Miracle-dz/UniFAR-FTUs huggingface dataset:
The Foreseeable Future: Self-Supervised Learning to Predict Dynamic Scenes for Indoor Navigation

We present a method for generating, predicting, and using spatiotemporal occupancy grid maps (SOGM), which embed future semantic information of real dynamic scenes. We present an autolabeling process that creates SOGMs from noisy real navigation data. We use a 3-D–2-D feedforward architecture, trained to predict the future time steps of SOGMs, given 3-D Lidar frames as input. Our pipeline is entirely self-supervised, thus enabling lifelong learning for real robots. The network is composed of a 3-D back-end that extracts rich features and enables the semantic segmentation of the lidar frames, and a 2-D front-end that predicts the future information embedded in the SOGM representation, potentially capturing the complexities and uncertainties of real-world multiagent interactions. We also design a navigation system that uses these predicted SOGMs within planning, after they have been transformed into spatiotemporal risk maps. We verify our navigation system's abilities in simulation, validate it on a real robot, study SOGM predictions on real data in various circumstances, and provide a novel indoor 3-D lidar dataset, collected during our experiments, which includes our automated annotations.
Novel phenomena of the Hartle-Hawking wave function

We ﬁnd a novel phenomenon in the solution to the Wheeler-DeWitt equation by solving numerically the equation with the Hartle-Hawking wave function as a boundary condition. In the slow-roll limit, as expected, the numerical solution gives the most dominant steepest-descent that describes the probability distribution for the initial condition of a universe. The probability is consistent with the Euclidean computations, and the overall shape of the wave function is consistent with analytical approximations. On the other hand, in the non-slow-roll limit, an unexpected novel phenomenon appears in the oscillatory regime corresponding to a classical domain of the solution: interferences among the propagating modes along the metric direction and the ﬁeld direction. Especially, in some circumstances, the probability of the oscillatory regime becomes even greater than that of the steepest-descent. Possible interpretations and conceptual issues of this phenomenon are discussed.
Light-weight probing of unsupervised representations for Reinforcement Learning

Unsupervised visual representation learning offers the opportunity to leverage large corpora of unlabeled trajectories to form useful visual representations, which can benefit the training of reinforcement learning (RL) algorithms. However, evaluating the fitness of such representations requires training RL algorithms which is computationally intensive and has high variance outcomes. To alleviate this issue, we design an evaluation protocol for unsupervised RL representations with lower variance and up to 600x lower computational cost. Inspired by the vision community, we propose two linear probing tasks: predicting the reward observed in a given state, and predicting the action of an expert in a given state. These two tasks are generally applicable to many RL domains, and we show through rigorous experimentation that they correlate strongly with the actual downstream control performance on the Atari100k Benchmark. This provides a better method for exploring the space of pretraining algorithms without the need of running RL evaluations for every setting. Leveraging this framework, we further improve existing self-supervised learning (SSL) recipes for RL, highlighting the importance of the forward model, the size of the visual backbone, and the precise formulation of the unsupervised objective.
Possible Explosive Dispersal Outflow in IRAS 16076-5134 Revealed with ALMA

We present 0.9 mm continuum and CO(3–2) line emission observations retrieved from the Atacama Large Millimeter/submillimeter Array archive toward the high-mass star formation region IRAS 16076-5134. We identify 14 dense cores with masses between 0.3 and 22 M ☉. We find an ensemble of filament-like CO(3–2) ejections from −62 to +83 km s−1 that appear to arise radially from a common central position, close to the dense core MM8. The ensemble of filaments has a quasi-isotropic distribution in the plane of the sky. The radial velocities of several filaments follow a linear velocity gradient, increasing from a common origin. Considering the whole ensemble of filaments, we estimate the total mass to be 138 and 216 M ☉, from its CO emission, for 70 K and 140 K, respectively. Also, assuming a constant velocity expansion for the filaments (of 83 km s−1), we estimate the dynamical age of the outflowing material (3500 yr), its momentum (∼104 M ☉ km s−1), and its kinetic energy (∼1048–49 erg). The morphology and kinematics presented by the filaments suggest the presence of a dispersal outflow with explosive characteristics in IRAS 16076-5134. In addition, we make a raw estimate of the lower limit of the frequency rate of the explosive dispersal outflows in the galaxy (one every 110 yr), considering a constant star formation rate and efficiency, with respect to the galactocentric radius of the galaxy. This may imply a comparable rate between dispersal outflows and supernovae (approximately one every 50 yr), which may be important for the energy budget of the and the link between dispersal outflows and high-mass star formation.
Flexible Vertical Federated Learning with Heterogeneous Parties

