bluuebunny/crossref_metadata_2025 huggingface dataset:
Query Scheduling Techniques and Power-Latency Trade-off Model for Large-Scale Search Engines
Authors: Freire Ana
Published: June 2015

Web search engines have to deal with a huge increase of information, demanded by high incoming query traffic. This situation has driven companies to build large, geographically distributed data centres housing thousands of servers and consuming enormous amounts of electricity. At this scale, even minor efficiency improvements may result in large financial and power savings.

This thesis represents a novel contribution to the state-of-the-art of Query Scheduling and Green Information Retrieval (Green IR), by assisting large-scale data centres to build more efficient and environmentally-friendly search engines.

The main contributions of this work are the following:

Query Scheduling. We introduce query efficiency predictors as suitable estimators to improve Query Scheduling. We estimate the processing time of the queries waiting in each query server and we calculate an approximate time that a new query must spend in each queue. Based on this estimation, the fastest query server is selected.

Green IR. Once we have developed new methods to improve the average response time of a search engine, we focus on reducing the power consumption of the whole system. This thesis proposes a mathematical model that establishes a trade-off between latency and power consumption. This model attempts to automatically adapt the number of active servers in the system based on the fluctuations of a daily query traffic flow.

Queueing Theory. We prove the limitation of Queueing Theory models for estimating the latency in search engines. As a consequence, we develop our trade-off model by predicting the latency using historical data. Results show the good performance of this approach.

IR evaluation. We attest that Simulation platforms are suitable for IR experimentation. We support this conclusion by establishing an exhaustive analysis of the current IR evaluation platforms.

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Text-Based Ephemeral Clustering for Web Image Retrieval on Mobile Devices
Authors: Moreno Jose G.
Published: June 2015

Text-Based Ephemeral Clustering for Web Image Retrieval on Mobile Devices.

Our specific contributions in this dissertation are: two algorithms, two datasets and an evaluation tool. The Dual C-means algorithm is the main product of these. Dual C-means can be seen as a generalization of our previous proposal the AGK-means. Both algorithms are based on word-word similarity metrics and on the classical K-means algorithm. A new dataset for a complete evaluation of search results clustering (SRC) algorithms is developed and presented. Similarly, a new Web image dataset is developed and used together with a new metric to measure the users' effort when a set of Web images is explored. Finally, we developed an evaluation tool for the SRC problem, in which we have implemented several classical and recent SRC metrics.

In order to validate our hypothesis, we performed a great deal of different experiments with the datasets mentioned above. Three valuable characteristics are evaluated in the proposed algorithms. First, clustering quality, in which classical and recent evaluation metrics are considered. Secondly, the labelling quality of each cluster is evaluated to make sure that all possible query intents are covered. Thirdly and finally, we evaluate the user's effort in exploring the clustered results presented as horizontal image strips. For these three, we use several datasets- some of which are built to evaluate individual or combinations of these characteristics.

We have made our conclusions on the numerous factors discussed in this dissertation. In essence, the proposed Dual C-means algorithm, is capable of obtaining proper clustering partitions and simultaneously ensuring high quality labels. When images are displayed on a mobile device's interface, our results indicate that the users' effort to explore the Web image results is reduced.
From Innovative I.S. Strategy to Customer Value
Authors: Chen Daniel Q. , Preston David S. , Tarafdar Monideepa
Published: June 2015

While information systems (IS) have traditionally been used primarily for firm-wide operational efficiency, emerging theoretical perspectives have shifted scholarly focus to IS as a force that can drive value through business innovation. This study examines how IS strategy creates customer value through an innovative business orientation and how IS leadership and the organizational context can influence the relationship between innovative IS and business strategies. Integrating several research streams under the umbrella of the IS strategic management literature, we develop and empirically validate a research model based on confirmatory analysis and structural equation modeling applied to survey data collected from senior IS executives in 165 organizations based in the United States and India. Our results suggest that organizations that have an innovative IS strategy are well positioned to engage in business innovation, which in turn yields greater customer value. Furthermore, we find that strong IS leadership and a creative organizational climate enhance the relationship between an innovative IS strategy and innovative business orientation.
Empirical Investigation on Relational Social Capital in a Virtual Community for Website Programming
Authors: Tan Chuan-Hoo , Sutanto Juliana , Tan Bernard C.Y.
Published: June 2015

