Miracle-dz/UniFAR-FTUs huggingface dataset:
Meta-analysis of real-time fMRI neurofeedback studies: how is brain regulation mediated?

An increasing number of studies using real-time fMRI neurofeedback have demonstrated that successful regulation of neural activity is possible in various brain regions. Since these studies focused on the regulated region(s), little is known about the underlying neuronal mechanisms associated with neurofeedback-guided control of brain activation, i.e. the regulating network. While the specificity of the activation during self-regulation is an important factor, no study has effectively determined the overarching network involved in self-regulation. In an effort to detect regions that are responsible for the act of brain regulation itself, we performed a meta-analysis of data involving different target regions based on studies from different research groups. We included twelve suitable studies that examined eight different target regions amounting to a total of 175 subjects and 899 neurofeedback runs. Data analysis included a standard first(single subject, extracting main paradigm) and secondlevel (single subject, all runs) general linear model (GLM) analysis of all participants taking into account the individual timing. Subsequently, at the third level, a random effects model GLM included all subjects of all studies, resulting in an overall mixed effects model. Since four of the twelve studies had a reduced field of view (FoV), we repeated the same analysis in a subsample of eight studies that had a well-overlapping FoV to obtain a more global picture of self-regulation. The GLM analysis revealed that the anterior insula as well as the basal ganglia, notably the striatum were consistently active during the regulation of brain activation across the studies. The AIC has been implicated in interoceptive awareness of the body and cognitive control. BG are involved in procedural learning, visuomotor integration and other higher cognitive processes including motivation. The larger FoV analysis yielded additional activations in the anterior cingulate cortex, the dorsolateral and ventrolateral prefrontal cortex, the temporo-parietal area and the visual association areas including the temporooccipital junction. In conclusion, we demonstrate that several key regions, most importantly the anterior insula and the basal ganglia, are consistently activated during selfregulation in real-time fMRI neurofeedback independent of the targeted regionof-interest. Our results imply that if the real-time fMRI neurofeedback studies target regions of this regulation network, such as the AIC, care should be given whether activation changes are related to successful regulation, or related to the regulation process per se. Furthermore, future research is needed to determine how activation within this regulation network is related to neurofeedback success.
Impact of climate change on renewable groundwater resources : assessing the benefits of avoided greenhouse gas emissions using selected CMIP 5 climate projections

Reduction of greenhouse gas (GHG) emissions to minimize climate change requires very significant societal effort. To motivate this effort, it is important to clarify the benefits of avoided emissions. To this end, we analysed the impact of four emissions scenarios on future renewable groundwater resources, which range from 1600 GtCO2 during the 21st century (RCP2.6) to 7300 GtCO2 (RCP8.5). Climate modelling uncertainty was taken into account by applying the bias-corrected output of a small ensemble of five CMIP5 global climate models (GCM) as provided by the ISI-MIP effort to the global hydrological model WaterGAP. Despite significant climate model uncertainty, the benefits of avoided emissions with respect to renewable groundwater resources (i.e. groundwater recharge (GWR)) are obvious. The percentage of projected global population (SSP2 population scenario) suffering from a significant decrease of GWR of more than 10% by the 2080s as compared to 1971–2000 decreases from 38% (GCM range 27–50%) for RCP8.5 to 24% (11–39%) for RCP2.6. The population fraction that is spared from any significant GWR change would increase from 29% to 47% if emissions were restricted to RCP2.6. Increases of GWR are more likely to occur in areas with below average population density, while GWR decreases of more than 30% affect especially (semi)arid regions, across all GCMs. Considering change of renewable groundwater resources as a function of mean global temperature (GMT) rise, the land area that is affected by GWR decreases of more than 30% and 70% increases linearly with global warming from 0 to 3 C. For each degree of GMT rise, an additional 4% of the global land area (except Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. 1 1748-9326/13/024023+14$33.00 c © 2013 IOP Publishing Ltd Printed in the UK Environ. Res. Lett. 8 (2013) 024023 F T Portmann et al Greenland and Antarctica) is affected by a GWR decrease of more than 30%, and an additional 1% is affected by a decrease of more than 70%.
A Non-Convex Optimization Approach to Correlation Clustering

We develop a non-convex optimization approach to correlation clustering using the Frank-Wolfe (FW) framework. We show that the basic approach leads to a simple and natural local search algorithm with guaranteed convergence. This algorithm already beats alternative algorithms by substantial margins in both running time and quality of the clustering. Using ideas from FW algorithms, we develop subsampling and variance reduction paradigms for this approach. This yields both a practical improvement of the algorithm and some interesting further directions to investigate. We demonstrate the performance on both synthetic and real world data sets.
Weighted graphlets and deep neural networks for protein structure classification