We propose flexible vertical federated learning (Flex-VFL), a distributed machine algorithm that trains a smooth, nonconvex function in a distributed system with vertically partitioned data. We consider a system with several parties that wish to collaboratively learn a global function. Each party holds a local dataset; the datasets have different features but share the same sample ID space. The parties are heterogeneous in nature: the parties' operating speeds, local model architectures, and optimizers may be different from one another and, further, they may change over time. To train a global model in such a system, Flex-VFL utilizes a form of parallel block coordinate descent (P-BCD), where parties train a partition of the global model via stochastic coordinate descent. We provide theoretical convergence analysis for Flex-VFL and show that the convergence rate is constrained by the party speeds and local optimizer parameters. We apply this analysis and extend our algorithm to adapt party learning rates in response to changing speeds and local optimizer parameters. Finally, we compare the convergence time of Flex-VFL against synchronous and asynchronous VFL algorithms, as well as illustrate the effectiveness of our adaptive extension.
Causal Bandits for Linear Structural Equation Models

This paper studies the problem of designing an optimal sequence of interventions in a causal graphical model to minimize cumulative regret with respect to the best intervention in hindsight. This is, naturally, posed as a causal bandit problem. The focus is on causal bandits for linear structural equation models (SEMs) and soft interventions. It is assumed that the graph's structure is known and has $N$ nodes. Two linear mechanisms, one soft intervention and one observational, are assumed for each node, giving rise to $2^N$ possible interventions. Majority of the existing causal bandit algorithms assume that at least the interventional distributions of the reward node's parents are fully specified. However, there are $2^N$ such distributions (one corresponding to each intervention), acquiring which becomes prohibitive even in moderate-sized graphs. This paper dispenses with the assumption of knowing these distributions or their marginals. Two algorithms are proposed for the frequentist (UCB-based) and Bayesian (Thompson Sampling-based) settings. The key idea of these algorithms is to avoid directly estimating the $2^N$ reward distributions and instead estimate the parameters that fully specify the SEMs (linear in $N$) and use them to compute the rewards. In both algorithms, under boundedness assumptions on noise and the parameter space, the cumulative regrets scale as $\tilde{\cal O} (d^{L+\frac{1}{2}} \sqrt{NT})$, where $d$ is the graph's maximum degree, and $L$ is the length of its longest causal path. Additionally, a minimax lower of $\Omega(d^{\frac{L}{2}-2}\sqrt{T})$ is presented, which suggests that the achievable and lower bounds conform in their scaling behavior with respect to the horizon $T$ and graph parameters $d$ and $L$.
Getting Quechua Closer to Final Users through Knowledge Graphs

Quechua language and Quechua knowledge gather millions of people around the world, especially in several countries in South America. Unfortunately, there are only a few resources available to Quechua communities, and they are mainly stored in PDF format. In this paper, the Quechua Knowledge Graph is envisioned and generated as an effort to get Quechua closer to the Quechua communities, researchers, and technology developers. Currently, there are 553636 triples stored in the Quechua Knowledge Graph, which is accessible on the Web, retrievable by machines, and curated by users. To showcase the deployment of the Quechua Knowledge Graph, use cases and future work are described.
Geostatistics for Large Datasets on Riemannian Manifolds: A Matrix-Free Approach

