Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-regularizing restricted Boltzmann machines

Focusing on the grand-canonical extension of the ordinary restricted Boltzmann machine, we suggest an energy-based model for feature extraction that uses a layer of hidden units with varying size. By an appropriate choice of the chemical potential and given a sufficiently large number of hidden resources the generative model is able to efficiently deduce the optimal number of hidden units required to learn the target data with exceedingly small generalization error. The formal simplicity of the grand-canonical ensemble combined with a rapidly converging ansatz in mean-field theory enable us to recycle well-established numerical algothhtims during training, like contrastive divergence, with only minor changes. As a proof of principle and to demonstrate the novel features of grand-canonical Boltzmann machines, we train our generative models on data from the Ising theory and MNIST.
Robust Data-driven Profile-based Pricing Schemes

To enable an effective electricity market, a good pricing scheme is of vital importance. Among many practical schemes, customized pricing is commonly believed to be able to best exploit the flexibility in the demand side. However, due to the large volume of consumers in the electricity sector, such task is simply too overwhelming. In this paper, we first compare two data driven schemes: one based on load profile and the other based on user's marginal system cost. Vulnerability analysis shows that the former approach may lead to loopholes in the electricity market while the latter is able to guarantee the robustness, which yields our robust data-driven pricing scheme. Although k-means clustering is NP-hard, by exploiting the structure of our problem, we design an efficient yet optimal k-means clustering algorithm to implement our proposed scheme.
HST/WFC3 grism observations of z ∼ 1 clusters: evidence for evolution in the mass–size relation of quiescent galaxies from post-starburst galaxies

Minor mergers have been proposed as the driving mechanism for the size growth of quiescent galaxies with decreasing redshift. The process whereby large star-forming galaxies quench and join the quiescent population at the large size end has also been suggested as an explanation for this size growth. Given the clear association of quenching with clusters, we explore this mechanism by studying the structural properties of 23 spectroscopically identified recently quenched (or ‘post-starburst’ (PSB)) cluster galaxies at z ∼ 1. Despite clear PSB spectral signatures implying rapid and violent quenching, 87 per cent of these galaxies have symmetric, undisturbed morphologies in the stellar continuum. Remarkably, they follow a mass–size relation lying midway between the star-forming and quiescent field relations, with sizes 0.1 dex smaller than z ∼ 1 star-forming galaxies at log(M*/M⊙) = 10.5. This implies a rapid change in the light profile without directly effecting the stellar distribution, suggesting changes in the mass-to-light ratio gradients across the galaxy are responsible. We develop fading toy models to explore how star-forming galaxies move across the mass–size plane as their stellar populations fade to match those of the PSBs. ‘Outside-in’ fading has the potential to reproduce the contraction in size and increase in bulge-dominance observed between star-forming and PSB cluster galaxies. Since cluster PSBs lie on the large size end of the quiescent mass–size relation, and our previous work shows cluster galaxies are smaller than field galaxies, the sizes of quiescent galaxies must grow both from the quenching of star-forming galaxies and dry minor mergers.
Towards Expressive Priors for Bayesian Neural Networks: Poisson Process Radial Basis Function Networks

While Bayesian neural networks have many appealing characteristics, current priors do not easily allow users to specify basic properties such as expected lengthscale or amplitude variance. In this work, we introduce Poisson Process Radial Basis Function Networks, a novel prior that is able to encode amplitude stationarity and input-dependent lengthscale. We prove that our novel formulation allows for a decoupled specification of these properties, and that the estimated regression function is consistent as the number of observations tends to infinity. We demonstrate its behavior on synthetic and real examples.
CineFilter: Unsupervised Filtering for Real Time Autonomous Camera Systems

Autonomous camera systems are often subjected to an optimization/filtering operation to smoothen and stabilize the rough trajectory estimates. Most common filtering techniques do reduce the irregularities in data; however, they fail to mimic the behavior of a human cameraman. Global filtering methods modeling human camera operators have been successful; however, they are limited to offline settings. In this paper, we propose two online filtering methods called Cinefilters, which produce smooth camera trajectories that are motivated by cinematographic principles. The first filter (CineConvex) uses a sliding window-based convex optimization formulation, and the second (CineCNN) is a CNN based encoder-decoder model. We evaluate the proposed filters in two different settings, namely a basketball dataset and a stage performance dataset. Our models outperform previous methods and baselines on both quantitative and qualitative metrics. The CineConvex and CineCNN filters operate at about 250fps and 1000fps, respectively, with a minor latency (half a second), making them apt for a variety of real-time applications.
Unified Generative Adversarial Networks for Controllable Image-to-Image Translation

