Miracle-dz/UniFAR-FTUs huggingface dataset:
BER Analysis of Square OSTBCs with LMMSE Channel Estimation in Arbitrarily Correlated Rayleigh Fading Channels

In this paper, we examine the bit error rate (BER) performance of square orthogonal space-time block codes (OSTBCs) under arbitrarily correlated Rayleigh fading channels. We consider a mismatched maximum-likelihood receiver that obtains the channel state information through pilot-based linear minimum mean-square error channel estimation. For PAM and QAM constellations, a closed-form approximation of the BER is presented, which yields very accurate BER results over a wide range of signal-to-noise ratios.
IEEE 802.11 MAC-Level FEC scheme with retransmission combining

In this paper, we evaluate and enhance the performance of a Forward Error Correction (FEC) scheme for IEEE 802.11 Medium Access Control (MAC). A novel retransmission combining technique is proposed to enhance the performance of the MAC-level FEC scheme. ,We also identify the problem with the IEEE 802.11a physical (PHY) layer when it is used with the MAC-level FEC. A new PHY frame format, backward compatible with the original format, is proposed to resolve the problem. Finally, we analytically evaluate the error performance of the MAC-level FEC, and its enhanced performance via retransmission combining and new 802.11a PHY frame format in AWGN environment. Additionally, we present and discuss the results from simulations using TCP/UDP traffic in more realistic channel environments.
Vegetation in clear-cuts depends on previous land use: a century-old grassland legacy

Plant species richness in central and northern European seminatural grasslands is often more closely linked to past than present habitat configuration, which is indicative of an extinction debt. In this study, we investigate whether signs of historical grassland management can be found in clear-cuts after at least 80 years as coniferous production forest by comparing floras between clear-cuts with a history as meadow and as forest in the 1870s in Sweden. Study sites were selected using old land-use maps and data on present-day clear-cuts. Species traits reflecting high capacities for dispersal and persistence were used to explain any possible links between the plants and the historical land use. Clear-cuts that were formerly meadow had, on average, 36% higher species richness and 35% higher richness of grassland indicator species, as well as a larger overall seed mass and lower anemochory, compared to clear-cuts with history as forest. We suggest that the plants in former meadows never disappeared after afforestation but survived as remnant populations. Many contemporary forests in Sweden were managed as grasslands in the 1800s. As conservation of remaining grassland fragments will not be enough to reduce the existing extinction debts of the flora, these young forests offer opportunities for grassland restoration at large scales. Our study supports the concept of remnant populations and highlights the importance of considering historical land use for understanding the distribution of grassland plant species in fragmented landscapes, as well as for policy-making and conservation.
Efficient multi-word locking using randomization

In this paper we examine the general multi-word lock problem, where processes are allowed to multilock arbitrary registers. Aiming for a highly efficient solution we propose a randomized algorithm which successfully breaks long dependency chains, the crucial factor for slowing down an execution. In the analysis we focus on the 2-word lock problem and show that in this special case an execution of our algorithm takes with high probability at most time O(Δ3 log n /log log n), where n is the number of registers and Δ the maximal number of processes interested in the same register (the contention). Furthermore, we implemented our algorithm for the general multi-word lock problem on an SGI Origin2000 machine, demonstrating that our algorithm is not only of theoretical interest.
Assessing the robustness of solutions to a multi-objective model of an energy management system aggregator

An approach for robustness analysis of non-dominated solutions to a multi-objective optimization model of an energy management system aggregator (EMSA) in face of uncertainty is presented. The EMSA is an intermediary entity between households and the System Operator (SO), capable of contributing to balance load and supply, and therefore coping with the intermittency of renewable energy sources (RES) and facilitating a load follows supply strategy in a Smart Grid environment. Household clusters provide load flexibility to satisfy system services requested by the SO, involving decreasing or increasing load in specific time slots. The EMSA multi-objective optimization model considers the maximization of profits and the minimization of the imbalance between the amounts of load flexibility provided by the end-user clusters to satisfy SO requests, taking into account revenues from the SO and payments to the clusters. A hybrid evolutionary approach combining Genetic Algorithms (GA) with Differential Evolution (DE) has been designed to deal with this model, and its behaviour subject to different scenarios of uncertainty is evaluated. The robustness analysis of non-dominated solutions produced by the hybrid evolutionary approach is based on the degree of robustness concept, taking into account the changes in the performance of the objective functions when small perturbations of the model nominal coefficients occur.
Timing and locations of reef fish spawning off the southeastern United States