Large or very large spatial (and spatio-temporal) datasets have become common place in many environmental and climate studies. These data are often collected in non-Euclidean spaces (such as the planet Earth) and they often present nonstationary anisotropies. This paper proposes a generic approach to model Gaussian Random Fields (GRFs) on compact Riemannian manifolds that bridges the gap between existing works on nonstationary GRFs and random fields on manifolds. This approach can be applied to any smooth compact manifolds, and in particular to any compact surface. By defining a Riemannian metric that accounts for the preferential directions of correlation, our approach yields an interpretation of the nonstationary geometric anisotropies as resulting from local deformations of the domain. We provide scalable algorithms for the estimation of the parameters and for optimal prediction by kriging and simulation able to tackle very large grids. Stationary and nonstationary illustrations are provided.
Robust and Efficient Depth-Based Obstacle Avoidance for Autonomous Miniaturized UAVs

Nanosize drones hold enormous potential to explore unknown and complex environments. Their small size makes them agile and safe for operation close to humans and allows them to navigate through narrow spaces. However, their tiny size and payload restrict the possibilities for onboard computation and sensing, making fully autonomous flight extremely challenging. The first step toward full autonomy is reliable obstacle avoidance, which has proven to be challenging by itself in a generic indoor environment. Current approaches utilize vision-based or 1-D sensors to support nanodrone perception algorithms. This article presents a lightweight obstacle avoidance system based on a novel millimeter form factor 64 pixels multizone time-of-flight (ToF) sensor and a generalized model-free control policy. In-field tests are based on the Crazyflie 2.1, extended by a custom multizone ToF deck, featuring a total flight mass of 35 g. The algorithm only uses 0.3% of the onboard processing power (${210}\,{\mu }\mathrm{{s}}$ execution time) with a frame rate of 15 f/s. The presented autonomous nanosize drone reaches 100% reliability at 0.5 m/s in a generic and previously unexplored indoor environment.
Second-order topological magneto-optical effects in noncoplanar antiferromagnets

The second-order magneto-optical eﬀects, represented by Voigt and Sch¨afer-Hubert eﬀects, are eﬀective methods to detect the spin textures in antiferromagnets, whereas the previous studies are usually limited to collinear antiferromagnets. In noncollinear antiferromagnets, the spin textures characterized by spin chirality have been revealed to play a critical role in many exciting physics. In particular, the ﬁrst-order topological magneto-optical eﬀects originated from scalar spin chirality have been discovered recently. In this work, using the ﬁrst-principles calculations and group theory analysis, we generalize the ﬁrst-order topological magneto-optical eﬀects to the second-order cases, that is, topological Voigt and Sch¨afer-Hubert eﬀects, by taking the noncoplanar 3Q spin state of γ Fe x Mn 1 − x alloy as an example. The conventional Voigt and Sch¨afer-Hubert eﬀects are comparatively studied in the collinear 1Q and 2Q spin states of γ -Fe x Mn 1 − x alloy. In addition, the natural linear birefringence due to crystal anisotropy is discussed in the strained 1Q, 2Q, and 3Q states, and a unique ﬁngerprint for experimentally distinguishing the second-order topological magneto-optical eﬀects and natural linear birefringence is identiﬁed. Our work brings a topological insight into the second-order magneto-optical eﬀects in noncoplanar antiferromagnets and also provides γ -Fe x Mn 1 − x as an attractive material platform for future experimental exploration. of an in-plane magnetized material (assuming x -axis). There exists an angle ( α ) between the electric ﬁeld of incident light ( E I ) and the magnetization direction of materials ( ˆ N for AFMs or ˆ M for FMs). The transmitted and reﬂected lights become elliptically polarized accompanying by the rotations of polarization planes. The rotation angles, θ V and θ SH , describe the deﬂections of E T and E R with respect to E I . The ellipticities, ε V and ε SH , are the quotient of the short and long axes of the ellipse.
Deciphering the extreme X-ray variability of the nuclear transient eRASSt J045650.3-203750. A likely repeating partial tidal disruption event

