Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Interpretable Shapelets for Time Series Classification through Adversarial Regularization

Times series classification can be successfully tackled by jointly learning a shapelet-based representation of the series in the dataset and classifying the series according to this representation. However, although the learned shapelets are discriminative, they are not always similar to pieces of a real series in the dataset. This makes it difficult to interpret the decision, i.e. difficult to analyze if there are particular behaviors in a series that triggered the decision. In this paper, we make use of a simple convolutional network to tackle the time series classification task and we introduce an adversarial regularization to constrain the model to learn more interpretable shapelets. Our classification results on all the usual time series benchmarks are comparable with the results obtained by similar state-of-the-art algorithms but our adversarially regularized method learns shapelets that are, by design, interpretable.
Effects of radiation pressure on the evaporative wind of HD 209458b

The role of radiation pressure in shaping exoplanet photoevaporation remains a topic of contention. Radiation pressure from the exoplanet’s host star has been proposed as a mechanism to drive the escaping atmosphere into a ‘cometary’ tail and explain the high velocities observed in systems where mass-loss is occurring. In this paper, we present results from high-resolution 3D hydrodynamic simulations of a planet similar to HD 209458b. We self-consistently launch a wind flowing outwards from the planet by calculating the ionization and heating resulting from incident high-energy radiation, and account for radiation pressure. We first present a simplified calculation, setting a limit on the Lyman-α flux required to drive the photoevaporated planetary material to larger radii and line-of-sight velocities. We then present the results of our simulations, which confirm the limits determined by our analytic calculation. We thus demonstrate that, within the limits of our hydrodynamic simulation and for the Lyman-α fluxes expected for HD 209458, radiation pressure is unlikely to significantly affect photoevaporative winds or to explain the high velocities at which wind material is observed, though further possibilities remain to be investigated.
“President Vows to Cut  Hair”: Dataset and Analysis of Creative Text Editing for Humorous Headlines

We introduce, release, and analyze a new dataset, called Humicroedit, for research in computational humor. Our publicly available data consists of regular English news headlines paired with versions of the same headlines that contain simple replacement edits designed to make them funny. We carefully curated crowdsourced editors to create funny headlines and judges to score a to a total of 15,095 edited headlines, with five judges per headline. The simple edits, usually just a single word replacement, mean we can apply straightforward analysis techniques to determine what makes our edited headlines humorous. We show how the data support classic theories of humor, such as incongruity, superiority, and setup/punchline. Finally, we develop baseline classifiers that can predict whether or not an edited headline is funny, which is a first step toward automatically generating humorous headlines as an approach to creating topical humor.
Intragenic deletions in 164 boys with Duchenne muscular dystrophy (DMD) studied with dystrophin cDNA

DNA from 164 unrelated Duchenne muscular dystrophy patients was screened with cDNA probes from the dystrophin gene. Molecular deletions were demonstrated in 82 (50%) subjects. Sixty‐two deletions (76%) were detected using cDNA probes Cf56a (cDNA 8) and Cf56b (cDNA 6–7) which map to the centre of the gene, while 22 deletions (27%) mapped to the 5′ end of the gene. In three subjects, the deletion extended from the 5′ end to the centre of the gene. One deletion was identified by probe 47–4 (cDNA 5b‐7) alone. In six of the deletions, junction fragments of altered size were observed. Using the three cDNA probes, RW2kb, Cf56a (cDNA 8) and Cf56b (cDNA 6–7), 99% of the deletions were detected. This will have implications for prenatal diagnosis in deletion families. Unlike Becker muscular dystrophy, where the deletions are more homogeneous, the deletions in the present study were heterogeneous both in size and position. No correlation between intelligence and either site or extent of deletion was found.
The discovery of weak coherent pulsations in the ultraluminous X-ray source NGC 1313 X-2

