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  1. Gatherings of thousands to millions of people frequently occur for an enormous variety of educational, social, sporting, and political events, and automated counting of these high-density crowds is useful for safety, management, and measuring significance of an event. In this work, we show that the regularly accepted labeling scheme of crowd density maps for training deep neural networks may not be the most effective one. We propose an alternative inverse k-nearest neighbor (i[Formula: see text]NN) map mechanism that, even when used directly in existing state-of-the-art network structures, shows superior performance. We also provide new network architecture mechanisms that we demonstrate in our own MUD-i[Formula: see text]NN network architecture, which uses multi-scale drop-in replacement upsampling via transposed convolutions to take full advantage of the provided i[Formula: see text]NN labeling. This upsampling combined with the i[Formula: see text]NN maps further improves crowd counting accuracy. We further analyze several variations of the i[Formula: see text]NN labeling mechanism, which apply transformations on the [Formula: see text]NN measure before generating the map, in order to consider the impact of camera perspective views, image resolutions, and the changing rates of the mapping functions. To alleviate the effects of crowd density changes in each image, we also introduce an attenuation mechanism in the i[Formula: see text]NN mapping. Experimentally, we show that inverse square root [Formula: see text]NN map variation (iR[Formula: see text]NN) provides the best performance. Discussions are provided on computational complexity, label resolutions, the gains in mapping and upsampling, and details of critical cases such as various crowd counts, uneven crowd densities, and crowd occlusions. 
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  2. Abstract

    Following Shin et al. (2023b), which is a part of the “Systematic KMTNet Planetary Anomaly Search” series (i.e., a search for planets in the 2016 KMTNet prime fields), we conduct a systematic search of the 2016 KMTNet subprime fields using a semi-machine-based algorithm to identify hidden anomalous events missed by the conventional by-eye search. We find four new planets and seven planet candidates that were buried in the KMTNet archive. The new planets are OGLE-2016-BLG-1598Lb, OGLE-2016-BLG-1800Lb, MOA-2016-BLG-526Lb, and KMT-2016-BLG-2321Lb, which show typical properties of microlensing planets, i.e., giant planets orbit M-dwarf host stars beyond their snow lines. For the planet candidates, we find planet/binary or 2L1S/1L2S degeneracies, which are an obstacle to firmly claiming planet detections. By combining the results of Shin et al. (2023b) and this work, we find a total of nine hidden planets, which is about half the number of planets discovered by eye in 2016. With this work, we have met the goal of the systematic search series for 2016, which is to build a complete microlensing planet sample. We also show that our systematic searches significantly contribute to completing the planet sample, especially for planet/host mass ratios smaller than 10−3, which were incomplete in previous by-eye searches of the KMTNet archive.

     
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  3. ABSTRACT

    In this work, we update and develop algorithms for KMTNet tender-love care (TLC) photometry in order to create a new, mostly automated, TLC pipeline. We then start a project to systematically apply the new TLC pipeline to the historic KMTNet microlensing events, and search for buried planetary signals. We report the discovery of such a planet candidate in the microlensing event MOA-2019-BLG-421/KMT-2019-BLG-2991. The anomalous signal can be explained by either a planet around the lens star or the orbital motion of the source star. For the planetary interpretation, despite many degenerate solutions, the planet is most likely to be a Jovian planet orbiting an M or K dwarf, which is a typical microlensing planet. The discovery proves that the project can indeed increase the sensitivity of historic events and find previously undiscovered signals.

