Speeding Up Maximal Causality Reduction with Static Dependency Analysis

Stateless Model Checking (SMC) offers a powerful approach to verifying multithreaded programs but suffers from the state-space explosion problem caused by the huge thread interleaving space. The pioneering reduction technique Partial Order Reduction (POR) mitigates this problem by pruning equivalent interleavings from the state space. However, limited by the happens-before relation, POR still explores redundant executions. The recent advance, Maximal Causality Reduction (MCR), shows a promising performance improvement over the existing reduction techniques, but it has to construct complicated constraints to ensure the feasibility of the derived execution due to the lack of dependency information. In this work, we present a new technique, which extends MCR with static analysis to reduce the size of the constraints, thus speeding up the exploration of the state space. We also address the redundancy problem caused by the use of static analysis. We capture the dependency between a read and a later event e in the trace from the system dependency graph and identify those reads that e is not control dependent on. Our approach then ignores the constraints over such reads to reduce the complexity of the constraints. The experimental results show that compared to MCR, the number of the constraints and the solving time by our approach are averagely reduced by 31.6% and 27.8%, respectively

Maximum penalized quasi-likelihood estimation of the diffusion function

We develop a maximum penalized quasi-likelihood estimator for estimating in a nonparametric way the diffusion function of a diffusion process, as an alternative to more traditional kernel-based estimators. After developing a numerical scheme for computing the maximizer of the penalized maximum quasi-likelihood function, we study the asymptotic properties of our estimator by way of simulation. Under the assumption that overnight London Interbank Offered Rates (LIBOR); the USD/EUR, USD/GBP, JPY/USD, and EUR/USD nominal exchange rates; and 1-month, 3-month, and 30-year Treasury bond yields are generated by diffusion processes, we use our numerical scheme to estimate the diffusion function.Comment: 17 pages, 4 figures, revised versio

SURFACE MOUNT TECHNOLOGY RJ45 STRETCHABLE PIN AGAINST PULL FORCE

Techniques are provided herein for a stretchable, spring-like structure that is added into the copper pin of Surface Mount Technology (SMT) RJ45. The structure can stretch when the RJ45 is pulled to mitigate the force on the RJ45 pin

Intrinsic Reliability improvement in Biaxially Strained SiGe p-MOSFETs

In this letter we not only show improvement in the performance but also in the reliability of 30nm thick biaxially strained SiGe (20%Ge) channel on Si p-MOSFETs. Compared to Si channel, strained SiGe channel allows larger hole mobility ({\mu}h) in the transport direction and alleviates charge flow towards the gate oxide. {\mu}h enhancement by 40% in SiGe and 100% in Si-cap SiGe is observed compared to the Si hole universal mobility. A ~40% reduction in NBTI degradation, gate leakage and flicker noise (1/f) is observed which is attributed to a 4% increase in the hole-oxide barrier height ({\phi}) in SiGe. Similar field acceleration factor ({\Gamma}) for threshold voltage shift ({\Delta}VT) and increase in noise ({\Delta}SVG) in Si and SiGe suggests identical degradation mechanisms.Comment: 4 figures, 3 pages, accepted for publication in IEEE ED

Evaluating e-portfolio Using by Learning Stages: A Case Study in an Interdisciplinary Program

This study conducts an investigation of posts in the e-portfolio platform of the program: “The interdisciplinary training program for talented college students in science.” Participants in this program were supposed to show their learning portfolios on this platform. Among the 2150 registered students, we randomly selected 126 students who have made at least 3 posts to become the target sample. By identifying the learning stages and posting styles shown by their posts, we find that students are mostly in the surface learning stages and weak in completing their learning portfolios. The results suggest that more strategies should be learned in e-portfolio use. In addition, some related issues about learning performance are also discussed

SmartTrack: Efficient Predictive Race Detection

Widely used data race detectors, including the state-of-the-art FastTrack algorithm, incur performance costs that are acceptable for regular in-house testing, but miss races detectable from the analyzed execution. Predictive analyses detect more data races in an analyzed execution than FastTrack detects, but at significantly higher performance cost. This paper presents SmartTrack, an algorithm that optimizes predictive race detection analyses, including two analyses from prior work and a new analysis introduced in this paper. SmartTrack's algorithm incorporates two main optimizations: (1) epoch and ownership optimizations from prior work, applied to predictive analysis for the first time; and (2) novel conflicting critical section optimizations introduced by this paper. Our evaluation shows that SmartTrack achieves performance competitive with FastTrack-a qualitative improvement in the state of the art for data race detection.Comment: Extended arXiv version of PLDI 2020 paper (adds Appendices A-E) #228 SmartTrack: Efficient Predictive Race Detectio