Miracle-dz/UniFAR-FTUs huggingface dataset:
Random field theory-based p-values: a review of the SPM implementation

P-values and null-hypothesis significance testing are popular data-analytical tools in functional neuroimaging. Sparked by the analysis of resting-state fMRI data, there has recently been a resurgence of interest in the validity of some of the p-values evaluated with the widely used software SPM. The default parametric p-values reported in SPM are based on the application of results from random field theory to statistical parametric maps, a framework we refer to as RFP. While RFP was established almost two decades ago and has since been applied in a plethora of fMRI studies, there does not exist a unified documentation of the mathematical and computational underpinnings of RFP as implemented in current versions of SPM. Here, we provide such a documentation with the aim of contributing to contemporary efforts towards higher levels of computational transparency in functional neuroimaging.
Power Allocation in Multi-User Cellular Networks: Deep Reinforcement Learning Approaches

The model-based power allocation has been investigated for decades, but this approach requires mathematical models to be analytically tractable and it has high computational complexity. Recently, the data-driven model-free approaches have been rapidly developed to achieve near-optimal performance with affordable computational complexity, and deep reinforcement learning (DRL) is regarded as one such approach having great potential for future intelligent networks. In this paper, a dynamic downlink power control problem is considered for maximizing the sum-rate in a multi-user wireless cellular network. Using cross-cell coordinations, the proposed multi-agent DRL framework includes off-line and on-line centralized training and distributed execution, and a mathematical analysis is presented for the top-level design of the near-static problem. Policy-based REINFORCE, value-based deep Q-learning (DQL), actor-critic deep deterministic policy gradient (DDPG) algorithms are proposed for this sum-rate problem. Simulation results show that the data-driven approaches outperform the state-of-art model-based methods on sum-rate performance. Furthermore, the DDPG outperforms the REINFORCE and DQL in terms of both sum-rate performance and robustness.
Contribution à l'étude des parois complexes intégrant des matériaux à changements de phase : modélisation, expérimentation, et évaluation de la performance énergétique globale

Cette these s'inscrit dans une demarche de maitrise de l'energie dans le secteur du bâtiment. Elle a pour but d'utiliser des solutions passives pour atteindre de hautes performances energetiques. Une des solutions proposee, est l'utilisation de Materiaux a Changements de Phase (MCP) dans les parois. Les materiaux a formes stabilisee solide-liquide, sont utilises pour stocker l'energie thermique sous forme de chaleur latente. Le but de l'etude est de mettre en evidence l'impact reel des MCP en terme de complement d'isolation thermique et de proposer un modele thermique pour predire son impact sur le champ de temperature et par consequent, sur le confort thermique. Une sequence experimentale en environnement naturel et a grande echelle a ete menee a l'ile de La Reunion, ou le climat est tropical et humide, avec un fort taux d'ensoleillement. Le rayonnement solaire etant important, il est necessaire de minimiser les sollicitations solaires sur l'ensemble du bâtiment et en particulier la toiture, qui constitue la surface la plus exposee. La presence de lames d'airs au niveau de la toiture, qualifie celle-ci de complexe. Il est alors necessaire de proposer une modelisation adaptee. Le modele est couple a un code de simulation thermique du bâtiment (ISOLAB) et permet de predire d'une part, les profils de temperatures de chacune des surfaces constituant l'enveloppe du bâtiment, et d'autre part, d'evaluer l'impact des MCP sur le confort thermique pour differentes configurations. Selon une methodologie, alliant Modelisation, Experimentation et Validation (MEV), la demarche a permis de valider l'experimentation dediee et d'evaluer la capacite du modele a predire l'ensemble des donnees issues de l'experimentation.
The adaptive interpolation method for proving replica formulas. Applications to the Curie–Weiss and Wigner spike models

In this contribution we give a pedagogic introduction to the newly introduced adaptive interpolation method to prove in a simple and unified way replica formulas for Bayesian optimal inference problems. Many aspects of this method can already be explained at the level of the simple Curie–Weiss spin system. This provides a new method of solution for this model which does not appear to be known. We then generalize this analysis to a paradigmatic inference problem, namely rank-one matrix estimation, also refered to as the Wigner spike model in statistics. We give many pointers to the recent literature where the method has been succesfully applied.
Efficient Tsunami Modeling on Adaptive Grids with Graphics Processing Units (GPUs)

