arXiv subjects

Li

Publications and source records attributed to Li.

At least 55 records · Page 3Linked to original sources

arXiv2021

Role of intermetallic phases in initiation and propagation of intergranular corrosion of an Al-Li-Cu-Mg alloy

Intermetallic phases in a recently developed Al-Li-Cu-Mg alloy have been investigated to understand their roles in the initiation and propagation processes of intergranular corrosion. Corrosion initiation involves trenching formation in the Al matrix adjacent to the large particles of Al7Cu2(Fe, Mn) phases. These phases containing Li are electrochemically active and susceptible to self-dissolution via a de-alloying mechanism during corrosion process. The subsurface particles of Al7Cu2(Fe, Mn) and Al20Cu2Mn3 phases act as the internal cathodes for continuous corrosion propagation along the particle-matrix interface and the associated grain boundaries. Corrosion propagation along the particle-matrix interface was facilitated by the anodic dissolution of the surrounding Al matrix due to the micro-galvanic interaction with the cathodic intermetallic phases. In addition, T1 (Al2CuLi) precipitates and the isolated particles of Al7Cu2(Fe, Mn) and Al20Cu2Mn3 phases were dissolved along the path of corrosion propagation. The dissolved metal ions were redeposited through the network of crevice.

cond-mat.mtrl-sci↗

arXiv2021

On a Class of Nonlocal SIR Models

We revisit the classic Susceptible-Infected-Recovered (SIR) epidemic model and one of its nonlocal variations recently developed in \cite{Guan}. We introduce several new approaches to derive exact analytical solutions in the classical situation and analyze the corresponding effective approximations in the nonlocal setting. An interesting new feature of the nonlocal models, compared with the classic SIR model, is the appearance of multiple peak solutions for the infected population. We provide several rigorous results on the existence and non-existence of peak solutions with sharp asymptotics.

math.AP↗

arXiv2021

Branching Dueling Q-Network Based Online Scheduling of a Microgrid With Distributed Energy Storage Systems

This letter investigates a Branching Dueling Q-Network (BDQ) based online operation strategy for a microgrid with distributed battery energy storage systems (BESSs) operating under uncertainties. The developed deep reinforcement learning (DRL) based microgrid online optimization strategy can achieve a linear increase in the number of neural network outputs with the number of distributed BESSs, which overcomes the curse of dimensionality caused by the charge and discharge decisions of multiple BESSs. Numerical simulations validate the effectiveness of the proposed method.

eess.SY↗

arXiv2021

Independent Action Models and Prediction of Combination Treatment Effects for Response Rate, Duration of Response and Tumor Size Change in Oncology Drug Development

An unprecedented number of new cancer targets are in development, and most are being developed in combination therapies. Early oncology development is strategically challenged in choosing the best combinations to move forward to late stage development. The most common early endpoints to be assessed in such decision-making include objective response rate, duration of response and tumor size change. In this paper, using independent-drug-action and Bliss-drug-independence concepts as a foundation, we introduce simple models to predict combination therapy efficacy for duration of response and tumor size change. These models complement previous publications using the independent action models (Palmer 2017, Schmidt 2020) to predict progression-free survival and objective response rate and serve as new predictive models to understand drug combinations for early endpoints. The models can be applied to predict the combination treatment effect for early endpoints given monotherapy data, or to estimate the possible effect of one monotherapy in the combination if data are available from the combination therapy and the other monotherapy. Such quantitative work facilitates efficient oncology drug development.

stat.AP↗

arXiv2020

Learned Indexes for a Google-scale Disk-based Database

There is great excitement about learned index structures, but understandable skepticism about the practicality of a new method uprooting decades of research on B-Trees. In this paper, we work to remove some of that uncertainty by demonstrating how a learned index can be integrated in a distributed, disk-based database system: Google's Bigtable. We detail several design decisions we made to integrate learned indexes in Bigtable. Our results show that integrating learned index significantly improves the end-to-end read latency and throughput for Bigtable.