As proteins with similar structures often have similar functions, analysis of protein structures can help predict protein functions and is thus important. We consider the problem of protein structure classification, which computationally classifies the structures of proteins into pre-defined groups. We develop a weighted network that depicts the protein structures, and more importantly, we propose the first graphlet-based measure that applies to weighted networks. Further, we develop a deep neural network (DNN) composed of both convolutional and recurrent layers to use this measure for classification. Put together, our approach shows dramatic improvements in performance over existing graphlet-based approaches on 36 real datasets. Even comparing with the state-of-the-art approach, it almost halves the classification error. In addition to protein structure networks, our weighted-graphlet measure and DNN classifier can potentially be applied to classification of other weighted networks in computational biology as well as in other domains.
Early Forecasting and Prevention of Cardio Vascular Disease Based on Human Life Style Factors

In the rapid growing world living fashion of human beings are distorted a lot and will influence the healthiness situation of human beings. The World Health Organization (WHO) statistics provides information that the cardio vascular diseases have an immense deal of interest inmedical research owing to its impact on person healthiness. This research study is intended torecognize and analyze the human life style factors in forecasting the cardio vascular disease with data mining techniques. The data collected from the range of citizens living in Bangaloreregion and the data obtainable inside online data repository are the input to this research analysis. Classification techniques K-means clustering algorithm and the decision tree approaches are used in this study and the precision, recall, accuracy and the F-measures are the measures estimated to prove the results with the different error measures RMSE, MAE and Kappa statistic values. This early forecasting process is proved with an accuracy of 94.56 percentages in K-means and 93.37 percentages with decision tree techniques.
Assessment of Tomographic SAR Processing Techniques for Forest Structure Estimation

The future SAR missions such as BIOMASS and Tandem-L will exploit the potential of Synthetic Aperture Radar Tomography to extract 3D forest structure information. Several algorithms can be applied for TomoSAR imaging. This paper analyses the performance of two non-parametric algorithms, Capon Beamforming and Compressive Sensing (CS), for forest structure applications, through a set of simulations reflecting different forest scenarios (distribution of canopy layers and temporal decorrealtion) and system parameters (baseline distribution, multilook, and noise). Results show that CS is in general more stable than Capon in front of system and scene variability, but may be more affected by artefacts.
Natural Language State Representation for Reinforcement Learning

Recent advances in Reinforcement Learning have highlighted the difficulties in learning within complex high dimensional domains. We argue that one of the main reasons that current approaches do not perform well, is that the information is represented sub-optimally. A natural way to describe what we observe, is through natural language. In this paper, we implement a natural language state representation to learn and complete tasks. Our experiments suggest that natural language based agents are more robust, converge faster and perform better than vision based agents, showing the benefit of using natural language representations for Reinforcement Learning.
Representation Learning of EHR Data via Graph-Based Medical Entity Embedding

Automatic representation learning of key entities in electronic health record (EHR) data is a critical step for healthcare informatics that turns heterogeneous medical records into structured and actionable information. Here we propose ME2Vec, an algorithmic framework for learning low-dimensional vectors of the most common entities in EHR: medical services, doctors, and patients. ME2Vec leverages diverse graph embedding techniques to cater for the unique characteristic of each medical entity. Using real-world clinical data, we demonstrate the efficacy of ME2Vec over competitive baselines on disease diagnosis prediction.
What is Transiting HD 139139?

The NASA mission Kepler has detected 28 transits with depths and durations hours in the light curve of HD139139 during a 87 days campaign . Their arrival times are erratic. Rappaport et al. (2019) discard ten explanations. It is not clear if the transits are for HD 139139 or for a star B at 3.3 arcsec. New radial velocity variation data give RV for HD139139 in 4 days (F. Bouchy and S. Udry, private communication) excluding a close-in planet orbiting HD 139139, as proposed earlier (Schneider 2019). (However, this explanation is still valid for the star B for which radial velocity data are very poor). Here I thus explore new tentative explanations and their likelihood: 1/ An eccentric transiters belt around HD 139139 2/ Interstellar transiters 3/ Solar System objects.
Plasmonic Nanocavity Modes: From Near-Field to Far-Field Radiation

