Miracle-dz/UniFAR-FTUs huggingface dataset:
Scavenging rate constants of hydrophilic antioxidants against multiple reactive oxygen species

Scavenging rate constants of eight hydrophilic antioxidants, including caffeic acid, chlorogenic acid, genistein, glutathione, N-acetylcysteine, rutin, trolox, and uric acid against multiple ROS, namely superoxide anion, hydroxyl radical, singlet oxygen, and alkoxyl radical were determined with the electron spin resonance method. Direct flash photolysis measurement of the second-order rate constant in the reaction of alkoxyl radical plus the spin trap 5,5-dimethyl-pyrroline N-oxide made it possible to evaluate scavenging rate constants in antioxidants. The magnitudes of scavenging rate constants were notably dependent on the character of each ROS and the overall rate constants were highest in hydroxyl radical scavenging and the lowest in superoxide anion. The highest scavenging rate constant against superoxide anion was obtained by chlorogenic acid (2.9 × 105 M−1 s−1) and the lowest was by N-acetylcysteine (5.0 × 102 M−1 s−1). For singlet oxygen, the highest was by glutathione (9.4 × 108 M−1 s−1) and the lowest was by uric acid (2.3 × 106 M−1 s−1). All other numbers are listed and illustrated. Redox potential measurements of the antioxidants indicated that the antioxidants are likely to react with superoxide anion and singlet oxygen through electron transfer processes.
Singularities of Optimal Control Problems on some Six Dimensional Lie groups

This paper considers the motion planning problem for oriented vehicles travelling at unit speed in a threedimensional space. A Lie group formulation arises naturally and the vehicles are modelled as kinematic control systems with drift defined on the orthonormal frame bundles of particular Riemannian manifolds, specifically the three-dimensional space forms Euclidean space E, the sphere S and the hyperboloid H. The corresponding frame bundles are equal to the Euclidean group of motions SE(3), the rotation group SO(4) and the Lorentz group SO(1, 3). The Maximum Principle of optimal control, shifts the emphasis for these systems to the associated Hamiltonian formalism. For an integrable case the extremal curves are explicitly expressed in terms of elliptic functions. In this paper, a study at the singularities of the extremal curves are given, which correspond to critical points of these elliptic functions. The extremal curves are characterized as the intersections of invariant surfaces and are illustrated graphically at the singular points. It is then shown that the projections of the extremals onto the base space, called elastica, at these singular points, are curves of constant curvature and torsion, which in turn implies that the oriented vehicles trace helices.
Correspondence and Affine Shape from Two Orthographic Views: Motion and Recognition

The paper presents a simple model for recovering affine shape and correspondence from two orthographic views of a 3D object. It is shown that four corresponding points along two orthographic views, taken under similar illumination conditions, determine affine shape and correspondence for all other points. The scheme is useful for purposes of visual recognition by generating novel views of an object given two model views. It is also shown that the scheme can handle objects with smooth boundaries, to a good approximation, without introducing any modifications or additional model views.
Classifying P300 responses to vowel stimuli for auditory brain-computer interface

A brain-computer interface (BCI) is a technology for operating computerized devices based on brain activity and without muscle movement. BCI technology is expected to become a communication solution for amyotrophic lateral sclerosis (ALS) patients. Recently the BCI2000 package application has been commonly used by BCI researchers. The P300 speller included in the BCI2000 is an application allowing the calculation of a classifier necessary for the user to spell letters or sentences in a BCI-speller paradigm. The BCI-speller is based on visual cues, and requires muscle activities such as eye movements, impossible to execute by patients in a totally locked-in state (TLS), which is a terminal stage of the ALS illness. The purpose of our project is to solve this problem, and we aim to develop an auditory BCI as a solution. However, contemporary auditory BCI-spellers are much weaker compared with a visual modality. Therefore there is a necessity for improvement before practical application. In this paper, we focus on an approach related to the differences in responses evoked by various acoustic BCI-speller related stimulus types. In spite of various event related potential waveform shapes, typically a classifier in the BCI speller discriminates only between targets and non-targets, and hence it ignores valuable and possibly discriminative features. Therefore, we expect that the classification accuracy could be improved by using an independent classifier for each of the stimulus cue categories. In this paper, we propose two classifier training methods. The first one uses the data of the five stimulus cues independently. The second method incorporates weighting for each stimulus cue feature in relation to all of them. The results of the experiments reported show the effectiveness of the second method for classification improvement.
Beyond position - spatial context for mobile information retrieval systems

Within context-aware mobile applications, location information usually plays a major role for information selection and adaptivity. In this paper, we explore the geospatial dimension between simple position-aware and fully context-aware information systems by examining in-depth the features of spatial context beyond mere position. We describe how these features can be used to create spatial queries in a mobile information retrieval system and further discuss the influence of spatial context to select and adapt the query results and its relation to mobile user's information needs.
An Investigation into ESL Learners’ Nature of Self-Regulated Learning in Out-of-Class Context: A Comparative Study

