Efficient Estimation of the Partly Linear Additive Hazards Model with Current Status Data

This paper focuses on efficient estimation, optimal rates of convergence and effective algorithms in the partly linear additive hazards regression model with current status data. We use polynomial splines to estimate both cumulative baseline hazard function with monotonicity constraint and nonparametric regression functions with no such constraint. We propose a simultaneous sieve maximum likelihood estimation for regression parameters and nuisance parameters and show that the resultant estimator of regression parameter vector is asymptotically normal and achieves the semiparametric information bound. In addition, we show that rates of convergence for the estimators of nonparametric functions are optimal. We implement the proposed estimation through a backfitting algorithm on generalized linear models. We conduct simulation studies to examine the finite‐sample performance of the proposed estimation method and present an analysis of renal function recovery data for illustration.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110752/1/sjos12108.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/110752/2/sjos12108-sup-0001-supinfo.pd

EFFECT OF ILLUMINATION ON THE OBSTACLE-CROSSING BEHAVIORS OF ELDERLY WOMEN

The purpose of this study was to determine how illumination affect elderly women when stepping over obstacles. A motion capture system was used to collect the kinematics data of 15 elderly women. The results revealed that the obstacle-crossing behavior of elderly women were affected by the illumination. Compare to the high illumination condition, the elderly women decreased their toe distance and heel distance (

Efficient Estimation of the Partly Linear Additive Hazards Model with Current Status Data

ABSTRACT. This paper focuses on efficient estimation, optimal rates of convergence and effective algorithms in the partly linear additive hazards regression model with current status data. We use polynomial splines to estimate both cumulative baseline hazard function with monotonicity constraint and nonparametric regression functions with no such constraint. We propose a simultaneous sieve maximum likelihood estimation for regression parameters and nuisance parameters and show that the resultant estimator of regression parameter vector is asymptotically normal and achieves the semiparametric information bound. In addition, we show that rates of convergence for the estimators of nonparametric functions are optimal. We implement the proposed estimation through a backfitting algorithm on generalized linear models. We conduct simulation studies to examine the finite-sample performance of the proposed estimation method and present an analysis of renal function recovery data for illustration

Effect of Chiral Symmetry Restoration on Pentaquark Θ+\Theta^+ Mass and Width at Finite Temperature and Density

We investigate the effect of chiral phase transition on the pentaquark $\Theta^+$ mass and width at one-loop level of $N\Theta^+K$ coupling at finite temperature and density. The behavior of the mass, especially the width in hadronic medium is dominated by the characteristics of chiral symmetry restoration at high temperature and high density. The mass and width shifts of positive-parity $\Theta^+$ are much larger than that of negative-parity one, which may be helpful to determine the parity of $\Theta^+$ in high energy nuclear collisions.Comment: 7 pages, 5 figure

Dedifferentiation process driven by radiotherapy-induced HMGB1/TLR2/YAP/HIF-1α signaling enhances pancreatic cancer stemness

Differentiated cancer cells reacquiring stem cell traits following radiotherapy may enrich cancer stem cells and accelerate tumor recurrence and metastasis. We are interested in the mechanistic role of dying cells-derived HMGB1 in CD133− pancreatic cancer cells dedifferentiation following radiotherapy. We firstly confirmed that X-ray irradiation induced differentiation of CD133− pancreatic cancer cells, from either sorted from patient samples or established cell lines, into cancer stem-like cells (iCSCs). Using an in vitro coculture model, X-ray irradiation induced dying cells to release HMGB1, which further promoted CD133− pancreatic cancer cells regaining stem cell traits, such as higher sphere forming ability and expressed higher level of stemness-related genes and proteins. Inhibiting the expression and activity of HMGB1 attenuated the dedifferentiation stimulating effect of irradiated, dying cells on C133− pancreatic cancer cells in vitro and in PDX models. Mechanistically, HMGB1 binding with TLR2 receptor functions in a paracrine manner to affect CD133− pancreatic cancer cells dedifferentiation via activating Hippo-YAP pathway and HIF-1α expression in oxygen independent manner in vitro and in vivo. We conclude that X-ray irradiation induces CD133− pancreatic cancer cell dedifferentiation into a CSC phenotype, and inhibiting HMGB1 may be a strategy to prevent CSC enrichment and further pancreatic carcinoma relapse.</p

Genome structure and evolutionary history of frankincense producing \u3ci\u3eBoswellia sacra\u3c/i\u3e

