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Dynamic Range (DR) is a pivotal characteristic of imaging systems. Current frame-based cameras struggle to achieve high dynamic range imaging due to the conflict between globally uniform exposure and spatially variant scene illumination. In this paper, we propose AsynHDR, a Pixel-Asynchronous HDR imaging system, based on key insights into the challenges in HDR imaging and the unique event-generating mechanism of Dynamic Vision Sensors (DVS). Our proposed AsynHDR system integrates the DVS with a set of LCD panels. The LCD panels modulate the irradiance incident upon the DVS by altering their transparency, thereby triggering the pixel-independent event streams. The HDR image is subsequently decoded from the event streams through our temporal-weighted algorithm. Experiments under standard test platform and several challenging scenes have verified the feasibility of the system in HDR imaging task.

Ego-to-exo video generation refers to generating the corresponding exocentric video according to the egocentric video, providing valuable applications in AR/VR and embodied AI. Benefiting from advancements in diffusion model techniques, notable progress has been achieved in video generation. However, existing methods build upon the spatiotemporal consistency assumptions between adjacent frames, which cannot be satisfied in the ego-to-exo scenarios due to drastic changes in views. To this end, this paper proposes an Intention-Driven Ego-to-exo video generation framework (IDE) that leverages action intention consisting of human movement and action description as view-independent representation to guide video generation, preserving the consistency of content and motion. Specifically, the egocentric head trajectory is first estimated through multi-view stereo matching. Then, cross-view feature perception module is introduced to establish correspondences between exo- and ego- views, guiding the trajectory transformation module to infer human full-body movement from the head trajectory. Meanwhile, we present an action description unit that maps the action semantics into the feature space consistent with the exocentric image. Finally, the inferred human movement and high-level action descriptions jointly guide the generation of exocentric motion and interaction content (i.e., corresponding optical flow and occlusion maps) in the backward process of the diffusion model, ultimately warping them into the corresponding exocentric video. We conduct extensive experiments on the relevant dataset with diverse exo-ego video pairs, and our IDE outperforms state-of-the-art models in both subjective and objective assessments, demonstrating its efficacy in ego-to-exo video generation.

Unconventional 1T′-phase transition metal dichalcogenides (TMDs) have aroused tremendous research interest due to their unique phase-dependent physicochemical properties and applications. However, due to the metastable nature of 1T′-TMDs, the controlled synthesis of 1T′-TMD monolayers (MLs) with high phase purity and stability still remains a challenge. Here we report that 4H-Au nanowires (NWs), when used as templates, can induce the quasi-epitaxial growth of high-phase-purity and stable 1T′-TMD MLs, including WS2, WSe2, MoS2 and MoSe2, via a facile and rapid wet-chemical method. The as-synthesized 4H-Au@1T′-TMD core–shell NWs can be used for ultrasensitive surface-enhanced Raman scattering (SERS) detection. For instance, the 4H-Au@1T′-WS2 NWs have achieved attomole-level SERS detections of Rhodamine 6G and a variety of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike proteins. This work provides insights into the preparation of high-phase-purity and stable 1T′-TMD MLs on metal substrates or templates, showing great potential in various promising applications. © The Author(s), under exclusive licence to Springer Nature Limited 2024.

In the current environment, psychological issues are prevalent and widespread, with social media serving as a key outlet for individuals to share their feelings. This results in the generation of vast quantities of data daily, where negative emotions have the potential to precipitate crisis situations. There is a recognized need for models capable of efficient analysis. While pre-trained language models have demonstrated their effectiveness broadly, there's a noticeable gap in pre-trained models tailored for specialized domains like psychology. To address this, we have collected a huge dataset from Chinese social media platforms and enriched it with publicly available datasets to create a comprehensive database encompassing 3.36 million text entries. To enhance the model's applicability to psychological text analysis, we integrated psychological lexicons into the pre-training masking mechanism. Building on an existing Chinese language model, we performed adaptive training to develop a model specialized for the psychological domain. We assessed our model's effectiveness across four public benchmarks, where it not only surpassed the performance of standard pre-trained models but also showed a inclination for making psychologically relevant predictions. Due to concerns regarding data privacy, the dataset will not be made publicly available. However, we have made the pre-trained models and codes publicly accessible to the community via: https://github.com/zwzzzQAQ/Chinese-MentalBERT.

Understanding egocentric human-object interaction (HOI) is a fundamental aspect of human-centric perception, facilitating applications like AR/VR and embodied AI. For the egocentric HOI, in addition to perceiving semantics e.g., ''what'' interaction is occurring, capturing ''where'' the interaction specifically manifests in 3D space is also crucial, which links the perception and operation. Existing methods primarily leverage observations of HOI to capture interaction regions from an exocentric view. However, incomplete observations of interacting parties in the egocentric view introduce ambiguity between visual observations and interaction contents, impairing their efficacy. From the egocentric view, humans integrate the visual cortex, cerebellum, and brain to internalize their intentions and interaction concepts of objects, allowing for the pre-formulation of interactions and making behaviors even when interaction regions are out of sight. In light of this, we propose harmonizing the visual appearance, head motion, and 3D object to excavate the object interaction concept and subject intention, jointly inferring 3D human contact and object affordance from egocentric videos. To achieve this, we present EgoChoir, which links object structures with interaction contexts inherent in appearance and head motion to reveal object affordance, further utilizing it to model human contact. Additionally, a gradient modulation is employed to adopt appropriate clues for capturing interaction regions across various egocentric scenarios. Moreover, 3D contact and affordance are annotated for egocentric videos collected from Ego-Exo4D and GIMO to support the task. Extensive experiments on them demonstrate the effectiveness and superiority of EgoChoir. Code and data will be open., Comment: 23 pages,10 figures

Video virtual try-on aims to transfer a clothing item onto the video of a target person. Directly applying the technique of image-based try-on to the video domain in a frame-wise manner will cause temporal-inconsistent outcomes while previous video-based try-on solutions can only generate low visual quality and blurring results. In this work, we present ViViD, a novel framework employing powerful diffusion models to tackle the task of video virtual try-on. Specifically, we design the Garment Encoder to extract fine-grained clothing semantic features, guiding the model to capture garment details and inject them into the target video through the proposed attention feature fusion mechanism. To ensure spatial-temporal consistency, we introduce a lightweight Pose Encoder to encode pose signals, enabling the model to learn the interactions between clothing and human posture and insert hierarchical Temporal Modules into the text-to-image stable diffusion model for more coherent and lifelike video synthesis. Furthermore, we collect a new dataset, which is the largest, with the most diverse types of garments and the highest resolution for the task of video virtual try-on to date. Extensive experiments demonstrate that our approach is able to yield satisfactory video try-on results. The dataset, codes, and weights will be publicly available. Project page: https://becauseimbatman0.github.io/ViViD.

