showing papers by "National University of Defense Technology published in 2016"
TL;DR: The state-of-the-art in deep learning algorithms in computer vision is reviewed by highlighting the contributions and challenges from over 210 recent research papers, and the future trends and challenges in designing and training deep neural networks are summarized.
1,733 citations
TL;DR: This work demonstrates the emergence of MBSs from coalescing Andreev bound states (ABSs) in a hybrid InAs nanowire with epitaxial Al, using a quantum dot at the end of thenanowire as a spectrometer and observed hybridization of the MBS with the end-dot bound state, which is in agreement with a numerical model.
Abstract: Hybrid nanowires combining semiconductor and superconductor materials appear well suited for the creation, detection, and control of Majorana bound states (MBSs). We demonstrate the emergence of MBSs from coalescing Andreev bound states (ABSs) in a hybrid InAs nanowire with epitaxial Al, using a quantum dot at the end of the nanowire as a spectrometer. Electrostatic gating tuned the nanowire density to a regime of one or a few ABSs. In an applied axial magnetic field, a topological phase emerges in which ABSs move to zero energy and remain there, forming MBSs. We observed hybridization of the MBS with the end-dot bound state, which is in agreement with a numerical model. The ABS/MBS spectra provide parameters that are useful for understanding topological superconductivity in this system.
965 citations
TL;DR: This paper compares ten popular local feature descriptors in the contexts of 3D object recognition, 3D shape retrieval, and 3D modeling and presents the performance results of these descriptors when combined with different 3D keypoint detection methods.
Abstract: A number of 3D local feature descriptors have been proposed in the literature. It is however, unclear which descriptors are more appropriate for a particular application. A good descriptor should be descriptive, compact, and robust to a set of nuisances. This paper compares ten popular local feature descriptors in the contexts of 3D object recognition, 3D shape retrieval, and 3D modeling. We first evaluate the descriptiveness of these descriptors on eight popular datasets which were acquired using different techniques. We then analyze their compactness using the recall of feature matching per each float value in the descriptor. We also test the robustness of the selected descriptors with respect to support radius variations, Gaussian noise, shot noise, varying mesh resolution, distance to the mesh boundary, keypoint localization error, occlusion, clutter, and dataset size. Moreover, we present the performance results of these descriptors when combined with different 3D keypoint detection methods. We finally analyze the computational efficiency for generating each descriptor.
503 citations
TL;DR: A multi-population based approach is proposed to realize the adapted ensemble of multiple strategies of differential evolution, thereby resulting in a new DE variant named multi- Population ensemble DE (MPEDE) which simultaneously consists of three mutation strategies.
426 citations
TL;DR: It is emphasized that information diffusion has great scientific depth and combines diverse research fields which makes it interesting for physicists as well as interdisciplinary researchers.
354 citations
TL;DR: In this paper, a p-g-n heterostructure formed by sandwiching graphene with a gapless band structure and wide absorption spectrum in an atomically thin p-n junction was proposed for broadband photodetection in the visible to short-wavelength infrared range at room temperature.
Abstract: van der Waals junctions of two-dimensional materials with an atomically sharp interface open up unprecedented opportunities to design and study functional heterostructures. Semiconducting transition metal dichalcogenides have shown tremendous potential for future applications due to their unique electronic properties and strong light-matter interaction. However, many important optoelectronic applications, such as broadband photodetection, are severely hindered by their limited spectral range and reduced light absorption. Here, we present a p-g-n heterostructure formed by sandwiching graphene with a gapless band structure and wide absorption spectrum in an atomically thin p-n junction to overcome these major limitations. We have successfully demonstrated a MoS2-graphene-WSe2 heterostructure for broadband photodetection in the visible to short-wavelength infrared range at room temperature that exhibits competitive device performance, including a specific detectivity of up to 10(11) Jones in the near-infrared region. Our results pave the way toward the implementation of atomically thin heterostructures for broadband and sensitive optoelectronic applications.
302 citations
TL;DR: A comprehensive evaluation on benchmark data sets reveals MRELBP’s high performance—robust to gray scale variations, rotation changes and noise—but at a low computational cost.
