Miracle-dz/UniFAR-FTUs huggingface dataset:
PAPER Special Section on Solid-State Circuit Design—Architecture, Circuit, Device and Design Methodology Reconfiguring Cache Associativity: Adaptive Cache Design for Wide-Range Reliable Low-Voltage Operation Using 7T/14T SRAM ∗

SUMMARY This paper presents an adaptive cache architecture for wide-range reliable low-voltage operations. The proposed associativityreconfigurable cache consists of pairs of cache ways so that it can exploit the recovery feature of the novel 7T/14T SRAM cell. Each pair has two operating modes that can be selected based upon the required voltage level of current operating conditions: normal mode for high performance and dependable mode for reliable low-voltage operations. We can obtain reliable low-voltage operations by application of the dependable mode to weaker pairs that cannot operate reliably at low voltages. Meanwhile leaving stronger pairs in the normal mode, we can minimize performance losses. Our chip measurement results show that the proposed cache can trade off its associativity with the minimum operating voltage. Moreover, it can decrease the minimum operating voltage by 140 mV achieving 67.48% and 26.70% reduction of the power dissipation and energy per instruction. Processor simulation results show that designing the on-chip caches using the proposed scheme results in 2.95% maximum IPC losses, but it can be chosen various performance levels. Area estimation results show that the proposed cache adds area overhead of 1.61% and 5.49% in 32-KB and 256-KB
A Trading Service for COTS Components

Component-based software development (CBSD) has gained recognition as one of the key technologies for the construction of high-quality, evolvable, large complex systems in a timely and affordable manner. In CBSD, the development effort becomes one of gradual discovery about the components, their capabilities and the incompatibilities that arise when they are used in concert. Thus, trading becomes one of the cornerstones of CBSD. However, most of the existing methods for CBSD do not make effective use of traders. In this paper, we analyze the required features for commercial off-the-shelf (COTS) components traders, and introduce COTStrader ,a n Internet-based trader for COTS components. In addition, we discuss how the COTStrader can be integrated into a spiral methodology for CBSD, providing partially automated support for building COTS-based systems.
Dynamical evidence for intermediate mass black holes in old globular clusters

We present an indirect dynamical evidence, based on the measure of the core to half mass radius ratio (rc/rh), that a significant fraction of globular clusters in our galaxy with an age greater than 10 half-mass relaxation times (t_rh) host an intermediate mass black hole (IMBH). In fact, after a few t_rh much of the memory about the details of initial conditions is erased and rc/rh is determined solely by the balance of the stellar encounters energy production in the core with the dissipation due to global expansion of the cluster. Here we compare the observed values of rc/rh from a sample of 57 galactic globular cluster, selected to be dynamically old and not strongly influenced by tidal forces, with the theoretical expectation for the rc/rh ratio based on analytical models and detailed numerical simulations. The simulations of the evolution of star clusters considered include combinations of single stars, primordial binaries and IMBH. For at least half of the clusters in our sample the observed rc/rh ratio appears to be too large to be explained without invoking the presence of an IMBH at the center of the system.
Deadline-Constrained MapReduce Scheduling Based on Graph Modelling

MapReduce is a software framework for processing data-intensive applications with a parallel manner in cloud computing systems. There are also an increasing number of MapReduce jobs that require deadline guarantees. The existing deadline-concerning scheduling schemes do not consider the two problems in the MapReduce computing environment: slot performance heterogeneity and job time variation. In this paper, we utilize the Bipartite Graph modeling to propose a new MapReduce Scheduler called the BGMRS. The BGMRS can obtain the optimal solution of the deadline-constrained scheduling problem by transforming the problem into a well-known graph problem: minimum weighted bipartite matching. The BGMRS has the following features. It considers the heterogeneous cloud computing environment, such that the computing resources of some nodes cannot meet the deadlines of some jobs. As the job progresses, the BGMRS can dynamically find different computing resources for running the job without violating the job deadline. This is beneficial in the computing resource utilization. The BGMRS can also trade the data locality off against the deadline to make more jobs with deadline guarantees. If the available computing resources of the system cannot meet all job deadlines, the BGMRS can minimize the number of jobs with the deadline violation. Finally, simulation experiments are performed to demonstrate the effectiveness of the BGMRS in the deadline-constrained scheduling.
Mobilizing Inclusion: Transforming the Electorate through Get-Out-the-Vote Campaigns

Which get-out-the-vote efforts actually succeed in ethnoracial communities-and why? Analyzing the results from hundreds of original experiments, the authors of this book offer a persuasive new theory to explain why some methods work while others don't. Exploring and comparing a wide variety of efforts targeting ethnoracial voters, Lisa Garcia Bedolla and Melissa R. Michelson present a new theoretical frame-the Social Cognition Model of voting, based on an individual's sense of civic identity-for understanding get-out-the-vote effectiveness. Their book will serve as a useful guide for political practitioners, for it offers concrete strategies to employ in developing future mobilization efforts.
A Quasi-Minimal Residual Variant of the Bi-CGSTAB Algorithm for Nonsymmetric Systems