The aim of the study was to investigate whether there were differences in the nature of use of technology for selfregulated language learning between learners from high and low proficiency levels. It employed the mixedmethods approach to understand the phenomenon. The quantitative findings indicate that there were no significant differences in the nature of use of technology between these learners. However, the qualitative findings reveal variance in the use of the Internet environments between the two groups of learners. The more proficient learners seemed to be able to regulate their learning experiences better than the low proficient group especially in terms of regulating emotions and social resources. Despite the variance in use, findings indicate that both groups’ engagement with technology mainly involves incidental learning. Thus, it is recommended that teachers should foster dialogue and reflection on the out-of-class engagement with technologies so that learners can further benefit from it.
The MIP Information Model - A semantic reference for Command & Control

The Multilateral Interoperability Programme (MIP), comprising 26 nations and NATO, has developed the MIP Information Model (MIM) to support semantic interoperability across C2 systems. The MIM is a platform-independent model in UML that focuses on describing semantic concepts rather than mandating a specific technical implementation. In this paper, we give an overview of the objectives and the scope of the MIM and explain how the use of UML stereotypes clarifies the semantics of its model elements. The MIM comes with an OWL 2 representation. We describe the mapping from UML to OWL, covering MIM-specific aspects. Finally, we show how the MIM elements are made available as linked data.
Multi-Viewpoint Synthesis from Uncalibrated Stereo Cameras

View synthesis from arbitrary viewpoints is an important component of virtual reality. This work addresses the problem of synthesizing a novel view from an arbitrary position given two images only (stereo pair). The proposed algorithm creates new viewpoints allowing six degrees of freedom to the virtual camera, i.e., translations along the three coordinate axis, pan, tilt and rotation around its own optical axis. The method generates new views for a totally non-calibrated stereo system. The novelty of the method relies in the placement of the virtual camera and its effect on the view synthesis. The synthesized views showed good results when compared with views produced by well-known viewpoint synthesis methods.
Implementation of Low-Memory Reference FFT on Digital Signal Processor

Problem statement: In order to improve and implement Fast Fourier Transform (FFT), in general, an efficient parallel form in digital signal processor is necessary. The butterfly structure is an important role in FFT, because its symmetry form is suitable for hardware implementation. Although it can perform a symmetric structure, the performance will be reduced under the data-dependent flow characteristic. Even though recent research which calls as Novel Memory Reference Reduction Methods (NMRRM) for FFT focus on reduce memory reference in twiddle factor, the data-dependent property still exists. Approach:In this study, we propose an approach for FFT implementation on DSP from analog device company (ADI) which is based on data-independent property and still hold the property of low-memory reference. We have applied the proposed method of radix-2 FFT algorithm in low-memory reference on ADI BlackFin561 DSP. Results: Experimental results show the method can reduce 44.36% clock cycles comparing with the NMRRM FFT implementation and keep the low-memory reference property. Conclusions/Recommendations: From our algorithm, the results can be accepted and realized for DSP-based embedded system. In further, we will try to implement on different DSP-based system in order to improve the algorithm values.
Immersive Policy Learning: An Interactive Course Experiment

Abstract An in-course experiment provided undergraduate public policy students with tangible experience in dealing with unfair, discriminatory, intrusive, and arbitrary policies and practices similar to those that legally exist in government. Students were subjected to in-course policies that gave preferential status and enhanced opportunities to some classmates while others were punished or handicapped. Each of the seemingly arbitrary conditions has parallels in U. S. legal, economic, and social systems. The experiment was designed to enhance student learning through an immersion in a simulated policy environment and to offer a personalized experience of dealing with unjust and arbitrary policies. Experimental and control group responses were analyzed with a grounded research approach. The authors found that the immersive environment led to deeper knowledge of the policy situation and an understanding of how to get involved in a policy area to effect change. This study illustrates potential applications for active learning, simulated empathy, and student empowerment.
Enhanced INS/Wi-Fi Integration for Indoor Vehicle Navigation

For low-cost and continuous indoor navigation, an INS/Wi-Fi (inertial navigation system/Wi-Fi) integrated system is expected to yield a synergetic effect resulting in higher navigation performance than using the standalone systems. In this paper, the authors explore the integration of Wi-Fi measurements with data from microelectromechanical systems based inertial measurement unit for indoor vehicle navigation. An enhanced integration, which employs adaptive Kalman filtering and vehicle constraints, is presented. One field experiment has been conducted in the corridor of a university building using a mobile robot vehicle. Two Wi-Fi based positioning approaches are considered for trajectory estimation. The numerical results show that the enhanced integrated systems provide higher navigation accuracy, compared to using corresponding standalone Wi-Fi positioning approaches and conventional INS/Wi-Fi integrations.
BAYESIAN INFERENCE OF POLARIZED COSMIC MICROWAVE BACKGROUND POWER SPECTRA FROM INTERFEROMETRIC DATA