Abstract: Local binary patterns (LBP) are considered among the most computationally efficient high-performance texture features. However, the LBP method is very sensitive to image noise and is unable to capture macrostructure information. To best address these disadvantages, in this paper, we introduce a novel descriptor for texture classification, the median robust extended LBP (MRELBP). Different from the traditional LBP and many LBP variants, MRELBP compares regional image medians rather than raw image intensities. A multiscale LBP type descriptor is computed by efficiently comparing image medians over a novel sampling scheme, which can capture both microstructure and macrostructure texture information. A comprehensive evaluation on benchmark data sets reveals MRELBP’s high performance—robust to gray scale variations, rotation changes and noise—but at a low computational cost. MRELBP produces the best classification scores of 99.82%, 99.38%, and 99.77% on three popular Outex test suites. More importantly, MRELBP is shown to be highly robust to image noise, including Gaussian noise, Gaussian blur, salt-and-pepper noise, and random pixel corruption.
289 citations
11 Apr 2016
TL;DR: Haxy is introduced, a platform for the collection, detection, and analysis of online misinformation and its related fact-checking efforts, and a preliminary analysis of a sample of public tweets containing both fake news and fact checking is presented.
Abstract: Massive amounts of misinformation have been observed to spread in uncontrolled fashion across social media. Examples include rumors, hoaxes, fake news, and conspiracy theories. At the same time, several journalistic organizations devote significant efforts to high-quality fact checking of online claims. The resulting information cascades contain instances of both accurate and inaccurate information, unfold over multiple time scales, and often reach audiences of considerable size. All these factors pose challenges for the study of the social dynamics of online news sharing. Here we introduce Hoaxy, a platform for the collection, detection, and analysis of online misinformation and its related fact-checking efforts. We discuss the design of the platform and present a preliminary analysis of a sample of public tweets containing both fake news and fact checking. We find that, in the aggregate, the sharing of fact-checking content typically lags that of misinformation by 10-20 hours. Moreover, fake news are dominated by very active users, while fact checking is a more grass-roots activity. With the increasing risks connected to massive online misinformation, social news observatories have the potential to help researchers, journalists, and the general public understand the dynamics of real and fake news sharing.
280 citations
TL;DR: Hoaxy as discussed by the authors is a platform for the collection, detection, and analysis of online misinformation and its related fact-checking efforts, which can help researchers, journalists, and the general public understand the dynamics of real and fake news sharing.
Abstract: Massive amounts of misinformation have been observed to spread in uncontrolled fashion across social media. Examples include rumors, hoaxes, fake news, and conspiracy theories. At the same time, several journalistic organizations devote significant efforts to high-quality fact checking of online claims. The resulting information cascades contain instances of both accurate and inaccurate information, unfold over multiple time scales, and often reach audiences of considerable size. All these factors pose challenges for the study of the social dynamics of online news sharing. Here we introduce Hoaxy, a platform for the collection, detection, and analysis of online misinformation and its related fact-checking efforts. We discuss the design of the platform and present a preliminary analysis of a sample of public tweets containing both fake news and fact checking. We find that, in the aggregate, the sharing of fact-checking content typically lags that of misinformation by 10--20 hours. Moreover, fake news are dominated by very active users, while fact checking is a more grass-roots activity. With the increasing risks connected to massive online misinformation, social news observatories have the potential to help researchers, journalists, and the general public understand the dynamics of real and fake news sharing.
278 citations
TL;DR: In this paper, high responsivity phototransistors based on few-layer rhenium disulfide (ReS2) are presented, where the maximum attainable photoresponsivity can reach as high as 88 600 A W−1, which is a record value compared to other individual 2D materials with similar device structures and two orders of magnitude higher than that of monolayer MoS2.
Abstract: 2D transition metal dichalcogenides are emerging with tremendous potential in many optoelectronic applications due to their strong light–matter interactions. To fully explore their potential in photoconductive detectors, high responsivity is required. Here, high responsivity phototransistors based on few-layer rhenium disulfide (ReS2) are presented. Depending on the back gate voltage, source drain bias and incident optical light intensity, the maximum attainable photoresponsivity can reach as high as 88 600 A W−1, which is a record value compared to other individual 2D materials with similar device structures and two orders of magnitude higher than that of monolayer MoS2. Such high photoresponsivity is attributed to the increased light absorption as well as the gain enhancement due to the existence of trap states in the few-layer ReS2 flakes. It further enables the detection of weak signals, as successfully demonstrated with weak light sources including a lighter and limited fluorescent lighting. Our studies underscore ReS2 as a promising material for future sensitive optoelectronic applications.
