MVFAN: Multi-View Feature Assisted Network for 4D Radar Object Detection

4D radar is recognized for its resilience and cost-effectiveness under adverse weather conditions, thus playing a pivotal role in autonomous driving. While cameras and LiDAR are typically the primary sensors used in perception modules for autonomous vehicles, radar serves as a valuable supplementary sensor. Unlike LiDAR and cameras, radar remains unimpaired by harsh weather conditions, thereby offering a dependable alternative in challenging environments. Developing radar-based 3D object detection not only augments the competency of autonomous vehicles but also provides economic benefits. In response, we propose the Multi-View Feature Assisted Network (\textit{MVFAN}), an end-to-end, anchor-free, and single-stage framework for 4D-radar-based 3D object detection for autonomous vehicles. We tackle the issue of insufficient feature utilization by introducing a novel Position Map Generation module to enhance feature learning by reweighing foreground and background points, and their features, considering the irregular distribution of radar point clouds. Additionally, we propose a pioneering backbone, the Radar Feature Assisted backbone, explicitly crafted to fully exploit the valuable Doppler velocity and reflectivity data provided by the 4D radar sensor. Comprehensive experiments and ablation studies carried out on Astyx and VoD datasets attest to the efficacy of our framework. The incorporation of Doppler velocity and RCS reflectivity dramatically improves the detection performance for small moving objects such as pedestrians and cyclists. Consequently, our approach culminates in a highly optimized 4D-radar-based 3D object detection capability for autonomous driving systems, setting a new standard in the field.Comment: 19 Pages, 7 figures, Accepted by ICONIP 202

Redemption from Range-view for Accurate 3D Object Detection

Most recent approaches for 3D object detection predominantly rely on point-view or bird's-eye view representations, with limited exploration of range-view-based methods. The range-view representation suffers from scale variation and surface texture deficiency, both of which pose significant limitations for developing corresponding methods. Notably, the surface texture loss problem has been largely ignored by all existing methods, despite its significant impact on the accuracy of range-view-based 3D object detection. In this study, we propose Redemption from Range-view R-CNN (R2 R-CNN), a novel and accurate approach that comprehensively explores the range-view representation. Our proposed method addresses scale variation through the HD Meta Kernel, which captures range-view geometry information in multiple scales. Additionally, we introduce Feature Points Redemption (FPR) to recover the lost 3D surface texture information from the range view, and Synchronous-Grid RoI Pooling (S-Grid RoI Pooling), a multi-scaled approach with multiple receptive fields for accurate box refinement. Our R2 R-CNN outperforms existing range-view-based methods, achieving state-of-the-art performance on both the KITTI benchmark and the Waymo Open Dataset. Our study highlights the critical importance of addressing the surface texture loss problem for accurate 3D object detection in range-view-based methods. Codes will be made publicly available

ThermRad: A Multi-modal Dataset for Robust 3D Object Detection under Challenging Conditions

Robust 3D object detection in extreme weather and illumination conditions is a challenging task. While radars and thermal cameras are known for their resilience to these conditions, few studies have been conducted on radar-thermal fusion due to the lack of corresponding datasets. To address this gap, we first present a new multi-modal dataset called ThermRad, which includes a 3D LiDAR, a 4D radar, an RGB camera and a thermal camera. This dataset is unique because it includes data from all four sensors in extreme weather conditions, providing a valuable resource for future research in this area. To validate the robustness of 4D radars and thermal cameras for 3D object detection in challenging weather conditions, we propose a new multi-modal fusion method called RTDF-RCNN, which leverages the complementary strengths of 4D radars and thermal cameras to boost object detection performance. To further prove the effectiveness of our proposed framework, we re-implement state-of-the-art (SOTA) 3D detectors on our dataset as benchmarks for evaluation. Our method achieves significant enhancements in detecting cars, pedestrians, and cyclists, with improvements of over 7.98%, 24.27%, and 27.15%, respectively, while achieving comparable results to LiDAR-based approaches. Our contributions in both the ThermRad dataset and the new multi-modal fusion method provide a new approach to robust 3D object detection in adverse weather and illumination conditions. The ThermRad dataset will be released.Comment: 12 pages, 5 figures, Proceedings of the IEEE/CVF International Conference on Computer Visio

