Miracle-dz/UniFAR-FTUs huggingface dataset:
Radiative Effects on the Diffusional Growth of Ice Particles in Cirrus Clouds

Abstract At Colorado State University the Regional Atmospheric Modeling System (RAMS) has been used to study the radiative effect on the diffusional growth of ice particles in cirrus clouds. Using soundings extracted from a mesoscale simulation of the 26 November 1991 cirrus event, the radiative effect was studied using a two-dimensional cloud-resolving model (CRM) version of RAMS, coupled to an explicit bin-resolving microphysics. The CRM simulations of the 26 November 1991 cirrus event demonstrate that the radiative impact on the diffusional growth (or sublimation) of ice crystals is significant. Even in a radiatively cooled atmospheric environment, ice particles may experience radiative warming because the net radiation received by an ice particle depends upon the emission from the particle, and the local upwelling and downwelling radiative fluxes. Model results show that radiative feedbacks on the diffusional growth of ice particles can be very complex. Radiative warming of an ice particle will restri.
Spectrum Sensing for OFDM Signals Using Pilot Induced Auto-Correlations

Orthogonal frequency division multiplex (OFDM) has been widely used in various wireless communications systems. Thus the detection of OFDM signals is of significant importance in cognitive radio and other spectrum sharing systems. A common feature of OFDM in many popular standards is that some pilot subcarriers repeat periodically after certain OFDM blocks. In this paper, sensing methods for OFDM signals are proposed by using such repetition structure of the pilots. Firstly, special properties for the auto-correlation (AC) of the received signals are identified, from which the optimal likelihood ratio test (LRT) is derived. However, this method requires the knowledge of channel information, carrier frequency offset (CFO) and noise power. To make the LRT method practical, we then propose an approximated LRT (ALRT) method that does not rely on the channel information and noise power, thus the CFO is the only remaining obstacle to the ALRT. To handle the problem, we propose a method to estimate the composite CFO and compensate its effect in the AC using multiple taps of ACs of the received signals. Computer simulations have shown that the proposed sensing methods are robust to frequency offset, noise power uncertainty, time delay uncertainty, and frequency selectiveness of the channel.
Mechanisms of age-related changes in forest production: the influence of physiological and successional changes

Net primary production (NPP) declines as forests age, but the causal role of decreased gross primary production (GPP), or increased autotrophic respiration (Ra) is still a matter of debate. This uncertainty complicates predicted responses to future climate, as higher atmospheric carbon dioxide (CO2) concentrations may amplify the carbon (C)sink in temperate forests if GPP controls the decline in NPP, but increased temperatures may decrease this C-sink if Ra controls the NPP decline. We quantified NPP in forests dominated by loblolly pine (Pinus taeda) in North Carolina, USA that varied from 14 to 115 years old. We used a sap-flow approach to quantify summer canopy photosynthesis by pines and later-successional hardwood trees, and measured wood CO2 efflux to investigate age-related changes in pine Ra. Despite increasing production by later-successional hardwoods, an 80% decline in pine NPP caused ecosystem NPP to decline with age by ! 40%. The decline in pine NPP was explained by reduced stomatal conductance and photosynthesis, supporting the hypothesis that increasing hydraulic limitation and declining GPP drove the age-related decline of NPP in this species. The difference between GPP and NPP indicated that pine Ra also declined with age; this was corroborated by measurements of reduced stem CO2 efflux with increasing age. These results indicate that C cycling in these successional temperate forests is controlled by C input from GPP, and elements of global change that increase GPP may increase the C-sink in aging warm-temperate pine forests.
Self-Determined Learning to Motivate Struggling Learners in Reading and Writing

Promoting self-determined learning through student-directed learning strategies has been documented to promote more positive school-related outcomes for upper elementary grade learners with disabilities and other students who are struggling. These strategies are typically introduced in multicomponent interventions combining several student-directed learning strategies such as self-monitoring, self-evaluation, antecedent cue regulation, and self-instruction. Such interventions have established efficacy in promoting a wide array of academic outcomes. Students’ motivation is consistently related to academic achievement, but it has been found to change over time, with intrinsic motivation’s having marked decreases into the later elementary years and into middle school. This article reviews the literature on the impact of promoting self-determination and self-determined learning strategies that can be used to promote more positive reading and writing outcomes to enable students to become autonomous learners.
Evidence and Interpretation in Language Learning Research: Opportunities for Collaboration With Computational Linguistics: Evidence and Interpretation

