Miracle-dz/UniFAR-FTUs huggingface dataset:
Duration robust sound event detection

Task 4 of the Dcase2018 challenge demonstrated that substantially more research is needed for a real-world application of sound event detection. Analyzing the challenge results it can be seen that most successful models are biased towards predicting long (e.g., over 5s) utterances. This work aims to investigate the performance impact of fixed sized window median filter post-processing and advocate the use of double thresholding as a more robust and predictable post-processing method. Further, four different temporal subsampling methods within the CRNN framework are proposed: mean-max, alpha-mean-max, Lp-norm and convolutional. We show that for this task subsampling the temporal resolution by a neural network enhances the F1 score as well as onset and offset accuracies. Our best single model achieves 30.1% F1 on the evaluation set and the best fusion model 32.5%, outperforming the previously best attempt by 0.1% while maintaining robustness to short events.
Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks

Deep neural networks are vulnerable to adversarial examples, which can mislead classifiers by adding imperceptible perturbations. An intriguing property of adversarial examples is their good transferability, making black-box attacks feasible in real-world applications. Due to the threat of adversarial attacks, many methods have been proposed to improve the robustness. Several state-of-the-art defenses are shown to be robust against transferable adversarial examples. In this paper, we propose a translation-invariant attack method to generate more transferable adversarial examples against the defense models. By optimizing a perturbation over an ensemble of translated images, the generated adversarial example is less sensitive to the white-box model being attacked and has better transferability. To improve the efficiency of attacks, we further show that our method can be implemented by convolving the gradient at the untranslated image with a pre-defined kernel. Our method is generally applicable to any gradient-based attack method. Extensive experiments on the ImageNet dataset validate the effectiveness of the proposed method. Our best attack fools eight state-of-the-art defenses at an 82% success rate on average based only on the transferability, demonstrating the insecurity of the current defense techniques.
A Statistical Investigation of Long Memory in Language and Music

Representation and learning of long-range dependencies is a central challenge confronted in modern applications of machine learning to sequence data. Yet despite the prominence of this issue, the basic problem of measuring long-range dependence, either in a given data source or as represented in a trained deep model, remains largely limited to heuristic tools. We contribute a statistical framework for investigating long-range dependence in current applications of deep sequence modeling, drawing on the well-developed theory of long memory stochastic processes. This framework yields testable implications concerning the relationship between long memory in real-world data and its learned representation in a deep learning architecture, which are explored through a semiparametric framework adapted to the high-dimensional setting.
How crystals form: A theory of nucleation pathways

A theory of nucleation that is able to predict nonclassical pathways and intermediates for crystallization is formulated. Recent advances in classical density functional theory are combined with stochastic process theory and rare event techniques to formulate a theoretical description of nucleation, including crystallization, that can predict nonclassical nucleation pathways based on no input other than the interaction potential of the particles making up the system. The theory is formulated directly in terms of the density field, thus forgoing the need to define collective variables. It is illustrated by application to diffusion-limited nucleation of macromolecules in solution for both liquid-liquid separation and crystallization. Both involve nonclassical pathways with crystallization, in particular, proceeding by a two-step mechanism consisting of the formation of a dense-solution droplet followed by ordering originating at the core of the droplet. Furthermore, during the ordering, the free-energy surface shows shallow minima associated with the freezing of liquid into solid shells, which may shed light on the widely observed metastability of nanoscale clusters.
Tau decays and dark-sector searches at BaBar

We present some recent measurements performed using 514 fb-1 of $e^+e^-$ collisions collected with the BaBar detector at the PEP-II asymmetric collider at SLAC. First we present a study of the decay tau -> K K_S nu, we measure the branching fraction B(tau -> K K_S nu), the mass spectrum of the K K_S system and obtain the spectral function. Our results are in agreement with and improve previous measurements. We then report a search for a dark boson A' produced in the reaction e+e- -> gamma A' in invisible final states A' -> chi anti-chi . We find no evidence for such processes and set 90% confidence level upper limits on the coupling strength of A' as a function of the A' mass m'. In particular, our limits exclude the values of the A' coupling suggested by the dark-photon interpretation of the muon (g-2) anomaly. Finally we report the search for a light non-Standard Model gauge boson A' coupling only to the second and third lepton families. Our results significantly improve current limits and further constrain the remaining region of the allowed parameter space.
Unveiling Women’s Needs and Expectations as Users of Bike Sharing Services: The H2020 DIAMOND Project

