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Searching for exotic spin-dependent interactions that beyond the standard model has been of interest for past decades and is crucial for unraveling the mysteries of the universe. Previous laboratory searches primarily focus on searching for either static or modulated energy-level shifts caused by exotic spin-dependent interactions. Here, we introduce a theoretical model based on thermal motion of particles, providing another efficient way to search for exotic spin-dependent interactions. The theoretical model indicates that as the exotic spin-dependent interactions are related with the relative displacements and velocities of atoms, atoms undergoing thermal motion would experience a fluctuating energy-level shift induced by the exotic interactions. Moreover, the resulting exotic energy-level shift noise could be sensed by high-sensitivity instruments. By using the model and taking the high-sensitivity atomic magnetometer as an example, we set the most stringent laboratory experiment constraints on eight different kinds of exotic spin- and velocity-dependent interactions, with five of which at the force range below 1 cm have not been covered previously. Furthermore, this theoretical model can be easily applied in other fields of quantum sensing, such as atomic clocks, atom interferometers and NV-diamond sensors, to further improve the laboratory constraints on exotic spin-dependent interactions.

Yan Yu,1,* Ti-wei Miao,1,* Wei Xiao,1 Bing Mao,1 Long-yi Du,1 Yan Wang,2 Juan-juan Fu1 1Division of Pulmonary Medicine, Department of Internal Medicine, Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, People’s Republic of China; 2Research Core Facility, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, People’s Republic of China*These authors contributed equally to this workCorrespondence: Juan-juan Fu, Division of Pulmonary Medicine, Department of Internal Medicine, Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Sichuan University, 37 Guoxue Street, Chengdu, Sichuan, 610041, People’s Republic of China, Tel +86-18980606822, Email fu.juanjuan@scu.edu.cnPurpose: The aim of this study is to uncover the anti-inflammatory propertity of andrographolide (AGP) in acute exacerbation of chronic obstructive pulmonary disease (AECOPD) and the underlying mechanisms related to the nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3) inflammasome pathway.Methods: An in vivo experiment was conducted on murine model of AECOPD through endotracheal atomization of elastase and lipopolysaccharide (LPS). Intraperitoneal AGP was administered four times. NLRP3 inflammasome pathway molecules were examined using real-time quantitative polymerase chain reaction (RT-qPCR) and Western blot analysis. By using enzyme-linked immunosorbent assay (ELISA), we tested interleukin (IL)-1β levels in bronchoalveolar lavage fluid. An in vitro study was conducted to determine how AGP impacts the NLRP3 inflammasome in THP-1 derived macrophages. The levels of molecules involved in the pathway were measured. Furthermore, molecular docking analyses were carried out to investigate the interactions between AGP and pathway targets.Results: In the in vivo study, NLRP3 inflammasome activation was observed in mice experiencing

Shi-Hua Yan,1,2 Yong Chen,3 Zhi-Qian Huang,1 Wen-Xi Zhong,1 Xiao-Tian Wang,1 Yang-Can Tang,1 Xu-Yi Zhao,1 Yu-Shan Wu,1 Chun Zhou,4 Wei Zhu,5 Wei Xiao,1,6 Xuan Li,2 Dong-Shu Zhang1,2 1School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, Guangdong, 510515, People’s Republic of China; 2Department of Traditional Chinese Medicine, The Tenth affiliated Hospital, Southern Medical University (Dongguan People’s Hospital), Dongguan, Guangdong, 523058, People’s Republic of China; 3Department of Rheumatology and Immunology, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, 563000, People’s Republic of China; 4School of Pharmaceutical Sciences; Guangdong Provincial Key Laboratory of Shock and Microcirculation, Southern Medical University, Guangzhou, Guangdong, 510515, People’s Republic of China; 5Guangzhou Center for Disease Control and Prevention, Guangzhou, 510440, People’s Republic of China; 6Key Laboratory of Glucolipid Metabolic Disorder, Ministry of Education, Guangdong Pharmaceutical University, Guangzhou, Guangdong, 510006, People’s Republic of ChinaCorrespondence: Dong-Shu Zhang, School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, Guangdong, 510515, People’s Republic of China, Email nyzds@sina.com Wei Xiao, Key Laboratory of Glucolipid Metabolic Disorder, Ministry of Education, Guangdong Pharmaceutical University, Guangzhou, Guangdong, 510006, People’s Republic of China, Email xw7688@smu.edu.cnPurpose: Acupoint autohemotherapy (A-AHT) has been proposed as an alternative and complementary treatment for atopic dermatitis (AD), yet the exact role of its blood component in terms of therapeutic efficacy and mechanism of action is still largely unknown.Methods: This study aimed to evaluate the therapeutic efficacies and action mechanisms of intramuscular injections of autologous whole blood (AWB) and mouse immunoglobulin G (IgG) (autologous or heterologous) at acupoints on 2,4-dinitrochlor

Jiaxing Zhang,1,2,* Liwei Gu,3,* Yumao Jiang,4,* Yun Ma,5,* Ziyue Zhang,3 Shengnan Shen,3 Shuo Shen,3 Qing Peng,6 Wei Xiao2,7 1Institute of Digestive Disease, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People’s Hospital, Qingyuan, Guang Dong, People’s Republic of China; 2School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, Guang Dong, People’s Republic of China; 3Artemisinin Research Center and Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China; 4Gannan Medical University, Ganzhou, Jiang Xi, People’s Republic of China; 5Department of Pharmacy, Nanfang Hospital, Southern Medical University, Guangzhou, Guang Dong, People’s Republic of China; 6Institute of Basic Medical Sciences, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China; 7Key Laboratory of Glucolipid Metabolic Disorder, Ministry of Education, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Qing Peng; Wei Xiao, Email pengqing85@163.com; xw7688@smu.edu.cnBackground: Acute kidney injury (AKI) is a syndrome, posing a substantial healthcare burden. The pathological basis of AKI is associated with inflammation and oxidative stress which cause additional damage to mitochondria. Artesunate (ATS) is a derivative of artemisinin isolated from Artemisia annua L. that is an effective treatment for malaria and favored for the prevention and treatment of kidney diseases. However, there are still challenges related to its efficacy, including poor water solubility, limited oral bioavailability and short half-life. Liposome-based nanoparticles are used for drug delivery due to their ideal biocompatibility and their ability to improve the bioavailability of specific drugs and enhance drug efficacy.Methods: In this study, a

