Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing the impact of hard faults in performance components of modern microprocessors

A growing portion of the silicon area of modern high-performance microprocessors is dedicated to components that increase performance but do not determine functional correctness. Permanent hardware faults in these components can lead to performance fluctuation (not necessarily degradation) and do not produce functional errors. Although this fact has been identified previously, extensive research has not yet been conducted to accurately classify and quantify permanent faults in these components over a set of CPU benchmarks or measure the magnitude of the performance impact. Depending on the results of such studies, performance-related components of microprocessors can be disabled in fine or coarse granularities, salvaging microprocessor functionality at different performance levels. This paper analyzes the impact of permanent faults in the arrays and control logic of key microprocessor performance components such as the branch predictor, branch target buffer, return address stack, and data and instruction prefetchers. We apply a statistically safe fault injection campaign for single faults in performance components on a modified version of the cycle-accurate x86 architectural simulator PTLsim running the SPEC CPU2006 suite. Our evaluation reveals significant differences in the effect of faults and their performance impacts across the components as well as within each component (different fields). We classify faults for all components and analyze their IPC impact in the arrays and control logic. Our analysis shows that a very large fraction (44% to 96%) of permanent faults in these components leads only to performance fluctuation. Observation confirms the intuition that there are no functionality errors; however, many cases of a single fault in a performance component can significantly degrade microprocessor performance (2-20%average IPC reduction for SPEC CPU2006).
Programmable control of bacterial gene expression with the combined CRISPR and antisense RNA system

A central goal of synthetic biology is to implement diverse cellular functions by predictably controlling gene expression. Though research has focused more on protein regulators than RNA regulators, recent advances in our understanding of RNA folding and functions have motivated the use of RNA regulators. RNA regulators provide an advantage because they are easier to design and engineer than protein regulators, potentially have a lower burden on the cell and are highly orthogonal. Here, we combine the CRISPR system from Streptococcus pyogenes and synthetic antisense RNAs (asRNAs) in Escherichia coli strains to repress or derepress a target gene in a programmable manner. Specifically, we demonstrate for the first time that the gene target repressed by the CRISPR system can be derepressed by expressing an asRNA that sequesters a small guide RNA (sgRNA). Furthermore, we demonstrate that tunable levels of derepression can be achieved (up to 95%) by designing asRNAs that target different regions of a sgRNA and by altering the hybridization free energy of the sgRNA–asRNA complex. This new system, which we call the combined CRISPR and asRNA system, can be used to reversibly repress or derepress multiple target genes simultaneously, allowing for rational reprogramming of cellular functions.
Optimal Algorithms for Testing Closeness of Discrete Distributions

We study the question of closeness testing for two discrete distributions. More precisely, given samples from two distributions p and q over an n-element set, we wish to distinguish whether p = q versus p is at least e-far from q, in either e1 or e2 distance. Batu et al [BFR+00, BFR+13] gave the first sub-linear time algorithms for these problems, which matched the lower bounds of [Val11] up to a logarithmic factor in n, and a polynomial factor of e. In this work, we present simple testers for both the e1 and e2 settings, with sample complexity that is information-theoretically optimal, to constant factors, both in the dependence on n, and the dependence on e for the e1 testing problem we establish that the sample complexity is Θ(max{n2/3/e4/3, n1/2/&epsilon2}).
Discriminative modelling of context-specific amino acid substitution probabilities

MOTIVATION Protein sequence searching and alignment are fundamental tools of modern biology. Alignments are assessed using their similarity scores, essentially the sum of substitution matrix scores over all pairs of aligned amino acids. We previously proposed a generative probabilistic method that yields scores that take the sequence context around each aligned residue into account. This method showed drastically improved sensitivity and alignment quality compared with standard substitution matrix-based alignment. RESULTS Here, we develop an alternative discriminative approach to predict sequence context-specific substitution scores. We applied our approach to compute context-specific sequence profiles for Basic Local Alignment Search Tool (BLAST) and compared the new tool (CS-BLASTdis) to BLAST and the previous context-specific version (CS-BLASTgen). On a dataset filtered to 20% maximum sequence identity, CS-BLASTdisis was 51% more sensitive than BLAST and 17% more sensitive than CS-BLASTgenin, detecting remote homologues at 10% false discovery rate. At 30% maximum sequence identity, its alignments contain 21 and 12% more correct residue pairs than those of BLAST and CS-BLASTgen, respectively. Clear improvements are also seen when the approach is combined with PSI-BLAST and HHblits. We believe the context-specific approach should replace substitution matrices wherever sensitivity and alignment quality are critical.
Function optimization in nonstationary environment using steady state genetic algorithms with aging of individuals