250 citations
TL;DR: The experimental results demonstrated capabilities and effectiveness of the proposed trajectory planning framework and algorithms to safely handle a variety of typical driving scenarios, such as static and moving objects avoidance, lane keeping, and vehicle following, while respecting the traffic rules.
Abstract: This paper focuses on the real-time trajectory planning problem for autonomous vehicles driving in realistic urban environments. To solve the complex navigation problem, we adopt a hierarchical motion planning framework. First, a rough reference path is extracted from the digital map using commands from the high-level behavioral planner. The conjugate gradient nonlinear optimization algorithm and the cubic B-spline curve are employed to smoothen and interpolate the reference path sequentially. To follow the refined reference path as well as handle both static and moving objects, the trajectory planning task is decoupled into lateral and longitudinal planning problems within the curvilinear coordinate framework. A rich set of kinematically feasible path candidates are generated to deal with the dynamic traffic both deliberatively and reactively. In the meanwhile, the velocity profile generation is performed to improve driving safety and comfort. After that, the generated trajectories are carefully evaluated by an objective function, which combines behavioral decisions by reasoning about the traffic situations. The optimal collision-free, smooth, and dynamically feasible trajectory is selected and transformed into commands executed by the low-level lateral and longitudinal controllers. Field experiments have been carried out with our test autonomous vehicle on the realistic inner-city roads. The experimental results demonstrated capabilities and effectiveness of the proposed trajectory planning framework and algorithms to safely handle a variety of typical driving scenarios, such as static and moving objects avoidance, lane keeping, and vehicle following, while respecting the traffic rules.
TL;DR: The strong nonlinear absorption and temperature-independent chromaticity of CsPbBr3 QDs observed in temperature range from 220 to 380 K will offer new opportunities in nonlinear photonics, light-harvesting, and light-emitting devices.
Abstract: Recently, lead halide perovskite quantum dots have been reported with potential for photovoltaic and optoelectronic applications due to their excellent luminescent properties. Herein excitonic photoluminescence (PL) excited by two-photon absorption in perovskite CsPbBr3 quantum dots (QDs) has been studied at a broad temperature range, from 80 to 380 K. Two-photon absorption has been investigated and the absorption coefficient is up to 0.085 cm/GW at room temperature. Moreover, the PL spectrum excited by two-photon absorption shows a linear blue-shift (0.32 meV/K) below the temperature of 220 K. However, for higher temperatures, the PL peak approaches a roughly constant value and shows temperature-independent chromaticity up to 380 K. This behavior is distinct from the general red-shift for semiconductors and can be attributed to the result of thermal expansion, electron–phonon interaction and structural phase transition around 360 K. The strong nonlinear absorption and temperature-independent chromaticity of CsPbBr3 QDs observed in temperature range from 220 to 380 K will offer new opportunities in nonlinear photonics, light-harvesting, and light-emitting devices.
08 Oct 2016
TL;DR: An unsupervised learning based approach to the ubiquitous computer vision problem of image matching that achieves surprising performance comparable to traditional empirically designed methods.
Abstract: This work presents an unsupervised learning based approach to the ubiquitous computer vision problem of image matching. We start from the insight that the problem of frame interpolation implicitly solves for inter-frame correspondences. This permits the application of analysis-by-synthesis: we first train and apply a Convolutional Neural Network for frame interpolation, then obtain correspondences by inverting the learned CNN. The key benefit behind this strategy is that the CNN for frame interpolation can be trained in an unsupervised manner by exploiting the temporal coherence that is naturally contained in real-world video sequences. The present model therefore learns image matching by simply “watching videos”. Besides a promise to be more generally applicable, the presented approach achieves surprising performance comparable to traditional empirically designed methods.
TL;DR: In this article, the transverse jet in supersonic crossflows has been summarized systematically from four aspects, namely single injection, multiport injection, interaction between jet and vortex generator, and interaction between the jet and shock wave.
TL;DR: This study confirms that traditional approaches to bug triaging and code review are feasible for pull-request reviewer recommendations on GitHub, and their performance can be improved significantly by combining them with information extracted from prior social interactions between developers on GitHub.
