Unsupervised RGB-to-Thermal Domain Adaptation via Multi-Domain Attention Network

This work presents a new method for unsupervised thermal image classification and semantic segmentation by transferring knowledge from the RGB domain using a multi-domain attention network. Our method does not require any thermal annotations or co-registered RGB-thermal pairs, enabling robots to perform visual tasks at night and in adverse weather conditions without incurring additional costs of data labeling and registration. Current unsupervised domain adaptation methods look to align global images or features across domains. However, when the domain shift is significantly larger for cross-modal data, not all features can be transferred. We solve this problem by using a shared backbone network that promotes generalization, and domain-specific attention that reduces negative transfer by attending to domain-invariant and easily-transferable features. Our approach outperforms the state-of-the-art RGB-to-thermal adaptation method in classification benchmarks, and is successfully applied to thermal river scene segmentation using only synthetic RGB images. Our code is made publicly available at https://github.com/ganlumomo/thermal-uda-attention

Towards a Work Breakdown Structure for Net Centric System of Systems Engineering and Management

As the system engineering industry sees an increasing focus on the lifecycle development, acquisition, and sustainment of net-centric Systems of Systems (SoS), organizations find that current processes and tools need to evolve to handle the increased scope, scale, and complexity of these efforts. One such tool, the Work Breakdown Structure (WBS) is important in planning, monitoring, and re-focusing of program activities as requirements and goals of the program evolve. This paper provides an overview of the limitations of current standard WBSs with respect to SoS efforts and presents a proposed WBS structure that more adequately reflects the evolving processes and cross-organizational complexities

Online Self-Supervised Thermal Water Segmentation for Aerial Vehicles

We present a new method to adapt an RGB-trained water segmentation network to target-domain aerial thermal imagery using online self-supervision by leveraging texture and motion cues as supervisory signals. This new thermal capability enables current autonomous aerial robots operating in near-shore environments to perform tasks such as visual navigation, bathymetry, and flow tracking at night. Our method overcomes the problem of scarce and difficult-to-obtain near-shore thermal data that prevents the application of conventional supervised and unsupervised methods. In this work, we curate the first aerial thermal near-shore dataset, show that our approach outperforms fully-supervised segmentation models trained on limited target-domain thermal data, and demonstrate real-time capabilities onboard an Nvidia Jetson embedded computing platform. Code and datasets used in this work will be available at: https://github.com/connorlee77/uav-thermal-water-segmentation.Comment: 8 pages, 4 figures, 3 table

RGB-X Object Detection via Scene-Specific Fusion Modules

Multimodal deep sensor fusion has the potential to enable autonomous vehicles to visually understand their surrounding environments in all weather conditions. However, existing deep sensor fusion methods usually employ convoluted architectures with intermingled multimodal features, requiring large coregistered multimodal datasets for training. In this work, we present an efficient and modular RGB-X fusion network that can leverage and fuse pretrained single-modal models via scene-specific fusion modules, thereby enabling joint input-adaptive network architectures to be created using small, coregistered multimodal datasets. Our experiments demonstrate the superiority of our method compared to existing works on RGB-thermal and RGB-gated datasets, performing fusion using only a small amount of additional parameters. Our code is available at https://github.com/dsriaditya999/RGBXFusion.Comment: Accepted to 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024

A preliminary study: Does relationship closeness with grandchildren correlate with the quality of life and physical health of Malaysian Chinese elderly?

The 13th Next-Generation Global Workshop第13回次世代グローバルワークショップテーマ: New Risks and Resilience in Asian Societies and the World 日程: 21-23 November, 2020 開催場所: ベトナム社会科学院(ハノイ)/Vietnam Academy of Social Sciences(No. 1 Lieu Giai street, Ba Dinh, Hanoi, Vietnam) ※Due to the COVID-19, the workshop will be held at ONLINE for overseas participants(not from Vietnam)/ONSITE for Vietnamese participants.This preliminary mixed-method study aimed to investigate whether grandparental childcare can contribute to elderly' quality of life and physical health. In the quantitative study, a total of 97 Chinese grandparents who are primary caregivers were recruited to examine the relations of relationship closeness, quality of life and physical health. Correlational analysis revealed that grandparent-grandchildren relationship closeness positively correlated with self-rated quality of life and physical health of the grandparents. In the qualitative interview, grandmothers (n = four Chinese grandmothers) who are primary caregivers were recruited to share their experience in taking care of grandchildren. Findings from the thematic analysis revealed that all grandmothers have a close relationship with their grandchildren. In specific, they are happy with the companionship of grandchildren even though they may feel physically tired in taking care of grandchildren. This preliminary study on skipped generation family provides insights into understanding the contribution of relationship closeness with grandchildren on the perceived quality of life and physical health of Malaysian grandparents who are the primary caregiver to their grandchildren

Spatiotemporal correlations of handset-based service usages

We study spatiotemporal correlations and temporal diversities of handset-based service usages by analyzing a dataset that includes detailed information about locations and service usages of 124 users over 16 months. By constructing the spatiotemporal trajectories of the users we detect several meaningful places or contexts for each one of them and show how the context affects the service usage patterns. We find that temporal patterns of service usages are bound to the typical weekly cycles of humans, yet they show maximal activities at different times. We first discuss their temporal correlations and then investigate the time-ordering behavior of communication services like calls being followed by the non-communication services like applications. We also find that the behavioral overlap network based on the clustering of temporal patterns is comparable to the communication network of users. Our approach provides a useful framework for handset-based data analysis and helps us to understand the complexities of information and communications technology enabled human behavior.Comment: 11 pages, 15 figure

