Miracle-dz/UniFAR-FTUs huggingface dataset:
fMRI signal modeling using Laguerre polynomials

In order to construct spatial activation plots from functional magnetic resonance imaging (fMRI) data, a complex spatio-temporal modeling problem must be solved. A crucial part of this process is the estimation of the hemodynamic response (HR) function, an impulse response relating the stimulus signal to the measured noisy response. The estimation of the HR is complicated by the presence of low frequency colored noise. The standard approach to modeling the HR is to use simple parametric models, although FIR models have been used. We pursue a nonparametric approach using orthonormal causal Laguerre polynomials which have become popular in the system identification literature. It also happens that the shape of the basis elements is similar to that of a typical HR. We thus expect to achieve a compact and so bias reduced and low noise representation of the HR. This is not the case in FIR modeling, because a low FIR order is unable to cover the whole length of the HR over its region of support while a high FIR order results in overestimation of signal and underestimation of noise leading to misleading interpretations.
SURFACE PARAMETER ESTIMATION USING BISTATIC POLARIMETRIC X-BAND MEASUREMENTS

The main purpose of this paper is to separately estimate the important surface parameters (soil moisture and roughness) by using full polarimetric bistatic measurements. The results provide a basis for new satellite application of future bistatic measurement systems such as the TanDEM-X satellite mission. Initially, bistatic X-band measurements, which have been recorded in the Bistatic Measurement Facility (BMF) at the DLR Oberpfafienhofen, Microwaves and Radar Institute, will be presented. The bistatic measurement sets are composed of soils with difierent well-known statistical roughness scales and difierent moistures. The BMF has been calibrated using the Isolated Antenna Calibration Technique (IACT). The validation of the calibration was achieved by measuring the re∞ectivity of fresh water. In the second part, the assessment of the surface parameters (soil moisture and surface roughness) using the well calibrated data introduced in the former related part, will be detailed. The validation of the specular algorithm by estimating the soil moisture of two surfaces with difierent roughness scales will be reported. Additionally, a new technique using the coherent term of the Integral Equation Method (IEM) to estimate the soil roughness will be presented, as well as the sensitivity of phase and re∞ectivity with regard to moisture variation and therefore the penetration depth was evaluated. Current results demonstrate a non-linear relationship
Precoder-aided iterative detection assisted multilevel coding and three-dimensional EXIT-chart analysis

A novel three-dimensional (3D) EXIT chart is developed for investigating the iterative behaviour of multilevel coding (MLC) invoking multistage decoding (MSD). The extrinsic information transfer characteristics of both the symbol-to-bit demapper used and those of the different protection constituent decoders suggest that potential improvements can be achieved by appropriately designing the demapper. The proposed 3D EXIT chart is then invoked for studying the precoder-aided multilevel coding scheme employing both MSD and parallel independent decoding (PID) for communicating over AWGN and uncorrelated Rayleigh fading channels with the aid of 8PSK modulation. At BER=10-5, the precoder was capable of enhancing the achievable Eb/N0 performance by 0.5 dB to 2.5 dB over AWGN and Rayleigh channels, respectively
Social Media Marketing: A Literature Review and Implications

This study carries out content analysis and systemizes articles on social media marketing in the Web of Science database. Forty-four studies were analyzed in accordance with a variation on the systematic review approach, involving synthesis- and interpretation-based assessment. The results demonstrate how most of the studies analyzed focus on the consumer perspective in terms of usage, share, and influence of social media on consumer decisions, and perceptions. The studies focusing on the firm's perspective centered not only on the usage of social media, but also on their implementation, optimization, and measurement of results. The majority of studies are quantitative and published in recent years. This study not only reached certain conclusions for both theory and practice, but also defined future lines of research according to the gaps detected by the study's results.
Developments in Diagnosis of Visceral Leishmaniasis in the Elimination Era

Visceral leishmaniasis (VL) is the most devastating parasitic infection worldwide causing high morbidity and mortality. Clinical presentation of VL ranges from asymptomatic or subclinical infection to severe and complicated symptomatic disease. A major challenge in the clinical management of VL is the weakness of health systems in disease endemic regions. People affected by VL mostly present to primary health care centers (PHCs), often late in their therapeutic itinerary. PHC physicians face a major challenge: they do not deal with a single disease issue but with patients presenting with complaints pointing to several diagnostic possibilities. Risk exists when some patients having less clinical manifestations are misdiagnosed. Therefore, field based accurate, sensitive, and cost effective rapid diagnostic tools that can detect disease in its mildest form are essential for effective control and reaching the goal of VL elimination. In this review, we discuss the current status and challenges of various diagnostic tools for the diagnosis of VL and assess their application in resource poor settings.
Implementation of the refined Deutsch-Jozsa algorithm on a three-bit NMR quantum computer

