Miracle-dz/UniFAR-FTUs huggingface dataset:
Association Mining of the Brain Data: An EEG Study

Many neuroscience researchers have explored how various parts of the brain are connected. No one has performed association mining using the brain data. In this study, we used SAS® Enterprise MinerTM 7.1 for association mining of the brain data collected by a 14-channel EEG device. An application of the association mining technique is presented in this novel context of brain activities and by linking our results to theories of cognitive neuroscience. The brain waves are collected while a user processes the information about Facebook, the most well known social networking site. The data is cleaned using Independent Component Analysis via an open source MATLAB package. Next, by applying the LORETA algorithm, activations at a fraction of a second are recorded. The data is codified into transactions to perform association mining. Results showing how various parts of brain that get excited while processing the information are reported. This study provides preliminary insights into how brain wave data may be analyzed by commonly available data mining techniques to enhance a researcher’s understanding of brain activation patterns.
Brand Suicide? Memory and Liking of Negative Brand Names

Negative brand names are surprisingly common in the marketplace (e.g., Poison perfume; Hell pizza, and Monster energy drink), yet their effects on consumer behavior are currently unknown. Three studies investigated the effects of negative brand name valence on brand name memory and liking of a branded product. Study 1 demonstrates that relative to non-negative brand names, negative brand names and their associated logos are better recognised. Studies 2 and 3 demonstrate that negative valence of a brand name tends to have a detrimental influence on product evaluation with evaluations worsening as negative valence increases. However, evaluation is also dependent on brand name arousal, with high arousal brand names resulting in more positive evaluations, such that moderately negative brand names are equally as attractive as some non-negative brand names. Study 3 shows evidence for affective habituation, whereby the effects of negative valence reduce with repeated exposures to some classes of negative brand name.
SENSOR-BASED SINGLE-USER ACTIVITY RECOGNITION

The focus of this work is to explore the possibilities of recognizing three common user activities (sitting, walking and running) with accelerometer data from smartphones. Among five common machine learning algorithms, Naïve Bayes classifier proved to be the best choice. Classification accuracy of more than 90% was achieved when phone is carried in a pocket. It is shown that this method is appropriate and that the phone’s orientation information is not needed. Finally, the classification of one day-long data set is presented.
Improved Wavelet-Based Illumination Normalization Algorithm for Face Recognition

The appearance of a face image is severely affected by illumination conditions that hinder the automatic face recognition process. To recognize faces under varying illuminations, we propose an improved wavelet-based normalization method so as to normalize illuminations. We decompose an image into its low frequency and high frequency components. Then different band coefficients are manipulated separately. We apply histogram equalization to the approximation (low frequency) coefficients and at the same time de-noise the detail (high frequency) coefficients adaptively and then accentuate them by multiplying by a scalar so as to enhance edges. A normalized image is obtained from the modified coefficients by inverse wavelet transform. Finally, We use PCA method to recognize normalized image with only one training image. The experimental results obtained by testing on the Yale Face Database B demonstrate the effectiveness of our method with significant improvement in the face recognition system.
HAWK: Real-World Implementation of High-Performance Heterogeneous Wireless Network for Internet Access

The co-existence and integration of different wireless networks will significantly advance the provision of ubiquitous and high-performance Internet services to mobile users. In this paper, we present HAWK, a real-world implementation of high performance heterogeneous wireless networks for ubiquitous Internet access. We have built a fully featured testbed comprised of different wireless networks, including Wireless Mesh Network (WMN), Wireless LAN (WLAN) and 3G network, and dual mode mobile clients that can access any of these networks. We have also developed protocols and algorithms for various system functions, including routing, intra-WMN handoff, inter-network handoff, and mobility adaptation middleware. Although many works can be found on heterogeneous wireless networks, most of them focus on protocol design and/or theoretical performance study. Very few of existing work reported real system development, of which most are limited to integration of WLANs and cellular networks. Our work reported in this paper provides insights into the design and implementation of heterogeneous wireless networks in the aspects of both hardware and software and will consequently drive heterogeneous wireless networks towards practical deployment.
A 4-fJ/Spike Artificial Neuron in 65 nm CMOS Technology