Event-based eye tracking has shown great promise with the high temporal resolution and low redundancy provided by the event camera. However, the diversity and abruptness of eye movement patterns, including blinking, fixating, saccades, and smooth pursuit, pose significant challenges for eye localization. To achieve a stable event-based eye-tracking system, this paper proposes a bidirectional long-term sequence modeling and time-varying state selection mechanism to fully utilize contextual temporal information in response to the variability of eye movements. Specifically, the MambaPupil network is proposed, which consists of the multi-layer convolutional encoder to extract features from the event representations, a bidirectional Gated Recurrent Unit (GRU), and a Linear Time-Varying State Space Module (LTV-SSM), to selectively capture contextual correlation from the forward and backward temporal relationship. Furthermore, the Bina-rep is utilized as a compact event representation, and the tailor-made data augmentation, called as Event-Cutout, is proposed to enhance the model's robustness by applying spatial random masking to the event image. The evaluation on the ThreeET-plus benchmark shows the superior performance of the MambaPupil, which secured the 1st place in CVPR'2024 AIS Event-based Eye Tracking challenge., Comment: Accepted by CVPR 2024 Workshop (AIS: Vision, Graphics and AI for Streaming), top solution of challenge Event-based Eye Tracking, see https://www.kaggle.com/competitions/event-based-eye-tracking-ais2024

Cognitive Behavioral Therapy (CBT) is an effective technique for addressing the irrational thoughts stemming from mental illnesses, but it necessitates precise identification of cognitive pathways to be successfully implemented in patient care. In current society, individuals frequently express negative emotions on social media on specific topics, often exhibiting cognitive distortions, including suicidal behaviors in extreme cases. Yet, there is a notable absence of methodologies for analyzing cognitive pathways that could aid psychotherapists in conducting effective interventions online. In this study, we gathered data from social media and established the task of extracting cognitive pathways, annotating the data based on a cognitive theoretical framework. We initially categorized the task of extracting cognitive pathways as a hierarchical text classification with four main categories and nineteen subcategories. Following this, we structured a text summarization task to help psychotherapists quickly grasp the essential information. Our experiments evaluate the performance of deep learning and large language models (LLMs) on these tasks. The results demonstrate that our deep learning method achieved a micro-F1 score of 62.34% in the hierarchical text classification task. Meanwhile, in the text summarization task, GPT-4 attained a Rouge-1 score of 54.92 and a Rouge-2 score of 30.86, surpassing the experimental deep learning model's performance. However, it may suffer from an issue of hallucination. We have made all models and codes publicly available to support further research in this field.

This survey reviews the AIS 2024 Event-Based Eye Tracking (EET) Challenge. The task of the challenge focuses on processing eye movement recorded with event cameras and predicting the pupil center of the eye. The challenge emphasizes efficient eye tracking with event cameras to achieve good task accuracy and efficiency trade-off. During the challenge period, 38 participants registered for the Kaggle competition, and 8 teams submitted a challenge factsheet. The novel and diverse methods from the submitted factsheets are reviewed and analyzed in this survey to advance future event-based eye tracking research., Comment: Qinyu Chen is the corresponding author

This paper provides a comprehensive review of the NTIRE 2024 challenge, focusing on efficient single-image super-resolution (ESR) solutions and their outcomes. The task of this challenge is to super-resolve an input image with a magnification factor of x4 based on pairs of low and corresponding high-resolution images. The primary objective is to develop networks that optimize various aspects such as runtime, parameters, and FLOPs, while still maintaining a peak signal-to-noise ratio (PSNR) of approximately 26.90 dB on the DIV2K_LSDIR_valid dataset and 26.99 dB on the DIV2K_LSDIR_test dataset. In addition, this challenge has 4 tracks including the main track (overall performance), sub-track 1 (runtime), sub-track 2 (FLOPs), and sub-track 3 (parameters). In the main track, all three metrics (ie runtime, FLOPs, and parameter count) were considered. The ranking of the main track is calculated based on a weighted sum-up of the scores of all other sub-tracks. In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking. In sub-track 2, the number of FLOPs was considered. The score calculated based on the corresponding FLOPs was used to determine the ranking. In sub-track 3, the number of parameters was considered. The score calculated based on the corresponding parameters was used to determine the ranking. RLFN is set as the baseline for efficiency measurement. The challenge had 262 registered participants, and 34 teams made valid submissions. They gauge the state-of-the-art in efficient single-image super-resolution. To facilitate the reproducibility of the challenge and enable other researchers to build upon these findings, the code and the pre-trained model of validated solutions are made publicly available at https://github.com/Amazingren/NTIRE2024_ESR/., Comment: The report paper of NTIRE2024 Efficient Super-resolution, accepted by CVPRW2024

Interaction intention anticipation aims to jointly predict future hand trajectories and interaction hotspots. Existing research often treated trajectory forecasting and interaction hotspots prediction as separate tasks or solely considered the impact of trajectories on interaction hotspots, which led to the accumulation of prediction errors over time. However, a deeper inherent connection exists between hand trajectories and interaction hotspots, which allows for continuous mutual correction between them. Building upon this relationship, a novel Bidirectional prOgressive Transformer (BOT), which introduces a Bidirectional Progressive mechanism into the anticipation of interaction intention is established. Initially, BOT maximizes the utilization of spatial information from the last observation frame through the Spatial-Temporal Reconstruction Module, mitigating conflicts arising from changes of view in first-person videos. Subsequently, based on two independent prediction branches, a Bidirectional Progressive Enhancement Module is introduced to mutually improve the prediction of hand trajectories and interaction hotspots over time to minimize error accumulation. Finally, acknowledging the intrinsic randomness in human natural behavior, we employ a Trajectory Stochastic Unit and a C-VAE to introduce appropriate uncertainty to trajectories and interaction hotspots, respectively. Our method achieves state-of-the-art results on three benchmark datasets Epic-Kitchens-100, EGO4D, and EGTEA Gaze+, demonstrating superior in complex scenarios.

In the social media, users frequently express personal emotions, a subset of which may indicate potential suicidal tendencies. The implicit and varied forms of expression in internet language complicate accurate and rapid identification of suicidal intent on social media, thus creating challenges for timely intervention efforts. The development of deep learning models for suicide risk detection is a promising solution, but there is a notable lack of relevant datasets, especially in the Chinese context. To address this gap, this study presents a Chinese social media dataset designed for fine-grained suicide risk classification, focusing on indicators such as expressions of suicide intent, methods of suicide, and urgency of timing. Seven pre-trained models were evaluated in two tasks: high and low suicide risk, and fine-grained suicide risk classification on a level of 0 to 10. In our experiments, deep learning models show good performance in distinguishing between high and low suicide risk, with the best model achieving an F1 score of 88.39%. However, the results for fine-grained suicide risk classification were still unsatisfactory, with an weighted F1 score of 50.89%. To address the issues of data imbalance and limited dataset size, we investigated both traditional and advanced, large language model based data augmentation techniques, demonstrating that data augmentation can enhance model performance by up to 4.65% points in F1-score. Notably, the Chinese MentalBERT model, which was pre-trained on psychological domain data, shows superior performance in both tasks. This study provides valuable insights for automatic identification of suicidal individuals, facilitating timely psychological intervention on social media platforms. The source code and data are publicly available.

