Inside the bubble 2.0

As a designer, I use speculation to observe. I use design to express. I want to hit people\u27s souls directly. I want to create unexpected sparks through relaxed storytelling

ACTS in Need: Automatic Configuration Tuning with Scalability Guarantees

To support the variety of Big Data use cases, many Big Data related systems expose a large number of user-specifiable configuration parameters. Highlighted in our experiments, a MySQL deployment with well-tuned configuration parameters achieves a peak throughput as 12 times much as one with the default setting. However, finding the best setting for the tens or hundreds of configuration parameters is mission impossible for ordinary users. Worse still, many Big Data applications require the support of multiple systems co-deployed in the same cluster. As these co-deployed systems can interact to affect the overall performance, they must be tuned together. Automatic configuration tuning with scalability guarantees (ACTS) is in need to help system users. Solutions to ACTS must scale to various systems, workloads, deployments, parameters and resource limits. Proposing and implementing an ACTS solution, we demonstrate that ACTS can benefit users not only in improving system performance and resource utilization, but also in saving costs and enabling fairer benchmarking

Nonlinear Weighted Directed Acyclic Graph and A Priori Estimates for Neural Networks

In an attempt to better understand structural benefits and generalization power of deep neural networks, we firstly present a novel graph theoretical formulation of neural network models, including fully connected, residual network (ResNet) and densely connected networks (DenseNet). Secondly, we extend the error analysis of the population risk for two layer network \cite{ew2019prioriTwo} and ResNet \cite{e2019prioriRes} to DenseNet, and show further that for neural networks satisfying certain mild conditions, similar estimates can be obtained. These estimates are a priori in nature since they depend sorely on the information prior to the training process, in particular, the bounds for the estimation errors are independent of the input dimension

An Empirical Study on the Efficiency of Financial Support for Economic Development in Henan Province Based on DEA Malmquist Index Method

The report of the 19th National People’s Congress points out that China’s social economy will gradually transition from the pursuit of high-speed operation to the pursuit of high-quality economic operation. The financial industries will strongly support the economic development in all aspects, however, studies show that the efficiency of finance in supporting economic development presents regional differences. This paper adopts the universally applicable DEA-Malmquist method and takes the panel data of Henan Province from 2013 to 2019 as the benchmark to calculate and study the input-output ratio of financial support for economic operation and development in each region of Henan Province, and the results show that the overall efficiency of financial service economy in each urban area of Henan Province has increased in the past seven years. According to the measured results in the first four parts of the most efficiency increase is analyzed, and based on the analysis results in the economic development of Henan province put forward some proposals in the new journey, increase the intensity of financial innovation and development, to carry out in-depth financial inclusion work, and to solve the problem that small and micro enterprises faced in raising funds and the slow pace of raising funds, in order to achieve a reasonable distribution of financial resources between the suppliers of funds and the demanders of funds

BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning

An ever increasing number of configuration parameters are provided to system users. But many users have used one configuration setting across different workloads, leaving untapped the performance potential of systems. A good configuration setting can greatly improve the performance of a deployed system under certain workloads. But with tens or hundreds of parameters, it becomes a highly costly task to decide which configuration setting leads to the best performance. While such task requires the strong expertise in both the system and the application, users commonly lack such expertise. To help users tap the performance potential of systems, we present BestConfig, a system for automatically finding a best configuration setting within a resource limit for a deployed system under a given application workload. BestConfig is designed with an extensible architecture to automate the configuration tuning for general systems. To tune system configurations within a resource limit, we propose the divide-and-diverge sampling method and the recursive bound-and-search algorithm. BestConfig can improve the throughput of Tomcat by 75%, that of Cassandra by 63%, that of MySQL by 430%, and reduce the running time of Hive join job by about 50% and that of Spark join job by about 80%, solely by configuration adjustment

