Miracle-dz/UniFAR-FTUs huggingface dataset:
Dissociation between the Electrophysiological Properties and Total Tissue Cyclic Guanosine Monophosphate Content of Guinea Pig Atria

The purpose of this study was to investigate the role of cyclic guanosine monophosphate (cyclic GMP) in mediating the direct electrophysiological effects of acetylcholine in guinea pig atria. Acetylcholine significantly diminished spontaneous rate of right atria without increasing cyclic GMP content. Reductions in rate following acetylcholine were augmented by pretreatment with physostig- mine, but cyclic GMP levels remained unchanged. In left atria, acetylcholine significantly shortened action potential duration within 5 seconds (both with and without physostigmine pretreatment), but cyclic GMP content was not significantly elevated. Cyclic GMP levels in right atria were significantly increased in response to acetylcholine when the Ca+2 content of the buffer was elevated from 1.25 mu to 2.5 mM; however, reductions in automaticity in the right atria were not augmented in the high Ca2+buffer. Marked increases in cyclic GMP content were produced by Na nitroprusside superfusion without changing automaticity of right atria or action potential duration of left atria. Finally, both right and left atria were superfused with cyclic GMP analogs (8-bromo cyclic GMP and dibutyryl cyclic GMP) at high concentrations (10−4) for 15 minutes without producing significant effects on spontaneous rate or action potential duration. These results failed to show a correlation between total tissue cyclic GMP content and the electrophysiological effects of acetylcholine on guinea pig atria. The reasons for this are either that cyclic GMP does not mediate directly the electrophysiological effects of acetylcholine, or that small changes in cyclic GMP concentrations, undetectable when total tissue nucleotide levels are measured, occur in discrete effector pools of the cardiac cell to mediate the intracellular effects of the choline ester. Ore Res 45: 225-233, 1979
Feature-based Thai Word Segmentation

Word segmentation is a problem in several Asian languages that have no explicit word boundary delimiter, e.g. Chinese, Japanese, Korean and Thai. We propose to use feature-based approaches for Thai word segmenta-tion. A feature can be anything that tests for speciic information in the context around the word in question, such as context words and collocations. To automatically extract such features from a training corpus, we employ two learning algorithms, namely RIP-PER and Winnow. Experimental results show that both algorithms appear to outper-form the existing Thai word segmentation methods, especially for context-dependent strings.
Edge-Based Intramode Selection for Depth-Map Coding in 3D-HEVC

The 3D video extension of High Efficiency Video Coding (3D-HEVC) is the state-of-the-art video coding standard for the compression of the multiview video plus depth format. In the 3D-HEVC design, new depth-modeling modes (DMMs) are utilized together with the existing intraprediction modes for depth intracoding. The DMMs can provide more accurate prediction signals and thereby achieve better compression efficiency. However, testing the DMMs in the intramode decision process causes a drastic increase in the computational complexity. In this paper, we propose a fast mode decision algorithm for depth intracoding. The proposed algorithm first performs a simple edge classification in the Hadamard transform domain. Then, based on the edge classification results, the proposed algorithm selectively omits unnecessary DMMs in the mode decision process. Experimental results demonstrate that the proposed algorithm speeds up the mode decision process by up to 37.65% with negligible loss of coding efficiency.
An Empirical Study on Statistical Disambiguation of Japanese Dependency Structures Using a Lexically Sensitive Language Model

We are proposing a new framework of statistical language modeling which integrates lexical association statistics with syntactic preference, while maintaining the modularity of those different statistics types, facilitating both training of the model and analysis of its behavior. In this paper, we report the result of an empirical evaluation of our model, where the model is applied to disambiguation of dependency structures of Japanese sentences. We also discussed the room remained for further improvement based on our error analysis.
Study of dynamic responses of an interconnected two-area all thermal power system with governor and boiler nonlinearities using BBO

This paper aims to investigate the application of biogeography based optimization (BBO) technique to load frequency control (LFC) problem for improving power system dynamics with governor dead band and boiler dynamics type of nonlinearities. Two-area all thermal types interconnected power system network is considered for design and analysis purpose. The thermal areas are equipped with single and double reheat turbine, simultaneously. The designed problem is considered as an optimization problem and BBO algorithm is applied to search optimal gains of classical PID-controller which is used in AGC problem. The behavior of the test system is also investigated with the analysis towards the different cost functions such as integral square error (ISE) and integral time absolute error (ITAE). Small load perturbation (SLP) is considered in area-1 for studying the dynamic behaviors of the designed power system unit. To validate the effectiveness and superiority of proposed controller, the performances are compared with the results obtained by differential evaluation (DE) and particle swarm optimization (PSO) techniques. Simulation study exhibits significant effect of the designed controller on the dynamics of concerned power system network.
An Improved File System Synchronous Algorithm

