Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficiently Guiding Imitation Learning Algorithms with Human Gaze

Human gaze is known to be an intention-revealing signal in human demonstrations of tasks. In this work, we use gaze cues from human demonstrators to enhance the performance of state-of-the-art inverse reinforcement learning (IRL) and behavioral cloning (BC) algorithms. We propose a novel approach for utilizing gaze data in a computationally efficient manner --- encoding the human's attention as part of an auxiliary loss function, without adding any additional learnable parameters to those models and without requiring gaze data at test time. The auxiliary loss encourages a network to have convolutional activations in regions where the human's gaze fixated. We show how to augment any existing convolutional architecture with our auxiliary gaze loss (coverage-based gaze loss or CGL) that can guide learning toward a better reward function or policy. We show that our proposed approach improves performance of both BC and IRL methods on a variety of Atari games. We also compare against two baseline methods for utilizing gaze data with imitation learning methods. Our approach outperforms a baseline method, called gaze-modulated dropout (GMD), and is comparable to another method (AGIL) which uses gaze as input to the network and thus increases the amount of learnable parameters.
A black hole inside dark matter and the rotation curves of galaxies

In this paper, we find a four-dimensional metric for a large black hole immersed in dark matter. Specifically, we look for and find a static spherically symmetric black hole solution to the Einstein equations which gives, in the Newtonian limit, the rotation curves of galaxies, including the flat region and the baryonic Tully–Fisher relation, and which has a regular horizon. We obtain as well the energy–momentum tensor of the dark matter sourcing this spacetime and it turns, in special, to satisfy the four energy conditions (dominant, weak, null and strong) everywhere outside the horizon. This black-hole-dark-matter system represents a successful simplified model for galaxies, opens a new area for exploring the relativistic regime of dark matter, and shows that the theory of General Relativity together with dark matter can account for the rotation curves of galaxies.
BMI: A Behavior Measurement Indicator for Fuel Poverty Using Aggregated Load Readings from Smart Meters

Fuel poverty affects between 50 and 125 million households in Europe and is a significant issue for both developed and developing countries globally. This means that fuel poor residents are unable to adequately warm their home and run the necessary energy services needed for lighting, cooking, hot water, and electrical appliances. The problem is complex but is typically caused by three factors; low income, high energy costs, and energy inefficient homes. In the United Kingdom (UK), 4 million families are currently living in fuel poverty. Those in series financial difficulty are either forced to self-disconnect or have their services terminated by energy providers. Fuel poverty contributed to 10,000 reported deaths in England in the winter of 2016-2107 due to homes being cold. While it is recognized by governments as a social, public health and environmental policy issue, the European Union (EU) has failed to provide a common definition of fuel poverty or a conventional set of indicators to measure it. This chapter discusses current fuel poverty strategies across the EU and proposes a new and foundational behavior measurement indicator designed to directly assess and monitor fuel poverty risks in households using smart meters, Consumer Access Device (CAD) data and machine learning. By detecting Activities of Daily Living (ADLS) through household appliance usage, it is possible to spot the early signs of financial difficulty and identify when support packages are required.
An efficient constraint based framework forhandling floating point SMT problems

This paper introduces the 2019 version of \us{}, a novel Constraint Programming framework for floating point verification problems expressed with the SMT language of SMTLIB. SMT solvers decompose their task by delegating to specific theories (e.g., floating point, bit vectors, arrays, .) the task to reason about combinatorial or otherwise complex constraints for which the SAT encoding would be cumbersome or ineffective. This decomposition and encoding processes lead to the obfuscation of the high-level constraints and a loss of information on the structure of the combinatorial model. In \us{}, constraints over the floats are first class objects, and the purpose is to expose and exploit structures of floating point domains to enhance the search process. A symbolic phase rewrites each SMTLIB instance to elementary constraints, and eliminates auxiliary variables whose presence is counterproductive. A diversification technique within the search steers it away from costly enumerations in unproductive areas of the search space. The empirical evaluation demonstrates that the 2019 version of \us{} is competitive on computationally challenging floating point benchmarks that induce significant search efforts even for other CP solvers. It highlights that the ability to harness both inference and search is critical. Indeed, it yields a factor 3 improvement over Colibri and is up to 10 times faster than SMT solvers. The evaluation was conducted over 214 benchmarks (The Griggio suite) which is a standard within SMTLIB.
Election Manipulation on Social Networks: Seeding, Edge Removal, Edge Addition