Within the objectives of the H2020 DIAMOND project, the paper investigates women’s needs and expectations as users of the bike-sharing service managed by Syndicat Mixte Autolib et Velib Métropole in the territory of Paris Region-Petite Couronne (France). The paper presents a thematic literature review focused on gender inclusion in bike-sharing schemes. The proposed methodological approach is based on (i) Geographic Information Systems for the analysis of geolocated open datasets related to land, sociodemographic and mobility characteristics of the areas surrounding each docking stations. This was aimed at identifying a short list of suitable bike-sharing docking stations, which were further characterized through: (ii) structured proprietary data focused on travel demand; (iii) onsite observations focused on universal design indicators; (iv) survey questionnaires focused on women’s concerns, needs and expectations; and (v) social media data from Twitter focused on the opinion of the end-users. Results showed that women use the VELIB’s bike-sharing service much less than men (about 30% of the total number of users), since they are more concerned about the following issues: accessibility (e.g., availability of bikes at the docking stations, distance to the nearest station, type and quality of the cycle paths); safety and security (e.g., perception of danger and insecurity while cycling and using the current bicycle infrastructures); social constraints (e.g., perceptions and cultural stigmatization associated with cycling and bike-sharing); weather and topography (e.g., impact of weather and the urban terrain on cycling and bike-sharing). The final aim of the H2020 DIAMOND project is to support the definition of guidelines and policies for the inclusion of women’s needs in the design of future bike-sharing services.
Orbital relaxation and excitation of planets tidally interacting with white dwarfs

Observational evidence of white dwarf planetary systems is dominated by the remains of exo-asteroids through accreted metals, debris discs, and orbiting planetesimals. However, exo-planets in these systems play crucial roles as perturbing agents, and can themselves be perturbed close to the white dwarf Roche radius. Here, we illustrate a procedure for computing the tidal interaction between a white dwarf and a near-spherical solid planet. This method determines the planet’s inward and/or outward drift, and whether the planet will reach the Roche radius and be destroyed. We avoid constant tidal lag formulations and instead employ the self-consistent secular Darwin–Kaula expansions from Boué & Efroimsky (2019), which feature an arbitrary frequency dependence on the quality functions. We adopt wide ranges of dynamic viscosities and spin rates for the planet in order to straddle many possible outcomes, and provide a foundation for the future study of individual systems with known or assumed rheologies. We find that (i) massive Super-Earths are destroyed more readily than minor planets (such as the ones orbiting WD 1145+017 and SDSS J1228+1040), (ii) low-viscosity planets are destroyed more easily than high-viscosity planets, and (iii) the boundary between survival and destruction is likely to be fractal and chaotic.
Air Quality Forecasting with Hybrid LSTM and Extended Stationary Wavelet Transform

Artificial intelligence (AI) technology-enhanced air quality forecasting is one of the most promising directions in the field of smart environment development. Despite recent advances in this area, two difficulties remain unsolved. First, multiple factors influence forecasting results, such as weather conditions, fuel usage and traffic conditions. These factors are usually unavailable in air quality sensor data. Second, traditional predicting models typically use the most recent training data, which neglects the historical data. In this study, we propose a hybrid deep learning model that embraces the merits of the stationary wavelet transform (SWT) and the nested long short term memory networks (NLSTM) to improve the prediction quality in the problem of hour-ahead air quality forecasting. The proposed method decomposes the original PM2.5 data into several more stationary sub-signals with different resolutions using an extended SWT algorithm. A framework that leverages several NLSTM recurrent neural networks is constructed to output forecasting results for different sub-signals, respectively. The final forecasting result is obtained by combining all sub-signal forecasting results using the inverse wavelet transform. Experiments on real-world data show that, accuracy-wise, our proposed method outperforms most of the existing prediction models in the literature. And the resulting forecasting curves of the proposed method are much closer to the real values without any lags, comparing with existing prediction models.
Koopman spectral analysis to capture underlying dynamic features in random pressure fields

Dynamic wind loads acting on structures by definition are statistically correlated both in space and time. However, conventional approaches for the analysis of distributed aerodynamic loads, such as the proper orthogonal decomposition (POD), can only present relevant spatial pattern that is encapsulated in a set of coherent structures. In this regard, this paper provides an operator-theoretic approach that describes a nonlinear/chaotic dynamical system in a functional space rather than the conventional phase space via the introduction of the Koopman operator. Subsequently, spectral analysis of the Koopman operator provides a spatial-temporal characterization of the time-dependent wind loads. We proposed an augmented dynamic mode decomposition (DMD) method to obtain the spectral decomposition results. Specifically, the proposed DMD algorithm is augmented by means of the delay coordinate embedding, and the identified eigen-tuples (eigenvalues, eigenvectors, and time evolution) can not only capture dominant spatial structures but also isolate each structure with a specific frequency and a corresponding growth/decay rate. To demonstrate the unique feature of this approach, we consider learning the evolution dynamics of the random wind pressure field over a scaled model of a finite height prism representing a tall building using limited wind tunnel data. POD analysis of the experimental data is also carried out. We systematically examine the DMD/POD learning results from aspects of algorithm convergence, data sufficiency, and modal analysis, highlighting their specialties and physical interpretations. The ensuing results from the learning process indicate that the proposed method can accurately identify a wide range of dynamical mechanisms, which are closely associated with physical phenomena.
On The Power of Curriculum Learning in Training Deep Networks