Zheng-Bin Pan,1,2 Zhi-Min Sheng,1 Miao Zhu,1 Zhong Mei,1 Yan-Ping Shen,1 Jin-Ping Liu,1 Xiao-Wei Qian1 1Department of Anesthesiology, Women’s Hospital, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China; 2Department of Anesthesiology, Shaoxing Maternity and Child Health Care Hospital, Shaoxing, People’s Republic of ChinaCorrespondence: Xiao-Wei Qian, Department of Anesthesiology, Women’s Hospital, Zhejiang University School of Medicine, Xueshi Road 1, Hangzhou, 310006, People’s Republic of China, Email qianxw@zju.edu.cnBackground: Norepinephrine has fewer negative effects on heart rate (HR) and cardiac output (CO) for treating postspinal hypotension (PSH) compared with phenylephrine during cesarean section. However, it remains unclear whether fetuses from patients with severe pre-eclampsia could benefit from the superiority of CO. The objective of this study was to compare the safety and efficacy of intermittent intravenous boluses of phenylephrine and norepinephrine used in equipotent doses for treating postspinal hypotension in patients with severe pre-eclampsia during cesarean section.Methods: A total of 80 patients with severe pre-eclampsia who developed PSH predelivery during cesarean section were included. Eligible patients were randomized at a 1:1 ratio to receive either phenylephrine or norepinephrine for treating PSH. The primary outcome was umbilical arterial pH. Secondary outcomes included other umbilical cord blood gas values, Apgar scores at 1 and 5 min, changes in hemodynamic parameters including CO, mean arterial pressure (MAP), HR, stroke volume (SV), and systemic vascular resistance (SVR), the number of vasopressor boluses required, and the incidence of bradycardia, hypertension, nausea, vomiting, and dizziness.Results: No significant difference was observed in umbilical arterial pH between the phenylephrine and norepinephrine groups (7.303± 0.38 vs 7.303± 0.44, respectively; P=0.978). Compared with the phenylep

Marine invertebrates are ecologically and economically important and have formed holobionts by evolving symbiotic relationships with cellular and acellular microorganisms that reside in and on their tissues. In recent decades, significant focus on symbiotic cellular microorganisms has led to the discovery of various functions and a considerable expansion of our knowledge of holobiont functions. Despite this progress, our understanding of symbiotic acellular microorganisms remains insufficient, impeding our ability to achieve a comprehensive understanding of marine holobionts. In this review, we highlight the abundant viruses, with a particular emphasis on bacteriophages; provide an overview of their diversity, especially in extensively studied sponges and corals; and examine their potential life cycles. In addition, we discuss potential phage-holobiont interactions of various invertebrates, including participating in initial bacterial colonization, maintaining symbiotic relationships, and causing or exacerbating the diseases of marine invertebrates. Despite the importance of this subject, knowledge of how viruses contribute to marine invertebrate organisms remains limited. Advancements in technology and greater attention to viruses will enhance our understanding of marine invertebrate holobionts. © 2024 Annual Reviews Inc.. All rights reserved.

In recent years, drones have found increased applications in a wide array of real-world tasks. Model predictive control (MPC) has emerged as a practical method for drone flight control, owing to its robustness against modeling errors/uncertainties and external disturbances. However, MPC's sensitivity to manually tuned parameters can lead to rapid performance degradation when faced with unknown environmental dynamics. This paper addresses the challenge of controlling a drone as it traverses a swinging gate characterized by unknown dynamics. This paper introduces a parameterized MPC approach named hyMPC that leverages high-level decision variables to adapt to uncertain environmental conditions. To derive these decision variables, a novel policy search framework aimed at training a high-level Gaussian policy is presented. Subsequently, we harness the power of neural network policies, trained on data gathered through the repeated execution of the Gaussian policy, to provide real-time decision variables. The effectiveness of hyMPC is validated through numerical simulations, achieving a 100\% success rate in 20 drone flight tests traversing a swinging gate, demonstrating its capability to achieve safe and precise flight with limited prior knowledge of environmental dynamics., Comment: To be published in Unmanned Systems

We address the security of a network of Connected and Automated Vehicles (CAVs) cooperating to safely navigate through a conflict area (e.g., traffic intersections, merging roadways, roundabouts). Previous studies have shown that such a network can be targeted by adversarial attacks causing traffic jams or safety violations ending in collisions. We focus on attacks targeting the V2X communication network used to share vehicle data and consider as well uncertainties due to noise in sensor measurements and communication channels. To combat these, motivated by recent work on the safe control of CAVs, we propose a trust-aware robust event-triggered decentralized control and coordination framework that can provably guarantee safety. We maintain a trust metric for each vehicle in the network computed based on their behavior and used to balance the tradeoff between conservativeness (when deeming every vehicle as untrustworthy) and guaranteed safety and security. It is important to highlight that our framework is invariant to the specific choice of the trust framework. Based on this framework, we propose an attack detection and mitigation scheme which has twofold benefits: (i) the trust framework is immune to false positives, and (ii) it provably guarantees safety against false positive cases. We use extensive simulations (in SUMO and CARLA) to validate the theoretical guarantees and demonstrate the efficacy of our proposed scheme to detect and mitigate adversarial attacks., Comment: arXiv admin note: substantial text overlap with arXiv:2305.16818

Quantum transport and localization are fundamental concepts in condensed matter physics. It is commonly believed that in one-dimensional systems, the existence of mobility edges is highly dependent on disorder. Recently, there has been a debate over the existence of an exact mobility edge in a modulated mosaic model without quenched disorder, the so-called mosaic Wannier-Stark lattice. Here, we experimentally implement such disorder-free mosaic photonic lattices using a silicon photonics platform. By creating a synthetic electric field, we could observe energy-dependent coexistence of both extended and localized states in a finite number of waveguides. The Wannier-Stark ladder emerges when the resulting potential is strong enough, and can be directly probed by exciting different spatial modes of the lattice. Our studies provide the experimental proof of coexisting sets of strongly localized and conducting (though weakly localized) states in finite-sized mosaic Wannier-Stark lattices, which hold the potential to encode high-dimensional quantum resources with compact and robust structures., QC 20231113

