Miracle-dz/UniFAR-FTUs huggingface dataset:
First Sunyaev–Zel’dovich mapping with the NIKA2 camera: Implication of cluster substructures for the pressure profile and mass estimate

The complete characterization of the pressure profile of high-redshift galaxy clusters, from their core to their outskirts, is a major issue for the study of the formation of large-scale structures. It is essential to constrain a potential redshift evolution of both the slope and scatter of the mass-observable scaling relations used in cosmology studies based on cluster statistics. In this paper, we present the first thermal Sunyaev–Zel’dovich (tSZ) mapping of a cluster from the sample of the New IRAM Kids Arrays (NIKA2) SZ large program that aims at constraining the redshift evolution of cluster pressure profiles and the tSZ-mass scaling relation. We observed the galaxy cluster PSZ2 G144.83+25.11 at redshift z = 0.58 with the NIKA2 camera, a dual-band (150 and 260 GHz) instrument operated at the Institut de Radioastronomie Millimtrique (IRAM) 30-m telescope. We identify a thermal pressure excess in the south-west region of PSZ2 G144.83+25.11 and a high-redshift sub-millimeter point source that affect the intracluster medium (ICM) morphology of the cluster. The NIKA2 data are used jointly with tSZ data acquired by the Multiplexed SQUID/TES Array at Ninety Gigahertz (MUSTANG), Bolocam, and Planck experiments in order to non-parametrically set the best constraints on the electronic pressure distribution from the cluster core (R ~ 0.02R500) to its outskirts (R ~ 3R500). We investigate the impact of the over-pressure region on the shape of the pressure profile and on the constraints on the integrated Compton parameter Y500. A hydrostatic mass analysis is also performed by combining the tSZ-constrained pressure profile with the deprojected electronic density profile from XMM-Newton. This allows us to conclude that the estimates of Y500 and M500 obtained from the analysis with and without masking the disturbed ICM region differ by 65% and 79%, respectively. This work highlights that NIKA2 will have a crucial impact on the characterization of the scatter of the Y500−M500 scaling relation due to its high potential to constrain the thermodynamic and morphological properties of the ICM when used in synergy with X-ray observations of similar angular resolution. This study also presents the typical products that will be delivered to the community for all clusters included in the NIKA2 tSZ Large Program.
CONVOLUTIONAL NEURAL NETWORK WITH MULTIPLE-WIDTH FREQUENCY-DELTA DATA AUGMENTATION FOR ACOUSTIC SCENE CLASSIFICATION

In this paper, we apply convolutional neural network on acoustic scene classification task of DCASE 2016. We propose multiwidth frequency-delta data augmentation which uses static melspectrogram as well as frequency-delta features as individual examples with same labels for the network input, and the experimental result shows that this method significantly improves the performance compare to the case of using static mel-spectrogram input only. In addition, we propose folded mean aggregation, which first multiplies output probabilities of static and delta augmentation data from the same window first prior to audio clip-wise aggregation, and we found that this method reduces the error rate further. The system exhibited a classification accuracy of 0.831 on the development set and 0.846 on the evaluation set.
Tuning the blueprint: how studies of implicit learning during speaking reveal the information processing components of the production system

ABSTRACT The “blueprint for the speaker” (Levelt, 1989) claimed that production is carried out by autonomous processing components. We present a framework for how those production components may change with experience and review studies that demonstrate limitations in what can be easily learned by adult speakers. Such limitations have been observed in the acquisition of new phonotactic constraints and in speakers’ ability to alter particular verbs’ biases for particular syntactic structures. We attribute these limitations to the learned componential structure of the production system.
ACOUSTIC SCENE CLASSIFICATION USING DEEP LEARNING

Acoustic Scene Classification (ASC) is the task of classifying audio samples on the basis of their soundscapes. This is one of the tasks taken up by Detection and Classification of Acoustic Scenes and Events 2016 (DCASE-2016) challenge. A labeled dataset of audio samples from various scenes is provided and solutions are invited. In this paper, use of Deep Neural Networks (DNN) is proposed for the task of ASC. Here, different methods for extracting features with different classification algorithms are explored. It is observed that DNN works significantly better as compared to other methods trained over the same set of features. It performs at par with the stateof-the-art techniques presented in DCASE-2013. It is concluded that the use of MFCC features with DNN works the best, giving 97.6 % cross-validation score on the development dataset-2016 data for a particular set of parameters for the DNN. Also training a DNN does not take larger run times compared to others methods.
Protein Crowding in Lipid Bilayers Gives Rise to Non-Gaussian Anomalous Lateral Diffusion of Phospholipids and Proteins