Training neural networks is traditionally done by providing a sequence of random mini-batches sampled uniformly from the entire training data. In this work, we analyze the effect of curriculum learning, which involves the non-uniform sampling of mini-batches, on the training of deep networks, and specifically CNNs trained for image recognition. To employ curriculum learning, the training algorithm must resolve 2 problems: (i) sort the training examples by difficulty; (ii) compute a series of mini-batches that exhibit an increasing level of difficulty. We address challenge (i) using two methods: transfer learning from some competitive ``teacher" network, and bootstrapping. In our empirical evaluation, both methods show similar benefits in terms of increased learning speed and improved final performance on test data. We address challenge (ii) by investigating different pacing functions to guide the sampling. The empirical investigation includes a variety of network architectures, using images from CIFAR-10, CIFAR-100 and subsets of ImageNet. We conclude with a novel theoretical analysis of curriculum learning, where we show how it effectively modifies the optimization landscape. We then define the concept of an ideal curriculum, and show that under mild conditions it does not change the corresponding global minimum of the optimization function.
Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration

Previous works utilized “smaller-norm-less-important” criterion to prune filters with smaller norm values in a convolutional neural network. In this paper, we analyze this norm-based criterion and point out that its effectiveness depends on two requirements that are not always met: (1) the norm deviation of the filters should be large; (2) the minimum norm of the filters should be small. To solve this problem, we propose a novel filter pruning method, namely Filter Pruning via Geometric Median (FPGM), to compress the model regardless of those two requirements. Unlike previous methods, FPGM compresses CNN models by pruning filters with redundancy, rather than those with“relatively less” importance. When applied to two image classification benchmarks, our method validates its usefulness and strengths. Notably, on CIFAR-10, FPGM reduces more than 52% FLOPs on ResNet-110 with even 2.69% relative accuracy improvement. Moreover, on ILSVRC-2012, FPGM reduces more than 42% FLOPs on ResNet-101 without top-5 accuracy drop, which has advanced the state-of-the-art. Code is publicly available on GitHub: https://github.com/he-y/filter-pruning-geometric-median
Hyperbolicity of links complements in Seifert fibered spaces

Let $\bar\gamma$ be a link in a Seifert fibered space $M$ over a hyperbolic $2$-orbifolds $\mathcal O$ that projects injectively to a filling multicurve of closed geodesics $\gamma$ in $\mathcal O. $ We prove that the complement $M_{\bar\gamma}$ of $\bar\gamma$ in $M$ admits a hyperbolic structure of finite volume and give combinatorial bounds of its volume.
Samples are not all useful: Denoising policy gradient updates using variance

Policy gradient algorithms in reinforcement learning rely on efficiently sampling an environment. Most sampling procedures are based solely on sampling the agent’s policy. However, other measures made available through these algorithms could be used in order to improve the sampling prior to each policy update. Following this line of thoughts, we propose a method where a transition is used in the gradient update if it meets a particular criterion, and rejected otherwise. This criterion is the fraction of variance explained (V), a measure of the discrepancy between a model and actual samples. V can be used to evaluate the impact each transition will have on the learning. This criterion refines sampling and improves the policy gradient algorithm. In this paper: (1) We introduce and explore V, the selection criterion used to improve the sampling procedure. (2) We conduct experiments across a variety of standard benchmark environments, including continuous control problems. Our results show better performance than if we did not use the V criterion for the policy gradient update. (3) We investigate why V gives a good evaluation for the selection of samples that will positively impact the learning. (4) We show how this criterion can be interpreted as a dynamic way to adjust the ratio between exploration and exploitation.
A Variational Auto-Encoder Model for Stochastic Point Processes

We propose a novel probabilistic generative model for action sequences. The model is termed the Action Point Process VAE (APP-VAE), a variational auto-encoder that can capture the distribution over the times and categories of action sequences. Modeling the variety of possible action sequences is a challenge, which we show can be addressed via the APP-VAE's use of latent representations and non-linear functions to parameterize distributions over which event is likely to occur next in a sequence and at what time. We empirically validate the efficacy of APP-VAE for modeling action sequences on the MultiTHUMOS and Breakfast datasets.
Two-level orthogonal screening designs with 80, 96, and 112 runs, and up to 29 factors