We propose a unified Generative Adversarial Network (GAN) for controllable image-to-image translation, i.e., transferring an image from a source to a target domain guided by controllable structures. In addition to conditioning on a reference image, we show how the model can generate images conditioned on controllable structures, e.g., class labels, object keypoints, human skeletons, and scene semantic maps. The proposed model consists of a single generator and a discriminator taking a conditional image and the target controllable structure as input. In this way, the conditional image can provide appearance information and the controllable structure can provide the structure information for generating the target result. Moreover, our model learns the image-to-image mapping through three novel losses, i.e., color loss, controllable structure guided cycle-consistency loss, and controllable structure guided self-content preserving loss. Also, we present the Fréchet ResNet Distance (FRD) to evaluate the quality of the generated images. Experiments on two challenging image translation tasks, i.e., hand gesture-to-gesture translation and cross-view image translation, show that our model generates convincing results, and significantly outperforms other state-of-the-art methods on both tasks. Meanwhile, the proposed framework is a unified solution, thus it can be applied to solving other controllable structure guided image translation tasks such as landmark guided facial expression translation and keypoint guided person image generation. To the best of our knowledge, we are the first to make one GAN framework work on all such controllable structure guided image translation tasks. Code is available at https://github.com/Ha0Tang/GestureGAN.
Contemporary Software Monitoring: A Systematic Literature Review

Contemporary software development strongly relies on software monitoring for different purposes, such as detecting abnormal behaviour or finding performance issues. These analyses are enabled by means of log data. The richness of log information has drawn the attention of researchers, who have put significant effort in software monitoring and log analysis techniques. Such knowledge, however, is currently spread. Moreover, we have no conceptual framework to explain the research field. In this paper, we perform a systematic literature review on logging techniques for software monitoring. More specifically, we explore the existing contemporary research on log engineering, infrastructure, and analysis. To that aim, we study 96 papers that appeared on top-level peer-reviewed conferences and journals. We then propose the Contemporary Logging Framework, a conceptual framework that maps the entire research field in four dimensions and 13 ramifications (i.e., research focuses). Finally, based on all the knowledge we gained, we propose a list of next steps that will help researchers in moving the field forward.
LSQ13ddu: a rapidly evolving stripped-envelope supernova with early circumstellar interaction signatures

This paper describes the rapidly evolving and unusual supernova LSQ13ddu, discovered by the La Silla-QUEST survey. LSQ13ddu displayed a rapid rise of just 4.8 ± 0.9 d to reach a peak brightness of −19.70 ± 0.02 mag in the LSQgr band. Early spectra of LSQ13ddu showed the presence of weak and narrow $\mathrm{ He}\, {\small I}$ features arising from interaction with circumstellar material (CSM). These interaction signatures weakened quickly, with broad features consistent with those seen in stripped-envelope SNe becoming dominant around two weeks after maximum. The narrow $\mathrm{ He}\, {\small I}$ velocities are consistent with the wind velocities of luminous blue variables but its spectra lack the typically seen hydrogen features. The fast and bright early light curve is inconsistent with radioactive 56Ni powering but can be explained through a combination of CSM interaction and an underlying 56Ni decay component that dominates the later time behaviour of LSQ13ddu. Based on the strength of the underlying broad features, LSQ13ddu appears deficient in He compared to standard SNe Ib.
PSR J0030+0451 Mass and Radius from NICER Data and Implications for the Properties of Neutron Star Matter

Neutron stars are not only of astrophysical interest, but are also of great interest to nuclear physicists because their attributes can be used to determine the properties of the dense matter in their cores. One of the most informative approaches for determining the equation of state (EoS) of this dense matter is to measure both a star’s equatorial circumferential radius Re and its gravitational mass M. Here we report estimates of the mass and radius of the isolated 205.53 Hz millisecond pulsar PSR J0030+0451 obtained using a Bayesian inference approach to analyze its energy-dependent thermal X-ray waveform, which was observed using the Neutron Star Interior Composition Explorer (NICER). This approach is thought to be less subject to systematic errors than other approaches for estimating neutron star radii. We explored a variety of emission patterns on the stellar surface. Our best-fit model has three oval, uniform-temperature emitting spots and provides an excellent description of the pulse waveform observed using NICER. The radius and mass estimates given by this model are km and (68%). The independent analysis reported in the companion paper by Riley et al. explores different emitting spot models, but finds spot shapes and locations and estimates of Re and M that are consistent with those found in this work. We show that our measurements of Re and M for PSR J0030+0451 improve the astrophysical constraints on the EoS of cold, catalyzed matter above nuclear saturation density.
Effects of chaos on the detectability of stellar streams

Observations show that stellar streams originating in satellite dwarf galaxies are frequent in the Universe. While such events are predicted by theory, it is not clear how many of the streams that are generated are washed out afterwards to the point in which it is impossible to detect them. Here, we study how these diffusion times are affected by the fact that typical gravitational potentials of the host galaxies can sustain chaotic orbits. We do this by comparing the behaviour of simulated stellar streams that reside in chaotic or non-chaotic regions of the phase space. We find that chaos does reduce the time interval in which streams can be detected. By analysing detectability criteria in configuration and velocity space, we find that the impact of these results on the observations depends on the quality of both the data and the underlying stellar halo model. For all the stellar streams, we obtain a similar upper limit to the detectable mass.
TERA: the Toxicological Effect and Risk Assessment Knowledge Graph.

