Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-time Processing of Multiplexed Data Acquired via Flexible Active Electrode Arrays

A new generation of flexible active electronics have been developed to record from biological tissue. These electrodes conform to biological surfaces, such as the brain, and can provide recordings of high spatial resolution. However, the previous data acquisition system for these electrodes suffered from limitations regarding the collection and storage of data. Multiplexed data was recorded from the electrodes and then it was saved. It possessed a limited display which provided little feedback to researchers during experiments. The data was later converted and loaded into MATLAB (The MathWorks, Natick, MA) and processing was done offline. A real-time online processing system has been built for the new generation of flexible active electronics. Raw voltage inputs are demultiplexed and filtered in real-time. Evoked response averages are generated for each electrode and a GUI (graphical user interface) is generated that displays a color map of real-time features. A 2D (2-dimensional) sparse noise plot is implemented. Demultiplexed data is saved to disk to allow for offline analysis. Experimenters can now use provided displays to analyze the results of experimentation in real-time and to adjust experimental parameters accordingly. The system has been tested and is being prepared to be used in future experiments.
TOWARDS A COLLABORATIVE KNOWLEDGE DISCOVERY SYSTEM FOR ENRICHING SEMANTIC INFORMATION ABOUT RISKS OF GEOSPATIAL DATA

The aim of this research is to design and implement a knowledge discovery system that facilitates, using a web 2.0 collaborative approach, the identification of new risks of geospatial data misuse based on a contributed knowledge repository fed by application domain experts. [ Context/Motivation ] This research is motivated by the irregularity of risk analysis efforts and the poor semantic of the collected information about risks. In the context of risk analysis during geospatial database design, the knowledge about risks of geospatial data misuse is typically held by domain application experts. The collection and record of that knowledge are usually considered as optional activities. It is usually performed through face-to-face risk assessment meetings and reports. Such techniques end up by restricting the scope of risk analysis to a set of obvious risks usually already identified. Besides, little consideration is devoted to the storage of risk information in an appropriate format for automatic reasoning and new risk information discovery. As a consequence, many foreseeable risky aspects inherent to the data remain overlooked leading to ill-defined specification and faulty decisions. [ Principal ideas/results ] In this paper, we present a contributed knowledge discovery system that aims at enriching the semantic information about risks of geospatial data misuse in order to identify foreseeable risks. The proposed web-based system relies on a systematic and more active involvement of users in risk analysis. The approach consists of 1) providing an overview of the related work in the domains of risk analysis within the context of geospatial database design, 2) presenting an ontology-based knowledge discovery system that helps experts in risks identification based on an upper-level risk ontology and on a structured representation of the domain-specific knowledge and, 3) presenting the components of the proposed system architecture and how it may be implemented and used in practice, and finally 4) we conclude by discussing the approach. [ Contribution ] A major outcome is that the proposed platform can help discovering implicit domain knowledge, and facilitating the identification of foreseeable risks of geospatial data misuse in a way to preventively improve the resulting fitness-for-use.
2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops Identifying and Measuring Stressors Present in Pre-hospital Care

In this paper we introduce and outline the results of a survey designed to identify the demands and stressors experienced by pre-hospital care providers. We then present a controlled study that investigates how the stressors identified as being present in pre-hospital care can be measured using unobtrusive sensors measuring heart rate and skin conductance. We highlight how these findings can have important implications for future work regarding the development of systems for pre­ hospital care providers.
Current BCI technologies in brain engineering

Various neuroimaging modalities have appeared to acquire brain signals for developing a brain-computer interface (BCI). In this article, we review studies on different modalities including both invasive and non-invasive techniques for the implementation of BCIs, for brain signals detection, decoding, feature extraction, and classification. We discuss their advantages, disadvantages, and implementation issues to design of BCIs. Finally, we focus on fundamental principles, recent developments, and applications by using optical imaging as a promising modality for BCI applications.
MATERNAL PARENTING AND DEPRESSION IN FEMALE VICTIMS OF DOMESTIC VIOLENCE OF RURAL AREAS OF PUNJAB, PAKISTAN

Objective: To explore the relationship of domestic violence with maternal par- enting and depression and further to find the predictors of depression in fe- male victims of domestic violence in rural area of Lahore, Punjab-Pakistan. Methodology: The current research was conducted in rural areas of Punjab including the villages of Qila Staar Shah, Kamahan, Attari, Badho Pulli, Rachna Town and Hadiyala Virkin. The sample included 200 married women with age range between 20-45 years. Purposive sampling technique was used for data collection. Domestic violence (both physical and psychological) was measured by The Karachi Domestic Violence Screening Scale (KDVSS; 2001), maternal de- pression by Center for Epidemiologic Studies Depression (CES-D; Radloff, 1977) and maternal parenting style by Parenting Practices Questionnaire (PPQ; Robin- son, 1995). Data were analyzed using correlation and regression analyses. Results: There was a significant positive correlation of domestic violence with authoritarian parenting (r =.65, p <.001) and depression (r =.35, p <.01) in the participants of study. Domestic violence was contributing 49%, 56% and 47% in the authoritarian parenting, authoritative parenting and depression in the victims of domestic violence, respectively. Conclusion: Domestic violence was associated with authoritarian parenting and depression in victims.
Development of universal gripping adapters: Sterile coupling of medical devices and robots using robotic fingers

