Miracle-dz/UniFAR-FTUs huggingface dataset:
Generalizing LTL Instructions via Future Dependent Options

In many real-world applications of control system and robotics, linear temporal logic (LTL) is a widely-used task speciﬁcation language which has a compositional grammar that naturally induces temporally extended behaviours across tasks, including conditionals and alternative realizations. An important problem in RL with LTL tasks is to learn task-conditioned policies which can zero-shot generalize to new LTL instruc- tions not observed in the training. However, because symbolic observation is often lossy and LTL tasks can have long time horizon, previous works can suffer from issues such as training sampling inefﬁciency and infeasibility or sub-optimality of the found solutions. In order to tackle these issues, this paper proposes a novel multi-task RL algorithm with improved learning efﬁciency and optimality. To achieve the global optimality of task completion, we propose to learn options dependent on the future subgoals via a novel off-policy approach. In order to propagate the rewards of satisfying future subgoals back more efﬁciently, we propose to train a multi-step value function conditioned on the subgoal sequence which is updated with Monte Carlo estimates of multi-step discounted returns. In experiments on three different domains, we evaluate the LTL generalization capability of the agent trained by the proposed method, showing its advantage over previous representative methods.
Metallicity–PAH Relation of MIR-selected Star-forming Galaxies in AKARI North Ecliptic Pole-wide Survey

We investigate the variation in the mid-infrared spectral energy distributions of 373 low-redshift (z < 0.4) star-forming galaxies, which reflects a variety of polycyclic aromatic hydrocarbon (PAH) emission features. The relative strength of PAH emission is parameterized as q PAH, which is defined as the mass fraction of PAH particles in the total dust mass. With the aid of continuous mid-infrared photometric data points covering 7–24 μm and far-infrared flux densities, q PAH values are derived through spectral energy distribution fitting. The correlation between q PAH and other physical properties of galaxies, i.e., gas-phase metallicity ( 12+log(O/H) ), stellar mass, and specific star-formation rate (sSFR) are explored. As in previous studies, q PAH values of galaxies with high metallicity are found to be higher than those with low metallicity. The strength of PAH emission is also positively correlated with the stellar mass and negatively correlated with the sSFR. The correlation between q PAH and each parameter still exists even after the other two parameters are fixed. In addition to the PAH strength, the application of metallicity-dependent gas-to-dust mass ratio appears to work well to estimate gas mass that matches the observed relationship between molecular gas and physical parameters. The result obtained will be used to calibrate the observed PAH luminosity-total infrared luminosity relation, based on the variation of MIR-FIR SED, which is used in the estimation of hidden star formation.
Statistical Properties and Uses of the Wavelet Variance Estimator for the Scale Analysis of Time Series

Abstract Many physical processes are an amalgam of components operating on different scales, and scientific questions about observed data are often inherently linked to understanding the behavior at different scales. We explore time-scale properties of time series through the variance at different scales derived using wavelet methods. The great advantage of wavelet methods over ad hoc modifications of existing techniques is that wavelets provide exact scale-based decomposition results. We consider processes that are stationary, nonstationary but with stationary dth order differences, and nonstationary but with local stationarity. We study an estimator of the wavelet variance based on the maximal-overlap (undecimated) discrete wavelet transform. The asymptotic distribution of this wavelet variance estimator is derived for a wide class of stochastic processes, not necessarily Gaussian or linear. The variance of this distribution is estimated using spectral methods. Simulations confirm the theoretical results. The utility of the methodology is demonstrated on two scientifically important series, the surface albedo of pack ice (a strongly non-Gaussian series) and ocean shear data (a nonstationary series).
Global mild solution with polynomial tail for the Boltzmann equation in the whole space

We are concerned with the Cauchy problem on the Boltzmann equation in the whole space. The goal is to construct global-in-time bounded mild solutions near Maxwellians with the perturbation admitting a polynomial tail in large velocities. The proof is based on the Caflisch's decomposition together with the $L^2- L^\infty$ interplay technique developed by Guo. The full range of both hard and soft potentials under the Grad's cutoff assumption can be covered. The main difficulty to be overcome in case of the whole space is the polynomial time decay of solutions which is much slower than the exponential rate in contrast with the torus case.
Effect of secondary emission on the argon plasma afterglow with large dust density

A zero-dimensional, space-averaged model for argon plasma afterglow with large dust density is developed. In the model, three groups of electrons in the plasma afterglow are assumed: (i) thermal electrons with Maxwellian distribution, (ii) energetic electrons generated by metastable-metastable collisions (metastable pooling), and (iii) secondary electrons generated at collisions of ions with the electrodes, which have sufficiently large negative voltages in the afterglow. The model calculates the time-dependencies for electron densities in plasma afterglow based on experimental decay times for metastable density and electrode bias. The effect of secondary emission on electron density in the afterglow is estimated by varying secondary emission yields. It is found that this effect is less important than metastable pooling. The case of dust-free plasma afterglow is considered also, and it is found that in the afterglow the effect of secondary emission may be more important than metastable pooling. The second.
Assessing the Capacity of Transformer to Abstract Syntactic Representations: A Contrastive Analysis Based on Long-distance Agreement