Abstract: Context: The pull-based model, widely used in distributed software development, offers an extremely low barrier to entry for potential contributors (anyone can submit of contributions to any project, through pull-requests). Meanwhile, the project's core team must act as guardians of code quality, ensuring that pull-requests are carefully inspected before being merged into the main development line. However, with pull-requests becoming increasingly popular, the need for qualified reviewers also increases. GitHub facilitates this, by enabling the crowd-sourcing of pull-request reviews to a larger community of coders than just the project's core team, as a part of their social coding philosophy. However, having access to more potential reviewers does not necessarily mean that it's easier to find the right ones (the "needle in a haystack" problem). If left unsupervised, this process may result in communication overhead and delayed pull-request processing.Objective: This study aims to investigate whether and how previous approaches used in bug triaging and code review can be adapted to recommending reviewers for pull-requests, and how to improve the recommendation performance.Method: First, we extend three typical approaches used in bug triaging and code review for the new challenge of assigning reviewers to pull-requests. Second, we analyze social relations between contributors and reviewers, and propose a novel approach by mining each project's comment networks (CNs). Finally, we combine the CNs with traditional approaches, and evaluate the effectiveness of all these methods on 84 GitHub projects through both quantitative and qualitative analysis.Results: We find that CN-based recommendation can achieve, by itself, similar performance as the traditional approaches. However, the mixed approaches can achieve significant improvements compared to using either of them independently.Conclusion: Our study confirms that traditional approaches to bug triaging and code review are feasible for pull-request reviewer recommendations on GitHub. Furthermore, their performance can be improved significantly by combining them with information extracted from prior social interactions between developers on GitHub. These results prompt for novel tools to support process automation in social coding platforms, that combine social (e.g., common interests among developers) and technical factors (e.g., developers' expertise).
TL;DR: This paper analyzes a family of frequently used scalarizing methods, the Lp methods, and shows that the p value is crucial to balance the selective pressure toward the Pareto optimal and the algorithm robustness to Pare to optimal front (PF) geometries.
Abstract: Decomposition-based algorithms have become increasingly popular for evolutionary multiobjective optimization. However, the effect of scalarizing methods used in these algorithms is still far from being well understood. This paper analyzes a family of frequently used scalarizing methods, the ${L} _{ {p}}$ methods, and shows that the ${p}$ value is crucial to balance the selective pressure toward the Pareto optimal and the algorithm robustness to Pareto optimal front (PF) geometries. It demonstrates that an ${L} _{ {p}}$ method that can maximize the search ability of a decomposition-based algorithm exists and guarantees that, given some weight, any solution along the PF can be found. Moreover, a simple yet effective method called Pareto adaptive scalarizing (PaS) approximation is proposed to approximate the optimal ${p}$ value. In order to demonstrate the effectiveness of PaS, we incorporate PaS into a state-of-the-art decomposition-based algorithm, i.e., multiobjective evolutionary algorithm based on decomposition (MOEA/D), and compare the resultant MOEA/D-PaS with some other MOEA/D variants on a set of problems with different PF geometries and up to seven conflicting objectives. Experimental results demonstrate that the PaS is effective.
TL;DR: In this paper, a comprehensive review of thermography NDT techniques for composites inspection was conducted based on an orderly and concise literature survey and detailed analysis, and some research trends were predicted.
TL;DR: This paper overviews data sources, analytical approaches, and application systems for social transportation, and suggests a few future research directions for this new social transportation field.
Abstract: Big data for social transportation brings us unprecedented opportunities for resolving transportation problems for which traditional approaches are not competent and for building the next-generation intelligent transportation systems. Although social data have been applied for transportation analysis, there are still many challenges. First, social data evolve with time and contain abundant information, posing a crucial need for data collection and cleaning. Meanwhile, each type of data has specific advantages and limitations for social transportation, and one data type alone is not capable of describing the overall state of a transportation system. Systematic data fusing approaches or frameworks for combining social signal data with different features, structures, resolutions, and precision are needed. Second, data processing and mining techniques, such as natural language processing and analysis of streaming data, require further revolutions in effective utilization of real-time traffic information. Third, social data are connected to cyber and physical spaces. To address practical problems in social transportation, a suite of schemes are demanded for realizing big data in social transportation systems, such as crowdsourcing, visual analysis, and task-based services. In this paper, we overview data sources, analytical approaches, and application systems for social transportation, and we also suggest a few future research directions for this new social transportation field.
TL;DR: In this article, a measurement of the flux and energy spectrum of electron antineutrinos from six 2.9 GWth nuclear reactors with six detectors deployed in two near (effective baselines 512 and 561 m) and one far (1579 m) underground experimental halls in the Daya Bay experiment was reported.