Detection of B-mode polarization of the cosmic microwave background (CMB) radiation is one of the frontiers of observational cosmology. Because they are an order of magnitude fainter than E-modes, it is quite a challenge to detect B-modes. Having more manageable systematics, interferometers prove to have a substantial advantage over imagers in detecting such faint signals. Here, we present a method for Bayesian inference of power spectra and signal reconstruction from interferometric data of the CMB polarization signal by using the technique of Gibbs sampling. We demonstrate the validity of the method in the flat-sky approximation for a simulation of an interferometric observation on a finite patch with incomplete uv-plane coverage, a finite beam size, and a realistic noise model. With a computational complexity of O(n3/2), n being the data size, Gibbs sampling provides an efficient method for analyzing upcoming cosmology observations.
A Privacy-Preserving Technique for Incremental Dataset on Cloud by Synthetic Data Perturbation

Cloud is an impetus technology revolution, allowing data providers to store their privatized Electronic Health Record (EHR) for further analysis and outlook with a compromised privacy, where the shared data being exposed to various adversary attacks and malware threats. There are several masking and randomization techniques that provide hints of privacy. In this paper, the Electronic Health Record (EHR) values are perturbed using logarithmic data perturbation and outsourced to the cloud.Aims. To develop a privacy-preserving technique for effective sharing ofElectronic Health Record (EHR) on a cloud by logarithmic data perturbation.Methods. Synthetic logarithmic transformation is applied to the sensitive values in a record before they are outsourced and for better privacy.Results. Synthetic Logarithmic Transformation along with the incremental anonymization produces effective result compared to classical anonymization.
Navigating Large-Scale Virtual Environments: What Differences Occur Between Helmet-Mounted and Desk-Top Displays?

Participants used a helmet-mounted display (HMD) and a desk-top (monitor) display to learn the layouts of two large-scale virtual environments (VEs) through repeated, direct navigational experience. Both VEs were virtual buildings containing more than seventy rooms. Participants using the HMD navigated the buildings significantly more quickly and developed a significantly more accurate sense of relative straight-line distance. There was no significant difference between the two types of display in terms of the distance that participants traveled or the mean accuracy of their direction estimates. Behavioral analyses showed that participants took advantage of the natural, head-tracked interface provided by the HMD in ways that included looking around more often while traveling through the VEs, and spending less time stationary in the VEs while choosing a direction in which to travel.
Structural and Functional Insights on the Myosin Superfamily

The myosin superfamily is a versatile group of molecular motors involved in the transport of specific biomolecules, vesicles and organelles in eukaryotic cells. The processivity of myosins along an actin filament and transport of intracellular ‘cargo’ are achieved by generating physical force from chemical energy of ATP followed by appropriate conformational changes. The typical myosin has a head domain, which harbors an ATP binding site, an actin binding site, and a light-chain bound ‘lever arm’, followed often by a coiled coil domain and a cargo binding domain. Evolution of myosins started at the point of evolution of eukaryotes, S. cerevisiae being the simplest one known to contain these molecular motors. The coiled coil domain of the myosin classes II, V and VI in whole genomes of several model organisms display differences in the length and the strength of interactions at the coiled coil interface. Myosin II sequences have long-length coiled coil regions that are predicted to have a highly stable dimeric interface. These are interrupted, however, by regions that are predicted to be unstable, indicating possibilities of alternate conformations, associations to make thick filaments, and interactions with other molecules. Myosin V sequences retain intermittent regions of strong and weak interactions, whereas myosin VI sequences are relatively devoid of strong coiled coil motifs. Structural deviations at coiled coil regions could be important for carrying out normal biological function of these proteins.
A GARCH forecasting model to predict day-ahead electricity prices

Price forecasting is becoming increasingly relevant to producers and consumers in the new competitive electric power markets. Both for spot markets and long-term contracts, price forecasts are necessary to develop bidding strategies or negotiation skills in order to maximize profits. This paper provides an approach to predict next-day electricity prices based on the Generalized Autoregressive Conditional Heteroskedastic (GARCH) methodology that is already being used to analyze time series data in general. A detailed explanation of GARCH models is presented and empirical results from the mainland Spain and California deregulated electricity-markets are discussed.
Putting fixed priority scheduling theory into engineering practice for safety critical applications

