Explicit Exchange Interaction and Decoherence Dynamics in One-Dimensional Quantum Systems

In this thesis, we investigate two aspects of one-dimensional interacting quantum systems. The first one is the effect of explicit antiferromagnetic exchange interactions on the quantum phase transitions of a one-dimensional itinerant electron system. The second one is the decoherence of a qubit coupled to a one-dimensional quantum spin system

Excerpts from: All the Time: lyric epistles

A poem about Kelowna; Vernon; Enderby; Ucluelet, and Shanghai, China.&nbsp

Analysis of the Narrative Perspective of Katherine Mansfield’s “The Garden Party”

Katharine Mansfield is a successful female writer in the literary history of the 20th century, who marks a new period of English short stories. She uses tremendous modernistic techniques and digs deep beneath the surface of life to show the causes of human happiness and despair in her works. “The Garden Party” is one of her most famous and representative short stories. Previous studies have mostly focused on its artistic methods, themes and characters, as well as the combination of all, but there are only few studies choosing its narrative perspectives as their study topic. This paper analyzed the narrative perspective in this story, focusing on the use of nonfocalization, internal focalization and covert progression and the effects they have. It is found that the change of ways of focalization combining with covert progression in this story forms a parallel of objective description and ironic description with the plot development, adds a new group to the relationship between the former implied author and target readers, and reveals two different ways (idealistic and realistic) of understanding this story, letting readers reflect on the behaviors of the upper-middle-class people and ironically pointing out their selfish nature

Uncertainty Estimation for 3D Dense Prediction via Cross-Point Embeddings

Dense prediction tasks are common for 3D point clouds, but the uncertainties inherent in massive points and their embeddings have long been ignored. In this work, we present CUE, a novel uncertainty estimation method for dense prediction tasks in 3D point clouds. Inspired by metric learning, the key idea of CUE is to explore cross-point embeddings upon a conventional 3D dense prediction pipeline. Specifically, CUE involves building a probabilistic embedding model and then enforcing metric alignments of massive points in the embedding space. We also propose CUE+, which enhances CUE by explicitly modeling cross-point dependencies in the covariance matrix. We demonstrate that both CUE and CUE+ are generic and effective for uncertainty estimation in 3D point clouds with two different tasks: (1) in 3D geometric feature learning we for the first time obtain well-calibrated uncertainty, and (2) in semantic segmentation we reduce uncertainty's Expected Calibration Error of the state-of-the-arts by 16.5%. All uncertainties are estimated without compromising predictive performance

STUN: Self-Teaching Uncertainty Estimation for Place Recognition

Place recognition is key to Simultaneous Localization and Mapping (SLAM) and spatial perception. However, a place recognition in the wild often suffers from erroneous predictions due to image variations, e.g., changing viewpoints and street appearance. Integrating uncertainty estimation into the life cycle of place recognition is a promising method to mitigate the impact of variations on place recognition performance. However, existing uncertainty estimation approaches in this vein are either computationally inefficient (e.g., Monte Carlo dropout) or at the cost of dropped accuracy. This paper proposes STUN, a self-teaching framework that learns to simultaneously predict the place and estimate the prediction uncertainty given an input image. To this end, we first train a teacher net using a standard metric learning pipeline to produce embedding priors. Then, supervised by the pretrained teacher net, a student net with an additional variance branch is trained to finetune the embedding priors and estimate the uncertainty sample by sample. During the online inference phase, we only use the student net to generate a place prediction in conjunction with the uncertainty. When compared with place recognition systems that are ignorant to the uncertainty, our framework features the uncertainty estimation for free without sacrificing any prediction accuracy. Our experimental results on the large-scale Pittsburgh30k dataset demonstrate that STUN outperforms the state-of-the-art methods in both recognition accuracy and the quality of uncertainty estimation.Comment: To appear at the 35th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2022

Risk Controlled Image Retrieval

Most image retrieval research focuses on improving predictive performance, but they may fall short in scenarios where the reliability of the prediction is crucial. Though uncertainty quantification can help by assessing uncertainty for query and database images, this method can provide only a heuristic estimate rather than an guarantee. To address these limitations, we present Risk Controlled Image Retrieval (RCIR), which generates retrieval sets that are guaranteed to contain the ground truth samples with a predefined probability. RCIR can be easily plugged into any image retrieval method, agnostic to data distribution and model selection. To the best of our knowledge, this is the first work that provides coverage guarantees for image retrieval. The validity and efficiency of RCIR is demonstrated on four real-world image retrieval datasets, including the Stanford CAR-196 (Krause et al. 2013), CUB-200 (Wah et al. 2011), the Pittsburgh dataset (Torii et al. 2013) and the ChestX-Det dataset (Lian et al. 2021)

A Robust Indoor Positioning System Based on the Procrustes Analysis and Weighted Extreme Learning Machine