In this article a new universal gripping adapter for a medical macro-micro telemanipulation system for the middle ear surgery is presented. The telemanipulation system should enable the surgeon to precisely manipulate tissue in interventions at the middle ear like the stapedotomy. The system proposed by Entsfellner et al. [1] contains of a joystick console, a robotic carrier system (macro-manipulator) with a robotic hand and three fingers, an active gripping adapter, a surgical micro-manipulator, and a microsurgical instrument. Using the adapter, no further modifications at the medical approved carrier system are necessary. The carrier system holds and guides the micro-manipulator with help of its robotic fingers. Both robotic systems can be sterilized using separate sterile foils. So the micro-manipulator can be attached to or removed from the carrier system at any time during an operation, and, if desired, exchanged with another device like, e.g., an endoscope. Furthermore, the control of the micro-manipulator by slightly moving the robotic fingers is possible and so no further cables or connections are necessary to transmit the control signals from the macro to the micro-manipulator. The surgeon can control the whole system telemanipulated by an OR (operating room) table mounted joystick console with touch display.
Effects of Resin Binder on Characteristics of Sintered Aluminum–Copper Nanopaste as High-Temperature Die-Attach Material

This paper aims to report an aluminum–copper (Al–Cu) die-attach system as a Pb-free alternative for electronic applications. The Al–Cu die-attach system is formulated by a mixture of Al and Cu nanoparticles and a constituent of organic compounds and sintered at 380 °C ± 10 °C without any pressure application. The effects of organic additives on physical, structural, and electrical properties are investigated to understand the die-attach quality of Al–Cu nanopaste. The formation of CuAl2 phase is discovered, which confirmed the joining of Al and Cu nanoparticles in sintered nanopaste layer. Solid-state fusion of Al and Cu can be seen in scanning electron microscope (SEM) analysis. Energy dispersive X-ray (EDX) analysis demonstrates sample of 0.25-g V006A has the lowest oxygen element at 15.36%. Sample with 0.25-g organic additives exhibits the smallest crystallite size and electrical resistivity, which were 8.15 nm and $21~\mu \Omega \cdot \text {cm}$ , which is favorable for high-temperature applications.
Large-Scale Tolerance Analysis

In this paper, we present an approach to the tolerance analysis of non-linear, time-invariant systems depending on statistical distributed parameters. The approach is based on an approximation of the performance function over the parameter domain which allows the determination of statistical properties of this function and provides appropriate information for the design adjustment. We show that parameter sensitivities and even higher order Taylor expansions of the performance function are not applicable in general, and propose an interpolation approach using power series. Finally, we give some remarks on the incorporation of our method in numerical simulation programs (e.g. spice), especially we discuss the reduction of computational efforts to outperform the Monte Carlo method, even for a larger number of parameters.
Improving the Multilingual User Experience of Wikipedia Using Cross-Language Name Search

Although Wikipedia has emerged as a powerful collaborative Encyclopedia on the Web, it is only partially multilingual as most of the content is in English and a small number of other languages. In real-life scenarios, non-English users in general and ESL/EFL users in particular, have a need to search for relevant English Wikipedia articles as no relevant articles are available in their language. The multilingual experience of such users can be significantly improved if they could express their information need in their native language while searching for English Wikipedia articles. In this paper, we propose a novel cross-language name search algorithm and employ it for searching English Wikipedia articles in a diverse set of languages including Hebrew, Hindi, Russian, Kannada, Bangla and Tamil. Our empirical study shows that the multilingual experience of users is significantly improved by our approach.
Bayesian Degree-Corrected Stochastic Block Models for Community Detection

Community detection in networks has drawn much attention in diverse fields, especially social sciences. Given its significance, there has been a large body of literature with approaches from many fields. Here we present a statistical framework that is representative, extensible, and that yields an estimator with good properties. Our proposed approach considers a stochastic blockmodel based on a logistic regression formulation with node correction terms. We follow a Bayesian approach that explicitly captures the community behavior via prior specification. We further adopt a data augmentation strategy with latent Polya-Gamma variables to obtain posterior samples. We conduct inference based on a principled, canonically mapped centroid estimator that formally addresses label non-identifiability and captures representative community assignments. We demonstrate the proposed model and estimation on real-world as well as simulated benchmark networks and show that the proposed model and estimator are more flexible, representative, and yield smaller error rates when compared to the MAP estimator from classical degree-corrected stochastic blockmodels.
The problem with ranking ensembles based on training or validation performance