A virtual community of interest has a specific and narrow topic of discussion. Therefore, these communities attract registered members who are focused on knowledge sharing. The current research examines whether network ties, which are an aspect of structural social capital that can be categorized into strong and weak social ties, can provide a non-trivial explanation for members? trust, reciprocity, and identification in a virtual community for website programming interest. This relationship enables us to examine a context in which members share a common goal of resolving programming problems through knowledge sharing in contrast with other community settings where only general topics are discussed (e.g., societal and emotional issues). Data were collected through a survey of a virtual community for website programming composed of 69 members. Affirming conventional perception, results of the study indicate that weak ties affect the level of generalized trust and facilitate group identification. Remarkably, the number of members? strong ties is not significantly related to the degree of their perceived norms regarding generalized reciprocity. Reciprocity refers to a mutual expectation that a benefit granted at present should be repaid in the future. The results suggest two key points. First, even for a virtual community of interest, weak ties overshadow strong ties in explaining the outcome variables. Second, reciprocity is not guaranteed even in a focused form of discussion with a non-social topic that involves specialized knowledge. Therefore, virtual community members should be cautious even if ties are strong. Overall, results imply that virtual community administrators, particularly those who manage specialized communities, should be attentive to the strong and weak ties that exist among the community members.
Newer Is Sometimes Better
Authors: Chen Ming , Hildebrand Dean , Kuenning Geoff , Shankaranarayana Soujanya , Singh Bharat , Zadok Erez
Published: June 2015

The popular Network File System (NFS) protocol is 30 years old. The latest version, NFSv4, is more than ten years old but has only recently gained stability and acceptance. NFSv4 is vastly different from its predecessors: it offers a stateful server, strong security, scalability/WAN features, and callbacks, among other things. Yet NFSv4's efficacy and ability to meet its stated design goals had not been thoroughly studied until now. This paper compares NFSv4.1's performance with NFSv3 using a wide range of micro- and macro-benchmarks on a testbed configured to exercise the core protocol features. We (1) tested NFSv4's unique features, such as delegations and statefulness; (2) evaluated performance comprehensively with different numbers of threads and clients, and different network latencies and TCP/IP features; (3) found, fixed, and reported several problems in Linux's NFSv4.1 implementation, which helped improve performance by up to 11X; and (4) discovered, analyzed, and explained several counter-intuitive results. Depending on the workload, NFSv4.1 was up to 67\% slower than NFSv3 in a low-latency network, but exceeded NFSv3's performance by up to 2.9X in a high-latency environment. Moreover, NFSv4.1 outperformed NFSv3 by up to 172X when delegations were used.
Joint Cyber and Physical Attacks on Power Grids
Authors: Soltan Saleh , Yannakakis Mihalis , Zussman Gil
Published: June 2015

Recent events demonstrated the vulnerability of power grids to cyber attacks and to physical attacks. Therefore, we focus on joint cyber and physical attacks and develop methods to retrieve the grid state information following such an attack. We consider a model in which an adversary attacks a zone by physically disconnecting some of its power lines and blocking the information flow from the zone to the grid's control center. We use tools from linear algebra and graph theory and leverage the properties of the power flow DC approximation to develop methods for information recovery. Using information observed outside the attacked zone, these methods recover information about the disconnected lines and the phase angles at the buses. We identify sufficient conditions on the zone structure and constraints on the attack characteristics such that these methods can recover the information. We also show that it is NP-hard to find an approximate solution to the problem of partitioning the power grid into the minimum number of attack-resilient zones. However, since power grids can often be represented by planar graphs, we develop a constant approximation partitioning algorithm for these graphs. Finally, we numerically study the relationships between the grid's resilience and its structural properties, and demonstrate the partitioning algorithm on real power grids. The results can provide insights into the design of a secure control network for the smart grid.
A Large-Scale Study of Flash Memory Failures in the Field
Authors: Meza Justin , Wu Qiang , Kumar Sanjev , Mutlu Onur
Published: June 2015

Servers use flash memory based solid state drives (SSDs) as a high-performance alternative to hard disk drives to store persistent data. Unfortunately, recent increases in flash density have also brought about decreases in chip-level reliability. In a data center environment, flash-based SSD failures can lead to downtime and, in the worst case, data loss. As a result, it is important to understand flash memory reliability characteristics over flash lifetime in a realistic production data center environment running modern applications and system software.

This paper presents the first large-scale study of flash-based SSD reliability in the field. We analyze data collected across a majority of flash-based solid state drives at Facebook data centers over nearly four years and many millions of operational hours in order to understand failure properties and trends of flash-based SSDs. Our study considers a variety of SSD characteristics, including: the amount of data written to and read from flash chips; how data is mapped within the SSD address space; the amount of data copied, erased, and discarded by the flash controller; and flash board temperature and bus power.

Based on our field analysis of how flash memory errors manifest when running modern workloads on modern SSDs, this paper is the first to make several major observations: (1) SSD failure rates do not increase monotonically with flash chip wear; instead they go through several distinct periods corresponding to how failures emerge and are subsequently detected, (2) the effects of read disturbance errors are not prevalent in the field, (3) sparse logical data layout across an SSD's physical address space (e.g., non-contiguous data), as measured by the amount of metadata required to track logical address translations stored in an SSD-internal DRAM buffer, can greatly affect SSD failure rate, (4) higher temperatures lead to higher failure rates, but techniques that throttle SSD operation appear to greatly reduce the negative reliability impact of higher temperatures, and (5) data written by the operating system to flash-based SSDs does not always accurately indicate the amount of wear induced on flash cells due to optimizations in the SSD controller and buffering employed in the system software. We hope that the findings of this first large-scale flash memory reliability study can inspire others to develop other publicly-available analyses and novel flash reliability solutions.
Transient and Steady-state Regime of a Family of List-based Cache Replacement Algorithms
Authors: Gast Nicolas , Van Houdt Benny
Published: June 2015