Ecological risk assessment requires large amounts of chemical effect data from laboratory experiments. Due to experimental effort and animal welfare concerns it is desired to extrapolate data from existing sources. To cover the required chemical effect data several data sources need to be integrated to enable their interoperability. In this paper we introduce the Toxicological Effect and Risk Assessment (TERA) knowledge graph, which aims at providing such integrated view, and the data preparation and steps followed to construct this knowledge graph. We also present the applications of TERA for chemical effect prediction and the potential applications within the Semantic Web community.
Towards Auction-Based Function Placement in Serverless Fog Platforms

The Function-as-a-Service (FaaS) paradigm has a lot of potential as a computing model for fog environments comprising both cloud and edge nodes. When the request rate exceeds capacity limits at the edge, some functions need to be offloaded from the edge towards the cloud.In this position paper, we propose an auction-based approach in which application developers bid on resources. This allows fog nodes to make a local decision about which functions to offload while maximizing revenue. For a first evaluation of our approach, we use simulation.
Totally Deep Support Vector Machines

Support vector machines (SVMs) have been successful in solving many computer vision tasks including image and video category recognition especially for small and mid-scale training problems. The principle of these non-parametric models is to learn hyperplanes that separate data belonging to different classes while maximizing their margins. However, SVMs constrain the learned hyperplanes to lie in the span of support vectors, fixed/taken from training data, and this reduces their representational power and may lead to limited generalization performances. In this paper, we relax this constraint and allow the support vectors to be learned (instead of being fixed/taken from training data) in order to better fit a given classification task. Our approach, referred to as deep total variation support vector machines, is parametric and relies on a novel deep architecture that learns not only the SVM and the kernel parameters but also the support vectors, resulting into highly effective classifiers. We also show (under a particular setting of the activation functions in this deep architecture) that a large class of kernels and their combinations can be learned. Experiments conducted on the challenging task of skeleton-based action recognition show the outperformance of our deep total variation SVMs w.r. t different baselines as well as the related work.
MAGSAC++, a Fast, Reliable and Accurate Robust Estimator

We propose MAGSAC++ and Progressive NAPSAC sampler, P-NAPSAC in short. In MAGSAC++, we replace the model quality and polishing functions of the original method by an iteratively re-weighted least-squares fitting with weights determined via marginalizing over the noise scale. MAGSAC++ is fast -- often an order of magnitude faster -- and more geometrically accurate than MAGSAC. P-NAPSAC merges the advantages of local and global sampling by drawing samples from gradually growing neighborhoods. Exploiting that nearby points are more likely to originate from the same geometric model, P-NAPSAC finds local structures earlier than global samplers. We show that the progressive spatial sampling in P-NAPSAC can be integrated with PROSAC sampling, which is applied to the first, location-defining, point. The methods are tested on homography and fundamental matrix fitting on six publicly available datasets. MAGSAC combined with P-NAPSAC sampler is superior to state-of-the-art robust estimators in terms of speed, accuracy and failure rate.
Peek Inside the Closed World: Evaluating Autoencoder-Based Detection of DDoS to Cloud

Machine-learning-based anomaly detection (ML-based AD) has been successful at detecting DDoS events in the lab. However published evaluations of ML-based AD have used only limited data and provided minimal insight into why it works. To address limited evaluation against real-world data, we apply autoencoder, an existing ML-AD model, to 57 DDoS attack events captured at 5 cloud IPs from a major cloud provider. We show that our models detect nearly all malicious flows for 2 of the 4 cloud IPs under attack (at least 99.99%) and detect most malicious flows (94.75% and 91.37%) for the remaining 2 IPs. Our models also maintain near-zero false positives on benign flows to all 5 IPs. Our primary contribution is to improve our understanding for why ML-based AD works on some malicious flows but not others. We interpret our detection results with feature attribution and counterfactual explanation. We show that our models are better at detecting malicious flows with anomalies on allow-listed features (those with only a few benign values) than flows with anomalies on deny-listed features (those with mostly benign values) because our models are more likely to learn correct normality for allow-listed features. We then show that our models are better at detecting malicious flows with anomalies on unordered features (that have no ordering among their values) than flows with anomalies on ordered features because even with incomplete normality, our models could still detect anomalies on unordered feature with high recall. Lastly, we summarize the implications of what we learn on applying autoencoder-based AD in production: training with noisy real-world data is possible, autoencoder can reliably detect real-world anomalies on well-represented unordered features and combinations of autoencoder-based AD and heuristic-based filters can help both.
Determining cross sections from transport coefficients using deep neural networks