We focus on the election manipulation problem through social influence, where a manipulator exploits a social network to make her most preferred candidate win an election. Influence is due to information in favor of and/or against one or multiple candidates, sent by seeds and spreading through the network according to the independent cascade model. We provide a comprehensive theoretical study of the election control problem, investigating two forms of manipulations: seeding to buy influencers given a social network and removing or adding edges in the social network given the set of the seeds and the information sent. In particular, we study a wide range of cases distinguishing in the number of candidates or the kind of information spread over the network. Our main result shows that the election manipulation problem is not affordable in the worst-case, even when one accepts to get an approximation of the optimal margin of victory, except for the case of seeding when the number of hard-to-manipulate voters is not too large, and the number of uncertain voters is not too small, where we say that a voter that does not vote for the manipulator's candidate is hard-to-manipulate if there is no way to make her vote for this candidate, and uncertain otherwise. We also provide some results showing the hardness of the problems in special cases. More precisely, in the case of seeding, we show that the manipulation is hard even if the graph is a line and that a large class of algorithms, including most of the approaches recently adopted for social-influence problems (e.g., greedy, degree centrality, PageRank, VoteRank), fails to compute a bounded approximation even on elementary networks, such as undirected graphs with every node having a degree at most two or directed trees. In the case of edge removal or addition, our hardness results also apply to election manipulation when the manipulator has an unlimited budget, being allowed to remove or add an arbitrary number of edges, and to the basic case of social influence maximization/minimization in the restricted case of finite budget. Interestingly, our hardness results for seeding and edge removal/addition still hold in a re-optimization variant, where the manipulator already knows an optimal solution to the problem and computes a new solution once a local modification occurs, e.g., the removal/addition of a single edge.
Double-differential inclusive charged-current 
νμ
 cross sections on hydrocarbon in MINERvA at 
⟨Eν⟩∼3.5  GeV

MINERvA reports inclusive charged-current cross sections for muon neutrinos on hydrocarbon in the NuMI beamline. We measured the double-differential cross section in terms of the longitudinal and transverse muon momenta, as well as the single-differential cross sections in those variables. The data used in this analysis correspond to an exposure of $3.34 \times 10^{20}$ protons on target with a peak neutrino energy of approximately 3.5 GeV. Measurements are compared to the GENIE, NuWro and GiBUU neutrino cross-section predictions, as well as a version of GENIE modified to produce better agreement with prior exclusive MINERvA measurements. None of the models or variants were able to successfully reproduce the data across the entire phase space, which includes areas dominated by each interaction channel.
Detecting and Recovering Adversarial Examples: An Input Sensitivity Guided Method

Deep neural networks undergo rapid development and achieve notable success in various tasks, including many security concerned scenarios. However, a considerable amount of works have proved its vulnerability in adversaries. To address this problem, we propose a Guided Robust and Efficient Defensive Model GRED integrating detection and recovery processes together. From the lens of the properties of gradient distribution of adversarial examples, our model detects malicious inputs effectively, as well as recovering the ground-truth label with high accuracy. Compared with commonly used adversarial training methods, our model is more efficient and outperforms state-of-the-art adversarial trained models by a large margin up to 99% on MNIST, 89 % on CIFAR-10 and 87% on ImageNet subsets. When exclusively compared with previous adversarial detection methods, the detector of GRED is robust under all threat settings with a detection rate of over 95% against most of the attacks. It is also demonstrated by empirical assessment that our model could increase attacking cost significantly resulting in either unacceptable time consuming or human perceptible image distortions.
NuSTAR Observations of G11.2–0.3

We present in this paper the hard X-ray view of the pulsar wind nebula in G11.2−0.3 and its central pulsar powered pulsar J1811−1925 as seen by NuSTAR. We complement the data with Chandra for a more complete picture and confirm the existence of a hard, power-law component in the shell with photon index , which we attribute to synchrotron emission. Our imaging observations of the shell show a slightly smaller radius at higher energies, consistent with Chandra results, and we find shrinkage as a function of increased energy along the jet direction, indicating that the electron outflow in the PWN may be simpler than that seen in other young PWNe. Combining NuSTAR with INTEGRAL, we find that the pulsar spectrum can be fit by a power law with up to 300 keV without evidence of curvature.
Introducing a Human-like Planner for Reaching in Cluttered Environments