In this paper we study the performance of a family of cache replacement algorithms. The cache is decomposed into lists. Items enter the cache via the first list. An item enters the cache via the first list and jumps to the next list whenever a hit on it occurs. The classical policies FIFO, RANDOM, CLIMB and its hybrids are obtained as special cases. We present explicit expressions for the cache content distribution and miss probability under the IRM model. We develop an algorithm with a time complexity that is polynomial in the cache size and linear in the number of items to compute the exact miss probability. We introduce lower and upper bounds on the latter that can be computed in a time that is linear in the cache size times the number of items.

We further introduce a mean field model to approximate the transient behavior of the miss probability and prove that this model becomes exact as the cache size and number of items tends to infinity. We show that the set of ODEs associated to the mean field model has a unique fixed point that can be used to approximate the miss probability in case the exact computation becomes too time consuming.

Using this approximation, we provide guidelines on how to select a replacement algorithm within the family considered such that a good trade-off is achieved between the cache reactivity and its steady-state hit probability. We simulate these cache replacement algorithms on traces of real data and show that they can outperform LRU. Finally, we also disprove the well-known conjecture that the CLIMB algorithm is the optimal finite-memory replacement algorithm under the IRM model.
Online Convex Optimization Using Predictions
Authors: Chen Niangjun , Agarwal Anish , Wierman Adam , Barman Siddharth , Andrew Lachlan L.H.
Published: June 2015

Making use of predictions is a crucial, but under-explored, area of online algorithms. This paper studies a class of online optimization problems where we have external noisy predictions available. We propose a stochastic prediction error model that generalizes prior models in the learning and stochastic control communities, incorporates correlation among prediction errors, and captures the fact that predictions improve as time passes. We prove that achieving sublinear regret and constant competitive ratio for online algorithms requires the use of an unbounded prediction window in adversarial settings, but that under more realistic stochastic prediction error models it is possible to use Averaging Fixed Horizon Control (AFHC) to simultaneously achieve sublinear regret and constant competitive ratio in expectation using only a constant-sized prediction window. Furthermore, we show that the performance of AFHC is tightly concentrated around its mean.
Computable Bounds in Fork-Join Queueing Systems
Authors: Rizk Amr , Poloczek Felix , Ciucu Florin
Published: June 2015

In a Fork-Join (FJ) queueing system an upstream fork station splits incoming jobs into N tasks to be further processed by N parallel servers, each with its own queue; the response time of one job is determined, at a downstream join station, by the maximum of the corresponding tasks' response times. This queueing system is useful to the modelling of multi-service systems subject to synchronization constraints, such as MapReduce clusters or multipath routing. Despite their apparent simplicity, FJ systems are hard to analyze.

This paper provides the first computable stochastic bounds on the waiting and response time distributions in FJ systems. We consider four practical scenarios by combining 1a) renewal and 1b) non-renewal arrivals, and 2a) non-blocking and 2b) blocking servers. In the case of non blocking servers we prove that delays scale as O(logN), a law which is known for first moments under renewal input only. In the case of blocking servers, we prove that the same factor of log N dictates the stability region of the system. Simulation results indicate that our bounds are tight, especially at high utilizations, in all four scenarios. A remarkable insight gained from our results is that, at moderate to high utilizations, multipath routing 'makes sense' from a queueing perspective for two paths only, i.e., response times drop the most when N = 2; the technical explanation is that the resequencing (delay) price starts to quickly dominate the tempting gain due to multipath transmissions.
Sponsored Data Plan
Authors: Zhang Liang , Wu Weijie , Wang Dan
Published: June 2015

Data traffic demand over the Internet is increasing rapidly, and it is changing the pricing model between Internet service providers (ISPs), content providers (CPs) and end users. One recent pricing proposal is sponsored data plan, i.e., when accessing contents from a particular CP, end users do not need to pay for that volume of traffic consumed, but the CP will sponsor for this data consumption. In this paper, our goal is to understand the rationale behind this new pricing model, as well as its impacts to the wireless data market, in particular, who will benefit and who will be hurt from this scheme. We build a two-class service model to analyze the consumers' traffic demand under the sponsored data plan with consideration of QoS. We use a two-stage Stackelberg game to characterize the interaction between CPs and the ISP and reveal a number of important findings. Our conclusions include: 1) When the ISP's capacity is sufficient, the sponsored data plan benefits consumers and CPs in the short run, but the ISP does not have incentives to further improve its service in the long run. 2) When ISP's capacity is insufficient, the ISP and end users may achieve a win- win trade, while the ISP and CPs always compete for the revenue. 3) The sponsored data plan may enlarge the un- balance in revenue distribution between different CPs; CPs with higher unit income and poorer technology support are more likely to prefer the sponsored data plan.
Queue-Proportional Rate Allocation with Per-Link Information in Multihop Networks
Authors: Li Bin , Srikant Rayadurgam
Published: June 2015