The paper discusses two types of opportunities for interdisciplinary collaboration between computational linguistics and language learning research. We target the connection be-tween data and theory in second language research and highlight opportunities to i) enrich the options for obtaining data, and ii) support the identiﬁcation and valid interpretation of relevant learner data. We ﬁrst characterize options, limitations, and potential for obtaining rich data on learning: from web-based intervention studies supporting the collection of experimentally controlled data to online workbooks facilitating large-scale, longitudinal corpus collection for a range of learning tasks and proﬁciency levels. We then turn to the question of how corpus data can systematically be used for second language research, focusing on the central role that linguistic corpus annotation plays in that regard. We show that learner language poses particular challenges to human and computational linguistic analysis and requires more interdisciplinary discussion of analysis frameworks and advances in annotation schemes.
Dynamic Cooperative Secondary Access in Hierarchical Spectrum Sharing Networks

We address the challenge of energy efficiency in hierarchical spectrum sharing networks with dynamic traffic. We consider a primary and a cognitive secondary transmitter-receiver pair, where the secondary transmitter can utilize cooperative transmission to relay primary traffic while superimposing its own information. The secondary user meets a dilemma in this scenario. By choosing cooperation, it can transmit a packet immediately, but it has to bear the additional cost of relaying. Otherwise, it can wait for the primary user to become idle, which increases the queuing delay that secondary packets experience. To solve this dilemma and trade off delay and energy consumption, we propose dynamic cooperative secondary access control that takes the state of the spectrum sharing network into account. We formulate the problem as a Markov decision process and prove the existence of a stationary policy that is average cost optimal. We evaluate reinforcement learning to find optimal transmission strategy when the traffic and link statistics are not known. We demonstrate that dynamic cooperation is necessary for the secondary system to be able to adapt to changing network conditions and show that optimal sequential decision can significantly improve the tradeoff of the energy consumption and the delay.
Batch fabrication of fiber-reinforced elastomer prepreg

Heightened interest in flexible (elastomeric) composite applications such as bio-mechanical devices, flexible underwater vehicles, and inflatable space structures highlight the need of improved fabrication techniques for fiber-reinforced elastomeric materials (FRE). Previous methods have generally been limited to a fairly low percentage of fibers in an elastomeric matrix, or used calendering manufacturing methods that are not generally uitable for non-tire fiber-rein-forced elastomeric composites applications. Other researchers have noted problems with fiber-elastomer adhesion. The current work demonstrates a method for making small batches of high quality fiber-reinforced elastomer pre-preg. Strengths of the current work include excellent fiber adhesion, medium to high fiber volume fractions, highly parallel fibers, use of traditional advanced composites fabrication methodologies, and reproducible ply thicknesses. The method combines standard techniques of filament winding, wet lay-up techniques, and autoclave curing with pertinent knowledge of elastomers to produce fiber-reinforced elastomer prepreg. Fiber-elastomer adhesion was enhanced by the proper choice of fibers, autoclave pressure, and the application of a primer. Fiber parallelism and straightness were accomplished by use of a filament winder. Fiber-reinforced elastomer prepreg and laminated specimens with fiber volume fractions of 12% to 62% were fabricated using fiberglass and cotton fibers, respectively Manufacturing quality was verified by tensile tests on fabricated specimens. Nonlinear material properties from the tests are being used to calibrate a modified nonlinear laminated plate model. Complete test results and nonlinear predictions are being reported separately.
Blending Makes the Difference: Comparison of Blended and Traditional Instruction on Students' Performance and Attitudes in Computer Literacy

Purpose of this study is to compare students’ course achievement and attitudes towards computers in computer literacy course between two course delivery methods: Blended and face-to-face (FTF). Using a pretest-posttest control group experimental design model, participants were assigned to experimental and control groups purposefully in order to achieve group equivalency. The study was conducted during the fall of the 2006-2007 academic year. Participants were 179 students from the Faculty of Education at Uludag University in Turkey. 86 students were taught with the blended mode of delivery, which involved using both FTF and online modes of instruction; 93 students were taught with FTF mode alone. At the beginning of the study, prior knowledge about computer literacy and attitudes towards computers were measured in both groups. At the end of the study, students’ final course scores and attitude scores towards computers were evaluated. Data were analyzed using independent samples t-tests. A statistically significant difference was found between the FTF and blended group (p<0.05). The analysis showed that the blended group was more successful than the traditional group in terms of both course achievement and attitudes towards computers.
VtkSMP: Task-based Parallel Operators for VTK Filters

NUMA nodes are potentially powerful but taking benefit of their capabilities is challenging due to their architecture (multiple computing cores, advanced memory hierarchy). They are nonetheless one of the key components to enable processing the ever growing amount of data produced by scientific simulations.In this paper we study the parallelization of patterns commonly used in vtk algorithms and propose a new multithreaded plugin for vtk that eases the development of parallel multi-core vtk filters. We specifically focus on task-based approaches and show that with a limited code refactoring effort we can take advantage of NUMA node capabilities. We experiment our patterns on a transform filter, base isosurface extraction filter and a min/max tree accelerated isosurface extraction. We support 3 programming environments, OpenMP, Intel TBB and X-Kaapi, and propose different algorithmic refinements according to the capabilities of the target environment. Results show that we can speed execution up to 30 times on a 48-core machine.
Pareto Frontiers of Sensor Networks for Localization

