Miracle-dz/UniFAR-FTUs huggingface dataset:
Deconvolution Analysis of FMRI Time Series Data

Program 3dDeconvolve was developed to provide deconvolution analysis of FMRI time series data. This has two primary applications: (1) estimation of the system impulse response function, and (2) multiple linear regression analysis of time series data. Given the input stimulus function(s), and the measured FMRI signal data, program 3dDeconvolve …rst estimates the impulse response function(s); the impulse response function(s) is then convolved with the stimulus time series to yield the estimated response. Various statistics are calculated to indicate the "goodness"of the …t. The capability of …tting multiple stimulus (or reference) waveforms dierenti- ates program 3dDeconvolve from the cross-correlation analysis programs (such as AFNI …m and 3d…m). Another way that program 3dDeconvolve diers from the cross-correlation analysis programs is in the model for the output waveform. The cross-correlation programs model the system response as a scaled version of a …xed waveform (such as a square wave or a sine wave). Program 3dDeconvolve uses a sum of scaled and time-delayed versions of the stimulus time series. The data itself de- termines (within limits) the functional form of the estimated response. In fact, the shape of the …tted waveform can vary from voxel to voxel. Program 3dDeconvolve is described in Section 1. Since program 3dDeconvolve was developed for use in a "batch"processing mode, it should be used in conjunction with the interactive program plug_deconvolve. See the documentation for program plug_deconvolve for a description of this interactive version. The details are contained in Section 2. Program RSFgen is a simple program for generating random stimulus functions. This capability may be useful for experimental design, and the evaluation of experi- mental designs. This program is described in Section 3. Program 3dConvolve, as the name implies, performs the inverse operation of pro- gram 3dDeconvolve. That is, program 3dConvolve convolves the input stimulus func- tions with the given system impulse response functions, in order to predict the output measured response. This program is described in Section 4.
A short note on dynamic programming and pricing American options by Monte Carlo simulation

There is an increasing interest in sampling-based pricing of American-style options. In fact, lattice or finite difference methods are naturally suited to coping with early exercise features, but there are limits in the number of stochastic factors they can deal with. These limits are due to the increase in the size of the grid or the lattice which is used to discretize the state space. On the contrary, one of the major strengths of Monte Carlo simulation is just the ability to price high-dimensional derivatives. Unfortunately, dealing with early exercise requires going backward in time, as at each point in the state space you must compare the value of immediate exercise with the value of keeping the option, which is simply the price of the option at that point. Hence, it would seem that one has to chase her tail a bit, since while running a simulation forward in time, you should already know the option value. Indeed, until a few years ago, it was a common belief that simulation could not be applied to American-style options. Actually, pricing an option with early exercise features requires solving a dynamic optimization problem by dynamic programming. Hence, in this short supplement we start with a brief account on dynamic programming (a streamlined version of Appendix D of [3]). Dynamic programming is arguably the most powerful concept in optimization, and its many potential applications are well illustrated in [1]. In section 1 we illustrate the principle behind dynamic programming with the simplest example, the shortest path problem in a network. Then, in section 2, we show the connection between this simple example and more general deterministic sequential decision processes. Actually, dynamic programming is able to cope also with stochastic programming problems, as those commonly encountered in finance. Despite its power, dynamic programming is limited by the so-called curse of dimensionality; in other words, the more state variables (in option pricing, the more stochastic factors), the higher the memory and time requirements of the approach. To overcome the curse of dimensionality, a great deal of effort has been devoted to the development of approximate solution methods [1, 2], which also include simulation-based methods. This has paved the way to simulation-based pricing for high-dimensional American-style options. An example of this line of research is [9]. Actually, other strategies have been pursued and are surveyed in chapter 6 of [8]. Here we illustrate (section 3) a simple least-square based method originally proposed by Longstaff and Schwartz [6]. Finally, we should also mention a recent book on Monte Carlo methods, [5], which we recommend to anyone interested in applying this methodology in finance.
Input Parameters Optimization in Swarm DS-CDMA Multiuser Detectors

In this paper, the uplink direct sequence code division multiple access (DS-CDMA) multiuser detection problem (MuD) is studied into heuristic perspective, named particle swarm optimization (PSO). Regarding different system improvements for future technologies, such as high-order modulation and diversity exploitation, a complete parameter optimization procedure for the PSO applied to MuD problem is provided, which represents the major contribution of this paper. Furthermore, the performance of the PSO-MuD is briefly analyzed via Monte-Carlo simulations. Simulation results show that, after convergence, the performance reached by the PSO-MuD is much better than the conventional detector, and somewhat close to the single user bound (SuB). Rayleigh flat channel is initially considered, but the results are further extend to diversity (time and spatial) channels.
Tracking Error Based On-line Trajectory Time Scaling

