Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding & Predicting User Lifetime with Machine Learning in an Anonymous Location-Based Social Network

In this work, we predict the user lifetime within the anonymous and location-based social network Jodel in the Kingdom of Saudi Arabia. Jodel’s location-based nature yields to the establishment of disjoint communities country-wide and enables for the first time the study of user lifetime in the case of a large set of disjoint communities. A user’s lifetime is an important measurement for evaluating and steering customer bases as it can be leveraged to predict churn and possibly apply suitable methods to circumvent potential user losses. We train and test off the shelf machine learning techniques with 5-fold crossvalidation to predict user lifetime as a regression and classification problem; identifying the Random Forest to provide very strong results. Discussing model complexity and quality trade-offs, we also dive deep into a time-dependent feature subset analysis, which does not work very well; Easing up the classification problem into a binary decision (lifetime longer than timespan x) enables a practical lifetime predictor with very good performance. We identify implicit similarities across community models according to strong correlations in feature importance. A single countrywide model generalizes the problem and works equally well for any tested community; the overall model internally works similar to others also indicated by its feature importances.
Comparison of Methods Generalizing Max- and Average-Pooling

Max- and average-pooling are the most popular pooling methods for downsampling in convolutional neural networks. In this paper, we compare different pooling methods that generalize both max- and average-pooling. Furthermore, we propose another method based on a smooth approximation of the maximum function and put it into context with related methods. For the comparison, we use a VGG16 image classification network and train it on a large dataset of natural high-resolution images (Google Open Images v5). The results show that none of the more sophisticated methods perform significantly better in this classification task than standard max- or average-pooling.
AttriMeter: An Attribute-guided Metric Interpreter for Person Re-Identification

Person Re-identiﬁcation (ReID) has achieved signiﬁcant improvement due to the adoption of Convolutional Neural Networks (CNNs). However, person ReID systems only provide a distance or similarity when matching two persons, which makes users hardly understand why they are similar or not. Therefore, we propose an Attri bute-guided Met ric In ter preter, named AttriMeter , to semantically and quantitatively explain the results of CNN-based ReID models. The AttriMeter has a pluggable structure that can be grafted on arbitrary target models, i.e., the ReID models that need to be interpreted. With an attribute decomposition head, it can learn to generate a group of attribute-guided attention maps (AAMs) from the target model. By applying AAMs to features of two persons from the target model, their distance will be decomposed into a set of attribute-guided components which can measure the contributions of individual attributes. Moreover, we design a distance distillation loss to guarantee the consistency between the results from the target model and the decomposed components from AttriMeter, and an attribute prior loss to eliminate the biases caused by the unbalanced distribution of attributes. Finally, extensive experiments and analysis on a variety of ReID models and datasets show the effectiveness of AttriMeter.
Sommerfeld-enhanced dark matter searches with dwarf spheroidal galaxies

We study observable signals from dark matter that self-annihilates via the Sommerfeld effect in dwarf spheroidal galaxies (dSphs). Since the effect of the Sommerfeld enhancement depends on the velocity of dark matter, it is crucial to determine the profile of dSphs to compute the J-factor, i.e., the line-of-sight integral of density squared. In our study we use the prior distributions of the parameters for satellite density profiles in order to determine the J-factor, making most out of the recent developments in the N-body simulations and semi-analytical modeling for the structure formation. As concrete models, we analyze fermionic dark matter that annihilates via a light scalar and Wino dark matter in supersymmetric models. We find that, with the more realistic prior distributions that we adopt in this study, the J-factor of the most promising dSphs is decreased by a factor of a few, compared with earlier estimates based on non-informative priors. Nevertheless, the Cherenkov Telescope Array should be able to detect the thermal Wino dark matter by pointing it toward best classical or ultrafaint dSphs for 500 hours.
Code smells: A Synthetic Narrative Review

Code smells are symptoms of poor design and implementation choices, which might hinder comprehension, increase code complexity and fault-proneness and decrease maintainability of software systems. The aim of our study was to perform a triangulation of bibliometric and thematic analysis on code smell literature production. The search was performed on Scopus (Elsevier, Netherlands) database using the search string code smells which resulted in 442 publications. The Go-to statement was the first bad code smells identified in software engineering history in 1968. The literature production trend has been positive. The most productive countries were the United States, Italy and Brazil. Eight research themes were identified: Managing software maintenance, Smell detection-based software refactoring, Architectural smells, Improving software quality with multi-objective approaches, Technical debt and its instance, Quality improvement/assurance with mining software repositories, Programming education, Integrating the concepts of anti-pattern, design defects and design smells. Some research gaps also emerged, namely, New uncatalogued smell identification; Smell propagation from architectural, design, code to test, and other possible smells; and Identification of good smells. The results of our study can help code smell researchers and practitioners understand the broader aspects of code smells research and its translation to practice.
Counterfactual Explanations for Oblique Decision Trees: Exact, Efficient Algorithms