Accounting for temporal changes in carbon dioxide (CO2) effluxes from freshwaters remains a challenge for global and regional carbon budgets. Here, we synthesize 171 site-months of flux measurements of CO2 based on the eddy covariance method from 13 lakes and reservoirs in the Northern Hemisphere, and quantify dynamics at multiple temporal scales. We found pronounced sub-annual variability in CO2 flux at all sites. By accounting for diel variation, only 11% of site-months were net daily sinks of CO2. Annual CO2 emissions had an average of 25% (range 3%–58%) interannual variation. Similar to studies on streams, nighttime emissions regularly exceeded daytime emissions. Biophysical regulations of CO2 flux variability were delineated through mutual information analysis. Sample analysis of CO2 fluxes indicate the importance of continuous measurements. Better characterization of short- and long-term variability is necessary to understand and improve detection of temporal changes of CO2 fluxes in response to natural and anthropogenic drivers. Our results indicate that existing global lake carbon budgets relying primarily on daytime measurements yield underestimates of net emissions., Correction in: Environmental Research Letters, vol. 18, issue 10, article number 109501DOI: 10.1088/1748-9326/acfb97

Meng-Chuan Zhao,1 Chan Wen,2 Ling Sun,2 Su-xia Duan,1 Ke-xin Zang,3 Le Wang,1 Xiao-Wei Cui4 1Department of Laborotory Medicine, Children’s Hospital of Hebei Province, Shijiazhuang, People’s Republic of China; 2Department of Medical, Children’s Hospital of Hebei Province, Shijiazhuang, People’s Republic of China; 3Hebei Medical University, Shijiazhuang, People’s Republic of China; 4Children’s Hospital of Hebei Province, Shijiazhuang, People’s Republic of ChinaCorrespondence: Xiao-Wei Cui, Children’s Hospital of Hebei Province, No. 133 of JianHua Street, Shijiazhuang, 050031, People’s Republic of China, Tel +86 18503292016, Fax +86 031185911109, Email cuixiaoweicxw@126.comBackground: This study aimed to assess the impact of the COVID-19 pandemic on the prevalence and clinical characteristics of seasonal human coronavirus (HCoV) infections among children hospitalized in Hebei, China.Methods: We examined nasopharyngeal aspirate (NPA) specimens for seasonal HCoVs from January 2018 to December 2021, at the Children’s Hospital of Hebei Province. We used a GeXP-based multiplex reverse transcription PCR assay for the detection of 11 common respiratory viruses (including seasonal HCoVs), chlamydia, and Mycoplasma pneumoniae. The demographic and clinical characteristics of children who tested positive for seasonal HCoVs were recorded and analyzed.Results: A total of 377 (1.96%) of the 19,248 specimens from 2018 to 2019 and 263 (1.96%) of the 13,426 specimens from 2020 to 2021 exhibited seasonal HCoVs. Compared to 2018 and 2019, the positive rate of seasonal HCoVs was lower from January to July of 2020 and increased beginning in August 2020, peaking in the autumn and winter. In 2020– 2021, nasal blockage and swollen adenoids were detected more frequently in children who tested positive for seasonal HCoVs. During 2018– 2019, however, the duration of fever was significantly longer, and cough and dyspnea were more prominent among children who had fallen ill

We report transparent quantum dot light-emitting diodes with a current focusing structure. By depositing a SiO2 thin film to form the current focusing structure, the DC density and luminance significantly increased to over 8700 mA/cm2 and 360 000 cd/m2, respectively. The emission spectra and current densities as functions of SiO2 thickness and aperture width have been investigated and discussed. This current focusing design is proved effective and can be further applied to other planar light-emitting diode devices.

Hua Tan, Ai-Fei Lou, Jian-Er Wu, Xin-Zhong Chen, Xiao-Wei Qian Department of Anesthesiology, Women’s Hospital, Zhejiang University School of Medicine, Hangzhou, People’s Republic of ChinaCorrespondence: Xiao-Wei Qian; Xin-Zhong Chen, Department of Anesthesiology, Women’s Hospital, Zhejiang University School of Medicine, Xueshi Road 1, Hangzhou, 310006, People’s Republic of China, Tel +86-571-87061501, Fax +86 571 87061878, Email qianxw@zju.edu.cn; chenxinz@zju.edu.cnPurpose: Remimazolam has demonstrated the potential as a valuable medication for procedural sedation. However, there were some shortcomings for higher doses of remimazolam during hysteroscopy in spite of less frequent adverse events. The aim of this study was to find the 50% and 95% effective dose (ED50 and ED95) of remimazolam when combined with propofol for intravenous sedation during day-surgery hysteroscopy.Patients and Methods: Patients were randomly assigned evenly (20 per group) to one of five different dosage of remimazolam: group A (0.05mg/kg), group B (0.075mg/kg), group C (0.1mg/kg), group D (0.125mg/kg) or group E (0.15mg/kg). Intravenous injection of sufentanil 0.1μg/kg was administered before sedative medication. Intravenous anesthesia was commenced with remimazolam. Subsequently, propofol was administered at 1mg/kg and maintained at 6mg/kg/h. Success was defined when the patient did not move during cervical dilation, had sufficient sedation as judged by SE < 60 and no requirement for rescue doses. The success rate, induce and average dosage of propofol, the induction time, total surgery time, recovery time, and adverse events were recorded. Estimate of ED50 and ED95 with 95% confidence interval (CI) was performed by probit regression.Results: The mean (95% CI) values for ED50 and ED95 of remimazolam in patients were 0.09 (0.08− 0.11) mg/kg and 0.21 (0.16– 0.35) mg/kg, respectively. There was no difference in the induction time, total surgery time, and recovery time among

