Miracle-dz/UniFAR-FTUs huggingface dataset:
Alteration of gene expression by exposure to a magnetic field at 23 kHz is not detected in astroglia cells

The increasing use of induction heating (IH) cooktops has roused public concern in Japan and Europe regarding potential health effects. The purpose of this study was to evaluate the effects of exposure to a magnetic field at 23 kHz (which is the maximum output power frequency of most IH cooktops) on gene expression in a human-fetus-derived astroglia cell line, SVGp12. The cells were exposed to the magnetic field at 2 mTrms [which is approximately 74 times higher than the reference level in the most recent International Commission on Non-Ionizing Radiation Protection (ICNIRP) guidelines], for 2, 4 and 6 h, using a previously reported exposure system. Gene expression was evaluated using an Agilent cDNA microarray. We did not detect any significant effects of the magnetic field on the gene expression profile. On the contrary, heat treatment at 43°C for 2 h used as a positive control significantly affected gene expression, including inducing heat shock proteins, which indicated that our protocol for microarray analysis was appropriate. From these results, we conclude that exposure of human-fetus-derived astroglia cells to an intermediate-frequency magnetic field at 23 kHz and 2 mTrms for up to 6 h does not induce detectable alteration of gene expression.
Improving event extraction using online learning strategy

The traditional mention-oriented model for event extraction cannot capture information of multiple event types in a sentence. To deal with this problem, we present a novel intensive model for event extraction task. Firstly, the model can filter out non-event sentences automatically by introducing some meaningful language features. Then, the model adopts an online learning strategy for event type ranking, which provides an alternative view of event type identification as multi-label classification so that we can achieve a predicted set of relevant event types for each sentence. The experimental evaluation verified that the model can improve the performance of the event extraction task.
Crystal structure of μ-peroxido-κ4 O 1,O 2:O 1′,O 2′-bis[(nitrato-κO)(2,2′:6′,2′′-terpyridine-κ3 N,N′,N′′)dioxidouranium(VI)]

In the title dimeric complex, [{UO2(NO3)(C15H11N3)}2O2], a peroxide ion bridges the two uranyl(VI) [O=U=O]2+ ions. The O—O bond length of the peroxide is 1.485 (6) Å and the mid-point of this bond is located at the inversion centre of the dimer. The U atom exhibits a distorted hexagonal–bipyramidal coordination geometry with two uranyl(VI) O atoms occupying the axial positions and one O atom of the monodentate nitrate ion, both O atoms of the peroxide ion and the three N atoms of the chelating tridentate 2,2′:6′,2′′-terpyridine (terpy) ligand in the equatorial positions. Two of the N atoms of the terpy ligand lie above and below the mean plane containing the equatorial ligand atoms and the U atom [deviations from the mean plane: maximum 0.500 (2), minimum −0.472 (2) and r.m.s. = 0.2910 Å]. The dihedral angle between the terpy ligand and the mean plane is 35.61 (7)°. The bond lengths around the U atom decrease in the order U—N > U—Onitrate > U—Operoxo > U=O. The dimeric complexes pack in a three-dimensional network held together by weak π–π interactions [centroid–centroid distance = 3.659 (3) Å] between pyridyl rings of the terpy ligands in neighbouring dimers, together with intermolecular C—H⋯O and C—H⋯π interactions. Weak intramolecular C—H⋯O interactions are also observed.
Tangent space intrinsic manifold regularization for data representation

A new regularization method called tangent space intrinsic manifold regularization is presented, which is intrinsic to data manifold and favors linear functions on the manifold. Fundamental elements involved in its formulation are local tangent space representations which we estimate by local principal component analysis, and the connections which relate adjacent tangent spaces. We exhibit its application to data representation where a nonlinear embedding in a low-dimensional space is found by solving an eigen-decomposition problem. Experimental results including comparisons with state-of-the-art techniques show the effectiveness of the proposed method.
Longitudinal, intermodality registration of quantitative breast PET and MRI data acquired before and during neoadjuvant chemotherapy: preliminary results.

