Miracle-dz/UniFAR-FTUs huggingface dataset:
ECG Authentication System Design Based on Signal Analysis in Mobile and Wearable Devices

We propose a practical system design for biometrics authentication based on electrocardiogram (ECG) signals collected from mobile or wearable devices. The ECG signals from such devices can be corrupted by noise as a result of movement, signal acquisition type, etc. This leads to a tradeoff between captured signal quality and ease of use. We propose the use of cross correlation of the templates extracted during the registration and authentication stages. The proposed approach can reduce the time required to achieve the target false acceptance rate (FAR) and false rejection rate (FRR). The proposed algorithms are implemented in a wearable watch for verification of feasibility. In the experiment results, the FAR and FRR are 5.2% and 1.9%, respectively, at approximately 3 s of authentication and 30 s of registration.
Organization and Variation Analysis of 5S rDNA in Different Ploidy-level Hybrids of Red Crucian Carp × Topmouth Culter

Through distant crossing, diploid, triploid and tetraploid hybrids of red crucian carp (Carassius auratus red var., RCC♀, Cyprininae, 2n = 100) × topmouth culter (Erythroculter ilishaeformis Bleeker, TC♂, Cultrinae, 2n = 48) were successfully produced. Diploid hybrids possessed 74 chromosomes with one set from RCC and one set from TC; triploid hybrids harbored 124 chromosomes with two sets from RCC and one set from TC; tetraploid hybrids had 148 chromosomes with two sets from RCC and two sets from TC. The 5S rDNA of the three different ploidy-level hybrids and their parents were sequenced and analyzed. There were three monomeric 5S rDNA classes (designated class I: 203 bp; class II: 340 bp; and class III: 477 bp) in RCC and two monomeric 5S rDNA classes (designated class IV: 188 bp, and class V: 286 bp) in TC. In the hybrid offspring, diploid hybrids inherited three 5S rDNA classes from their female parent (RCC) and only class IV from their male parent (TC). Triploid hybrids inherited class II and class III from their female parent (RCC) and class IV from their male parent (TC). Tetraploid hybrids gained class II and class III from their female parent (RCC), and generated a new 5S rDNA sequence (designated class I–N). The specific paternal 5S rDNA sequence of class V was not found in the hybrid offspring. Sequence analysis of 5S rDNA revealed the influence of hybridization and polyploidization on the organization and variation of 5S rDNA in fish. This is the first report on the coexistence in vertebrates of viable diploid, triploid and tetraploid hybrids produced by crossing parents with different chromosome numbers, and these new hybrids are novel specimens for studying the genomic variation in the first generation of interspecific hybrids, which has significance for evolution and fish genetics.
Neurobiology of mammalian olfactory learning that occurs during sensitive periods

This review examines the organizational principles underlying olfactory learning in three specialized contexts that occur during sensitive periods of enhanced neural plasticity and emphasizes some of their common features. All three forms of olfactory learning are associated with neural changes in the olfactory bulb (OB) at the first stage of sensory processing. These changes require the association of the olfactory and somatosensory signals in the OB. They all depend on somatosensory stimula- tion-induced release of noradrenaline that induces structural and functional changes at mitral-granule cell reciprocal synapses in the OB, resulting in increases in inhibitory transmission. In the accessory olfactory bulb, this represents the enhanced self-inhibition of mitral cells, which selectively disrupts the transmission of the mating male's pregnancy-blocking signal at this level. In contrast, an extensive network of secondary dendrites of mitral cells in the main olfactory bulb probably results in a sharpening of the odor-induced pattern of activity, due to increases in lateral inhibition, leading to offspring recognition in sheep and neonatal learning in rats and rabbits. These findings show that inhibitory interneurons play a critical role in olfactory learning. Further work on how these neurons shape olfactory circuit function could provide important clues to understand memory func- tions of interneurons in other systems. Moreover, recent research has suggested that three forms of olfactory learning are con- trolled by synergistic, redundant, and distributed neural mechanisms. This has general implications regarding the mechanisms that may contribute to the robustness of memories (Current Zoology 56 (6): 819-833, 2010).
The present and future of mental health care in Japan

