Miracle-dz/UniFAR-FTUs huggingface dataset:
36 th SAW Consensus Summary 389 Indices of Abundance Fishery Independent Indices

The Atlantic Coast striped bass stock is assessed with two separate methods: 1) catch-age based virtual population analysis, and 2) tag release-recovery based survival estimation. Each program is presented in this report as separate segments. The VPA analysis, prepared by the Stock Assessment Subcommittee, is used to evaluate fishing mortality for the mixed coastal stock and provide estimates of abundance and biomass. The tagging analysis, prepared by the Tagging Workgroup, is used to evaluate fishing mortality for specific stocks and averaged results are used to develop a mixed stock mortality estimate. Fishing mortality rates from both programs are compared. A summary of the Chesapeake Bay tag-based direct enumeration study, used to evaluate compliance of the Chesapeake Bay management program with FMP mortality targets, is also presented. The ASMFC Striped Bass stock assessment subcommittee and Technical Committee met in September 2002 to evaluate the status of the striped bass resource. The first analytical assessment using virtual population analysis (VPA) was conducted in 1997 (for years 1982-1996) and reviewed by the 26 th Stock Assessment Review Committee at the Northeast Fisheries Science Center. The results of the review were reported in the proceedings of the 26 th Northeast Regional Stock Assessment Workshop (26 th SAW): SARC Consensus Summary of Assessments (NEFSC Ref. Document 98-03). This report represents the latest in the series of annual assessments with the inclusion of the 2001 catch and survey data. Commercial Fishery Commercial landings in 2001 totaled 941.7 thousand fish and 6.2 million pounds (2,826 mt) (Table D1, Table D2). The landings represent a decline of 109.5 thousand fish (10.4%) and of 395.7 thousand pounds (6%) compared to 2000 (Table D8). The Chesapeake Region (Maryland, PRFC, and Virginia) accounts for most of the commercial harvest, 65% by weight and 82% by number (Table D3). Overall, commercial harvest represented 22% by number and 24% by weight of total harvest in 2001, and 29 % of total catch in number (harvest + discard) (Figure D1, Table D2). Commercial harvest was comprised primarily of fish ages 4 to 6 (60% of commercial harvest). Ages 3 through 8 comprised 88.5% of the harvest. Direct measurements of commercial discards of striped bass were not available. For past assessments that incorporated 1982-97, the estimates were based on the ratio of commercial to recreational released fish tag recovery data, scaled by total recreational discards: CD = RD*(CT/RT) where: CD is an estimate of the …
Modeling of graphene nanoribbon FET and analysis of its electrical properties

Graphene devices can be used to electromagnetic information field base on their peculiar electromagnetic properties. These devices, which have advantages in terms of small size, high switching speed, and high switching reliability, will be instead of the variable capacitances or mechanical contacts. In this paper, we present a modeling of graphene nanoribbon field-effect transistor, and a simulation of graphene devices in weak electrostatic fields, based on the self-consistent solution of Poisson and Schrödinger equations within the non-equilibrium Green's function formalism and a tight-binding Hamiltonian. The simulation results analyze the electrical characteristics of GNR-FET with different structures.
Dimer order with striped correlations in the J 1 -J 2 Heisenberg model

Ground state energies for plaquette and dimer order in the $J_1-J_2$ square-lattice spin-half Heisenberg model are compared using series expansion methods. We find that these energies are remarkably close to each other at intermediate values of $J_2/J_1$, where the model is believed to have a quantum disordered ground state. They join smoothly with those obtained from the Ising expansions for the 2-sublattice N\'eel-state at $J_2/J_1 \approx 0.4$, suggesting a second order transition from a N\'eel state to a quantum disordered state, whereas they cross the energy for the 4-sublattice ordered state at $J_2/J_1 \approx 0.6$ at a large angle, implying a first order transition to the 4-sublattice magnetic state. The strongest evidence that the plaquette phase is not realized in this model comes from the analysis of the series for the singlet and triplet excitation spectra, which suggest an instability in the plaquette phase. Thus, our study supports the recent work of Kotov et al, which presents a strong picture for columnar dimer order in this model. We also discuss the striped nature of spin correlations in this phase, with substantial resonance all along columns of dimers.
Genetic algorithm for dynamic path planning

Optimization in dynamically changing environments is a hard problem. Path planning for mobile robots is a complex problem that not only guarantees a collision-free with minimum traveling distance but also requires smoothness and clearances. This paper presents a genetic algorithm approach for solving the path planning problem in stochastic mobile robot environments. The genetic algorithm planner (GAP) is based on a variable length representation, where different evolutionary operators are applied. A generic fitness function is used to combine all the objectives of the problem. In order to make the algorithm suitable for both static and dynamic environments, problem specific domain knowledge is used.
A high performance multi-modal traffic simulation platform and its case study with the Dublin city