This paper presents an online time scaling concept for robotic manipulators. The task is to ensure the fastest possible motion along a trajectory with a given geometry in the robot workspace. The other requirement is tracking accuracy. Robots should move not only with high speed but with high precision as well. The speed of the motion cannot be arbitrary fast. It is delimited by the saturation of the actuators and by some other (e.g. security) constraints. Offline time scaling solutions change the speed of the motion without changing its geometry, thus these constraints are not violated. If the exact torque bounds are not known in advance, another method should be used. This work gives an online time scaling algorithm which is based on the tracking error. Simulations demonstrate the usefulness of the algorithm proposed
Comparing Fuzzy and Intelligent PI Controllers in Stop-and-Go Manoeuvres

The aim of this work was twofold: on the one hand, to describe a comparative study of two intelligent control techniques-fuzzy and intelligent proportional-integral (PI) control, and on the other, to try to provide an answer to an as yet unsolved topic in the automotive sector-stop-and-go control in urban environments at very low speeds. Commercial vehicles exhibit nonlinear behavior and therefore constitute an excellent platform on which to check the controllers. This paper describes the design, tuning, and evaluation of the controllers performing actions on the longitudinal control of a car-the throttle and brake pedals-to accomplish stop-and-go manoeuvres. They are tested in two steps. First, a simulation model is used to design and tune the controllers, and second, these controllers are implemented in the commercial vehicle-which has automatic driving capabilities-to check their behavior. A stop-and-go manoeuvre is implemented with the two control techniques using two cooperating vehicles.
Safety system based on Linear Discriminant Analysis

A Linear Discriminant Analysis Classifier for home security system we describe in this paper. Images were taken in uncontrolled indoor environment using video cameras of various qualities. Database contains 4,005 static images (in visible and infrared spectrum) of 267 subjects. Images from different quality cameras should mimic real-world conditions and enable robust face recognition algorithms testing, emphasizing different law enforcement and surveillance use case scenarios. In addition to database description, this paper also elaborates on possible uses of the database and proposes a testing protocol. A baseline Haar Cascade Method for face recognition algorithm was tested following the proposed protocol based on LDA Classifier. Other researchers can use these test results as a control algorithm performance score when testing their own algorithms on this dataset. Database is available to research community through the procedure described at http://www.lrv. fri.uni-lj.si/facedb.html.
Variation in HIV-1 set-point viral load: Epidemiological analysis and an evolutionary hypothesis

The natural course of HIV-1 infection is characterized by a high degree of heterogeneity in viral load, not just within patients over time, but also between patients, especially during the asymptomatic stage of infection. Asymptomatic, or set-point, viral load has been shown to correlate with both decreased time to AIDS and increased infectiousness. The aim of this study is to characterize the epidemiological impact of heterogeneity in set-point viral load. By analyzing two cohorts of untreated patients, we quantify the relationships between both viral load and infectiousness and the duration of the asymptomatic infectious period. We find that, because both the duration of infection and infectiousness determine the opportunities for the virus to be transmitted, this suggests a trade-off between these contributions to the overall transmission potential. Some public health implications of variation in set-point viral load are discussed. We observe that set-point viral loads are clustered around those that maximize the transmission potential, and this leads us to hypothesize that HIV-1 could have evolved to optimize its transmissibility, a form of adaptation to the human host population. We discuss how this evolutionary hypothesis can be tested, review the evidence available to date, and highlight directions for future research.
Thermodynamic structure of collision-dominated expanding plasma: Heating of interplanetary coronal mass ejections

[1] We investigate the thermodynamic structure of interplanetary coronal mass ejections (ICMEs) using combined surveys of the ejecta between 0.3 and 20 AU. ICMEs are shown to have a moderate expansion in the solar wind compared with theoretical predictions. The expansion seems to be governed by a polytrope with γ ∼ 1.3 in this distance range. We find that Coulomb collisions are important contributors to the ion-ion equilibration process in the ICME plasma. The alpha-proton differential speed quickly drops to below 10 km s−1 due to strong Coulomb collisions. However, the two species of particles are far from thermal equilibrium with a temperature ratio Tα/Tp = 4–6, suggestive of a preferential heating of alpha particles. The plasma heating rate as a function of heliocentric distance required for the temperature profile is deduced by taking into account the expansion and energy transfer between protons and alphas via Coulomb collisions. The turbulence dissipation rate is also inferred from the inertial range power spectrum of magnetic fluctuations within ICMEs. Comparison of the turbulence dissipation rate with the required heating rate shows that turbulence dissipation seems sufficient to explain the ICME heating. Sources powering the turbulence are also investigated by examining the instabilities induced by temperature anisotropies and energy deposition by pickup ions.
Stochastic Herding by Institutional Investment Managers