We consider counterfactual explanations, the problem of minimally adjusting features in a source input instance so that it is classified as a target class under a given classifier. This has become a topic of recent interest as a way to query a trained model and suggest possible actions to overturn its decision. Mathematically, the problem is formally equivalent to that of finding adversarial examples, which also has attracted significant attention recently. Most work on either counterfactual explanations or adversarial examples has focused on differentiable classifiers, such as neural nets. We focus on classification trees, both axis-aligned and oblique (having hyperplane splits). Although here the counterfactual optimization problem is nonconvex and nondifferentiable, we show that an exact solution can be computed very efficiently, even with high-dimensional feature vectors and with both continuous and categorical features, and demonstrate it in different datasets and settings. The results are particularly relevant for finance, medicine or legal applications, where interpretability and counterfactual explanations are particularly important.
Fashion and colour trends in the 1990s—forecasting into the future

Colour as an indicator of social mood, environment and culture has significant commercial implications. Different colour palettes can be attributed to different eras and fashions. Julie Buddy discusses the role of the colour forecaster and indicates emerging trends for the 1990s. Julie Buddy is a colour and fashion consultant for Informada, Suite 6, Browning Gardens, London W8, UK.
Learning Multimodal Contact-Rich Skills from Demonstrations Without Reward Engineering

Everyday contact-rich tasks, such as peeling, cleaning, and writing, demand multimodal perception for effective and precise task execution. However, these present a novel challenge to robots as they lack the ability to combine these multimodal stimuli for performing contact-rich tasks. Learning-based methods have attempted to model multi-modal contact-rich tasks, but they often require extensive training examples and task-specific reward functions which limits their practicality and scope. Hence, we propose a generalizable model-free learning-from-demonstration framework for robots to learn contact-rich skills without explicit reward engineering. We present a novel multi-modal sensor data representation which improves the learning performance for contact-rich skills. We performed training and experiments using the real-life Sawyer robot for three everyday contact-rich skills – cleaning, writing, and peeling. Notably, the framework achieves a success rate of 100% for the peeling and writing skill, and 80% for the cleaning skill. Hence, this skill learning framework can be extended for learning other physical manipulation skills.
Expectation-Maximization-Aided Hybrid Generalized Expectation Consistent for Sparse Signal Reconstruction

The reconstruction of sparse signal is an active area of research. Different from a typical i.i. d. assumption, this paper considers a non-independent prior of group structure. For this more practical setup, we propose EM-aided HyGEC, a new algorithm to address the stability issue and the hyper-parameter issue facing the other algorithms. The instability problem results from the ill condition of the transform matrix, while the unavailability of the hyper-parameters is a ground truth that their values are not known beforehand. The proposed algorithm is built on the paradigm of HyGAMP (proposed by Rangan et al.) but we replace its inner engine, the GAMP, by a matrix-insensitive alternative, the GEC, so that the first issue is solved. For the second issue, we take expectation-maximization as an outer loop, and together with the inner engine HyGEC, we learn the value of the hyper-parameters. Effectiveness of the proposed algorithm is also verified by means of numerical simulations.
The Difficulty in Scaling Blockchains: A Simple Explanation

Blockchains have become immensely popular and are high on the list of national and international research and innovation agenda's. This is partly caused by the numerous interesting applications, combined with the promise of full decentralization and high scalability (among others). Keeping that promise, however, is technically extremely demanding and has already required substantial scientific efforts, which so far have not been overwhelmingly successful. In this paper, we provide a laymen's description of what may turn out to be a fundamental hurdle in attaining blockchains that combine scalability, high transaction processing capabilities, and are indeed fully decentralized.
A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness

Alongside the well-publicized accomplishments of deep neural networks there has emerged an apparent bug in their success on tasks such as object recognition: with deep models trained using vanilla methods, input images can be slightly corrupted in order to modify output predictions, even when these corruptions are practically invisible. This apparent lack of robustness has led researchers to propose methods that can help to prevent an adversary from having such capabilities. The state-of-the-art approaches have incorporated the robustness requirement into the loss function, and the training process involves taking stochastic gradient descent steps not using original inputs but on adversarially-corrupted ones. In this paper we propose a multiclass boosting framework to ensure adversarial robustness. Boosting algorithms are generally well-suited for adversarial scenarios, as they were classically designed to satisfy a minimax guarantee. We provide a theoretical foundation for this methodology and describe conditions under which robustness can be achieved given a weak training oracle. We show empirically that adversarially-robust multiclass boosting not only outperforms the state-of-the-art methods, it does so at a fraction of the training time.
Stimulated Emission Depletion Microscopy with Color Centers in Hexagonal Boron Nitride

Stimulated emission depletion, or STED microscopy is a well-established super-resolution technique, but is ultimately limited by the chosen flourophore. Here we demonstrate STED microscopy with color centers in nanoscale flakes of hexagonal boron nitride using time-gated continuous wave STED. For color centers with zero phonon line emission around 580 nm we measure a STED cross-section of (5.5 $\pm$ 3.2) x $10^{-17} \mbox{cm}^{2}$, achieve a resolution of $\sim$ 50 nm and resolve two color centers separated by 250 nm, which is less than the diffraction limit. The achieved resolution is limited by the numerical aperture of the objective lens (0.8) and the available laser power, and we predict that a resolution of sub-10 nm can be achieved with an oil immersion objective lens, similar to state-of-the-art resolution obtained with nitrogen vacancy centers in diamond.
Quantifying non-Markovianity via conditional mutual information

In this paper, we study measures of quantum non-Markovianity based on the conditional mutual information. We obtain such measures by considering multiple parts of the total environment such that the conditional mutual informations can be defined in this multipartite setup. The benefit of this approach is that the conditional mutual information is closely related to recovery maps and Markov chains; we also point out its relations with the change of distinguishability. Moreover, we show how to extend the non-Markovianity measures to the case in which the initial system-environment state is correlated.
Control of Spatial Organization of Electrospun Fibers in a Carbon Felt for Enhanced Bioelectrode Performance.

Electrospun carbon fiber electrodes showing high performance for bio-electrochemical applications were developed. Easy to handle and manipulate aligned and unaligned carbon fibers with a mean diameter of (330±50) nm were synthesized through an electrospinning technique. Electrical resistivity measurements, which are a challenge that has not been much explored in the case of fibrous materials, were realized through two different techniques, and a study of contact resistances between electrical clips and the carbon fibers was performed. To target the creation of a bioelectrode, carbon fibers were characterized electrochemically by cyclic voltammetry. After being modified with the enzyme laccase, its response to oxygen electroreduction was studied. Aligned fibers present a cathodic current that is 30 % higher than that of randomly distributed fibers. Overall, the results show that aligned fibers are more appropriate for bio-electrochemical applications when exploiting anisotropic spatial organization.
Generalized Rough Polyharmonic Splines for Multiscale PDEs with Rough Coefficients

In this paper, we demonstrate the construction of generalized Rough Polyhamronic Splines (GRPS) within the Bayesian framework, in particular, for multiscale PDEs with rough coefficients. The optimal coarse basis can be derived automatically by the randomization of the original PDEs with a proper prior distribution and the conditional expectation given partial information on edge or derivative measurements. We prove the (quasi)-optimal localization and approximation properties of the obtained bases, and justify the theoretical results with numerical experiments.
On the Generalisation Capabilities of Fisher Vector based Face Presentation Attack Detection

In the last decades, the broad development experienced by biometric systems has unveiled several threats which may decrease their trustworthiness. Those are attack presentations which can be easily carried out by a non-authorised subject to gain access to the biometric system. In order to mitigate those security concerns, most face Presentation Attack Detection techniques have reported a good detection performance when they are evaluated on known Presentation Attack Instruments (PAI) and acquisition conditions, in contrast to more challenging scenarios where unknown attacks are included in the test set. For those more realistic scenarios, the existing algorithms face difficulties to detect unknown PAI species in many cases. In this work, we use a new feature space based on Fisher Vectors, computed from compact Binarised Statistical Image Features histograms, which allow discovering semantic feature subsets from known samples in order to enhance the detection of unknown attacks. This new representation, evaluated for challenging unknown attacks taken from freely available facial databases, shows promising results: a BPCER100 under 17% together with an AUC over 98% can be achieved in the presence of unknown attacks. In addition, by training a limited number of parameters, our method is able to achieve state-of-the-art deep learning-based approaches for cross-dataset scenarios.
Causes of delayed outbreak responses and their impacts on epidemic spread