Motivated by a recent method of Freund [SIAM J. Sci. Comput. , 14 (1993), pp. 470–482], who introduced a quasi-minimal residual (QMR) version of the conjugate gradients squared (CGS) algorithm, a QMR variant of the biconjugate gradient stabilized (Bi-CGSTAB) algorithm of van der Vorst that is called QMRCGSTAB, is proposed for solving nonsymmetric linear systems. The motivation for both QMR variants is to obtain smoother convergence behavior of the underlying method. The authors illustrate this by numerical experiments that also show that for problems on which Bi-CGSTAB performs better than CGS, the same advantage carries over to QMRCGSTAB.
Airline-catering plant material handling system analysis with simulation and scaled animation

The authors discuss the applications of simulation and scaled animation at Sky Chefs Inc., and particularly highlight the analysis of complex power and free (P&F) material handling systems (MHSs). Results of analysis related to the design and validation of the system are also presented. A detailed simulation model is developed with GPSS/H language for the P&F conveyor system for the Sky Chefs Miami facility. Graphic animation is interfaced to the simulation model with a general animation software PROOF. With the aid of PROOF, the scaled animation trace file generated from the GPSS/H simulation model made it possible to see the behavior and performance of the designed system in a realistic, smooth, and dynamic environment during the entire life cycle of the system's development. Animation also gives the systems engineers operational experience with the system and improves the communications between internal/external supplies/customers. <<ETX>>
Accurate Identification of ALK Positive Lung Carcinoma Patients: Novel FDA-Cleared Automated Fluorescence In Situ Hybridization Scanning System and Ultrasensitive Immunohistochemistry

Background Based on the excellent results of the clinical trials with ALK-inhibitors, the importance of accurately identifying ALK positive lung cancer has never been greater. However, there are increasing number of recent publications addressing discordances between FISH and IHC. The controversy is further fuelled by the different regulatory approvals. This situation prompted us to investigate two ALK IHC antibodies (using a novel ultrasensitive detection-amplification kit) and an automated ALK FISH scanning system (FDA-cleared) in a series of non-small cell lung cancer tumor samples. Methods Forty-seven ALK FISH-positive and 56 ALK FISH-negative NSCLC samples were studied. All specimens were screened for ALK expression by two IHC antibodies (clone 5A4 from Novocastra and clone D5F3 from Ventana) and for ALK rearrangement by FISH (Vysis ALK FISH break-apart kit), which was automatically captured and scored by using Bioview's automated scanning system. Results All positive cases with the IHC antibodies were FISH-positive. There was only one IHC-negative case with both antibodies which showed a FISH-positive result. The overall sensitivity and specificity of the IHC in comparison with FISH were 98% and 100%, respectively. Conclusions The specificity of these ultrasensitive IHC assays may obviate the need for FISH confirmation in positive IHC cases. However, the likelihood of false negative IHC results strengthens the case for FISH testing, at least in some situations.
Multirelational Topic Models

In this paper we propose the multirelational topic model (MRTM) for multiple types of link modeling such as citation and coauthor links in document networks. In the citation network, the MRTM models the citation link between each pair of documents as a binary variable conditioned on their topic distributions. In the coauthor network, the MRTM models the coauthor link between each pair of authors as a binary variable conditioned on their expertise distributions. The topic discovery is collectively regularized by multiple relations in both citation and coauthor networks. This model can summarize topics from the document network, predict citation links between documents and coauthor links between authors. Efficient inference and learning algorithms are derived based on Gibbs sampling. Experiments demonstrate that the MRTM significantly outperforms other state-of-the-art single-relational link modeling methods for large scientific document networks.
A Density based clustering with Artificial Immunity inspired preprocessing

In this paper we propose an algorithm which can identify varied shaped clusters from wide variety of input dataset with high degree of accuracy in presence of noise. The initial data processing module adopts a novel approach of Artificial Immune system to reduce data redundancy while preserving the original data patterns. The clustering module pursues a density based approach to identify clusters from the compressed dataset produced by the preprocessing module. We introduced several new concepts like selective Antigenic binding, Local Reachability Factor, Global Reachability Factor to effectively recognize clusters with varied shape, varied density and low intercluster separation with acceptable computational cost. We performed experimental evaluation of our algorithm with wide variety of real and synthetic dataset and obtained higher cluster success rate for all dataset when compared to DBSCAN.
Intrusion detection using fuzzy window Markov model

