Distributed Estimation and Inference for Spatial Autoregression Model with Large Scale Networks

The rapid growth of online network platforms generates large-scale network data and it poses great challenges for statistical analysis using the spatial autoregression (SAR) model. In this work, we develop a novel distributed estimation and statistical inference framework for the SAR model on a distributed system. We first propose a distributed network least squares approximation (DNLSA) method. This enables us to obtain a one-step estimator by taking a weighted average of local estimators on each worker. Afterwards, a refined two-step estimation is designed to further reduce the estimation bias. For statistical inference, we utilize a random projection method to reduce the expensive communication cost. Theoretically, we show the consistency and asymptotic normality of both the one-step and two-step estimators. In addition, we provide theoretical guarantee of the distributed statistical inference procedure. The theoretical findings and computational advantages are validated by several numerical simulations implemented on the Spark system. Lastly, an experiment on the Yelp dataset further illustrates the usefulness of the proposed methodology

On decoder-only architecture for speech-to-text and large language model integration

Large language models (LLMs) have achieved remarkable success in the field of natural language processing, enabling better human-computer interaction using natural language. However, the seamless integration of speech signals into LLMs has not been explored well. The "decoder-only" architecture has also not been well studied for speech processing tasks. In this research, we introduce Speech-LLaMA, a novel approach that effectively incorporates acoustic information into text-based large language models. Our method leverages Connectionist Temporal Classification and a simple audio encoder to map the compressed acoustic features to the continuous semantic space of the LLM. In addition, we further probe the decoder-only architecture for speech-to-text tasks by training a smaller scale randomly initialized speech-LLaMA model from speech-text paired data alone. We conduct experiments on multilingual speech-to-text translation tasks and demonstrate a significant improvement over strong baselines, highlighting the potential advantages of decoder-only models for speech-to-text conversion

Dynamic multi-objective sequence-wise recommendation framework via deep reinforcement learning

Abstract Sequence-wise recommendation, where recommend exercises to each student step by step, is one of the most exciting tasks in the field of intelligent tutoring systems (ITS). It is important to develop a personalized sequence-wise recommendation framework that immerses students in learning and helps them acquire as much necessary knowledge as possible, rather than merely focusing on providing non-mastered exercises, which is referred to optimize a single objective. However, due to the different knowledge levels of students and the large scale of exercise banks, it is difficult to generate a personalized exercise recommendation for each student. To fully exploit the multifaceted beneficial information collected from e-learning platforms, we design a dynamic multi-objective sequence-wise recommendation framework via deep reinforcement learning, i.e., DMoSwR-DRL, which automatically select the most suitable exercises for each student based on the well-designed domain-objective rewards. Within this framework, the interaction between students and exercises can be explicitly modeled by integrating the actor–critic network and the state representation component, which can greatly help the agent perform effective reinforcement learning. Specifically, we carefully design a state representation module with dynamic recurrent mechanism, which integrates concept information and exercise difficulty level, thus generating a continuous state representation of the student. Subsequently, a flexible reward function is designed to simultaneously optimize the four domain-specific objectives of difficulty, novelty, coverage, and diversity, providing the students with a trade-off sequence-wise recommendation. To set up the online evaluation, we test DMoSwR-DRL on a simulated environment which can model qualitative development of knowledge level and predicts their performance for a given exercise. Comprehensive experiments are conducted on four classical exercise-answer datasets, and the results show the effectiveness and advantages of DMoSwR-DRL in terms of recommendation quality

