Miracle-dz/UniFAR-FTUs huggingface dataset:
A two-stage model for negative bias temperature instability

Based on the established properties of the most commonly observed defect in amorphous oxides, the E′ center, we suggest a coupled two-stage model to explain the negative bias temperature instability. We show that a full model that includes the creation of E′ centers from their neutral oxygen vacancy precursors and their ability to be repeatedly charged and discharged prior to total annealing is required to describe the first stage of degradation. In the second stage a positively charged E′ center can trigger the depassivation of Pb centers at the Si/SiO2 interface or KN centers in oxynitrides to create an unpassivated silicon dangling bond. We evaluate the new model to experimental data obtained from three vastly different technologies (thick SiO2, SiON, and HK) and obtain very promising results.
Manifold Learning for Image Denoising

This paper presents a novel scheme for image denoising. In spite of the sophistication of recently developed double-density discrete wavelet transforms (double-density DWTs), it still produces artifacts or destroys fine structures by blurring the data. Inspired by recent manifold learning methods, especially the locally linear embedding (LLE), we discover the underlying fact that image patches in noisy and denoised images construct manifolds with similar local geometry in these two distinct spaces. Therefore, we characterize local geometry by measuring how an image patch represented by a feature vector can be reconstructed by its nearest neighbors in feature space. Besides using the training image patches to construct the embedding, we also propose to overlap the target denoised image patches to satisfy local compatibility and smoothness constraints. In our method, double-density DWTs is incorporated with LLE for the purpose of denoising. The experimental results show that our method is flexible with noise type and achieves state-of-the-art performance particularly in terms of preserving the fine structures
Analytical Models and Performance Evaluation of Drive-thru Internet Systems

Drive-thru Internet systems are multiple-access wireless networks in which users in moving vehicles can connect to a roadside access point (AP) to obtain Internet connectivity for some period of time as the vehicles pass through the AP's coverage range. In order to evaluate the type of communication services and the quality-of-service that these systems can provide, in this paper, we investigate the data communication performance of a vehicle in Drive-thru Internet systems. In particular, we derive analytical models with tractable solutions to characterize the average and the distribution of the number of bytes downloaded by a vehicle by the end of its sojourn through an AP's coverage range, in the presence of other vehicles contending for the same AP's resources. Our models are able to quantify the impact of road traffic density, vehicle speed, service penetration rate, AP's transmission range and the corresponding bit rate, on the amount of data downloaded by an individual vehicle. In terms of analysis technique, we map the study of our vehicular data downloading process into the transient analysis of a series of Markov reward processes. Our use of Markov reward model is novel in the sense that we only select from the corresponding Markov chain, a subset of relevant sample paths that matches the required behavior of our vehicular flow model. We also validate our proposed analytical models through extensive simulations, driven by empirical vehicular traffic traces. We believe our work offers a unique analytical framework based on which the interplay between vehicular traffic parameters and a vehicle's data communication performance in a Drive-thru Internet system can be studied and optimized in a systematic, quantitative manner.
Scaling Task Graphs for Network Processors

Modern network processors (NPs) are highly multithreaded chip multiprocessors (CMPs), supporting a wide variety of mechanisms for on-chip storage and inter-task communication. Real network processor applications are hard to program and must be tailored to fit the resources of the underlying NP, motivating an automated approach to mapping multithreaded applications to NPs. In this paper we propose and evaluate compiler-based automated task and data management techniques to scale the throughput of network processing task graphs onto NPs. We evaluate these techniques using a NP simulation infrastructure based on realistic NP applications, and present an approach to discovering performance bottlenecks. Finally we demonstrate how our techniques enhance throughput-scaling for NPs.
Cognitive delay in children with tuberous sclerosis in a developing country: Clinical correlations

Abstract Cognitive delay is a frequent neurobehavioral manifestation in children with tuberous sclerosis complex (TSC). This study aimed to determine the relationships between cognitive delay with respect to seizure characteristics, interictal electroencephalography (EEG) changes, behavioral abnormalities and brain tubers’ count and location. This study included 24 children with TSC with a mean age of 6.2 ± 2.55 yr. Children underwent clinical, psychometric, interictal EEG and magnetic resonance imaging or magnetic resonance imaging of the brain. Nearly 58.33% of children had cognitive delay and 45.83% had autism. Compared to children without cognitive delay, children with cognitive delay were younger (P < 0.01), had higher frequencies of seizures (particularly infantile spasms with onset predominantly below 6 mo of age) (P < 0.01), severe epileptogenic EEG changes, high tuber burden (particularly in the left hemisphere with frontal and temporal predominance) (P < 0.01) and frequent behavioral abnormalities as autism (P < 0.001) and attention deficit hyperactivity disorders (P < 0.05). Cognitive scores were correlated with age of seizures’ onset (r = 0.432, P = 0.01), presence of infantile spasms (r = −0.675, P = 0.001), autism (r = −0.350, P = 0.05), and number of brain tubers (r = −0.865, P = 0.0001) but non correlated to age nor gender. In multivariate analysis, only the age of seizure onset (odds ratio 1.90; 95% confidence interval 1.02 to 3.55, P = 0.044) and the number of brain tubers (odds ratio 2.36; 95% confidence interval 0.95 to 5.81; P = 0.06) increased the odds for cognitive delay among TSC patients. The knowledge of the clinical features of TSC can help in early diagnosis and management of patients with a multidisciplinary consultation.
Complex Radar Cross Section Measurements of the Human Body for Breath-Activity Monitoring Applications