(Abridged) In this paper, we present the results of an exceptional repeating X-ray nuclear transient, eRASSt J045650.3-203750 (hereafter J0456-20), uncovered by SRG/eROSITA in a quiescent galaxy at redshift of z~0.077. The main results are: 1) J0456-20 cycles through four distinctive phases: an X-ray rising phase leading into an X-ray plateau phase which lasts for ~2 months. This is terminated by a rapid X-ray flux drop phase during which the X-ray flux can drastically drop by more than a factor of 100 within 1 week followed by an X-ray faint state for about two months before it starts the X-ray rising phase again; 2) the X-ray spectra are generally soft in the rising phase with a photon index>3.0, and become harder as the X-ray flux increases. There is evidence of a multi-colour disk with inner region temperature of $T_\text{in}=70$ eV at the beginning of the X-ray rising phase. The high quality XMM-Newton data suggest that a warm and hot corona could be responsible for the X-ray emission, through inverse Comptonisation of soft disk seed photons, during the plateau phase and at the bright end of the rising phase; 3) J0456-20 shows only moderate UV variability and no significant optical variability; 4) radio emission is only detected (as yet) in the X-ray plateau phase, and shows a rapid decline on a time-scale of 2 weeks. We conclude that J0456-20 is likely a repeating nuclear transient with a tentative recurrence time of ~223 days. We discuss several possibilities to explain J0456-20's observational properties, and currently favour a repeating partial tidal disruption event (TDE) as the most likely scenario. The long-term X-ray evolution is explained as a transition between a thermal disk-dominated soft state and a steep power-law state, implying that the corona can be formed within a few months and destroyed within a few weeks.
The Effect of Frequency Droop Damping on System Parameters and Battery Sizing During Load Change Condition

Inverter-based resources (IBR) have been widely studied for their advantages on the current power systems. This increase in the penetration of renewable energy has raised some concerns about the stability of the existing grid. Historically, power systems are dominated by synchronous generators that can easily react to system instability due to high inertia and damping characteristics. However, with IBR, the control of the inverter plays a crucial role in contributing to the system stability and enhancing the functionality of the inverters. One of these novel control methods is droop control. Droop characteristics are used to control voltage, frequency, and active and reactive power. This paper presents the impact of frequency droop damping on system frequency, real power, and the rate of change of frequency with distributed energy resources. Also, battery sizing is suggested based on the results. The results also show the need for optimal selection for the frequency droop damping to fulfill the appropriate battery size in terms of cost and performance. The simulations are carried out in an electromagnetic transient program (EMTP).
Multivariate manifold-valued curve regression in time

Fr\'echet global regression is extended to the context of Riemannian--manifold--valued bivariate curve data correlated in time. Specifically, our Fr\'echet predictor is the minimizer of a weighted $L^{2}$--energy function. We restrict our attention to the case of a compact and connected Riemannian submanifold of a Euclidean space. Suitable regularity assumptions, and dispersion restrictions are considered on the manifold continuous curves defining the state space of the regressor and response processes. Under stationarity and ergodicity, uniform weak--consistency of the functional Fr\'echet predictor is proved. A simulated example in the sphere illustrates the finite--sample--size performance of the proposed manifold--valued Fr\'echet curve predictor. A real data example is considered, where the time--varying spherical coordinates of the magnetic field vector are predicted from the geocentric latitude and longitude of the satellite NASA's MAGSAT spacecraft.
Reinforcement Learning based Multi-connectivity Resource Allocation in Factory Automation Systems