We implemented the refined Deutsch-Jozsa algorithm on a three-bit nuclear magnetic resonance quantum computer. All of the balanced and constant functions were realized exactly. The results agree well with theoretical predictions and clearly distinguish the balanced functions from constant functions. Efficient refocusing schemes were proposed for the soft z pulse and J coupling, and it is shown that the thermal equilibrium state gives the same answer as the pure state for this algorithm.
Interacting many-body systems as non-cooperative games

We explore the possibility that physical phenomena arising from interacting multi-particle systems, can be usefully interpreted in terms of multi-player games. We show how non-cooperative phenomena can emerge from Ising Hamiltonians, even though the individual spins behave cooperatively. Our findings establish a mapping between two fundamental models from condensed matter physics and game theory.
The Impact of Household Level Heterogeneity in Reference Price Effects on Optimal Retailer Pricing Policies

The field of marketing has witnessed substantial improvement in modeling household level heterogeneity. However, relatively little has been written about how modeling household heterogeneity translates into better marketing decisions. In this paper, we study the impact of household level heterogeneity in reference price effects on a retailer's pricing policy. Reference prices are certain anchors or standards that households use to compare the observed purchase price of a product against. If the observed price is greater than the reference price it is perceived as a “loss” and if it is smaller than the reference price it is perceived as a “gain”. In order to study the impact of heterogeneity in reference price effects on retail pricing, we test a nested logit model under two alternative reference price (memory and stimulus based) and heterogeneity (finite mixture and hierarchical Bayes) specifications. In the empirical analysis, we find that households are quite heterogeneous in terms of their gain and loss effects. For some households a gain has higher impact than a corresponding loss, while the opposite is true for others. Using individual level estimates we then develop a normative pricing policy for a retailer maximizing category profit. Our results indicate that the optimal pricing policy derived from the heterogeneous case is qualitatively different, and more profitable, than the case when heterogeneity is ignored. We show that for an important marketing problem pertaining to a retailer, the optimal pricing decisions for various brands in a category are inextricably related to household heterogeneity in reference effects and brand preference.
Bad Loans and Entry into Local Credit Markets

Is deregulation sufficient to grant free entry in local credit markets? Economic theory suggests at least two ways in which asymmetric information between incumbents and entrants can work as an endogenous barrier to entry. First, entrantsi? ½ pool of applicants contains a larger share of potential customers who are not creditworthy because it includes all those would-be borrowers who were previously rejected by mature banks in the market. Second, since a substantial amount of the information used by banks to screen loan applicants and monitor borrowers is generated through repeated interaction with their customers and the local business community, incumbentsi? ½ creditworthiness tests are likely to be more accurate. Other things being equal, entrants are therefore expected to experience higher loan default rates than incumbents. Using a unique database of 7,275 observations on 729 individual banksi? ½ lending in 95 Italian local markets, we find that both adverse selection and informational disadvantage play a significant role in explaining entrantsi? ½ loan default rates. We argue that these endogenous barriers can help to explain why in many local credit markets by domestic and foreign banks was slow, even after substantial deregulation.
Best Conventional Therapy Versus Modular Impairment-Oriented Training for Arm Paresis After Stroke: A Single-Blind, Multicenter Randomized Controlled Trial

Background. The study investigated whether passive splinting or active motor training as either individualized best conventional therapy or as standardized impairment-oriented training (IOT) would be superior in promoting motor recovery in subacute stroke patients with mildly or severely paretic arms. Methods. A total of 148 anterior circulation ischemic stroke patients were randomly assigned to 45 minutes of additional daily arm therapy over 3 to 4 weeks as either ( a) passive therapy with inflatable splints or active arm motor therapy as either (b) individualized best conventional therapy (CONV) or ( c) standardized IOT, that is Arm BASIS training for severe paresis or Arm Ability training for mild paresis. Main outcome measures included the following: Fugl-Meyer arm motor score (severely paretic arms) and the TEMPA time scores (mildly affected arms). Pre—post (immediate effects) and pre—4 weeks follow-up analyses (long-term effects) were performed. Results. Overall improvements were documented (mean baseline and change scores efficacy: Fugl-Meyer, arm motor scores, 24.4, +9.1 points; TEMPA, 119, −26.6 seconds; P < .0001), but with no differential effects between splint therapy and the combined active motor rehabilitation groups. Both efficacy and effectiveness analyses indicated, however, bigger immediate motor improvements after IOT as compared with best conventional therapy (Fugl-Meyer, arm motor scores: IOT +12.3, CONV +9.2 points; TEMPA: IOT −31.1 seconds, CONV −20.5 seconds; P = .0363); for mildly affected patients long-term effects could also be substantiated. Conclusions. Specificity of active training seemed more important for motor recovery than intensity (therapy time). The comprehensive modular IOT approach promoted motor recovery in patients with either severe or mild arm paresis.
Optimistic parallelization of communicating sequential processes

