An investigation on the Factors Influencing the dissemination of WeChat Push Based on HSM and the Prediction of its Content Hotspot

With the continuous development of information technology, the carrier of we-media has emerged. The WeChat Subscription Accounts has quickly led the other we-media platforms. During the six years of its emergence, WeChat Subscription Accounts have attracted a lot of traffic and brought huge profit margins. Based on the above background, this study combines the heuristic-systematic model of information processing to classify the heuristic and systematic factors that influence the dissemination of WeChat push. Analyze the factors affecting WeChat push transmission, supplement relevant theories, and provide suggestions for WeChat Subscription Accounts operators

Maximizing charge dynamics in ZnIn2S4/CN Van der Waals heterojunction for optimal hydrogen production from photoreforming of glucose

Biomass photoreforming stands out as a promising avenue for green hydrogen, leveraging solar energy for the generation and transformation of clean and renewable energy resources. The pursuit of efficient photocatalysts is motivated by the unsatisfied hydrogen evolution performance arising from the complex and stubborn structure of biomass. Herein, we loaded 2-dimensional (2D) ZnIn2S4 onto 2D carbon nitride nanosheets, resulting in the formation of Van der Waals (VDW) heterojunctions (ZIS/CN). Band structure and morphology of CN were rationally tailored through precursor engineering to effectively magnify interfacial internal electric field and minimize diffusion pathway within the VDW heterostructure, realizing optimal charge dynamics in ZIS/DCN. As a result, intensified H2 generation was achieved, which was 350 times higher than pure DCN and outperformed ZIS at the same unit mass. This work offers design principles for VDW heterostructured photocatalysts and accelerates the transition towards a more sustainable manner in biomass reforming

Nanocomposite electret with surface potential self-recovery from water dipping for environmentally stable energy harvesting

Due to their high charge densities, electret materials have gained extensive attention in recent years for their abilities to harvest mechanical energy. However, the environmental stability of electret materials is still a major concern for real applications. Here, we report a thin-film nanocomposite electret material (NCEM) that exhibits immediate and effective self-recovery of the surface potential after water dipping. The NCEM is composed of a polytetrafluoroethylene (PTFE) film, a nanocomposite film with PTFE nanoparticles as the nanofiller and polydimethylsiloxane (PDMS) as the matrix. The surface potential of the NCEM resulting from corona charging could be stably maintained with very little decay of 2% after 25 days. More importantly, the surface potential exhibited quick self-recovery to 75% and 90% of its initial value after 10 min and 60 min, respectively, when the NCEM was removed from water. A 70% self-recovery was still observed even when the NCEM was dipped in water for 200 cycles. When used in electret nanogenerators (ENGs), the electric output recovered to 90% even when the ENG experienced water dipping. Therefore, this work presents a key step towards developing high-performance and environmentally stable energy harvesting nanogenerators that can survive harsh conditions for real applications

GPT4Video: A Unified Multimodal Large Language Model for lnstruction-Followed Understanding and Safety-Aware Generation

While the recent advances in Multimodal Large Language Models (MLLMs) constitute a significant leap forward in the field, these models are predominantly confined to the realm of input-side multimodal comprehension, lacking the capacity for multimodal content generation. To fill this gap, we present GPT4Video, a unified multi-model framework that empowers Large Language Models (LLMs) with the capability of both video understanding and generation. Specifically, we develop an instruction-following-based approach integrated with the stable diffusion generative model, which has demonstrated to effectively and securely handle video generation scenarios. GPT4Video offers the following benefits: 1) It exhibits impressive capabilities in both video understanding and generation scenarios. For example, GPT4Video outperforms Valley by 11.8\% on the Video Question Answering task, and surpasses NExt-GPT by 2.3\% on the Text to Video generation task. 2) it endows the LLM/MLLM with video generation capabilities without requiring additional training parameters and can flexibly interface with a wide range of models to perform video generation. 3) it maintains a safe and healthy conversation not only in output-side but also the input side in an end-to-end manner. Qualitative and qualitative experiments demonstrate that GPT4Video holds the potential to function as a effective, safe and Humanoid-like video assistant that can handle both video understanding and generation scenarios

Hijacking Attacks against Neural Networks by Analyzing Training Data

Backdoors and adversarial examples are the two primary threats currently faced by deep neural networks (DNNs). Both attacks attempt to hijack the model behaviors with unintended outputs by introducing (small) perturbations to the inputs. Backdoor attacks, despite the high success rates, often require a strong assumption, which is not always easy to achieve in reality. Adversarial example attacks, which put relatively weaker assumptions on attackers, often demand high computational resources, yet do not always yield satisfactory success rates when attacking mainstream black-box models in the real world. These limitations motivate the following research question: can model hijacking be achieved more simply, with a higher attack success rate and more reasonable assumptions? In this paper, we propose CleanSheet, a new model hijacking attack that obtains the high performance of backdoor attacks without requiring the adversary to tamper with the model training process. CleanSheet exploits vulnerabilities in DNNs stemming from the training data. Specifically, our key idea is to treat part of the clean training data of the target model as "poisoned data," and capture the characteristics of these data that are more sensitive to the model (typically called robust features) to construct "triggers." These triggers can be added to any input example to mislead the target model, similar to backdoor attacks. We validate the effectiveness of CleanSheet through extensive experiments on 5 datasets, 79 normally trained models, 68 pruned models, and 39 defensive models. Results show that CleanSheet exhibits performance comparable to state-of-the-art backdoor attacks, achieving an average attack success rate (ASR) of 97.5% on CIFAR-100 and 92.4% on GTSRB, respectively. Furthermore, CleanSheet consistently maintains a high ASR, when confronted with various mainstream backdoor defenses.Comment: Full version with major polishing, compared to the Usenix Security 2024 editio