As Moore's law reaches its end, traditional computing technology based on the Von Neumann architecture is facing fundamental limits. Among them is poor energy efficiency. This situation motivates the investigation of different processing information paradigms, such as the use of spiking neural networks (SNNs), which also introduce cognitive characteristics. As applications at very high scale are addressed, the energy dissipation needs to be minimized. This effort starts from the neuron cell. In this context, this paper presents the design of an original artificial neuron, in standard 65 nm CMOS technology with optimized energy efficiency. The neuron circuit response is designed as an approximation of the Morris-Lecar theoretical model. In order to implement the non-linear gating variables, which control the ionic channel currents, transistors operating in deep subthreshold are employed. Two different circuit variants describing the neuron model equations have been developed. The first one features spike characteristics, which correlate well with a biological neuron model. The second one is a simplification of the first, designed to exhibit higher spiking frequencies, targeting large scale bio-inspired information processing applications. The most important feature of the fabricated circuits is the energy efficiency of a few femtojoules per spike, which improves prior state-of-the-art by two to three orders of magnitude. This performance is achieved by minimizing two key parameters: the supply voltage and the related membrane capacitance. Meanwhile, the obtained standby power at a resting output does not exceed tens of picowatts. The two variants were sized to 200 and 35 μm2 with the latter reaching a spiking output frequency of 26 kHz. This performance level could address various contexts, such as highly integrated neuro-processors for robotics, neuroscience or medical applications.
Monosynaptic and Polysynaptic Feed-Forward Inputs to Mitral Cells from Olfactory Sensory Neurons

Olfactory sensory neurons (OSNs) expressing the same odorant receptor converge in specific glomeruli where they transmit olfactory information to mitral cells. Surprisingly, synaptic mechanisms underlying mitral cell activation are still controversial. Using patch-clamp recordings in mouse olfactory bulb slices, we demonstrate that stimulation of OSNs produces a biphasic postsynaptic excitatory response in mitral cells. The response was initiated by a fast and graded monosynaptic input from OSNs and followed by a slower component of feedforward excitation, involving dendro-dendritic interactions between external tufted, tufted and other mitral cells. The mitral cell response occasionally lacked the fast OSN input when few afferent fibers were stimulated. We also show that OSN stimulation triggers a strong and slow feedforward inhibition that shapes the feedforward excitation but leaves unaffected the monosynaptic component. These results confirm the existence of direct OSN to mitral cells synapses but also emphasize the prominence of intraglomerular feedforward pathways in the mitral cell response.
On Fuzzy Distances and Their Applications on Cost Function ) ~ , ~ ( ~ jj DCL

This paper considers a single machine scheduling problem with fuzzy due date and fuzzy processing time (SMSPFDP). We using fuzzy distance function concepts which introduced by Lam and Cai. The objective is to minimize the maximum fuzzy lateness. In this paper we develop compare and test different local search method (Threshold accepted (TA), Tabu search (TS), Ant colony optimization (ACO) and Memetic algorithm (MA)) computational experience 1000 jobs with reasonable time. Also computational show ACO gives the best solution.
in Middle Eastern and North African Economies Quinlan School of Business 9-1-2007 Financial Sector Deepening and Economic Growth : Evidence from Turkey