Humans, in comparison to robots, are remarkably adept at reaching for objects in cluttered environments. The best existing robot planners are based on random sampling in configuration space -- which becomes excessively high-dimensional with a large number of objects. Consequently, most of these planners suffer from limited object manipulation. We address this problem by learning high-level manipulation planning skills from humans and transfer these skills to robot planners. We used virtual reality to generate data from human participants whilst they reached for objects on a cluttered table top. From this, we devised a qualitative representation of the task space to abstract human decisions, irrespective of the number of objects in the way. Based on this representation, human demonstrations were segmented and used to train decision classifiers. Using these classifiers, our planner produced a list of waypoints in task space. These waypoints provide a high-level plan, which can be transferred to an arbitrary robot model and used to initialize a local trajectory optimiser. We evaluated this approach through testing on unseen human VR data, a physics-based robot simulation and real robot experiments. We find that this human-like planner outperforms a state-of-the-art standard trajectory optimisation algorithm and is able to generate effective strategies for rapid planning, irrespective of the number of objects in a cluttered environment. Our dataset and source code are publicly available.
Inverse Graphics GAN: Learning to Generate 3D Shapes from Unstructured 2D Data

Recent work has shown the ability to learn generative models for 3D shapes from only unstructured 2D images. However, training such models requires differentiating through the rasterization step of the rendering process, therefore past work has focused on developing bespoke rendering models which smooth over this non-differentiable process in various ways. Such models are thus unable to take advantage of the photo-realistic, fully featured, industrial renderers built by the gaming and graphics industry. In this paper we introduce the first scalable training technique for 3D generative models from 2D data which utilizes an off-the-shelf non-differentiable renderer. To account for the non-differentiability, we introduce a proxy neural renderer to match the output of the non-differentiable renderer. We further propose discriminator output matching to ensure that the neural renderer learns to smooth over the rasterization appropriately. We evaluate our model on images rendered from our generated 3D shapes, and show that our model can consistently learn to generate better shapes than existing models when trained with exclusively unstructured 2D images.
On Material Optimisation for Nonlinearly Elastic Plates and Shells

This paper investigates the optimal distribution of hard and soft material on elastic plates. In the class of isometric deformations stationary points of a Kirchhoff plate functional with incorporated material hardness function are investigated and a compliance cost functional is taken into account. Under symmetry assumptions on the material distribution and the load it is shown that cylindrical solutions are stationary points. Furthermore, it is demonstrated that the optimal design of cylindrically deforming, clamped rectangular plates is non trivial, i.e. with a material distribution which is not just depending on one axial direction on the plate. Analytical results are complemented with numerical optimization results using a suitable finite element discretization and a phase field description of the material phases. Finally, using numerical methods an outlook on the optimal design of non isometrically deforming plates and shells is given.
Role of nonlocality in exchange correlation for magnetic two-dimensional van der Waals materials

To obtain accurate independent-particle descriptions for ferromagnetic two-dimensional van der Waals materials, we apply the quasiparticle self-consistent $GW$ (QSGW) method to ${\mathrm{VI}}_{3}, {\mathrm{CrI}}_{3}, {\mathrm{CrGeTe}}_{3}$, and ${\mathrm{Fe}}_{3}{\mathrm{GeTe}}_{2}$. QSGW provides a description of the nonlocal exchange-correlation term in the one-particle Hamiltonian. The nonlocal term is important not only as the $U$ of density functional theory $(\mathrm{DFT})+U$ but also for differentiating occupied and unoccupied states in semiconductors. We show the limitations of $\mathrm{DFT}+U$ in mimicking QSGW.
Robust Unsupervised Neural Machine Translation with Adversarial Training

Unsupervised neural machine translation (UNMT) has recently attracted great interest in the machine translation community, achieving only slightly worse results than supervised neural machine translation. However, in real-world scenarios, there usually exists minor noise in the input sentence and the neural translation system is sensitive to the small perturbations in the input, leading to poor performance. In this paper, we first define two types of noises and empirically show the effect of these noisy data on UNMT performance. Moreover, we propose adversarial training methods to improve the robustness of UNMT in the noisy scenario. To the best of our knowledge, this paper is the first work to explore the robustness of UNMT. Experimental results on several language pairs show that our proposed methods substantially outperform conventional UNMT systems in the noisy scenario.
First Constraints on Compact Dark Matter from Fast Radio Burst Microstructure

