Exact Single-Source SimRank Computation on Large Graphs

SimRank is a popular measurement for evaluating the node-to-node similarities based on the graph topology. In recent years, single-source and top-$k$ SimRank queries have received increasing attention due to their applications in web mining, social network analysis, and spam detection. However, a fundamental obstacle in studying SimRank has been the lack of ground truths. The only exact algorithm, Power Method, is computationally infeasible on graphs with more than $10^6$ nodes. Consequently, no existing work has evaluated the actual trade-offs between query time and accuracy on large real-world graphs. In this paper, we present ExactSim, the first algorithm that computes the exact single-source and top-$k$ SimRank results on large graphs. With high probability, this algorithm produces ground truths with a rigorous theoretical guarantee. We conduct extensive experiments on real-world datasets to demonstrate the efficiency of ExactSim. The results show that ExactSim provides the ground truth for any single-source SimRank query with a precision up to 7 decimal places within a reasonable query time.Comment: ACM SIGMOD 202

An Effective Two-stage Training Paradigm Detector for Small Dataset

Learning from the limited amount of labeled data to the pre-train model has always been viewed as a challenging task. In this report, an effective and robust solution, the two-stage training paradigm YOLOv8 detector (TP-YOLOv8), is designed for the object detection track in VIPriors Challenge 2023. First, the backbone of YOLOv8 is pre-trained as the encoder using the masked image modeling technique. Then the detector is fine-tuned with elaborate augmentations. During the test stage, test-time augmentation (TTA) is used to enhance each model, and weighted box fusion (WBF) is implemented to further boost the performance. With the well-designed structure, our approach has achieved 30.4% average precision from 0.50 to 0.95 on the DelftBikes test set, ranking 4th on the leaderboard.Comment: 4 pages, 2 figure

Optimal Dynamic Subset Sampling: Theory and Applications

We study the fundamental problem of sampling independent events, called subset sampling. Specifically, consider a set of $n$ events $S=\{x_1, \ldots, x_n\}$, where each event $x_i$ has an associated probability $p(x_i)$. The subset sampling problem aims to sample a subset $T \subseteq S$, such that every $x_i$ is independently included in $S$ with probability $p_i$. A naive solution is to flip a coin for each event, which takes $O(n)$ time. However, the specific goal is to develop data structures that allow drawing a sample in time proportional to the expected output size $\mu=\sum_{i=1}^n p(x_i)$, which can be significantly smaller than $n$ in many applications. The subset sampling problem serves as an important building block in many tasks and has been the subject of various research for more than a decade. However, most of the existing subset sampling approaches are conducted in a static setting, where the events or their associated probability in set $S$ is not allowed to be changed over time. These algorithms incur either large query time or update time in a dynamic setting despite the ubiquitous time-evolving events with changing probability in real life. Therefore, it is a pressing need, but still, an open problem, to design efficient dynamic subset sampling algorithms. In this paper, we propose ODSS, the first optimal dynamic subset sampling algorithm. The expected query time and update time of ODSS are both optimal, matching the lower bounds of the subset sampling problem. We present a nontrivial theoretical analysis to demonstrate the superiority of ODSS. We also conduct comprehensive experiments to empirically evaluate the performance of ODSS. Moreover, we apply ODSS to a concrete application: influence maximization. We empirically show that our ODSS can improve the complexities of existing influence maximization algorithms on large real-world evolving social networks.Comment: ACM SIGKDD 202