Weakly supervised object localization (WSOL) is a challenging task aiming to localize objects with only image-level supervision. Recent works apply visual transformer to WSOL and achieve significant success by exploiting the long-range feature dependency in self-attention mechanism. However, existing transformer-based methods synthesize the classification feature maps as the localization map, which leads to optimization conflicts between classification and localization tasks. To address this problem, we propose to learn a task-specific spatial-aware token (SAT) to condition localization in a weakly supervised manner. Specifically, a spatial token is first introduced in the input space to aggregate representations for localization task. Then a spatial aware attention module is constructed, which allows spatial token to generate foreground probabilities of different patches by querying and to extract localization knowledge from the classification task. Besides, for the problem of sparse and unbalanced pixel-level supervision obtained from the image-level label, two spatial constraints, including batch area loss and normalization loss, are designed to compensate and enhance this supervision. Experiments show that the proposed SAT achieves state-of-the-art performance on both CUB-200 and ImageNet, with 98.45% and 73.13% GT-known Loc, respectively. Even under the extreme setting of using only 1 image per class from ImageNet for training, SAT already exceeds the SOTA method by 2.1% GT-known Loc. Code and models are available at https://github.com/wpy1999/SAT., Comment: Accepted by ICCV 2023. Code:https://github.com/wpy1999/SAT

Grounding 3D object affordance seeks to locate objects' ''action possibilities'' regions in the 3D space, which serves as a link between perception and operation for embodied agents. Existing studies primarily focus on connecting visual affordances with geometry structures, e.g. relying on annotations to declare interactive regions of interest on the object and establishing a mapping between the regions and affordances. However, the essence of learning object affordance is to understand how to use it, and the manner that detaches interactions is limited in generalization. Normally, humans possess the ability to perceive object affordances in the physical world through demonstration images or videos. Motivated by this, we introduce a novel task setting: grounding 3D object affordance from 2D interactions in images, which faces the challenge of anticipating affordance through interactions of different sources. To address this problem, we devise a novel Interaction-driven 3D Affordance Grounding Network (IAG), which aligns the region feature of objects from different sources and models the interactive contexts for 3D object affordance grounding. Besides, we collect a Point-Image Affordance Dataset (PIAD) to support the proposed task. Comprehensive experiments on PIAD demonstrate the reliability of the proposed task and the superiority of our method. The project is available at https://github.com/yyvhang/IAGNet., Comment: ICCV2023, camera-ready version

Semantically coherent out-of-distribution (SCOOD) detection aims to discern outliers from the intended data distribution with access to unlabeled extra set. The coexistence of in-distribution and out-of-distribution samples will exacerbate the model overfitting when no distinction is made. To address this problem, we propose a novel uncertainty-aware optimal transport scheme. Our scheme consists of an energy-based transport (ET) mechanism that estimates the fluctuating cost of uncertainty to promote the assignment of semantic-agnostic representation, and an inter-cluster extension strategy that enhances the discrimination of semantic property among different clusters by widening the corresponding margin distance. Furthermore, a T-energy score is presented to mitigate the magnitude gap between the parallel transport and classifier branches. Extensive experiments on two standard SCOOD benchmarks demonstrate the above-par OOD detection performance, outperforming the state-of-the-art methods by a margin of 27.69% and 34.4% on FPR@95, respectively., Comment: Accepted by CVPR2023

Problem, research strategy, and findings: Planners need to read plans to learn and adapt current practice. Planners may struggle to find time to read and study lengthy planning documents, especially in emerging areas such as climate change and urban resilience. Recently, natural language processing (NLP) has shown promise in processing big textual data. We asked whether planners could use NLP techniques to more efficiently extract useful and reliable information from planning documents. By analyzing 78 resilience plans from the 100 Resilient Cities Network, we found that results generated from topic modeling, which is an NLP technique, coincided to a large extent (80%) with those from the conventional content analysis approach. Topic modeling was generally effective and efficient in extracting the main information of plans, whereas the content analysis approach could find more in-depth details but at the expense of considerable time and effort. We further propose a transferrable model for cutting-edge planners to more efficiently read and study a large collection of plans using machine learning. Our methodology has limitations: Both topic modeling and content analysis can be subject to human bias and generate unreliable results; NLP text processing techniques may create inaccurate results due to their specific method limitations; and the transferable approach can be only applied to big textual data where there are enough sufficiently long documents. Takeaway for practice: NLP represents a valuable addition to the planner’s toolbox. Topic modeling coupled with other NLP techniques can help planners to effectively discover key topics in plans, identify planning priorities and plans of specific emphasis, and find relevant policies.

As a key structural parameter, phase depicts the arrangement of atoms in materials. Normally, a nanomaterial exists in its thermodynamically stable crystal phase. With the development of nanotechnology, nanomaterials with unconventional crystal phases, which rarely exist in their bulk counterparts, or amorphous phase have been prepared using carefully controlled reaction conditions. Together these methods are beginning to enable phase engineering of nanomaterials (PEN), i.e., the synthesis of nanomaterials with unconventional phases and the transformation between different phases, to obtain desired properties and functions. This Review summarizes the research progress in the field of PEN. First, we present representative strategies for the direct synthesis of unconventional phases and modulation of phase transformation in diverse kinds of nanomaterials. We cover the synthesis of nanomaterials ranging from metal nanostructures such as Au, Ag, Cu, Pd, and Ru, and their alloys; metal oxides, borides, and carbides; to transition metal dichalcogenides (TMDs) and 2D layered materials. We review synthesis and growth methods ranging from wet-chemical reduction and seed-mediated epitaxial growth to chemical vapor deposition (CVD), high pressure phase transformation, and electron and ion-beam irradiation. After that, we summarize the significant influence of phase on the various properties of unconventional-phase nanomaterials. We also discuss the potential applications of the developed unconventional-phase nanomaterials in different areas including catalysis, electrochemical energy storage (batteries and supercapacitors), solar cells, optoelectronics, and sensing. Finally, we discuss existing challenges and future research directions in PEN. © 2023 American Chemical Society