The authors explore the utility of the concept of aging of individuals in the context of steady state GAs for nonstationary function optimization. Age of an individual is used as an additional factor in addition to the objective functional value in order to determine its effective fitness value. Age of a newly generated individual is taken as zero, and in every iteration it is increased by one. Individuals undergoing genetic operations are selected based on the effective fitness value, which changes dynamically. This helps to maintain diversity in the population and is useful to trace changes in environment. Simulation results show some promise for the utility of the present technique for nonstationary function optimization.
FEBA: An Action-Based Feature Extraction Framework for Behavioural Identification and Authentication

While the usage of behavioural features for authentication purposes is gaining more and more consensus in the community, there is less consensus on which specific behavioural traits may be useful in eventually different settings. This calls for flexible tools which the application developer can leverage to automate the extraction and management of behavioural features for identification and authentication. This paper specifically describes a framework called FEBA (Feature Extraction Based on Action), which to the best of our knowledge is the first open-source framework that provides the developer with simple and flexible means to: i) define application-specific actions, ii) recognize actions based on the received raw data, and iii) finally extract the action-specific features. We have built a complete implementation of FEBA, and made it available online to facilitate future research in such context. To prove the performance of FEBA, we provide an experimental evaluation of a use case scenario, i.e., mouse movements feature extraction and pattern recognition. We believe that FEBA will help researchers and developers to design and implement novel behavioural authentication mechanisms.
The Initial Conditions Problem in Dynamic, Nonlinear Panel Data Models with Unobserved Heterogeneity

A rotary plug valve for controlling fluid flow including a cylindrical housing having inlet and outlet ports and a plug rotatably disposed within the housing having a V-shaped passageway therein such that the inlet and outlet ends of the passageway register with the inlet and outlet ports of the housing for the flow of fluid therethrough. When the inlet and outlet ports in the housing are taken out of registry with the inlet and outlet ends of the plug by rotation of the latter, fluid flow through the valve is terminated.
Impact of L2S interface on system level evaluation for LTE system

The wireless communication systems have been developed and evaluated mainly based on computer simulation. One challenge has arisen that the whole simulation of the wireless system becomes almost impractical to simulate in a one simulation scheme due to the complexity of the system, the huge number of optimization parameters and huge time consumption. The proposed solution is to divide the whole simulation to two main levels which is commonly called the Link Level Simulation (LLS) and the System Level Simulation (SLS). Further, an interface is needed for the connection between the two levels and the interface is needed to transfer the measured data between the two levels. The interface is called Link-to-System (L2S) interface. The interface plays also as an abstraction model of the link level performance and it is being used in the system level simulation. The L2S interface plays a crucial role in the evaluation of the wireless system in terms of accuracy, simplicity and generality. The L2S interface accuracy and performance depends on different mapping models such as Exponential Effective SINR Mapping (EESM) model and Mutual Information Effective SINR Mapping (MIESM) model. Therefore, this paper investigates the impact of L2S interface on the system level evaluation. As a result, the MIESM and EESM model in terms of throughput gain show a high performance in the overall performance of wireless system evaluation utilizing L2S interface technique.
Results of the Active Learning Challenge

We organized a machine learning challenge on \active learning", addressing problems where labeling data is expensive, but large amounts of unlabeled data are available at low cost. Examples include handwriting and speech recognition, document classication, vision tasks, drug design using recombinant molecules and protein engineering. The algorithms may place a limited number of queries to get new sample labels. The design of the challenge and its results are summarized in this paper and the best contributions made by the participants are included in these proceedings. The website of the challenge remains open as a resource for students and researchers (http://clopinet.com/al).
FCPre: Extending the Arora-Kulkarni Method of Automatic Addition of Fault-Tolerance

Synthesizing fault-tolerant systems from fault-intolerant systems simplifies design of fault-tolerance. Arora and Kulkarni developed a method and a tool to synthesize fault-tolerance under the assumption that specifications are not history-dependent (fusion-closed). Later, Gartner and Jhumka removed this assumption by presenting a modular extension of the Arora-Kulkarni method. This paper presents an implementation of the Gartner-Jhumka method which is evaluated on several examples. As additional safety net, we have added automatic verification of the results using the model checker Spin. In the context of this work, a fault in the Gartner-Jhumka method has been found. Though this fault is rare and does not cause incorrect results, there might be no result at all
Are you still there? - A lightweight algorithm to monitor node presence in self-configuring networks

