Efficient Image-Text Retrieval via Keyword-Guided Pre-Screening

Under the flourishing development in performance, current image-text retrieval methods suffer from $N$-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 $O(1)$ 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~cm$\times$10~cm and a thickness of 320~$\mu$m. The electrical properties of the interposer are carefully evaluated at room temperature, and its performance is also examined at -110~$^\circ$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

第944回千葉医学会例会・第1外科教室談話会

Reference Search Summary (DOCX 12 kb

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