Biomembranes are exceptionally crowded with proteins with typical protein-to-lipid ratios being around 1 ∶ 50 − 1 ∶ 100 . Protein crowding has a decisive role in lateral membrane dynamics as shown by recent experimental and computational studies that have reported anomalous lateral diffusion of phospholipids and membrane proteins in crowded lipid membranes. Based on extensive simulations and stochastic modeling of the simulated trajectories, we here investigate in detail how increasing crowding by membrane proteins reshapes the stochastic characteristics of the anomalous lateral diffusion in lipid membranes. We observe that correlated Gaussian processes of the fractional Langevin equation type, identified as the stochastic mechanism behind lipid motion in noncrowded bilayer, no longer adequately describe the lipid and protein motion in crowded but otherwise identical membranes. It turns out that protein crowding gives rise to a multifractal, non-Gaussian, and spatiotemporally heterogeneous anomalous lateral diffusion on time scales from nanoseconds to, at least, tens of microseconds. Our investigation strongly suggests that the macromolecular complexity and spatiotemporal membrane heterogeneity in cellular membranes play critical roles in determining the stochastic nature of the lateral diffusion and, consequently, the associated dynamic phenomena within membranes. Clarifying the exact stochastic mechanism for various kinds of biological membranes is an important step towards a quantitative understanding of numerous intramembrane dynamic phenomena.
Beta-amyloid imaging in dementia

Alzheimer's disease (AD) is a neurodegenerative disorder associated with extracellular plaques, composed of amyloid-beta (Aβ), in the brain. Although the precise mechanism underlying the neurotoxicity of Aβ has not been established, Aβ accumulation is the primary event in a cascade of events that lead to neurofibrillary degeneration and dementia. In particular, the Aβ burden, as assessed by neuroimaging, has proved to be an excellent predictive biomarker. Positron emission tomography, using ligands such as 11C-labeled Pittsburgh Compound B or 18F-labeled tracers, such as 18F-florbetaben, 18F-florbetapir, and 18F-flutemetamol, which bind to Aβ deposits in the brain, has been a valuable technique for visualizing and quantifying the deposition of Aβ throughout the brain in living subjects. Aβ imaging has very high sensitivity for detecting AD pathology. In addition, it can predict the progression from mild cognitive impairment to AD, and contribute to the development of disease-specific therapies.
The RAMPE Project : Interactive , Auditive Information System for the Mobility of Blind People in Public Transports

The RAMPE project aims to design, realize and experiment a system for the assistance and information of blind people so that they can increase their mobility and autonomy in public transport. It is intended to equip bus or tramway stops or to be installed in poles of transport interactions. It is based on smart hand-held Personal Digital Assistant (PDA) with embedded speech synthesis and able to communicate by a wireless WiFi connection with fixed equipment in the bus or tram stations. The main characteristics of the smart hand-held devices are: they can present and filter vocal messages, they can adapt themselves to the type of information system available at the stations, they can react to real-time information sent by the stations. A special care has been given to the design of the man machine interface and to the management of priorities in the real-time vocal information application.
Uncertainty-Aware Path Planning for Navigation on Road Networks Using Augmented MDPs

Although most robots use probabilistic algorithms to solve state estimation problems, path planning is often performed without considering the uncertainty about the robot’s position. Uncertainty, however, matters in planning, but considering it often leads to computationally expensive algorithms. In this paper, we investigate the problem of path planning considering the uncertainty in the robot’s belief about the world, in its perceptions and in its action execution. We propose the use of an uncertainty-augmented Markov Decision Process to approximate the underlying Partially Observable Markov Decision Process, and we employ a localization prior to estimate how the belief about the robot’s position propagates through the environment. This yields to a planning approach that generates navigation policies able to make decisions according to the degree of uncertainty while being computationally tractable. We implemented our approach and thoroughly evaluated it on different navigation problems. Our experiments suggest that we are able to compute policies that are more effective than approaches that ignore the uncertainty, and that also outperform policies that always take the safest actions.
Many-particle entanglement

The quantum state of a system of k particles can be viewed as a tensor of order k. Local stochastic operations on a quantum state correspond to the application of linear maps to the indices of the corresponding tensor and are studied in entanglement theory and implemented in current quantum information science experiments. Interestingly, the notion of tensor transformation is at the heart of the study of the computational complexity of algebraic problems such as the multiplication of matrices. Strassen’s breakthrough algorithm for the multiplication of d-by-d matrices that runs faster than your standard d high-school algorithm spurred a whole development of tensor theory. In these notes (based on lectures given at the Boulder summer school on condensed matter physics 2018), I will explain these connections in the case k = 3 and show how they elucidate our understanding of both entanglement and matrix multiplication. For more recent developments on the subject, please see my presentations on tensors at https://prezi. com/user/christandl/. I will review these connections and present recent results on quantum information-inspired functionals that can serve as obstructions for asymptotic tensor transformations.
Declarative Metadata Management : A Missing Piece in End-To-End Machine Learning