An experimental setup for complex mono-static radar cross section (RCS) measurements in the 1-10-GHz-frequency band, employing a suitably modified semianechoic chamber, is presented and characterized. The foreseen application is the measurement of the complex RCS of the human body during respiratory activity, to ease the design and optimization of ultrawideband (UWB) radar systems for breath-activity monitoring. The proposed RCS test range is calibrated by means of a readily available aluminum flat panel and its performance is tested against canonical targets, evaluating uncertainty in magnitude, and phase measurements. The setup is then employed to carry out investigations on the complex RCS of a volunteer, focusing on its changes resulting from breath activity. Applying the measured RCS patterns to a specifically developed model, the feasibility of the UWB radar approach for achieving a continuous contact-less monitoring of breath activity in a subject at rest is clearly demonstrated. Finally, experimental tests of the application of the proposed radar technique to real-world scenarios are shown, and the safety of RCS measurements and UWB radar monitoring, with reference to exposure of the monitored subject to the radiated electromagnetic fields, is evaluated.
Crawling BitTorrent DHTs for Fun and Profit

This paper presents two kinds of attacks based on crawling the DHTs used for distributed BitTorrent tracking. First, we show how pirates can use crawling to rebuild BitTorrent search engines just a few hours after they are shut down (crawling for fun). Second, we show how content owners can use related techniques to monitor pi-rates' behavior in preparation for legal attacks and negate any perceived anonymity of the decentralized BitTorrent architecture (crawling for profit). We validate these attacks and measure their performance with a crawler we developed for the Vuze DHT. We find that we can establish a search engine with over one million torrents in under two hours using a single desktop PC. We also track 7.9 million IP addresses downloading 1.5 million torrents over 16 days. These results imply that shifting from centralized BitTorrent tracking to DHT-based tracking will have mixed results for the file sharing arms race. While it will likely make illicit torrents harder to quash, it will not help users hide their activities.
Theoretical analysis of the high-rate vector quantization of LPC parameters

The paper presents a theoretical analysis of high-rate vector quantization (VQ) systems that use suboptimal, mismatched distortion measures, and describes the application of the analysis to the problem of quantizing the linear predictive coding (LPC) parameters in speech coding systems. First, it is shown that in many high-rate VQ systems the quantization distortion approaches a simple quadratically weighted error measure, where the weighting matrix is a "sensitivity matrix" that is an extension of the concept of the scalar sensitivity. The approximate performance of VQ systems that train and quantize using mismatched distortion measures is derived, and is used to construct better distortion measures. Second, these results are used to determine the performance of LPC vector quantizers, as measured by the log spectral distortion (LSD) measure, which have been trained using other error measures, such as mean-squared (MSE) or weighted mean-squared error (WMSE) measures of LEPC parameters, reflection coefficients and transforms thereof, and line spectral pair (LSP) frequencies. Computationally efficient algorithms for computing the sensitivity matrices of these parameters are described. In particular, it is shown that the sensitivity matrix for the LSP frequencies is diagonal, implying that a WMSE measured LSP frequencies converges to the LSD measure in high-rate VQ systems. Experimental results to support the theoretical performance estimates are provided. >
NOTES AND CORRESPONDENCE A Simple Model Study of ENSO Suppression by External Periodic Forcing

The study of a forced delayed oscillator ENSO model suggests that the intensity of ENSO can be suppressed significantly by an external periodic forcing due to the nonlinear mechanism of frequency entrainment. This suppression of ENSO is most effective for ENSOs in the regime of unstable self-exciting oscillation and for forcing of frequencies close to that of ENSO. In particular, an annual cycle forcing can suppress ENSO substantially. This ENSO suppression effect by an external annual cycle is in contrast to the effect of the seasonal change of the coupled instability: the latter predominantly generates the seasonal phase locking of ENSO but has little effect on the amplitude of ENSO. Potential implications are also discussed for the evolution of ENSO in the Holocene and the observed monsoon‐ENSO relationship.
Industrial application of exact Boolean operations for meshes