The backpressure scheduling algorithm for multihop wireless networks is known to be throughput optimal, but it requires each node to maintain per-destination queues. Recently, a clever generalization of processor sharing has been proposed which is also throughput optimal, but which only uses per-link queues. Here we propose another algorithm called Queue Proportional Rate Allocation (QPRA) which also only uses per-link queues, and allocates service rates to links in proportion to their queue-lengths and employs the Serve-In-Random-Order (SIRO) discipline within each link. Through fluid limit techniques and using a novel Lyapunov function, we show that the QPRA achieves the maximum throughput. We demonstrate an advantage of QPRA by showing that, for the so-called primary interference model, it is able to develop a low-complexity scheduling scheme which approximates QPRA and achieves a constant fraction of the maximum throughput region, independent of network size.
Hyper-Compact Virtual Estimators for Big Network Data Based on Register Sharing
Authors: Xiao Qingjun , Chen Shigang , Chen Min , Ling Yibei
Published: June 2015

Cardinality estimation over big network data consisting of numerous flows is a fundamental problem with many practical applications. Traditionally the research on this problem focused on using a small amount of memory to estimate each flow's cardinality from a large range (up to $10^9$). However, although the memory needed for each flow has been greatly compressed, when there is an extremely large number of flows, the overall memory demand can still be very high, exceeding the availability under some important scenarios, such as implementing online measurement modules in network processors using only on-chip cache memory. In this paper, instead of allocating a separated data structure (called
estimator
) for each flow, we take a different path by viewing all the flows together as a whole: Each flow is allocated with a virtual estimator, and these virtual estimators share a common memory space. We discover that sharing at the register (multi-bit) level is superior than sharing at the bit level. We propose a framework of virtual estimators that allows us to apply the idea of sharing to an array of cardinality estimation solutions, achieving far better memory efficiency than the best existing work. Our experiment shows that the new solution can work in a tight memory space of less than 1 bit per flow or even one tenth of a bit per flow --- a quest that has never been realized before.
Combining Phase Identification and Statistic Modeling for Automated Parallel Benchmark Generation
Authors: Jin Ye , Ma Xiaosong , Liu Mingliang , Liu Qing , Logan Jeremy , Podhorszki Norbert , Choi Jong Youl , Klasky Scott
Published: June 2015

Parallel application benchmarks are indispensable for evaluating/optimizing HPC software and hardware. However, it is very challenging and costly to obtain high-fidelity benchmarks reflecting the scale and complexity of state-of-the-art parallel applications. Hand-extracted synthetic benchmarks are time- and labor-intensive to create. Real applications themselves, while offering most accurate performance evaluation, are expensive to compile, port, reconfigure, and often plainly inaccessible due to security or ownership concerns. This work contributes APPrime, a novel tool for trace-based automatic parallel benchmark generation. Taking as input standard communication-I/O traces of an application's execution, it couples accurate automatic phase identification with statistical regeneration of event parameters to create compact, portable, and to some degree reconfigurable parallel application benchmarks. Experiments with four NAS Parallel Benchmarks (NPB) and three real scientific simulation codes confirm the fidelity of APPrime benchmarks. They retain the original applications' performance characteristics, in particular their relative performance across platforms. Also, the result benchmarks, already released online, are much more compact and easy-to-port compared to the original applications.
Localized Epidemic Detection in Networks with Overwhelming Noise
Authors: Meirom Eli A. , Milling Chris , Caramanis Constantine , Mannor Shie , Shakkottai Sanjay , Orda Ariel
Published: June 2015

We consider the problem of detecting an epidemic in a population where individual diagnoses are extremely noisy. We show that exclusively local, approximate knowledge of the contact network suffices to accurately detect the epidemic. The motivation for this problem is the plethora of examples (influenza strains in humans, or computer viruses in smartphones, etc.) where reliable diagnoses are scarce, but noisy data plentiful. In flu or phone-viruses, exceedingly few infected people/phones are professionally diagnosed (only a small fraction go to a doctor) but less reliable
secondary signatures
(e.g., people staying home, or greater-than-typical upload activity) are more readily available.

Our algorithm requires only local-neighbor knowledge of this graph, and in a broad array of settings that we describe, succeeds even when false negatives and false positives make up an overwhelming majority of the data available. Our results show it succeeds in the presence of partial information about the contact network, and also when are many (hundreds, in our examples) of initial patients-zero.
Deterministic Near-Optimal P2P Streaming
Authors: Bojja Venkatakrishnan Shaileshh , Viswanath Pramod
Published: June 2015