We argue for the necessity of managing the metadata and lineage of common artifacts in machine learning (ML). We discuss a recently presented lightweight system built for this task, which accelerates users in their ML workflows, and provides a basis for comparability and repeatability of ML experiments. This system tracks the lineage of produced artifacts in ML workloads and automatically extracts metadata such as hyperparameters of models, schemas of datasets and layouts of deep neural networks. It provides a general declarative representation of common ML artifacts, is integrated with popular frameworks such as MXNet, SparkML and scikit-learn, and meets the demands of various production use cases at Amazon. ACM Reference Format: Sebastian Schelter, Joos-Hendrik Böse, Johannes Kirschnick, Thoralf Klein, Stephan Seufert. 2018. Declarative Metadata Management: A Missing Piece in End-To-End Machine Learning. In Proceedings of SysML Conference, Stanford, USA, Feb 2018 (SYSML’18), 3 pages. https://doi.org/10.475/123_4
A data-driven approach to exploring similarities of tourist attractions through online reviews

ABSTRACT The motivation for tourists to visit a city is often driven by the uniqueness of the attractions accessible within the region. The draw to these locations varies by visitor as some travellers are interested in a single specific attraction while others prefer thematic travel. Tourists today have access to detailed experiences of other visitors to these locations in the form of user-contributed text reviews, opinions, photographs, and videos, all contributed through online tourism platforms. The data available through these platforms offer a unique opportunity to examine the similarities and difference between these attractions, their cities, and the visitors that contribute the reviews. In this work, we take a data-driven approach to assessing similarity through textual analysis of user-contributed reviews, uncovering nuanced differences and similarities in the ways that reviewers write about attractions and cities.
Towards Smart and Sustainable Future Cities Based on Internet of Things for Developing Countries: What Approach for Africa?

This article aims ﬁrst at reviewing and discussing previous works on Internet of Things based sustainable smart cities. Secondly, it proposes an ideological and technical framework that better suits communities in future cities. At the same time, we provide a reference model that can generally apply to developing countries and speciﬁcally to Africa. This work represents a ﬁrst step towards future cities and should at the same time constitute a reference base for researchers working on the same topic. Some Indexes have been leveraged to carry out this work
Development and assessment of a daily time-step continuous simulation modelling approach for design flood estimation at ungauged locations: ACRU model and Thukela Catchment case study

The estimation of design floods is necessary for the design of hydraulic structures. Commonly used event-based approaches to design flood estimation have several limitations, which include the estimation of antecedent soil moisture conditions and the assumption that the exceedance probability of the design flood is the same as the exceedance probability of the design rainfall. Many of the limitations of event-based approaches may be overcome by the use of continuous simulation modelling for design flood estimation. This paper contains a brief summary on the development and assessment of a continuous simulation modelling system for design flood estimation in ungauged catchments. These developments include an investigation into the appropriate spatial scale of model configuration for optimum performance of the system, the temporal disaggregation of daily rainfall for hydrograph generation, flood routing in ungauged catchments and the use of radar information and rain-gauge data to improve the estimation of catchment rainfall. Results from the application of the system for design flood estimation in the Thukela Catchment in South Africa are presented and discussed. The results from the study highlight the challenges of hydrological modelling in an operational catchment and the need for reliable rainfall and runoff data. From the results obtained, it is concluded that reasonable and consistent estimates of design floods in the Thukela Catchment, particularly in smaller sub-catchments, can be obtained using the ACRU model. Keywords: Design flood estimation; continuous simulation modelling, Thukela Catchment
An Image Fingerprinting System Based on Color Histogram of Affine Covariant Region

In this paper, we propose a content-based image fingerprinting system which uses color histograms of affine covariant regions as a local fingerprint of an image. We focus on the robustness for various distortions and computational efficiency. Given a query, the system first extracts local fingerprints from affine covariant regions. The local fingerprints are based on the color histogram, and the affine covariant regions are detected using the maximally stable extremal region (MSER) detector. After extracting local fingerprints, the system searches local fingerprints in fingerprint database for the nearest neighbor of the query local fingerprints. Then, the query image is identified. Experimental results show that that our system is robust against cropping, resizing, rotation, and small color changes with efficient computational cost.
A Revised Underwater Image Formation Model

