Emotional Marketing for Chinese Women

With the development of social economy, people's consumption level has improved; the level of consumption has changed from the initial demand for the basic functions to the pursuit of added value, which contributed to emotional marketing. Emotional marketing is the combination of personal emotion and product marketing to meet the emotional needs of consumers. In the 21st century, women have occupied an increasingly important position in social life. With the improving of women’s economic status and family status, 21st century has become the dominant era of female consumption. Females have become the main motive force for the development of the market. The purpose of this thesis is to help find some emotional marketing strategies of clothing. Shop owners are concerned with women’s consumer behavior and psychological characteristics, so emotional marketing is more used with female consumers. By analyzing female consumer behavior and consumer psychology, combined with the theory of emotional marketing, the intention is to help shop owners find emotional marketing strategies. There is an interview and a questionnaire in the thesis. In the interview, a shop owner told women caring about when their buying clothes and some strategies that she had. Women’s buying behavior and their emotion has been asked in the questionnaire. From the questionnaire, the main result is different ages consumers have different shopping psychological characteristics and emotion. Therefore, the research of this article has important significance to the female consumption market

Urbanisation and Fiscal Risks in China

China has witnessed rapid urbanisation over the past three decades. It has been generally successful in mobilising resources and providing the infrastructure that cities need to grow their economies. The central government has played a major role in China’s urbanisation through setting the overall development direction, land granting, and policy formulation. However, the responsibilities of infrastructure financing have been gradually shifted to local governments, and consequently, local budgetary systems face substantial funding challenges. While the decentralised structure of Chinese politics provides strong incentives for local officials to take the lead in urbanising China, fiscal institutions place heavy financial burdens on local governments. This thesis studies two major problems that arose from China’s urbanisation process. In terms of theoretical contribution, the thesis both advances the theories of Chinese style fiscal federalism and provides new evidence to enhance its explaining power. The first study is on China’s infrastructure financing and local government debt. It finds that local government debt for infrastructure is positively affected by the land demand from the private sector. Furthermore, land finance is positively related to the level of local government debt. The results reveal that the visible hand of local governments works creatively to meet infrastructure development targets handed down by the ‘iron hand’ of the central government. The second study is on local government financing vehicles’ (LGFVs) borrowing costs and land finance. It finds that local governments with higher land leasing revenue could bring down the borrowing costs of local LGFVs, while a higher ratio of land revenue to fiscal revenue would raise LGFVs’ borrowing costs. A booming local land market would push up the value of land assets held by LGFVs and therefore strengthen its ‘collateral channel’, enabling LGFVs to borrow at a lower cost. The thesis’ findings can help investors better identify the risks associated with LGFV bonds and enable local government borrowing at a lower cost

A dynamic mode of mitotic bookmarking by transcription factors.

During mitosis, transcription is shut off, chromatin condenses, and most transcription factors (TFs) are reported to be excluded from chromosomes. How do daughter cells re-establish the original transcription program? Recent discoveries that a select set of TFs remain bound on mitotic chromosomes suggest a potential mechanism for maintaining transcriptional programs through the cell cycle termed mitotic bookmarking. Here we report instead that many TFs remain associated with chromosomes in mouse embryonic stem cells, and that the exclusion previously described is largely a fixation artifact. In particular, most TFs we tested are significantly enriched on mitotic chromosomes. Studies with Sox2 reveal that this mitotic interaction is more dynamic than in interphase and is facilitated by both DNA binding and nuclear import. Furthermore, this dynamic mode results from lack of transcriptional activation rather than decreased accessibility of underlying DNA sequences in mitosis. The nature of the cross-linking artifact prompts careful re-examination of the role of TFs in mitotic bookmarking

A stable mode of bookmarking by TBP recruits RNA polymerase II to mitotic chromosomes.

Maintenance of transcription programs is challenged during mitosis when chromatin becomes condensed and transcription is silenced. How do the daughter cells re-establish the original transcription program? Here, we report that the TATA-binding protein (TBP), a key component of the core transcriptional machinery, remains bound globally to active promoters in mouse embryonic stem cells during mitosis. Using live-cell single-molecule imaging, we observed that TBP mitotic binding is highly stable, with an average residence time of minutes, in stark contrast to typical TFs with residence times of seconds. To test the functional effect of mitotic TBP binding, we used a drug-inducible degron system and found that TBP promotes the association of RNA Polymerase II with mitotic chromosomes, and facilitates transcriptional reactivation following mitosis. These results suggest that the core transcriptional machinery promotes efficient transcription maintenance globally

Human Sensing via Passive Spectrum Monitoring

Human sensing is significantly improving our lifestyle in many fields such as elderly healthcare and public safety. Research has demonstrated that human activity can alter the passive radio frequency (PRF) spectrum, which represents the passive reception of RF signals in the surrounding environment without actively transmitting a target signal. This paper proposes a novel passive human sensing method that utilizes PRF spectrum alteration as a biometrics modality for human authentication, localization, and activity recognition. The proposed method uses software-defined radio (SDR) technology to acquire the PRF in the frequency band sensitive to human signature. Additionally, the PRF spectrum signatures are classified and regressed by five machine learning (ML) algorithms based on different human sensing tasks. The proposed Sensing Humans among Passive Radio Frequency (SHAPR) method was tested in several environments and scenarios, including a laboratory, a living room, a classroom, and a vehicle, to verify its extensiveness. The experimental results show that the SHAPR method achieved more than 95% accuracy in the four scenarios for the three human sensing tasks, with a localization error of less than 0.8 m. These results indicate that the SHAPR technique can be considered a new human signature modality with high accuracy, robustness, and general applicability