Despite existing constraints, it remains possible that up to 35% of all dark matter is comprised of compact objects, such as the black holes in the 10–100 M⊙ range whose existence has been confirmed by LIGO. The strong gravitational lensing of transients such as fast radio bursts (FRBs) and gamma-ray bursts has been suggested as a more sensitive probe for compact dark matter than intensity fluctuations observed in microlensing experiments. Recently the Australian Square Kilometre Array Pathfinder has reported burst substructure down to 15 μs timescales in FRBs in the redshift range 0.3–0.5. We investigate here the implications of this for the detectability of compact dark matter by FRBs. We find that a sample size of ∼130 FRBs would be required to constrain compact dark matter to less than the existing 35% limit with 95% confidence, if it were distributed along ≳1 Gpc-long FRB sightlines through the cosmic web. Conversely, existing constraints on the fraction of compact dark matter permit as many as 1 in ≈40 of all z ≲ 0.4 FRBs to exhibit microlensed burst structure. Approximately 170 FRBs intercepting halos within ∼50 kpc would be required to exclude the fraction of compact dark matter in each intercepted halo to a similar level. Furthermore, we consider the cumulative effects of lensing of the FRB signal by a macroscopic dark matter distribution. We conclude that lensing from a uniform distribution of compact objects is likely not observable, but suggest that FRBs may set meaningful limits on power-law distributions of dark matter.
Beyond the Trees: Resilient Multipath for Last-mile WISP Networks

Expanding the reach of the Internet is a topic of widespread interest today. Google and Facebook, among others, have begun investing substantial research efforts toward expanding Internet access at the edge. Compared to data center networks, which are relatively over-engineered, last-mile networks are highly constrained and end up being ultimately responsible for the performance issues that impact the user experience. The most viable and cost-effective approach for providing last-mile connectivity has proved to be Wireless ISPs (WISPs), which rely on point-to-point wireless backhaul infrastructure to provide connectivity using cheap commodity wireless hardware. However, individual WISP network links are known to have poor reliability and the networks as a whole are highly cost constrained. Motivated by these observations, we propose Wireless ISPs with Redundancy (WISPR), which leverages the cost-performance tradeoff inherent in commodity wireless hardware to move toward a greater number of inexpensive links in WISP networks thereby lowering costs. To take advantage of this new path diversity, we introduce a new, general protocol that provides increased performance, reliability, or a combination of the two.
ResNets, NeuralODEs and CT-RNNs are Particular Neural Regulatory Networks

This paper shows that ResNets, NeuralODEs, and CT-RNNs, are particular neural regulatory networks (NRNs), a biophysical model for the nonspiking neurons encountered in small species, such as the C.elegans nematode, and in the retina of large species. Compared to ResNets, NeuralODEs and CT-RNNs, NRNs have an additional multiplicative term in their synaptic computation, allowing them to adapt to each particular input. This additional flexibility makes NRNs $M$ times more succinct than NeuralODEs and CT-RNNs, where $M$ is proportional to the size of the training set. Moreover, as NeuralODEs and CT-RNNs are $N$ times more succinct than ResNets, where $N$ is the number of integration steps required to compute the output $F(x)$ for a given input $x$, NRNs are in total $M\,{\cdot}\,N$ more succinct than ResNets. For a given approximation task, this considerable succinctness allows to learn a very small and therefore understandable NRN, whose behavior can be explained in terms of well established architectural motifs, that NRNs share with gene regulatory networks, such as, activation, inhibition, sequentialization, mutual exclusion, and synchronization. To the best of our knowledge, this paper unifies for the first time the mainstream work on deep neural networks with the one in biology and neuroscience in a quantitative fashion.
Mixed Strategies for Robust Optimization of Unknown Objectives

We consider robust optimization problems, where the goal is to optimize an unknown objective function against the worst-case realization of an uncertain parameter. For this setting, we design a novel sample-efficient algorithm GP-MRO, which sequentially learns about the unknown objective from noisy point evaluations. GP-MRO seeks to discover a robust and randomized mixed strategy, that maximizes the worst-case expected objective value. To achieve this, it combines techniques from online learning with nonparametric confidence bounds from Gaussian processes. Our theoretical results characterize the number of samples required by GP-MRO to discover a robust near-optimal mixed strategy for different GP kernels of interest. We experimentally demonstrate the performance of our algorithm on synthetic datasets and on human-assisted trajectory planning tasks for autonomous vehicles. In our simulations, we show that robust deterministic strategies can be overly conservative, while the mixed strategies found by GP-MRO significantly improve the overall performance.
Optimization with Momentum: Dynamical, Control-Theoretic, and Symplectic Perspectives

We analyze the convergence rate of various momentum-based optimization algorithms from a dynamical systems point of view. Our analysis exploits fundamental topological properties, such as the continuous dependence of iterates on their initial conditions, to provide a simple characterization of convergence rates. In many cases, closed-form expressions are obtained that relate algorithm parameters to the convergence rate. The analysis encompasses discrete time and continuous time, as well as time-invariant and time-variant formulations, and is not limited to a convex or Euclidean setting. In addition, the article rigorously establishes why symplectic discretization schemes are important for momentum-based optimization algorithms, and provides a characterization of algorithms that exhibit accelerated convergence.