The Cost of Parallelizing Boosting

We study the cost of parallelizing weak-to-strong boosting algorithms for learning, following the recent work of Karbasi and Larsen. Our main results are two-fold: - First, we prove a tight lower bound, showing that even "slight" parallelization of boosting requires an exponential blow-up in the complexity of training. Specifically, let $\gamma$ be the weak learner's advantage over random guessing. The famous \textsc{AdaBoost} algorithm produces an accurate hypothesis by interacting with the weak learner for $\tilde{O}(1 / \gamma^2)$ rounds where each round runs in polynomial time. Karbasi and Larsen showed that "significant" parallelization must incur exponential blow-up: Any boosting algorithm either interacts with the weak learner for $\Omega(1 / \gamma)$ rounds or incurs an $\exp(d / \gamma)$ blow-up in the complexity of training, where $d$ is the VC dimension of the hypothesis class. We close the gap by showing that any boosting algorithm either has $\Omega(1 / \gamma^2)$ rounds of interaction or incurs a smaller exponential blow-up of $\exp(d)$. -Complementing our lower bound, we show that there exists a boosting algorithm using $\tilde{O}(1/(t \gamma^2))$ rounds, and only suffer a blow-up of $\exp(d \cdot t^2)$. Plugging in $t = \omega(1)$, this shows that the smaller blow-up in our lower bound is tight. More interestingly, this provides the first trade-off between the parallelism and the total work required for boosting.Comment: appeared in SODA 202

Adaptive Semantic-Visual Tree for Hierarchical Embeddings

Merchandise categories inherently form a semantic hierarchy with different levels of concept abstraction, especially for fine-grained categories. This hierarchy encodes rich correlations among various categories across different levels, which can effectively regularize the semantic space and thus make predictions less ambiguous. However, previous studies of fine-grained image retrieval primarily focus on semantic similarities or visual similarities. In a real application, merely using visual similarity may not satisfy the need of consumers to search merchandise with real-life images, e.g., given a red coat as a query image, we might get a red suit in recall results only based on visual similarity since they are visually similar. But the users actually want a coat rather than suit even the coat is with different color or texture attributes. We introduce this new problem based on photoshopping in real practice. That's why semantic information are integrated to regularize the margins to make "semantic" prior to "visual". To solve this new problem, we propose a hierarchical adaptive semantic-visual tree (ASVT) to depict the architecture of merchandise categories, which evaluates semantic similarities between different semantic levels and visual similarities within the same semantic class simultaneously. The semantic information satisfies the demand of consumers for similar merchandise with the query while the visual information optimizes the correlations within the semantic class. At each level, we set different margins based on the semantic hierarchy and incorporate them as prior information to learn a fine-grained feature embedding. To evaluate our framework, we propose a new dataset named JDProduct, with hierarchical labels collected from actual image queries and official merchandise images on an online shopping application. Extensive experimental results on the public CARS196 and CUB

Self-supervised Video Representation Learning with Motion-Aware Masked Autoencoders

Masked autoencoders (MAEs) have emerged recently as art self-supervised spatiotemporal representation learners. Inheriting from the image counterparts, however, existing video MAEs still focus largely on static appearance learning whilst are limited in learning dynamic temporal information hence less effective for video downstream tasks. To resolve this drawback, in this work we present a motion-aware variant -- MotionMAE. Apart from learning to reconstruct individual masked patches of video frames, our model is designed to additionally predict the corresponding motion structure information over time. This motion information is available at the temporal difference of nearby frames. As a result, our model can extract effectively both static appearance and dynamic motion spontaneously, leading to superior spatiotemporal representation learning capability. Extensive experiments show that our MotionMAE outperforms significantly both supervised learning baseline and state-of-the-art MAE alternatives, under both domain-specific and domain-generic pretraining-then-finetuning settings. In particular, when using ViT-B as the backbone our MotionMAE surpasses the prior art model by a margin of 1.2% on Something-Something V2 and 3.2% on UCF101 in domain-specific pretraining setting. Encouragingly, it also surpasses the competing MAEs by a large margin of over 3% on the challenging video object segmentation task. The code is available at https://github.com/happy-hsy/MotionMAE.Comment: 17 pages, 6 figure

Biochemical characterization of a thermostable DNA ligase from the hyperthermophilic euryarchaeon Thermococcus barophilus Ch5

International audienc

The realization logic of rural revitalization: Coupled coordination analysis of development and governance.

BackgroundSocialism with Chinese characteristics has entered a new stage. The principal social contradiction is the uneven development of urban and rural areas. The rural revitalization strategy has emerged as time has required. The realization of rural revitalization not only requires development to lay the foundation of the countryside but also requires governance to lead the development of the countryside. Development and governance are two indispensable aspects of rural revitalization. However, China's rural areas have long been in a state of development without governance, and this situation must change. Therefore, systematically exploring the relationship between development and governance is the key to solving the current shortcomings in rural areas.MethodsBased on the data from the statistical yearbook, the study constructed a set of evaluation indicators for rural development governance and revitalization and verified the model's effectiveness.The entropy method and the assessment model were used to calculate the comprehensive score of rural development, governance, and revitalization. The relationship between rural development and governance was analyzed using a coupled coordination model. The regression analysis model was used to explore the relationship between the coupling results of rural development, governance, and rural revitalization.ResultsFrom the comprehensive results, both development and governance show an upward trend, but the upward trend of development is better. From the analysis of coupling coordination between development and governance, the C value is in good condition, the T value fluctuates wildly, and the D fluctuates with the fluctuation of T. Judging from the comprehensive score of rural revitalization, it also shows an upward trend year by year. Judging from the regression analysis results of coupling coordination degree and rural revitalization comprehensive score, coupling coordination degree will significantly impact the rural revitalization evaluation value.ConclusionsThe study found that current rural development and governance present a spiral coupling coordination relationship, and the degree of coupling coordination significantly correlates with rural revitalization. Based on the research conclusions, the study further proposes three paths to promote the coupling and coordination of development and governance. The first is an organizational isomorphism, which builds a coupled coordination system for rural development and governance. The second is to tilt resources and improve the supply of connected and coordinated factors for rural development and governance. The third is the operating mechanism to optimize rural development and governance's coupling and coordination path

Categorical Neighbour Correlation Coefficient (CnCor) for Detecting Relationships between Categorical Variables

Categorical data is common and, however, special in that its possible values exist only on a nominal scale so that many statistical operations such as mean, variance, and covariance become not applicable. Following the basic idea of the neighbour correlation coefficient (nCor), in this study, we propose a new measure named the categorical nCor (CnCor) to examine the association between categorical variables through using indicator functions to reform the distance metric and product-moment correlation coefficient. The proposed measure is easy to compute, and enables a direct test of statistical dependence without the need of converting the qualitative variables to quantitative ones. Compare to previous approaches, it is much more robust and effective in dealing with multi-categorical target variables especially when highly nonlinear relationships occurs in the multivariate case. We also applied the CnCor to implementing feature selection by the scheme of backward elimination. Finally, extensive experiments performed on both synthetic and real-world datasets are conducted to demonstrate the outstanding performance of the proposed methods, and draw comparisons with state-of-the-art association measures and feature selection algorithms