CoT-BERT: Enhancing Unsupervised Sentence Representation through Chain-of-Thought

Unsupervised sentence representation learning aims to transform input sentences into fixed-length vectors enriched with intricate semantic information while obviating the reliance on labeled data. Recent progress within this field, propelled by contrastive learning and prompt engineering, has significantly bridged the gap between unsupervised and supervised strategies. Nonetheless, the potential utilization of Chain-of-Thought, remains largely untapped within this trajectory. To unlock latent capabilities within pre-trained models, such as BERT, we propose a two-stage approach for sentence representation: comprehension and summarization. Subsequently, the output of the latter phase is harnessed as the vectorized representation of the input sentence. For further performance enhancement, we meticulously refine both the contrastive learning loss function and the template denoising technique for prompt engineering. Rigorous experimentation substantiates our method, CoT-BERT, transcending a suite of robust baselines without necessitating other text representation models or external databases

Research on Tracking and Synchronization of Uncertain Chaotic Systems

The tracking and synchronization problem of uncertain chaotic system, which is considered to be applied in secure communication in the future by many researchers, is considered in this paper. A double integral sliding mode controller is adopted to cope with the uncertainties of the chaotic system. Adaptive and robust strategies, such as Nussbaum gain method, are used to solve the unmodeled dynamic problem and unknown control direction problem. Meanwhile, the stability of the whole system is guaranteed by constructing of a big Lyapunov function for the whole system. Finally, a four dimension super-chaotic system is used as an example to do the numerical simulation and it testifies the rightness and effectiveness of the proposed method

Solar Thermal Energy Storage Using Paraffins as Phase Change Materials for Air Conditioning in the Built Environment

Thermal energy storage (TES) using phase change materials (PCMs) has received increasing attention since the last decades, due to its great potential for energy savings and energy management in the building sector. As one of the main categories of organic PCMs, paraffins exhibit favourable phase change temperatures for solar thermal energy storage. Its application is therefore effective to overcome the intermittent problem of solar energy utilisation, thereby reducing the power consumption of heating, ventilation and air conditioning (HVAC) systems and domestic hot water (DHW) systems. This chapter reviews the development and performance evaluation of solar thermal energy storage using paraffin-based PCMs in the built environment. Two case studies of solar-assisted radiant heating and desiccant cooling systems with integrated paraffin-based PCM TES were also presented. The results showed that paraffin-based PCM TES systems can rationalise the utilisation of solar thermal energy for air conditioning while maintaining a comfortable indoor environment

Spatio-temporal Tendency Reasoning for Human Body Pose and Shape Estimation from Videos

In this paper, we present a spatio-temporal tendency reasoning (STR) network for recovering human body pose and shape from videos. Previous approaches have focused on how to extend 3D human datasets and temporal-based learning to promote accuracy and temporal smoothing. Different from them, our STR aims to learn accurate and natural motion sequences in an unconstrained environment through temporal and spatial tendency and to fully excavate the spatio-temporal features of existing video data. To this end, our STR learns the representation of features in the temporal and spatial dimensions respectively, to concentrate on a more robust representation of spatio-temporal features. More specifically, for efficient temporal modeling, we first propose a temporal tendency reasoning (TTR) module. TTR constructs a time-dimensional hierarchical residual connection representation within a video sequence to effectively reason temporal sequences' tendencies and retain effective dissemination of human information. Meanwhile, for enhancing the spatial representation, we design a spatial tendency enhancing (STE) module to further learns to excite spatially time-frequency domain sensitive features in human motion information representations. Finally, we introduce integration strategies to integrate and refine the spatio-temporal feature representations. Extensive experimental findings on large-scale publically available datasets reveal that our STR remains competitive with the state-of-the-art on three datasets. Our code are available at https://github.com/Changboyang/STR.git.Comment: Accepted by BMVC202

FedABC: Targeting Fair Competition in Personalized Federated Learning

Federated learning aims to collaboratively train models without accessing their client's local private data. The data may be Non-IID for different clients and thus resulting in poor performance. Recently, personalized federated learning (PFL) has achieved great success in handling Non-IID data by enforcing regularization in local optimization or improving the model aggregation scheme on the server. However, most of the PFL approaches do not take into account the unfair competition issue caused by the imbalanced data distribution and lack of positive samples for some classes in each client. To address this issue, we propose a novel and generic PFL framework termed Federated Averaging via Binary Classification, dubbed FedABC. In particular, we adopt the ``one-vs-all'' training strategy in each client to alleviate the unfair competition between classes by constructing a personalized binary classification problem for each class. This may aggravate the class imbalance challenge and thus a novel personalized binary classification loss that incorporates both the under-sampling and hard sample mining strategies is designed. Extensive experiments are conducted on two popular datasets under different settings, and the results demonstrate that our FedABC can significantly outperform the existing counterparts.Comment: 9 pages,5 figure

Spectroscopic Signature of Oxidized Oxygen States in Peroxides

Recent debates on the oxygen redox behaviors in battery electrodes have triggered a pressing demand for the reliable detection and understanding of non-divalent oxygen states beyond conventional absorption spectroscopy. Here, enabled by high-efficiency mapping of resonant inelastic X-ray scattering (mRIXS) coupled with first-principles calculations, we report distinct mRIXS features of the oxygen states in Li2O, Li2CO3, and especially, Li2O2, which are successfully reproduced and interpreted theoretically. mRIXS signals are dominated by valence-band decays in Li2O and Li2CO3. However, the oxidized oxygen in Li2O2 leads to partially unoccupied O-2p states that yield a specific intra-band excitonic feature in mRIXS. Such a feature displays a specific emission energy in mRIXS, which disentangles the oxidized oxygen states from the dominating transition-metal/oxygen hybridization features in absorption spectroscopy, thus providing critical hints for both detecting and understanding the oxygen redox reactions in transition-metal oxide based battery materials.Comment: 25 pages, 4 figures, plus 11 pages of Supplementary Information with 4 figure

Dissociate lattice oxygen redox reactions from capacity and voltage drops of battery electrodes.

The oxygen redox (OR) activity is conventionally considered detrimental to the stability and kinetics of batteries. However, OR reactions are often confused by irreversible oxygen oxidation. Here, based on high-efficiency mapping of resonant inelastic x-ray scattering of both the transition metal and oxygen, we distinguish the lattice OR in Na0.6[Li0.2Mn0.8]O2 and compare it with Na2/3[Mg1/3Mn2/3]O2. Both systems display strong lattice OR activities but with distinct electrochemical stability. The comparison shows that the substantial capacity drop in Na0.6[Li0.2Mn0.8]O2 stems from non-lattice oxygen oxidations, and its voltage decay from an increasing Mn redox contribution upon cycling, contrasting those in Na2/3[Mg1/3Mn2/3]O2. We conclude that lattice OR is not the ringleader of the stability issue. Instead, irreversible oxygen oxidation and the changing cationic reactions lead to the capacity and voltage fade. We argue that lattice OR and other oxygen activities should/could be studied and treated separately to achieve viable OR-based electrodes