Effects of tidal-forcing variations on tidal properties along a narrow convergent estuary

A 1D analytical framework is implemented in a narrow convergent estuary that is 78 km in length (the Guadiana, Southern Iberia) to evaluate the tidal dynamics along the channel, including the effects of neap-spring amplitude variations at the mouth. The close match between the observations (damping from the mouth to ∼ 30 km, shoaling upstream) and outputs from semi-closed channel solutions indicates that the M2 tide is reflected at the estuary head. The model is used to determine the contribution of reflection to the dynamics of the propagating wave. This contribution is mainly confined to the upper one third of the estuary. The relatively constant mean wave height along the channel (< 10% variations) partly results from reflection effects that also modify significantly the wave celerity and the phase difference between tidal velocity and elevation (contradicting the definition of an “ideal” estuary). Furthermore, from the mouth to ∼ 50 km, the variable friction experienced by the incident wave at neap and spring tides produces wave shoaling and damping, respectively. As a result, the wave celerity is largest at neap tide along this lower reach, although the mean water level is highest in spring. Overall, the presented analytical framework is useful for describing the main tidal properties along estuaries considering various forcings (amplitude, period) at the estuary mouth and the proposed method could be applicable to other estuaries with small tidal amplitude to depth ratio and negligible river discharge.info:eu-repo/semantics/publishedVersio

Interplay between Lefty and Nodal signaling is essential for the organizer and axial formation in amphioxus embryos

Abstract(#br)The organizer is an essential signaling center required for axial formation during vertebrate embryonic development. In the basal chordate amphioxus, the dorsal blastopore lip of the gastrula has been proposed to be homologous to the vertebrate organizer. Lefty is one of the first genes to be expressed in the organizer. The present results show that Lefty overexpression abolishes the organizer; the embryos were severely ventralized and posteriorized, and failed to develop anterior and dorsal structures. In Lefty knockouts the organizer is enlarged, and anterior and dorsal structures are expanded. Different from Lefty morphants in vertebrates, amphioxus Lefty mutants also exhibited left-right defects. Inhibition of Nodal with SB505124 partially rescued the effects of Lefty loss-of-function on morphology. In addition, while SB505124 treatment blocked Lefty expression in the cleavage stages of amphioxus embryos, activation of Nodal signaling with Activin protein induced ectopic Lefty expression at these stages. These results show that the interplay between Lefty and Nodal signaling plays an essential role in the specification of the amphioxus organizer and axes

Confined FeNi alloy nanoparticles in carbon nanotubes for photothermal oxidative dehydrogenation of ethane by carbon dioxide

Oxidative dehydrogenation of ethane with CO2 (ODEC) is an attractive reaction for reduction of carbon footprints and ethene production. In this work, we present photothermal catalysis on confined bimetal catalysts for ODEC. Carbon nanotubes confined non-noble bimetal alloy (i.e., CoNi@CNTs and FeNi@CNTs) catalysts were prepared and FeNi@CNTs showed effective performance in photothermal catalytic ODEC to ethene. Experiments and simulations reveal that UV and visible lights (420 – 490 nm) are responsible for ODEC and non-oxidative dehydrogenation of ethane, respectively, to ethene. Additionally, ODEC to ethene is preferred to C-C cracking to methane on FeNi@CNTs in light ( \u3e 490 nm)-induced thermocatalysis. The photothermal effect turns more significant when introduced into thermocatalytic ODEC (500 °C), with ethene generation at one order of magnitude. This work advances new mechanism of photo-mediated catalysis and sheds light on utilization of full-spectrum solar energy and non-noble metallic catalysts for ethene production and CO2 recycling at moderate conditions

NTU4DRadLM: 4D Radar-centric Multi-Modal Dataset for Localization and Mapping

Simultaneous Localization and Mapping (SLAM) is moving towards a robust perception age. However, LiDAR- and visual- SLAM may easily fail in adverse conditions (rain, snow, smoke and fog, etc.). In comparison, SLAM based on 4D Radar, thermal camera and IMU can work robustly. But only a few literature can be found. A major reason is the lack of related datasets, which seriously hinders the research. Even though some datasets are proposed based on 4D radar in past four years, they are mainly designed for object detection, rather than SLAM. Furthermore, they normally do not include thermal camera. Therefore, in this paper, NTU4DRadLM is presented to meet this requirement. The main characteristics are: 1) It is the only dataset that simultaneously includes all 6 sensors: 4D radar, thermal camera, IMU, 3D LiDAR, visual camera and RTK GPS. 2) Specifically designed for SLAM tasks, which provides fine-tuned ground truth odometry and intentionally formulated loop closures. 3) Considered both low-speed robot platform and fast-speed unmanned vehicle platform. 4) Covered structured, unstructured and semi-structured environments. 5) Considered both middle- and large- scale outdoor environments, i.e., the 6 trajectories range from 246m to 6.95km. 6) Comprehensively evaluated three types of SLAM algorithms. Totally, the dataset is around 17.6km, 85mins, 50GB and it will be accessible from this link: https://github.com/junzhang2016/NTU4DRadLMComment: 2023 IEEE International Intelligent Transportation Systems Conference (ITSC 2023

Power Pylon Reconstruction Based on Abstract Template Structures Using Airborne LiDAR Data

Paper describes the development and testing of a three-dimensional reconstruction method using airborne LiDAR (Light Detection And Ranging) point cloud