Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Supervised Discovering of Interpretable Features for Reinforcement Learning

Deep reinforcement learning (RL) has recently led to many breakthroughs on a range of complex control tasks. However, the agent’s decision-making process is generally not transparent. The lack of interpretability hinders the applicability of RL in safety-critical scenarios. While several methods have attempted to interpret vision-based RL, most come without detailed explanation for the agent’s behavior. In this paper, we propose a self-supervised interpretable framework, which can discover interpretable features to enable easy understanding of RL agents even for non-experts. Specifically, a self-supervised interpretable network (SSINet) is employed to produce fine-grained attention masks for highlighting task-relevant information, which constitutes most evidence for the agent’s decisions. We verify and evaluate our method on several Atari 2600 games as well as Duckietown, which is a challenging self-driving car simulator environment. The results show that our method renders empirical evidences about how the agent makes decisions and why the agent performs well or badly, especially when transferred to novel scenes. Overall, our method provides valuable insight into the internal decision-making process of vision-based RL. In addition, our method does not use any external labelled data, and thus demonstrates the possibility to learn high-quality mask through a self-supervised manner, which may shed light on new paradigms for label-free vision learning such as self-supervised segmentation and detection.
Online stochastic gradient descent on non-convex losses from high-dimensional inference

Stochastic gradient descent (SGD) is a popular algorithm for optimization problems arising in high-dimensional inference tasks. Here one produces an estimator of an unknown parameter from independent samples of data by iteratively optimizing a loss function. This loss function is random and often non-convex. We study the performance of the simplest version of SGD, namely online SGD, from a random start in the setting where the parameter space is high-dimensional. We develop nearly sharp thresholds for the number of samples needed for consistent estimation as one varies the dimension. Our thresholds depend only on an intrinsic property of the population loss which we call the information exponent. In particular, our results do not assume uniform control on the loss itself, such as convexity or uniform derivative bounds. The thresholds we obtain are polynomial in the dimension and the precise exponent depends explicitly on the information exponent. As a consequence of our results, we find that except for the simplest tasks, almost all of the data is used simply in the initial search phase to obtain non-trivial correlation with the ground truth. Upon attaining non-trivial correlation, the descent is rapid and exhibits law of large numbers type behaviour. We illustrate our approach by applying it to a wide set of inference tasks such as phase retrieval, parameter estimation for generalized linear models, spiked matrix models, and spiked tensor models, as well as for supervised learning for single-layer networks with general activation functions.
Fast & Slow Learning: Incorporating Synthetic Gradients in Neural Memory Controllers

Neural Memory Networks (NMNs) have received increased attention in recent years compared to deep architectures that use a constrained memory. Despite their new appeal, the success of NMNs hinges on the ability of the gradient-based optimiser to perform incremental training of the NMN controllers, determining how to leverage their high capacity for knowledge retrieval. This means that while excellent performance can be achieved when the training data is consistent and well distributed, rare data samples are hard to learn from as the controllers fail to incorporate them effectively during model training. Drawing inspiration from the human cognition process, in particular the utilisation of neuromodulators in the human brain, we propose to decouple the learning process of the NMN controllers to allow them to achieve flexible, rapid adaptation in the presence of new information. This trait is highly beneficial for meta-learning tasks where the memory controllers must quickly grasp abstract concepts in the target domain, and adapt stored knowledge. This allows the NMN controllers to quickly determine which memories are to be retained and which are to be erased, and swiftly adapt their strategy to the new task at hand. Through both quantitative and qualitative evaluations on multiple public benchmarks, including classification and regression tasks, we demonstrate the utility of the proposed approach. Our evaluations not only highlight the ability of the proposed NMN architecture to outperform the current state-of-the-art methods, but also provide insights on how the proposed augmentations help achieve such superior results. In addition, we demonstrate the practical implications of the proposed learning strategy, where the feedback path can be shared among multiple neural memory networks as a mechanism for knowledge sharing.
Discrete solution pools and noise-contrastive estimation for predict-and-optimize

Numerous real-life decision-making processes involve solving a combinatorial optimization problem with uncertain input that can be estimated from historic data. There is a growing interest in decision-focused learning methods, where the loss function used for learning to predict the uncertain input uses the outcome of solving the combinatorial problem over a set of predictions. Different surrogate loss functions have been identified, often using a continuous approximation of the combinatorial problem. However, a key bottleneck is that to compute the loss, one has to solve the combinatorial optimisation problem for each training instance in each epoch, which is computationally expensive even in the case of continuous approximations. We propose a different solver-agnostic method for decision-focused learning, namely by considering a pool of feasible solutions as a discrete approximation of the full combinatorial problem. Solving is now trivial through a single pass over the solution pool. We design several variants of a noise-contrastive loss over the solution pool, which we substantiate theoretically and empirically. Furthermore, we show that by dynamically re-solving only a fraction of the training instances each epoch, our method performs on par with the state of the art, whilst drastically reducing the time spent solving, hence increasing the feasibility of predict-and-optimize for larger problems.
Self-Supervised Out-of-Distribution Detection in Brain CT Scans