This paper is concerned with the analysis and redesign of a distributed algorithm to monitor the availability of nodes in self-configuring networks. The simple scheme to regularly probe a node - "are you still there? " - may easily lead to over- or underloading. The essence of the algorithm is therefore to automatically adapt the probing frequency. We show that a self-adaptive scheme to control the probe load, originally proposed as an extension to the UpnP/spl trade/ (universal plug and play) standard, leads to an unfair treatment of nodes: some nodes probe fast while others almost starve. An alternative distributed algorithm is proposed that overcomes this problem and that tolerates highly dynamic network topology changes. The algorithm is very simple and can be implemented on large networks of small computing devices such as mobile phones, PDAs, and so on.
Supplementary Material for “ Learning with Partially Absorbing RandomWalks ”

We propose a novel stochastic process that is with probability αi being absorbed at current state i, and with probability 1 − αi follows a random edge out of it. We analyze its properties and show its potential for exploring graph structures. We prove that under proper absorption rates, a random walk starting from a set S of low conductance will be mostly absorbed in S. Moreover, the absorption probabilities vary slowly inside S, while dropping sharply outside, thus implementing the desirable cluster assumption for graph-based learning. Remarkably, the partially absorbing process unifies many popular models arising in a variety of contexts, provides new insights into them, and makes it possible for transferring findings from one paradigm to another. Simulation results demonstrate its promising applications in retrieval and classification.
Towards Graph Containment Search and Indexing

Given a set of model graphs D and a query graph q, containment search aims to find all model graphs g e D such that q contains g (q ⊇ g). Due to the wide adoption of graph models, fast containment search of graph data finds many applications in various domains. In comparison to traditional graph search that retrieves all the graphs containing q (q ⊆ g), containment search has its own indexing characteristics that have not yet been examined. In this paper, we perform a systematic study on these characteristics and propose a contrast subgraph-based indexing model, called cIndex. Contrast subgraphs capture the structure differences between model graphs and query graphs, and are thus perfect for indexing due to their high selectivity. Using a redundancy-aware feature selection process, cIndex can sort out a set of significant and distinctive contrast subgraphs and maximize its indexing capability. We show that it is NP-complete to choose the best set of indexing features, and our greedy algorithm can approximate the one-level optimal index within a ratio of 1-- 1/e. Taking this solution as a base indexing model, we further extend it to accommodate hierarchical indexing methodologies and apply data space clustering and sampling techniques to reduce the index construction time. The proposed methodology provides a general solution to containment search and indexing, not only for graphs, but also for any data with transitive relations as well. Experimental results on real test data show that cIndex achieves near-optimal pruning power on various containment search workloads, and confirms its obvious advantage over indices built for traditional graph search in this new scenario.
Mass characterization in mammograms using an optimal ensemble classifier

Mass is the most common indicator in mammograms, especially in the early stages of breast cancer. Due to subtle nature of the masses, there is a considerable overlap between the malignant and benign mass characteristics. In this work, a Computer Aided Diagnosis (CADx) system that employs ensemble classifier has been proposed to improve mass characterization. Genetic algorithm (GA), an optimization technique, was employed to select the optimal ensemble. Multicollinearity among classifiers has to be resolved while forming the ensemble. Combining the classifiers that are highly correlated will not guarantee an improved performance when compared to individual classifiers. Variation Inflation Factor (VIF) analysis is incorporated in this work for detecting multicollinearity among classifiers.
Large System Analysis of Base Station Cooperation for Power Minimization

This paper focuses on a large-scale multi-cell multi-user MIMO system in which L base stations (BSs) of N antennas each communicate with K single-antenna user equipments. We consider the design of the linear precoder that minimizes the total power consumption while ensuring target user rates. Three configurations with different degrees of cooperation among BSs are considered: the coordinated beamforming scheme (only channel state information is shared among BSs), the coordinated multipoint MIMO processing technology or network MIMO (channel state and data cooperation), and a single-cell beamforming scheme (only local channel state information is used for beamforming, while channel state cooperation is needed for power allocation). The analysis is conducted assuming that N and K$ grow large with a non trivial ratio K/N, and imperfect channel state information (modeled by the generic Gauss-Markov formulation form) is available at the BSs. Tools of random matrix theory are used to compute, in explicit form, deterministic approximations for: i) the parameters of the optimal precoder; ii) the powers needed to ensure target rates; and iii) the total transmit power. These results are instrumental to get further insight into the structure of the optimal precoders and also to reduce the implementation complexity in large-scale networks. Numerical results are used to validate the asymptotic analysis in the finite system regime and to make comparisons among the different configurations.
Service Choreography, SBVR, and Time