PURPOSE The authors propose a method whereby serially acquired DCE-MRI, DW-MRI, and FDG-PET breast data sets can be spatially and temporally coregistered to enable the comparison of changes in parameter maps at the voxel level. METHODS First, the authors aligned the PET and MR images at each time point rigidly and nonrigidly. To register the MR images longitudinally, the authors extended a nonrigid registration algorithm by including a tumor volume-preserving constraint in the cost function. After the PET images were aligned to the MR images at each time point, the authors then used the transformation obtained from the longitudinal registration of the MRI volumes to register the PET images longitudinally. The authors tested this approach on ten breast cancer patients by calculating a modified Dice similarity of tumor size between the PET and MR images as well as the bending energy and changes in the tumor volume after the application of the registration algorithm. RESULTS The median of the modified Dice in the registered PET and DCE-MRI data was 0.92. For the longitudinal registration, the median tumor volume change was -0.03% for the constrained algorithm, compared to -32.16% for the unconstrained registration algorithms (p = 8 × 10(-6)). The medians of the bending energy were 0.0092 and 0.0001 for the unconstrained and constrained algorithms, respectively (p = 2.84 × 10(-7)). CONCLUSIONS The results indicate that the proposed method can accurately spatially align DCE-MRI, DW-MRI, and FDG-PET breast images acquired at different time points during therapy while preventing the tumor from being substantially distorted or compressed.
Noise Robust Pitch Tracking by Subband Autocorrelation Classification

Speech pitch tracking is one of the elementary tasks of the Computational Auditory Scene Analysis (CASA). While a human can easily listen to the voiced pitch in highly noisy recordings, the performance of automatic speech pitch tracking degrades in unknown noisy audio conditions. Traditional pitch trackers use either autocorrelation or the Fourier transform to calculate periodicity, which works well for clean recordings. For noisy recordings, however, the accuracy of these pitch trackers degrades in general. For example, the information in parts of the frequency spectrum may be lost due to analog radio band transmission and/or contain additive noise of various kinds. Instead of explicitly using the most obvious features of autocorrelation, we propose a trained classier-based approach, which we call Subband Autocorrelation Classification (SAcC). A multi-layer perceptron (MLP) classier is trained on the principal components of the autocorrelations of subbands from an auditory filterbank. The output of the MLP classifier is temporally smoothed to produce the pitch track by finding the Viterbi path of a Hidden Markov Model (HMM). Training on various types of noisy speech recordings leads to a great increase in performance over state-of-the-art algorithms, according to both the traditional Gross Pitch Error (GPE) measure, and a proposed novel Pitch Tracking Error (PTE) which more fully reflects the accuracy of both pitch estimation/extraction and voicing detection in a single measure. To verify the generalization and specificity of SAcC, we test SAcC on a real world problem that has a large-scale noisy speech corpus. The data is from the DARPA Robust Automatic Transcription of Speech (RATS) program. The experiments on the performance evaluation of SAcC pitch tracking confirm the generalization power of SAcC across various unknown noise conditions and distinct speech corpora. We also report the use of SAcC output adds a significant improvement to a Speaker Identification (SID) system for RATS as well, suggesting the potential contribution of SAcC pitch tracking in the higher-level tasks.
Parallel Algorithms for Unsupervised Tagging

We propose a new method for unsupervised tagging that finds minimal models which are then further improved by Expectation Maximization training. In contrast to previous approaches that rely on manually specified and multi-step heuristics for model minimization, our approach is a simple greedy approximation algorithm DMLC (Distributed-Minimum-Label-Cover) that solves this objective in a single step. We extend the method and show how to efficiently parallelize the algorithm on modern parallel computing platforms while preserving approximation guarantees. The new method easily scales to large data and grammar sizes, overcoming the memory bottleneck in previous approaches. We demonstrate the power of the new algorithm by evaluating on various sequence labeling tasks: Part-of-Speech tagging for multiple languages (including low-resource languages), with complete and incomplete dictionaries, and supertagging, a complex sequence labeling task, where the grammar size alone can grow to millions of entries. Our results show that for all of these settings, our method achieves state-of-the-art scalable performance that yields high quality tagging outputs.
Heterogeneity and anisotropy of the lithosphere of SE Tibet from surface wave array tomography