We consider live-streaming over a peer-to-peer network in which peers are allowed to enter or leave the system adversarially and arbitrarily. Previous approaches for streaming have either used randomized distribution graphs or structured trees with randomized maintenance algorithms. Randomized graphs handle peer churn well but have only probabilistic connectivity guarantees, while structured trees have good connectivity but have proven hard to maintain under peer churn. We improve upon both approaches by presenting a novel distribution structure with a
deterministic
and distributed algorithm for maintenance under peer churn. The algorithm has a constant repair time for connectivity, and near optimal delay. As opposed to order results, the guarantees provided by our algorithm are exact and hold for any network size.
Flexible Transformations For Learning Big Data
Authors: Mirhoseini Azalia , Songhori Ebrahim M. , Darvish Rouhani Bita , Koushanfar Farinaz
Published: June 2015

This paper proposes a domain-specific solution for iterative learning of big and
dense (non-sparse)
datasets. A large host of learning algorithms, including linear and regularized regression techniques, rely on iterative updates on the data connectivity matrix in order to converge to a solution. The performance of such algorithms often severely degrade when it comes to large and dense data. Massive dense datasets not only induce obligatory large number of arithmetics, but they also incur unwanted message passing cost across the processing nodes. Our key observation is that despite the seemingly dense structures, in many applications, data can be transformed into a new space where sparse structures become revealed. We propose a scalable data transformation scheme that enables creating versatile sparse representations of the data. The transformation can be tuned to benefit the underlying platform's cost and constraints. Our evaluations demonstrate significant improvement in energy usage, runtime, and mem
DeltaTree
Authors: Umar Ibrahim , Anshus Otto Johan , Ha Phuong Hoai
Published: June 2015

Like other fundamental abstractions for high-performance computing, search trees need to support both high concurrency and data locality. However, existing locality-aware search trees based on the van Emde Boas layout (vEB-based trees), poorly support
concurrent
(update) operations.

We present DeltaTree, a practical locality-aware concurrent search tree that integrates both locality-optimization techniques from vEB-based trees, and concurrency optimization techniques from highly-concurrent search trees. As a result, DeltaTree minimizes data transfer from memory to CPU and supports high concurrency. Our experimental evaluation shows that DeltaTree is up to 50% faster than highly concurrent B-trees on a commodity Intel high performance computing (HPC) platform and up to 65% faster on a commodity ARM embedded platform.
Speeding up Glauber Dynamics for Random Generation of Independent Sets
Authors: Varloot Rémi , Bušíüć Ana , Bouillard Anne
Published: June 2015

The maximum independent set (MIS) problem is a well-studied combinatorial optimization problem that naturally arises in many applications, such as wireless communication, information theory and statistical mechanics.

MIS problem is NP-hard, thus many results in the literature focus on fast generation of maximal independent sets of high cardinality. One possibility is to combine Gibbs sampling with coupling from the past arguments to detect convergence to the stationary regime. This results in a sampling procedure with time complexity that depends on the mixing time of the Glauber dynamics Markov chain.

We propose an adaptive method for random event generation in the Glauber dynamics that considers only the events that are effective in the coupling from the past scheme, accelerating the convergence time of the Gibbs sampling algorithm.

The full paper is available on arXiv.
Online Electricity Cost Saving Algorithms for Co-Location Data Centers
Authors: Zhang Linquan , Li Zongpeng , Wu Chuan , Ren Shaolei
Published: June 2015

This work studies the online electricity cost minimization problem at a co-location data center. A co-location data center serves multiple tenants who rent the physical infrastructure within the data center to run their respective cloud computing services. Consequently, the co-location operator has no direct control over power consumption of its tenants, and an efficient mechanism is desired for eliciting desirable consumption patterns from the co-location tenants. Electricity billing faced by a data center is nowadays based on both the total volume consumed and the peak consumption rate. This leads to an interesting new combinatorial optimization structure on the electricity cost optimization problem, which also exhibits an online nature due to the definition of peak consumption. We model and solve the problem through two approaches: the
pricing
approach and the
auction
approach. For the former, we design an offline 2-approximation algorithm as well as an online algorithm with a small competitive ratio in most practical settings. For the latter, we design an efficient (2+
c
)-competitive online algorithm, where c is a system dependent parameter close to 1.49, and then convert it into an efficient mechanism that executes in an online fashion, runs in polynomial time, and guarantees truthful bidding and (2+2
c
)-competitive in social cost.
Web Transparency for Complex Targeting
Authors: Ducoffe Guillaume , Lécuyer Mathias , Chaintreau Augustin , Geambasu Roxana
Published: June 2015

Big Data promises important societal progress but exacerbates the need for due process and accountability. Companies and institutions can now discriminate between users at an individual level using collected data or past behavior. Worse, today they can do so in near perfect opacity. The nascent field of
web transparency
aims to develop the tools and methods necessary to reveal how information is used, however today it lacks robust tools that let users and investigators identify targeting using multiple inputs.