     
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  4. Aims. We investigate the data collected by the high-cadence microlensing surveys during the 2022 season in search of planetary signals appearing in the light curves of microlensing events. From this search, we find that the lensing event MOA-2022-BLG-249 exhibits a brief positive anomaly that lasted for about one day, with a maximum deviation of ~0.2 mag from a single-source, single-lens model. Methods. We analyzed the light curve under the two interpretations of the anomaly: one originated by a low-mass companion to the lens (planetary model) and the other originated by a faint companion to the source (binary-source model). Results. We find that the anomaly is better explained by the planetary model than the binary-source model. We identified two solutions rooted in the inner-outer degeneracy and for both of them, the estimated planet-to-host mass ratio, q ~ 8 × 10 −5 , is very small. With the constraints provided by the microlens parallax and the lower limit on the Einstein radius, as well as the blend-flux constraint, we find that the lens is a planetary system, in which a super-Earth planet, with a mass of (4.83 ± 1.44) Μ ⊕ , orbits a low-mass host star, with a mass of (0.18 ± 0.05) M ⊙ , lying in the Galactic disk at a distance of (2.00 ± 0.42) kpc. The planet detection demonstrates the elevated microlensing sensitivity of the current high-cadence lensing surveys to low-mass planets. 
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    Free, publicly-accessible full text available June 1, 2024
  5. null (Ed.)
    This paper describes the interface and testing of an indoor navigation app - ASSIST - that guides blind & visually impaired (BVI) individuals through an indoor environment with high accuracy while augmenting their understanding of the surrounding environment. ASSIST features personalized interfaces by considering the unique experiences that BVI individuals have in indoor wayfinding and offers multiple levels of multimodal feedback. After an overview of the technical approach and implementation of the first prototype of the ASSIST system, the results of two pilot studies performed with BVI individuals are presented – a performance study to collect data on mobility (walking speed, collisions, and navigation errors) while using the app, and a usability study to collect user evaluation data on the perceived helpfulness, safety, ease-of-use, and overall experience while using the app. Our studies show that ASSIST is useful in providing users with navigational guidance, improving their efficiency and (more significantly) their safety and accuracy in wayfinding indoors. Findings and user feed-back from the studies confirm some of the previous results, while also providing some new insights into the creation of such an app, including the use of customized user interfaces and expanding the types of information provided. 
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  6. ABSTRACT We present the observations and analysis of a high-magnification microlensing planetary event, KMT-2022-BLG-0440, for which the weak and short-lived planetary signal was covered by both the KMTNet survey and follow-up observations. The binary-lens models with a central caustic provide the best fits, with a planet/host mass ratio, q = 0.75–1.00 × 10−4 at 1σ. The binary-lens models with a resonant caustic and a brown-dwarf mass ratio are both excluded by Δχ2 > 70. The binary-source model can fit the anomaly well but is rejected by the ‘colour argument’ on the second source. From Bayesian analyses, it is estimated that the host star is likely a K or M dwarf located in the Galactic disc, the planet probably has a Neptune-mass, and the projected planet-host separation is $1.9^{+0.6}_{-0.7}$ or $4.6^{+1.4}_{-1.7}$  au, subject to the close/wide degeneracy. This is the third q < 10−4 planet from a high-magnification planetary signal (A ≳ 65). Together with another such planet, KMT-2021-BLG-0171Lb, the ongoing follow-up program for the KMTNet high-magnification events has demonstrated its ability to detect high-magnification planetary signals for q < 10−4 planets, which are challenging for the current microlensing surveys. 
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  7. Gatherings of thousands to millions of people frequently occur for an enormous variety of events, and automated counting of these high-density crowds is useful for safety, management, and measuring significance of an event. In this work, we show that the regularly accepted labeling scheme of crowd density maps for training deep neural networks is less effective than our alternative inverse k-nearest neighbor (i$k$NN) maps, even when used directly in existing state-of-the-art network structures. We also provide a new network architecture MUD-i$k$NN, which uses multi-scale upsampling via transposed convolutions to take full advantage of the provided i$k$NN labeling. This upsampling combined with the i$k$NN maps further improves crowd counting accuracy. Our new network architecture performs favorably in comparison with the state-of-the-art. However, our labeling and upsampling techniques are generally applicable to existing crowd counting architectures. 
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  8. Abstract

    As a part of the “Systematic KMTNet Planetary Anomaly Search” series, we report five new planets (namely, OGLE-2016-BLG-1635Lb, MOA-2016-BLG-532Lb, KMT-2016-BLG-0625Lb, OGLE-2016-BLG-1850Lb, and KMT-2016-BLG-1751Lb) and one planet candidate (KMT-2016-BLG-1855), which were found by searching 2016 KMTNet prime fields. Theseburiedplanets show a wide range of masses from Earth-class to super-Jupiter-class and are located in both the disk and the bulge. The ultimate goal of this series is to build a complete planet sample. Because our work provides a complementary sample to other planet detection methods, which have different detection sensitivities, our complete sample will help us to obtain a better understanding of planet demographics in our Galaxy.

     
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  9. Abstract We analyze the MOA-2020-BLG-208 gravitational microlensing event and present the discovery and characterization of a new planet, MOA-2020-BLG-208Lb, with an estimated sub-Saturn mass. With a mass ratio q = 3.17 − 0.26 + 0.28 × 10 − 4 , the planet lies near the peak of the mass-ratio function derived by the MOA collaboration and near the edge of expected sample sensitivity. For these estimates we provide results using two mass-law priors: one assuming that all stars have an equal planet-hosting probability, and the other assuming that planets are more likely to orbit around more massive stars. In the first scenario, we estimate that the lens system is likely to be a planet of mass m planet = 46 − 24 + 42 M ⊕ and a host star of mass M host = 0.43 − 0.23 + 0.39 M ⊙ , located at a distance D L = 7.49 − 1.13 + 0.99 kpc . For the second scenario, we estimate m planet = 69 − 34 + 37 M ⊕ , M host = 0.66 − 0.32 + 0.35 M ⊙ , and D L = 7.81 − 0.93 + 0.93 kpc . The planet has a projected separation as a fraction of the Einstein ring radius s = 1.3807 − 0.0018 + 0.0018 . As a cool sub-Saturn-mass planet, this planet adds to a growing collection of evidence for revised planetary formation models. 
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