89 research outputs found
Sparse Coding Based Dense Feature Representation Model for Hyperspectral Image Classification
We present a sparse coding based dense feature representation model (a preliminary version of the paper was presented at the SPIE Remote Sensing Conference, Dresden, Germany, 2013) for hyperspectral image (HSI) classification. The proposed method learns a new representation for each pixel in HSI through the following four steps: sub-band construction, dictionary learning, encoding, and feature selection. The new representation usually has a very high dimensionality requiring a large amount of computational resources. We applied the l1/lq regularized multiclass logistic regression technique to reduce the size of the new representation. We integrated the method with a linear support vector machine (SVM) and a composite kernels SVM (CKSVM) to discriminate different types of land cover. We evaluated the proposed algorithm on three well-known HSI datasets and compared our method to four recently developed classification methods: SVM, CKSVM, simultaneous orthogonal matching pursuit, and image fusion and recursive filtering. Experimental results show that the proposed method can achieve better overall and average classification accuracies with a much more compact representation leading to more efficient sparse models for HSI classification
Moving beyond Deletions: Program Simplification via Diverse Program Transformations
To reduce the complexity of software, Developers manually simplify program
(known as developer-induced program simplification in this paper) to reduce its
code size yet preserving its functionality but manual simplification is
time-consuming and error-prone. To reduce manual effort, rule-based approaches
(e.g., refactoring) and deletion-based approaches (e.g., delta debugging) can
be potentially applied to automate developer-induced program simplification.
However, as there is little study on how developers simplify programs in
Open-source Software (OSS) projects, it is unclear whether these approaches can
be effectively used for developer-induced program simplification. Hence, we
present the first study of developer-induced program simplification in OSS
projects, focusing on the types of program transformations used, the
motivations behind simplifications, and the set of program transformations
covered by existing refactoring types. Our study of 382 pull requests from 296
projects reveals that there exist gaps in applying existing approaches for
automating developer-induced program simplification. and outlines the criteria
for designing automatic program simplification techniques. Inspired by our
study and to reduce the manual effort in developer-induced program
simplification, we propose SimpT5, a tool that can automatically produce
simplified programs (semantically-equivalent programs with reduced source lines
of code). SimpT5 is trained based on our collected dataset of 92,485 simplified
programs with two heuristics: (1) simplified line localization that encodes
lines changed in simplified programs, and (2)checkers that measure the quality
of generated programs. Our evaluation shows that SimpT5 are more effective than
prior approaches in automating developer-induced program simplification
JoTR: A Joint Transformer and Reinforcement Learning Framework for Dialog Policy Learning
Dialogue policy learning (DPL) is a crucial component of dialogue modelling.
Its primary role is to determine the appropriate abstract response, commonly
referred to as the "dialogue action". Traditional DPL methodologies have
treated this as a sequential decision problem, using pre-defined action
candidates extracted from a corpus. However, these incomplete candidates can
significantly limit the diversity of responses and pose challenges when dealing
with edge cases, which are scenarios that occur only at extreme operating
parameters. To address these limitations, we introduce a novel framework, JoTR.