Solving the shallow water equations efficiently is critical to the study of natural hazards induced by tsunami and storm surge, since it provides more response time in an early warning system and allows more runs to be done for probabilistic assessment where thousands of runs may be required. Using Adaptive Mesh Refinement (AMR) speeds up the process by greatly reducing computational demands, while accelerating the code using the Graphics Processing Unit (GPU) does so through using faster hardware. Combining both, we present an efficient CUDA implementation of GeoClaw, an open source Godunov-type high-resolution finite volume numerical scheme on adaptive grids for shallow water system with varying topography. The use of AMR and spherical coordinates allows modeling transoceanic tsunami simulation. Numerical experiments on several realistic tsunami modeling problems illustrate the correctness and efficiency of the code, which implements a simplified dimensionally-split version of the algorithms. This implementation is shown to be accurate and faster than the original when using CPUs alone. The GPU implementation, when running on a single GPU, is observed to be 3.6 to 6.4 times faster than the original model running in parallel on a 16-core CPU. Three metrics are proposed to evaluate relative performance of the model, which shows efficient usage of hardware resources.
Mining Within-Trial Oscillatory Brain Dynamics to Address the Variability of Optimized Spatial Filters

Data-driven spatial filtering algorithms optimize scores, such as the contrast between two conditions to extract oscillatory brain signal components. Most machine learning approaches for the filter estimation, however, disregard within-trial temporal dynamics and are extremely sensitive to changes in training data and involved hyperparameters. This leads to highly variable solutions and impedes the selection of a suitable candidate for, e.g., neurotechnological applications. Fostering component introspection, we propose to embrace this variability by condensing the functional signatures of a large set of oscillatory components into homogeneous clusters, each representing specific within-trial envelope dynamics. The proposed method is exemplified by and evaluated on a complex hand force task with a rich within-trial structure. Based on electroencephalography data of 18 healthy subjects, we found that the components’ distinct temporal envelope dynamics are highly subject-specific. On average, we obtained seven clusters per subject, which were strictly confined regarding their underlying frequency bands. As the analysis method is not limited to a specific spatial filtering algorithm, it could be utilized for a wide range of neurotechnological applications, e.g., to select and monitor functionally relevant features for brain–computer interface protocols in stroke rehabilitation.
Energy Confused Adversarial Metric Learning for Zero-Shot Image Retrieval and Clustering

Deep metric learning has been widely applied in many computer vision tasks, and recently, it is more attractive in zeroshot image retrieval and clustering (ZSRC) where a good embedding is requested such that the unseen classes can be distinguished well. Most existing works deem this ’good’ embedding just to be the discriminative one and thus race to devise powerful metric objectives or hard-sample mining strategies for leaning discriminative embedding. However, in this paper, we first emphasize that the generalization ability is a core ingredient of this ’good’ embedding as well and largely affects the metric performance in zero-shot settings as a matter of fact. Then, we propose the Energy Confused Adversarial Metric Learning (ECAML) framework to explicitly optimize a robust metric. It is mainly achieved by introducing an interesting Energy Confusion regularization term, which daringly breaks away from the traditional metric learning idea of discriminative objective devising, and seeks to ’confuse’ the learned model so as to encourage its generalization ability by reducing overfitting on the seen classes. We train this confusion term together with the conventional metric objective in an adversarial manner. Although it seems weird to ’confuse’ the network, we show that our ECAML indeed serves as an efficient regularization technique for metric learning and is applicable to various conventional metric methods. This paper empirically and experimentally demonstrates the importance of learning embedding with good generalization, achieving state-of-theart performances on the popular CUB, CARS, Stanford Online Products and In-Shop datasets for ZSRC tasks. Code available at http://www.bhchen. cn/.
Moment-to-Moment Detection of Internal Thought from Eye Vergence Behaviour

Internal thought refers to the process of directing attention away from a primary visual task to internal cognitive processing. Internal thought is a pervasive mental activity and closely related to primary task performance. As such, automatic detection of internal thought has significant potential for user modelling in intelligent interfaces, particularly for e-learning applications. Despite the close link between the eyes and the human mind, only a few studies have investigated vergence behaviour during internal thought and none has studied moment-to-moment detection of internal thought from gaze. While prior studies relied on long-term data analysis and required a large number of gaze characteristics, we describe a novel method that is computationally light-weight and that only requires eye vergence information that is readily available from binocular eye trackers. We further propose a novel paradigm to obtain ground truth internal thought annotations that exploits human blur perception. We evaluate our method for three increasingly challenging detection tasks: (1) during a controlled math-solving task, (2) during natural viewing of lecture videos, and (3) during daily activities, such as coding, browsing, and reading. Results from these evaluations demonstrate the performance and robustness of vergence-based detection of internal thought and, as such, open up new directions for research on interfaces that adapt to shifts of mental attention.
Spec-ResNet: A General Audio Steganalysis scheme based on Deep Residual Network of Spectrogram