Learning 3D human-object interaction relation is pivotal to embodied AI and interaction modeling. Most existing methods approach the goal by learning to predict isolated interaction elements, e.g., human contact, object affordance, and human-object spatial relation, primarily from the perspective of either the human or the object. Which underexploit certain correlations between the interaction counterparts (human and object), and struggle to address the uncertainty in interactions. Actually, objects' functionalities potentially affect humans' interaction intentions, which reveals what the interaction is. Meanwhile, the interacting humans and objects exhibit matching geometric structures, which presents how to interact. In light of this, we propose harnessing these inherent correlations between interaction counterparts to mitigate the uncertainty and jointly anticipate the above interaction elements in 3D space. To achieve this, we present LEMON (LEarning 3D huMan-Object iNteraction relation), a unified model that mines interaction intentions of the counterparts and employs curvatures to guide the extraction of geometric correlations, combining them to anticipate the interaction elements. Besides, the 3D Interaction Relation dataset (3DIR) is collected to serve as the test bed for training and evaluation. Extensive experiments demonstrate the superiority of LEMON over methods estimating each element in isolation., Comment: accept by CVPR2024

Full-spectrum out-of-distribution (F-OOD) detection aims to accurately recognize in-distribution (ID) samples while encountering semantic and covariate shifts simultaneously. However, existing out-of-distribution (OOD) detectors tend to overfit the covariance information and ignore intrinsic semantic correlation, inadequate for adapting to complex domain transformations. To address this issue, we propose a Likelihood-Aware Semantic Alignment (LSA) framework to promote the image-text correspondence into semantically high-likelihood regions. LSA consists of an offline Gaussian sampling strategy which efficiently samples semantic-relevant visual embeddings from the class-conditional Gaussian distribution, and a bidirectional prompt customization mechanism that adjusts both ID-related and negative context for discriminative ID/OOD boundary. Extensive experiments demonstrate the remarkable OOD detection performance of our proposed LSA especially on the intractable Near-OOD setting, surpassing existing methods by a margin of $15.26\%$ and $18.88\%$ on two F-OOD benchmarks, respectively., Comment: 16 pages, 7 figures

Weakly supervised object localization and semantic segmentation aim to localize objects using only image-level labels. Recently, a new paradigm has emerged by generating a foreground prediction map (FPM) to achieve pixel-level localization. While existing FPM-based methods use cross-entropy to evaluate the foreground prediction map and to guide the learning of the generator, this paper presents two astonishing experimental observations on the object localization learning process: For a trained network, as the foreground mask expands, 1) the cross-entropy converges to zero when the foreground mask covers only part of the object region. 2) The activation value continuously increases until the foreground mask expands to the object boundary. Therefore, to achieve a more effective localization performance, we argue for the usage of activation value to learn more object regions. In this paper, we propose a Background Activation Suppression (BAS) method. Specifically, an Activation Map Constraint (AMC) module is designed to facilitate the learning of generator by suppressing the background activation value. Meanwhile, by using foreground region guidance and area constraint, BAS can learn the whole region of the object. In the inference phase, we consider the prediction maps of different categories together to obtain the final localization results. Extensive experiments show that BAS achieves significant and consistent improvement over the baseline methods on the CUB-200-2011 and ILSVRC datasets. In addition, our method also achieves state-of-the-art weakly supervised semantic segmentation performance on the PASCAL VOC 2012 and MS COCO 2014 datasets. Code and models are available at https://github.com/wpy1999/BAS-Extension., Comment: Accepted by IJCV. arXiv admin note: text overlap with arXiv:2112.00580

In the realm of social media, users frequently convey personal sentiments, with some potentially indicating cognitive distortions or suicidal tendencies. Timely recognition of such signs is pivotal for effective interventions. In response, we introduce two novel annotated datasets from Chinese social media, focused on cognitive distortions and suicidal risk classification. We propose a comprehensive benchmark using both supervised learning and large language models, especially from the GPT series, to evaluate performance on these datasets. To assess the capabilities of the large language models, we employed three strategies: zero-shot, few-shot, and fine-tuning. Furthermore, we deeply explored and analyzed the performance of these large language models from a psychological perspective, shedding light on their strengths and limitations in identifying and understanding complex human emotions. Our evaluations underscore a performance difference between the two approaches, with the models often challenged by subtle category distinctions. While GPT-4 consistently delivered strong results, GPT-3.5 showed marked improvement in suicide risk classification after fine-tuning. This research is groundbreaking in its evaluation of large language models for Chinese social media tasks, accentuating the models' potential in psychological contexts. All datasets and code are made available., Comment: 17 pages

In the contemporary landscape of social media, an alarming number of users express negative emotions, some of which manifest as strong suicidal intentions. This situation underscores a profound need for trained psychological counselors who can enact effective mental interventions. However, the development of these professionals is often an imperative but time-consuming task. Consequently, the mobilization of non-professionals or volunteers in this capacity emerges as a pressing concern. Leveraging the capabilities of artificial intelligence, and in particular, the recent advances in large language models, offers a viable solution to this challenge. This paper introduces a novel model constructed on the foundation of large language models to fully assist non-professionals in providing psychological interventions on online user discourses. This framework makes it plausible to harness the power of non-professional counselors in a meaningful way. A comprehensive study was conducted involving ten professional psychological counselors of varying expertise, evaluating the system across five critical dimensions. The findings affirm that our system is capable of analyzing patients' issues with relative accuracy and proffering professional-level strategies recommendations, thereby enhancing support for non-professionals. This research serves as a compelling validation of the application of large language models in the field of psychology and lays the groundwork for a new paradigm of community-based mental health support.

Affordance grounding aims to locate objects' "action possibilities" regions, which is an essential step toward embodied intelligence. Due to the diversity of interactive affordance, the uniqueness of different individuals leads to diverse interactions, which makes it difficult to establish an explicit link between object parts and affordance labels. Human has the ability that transforms the various exocentric interactions into invariant egocentric affordance to counter the impact of interactive diversity. To empower an agent with such ability, this paper proposes a task of affordance grounding from exocentric view, i.e., given exocentric human-object interaction and egocentric object images, learning the affordance knowledge of the object and transferring it to the egocentric image using only the affordance label as supervision. However, there is some "interaction bias" between personas, mainly regarding different regions and different views. To this end, we devise a cross-view affordance knowledge transfer framework that extracts affordance-specific features from exocentric interactions and transfers them to the egocentric view. Specifically, the perception of affordance regions is enhanced by preserving affordance co-relations. In addition, an affordance grounding dataset named AGD20K is constructed by collecting and labeling over 20K images from $36$ affordance categories. Experimental results demonstrate that our method outperforms the representative models regarding objective metrics and visual quality. Code is released at https://github.com/lhc1224/Cross-view-affordance-grounding., Comment: arXiv admin note: text overlap with arXiv:2203.09905