We present a transparent program transformation which converts a sequential execution of S1; SZ by a procesl; in a multiprocess environment into an optimistic parallel execution of SI and S2. Such a transformation is valuable in the case where S1 and SZ cannot be para.llelized by static analysis either because S2 reads a value from S1 or because S1 and S2 each interact with an external process. The optimistic transformation works under a weaker set of conditions: (1) if the value Sz reads from S1 can usually, but not always, be correctly guessed ahead of time, and (2) if S1 and S2 interact with an external process, conflicts which violate the ordering of S1 and % are possible but rare. Practical applications of this approach include executing the likely outcome of a test in pwdlel with making the test, and converting sequences of calls into streams of asynchronous sends. We analyze the problem using the framework of guarded cornputatiow, in which each computation is tagged with the set of guesses on which it depends. We present an algorithm for managing communications, thread creation, committing, and aborting in the transformed program. We contrast our approach with related work.
New class of exact solutions in string theory.

We prove that a large class of leading-order string solutions which generalize both the plane-wave and fundamental string backgrounds are, in fact, exact solutions to all orders in [alpha][prime]. These include, in particular, the traveling waves along the fundamental string. The key features of these solutions are a null symmetry and a chiral coupling of the string to the background. Using dimensional reduction, one finds that the extremal electric dilatonic black holes and their recently discovered generalizations with NUT charge and rotation are also exact solutions. We show that our bosonic solutions are also exact solutions of the heterotic string theory with no extra gauge field background.
Motion Correspondence Using a Neural Network

Identifying corresponding features in an image sequence is an important issue in motion analysis. We present a solution based on the assumption of smooth motion using point features. Local constraints are used to make the method robust against occlusion and imperfect feature extraction. A global cost function is defined which is minimised by a mapping of feature points onto a 2-D Hopfield neural network. Three variants of the Hopfield network model are considered. Results obtained using synthetic and natural image data show that this method is robust against occlusion and poor feature detection.
Customer loyalty and customer loyalty programs

Customer loyalty presents a paradox. Many see it as primarily an attitude‐based phenomenon that can be influenced significantly by customer relationship management initiatives such as the increasingly popular loyalty and affinity programs. However, empirical research shows that loyalty in competitive repeat‐purchase markets is shaped more by the passive acceptance of brands than by strongly‐held attitudes about them. From this perspective, the demand‐enhancing potential of loyalty programs is more limited than might be hoped. Reviews three different perspectives on loyalty, and relates these to a framework for understanding customer loyalty that encompasses customer brand commitment, customer brand acceptance and customer brand buying. Uses this framework to analyze the demand‐side potential of loyalty programs. Discusses where these programs might work and where they are unlikely to succeed on any large scale. Provides a checklist for marketers.
Study on the optimization of ring type composite sensing structure

Cutting force is an important parameter to describe the mechanical processing chi removal process, and its accurate measurement is a significant basis for judging process state. Compared with octagonal ring, ring type composite sensing structure has the advantages of high sensitivity, convenient installation, easy to install sensors, and the measurement accuracy of fiber Bragg grating sensor will not be affected by pasting skill. In this study, under different per-tightening condition of fiber Bragg grating sensor located in annulus elastic body with two ends fixed, the static characteristics of the ring type composite sensing structure are analyzed. The optimal pre-tightening value of fiber Bragg grating sensor in the ring type composite sensing structure has been obtained by a large number of experiments. Then the structure of annulus elastic body has been optimized, and fiber grating pre-tightening device has been developed to replace preloading device.
Active speaker detection with audio-visual co-training

In this work, we show how to co-train a classifier for active speaker detection using audio-visual data. First, audio Voice Activity Detection (VAD) is used to train a personalized video-based active speaker classifier in a weakly supervised fashion. The video classifier is in turn used to train a voice model for each person. The individual voice models are then used to detect active speakers. There is no manual supervision - audio weakly supervises video classification, and the co-training loop is completed by using the trained video classifier to supervise the training of a personalized audio voice classifier.
Precise and Efficient Retrieval of Captioned Images: The MARIE Project

