Analyzing the impact of storage shortage on data availability in decentralized online social networks

Maintaining data availability is one of the biggest challenges in decentralized online social networks (DOSNs). The existing work often assumes that the friends of a user can always contribute to the sufficient storage capacity to store all data. However, this assumption is not always true in today’s online social networks (OSNs) due to the fact that nowadays the users often use the smart mobile devices to access the OSNs. The limitation of the storage capacity in mobile devices may jeopardize the data availability. Therefore, it is desired to know the relation between the storage capacity contributed by the OSN users and the level of data availability that the OSNs can achieve. This paper addresses this issue. In this paper, the data availability model over storage capacity is established. Further, a novel method is proposed to predict the data availability on the fly. Extensive simulation experiments have been conducted to evaluate the effectiveness of the data availability model and the on-the-fly prediction

A Deep Learning Prediction Model Based on Extreme-Point Symmetric Mode Decomposition and Cluster Analysis

Aiming at the irregularity of nonlinear signal and its predicting difficulty, a deep learning prediction model based on extreme-point symmetric mode decomposition (ESMD) and clustering analysis is proposed. Firstly, the original data is decomposed by ESMD to obtain the finite number of intrinsic mode functions (IMFs) and residuals. Secondly, the fuzzy c-means is used to cluster the decomposed components, and then the deep belief network (DBN) is used to predict it. Finally, the reconstructed IMFs and residuals are the final prediction results. Six kinds of prediction models are compared, which are DBN prediction model, EMD-DBN prediction model, EEMD-DBN prediction model, CEEMD-DBN prediction model, ESMD-DBN prediction model, and the proposed model in this paper. The same sunspots time series are predicted with six kinds of prediction models. The experimental results show that the proposed model has better prediction accuracy and smaller error

From Genome-wide Probabilistic Functional Networks to a Model for Physical Chromosome Interactions

It’s increasingly clear that the three-dimensional genome organization is dependent on the functional cell activities. In this thesis, the relationships between genome function, gene transcription activities for specific, and genome physical structure are investigated from different perspectives. From genomewide probabilistic functional networks, we develop two different models for the three-dimensional genome architecture in Escherichia coli as well as in Saccharomyces cerevisiae. To begin with, we explore and confirm the correlation between gene transcription and genome organization by investigating the chimeric transcripts induced by the ‘transcription-induced chimerism’ (TIC). Transcription-induced chimeras involve heterogeneous genes localized on different chromosomes, or on the same chromosome with a large genomic distance. When these genes are concurrently transcribed with a spatial proximity, their transcripts are more likely to be ligated as fusion products. By using bioinformatic approaches, we glean and validate chimeric transcripts from the Expressed Sequence Tag (EST) databases of human and mouse, and use them as the probe to identify physical contacts within the same chromosome or between different chromosomes. The chromosomal contact pattern extracted from the identified fusion transcripts is in agreement with the results from other independent experiments. The utilization of the chimeric transcripts in identifying chromosomal physical interactions shed light on the association between genome structure and genome function. In a subsequent step, we postulate and test one prospective mechanism for the formation of chromosomal domains in the prokaryotic organism, E. coli. A genome folding model, which accounts for the role of gene transcriptional regulatory network (TRN) along with the nucleus confinement, is developed. Considering the stochastic nature of TF-promoter binding, we assume that transcription factors (TFs) and corresponding target genes (TGs) could stay in physical proximity for rapid targeting and more efficient regulation. We validate this model via numerical simulations and re-construct the ordering and the precise subnuclear distribution of the genetic loci that are experimentally screened. With this model, we contribute to a deeper understanding of the spatial chromosome organization in E. coli. Last but not least, inspired by the findings from E. coli, we hold that a compatible interacting way between gene transcription and chromosome organization might exist in eukaryotes as well. Different from prokaryotes, eukaryotic organisms undertake their gene transcription and mRNA translation activities within different cell compartments. For this reason, we postulate a functiondependent genome structure model for budding yeast, in which we assume that genes with highly similar transcriptional control profiles might be recruited to the same subnuclear compartment enriched with specific transcription factors for their expression control. We test this idea with a simple eukaryotic organism, S. cerevisiae. The chromosomal interaction patterns and the folding behavior generated by this model are consistent with the experimental observations. We show that the transcriptional regulatory network has a close linkage with the genome organization in budding yeast, which is fundamental and instrumental to later studies on other more complex eukaryotes

Earnings Management for Second-time IPOs: Evidence from China

In China’s IPO market, firms that fail in their first IPO application make considerable adjustments before making their second IPO application. Examining firms that applied for IPOs during 2004-2018, we find that failed IPO applicant firms “package” themselves to obtain approval of the China Securities Regulatory Commission (CSRC) by reducing accrual earnings management and increasing real earnings management. In addition, after a successful second IPO application, these firms relax their vigilance vis-à-vis the CSRC and increase both accrual and real earnings management. This pre-IPO “packaging” behavior deceives investors, leading to higher IPO prices and higher post-IPO returns

Design of the Tsinghua Tabletop Kibble Balance

The Kibble balance is a precision instrument for realizing the mass unit, the kilogram, in the new international system of units (SI). In recent years, an important trend for Kibble balance experiments is to go tabletop, in which the instrument's size is notably reduced while retaining a measurement accuracy of $10^{-8}$. In this paper, we report a new design of a tabletop Kibble balance to be built at Tsinghua University. The Tsinghua Kibble balance aims to deliver a compact instrument for robust mass calibrations from 10 g to 1 kg with a targeted measurement accuracy of 50 $\mu$g or less. Some major features of the Tsinghua Kibble balance system, including the design of a new magnet, one-mode measurement scheme, the spring-compensated magnet moving mechanism, and magnetic shielding considerations, are discussed.Comment: 8 pages, 9 figure

TENSILE: A Tensor granularity dynamic GPU memory scheduling method towards multiple dynamic workloads system

Recently, deep learning has been an area of intense research. However, as a kind of computing-intensive task, deep learning highly relies on the scale of GPU memory, which is usually prohibitive and scarce. Although there are some extensive works have been proposed for dynamic GPU memory management, they are hard to be applied to systems with multiple dynamic workloads, such as in-database machine learning systems. In this paper, we demonstrated TENSILE, a method of managing GPU memory in tensor granularity to reduce the GPU memory peak, considering the multiple dynamic workloads. TENSILE tackled the cold-starting and across-iteration scheduling problem existing in previous works. We implement TENSILE on a deep learning framework built by ourselves and evaluated its performance. The experiment results show that TENSILE can save more GPU memory with less extra time overhead than prior works in both single and multiple dynamic workloads scenarios