Ba-enhanced stars are interesting probes of stellar astrophysics and Galactic formation history. In this work, we investigate the chemistry and kinematics for a large sample of Ba-enhanced ([Ba/Fe]$>$1.0) dwarf and subgiant stars with $5000 < T_{\rm eff }< 6700$\,K from LAMOST. We find that both stellar internal evolution process and external mass exchange due to binary evolution are responsible for the origins of the Ba-enhancement of our sample stars. About one third of them exhibit C and N enhancement and ultraviolet brightness excess, indicating they are products of binary evolution. The remaining Ba-enhanced stars with normal C and N abundances are mostly warm stars with $T_{\rm eff} > 6000$\,K. They are likely consequences of stellar internal elemental transport processes, but they show very different elemental patterns to the hotter Am/Fm stars. Our results reveal a substantially lack of high-[$\alpha$/Fe] Ba-enhanced stars in the [Fe/H]--[$\alpha$/Fe] plane, which we dub as a {\em high-$\alpha$ desert}. We suggest it is due to a lower efficiency for producing Ba-enhanced stars by low-mass AGB progenitors in binary systems. Our results call for detailed modellings for these Ba-enhanced stellar peculiars, in the context of both stellar internal elemental transport and external mass accretion., Comment: 20 pages, 17 figures. Accepted for publication in ApJ

Intracerebral hemorrhage is often accompanied by oxidative stress induced by reactive oxygen species, which causes abnormal mitochondrial function and secondary reactive oxygen species generation. This creates a vicious cycle leading to reactive oxygen species accumulation, resulting in progression of the pathological process. Therefore, breaking the cycle to inhibit reactive oxygen species accumulation is critical for reducing neuronal death after intracerebral hemorrhage. Our previous study found that increased expression of nicotinamide adenine dinucleotide phosphate oxidase 4 (NADPH oxidase 4, NOX4) led to neuronal apoptosis and damage to the blood-brain barrier after intracerebral hemorrhage. The purpose of this study was to investigate the role of NOX4 in the circle involving the neuronal tolerance to oxidative stress, mitochondrial reactive oxygen species and modes of neuronal death other than apoptosis after intracerebral hemorrhage. We found that NOX4 knockdown by adeno-associated virus (AAV-NOX4) in rats enhanced neuronal tolerance to oxidative stress, enabling them to better resist the oxidative stress caused by intracerebral hemorrhage. Knockdown of NOX4 also reduced the production of reactive oxygen species in the mitochondria, relieved mitochondrial damage, prevented secondary reactive oxygen species accumulation, reduced neuronal pyroptosis and contributed to relieving secondary brain injury after intracerebral hemorrhage in rats. Finally, we used a mitochondria-targeted superoxide dismutase mimetic to explore the relationship between reactive oxygen species and NOX4. The mitochondria-targeted superoxide dismutase mimetic inhibited the expression of NOX4 and neuronal pyroptosis, which is similar to the effect of AAV-NOX4. This indicates that NOX4 is likely to be an important target for inhibiting mitochondrial reactive oxygen species production, and NOX4 inhibitors can be used to alleviate oxidative stress response induced by intracerebral hemorrhage.

We present Learned Risk Metric Maps (LRMM) for real-time estimation of coherent risk metrics of high dimensional dynamical systems operating in unstructured, partially observed environments. LRMM models are simple to design and train -- requiring only procedural generation of obstacle sets, state and control sampling, and supervised training of a function approximator -- which makes them broadly applicable to arbitrary system dynamics and obstacle sets. In a parallel autonomy setting, we demonstrate the model's ability to rapidly infer collision probabilities of a fast-moving car-like robot driving recklessly in an obstructed environment; allowing the LRMM agent to intervene, take control of the vehicle, and avoid collisions. In this time-critical scenario, we show that LRMMs can evaluate risk metrics 20-100x times faster than alternative safety algorithms based on control barrier functions (CBFs) and Hamilton-Jacobi reachability (HJ-reach), leading to 5-15\% fewer obstacle collisions by the LRMM agent than CBFs and HJ-reach. This performance improvement comes in spite of the fact that the LRMM model only has access to local/partial observation of obstacles, whereas the CBF and HJ-reach agents are granted privileged/global information. We also show that our model can be equally well trained on a 12-dimensional quadrotor system operating in an obstructed indoor environment. The LRMM codebase is provided at https://github.com/mit-drl/pyrmm.

Missing information is a common issue of dialogue summarization where some information in the reference summaries is not covered in the generated summaries. To address this issue, we propose to utilize natural language inference (NLI) models to improve coverage while avoiding introducing factual inconsistencies. Specifically, we use NLI to compute fine-grained training signals to encourage the model to generate content in the reference summaries that have not been covered, as well as to distinguish between factually consistent and inconsistent generated sentences. Experiments on the DialogSum and SAMSum datasets confirm the effectiveness of the proposed approach in balancing coverage and faithfulness, validated with automatic metrics and human evaluations. Additionally, we compute the correlation between commonly used automatic metrics with human judgments in terms of three different dimensions regarding coverage and factual consistency to provide insight into the most suitable metric for evaluating dialogue summaries., Comment: Accepted by Findings of EACL 2023

We address the problem of controlling Connected and Automated Vehicles (CAVs) in conflict areas of a traffic network subject to hard safety constraints. It has been shown that such problems can be solved through a combination of tractable optimal control problems and Control Barrier Functions (CBFs) that guarantee the satisfaction of all constraints. These solutions can be reduced to a sequence of Quadratic Programs (QPs) which are efficiently solved on line over discrete time steps. However, guaranteeing the feasibility of the CBF-based QP method within each discretized time interval requires the careful selection of time steps which need to be sufficiently small. This creates computational requirements and communication rates between agents which may hinder the controller's application to real CAVs. In this paper, we overcome this limitation by adopting an event-triggered approach for CAVs in a conflict area such that the next QP is triggered by properly defined events with a safety guarantee. We present a laboratory-scale test bed we have developed to emulate merging roadways using mobile robots as CAVs which can be used to demonstrate how the event-triggered scheme is computationally efficient and can handle measurement uncertainties and noise compared to time-driven control while guaranteeing safety., Comment: arXiv admin note: substantial text overlap with arXiv:2203.12089, arXiv:2209.13053