Abstract: This Letter reports a measurement of the flux and energy spectrum of electron antineutrinos from six 2.9 GWth nuclear reactors with six detectors deployed in two near (effective baselines 512 and 561 m) and one far (1579 m) underground experimental halls in the Daya Bay experiment. Using 217 days of data, 296 721 and 41 589 inverse β decay (IBD) candidates were detected in the near and far halls, respectively. The measured IBD yield is (1.55±0.04) ×10(-18) cm(2) GW(-1) day(-1) or (5.92±0.14) ×10(-43) cm(2) fission(-1). This flux measurement is consistent with previous short-baseline reactor antineutrino experiments and is 0.946±0.022 (0.991±0.023) relative to the flux predicted with the Huber-Mueller (ILL-Vogel) fissile antineutrino model. The measured IBD positron energy spectrum deviates from both spectral predictions by more than 2σ over the full energy range with a local significance of up to ∼4σ between 4-6 MeV. A reactor antineutrino spectrum of IBD reactions is extracted from the measured positron energy spectrum for model-independent predictions.
TL;DR: In this article, a system is proposed to generate vortex electromagnetic (EM) beams in the microwave band, which generates high-order vortex beams at the X-frequency band for the first time.
Abstract: A system is proposed to generate vortex electromagnetic (EM) beams in the microwave band, which generates high-order vortex beams at the X-frequency band for the first time. First, the orbital angular momentum (OAM)-generating system is designed and the signal model based on the uniform circular array is presented. Subsequently, the mathematical model with array error contributions is established and, comprehensively, numerical simulations are conducted to analyze how amplitude and phase errors affect the radiation field and the EM vortex imaging. The experimental results validate that the proposed system can readily generate vortex beams of high quality, which are in agreement with the simulated results. The work paves the way to applications of OAM-carrying beams as well as a novel information-rich radar paradigm.
TL;DR: Transmission electron microscopy results demonstrate the behavior is caused by the overgrowth of the conductive filament into the Pt electrode, and the CF overgrowth phenomenon is suppressed and the negative-SET behavior is eliminated by inserting an impermeable graphene layer.
Abstract: Negative-SET behavior is observed in various cation-based memories, which degrades the device reliability. Transmission electron microscopy results demonstrate the behavior is caused by the overgrowth of the conductive filament (CF) into the Pt electrode. The CF overgrowth phenomenon is suppressed and the negative-SET behavior is eliminated by inserting an impermeable graphene layer. The graphene-based devices show high reliability and satisfying performance.
TL;DR: A bifunctional catalyst, CFP/NiCo2O4/Co0.57Ni0.43LMOs, which will be very promising in overall water splitting and present better durability under harsh OER and HER cycling conditions than commercial Ir/C and Pt/C.
Abstract: Water splitting via the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) in producing H2 and O2 is a very important process in the energy field. Developing an efficient catalyst which can be applied to both HER and OER is crucial. Here, a bifunctional catalyst, CFP/NiCo2O4/Co0.57Ni0.43LMOs, has been successfully fabricated. It exhibits remarkable performance for OER in 0.1 M KOH producing a current density of 10 mA cm−2 at an overpotential of 0.34 V (1.57 V vs. RHE), better than that of the commercial Ir/C (20%) catalyst. Simultaneously, it also exhibits good catalytic performance for HER in 0.5 M H2SO4 producing a current density of 10 mA cm−2 at an overpotential of 52 mV and a Tafel slope of 34 mV dec−1, approaching that of the commercial Pt/C (20%) nanocatalyst. Particularly, CFP/NiCo2O4/Co0.57Ni0.43LMOs present better durability under harsh OER and HER cycling conditions than commercial Ir/C and Pt/C. Furthermore, an H-type electrolyzer was fabricated by applying CFP/NiCo2O4/Co0.57Ni0.43LMOs as the cathode and anode electrocatalyst, which can be driven by a single-cell battery. This bifunctional catalyst will be very promising in overall water splitting.
TL;DR: In this paper, the inlet boundary conditions at the entrance of the isolator and the jet-to-crossflow pressure ratio both have a large impact on the Ram-To-Scram mode transition.
TL;DR: In this article, an adaptive fuzzy gain-scheduling sliding mode control (AFGS-SMC) approach is proposed for attitude regulation of UAVs with parametric uncertainties and external disturbances.
