Socially-Compatible Behavior Design of Autonomous Vehicles with Verification on Real Human Data

As more and more autonomous vehicles (AVs) are being deployed on public roads, designing socially compatible behaviors for them is becoming increasingly important. In order to generate safe and efficient actions, AVs need to not only predict the future behaviors of other traffic participants, but also be aware of the uncertainties associated with such behavior prediction. In this paper, we propose an uncertain-aware integrated prediction and planning (UAPP) framework. It allows the AVs to infer the characteristics of other road users online and generate behaviors optimizing not only their own rewards, but also their courtesy to others, and their confidence regarding the prediction uncertainties. We first propose the definitions for courtesy and confidence. Based on that, their influences on the behaviors of AVs in interactive driving scenarios are explored. Moreover, we evaluate the proposed algorithm on naturalistic human driving data by comparing the generated behavior against ground truth. Results show that the online inference can significantly improve the human-likeness of the generated behaviors. Furthermore, we find that human drivers show great courtesy to others, even for those without right-of-way. We also find that such driving preferences vary significantly in different cultures.Comment: Accepted by IEEE Robotics and Automation Letters. January 202

Monitoring of Tsunami/Earthquake Damages by Polarimetric Microwave Remote Sensing Technique

Polarization characterizes the vector state of EM wave. When interacting with polarized wave, rough natural surface often induces dominant surface scattering; building also presents dominant double-bounce scattering. Tsunami/earthquake causes serious destruction just by inundating the land surface and destroying the building. By analyzing the change of surface and double-bounce scattering before and after disaster, we can achieve a monitoring of damages. This constitutes one basic principle of polarimetric microwave remote sensing of tsunami/earthquake. The extraction of surface and double-bounce scattering from coherency matrix is achieved by model-based decomposition. The general four-component scattering power decomposition with unitary transformation (G4U) has been widely used in the remote sensing of tsunami/earthquake to identify surface and double-bounce scattering because it can adaptively enhance surface or double-bounce scattering. Nonetheless, the strict derivation in this chapter conveys that G4U cannot always strengthen the double-bounce scattering in urban area nor strengthen the surface scattering in water or land area unless we adaptively combine G4U and its duality for an extended G4U (EG4U). Experiment on the ALOS-PALSAR datasets of 2011 great Tohoku tsunami/earthquake demonstrates not only the outperformance of EG4U but also the effectiveness of polarimetric remote sensing in the qualitative monitoring and quantitative evaluation of tsunami/earthquake damages

Design of Ultra-Wideband MIMO Antenna for Breast Tumor Detection

A MIMO antenna composed by microstrip line-fed circular slot antenna is proposed. This antenna is used in ultra-wideband microwave imaging systems aimed for early breast cancer detection. The antenna is designed to operate across the ultra-wideband frequency band in the air. The mutual coupling between the antenna elements has been investigated to be low enough for MIMO medical imaging applications. Both the simulation and measurement results are shown to illustrate the performances of the proposed antenna

HMGA1 variant IVS5-13insC is associated with insulin resistance and type 2 diabetes: an updated meta-analysis

Background: High-mobility group A1 (HMGA1) polymorphism has been suspected as a gene variant associated with type 2 diabetes (T2D). However, conflicting outcomes have been reported. Objective: This meta-analysis aimed to predict the association between the HMGA1 variant IVS5-13insC and T2D. Methods: Statistical analyses were performed using Stata/SE 12.0 software. Results: A total of 11 case-control studies in 6 articles were included. Results suggested that the HMGA1 variant IVS5-13insC was associated with an increased risk of insulin resistance (OR = 0.61, 95% CI 0.56 to 0.66, P < 0.0001), T2D (OR = 0.67, 95% CI 0.61 to 0.73, P < 0.0001), particularly for Caucasians with increased risks of T2D (OR = 0.56, 95% CI 0.49 to 0.65, P < 0.0001) compared with wild-type subjects. Conclusion: This meta-analysis indicated that the HMGA1 variant IVS5-13insC can be a risk factor of T2D development, particularly among Caucasians. Significant risks were also found (Asian: OR = 0.74, 95% CI: 0.63 to 0.86, P < 0.0001, Hispanic-American: OR = 0.81, 95% CI: 0.65 to 1.01, P < 0.0001) in non-Caucasian population. However, ethnical studies should be conducted to reveal whether the HMGA1 variant IVS5-13insC is associated with an increased risk of T2D.Keywords: HMGA1, type 2 diabetes, insulin resistance, variant, meta-analysis