Diffusion model-based approaches have shown promise in data-driven planning, but there are no safety guarantees, thus making it hard to be applied for safety-critical applications. To address these challenges, we propose a new method, called SafeDiffuser, to ensure diffusion probabilistic models satisfy specifications by using a class of control barrier functions. The key idea of our approach is to embed the proposed finite-time diffusion invariance into the denoising diffusion procedure, which enables trustworthy diffusion data generation. Moreover, we demonstrate that our finite-time diffusion invariance method through generative models not only maintains generalization performance but also creates robustness in safe data generation. We test our method on a series of safe planning tasks, including maze path generation, legged robot locomotion, and 3D space manipulation, with results showing the advantages of robustness and guarantees over vanilla diffusion models., Comment: 19 pages, website: https://safediffuser.github.io/safediffuser

We address the security of a network of Connected and Automated Vehicles (CAVs) cooperating to navigate through a conflict area. Adversarial attacks such as Sybil attacks can cause safety violations resulting in collisions and traffic jams. In addition, uncooperative (but not necessarily adversarial) CAVs can also induce similar adversarial effects on the traffic network. We propose a decentralized resilient control and coordination scheme that mitigates the effects of adversarial attacks and uncooperative CAVs by utilizing a trust framework. Our trust-aware scheme can guarantee safe collision free coordination and mitigate traffic jams. Simulation results validate the theoretical guarantee of our proposed scheme, and demonstrate that it can effectively mitigate adversarial effects across different traffic scenarios., Comment: Keywords: Resilient control and coordination, Cybersecurity, Safety guaranteed coordination, Connected And Autonomous Vehicles

In this paper, we consider the problem of learning a neural network controller for a system required to satisfy a Signal Temporal Logic (STL) specification. We exploit STL quantitative semantics to define a notion of robust satisfaction. Guaranteeing the correctness of a neural network controller, i.e., ensuring the satisfaction of the specification by the controlled system, is a difficult problem that received a lot of attention recently. We provide a general procedure to construct a set of trainable High Order Control Barrier Functions (HOCBFs) enforcing the satisfaction of formulas in a fragment of STL. We use the BarrierNet, implemented by a differentiable Quadratic Program (dQP) with HOCBF constraints, as the last layer of the neural network controller, to guarantee the satisfaction of the STL formulas. We train the HOCBFs together with other neural network parameters to further improve the robustness of the controller. Simulation results demonstrate that our approach ensures satisfaction and outperforms existing algorithms., Comment: Submitted to CDC 2023

For driving safely and efficiently in highway scenarios, autonomous vehicles (AVs) must be able to predict future behaviors of surrounding object vehicles (OVs), and assess collision risk accurately for reasonable decision-making. Aiming at autonomous driving in highway scenarios, a predictive collision risk assessment method based on trajectory prediction of OVs is proposed in this paper. Firstly, the vehicle trajectory prediction is formulated as a sequence generation task with long short-term memory (LSTM) encoder-decoder framework. Convolutional social pooling (CSP) and graph attention network (GAN) are adopted for extracting local spatial vehicle interactions and distant spatial vehicle interactions, respectively. Then, two basic risk metrics, time-to-collision (TTC) and minimal distance margin (MDM), are calculated between the predicted trajectory of OV and the candidate trajectory of AV. Consequently, a time-continuous risk function is constructed with temporal and spatial risk metrics. Finally, the vehicle trajectory prediction model CSP-GAN-LSTM is evaluated on two public highway datasets. The quantitative results indicate that the proposed CSP-GAN-LSTM model outperforms the existing state-of-the-art (SOTA) methods in terms of position prediction accuracy. Besides, simulation results in typical highway scenarios further validate the feasibility and effectiveness of the proposed predictive collision risk assessment method., Comment: manuscript submitted to IEEE Transactions on Intelligent Transportation Systems

Modern end-to-end learning systems can learn to explicitly infer control from perception. However, it is difficult to guarantee stability and robustness for these systems since they are often exposed to unstructured, high-dimensional, and complex observation spaces (e.g., autonomous driving from a stream of pixel inputs). We propose to leverage control Lyapunov functions (CLFs) to equip end-to-end vision-based policies with stability properties and introduce stability attention in CLFs (att-CLFs) to tackle environmental changes and improve learning flexibility. We also present an uncertainty propagation technique that is tightly integrated into att-CLFs. We demonstrate the effectiveness of att-CLFs via comparison with classical CLFs, model predictive control, and vanilla end-to-end learning in a photo-realistic simulator and on a real full-scale autonomous vehicle., Comment: First two authors contributed equally; L4DC 2023

This paper studies safety guarantees for systems with time-varying control bounds. It has been shown that optimizing quadratic costs subject to state and control constraints can be reduced to a sequence of Quadratic Programs (QPs) using Control Barrier Functions (CBFs). One of the main challenges in this method is that the CBF-based QP could easily become infeasible under tight control bounds, especially when the control bounds are time-varying. The recently proposed adaptive CBFs have addressed such infeasibility issues, but require extensive and non-trivial hyperparameter tuning for the CBF-based QP and may introduce overshooting control near the boundaries of safe sets. To address these issues, we propose a new type of adaptive CBFs called Auxiliary-Variable Adaptive CBFs (AVCBFs). Specifically, we introduce an auxiliary variable that multiplies each CBF itself, and define dynamics for the auxiliary variable to adapt it in constructing the corresponding CBF constraint. In this way, we can improve the feasibility of the CBF-based QP while avoiding extensive parameter tuning with non-overshooting control since the formulation is identical to classical CBF methods. We demonstrate the advantages of using AVCBFs and compare them with existing techniques on an Adaptive Cruise Control (ACC) problem with time-varying control bounds., Comment: 8 pages, 4 figures. arXiv admin note: text overlap with arXiv:2310.00238