cs.DB↗

arXiv2020

Data Troubles in Sentence Level Confidence Estimation for Machine Translation

The paper investigates the feasibility of confidence estimation for neural machine translation models operating at the high end of the performance spectrum. As a side product of the data annotation process necessary for building such models we propose sentence level accuracy $SACC$ as a simple, self-explanatory evaluation metric for quality of translation. Experiments on two different annotator pools, one comprised of non-expert (crowd-sourced) and one of expert (professional) translators show that $SACC$ can vary greatly depending on the translation proficiency of the annotators, despite the fact that both pools are about equally reliable according to Krippendorff's alpha metric; the relatively low values of inter-annotator agreement confirm the expectation that sentence-level binary labeling $good$ / $needs\ work$ for translation out of context is very hard. For an English-Spanish translation model operating at $SACC = 0.89$ according to a non-expert annotator pool we can derive a confidence estimate that labels 0.5-0.6 of the $good$ translations in an "in-domain" test set with 0.95 Precision. Switching to an expert annotator pool decreases $SACC$ dramatically: $0.61$ for English-Spanish, measured on the exact same data as above. This forces us to lower the CE model operating point to 0.9 Precision while labeling correctly about 0.20-0.25 of the $good$ translations in the data. We find surprising the extent to which CE depends on the level of proficiency of the annotator pool used for labeling the data. This leads to an important recommendation we wish to make when tackling CE modeling in practice: it is critical to match the end-user expectation for translation quality in the desired domain with the demands of annotators assigning binary quality labels to CE training data.

cs.CL↗

arXiv2020

Practical Perspectives on Quality Estimation for Machine Translation

Sentence level quality estimation (QE) for machine translation (MT) attempts to predict the translation edit rate (TER) cost of post-editing work required to correct MT output. We describe our view on sentence-level QE as dictated by several practical setups encountered in the industry. We find consumers of MT output---whether human or algorithmic ones---to be primarily interested in a binary quality metric: is the translated sentence adequate as-is or does it need post-editing? Motivated by this we propose a quality classification (QC) view on sentence-level QE whereby we focus on maximizing recall at precision above a given threshold. We demonstrate that, while classical QE regression models fare poorly on this task, they can be re-purposed by replacing the output regression layer with a binary classification one, achieving 50-60\% recall at 90\% precision. For a high-quality MT system producing 75-80\% correct translations, this promises a significant reduction in post-editing work indeed.

cs.CL↗

arXiv2020

Single-shot x-ray speckle-based imaging of a single-material object

We develop a means for speckle-based phase imaging of the projected thickness of a single-material object, under the assumption of illumination by spatially random time-independent x-ray speckles. These speckles are generated by passing x rays through a suitable spatially random mask. The method makes use of a single image obtained in the presence of the object, which serves to deform the illuminating speckle field relative to a reference speckle field (which only needs to be measured once) obtained in the presence of the mask and the absence of the object. The method implicitly rather than explicitly tracks speckles, and utilizes the transport-of-intensity equation to give a closed-form solution to the inverse problem of determining the complex transmission function of the object. Implementation using x-ray synchrotron data shows the method to be robust and efficient with respect to noise. Applications include x-ray phase--amplitude radiography and tomography, as well as time-dependent imaging of dynamic and radiation-sensitive samples using low-flux sources.

physics.med-ph↗

arXiv2019

RED: A ReRAM-based Deconvolution Accelerator

Deconvolution has been widespread in neural networks. For example, it is essential for performing unsupervised learning in generative adversarial networks or constructing fully convolutional networks for semantic segmentation. Resistive RAM (ReRAM)-based processing-in-memory architecture has been widely explored in accelerating convolutional computation and demonstrates good performance. Performing deconvolution on existing ReRAM-based accelerator designs, however, suffers from long latency and high energy consumption because deconvolutional computation includes not only convolution but also extra add-on operations. To realize the more efficient execution for deconvolution, we analyze its computation requirement and propose a ReRAM-based accelerator design, namely, RED. More specific, RED integrates two orthogonal methods, the pixel-wise mapping scheme for reducing redundancy caused by zero-inserting operations and the zero-skipping data flow for increasing the computation parallelism and therefore improving performance. Experimental evaluations show that compared to the state-of-the-art ReRAM-based accelerator, RED can speed up operation 3.69x~1.15x and reduce 8%~88.36% energy consumption.