Quasars Probing Quasars. VI. Excess H I Absorption within one Proper Mpc of z ~ 2 Quasars

With close pairs of quasars at different redshifts, a background quasar sightline can be used to study a foreground quasar's environment in absorption. We use a sample of 650 projected quasar pairs to study the H I Lyα absorption transverse to luminous, z ~ 2 quasars at proper separations of 30 kpc 10^(17.3) cm^(-2) at separations R_⊥ < 200 kpc, which decreases to ~20% at R_⊥ ≃ 1 Mpc, but still represents a significant excess over the cosmic average. This excess of optically thick absorption can be described by a quasar-absorber cross-correlation function ξ_(QA)(r) = (r/r_0)^γ with a large correlation length r_0=12.5^(+2.7)_(-1.4)h^(-1)Mpc(comoving) and y =1.68^(+0.14)_(-0.30). The H I absorption measured around quasars exceeds that of any previously studied population, consistent with quasars being hosted by massive dark matter halos M_(halo) ≈ 10^(12.5) M_☉ at z ~ 2.5. The environments of these massive halos are highly biased toward producing optically thick gas, and may even dominate the cosmic abundance of Lyman limit systems and hence the intergalactic opacity to ionizing photons at z ~ 2.5. The anisotropic absorption around quasars implies the transverse direction is much less likely to be illuminated by ionizing radiation than the line-of-sight

Baryon Acoustic Oscillations in the Ly{\alpha} forest of BOSS DR11 quasars

We report a detection of the baryon acoustic oscillation (BAO) feature in the flux-correlation function of the Ly{\alpha} forest of high-redshift quasars with a statistical significance of five standard deviations. The study uses 137,562 quasars in the redshift range $2.1\le z \le 3.5$ from the Data Release 11 (DR11) of the Baryon Oscillation Spectroscopic Survey (BOSS) of SDSS-III. This sample contains three times the number of quasars used in previous studies. The measured position of the BAO peak determines the angular distance, $D_A(z=2.34)$ and expansion rate, $H(z=2.34)$, both on a scale set by the sound horizon at the drag epoch, $r_d$. We find $D_A/r_d=11.28\pm0.65(1\sigma)^{+2.8}_{-1.2}(2\sigma)$ and $D_H/r_d=9.18\pm0.28(1\sigma)\pm0.6(2\sigma)$ where $D_H=c/H$. The optimal combination, $\sim D_H^{0.7}D_A^{0.3}/r_d$ is determined with a precision of $\sim2\%$. For the value $r_d=147.4~{\rm Mpc}$, consistent with the CMB power spectrum measured by Planck, we find $D_A(z=2.34)=1662\pm96(1\sigma)~{\rm Mpc}$ and $H(z=2.34)=222\pm7(1\sigma)~{\rm km\,s^{-1}Mpc^{-1}}$. Tests with mock catalogs and variations of our analysis procedure have revealed no systematic uncertainties comparable to our statistical errors. Our results agree with the previously reported BAO measurement at the same redshift using the quasar-Ly{\alpha} forest cross-correlation. The auto-correlation and cross-correlation approaches are complementary because of the quite different impact of redshift-space distortion on the two measurements. The combined constraints from the two correlation functions imply values of $D_A/r_d$ and $D_H/r_d$ that are, respectively, 7% low and 7% high compared to the predictions of a flat $\Lambda$CDM cosmological model with the best-fit Planck parameters. With our estimated statistical errors, the significance of this discrepancy is $\approx 2.5\sigma$.Comment: Accepted for publication in A&A. 17 pages, 18 figure

The SDSS-III Baryon Oscillation Spectroscopic Survey: Quasar Target Selection for Data Release Nine

The SDSS-III Baryon Oscillation Spectroscopic Survey (BOSS), a five-year spectroscopic survey of 10,000 deg^2, achieved first light in late 2009. One of the key goals of BOSS is to measure the signature of baryon acoustic oscillations in the distribution of Ly-alpha absorption from the spectra of a sample of ~150,000 z>2.2 quasars. Along with measuring the angular diameter distance at z\approx2.5, BOSS will provide the first direct measurement of the expansion rate of the Universe at z > 2. One of the biggest challenges in achieving this goal is an efficient target selection algorithm for quasars over 2.2 < z < 3.5, where their colors overlap those of stars. During the first year of the BOSS survey, quasar target selection methods were developed and tested to meet the requirement of delivering at least 15 quasars deg^-2 in this redshift range, out of 40 targets deg^-2. To achieve these surface densities, the magnitude limit of the quasar targets was set at g <= 22.0 or r<=21.85. While detection of the BAO signature in the Ly-alpha absorption in quasar spectra does not require a uniform target selection, many other astrophysical studies do. We therefore defined a uniformly-selected subsample of 20 targets deg^-2, for which the selection efficiency is just over 50%. This "CORE" subsample will be fixed for Years Two through Five of the survey. In this paper we describe the evolution and implementation of the BOSS quasar target selection algorithms during the first two years of BOSS operations. We analyze the spectra obtained during the first year. 11,263 new z>2.2 quasars were spectroscopically confirmed by BOSS. Our current algorithms select an average of 15 z > 2.2 quasars deg^-2 from 40 targets deg^-2 using single-epoch SDSS imaging. Multi-epoch optical data and data at other wavelengths can further improve the efficiency and completeness of BOSS quasar target selection. [Abridged]Comment: 33 pages, 26 figures, 12 tables and a whole bunch of quasars. Submitted to Ap