This paper describes a highly scalable multi-modal traffic simulation platform and its case study in Dublin city. By leveraging various sources of open and administrative data for the Greater Dublin Region, we have built a city operating system like platform that simulates not only private cars but also public buses and trains. Our performance study demonstrates that our simulator is highly scalable by achieving 15.5 times faster than real-world time with 12 parallel threads. This is the first effort that has provided a high-performance and high-scalability traffic simulation on a distributed-memory environment and demonstrated the validity of the approach using real data sets.
A Unified Approach to Model Non-Functional Properties of Mobile Context-Aware Software

Modeling context-awareness is becoming a primary activity for software engineers that design applications for mobile devices. In fact, software applications running on such devices need to be aware of their context (that may rapidly change) to adapt their services and offer the best quality (intended as a combination of non-functional properties) in any context. Thus the need of instruments to manage mobility, context-awareness and non-functional characteristics is critical to build software systems in ubiquitous and mobile domain. In this paper we introduce a framework to uniformly model different types of mobility and context-awareness so that the modeling and analysis of non-functional properties of such systems can be supported in an integrated environment. To enable non-functional analysis, we devise the integration of non-functional parameters in the modeling framework. In particular, we present an UML implementation of our framework within the MagicDraw modeling environment, with the support of existing UML profiles for modeling context-awareness and non-functional properties. We finally show an example of modeling and analysis of reliability in the eHealth domain.
Ensemble based 3D human motion classification

Due to the rapid development of motion capture technology, more and more human motion databases appear. In order to effectively and efficiently manage human motion database, human motion classification is necessary. In this paper, we propose an ensemble based human motion classification approach (EHMCA). Specifically, EHMCA first extracts the descriptors from human motion sequences. Then, singular value decomposition (SVD) is adopted to reduce the dimensionality of all the feature vectors. In the following step, a cluster ensemble approach is designed to construct the consensus matrix from the feature vectors. Finally, the normalized cut algorithm is applied to partition the consensus matrix and assign the feature vectors into the corresponding clusters. Experiments on the CMU database illustrate that the proposed approach achieves good performance.
CloudaSec: A novel public-key based framework to handle data sharing security in clouds

Recent years have witnessed the trend of leveraging cloud-based services for large scale content storage, processing, and distribution. Data security and privacy are among top concerns for the public cloud environments. Towards these security challenges, we propose and implement CloudaSec framework for securely sharing outsourced data via the public cloud. CloudaSec ensures the confidentiality of content in the public cloud environments with flexible access control policies for subscribers and efficient revocation mechanisms. CloudaSec proposes several cryptographic tools for data owners, based on a novel content hash keying system, by leveraging the Elliptic Curve Cryptography (ECC). The separation of subscription-based key management and confidentiality-oriented asymmetric encryption policies uniquely enables flexible and scalable deployment of the solution as well as strong security for outsourced data in cloud servers. Through experimental evaluation, we demonstrate the efficiency and scalability of CloudaSec, build upon OpenStack Swift testbed.
The Interferon-Stimulated Gene Ifi27l2a Restricts West Nile Virus Infection and Pathogenesis in a Cell-Type- and Region-Specific Manner

ABSTRACT The mammalian host responds to viral infections by inducing expression of hundreds of interferon-stimulated genes (ISGs). While the functional significance of many ISGs has yet to be determined, their cell type and temporal nature of expression suggest unique activities against specific pathogens. Using a combination of ectopic expression and gene silencing approaches in cell culture, we previously identified Ifi27l2a as a candidate antiviral ISG within neuronal subsets of the central nervous system (CNS) that restricts infection by West Nile virus (WNV), an encephalitic flavivirus of global concern. To investigate the physiological relevance of Ifi27l2a in the context of viral infection, we generated Ifi27l2a −/− mice. Although adult mice lacking Ifi27l2a were more vulnerable to lethal WNV infection, the viral burden was greater only within the CNS, particularly in the brain stem, cerebellum, and spinal cord. Within neurons of the cerebellum and brain stem, in the context of WNV infection, a deficiency of Ifi27l2a was associated with less cell death, which likely contributed to sustained viral replication and higher titers in these regions. Infection studies in a primary cell culture revealed that Ifi27l2a −/− cerebellar granule cell neurons and macrophages but not cerebral cortical neurons, embryonic fibroblasts, or dendritic cells sustained higher levels of WNV infection than wild-type cells and that this difference was greater under conditions of beta interferon (IFN-β) pretreatment. Collectively, these findings suggest that Ifi27l2a has an antiviral phenotype in subsets of cells and that at least some ISGs have specific inhibitory functions in restricted tissues. IMPORTANCE The interferon-stimulated Ifi27l2a gene is expressed differentially within the central nervous system upon interferon stimulation or viral infection. Prior studies in cell culture suggested an antiviral role for Ifi27l2a during infection by West Nile virus (WNV). To characterize its antiviral activity in vivo, we generated mice with a targeted gene deletion of Ifi27l2a. Based on extensive virological analyses, we determined that Ifi27l2a protects mice from WNV-induced mortality by contributing to the control of infection of the hindbrain and spinal cord, possibly by regulating cell death of neurons. This antiviral activity was validated in granule cell neurons derived from the cerebellum and in macrophages but was not observed in other cell types. Collectively, these data suggest that Ifi27l2a contributes to innate immune restriction of WNV in a cell-type- and tissue-specific manner.
Understanding Feature Selection in Functional Magnetic Resonance Imaging