The widespread application of audio and video communication technology make the compressed audio data flowing over the Internet, and make it become an important carrier for covert communication. There are many steganographic schemes emerged in the mainstream audio compression data, such as AAC and MP3, followed by many steganalysis schemes. However, these steganalysis schemes are only effective in the specific embedded domain. In this paper, a general steganalysis scheme Spec-ResNet (Deep Residual Network of Spectrogram) is proposed to detect the steganography schemes of different embedding domain for AAC and MP3. The basic idea is that the steganographic modification of different embedding domain will all introduce the change of the decoded audio signal. In this paper, the spectrogram, which is the visual representation of the spectrum of frequencies of audio signal, is adopted as the input of the feature network to extract the universal features introduced by steganography schemes; Deep Neural Network Spec-ResNet is well-designed to represent the steganalysis feature; and the features extracted from different spectrogram windows are combined to fully capture the steganalysis features. The experiment results show that the proposed scheme has good detection accuracy and generality. The proposed scheme has better detection accuracy for three different AAC steganographic schemes and MP3Stego than the state-of-arts steganalysis schemes which are based on traditional hand-crafted or CNN-based feature. To the best of our knowledge, the audio steganalysis scheme based on the spectrogram and deep residual network is first proposed in this paper. The method proposed in this paper can be extended to the audio steganalysis of other codec or audio forensics.
CASCLIK: CasADi-Based Closed-Loop Inverse Kinematics

A Python module for rapid prototyping of constraint-based closed-loop inverse kinematics controllers is presented. The module allows for combining multiple tasks that are resolved with a quadratic, nonlinear, or model predictive optimization-based approach, or a set-based task-priority inverse kinematics approach. The optimization-based approaches are described in relation to the set-based task approach, and a novel multidimensional "in tangent cone" function is presented for set-based tasks. A ROS component is provided, and the controllers are tested with matching a pose using either transformation matrices or dual quaternions, trajectory tracking while remaining in a bounded workspace, maximizing manipulability during a tracking task, tracking an input marker's position, and force compliance.
We Are Not Your Real Parents: Telling Causal from Confounded using MDL

Given data over variables (X1,.,Xm,Y) we consider the problem of finding out whether X jointly causes Y or whether they are all confounded by an unobserved latent variable Z. To do so, we take an information-theoretic approach based on Kolmogorov complexity. In a nutshell, we follow the postulate that first encoding the true cause, and then the effects given that cause, results in a shorter description than any other encoding of the observed variables. The ideal score is not computable, and hence we have to approximate it. We propose to do so using the Minimum Description Length (MDL) principle. We compare the MDL scores under the models where X causes Y and where there exists a latent variables Z confounding both X and Y and show our scores are consistent. To find potential confounders we propose using latent factor modeling, in particular, probabilistic PCA (PPCA). Empirical evaluation on both synthetic and real-world data shows that our method, CoCa, performs very well -- even when the true generating process of the data is far from the assumptions made by the models we use. Moreover, it is robust as its accuracy goes hand in hand with its confidence.
Application development for touch-screen interfaces

The way we interact with computers will soon change dramatically. The input interfaces are becoming more and more intuitive and one of the latest technologies is a touch-screen interface capable of dealing with multiple inputs simultaneously. The purpose of this project was to create a basic application framework for future projects dealing with this interface, as well as to create a couple of example programs utilizing the framework. The result is a working application framework, making it easy to create new applications for this interface, as well as five different example applications. This report describes my work and my experiences with this new interface. Sammanfattning Sattet som vi interagerar med datorer kommer snart att forandras drastiskt. Styrenheterna blir allt mer intuitiva och en av de senaste teknologierna ar en tryckkanslig bildskarm som klarar av flera inmatningar samtidigt. Syftet med detta projekt var att skapa en grund for programutveckling for framtida projekt som ska jobba mot detta nya granssnitt, samt skapa nagra enkla exempelprogram som anvander sig av denna grund. Resultatet ar en fungerande bas for programutveckling, som gor det latt att skapa nya applikationer for detta granssnitt, samt fem olika exempelapplikationer. Den har rapporten beskriver mitt arbete och mina erfarenheter med det har nya granssnittet. Preface This thesis project was done at gsCEPT, Lulea University of Technology, campus Skelleftea, during a period of 10 weeks in the spring of 2007. The university had recently looked into the FTIR technology and was in the act of acquiring the equipment needed for it. We were a total of four students who got the assignment to develop different parts for a touch-screen interface as well as some proof-of-concept software. My part was to develop an application framework and some proof-of-concept applications to show off the possibilities of the interface. I would like to thank Johannes Hirche at LTU who has been my mentor during this project. I’d also like to thank Patrik Holmlund (LTU) and Arash Vahdat (LTU) for giving me the opportunity to work with this project.
Determinants of exercise participation in relationship to severity of clinical depression in adult female psychiatric in-patients [online]