We propose joint user association, channel assignment and power allocation for mobile robot Ultra-Reliable and Low Latency Communications (URLLC) based on multi-connectivity and reinforcement learning. The mobile robots require control messages from the central guidance system at regular intervals. We use a two-phase communication scheme where robots can form multiple clusters. The robots in a cluster are close to each other and can have reliable Device-to-Device (D2D) communications. In Phase I, the APs transmit the combined payload of a cluster to the cluster leader within a latency constraint. The cluster leader broadcasts this message to its members in Phase II. We develop a distributed Multi-Agent Reinforcement Learning (MARL) algorithm for joint user association and resource allocation (RA) for Phase I. The cluster leaders use their local Channel State Information (CSI) to decide the APs for connection along with the sub-band and power level. The cluster leaders utilize multi-connectivity to connect to multiple APs to increase their reliability. The objective is to maximize the successful payload delivery probability for all robots. Illustrative simulation results indicate that the proposed scheme can approach the performance of the centralized algorithm and offer a substantial gain in reliability as compared to single-connectivity (when cluster leaders are able to connect to 1 AP).
Can a Hebbian-like learning rule be avoiding the curse of dimensionality in sparse distributed data?

It is generally assumed that the brain uses something akin to sparse distributed representations. These representations, however, are high-dimensional and consequently they affect classification performance of traditional Machine Learning models due to"the curse of dimensionality". In tasks for which there is a vast amount of labeled data, Deep Networks seem to solve this issue with many layers and a non-Hebbian backpropagation algorithm. The brain, however, seems to be able to solve the problem with few layers. In this work, we hypothesize that this happens by using Hebbian learning. Actually, the Hebbian-like learning rule of Restricted Boltzmann Machines learns the input patterns asymmetrically. It exclusively learns the correlation between non-zero values and ignores the zeros, which represent the vast majority of the input dimensionality. By ignoring the zeros"the curse of dimensionality"problem can be avoided. To test our hypothesis, we generated several sparse datasets and compared the performance of a Restricted Boltzmann Machine classifier with some Backprop-trained networks. The experiments using these codes confirm our initial intuition as the Restricted Boltzmann Machine shows a good generalization performance, while the Neural Networks trained with the backpropagation algorithm overfit the training data.
NeuralSI: Structural Parameter Identification in Nonlinear Dynamical Systems

Structural monitoring for complex built environments often suffers from mismatch between design, laboratory testing, and actual built parameters. Additionally, real-world structural identification problems encounter many challenges. For example, the lack of accurate baseline models, high dimensionality, and complex multivariate partial differential equations (PDEs) pose significant difficulties in training and learning conventional data-driven algorithms. This paper explores a new framework, dubbed NeuralSI, for structural identification by augmenting PDEs that govern structural dynamics with neural networks. Our approach seeks to estimate nonlinear parameters from governing equations. We consider the vibration of nonlinear beams with two unknown parameters, one that represents geometric and material variations, and another that captures energy losses in the system mainly through damping. The data for parameter estimation is obtained from a limited set of measurements, which is conducive to applications in structural health monitoring where the exact state of an existing structure is typically unknown and only a limited amount of data samples can be collected in the field. The trained model can also be extrapolated under both standard and extreme conditions using the identified structural parameters. We compare with pure data-driven Neural Networks and other classical Physics-Informed Neural Networks (PINNs). Our approach reduces both interpolation and extrapolation errors in displacement distribution by two to five orders of magnitude over the baselines. Code is available at https://github.com/human-analysis/neural-structural-identification
Selective manipulation of disentangled representations for privacy-aware facial image processing

Camera sensors are increasingly being combined with machine learning to perform various tasks such as intelligent surveillance. Due to its computational complexity, most of these machine learning algorithms are offloaded to the cloud for processing. However, users are increasingly concerned about privacy issues such as function creep and malicious usage by third-party cloud providers. To alleviate this, we propose an edge-based filtering stage that removes privacy-sensitive attributes before the sensor data are transmitted to the cloud. We use state-of-the-art image manipulation techniques that leverage disentangled representations to achieve privacy filtering. We define opt-in and opt-out filter operations and evaluate their effectiveness for filtering private attributes from face images. Additionally, we examine the effect of naturally occurring correlations and residual information on filtering. We find the results promising and believe this elicits further research on how image manipulation can be used for privacy preservation.