Because the computer systems audit is a series of system commands or events, which we name states, a very natural method of detecting intrusion is to use a Markov model to calculate the audit series anomaly. Some researchers have done some work based on stationary Markov theory. Stationary Markov theory, however, has its shortcoming of incorrect assumption, and that can directly affect intrusion detection effect, because some "noise" can often make the state series change. To solve this problem, a window Markov (WM) model is proposed. In the WM model, the next state of time t does not depend on the state of time t+1 but on the states in a time window of [t+1, t+m]. In the WM model, it is not reasonable to give every state in the window equal evaluation to be the next state of time t, so we create a fuzzy window Markov (FWM) model. In the FWM model the states in the time window have different fuzzy evaluation values to indicate the probability to be the next state of time t. To test our FWM model, we have carried out an experiment based on the datasets collected at UNM by Forrest. Results show that the FWM model can work effectively.
Design of optimized reversible binary adder/subtractor and BCD adder

Reversible logic has gained the interest of many researchers due to its applicability in emerging low power technologies such as Quantum computing, QCA, optical computing etc., Adders/Subtractors are basic design components of any processor. Optimized design of these adders results in efficient processors. In this work we propose optimized Binary adders/subtractors and BCD adders. The adders/subtractors designed in this work are optimized for Quantum cost and Delay. We also propose a generic design of n-bit adders and subtractors. In this work, we explore the use of Negative control lines for detecting overflow logic of BCD adder which considerably reduces Quantum cost, delay and gate count which result in high speed BCD adder with optimized area which give way to lot of scope in the field of reversible computing in near future.
Visual Concept Detection and Real Time Object Detection

Bag-of-words model is implemented and tried on 10-class visual concept detection problem. The experimental results show that "DURF+ERT+SVM" outperforms "SIFT+ERT+SVM" both in detection performance and computation efficiency. Besides, combining DURF and SIFT results in even better detection performance. Real-time object detection using SIFT and RANSAC is also tried on simple objects, e.g. drink can, and good result is achieved.
Sparse Matrix Prediction Filling in Collaborative Filtering

Collaborative filtering is one of the most successful techniques that attempts to recommend items (such as music, movies, web sites) that are likely of interest to the people. However, Existing CF technique may work poorly due to the sparse attribute inherent to the rating data. In this paper, a new mechanism that combines the user-based rating and item attribute-based is presented. First, we use the inherent item attributes to construct Boolean matrix. Second, we propose a novel blank unrated element prediction approach to compute the similarity of items by comparing the Euclidean distance between two items. Case studies show that our approach contributes to predict the unrated blank data for sparse matrix. The filling-in accuracy is also acceptable and reasonable.
On the performance of an optimized NLMS algorithm

The recently proposed joint-optimized normalized least-mean-square (JO-NLMS) algorithm was developed in the context of a state variable model. Moreover, following the minimization of the system misalignment and using an iterative procedure for adjusting the system model parameter, this algorithm is able to achieve a proper compromise between the performance criteria (i.e., fast convergence/tracking and low misadjustment). In this paper, we present a performance analysis of the JO-NLMS algorithm, outlining some relations between its main parameters and indicating its good behavior.
Gaze-based awareness in complex healthcare environments 1 st Author

Medical staff work in collaborative environments and require information regarding workmates, patients and resources as well as data related to the completion of ongoing tasks. Healthcare systems provide a large quantity of information and current applications usually involve the simultaneous use of many different devices. A system might monitor several patients, provide alerts and warnings as well as information on pending tasks, and many other demanding workloads. It is therefore an open question whether a professional is able to attend a rehabilitation process involving technology and still be able to remain aware of all notifications provided by different devices. In this paper, gaze-based awareness is presented as a natural evolution, through current technology, of the common awareness concept. The key concept consists of considering users’ gaze as fundamental to personalizing the way to subtle notify users about changes on unattended screens. To this end, different levels of subtlety of notification are considered based on where the user is looking together and the user’s work conditions. We present a realization of gaze-based awareness using a real healthcare system named Ubi4health in which this awareness has been considered an essential element during development.
Multidimensional matching and fast search in suffix trees

We show how to construct a suffix tree of a text string t in linear time, after sorting the characters in the text, so that a search for pattern p take time O(p + log t), independent of the alphabet size, thereby matching the asymptotic performance of suffix arrays. Using these suffix trees or suffix arrays we then give linear time algorithms for pattern matching in any fixed dimension.
Why Consumers Engage in Virtual New Product Developments Initiated by Producers