This framework is unique as it leverages a text-to-text Transformer-based model
to generate flexible dialogue actions. Unlike traditional methods, JoTR
formulates a word-level policy that allows for a more dynamic and adaptable
dialogue action generation, without the need for any action templates. This
setting enhances the diversity of responses and improves the system's ability
to handle edge cases effectively. In addition, JoTR employs reinforcement
learning with a reward-shaping mechanism to efficiently finetune the word-level
dialogue policy, which allows the model to learn from its interactions,
improving its performance over time. We conducted an extensive evaluation of
JoTR to assess its effectiveness. Our extensive evaluation shows that JoTR
achieves state-of-the-art performance on two benchmark dialogue modelling
tasks, as assessed by both user simulators and human evaluators.Comment: Our code, models and other related resources are publicly available
at https://github.com/KwanWaiChung/JoT
Adaptive Graph Construction for Isomap Manifold Learning
Isomap is a classical manifold learning approach that preserves geodesic distance of nonlinear data sets. One of the main drawbacks of this method is that it is susceptible to leaking, where a shortcut appears between normally separated portions of a manifold. We propose an adaptive graph construction approach that is based upon the sparsity property of the â„“1 norm. The â„“1 enhanced graph construction method replaces k-nearest neighbors in the classical approach. The proposed algorithm is first tested on the data sets from the UCI data base repository which showed that the proposed approach performs better than the classical approach. Next, the proposed approach is applied to two image data sets and achieved improved performances over standard Isomap
Potential of Core-Collapse Supernova Neutrino Detection at JUNO
JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve
Detection of the Diffuse Supernova Neutrino Background with JUNO
As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO
Real-time Monitoring for the Next Core-Collapse Supernova in JUNO
Core-collapse supernova (CCSN) is one of the most energetic astrophysical
events in the Universe. The early and prompt detection of neutrinos before
(pre-SN) and during the SN burst is a unique opportunity to realize the
multi-messenger observation of the CCSN events. In this work, we describe the
monitoring concept and present the sensitivity of the system to the pre-SN and
SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is
a 20 kton liquid scintillator detector under construction in South China. The
real-time monitoring system is designed with both the prompt monitors on the
electronic board and online monitors at the data acquisition stage, in order to
ensure both the alert speed and alert coverage of progenitor stars. By assuming
a false alert rate of 1 per year, this monitoring system can be sensitive to
the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos
up to about 370 (360) kpc for a progenitor mass of 30 for the case
of normal (inverted) mass ordering. The pointing ability of the CCSN is
evaluated by using the accumulated event anisotropy of the inverse beta decay
interactions from pre-SN or SN neutrinos, which, along with the early alert,
can play important roles for the followup multi-messenger observations of the
next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure
A Scalable Platform for Data-Intensive Visualization
A huge variety of social applications, such as Twitter and Instagram, have been developed over the last few decades. With the introduction of these online social networks, there has never been a better time to research human interaction on a worldwide scale. The goal of this projectis to use a scalable and high-performance Twitter data visualization platform to investigate Twitter data on a given topic in real-time. To create a scalable Twitter data visualization platform, we write a basic version of the system using the Twitter Developer Platform's real-time and non-real-time APIs, optimize the frontend and backend performance with various components, and devise a benchmarking testing scheme to see if the application meets the scalability and high-performance requirements. Our results demonstrate an improvement over the basic version, indicating that a scalable Twitter data visualization platform has been built. However, since it relies on Twitter API to collect data, it will be constrained by the rate limit of Twitter API
A Scalable Platform for Data-Intensive Visualization
A huge variety of social applications, such as Twitter and Instagram, have been developed over the last few decades. With the introduction of these online social networks, there has never been a better time to research human interaction on a worldwide scale. The goal of this projectis to use a scalable and high-performance Twitter data visualization platform to investigate Twitter data on a given topic in real-time. To create a scalable Twitter data visualization platform, we write a basic version of the system using the Twitter Developer Platform's real-time and non-real-time APIs, optimize the frontend and backend performance with various components, and devise a benchmarking testing scheme to see if the application meets the scalability and high-performance requirements. Our results demonstrate an improvement over the basic version, indicating that a scalable Twitter data visualization platform has been built. However, since it relies on Twitter API to collect data, it will be constrained by the rate limit of Twitter API
DETECTING AND COUNTING VEHICLES FROM SMALL LOW-COST UAV IMAGES
ABSTRACT In recent years, many civil users have been interested in unmanned aerial vehicle (UAV) for traffic monitoring and traffic data collection because they have the ability to cover a large area, focus resources on the current problems, travel at higher speeds than ground vehicles, and are not restricted to traveling on the road network. This paper presents a method for detecting and counting vehicles from UAV video flow. The algorithm for vision-based detection and counting of vehicles in monocular image sequences for traffic scenes have been developed. In the algorithm, video frame-to-frame matching to track vehicle is one of important steps. Dynamic vehicles are identified using both background elimination and background registration techniques. The background elimination method uses concept of least squares to compare the accuracies of the current algorithm with the already existing algorithms. The background registration method uses background subtraction which improves the adaptive background mixture model and makes the system learn faster and more accurately, as well as adapt effectively to changing environments. In addition, because of high data sampling rates of video flow, resampling of video flow is also analyzed and discussed. The objective of this research is to monitor activities at traffic intersections for detecting congestions, and then predict the traffic flow
- …