Livestock diseases have devastating consequences economically, socially and politically across the globe. In certain systems, pathogens remain viable after host death, which enables residual transmissions from infected carcasses. Rapid culling and carcass disposal are well-established strategies for stamping out an outbreak and limiting its impact; however, wait-times for these procedures, i.e. response delays, are typically farm-specific and time-varying due to logistical constraints. Failing to incorporate variable response delays in epidemiological models may understate outbreak projections and mislead management decisions. We revisited the 2001 foot-and-mouth epidemic in the United Kingdom and sought to understand how misrepresented response delays can influence model predictions. Survival analysis identified farm size and control demand as key factors that impeded timely culling and disposal activities on individual farms. Using these factors in the context of an existing policy to predict local variation in response times significantly affected predictions at the national scale. Models that assumed fixed, timely responses grossly underestimated epidemic severity and its long-term consequences. As a result, this study demonstrates how general inclusion of response dynamics and recognition of partial controllability of interventions can help inform management priorities during epidemics of livestock diseases.
Ocean acidification effects on fish hearing

Humans are rapidly changing the marine environment through a multitude of effects, including increased greenhouse gas emissions resulting in warmer and acidified oceans. Elevated CO2 conditions can cause sensory deficits and altered behaviours in marine organisms, either directly by affecting end organ sensitivity or due to likely alterations in brain chemistry. Previous studies show that auditory-associated behaviours of larval and juvenile fishes can be affected by elevated CO2 (1000 µatm). Here, using auditory evoked potentials (AEP) and micro-computer tomography (microCT) we show that raising juvenile snapper, Chrysophyrs auratus, under predicted future CO2 conditions resulted in significant changes to their hearing ability. Specifically, snapper raised under elevated CO2 conditions had a significant decrease in low frequency (less than 200 Hz) hearing sensitivity. MicroCT demonstrated that these elevated CO2 snapper had sacculus otolith's that were significantly larger and had fluctuating asymmetry, which likely explains the difference in hearing sensitivity. We suggest that elevated CO2 conditions have a dual effect on hearing, directly effecting the sensitivity of the hearing end organs and altering previously described hearing induced behaviours. This is the first time that predicted future CO2 conditions have been empirically linked through modification of auditory anatomy to changes in fish hearing ability. Given the widespread and well-documented impact of elevated CO2 on fish auditory anatomy, predictions of how fish life-history functions dependent on hearing may respond to climate change may need to be reassessed.
Rapid characterization of hypanthium and seed in wild and cultivated rosehip: application of Raman microscopy combined with multivariate analysis

Rosehip (pseudo-fruit) of dog rose (Rosa canina L.) is highly valued, and owing to nutritional and sensory properties it has a significant place in the food industry. This work represents an innovative report focusing on the evaluation of the phytochemical composition of rosehips (hypanthium and seed) grown in different locations in Serbia, using Raman microspectroscopy combined with multivariate data analysis. Some significant differences arose between the analysed rosehip samples with regard to the chemical profile of both hypanthium parenchyma cells and seed, although no evident discrimination was recorded between the samples of wild and cultivated rosehip. The differences between the hypanthium samples compared were mainly determined by the content of carotenoids, phenolic compounds and polysaccharides, whereas phenolics, polysaccharides (pectin, cellulose and hemicellulose) and lipids (to a lower extent) contributed to the seed sample discrimination. The differences observed between the rosehip samples may be attributed to abiotic factors (growing, ripening and storage conditions), which had a significant impact on the carotenoid and polyphenols biosynthesis.
Evolutionary versatility of the avian neck

Bird necks display unparalleled levels of morphological diversity compared to other vertebrates, yet it is unclear what factors have structured this variation. Using three-dimensional geometric morphometrics and multivariate statistics, we show that the avian cervical column is a hierarchical morpho-functional appendage, with varying magnitudes of ecologically driven osteological variation at different scales of organization. Contrary to expectations given the widely varying ecological functions of necks in different species, we find that regional modularity of the avian neck is highly conserved, with an overall structural blueprint that is significantly altered only by the most mechanically demanding ecological functions. Nevertheless, the morphologies of vertebrae within subregions of the neck show more prominent signals of adaptation to ecological pressures. We also find that both neck length allometry and the nature of neck elongation in birds are different from other vertebrates. In contrast with mammals, neck length scales isometrically with head mass and, contrary to previous work, we show that neck elongation in birds is achieved predominantly by increasing vertebral lengths rather than counts. Birds therefore possess a cervical spine that may be unique in its versatility among extant vertebrates, one that, since the origin of flight, has adapted to function as a surrogate forelimb in varied ecological niches.