Urban resilience efficiency is an important indicator to explore the relationship between resource consumption and urban resilience, shedding new light on the study of urban sustainable development. Based on the panel data of 2008, 2012, and 2017, this paper makes a spatiotemporal assessment on the urban resilience efficiency of 126 cities in the Yangtze River Economic Belt (YREB) in China by applying an entropy weight-TOPSIS method and a slack-based measure (SBM) model. Combined with the analysis of a geographically weighted regression model (GWR), the influencing factors on resilience efficiency are also investigated. The results show that both the resource consumption index (RC, inputs) and the urban resilience index (UR, outputs) presented a steady upward trend, and their spatial distribution characteristics were similar, showing a gradual decrease from the eastern coastal cities to the central and western inland cities. Derived from inputs and outputs, the mean values of resilience efficiency index (RE) in three periods were 0.3149, 0.2906, and 0.1625, respectively, revealing that there had been a noticeable decline. Spatially, its spatial distribution has evolved from a relatively balanced pattern to an unbalanced one, showing a gradual decrease from west to east. The results of the GWR model analysis indicate that the total electricity consumption and area of construction land had a considerable correlation with the overall urban resilience of the YREB. Furthermore, total quantity of water supply and science and technology (S&T) expenditure continued to be the main driving factors on urban resilience of the upstream cities. The midstream regions mainly depended on the scale of construction land, and the influencing factors are relatively single. The influencing factors in the downstream areas have changed from dominance of resources and capital factors to the single dominance of resource factors, and total electricity consumption had a strong explanatory powe

Camouflage is a common visual phenomenon, which refers to hiding the foreground objects into the background images, making them briefly invisible to the human eye. Previous work has typically been implemented by an iterative optimization process. However, these methods struggle in 1) efficiently generating camouflage images using foreground and background with arbitrary structure; 2) camouflaging foreground objects to regions with multiple appearances (e.g. the junction of the vegetation and the mountains), which limit their practical application. To address these problems, this paper proposes a novel Location-free Camouflage Generation Network (LCG-Net) that fuse high-level features of foreground and background image, and generate result by one inference. Specifically, a Position-aligned Structure Fusion (PSF) module is devised to guide structure feature fusion based on the point-to-point structure similarity of foreground and background, and introduce local appearance features point-by-point. To retain the necessary identifiable features, a new immerse loss is adopted under our pipeline, while a background patch appearance loss is utilized to ensure that the hidden objects look continuous and natural at regions with multiple appearances. Experiments show that our method has results as satisfactory as state-of-the-art in the single-appearance regions and are less likely to be completely invisible, but far exceed the quality of the state-of-the-art in the multi-appearance regions. Moreover, our method is hundreds of times faster than previous methods. Benefitting from the unique advantages of our method, we provide some downstream applications for camouflage generation, which show its potential. The related code and dataset will be released at https://github.com/Tale17/LCG-Net.

Affordance grounding, a task to ground (i.e., localize) action possibility region in objects, which faces the challenge of establishing an explicit link with object parts due to the diversity of interactive affordance. Human has the ability that transform the various exocentric interactions to invariant egocentric affordance so as to counter the impact of interactive diversity. To empower an agent with such ability, this paper proposes a task of affordance grounding from exocentric view, i.e., given exocentric human-object interaction and egocentric object images, learning the affordance knowledge of the object and transferring it to the egocentric image using only the affordance label as supervision. To this end, we devise a cross-view knowledge transfer framework that extracts affordance-specific features from exocentric interactions and enhances the perception of affordance regions by preserving affordance correlation. Specifically, an Affordance Invariance Mining module is devised to extract specific clues by minimizing the intra-class differences originated from interaction habits in exocentric images. Besides, an Affordance Co-relation Preserving strategy is presented to perceive and localize affordance by aligning the co-relation matrix of predicted results between the two views. Particularly, an affordance grounding dataset named AGD20K is constructed by collecting and labeling over 20K images from 36 affordance categories. Experimental results demonstrate that our method outperforms the representative models in terms of objective metrics and visual quality. Code: github.com/lhc1224/Cross-View-AG., Comment: CVPR2022

Non-exemplar class-incremental learning is to recognize both the old and new classes when old class samples cannot be saved. It is a challenging task since representation optimization and feature retention can only be achieved under supervision from new classes. To address this problem, we propose a novel self-sustaining representation expansion scheme. Our scheme consists of a structure reorganization strategy that fuses main-branch expansion and side-branch updating to maintain the old features, and a main-branch distillation scheme to transfer the invariant knowledge. Furthermore, a prototype selection mechanism is proposed to enhance the discrimination between the old and new classes by selectively incorporating new samples into the distillation process. Extensive experiments on three benchmarks demonstrate significant incremental performance, outperforming the state-of-the-art methods by a margin of 3%, 3% and 6%, respectively., Comment: Camera_Ready Version for CVPR 2022

Affordance detection, which refers to perceiving objects with potential action possibilities in images, is a challenging task since the possible affordance depends on the person's purpose in real-world application scenarios. The existing works mainly extract the inherent human-object dependencies from image/video to accommodate affordance properties that change dynamically. In this paper, we explore to perceive affordance from a vision-language perspective and consider the challenging phrase-based affordance detection problem,i.e., given a set of phrases describing the action purposes, all the object regions in a scene with the same affordance should be detected. To this end, we propose a cyclic bilateral consistency enhancement network (CBCE-Net) to align language and vision features progressively. Specifically, the presented CBCE-Net consists of a mutual guided vision-language module that updates the common features of vision and language in a progressive manner, and a cyclic interaction module (CIM) that facilitates the perception of possible interaction with objects in a cyclic manner. In addition, we extend the public Purpose-driven Affordance Dataset (PAD) by annotating affordance categories with short phrases. The contrastive experimental results demonstrate the superiority of our method over nine typical methods from four relevant fields in terms of both objective metrics and visual quality. The related code and dataset will be released at \url{https://github.com/lulsheng/CBCE-Net}.

Two-dimensional (2D) metal nanomaterials have gained ever-growing research interest owing to their fascinating physicochemical properties and promising application, especially in the field of electrocatalysis. In this review, we briefly introduce the recent advances in wet-chemical synthesis of 2D metal nanomaterials. Subsequently, the catalytic performances of 2D metal nanomaterials in a variety of electrochemical reactions are illustrated. Finally, we summarize current challenges and highlight our perspectives on preparing high-performance 2D metal electrocatalysts. © 2022 The Author(s) 2021. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd.