In this paper, we formalize for the first time the problem of detecting and identifying targeting on
combinations of inputs
and provide the first algorithm that is asymptotically exact. This algorithm is designed to serve as a theoretical foundational block to build future scalable and robust web transparency tools. It offers three key properties. First, our algorithm is
service agnostic
and applies to a variety of settings under a broad set of assumptions. Second, our algorithm's analysis delineates a
theoretical detection limit
that characterizes which forms of targeting can be distinguished from noise and which cannot. Third, our algorithm establishes
fundamental tradeoffs
that lead the way to new metrics for the science of web transparency.
Performance Characterization of Hyperscale Applicationson on NVMe SSDs
Authors: Xu Qiumin , Siyamwala Huzefa , Ghosh Mrinmoy , Awasthi Manu , Suri Tameesh , Guz Zvika , Shayesteh Anahita , Balakrishnan Vijay
Published: June 2015

The storage subsystem has undergone tremendous innovation in order to keep up with the ever-increasing demand for throughput. NVMe based SSDs are the latest development in this domain, delivering unprecedented performance in terms of both latency and peak bandwidth. Given their superior performance, NVMe drives are expected to be particularly beneficial for I/O intensive applications in datacenter installations. In this paper we identify and analyze the different factors leading to the better performance of NVMe SSDs. Then, using databases as the prominent use-case, we show how these would translate into real-world benefits. We evaluate both a relational database (MySQL) and a NoSQL database (Cassandra) and demonstrate significant performance gains over best-in-class enterprise SATA SSDs: from 3.5x for TPC-C and up to 8.5x for Cassandra.
Viral transduction of renin rapidly establishes persistent hypertension in diverse murine strains
Authors: Harlan Shannon M. , Ostroski Robert A. , Coskun Tamer , Yantis Loudon D. , Breyer Matthew D. , Heuer Josef G.
Published: June 2015

Mice provide a unique platform to dissect disease pathogenesis, with the availability of recombinant inbred strains and diverse genetically modified strains. Leveraging these reagents to elucidate the mechanisms of hypertensive tissue injury has been hindered by difficulty establishing persistent hypertension in these inbred lines. ANG II infusion provides relatively short-term activation of the renin-angiotensinogen system (RAS) with concomitant elevated arterial pressure. Longer-duration studies using renin transgenic mice are powerful models of chronic hypertension, yet are limited by the genetic background on which the transgene exists and the exposure throughout development. The present studies characterized hypertension produced by transduction with a renin-coding adeno-associated virus (ReninAAV). ReninAAV mice experienced elevated circulating renin with concurrent elevations in arterial pressure. Following a single injection of ReninAAV, arterial pressure increased on average +56 mmHg, an increase that persisted for at least 12 wk in three distinct and widely used strains of adult mice: 129/S6, C56BL/6, and DBA/2J. This was accomplished without surgical implantation of pumps or complex breeding and backcrossing. In addition, ReninAAV mice developed pathophysiological changes associated with chronic hypertension, including increased heart weight and albuminuria. Thus ReninAAV provides a unique tool to study the onset of and effects of persistent hypertension in diverse murine models. This model should facilitate our understanding of the pathogenesis of hypertensive injury.
Cyclophosphamide-induced vasopressin-independent activation of aquaporin-2 in the rat kidney
Authors: Kim Sua , Choi Hyo-Jung , Jo Chor Ho , Park Joon-Sung , Kwon Tae-Hwan , Kim Gheun-Ho
Published: June 2015

Because cyclophosphamide-induced hyponatremia was reported to occur without changes in plasma vasopressin in a patient with central diabetes insipidus, we hypothesized that cyclophosphamide or its active metabolite, 4-hydroperoxycyclophosphamide (4-HC), may directly dysregulate the expression of water channels or sodium transporters in the kidney. To investigate whether intrarenal mechanisms for urinary concentration are activated in vivo and in vitro by treatment with cyclophosphamide and 4-HC, respectively, we used water-loaded male Sprague-Dawley rats, primary cultured inner medullary collecting duct (IMCD) cells, and IMCD suspensions prepared from male Sprague-Dawley rats. In cyclophosphamide-treated rats, significant increases in renal expression of aquaporin-2 (AQP2) and Na-K-2Cl cotransporter type 2 (NKCC2) were shown by immunoblot analysis and immunohistochemistry. Apical translocation of AQP2 was also demonstrated by quantitative immunocytochemistry. In both rat kidney and primary cultured IMCD cells, significant increases in AQP2 and vasopressin receptor type 2 (V2R) mRNA expression were demonstrated by real-time quantitative PCR analysis. Confocal laser-scanning microscopy revealed that apical translocation of AQP2 was remarkably increased when primary cultured IMCD cells were treated with 4-HC in the absence of vasopressin stimulation. Moreover, AQP2 upregulation and cAMP accumulation in response to 4-HC were significantly reduced by tolvaptan cotreatment in primary cultured IMCD cells and IMCD suspensions, respectively. We demonstrated that, in the rat kidney, cyclophosphamide may activate V2R and induce upregulation of AQP2 in the absence of vasopressin stimulation, suggesting the possibility of drug-induced nephrogenic syndrome of inappropriate antidiuresis (NSIAD).
Methods to estimate aspects of physical activity and sedentary behavior from high-frequency wrist accelerometer measurements
Authors: Staudenmayer John , He Shai , Hickey Amanda , Sasaki Jeffer , Freedson Patty
Published: June 2015