Medical imaging data suffers from the limited availability of annotation because annotating 3D medical data is a time-consuming and expensive task. Moreover, even if the annotation is available, supervised learning-based approaches suffer highly imbalanced data. Most of the scans during the screening are from normal subjects, but there are also large variations in abnormal cases. To address these issues, recently, unsupervised deep anomaly detection methods that train the model on large-sized normal scans and detect abnormal scans by calculating reconstruction error have been reported. In this paper, we propose a novel self-supervised learning technique for anomaly detection. Our architecture largely consists of two parts: 1) Reconstruction and 2) predicting geometric transformations. By training the network to predict geometric transformations, the model could learn better image features and distribution of normal scans. In the test time, the geometric transformation predictor can assign the anomaly score by calculating the error between geometric transformation and prediction. Moreover, we further use self-supervised learning with context restoration for pretraining our model. By comparative experiments on clinical brain CT scans, the effectiveness of the proposed method has been verified.
Separations and Transmutation Criteria to Improve Utilization of a Geologic Repository

Abstract This paper describes the results of a study that uses the thermal performance of the repository to establish chemical separations and transmutation criteria for commercial spent nuclear fuel of benefit to a geologic repository, as measured by the allowable increase in utilization of repository space. The method for determining the chemical elements to be separated is based on the thermal performance of the repository. The important chemical elements are identified, the order of importance of the separated elements is established, and the relationship between the efficiency of the chemical separation and the resulting increase in utilization is determined. The proposed repository at Yucca Mountain is used as an example of a geologic repository for the purposes of illustrating the magnitude of the benefits that are possible and the implications for repository size and operation. This work is being done in support of the U.S. Department of Energy Advanced Fuel Cycle Initiative, where numerous reactor, processing, and recycling strategies are being examined to determine the impact on issues important to the viability of nuclear electricity generation, including the disposal of spent nuclear fuel and nuclear waste.
The evolution of the low-frequency radio AGN population to z ≃ 1.5 in the ELAIS N1 field

We study the cosmic evolution of radio sources out to z ' 1.5 using a GMRT 610 MHz survey covering ∼1.86 deg2 of the ELAIS N1 field with a minimum/median rms noise 7.1/19.5 μJy / beam and an angular resolution of 6 arcsec. We classify sources as star forming galaxies (SFGs), radio-quiet (RQ) and radio-loud (RL) Active Galactic Nuclei (AGN) using a combination of multi-wavelength diagnostics and find evidence in support of the radio emission in SFGs and RQ AGN arising from star formation, rather than AGN-related processes. At high luminosities, however, both SFGs and RQ AGN display a radio excess when comparing radio and infrared star formation rates. The vast majority of our sample lie along the SFR −M★ "main sequence" at all redshifts when using infrared star formation rates. We derive the 610 MHz radio luminosity function for the total AGN population, constraining its evolution via continuous models of pure density and pure luminosity evolution with Φ ∝ ( 1 + z) (2.25±0.38)−(0.63±0.35)z and L610MHz ∝ ( 1 + z) (3.45±0.53)−(0.55±0.29)z respectively. For our RQ and RL AGN, we find a fairly mild evolution with redshift best fitted by pure luminosity evolution with L610MHz ∝ ( 1 + z) (2.81±0.43)−(0.57±0.30)z for RQ AGN and L610MHz ∝ ( 1 + z) (3.58±0.54)−(0.56±0.29)z for RL AGN. The 610 MHz radio AGN population thus comprises two differently evolving populations whose radio emission is mostly SF-driven or AGN-driven respectively.
$(f,\Gamma)$-Divergences: Interpolating between $f$-Divergences and Integral Probability Metrics

We develop a rigorous and general framework for constructing information-theoretic divergences that subsume both $f$-divergences and integral probability metrics (IPMs), such as the $1$-Wasserstein distance. We prove under which assumptions these divergences, hereafter referred to as $(f,\Gamma)$-divergences, provide a notion of `distance' between probability measures and show that they can be expressed as a two-stage mass-redistribution/mass-transport process. The $(f,\Gamma)$-divergences inherit features from IPMs, such as the ability to compare distributions which are not absolutely continuous, as well as from $f$-divergences, namely the strict concavity of their variational representations and the ability to control heavy-tailed distributions for particular choices of $f$. When combined, these features establish a divergence with improved properties for estimation, statistical learning, and uncertainty quantification applications. Using statistical learning as an example, we demonstrate their advantage in training generative adversarial networks (GANs) for heavy-tailed, not-absolutely continuous sample distributions. We also show improved performance and stability over gradient-penalized Wasserstein GAN in image generation.
Distill2Vec: Dynamic Graph Representation Learning with Knowledge Distillation

