Differential Flatness of Lifting-Wing Quadcopters Subject to Drag and Lift for Accurate Tracking

In this paper, we propose an effective unified control law for accurately tracking agile trajectories for lifting-wing quadcopters with different installation angles, which have the capability of vertical takeoff and landing (VTOL) as well as high-speed cruise flight. First, we derive a differential flatness transform for the lifting-wing dynamics with a nonlinear model under coordinated turn condition. To increase the tracking performance on agile trajectories, the proposed controller incorporates the state and input variables calculated from differential flatness as feedforward. In particular, the jerk, the 3-order derivative of the trajectory, is converted into angular velocity as a feedforward item, which significantly improves the system bandwidth. At the same time, feedback and feedforward outputs are combined to deal with external disturbances and model mismatch. The control algorithm has been thoroughly evaluated in the outdoor flight tests, which show that it can achieve accurate trajectory tracking

Better Safe than Sorry: Pre-training CLIP against Targeted Data Poisoning and Backdoor Attacks

Contrastive Language-Image Pre-training (CLIP) on large image-caption datasets has achieved remarkable success in zero-shot classification and enabled transferability to new domains. However, CLIP is extremely more vulnerable to targeted data poisoning and backdoor attacks, compared to supervised learning. Perhaps surprisingly, poisoning 0.0001% of CLIP pre-training data is enough to make targeted data poisoning attacks successful. This is four orders of magnitude smaller than what is required to poison supervised models. Despite this vulnerability, existing methods are very limited in defending CLIP models during pre-training. In this work, we propose a strong defense, SAFECLIP, to safely pre-train CLIP against targeted data poisoning and backdoor attacks. SAFECLIP warms up the model by applying unimodal contrastive learning (CL) on image and text modalities separately. Then, it carefully divides the data into safe and risky subsets. SAFECLIP trains on the risky data by applying unimodal CL to image and text modalities separately, and trains on the safe data using the CLIP loss. By gradually increasing the size of the safe subset during the training, SAFECLIP effectively breaks targeted data poisoning and backdoor attacks without harming the CLIP performance. Our extensive experiments show that SAFECLIP decrease the attack success rate of targeted data poisoning attacks from 93.75% to 0% and that of the backdoor attacks from 100% to 0%, without harming the CLIP performance on various datasets

Do Multi-hop Readers Dream of Reasoning Chains?

General Question Answering (QA) systems over texts require the multi-hop reasoning capability, i.e. the ability to reason with information collected from multiple passages to derive the answer. In this paper we conduct a systematic analysis to assess such an ability of various existing models proposed for multi-hop QA tasks. Specifically, our analysis investigates that whether providing the full reasoning chain of multiple passages, instead of just one final passage where the answer appears, could improve the performance of the existing QA models. Surprisingly, when using the additional evidence passages, the improvements of all the existing multi-hop reading approaches are rather limited, with the highest error reduction of 5.8% on F1 (corresponding to 1.3% absolute improvement) from the BERT model. To better understand whether the reasoning chains could indeed help find correct answers, we further develop a co-matching-based method that leads to 13.1% error reduction with passage chains when applied to two of our base readers (including BERT). Our results demonstrate the existence of the potential improvement using explicit multi-hop reasoning and the necessity to develop models with better reasoning abilities.Comment: Accepted by MRQA Workshop 201

GANHead: Towards Generative Animatable Neural Head Avatars

To bring digital avatars into people's lives, it is highly demanded to efficiently generate complete, realistic, and animatable head avatars. This task is challenging, and it is difficult for existing methods to satisfy all the requirements at once. To achieve these goals, we propose GANHead (Generative Animatable Neural Head Avatar), a novel generative head model that takes advantages of both the fine-grained control over the explicit expression parameters and the realistic rendering results of implicit representations. Specifically, GANHead represents coarse geometry, fine-gained details and texture via three networks in canonical space to obtain the ability to generate complete and realistic head avatars. To achieve flexible animation, we define the deformation filed by standard linear blend skinning (LBS), with the learned continuous pose and expression bases and LBS weights. This allows the avatars to be directly animated by FLAME parameters and generalize well to unseen poses and expressions. Compared to state-of-the-art (SOTA) methods, GANHead achieves superior performance on head avatar generation and raw scan fitting.Comment: Camera-ready for CVPR 2023. Project page: https://wsj-sjtu.github.io/GANHead

Histamine induced high mobility group box-1 release from vascular endothelial cells through H-1 receptor