In order to timely synchronize the nodes in unstable network environment and reduce the space overhead of the operation log, we mix the operation transfer-based and state transfer-based synchronous models. In this paper, for file system synchronization, we design an improved file system synchronous algorithm -- the combination of state transform algorithm and operation merge algorithm. The experimental results show that the improved algorithm can completely merge file system changes into updates operation in millisecond. The key to mix the two ways is to unify updating granularity and semantic level, which will be taken by both models. The mixed model can meet the demand of instant response and the less resource-consuming at the same time.
Regionalization of the conterminous U . S . into hierarchical landscape pattern types

We present a pattern-based regionalization of the conterminous U.S. – a partition of the country into a number of mutually exclusive and exhaustive regions that maximizes the intra-region stationarity of land cover patterns and inter-region disparity between those patterns. The result is a discretization of land surface into a number of landscape pattern types (LPTs). First, the entire NLCD is partitioned into a grid of 500  500 pixels blocks (15 km scale). The NLCD classes of pixels within a block forms a local landscape pattern which is mathematically represented by a histogram of co-occurrence features. Using the Jensen-Shannon divergence as dissimilarity function between the patterns we cluster the local landscape patterns into several LPTs. The broad-extent maps of progressively more generalized LPTs are shown and discussed.
Improving robot navigation through self‐supervised online learning

In mobile robotics, there are often features that, while potentially powerful for improving navigation, prove difficult to profit from as they generalize poorly to novel situations. Overhead imagery data, for instance, have the potential to greatly enhance autonomous robot navigation in complex outdoor environments. In practice, reliable and effective automated interpretation of imagery from diverse terrain, environmental conditions, and sensor varieties proves challenging. Similarly, fixed techniques that successfully interpret on‐board sensor data across many environments begin to fail past short ranges as the density and accuracy necessary for such computation quickly degrade and features that are able to be computed from distant data are very domain specific. We introduce an online, probabilistic model to effectively learn to use these scope‐limited features by leveraging other features that, while perhaps otherwise more limited, generalize reliably. We apply our approach to provide an efficient, self‐supervised learning method that accurately predicts traversal costs over large areas from overhead data. We present results from field testing on‐board a robot operating over large distances in various off‐road environments. Additionally, we show how our algorithm can be used offline with overhead data to produce a priori traversal cost maps and detect misalignments between overhead data and estimated vehicle positions. This approach can significantly improve the versatility of many unmanned ground vehicles by allowing them to traverse highly varied terrains with increased performance. © 2007 Wiley Periodicals, Inc.
Endogenous Capital Augmenting Technical Change and the Shape of Aggregate Production Function

In a neoclassical growth model with a constant elasticity of substitution production function, the conventional wisdom claims that capital augmenting technical change is labor biased and ceteris paribus increases the labor share when capital and labor are gross compliments. We construct an endogenous biased technical change model to challenge that interpretation. We show that capital augmenting technical change is capital biased and ceteris paribus decreases the labor share irrespective of the size of the elasticity of substitution. And we also provide a microfoundation for the induced aggregate production function to take the form of AK in the long-run.
Mapping Networks of Terrorist Cells

This paper looks at the difficulty in mapping covert networks. Analyzing networks after an event is fairly easy for prosecution purposes. Mapping covert networks to prevent criminal activity is much more difficult. We examine the network surrounding the tragic events of September 11th, 2001. Through public data we are able to map a portion of the network centered around the 19 dead hijackers. This map gives us some insight into the terrorist organization, yet it is incomplete. Suggestions for further work and research are offered.
Cryptic Virulence and Avirulence Alleles Revealed by Controlled Sexual Recombination in Pea Aphids

Although aphids are worldwide crop pests, little is known about aphid effector genes underlying virulence and avirulence. Here we show that controlling the genetics of both aphid and host can reveal novel recombinant genotypes with previously undetected allelic variation in both virulence and avirulence functions. Clonal F1 progeny populations were derived from reciprocal crosses and self-matings between two parental genotypes of pea aphid (Acyrthosiphon pisum) differing in virulence on a Medicago truncatula host carrying the RAP1 and RAP2 resistance genes. These populations showed Mendelian segregation consistent with aphid performance being controlled largely by a dominant virulence allele derived from only one parent. Altered segregation ratios on near-isogenic host genotypes differing in the region carrying RAP1 were indicative of additional heritable functions likely related to avirulence genes originating from both parents. Unexpectedly, some virulent F1 progeny were recovered from selfing of an avirulent parent, suggesting a reservoir of cryptic alleles. Host chlorosis was associated with virulence, whereas necrotic hypersensitive-like response was not. No maternal inheritance was found for any of these characteristics, ruling out sex-linked, cytoplasmic, and endosymbiotic factors. Our results demonstrate the tractability of dissecting the genetic basis of pest-host resistance mechanisms and indicate that the annual sexual cycle in aphids may lead to frequent novel genotypes with both increased and decreased virulence. Availability of genomes for both pest and host can facilitate definition of cognate gene-for-gene relationships, potentially leading to selection of crop genotypes with multiple resistance traits.
Person Movement Prediction Using Neural Networks