Indoor positioning system (IPS) has become one of the most attractive research fields due to the increasing demands on location-based services (LBSs) in indoor environments. Various IPSs have been developed under different circumstances, and most of them adopt the fingerprinting technique to mitigate pervasive indoor multipath effects. However, the performance of the fingerprinting technique severely suffers from device heterogeneity existing across commercial off-the-shelf mobile devices (e.g., smart phones, tablet computers, etc.) and indoor environmental changes (e.g., the number, distribution and activities of people, the placement of furniture, etc.). In this paper, we transform the received signal strength (RSS) to a standardized location fingerprint based on the Procrustes analysis, and introduce a similarity metric, termed signal tendency index (STI), for matching standardized fingerprints. An analysis of the capability of the proposed STI to handle device heterogeneity and environmental changes is presented. We further develop a robust and precise IPS by integrating the merits of both the STI and weighted extreme learning machine (WELM). Finally, extensive experiments are carried out and a performance comparison with existing solutions verifies the superiority of the proposed IPS in terms of robustness to device heterogeneity

MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless Sensing

4D human perception plays an essential role in a myriad of applications, such as home automation and metaverse avatar simulation. However, existing solutions which mainly rely on cameras and wearable devices are either privacy intrusive or inconvenient to use. To address these issues, wireless sensing has emerged as a promising alternative, leveraging LiDAR, mmWave radar, and WiFi signals for device-free human sensing. In this paper, we propose MM-Fi, the first multi-modal non-intrusive 4D human dataset with 27 daily or rehabilitation action categories, to bridge the gap between wireless sensing and high-level human perception tasks. MM-Fi consists of over 320k synchronized frames of five modalities from 40 human subjects. Various annotations are provided to support potential sensing tasks, e.g., human pose estimation and action recognition. Extensive experiments have been conducted to compare the sensing capacity of each or several modalities in terms of multiple tasks. We envision that MM-Fi can contribute to wireless sensing research with respect to action recognition, human pose estimation, multi-modal learning, cross-modal supervision, and interdisciplinary healthcare research.Comment: The paper has been accepted by NeurIPS 2023 Datasets and Benchmarks Track. Project page: https://ntu-aiot-lab.github.io/mm-f

Effects of on-Board Unit on Driving Behavior in Connected Vehicle Traffic Flow

Connected vehicle technology has potentials to increase traffic safety, reduce traffic pollution, and ease traffic congestion. In the connected vehicle environment, the information interaction among people, cars, roads, and the environment is significantly enhanced, and driver behavior will change accordingly due to increased external stimulation. This paper designed a Vehicle-to-Vehicle (V2V) on-board unit (OBU) based on driving demand. In addition, a simulation platform for the interconnection and communication between the OBU and simulator was built. Thirty-one test drivers were investigated to drive an instrumented vehicle in four scenarios, with and without the OBU under two different traffic states. Collected trajectory data of the subject vehicle and the vehicle in front, as well as sociodemographic characteristics of the test drivers were used to evaluate the potential impact of such OBUs on driving behavior and traffic safety. Car-following behavior is an essential component of microsimulation models. This paper also investigated the impacts of the V2V OBU on car-following behaviors. Considering the car-following related indicators, the k-Means algorithm was used to categorize different car-following modes. The results show that the OBU has a positive impact on drivers in terms of speed, front distance, and the time to stable regime. Furthermore, drivers’ opinions show that the system is acceptable and useful in general. Document type: Articl

Reduction of the HIV Protease Inhibitor-Induced ER Stress and Inflammatory Response by Raltegravir in Macrophages

Background HIV protease inhibitor (PI), the core component of highly active antiretroviral treatment (HAART) for HIV infection, has been implicated in HAART-associated cardiovascular complications. Our previous studies have demonstrated that activation of endoplasmic reticulum (ER) stress is linked to HIV PI-induced inflammation and foam cell formation in macrophages. Raltegravir is a first-in-its-class HIV integrase inhibitor, the newest class of anti-HIV agents. We have recently reported that raltegravir has less hepatic toxicity and could prevent HIV PI-induced dysregulation of hepatic lipid metabolism by inhibiting ER stress. However, little information is available as to whether raltegravir would also prevent HIV PI-induced inflammatory response and foam cell formation in macrophages. Methodology and Principal Findings In this study, we examined the effect of raltegravir on ER stress activation and lipid accumulation in cultured mouse macrophages (J774A.1), primary mouse macrophages, and human THP-1-derived macrophages, and further determined whether the combination of raltegravir with existing HIV PIs would potentially exacerbate or prevent the previously observed activation of inflammatory response and foam cell formation. The results indicated that raltegravir did not induce ER stress and inflammatory response in macrophages. Even more interestingly, HIV PI-induced ER stress, oxidative stress, inflammatory response and foam cell formation were significantly reduced by raltegravir. High performance liquid chromatography (HPLC) analysis further demonstrated that raltegravir did not affect the uptake of HIV PIs in macrophages. Conclusion and Significance Raltegravir could prevent HIV PI-induced inflammatory response and foam cell formation by inhibiting ER stress. These results suggest that incorporation of this HIV integrase inhibitor may reduce the cardiovascular complications associated with current HAART