Approaches towards advanced brain age prediction models

As the global population ages, it becomes crucial for the early detection and prevention of neurological aspects of ageing, such as cognitive decline. The human brain ageing process is biologically complex and could be affected by various factors. Therefore, the determination of a person’s brain biological ageing process holds important clinical implications, which reflect that person’s brain health and may indicate the risk of age-associated brain diseases. To quantitatively measure the brain biological ageing process, brain age, defined as the biological age of the brain, has been proposed, which has demonstrated huge potential in clinical diagnosis. Brain age can be estimated by brain age prediction models, which take in brain-ageing related information, such as brain MR scans, and adopt machine learning models to make predictions. Despite the rapid research advancements in brain age prediction over the past decade, brain age prediction framework has yet to mature before the implementation in clinical practice. In this thesis, we propose three novel approaches, each focused on a distinct perspective, to make the brain age prediction model a more reliable, practical, and accurate tool for clinical diagnosis. Our contribution is three-fold: firstly, we propose a skewed loss function to correct a commonly-observed regression bias in brain age prediction models. The skewed loss function unifies the model training and bias correction stages, achieving improved accuracy compared with the state-of-the-art practices in the literature. A dynamic training algorithm is further proposed for the skewed loss function. It adopts a heuristic approach to iteratively tune the hyperparameters of the skewed loss function, which has been proven to be robust to different datasets, model architectures and problem domains. The proposed skewed loss function makes the model produce unbiased estimations of brain age, which makes brain age prediction a reliable and trustworthy tool for clinical use. Secondly, we generalise brain age prediction for clinical-grade low-resolution MR images, which makes it a practical and accessible tool for hospital settings. We propose an integrated workflow by combining brain super-resolution and age prediction models. Clinical MR images are firstly super-resolved, before being fed into a pre-trained age prediction model, which have been proven to achieve negligible differences in predicting age compared with high-resolution images. A non-uniform sampling strategy is also demonstrated to improve the image reconstruction quality especially in the high-frequency regions of the brain. Lastly, we demonstrate the strength of adopting a multi-modal approach for predicting brain age more accurately compared with uni-modal models. Structural T1-weighted MR images and diffusion MRI measures of the brain microstructures are adopted to provide the model with a more complete picture of brain ageing. A tract-wise training approach is also proposed for predicting brain age from diffusion MRI measures, which performs feature selections in the model training process. It prioritises more age-sensitive features and discards less useful ones for brain age prediction, which achieves an improved accuracy on a relatively small dataset

Enhancing Drug Delivery Precision: Development and Optimization of Nanoparticle-Based Formulations for Targeted Therapy in Preclinical Models

In recent years, the utilization of nanoparticles has proliferated across a wide spectrum of clinical domains. Nanoparticles have been engineered to surmount the constraints associated with free therapeutics and negotiate biological barriers—systemic, microenvironmental, and cellular—that exhibit heterogeneity across diverse patient cohorts and diseases. Mitigating this patient heterogeneity has also been facilitated through precision therapeutics, where tailored interventions have augmented therapeutic effectiveness. Nonetheless, current nanoparticle development predominantly emphasizes the refinement of delivery platforms with a uniform approach. As lipid-based, polymeric, and inorganic nanoparticles undergo increasingly nuanced engineering, there arises the potential for tailoring them to drug delivery in a more personalized manner, ushering in the era of precision medicine. In this Review, we deliberate on sophisticated nanoparticle designs employed in both generalized and precision applications, offering insights into their potential for enhancing precision therapies. We concentrate on advancements in nanoparticle design that surmount heterogeneous barriers to delivery, positing that intelligent nanoparticle design can enhance efficacy in broad delivery applications while facilitating customized designs for precision applications, thereby ultimately enhancing overall patient outcomes

Enhancing Drug Delivery Precision: Development and Optimization of Nanoparticle-Based Formulations for Targeted Therapy in Preclinical Models

In recent years, the utilization of nanoparticles has proliferated across a wide spectrum of clinical domains. Nanoparticles have been engineered to surmount the constraints associated with free therapeutics and negotiate biological barriers—systemic, microenvironmental, and cellular—that exhibit heterogeneity across diverse patient cohorts and diseases. Mitigating this patient heterogeneity has also been facilitated through precision therapeutics, where tailored interventions have augmented therapeutic effectiveness. Nonetheless, current nanoparticle development predominantly emphasizes the refinement of delivery platforms with a uniform approach. As lipid-based, polymeric, and inorganic nanoparticles undergo increasingly nuanced engineering, there arises the potential for tailoring them to drug delivery in a more personalized manner, ushering in the era of precision medicine. In this Review, we deliberate on sophisticated nanoparticle designs employed in both generalized and precision applications, offering insights into their potential for enhancing precision therapies. We concentrate on advancements in nanoparticle design that surmount heterogeneous barriers to delivery, positing that intelligent nanoparticle design can enhance efficacy in broad delivery applications while facilitating customized designs for precision applications, thereby ultimately enhancing overall patient outcomes