This paper analyzes the effects of financial sector deepening on economic growth using a province-level data set for 1996-2001 on Turkey. This period is associated with a weakly regulated and relatively unsupervised expansion of the banking sector which led to the 2001 financial crisis. Contrary to findings in the previous literature, our results indicate a strong negative relationship between financial deepening—both public and private—and economic growth. In light of the developments in the period of analysis, this result is not surprising, as the main function of the banking sector at that time was to provide financing for the Turkish Treasury, which channeled these funds to the government—albeit mainly for rent distribution purposes. However, it is important to note that the growth of private banking sector needs yet to be examined separately, as government ownership of banks may distort the development of the banking sector as a whole. Yet, it is possible to conclude that financial development may not always contribute to economic growth, and the conditions under which such a contribution takes place should be investigated further.
SCIENTIFIC COMMITTEE FOURTH REGULAR SESSION 11-22 August 2008 Port Moresby, Papua New Guinea STOCK STRUCTURE OF SWORDFISH (Xiphias gladius) IN THE PACIFIC OCEAN USING MICROSATELLITE DNA MARKERS WCPFC-SC4-2008/BI-WP-4

The genetic structure of swordfish (Xiphias gladius) in the Pacific Ocean was assessed by analyzing 594 individuals from 6 different regions, genotyped with 13 microsatellite loci. The results showed very low genetic differentiation among the different geographical areas, which was not statistically significant. There was a slight evidence for isolation by distance across a U-shaped corridor, as it had been demonstrated by Reeb et al. (2000) using mtDNA, but more samples and a better geographical coverage are necessary to support this finding. These data confirmed the low genetic differentiation of swordfish within Pacific, which has been already found with other genetic markers.
Signaling Mechanisms and Physical Structure of Biofilm Growth in Synechocystis sp. Strain PCC 6803

Chemical signaling has been found in the past to play a central role in bacterial biofilm formation, and may be important for the cyanobacterium Synechocystis sp. Strain PCC 6803. We investigate the cell‐cell communication system of this species and whether it involves acyl homoserine lactone (AHL) signaling molecules. We use the biosensor, Chromobacterium violaceum CV026, which produces the violacein pigment in the presence of AHLs (McClean, et al., 1997). This biosensor was found to be a sensitive and quantitative method of detecting a variety of AHL chain lengths. Optimal growth temperature is 29°C and growth is not affected at low concentrations of acid, which is required for AHL stability. The physical structure of the biofilms of a super motile cyanobacterial mutant is also being analyzed using confocal microscopy. The biofilms begin as pillars 20–30um tall, grow to 50um deep and spread across the surface. The motile cyanobacterial cells exhibit unique biofilm formation and structure, and we are determining whether the process involves AHL signaling. This research is supported by the Wellesley College Biological Sciences Department and a Jerome A. Schiff Fellowship.
A Spontaneous Animal Model of Intestinal Dysmotility Evoked by Inflammatory Nitrergic Dysfunction

Background and Aims Recent reports indicate the presence of low grade inflammation in functional gastrointestinal disorders (FGID), in these cases often called “post-inflammatory” FGIDs. However, suitable animal models to study these disorders are not available. The Biobreeding (BB) rat consists of a diabetes-resistant (BBDR) and a diabetes-prone (BBDP) strain. In the diabetes-prone strain, 40–60% of the animals develop diabetes and concomitant nitrergic dysfunction. Our aim was to investigate the occurrence of intestinal inflammation, nitrergic dysfunction and intestinal dysmotility in non-diabetic animals. Methods Jejunal inflammation (MPO assay, Hematoxylin&Eosin staining and inducible nitric oxide synthase (iNOS) mRNA expression), in vitro jejunal motility (video analysis) and myenteric neuronal numbers (immunohistochemistry) were assessed in control, normoglycaemic BBDP and diabetic BBDP rats. To study the impact of iNOS inhibition on these parameters, normoglycaemic BBDP rats were treated with aminoguanidine. Results Compared to control, significant polymorphonuclear (PMN) cell infiltration, enhanced MPO activity, increased iNOS mRNA expression and a decreased ratio of nNOS to Hu-C/D positive neurons were observed in both normoglycaemic and diabetic BBDP rats. Aminoguanidine treatment decreased PMN infiltration, iNOS mRNA expression and MPO activity. Moreover, it restored the ratio of nNOS to Hu-C/D positive nerves in the myenteric plexus and decreased the abnormal jejunal elongation and dilation observed in normoglycaemic BBDP rats. Conclusions Aminoguanidine treatment counteracts the inflammation-induced nitrergic dysfunction and prevents dysmotility, both of which are independent of hyperglycaemia in BB rats. Nitrergic dysfunction may contribute to the pathophysiology of “low-grade inflammatory” FGIDs. Normoglycaemic BBDP rats may be considered a suitable animal model to study the pathogenesis of FGIDs.
ASSESSING URBAN MICROCLIMATE USING MULTI-AGENT SIMULATIONS : A NEW APPROACH TO ANSWER AN OLD PROBLEM ?