In this article, I investigate why consumers engage in virtual new product developments initiated by producers. Drawing on motivation research found in related fields such as leisure, online communities, user innovation, and survey participation several intrinsic and extrinsic motives can be identified that may induce consumers’ engagement. In this empirical study, 825 consumers participating in a virtual development project were asked about their motivations. Six motivational factors could be extracted. Intrinsic interest in the innovation activity and curiosity are found to be the most important motives for consumers’ willingness to engage in further virtual development activities.
Multiple Resistance Mechanisms among Aspergillus fumigatus Mutants with High-Level Resistance to Itraconazole

ABSTRACT A collection of Aspergillus fumigatus mutants highly resistant to itraconazole (RIT) at 100 μg ml−1 were selected in vitro (following UV irradiation as a preliminary step) to investigate mechanisms of drug resistance in this clinically important pathogen. Eight of the RIT mutants were found to have a mutation at Gly54 (G54E, -K, or -R) in the azole target gene CYP51A. Primers designed for highly conserved regions of multidrug resistance (MDR) pumps were used in reverse transcriptase PCR amplification reactions to identify novel genes encoding potential MDR efflux pumps in A. fumigatus. Two genes, AfuMDR3 and AfuMDR4, showed prominent changes in expression levels in many RIT mutants and were characterized in more detail. Analysis of the deduced amino acid sequence encoded by AfuMDR3 revealed high similarity to major facilitator superfamily transporters, while AfuMDR4 was a typical member of the ATP-binding cassette superfamily. Real-time quantitative PCR with molecular beacon probes was used to assess expression levels of AfuMDR3 and AfuMDR4. Most RIT mutants showed either constitutive high-level expression of both genes or induction of expression upon exposure to itraconazole. Our results suggest that overexpression of one or both of these newly identified drug efflux pump genes of A. fumigatus and/or selection of drug target site mutations are linked to high-level itraconazole resistance and are mechanistic considerations for the emergence of clinical resistance to itraconazole.
Plant responses to elevated temperatures: a field study on phenological sensitivity and fitness responses to simulated climate warming

Significant changes in plant phenology have been observed in response to increases in mean global temperatures. There are concerns that accelerated phenologies can negatively impact plant populations. However, the fitness consequence of changes in phenology in response to elevated temperature is not well understood, particularly under field conditions. We address this issue by exposing a set of recombinant inbred lines of Arabidopsis thaliana to a simulated global warming treatment in the field. We find that plants exposed to elevated temperatures flower earlier, as predicted by photothermal models. However, contrary to life‐history trade‐off expectations, they also flower at a larger vegetative size, suggesting that warming probably causes acceleration in vegetative development. Although warming increases mean fitness (fruit production) by ca. 25%, there is a significant genotype‐by‐environment interaction. Changes in fitness rank indicate that imminent climate change can cause populations to be maladapted in their new environment, if adaptive evolution is limited. Thus, changes in the genetic composition of populations are likely, depending on the species’ generation time and the speed of temperature change. Interestingly, genotypes that show stronger phenological responses have higher fitness under elevated temperatures, suggesting that phenological sensitivity might be a good indicator of success under elevated temperature at the genotypic level as well as at the species level.
Towards a survival analysis of database framework usage in Java projects

Many software projects rely on a relational database in order to realize part of their functionality. Various database frameworks and object-relational mappings have been developed and used to facilitate data manipulation. Little is known about whether and how such frameworks co-occur, how they complement or compete with each other, and how this changes over time. We empirically studied these aspects for 5 Java database frameworks, based on a corpus of 3,707 GitHub Java projects. In particular, we analysed whether certain database frameworks co-occur frequently, and whether some database frameworks get replaced over time by others. Using the statistical technique of survival analysis, we explored the survival of the database frameworks in the considered projects. This provides useful evidence to software developers about which frameworks can be used successfully in combination and which combinations should be avoided.
Business Intelligence Reporting

Business intelligence (BI) refers to computer-based techniques used in spotting, digging-out, and analyzing business data.BI technologies provide historical, current, and predictive views of business operations. Common functions of Business Intelligence technologies are reporting, online analytical processing, data mining, business performance management, text mining, predictive analytics. The following project concentrates on developing a business intelligence tool that can be prove to be very useful in the healthcare sector. In this project we propose the introduction of a recently developed technology known as data mining to laboratory database and describe the medical data mining process which entails transfer of the database from a comprehensive computer-based patient record system (CRPS) into a data warehouse server , creation of a dataset for analysis by extracting ,cleaning selected variables and mining of data using exploratory factor analysis. A computerized patient record system (CRPS) now contains enough laboratorial data to help us find the relationships between multiple variables and the outcomes they influence. The new paradigm proposed in this project for determining complex associations which influence medical outcomes by combining data mining with computerized patient record merits further study. The role of Information Technology in health care is well established. Continuing advancement in IT applications in the area of healthcare has raised people's expectations for better and less expensive healthcare. Computerization of hospital information systems (HIS) has provided an easy access to valuable medical information.