This paper demonstrates that the behavior of institutional investors around the downturn of the U.S. equity markets in 2007 is consistent with stochastic herding in attempts to time the market. We consider a model of large number of institutional investment managers who simultaneously decide whether to remain invested in an assets or liquidate their positions. Each fund manager receives imperfect information about the market’s ability to supply liquidity and chooses whether or not to sell the security based on her private information as well as the actions of others. Due to feedback effects the equilibrium is stochastic and the “aggregate action” is characterized by a power-law probability distribution with exponential truncation predicting occasional “explosive” sell-out events. We examine highly disaggregated institutional ownership data of publicly traded stocks to find that stochastic herding explains the underlying data generating mechanism. Furthermore, consistent with market-timing considerations, the distribution parameter measuring the degree of herding rises sharply immediately prior the sell-out phase. The sell-out phase is consistent with the transition from subcritical to supercritical phase, whereby the system swings sharply to a new equilibrium. Specifically, exponential truncation vanishes as the distribution of fund manager actions becomes centered around the same action – all sell. JEL classification codes: D8, G2, G14 ∗We are grateful for the support from the Research Center for Price Dynamics funded by a JSPS Grant-in-Aid for Creative Scientific Research (18GS0101). †The authors would like to thank Theodoros Stamatiou and the participants of the University of California Santa Cruz Economics Department Seminar for their comments and suggestions.
Hil validation of an embedded system acting as a nonlinear Takagi-Sugeno state observer on an Arduino board

The article presents a method for validating a physical Embedded state observer by using a Hardware In The Loop architecture. As an application, a two water tank system was chosen. For the validation of the observer, a software has been employed, specifically LMS AMESim, interfaced by Simulink on a Windows operating system. Even though Windows is not well suited for real time operations, it is the most common operating system and it has proven to be robust enough to sustain our tests. The article also shows the capability of small scaled microcontroller based systems to handle complex algorithms. It also promotes the simplicity of implementing Takagi-Sugeno algorithms in small scaled numerical systems. Although tests were made on a Arduino UNO version, by adding the benefits of the powerful, in development, ArduinoTRE Board, the power bonus of Texas Instruments processor allows more complexity as well as signal filtering and faster and more secure communication protocols.
Media on the Move : Global Flow and Contra-Flow

Part I: Contextualising Contra-Flow 1. Mapping Global Flow and Contra-Flow 2. Diasporas and Contra-Flows Beyond National Centricism 3. Thinking Through Contra-flows: Perspectives from Post-colonial and Transnational Cultural Studies Part II: The Non-Western Media in Motion 4. Contra-Flow or the Cultural Logic of Uneven Globalization: Japanese Media in the Global Agora 5. Bollywood and the Frictions of Global Mobility 6. Brazil and the Globalization of Telenovelas 7. Challenger or Lackey? The Politics of News on al-Jazeera Part III: Regional Perspectives on Flow and Contra-Flow 8. The Rising Korean 'Wave': Korean Media Go Global 9. South Africa as a Regional Media Power 10. Flows and Contra-Flows in Transitional Societies 11. Facing the Challenge of Global Competition: Chinese News in Transition Part IV: Moving Media - From the Margins to the Mainstream? 12. Alternative Re-Framing of Mainstream Media Frames 13. Transnational Feminism and the Revolutionary Association of the Women of Afghanistan 14. The Islamic Internet: Authority, Authenticity and Reform
Feedback-based handwriting recognition from inertial sensor data for wearable devices

This paper presents a novel interactive method for recognizing handwritten words, using the inertial sensor data available on smart watches. The goal is to allow the user to write with a finger, and use the smart watch sensor signals to infer what the user has written. Past work has exploited the similarity of handwriting recognition to speech recognition in order to deploy HMM based methods. In contrast to speech recognition, however, in our scenario, the user can see the individual letters that are recognized on a sequential basis, and provide feedback or corrections after each letter. In this paper, we exploit this key difference to improve the input mechanism over a classical source-channel model. For a small increase in the amount of time required to input a word, we improve recognition accuracy from 59.6% to 91.4% with an implicit feedback mechanism, and to 100% with an explicit feedback mechanism.
An Optimal Cache-Oblivious Priority Queue and Its Application to Graph Algorithms

