Miracle-dz/UniFAR-FTUs huggingface dataset:
3D comparison of low, intermediate, and advanced hippocampal atrophy in MCI

We applied the hippocampal radial atrophy mapping technique to the baseline and follow‐up magnetic resonance image data of 169 amnestic mild cognitive impairment (MCI) participants in the imaging arm of the Alzheimer's Disease Cooperative Study MCI Donepezil/Vitamin E trial. Sixty percent of the subjects with none to mild hippocampal atrophy rated with the visual medial temporal atrophy rating scale (MTA score < 2) and 33.8% of the subjects with moderate to severe (MTA ≥ 2) hippocampal atrophy converted to Alzheimer's disease (AD) during 3‐year follow‐up. MTA ≥ 2 showed a trend for greater left sided hippocampal atrophy versus MTA < 2 groups at baseline (Pcorrected = 0.08). Higher MTA scores were associated with progressive atrophy of the subiculum and the CA1‐3 subregions. The MTA < 2 group demonstrated significant bilateral atrophy progression at follow‐up (left Pcorrected = 0.008; right Pcorrected = 0.05). Relative to MTA < 2 nonconverters, MTA < 2 converters showed further involvement of the subiculum and CA1 and additional involvement of CA2‐3 at follow‐up. Right CA1 atrophy was significantly associated with conversion to dementia (for 1 mm greater right CA1 radial distance subjects had 50% reduced hazard for conversion). Greater CA1 and subicular atrophy can be demonstrated early and is predictive of future conversion to AD, whereas CA2‐3 involvement becomes more evident as the disease progresses. Hum Brain Mapp, 2010. © 2010 Wiley‐Liss, Inc.
Red Sox versus Yankees: Sports Team Rivalry, Sports Symbols, and Distance Performance

Abstract Building on the literature on psychological distance and visual illusion, the authors examine whether visual exposure to sports team symbols affect distance performance, depending on whether the symbols represent favored or rival teams. Boston Red Sox fans are used to test the hypothesis in the context of the famous Red Sox–Yankees rivalry. Individuals who are primed with the symbol of their favored (rival) sports team are predicted to hit/throw a ball short (long) of the target. In Study 1, Red Sox fans putt a golf ball imprinted with a Red Sox (Yankees) logo, and are found to putt short (long) of the goal. In Study 2, Red Sox fans photograph a bobble head wearing a Red Sox (Yankee) jersey and then are found to throw a basketball nearer to (farther from) themselves.
Multimodal Silent Speech Interface based on Video, Depth, Surface Electromyography and Ultrasonic Doppler: Data Collection and First Recognition Results

Silent Speech Interfaces use data from the speech production process, such as visual information of face movements. However, using a single modality limits the amount of available information. In this study we start to explore the use of multiple data input modalities in order to acquire a more complete representation of the speech production model. We have selected 4 non-invasive modalities – Visual data from Video and Depth, Surface Electromyography and Ultrasonic Doppler - and created a system that explores the synchronous combination of all 4, or of a subset of them, into a multimodal Silent Speech Interface (SSI). This paper describes the system design, data collection and first word recognition results. As the first acquired corpora are necessarily small for this SSI, we use for classification an example based recognition approach based on Dynamic Time Warping followed by a weighted k-Nearest Neighbor classifier. The first classification results using different vocabularies, with digits, a small set of commands related to Ambient Assisted Living and minimal nasal pairs, show that word recognition benefits can be obtained from a multimodal approach.
Empirical analysis of Corporate Environmental Responsibility

Under the current situation of environmental pollution and resources shortage, the research of Corporate Environmental Responsibility (CER) is of great importance to promote the sustainable development of society. The thesis adopts the perspective of the Symbiotic Theory and environmental management to make the empirical analysis of CER. Through the correlation analysis, principal component analysis, factor ordering and variables reappearance of key factors, 7 key indexes are identified in the original 35 variables. This paper aims to provide reference for enterprise environmental management and forward the study of Corporate Social Responsibility.
Magnitude-Frequency Distribution of Hummocks on Rockslide-Debris Avalanche Deposits and Its Geomorphological Significance