Floating binary planets from ejections during close stellar encounters

The discovery of planetary systems beyond our solar system has posed challenges to established theories of planetary formation. Planetary orbits display a variety of architectures not predicted by first principles, and free-floating planets appear ubiquitous. The recent discovery of candidate Jupiter Mass Binary Objects (JuMBOs) by the James Webb Space Telescope (JWST) further expanded this enigma. Here, by means of high-accuracy, direct $N$-body simulations, we evaluate the possibility that JuMBOs may form as a result of ejection after a close stellar flyby. We consider a system of two Jupiter-like planets moving in circular orbits with velocities $v_1$ and $v_2$ at distances $a_1$ and $a_2$ around a Sun-like star. The interloper is another Sun-like star approaching with asymptotic velocity $v_\infty$. We find that JuMBOs can indeed be formed upon ejection if the two planets are nearly aligned as the interloper reaches the closest approach. The ratio of the cross section of JuMBOs production to that of single ejected free-floating planets can approach $\sim 20\%$ for $v_\infty/v_2 \sim 0.1 - 0.2$ and $a_1/a_2\sim 0.75-0.8$. JuMBOs formed via this channel are expected to have an average semi-major axis comparable to $\Delta a = (a_2-a_1)$ and high eccentricity, with a distinctive superthermal distribution which can help to observationally identify this formation channel and distinguish it from primordial formation. If the ejection channel is confirmed for these or future JWST observations, these JuMBOs will directly inform us of the conditions where these giant planets formed in protoplanetary disks, putting stringent constraints on the giant planet formation theory.Comment: 18 pages, 5 figures. Videos are available at https://yihanwangastro.github.io/#posts

Technical and Economic Feasibility Analysis of a Conceptual Subsea Freight Glider for CO2 Transportation

This study analyses the technical and economic aspects of a novel subsea freight glider (SFG). The SFG is an excellent replacement for tanker ships and submarine pipelines transporting liquefied CO2. The main aim of the SFG is to ship CO2 from an offshore facility to an underwater well where the gas can be injected; as an advantage, the SFG vehicle may be used to transport all kinds of cargo. The SFG travels below the sea surface, making the vessel weather-independent. The research is divided into two steps. Firstly, the technical feasibility analysis is performed by designing a baseline design with a length of 56.5 m, a beam of 5.5 m, and a cargo volume of 1194 m3. The SFG is developed using DNVGL-RU-NAVAL-Pt4Ch1, which was initially created for military submarine designs. Two additional half-scaled 469 m3 and double-scaled 2430 m3 models are created when the baseline design fulfils the technical requirements. Secondly, the economic analysis is carried out using the freely accessible MUNIN D9.3 and ZEP reports. The economic feasibility analysis is illustrated through a case study with a CO2 transport capacity range of 0.5 to 2.5 mtpa (million tons per annum) and a transport length range of 180 km to 1500 km. The prices of CO2 per ton for the SFG, crew and autonomous tankers, and offshore pipelines are comprehensively compared. According to the results, SFGs with capacities of 469 m3, 1194 m3, and 2430 m3 are technically possible to manufacture. Moreover, the SFGs are competitive with a smaller CO2 capacity of 0.5 mtpa at distances of 180 and 500 km and a capacity of 1 mtpa at a distance of 180 km.publishedVersio

Community Perspectives of Tourism Benefits - The Link to Conservation Attitudes and Livelihoods

Tourism has often been seen as an approach to link conservation and development. Its potential rests in providing economic benefits while maintaining environmental integrity (Stem et al, 2003). In protected areas, nature-based tourism offers the economic justification for establishing protected area (Brandon, K. 1996) through park entry fees and other tourism related activities. The economic benefits provided through tourism development also help generate conservation support from local communities and can be used to improve conservation efforts. Numerous studies have shown that the incentives for local people to support conservation are recipient of tangible economic benefits. However, despite this popular notion, it is remains to be seen what role tourism benefits play in conservation attitudes, and its interactions with other social economic factors that are in play. In face of a lack of information on the role tourism plays in livelihoods improvement and conservation attitudes, this study attempts to explore how tourism in protected area help link biodiversity conservation with community development. It evaluates the positive and negative effects tourism has had on local residents, and explores whether those factors lead to positive conservation attitudes. The study took place in buffer zone community in Chitwan National Park, Nepal’s first protected area and one of the most popular tourist destinations in the nation. By providing the context of this study, the paper introduces the study method and analyzes the results it revealed. Such information will provide policy implications for governments and future tourism operators to prioritize factors that generate supportive attitudes towards tourism and greater support for conservation. The study also helps to develop a better understanding of community needs for designing future development projects that meet community expectations.Master of ScienceSchool for Environment and SustainabilityUniversity of Michiganhttps://deepblue.lib.umich.edu/bitstream/2027.42/137969/1/Wang_Yihan_Practicum_2017.pd