cs.ET↗

arXiv2019

An Overview of In-memory Processing with Emerging Non-volatile Memory for Data-intensive Applications

The conventional von Neumann architecture has been revealed as a major performance and energy bottleneck for rising data-intensive applications. %, due to the intensive data movements. The decade-old idea of leveraging in-memory processing to eliminate substantial data movements has returned and led extensive research activities. The effectiveness of in-memory processing heavily relies on memory scalability, which cannot be satisfied by traditional memory technologies. Emerging non-volatile memories (eNVMs) that pose appealing qualities such as excellent scaling and low energy consumption, on the other hand, have been heavily investigated and explored for realizing in-memory processing architecture. In this paper, we summarize the recent research progress in eNVM-based in-memory processing from various aspects, including the adopted memory technologies, locations of the in-memory processing in the system, supported arithmetics, as well as applied applications.

cs.AR↗

arXiv2019

Thread Batching for High-performance Energy-efficient GPU Memory Design

Massive multi-threading in GPU imposes tremendous pressure on memory subsystems. Due to rapid growth in thread-level parallelism of GPU and slowly improved peak memory bandwidth, the memory becomes a bottleneck of GPU's performance and energy efficiency. In this work, we propose an integrated architectural scheme to optimize the memory accesses and therefore boost the performance and energy efficiency of GPU. Firstly, we propose a thread batch enabled memory partitioning (TEMP) to improve GPU memory access parallelism. In particular, TEMP groups multiple thread blocks that share the same set of pages into a thread batch and applies a page coloring mechanism to bound each stream multiprocessor (SM) to the dedicated memory banks. After that, TEMP dispatches the thread batch to an SM to ensure high-parallel memory-access streaming from the different thread blocks. Secondly, a thread batch-aware scheduling (TBAS) scheme is introduced to improve the GPU memory access locality and to reduce the contention on memory controllers and interconnection networks. Experimental results show that the integration of TEMP and TBAS can achieve up to 10.3% performance improvement and 11.3% DRAM energy reduction across diverse GPU applications. We also evaluate the performance interference of the mixed CPU+GPU workloads when they are run on a heterogeneous system that employs our proposed schemes. Our results show that a simple solution can effectively ensure the efficient execution of both GPU and CPU applications.

cs.AR↗

arXiv2018

Exploiting Spin-Orbit Torque Devices as Reconfigurable Logic for Circuit Obfuscation

Circuit obfuscation is a frequently used approach to conceal logic functionalities in order to prevent reverse engineering attacks on fabricated chips. Efficient obfuscation implementations are expected with lower design complexity and overhead but higher attack difficulties. In this paper, an emerging obfuscation approach is proposed by leveraging spinorbit torque (SOT) devices based look-up-tables (LUTs) as reconfigurable logic to replace the carefully selected gates. It is essentially impossible to identify the obfuscated gate with SOTs inside according to the physical geometry characteristics because the configured functionalities are represented by magnetization states. Such an obfuscation approach makes the circuit security further improved with high exponential attack complexities. Experiments on MCNC and ISCAS 85/89 benchmark suits show that the proposed approach could reduce the area overheads due to obfuscation by 10% averagely.

cs.ET↗

arXiv2017

Half-lives of neutron-rich $^{128-130}$Cd

The $β$-decay half-lives of $^{128\text{--}130}$Cd have been measured with the newly commissioned GRIFFIN $γ$-ray spectrometer at the TRIUMF-ISAC facility. The time structures of the most intense $γ$-rays emitted following the $β$-decay were used to determine the half-lives of $^{128}$Cd and $^{130}$Cd to be $T_{1/2}= 246.2(21)$ ms and $T_{1/2}= 126(4)$ ms, respectively. The half-lives of the 3/2$^+$ and 11/2$^-$ states of $^{129}$Cd were measured to be $T_{1/2}(3/2^+)= 157(8)$ ms and $T_{1/2}(11/2^-)= 147(3)$ ms. The half-lives of the Cd isotopes around the $N=82$ shell closure are an important ingredient in astrophysical simulations to derive the magnitude of the second $r$-process abundance peak in the $A\sim130$ region. Our new results are compared with recent literature values and theoretical calculations.