A magnitude-frequency analysis of rockslide-debris avalanche deposits was performed. Hummocks are conical mounds formed in debris avalanche deposits from the catastrophic sector collapse of a mountain (often volcanic) that represent relatively cohesive fragments of the mountain edifice. Examination of 17 debris avalanche deposits in Japan and the Philippines showed that, in general, the larger the magnitude of the hummocks, the smaller their frequency. Hummocks followed an exponential distribution: log10N(x) = a – bx, where N(x) is the cumulative number of hummocks with magnitude ≥ x and a and b are constants; x is equal to log10A, where A is the area of a hummock. The constants a and b were positively correlated. The value of b, which differs among avalanches and in this analysis ranged between 1 and 3, may be controlled by the mobility of the debris avalanche. Avalanches with higher mobility (relatively longer runout) have higher b and potentially produce more numerous fragments forming hummocks (i.e., higher a). From the above correlation, the magnitude-frequency relationship can be used to roughly estimate the original height of the collapsed volcanic body, if the runout distance of the rockslide–debris avalanche can be estimated with sufficient accuracy.
Space-based AIS multi-user separation based on the improved complex value FastICA

The coverage of the space-based automatic identification system (AIS) is wide and it can be used to monitor the global ships effectively. It also brings a new challenge that the collision of AIS signals from different cells results in loss of all collided message. In order to solve this problem, this paper proposes a multi-user separation scheme based on improved complex value FastICA. In this paper, we give full consideration to the communication mode and signal characteristics of the system. Meanwhile, a step factor is introduced to the complex value FastICA to reduce the algorithm's sensitivity to initial value. In order to reduce the amount of calculation, the introduced step factor searched based on Wolfe-Powell. Simulation results show that the proposed scheme can separate the collided signals effectively.
A discrete-time relativistic Toda lattice

For each of the two simplest Hamiltonian flows from the relativistic Toda hierarchy we introduce two integrable symplectic discretizations. All four discrete-time systems are demonstrated to belong to the same hierarchy and to exemplify the general scheme for symplectic maps on groups equipped with quadratic Poisson brackets. The initial-value problem for the difference equations is solved in terms of a factorization problem in a group. Interpolating Hamiltonian flows are found for all maps.
Nanotesla torque magnetometry using a microcantilever

We present a novel ultrasensitive magnetometry technique using a micromachined magnetic antilever that is brought in resonance. The induced magnetic moment generates a torque on the cantilever, thereby effectively stiffening the cantilever spring constant and changing its resonance frequency. Experiments are in good correspondence with the presented analytical model for this frequency shift, predicting the detection of nanotesla magnetic fields.
Raphé Neurons Stimulate Respiratory Circuit Activity by Multiple Mechanisms via Endogenously Released Serotonin and Substance P

Brainstem serotonin (5-HT) neurons modulate activity of many neural circuits in the mammalian brain, but in many cases endogenous mechanisms have not been resolved. Here, we analyzed actions of raphé 5-HT neurons on respiratory network activity including at the level of the pre-Bötzinger complex (pre-BötC) in neonatal rat medullary slices in vitro, and in the more intact nervous system of juvenile rats in arterially perfused brainstem–spinal cord preparations in situ. At basal levels of activity, excitation of the respiratory network via simultaneous release of 5-HT and substance P (SP), acting at 5-HT2A/2C, 5-HT4, and/or neurokinin-1 receptors, was required to maintain inspiratory motor output in both the neonatal and juvenile systems. The midline raphé obscurus contained spontaneously active 5-HT neurons, some of which projected to the pre-BötC and hypoglossal motoneurons, colocalized 5-HT and SP, and received reciprocal excitatory connections from the pre-BötC. Experimentally augmenting raphé obscurus activity increased motor output by simultaneously exciting pre-BötC and motor neurons. Biophysical analyses in vitro demonstrated that 5-HT and SP modulated background cation conductances in pre-BötC and motor neurons, including a nonselective cation leak current that contributed to the resting potential, which explains the neuronal depolarization that augmented motor output. Furthermore, we found that 5-HT, but not SP, can transform the electrophysiological phenotype of some pre-BötC neurons to intrinsic bursters, providing 5-HT with an additional role in promoting rhythm generation. We conclude that raphé 5-HT neurons excite key circuit components required for generation of respiratory motor output.
A potential therapeutic effect of CYP2C8 overexpression on anti-TNF-α activity