Although the shift from hospital-based psychiatric care to community care began in 1960's, Japan remains the only country in the developed world where deinstitutionalization has yet to significantly occur. As a consequence, elderly and chronic patients who have been living in Japanese psychiatric hospitals for a long time are the typical psychiatric inpatients. Japan has a universal health care insurance system that mental health care should be available and affordable, at least at a minimum level, to all citizens. Recently, however, the health and social security system has been eroded by a stagnant economy and the medical and social demands of an aging population. At the beginning of the 21st century, Japan will have the largest aging population that the world has ever witnessed. It is estimated that by 2010 one-third of the entire household members will be 65 years of age or older (Japan Ministry of Health and Welfare, 1997). The more elderly the population of a society, the higher prevalence of dementia they will have. By 2000, it is estimated that those elderly with dementia will number 1.55 million (Japan Ministry of Health and Welfare, 1999). Where the mentally ill can be cared for, outside of hospitals, is of critical policy concern in Japan.
On worst case performance of collision avoidance systems

Automotive Collision Avoidance and Mitigation (CA/CM) systems help drivers to avoid collisions through autonomous interventions by braking or steering. If the decision to intervene is made too early, the intervention can become a nuisance to the driver and if the decision is made too late, the safety benefits of the intervention will be reduced. Decision timing is thus crucial for the successful operation of a CA/CM system. The decision to intervene is commonly taken when a threat function reaches a specific threshold. The dimensionality of the input state space for the threat function is in general very large making exhaustive evaluation in real vehicles expensive and time consuming. This paper presents a method for efficient estimation of a lower bound on CA/CM system performance, i.e. the worst case performance. The method is applied on an example system for a set of longitudinal single object escape scenarios. Results show significant variation in worst case decision timing across scenarios.
Multi Facet Learning in Hilbert Spaces

We extend the kernel based learning framework to learning from linear functionals, such as partial derivatives. The learning problem is formulated as a generalized regularized risk minimization problem, possibly involving several different functionals. We show how to reduce this to conventional kernel based learning methods and explore a specific application in Computational Condensed Matter Physics.
Performance Enhancement of Subspace Detection With a Diversely Polarized Antenna

Based on a diversely polarized antenna, we address the problems of adaptive detection and performance enhancement in partially homogeneous environments where the test and training data samples share the same noise covariance matrix up to an unknown scaling factor. The matched subspace detector and adaptive subspace detector are employed to handle the detection problem for the cases of known and unknown noise covariance matrix structure, respectively. The performance of the two detectors is evaluated in terms of their probabilities of false alarm and detection. In particular, a waveform design algorithm to enhance the detection performance of the two detectors is proposed. A numerical analysis is presented to demonstrate the potential performance improvement obtained with this algorithm.
An embodied agent that sends nonverbal conversational signals consistent with those of the partner during a dialogue

In this article, we discuss how to control nonverbal conversational signals (NCS) including eye gazes, nods and facial expressions displayed by a pair of embodied agents in dialogue. Works on information agent proposes to employ embodied agents for presenting information to the viewers. Since embodied agents have faces and bodies in order to be "embodied", the viewers read various meanings in the NCS on the faces and the bodies of the agents, even if the agents are not actually designed to send NCS to their viewers but only to speak. In order to cope with this problem, it has been proposed in some previous work to make the NCS appropriate for the speech utterances or the task of the agent. However, when two embodied agents are in dialogue, we also need to consider interdependences between NCS displayed by those agents. We discuss how to maintain those interdependencies for producing dialogues between a pair of embodied agents for information presentation.
MOBILE COMPUTING WITH WEB 2.0: CURRENT STATE-OF-THE-ART, ISSUES AND CHALLENGES

The mobile computing with web 2.0 is considered by many as the next big wave riding in the field of mobile computing. Many industry leaders in the web, telecom, and cell phone manufacturing arena are taking active interest in exploiting the power of web 2.0 and applying it on the mobile platform to bring to the users a set of new and innovative services. With the advanced mobile web services, there are challenges for the design and implementation of the user interfaces for these services. Further, issues such as the lack of open access from telecom carriers and lack of open standards for application development represent major hindrances to mobile web 2.0 becoming a true success in the mobile computing world. This paper describes essential characteristics of mobile web 2.0, mobile web 2.0 applications, future possibilities and challenges of mobile computing with Web 2.0.
Adaptive Variable Structure Maneuvering Control and Vibration Reduction of Three-axis Stabilized Flexible Spacecraft