Go beyond End-to-End Training: Boosting Greedy Local Learning with Context Supply

Traditional end-to-end (E2E) training of deep networks necessitates storing intermediate activations for back-propagation, resulting in a large memory footprint on GPUs and restricted model parallelization. As an alternative, greedy local learning partitions the network into gradient-isolated modules and trains supervisely based on local preliminary losses, thereby providing asynchronous and parallel training methods that substantially reduce memory cost. However, empirical experiments reveal that as the number of segmentations of the gradient-isolated module increases, the performance of the local learning scheme degrades substantially, severely limiting its expansibility. To avoid this issue, we theoretically analyze the greedy local learning from the standpoint of information theory and propose a ContSup scheme, which incorporates context supply between isolated modules to compensate for information loss. Experiments on benchmark datasets (i.e. CIFAR, SVHN, STL-10) achieve SOTA results and indicate that our proposed method can significantly improve the performance of greedy local learning with minimal memory and computational overhead, allowing for the boost of the number of isolated modules. Our codes are available at https://github.com/Tab-ct/ContSup.Comment: 9 figures, 12 table

SDiT: Spiking Diffusion Model with Transformer

Spiking neural networks (SNNs) have low power consumption and bio-interpretable characteristics, and are considered to have tremendous potential for energy-efficient computing. However, the exploration of SNNs on image generation tasks remains very limited, and a unified and effective structure for SNN-based generative models has yet to be proposed. In this paper, we explore a novel diffusion model architecture within spiking neural networks. We utilize transformer to replace the commonly used U-net structure in mainstream diffusion models. It can generate higher quality images with relatively lower computational cost and shorter sampling time. It aims to provide an empirical baseline for research of generative models based on SNNs. Experiments on MNIST, Fashion-MNIST, and CIFAR-10 datasets demonstrate that our work is highly competitive compared to existing SNN generative models

INVESTIGATION ON THE CURRENT SITUATION AND COUNTERMEASURES OF EXTRACURRICULAR READING FOR PRIMARY SCHOOL STUDENTS: A CASE STUDY OF ZHEJIANG, CHINA

Based on a survey of 697 students in primary schools of Hangzhou, Ningbo, and Jiaxing in Zhejiang Province, this paper found that: (1) 25% of parents support their children to read reference books and regular parent-child reading, but most of the parents are afraid it may negatively influence school curriculum so they limit or against extracurricular reading and do not carry out the parent-child reading; only 27% of teachers often assign extracurricular reading tasks, most rarely or never, parents and schools currently pay little attention to extracurricular reading. (2) Eighty one percent of children spent 2 hours or more on homework or reviewing lessons every day, 88% of them watched about 1 hour of TV every day, and 68% of them played video games for about 1 hour. Except eating, sleeping and commuting, they have an average of 4-5 hours of free time outside school, so more than 50% of primary school students spend less than 0.5 hour reading Chinese books every day. (3) Nearly 80% of children read 2-5 Chinese books every month and spend around ￥500 on books every year; in addition, more than 60% of the respondents read paper books, and the reading of extracurricular English books is close to zero, which caused their limited reading volume, narrow scope of knowledge, restricted international vision, and inadequate reading habits. The situation extracurricular reading is not ideal, which may seriously affect the future academic and career development. To improve the unfavorable situation, this paper put forward the following suggestions: (1) all levels of government should allocate fund to establish and improve bookshelves in school classroom and community library, promote "home - school - community cooperation reading plan", let the children at school can have more 0.5-1 hours of reading at noon, and can approach in the community library for reading on weekend and holidays; (2) distribute free books to low-income families and advocate encouraging parents to spend half an hour reading with their children; (3) the school attaches great importance to the extracurricular reading education, organize pupils in class after school every afternoon 1 hour or so of intensive reading, storytelling and drama class PK activities such as English, both can effectively improve the student's reading interest, also solved the question which is many parents unable to pick up their kids during work at 3:30 p.m.; (4) promote digital reading, reduce the cost of reading, and correctly understand the impact of digital reading on eyesight.  Article visualizations