We report the detection of weak pulsations from the archetypal ultraluminous X-ray source (ULX) NGC 1313 X-2. Acceleration searches reveal sinusoidal pulsations in segments of two out of six new deep observations of this object, with a period of ∼1.5 s and a pulsed fraction of ${\sim } 5{{\ \rm per\ cent}}$. We use Monte Carlo simulations to demonstrate that the individual detections are unlikely to originate in false Poisson noise detections given their very close frequencies; their strong similarity to other pulsations detected from ULXs also argues they are real. The presence of a large bubble nebula surrounding NGC 1313 X-2 implies an age of order 1 Myr for the accreting phase of the ULX, which implies that the neutron star’s (NS) magnetic field has not been suppressed over time by accreted material, nor has the NS collapsed into a black hole, despite an average energy output into the nebula two orders of magnitude above Eddington. This argues that most of the accreted material has been expelled over the lifetime of the ULX, favouring physical models including strong winds and/or jets for NS ULXs.
Evaluating Differentially Private Machine Learning in Practice

Differential privacy is a strong notion for privacy that can be used to prove formal guarantees, in terms of a privacy budget, $\epsilon$, about how much information is leaked by a mechanism. However, implementations of privacy-preserving machine learning often select large values of $\epsilon$ in order to get acceptable utility of the model, with little understanding of the impact of such choices on meaningful privacy. Moreover, in scenarios where iterative learning procedures are used, differential privacy variants that offer tighter analyses are used which appear to reduce the needed privacy budget but present poorly understood trade-offs between privacy and utility. In this paper, we quantify the impact of these choices on privacy in experiments with logistic regression and neural network models. Our main finding is that there is a huge gap between the upper bounds on privacy loss that can be guaranteed, even with advanced mechanisms, and the effective privacy loss that can be measured using current inference attacks. Current mechanisms for differentially private machine learning rarely offer acceptable utility-privacy trade-offs with guarantees for complex learning tasks: settings that provide limited accuracy loss provide meaningless privacy guarantees, and settings that provide strong privacy guarantees result in useless models. Code for the experiments can be found here: this https URL
Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewards

Classical multi-armed bandit problems use the expected value of an arm as a metric to evaluate its goodness. However, the expected value is a risk-neutral metric. In many applications like finance, one is interested in balancing the expected return of an arm (or portfolio) with the risk associated with that return. In this paper, we consider the problem of selecting the arm that optimizes a linear combination of the expected reward and the associated Conditional Value at Risk (CVaR) in a fixed budget best-arm identification framework. We allow the reward distributions to be unbounded or even heavy-tailed. For this problem, our goal is to devise algorithms that are entirely distribution oblivious, i.e., the algorithm is not aware of any information on the reward distributions, including bounds on the moments/tails, or the suboptimality gaps across arms. In this paper, we provide a class of such algorithms with provable upper bounds on the probability of incorrect identification. In the process, we develop a novel estimator for the CVaR of unbounded (including heavy-tailed) random variables and prove a concentration inequality for the same, which could be of independent interest. We also compare the error bounds for our distribution oblivious algorithms with those corresponding to standard non-oblivious algorithms. Finally, numerical experiments reveal that our algorithms perform competitively when compared with non-oblivious algorithms, suggesting that distribution obliviousness can be realised in practice without incurring a significant loss of performance.
Dust condensation in evolving discs and the composition of planetary building blocks