The main purpose of this study was to determine whether it is possible to somehow use results on training or validation data to estimate ensemble performance on novel data. With the specific setup evaluated; i.e. using ensembles built from a pool of independently trained neural networks and targeting diversity only implicitly, the answer is a resounding no. Experimentation, using 13 UCI datasets, shows that there is in general nothing to gain in performance on novel data by choosing an ensemble based on any of the training measures evaluated here. This is despite the fact that the measures evaluated include all the most frequently used; i.e. ensemble training and validation accuracy, base classifier training and validation accuracy, ensemble training and validation AUC and two diversity measures. The main reason is that all ensembles tend to have quite similar performance, unless we deliberately lower the accuracy of the base classifiers. The key consequence is, of course, that a data miner can do no better than picking an ensemble at random. In addition, the results indicate that it is futile to look for an algorithm aimed at optimizing ensemble performance by somehow selecting a subset of available base classifiers.
An efficient instance hiding scheme

Delegating computation, which is applicable to many practical contexts such as cloud computing or pay-TV system, concerns the task where a computationally weak client wants to securely compute a very complex function f on a given input with the help of a remote computationally strong but untrusted server. The requirement is that the computation complexity of the client is much more efficient than that of f, ideally it should be in constant time or in NC0. This task has been investigated in several contexts such as instance hiding, randomized encoding, fully homomorphic encryption, garbling schemes, and verifiable scheme. In this work, we specifically consider the context where only the client has an input and gets an output, also called instance hiding. Concretely, we first give a survey of delegating computation, we then propose an efficient instance hiding scheme with passive input privacy. In our scheme, the computation complexity of the client is in NC0 and that of the server is exactly the same as the original function f. Regarding communication complexity, the client in our scheme just needs to transfer 4|f| + |x| bits to the server, where |f| is the size of the circuit representing f and |x| is the length of the input of f.
Simultaneous Learning of Action and Space Hierarchies in Reinforcement Learning

This paper presents a new method for the autonomous construction of hierarchical action and state representations in reinforcement learning, aimed at accel erating learning and extending the scope of such systems. In this approach, the agent uses information acquired while learning one task to discover subgoals for similar tasks by analyzing the learned policy using Monte Carlo sampling. The agent is able to transfer this knowledge to subsequent tasks and to accelerate learning by creating corresponding subtask policies as abstract actions (options). At the same time, the subgoal actions are used to construct a more abstract state representation using action-dependent sta te space partitioning, adding a new level to the state space hierarchy. This level serves as the initial representation for new learning tasks. In order to ensure that tasks are learnable, value functions are built simultaneously at different levels of hierarchy and inconsistencies are used to identify actions to be used to refine relevant portions of the abstract state space.
Corporate Financial Policies and Informed Trading

This paper uses informed trading to examine information content of earnings and dividends announcements. The major innovation of this research design is a combination of assessing information contents of dividends and earnings announcements based on their surprises and estimating informed trading to understand market reaction. The premise of this study is that if announcements contain information it should reflect on traders’ trading patterns. Based on this premise, I find evidence of significant change in informed trading for dividend announcements but not for earnings announcements when the announcements are made concurrently, an issue which the existing literature does not address. I also find significant differences in informed trading when dividend announcements contain surprises but not when dividends announcements contain no surprises, which the extent literature provides no empirical evidence. These results collectively imply that dividends are better signal than earnings to market participants. More importantly, contrary to recent findings that suggest a decline in information content of dividends, the results confirm significant information content of dividends. This study highlights the importance of identification and control of information environment and disaggregating dividends announcements based on its surprises while studying information content of announcement.
Sole based tactile information display for visually impaired pedestrian navigation

Well understanding guidance instructions when using an electronic orientation aids is vital for people with visual impairments to reach destinations or avoid obstacles and hazards. In this paper we describes a study about how to make use of a tactile sole with 5 vibrators to present spatial information for visually impaired people while using an electronic orientation aid. We designed and evaluated a sole based tactile information display that delivers directional information in real time through shoes. A Wizard-of-Oz preliminary evaluation with 2 blindfolded subjects has been conducted. Result shows that designed haptic sole is a safer, non-intrusive and promising approach that can be used to simplify guidance instructions.
NOTES AND CORRESPONDENCE Improvement of Advanced Microwave Sounding Unit Tropical Cyclone Intensity and Size Estimation Algorithms