Weakly supervised object localization (WSOL) aims to localize objects using only image-level labels. Recently a new paradigm has emerged by generating a foreground prediction map (FPM) to achieve localization task. Existing FPM-based methods use cross-entropy (CE) to evaluate the foreground prediction map and to guide the learning of generator. We argue for using activation value to achieve more efficient learning. It is based on the experimental observation that, for a trained network, CE converges to zero when the foreground mask covers only part of the object region. While activation value increases until the mask expands to the object boundary, which indicates that more object areas can be learned by using activation value. In this paper, we propose a Background Activation Suppression (BAS) method. Specifically, an Activation Map Constraint module (AMC) is designed to facilitate the learning of generator by suppressing the background activation value. Meanwhile, by using the foreground region guidance and the area constraint, BAS can learn the whole region of the object. In the inference phase, we consider the prediction maps of different categories together to obtain the final localization results. Extensive experiments show that BAS achieves significant and consistent improvement over the baseline methods on the CUB-200-2011 and ILSVRC datasets. Code and models are available at https://github.com/wpy1999/BAS., Comment: Accepted by CVPR 2022. Code: https://github.com/wpy1999/BAS

It is crucial to understand the robustness of text detection models with regard to extensive corruptions, since scene text detection techniques have many practical applications. For systematically exploring this problem, we propose two datasets from which to evaluate scene text detection models: ICDAR2015-C (IC15-C) and CTW1500-C (CTW-C). Our study extends the investigation of the performance and robustness of the proposed region proposal, regression and segmentation-based scene text detection frameworks. Furthermore, we perform a robustness analysis of six key components: pre-training data, backbone, feature fusion module, multi-scale predictions, representation of text instances and loss function. Finally, we present a simple yet effective data-based method to destroy the smoothness of text regions by merging background and foreground, which can significantly increase the robustness of different text detection networks. We hope that this study will provide valid data points as well as experience for future research. Benchmark, code and data will be made available at \url{https://github.com/wushilian/robust-scene-text-detection-benchmark}.

Visual affordance grounding aims to segment all possible interaction regions between people and objects from an image/video, which is beneficial for many applications, such as robot grasping and action recognition. However, existing methods mainly rely on the appearance feature of the objects to segment each region of the image, which face the following two problems: (i) there are multiple possible regions in an object that people interact with; and (ii) there are multiple possible human interactions in the same object region. To address these problems, we propose a Hand-aided Affordance Grounding Network (HAGNet) that leverages the aided clues provided by the position and action of the hand in demonstration videos to eliminate the multiple possibilities and better locate the interaction regions in the object. Specifically, HAG-Net has a dual-branch structure to process the demonstration video and object image. For the video branch, we introduce hand-aided attention to enhance the region around the hand in each video frame and then use the LSTM network to aggregate the action features. For the object branch, we introduce a semantic enhancement module (SEM) to make the network focus on different parts of the object according to the action classes and utilize a distillation loss to align the output features of the object branch with that of the video branch and transfer the knowledge in the video branch to the object branch. Quantitative and qualitative evaluations on two challenging datasets show that our method has achieved stateof-the-art results for affordance grounding. The source code will be made available to the public.

Affordance detection refers to identifying the potential action possibilities of objects in an image, which is a crucial ability for robot perception and manipulation. To empower robots with this ability in unseen scenarios, we first study the challenging one-shot affordance detection problem in this paper, i.e., given a support image that depicts the action purpose, all objects in a scene with the common affordance should be detected. To this end, we devise a One-Shot Affordance Detection Network (OSAD-Net) that firstly estimates the human action purpose and then transfers it to help detect the common affordance from all candidate images. Through collaboration learning, OSAD-Net can capture the common characteristics between objects having the same underlying affordance and learn a good adaptation capability for perceiving unseen affordances. Besides, we build a large-scale Purpose-driven Affordance Dataset v2 (PADv2) by collecting and labeling 30k images from 39 affordance and 103 object categories. With complex scenes and rich annotations, our PADv2 dataset can be used as a test bed to benchmark affordance detection methods and may also facilitate downstream vision tasks, such as scene understanding, action recognition, and robot manipulation. Specifically, we conducted comprehensive experiments on PADv2 dataset by including 11 advanced models from several related research fields. Experimental results demonstrate the superiority of our model over previous representative ones in terms of both objective metrics and visual quality. The benchmark suite is available at https://github.com/lhc1224/OSAD Net.

Few-shot class-incremental learning is to recognize the new classes given few samples and not forget the old classes. It is a challenging task since representation optimization and prototype reorganization can only be achieved under little supervision. To address this problem, we propose a novel incremental prototype learning scheme. Our scheme consists of a random episode selection strategy that adapts the feature representation to various generated incremental episodes to enhance the corresponding extensibility, and a self-promoted prototype refinement mechanism which strengthens the expression ability of the new classes by explicitly considering the dependencies among different classes. Particularly, a dynamic relation projection module is proposed to calculate the relation matrix in a shared embedding space and leverage it as the factor for bootstrapping the update of prototypes. Extensive experiments on three benchmark datasets demonstrate the above-par incremental performance, outperforming state-of-the-art methods by a margin of 13%, 17% and 11%, respectively., Comment: Accepted by CVPR 2021

Affordance detection refers to identifying the potential action possibilities of objects in an image, which is an important ability for robot perception and manipulation. To empower robots with this ability in unseen scenarios, we consider the challenging one-shot affordance detection problem in this paper, i.e., given a support image that depicts the action purpose, all objects in a scene with the common affordance should be detected. To this end, we devise a One-Shot Affordance Detection (OS-AD) network that firstly estimates the purpose and then transfers it to help detect the common affordance from all candidate images. Through collaboration learning, OS-AD can capture the common characteristics between objects having the same underlying affordance and learn a good adaptation capability for perceiving unseen affordances. Besides, we build a Purpose-driven Affordance Dataset (PAD) by collecting and labeling 4k images from 31 affordance and 72 object categories. Experimental results demonstrate the superiority of our model over previous representative ones in terms of both objective metrics and visual quality. The benchmark suite is at ProjectPage.

Few-shot segmentation aims at assigning a category label to each image pixel with few annotated samples. It is a challenging task since the dense prediction can only be achieved under the guidance of latent features defined by sparse annotations. Existing meta-learning method tends to fail in generating category-specifically discriminative descriptor when the visual features extracted from support images are marginalized in embedding space. To address this issue, this paper presents an adaptive tuning framework, in which the distribution of latent features across different episodes is dynamically adjusted based on a self-segmentation scheme, augmenting category-specific descriptors for label prediction. Specifically, a novel self-supervised inner-loop is firstly devised as the base learner to extract the underlying semantic features from the support image. Then, gradient maps are calculated by back-propagating self-supervised loss through the obtained features, and leveraged as guidance for augmenting the corresponding elements in embedding space. Finally, with the ability to continuously learn from different episodes, an optimization-based meta-learner is adopted as outer loop of our proposed framework to gradually refine the segmentation results. Extensive experiments on benchmark PASCAL-$5^{i}$ and COCO-$20^{i}$ datasets demonstrate the superiority of our proposed method over state-of-the-art., Comment: Accepted to IJCAI 2020