Die Forschungsergebnisse unterstutzen die Theorie, dass eine Verbindung zwischen physischer (In-)Aktivitat und Depression bei Psychiatriepatienten besteht. Folglich konnen Strategien zur Ubungsakzeptanz als wichtiger Beitrag betrachtet werden, um die Reduzierung von Depressionssymptomen bei diesen Patienten zu erklaren. Die vorliegende Studie untersucht die Beziehung zwischen Strategien zur Ubungsakzeptanz und der Schwere von Depressionssymptomen bei einer Testgruppe weiblicher Patienten in stationarer Behandlung, die an klinischen Depressionen leiden. In der Datenanalyse sind 38 Patientinnen erfasst. Selbst gemeldete physische Aktivitat, entscheidende Faktoren fur soziale Unterstutzung, Hindernisse und die Vorteile physischer Aktivitat liefern einen wichtigen Beitrag zur Erklarung von Depressionen bei der Einlieferung (R2adj = 0,26). Ungeachtet der Einschrankungen, denen derartige Querschnittsuntersuchungen unterliegen, kann die Erforschung der Bedeutung der entscheidenden Faktoren physischer Aktivitat fur Depressionen zur Steigerung der Beteiligungsquote beitragen und, im Gegenzug, dazu fuhren, dass das Potential der Ubungen dabei hilft, Veranderungen der Depressionswerte vorauszusagen.
Local Patterns for Face Recognition

The main objective of the local pattern is to describe the image with high discriminative features so that the local pattern descriptors are more suitable for face recognition. The word “ local ” represents the measured image with the subregion and is the key in this chapter. Regardless of the techniques proposed, the local pattern is one of the most interesting areas in face recognition. The local facial descriptor is a local pattern that generates the descriptor by considering the subregion of an image. Techniques based on various combination methods from the local facial descriptors are not unusual. This chapter is concerned primarily to help the reader to develop a basic understanding of the local pattern descriptors and how they apply to face recognition. We begin to describe the outline of the local pattern in face recognition and its relative facial descriptors. Next, we give an introduction to the popular local patterns and establish examples to demonstrate the process of each method. To the end of this chapter, we conclude those methods with a discussion of issues related to the properties of the local patterns.
Proﬁling in Games: Understanding Behavior from Telemetry

The availability of large scale game behavioral data has led to increased attention to business intelligence in game development and -research. The analysis of player behavior has rapidly emerged to become an integrated component of game development, and is used to address a range of challenges such as improving game design, managing monetization, predicting behavior, recommending games, detecting fraud, or building a general understanding of human behavior in online environments. Key challenges in contemporary game analytics are pattern ﬁnding in behavioral telemetry and the construction of actionable models of behavior based on patterns. When applied to players, this process can be broadly referred to as proﬁling. There are a number of challenges in behavioral proﬁling, including the constant growth in data volume, the variety, dimensionality and volatility of big data in game development. In this chapter, current work in behavioral proﬁling in digital games is described and analyzed and the state-of-the-art is established. The techniques used for behavioral proﬁling in games are described across descriptive statistics and machine learning. Furthermore, a number of examples are provided that cover a range of techniques from machine learning: conventional clustering, archetypal analysis, decomposition into directional components, and decision trees. Each of these have speciﬁc advantages in overcoming the challenge of extracting interpretable behavioral proﬁles from behavioral game telemetry data.
Adaptive control of pure-feedback systems in the presence of parametric uncertainties

The purpose of this study is to investigate an adaptive control approach for completely nonaffine pure-feedback systems with linear/nonlinear parameterization. In this approach, the parameter separation technique and the idea of the positive function of linearly connected parameters are coupled effectively with the combination of backstepping and time-scale separation. Subsequently, a fast dynamical equation is derived from the original subsystem, where the solution is sought to approximate the corresponding ideal virtual/actual control inputs. Furthermore, the adaptation law of unknown parameters can be derived based on Lyapunov theory in the backstepping technique and there is no need to design a state predictor for this purpose. Therefore, it results in higher accuracy and avoids complexity. The closed-loop stability and the state regulation of these systems are proved. Finally, the simulation results are provided to demonstrate the effectiveness of the proposed approach.