Partial condensation of dust from the Solar nebula is likely responsible for the diverse chemical compositions of chondrites and rocky planets/planetesimals in the inner Solar system. We present a forward physical–chemical model of a protoplanetary disc to predict the chemical compositions of planetary building blocks that may form from such a disc. Our model includes the physical evolution of the disc and the condensation, partial advection, and decoupling of the dust within it. The chemical composition of the condensate changes with time and radius. We compare the results of two dust condensation models: one where an element condenses when the mid-plane temperature in the disc is lower than the 50 per cent condensation temperature ($\rm T_{50}$) of that element and the other where the condensation of the dust is calculated by a Gibbs free energy minimization technique assuming chemical equilibrium at local disc temperature and pressure. The results of two models are generally consistent with some systematic differences of ∼10 per cent depending upon the radial distance and an element’s condensation temperature. Both models predict compositions similar to CM, CO, and CV chondrites provided that the decoupling time-scale of the dust is of the order of the evolution time-scale of the disc or longer. If the decoupling time-scale is too short, the composition deviates significantly from the measured values. These models may contribute to our understanding of the chemical compositions of chondrites, and ultimately the terrestrial planets in the Solar system, and may constrain the potential chemical compositions of rocky exoplanets.
An adaptive finite element method for the sparse optimal control of fractional diffusion

We propose and analyze an a posteriori error estimator for a partial differential equation (PDE)‐constrained optimization problem involving a nondifferentiable cost functional, fractional diffusion, and control‐constraints. We realize fractional diffusion as the Dirichlet‐to‐Neumann map for a nonuniformly PDE and propose an equivalent optimal control problem with a local state equation. For such an equivalent problem, we design an a posteriori error estimator which can be defined as the sum of four contributions: two contributions related to the approximation of the state and adjoint equations and two contributions that account for the discretization of the control variable and its associated subgradient. The contributions related to the discretization of the state and adjoint equations rely on anisotropic error estimators in weighted Sobolev spaces. We prove that the proposed a posteriori error estimator is locally efficient and, under suitable assumptions, reliable. We design an adaptive scheme that yields, for the examples that we perform, optimal experimental rates of convergence.
Association of HTR2A polymorphisms with chronic widespread pain and the extent of musculoskeletal pain: results from two population-based cohorts.

OBJECTIVE The aim of the current study was to investigate whether genetic variation in genes across the serotoninergic system is associated with chronic widespread pain (CWP) and the number of pain sites reported. METHODS A discovery cohort, with pain data at 3 time points, was used to investigate genetic associations with 2 phenotypes: 1) CWP (at ≥ 2 time points; n = 164) compared with pain-free controls (at 3 time points; n = 172), and 2) the maximum number of pain sites reported at any 1 of the 3 time points (range of sites 0-29; n = 989). A cohort of 2,285 men for whom a DNA sample and pain data were available (including 203 CWP cases and 929 controls) was used for validation. Pairwise tagging (r(2) > 0.8) single-nucleotide polymorphisms (SNPs) were genotyped. Logistic and zero-inflated negative binomial regression analyses were used to test for SNP associations with CWP and the number of pain sites, respectively. RESULTS SNPs in HTR2A were associated with both pain phenotypes in the discovery cohort, and a number of these SNP associations were replicated in the validation cohort, some of which were attenuated after adjustment for depression. There was an increased likelihood of having CWP in subjects with 1 or 2 copies of the T allele of rs12584920 (odds ratio [OR] 1.64, 95% confidence interval [95% CI] 1.01-2.60 [P = 0.03] in the discovery cohort, and OR 1.46, 95% CI 1.07-2.00 [P = 0.018] in the validation cohort). A similar association was observed between rs17289394 and the maximum number of pain sites reported in both cohorts. Results from a meta-analysis of the data from the 2 cohorts further strengthened these findings. CONCLUSION The findings of this study support the role of HTR2A in the genetic predisposition to musculoskeletal pain.
Self-Supervised Deep Learning on Point Clouds by Reconstructing Space

Point clouds provide a flexible and natural representation usable in countless applications such as robotics or self-driving cars. Recently, deep neural networks operating on raw point cloud data have shown promising results on supervised learning tasks such as object classification and semantic segmentation. While massive point cloud datasets can be captured using modern scanning technology, manually labelling such large 3D point clouds for supervised learning tasks is a cumbersome process. This necessitates methods that can learn from unlabelled data to significantly reduce the number of annotated samples needed in supervised learning. We propose a self-supervised learning task for deep learning on raw point cloud data in which a neural network is trained to reconstruct point clouds whose parts have been randomly rearranged. While solving this task, representations that capture semantic properties of the point cloud are learned. Our method is agnostic of network architecture and outperforms current unsupervised learning approaches in downstream object classification tasks. We show experimentally, that pre-training with our method before supervised training improves the performance of state-of-the-art models and significantly improves sample efficiency.
High-Quality Self-Supervised Deep Image Denoising