The advent of functional Magnetic Resonance Imaging (fMRI) has provided researchers with a method of probing the activity of the brain with fine granularity. One goal of fMRI research is to use brain activity to classify the task a subject is performing. Due to the vast quantity of data in fMRI studies, feature selection techniques are used to select relevant brain areas (features) as input to a classifier. In this problem setting, features can be selected through two methods: (1) discriminability tests that compare the feature’s activity between the specific tasks to be classified or (2) activity tests that compare the task activity against activity during a rest condition. A surprising result is that features chosen using activity tests often provide better classification accuracy than discriminability tests, despite receiving no information pertinent to the classification task. The goal of my research has been to use a plausible model of fMRI data coupled with mathematical analysis and experiments using synthetic data to explain this phenomenon. Realistic parameters to this model allow very specific predictions about the performance of different feature selection methods. Subsequently, this approach is validated and extended through the investigation of experimental data in a semantic categories experiment. Finally, the results of this approach are used to explore alternative feature selection methods that have the potential to outperform activity-based feature selection. A case study comparing feature selection methods in experimental data serves to underscore the applications of this research. Although this research examines fMRI data, the research methodology, analytical approach, and conclusions of this work have the potential to apply to a broad spectrum of problems and provide insight into the general problem of dimensionality reduction.
High capacity reversible data hiding based on histogram shifting for medical images

This paper presents a modified histogram shifting algorithm for reversible medical image watermarking to increase the hiding capacity. It is based on hierarchically dividing a cover image into smaller blocks for data embedding using the histogram shifting technique. To yield an optimal data hiding result, a technique for recursive looking-ahead estimation of the maximum data hiding volume at the lowest level of the block-division tree structure is used. The block divisions are performed and optimal division is selected based on the hiding capacity. A modified block division scheme is chosen for the medical images having large dark areas. In this method high data hiding capacity and high stego-image quality are achieved.
Trust-region methods for nonconvex sparse recovery optimization

We solve the ℓ2-ℓp sparse recovery problem by transforming the objective function into an unconstrained differentiable function and apply a limited-memory trust-region method. Unlike gradient projection-type methods, which uses only the current gradient, our approach uses gradients from previous iterations to obtain a more accurate Hessian approximation. Preliminary numerical experiments with simulated compressive sensing 1D data are provided to illustrate that our proposed approach eliminates spurious solutions more effectively while improving the computational time to converge in comparison to standard approaches.
MAST: Modeling and Analysis Suite for Real Time Applications

This paper describes a model for representing the temporal and logical elements of real-time applications, called MAST. This model allows a very rich description of the system, including the effects of event or message-based synchronization, multiprocessor and distributed architectures as well as shared resource synchronization. The model is directly obtainable from a description of the system design using a UML tool. A system representation using this model is analyzable through a set of tools that has been developed within the MAST suite, including worst-case schedulability analysis for hard timing requirements, and discrete-event simulation for soft timing requirements. Although the current model only includes fixed priority systems, it is conceived as an open model and is easily extensible to accommodate other kinds of systems.
Voltage Smoothing: Characterizing and Mitigating Voltage Noise in Production Processors via Software-Guided Thread Scheduling