Fund Network Centrality, Hard-to-Value Portfolio, and Investment Performance

Based on the quarterly data of mutual funds in China from the fourth quarter of 2004 to the fourth quarter of 2019, this paper constructs a series of complex bipartite networks based on the overlapped portfolios of mutual funds and then explores the influences of fund network position on mutual fund’s investment behavior and performance. This paper finds that a mutual fund with shorter information transmission path to other entities in the fund network (i.e., having higher closeness centrality) or with stronger ties with those entities in important information positions (i.e., having higher eigenvector centrality) will achieve better investment performance. However, a stronger mediating role over the potential information flow of the fund network (i.e., having higher betweenness centrality) cannot help a mutual fund increase performance. The empirical results also indicate that a mutual fund holding stock portfolios with high valuation difficulties caused by the market or fundamental information uncertainty will achieve better investment performance, while holding hard-to-value portfolios caused by limited public information will reduce the performance of the fund. Furthermore, high closeness centrality or eigenvector centrality can help mutual funds deal with the disclose problems of public information, thus reducing the likelihood of a mutual fund holding hard-to-value portfolios caused by limited public information to achieve worse performance. Eigenvector centrality brings information advantages about company fundamentals, so it is easier for a mutual fund with high eigenvector centrality to profit from holding hard-to-value portfolios caused by the fundamental information uncertainty. The conclusions of this paper can enhance our understanding of the fund network and its information mechanism and shed new light on mutual fund’s information advantages and related asset allocation strategies

Application of an Architect-Friendly Digital Design Approach to the Wind Environment of Campus Dormitory Buildings

Good natural ventilation can improve the comfort of campus dormitories and effectively avoid pollution caused by particle accumulation. Parametric design can effectively address the feedback and connection between building performance analysis and design. This study employs an architect-friendly digital design method based on the Rhino/Grasshopper parametric platform. It takes campus dormitories in the cold region as a case, using parameterized digital tools, such as the Butterfly plugin to simulate wind performance under three influencing factors: building layout, opening position, and building façade (shape and spoiler). Finally, the optimal design that can simultaneously meet the local winter and summer wind environment requirements is selected and validated. In addition, the reasonable design of external balconies and bathrooms in a dormitory can form buffer spaces to achieve effective wind shelter and insulation effects in cold regions. This article describes how to use digital tools to quickly and easily optimize the design of building forms based on wind simulations to promote campus sustainability

Fully adaptive recommendation paradigm: top-enhanced recommender distillation for intelligent education systems

Abstract Top-N recommendation has received great attention in assisting students in providing personalized learning guidance on the required subject/domain. Generally, existing approaches mainly aim to maximize the overall accuracy of the recommendation list while ignoring the accuracy of highly ranked recommended exercises, which seriously affects the students’ learning enthusiasm. Motivated by the Knowledge Distillation (KD) technique, we skillfully design a fully adaptive recommendation paradigm named Top-enhanced Recommender Distillation framework (TERD) to improve the recommendation effect of the top positions. Specifically, the proposed TERD transfers the knowledge of an arbitrary recommender (teacher network), and injects it into a well-designed student network. The prior knowledge provided by the teacher network, including student-exercise embeddings, and candidate exercise subsets, are further utilized to define the state and action space of the student network (i.e., DDQN). In addition, the student network introduces a well-designed state representation scheme and an effective individual ability tracing model to enhance the recommendation accuracy of top positions. The developed TERD follows a flexible model-agnostic paradigm that not only simplifies the action space of the student network, but also promotes the recommendation accuracy of the top position, thus enhancing the students’ motivation and engagement in e-learning environment. We implement our proposed approach on three well-established datasets and evaluate its Top-enhanced performance. The experimental evaluation on three publicly available datasets shows that our proposed TERD scheme effectively resolves the Top-enhanced recommendation issue

Evaluation of the Treatment Effect of Aloe vera Fermentation in Burn Injury Healing Using a Rat Model