Managed reef fish in the Atlantic Ocean of the southeastern United States (SEUS) support a multi-billion dollar industry. There is a broad interest in locating and protecting spawning fish from harvest, to enhance productivity and reduce the potential for overfishing. We assessed spatiotemporal cues for spawning for six species from four reef fish families, using data on individual spawning condition collected by over three decades of regional fishery-independent reef fish surveys, combined with a series of predictors derived from bathymetric features. We quantified the size of spawning areas used by reef fish across many years and identified several multispecies spawning locations. We quantitatively identified cues for peak spawning and generated predictive maps for Gray Triggerfish (Balistes capriscus), White Grunt (Haemulon plumierii), Red Snapper (Lutjanus campechanus), Vermilion Snapper (Rhomboplites aurorubens), Black Sea Bass (Centropristis striata), and Scamp (Mycteroperca phenax). For example, Red Snapper peak spawning was predicted in 24.7–29.0°C water prior to the new moon at locations with high curvature in the 24–30 m depth range off northeast Florida during June and July. External validation using scientific and fishery-dependent data collections strongly supported the predictive utility of our models. We identified locations where reconfiguration or expansion of existing marine protected areas would protect spawning reef fish. We recommend increased sampling off southern Florida (south of 27° N), during winter months, and in high-relief, high current habitats to improve our understanding of timing and location of reef fish spawning off the southeastern United States.
Model-Based QoS-Enabled Self-Healing Web Services

Failures during Web service execution may depend on a wide variety of causes, such as network faults, server crashes, or application-related errors, such as unavailability of a requested web service, errors in the orchestration of choreography of applications, missing data or parameters in an execution flow, or low quality of service (QoS). In this paper, we propose a healing architecture able to handle web service faults in a self- healing way, discussing infrastructural faults and web service and Web application faults. The self-healing architecture manages repair actions, such as substitution of a faulty service or duplication of overloaded services. Implemented prototypes involving QoS in coordinated web services are illustrated and discussed.
Loss of Branched O-Mannosyl Glycans in Astrocytes Accelerates Remyelination

In demyelinating diseases such as multiple sclerosis, a critical problem is failure of remyelination, which is important for protecting axons against degeneration and restoring conduction deficits. However, the underlying mechanism of demyelination/remyelination remains unclear. N-acetylglucosaminyltransferase-IX (GnT-IX; also known as GnT-Vb) is a brain-specific glycosyltransferase that catalyzes the branched formation of O-mannosyl glycan structures. O-Mannosylation of α-dystroglycan is critical for its function as an extracellular matrix receptor, but the biological significance of its branched structures, which are exclusively found in the brain, is unclear. In this study, we found that GnT-IX formed branched O-mannosyl glycans on receptor protein tyrosine phosphatase β (RPTPβ) in vivo. Since RPTPβ is thought to play a regulatory role in demyelinating diseases, GnT-IX-deficient mice were subjected to cuprizone-induced demyelination. Cuprizone feeding for 8 weeks gradually promoted demyelination in wild-type mice. In GnT-IX-deficient mice, the myelin content in the corpus callosum was reduced after 4 weeks of treatment, but markedly increased at 8 weeks, suggesting enhanced remyelination under GnT-IX deficiency. Furthermore, astrocyte activation in the corpus callosum of GnT-IX-deficient mice was significantly attenuated, and an oligodendrocyte cell lineage analysis indicated that more oligodendrocyte precursor cells differentiated into mature oligodendrocytes. Together, branched O-mannosyl glycans in the corpus callosum in the brain are a necessary component of remyelination inhibition in the cuprizone-induced demyelination model, suggesting that modulation of O-mannosyl glycans is a likely candidate for therapeutic strategies.
Sparse model construction using coordinate descent optimization

We propose a new sparse model construction method aimed at maximizing a model's generalisation capability for a large class of linear-in-the-parameters models. The coordinate descent optimization algorithm is employed with a modified l1- penalized least squares cost function in order to estimate a single parameter and its regularization parameter simultaneously based on the leave one out mean square error (LOOMSE). Our original contribution is to derive a closed form of optimal LOOMSE regularization parameter for a single term model, for which we show that the LOOMSE can be analytically computed without actually splitting the data set leading to a very simple parameter estimation method. We then integrate the new results within the coordinate descent optimization algorithm to update model parameters one at the time for linear-in-the-parameters models. Consequently a fully automated procedure is achieved without resort to any other validation data set for iterative model evaluation. Illustrative examples are included to demonstrate the effectiveness of the new approaches.
Echolocating Big Brown Bats, Eptesicus fuscus, Modulate Pulse Intervals to Overcome Range Ambiguity in Cluttered Surroundings