Possibility of experimental study on nonleptonic Bc∗B_{c}^{\ast} weak decays

The ground vector $B_{c}^{\ast}$ meson has not yet been experimentally discovered until now. Besides the dominant electromagnetic decays, nonleptonic weak decays provide another choice to search for the mysterious $B_{c}^{\ast}$ mesons. Inspired by the potential prospects of $B_{c}^{\ast}$ meson in the future high-luminosity colliders, nonleptonic $B_{c}^{\ast}$ weak decays induced by bottom and charm quark decays are studied within SM by using naive factorization approach. It is found that for $B_{c}^{\ast}$ ${\to}$ $B_{s,d}{\pi}$, $B_{s,d}^{\ast}{\pi}$, $B_{s,d}{\rho}$, $B_{s}K$, $B_{s}^{\ast}K$, $B_{s}K^{\ast}$, ${\eta}_{c}(1S,2S){\pi}$, ${\eta}_{c}(1S,2S){\rho}$ and ${\psi}(1S,2S){\pi}$ decays, a few hundred and even thousand of events might be observable at CEPC, FCC-ee and LHCb@HL-LHC experiments.Comment: 15 page

Conservative State Value Estimation for Offline Reinforcement Learning

Offline reinforcement learning faces a significant challenge of value over-estimation due to the distributional drift between the dataset and the current learned policy, leading to learning failure in practice. The common approach is to incorporate a penalty term to reward or value estimation in the Bellman iterations. Meanwhile, to avoid extrapolation on out-of-distribution (OOD) states and actions, existing methods focus on conservative Q-function estimation. In this paper, we propose Conservative State Value Estimation (CSVE), a new approach that learns conservative V-function via directly imposing penalty on OOD states. Compared to prior work, CSVE allows more effective in-data policy optimization with conservative value guarantees. Further, we apply CSVE and develop a practical actor-critic algorithm in which the critic does the conservative value estimation by additionally sampling and penalizing the states \emph{around} the dataset, and the actor applies advantage weighted updates extended with state exploration to improve the policy. We evaluate in classic continual control tasks of D4RL, showing that our method performs better than the conservative Q-function learning methods and is strongly competitive among recent SOTA methods

Mlsp : A bioinformatics tool for predicting molecular subtypes and prognosis in patients with breast cancer

The molecular landscape in breast cancer is characterized by large biological heterogeneity and variable clinical outcomes. Here, we performed an integrative multi-omics analysis of patients diagnosed with breast cancer. Using transcriptomic analysis, we identified three subtypes (cluster A, cluster B and cluster C) of breast cancer with distinct prognosis, clinical features, and genomic alterations: Cluster A was asso-ciated with higher genomic instability, immune suppression and worst prognosis outcome; cluster B was associated with high activation of immune-pathway, increased mutations and middle prognosis out-come; cluster C was linked to Luminal A subtype patients, moderate immune cell infiltration and best prognosis outcome. Combination of the three newly identified clusters with PAM50 subtypes, we pro-posed potential new precision strategies for 15 subtypes using L1000 database. Then, we developed a robust gene pair (RGP) score for prognosis outcome prediction of patients with breast cancer. The RGP score is based on a novel gene-pairing approach to eliminate batch effects caused by differences in heterogeneous patient cohorts and transcriptomic data distributions, and it was validated in ten cohorts of patients with breast cancer. Finally, we developed a user-friendly web-tool (https://sujiezhulab.shi-nyapps.io/BRCA/) to predict subtype, treatment strategies and prognosis states for patients with breast cancer.(c) 2022 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creative-commons.org/licenses/by-nc-nd/4.0/).Peer reviewe

Low-Cost Efficient Magnetic Adsorbent for Phosphorus Removal from Water

Adsorption using magnetic adsorbents makes the phosphorus removal from water simple and efficient. However, most of the reported magnetic adsorbents use chemically synthesized nanoparticles as magnetic cores, which are expensive and environmentally unfriendly. Replacing the nanomagnetic cores by cheap and green magnetic materials is essential for the wide application of this technique. In this paper, coal-fly-ash magnetic spheres (MSs) were processed to produce a cheap and eco-friendly magnetic core. A magnetic adsorbent, ZrO2 coated ball-milled MS (BMS@ZrO2), was prepared through a simple chemical precipitation method. Careful structural investigations indicate that a multipore structural amorphous ZrO2 layer has grown on the MS core. The specific surface area of BMS@ZrO2 is 48 times larger than that of the MS core. The highest phosphorus adsorption is tested as 16.47 mg g-1 at pH = 2. The BMS@ZrO2 adsorbent has a saturation magnetization as high as 33.56 emu g-1, enabling efficient magnetic separation. Zeta potential measurements and X-ray photoelectron spectroscopy analysis reveal that the phosphorus adsorption of BMS@ZrO2 is triggered by the electrostatic attraction and the ligand exchange mechanism. The BMS@ZrO2 adsorbent could be reused several times after proper chemical treatment