231 research outputs found
Efficient Image-Text Retrieval via Keyword-Guided Pre-Screening
Under the flourishing development in performance, current image-text
retrieval methods suffer from -related time complexity, which hinders their
application in practice. Targeting at efficiency improvement, this paper
presents a simple and effective keyword-guided pre-screening framework for the
image-text retrieval. Specifically, we convert the image and text data into the
keywords and perform the keyword matching across modalities to exclude a large
number of irrelevant gallery samples prior to the retrieval network. For the
keyword prediction, we transfer it into a multi-label classification problem
and propose a multi-task learning scheme by appending the multi-label
classifiers to the image-text retrieval network to achieve a lightweight and
high-performance keyword prediction. For the keyword matching, we introduce the
inverted index in the search engine and create a win-win situation on both time
and space complexities for the pre-screening. Extensive experiments on two
widely-used datasets, i.e., Flickr30K and MS-COCO, verify the effectiveness of
the proposed framework. The proposed framework equipped with only two embedding
layers achieves querying time complexity, while improving the retrieval
efficiency and keeping its performance, when applied prior to the common
image-text retrieval methods. Our code will be released.Comment: 11 pages, 7 figures, 6 table
RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search
Text-based person search aims to retrieve the specified person images given a
textual description. The key to tackling such a challenging task is to learn
powerful multi-modal representations. Towards this, we propose a Relation and
Sensitivity aware representation learning method (RaSa), including two novel
tasks: Relation-Aware learning (RA) and Sensitivity-Aware learning (SA). For
one thing, existing methods cluster representations of all positive pairs
without distinction and overlook the noise problem caused by the weak positive
pairs where the text and the paired image have noise correspondences, thus
leading to overfitting learning. RA offsets the overfitting risk by introducing
a novel positive relation detection task (i.e., learning to distinguish strong
and weak positive pairs). For another thing, learning invariant representation
under data augmentation (i.e., being insensitive to some transformations) is a
general practice for improving representation's robustness in existing methods.
Beyond that, we encourage the representation to perceive the sensitive
transformation by SA (i.e., learning to detect the replaced words), thus
promoting the representation's robustness. Experiments demonstrate that RaSa
outperforms existing state-of-the-art methods by 6.94%, 4.45% and 15.35% in
terms of Rank@1 on CUHK-PEDES, ICFG-PEDES and RSTPReid datasets, respectively.
Code is available at: https://github.com/Flame-Chasers/RaSa.Comment: Accepted by IJCAI 2023. Code is available at
https://github.com/Flame-Chasers/RaS
Development of silicon interposer: towards an ultralow radioactivity background photodetector system
It is of great importance to develop a photodetector system with an ultralow
radioactivity background in rare event searches. Silicon photomultipliers
(SiPMs) and application-specific integrated circuits (ASICs) are two ideal
candidates for low background photosensors and readout electronics,
respectively, because they are mainly composed of silicon, which can achieve
good radio-purity without considerable extra effort. However, interposers, used
to provide mechanical support and signal routes between the photosensor and the
electronics, are a bottleneck in building ultralow background photodetectors.
Silicon and quartz are two candidates to construct the low background
interposer because of their good radio-purity; nevertheless, it is non-trivial
to produce through silicon vias (TSV) or through quartz vias (TQV) on the large
area silicon or quartz wafer. In this work, based on double-sided TSV
interconnect technology, we developed the first prototype of a silicon
interposer with a size of 10~cm10~cm and a thickness of 320~m. The
electrical properties of the interposer are carefully evaluated at room
temperature, and its performance is also examined at -110~C with an
integrated SiPM on the interposer. The testing results reveal quite promising
performance of the prototype, and the single photoelectron signals can be
clearly observed from the SiPM. The features of the observed signals are
comparable with those from the SiPM mounted on a normal FR4-based PCB. Based on
the success of the silicon interposer prototype, we started the follow-up
studies that aimed to further improve the performance and yield of the silicon
interposer, and eventually to provide a solution for building an ultralow
background photodetector system
Version-sensitive mobile app recommendation
National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ
MgH2 nanoparticles confined in reduced graphene oxide pillared with organosilica: a novel type of hydrogen storage material
Hydrogen is a promising energy carrier that can push forward the energy
transition because of its high energy density (142 MJ kg-1), variety of
potential sources, low weight and low environmental impact, but its storage for
automotive applications remains a formidable challenge. MgH2, with its high
gravimetric and volumetric density, presents a compelling platform for hydrogen
storage; however, its utilization is hindered by the sluggish kinetics of
hydrogen uptake/release and high temperature operation. Herein we show that a
novel layered heterostructure of reduced graphene oxide and organosilica with
high specific surface area and narrow pore size distribution can serve as a
scaffold to host MgH2 nanoparticles with a narrow diameter distribution around
~2.5 nm and superior hydrogen storage properties to bulk MgH2. Desorption
studies showed that hydrogen release starts at 50 {\deg}C, with a maximum at
348 {\deg}C and kinetics dependent on particle size. Reversibility tests
demonstrated that the dehydrogenation kinetics and re-hydrogenation capacity of
the system remains stable at 1.62 wt.% over four cycles at 200 {\deg}C. Our
results prove that MgH2 confinement in a nanoporous scaffold is an efficient
way to constrain the size of the hydride particles, avoid aggregation and
improve kinetics for hydrogen release and recharging
PSR J1926-0652: A Pulsar with Interesting Emission Properties Discovered at FAST
We describe PSR J1926-0652, a pulsar recently discovered with the
Five-hundred-meter Aperture Spherical radio Telescope (FAST). Using sensitive
single-pulse detections from FAST and long-term timing observations from the
Parkes 64-m radio telescope, we probed phenomena on both long and short time
scales. The FAST observations covered a wide frequency range from 270 to 800
MHz, enabling individual pulses to be studied in detail. The pulsar exhibits at
least four profile components, short-term nulling lasting from 4 to 450 pulses,
complex subpulse drifting behaviours and intermittency on scales of tens of
minutes. While the average band spacing P3 is relatively constant across
different bursts and components, significant variations in the separation of
adjacent bands are seen, especially near the beginning and end of a burst. Band
shapes and slopes are quite variable, especially for the trailing components
and for the shorter bursts. We show that for each burst the last detectable
pulse prior to emission ceasing has different properties compared to other
pulses. These complexities pose challenges for the classic carousel-type
models.Comment: 13pages with 12 figure
Mitigation of severe urban haze pollution by a precision air pollution control approach
Severe and persistent haze pollution involving fine particulate matter (PM_(2.5)) concentrations reaching unprecedentedly high levels across many cities in China poses a serious threat to human health. Although mandatory temporary cessation of most urban and surrounding emission sources is an effective, but costly, short-term measure to abate air pollution, development of long-term crisis response measures remains a challenge, especially for curbing severe urban haze events on a regular basis. Here we introduce and evaluate a novel precision air pollution control approach (PAPCA) to mitigate severe urban haze events. The approach involves combining predictions of high PM_(2.5) concentrations, with a hybrid trajectory-receptor model and a comprehensive 3-D atmospheric model, to pinpoint the origins of emissions leading to such events and to optimize emission controls. Results of the PAPCA application to five severe haze episodes in major urban areas in China suggest that this strategy has the potential to significantly mitigate severe urban haze by decreasing PM_(2.5) peak concentrations by more than 60% from above 300 μg m^(−3) to below 100 μg m^(−3), while requiring ~30% to 70% less emission controls as compared to complete emission reductions. The PAPCA strategy has the potential to tackle effectively severe urban haze pollution events with economic efficiency
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