It has been shown that optimizing quadratic costs while stabilizing affine control systems to desired (sets of) states subject to state and control constraints can be reduced to a sequence of Quadratic Programs (QPs) by using Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs). In this paper, we employ machine learning techniques to ensure the feasibility of these QPs, which is a challenging problem, especially for high relative degree constraints where High Order CBFs (HOCBFs) are required. To this end, we propose a sampling-based learning approach to learn a new feasibility constraint for CBFs; this constraint is then enforced by another HOCBF added to the QPs. The accuracy of the learned feasibility constraint is recursively improved by a recurrent training algorithm. We demonstrate the advantages of the proposed learning approach to constrained optimal control problems with specific focus on a robot control problem and on autonomous driving in an unknown environment., Comment: 8 pages, to appear in ECC 2023

Jasmonates (JAs) are phytohormones that finely regulate critical biological processes, including plant development and defense. JASMONATE ZIM-DOMAIN (JAZ) proteins are crucial transcriptional regulators that keep JA-responsive genes in a repressed state. In the presence of JA-Ile, JAZ repressors are ubiquitinated and targeted for degradation by the ubiquitin/proteasome system, allowing the activation of downstream transcription factors and, consequently, the induction of JA-responsive genes. A growing body of evidence has shown that JA signaling is crucial in defending against plant viruses and their insect vectors. Here, we describe the interaction of C2 proteins from two tomato-infecting geminiviruses from the genus Begomovirus, tomato yellow leaf curl virus (TYLCV) and tomato yellow curl Sardinia virus (TYLCSaV), with the transcriptional repressor JAZ8 from Arabidopsis thaliana and its closest orthologue in tomato, SlJAZ9. Both JAZ and C2 proteins colocalize in the nucleus, forming discrete nuclear speckles. Overexpression of JAZ8 did not lead to altered responses to TYLCV infection in Arabidopsis; however, knock-down of JAZ8 favors geminiviral infection. Low levels of JAZ8 likely affect the viral infection specifically, since JAZ8-silenced plants neither display obvious developmental phenotypes nor present differences in their interaction with the viral insect vector. In summary, our results show that the geminivirus-encoded C2 interacts with JAZ8 in the nucleus, and suggest that this plant protein exerts an anti-geminiviral effect.

Incorporating both flexible and rigid components in robot designs offers a unique solution to the limitations of traditional rigid robotics by enabling both compliance and strength. This paper explores the challenges and solutions for controlling soft-rigid hybrid robots, particularly addressing the issue of self-contact. Conventional control methods prioritize precise state tracking, inadvertently increasing the system's overall stiffness, which is not always desirable in interactions with the environment or within the robot itself. To address this, we investigate the application of Control Barrier Functions (CBFs) and High Order CBFs to manage self-contact scenarios in serially connected soft-rigid hybrid robots. Through an analysis based on Piecewise Constant Curvature (PCC) kinematics, we establish CBFs within a classical control framework for self-contact dynamics. Our methodology is rigorously evaluated in both simulation environments and physical hardware systems. The findings demonstrate that our proposed control strategy effectively regulates self-contact in soft-rigid hybrid robotic systems, marking a significant advancement in the field of robotics., Comment: 6 pages, 6 figures, submitted to IEEE Robosoft 2024 Conference

As autonomous driving technology matures, end-to-end methodologies have emerged as a leading strategy, promising seamless integration from perception to control via deep learning. However, existing systems grapple with challenges such as unexpected open set environments and the complexity of black-box models. At the same time, the evolution of deep learning introduces larger, multimodal foundational models, offering multi-modal visual and textual understanding. In this paper, we harness these multimodal foundation models to enhance the robustness and adaptability of autonomous driving systems, enabling out-of-distribution, end-to-end, multimodal, and more explainable autonomy. Specifically, we present an approach to apply end-to-end open-set (any environment/scene) autonomous driving that is capable of providing driving decisions from representations queryable by image and text. To do so, we introduce a method to extract nuanced spatial (pixel/patch-aligned) features from transformers to enable the encapsulation of both spatial and semantic features. Our approach (i) demonstrates unparalleled results in diverse tests while achieving significantly greater robustness in out-of-distribution situations, and (ii) allows the incorporation of latent space simulation (via text) for improved training (data augmentation via text) and policy debugging. We encourage the reader to check our explainer video at https://www.youtube.com/watch?v=4n-DJf8vXxo&feature=youtu.be and to view the code and demos on our project webpage at https://drive-anywhere.github.io/., Comment: Project webpage: https://drive-anywhere.github.io Explainer video: https://www.youtube.com/watch?v=4n-DJf8vXxo&feature=youtu.be

Game-theoretic motion planners are a powerful tool for the control of interactive multi-agent robot systems. Indeed, contrary to predict-then-plan paradigms, game-theoretic planners do not ignore the interactive nature of the problem, and simultaneously predict the behaviour of other agents while considering change in one's policy. This, however, comes at the expense of computational complexity, especially as the number of agents considered grows. In fact, planning with more than a handful of agents can quickly become intractable, disqualifying game-theoretic planners as possible candidates for large scale planning. In this paper, we propose a planning algorithm enabling the use of game-theoretic planners in robot systems with a large number of agents. Our planner is based on the reality of locality of information and thus deploys local games with a selected subset of agents in a receding horizon fashion to plan collision avoiding trajectories. We propose five different principled schemes for selecting game participants and compare their collision avoidance performance. We observe that the use of Control Barrier Functions for priority ranking is a potent solution to the player selection problem for motion planning., Comment: to be published in IROS 2023 conference proceedings

Audio coding is an essential module in the real-time communication system. Neural audio codecs can compress audio samples with a low bitrate due to the strong modeling and generative capabilities of deep neural networks. To address the poor high-frequency expression and high computational cost and storage consumption, we proposed an integrated framework that utilizes a neural network to model wide-band components and adopts traditional signal processing to compress high-band components according to psychological hearing knowledge. Inspired by auditory perception theory, a perception-based loss function is designed to improve harmonic modeling. Besides, generative adversarial network (GAN) compression is proposed for the first time for neural audio codecs. Our method is superior to prior advanced neural codecs across subjective and objective metrics and allows real-time inference on desktop and mobile.