Abstract Due to recent advances in the development of laboratory equipment, large screening experiments can now be conducted to study the joint impact of up to a few dozen factors. While much is known about orthogonal designs involving 64 and 128 runs, there is a lack of literature on screening designs with intermediate run sizes. In this article, we therefore construct screening designs with 80, 96 and 112 runs which allow the main effects to be estimated independently from the two-factor interactions and limit the aliasing among the interactions. We motivate our work using a 14-factor tuberculosis inhibition experiment and compare our new designs with alternatives from the literature using simulations.
Asymptotic generalized Fermat’s last theorem over number fields

Recent work of Freitas and Siksek showed that an asymptotic version of Fermat’s Last Theorem (FLT) holds for many totally real fields. This result was extended by Deconinck to the generalized Fermat equation of the form [Formula: see text], where [Formula: see text] are odd integers belonging to a totally real field. Later Şengün and Siksek showed that the asymptotic FLT holds over number fields assuming two standard modularity conjectures. In this work, combining their techniques, we show that the generalized Fermat’s Last Theorem (GFLT) holds over number fields asymptotically assuming the standard conjectures. We also give three results which show the existence of families of number fields on which asymptotic versions of FLT or GFLT hold. In particular, we prove that the asymptotic GFLT holds for a set of imaginary quadratic number fields of density 5/6.
Inner and outer rings are not strongly coupled with stellar bars

Rings are distinctive features of many disc galaxies and their location and properties are closely related to the disc dynamics. In particular, rings are often associated to stellar bars, but the details of this connection are far from clear. We have studied the frequency and dimensions of inner and outer rings in the local Universe as a function of disc parameters and the amplitude of non-axisymmetries. We used the 1320 not highly inclined disc galaxies (i < 65°) from the S4G survey. The ring fraction increases with bar Fourier density amplitude: this can be interpreted as evidence for the role of bars in ring formation. The sizes of inner rings are positively correlated with bar strength: this can be linked to the radial displacement of the 1/4 ultraharmonic resonance while the bar grows and the pattern speed decreases. The ring’s intrinsic ellipticity is weakly controlled by the non-axisymmetric perturbation strength: this relation is not as strong as expected from simulations, especially when we include the dark matter halo in the force calculation. The ratio of outer-to-inner ring semi-major axes is uncorrelated with bar strength: this questions the manifold origin of rings. In addition, we confirm that (i) ∼1/3 (∼1/4) of the galaxies hosting inner (outer) rings are not barred; (ii) on average, the sizes and shapes of rings are roughly the same for barred and non-barred galaxies; and (iii) the fraction of inner (outer) rings is a factor of 1.2−1.4 (1.65−1.9) larger in barred galaxies than in their non-barred counterparts. Finally, we apply unsupervised machine learning (self-organising maps, SOMs) to show that, among early-type galaxies, ringed or barred galaxies cannot be univocally distinguished based on 20 internal and external fundamental parameters. We confirm, with the aid of SOMs, that rings are mainly hosted by red, massive, gas-deficient, dark-matter poor, and centrally concentrated galaxies. We conclude that the present-day coupling between rings and bars is not as robust as predicted by numerical models, and diverse physical mechanisms and timescales determine ring formation and evolution.
Does it Swing? Microtiming Deviations and Swing Feeling in Jazz

Jazz music that swings has the fascinating power to elicit a pleasant sensation of flow in listeners and the desire to synchronize body movements with the music. Whether microtiming deviations (MTDs), i.e. small timing deviations below the bar or phrase level, enhance the feeling of swing is highly debated in the current literature. Studies on other groove related genres did not find evidence for a positive impact of MTDs. The present study addresses jazz music and swing in particular, as there is some evidence that microtiming patterns are genre-specific. We recorded twelve piano jazz standards played by a professional pianist and manipulated the natural MTDs of the recordings in systematic ways by quantizing, expanding and inverting them. MTDs were defined with respect to a grid determined by the average swing ratio. The original and manipulated versions were presented in an online survey and evaluated by 160 listeners with various musical skill levels and backgrounds. Across pieces the quantized versions (without MTDs) were rated slightly higher and versions with expanded MTDs were rated lower with regard to swing than the original recordings. Unexpectedly, inversion had no impact on swing ratings except for two pieces. Our results suggest that naturally fluctuating MTDs are not an essential factor for the feeling of swing.