This paper proposes a robust control algorithm for stabilization of a three-axis flexible spacecraft in the presence of model uncertainty, external disturbances and control input nonlinearities. This control algorithm is based on variable structure output feedback control design technique that explicitly accounts for the control input nonlinearities in the stability analysis. Asymptotically and exponentially stable design methods are investigated for constructing the controller to stabilized uncertain system with input nonlinearities. Two kinds of the controller are presented that both ensures the global reaching condition of the sliding mode of the spacecraft dynamics system. Moreover, in the sliding mode, the dynamics system under study still bears the insensitivity to the uncertainties and disturbances as the system with linear input. An additional attractive feature of the control method is that the structure of controller is independent of the elastic mode dynamics of the spacecraft, since in practice the measurement of flexible modes is not easy or feasible. It is also shown that an adaptive version of the proposed controller is achieved through removing requirements of knowing the bounds of the uncertainties and perturbations in advance. Furthermore, a modified adaptation control law is given to improve the adaptive performances such that a new controller is designed which can guarantee the boundedness of the closed-loop system. Numerical simulations show that the precise attitude and vibration suppression can be accomplished using the derived controller for both cases with and without adaptive control.
Information Content of Advertising: Theory and Empirical Evidence

We propose a theoretical model of advertising content, based on an informationpersuasion trade-off, where the “persuasive” content of an advertisement is defined as the content that is not objective information. Brands must allocate available time in an advertisement between imparting objective information about the product’s attributes and other persuasive methods. Our theoretical model gives rise to a structural ordered probit model, which we estimate with data from the OTC Analgesics Industry. We test four predictions of the model. We find that stronger vertical differentiation is positively associated with the delivery of more product information in a brand’s advertisements: Brands with higher levels of quality (on each of the quality dimensions for which we have data) will include more information cues in their advertisements. We also find that comparative advertisements contain significantly more product information relative to noncomparative advertisements. Brands with higher market shares and brands competing against generic substitutes with higher market shares have less information content in their advertising. The proposed method of measuring and analyzing information content of advertising extends and improves upon existing techniques for measuring advertising information.
Biocellulose based materials for organic field effect transistors

Biocellulose and biocellulose composites are being investigated as substrates and as gate dielectrics for organic field effect transistors. Spin coated films of regioregular poly(3-hexylthiophene) (RR-P3HT) and thermally evaporated pentacene were used as semiconducting materials. Preliminary results on device characteristics are reported.
Extraction of Dynamics-correlated Factors from Image Features in Pushing Motion of a Mobile Robot

It is important for autonomous robots to improve capability of extracting information that is relevant to their motions. This paper presents an extraction and estimation of factors that affect behavior of object from image features in object pushing manipulation by a two-wheeled robot. Motions of image features (SIFT keypoints) are approximated with variance. By detecting correlation between the variance and positions of the keypoints, the robot can detect keypoints whose positions affect behaviour of some keypoints. Position information of the keypoints is expected to be useful for the robot to decide its pushing motion. The proposed scheme was verified in experiment with a camera-mounted mobile robot which has no pre-defined knowledge about its environment.
When spatial and temporal locality collide: the case of the missing cache hits

Even the simplest hardware, running the simplest programs, can behave in the strangest of ways. Tracking down the cause of a performance anomaly without the complete hardware reference of a processor is a prime example of black-box architectural exploration. When doubling the work of a simple benchmark program, that was run on a single core of Tilera's TILEPro64 processor, did not double the number of consumed cycles, a mystery was unveiled. After ruling out different levels of optimization for the two programs, a cycle-accurate simulation attributed the sub-optimal performance to an abnormally high number of L1 data cache misses. Further investigation showed that the processor stalled on every Read-After-Write instruction sequence when the following two conditions were met: 1) there are 0 or 1 instructions between the write and the read instruction and 2) the read and the write instructions target distinct memory locations that share an L1 cache line. We call this performance pitfall a RAW hiccup. We describe two countermeasures, memory padding and the explicit introduction of pipeline bubbles, that sidestep the RAW hiccup. This experience paper serves as a useful troubleshooting guide for uncovering anomalous performance issues when the hardware design under study is unavailable.
Interprofessional Collaborative Practice to Improve Patient Outcomes: A Pilot Study

This project focused on a pilot project implemented during the 2013-2014 academic year. The overall purpose was to facilitate interprofessional collaborative practice innovations, through the development of leadership, core competencies, and the use of technology, especially among nurses. Nursing, medicine, and physician assistant students were educated on the IOM competencies for interprofessional teams and the core competencies identified by the Interprofessional Education Collaborative Expert Panel [1] to develop knowledge, skills, and attitudes needed to practice in the collaborative practice environments. The project addressed four goals: Develop faculty expertise and leadership in interprofessional collaborative practice to provide a current, high quality education to nursing, physician assistant and medical students; Implement a culturally responsive and respectful collaborative interprofessional practice curriculum to prepare nurses, physician assistants, and medical students to deliver high quality, efficient, team-based care in a dynamically evolving environment; Focus interprofessional collaborative practice education on models and practices that lead to improvement in patient outcomes; and Evaluate the program and disseminate best practices. Findings from this pilot include strategies to engage different health professions’ students and faculty, partnering with community agencies, building an effective interprofessional team to guide the project, and seeking funding for extension and expansion of the offerings.
A Novel Method to Fix Numbers of Hidden Neurons in Deep Neural Networks