Epoxyeicosatrienoic acids (EETs) are generated from arachidonic acid catalysed by cytochrome P450 (CYP) epoxygenases. In addition to regulating vascular tone EETs may alleviate inflammation and ROS. The present study was conducted to determine whether CYP2C8 gene overexpression was able to increase the level of EETs, and subsequently prevent TNF-α induced inflammation and reactive oxygen species (ROS) in human umbilical vein endothelial cells (HUVECs) and macrophages. Peroxisome proliferator-activated receptor γ (PPARγ) activation, nuclear factor-κB (NF-κB) activation, endothelial nitric oxide synthase (eNOS) activation, gp-91 activation, and inflammatory cytokine expression were detected by western blot analysis or enzyme-linked immunosorbent assay. Intracellular reactive oxygen species (ROS) was measured by flow cytometry, while the migration of vascular smooth muscle cells (VSMCs) was detected by Transwell assay. pCMV-mediated CYP2C8 overexpression and its metabolites, EETs, markedly suppressed TNF-α induced inflammatory cytokines IL-6 and MCP-1 expression via the activation of NF-κB and degradation of IκBα. Moreover, pretreatment with 11,12-EET significantly blocked TNF-α-induced ROS production. CYP2C8-derived EETs also effectively alleviated the migration of VSMCs and improved the function of endothelial cells through the upregulation of eNOS, which was significantly decreased under the stimulation of TNF-α. Furthermore, these protective effects observed were mediated by PPARγ activation. To the best of our knowledge, the results of the present study demonstrated for the first time that CYP2C8-derived EETs exerted antivascular inflammatory and anti-oxidative effects, at least in part, through the activation of PPARγ. Thus, the CYP2C8 gene may be useful in the prevention and treatment of vascular inflammatory diseases.
A New Layer for Automatic Ontology Creation from Text by Using Linked Data and Implied Information

Manual creation of ontologies is often a time-consuming, tedious, erroneous and costly process which is also dependent on the domain engineer’s knowledge. Automation of this process is an important task which solves the problems of time-consumption, tediousness and, to some extent, expensiveness. Context-awareness is a huge problem of methods proposed in the ontology engineering scope. This problem also exists in the case of automatic ontology creation from texts. In this paper we propose a new layer for the ontology learning stack to solve this problem. This proposed layer with its in-built algorithm uses Linked Data in order to make use of “hidden” or implied information in texts, resulting in the extraction of new ontological relations which cannot be otherwise extracted. We use a real example and perform the algorithm on it to illustrate the correctness and accuracy of our proposed approach. This proposed approach thus enriches the non-taxonomic relations in ontologies created from texts. At the end, we discuss the growing potential of our approach.
Architectural Refactoring for Fast and Modular Bioinformatics Sequence Search

Bioinformaticists use the Basic Local Alignment Search Tool (BLAST) to characterize an unknown sequence by comparing it against a database of known sequences, thus detecting evolutionary relationships and biological properties. mpiBLAST is a widely-used, high-performance, open-source parallelization of BLAST that runs on a computer cluster delivering super-linear speedups. However, the Achilles heel of mpiBLAST is its lack of modularity, adversely affecting maintainability and extensibility; an effective architectural refactoring will benefit both users and developers. This paper describes our experiences in the architectural refactoring of mpiBLAST into a modular, high-performance software package. Our evaluation of five component-oriented designs culminated in a design that enables modularity while retaining high-performance. Furthermore, we achieved this refactoring effectively and efficiently using eXtreme Programming techniques. These experiences will be of value to software engineers faced with the challenge of creating maintainable and extensible, high-performance, bioinformatics software.
Introducing SSDs to the Hadoop MapReduce Framework