This paper studies safe driving interactions between Human-Driven Vehicles (HDVs) and Connected and Automated Vehicles (CAVs) in mixed traffic where the dynamics and control policies of HDVs are unknown and hard to predict. In order to address this challenge, we employ event-triggered Control Barrier Functions (CBFs) to estimate the HDV model online, construct data-driven and state-feedback safety controllers, and transform constrained optimal control problems for CAVs into a sequence of event-triggered quadratic programs. We show that we can ensure collision-free between HDVs and CAVs and demonstrate the robustness and flexibility of our framework on different types of human drivers in lane-changing scenarios while guaranteeing safety with human-in-the-loop interactions.

This paper studies safety and feasibility guarantees for systems with tight control bounds. It has been shown that stabilizing an affine control system while optimizing a quadratic cost and satisfying state and control constraints can be mapped to a sequence of Quadratic Programs (QPs) using Control Barrier Functions (CBF) and Control Lyapunov Functions (CLF). One of the main challenges in this method is that the QP could easily become infeasible under safety constraints of high relative degree, especially under tight control bounds. Recent work focused on deriving sufficient conditions for guaranteeing feasibility. The existing results are case-dependent. In this paper, we consider the general case. We define a feasibility constraint and propose a new type of CBF to enforce it. Our method guarantees the feasibility of the above mentioned QPs, while satisfying safety requirements. We demonstrate the proposed method on an Adaptive Cruise Control (ACC) problem for a heterogeneous platoon with tight control bounds, and compare our method to existing CBF-CLF approaches. The results show that our proposed approach can generate gradually transitioned control (without abrupt changes) with guaranteed feasibility and safety., Comment: 8 pages, 2 figures. arXiv admin note: text overlap with arXiv:2304.00372

This paper addresses the problem of safety-critical control for non-affine control systems. It has been shown that optimizing quadratic costs subject to state and control constraints can be sub-optimally reduced to a sequence of quadratic programs (QPs) by using Control Barrier Functions (CBFs). Our recently proposed High Order CBFs (HOCBFs) can accommodate constraints of arbitrary relative degree. The main challenges in this approach are that it requires affine control dynamics and the solution of the CBF-based QP is sub-optimal since it is solved point-wise. To address these challenges, we incorporate higher-order CBFs into neural ordinary differential equation-based learning models as differentiable CBFs to guarantee safety for non-affine control systems. The differentiable CBFs are trainable in terms of their parameters, and thus, they can address the conservativeness of CBFs such that the system state will not stay unnecessarily far away from safe set boundaries. Moreover, the imitation learning model is capable of learning complex and optimal control policies that are usually intractable online. We illustrate the effectiveness of the proposed framework on LiDAR-based autonomous driving and compare it with existing methods., Comment: 8 pages, accepted in IEEE CDC 2023

The pursuit of a high-capacity anode material has been urgently required for commercializing sodium-ion batteries with a high energy density and an improved working safety. In the absence of thermodynamically stable sodium intercalated compounds with graphite, constructing nanostructures with expanded interlayer distances is still the mainstream option for developing high-performance carbonaceous anodes. In this regard, a surface-functionalized and pore-forming strategy through a facile CO2 thermal etching route was rationally adopted to engineer negligible oxygenated functionalities on commercial carbon for boosting the sodium storage process. Benefitted from the abundant ionic/electronic pathways and more active reaction sites in the microporous structure with noticeable pseudocapacitive behaviors, the functionalized porous carbon could achieve a highly reversible capacity of 505 mA h g-1 at 50 mA g-1, an excellent rate performance of 181 mA h g-1 at 16,000 mA g-1, and an exceptional rate cycle stability of 176 mA h g-1 at 3200 mA g-1 over 1000 cycles. These outstanding electrochemical properties should be ascribed to a synergistic mechanism, fully utilizing the graphitic and amorphous structures for synchronous intercalations of sodium ions and solvated sodium ion compounds, respectively. Additionally, the controllable generation and evolution of a robust but thin solid electrolyte interphase film with the emergence of obvious capacitive reactions on the defective surface, favoring the rapid migration of sodium ions and solvated species, also contribute to a remarkable electrochemical performance of this porous carbon black.

PURPOSE: Major adverse cardiac events (MACEs) because of immune checkpoint inhibitors (ICIs) are infrequent immune-related adverse events (irAEs) that comprise a spectrum of cardiac toxicities with variable manifestations. ICI-related MACEs can lead to significant morbidity and mortality, hence the need to better define presentations of MACEs and their association with noncardiac irAEs in ICI-treated patients. METHODS: We conducted a retrospective pooled analysis of MACE captured in the serious adverse events reporting database of the National Cancer Institute-Cancer Therapy Evaluation Program for National Cancer Institute-sponsored investigational clinical trials between June 2015 and December 2019. Patients were eligible if they had been treated with anti-programmed cell death protein-1 (anti-PD-1)/programmed cell death-ligand 1 (anti-PD-L1) alone or with additional anticancer therapies. RESULTS: A total of 6,925 participants received anti-PD-(L)1-based therapies; 48% (n = 3,354) were treated with single-agent anti-PD-(L)1 therapy. Of 6,925 patients, 0.6% (n = 40) qualified as ICI-related MACE, with 77.5% (n = 31 of 40) being ≥ grade 3. Myocarditis accounted for 45% (n = 18 of 40) of total ICI-MACEs. Concurrent multisystem involvement with other noncardiac irAEs was seen in 65% (n = 26 of 40). Most patients with myocarditis (83%, n = 15 of 18) had one or more noncardiac irAEs associated. Incidence of MACE was higher with anti-PD-(L)1 + targeted therapies compared with anti-PD-(L)1 + anti-cytotoxic T-cell lymphocyte-4 combinations (2.1% v 0.9%, P = .08). There was a higher incidence of myocarditis with anti-PD-(L)1-based combination therapies versus single-agent anti-PD-(L)1 therapies (0.36%, n = 13 of 3,571 v 0.15%, n = 5 of 3,354, P = .08). Deaths related to myocarditis were identified in 22.5% (n = 4 of 18). All four patients who died had concurrent myositis. CONCLUSION: Increasing patient and prescriber awareness in understanding patterns of ICI-MACE and associ