We present an algorithm for robust Boolean operations of triangulated solids, which is suitable for real-word industrial applications involving meshes with large numbers of triangles. In order to avoid potential robustness problems, which may be caused by (almost) degenerate triangles or by intersections of nearly co-planar triangles, we use filtered exact arithmetic, based on the libraries CGAL and GNU Multi Precision Arithmetic Library. The method consists of two major steps: First we compute the exact intersection of the meshes using a sweep plane algorithm. Second we apply mesh cleaning methods which allow us to generate output which can safely be represented using floating point numbers. The performance of the method is demonstrated by several examples which are taken from applications at ECS Magna Powertrain.
Scattering-model-based speckle filtering of polarimetric SAR data

A new concept in polarimetric synthetic aperture radar (POLSAR) speckle filtering that preserves the dominant scattering mechanism of each pixel is proposed in this paper. The basic principle is to select pixels of the same scattering characteristics to be included in the filtering process. To achieve this, the algorithm first applies the Freeman and Durden decomposition to separate pixels into three dominant scattering categories: surface, double bounce, and volume, and then unsupervised classification is applied. Speckle filtering is performed using the classification map as a mask. A single-look or multilook pixel centered in a 9 /spl times/ 9 window is filtered by including only pixels in the same and two neighboring classes from the same scattering category. This filter is effective in speckle reduction, while perfectly preserving strong point target signatures, and retains edges, linear, and curved features in the POLSAR data. The effect of speckle filtering on scattering characteristics, such as entropy, anisotropy, and alpha angle, will be discussed.
Improving the Accuracy of Question Classification with Machine Learning

Question classification is an important phase in question answering systems. In this paper, we propose to apply i) hierarchical classifiers, ii) hierarchical classifiers in combination with semi-supervised learning and iii) hierarchy expansion for question classification for improving the precision. When the number of classes is large, the performance of classification algorithms may be affected. In order to improve the performance by reducing the number of classes for each classifier, we propose to use hierarchical classifiers according to the question taxonomy, in which each internal node is attached a classifier. We try to use semi-supervised learning to consume unlabeled questions with expectation to improve the performance of classifiers in the hierarchy. We explored different applications of learning methods in for each classifier of the hierarchy: a) supervised learning for all classifiers at all levels; b) semi-supervised learning for the first-level classifier and supervised learning for other classifiers; c) semi-supervised learning for all classifiers. The experiments show that the first method (a) has better results than those of flat classification; the second method (b) produces better results than those of the first method while the effort to increase the performance of fine classifiers in the last method (c) is not so successful. As another effort, we propose to automatically group question classes by clustering in order to expand a node which has a large number of classes in the question taxonomy. The experiment also shows that the overall precision is improved.
Effects of coupling and heterogeneity in the pre-Botzinger complex cells using first return maps

The preBotzinger complex located at the ventrolateral medulla in the brainstem is believedto have an important role in generating the respiratory rhythm in mammals, specially theinspiratory process [56]. Keeping this in mind, we will study a small network of such cellsby means of a minimal model suggested and experimentally tested by Butera et al [6, 7]. Athorough analysis of the Butera model was done for two very small networks of pre-Botzingercells: a self coupled single cell and a network of two coupled cells [5]. In order to understandthe role of coupling and heterogeneity in these two particular networks we reduce the selfcoupled single cell network to a one dimensional map using a similar approach as in [37]. Using this one dimensional map, some analytical conditions for switching from one regime toanother are determined and numerical results are shown. Using the same idea as for the selfcoupled single cell case, two identical coupled cells are reduced to a two dimensional iteratedmap which is a composition of many one dimensional maps. Using the form of these maps,mechanisms for the transition between previously observed regimes [5] are determined andlinear analysis is performed for a particular set of parameters.Introducing heterogeneity on the network of two coupled identical cells, for a fixed levelof synaptic input, shows that depending on the level of the synaptic input some differentbehaviors arise which were not previously observed in a network of homogenous cells [5]. These results suggest that introducing heterogeneity can increase the range in the parameterspace for which cells are bursting. This is desirable, since from experiments it is observedthat bursting is associated with the inspiratory rhythm of respiration.
Does It Matter Where You Are Hospitalized for Inflammatory Bowel Disease? A Nationwide Analysis of Hospital Volume