This investigation developed models to estimate aspects of physical activity and sedentary behavior from three-axis high-frequency wrist-worn accelerometer data. The models were developed and tested on 20 participants ( n = 10 males, n = 10 females, mean age = 24.1, mean body mass index = 23.9), who wore an ActiGraph GT3X+ accelerometer on their dominant wrist and an ActiGraph GT3X on the hip while performing a variety of scripted activities. Energy expenditure was concurrently measured by a portable indirect calorimetry system. Those calibration data were then used to develop and assess both machine-learning and simpler models with fewer unknown parameters (linear regression and decision trees) to estimate metabolic equivalent scores (METs) and to classify activity intensity, sedentary time, and locomotion time. The wrist models, applied to 15-s windows, estimated METs [random forest: root mean squared error (rSME) = 1.21 METs, hip: rMSE = 1.67 METs] and activity intensity (random forest: 75% correct, hip: 60% correct) better than a previously developed model that used counts per minute measured at the hip. In a separate set of comparisons, the simpler decision trees classified activity intensity (random forest: 75% correct, tree: 74% correct), sedentary time (random forest: 96% correct, decision tree: 97% correct), and locomotion time (random forest: 99% correct, decision tree: 96% correct) nearly as well or better than the machine-learning approaches. Preliminary investigation of the models' performance on two free-living people suggests that they may work well outside of controlled conditions.
Estrogen Effects on Cognitive and Synaptic Health Over the Lifecourse
Authors: Hara Yuko , Waters Elizabeth M. , McEwen Bruce S. , Morrison John H.
Published: June 2015

Estrogen facilitates higher cognitive functions by exerting effects on brain regions such as the prefrontal cortex and hippocampus. Estrogen induces spinogenesis and synaptogenesis in these two brain regions and also initiates a complex set of signal transduction pathways via estrogen receptors (ERs). Along with the classical genomic effects mediated by activation of ER α and ER β, there are membrane-bound ER α, ER β, and G protein-coupled estrogen receptor 1 (GPER1) that can mediate rapid nongenomic effects. All key ERs present throughout the body are also present in synapses of the hippocampus and prefrontal cortex. This review summarizes estrogen actions in the brain from the standpoint of their effects on synapse structure and function, noting also the synergistic role of progesterone. We first begin with a review of ER subtypes in the brain and how their abundance and distributions are altered with aging and estrogen loss (e.g., ovariectomy or menopause) in the rodent, monkey, and human brain. As there is much evidence that estrogen loss induced by menopause can exacerbate the effects of aging on cognitive functions, we then review the clinical trials of hormone replacement therapies and their effectiveness on cognitive symptoms experienced by women. Finally, we summarize studies carried out in nonhuman primate models of age- and menopause-related cognitive decline that are highly relevant for developing effective interventions for menopausal women. Together, we highlight a new understanding of how estrogen affects higher cognitive functions and synaptic health that go well beyond its effects on reproduction.
Occult Breast Lobular Carcinoma with Numerous Circulating Tumor Cells in Peripheral Blood
Authors: Ogura Kanako , Amano Maki , Matsumoto Toshiharu , Sakaguchi Asumi , Kosaka Taijiro , Kitabatake Toshiaki , Kojima Kuniaki
Published: June 2015

We experienced a very rare case of occult breast lobular carcinoma with numerous circulating tumor cells in peripheral blood. The diagnosis was very difficult because there were no symptoms of breast cancer and the preceding chief complaints such as general fatigue and weight loss or abnormality of peripheral blood findings were suggestive of a hematological disease. We could make a correct diagnosis of this case by checking the findings of complete blood count and bone marrow biopsy at the same time using immunohistochemistry.
Transverse Vibration of Axially Moving Functionally Graded Materials Based on Timoshenko Beam Theory
Authors: Sui Suihan , Chen Ling , Li Cheng , Liu Xinpei
Published: June 2015

The transverse free vibration of an axially moving beam made of functionally graded materials (FGM) is investigated using a Timoshenko beam theory. Natural frequencies, vibration modes, and critical speeds of such axially moving systems are determined and discussed in detail. The material properties are assumed to vary continuously through the thickness of the beam according to a power law distribution. Hamilton’s principle is employed to derive the governing equation and a complex mode approach is utilized to obtain the transverse dynamical behaviors including the vibration modes and natural frequencies. Effects of the axially moving speed and the power-law exponent on the dynamic responses are examined. Some numerical examples are presented to reveal the differences of natural frequencies for Timoshenko beam model and Euler beam model. Moreover, the critical speed is determined numerically to indicate its variation with respect to the power-law exponent, axial initial stress, and length to thickness ratio.
Oral Administration of Escin Inhibits Acute Inflammation and Reduces Intestinal Mucosal Injury in Animal Models
Authors: Li Minmin , Lu Chengwen , Zhang Leiming , Zhang Jianqiao , Du Yuan , Duan Sijin , Wang Tian , Fu Fenghua
Published: June 2015