[1] Understanding the geotectonic evolution of the southeastern Tibetan plateau requires knowledge about the structure of the lithosphere. Using data from 77 broadband stations in SW China, we invert Rayleigh wave phase velocity dispersion curves from ambient noise interferometry (T =1 0–40 s) and teleseismic surface waves (T =2 0–150 s) for 3‐D heterogeneity and azimuthal anisotropy in the lithosphere to ∼150 km depth. Our surface wave array tomography reveals (1) deep crustal zones of anomalously low shear wave speed and (2) substantial variations with depth of the pattern of azimuthal anisotropy. Upper crustal azimuthal anisotropy reveals a curvilinear pattern around the eastern Himalayan syntaxis, with fast directions generally parallel to the main strike slip faults. The mantle pattern of azimuthal anisotropy is different from that in the crust and varies from north to south. The tomographically inferred 3‐D variation in azimuthal anisotropy helps constrain the source region of shear wave splitting. South of ∼26°N (off the high plateau) most of the observed splitting can be accounted for by upper mantle anisotropy, but for stations on the plateau proper (with thick crust) crustal anisotropy cannot be ignored. On long wavelengths, the pattern of azimuthal anisotropy in the crust differs from that in the mantle. This is easiest explained if deformation varies with depth. The deep crustal zones of low shear wave speed (and, presumably, mechanical strength) may represent loci of ductile deformation. But their lateral variation suggests that in SE Tibet (localized) crustal channel flow and motion along the major strike slip faults are both important.
Brain-computer interface using P300: a gaming approach for neurocognitive impairment diagnosis

This paper proposes a novel mobile healthcare system for remotely monitoring neuro-cognitive functions of impaired subjects and proposing possible treatments. Currently, only hospital centers perform similar analyses through fixed and wired electroencephalography (EEG) inspection. The solution proposed here works wirelessly and improves its accuracy learning by performances of the subject playing a game/test. The system is based on spatio-temporal detection and characterization of a specific brain potential named P300. It includes: i) a wearable wireless EEG device; ii) a gateway (tablet or smartphone) processing gathered data, also providing the test/game to the user. Given the above hardware settings, a new algorithm, named tuned-Residue Iteration Decomposition (t-RIDE), provides spatiotemporal features of P300s and a semantic-based reasoner allows taking into account factors which could modify the test if performed in non-standard conditions. The system has been adopted with 12 subjects involved in three different cognitive tasks with increasing difficulty. Fast diagnosis of cognitive deficits is reached, including mild and heavy impairments cases: t-RIDE processing is performed in 1.95s (after 79 iterations for convergence) whereas semantic matchmaking routine requires 2.5ms in the worst case. A case study for an Alzheimer injured patient is reported to corroborate and clarify the proposed approach.
On the phase-noise and phase-error performances of multiphase LC CMOS VCOs

This paper presents an analysis of phase noise in multiphase LC oscillators, and measurement results for several CMOS quadrature-voltage-controlled-oscillators (QVCOs) working in the 2-GHz frequency range. The phase noise data for a so-called BS-QVCO (-140 dBc/Hz or less at 3 MHz frequency offset from the carrier, for a power consumption of 20.8 mW and a figure-of-merit of 184 dBc/Hz) show that phase noise performances are close to the previously derived limits. A systematic cause of departure from ideal quadrature between QVCO signals is also analyzed and measured experimentally, and a compact LC-tank layout that removes this source of phase error is proposed. A TS-QVCO designed with this technique shows a phase-noise figure-of-merit improvement of 4 dB, compared to a previous implementation. The measured equivalent phase error for all QVCOs is between 0.6/spl deg/ and 1/spl deg/.
Web-pages sojourn times in high speed networks