A compact protein with a size of <1,000 amino acids, the CRISPR-associated protein CasX is a fundamentally distinct RNA-guided nuclease when compared to Cas9 and Cas12a. Although it can induce RNA-guided genome editing in mammalian cells, the activity of CasX is less robust than that of the widely used S. pyogenes Cas9. Here, we show that structural features of two CasX homologs and their guide RNAs affect the R-loop complex assembly and DNA cleavage activity. Cryo-EM-based structural engineering of either the CasX protein or the guide RNA produced two new CasX genome editors (DpbCasX-R3-v2 and PlmCasX-R1-v2) with significantly improved DNA manipulation efficacy. These results advance both the mechanistic understanding of CasX and its application as a genome-editing tool.

A simple and sensitive fluoroimmunoassay (FIA) based on a heavy-chain antibody (VHH) for rapid detection of fenitrothion was developed. A VHH library was constructed from an immunized alpaca, and one clone recognizing fenitrothion (namely, VHHjd8) was achieved after careful biopanning. It was biotinylated by fusing with the Avi tag and biotin ligase to obtain a fusion protein (VHHjd8-BT), showing both binding capacity to fenitrothion and the streptavidin poly-horseradish peroxidase conjugate (SA-polyHRP). Based on a competitive assay format, the absorbance spectrum of oxidized 3,3',5,5'-tetramethylbenzidine generated by SA-polyHRP overlapped the emission spectrum of carbon dots, which resulted in quenching of signals due to the inner-filter effect. The developed FIA showed an IC50 value of 1.4 ng/mL and a limit of detection of 0.03 ng/mL, which exhibited 15-fold improvement compared with conventional enzyme-linked immunosorbent assay. The recovery test of FIA was validated by standard GC-MS/MS, and the results showed good consistency, indicating that the assay is an ideal tool for rapid screening of fenitrothion in bulk food samples.

Chan Wen,1,&ast; Ling Sun,1,&ast; Meng-Chuan Zhao,2 Su-Xia Duan,2 Le Wang,2 Xiao-Wei Cui3 1Department of Medical, Children’s Hospital of Hebei Province, Shijiazhuang, 050031, People’s Republic of China; 2Department of Laboratory Medicine, Children’s Hospital of Hebei Province, Shijiazhuang, 050031, People’s Republic of China; 3Children’s Hospital of Hebei Province, Shijiazhuang, 050031, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xiao-Wei Cui, Children’s Hospital of Hebei Province, No. 133 of Jianhua Street, Yuhua District, Shijiazhuang, 050031, People’s Republic of China, Tel +86 18503292016, Fax +86 031185911109, Email cuixiaoweicxw@126.comObjective: This study aims to analyze the clinical characteristics of hospitalized children infected with HCoV-NL63, OC43, 229E, HKU1 and provide the basis for disease diagnosis and treatment.Methods: A retrospective analysis was conducted on clinical manifestations, imaging data, and treatment measures of hospitalized children with positive HCoV-NL63, OC43, 229E, HKU1 from 2015 to 2020.Results: A total of 1062 children aged 33 days to 12 years were analyzed, including 879 (82.77%) between 33 days to three years. Lower respiratory tract infections were the most common in 698 children positive for HCoVs (65.72%). The incidences of runny nose, cough, pharyngeal hyperemia, and fine crackles in the mild case group (n = 894, 84.18%) were significantly higher than in the severe case group, and the differences were statistically significant (P < 0.01). The incidences of gasp, stridor, and convulsions, the proportion of underlying diseases, such as congenital heart disease, laryngomalacia, and general developmental disorders, anemia, and abnormal liver function, and mixed infections in the severe group (n = 168, 15.82%) were significantly higher than in the mild group, and the differences were statistically significant (P < 0.01 or P < 0.05). Imaging m

The advancement of robots, particularly those functioning in complex human-centric environments, relies on control solutions that are driven by machine learning. Understanding how learning-based controllers make decisions is crucial since robots are often safety-critical systems. This urges a formal and quantitative understanding of the explanatory factors in the interpretability of robot learning. In this paper, we aim to study interpretability of compact neural policies through the lens of disentangled representation. We leverage decision trees to obtain factors of variation [1] for disentanglement in robot learning; these encapsulate skills, behaviors, or strategies toward solving tasks. To assess how well networks uncover the underlying task dynamics, we introduce interpretability metrics that measure disentanglement of learned neural dynamics from a concentration of decisions, mutual information and modularity perspective. We showcase the effectiveness of the connection between interpretability and disentanglement consistently across extensive experimental analysis.

Recently, UAVs endowed with high mobility, low cost, and remote control have promoted the development of UAV-assisted real-time video/image acquisition applications, which have a high demand for both transmission rate and image resolution. However, in conventional vertical photography model, the UAV should fly to the top of ground targets (GTs) to capture images, thus enlarge the transmission delay. In this paper, we propose an oblique photography model, which allows the UAV to capture images of GTs from a far distance while still satisfying the predetermined resolution requirement. Based on the proposed oblique photography model, we further study the UAV placement problem in the cellular-connected UAV-assisted image acquisition system, which aims at minimizing the data transmission delay under the condition of satisfying the predetermined image resolution requirement. Firstly, the proposed scheme is first formulated as an intractable non-convex optimization problem. Then, the original problem is simplified to obtain a tractable suboptimal solution with the help of the block coordinate descent and the successive convex approximation techniques. Finally, the numerical results are presented to show the effectiveness of the proposed scheme. The numerical results have shown that the proposed scheme can largely save the transmission time as compared to the conventional vertical photography model., Comment: submitted to ieee for possible publication. arXiv admin note: text overlap with arXiv:2006.14438 by other authors