In the past decade, advances in nanotechnology have led to the development of plasmonic nanocavities which facilitate light-matter strong coupling in ambient conditions. The most robust example is the nanoparticle-on-mirror (NPoM) structure whose geometry is controlled with subnanometer precision. The excited plasmons in such nanocavities are extremely sensitive to the exact morphology of the nanocavity, giving rise to unexpected optical behaviors. So far, most theoretical and experimental studies on such nanocavities have been based solely on their scattering and absorption properties. However, these methods do not provide a complete optical description of a NPoM. Here, the NPoM is treated as an open non-conservative system supporting a set of photonic quasinormal modes (QNMs). By investigating the morphology-dependent optical properties of nanocavities, we propose a simple yet comprehensive nomenclature based on spherical harmonics and report spectrally overlapping bright and dark nanogap eigenmodes. The near-field and far-field optical properties of NPoMs are explored and reveal intricate multi-modal interactions.
A differential approximation model for passive scalar turbulence

Two-dimensional passive scalar turbulence is studied by means of a k-space diffusion model based on a third order differential approximation. This simple description of local nonlinear interactions in Fourier space is shown to provide a general expression, in line with previous seminal works, and appears to be suitable for various 2D turbulence problems. Steady state solutions for the spectral energy density of the flow are shown to recover the Kraichnan–Kolmogorov phenomenology of the dual cascade, while various passive scalar spectra, such as Batchelor or Obukhov–Corssin spectra are recovered as steady state solutions of the spectral energy density of the passive scalar. These analytical results are then corroborated by numerical solutions of the time evolving problem with energy and passive scalar injection and dissipation on a logarithmic wavenumber space grid over a large range of scales. The particular power law spectra that are obtained are found to depend mainly on the location of the kinetic and passive scalar energy injections. In particular, it is shown that by injecting the energy simultaneously at large and small scales, wave number spectra consistent with those of Nastrom–Gage can be obtained.
Effects of niche overlap on coexistence, fixation and invasion in a population of two interacting species

Synergistic and antagonistic interactions in multi-species populations—such as resource sharing and competition—result in remarkably diverse behaviours in populations of interacting cells, such as in soil or human microbiomes, or clonal competition in cancer. The degree of inter- and intra-specific interaction can often be quantified through the notion of an ecological ‘niche’. Typically, weakly interacting species that occupy largely distinct niches result in stable mixed populations, while strong interactions and competition for the same niche result in rapid extinctions of some species and fixations of others. We investigate the transition of a deterministically stable mixed population to a stochasticity-induced fixation as a function of the niche overlap between the two species. We also investigate the effect of the niche overlap on the population stability with respect to external invasions. Our results have important implications for a number of experimental systems.
Distributed Learning of Fully Connected Neural Networks using Independent Subnet Training

Distributed machine learning (ML) can bring more computational resources to bear than single-machine learning, thus enabling reductions in training time. Distributed learning partitions models and data over many machines, allowing model and dataset sizes beyond the available compute power and memory of a single machine. In practice though, distributed ML is challenging when distribution is mandatory, rather than chosen by the practitioner. In such scenarios, data could unavoidably be separated among workers due to limited memory capacity per worker or even because of data privacy issues. There, existing distributed methods will utterly fail due to dominant transfer costs across workers, or do not even apply. We propose a new approach to distributed fully connected neural network learning, called independent subnet training (IST), to handle these cases. In IST, the original network is decomposed into a set of narrow subnetworks with the same depth. These subnetworks are then trained locally before parameters are exchanged to produce new subnets and the training cycle repeats. Such a naturally "model parallel" approach limits memory usage by storing only a portion of network parameters on each device. Additionally, no requirements exist for sharing data between workers (i.e., subnet training is local and independent) and communication volume and frequency are reduced by decomposing the original network into independent subnets. These properties of IST can cope with issues due to distributed data, slow interconnects, or limited device memory, making IST a suitable approach for cases of mandatory distribution. We show experimentally that IST results in training times that are much lower than common distributed learning approaches.
Fourier integrals and Sobolev embedding on rearrangement-invariant quasi-Banach function spaces

We extend the mapping properties for the fractional integral operators, the convolution operators, the Fourier integral operators and the oscillatory integral operators to rearrangement-invariant quasiBanach functions spaces (r.i.q.B.f.s.). We also generalize the Fourier restriction theorem and the Sobolev embedding theorem to r.i. q.B. f.s. We obtain the above results by introducing two families of r.i. q.B. f.s. Furthermore, these two families of r.i. q.B. f.s. also give us some embedding and interpolation results of Triebel-Lizorkin type spaces and Hardy type spaces built on r.i. q.B. f.s.
Model-Free Recurrent Reinforcement Learning for AUV Horizontal Control