Ubiquitous systems use context information to adapt appliance behavior to human needs. Even more convenience is reached if the appliance foresees the user’s desires and acts proactively. This paper proposes neural prediction techniques to anticipate a person’s next movement. We focus on neural predictors (multi-layer perceptron with back-propagation learning) with and without pre-training. The optimal configuration of the neural network is determined by evaluating movement sequences of real persons within an office building. The simulation results, obtained with one of the pre-trained neural predictors, show accuracy in next location prediction reaching up to 92%.
Analysis and Design of Gain Enhanced Cascode Stage Utilizing a New Passive Compensation Network

This paper analyzes the influence of the Miller effect on the gain performance of the cascode stage. A passive compensation network is proposed to cancel Cbc at the millimeter-wave range without distinct penalty on noise figure and stability. Using this passive compensation network, the cascode stage can achieve a higher gain in a wider bandwidth than other cascode topologies. Furthermore, a W-band amplifier based on the new cascode stage is designed and fabricated using the 0.13-μm BiCMOS process successfully. The proposed passive compensation network has a potential to be used in other building blocks, which need improvement through Miller effect cancellation.
Visual crowding and category specific deficits for pictorial stimuli: A neural network model

This paper describes a series of modular neural network simulations of visual object processing. In a departure from much previous work in this domain, the model described here comprises both supervised and unsupervised modules and processes real pictorial representations of items from different object categories. The unsupervised module carries out bottom-up encoding of visual stimuli, thereby developing a “perceptual” representation of each presented picture. The supervised component then classifies each perceptual representation according to a target semantic category. Model performance was assessed (1) during learning, (2) under generalisation to novel instances, and (3) after lesion damage at different stages of processing. Strong category effects were observed throughout the different experiments, with living things and musical instruments eliciting greater recognition failures relative to other categories. This pattern derives from within-category similarity effects at the level of perceptual representation and our data support the view that visual crowding can be a potentially important factor in the emergence of some category-specific impairments. The data also accord with the cascade model of object recognition, since increased competition between perceptual representations resulted in category-specific impairments even when the locus of damage was within the semantic component of the model. Some strengths and limitations of this modelling approach are discussed and the results are evaluated against some other accounts of category-specific recognition failure.
Simple and precise static analysis of untrusted Linux kernel extensions

Extended Berkeley Packet Filter (eBPF) is a Linux subsystem that allows safely executing untrusted user-defined extensions inside the kernel. It relies on static analysis to protect the kernel against buggy and malicious extensions. As the eBPF ecosystem evolves to support more complex and diverse extensions, the limitations of its current verifier, including high rate of false positives, poor scalability, and lack of support for loops, have become a major barrier for developers. We design a static analyzer for eBPF within the framework of abstract interpretation. Our choice of abstraction is based on common patterns found in many eBPF programs. We observed that eBPF programs manipulate memory in a rather disciplined way which permits analyzing them successfully with a scalable mixture of very-precise abstraction of certain bounded regions with coarser abstractions of other parts of the memory. We use the Zone domain, a simple domain that tracks differences between pairs of registers and offsets, to achieve precise and scalable analysis. We demonstrate that this abstraction is as precise in practice as more costly abstract domains like Octagon and Polyhedra. Furthermore, our evaluation, based on hundreds of real-world eBPF programs, shows that the new tool generates no more false alarms than the existing Linux verifier, while it supports a wider class of programs (including programs with loops) and has better asymptotic complexity.
The Complexity of Fuzzy Description Logics over Finite Lattices with Nominals

The complexity of reasoning in fuzzy description logics (DLs) over finite lattices usually does not exceed that of the underlying classical DLs. This has recently been shown for the logics between L-IALC and L-ISCHI using a combination of automata- and tableau-based techniques. In this report, this approach is modified to deal with nominals and constants in L-ISCHOI. Reasoning w.r. t. general TBoxes is ExpTime-complete, and PSpace-completeness is shown under the restriction to acyclic terminologies in two sublogics. The latter implies two previously unknown complexity results for the classical DLs ALCHO and SO.
Smooth Adaptive Soft Handover Algorithm for Multimedia Streaming over Wireless Networks