Solid State Drive (SSD) cost-per-bit continues to decrease. Consequently, system architects increasingly consider replacing Hard Disk Drives (HDDs) with SSDs to accelerate Hadoop MapReduce processing. When attempting this, system architects usually realize that SSD characteristics and today's Hadoop framework exhibit mismatches that impede indiscriminate SSD integration. Hence, cost-effective SSD utilization has proved challenging within many Hadoop environments. This paper compares SSD performance to HDD performance within a Hadoop MapReduce framework. It identifies extensible best practices that can exploit SSD benefits within Hadoop frameworks when combined with high network bandwidth and increased parallel storage access. Terasort benchmark results demonstrate that SSDs presently deliver significant cost-effectiveness when they store intermediate Hadoop data, leaving HDDs to store Hadoop Distributed File System (HDFS) source data.
AOCD: An Adaptive Outlier Based Coordinated Scan Detection Approach

Coordinated attacks are distributed in nature because they attempt to compromise a target machine from multiple sources. It is important for network defenders and administrators to detect these scans as possible preliminaries to more serious attacks. However, it is very di±cult to detect malicious scans based on port specific behavior alone. In this paper, we present an Adaptive Outlier based approach for Coordinated scan Detection (AOCD) at an early stage with high accuracy. It is an outlier score based adaptive network anomaly detection approach that considers sets of normal instances during training. We use both normal and port scan instances for testing purpose. We achieve higher detection accuracy and low false positive rate on real-life and KDDcup99 probe datasets in comparison with existing techniques.
Three qubits can be entangled in two inequivalent ways

Invertible local transformations of a multipartite system are used to define equivalence classes in the set of entangled states. This classification concerns the entanglement properties of a single copy of the state. Accordingly, we say that two states have the same kind of entanglement if both of them can be obtained from the other by means of local operations and classical communication (LOCC) with nonzero probability. When applied to pure states of a three-qubit system, this approach reveals the existence of two inequivalent kinds of genuine tripartite entanglement, for which the Greenberger-Horne-Zeilinger state and a W state appear as remarkable representatives. In particular, we show that the W state retains maximally bipartite entanglement when any one of the three qubits is traced out. We generalize our results both to the case of higher-dimensional subsystems and also to more than three subsystems, for all of which we show that, typically, two randomly chosen pure states cannot be converted into each other by means of LOCC, not even with a small probability of success.
Designing Inflation Targets

This paper discusses some operational issues regarding the design of monetary frameworks in general and inflation targets in particular. Among inflation targeters,1 these issues are well-known and manifold (see, inter alia, Yates (1995), McCallum (1995), Debelle (1997), Siklos (1997) and the country contributions in the volumes by Haldane (1995) and Leiderman and Svensson (1995)). They include: (a) At what level to set an inflation target?
Toward noise-tolerant acoustic models

State-of-the-art acoustic models, relying on hidden Markov models (HMMs), are heavily noise-sensitive: recognition performance drops whenever a signiﬁcant difference in acoustic conditions holds between the training and the test environments. Practical, yet partial, attempts to tackle the problem are usually based on noise reduction via spectral subtraction, blind source separation, parameter adaptation/normalization, HMM retrain-ing, etc. But the relevance of developing acoustic models that are intrinsically robust has to be stressed. Robustness to noise is related to the generalization capabilities of the model. Artiﬁcial neural networks (ANNs) appear to be a promising alterna-tive, but they historically failed as a general paradigm for speech recognition [1]. This paper faces the problem by (i) investigat-ing the recognition performance of the ANN/HMM hybrid proposed by the authors [2] over tasks with noisy signals, and (ii) proposing an explicit “soft” weight grouping technique, capable to improve its robustness. Experiments over noisy speaker-independent connected-digits strings are presented. In particu-lar, results on the VODIS II/SpeechDatCar database, collected in a real car environment, show the dramatic gain over the standard HMM, as well as over Bourlard and Morgan’s hybrid.
A Two-Level Parallel Preconditioner Based on Sparse Approximate Inverses