Boswellia sacra Flueck (family Burseraceae) tree is wounded to produce frankincense. We report its de novo assembled genome (667.8 Mb) comprising 18,564 high-confidence protein-encoding genes. Comparing conserved single-copy genes across eudicots suggest \u3e97% gene space assembly of B. sacra genome. Evolutionary history shows B. sacra gene-duplications derived from recent paralogous events and retained from ancient hexaploidy shared with other eudicots. The genome indicated a major expansion of Gypsy retroelements in last 2 million years. The B. sacra genetic diversity showed four clades intermixed with a primary genotype—dominating most resin-productive trees. Further, the stemtranscriptome revealed that wounding concurrently activates phytohormones signaling, cell wall fortification, and resin terpenoid biosynthesis pathways leading to the synthesis of boswellic acid—a key chemotaxonomic marker of Boswellia. The sequence datasets reported here will serve as a foundation to investigate the genetic determinants of frankincense and other resin-producing species in Burseraceae

A Forwarding Latency Optimization Method for Software Data Plane Based on Spin-Polling

Modern software data planes use spin-polling and batch processing mechanisms to significantly improve maximum throughput and forwarding latency. The user-level IO queue-based spin polling mechanism has a higher response speed than the traditional interrupt mechanism. The batch mechanism enables the software data plane to achieve higher throughput by amortizing the IO overhead over multiple packets. However, the software data plane under the spin-polling mechanism keeps running at full speed regardless of the input traffic rate, resulting in significant performance waste. At the same time, we find that the batch processing mechanism does not cope well with different input traffic, mainly reflected in the forwarding latency. The purpose of this paper is to optimize the forwarding latency by leveraging the wasted performance. We propose a forwarding latency optimization scheme for the software data plane based on the spin polling mechanism in this paper. First, we calculate the CPU utilization of the software data plane according to the number of cycles the CPU spends on the valuable task. Then, our scheme controls the Tx queues and dynamically adjusts the output batch size based on the CPU utilization to optimize the forwarding latency of the software data plane. Compared with the original software data plane, the evaluation result shows that the forwarding latency can be reduced by 3.56% to 45% (in a single queue evaluation) and 4.35% to 55.54% (in a multiple queue evaluation)

A Forwarding Latency Optimization Method for Software Data Plane Based on Spin-Polling

Modern software data planes use spin-polling and batch processing mechanisms to significantly improve maximum throughput and forwarding latency. The user-level IO queue-based spin polling mechanism has a higher response speed than the traditional interrupt mechanism. The batch mechanism enables the software data plane to achieve higher throughput by amortizing the IO overhead over multiple packets. However, the software data plane under the spin-polling mechanism keeps running at full speed regardless of the input traffic rate, resulting in significant performance waste. At the same time, we find that the batch processing mechanism does not cope well with different input traffic, mainly reflected in the forwarding latency. The purpose of this paper is to optimize the forwarding latency by leveraging the wasted performance. We propose a forwarding latency optimization scheme for the software data plane based on the spin polling mechanism in this paper. First, we calculate the CPU utilization of the software data plane according to the number of cycles the CPU spends on the valuable task. Then, our scheme controls the Tx queues and dynamically adjusts the output batch size based on the CPU utilization to optimize the forwarding latency of the software data plane. Compared with the original software data plane, the evaluation result shows that the forwarding latency can be reduced by 3.56% to 45% (in a single queue evaluation) and 4.35% to 55.54% (in a multiple queue evaluation)

An Adaptive Throughput-First Packet Scheduling Algorithm for DPDK-Based Packet Processing Systems

The continuous increase in network traffic has sharply increased the demand for high-performance packet processing systems. For a high-performance packet processing system based on multi-core processors, the packet scheduling algorithm is critical because of the significant role it plays in load distribution, which is related to system throughput, attracting intensive research attention. However, it is not an easy task since the canonical flow-level packet scheduling algorithm is vulnerable to traffic locality, while the packet-level packet scheduling algorithm fails to maintain cache affinity. In this paper, we propose an adaptive throughput-first packet scheduling algorithm for DPDK-based packet processing systems. Combined with the feature of DPDK burst-oriented packet receiving and transmitting, we propose using Subflow as the scheduling unit and the adjustment unit making the proposed algorithm not only maintain the advantages of flow-level packet scheduling algorithms when the adjustment does not happen but also avoid packet loss as much as possible when the target core may be overloaded Experimental results show that the proposed method outperforms Round-Robin, HRW (High Random Weight), and CRC32 on system throughput and packet loss rate