OBJECTIVE: To examine if a high hospital volume was associated with superior outcomes in inflammatory bowel disease (IBD) patients requiring hospitalization.METHODS: This was a cross-sectional study using data from the Nationwide Inpatient Sample (NIS 2004). IBD-related hospitalizations were identified using appropriate International Classification of Diseases, Ninth revision, Clinical modification (ICD-9-CM) codes. Hospital volume was divided into low, medium, and high by assigning the threshold cutoff values of 1–50, 51–150, and >150 annual IBD hospitalizations, respectively. Our primary outcomes were in-hospital mortality, length of stay, and postoperative complications and stay.RESULTS: Patients at high-volume centers were more likely to be hospitalized with fistulizing or stricturing disease. The adjusted mortality was lower for IBD-related discharges from high-volume centers for those undergoing abdominal surgery (odds ratio [OR] 0.38, 95% confidence interval [CI] 0.18–0.78), but not among those who did not undergo surgery (OR 0.90, 95% CI 0.53–1.52). Patients at high-volume centers were also more likely to undergo surgery (OR 2.24, 95% CI 1.40–3.58). These differences were more prominent in Crohn's disease than in ulcerative colitis.CONCLUSION: Hospitals with a high annual IBD volume have lower in-hospital mortality among surgical IBD patients. This suggests a need for future research into identifying the quality-of-care measures in IBD and instituting appropriate interventions to improve overall IBD outcomes.
Study of Raman amplification in DPP-BOTDA sensing employing Simplex coding for sub-meter scale spatial resolution over long fiber distances

The impact of Raman amplification and Simplex coding is studied in combination with differential pulse-width pair Brillouin optical time-domain analysis (DPP-BOTDA) to achieve sub-meter spatial resolution over very long sensing distances. An optimization of the power levels for the Raman pumps, Brillouin pump and signal has been carried out through numerical simulations, maximizing the signal levels and avoiding at the same time nonlinear effects and pump depletion. A reduction of acoustic-wave-induced distortions in the Brillouin gain spectrum down to negligible levels has also been achieved by numerical optimization of the pulse width and duty cycle of return-to-zero Simplex coding, providing significant signal-to-noise ratio enhancement. Strain–temperature sensing over 93 km standard SMF is achieved with a strain/temperature accuracy of 34µε/1.7 °C, and 50 cm spatial resolution throughout the fiber length.
Frontiers in Integrative Neuroscience Integrative Neuroscience

What is the most sensitive measure of water maze probe test performance? The water maze is commonly used to assay spatial cognition, or, more generally, learning and memory in experimental rodent models. In the water maze, mice or rats are trained to navigate to a platform located below the water's surface. Spatial learning is then typically assessed in a probe test, where the platform is removed from the pool and the mouse or rat is allowed to search for it. Performance in the probe test may then be evaluated using either occupancy-based (percent time in a virtual quadrant [Q] or zone [Z] centered on former platform location), error-based (mean proximity to former platform location [P]) or counting-based (platform crossings [X]) measures. While these measures differ in their popularity, whether they differ in their ability to detect group differences is not known. To address this question we compiled fi ve separate databases, containing more than 1600 mouse probe tests. Random selection of individual trials from respective databases then allowed us to simulate experiments with varying sample and effect sizes. Using this Monte Carlo-based method, we found that the P measure consistently outperformed the Q, Z and X measures in its ability to detect group differences. This was the case regardless of sample or effect size, and using both parametric and non-parametric statistical analyses. The relative superiority of P over other commonly used measures suggests that it is the most appropriate measure to employ in both low-and high-throughput water maze screens. the platform is not visible, the mouse must locate it using an array of distal, visual cues surrounding the pool. As training progresses, the latency to fi nd the platform typically decreases. Such decreased escape latencies would most commonly refl ect the adoption of a focal search strategy (i.e., a search strategy centered on the former platform location with little variance). However, reduced escape latencies may also refl ect the adoption of non-spatial strategies (e.g., mice might learn to swim in concentric circles a fi xed distance from the wall) Therefore, to discriminate spatial and non-spatial strategies mice are usually given a probe test, where the platform is removed from the pool and the mouse is allowed to search for it, typically over a 60-s period. Mice having adopted a spatial strategy will search focally near the former location of platform. Tracking software is routinely used to precisely record the position …
Reservoir Computing for Prediction of the Spatially-Variant Point Spread Function

A new method is presented which provides prediction of the spatially variant point spread function for the restoration of astronomical images, distorted by atmospheric turbulence when viewed using ground-based telescopes. Our approach uses reservoir computing to firstly learn the spatio-temporal evolution of aberrations caused by turbulence, and secondly, predicts the space-varying point spread function (PSF) for application of widely-used deconvolution algorithms, resulting in the restoration of astronomical images. In this article, a reservoir-based, recurrent neural network is used to predict modal aberrations that comprise the spatially variant PSF over a wide field-of-view using a time-series ensemble from multiple reference beacons.