Burn injury is a growing medical problem associated with public health, and few effective agents are available for treatment of this disease. In the present study, a burn injury rat model was developed and the accelerated effect of Aloe vera fermentation on burn injury healing was evaluated. Our results indicated that Aloe vera fermentation could markedly reduce the DPPH (56.12%), O2⋅− (93.5%), ⋅OH (76.12%), Fe2+ chelation (82%), and oxygen-reduction activity (0.28 μg/ml) and significantly inhibited the growth of pathogens S. typhimurium ATCC 13311 (inhibition zone diameter: 14 mm), S. enteritidis ATCC13076 (IZD: 13 mm), S. flexneri ATCC 12022 (IZD: 18 mm), E. coli 44102 (IZD: 10 mm), L. monocytogenes ATCC 19111 (IZD: 18 mm), S. dysenteriae 301 (IZD: 20 mm), S. aureus COWAN1 (IZD: 19 mm), and P. acnes ATCC 11827 (IZD: 25 mm) in vitro. The in vivo results indicated that Aloe vera fermentation produced more eosinophils and fibroblasts and less vessel proliferation compared with the model group on the 14th day, which had greatly accelerated burn injury healing via shedding of the scab and promoting hair growth. ELISA results indicated that Aloe vera fermentation had significantly reduced the production of proinflammatory factors TNF-α and IL-1β (p<0.05) and greatly enhanced the yield of anti-inflammatory factor IL-4 in animal serum (p<0.05). In addition, the high-throughput sequencing results indicated that Aloe vera fermentation obviously increased the percentage of Firmicutes (65.86% vs. 49.76%), while reducing the number of Bacteroidetes (27.60% vs. 45.15%) compared with the M group at the phylum level. At the genus level, Aloe vera fermentation increased the probiotic bacteria Lactobacillus (3.13% vs. 2.09%) and reduced the pathogens Prevotella (10.60% vs.18.24%) and Blautia (2.91% vs. 16.41%) compared with the M group. Therefore, we concluded that the use of Aloe vera fermentation significantly accelerates burn injury healing via reduction of the severity of inflammation and through modification of gut microbiota

Multi-objective inverse design of finned heat sink system with physics-informed neural networks

This study proposes a new inverse design method that utilizes a physics-informed neural network (PINN) to parameterize the geometric and operating inputs, enabling the identification of optimal heat sink designs by starting with the desired objectives and working backward. A specialized hybrid PINN is designed to accurately approximate the governing equations of the conjugate heat transfer processes. On this basis, a surrogate model derived from the hybrid PINN is constructed and integrated with multi-objective optimization and decision-making algorithms. The results of an example finned heat sink system are presented, showcasing the accelerated search for Pareto-optimal designs. The proposed method nearly halved the search time to approximately 113.9 h in comparison with the traditional methods. Moreover, three representative scenarios—high-performance design, equilibrium design, and low-cost design —were compared to visualize the real-time changes in the multiphysics field, facilitating improved physical inspection and understanding of the optimal designs.</p

Emotional learning retroactively promotes memory integration through rapid neural reactivation and reorganization

Neutral events preceding emotional experiences can be better remembered, likely by assigning them as significant to guide possible use in future. Yet, the neurobiological mechanisms of how emotional learning enhances memory for past mundane events remain unclear. By two behavioral studies and one functional magnetic resonance imaging study with an adapted sensory preconditioning paradigm, we show rapid neural reactivation and connectivity changes underlying emotion-charged retroactive memory enhancement. Behaviorally, emotional learning retroactively enhanced initial memory for neutral associations across the three studies. Neurally, emotional learning potentiated trial-specific reactivation of overlapping neural traces in the hippocampus and stimulus-relevant neocortex. It further induced rapid hippocampal-neocortical functional reorganization supporting such retroactive memory benefit, as characterized by enhanced hippocampal-neocortical coupling modulated by the amygdala during emotional learning, and a shift of hippocampal connectivity from stimulus-relevant neocortex to distributed transmodal prefrontal-parietal areas at post-learning rests. Together, emotional learning retroactively promotes memory integration for past neutral events through stimulating trial-specific reactivation of overlapping representations and reorganization of associated memories into an integrated network to foster its priority for future use