In this paper, we tackle one-shot texture retrieval: given an example of a new reference texture, detect and segment all the pixels of the same texture category within an arbitrary image. To address this problem, we present an OS-TR network to encode both reference and query image, leading to achieve texture segmentation towards the reference category. Unlike the existing texture encoding methods that integrate CNN with orderless pooling, we propose a directionality-aware module to capture the texture variations at each direction, resulting in spatially invariant representation. To segment new categories given only few examples, we incorporate a self-gating mechanism into relation network to exploit global context information for adjusting per-channel modulation weights of local relation features. Extensive experiments on benchmark texture datasets and real scenarios demonstrate the above-par segmentation performance and robust generalization across domains of our proposed method., Comment: ijcai2019-lastest

We demonstrate that the double-shell coating of graphene and Li4SiO4 on commercial Si nanoparticles as an effective strategy for improving the anode of lithium ion batteries to overcome the two critical concerns, i.e. rapid capacity decay and inferior coulombic efficiency caused by the large-volume changes. It is proven that the double-shell coating enables the formation of a stable hybrid solid electrolyte interphase, leading to much higher coulombic efficiency and longer cycling stability of the Si anodes. Furthermore, the rate performance of Si is significantly enhanced by the outstanding electrical conductivity of inner graphene layers and the excellent ionic conductivity of Li4SiO4 out-shell. The overall results suggest that this new strategy holds promising perspectives in optimizing electrochemical performances of Si anodes, which should promote their practical applications for next-generation lithium ion batteries with increasingly demanded energy density.

Objective: Restoring intracellular cGMP and inducing NO-synthesis attenuates ischemia-associated early pulmonary allograft dysfunction. Phosphodiesterase-5 (PDE), predominantly expressed in lung tissue, plays a pivotal role in modulating the cGMP/NO-synthase pathway in endothelial and epithelial cells. In this study, we evaluate the effect of employing sildenafil (Viagra®), a specific inhibitor of PDE-5, to counteract ischemia/reperfusion (I/R) injury in a single lung transplantation model of extended ischemia. Methods: Donor animals (weight matched outbred pigs, 28-35 kg) in the treatment group (I) (n = 5) were injected with 0.7 mg sildenafil/kg into the pulmonary artery (PA) prior to inflow occlusion. For perfusion, Perfadex®, containing 0.7 mg sildenafil/l was used, and the graft stored at 1 °C in the perfusion solution. After 24 h ischemia, unilateral left lung transplantation was performed. Starting at reperfusion, group I received continuous sildenafil (0.7 mg sildenafil/kg), over 6 h. Except for the sildenafil application, the control group (II) (n = 4) was treated identically (PGE1 was injected into the PA). One hour after reperfusion, the right main bronchus (MB) and right PA were occluded. Over the next 5 h, cardiopulmonary parameters (systemic aterial, PA, central venous, left atrial pressure, pCO2, pO2) were measured, including extravascular lung water (EVLW). Thiobarbituric acid-reactive substance assay (TBARS) and myeloperoxidase (MPO) analysis from lung tissue were run. Results: All recipients of group I survived the 6-h reperfusion period; in contrast, all control animals died within 1-2 h after occlusion of the right side. In comparison to a marked rise in pulmonary vascular resistance (PVR) in group II (>1000 dyne s cm−5), PVR in group I remained stable, moderately elevated from baseline (baseline: 150-180 dyne s cm−5 vs endpoint: 1000 dyne s cm−5). EVLW in group I did not increase during reperfusion (baseline: 6.75 ± 1.4 mg/kg vs endpoint: 6.7 ± 1

The deformation characteristics of MD-PMMA vary greatly at different temperatures. In the paper, whether a uniform model could be used to describe these complex characteristics was discussed. Tensile properties of MD-PMMA at the temperatures of -50˚C, -25˚C, 20˚C, 60˚C, 90˚C were experimentally investigated. The entire deformation processes of PMMA were divided into four stages: elastic stage, viscoelastic stage, yielding stage and post-yielding stage. Strain softening and strain hardening phenomenon occurred in the yielding and post-yielding stage, it was the results of the competition between loading rate and plastic strain rate. A nonlinear model of activation dashpot was constructed, in the model, the evolution rate of plastic deformation was defined by Eyring’s theory, and the actual stress was the difference between external applied stress and internal resistance stress caused by plastic strain. The above activation dashpot serially connected with the standard linear model (SLM) to identify elastic and viscoelastic characteristics. A two iterations integral algorithm was proposed to simplify the inter-coupling between the internal stress and the plastic strain, and the unknown parameters in the model could be easily fitted by the experimental data. This uniform viscoelastic-plastic model was demonstrated that could predict different deformation behaviors at a wide temperature range.

Mojiang is a large Au-Ni deposit which occurs in the contact zone between ultramafic intrusions and metasedimentary rocks. The data indicate that the deposit is a composite deposit composed mainly of early- stage magmatic-type Ni ores and late-stage hydrothermal-type Au-Ni ores. The ultramafic intrusions were derived directly from the mantle and are products of the partial melting of a depleted mantle to different degrees. The mantle underwent extraction of mafic magma and metasomatism, and the primary ultramafic magma was sulphur saturated., Mojiang is a large Au-Ni deposit which occurs in the contact zone between ultramafic intrusions and metasedimentary rocks. The data indicate that the deposit is a composite deposit composed mainly of early- stage magmatic-type Ni ores and late-stage hydrothermal-type Au-Ni ores. The ultramafic intrusions were derived directly from the mantle and are products of the partial melting of a depleted mantle to different degrees. The mantle underwent extraction of mafic magma and metasomatism, and the primary ultramafic magma was sulphur saturated.

The Chinese deposit occurs in a deep-seated fault mylonite zone. Melt inclusions, fluid-melt inclusions and organic inclusions from ore-bearing quartz veins and mylonites at the 0, 80 and 160 m levels have provided new evidence for the deposit's genesis and for the mechanisms of gold deposit metallogeny in shear zones. Homogenisation temperatures and compositions of the inclusions were determined, Au grades and Au/Ag ratios at different levels correlated with contents of K, Na, Ca, SO4, HCO3, Cl, H2O and C2H2, and the electron microscopy and energy spectra of daughter minerals in the vein melt inclusions analysed. It is concluded that regional metamorphism and shearing played an important role in deposit formation, giving rise to multi-stage aluminium silicate melts during long-term geotechnical reworking of the metasedimentary rocks., The Chinese deposit occurs in a deep-seated fault mylonite zone. Melt inclusions, fluid-melt inclusions and organic inclusions from ore-bearing quartz veins and mylonites at the 0, 80 and 160 m levels have provided new evidence for the deposit's genesis and for the mechanisms of gold deposit metallogeny in shear zones. Homogenisation temperatures and compositions of the inclusions were determined, Au grades and Au/Ag ratios at different levels correlated with contents of K, Na, Ca, SO4, HCO3, Cl, H2O and C2H2, and the electron microscopy and energy spectra of daughter minerals in the vein melt inclusions analysed. It is concluded that regional metamorphism and shearing played an important role in deposit formation, giving rise to multi-stage aluminium silicate melts during long-term geotechnical reworking of the metasedimentary rocks.