We propose the use of structured natural language (English) in specifying service choreographies, focusing on the what rather than the how of the required coordination of participant services in realising a business application scenario. The declarative approach we propose uses the OMG standard Semantics of Business Vocabulary and Rules (SBVR) as a modelling language. The service choreography approach has been proposed for describing the global orderings of the invocations on interfaces of participant services. We therefore extend SBVR with a notion of time which can capture the coordination of the participant services, in terms of the observable message exchanges between them. The extension is done using existing modelling constructs in SBVR, and hence respects the standard specification. The idea is that users - domain specialists rather than implementation specialists - can verify the requested service composition by directly reading the structured English used by SBVR. At the same time, the SBVR model can be represented in formal logic so it can be parsed and executed by a machine.
Managing mutex variables in a cache-coherent shared-memory system for FPGAs

Modern FPGAs have the ability to place many processing elements on a single die that can access shared memory. In a multiprocessing system, mutex variables are often used to provide proper synchronization and access to memory locations shared by the processing elements. This paper introduces a novel technique to manage mutex variables in caches for FPGAs, and is compared to an off-the-shelf system built mostly by components from an FPGA vendor. Results show that a cache-coherent system using our proposed technique performs more barriers per second than the off-the-shelf system with comparable hardware resources while also providing coherent caches.
Sequential Pattern Based Temporal Contour Representations for Content-Based Multimedia Timeline Analysis

Temporal contour shapes are closely linked to the narrative structure of multimedia content and provide important reference points in content-based multimedia timeline analysis. In this paper, multimedia timeline is extracted from content as time varying video and audio signal features. A temporal contour representation is implemented based on sequential pattern discovery algorithm for modeling the variation contours of multimedia features. The proposed contour representation extracts repetitive temporal patterns from a hierarchy of time resolutions or from synchronized video/audio feature dimensions. The statistically significant contour components, depicting the dominant timeline shapes, are utilized as a structural or analytical representation of the timeline. The modeling performance of this proposed temporal modeling framework is demonstrated through empirical validation and subjective evaluations.
Rotor fault diagnosis in a Squirrel-Cage Induction Machine using support vector

This paper deals with the diagnosis of electrical defects of Squirrel-Cage Rotor Induction Machines (IMs). The failures of induction machine and the diagnosis methods are presented. Among the methods, Motor Current Signature Analysis (MCSA) is used in the experimental study to detect broken rotor bars and end-ring segment. The load level and the load effects on the diagnosis are also studied. Support vector machines (SVM) is applied to classify faults.
Singularity Points Detection in Fingerprint Images

efficient algorithm for singular points (core and delta) detection in fingerprint images is proposed. The algorithm is based on an efficient maximum variation in local orientation field calculation method. The method was tested with FVC- 2000 fingerprint database and the results were compared visually to the results obtained by human experts. The algorithm is capable of detecting singular points with precision and less computational time. The proposed algorithm outperforms existing algorithms in detection accuracy and calculation speed.
Privacy Amplification Against Active Quantum Adversaries

Privacy amplification is the task by which two cooperating parties transform a shared weak secret, about which an eavesdropper may have side information, into a uniformly random string uncorrelated from the eavesdropper. Privacy amplification against passive adversaries, where it is assumed that the communication is over a public but authenticated channel, can be achieved in the presence of classical as well as quantum side information by a single-message protocol based on strong extractors. In 2009 Dodis and Wichs devised a two-message protocol to achieve privacy amplification against active adversaries, where the public communication channel is no longer assumed to be authenticated, through the use of a strengthening of strong extractors called non-malleable extractors which they introduced. Dodis and Wichs only analyzed the case of classical side information. We consider the task of privacy amplification against active adversaries with quantum side information. Our main result is showing that the Dodis-Wichs protocol remains secure in this scenario provided its main building block, the non-malleable extractor, satisfies a notion of quantum-proof non-malleability which we introduce. We show that an adaptation of a recent construction of non-malleable extractors due to Chattopadhyay et al. is quantum proof, thereby providing the first protocol for privacy amplification that is secure against active quantum adversaries. Our protocol is quantitatively comparable to the near-optimal protocols known in the classical setting.