TL;DR: This analysis provides an unprecedented level of information about human movement after a natural disaster, provided within a very short timeframe after the Nepal earthquake occurred, and reveals patterns revealed that are almost impossible to find through other methods.
Abstract: INTRODUCTION: Sudden impact disasters often result in the displacement of large numbers of people. These movements can occur prior to events, due to early warning messages, or take place post-event due to damages to shelters and livelihoods as well as a result of long-term reconstruction efforts. Displaced populations are especially vulnerable and often in need of support. However, timely and accurate data on the numbers and destinations of displaced populations are extremely challenging to collect across temporal and spatial scales, especially in the aftermath of disasters. Mobile phone call detail records were shown to be a valid data source for estimates of population movements after the 2010 Haiti earthquake, but their potential to provide near real-time ongoing measurements of population displacements immediately after a natural disaster has not been demonstrated. METHODS: A computational architecture and analytical capacity were rapidly deployed within nine days of the Nepal earthquake of 25th April 2015, to provide spatiotemporally detailed estimates of population displacements from call detail records based on movements of 12 million de-identified mobile phones users. RESULTS: Analysis shows the evolution of population mobility patterns after the earthquake and the patterns of return to affected areas, at a high level of detail. Particularly notable is the movement of an estimated 390,000 people above normal from the Kathmandu valley after the earthquake, with most people moving to surrounding areas and the highly-populated areas in the central southern area of Nepal. DISCUSSION: This analysis provides an unprecedented level of information about human movement after a natural disaster, provided within a very short timeframe after the earthquake occurred. The patterns revealed using this method are almost impossible to find through other methods, and are of great interest to humanitarian agencies. Language: en
TL;DR: In this paper, the authors use two datasets of individual mobility trajectories from six million de-identified mobile phone users in Bangladesh over three months and two years respectively to quantify incidence, direction, duration and seasonality of migration episodes in Bangladesh.
Abstract: Climate change is likely to drive migration from environmentally stressed areas. However quantifying short and long-term movements across large areas is challenging due to difficulties in the collection of highly spatially and temporally resolved human mobility data. In this study we use two datasets of individual mobility trajectories from six million de-identified mobile phone users in Bangladesh over three months and two years respectively. Using data collected during Cyclone Mahasen, which struck Bangladesh in May 2013, we show first how analyses based on mobile network data can describe important short-term features (hours–weeks) of human mobility during and after extreme weather events, which are extremely hard to quantify using standard survey based research. We then demonstrate how mobile data for the first time allow us to study the relationship between fundamental parameters of migration patterns on a national scale. We concurrently quantify incidence, direction, duration and seasonality of migration episodes in Bangladesh. While we show that changes in the incidence of migration episodes are highly correlated with changes in the duration of migration episodes, the correlation between in- and out-migration between areas is unexpectedly weak. The methodological framework described here provides an important addition to current methods in studies of human migration and climate change.
Book•
13 Sep 2016TL;DR: A Survey of Query Auto Completion in Information Retrieval is an ideal reference on the topic and provides researchers who are working on query auto completion or related problems in the eld of information retrieval with a good overview and analysis of state-of-the-art QAC approaches.
Abstract: In information retrieval, query auto completion (QAC), also known as type-ahead and auto-complete suggestion, refers to the following functionality: given a prex consisting of a number of characters entered into a search box, the user interface proposes alternative ways of extending the prex to a full query. QAC helps users to formulate their query when they have an intent in mind but not a clear way of expressing this in a query. It helps to avoid possible spelling mistakes, especially on devices with small screens. It saves keystrokes and cuts down the search duration of users which implies a lower load on the search engine, and results in savings in machine resources and maintenance. Because of the clear benets of QAC, a considerable number of algorithmic approaches to QAC have been proposed in the past few years. Query logs have proven to be a key asset underlying most of the recent research. This monograph surveys this research. It focuses on summarizing the literature on QAC and provides a general understanding of the wealth of QAC approaches that are currently available. A Survey of Query Auto Completion in Information Retrieval is an ideal reference on the topic. Its contributions can be summarized as follows: It provides researchers who are working on query auto completion or related problems in the eld of information retrieval with a good overview and analysis of state-of-the-art QAC approaches. In particular, for researchers new to the eld, the survey can serve as an introduction to the state-of-the-art. It also offers a comprehensive perspective on QAC approaches by presenting a taxonomy of existing solutions. In addition, it presents solutions for QAC under different conditions such as available high-resolution query logs, in-depth user interactions with QAC using eye-tracking, and elaborate user engagements in a QAC process. It also discusses practical issues related to QAC. Lastly, it presents a detailed discussion of core challenges and promising open directions in QAC.