Inter-network mobility is achieved by allowing a mobile node to change its point of attachment to the network while preserving connectivity to its corresponding nodes. Most handover solutions proposed in the literature directly change the whole data flow from one network to another relying on only one network to transfer the entire data stream. These solutions involve a certain amount of quality degradation due to increasing loss and delay and suffer in terms of scalability, efficient resource allocation and resilience to different mobile node speeds. This paper proposes the Smooth Adaptive Soft-Handover Algorithm (SASHA) which increases the quality of the multimedia delivery process when performing handover in heterogeneous wireless environment by gracefully transferring the load from one connection to the other.
Argyrophil cell density in the oxyntic mucosa is higher in female than in male morbidly obese patients

Obesity is a multifactorial disorder often associated with many important diseases such as diabetes, hypertension and other metabolic syndrome conditions. Argyrophil cells represent almost the total population of endocrine cells of the human gastric mucosa and some reports have described changes of specific types of these cells in patients with obesity and metabolic syndrome. The present study was designed to evaluate the global population of argyrophil cells of the gastric mucosa of morbidly obese and dyspeptic non-obese patients. Gastric biopsies of antropyloric and oxyntic mucosa were obtained from 50 morbidly obese patients (BMI >40) and 50 non-obese patients (17 dyspeptic overweight and 33 lean individuals) and processed for histology and Grimelius staining for argyrophil cell demonstration. Argyrophil cell density in the oxyntic mucosa of morbidly obese patients was higher in female (238.68 ± 83.71 cells/mm2) than in male patients (179.31 ± 85.96 cells/mm2) and also higher in female (214.20 ± 50.38 cells/mm2) than in male (141.90 ± 61.22 cells/mm2) morbidly obese patients with metabolic syndrome (P = 0.01 and P = 0.02, respectively). In antropyloric mucosa, the main difference in argyrophil cell density was observed between female morbidly obese patients with (167.00 ± 69.30 cells/mm2) and without (234.00 ± 69.54 cells/mm2) metabolic syndrome (P = 0.001). In conclusion, the present results show that the number of gastric argyrophil cells could be under gender influence in patients with morbid obesity. In addition, gastric argyrophil cells seem to behave differently among female morbidly obese patients with and without metabolic syndrome.
APPLYING FUZZY ANALYTIC HIERARCHY PROCESS TO EVALUATE SUCCESS FACTORS OF E-COMMERCE

: With the fast development of the E-commerce, it becomes critical to set up an E-commerce Evaluation criteria system. Fuzzy Analytic Hierarchy Process is a new multi-criteria evaluation method evolved from Saaty’s AHP. This paper aimed to find out the key factors that affect success in E-commerce using fuzzy AHP, and give an evaluation method for E-commerce in order to help researches and managers to determine the drawbacks and opportunities.
A 246 GHz fundamental source with a peak output power of 2.8 dBm

A 246 GHz source for imaging and incoherent high data rate communication applications is presented. The source consists of a fundamental free-running Colpitts oscillator cascaded with a differential common base (CB) buffer stage and a 3-stage power amplifier (PA). The circuit has been fabricated in a 130 nm SiGe HBT technology with fT/fmax of 300/500 GHz. At 246 GHz, the measured output power is 2.8 dBm and the overall DC to RF conversion efficiency is 0.43%. The available output power from the oscillator alone is -4.2 dBm (after correcting for the PA gain of 10 dB and another 3 dB loss due to output pad and balun at 246 GHz).
Why Fuzzy Partition in F-Transform?

In many application problems, F-transform algorithms are very efficient. In F-transform techniques, we replace the original signal or image with a finite number of weighted averages. The use of weighted average can be naturally explained, e.g., by the fact that this is what we get anyway when we measure the signal. However, most successful applications of F-transform have an additional not-so-easy-to-explain feature: the partition requirement, that the sum of all the related weighting functions is a constant. In this paper, we show that this seemingly difficult-to-explain requirement can also be naturally explained in signal-measuring terms: namely, this requirement can be derived from the natural desire to have all the signal values at different moments of time estimated with the same accuracy.
BeCecil, A Core Object-Oriented Language with Block Structure and Multimethods: Semantics and Typing

We present and analyze the semantics and static type system for BeCecil, a theoretical (core) language with multimethods. BeCecil is a simple and orthogonal version of object-oriented languages like Cecil, CLOS, and Dylan. BeCecil has a new, simple mechanism for information hiding, which allows subclassing and yet can preserve representation invariants. BeCecil is also block-structured; within a block, one can extend a generic function with new multimethods, which may come from other generic functions. The inheritance relationships of objects may be extended in any block, and are statically scoped. The type system separates classes from types, and inheritance from subtyping. Subtype relationships are also extensible and statically scoped. These features combine to make BeCecil unusually expressive, while still allowing static typechecking.