Parameter variations have become a dominant challenge in microprocessor design. Voltage variation is especially daunting because it happens so rapidly. We measure and characterize voltage variation in a running Intel Core2 Duo processor. By sensing on-die voltage as the processor runs single-threaded, multi-threaded, and multi-program workloads, we determine the average supply voltage swing of the processor to be only 4 percent, far from the processor's 14percent worst-case operating voltage margin. While such large margins guarantee correctness, they penalize performance and power efficiency. We investigate and quantify the benefits of designing a processor for typical-case (rather than worst-case) voltage swings, assuming that a fail-safe mechanism protects it from infrequently occurring large voltage fluctuations. With today's processors, such resilient designs could yield 15 percent to 20 percent performance improvements. But we also show that in future systems, these gains could be lost as increasing voltage swings intensify the frequency of fail-safe recoveries. After characterizing micro architectural activity that leads to voltage swings within multi-core systems, we show that a voltage-noise-aware thread scheduler in software can co-schedule phases of different programs to mitigate error recovery overheads in future resilient processor designs.
Design considerations of coupling inductance for active power filters

A method for calculating the coupling inductance of active power filters (APFs) is proposed in this paper. The current ripple is defined and its relationship with the coupling inductance value is deduced based on error analyses. Hence, the lower boundary of the inductor value could be determined from the current ripple limit. A method to estimate the upper boundary of the inductor value is also proposed based on the capability to track compensating current reference. It is suggested to select the coupling inductor value close to the lower boundary for the APF. The upper boundary is used to check if any further improvement is needed for getting the balance between tracking speed and suppressing current ripple. The converter topology and modulation signal scheme are also considered in selecting the coupling inductor. Simulation and experimental results are provided to show the validity of the proposed method. Comparisons with previous methods are also given. The proposed method may also work for other inductor-coupled system, such as STATCOM.
Critical Success Factors for Successful Enterprise Resource Planning Implementation at Indian SMEs

Small and medium-size enterprises (SMEs) have been receiving lesser focus from the software vendors than large enterprises (LEs). Research on the implementation of ERP in certain European countries shows that the job of implementing an Enterprise Resource Planning (ERP) is a riskier business for SMEs than for LEs. In this paper, a framework has been adopted to cover both the national (Indian) and the organization size (SMEs) aspects to identify and rank the Critical Success Factors (CSFs) that influence the success of ERP implementation at Indian SMEs. This paper attempts to build a consensus from the previous research and to derive a unified ERP implementation success model of the critical success factors to guide a successful ERP implementation at Indian SMEs that differs from existing models in that it has a broader and more holistic focus due to the coverage of thirty CSFs. A model is developed with the help of quantitative survey based method to identify and rank the thirty critical success factors and then a framework has been proposed in terms of recommendations for managing these CSFs. Two close ended questionnaires were used to collect the data from the Indian ERP consultants, those who are having experience of ERP implementation in India for almost all types of Indian industries including Indian SMEs. The data collected were analyzed using statistical techniques such as factor analysis and non parametric tests. This paper argues that ERP implementation at Indian SMEs should extend its scope beyond the configuration to the process, enterprise, technology, vendor, end-user, human resource, performance, quality, strategy and project related issues by considering these thirty critical success factors for the success of ERP implementation at Indian SMEs that may put Indian SMEs on the competitive position. The ERP implementation success model simplifies the functionality of ERP implementation at Indian SMEs. The simplification of system makes easier to understand the ERP requirements. Although limited sample size restricted to Indian SMEs may limit generalization but still these factors can be put into practice to facilitate the process of successful ERP implementations at Indian SMEs. It is hoped that this research will help to bridge the current literature gap and provide practical advice for both academics and practitioners for successful ERP implementation at Indian SMEs.
Systems Neuroscience Approaches to Measure Brain Mechanisms Underlying Resilience—Towards Optimizing Performance

Extreme environments are characterized as those situations which place a high demand on the physiological, affective, cognitive, and/or social processing resources of the individual. Extreme environments strongly perturb the body and mind, which in turn initiate complex cognitive and affective response strategies. Different types of extreme environments may share some aspects but can also have unique demand characteristics. For example, exposure to the cold and isolated environment of an Antarctic expedition may result in extreme social and sensory deprivation, whereas exposure to military combat operations may entail extreme sensory overload. It is clear that there are many different types of extreme environments or situations, but it is less clear that an individual’s cognitive and affective responses are as varied as the different types of extreme environments.
Jointly optimal quantization of parameters in sinusoidal audio coding

In this paper, we present new methods for jointly optimal scalar quantization of parameters in sinusoidal audio coding. The sinusoidal amplitudes, frequencies, and phases used to represent (the tonal component of) an audio signal are quantized such as to minimize a single-letter perceptual distortion measure under a given bit-rate constraint. The asymptotically optimal scalar quantizers are derived analytically using high-rate theory. We present different practical methods, where quantization accuracy of some parameters is made dependent on quantized values of other parameters. The choice of method depends on the availability of the frequency-masking threshold, defining the perceptual importance of the individual sinusoids, at the decoder. The high performance of our quantizers is demonstrated by means of a listening test