We present a neural network for the solution of the inverse swarm problem of deriving cross sections from swarm transport data. To account for the uncertainty inherent to this somewhat ill-posed inverse problem, we train the neural network using cross sections from the LXCat project, paired with associated transport coefficients found by the numerical solution of Boltzmann’s equation. The use of experimentally measured and theoretically calculated cross sections for training encourages the network to avoid unphysical solutions, such as those containing spurious energy-dependent oscillations. We successfully apply this machine learning approach to simulated swarm data for electron transport in helium, separately determining its elastic momentum transfer and ionisation cross sections to within an accuracy of 4% over the range of energies considered. Our attempt to extend our method to argon was less successful, although the reason for that observation is well-understood. Finally, we explore the feasibility of simultaneously determining cross sections of helium using this approach. We have some success here, determining elastic, total n = 2 excitation and ionisation cross sections to 10%, 20% and 25% accuracy, respectively. We are unsuccessful in properly unfolding the separate n = 2 singlet and triplet excitation cross sections of helium, but this is as expected given their similar threshold energies.
Single-Stage Regulated Resonant WPT Receiver With Low Input Harmonic Distortion

Resonant rectifier topologies would be a promising candidate for achieving simple, compact, and reliable single-stage wireless power transfer (WPT) receiver if not for the lack of good dc regulation capability. This article investigates the problems that prevent the feasibility of single-stage dc regulation in resonant rectifier topologies. A possible solution is the proposed differential resonant rectifier topology, of which the rectifier is designed to have a relatively constant ac voltage, and that phase-shift control is used to achieve relatively good output regulation. Design considerations on the reactive component sizing, magnetic component design, frequency and phase synchronization, small-signal modeling, and closed-loop feedback control design, are discussed. Experimental results verified that the proposed WPT receiver system can achieve single-stage ac rectification and dc regulation while attaining the key features of low harmonic distortion in its ac output voltage, continuous dc current, and zero-voltage-switching operation over a wide operating range.
Phonon-mediated superconductivity in doped monolayer materials

Insight into why superconductivity in pristine and doped monolayer graphene seems strongly suppressed has been central for the recent years' various creative approaches to realize superconductivity in graphene and graphene-like systems. We provide further insight by studying electron-phonon coupling and superconductivity in doped monolayer graphene and hexagonal boron nitride based on intrinsic phonon modes. Solving the graphene gap equation using a detailed model for the effective attraction based on electron tight binding and phonon force constant models, the various system parameters can be tuned at will. Consistent with results in the literature, we find slight gap modulations along the Fermi surface, and the high energy phonon modes are shown to be the most significant for the superconductivity instability. The Coulomb interaction plays a major role in suppressing superconductivity at realistic dopings. Motivated by the direct onset of a large density of states at the Fermi surface for small charge dopings in hexagonal boron nitride, we also calculate the dimensionless electron-phonon coupling strength there, but the comparatively large density of states cannot immediately be capitalized on, and the charge doping necessary to obtain significant electron-phonon coupling is similar to the value in graphene.
Unsupervised classification of acoustic emissions from catalogs and fault time-to-failure prediction

When a rock is subjected to stress it deforms by creep mechanisms that include formation and slip on small-scale internal cracks. Intragranular cracks and slip along grain contacts release energy as elastic waves called acoustic emissions (AE). Early research into AEs envisioned that these signals could be used in the future to predict rock falls, mine collapse, or even earthquakes. Today, nondestructive testing, a field of engineering, involves monitoring the spatio-temporal evolution of AEs with the goal of predicting time-to-failure for manufacturing tools and infrastructure. The monitoring process involves clustering AEs by damage mechanism (e.g. matrix cracking, delamination) to track changes within the material. In this study, we aim to adapt aspects of this process to the task of generalized earthquake prediction. Our data are generated in a laboratory setting using a biaxial shearing device and a granular fault gouge that mimics the conditions around tectonic faults. In particular, we analyze the temporal evolution of AEs generated throughout several hundred laboratory earthquake cycles. We use a Conscience Self-Organizing Map (CSOM) to perform topologically ordered vector quantization based on waveform properties. The resulting map is used to interactively cluster AEs according to damage mechanism. Finally, we use an event-based LSTM network to test the predictive power of each cluster. By tracking cumulative waveform features over the seismic cycle, the network is able to forecast the time-to-failure of the fault.