Digital Ethics in Federated Learning

The Internet of Things (IoT) consistently generates vast amounts of data, sparking increasing concern over the protection of data privacy and the limitation of data misuse. Federated learning (FL) facilitates collaborative capabilities among multiple parties by sharing machine learning (ML) model parameters instead of raw user data, and it has recently gained significant attention for its potential in privacy preservation and learning efficiency enhancement. In this paper, we highlight the digital ethics concerns that arise when human-centric devices serve as clients in FL. More specifically, challenges of game dynamics, fairness, incentive, and continuity arise in FL due to differences in perspectives and objectives between clients and the server. We analyze these challenges and their solutions from the perspectives of both the client and the server, and through the viewpoints of centralized and decentralized FL. Finally, we explore the opportunities in FL for human-centric IoT as directions for future development

Passive Radio Frequency-based 3D Indoor Positioning System via Ensemble Learning

Passive radio frequency (PRF)-based indoor positioning systems (IPS) have attracted researchers' attention due to their low price, easy and customizable configuration, and non-invasive design. This paper proposes a PRF-based three-dimensional (3D) indoor positioning system (PIPS), which is able to use signals of opportunity (SoOP) for positioning and also capture a scenario signature. PIPS passively monitors SoOPs containing scenario signatures through a single receiver. Moreover, PIPS leverages the Dynamic Data Driven Applications System (DDDAS) framework to devise and customize the sampling frequency, enabling the system to use the most impacted frequency band as the rated frequency band. Various regression methods within three ensemble learning strategies are used to train and predict the receiver position. The PRF spectrum of 60 positions is collected in the experimental scenario, and three criteria are applied to evaluate the performance of PIPS. Experimental results show that the proposed PIPS possesses the advantages of high accuracy, configurability, and robustness.Comment: DDDAS 202

Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges

Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles (CAV), including perception, planning, and control. However, its reliance on vehicular data for model training presents significant challenges related to in-vehicle user privacy and communication overhead generated by massive data volumes. Federated learning (FL) is a decentralized ML approach that enables multiple vehicles to collaboratively develop models, broadening learning from various driving environments, enhancing overall performance, and simultaneously securing local vehicle data privacy and security. This survey paper presents a review of the advancements made in the application of FL for CAV (FL4CAV). First, centralized and decentralized frameworks of FL are analyzed, highlighting their key characteristics and methodologies. Second, diverse data sources, models, and data security techniques relevant to FL in CAVs are reviewed, emphasizing their significance in ensuring privacy and confidentiality. Third, specific and important applications of FL are explored, providing insight into the base models and datasets employed for each application. Finally, existing challenges for FL4CAV are listed and potential directions for future work are discussed to further enhance the effectiveness and efficiency of FL in the context of CAV

Training Stronger Spiking Neural Networks with Biomimetic Adaptive Internal Association Neurons

As the third generation of neural networks, spiking neural networks (SNNs) are dedicated to exploring more insightful neural mechanisms to achieve near-biological intelligence. Intuitively, biomimetic mechanisms are crucial to understanding and improving SNNs. For example, the associative long-term potentiation (ALTP) phenomenon suggests that in addition to learning mechanisms between neurons, there are associative effects within neurons. However, most existing methods only focus on the former and lack exploration of the internal association effects. In this paper, we propose a novel Adaptive Internal Association~(AIA) neuron model to establish previously ignored influences within neurons. Consistent with the ALTP phenomenon, the AIA neuron model is adaptive to input stimuli, and internal associative learning occurs only when both dendrites are stimulated at the same time. In addition, we employ weighted weights to measure internal associations and introduce intermediate caches to reduce the volatility of associations. Extensive experiments on prevailing neuromorphic datasets show that the proposed method can potentiate or depress the firing of spikes more specifically, resulting in better performance with fewer spikes. It is worth noting that without adding any parameters at inference, the AIA model achieves state-of-the-art performance on DVS-CIFAR10~(83.9\%) and N-CARS~(95.64\%) datasets.Comment: Accepted by ICASSP 202

Training Robust Spiking Neural Networks on Neuromorphic Data with Spatiotemporal Fragments

Neuromorphic vision sensors (event cameras) are inherently suitable for spiking neural networks (SNNs) and provide novel neuromorphic vision data for this biomimetic model. Due to the spatiotemporal characteristics, novel data augmentations are required to process the unconventional visual signals of these cameras. In this paper, we propose a novel Event SpatioTemporal Fragments (ESTF) augmentation method. It preserves the continuity of neuromorphic data by drifting or inverting fragments of the spatiotemporal event stream to simulate the disturbance of brightness variations, leading to more robust spiking neural networks. Extensive experiments are performed on prevailing neuromorphic datasets. It turns out that ESTF provides substantial improvements over pure geometric transformations and outperforms other event data augmentation methods. It is worth noting that the SNNs with ESTF achieve the state-of-the-art accuracy of 83.9\% on the CIFAR10-DVS dataset.Comment: Accepted by ICASSP 202