Dynamic graph representation learning strategies are based on different neural architectures to capture the graph evolution over time. However, the underlying neural architectures require a large amount of parameters to train and suffer from high online inference latency, that is several model parameters have to be updated when new data arrive online. In this study we propose Distill2Vec, a knowledge distillation strategy to train a compact model with a low number of trainable parameters, so as to reduce the latency of online inference and maintain the model accuracy high. We design a distillation loss function based on Kullback-Leibler divergence to transfer the acquired knowledge from a teacher model trained on offline data, to a small-size student model for online data. Our experiments with publicly available datasets show the superiority of our proposed model over several state-of-the-art approaches with relative gains up to 5% in the link prediction task. In addition, we demonstrate the effectiveness of our knowledge distillation strategy, in terms of number of required parameters, where Distill2Vec achieves a compression ratio up to 7:100 when compared with baseline approaches. For reproduction purposes, our implementation is publicly available at https://stefanosantaris.github. io/Distill2Vec.
On persistence properties in weighted spaces for solutions of the fractional Korteweg–de Vries equation

Persistence problems in weighted spaces have been studied for different dispersive models involving non-local operators. Generally, these models do not propagate polynomial weights of arbitrary magnitude, and the maximum decay rate is associated with the dispersive part of the equation. Altogether, this analysis is complemented by unique continuation principles that determine optimal spatial decay. This work is intended to establish the above questions for a weakly dispersive perturbation of the inviscid Burgers equation. More precisely, we consider the fractional Korteweg–de Vries equation, which comprises the Burgers–Hilbert equation and dispersive effects weaker than those of the Benjamin–Ono equation.
Implications of new physics in $B \to K_1 \mu^+ \mu^-$ decay processes

In recent times, several discrepancies at the level of $(2-3)\sigma$ have been observed in the decay processes mediated by flavour changing neutral current (FCNC) transitions $b \to s \ell^+ \ell^-$, which may be considered as the smoking-gun signal of New Physics (NP). These intriguing hints of NP have attracted a lot of attention and many attempts are made to look for the possible NP signature in other related processes, which are mediated through the same quark-level transitions. In this work, we perform a comprehensive analysis of the FCNC decays of $B$ meson to axial vector mesons $ K_1(1270)$ and $ K_1(1400)$, which are admixture of the $1^3P_1$ and $1^1P_1$ states $K_{1A}$ and $K_{1B}$, in a model independent framework. Using the $ B \to K_1$ form factors evaluated in the light cone sum rule approach, we investigate the rare exclusive semileptonic decays $ B \to K_1(1270) \mu^+ \mu^-$ and $ B \to K_1(1400) \mu^+ \mu^-$. Considering all the possible relevant operators for $b \to s \ell^+ \ell^-$ transitions, we study their effects on various observables such as branching fractions, lepton flavor universality violating ratio ($R_{K_1})$, forward-backward asymmetries, and lepton polarization asymmetries of these processes. These results will not only enhance the theoretical understanding of the mixing angle but also serve as a good tool for probing New Physics.
A Nonconvex Framework for Structured Dynamic Covariance Recovery

We propose a flexible yet interpretable model for high-dimensional data with time-varying second order statistics, motivated and applied to functional neuroimaging data. Motivated by the neuroscience literature, we factorize the covariances into sparse spatial and smooth temporal components. While this factorization results in both parsimony and domain interpretability, the resulting estimation problem is nonconvex. To this end, we design a two-stage optimization scheme with a carefully tailored spectral initialization, combined with iteratively refined alternating projected gradient descent. We prove a linear convergence rate up to a nontrivial statistical error for the proposed descent scheme and establish sample complexity guarantees for the estimator. We further quantify the statistical error for the multivariate Gaussian case. Empirical results using simulated and real brain imaging data illustrate that our approach outperforms existing baselines.
Invertible CNN-Based Super Resolution with Downsampling Awareness

Single image super resolution involves artificially increasing the resolution of an image. Recently, convolutional neural networks have been demonstrated as very powerful tools for this problem. These networks are typically trained by artificially degrading high resolution images and training the neural network to reproduce the original. Because these neural networks are learning an inverse function for an image downsampling scheme, their high-resolution outputs should ideally re-produce the corresponding low-resolution input when the same downsampling scheme is applied. This constraint has not historically been explicitly and strictly imposed during training however. Here, a method for "downsampling aware" super resolution networks is proposed. A differentiable operator is applied as the final output layer of the neural network that forces the downsampled output to match the low resolution input data under 2D-average downsampling. It is demonstrated that appending this operator to a selection of state-of-the-art deep-learning-based super resolution schemes improves training time and overall performance on most of the common image super resolution benchmark datasets. In addition to this performance improvement for images, this method has potentially broad and significant impacts in the physical sciences. This scheme can be applied to data produced by medical scans, precipitation radars, gridded numerical simulations, satellite imagers, and many other sources. In such applications, the proposed method's guarantee of strict adherence to physical conservation laws is of critical importance.
Scribble-Supervised Semantic Segmentation by Random Walk on Neural Representation and Self-Supervision on Neural Eigenspace