In this paper, aiming at the problems of 2-DOF horizontal motion control with high precision for autonomous underwater vehicle(AUV) trajectory tracking tasks, deep reinforcement learning controllers are applied to these conditions. These control problems are considered as a POMDP (Partially Observable Markov Decision Process). Model-free reinforcement learning(RL) algorithms for continuous control mission based on Deterministic Policy Gradient(DPG) allows robots learn from received delayed rewards when interacting with environments. Recurrent neural networks LSTM (Long Short-Term Memory) are involved into the reinforcement learning algorithm. Through this deep reinforcement learning algorithm, AUVs learn from sequences of dynamic information. The horizontal trajectory tracking tasks are described by LOS method and the motion control are idealized as a SISO model. Tanh-estimators are presented as data normalization. Moreover, AUV horizontal trajectory tracking and motion control simulation results demonstrate this algorithm gets better accuracy compared with the PID method and other non-recurrent methods. Efforts show the efficiency and effectiveness of the improved deep reinforcement learning algorithm.
Transformer-XL: Language Modeling with Longer-Term Dependency

We propose a novel neural architecture, Transformer-XL, for modeling longerterm dependency. To address the limitation of fixed-length contexts, we introduce a notion of recurrence by reusing the representations from the history. Empirically, we show state-of-the-art (SoTA) results on both word-level and character-level language modeling datasets, including WikiText-103, One Billion Word, Penn Treebank, and enwiki8. Notably, we improve the SoTA results from 1.06 to 0.99 in bpc on enwiki8, from 33.0 to 18.9 in perplexity on WikiText-103, and from 28.0 to 23.5 in perplexity on One Billion Word. Performance improves when the attention length increases during evaluation, and our best model attends to up to 1,600 words and 3,800 characters. To quantify the effective length of dependency, we devise a new metric and show that on WikiText-103 Transformer-XL manages to model dependency that is about 80% longer than recurrent networks and 450% longer than Transformer. Moreover, Transformer-XL is up to 1,800+ times faster than vanilla Transformer during evaluation.
Design and Simulation of Fuzzy Logic Controlled Car Parking Assist System

The simulation of the autonomous self-parking car model is introduced in this paper. The system using a fuzzy logic controller in monitoring the car into a parking spot. The car will be manoeuvred to the parking space along a curvilinear path called path planning that designed to prevent crashing with the neighbouring cars or obstacles. In this project, it has used the Simulink in MATLAB to compute the system. The Simulink has an easier way to set up the rules in fuzzy control. The system imitates human thinking in performing the parking whereby it controls the steering angle and speed of the car corresponded to the situation of the car.
Models and Algorithms For Operations Management Applications

In the last decades, there has been a huge evolution in the development and application of Derivative Free Optimization (DFO) techniques. One of the major emerging DFO application fields are the optimal design of industrial products and machines and the development of efficient computer software. This Ph.D. thesis focuses on the exploration of the emerging DFO techniques, oriented especially in the optimization of real-world industrial problem. In this thesis is firstly presented a brief overview of the main DFO techniques and application. Then, are reported two works describing the development of DFO approaches aimed to tackle the optimization of Computer Vision Algorithms (CVA), employed in the automatic defect detection of pieces produced by a real-world industries.
Defining Undesign in a Designed Universe

The argument from design, recast today in the Intelligent Design movement, relies critically on the contrast of designed things with undesigned things. This poses a problem for Christians, however, because they affirm that God designed the whole universe. How then can we call anything undesigned? I argue that this problem is equivalent to the problem of free will, or the problem of moral evil, and as such can be addressed by the same philosophical frameworks developed in the past for addressing those issues, in particular the notions of different levels of description and Augustine’s different levels of giftedness.
Institutional Repository

The European General Data Protection Regulation (GDPR) brings new challenges for companies, who must demonstrate that their systems and business processes comply with usage constraints specified by data subjects. However, due to the lack of standards, tools, and best practices, many organizations struggle to adapt their infrastructure and processes to ensure and demonstrate that all data processing is in compliance with users’ given consent. The SPECIAL EU H2020 project has developed vocabularies that can formally describe data subjects’ given consent as well as methods that use this description to automatically determine whether processing of the data according to a given policy is compliant with the given consent. Whereas this makes it possible to determine whether processing was compliant or not, integration of the approach into existing line of business applications and ex-ante compliance checking remains an open challenge. In this short paper, we demonstrate how the SPECIAL consent and compliance framework can be integrated into Linked Widgets, a mashup platform, in order to support privacy-aware ad-hoc integration of personal data. The resulting environment makes it possible to create data integration and processing workflows out of components that inherently respect usage policies of the data that is being processed and are able to demonstrate compliance. We provide an overview of the necessary meta data and orchestration towards a privacy-aware linked data mashup platform that automatically respects subjects’ given consents. The evaluation results show the potential of our approach for ex-ante usage policy compliance checking within the Linked Widgets Platforms and beyond.