BackgroundSystemic allergic reaction is characterized by vasodilation and vascular leakage, which causes a rapid, precipitous and sustained decrease in arterial blood pressure with a concomitant decrease of cardiac output. Histamine is a major mediator released by mast cells in allergic inflammation and response. It causes a cascade of inflammation and strongly increases vascular permeability within minutes through its four G-protein-coupled receptors (GPCRs) on endothelial cells. High mobility group box-1 (HMGB1), a nonhistone chromatin-binding nuclear protein, can be actively secreted into the extracellular space by endothelial cells. HMGB1 has been reported to exert pro-inflammatory effects on endothelial cells and to increase vascular endothelial permeability. However, the relationship between histamine and HMGB1-mediated signaling in vascular endothelial cells and the role of HMGB1 in anaphylactic-induced hypotension have never been studied. Methods and resultsEA.hy 926 cells were treated with different concentrations of histamine for the indicated periods. The results showed that histamine induced HMGB1 translocation and release from the endothelial cells in a concentration- and time-dependent manner. These effects of histamine were concentration-dependently inhibited by d-chlorpheniramine, a specific H-1 receptor antagonist, but not by H-2 or H-3/4 receptor antagonists. Moreover, an H-1-specific agonist, 2-pyridylethylamine, mimicked the effects of histamine, whereas an H-2-receptor agonist, 4-methylhistamine, did not. Adrenaline and noradrenaline, which are commonly used in the clinical treatment of anaphylactic shock, also inhibited the histamine-induced HMGB1 translocation in endothelial cells. We therefore established a rat model of allergic shock by i.v. injection of compound 48/80, a potent histamine-releasing agent. The plasma HMGB1 levels in compound 48/80-injected rats were higher than those in controls. Moreover, the treatment with anti-HMGB1 antibody successfully facilitated the recovery from compound 48/80-induced hypotension. ConclusionHistamine induces HMGB1 release from vascular endothelial cells solely through H-1 receptor stimulation. Anti-HMGB1 therapy may provide a novel treatment for life-threatening systemic anaphylaxis

The Multivesicular Body and Autophagosome Pathways in Plants

In eukaryotic cells, the endomembrane system consists of multiple membrane-bound organelles, which play essential roles in the precise transportation of various cargo proteins. In plant cells, vacuoles are regarded as the terminus of catabolic pathways whereas the selection and transport of vacuolar cargoes are mainly mediated by two types of organelles, multivesicular bodies (MVBs) also termed prevacuolar compartments (PVCs) and autophagosomes. MVBs are single-membrane bound organelles with intraluminal vesicles and mediate the transport between the trans-Golgi network (TGN) and vacuoles, while autophagosomes are double-membrane bound organelles, which mediate cargo delivery to the vacuole for degradation and recycling during autophagy. Great progress has been achieved recently in identification and characterization of the conserved and plant-unique regulators involved in the MVB and autophagosome pathways. In this review, we present an update on the current knowledge of these key regulators and pay special attention to their conserved protein domains. In addition, we discuss the possible interplay between the MVB and autophagosome pathways in regulating vacuolar degradation in plants

Brasilianoids A–F, New Meroterpenoids From the Sponge-Associated Fungus Penicillium brasilianum

3,5-Dimethylorsellinic acid (DMOA) derived meroterpenoids comprise an unique class of natural products with diverse scaffolds and with a broad spectrum of bioactivities. Bioinformatics analysis of the gene clusters in association with the qRT-PCR detection of the amplification of two key genes led to speculate that the sponge associated fungus Penicillium brasilianum WZXY-m122-9 is a potential producer of meroterpenoids. Chromatographic separation of the EtOAc extract of this fungal strain on a large-scale fermentation resulted in the isolation of six new DMOA-related meroterpenoids with trivial names of brasilianoids A–F (1-6), together with preaustinoid D and preaustinoid A2. The structures were determined by extensive analyses of spectroscopic data, including the X-ray diffraction and the ECD data for configurational assignment. Brasilianoids A and F showed an unprecedented skeleton with a γ-lactone in ring A, while brasilianoids B–C featured a 7/6/6/5/5 pentacyclic ring system finding in nature for the first time. The biosynthetic relationship among the isolated compounds was postulated. Compound 1 significantly stimulated the expression of filaggrin and caspase-14 in HaCaT cells in dose-dependent manner, while compounds 2 and 3 showed moderate inhibition against NO production in LPS-induced RAW 264.7 macrophages

Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesis

We tackle the human motion imitation, appearance transfer, and novel view synthesis within a unified framework, which means that the model once being trained can be used to handle all these tasks. The existing task-specific methods mainly use 2D keypoints (pose) to estimate the human body structure. However, they only expresses the position information with no abilities to characterize the personalized shape of the individual person and model the limbs rotations. In this paper, we propose to use a 3D body mesh recovery module to disentangle the pose and shape, which can not only model the joint location and rotation but also characterize the personalized body shape. To preserve the source information, such as texture, style, color, and face identity, we propose a Liquid Warping GAN with Liquid Warping Block (LWB) that propagates the source information in both image and feature spaces, and synthesizes an image with respect to the reference. Specifically, the source features are extracted by a denoising convolutional auto-encoder for characterizing the source identity well. Furthermore, our proposed method is able to support a more flexible warping from multiple sources. In addition, we build a new dataset, namely Impersonator (iPER) dataset, for the evaluation of human motion imitation, appearance transfer, and novel view synthesis. Extensive experiments demonstrate the effectiveness of our method in several aspects, such as robustness in occlusion case and preserving face identity, shape consistency and clothes details. All codes and datasets are available on https://svip-lab.github.io/project/impersonator.htmlComment: accepted by ICCV201

Local Microstructure Characterization of Rare Earth-Doped PMMA with Low-Ion Content by Fluorescence EXAFS

ABSTRACT: Fluorescence-extended X-ray absorption fine structure (EXAFS), and emission spectrum and excitation spectrum (ESES) were used to characterize the local structure of rare earth-doped poly(methyl methacrylate)s (RePMMAs) with ion concentration of 600 -1000 ppm