The MARIE project has explored knowledge-based information retrieval of captioned images of the kind found in picture libraries and on the Internet. It exploits the idea that images are easier to understand with context, especially descriptive text near them, but it also does image analysis. The MARIE approach has five parts: (1) find the images and captions; (2) parse and interpret the captions; (3) segment the images into regions of homogeneous characteristics and classify them; (4) correlate caption interpretation with image interpretation using the idea of focus; and (5) optimize query execution at run time. MARIE emphasizes domain-independent methods for portability at the expense of some performance, although some domain specification is still required. Experiments show MARIE prototypes are more accurate than simpler methods, although the task is very challenging and more work is needed. Its processing is illustrated in detail on part of an Internet World Wide Web page.
A scheme to control neutral point voltage variation in three-level converter

The three-level technology of converters has the weak point of the neutral point voltage potential unbalance. This paper describes a scheme to control neutral point voltage variation in three-level converter. The scheme solves the problem by controlling the three-level rectifier. The parameters in it can be reconstructed and it avoids adding complexity to the inverter. The simulation at the last part demonstrates validity of the proposed scheme.
Pilot-assisted channel estimation for STBC-based wireless MIMO-OFDM systems

With equipping multiple antennas at both transmitter and receiver ends, the desired signals in the OFDM wireless communications could be transmitted and received through multiple uncorrelated channels for achieving twofold benefits and high flexibility. For this kind of MIMO-OFDM systems, if the assistance of channel estimation is under consideration, the overall system performance is able to be further enhanced. This paper proposed a pilot-symbol-assisted channel estimation technique for MIMO architecture by assigning known pilot symbols orthogonally between different transmitting antennas. The mixed transmitted signals could be completely separated at the receiver end due to the orthogonal property of pilot blocks. Some simulation results are given to illustrate the superior overall performance that can beachieved by the proposed channel estimation algorithm in MIMO-OFDM systems under COST 207 mobile wireless environments.
The design of active workspaces

This thesis investigates the creation of context-specific environments designed to assist people engaged in productive tasks. These Active Workspaces are an emerging class of applications that combine interaction design, innovative input and output techniques and multi-media coaching methodologies to teach people to create things in the physical domain. The design and implementation of two systems that exemplify the features, process and goals of Active Workspace design are detailed and evaluated. CounterActive is an interactive kitchen counter that guides users of varying age and skill level through the preparation of several recipes. CounterActive enlivens the experience of cooking with instructional videos, illustrative photos, entertaining music and sounds. Origami Desk is an exhibit that teaches people how to fold paper into beautiful boxes and cranes. The desk not only projects animations and videos for the user to mimic, but also monitors the folding of the paper to enable performance feedback. These project descriptions are accompanied by generalized principles for the design of Active Workspaces, process guidelines, and as well as analysis of relevant technologies and ruminations on possible future applications in this arena. Thesis Supervisor: Dr. Michael J. Hawley Media Laboratory The Design of Active Workspaces
An Improved Gene Expression Programming(GEP) Algorithm Based on Classification

This paper presents an improved gene expression programming (GEP) algorithm, which combines the thought of classification and the original GEP operations. Meanwhile it designs a heuristic accelerating searching strategy and a diversity operator, which imports the thought of greedy algorithm and simulated annealing respectively. Experimental results based on comparisons between the improved GEP algorithm and the original GEP algorithm indicate that the improved GEP algorithm solves the contradiction between population diversity and algorithm convergence which has a faster convergence and better ability of searching optimization.
Measuring Intelligence through Games

Abstract Artiﬁcial general intelligence (AGI) refers to research aimed at tackling the full problemof artiﬁcial intelligence, that is, create truly intelligent agents. This sets it apart from mostAI research which aims at solving relatively narrow domains, such as character recognition,motion planning, or increasing player satisfaction in games. But how do we know when anagent is truly intelligent? A common point of reference in the AGI community is Legg andHutter’s formal deﬁnition of universal intelligence, which has the appeal of simplicity andgenerality but is unfortunately incomputable.Games of various kinds are commonly used as benchmarks for “narrow” AI research,as they are considered to have many important properties. We argue that many of theseproperties carry over to the testing of general intelligence as well. We then sketch how suchtesting could practically be carried out. The central part of this sketch is an extension ofuniversal intelligence to deal with ﬁnite time, and the use of sampling of the space of gamesexpressed in a suitably biased game description language.Keywords: measure of intelligence, games
The list Viterbi training algorithm and its application to keyword search over databases

Hidden Markov Models (HMMs) are today employed in a variety of applications, ranging from speech recognition to bioinformatics. In this paper, we present the List Viterbi training algorithm, a version of the Expectation-Maximization (EM) algorithm based on the List Viterbi algorithm instead of the commonly used forward-backward algorithm. We developed the batch and online versions of the algorithm, and we also describe an interesting application in the context of keyword search over databases, where we exploit a HMM for matching keywords into database terms. In our experiments we tested the online version of the training algorithm in a semi-supervised setting that allows us to take into account the feedbacks provided by the users.