Universality and Limitations of Prompt Tuning

Despite the demonstrated empirical efficacy of prompt tuning to adapt a pretrained language model for a new task, the theoretical underpinnings of the difference between "tuning parameters before the input" against "the tuning of model weights" are limited. We thus take one of the first steps to understand the role of soft-prompt tuning for transformer-based architectures. By considering a general purpose architecture, we analyze prompt tuning from the lens of both: universal approximation and limitations with finite-depth fixed-weight pretrained transformers for continuous-valued functions. Our universality result guarantees the existence of a strong transformer with a prompt to approximate any sequence-to-sequence function in the set of Lipschitz functions. The limitations of prompt tuning for limited-depth transformers are first proved by constructing a set of datasets, that cannot be memorized by a prompt of any length for a given single encoder layer. We also provide a lower bound on the required number of tunable prompt parameters and compare the result with the number of parameters required for a low-rank update (based on LoRA) for a single-layer setting. We finally extend our analysis to multi-layer settings by providing sufficient conditions under which the transformer can at best learn datasets from invertible functions only. Our theoretical claims are also corroborated by empirical results

Determination of extreme responses of USFG’s equilibrium glide path hovering in ocean current

The UiS subsea-freight glider (USFG) is a state-of-the-art autonomous vessel designed to be an alternative to existing transportation technologies and serve the demands of small-scale fields for CO2 transportation. Generally, these smaller fields cannot economically justify the costs of large tankers, cargo ships, or underwater pipelines on the seabed, as the transport volume is nominal compared to larger fields. The USFG can travel underwater at an operational depth of 200 m, allowing the glider to carry freight operations without considering ideal weather windows. It can manoeuvre itself underwater by monitoring the flow between the ballast tanks aboard. During the entire mission of the USFG, from capturing to injection locations, it follows a pre-laid saw-tooth path while experiencing transient loads from the ocean current. The extreme surge and heave responses of the USFG are vital for its design. Extreme motion along the surge direction affects the range of the glider (vital for battery design) and the dynamic controller parameters concerning manoeuvrability. For this paper, the averaged conditional exceedance rate (ACER) is employed to scrutinize the extreme motion (surge direction) of the USFG while gliding to a defined depth. This is done when the glider is exposed to an average current velocity of 0.5 m/s and 1.0 m/s. The data used for analysis in this study is obtained from the time-domain simulations carried out on a two-dimensional mathematical model developed in Simulink. The presented ACER method efficiently uses the available data points and accurately predicts the extreme surge responses precisely and accurately. This study can effectively promote the design improvement of the USFG; thus, the safety and economic benefits can be essentially enhanced.publishedVersio

Beyond Wealth and Health: Psycho-Social Factors and Retirement Planning and Expectations in the U.S.

Thesis advisor: Christina MatzRetirement is a significant transition in an individual’s life course. More and more people are working past traditional retirement ages. Planning before retirement has been shown to relate to a number of positive outcomes and lead to a smoother transition to a retired life, such as more retirement savings, better retirement satisfaction, better social life, health, and mental health. However, most of the studies about retirement to date have focused on the impact of health and wealth in preparing for a successful retirement. This dissertation examines three issues related to retirement planning and expectations: (1) How do work and family relationships relate to having a plan to reduce or stop work and expected retirement timing in late life, and are there gender and occupational differences in these relationships? (2) How do workplace experiences relate to expectations to retire earlier or later than what is normative in different occupations? (3) Does sense of control explain the relationship between involuntary retirement and retirement satisfaction? To answer the three questions, the author adopts the role theory, the age norm theory, and the theory of self-efficacy to explain the background and findings. The data for this dissertation comes from the Health and Retirement Study (HRS), a nationally representative dataset that captures the information about the health and retirement issues among adults over age 50 in the U.S. This proposed study uses pooled cross-sectional data from waves 2012 and 2014. Ordinary least squares (OLS) regression and logistic regression were used to examine the effect of work and family relationships and the plans/retirement timing of pre-retirees. Multinomial logistic regression was used to examine workplace factors that contribute to the non-normative retirement age expectations. Mediation analysis was used to study how personal mastery, perceived constraints, and domain-specific control mediates the relationship between involuntary retirement and retirement satisfaction.Thesis (PhD) — Boston College, 2019.Submitted to: Boston College. Graduate School of Social Work.Discipline: Social Work