Processor sharing (PS) queuing systems have been historically applied for the estimation of file Sojourn times. We present a model for Web-pages sojourn times at the session-layer by incorporating the time spent by TCP in its slow start and congestion avoidance phases. The model is a function of not only the residual capacity of the bottleneck link but also of the packet loss probability and round trip time (RTT) delay. We show that the model effectively captures the Web-pages sojourn times in high speed networks.
Delineating social network data anonymization via random edge perturbation

Social network data analysis raises concerns about the privacy of related entities or individuals. To address this issue, organizations can publish data after simply replacing the identities of individuals with pseudonyms, leaving the overall structure of the social network unchanged. However, it has been shown that attacks based on structural identification (e.g., a walk-based attack) enable an adversary to re-identify selected individuals in an anonymized network. In this paper we explore the capacity of techniques based on random edge perturbation to thwart such attacks. We theoretically establish that any kind of structural identification attack can effectively be prevented using random edge perturbation and show that, surprisingly, important properties of the whole network, as well as of subgraphs thereof, can be accurately calculated and hence data analysis tasks performed on the perturbed data, given that the legitimate data recipient knows the perturbation probability as well. Yet we also examine ways to enhance the walk-based attack, proposing a variant we call probabilistic attack. Nevertheless, we demonstrate that such probabilistic attacks can also be prevented under sufficient perturbation. Eventually, we conduct a thorough theoretical study of the probability of success of any}structural attack as a function of the perturbation probability. Our analysis provides a powerful tool for delineating the identification risk of perturbed social network data; our extensive experiments with synthetic and real datasets confirm our expectations.
RAN and Double-Deficit Theory

Lervag and Hulme’s neuro-developmental theory and Wolf and Bowers’s double-deficit hypothesis were examined in this longitudinal study. A total of 130 children were tested in preschool and followed through fifth grade, when 84 remained in the study. During preschool and kindergarten the participants were given tests of end-sound discrimination (phonological awareness; PA) and the rapid naming of objects (rapid automatic naming; RAN) and were placed into the four groupings of the double-deficit hypothesis. The growth curves for the four groups with the subtests of word reading, pseudoword reading, and comprehension supported the double-deficit hypothesis. The RAN objects scores of preschool and kindergarten predicted reading at every age level and offered support for Lervag and Hulme’s neuro-developmental theory. It was concluded that both RAN and PA predicted reading in the English language throughout the elementary school years and that the early assessments of these variables were more diagnostic than measures at later ages.
Finding communities by clustering a graph into overlapping subgraphs

We present a new approach to the problem of finding communities: a community is a subset of actors who induce a locally optimal subgraph with respect to a density function defined on subsets of actors. Two different subsets with significant overlap can both be locally optimal, and in this way we may obtain overlapping communities. We design, implement, and test two novel efficient algorithms, RaRe and IS, which find communities according to our definition. These algorithms are shown to work effectively on both synthetic and real-world graphs, and also are shown to outperform a well-known k-neighborhood heuristic.
THE CANADIAN JOURNAL OF NEUROLOGICAL SCIENCES SPECIAL FEATURE Traditional Chinese Herbs in Treatment of Neurological and Neurosurgical Disorders

EDITOR'S NOTE With the rapid increase in communication and exchange of ideas between China and North America during the past decade, there has been considerable interest amongst Western physicians in traditional Chinese medicine and the use of herbal preparations for a variety of medical problems. The exact chemical constituents of many of these preparations are still unknown and few of the treatments have been tested in rigorous controlled clinical trials. Nevertheless it was considered timely to present a review of the use of herbal medicines in certain neurological problems, and Drs. Ding and He were invited to prepare this special feature for the CJNS. Drs. Ding and He are presently both working as visiting scientists in North America — Dr. Ding in the Department of Clinical Neurosciences at University of Calgary and Dr. He in the Department of Neurology at University of Wisconsin. Traditional Chinese medicine and herbs have been widely used for at least 3,000 years. During this time there has been a rich accumulation of experience using herbal medication to treat a variety of diseases. Numerous books were written by ancient Chinese physicians and they are still in use today. Since 1949, the further development of Chinese medicine has been encouraged and in some areas important contributions to the health care of the Chinese people have resulted from combining traditional Chinese and Western medicine. Since effective treatments are still not available for many neurological disorders, there has been a natural tendency to attempt treatment of some of these conditions with traditional herbal medicine. This article will briefly review some of the experience gained from using Chinese herbs in the fields of neurology and neurosurgery.
Real-Time Constraints