We describe a novel method for training high-quality image denoising models based on unorganized collections of corrupted images. The training does not need access to clean reference images, or explicit pairs of corrupted images, and can thus be applied in situations where such data is unacceptably expensive or impossible to acquire. We build on a recent technique that removes the need for reference data by employing networks with a "blind spot" in the receptive field, and significantly improve two key aspects: image quality and training efficiency. Our result quality is on par with state-of-the-art neural network denoisers in the case of i.i. d. additive Gaussian noise, and not far behind with Poisson and impulse noise. We also successfully handle cases where parameters of the noise model are variable and/or unknown in both training and evaluation data.
A Longitudinal Analysis of Online Ad-Blocking Blacklists

Websites employ third-party ads and tracking services leveraging cookies and JavaScript code, to deliver ads and track users’ behavior, causing privacy concerns. To limit online tracking and block advertisements, several ad-blocking (black) lists have been curated consisting of URLs and domains of well-known ads and tracking services. Using Internet Archive’s Wayback Machine in this paper, we collect a retrospective view of the Web to analyze the evolution of ads and tracking services and evaluate the effectiveness of ad-blocking blacklists. We propose metrics to capture the efficacy of ad-blocking blacklists to investigate whether these blacklists have been reactive or proactive in tackling the online ad and tracking services. We introduce a stability metric to measure the temporal changes in ads and tracking domains blocked by ad-blocking blacklists and a diversity metric to measure the ratio of new ads and tracking domains detected. We observe that ads and tracking domains in websites change over time, and among the ad-blocking blacklists that we investigated, our analysis reveals that some blacklists were more informed with the existence of ads and tracking domains, but their rate of change was slower than other blacklists. Our analysis also shows that Alexa top 5K websites in the US, Canada, and the UK have the most number of ads and tracking domains per website, and have the highest proactive scores. This suggests that ad-blocking blacklists are updated by prioritizing ads and tracking domains reported in the popular websites from these countries.
Deligne–Lusztig duality on the stack of local systems

Abstract In the setting of the geometric Langlands conjecture, we argue that the phenomenon of divergence at infinity on BunG{\operatorname{Bun}_{G}} (that is, the difference between ! {! }-extensions and *{*}-extensions) is controlled, Langlands-dually, by the locus of semisimple Gˇ{{\check{G}}}-local systems. To see this, we first rephrase the question in terms of Deligne–Lusztig duality and then study the Deligne–Lusztig functor 𝖣𝖫Gspec{\mathsf{DL}_{G}^{\mathrm{spec}}} acting on the spectral Langlands DG category IndCoh𝒩⁢(LSG){{\mathrm{IndCoh}}_{\mathcal{N}}({\mathrm{LS}}_{G})}. We prove that 𝖣𝖫Gspec{\mathsf{DL}_{G}^{\mathrm{spec}}} is the projection IndCoh𝒩⁢(LSG)↠QCoh⁢(LSG){{\mathrm{IndCoh}}_{\mathcal{N}}({\mathrm{LS}}_{G})\twoheadrightarrow{\mathrm{% QCoh}}({\mathrm{LS}}_{G})}, followed by the action of a coherent D-module StG∈𝔇⁢(LSG){{\mathrm{St}}_{G}\in\mathfrak{D}({\mathrm{LS}}_{G})}, which we call the SteinbergD-module. We argue that StG{{\mathrm{St}}_{G}} might be regarded as the dualizing sheaf of the locus of semisimple G-local systems. We also show that 𝖣𝖫Gspec{\mathsf{DL}_{G}^{\mathrm{spec}}}, while far from being conservative, is fully faithful on the subcategory of compact objects.
SpikeGrad: An ANN-equivalent Computation Model for Implementing Backpropagation with Spikes