HoxA9 binds and represses the Cebpa +8 kb enhancer

C/EBPα plays a key role in specifying myeloid lineage development. HoxA9 is expressed in myeloid progenitors, with its level diminishing during myeloid maturation, and HOXA9 is over-expressed in a majority of acute myeloid leukemia cases, including those expressing NUP98-HOXD13. The objective of this study was to determine whether HoxA9 directly represses Cebpa gene expression. We find 4-fold increased HoxA9 and 5-fold reduced Cebpa in marrow common myeloid and LSK progenitors from Vav-NUP98-HOXD13 transgenic mice. Conversely, HoxA9 decreases 5-fold while Cebpa increases during granulocytic differentiation of 32Dcl3 myeloid cells. Activation of exogenous HoxA9-ER in 32Dcl3 cells reduces Cebpa mRNA even in the presence of cycloheximide, suggesting direct repression. Cebpa transcription in murine myeloid cells is regulated by a hematopoietic-specific +37 kb enhancer and by a more widely active +8 kb enhancer. ChIP-Seq analysis of primary myeloid progenitor cells expressing exogenous HoxA9 or HoxA9-ER demonstrates that HoxA9 localizes to both the +8 kb and +37 kb Cebpa enhancers. Gel shift analysis demonstrates HoxA9 binding to three consensus sites in the +8 kb enhancer, but no affinity for the single near-consensus site present in the +37 kb enhancer. Activity of a Cebpa +8 kb enhancer/promoter-luciferase reporter in 32Dcl3 or MOLM14 myeloid cells is increased ~2-fold by mutation of its three HOXA9-binding sites, suggesting that endogenous HoxA9 represses +8 kb Cebpa enhancer activity. In contrast, mutation of five C/EBPα-binding sites in the +8 kb enhancer reduces activity 3-fold. Finally, expression of a +37 kb enhancer/promoter-hCD4 transgene reporter is reduced ~2-fold in marrow common myeloid progenitors when the Vav-NUP98-HOXD13 transgene is introduced. Overall, these data support the conclusion that HoxA9 represses Cebpa expression, at least in part via inhibition of its +8 kb enhancer, potentially allowing normal myeloid progenitors to maintain immaturity and contributing to the pathogenesis of acute myeloid leukemia associated with increased HOXA9

Stratified Rule-Aware Network for Abstract Visual Reasoning

Abstract reasoning refers to the ability to analyze information, discover rules at an intangible level, and solve problems in innovative ways. Raven's Progressive Matrices (RPM) test is typically used to examine the capability of abstract reasoning. The subject is asked to identify the correct choice from the answer set to fill the missing panel at the bottom right of RPM (e.g., a 3$\times$3 matrix), following the underlying rules inside the matrix. Recent studies, taking advantage of Convolutional Neural Networks (CNNs), have achieved encouraging progress to accomplish the RPM test. However, they partly ignore necessary inductive biases of RPM solver, such as order sensitivity within each row/column and incremental rule induction. To address this problem, in this paper we propose a Stratified Rule-Aware Network (SRAN) to generate the rule embeddings for two input sequences. Our SRAN learns multiple granularity rule embeddings at different levels, and incrementally integrates the stratified embedding flows through a gated fusion module. With the help of embeddings, a rule similarity metric is applied to guarantee that SRAN can not only be trained using a tuplet loss but also infer the best answer efficiently. We further point out the severe defects existing in the popular RAVEN dataset for RPM test, which prevent from the fair evaluation of the abstract reasoning ability. To fix the defects, we propose an answer set generation algorithm called Attribute Bisection Tree (ABT), forming an improved dataset named Impartial-RAVEN (I-RAVEN for short). Extensive experiments are conducted on both PGM and I-RAVEN datasets, showing that our SRAN outperforms the state-of-the-art models by a considerable margin.Comment: AAAI 2021 paper. Code: https://github.com/husheng12345/SRA

Changes in snow depth under elevation‐dependent warming over the Tibetan Plateau

Abstract Snow plays an essential role in regulating climate change, the hydrological cycle, and various biological processes. Passive microwave snow depth data and gridded data from the Climate Research Unit (CRU_TS4.04) are utilized in this study to investigate spatiotemporal variations of snow depth over the Tibetan Plateau (TP), with special focus on the vertical dimension. The response of snow to elevation‐dependent warming (EDW) is determined accordingly. High mountains experience more rapid warming than lower elevations. During 1980–2014, the total snow depth over the TP decreased; areas with the most significant decreasing trends are mainly concentrated in the northwestern and southwestern parts of the TP. The plateau‐wide decrease in snow depth (−0.24 cm/decade) is mainly affected by increasing temperature (0.30°C/decade). The reduction in snow depth trend intensifies as sub‐regional mean elevation increases from 3,332 m (IID2) to 5,074 m (ID1). A stronger snow depth decrease in high‐elevation sub‐regions generally corresponds to higher warming rates, which demonstrates EDW. The most pronounced correlation between snow depth decrease rate and elevation occurs in the southeastern TP, which covers the largest elevation range on the plateau (from 2,000 to 6,000 m)