In this paper, we propose a novel method which can automatically find the preferable hidden neuron numbers in deep neural networks. This method is completed by two cooperating algorithms: Principle Components Analysis (PCA) and Reinforcement Learning (RL). PCA is used to find a range of hidden neuron numbers, and RL is applied to search a better number of hidden neurons and update the searching points. The training process is layer wisely conducted and finally formed a deep neural network. Testing on the MNIST dataset shows, the algorithm can automatically fix the number of hidden neurons layer wisely in deep neural networks and achieve an accuracy of 98.24%, which shows that our method is effective in selection of hidden neuron numbers.
A Physical Layer Implementation of Reconfigurable Radio

The next generation of wireless communications will demand the use of software radio technology as the basic architecture to support multi-standard, multi-mode and future-proof radio designs. Software-defined radios are configurable devices in which the physical layer can be reprogrammed to support various standards. Field programmable architectures provide a suitable platform to achieve such run-time reconfigurations of the physical layer of the radio. This thesis explores the use of FPGAs in the design of reconfigurable radios. The results presented here demonstrate how FPGAs can be used to provide the flexibility, performance, efficiency and better resource utilization while meeting the speed and area constraints set by a particular design. The partial reconfiguration feature available in the state-of-the art FPGAs has been exploited to implement the baseband physical layer of reconfigurable radio which can be altered to support various modulations schemes for different wireless standards. The design flow for partial reconfiguration along with the implementation results on two different FPGA platforms is presented. The experiments presented in this thesis make use of System Generator for DSP, a productivity tool from Xilinx, to design and to simulate system-level models in a MATLAB/Simulink environment, and to obtain timing and resource utilization results before implementing the design on actual hardware. iii Acknowledgements I would like to express my sincere gratitude to Dr. Peter Athanas for being my advisor and for providing me with the opportunity to conduct research in the field of my interest. His continual support and guidance throughout my research work in Configurable Computing Laboratory at Virginia Tech made this thesis a success. I thank Dr. Mark Jones and Dr. Jeffrey Reed for their support. I appreciate their willingness to serve on my committee. I am really grateful to all the members of CCM Lab, especially the SecCom team, who helped me at every stage of my research. I am indebted to Dr. and Jason Zimmerman who helped me get started on this project. I would also like tooperation. Finally, I dedicate my thesis to my grandfather and parents for their endless support and blessings without which I wouldn't be where I am in life.
Near-Optimal Active Learning of Multi-Output Gaussian Processes

This paper addresses the problem of active learning of a multi-output Gaussian process (MOGP) model representing multiple types of coexisting correlated environmental phenomena. In contrast to existing works, our active learning problem involves selecting not just the most informative sampling locations to be observed but also the types of measurements at each selected location for minimizing the predictive uncertainty (i.e., posterior joint entropy) of a target phenomenon of interest given a sampling budget. Unfortunately, such an entropy criterion scales poorly in the numbers of candidate sampling locations and selected observations when optimized. To resolve this issue, we first exploit a structure common to sparse MOGP models for deriving a novel active learning criterion. Then, we exploit a relaxed form of submodularity property of our new criterion for devising a polynomial-time approximation algorithm that guarantees a constant-factor approximation of that achieved by the optimal set of selected observations. Empirical evaluation on real-world datasets shows that our proposed approach outperforms existing algorithms for active learning of MOGP and single-output GP models.
Data Modelling with Gaussian Process in Sensor Networks for Urban Environmental Monitoring

In this paper, the multidimensional output Gaussian process (GP) is applied to model urban environmental data collected by sensor networks. Measurements from sensors at different locations are correlated. Moreover, we observe that the pollution level in urban area is highly coupled with human activities and shows periodic patterns accordingly. Based on these observations, we discuss the design of mean and kernel functions with two approaches: (1) composed kernel and maximum likelihood estimation of hyper-parameters, (2) Wiener-Khinchin theorem based approximation of sample covariances. To validate the models, the accuracy of interpolations given by different approaches are compared. The experimental results show that, for the application of interpolation, the dependent GP with the approximated sample covariances as kernels can provide better performance than the independent GP model with composed kernels.