Event-based neuromorphic systems promise to reduce the energy consumption of deep learning tasks by replacing expensive floating point operations on dense matrices by low power sparse and asynchronous operations on spike events. While these systems can be trained increasingly well using approximations of the back-propagation algorithm, these implementations usually require high precision errors for training and are therefore incompatible with the typical communication infrastructure of neuromorphic circuits. In this work, we analyze how the gradient can be discretized into spike events when training a spiking neural network. To accelerate our simulation, we show that using a special implementation of the integrate-and-fire neuron allows us to describe the accumulated activations and errors of the spiking neural network in terms of an equivalent artificial neural network, allowing us to largely speed up training compared to an explicit simulation of all spike events. This way we are able to demonstrate that even for deep networks, the gradients can be discretized sufficiently well with spikes if the gradient is properly rescaled. This form of spike-based backpropagation enables us to achieve equivalent or better accuracies on the MNIST and CIFAR10 dataset than comparable state-of-the-art spiking neural networks trained with full precision gradients. The algorithm, which we call SpikeGrad, is based on accumulation and comparison operations and can naturally exploit sparsity in the gradient computation, which makes it an interesting choice for a spiking neuromorphic systems with on-chip learning capacities.
Predicting gender from blog posts

Blogs are informal, personal writings that people post on their own blog sites. Nowadays, blogging is an important online activity. People share blogs with their friends and family members. The topics of blog posting cover almost everything, ranging from personal life, political opinions, recipes, product reviews, or even just random rants. Although some bloggers review their biologically information on their blog site, many don’t make such information public. Thereful, automatically classifying the gender or age of a blog author may have important applications in many commercial domains, such as targeted advertising and product development [8]. In blog search, this information may help people find blogs that they are interested in. From a research perspective, blog author gender classification is also an interesting problem. Blog posts are typically short and unstructured. They differ tremendously from formal texts, since they may have informal sentences, grammer errors, slang words and phrases, and wrong spellings. These characteristics of blog posts may complicate any classification or categorization attempts. The goal of our project is to identify author gender of blogs coming from a wide variety of sources. We are interested in knowing how well we can tackle this problem, what methods and features are most effective in this task.
Multiwavelength analysis of brightness variations of 3C 279: probing the relativistic jet structure and its evolution

Aims. We studied the correlation between brightness and polarization variations in 3C 279 at different wavelengths, over time intervals long enough to cover the time lags due to opacity effects. We used these correlations together with VLBI images to constrain the radio and high energy source position. Methods. We made 7 mm radio continuum and R-band polarimetric observations of 3C 279 between 2009 and 2014. The radio observations were performed at the Itapetinga Radio Observatory, while the polarimetric data were obtained at Pico dos Dias Observatory, both in Brazil. We compared our observations with the γ-ray Fermi/LAT and R-band SMARTS light curves. Results. We found a good correlation between 7 mm and R-band light curves, with a delay of 170 ± 30 days in radio, but no correlation with the γ rays. However, a group of several γ-ray flares in April 2011 could be associated with the start of the 7 mm strong activity observed at the end of 2011. We also detected an increase in R-band polarization degree and rotation of the polarization angle simultaneous with these flares. Contemporaneous VLBI images at the same radio frequency show two new strong components close to the core, ejected in directions very different from that of the jet. Conclusions. The good correlation between radio and R-band variability suggests that their origin is synchrotron radiation. The lack of correlation with γ-rays produced by the Inverse Compton process on some occasions could be due to the lack of low energy photons in the jet direction or to absorption of the high energy photons by the broad line region clouds. The variability of the polarization parameters during flares can be easily explained by the combination of the jet polarization parameters and those of newly formed jet components.