We develop a theory to predict the localization performance of randomly distributed sensor networks consisting of various sensor modalities when only a constant active subset of sensors that minimize localization error is used for estimation. The characteristics of the modalities include measurement type (bearing or range) and error, sensor reliability, FOV, sensing range, and mobility. We show that the localization performance of a sensor network is a function of a weighted sum of the total number of each sensor modality. We also show that optimization of this weighted sum is independent of how the sensor management strategy chooses the active sensors. We combine the utility objective with other objectives, such as lifetime, coverage and reliability to determine the best mix of sensors for an optimal sensor network design. The Pareto efficient frontier of the multi objectives are obtained with a dynamic program, which also accommodates additional convex constraints.
The Effect of Water, Sugars, and Proteins on the Pattern of Ice Nucleation and Propagation in Acclimated and Nonacclimated Canola Leaves1

Infrared video thermography was used to observe ice nucleation temperatures, patterns of ice formation, and freezing rates in nonacclimated and cold acclimated leaves of a spring (cv Quest) and a winter (cv Express) canola (Brassica napus). Distinctly different freezing patterns were observed, and the effect of water content, sugars, and soluble proteins on the freezing process was characterized. When freezing was initiated at a warm subzero temperature, ice growth rapidly spread throughout nonacclimated leaves. In contrast, acclimated leaves initiated freezing in a horseshoe pattern beginning at the uppermost edge followed by a slow progression of ice formation across the leaf. However, when acclimated leaves, either previously killed by a slow freeze (2°C h−1) or by direct submersion in liquid nitrogen, were refrozen their freezing pattern was similar to nonacclimated leaves. A novel technique was developed using filter paper strips to determine the effects of both sugars and proteins on the rate of freezing of cell extracts. Cell sap from nonacclimated leaves froze 3-fold faster than extracts from acclimated leaves. The rate of freezing in leaves was strongly dependent upon the osmotic potential of the leaves. Simple sugars had a much greater effect on freezing rate than proteins. Nonacclimated leaves containing high water content did not supercool as much as acclimated leaves. Additionally, wetted leaves did not supercool as much as nonwetted leaves. As expected, cell solutes depressed the nucleation temperature of leaves. The use of infrared thermography has revealed that the freezing process in plants is a complex process, reminding us that many aspects of freezing tolerance occur at a whole plant level involving aspects of plant structure and metabolites rather than just the expression of specific genes alone.
Integrated optical interconnect for on-chip data transport

The evolution of integrated circuit technology is causing system designs to move towards communication-based architectures. However, metallic interconnect networks can be very costly in terms of power and silicon area and can thus become a bottleneck in system on chip design. Integrated optical interconnect has been identified by the ITRS as a potential solution to overcome predicted interconnect limitations. This paper firstly presents the technological and device aspects of integrated optical interconnect for on-chip data transport. The second part of the paper concentrates on simulation-based quantitative comparisons of electrical to optical interconnect at the physical link level, and it is demonstrated that a ten-fold reduction in interconnect power consumption can be achieved through the use of optics. Finally some more advanced concepts are introduced in the form of optical networks on chip
Performance of distributed compressive sensing channel feedback in multi-user massive MIMO

Large scale multiple-input multiple-output (MIMO) system is draining attention for its huge spectral efficiency. However, this is only applicable if the base-station is provided with an accurate channel-state information (CSI). In practical cases, the feedback channel is limited, especially when a large antenna array is used making CSI compression mandatory. Therefore, compressive sensing (CS) can be applied to massive MIMO systems, where spatial correlation between the antenna array elements is exploited to obtain sparse representations of the downlink channel. Moreover, the correlation among nearby users seeing similar scatterers can be utilized to recover the CSI at the base-station using distributed compressive sensing (DCS) with a limited amount of errors. Our unique link-level simulation shows the suitability of CS and DCS to reduce the amount of channel feedback to less than 37% and still achieves a very low symbol-error ratio (SER) error floor and high per-user capacity.
Adaptive Neural Sliding Mode Control of Active Power Filter

A radial basis function (RBF) neural network adaptive sliding mode control system is developed for the current compensation control of three-phase active power filter (APF). The advantages of the adaptive control, neural network control, and sliding mode control are combined together to achieve the control task; that is, the harmonic current of nonlinear load can be eliminated and the quality of power system can be well improved. Sliding surface coordinate function and sliding mode controller are used as input and output of the RBF neural network, respectively. The neural network control parameters are online adjusted through gradient method and Lyapunov theory. Simulation results demonstrate that the adaptive RBF sliding mode control can compensate harmonic current effectively and has strong robustness to disturbance signals.
The Mini Mental State Examination at the Time of Alzheimer's Disease and Related Disorders Diagnosis, According to Age, Education, Gender and Place of Residence: A Cross-Sectional Study among the French National Alzheimer Database