nucl-ex↗

arXiv2017

Spintronics based Stochastic Computing for Efficient Bayesian Inference System

Bayesian inference is an effective approach for solving statistical learning problems especially with uncertainty and incompleteness. However, inference efficiencies are physically limited by the bottlenecks of conventional computing platforms. In this paper, an emerging Bayesian inference system is proposed by exploiting spintronics based stochastic computing. A stochastic bitstream generator is realized as the kernel components by leveraging the inherent randomness of spintronics devices. The proposed system is evaluated by typical applications of data fusion and Bayesian belief networks. Simulation results indicate that the proposed approach could achieve significant improvement on inference efficiencies in terms of power consumption and inference speed.

cs.ET↗

arXiv2013

On the k-Atomicity-Verification Problem

Modern Internet-scale storage systems often provide weak consistency in exchange for better performance and resilience. An important weak consistency property is k-atomicity, which bounds the staleness of values returned by read operations. The k-atomicity-verification problem (or k-AV for short) is the problem of deciding whether a given history of operations is k-atomic. The 1-AV problem is equivalent to verifying atomicity/linearizability, a well-known and solved problem. However, for k > 2, no polynomial-time k-AV algorithm is known. This paper makes the following contributions towards solving the k-AV problem. First, we present a simple 2- AV algorithm called LBT, which is likely to be efficient (quasilinear) for histories that arise in practice, although it is less efficient (quadratic) in the worst case. Second, we present a more involved 2-AV algorithm called FZF, which runs efficiently (quasilinear) even in the worst case. To our knowledge, these are the first algorithms that solve the 2-AV problem fully. Third, we show that the weighted k-AV problem, a natural extension of the k-AV problem, is NP-complete.

cs.DC↗

arXiv2013

Bit Level Soft Decision Decoding of Triple Parity Reed Solomon Codes through Automorphism Groups

This paper discusses bit-level soft decoding of triple-parity Reed-Solomon (RS) codes through automorphism permutation. A new method for identifying the automorphism groups of RS binary images is first developed. The new algorithm runs effectively, and can handle more RS codes and capture more automorphism groups than the existing ones. Utilizing the automorphism results, a new bit-level soft-decision decoding algorithm is subsequently developed for general $(n,n-3,4)$ RS codes. Simulation on $(31,28,4)$ RS codes demonstrates an impressive gain of more than 1 dB at the bit error rate of $10^{-5}$ over the existing algorithms.

cs.IT↗

arXiv2011

Efficient Image Transmission Through Analog Error Correction

This paper presents a new paradigm for image transmission through analog error correction codes. Conventional schemes rely on digitizing images through quantization (which inevitably causes significant bandwidth expansion) and transmitting binary bit-streams through digital error correction codes (which do not automatically differentiate the different levels of significance among the bits). To strike a better overall performance in terms of transmission efficiency and quality, we propose to use a single analog error correction code in lieu of digital quantization, digital code and digital modulation. The key is to get analog coding right. We show that this can be achieved by cleverly exploiting an elegant "butterfly" property of chaotic systems. Specifically, we demonstrate a tail-biting triple-branch baker's map code and its maximum-likelihood decoding algorithm. Simulations show that the proposed analog code can actually outperform digital turbo code, one of the best codes known to date. The results and findings discussed in this paper speak volume for the promising potential of analog codes, in spite of their rather short history.

cs.MM↗

arXiv2011

A New Class of MDS Erasure Codes Based on Graphs

Maximum distance separable (MDS) array codes are XOR-based optimal erasure codes that are particularly suitable for use in disk arrays. This paper develops an innovative method to build MDS array codes from an elegant class of nested graphs, termed \textit{complete-graph-of-rings (CGR)}. We discuss a systematic and concrete way to transfer these graphs to array codes, unveil an interesting relation between the proposed map and the renowned perfect 1-factorization, and show that the proposed CGR codes subsume B-codes as their "contracted" codes. These new codes, termed \textit{CGR codes}, and their dual codes are simple to describe, and require minimal encoding and decoding complexity.

cs.IT↗