The present study aimed to investigate the effects of oral administration of escin on acute inflammation and intestinal mucosal injury in animal models. The effects of escin on carrageenan-induced paw edema in a rat model of acute inflammation, cecal ligation and puncture (CLP) induced intestinal mucosal injury in a mouse model, were observed. It was shown that oral administration of escin inhibits carrageenan-induced paw edema and decreases the production of prostaglandin E2 (PGE2) and cyclooxygenase- (COX-) 2. In CLP model, low dose of escin ameliorates endotoxin induced liver injury and intestinal mucosal injury and increases the expression of tight junction protein claudin-5 in mice. These findings suggest that escin effectively inhibits acute inflammation and reduces intestinal mucosal injury in animal models.
Asphalt Concrete Mixtures: Requirements with regard to Life Cycle Assessment
Authors: Mikolaj Jan , Schlosser Frantisek , Remek Lubos , Chytcakova Aurelia
Published: June 2015

Design of asphalt concrete, required properties of constituent materials and their mixing ratios, is of tremendous significance and should be implemented with consideration given to the whole life cycle of those materials and the final construction. Conformity with requirements for long term performance of embedded materials is the general objective of the Life Cycle Assessment (LCA). Therefore, within the assessment, material properties need to be evaluated with consideration given to the whole service life—from the point of embedding in the construction until their disposal or recycling. The evaluation focuses on verification of conformity with criteria set for these materials and should guarantee serviceability and performance during their whole service life. Recycling and reuse of asphalt concrete should be preferred over disposal of the material. This paper presents methodology for LCA of asphalt concrete. It was created to ensure not only applicability of the materials in the initial stage, at the point of their embedding, but their suitability in terms of normatively prescribed service performance of the final construction. Methods described and results are presented in a case study for asphalt mixture AC 11; I design.
PKDIP: Efficient Public-Key-Based Data Integrity Protection for Wireless Image Sensors
Authors: Wan Changsheng , Zhang Juan , Huang Jie
Published: June 2015

Due to limited energy of “wireless image sensors (WISs),” existing data integrity protection mechanisms typically employ a hash-function-based signing algorithm to generate “message authentication codes (MACs)” for long image frames. However, hash-function-based signing algorithm requires the WIS and the “end user (EU)” sharing a secret, which leads to a new security issue: Once the EU becomes malicious due to some reasons, it will be able to forge the WIS’s data since it holds the shared secret. Therefore, public-key cryptography is desirable. Unfortunately, public-key cryptographic operations are quite time-consuming for energy-restrained WISs. Facing this dilemma, we present a novel data integrity protection protocol named PKDIP in this paper. Similar to the mechanisms of this field, PKDIP generates MACs for data integrity protection. However, different from other well-known approaches, PKDIP introduces the “Montgomery Modular Multiplication (MontMM)” technique to current public-key-based signing algorithms. Since MontMM is much more efficient than hash functions, PKDIP can reduce the signing cost significantly. Experimental results show PKDIP can even be more efficient than hash-function-based schemes.
Measurements of Building Transmission Loss and Delay Spread at 2.5 GHz
Authors: Gonsioroski L. H. , da Silva Mello L.
Published: June 2015

This paper presents the results of measurements of signal transmission loss at 2.5 GHz through 10 urban buildings. This allows the characterization of different types of buildings by effective attenuation constants and consideration of the contribution of the transmitted signal in microcell coverage predictions. Power delay profiles (PDPs) of the received signal were also measured and used to determine the time dispersion parameters of the channel, including the mean excess delay and the rms delay spread.
Effect ofLuteolinon 11Beta-Hydroxysteroid Dehydrogenase in Rat Liver and Kidney
Authors: Tang Lei , Deng Bin , Shi Lijuan , Wei Binghua , Ren Bin , Fu Xiaohua
Published: June 2015

11Beta-hydroxysteroid dehydrogenase (11β-HSD) enzymes control the glucocorticoid (GC) signaling, which is essential in regulating homeostasis. Our previous study revealed thatEclipta prostrata(EP) affected the activity and expression of 11β-HSD enzymes which might improve the efficacy and reduce the adverse drug effects of glucocorticoid in patients undergoing combinational therapy. However, it is still unclear which composition of EP plays a major role and how it works. In this paper, we choseLuteolinwhich is one of the main ingredients of EP and evaluated its effect and metabolism in combination with prednisone. The effects of different concentrations ofLuteolinextract on prednisone/prednisolone metabolism indicated the enzyme activity of 11β-HSD, so the production rate (pmol/min per mg protein) of metabolites was used to indicate enzyme activity. Furthermore, we explored the influence ofLuteolinon gene and protein expressions of 11β-HSD I/II in rat liver and kidney tissue. Our results showed that oral administration ofLuteolinsignificantly increased the gene and protein expressions of hepatic 11β-HSD I and renal 11β-HSD II, which may improve the efficacy and reduce the adverse drug effect of glucocorticoid in clinical application. A potential clinical value ofLuteolinwould also be indicated in combination therapy with prednisone for the treatment of nephrotic syndrome.