The re-discovery of the urban environment as an important component of daily life induced an increased interest in methods for assessing the quality of open spaces with respect to their contribution to the quality of urban life. A wide range of studies ranging from field experiments up to the application of numerical models have been conducted in order to understand and predict the interactions between the urban design, local microclimate and human comfort. Unfortunately, like in most other cases in which individual human decisions are involved, the relationship between the stimulating factors and the observed behaviour patterns are far from being trivial. Using the technique of Multi-Agent simulation, new insights into the complex interactions between microclimate, the thermal comfort of an individual pedestrian and the emergent pedestrian behaviour patterns can be obtained. In this paper the outline of the climBOT model system and its possible application to assess urban microclimate is presented.
WAITING TO PERSUADE BY MUHAMET YILDIZ COWLES FOUNDATION PAPER NO. 1086 COWLES FOUNDATION FOR RESEARCH IN ECONOMICS

I analyze a sequential bargaining model in which players are optimistic about their bargaining power (measured as the probability of making offers), but learn as they play the game. I show that there exists a uniquely predetermined settlement date, such that in equilibrium the players always reach an agreement at that date, but never reach one before it. Given any discount rate, if the learning is sufficiently slow, the players agree immediately. I show that, for any speed of learning, the agreement is delayed arbitrarily long, provided that the players are sufficiently patient. Therefore, although excessive optimism alone cannot cause delay, it can cause long delays if the players are expected to learn.
A Generic Framework for Optimal Mobile Sensor Redeployment

Wireless sensor networks (WSNs) are widely used for various applications. In some applications, it is very likely that the number of sensors is limited, the initial deployment is random, and deployment “holes” (i.e., subareas where there is no sensor) may exist. Therefore, a major challenge in WSNs is to improve the network coverage. A viable solution is to enhance some sensors with mobility capability so that they can move inside the WSNs after the initial deployment, which is referred to as mobile sensor redeployment. In this paper, we target at a generic framework for the optimal mobile sensor redeployment problem in WSNs. In particular, in this paper, the area of a WSN is partitioned into a number of grids, and the gap of each grid is defined as the difference of the number of sensors in the grid from the desired number of sensors. Then, the mobile sensor redeployment problem is formulated as an optimization problem with three optimization requirements: to minimize the sum of gaps of all grids, to minimize the Lp-norm (1 ≤ p ≤ ∞) of the gap vector (where Lp-norm represents a family of optimization objectives when p takes different values), and to minimize the total movement cost of all mobile sensors. Bipartite matching-based redeployment (BMrD) algorithms are provided to solve the optimization problems with different values of p. In this paper, the framework is generic for mobile sensor redeployment because 1) the mobility capabilities of the sensors, the initial sensor distribution, the movement cost measures, and the sensor types are all arbitrary; 2) the coverage requirements in different grids can be heterogeneous; and 3) the BMrD algorithms are generic for different p values. The effectiveness of the BMrD algorithms is validated through theoretical proofs and extensive simulations.
Visualization of spatio - temporal patterns of public transport data