Melt inclusions and fluid-melt inclusions have been found in ore-bearing quartz and mylonites at Qiaogashan in Xinjiang and Hetai in Guangdong, which are hosted by shear zones in Silurian-Devonian strata and Sinian strata respectively. The homogenisation temperatures of the melts and fluid inclusions and the daughter minerals of the melts have thrown new light on the genesis of this type of gold deposit., Melt inclusions and fluid-melt inclusions have been found in ore-bearing quartz and mylonites at Qiaogashan in Xinjiang and Hetai in Guangdong, which are hosted by shear zones in Silurian-Devonian strata and Sinian strata respectively. The homogenisation temperatures of the melts and fluid inclusions and the daughter minerals of the melts have thrown new light on the genesis of this type of gold deposit.

The deposit occurs in hydrothermally eruptive breccia in the Lower Carboniferous Dahalajunshan Formation. Ore-forming processes can be divided into hydrothermal-eruptive sedimentary and metasomatic stages. During hydrothermal sedimentation, hydrothermally eruptive breccia and other hydrothermal sediments were formed and gold was concentrated. Circulation of underground water caused the metasomatism of the hydrothermally eruptive breccia, thus concentrating gold again and forming the hot spring gold deposit. Ore-bearing rocks are rich in Au, Ag, As, Sb, Hg and Bi and poor in Cu, Pb and Zn. Mineralisation occurred during the Early Carboniferous near the surface, at temperatures of around 80-90 degrees C and salinities lower than 1% NaCl., The deposit occurs in hydrothermally eruptive breccia in the Lower Carboniferous Dahalajunshan Formation. Ore-forming processes can be divided into hydrothermal-eruptive sedimentary and metasomatic stages. During hydrothermal sedimentation, hydrothermally eruptive breccia and other hydrothermal sediments were formed and gold was concentrated. Circulation of underground water caused the metasomatism of the hydrothermally eruptive breccia, thus concentrating gold again and forming the hot spring gold deposit. Ore-bearing rocks are rich in Au, Ag, As, Sb, Hg and Bi and poor in Cu, Pb and Zn. Mineralisation occurred during the Early Carboniferous near the surface, at temperatures of around 80-90 degrees C and salinities lower than 1% NaCl.

The deposit is hosted in the Hetai ductile shear zone. Ore formation can be divided into the ductile shear metamorphic ore-forming stage of the ductile shear metamorphic metallogenic epoch and the gold quartz vein stage, gold sulphide stage and galena-sphalerite-carbonate vein stage of the hydrothermal alteration metallogenic epoch. Gold mineralisation occurred mainly in the gold quartz vein stage and the gold sulphide stage. The two types of ores thus formed are altered mylonites and sulphide-rich quartz veins. Individual mineral quartz in sulphide-rich quartz veins contain many fluid inclusions consisting mainly of CO2-rich liquid, fluid-melt inclusions and melt inclusions. Quartz was melted to obtain Rb-Sr isochron ages of quartz in the sulphide-rich veins. The age was found to be 172 +/- 2 Ma and the initial Sr87/Sr86 ratio was around 0.72816., The deposit is hosted in the Hetai ductile shear zone. Ore formation can be divided into the ductile shear metamorphic ore-forming stage of the ductile shear metamorphic metallogenic epoch and the gold quartz vein stage, gold sulphide stage and galena-sphalerite-carbonate vein stage of the hydrothermal alteration metallogenic epoch. Gold mineralisation occurred mainly in the gold quartz vein stage and the gold sulphide stage. The two types of ores thus formed are altered mylonites and sulphide-rich quartz veins. Individual mineral quartz in sulphide-rich quartz veins contain many fluid inclusions consisting mainly of CO2-rich liquid, fluid-melt inclusions and melt inclusions. Quartz was melted to obtain Rb-Sr isochron ages of quartz in the sulphide-rich veins. The age was found to be 172 +/- 2 Ma and the initial Sr87/Sr86 ratio was around 0.72816.

Ride-hailing services are often seen as benefiting the middle classes and other well-off Americans. But can they also serve people living in low-density areas, especially those who do not own vehicles? In new research, Shengxiao (Alex) Li, Wei Zhai, Junfeng Jiao, and Chao (Kenneth) Wang examined how ride-hailing services have reshaped transportation across neighborhoods in Austin, Texas. They find that while ride-hailing services mainly serve those who live downtown, where transit services are available, they also have the potential to help people living in low-income and low-density neighborhoods, and those without vehicles.

Objective: Restoring intracellular cGMP and inducing NO-synthesis attenuates ischemia-associated early pulmonary allograft dysfunction. Phosphodiesterase-5 (PDE), predominantly expressed in lung tissue, plays a pivotal role in modulating the cGMP/NO-synthase pathway in endothelial and epithelial cells. In this study, we evaluate the effect of employing sildenafil (Viagra®), a specific inhibitor of PDE-5, to counteract ischemia/reperfusion (I/R) injury in a single lung transplantation model of extended ischemia. Methods: Donor animals (weight matched outbred pigs, 28-35 kg) in the treatment group (I) (n = 5) were injected with 0.7 mg sildenafil/kg into the pulmonary artery (PA) prior to inflow occlusion. For perfusion, Perfadex®, containing 0.7 mg sildenafil/l was used, and the graft stored at 1 °C in the perfusion solution. After 24 h ischemia, unilateral left lung transplantation was performed. Starting at reperfusion, group I received continuous sildenafil (0.7 mg sildenafil/kg), over 6 h. Except for the sildenafil application, the control group (II) (n = 4) was treated identically (PGE1 was injected into the PA). One hour after reperfusion, the right main bronchus (MB) and right PA were occluded. Over the next 5 h, cardiopulmonary parameters (systemic aterial, PA, central venous, left atrial pressure, pCO2, pO2) were measured, including extravascular lung water (EVLW). Thiobarbituric acid-reactive substance assay (TBARS) and myeloperoxidase (MPO) analysis from lung tissue were run. Results: All recipients of group I survived the 6-h reperfusion period; in contrast, all control animals died within 1-2 h after occlusion of the right side. In comparison to a marked rise in pulmonary vascular resistance (PVR) in group II (>1000 dyne s cm−5), PVR in group I remained stable, moderately elevated from baseline (baseline: 150-180 dyne s cm−5 vs endpoint: 1000 dyne s cm−5). EVLW in group I did not increase during reperfusion (baseline: 6.75 ± 1.4 mg/kg vs endpoint: 6.7 ± 1