Big brown bats (Eptesicus fuscus) emit trains of brief, wideband frequency-modulated (FM) echolocation sounds and use echoes of these sounds to orient, find insects, and guide flight through vegetation. They are observed to emit sounds that alternate between short and long inter-pulse intervals (IPIs), forming sonar sound groups. The occurrence of these strobe groups has been linked to flight in cluttered acoustic environments, but how exactly bats use sonar sound groups to orient and navigate is still a mystery. Here, the production of sound groups during clutter navigation was examined. Controlled flight experiments were conducted where the proximity of the nearest obstacles was systematically decreased while the extended scene was kept constant. Four bats flew along a corridor of varying widths (100, 70, and 40 cm) bounded by rows of vertically hanging plastic chains while in-flight echolocation calls were recorded. Bats shortened their IPIs for more rapid spatial sampling and also grouped their sounds more tightly when flying in narrower corridors. Bats emitted echolocation calls with progressively shorter IPIs over the course of a flight, and began their flights by emitting shorter starting IPI calls when clutter was denser. The percentage of sound groups containing 3 or more calls increased with increasing clutter proximity. Moreover, IPI sequences having internal structure become more pronounced when corridor width narrows. A novel metric for analyzing the temporal organization of sound sequences was developed, and the results indicate that the time interval between echolocation calls depends heavily on the preceding time interval. The occurrence of specific IPI patterns were dependent upon clutter, which suggests that sonar sound grouping may be an adaptive strategy for coping with pulse-echo ambiguity in cluttered surroundings.
A General Paradigm for Normalizing Metamorphic Malwares

Nowadays malwares are one of the most important problems of computer society and even people society according to the expansion of computer applications in every dimension of our life. Malware is a malicious code that can harm computer systems and thus makes disorder in their performance. In order to escape from malware detectors, malwares use some obfuscation methods to change their appearance. This problem cannot be solved using traditional malware detection methods since these methods are highly dependent on malware's signatures. So normalization (de-obfuscation) methods have been proposed to confront with these problems. In this paper we propose a general malware normalizer that can store lots of obfuscation methods in the form of automata structures and use them for normalizing metamorphic malwares. Each obfuscation method is modeled using an Augmented DFA, ADFA in short. This paradigm searches the occurrence of obfuscated codes in the source code by traversing these ADFAs. If an obfuscated code is detected in the code, it will be normalized in the next phase and thus the obfuscated malware will be detected easily by traditional malware detectors. The main contribution of this paper is its high generality. It can normalize a wide range of obfuscation methods against current methods that are proposed for confronting with one or a limited set of obfuscation methods. The presented approach is developed and tested on a diverse set of malwares and the results are promising for detecting metamorphic malwares.
Documents de Travail du Centre d ’ Economie de la Sorbonne Price Dynamics in a Market with Heterogeneous Investment Horizons and Boundedly Rational Traders

This paper studies the effects of multiple investment horizons and investors’ bounded rationality on the price dynamics. We consider a pure exchange economy with one risky asset, populated with agents maximizing CRRA-type expected utility of wealth over discrete investment periods. An investor’s demand for the risky asset may depend on the historical returns, so that our model encompasses a wide range of behaviorist patterns. The necessary conditions, under which the risky return can be a stationary iid process, are established. The compatibility of these conditions with different types of demand functions in the heterogeneous agents’ framework are explored. We find that conditional volatility of returns cannot be constant in many generic situations, especially if agents with different investment horizons operate on the market. In the latter case the return process can display conditional heteroscedasticity, even if all investors are so-called “fundamentalists” and their demand for the risky asset is subject to exogenous iid shocks. We show that the heterogeneity of investment horizons can be a possible explanation of different stylized patterns in stock returns, in particular, mean-reversion and volatility clustering.
Properties of the kurtosis performance surface in linear estimation: application to adaptive filtering

The paper presents an analysis of the kurtosis performance surface as applied to linear estimation. The analysis concentrates on a modified kurtosis (MK) function used in implementations of the least mean kurtosis (LMK) adaptive algorithm. The MK function is shown to make the LMK algorithm applicable even for Gaussian inputs. The minimum of the MK function is derived and shown to be unique and to correspond to the Wiener solution of the mean square error (MSE) estimation problem. A quantitative comparison of the MSE and MK functions explains why the LMK adaptive algorithm is faster than MSE-based algorithms during the initial learning phase, becoming slower as it approaches steady-state.
Adding Double Progressive Widening to Upper Confidence Trees to Cope with Uncertainty in Planning Problems

Current state of the art methods in energy policy planning only approximate the problem (Linear Programming on a finite sample of scenarios, Dynamic Programming on an approximation of the problem, etc). Monte-Carlo Tree Search (MCTS [3]) seems to be a potential candidate to converge to an exact solution of these problems ([2]). But how fast, and how do key parameters (double/simple progressive widening) influence the rate of convergence (or even the convergence itself), are still open questions. Also, MCTS completely ignores the features of the problem, including the scale of the objective function. In this paper, we present MCTS, and its extension to continuous/stochastic domains. We show that on problems with continuous action spaces and infinite support of random variables, the "vanilla" version of MCTS fails. We also show how the double progressive widening technique success[2] relies on its widening coefficient. We also study the impact of an unknown variance of the random variables, to see if it affects the optimal choice of the widening coefficients.
Applications and Evaluation of In-memory MapReduce