In this paper, we discuss geovisualization techniques to explore spatio-temporal patterns formed by people traveling through public transport system (PTS). The Spatio-temporal reasoning by PTS operators/policy makers to extract patterns from public transport system data is studied. The resulting questions are related to basic visual tasks like locate, identify, associate and compare. These visual tasks are incorporated in a set of visual tools. A visual environment is developed based on time geographys space-time cube and multiple dynamically linked views based on above visual tools that offer alternative perspectives on the data and patterns extracted. A preliminary usability test for the visual environment is conducted with the PTS experts and its results are presented.
An entanglement measure for n qubits

Coffman et al. [Phys. Rev. A 61, 052306 (2000)] introduced the residual entanglement for three qubits. In this paper, we present the entanglement measure τ(ψ) for even n qubits; for odd n qubits, we propose the residual entanglement τ(i)(ψ) with respect to qubit i and the odd n-tangle R(ψ) by averaging the residual entanglement with respect to each qubit. In this paper, we show that these measures are LU-invariant, entanglement monotones, invariant under permutations of the qubits, and multiplicative in some cases.
An integral time calculation model for agile satellite TDICCD camera

As a key parameter of agile satellite TDICCD camera, the integral time calculation accuracy affects the satellite imaging quality directly. The traditional integral time calculation model is unable to satisfy the accuracy requirement for attitude changes flexibility and integral time changes severely during the satellite imaging. This paper describes an integral time calculation model that improved in aspects of calculation of photography point ground velocity and calculation of height from satellite to photography point. Calculation of height from satellite to photography point adopted digital elevation model (DEM) to correct the ideal value. In addition to, the model added attitude angular velocity's impact to the satellite body coordinate system angular velocity vector to photography point ground velocity calculation. The approach designed implementation scheme of the whole algorithm on OBDH subsystem onboard and took simulation calculation under the embedded environment. The analysis results manifests the approach could meet the requirement of agile satellite camera imaging quality since the difference is within 0.05 microseconds through comparison with STK simulation calculation results.
Dynamic classifier aggregation using fuzzy integral with interaction-sensitive fuzzy measure

In classifier combining, predictions of several classifiers are aggregated into a single prediction in order to improve the classification quality. Among others, fuzzy integrals are commonly used as aggregation operators. Usually, Sugeno lambda-measure is used as the fuzzy measure of the integral. However, interaction between the classifiers in the team (diversity), an important property in classifier combining, cannot be modeled by such fuzzy measure. In this paper, we present an interaction-sensitive fuzzy measure (ISFM), which can incorporate the diversity of the team into the aggregation process. Experimental results on 27 datasets show that the Choquet integral w.r. t. the ISFM outperforms the Choquet integral w.r. t. the Sugeno-lambda measure.
Evaluation of Different EEG Acquisition Systems Concerning Their Suitability for Building a Brain–Computer Interface: Case Studies

One important aspect in non-invasive brain–computer interface (BCI) research is to acquire the electroencephalogram (EEG) in a proper way. From an end-user perspective, it means with maximum comfort and without any extra inconveniences (e.g., washing the hair), whereas from a technical perspective, the signal quality has to be optimal to make the BCI work effectively and efficiently. In this work, we evaluated three different commercially available EEG acquisition systems that differ in the type of electrodes (gel-, water-, and dry-based), the amplifier technique, and the data transmission method. Every system was tested regarding three different aspects, namely, technical, BCI effectiveness and efficiency (P300 communication and control), and user satisfaction (comfort). We found that water-based system had the lowest short circuit noise level, the hydrogel-based system had the highest P300 spelling accuracies, and the dry electrode-based system caused the least inconveniences. Therefore, building a reliable BCI is possible with all the evaluated systems, and it is on the user to decide which system meets the given requirements best.
Study of P2P flow detection technology

To identify and control P2Pflow more infectively, the industry went through Multi-level explores and put forward many infective P2P flow detection methods. They are the method based on the port, the method based on deep data, the method based on machine learning and the method based on network behavior. In the paper, we sumerize and analyze those methods, and compared both the advantages and disadvantages between them At last, it analyzed the further step of P2P Flow Detection Technology.