TL;DR: An all-optical scheme for overdense pair production by two counter-propagating lasers irradiating near-critical-density plasmas at only ∼1022 W cm−2, which could be basis for future compact high-luminosity electron–positron colliders.
Abstract: Pair production can be triggered by high-intensity lasers via the Breit-Wheeler process. However, the straightforward laser-laser colliding for copious numbers of pair creation requires light intensities several orders of magnitude higher than possible with the ongoing laser facilities. Despite the numerous proposed approaches, creating high-energy-density pair plasmas in laboratories is still challenging. Here we present an all-optical scheme for overdense pair production by two counter-propagating lasers irradiating near-critical-density plasmas at only ∼1022 W cm-2. In this scheme, bright γ-rays are generated by radiation-trapped electrons oscillating in the laser fields. The dense γ-photons then collide with the focused counter-propagating lasers to initiate the multi-photon Breit-Wheeler process. Particle-in-cell simulations indicate that one may generate a high-yield (1.05 × 1011) overdense (4 × 1022 cm-3) GeV positron beam using 10 PW scale lasers. Such a bright pair source has many practical applications and could be basis for future compact high-luminosity electron-positron colliders.
Posted Content•
TL;DR: DESI (Dark Energy Spectropic Instrument) as mentioned in this paper is a ground-based dark energy experiment that will study baryon acoustic oscillations and the growth of structure through redshift-space distortions with a wide-area galaxy and quasar redshift survey.
Abstract: DESI (Dark Energy Spectropic Instrument) is a Stage IV ground-based dark energy experiment that will study baryon acoustic oscillations and the growth of structure through redshift-space distortions with a wide-area galaxy and quasar redshift survey. The DESI instrument is a robotically-actuated, fiber-fed spectrograph capable of taking up to 5,000 simultaneous spectra over a wavelength range from 360 nm to 980 nm. The fibers feed ten three-arm spectrographs with resolution $R= \lambda/\Delta\lambda$ between 2000 and 5500, depending on wavelength. The DESI instrument will be used to conduct a five-year survey designed to cover 14,000 deg$^2$. This powerful instrument will be installed at prime focus on the 4-m Mayall telescope in Kitt Peak, Arizona, along with a new optical corrector, which will provide a three-degree diameter field of view. The DESI collaboration will also deliver a spectroscopic pipeline and data management system to reduce and archive all data for eventual public use.
TL;DR: In this paper, a comprehensive review of NDT techniques for wind turbine blade inspection is reported based on an orderly and concise literature survey, where the development of visual, sonic and ultrasonic, optical, electromagnetic, thermal and radiographic NDT for composite WTB inspection were reviewed.
Abstract: Wind energy is one of the fastest growing renewable energy resources. It is distinctly important to increase reliability and availability of wind turbines and further to reduce the wind energy cost. Blades are considered to be one of the most critical components in wind turbine system because they convert Kinetic energy of wind into useable power. Blades are fabricated by carbon fiber reinforced polymer (CFRP) or glass fiber reinforced polymer (GFRP). Flaws and damages are inevitable during either fabrication or lifetime of a composite blade. Thus, non-destructive testing (NDT) and structural health monitoring (SHM) for wind turbine blade (WTB) are required to prevent failures and increase reliability in both manufacturing quality control and in-service inspection. In this work, a fully, in-depth and comprehensive review of NDT techniques for WTB inspection was reported based on an orderly and concise literature survey. Firstly, typical flaw and damage occurring in manufacturing progress and in service of WTB were introduced. Next, the developments of visual, sonic and ultrasonic, optical, electromagnetic, thermal and radiographic NDT for composite WTB inspection were reviewed. Thereafter, strengths and limitations of NDT techniques were concluded through comparison studies. In the end, some research trends in WTB NDT have been predicted, for example in combination with SHM. This work will provide a guide for NDT and SHM of WTB, which plays an important role in wind turbine safety control and wind energy cost savings. In addition, this work can benefit the NDT development in the field of renewable energy, such as solar energy, and energy conservation field, such as building diagnosis.