In-memory storage techniques provide cloud applications with cheap, fast and large-scale RAM-based storage. By replicating data and providing adequate consistency control mechanisms, in-memory storage can simplify the design and implementation of highly scalable distributed applications. We argue that in-memory storage can increase the flexibility of the MapReduce parallel programming model without requiring additional communication facilities to propagate data updates. In this paper, we present several applications for our in-memory MapReduce framework from diverse problem domains including iterative and on-line data processing.
Psychological Experience Dynamics of Students with Glossophobia through Narrative Counseling as seen from Gender: A Qualitative Study

Psychological experience can lead to the fear of speech anxiety (glossophobia). A number of studies explain that 75% people in the world have suffered glossophobia. The studies also revealed that, compared with men, women have higher tendency to experience glossophobia. The aim of the present study was investigating psychological experience dynamics and the role of gender that influence the emergence of glossophobia through narrative counseling. The data were collected qualitatively through observation and depth interview on five students of Public Senior High School 13 Surabaya. Furthermore, the data were analyzed thematically on three components of psychological experience dynamics: fear, anxiety, and negative thinking. The results of the analysis showed that most of the students are afraid to be laughed and humiliated. They are also anxious of past bad experience that would be repeated when speaking in public place. Moreover, excessive affection and cognitive responses of the female students are the trigger of experiencing glossophobia. Based on the interpretation, it can be concluded that misinterpretation of psychological experience and low-self-control of the students are able to determine glossophobia easily.
Spherically actuated platform manipulator

This article presents the inverse and forward pose and rate kinematics solutions for a novel 6-DOF platform manipulator, actuated by two base-mounted spherical actuators. The moving platform is connected to the fixed base by two identical spherical-prismatic-universal serial chain legs. The S-joint is active, and the remaining two joints in each chain are passive. An analytical solution is presented for the inverse pose problems, a semi-analytical solution is presented for the rate problems, and the numerical Newton–Raphson technique is employed to solve the forward pose problem. Unfortunately, the passive joint variables cannot be ignored in the kinematics solutions as they can for the Gough–Stewart platform. Examples are presented and hardware has been built, using two Rosheim Omni-Wrists on loan from NASA as the spherical actuators. © 2001 John Wiley & Sons, Inc.
Research of image retrieval based on color and shape features

A new image retrieval algorithm based on color of image blocks and moment invariant of image was presented. First of all, the image is converted to HSV color space, then the image is separated into sub-blocks, and it gets the color histogram in each block as the image color feature. Meanwhile, it uses seven HU moment invariant and eccentricity to describe shape feature. Finally, the similarity is calculated based on the color histogram of each block and moment invariant. Experiment results show this method is very effective.
Comparison of structure‐based and threading‐based approaches to protein functional annotation

To exploit the vast amount of sequence information provided by the Genomic revolution, the biological function of these sequences must be identified. As a practical matter, this is often accomplished by functional inference. Purely sequence‐based approaches, particularly in the “twilight zone” of low sequence similarity levels, are complicated by many factors. For proteins, structure‐based techniques aim to overcome these problems; however, most require high‐quality crystal structures and suffer from complex and equivocal relations between protein fold and function. In this study, in extensive benchmarking, we consider a number of aspects of structure‐based functional annotation: binding pocket detection, molecular function assignment and ligand‐based virtual screening. We demonstrate that protein threading driven by a strong sequence profile component greatly improves the quality of purely structure‐based functional annotation in the “twilight zone. ” By detecting evolutionarily related proteins, it considerably reduces the high false positive rate of function inference derived on the basis of global structure similarity alone. Combined evolution/structure‐based function assignment emerges as a powerful technique that can make a significant contribution to comprehensive proteome annotation. Proteins 2010. © 2009 Wiley‐Liss, Inc.
Optimal Monetary Policy and Downward Nominal Wage Rigidity in Frictional Labor Markets

This paper studies the optimal long-run inflation rate in a labor search and matching framework in the presence of downward nominal wage rigidity. Optimal monetary policy features positive inflation in the long run; the optimal annual long-run inflation rate for the U.S. economy is slightly below 1 percent with a money demand motive and around 2 percent otherwise. Positive inflation facilitates real wage adjustments and hence it eases job creation and prevents excessive increase in unemployment following adverse productivity shocks. The findings of the paper can also be related to standard Ramsey theory of “wedge smoothing”; with positive inflation under sticky prices, the size and the volatility of the intertemporal wedge are significantly reduced.