Scribble-supervised semantic segmentation has gained much attention recently for its promising performance without high-quality annotations. Many approaches have been proposed. Typically, they handle this problem to either introduce a well-labeled dataset from another related task, turn to iterative refinement and post-processing with the graphical model, or manipulate the scribble label. This work aims to achieve semantic segmentation supervised by scribble label directly without auxiliary information and other intermediate manipulation. Specifically, we impose diffusion on neural representation by random walk and consistency on neural eigenspace by self-supervision, which forces the neural network to produce dense and consistent predictions over the whole dataset. The random walk embedded in the network will compute a probabilistic transition matrix, with which the neural representation diffused to be uniform. Moreover, given the probabilistic transition matrix, we apply the self-supervision on its eigenspace for consistency in the image's main parts. In addition to comparing the common scribble dataset, we also conduct experiments on the modified datasets that randomly shrink and even drop the scribbles on image objects. The results demonstrate the superiority of the proposed method and are even comparable to some full-label supervised ones. The code and datasets are available at this https URL.
Leveraging the Defects Life Cycle to Label Affected Versions and Defective Classes

Two recent studies explicitly recommend labeling defective classes in releases using the affected versions (AV) available in issue trackers (e.g., Jira). This practice is coined as the realistic approach. However, no study has investigated whether it is feasible to rely on AVs. For example, how available and consistent is the AV information on existing issue trackers? Additionally, no study has attempted to retrieve AVs when they are unavailable. The aim of our study is threefold: (1) to measure the proportion of defects for which the realistic method is usable, (2) to propose a method for retrieving the AVs of a defect, thus making the realistic approach usable when AVs are unavailable, (3) to compare the accuracy of the proposed method versus three SZZ implementations. The assumption of our proposed method is that defects have a stable life cycle in terms of the proportion of the number of versions affected by the defects before discovering and fixing these defects. Results related to 212 open-source projects from the Apache ecosystem, featuring a total of about 125,000 defects, reveal that the realistic method cannot be used in the majority (51%) of defects. Therefore, it is important to develop automated methods to retrieve AVs. Results related to 76 open-source projects from the Apache ecosystem, featuring a total of about 6,250,000 classes, affected by 60,000 defects, and spread over 4,000 versions and 760,000 commits, reveal that the proportion of the number of versions between defect discovery and fix is pretty stable (standard deviation <2)—across the defects of the same project. Moreover, the proposed method resulted significantly more accurate than all three SZZ implementations in (i) retrieving AVs, (ii) labeling classes as defective, and (iii) in developing defects repositories to perform feature selection. Thus, when the realistic method is unusable, the proposed method is a valid automated alternative to SZZ for retrieving the origin of a defect. Finally, given the low accuracy of SZZ, researchers should consider re-executing the studies that have used SZZ as an oracle and, in general, should prefer selecting projects with a high proportion of available and consistent AVs.
Radio observations of HD 80606 near planetary periastron

Context. All the giant planets in the Solar System generate radio emission via electron cyclotron maser instability, giving rise most notably to Jupiter’s decametric emissions. An interaction with the solar wind is at least partially responsible for all of these Solar System electron cyclotron masers. HD 80606b is a giant planet with a highly eccentric orbit, leading to predictions that its radio emission may be enhanced substantially near periastron. Aims. This paper reports observations with the Low Frequency Array (LOFAR) of HD 80606b near its periastron in an effort to detect radio emissions generated by an electron cyclotron maser instability in the planet’s magnetosphere. Methods. The reported observations are at frequencies between 30 and 78 MHz, and they are distinguished from most previous radio observations of extrasolar planets by two factors: (i) they are at frequencies near 50 MHz, much closer to the frequencies at which Jupiter emits (ν < 40 MHz) and lower than most previously reported observations of extrasolar planets; and (ii) sensitivities of approximately a few millijanskys have been achieved, an order of magnitude or more below nearly all previous extrasolar planet observations below 100 MHz. Results. We do not detect any radio emissions from HD 80606b and use these observations to place new constraints on its radio luminosity. We also revisit whether the observations were conducted at a time when HD 80606b was super-Alfvénic relative to the host star’s stellar wind, which experience from the Solar System illustrates is a state in which an electron cyclotron maser emission can be sustained in a planet’s magnetic polar regions.