Real-time constraints are restrictions on the timings of events, such that they occur on-time. A system with real-time constraints is called a real-time system. Not merely the performance of such systems, but also their feasibility depends on the satisfaction of real-time constraints. Hard constraints must be satisfied for system correctness, while the violation of soft constraints only degrades a system. Specification of real-time constraints requires either some extensions of programming languages through annotations and logic expressions, or the use of temporal logics or some formal declarative language with temporal constructs. Stringent timing restrictions complicate system design and verification. The time model can be dense or discrete, thus giving different methods for real-time system synthesis and verification. This article surveys the specification, design, and verification of real-time constraints and systems. The most important technique for guaranteeing real-time, namely scheduling, is briefly surveyed. Different system models are presented for handling real-time constraints such as Petri nets, timed automata, process algebra and object-oriented model. Design techniques for real-time hardware systems and for real-time software applications are introduced and discussed. Hardware-software codesign is also introduced. Verification techniques for real-time constraints, such as model checking, are also presented. † The author will be moving to the Department of Computer Science and Information Engineering, National Chung-Cheng University, Chiayi, Taiwan, starting February 2001. The work was supported by several research grants from the National Science Council, Taipei, Taiwan.
Computing with Populations of Monotonically Tuned Neurons

The parametric variation in neuronal discharge according to the values of sensory or motor variables strongly influences the collective behavior of neuronal populations. A multitude of studies on the populations of broadly tuned neurons (e.g., cosine tuning) have led to such well-known computational principles as population coding, noise suppression, and line attractors. Much less is known about the properties of populations of monotonically tuned neurons. In this letter, we show that there exists an efficient weakly biased linear estimator for monotonic populations and that neural processing based on linear collective computation and least-square error learning in populations of intensity-coded neurons has specific generalization capacities.
Performances analysis of Algebraic Space Time Code under correlated and uncorrelated channels

With their very Algebraic-construction based on Quaternionic algebra, Algebraic Space Time Codes (ASTC), called the Golden codes, have a full rate, full diversity and non-vanishing constant minimum determinant for increasing spectral efficiency. They have also uniform average transmitted energy per antenna and good shaping, readily lend themselves to high data rate situations. In this paper, we first analyze the performances of the ASTC codes in correlated Rayleigh channel. We consider a coherent demodulator using different decoding schemes and we analyze the Bit Error Rate (BER). In order to increase the spectral efficiency and to maximize the coding gain, ASTC have been proposed for MIMO flat fading channels. To deal with the frequency selectivity, we use the OFDM modulation. So we analyze the performances of an ASTC-MIMO-OFDM system in terms of BER. Finally, we investigate the impact of spatial correlation on the ASTC code design in terms of BER and capacity.
Monte Carlo simulations of carbon nanotube networks for optoelectronic applications

Carbon nanotube networks are compatible with silicon and possess features of light modulation, detection and emission in the transparency band of silicon. This makes them excellent candidates as active material for silicon photonics. However, the ubiquitous presence of residual metallic nanotubes in nanotube networks is a strong issue for this vision. In this work, we perform Monte Carlo simulations of the electrical properties of nanotube networks, by extracting and simulating an equivalent netlist of the networks. The results allow us to identify the appropriate densities of nanotubes not affected by the metallic nanotube issue, and to propose a first design rule for nanotube-based optoelectronics.