We introduce a novel strategy for parallel preconditioning of large-scale linear systems by means of a two-level factorized sparse approximate inverse algorithm. Using graph partitioning and incomplete biconjugation we are able to obtain a highly parallel preconditioner. The algorithm has been implemented using MPI on a SGI Origin 2000 computer at Los Alamos National Laboratory and is currently being used to solve unstructured linear systems with up to a few million unknowns from a variety of applications. The numerical experiments demonstrate the excellent scalability of the algorithm for suuciently large problems.
Ph.D. Thesis Proposal: Motion Planning Of Multiple Agents in Virtual Environments

Motion planning has been traditionally studied in the area of robotics. In recent years the techniques are increasingly used in virtual environments and games. In many such applications agents move around and their motion must be planned. We define an agent as anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators [1]. This definition applies to humans, robotic agents, but also to software agents. There are four different types of motion in virtual environments and games: navigation, animation, manipulation, and camera motion. We are interested in navigation, i.e., agents must find their route to a particular goal while avoiding collisions with obstacles and other agents. Virtual environments and games offer a challenging problem setting because of the following aspects [2, 3]:
Estimation of vocal tract parameters for the classification of speech under stress

In this work, we propose a method for the classification of speech under stress that is based on a physical model. Using this method, the characteristics of the vocal folds and the vocal tract are taken into consideration, based on the process of speech production. In addition to vocal fold parameters, we estimate parameters of the vocal tract representing cross-sectional areas and vocal tract length, by fitting a two-mass model to real speech. Results show that calculation of vocal tract length for each speaker can improve the accuracy of the estimation of other physical parameters. Analysis is performed under vowel-dependent and vowel-independent conditions, showing that the proposed physical features are effective for the classification of neutral and stressed speech.
GSTM1, smoking and lung cancer: a case-control study.

BACKGROUND We conducted a case-control study to examine the risk of lung cancer in relation to GSTM1 polymorphism and cigarette smoking (primarily of black tobacco) in a French population. METHODS The 611 subjects were 301 incident lung cancer cases and 310 hospital controls. We were able to constitute a DNA bank for 547 subjects (89.5%) and gather detailed information on smoking history for all of them. Results presented here concern 247 cases and 254 controls. RESULTS Taking non- or light smokers as the reference category, we estimated odds ratios (OR) of 4.2 (95% CI: 2.6-6.7) and 5.2 (95% CI: 3.3-8.3) for the medium and heavy smokers respectively. On the other hand we estimated that the crude OR associating GSTM1 with lung cancer was 1.3 (95% CI: 0.9-1.8). Furthermore our data do not depart significantly from a multiplicative model of the combined effects of smoking and GSTM1 deficiency. CONCLUSIONS We conclude that smoking and the GSTM1 gene are each a risk factor for lung cancer, and that their combined effect does not differ significantly from that of a multiplicative model.
On the Purity of the ZZ Ceti Instability Strip: Discovery of More Pulsating DA White Dwarfs on the Basis of Optical Spectroscopy

We report the discovery of two new ZZ Ceti pulsators, LP 133-144 and HE 1258+0123, selected on the basis of model atmosphere fits to optical spectroscopic data. The atmospheric parameters for LP 133-144 (Teff = 11,800 ± 200 K and log g = 7.87 ± 0.05) and for HE 1258+0123 (Teff = 11,410 ± 200 K and log g = 8.04 ± 0.05) place them within the empirical boundaries of the ZZ Ceti instability strip. This brings the number of known ZZ Ceti stars to a total of 36, a quarter of which have now been discovered using the spectroscopic approach for estimating their atmospheric parameters. This method has had a 100% success rate so far in predicting the variability of candidate ZZ Ceti stars. We have also analyzed additional spectra of known nonvariable white dwarfs in the vicinity of the ZZ Ceti instability strip. Our study further strengthens the idea that ZZ Ceti stars occupy a pure region in the log g-Teff plane, a region where no nonvariable stars are found. This result supports the thesis that ZZ Ceti pulsators represent a phase through which all DA stars must evolve.