Succinct Explanations With Cascading Decision Trees

The decision tree is one of the most popular and classical machine learning models from the 1980s. However, in many practical applications, decision trees tend to generate decision paths with excessive depth. Long decision paths often cause overfitting problems, and make models difficult to interpret. With longer decision paths, inference is also more likely to fail when the data contain missing values. In this work, we propose a new tree model called Cascading Decision Trees to alleviate this problem. The key insight of Cascading Decision Trees is to separate the decision path and the explanation path. Our experiments show that on average, Cascading Decision Trees generate 63.38% shorter explanation paths, avoiding overfitting and thus achieve higher test accuracy. We also empirically demonstrate that Cascading Decision Trees have advantages in the robustness against missing values

Mutations in porin LamB contribute to ceftazidime-avibactam resistance in KPC-producing Klebsiella pneumoniae.

Ceftazidime-avibactam (CAZ-AVI) shows promising activity against carbapenem-resistant Klebsiella pneumoniae (CRKP), however, CAZ-AVI resistance have emerged recently. Mutations in KPCs, porins OmpK35 and/or OmpK36, and PBPs are known to contribute to the resistance to CAZ-AVI in CRKP. To identify novel CAZ-AVI resistance mechanism, we generated 10 CAZ-AVI-resistant strains from 14 CAZ-AVI susceptible KPC-producing K. pneumoniae (KPC-Kp) strains through in vitro multipassage resistance selection using low concentrations of CAZ-AVI. Comparative genomic analysis for the original and derived mutants identified CAZ-AVI resistance-associated mutations in KPCs, PBP3 (encoded by ftsI), and LamB, an outer membrane maltoporin. CAZ-AVI susceptible KPC-Kp strains became resistant when complemented with mutated blaKPC genes. Complementation experiments also showed that a plasmid borne copy of wild-type lamB or ftsI gene reduced the MIC value of CAZ-AVI in the induced resistant strains. In addition, blaKPC expression level increased in four of the six CAZ-AVI-resistant strains without KPC mutations, indicating a probable association between increased blaKPC expression and increased resistance in these strains. In conclusion, we here identified a novel mechanism of CAZ-AVI resistance associated with mutations in porin LamB in KPC-Kp

Fast Submodular Function Maximization

Submodular functions have many real-world applications, such as document summarization, sensor placement, and image segmentation. For all these applications, the key building block is how to compute the maximum value of a submodular function efficiently. We consider both the online and offline versions of the problem: in each iteration, the data set changes incrementally or is not changed, and a user can issue a query to maximize the function on a given subset of the data. The user can be malicious, issuing queries based on previous query results to break the competitive ratio for the online algorithm. Today, the best-known algorithm for online submodular function maximization has a running time of $O(n k d^2)$ where $n$ is the total number of elements, $d$ is the feature dimension and $k$ is the number of elements to be selected. We propose a new method based on a novel search tree data structure. Our algorithm only takes $\widetilde{O}(nk + kd^2 + nd)$ time

Temporally Adaptive Restricted Boltzmann Machine for Background Modeling

We examine the fundamental problem of background modeling which is to model the background scenes in video sequences and segment the moving objects from the background. A novel approach is proposed based on the Restricted Boltzmann Machine (RBM) while exploiting the temporal nature of the problem. In particular, we augment the standard RBM to take a window of sequential video frames as input and generate the background model while enforcing the background smoothly adapting to the temporal changes. As a result, the augmented temporally adaptive model can generate stable background given noisy inputs and adapt quickly to the changes in background while keeping all the advantages of RBMs including exact inference and effective learning procedure. Experimental results demonstrate the effectiveness of the proposed method in modeling the temporal nature in background

Association between Personal Social Capital and Loneliness among Widowed Older People

To explore the association between the personal social capital and loneliness among the widowed older adults in China. Data from 1497 widowed older adults were extracted from China’s Health-Related Quality of Life Survey for Older Adults 2018. The Chinese version of the Personal Social Capital Scale (PSCS-16) was used to evaluate the participants’ status of bonding and bridging social capital (BOC and BRC). Loneliness was assessed by the short-form UCLA Loneliness Scale (ULS-8). Multiple linear regression models were established to examine the relationship between social capital and loneliness. The BOC and BRC of rural widowed older people were significantly lower than those of widowed older people in urban areas, while loneliness of rural widowed older people was higher than that of widowed older people in urban areas. The result of the final model showed that loneliness of rural participants was significantly associated with both BOC (B = 0.141, p = 0.001) and BRC (B = −0.116, p = 0.003). The loneliness of the urban widowed sample had no association with both BOC and BRC (p > 0.05). These findings suggested that more social support and compassionate care should be provided to enrich the personal social capital and thus to reduce loneliness of widowed older adults, especially those in rural areas