Previous work, in which Advanced Microwave Sounding Unit (AMSU) data from the Atlantic Ocean and east Pacific Ocean basins during 1999–2001 were used to provide objective estimates of 1-min maximum sustained surface winds, minimum sea level pressure, and the radii of 34-, 50-, and 64-kt (1 kt 0.5144 ms 1 ) winds in the northeast, southeast, southwest, and northwest quadrants of tropical cyclones, is updated to reflect larger datasets, improved statistical analysis techniques, and improved estimation through dependent variable transforms. A multiple regression approach, which utilizes best-subset predictor selection and cross validation, is employed to develop the estimation models, where the dependent data (i.e., maximum sustained winds, minimum pressure, wind radii) are from the extended best track and the independent data consist of AMSU-derived parameters that give information about retrieved pressure, winds, temperature, moisture, and satellite resolution. The developmental regression models result in mean absolute errors (MAE) of 10.8 kt and 7.8 hPa for estimating maximum winds and minimum pressure, respectively. The MAE for the 34-, 50-, and 64-kt azimuthally averaged wind radii are 16.9, 13.3, and 6.8 n mi (1 n mi 1852 m), respectively.
From [R, G, B] to Surface Reflectance: Computing Color Constant Descriptors in Images

This paper addresses the issue of color constancy, which is the the perceptual ability of the human visual system to assign the same colors to objects under different lighting conditions. We propose a method, based on finite-dimensional linear models of reflectance and illumination, which allows the transformation [R,G,B] images into color constant images. In contrast to previous work, we show that good results can be obtained using a 3-receptor system and some knowledge about the spectral properties of natural materials and illuminants. In the method developed, an estimate of illuminant in the scene is computed, which allows the computation of color constant descriptors of the pixel values in the image. In addition, we show a method of computing the actual reflectances of the materials in the scene out of the computed color descriptors.
Performance Analysis of Some New CFAR Detectors under Clutter

In order to improve the radar’s detection performance under Pearson distributed background clutte r , the problem of designing CFAR detectors is studied in this paper. Three kind s of distributed fuzzy CA scheme and two kinds of OS scheme are proposed. The numerical simulations prove that CA CFAR detector based on algebraic sum fusion rule provides more robust detection performance in homogenous background clutter , CASO based on algebraic-sum rule shows detection improvement in multi-target environment, CAGO based on MIN rule provides better false-alarm-control capacity und er clutter-edge condition , and OSSO CFAR offers better detection performance than OS CFAR in multiple interrupt- target situation.
Contraction of Riccati flows applied to the convergence analysis of the max-plus curse of dimensionality free method

Max-plus based methods have been recently explored for solution of first-order Hamilton-Jacobi-Bellman equations by several authors. In particular, McEneaney's curse-of-dimensionality free method applies to the equations where the Hamiltonian takes the form of a (pointwise) maximum of linear/quadratic forms. In previous works of McEneaney and Kluberg, the approximation error of the method was shown to be O(1/(Nτ))+O(√τ) where τ is the time discretization step and N is the number of iterations. Here we use a recently established contraction result of the indefinite Riccati flow in Thompson's metric to show that under different technical assumptions, still covering an important class of problems, the total error incorporating a pruning procedure of error order τ2 is O(e-αNτ)+O(τ) for some α > 0 related to the contraction rate of the indefinite Riccati flow.
An adaptive classifier design for high-dimensional data analysis with a limited training data set

Proposes a self-learning and self-improving adaptive classifier to mitigate the problem of small training sample size that can severely affect the recognition accuracy of classifiers when the dimensionality of the multispectral data is high. This proposed adaptive classifier utilizes classified samples (referred to as semilabeled samples) in addition to original training samples iteratively. In order to control the influence of semilabeled samples, the proposed method gives full weight to the training samples and reduced weight to semilabeled samples. The authors show that by using additional semilabeled samples that are available without extra cost, the additional class label information may be extracted and utilized to enhance statistics estimation and hence improve the classifier performance, and therefore the Hughes phenomenon (peak phenomenon) may be mitigated. Experimental results show this proposed adaptive classifier can improve the classification accuracy as well as representation of estimated statistics significantly.
Learning Activity-dependent Dynamic Bayesian Networks with Qualitative Constraints for Activity Recognition

In this paper, we propose solutions for learning activity-dependent dynamic Bayesian network (DBN) for human activity recognition. As our model is designed to capture the underlying state dependencies among multiple features, a DBN with unique structure and parametrization is learned for each activity to encode its speciﬁc state dependencies. To alleviate the common problem of lack of sufﬁcient training data, we propose a new structure learning algorithm, constrained structural EM (CSEM), to learn the activity-dependent model structures combining the training data with qualitative and generic domain knowledge. Expressed as the