The aim of this study was firstly to describe the MMSE (Mini-Mental State Examination) score upon initial diagnosis of Alzheimer's disease and related disorders among the French population, according to age. Secondly, education, gender and place of residence were studied as factors potentially associated with delayed Alzheimer's disease diagnosis. Design we conducted a cross sectional analysis of the French National Alzheimer database (BNA). Data from 2008 to 2012 were extracted. Patients were selected at the moment of their first diagnosis of AD (n = 39,451). Results The MMSE score at initial diagnosis dropped significantly with increasing age. The test score increased with the degree of educational background regardless of age. Gender and place of residence were significantly related to the MMSE score, women and persons living in medical institutions having lower MMSE scores under the age of 90 years and at all educational levels. Conclusions Health care professionals should be aware of these risk factors in order to maximize chances of earliest possible diagnosis of Alzheimer's disease and related disorders.
Distributed Evolutionary Monte Carlo with Applications to Bayesian Analysis

Sampling from multimodal and high dimensional target distribution posits a great challenge in Bayesian analysis. This paper combines the attractive features of the distributed genetic algorithm and the Markov Chain Monte Carlo, resulting in a new Monte Carlo algorithm Distributed Evolutionary Monte Carlo (DEMC) for real-valued problems. DEMC evolves a population of the Markov chains through genetic operators to explore the target function efficiently. The promising potential of the DEMC algorithm is illustrated by applying it to multimodal samples, Bayesian Neural Network and logistic regression inference.
Stochastic Ordering of Fading Channels Through the Shannon Transform

A new stochastic order between two fading distributions is introduced. A fading channel dominates another in the ergodic capacity ordering sense, if the Shannon transform of the first is greater than that of the second at all values of average signal-to-noise ratio. It is shown that some parametric fading models, such as the Nakagami-m, Rician, and Hoyt are distributions that are monotonic in their line of sight parameters with respect to the ergodic capacity order. Some operations under which the ergodic capacity order is preserved are also discussed. Through these properties of the ergodic capacity order, it is possible to compare under two different fading scenarios, the ergodic capacity of a composite system involving multiple fading links with coding/decoding capabilities only at the transmitter/receiver. Such comparisons can be made even in cases when a closed form expression for the ergodic capacity of the composite system is not analytically tractable. Applications to multiple access channels, and extensions to multiple-input multiple-output systems are also discussed.
Enhanced moving object detection using tracking system for video surveillance purposes

Foreground extraction and moving object detection are often used in human tracking systems. However those methods are not able to produce accurate results when objects are too close or when occlusions happen since the result is generally a single big blob which contains all the different objects. In this paper we propose a novel and efficient moving object detection enhancement method. Indeed, by using the results of the previous iteration tracking we are able to keep a correct number of moving objects by removing useless blobs and by splitting those which contain more than one tracker. The method can also speed up the tracking by creating a link between a tracker and a blob to avoid unnecessary processing in some situations.
Efficient Strongly Unforgeable ID-Based Signature Without Random Oracles

Up to date, a large number of ID-based signature (IBS) schemes based on bilinear pairings have been proposed. Most of these IBS schemes possess existential unforgeability under adaptive chosen-message attacks, among which some offer strong unforgeability. An IBS scheme is said to be strongly unforgeable if it possesses existential unforgeability and an adversary who is given signatures of the IBS scheme on some message m is unable to generate a new signature on m. Strong unforgeable IBS schemes can be used to construct many important ID-based cryptographic schemes. However, the existing strongly unforgeable IBS schemes lack efficiency for the signature size and the computation cost of verification phase. In this paper, we propose an efficient strongly unforgeable IBS scheme without random oracles. Under the computational DiffieHellman and collision resistant hash assumptions, we demonstrate that the proposed IBS scheme possesses strong unforgeability against adaptive chosen-message attacks. When compared with previously proposed strongly unforgeable IBS schemes, our scheme has better performance in terms of signature size and computation cost.
A Novel Approach to Saliency Detection Model and Its Applications in Image Compression Miss .

Salient region detection in images is very useful for image processing applications like image segmentation, object detection and recognition. In this paper, an improved approach to detect salient region is presented Using saliency detection technique. Existing saliency detection models are built in the uncompressed domain. Since most images over Internet are typically stored in the compressed domain such as joint photographic experts group,a novel saliency detection model in the compressed domain in this paper. The intensity, color, and texture features of the image are extracted . Detection of irregular visual patterns in images and in video sequences is useful for a variety of tasks. Detecting suspicious behaviors or unusual objects is important for surveillance and monitoring. Identifying spatial saliency in images is useful for quality control and automatic inspection.