We develop an optimal cache-oblivious priority queue data structure, supporting insertion, deletion, and delete-min operations in $O(\frac{1}{B}\log_{M/B}\frac{N}{B})$ amortized memory transfers, where $M$ and $B$ are the memory and block transfer sizes of any two consecutive levels of a multilevel memory hierarchy. In a cache-oblivious data structure, $M$ and $B$ are not used in the description of the structure. Our structure is as efficient as several previously developed external memory (cache-aware) priority queue data structures, which all rely crucially on knowledge about $M$ and $B$. Priority queues are a critical component in many of the best known external memory graph algorithms, and using our cache-oblivious priority queue we develop several cache-oblivious graph algorithms.
WIRELESS MESH NETWORKS : OPPORTUNITIES AND CHALLENGES

Wireless mesh networks have the potential to deliver Internet broadband access, wireless local area network coverage and network connectivity for stationary or mobile hosts at low costs both for network operators and customers. The core technology involves a network of wireless routers relaying each others’ packets in a multihop fashion. Many variations on targeted applications and implementation choices offer different opportunities to emerging companies in this emerging area. In this article, we will present an introduction to wireless mesh networks and present both the benefits enabled by this technology and the main hurdles that have to be overcome.
How many solutions does a SAT instance have?

Our goal is to investigate the solution space of a given Boolean satisfiability (SAT) instance. In particular, we are interested in determining the size of the solution space - the number of truth assignments that make the SAT instance true - and finding all such truth assignments, if possible. This apparently hard problem has both theoretical and practical values. We propose an exact algorithm based on exhaustive search that solves the instance once and finds all solutions (SOFAS) and several sampling techniques that estimate the size of the solution space. SOFAS works better for SAT instances of small size with a 5X-100X speed-up over the brute force search algorithm. The sampling techniques estimate the solution space reasonably well for standard SAT benchmarks.
Southern Ocean transformation in a coupled model with and without eddy mass fluxes

A coupled air–sea general circulation model is used to simulate the global circulation. Differentparameterizations of lateral mixing in the ocean by eddies, horizontal, isopycnal, and isopycnalplus eddy advective flux, are compared from the perspective of water mass transformation inthe Southern Ocean. The different mixing physics imply different buoyancy equilibria in thesurface mixed layer, different transformations, and therefore a variety of meridional overturningstreamfunctions. The coupled-model approach avoids strong artificial water mass transformationassociated with relaxation to prescribed mixed layer conditions. Instead, transformationresults from the more physical non-local, nonlinear interdependence of sea-surface temperature, air—sea fluxes, and circulation in the model’s atmosphere and ocean. The developmentof a stronger mid-depth circulation cell and associated upwelling when eddy fluxes arepresent, is examined. The strength of overturning is diagnosed in density coordinates using thetransformation framework.
The Economics of Conflicts of Interest in Financial Institutions

A conflict of interest exists when a party to a transaction could potentially make a gain from taking actions that are detrimental to the other party in the transaction. This paper examines the economics of conflicts of interest in financial institutions and reviews the growing empirical literature (mostly focused on analysts) on the economic implications of these conflicts. Economic analysis shows that, although conflicts of interest are omnipresent when contracting is costly and parties are imperfectly informed, there are important factors that mitigate their impact and, strikingly, it is possible for customers of financial institutions to benefit from the existence of such conflicts. The empirical literature reaches conclusions that differ across types of conflicts of interest, but overall these conclusions are more ambivalent and certainly more benign than the conclusions drawn by journalists and politicians from mostly anecdotal evidence. Though much has been made of conflicts of interest arising from investment banking activities, there is no consensus in the empirical literature supporting the view that conflicts resulting from these activities had a systematic adverse impact on customers of financial institutions.
Pain assessment using neural network classifier

Both the timing of facial actions and the configuration are important in emotion expression and recognition. To investigate the timing and configuration of pain facial actions, in this paper, pain assessment is explained and reviewed for detecting based on skin detection using color detection and SVD as feature extraction. Finally a Neural Network classifier used to classify the movements. From the experiments, it is found that the identification rate of reaches 93.75%.