Describes the approach proposed by the York University Technology Centre for introducing fixed-priority scheduling into industrial safety-critical hard real-time systems. The work has been performed within the context of a class A (safety-critical) system as defined by civil aircraft software standard DO178B. Traditionally, class A systems have been scheduled by a cyclic executive. However, many such systems can be re-designed using a fixed-priority scheduler. This saves time and money, with no significant increase in risk. Also, significant technical benefits are apparent. This paper describes the timing requirements of the system, provides a potential scheduling approach (including appropriate timing analysis), and outlines an approach for gathering the necessary evidence for presentation to certification authorities.
Channel Estimation and Symbol Detection Using Superimposed Pilot for Rapidly Time-Varying OFDM System

A superimposed-training (ST) based rapidly time-varying frequency selective channel estimation scheme for orthogonal frequency division multiplexing (OFDM) system is investigated. The time-varying coefficients of the rapidly time-varying channel are modeled by exponential basis expansion. Meanwhile, the optimal training sequences are derived with respect to minimizing the mean square error (MSE). Furthermore, an iterative technique for symbol detector is proposed to cancel the effects of the embedded training on symbol recovery. Simulation results confirm the effectiveness of the proposed approach, even for the rapidly time-varying channel with a great Doppler frequency.
Supervised Dictionary Learning for Inferring Concurrent Brain Networks

Task-based fMRI (tfMRI) has been widely used to explore functional brain networks via predefined stimulus paradigm in the fMRI scan. Traditionally, the general linear model (GLM) has been a dominant approach to detect task-evoked networks. However, GLM focuses on task-evoked or event-evoked brain responses and possibly ignores the intrinsic brain functions. In comparison, dictionary learning and sparse coding methods have attracted much attention recently, and these methods have shown the promise of automatically and systematically decomposing fMRI signals into meaningful task-evoked and intrinsic concurrent networks. Nevertheless, two notable limitations of current data-driven dictionary learning method are that the prior knowledge of task paradigm is not sufficiently utilized and that the establishment of correspondences among dictionary atoms in different brains have been challenging. In this paper, we propose a novel supervised dictionary learning and sparse coding method for inferring functional networks from tfMRI data, which takes both of the advantages of model-driven method and data-driven method. The basic idea is to fix the task stimulus curves as predefined model-driven dictionary atoms and only optimize the other portion of data-driven dictionary atoms. Application of this novel methodology on the publicly available human connectome project (HCP) tfMRI datasets has achieved promising results.
Mass detection in digital mammograms using Gabor filter bank

Digital Mammograms are currently the most effective imaging modality for early detection of breast cancer but the number of false negatives and false positives is high. Mass is one type of breast lesion and the detection of masses is highly challenged problem. Almost all methods that have been proposed so far suffer from high number of false positives and false negatives. In this paper, a method for detecting true masses is presented, especially, for the reduction of false positives and false negatives. The key idea of the proposal is the use of Gabor filter banks for extracting the most representative and discriminative local spatial textural properties of masses that are present in mammograms at different orientations and scales. The system is evaluated on 512 (256 normal+256 true mass) regions of interests (ROIs) extracted from digital mammograms of DDSM database. We performed experiments with Gabor filter banks having different numbers of orientations and scales to find the best parameter setting. Using a powerful feature selection technique and support vector machines (SVM) with 10-fold cross validation, we report to achieve Az = 0.995±0.011, the area under ROC. Comparison with state-of-the-art techniques suggests that the proposed system outperforms similar methods, which are based on texture description, and the difference is statistically significant.
Impact of network conditions on QoS routing algorithms

QoS Routing has been studied to provide evidence that it can increase network utilization compared to routing that is insensitive to QoS traffic requirements. This paper aims to discover in detail the relationship between the routing algorithms and network conditions (traffic load and the scale of the network topology) both in fixed and random source-destination pairs in different topologies. We demonstrate that network scale affects the performance, and give evaluation of different locations of source-destination pairs. We also point out the importance of using length of route as a metric to evaluate algorithms. This has seldom been used in previous simulation studies.
Real-time Gesture Recognition with the Perseus

Interpersonal communication involves more than simply spoken information. Gestures are commonly used to more eeciently and precisely communicate. An important gesture because of its descriptive power and frequency of use is pointing. To produce a more natural and powerful human-robot interface, we have developed a purposive visual architecture called Perseus and have used it to locate objects a person is pointing to. With Perseus, in real-time, we are able to determine when a person enters the scene, track the relevant parts of the person including the hands and head, and recognize when she is pointing. Once the person points, the object pointed to is located. The Perseus architecture allows knowledge about the task and context to be used at all levels of visual analysis for improved performance. This knowledge is explicitly represented in the Perseus system to facilitate the extension of Perseus to other tasks and environments. In this paper we describe Perseus and how it is used to solve this task. Previous work on Perseus is extended by describing a more sophisticated representation of the person and superior segmenting and tracking methods. Experiments showing the success of the Perseus system with numerous naive users in varied environments is presented.