The current underwater image formation model descends from atmospheric dehazing equations where attenuation is a weak function of wavelength. We recently showed that this model introduces significant errors and dependencies in the estimation of the direct transmission signal because underwater, light attenuates in a wavelength-dependent manner. Here, we show that the backscattered signal derived from the current model also suffers from dependencies that were previously unaccounted for. In doing so, we use oceanographic measurements to derive the physically valid space of backscatter, and further show that the wideband coefficients that govern backscatter are different than those that govern direct transmission, even though the current model treats them to be the same. We propose a revised equation for underwater image formation that takes these differences into account, and validate it through in situ experiments underwater. This revised model might explain frequent instabilities of current underwater color reconstruction models, and calls for the development of new methods.
Continental‐scale macrofungal assemblage patterns correlate with climate, soil carbon and nitrogen deposition

Macroecological scales of species compositional trends are well documented for a variety of plant and animal groups, but remain sparse for fungi, despite their ecological importance in carbon and nutrient cycling. It is, thus, essential to understand the composition of fungal assemblages across broad geographical scales and the underlying drivers. Our overall aim was to describe these patterns for fungi across two nutritional modes (saprotrophic and ectomycorrhizal). Furthermore, we aimed to elucidate the temporal component of fruiting patterns and to relate these to soil carbon and nitrogen deposition.
Investigating the Predictive Role of Authenticity on Subjective Vitality with Structural Equation Modelling.

Authenticity is a basic personality characteristic that has an important influence on both the psychological and social lives of individuals. Subjective vitality also assumes a facilitative role regarding positive mental health indicators. Therefore, the purpose of this study is to investigate the predictive role of authenticity on subjective vitality. The participants consisted of 329 university students who completed the Authenticity Scale as well as the Subjective Vitality Scale. The findings of correlation analysis showed that subjective vitality was negatively correlated with two sub-factors of authenticity: accepting external influence and self-alienation. Conversely, the authentic living factor of authenticity was positively related to subjective vitality. In addition, the results from structural equation modelling showed that subjective vitality was positively predicted by authentic living, whe- reas it was negatively predicted by accepting external influence and self-alienation. These findings, discussed in light of the related literature and dependent recommendations, illustrate the predictive role of authenticity on subjective vitality.
Cooperative video streaming with dynamic compression at the terminal nodes

Media content distribution constitutes a growing share of the services on the Internet. In recent years methods for cooperative media distribution has been a research area with high activity. Concurrently wireless internet connections have emerged. Wireless connection technologies, e.g. Wimax, have properties which make them suboptimal for cooperative distribution. In particular, the asymmetric relationship between the uplink and the downlink bandwidth makes the cooperative distribution difficult. Today two distinct source coding approaches are used; Layered Coding (LC) and Multiple Description Coding (MDC). A promising concept, termed MDC with Conditional Compression (MDC-CC), has been proposed in [1]. MDC-CC essentially acts as an adaptive hybrid between LC and MDC and is particularly interesting when combined with cooperative distribution. In this paper a scheme utilizing the concept of MDC-CC is proposed. The functionality of the scheme has been described and performance estimates using different distributions of redundancy has revealed that the proposed scheme has great potential.
Depth Map Upsampling using Color Image Characteristics

In this paper, we present an upsampling method of lowresolution depth maps using color segment information. After we supply the initial depth measurement considering the corresponding color segment information, we define an energy function for depth map upsampling based on the depth measurement and color image characteristics. Then, we obtain high-resolution depth maps by belief propagation optimization. Experimental results show that the proposed method outperforms other approaches for depth map upsampling in terms of the bad pixel rate. KeywordsDepth map, depth upsampling, Markov random field, 3D image
Deep Hashing via Discrepancy Minimization

This paper presents a discrepancy minimizing model to address the discrete optimization problem in hashing learning. The discrete optimization introduced by binary constraint is an NP-hard mixed integer programming problem. It is usually addressed by relaxing the binary variables into continuous variables to adapt to the gradient based learning of hashing functions, especially the training of deep neural networks. To deal with the objective discrepancy caused by relaxation, we transform the original binary optimization into differentiable optimization problem over hash functions through series expansion. This transformation decouples the binary constraint and the similarity preserving hashing function optimization. The transformed objective is optimized in a tractable alternating optimization framework with gradual discrepancy minimization. Extensive experimental results on three benchmark datasets validate the efficacy of the proposed discrepancy minimizing hashing.
PRODUCING AN EDUCATIONALLY EFFECTIVE AND USABLE TOOL FOR LEARNING, THE CASE OF JELIOT FAMILY

Jeliot Family is a group of program visualization tools that are designed to aid students to learn programming. Jeliot family’s key feature is the fully or semi-automatic visualization of the programs’ data and control flows. This means that the animation is produced from the original program code without any modifications. Thus, students and users can concentrate in programming correctly and not in how to implement the visualization. In addition to this in semi-automatic visualization the user can decide during the animation what kind of animation primitives (e.g. numbers, bounding boxes, vertical bars) he/she wants to use for the values in the program.