Many studies have shown that transformers are able to predict subject-verb agreement, demonstrating their ability to uncover an abstract representation of the sentence in an unsupervised way. Recently, Li et al. (2021) found that transformers were also able to predict the object-past participle agreement in French, the modeling of which in formal grammar is fundamentally different from that of subject-verb agreement and relies on a movement and an anaphora resolution. To better understand transformers’ internal working, we propose to contrast how they handle these two kinds of agreement. Using probing and counterfactual analysis methods, our experiments on French agreements show that (i) the agreement task suffers from several confounders that partially question the conclusions drawn so far and (ii) transformers handle subject-verb and object-past participle agreements in a way that is consistent with their modeling in theoretical linguistics.
Constraints From Dwarf Galaxies on Black Hole Seeding and Growth Models With Current and Future Surveys

Dwarf galaxies are promising test beds for constraining models of supermassive and intermediate-mass black holes (MBHs) via their BH occupation fraction (BHOF). Disentangling seeding from the confounding effects of mass assembly over a Hubble time is a challenging problem that we tackle in this study with a suite of semianalytical models (SAMs). We show how the measured BHOF depends on the lowest BH mass or active galactic nucleus (AGN) luminosity achieved by a survey. To tell seeding models apart, we need to detect or model all AGNs brighter than 1037 erg s−1 in galaxies of M * ∼ 108−10 M ⊙. Shallower surveys, like eRASS, cannot distinguish between seed models even with the compensation of a much larger survey volume. We show that the AMUSE survey, with its inference of the MBH population underlying the observed AGNs, strongly favors heavy seed models, growing with either a power-law Eddington ratio distribution function or one in which BH accretion is tied to the star formation rate (i.e., the AGN-main sequence, AGN-MS, model). These two growth channels can then be distinguished by the AGN luminosity function at >1040 erg s−1, with the AGN-MS model requiring more accretion than observed at z ∼ 0. Thus, current X-ray observations favor heavy seeds whose Eddington ratios follow a power-law distribution. The different models also predict different radio scaling relations, which we quantify using the fundamental plane of BH activity. We close with recommendations for the design of upcoming multiwavelength campaigns that can optimally detect MBHs in dwarf galaxies.
Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Training large, deep neural networks to convergence can be prohibitively expensive. As a result, often only a small selection of popular, dense models are reused across different contexts and tasks. Increasingly, sparsely activated models, which seek to decouple model size from computation costs, are becoming an attractive alternative to dense models. Although more efficient in terms of quality and computation cost, sparse models remain data-hungry and costly to train from scratch in the large scale regime. In this work, we propose sparse upcycling -- a simple way to reuse sunk training costs by initializing a sparsely activated Mixture-of-Experts model from a dense checkpoint. We show that sparsely upcycled T5 Base, Large, and XL language models and Vision Transformer Base and Large models, respectively, significantly outperform their dense counterparts on SuperGLUE and ImageNet, using only ~50% of the initial dense pretraining sunk cost. The upcycled models also outperform sparse models trained from scratch on 100% of the initial dense pretraining computation budget.
ShadowDiffusion: When Degradation Prior Meets Diffusion Model for Shadow Removal

Recent deep learning methods have achieved promising results in image shadow removal. However, their restored images still suffer from unsatisfactory boundary artifacts, due to the lack of degradation prior embedding and the deficiency in modeling capacity. Our work addresses these issues by proposing a unified diffusion framework that integrates both the image and degradation priors for highly effective shadow removal. In detail, we first propose a shadow degradation model, which inspires us to build a novel unrolling diffusion model, dubbed ShandowDiffusion. It remarkably improves the model's capacity in shadow removal via progressively refining the desired output with both degradation prior and diffusive generative prior, which by nature can serve as a new strong baseline for image restoration. Furthermore, ShadowDiffusion progressively refines the estimated shadow mask as an auxiliary task of the diffusion generator, which leads to more accurate and robust shadow-free image generation. We conduct extensive experiments on three popular public datasets, including ISTD, ISTD+, and SRD, to validate our method's effectiveness. Compared to the state-of-the-art methods, our model achieves a significant improvement in terms of PSNR, increasing from 31.69dB to 34. 73dB over SRD dataset. 11https://github.com/GuoLanqing/ShadowDiffusion
On the Contribution of Released Latent Heat to Changes in Available Potential Energy

Abstract Using quasi-geostrophic theory, equations are derived separating from other factors the contribution of release of latent heat to changes in the available potential energy. A synoptic case study of a major winter cyclone and an examination of the equation for the amplification of the vertical velocity associated with release of latent heat both suggest the same conclusion: in the lower and middle troposphere, the positive contribution of heating is normally significantly larger than the negative effect arising from the enhanced vertical motion. Thus release of latent heat should lessen the tendency for vertical circulations to destroy existing temperature contrasts.
Implementing Neural Network-Based Equalizers in a Coherent Optical Transmission System Using Field-Programmable Gate Arrays

In this work, we demonstrate the offline FPGA realization of both recurrent and feedforward neural network (NN)-based equalizers for nonlinearity compensation in coherent optical transmission systems. First, we present a realization pipeline showing the conversion of the models from Python libraries to the FPGA chip synthesis and implementation. Then, we review the main alternatives for the hardware implementation of nonlinear activation functions. The main results are divided into three parts: a performance comparison, an analysis of how activation functions are implemented, and a report on the complexity of the hardware. The performance in Q-factor is presented for the cases of bidirectional long-short-term memory coupled with convolutional NN (biLSTM + CNN) equalizer, CNN equalizer, and standard 1-StpS digital back-propagation (DBP) for the simulation and experiment propagation of a single channel dual-polarization (SC-DP) 16QAM at 34 GBd along 17 × 70 km of LEAF. The biLSTM+CNN equalizer provides a similar result to DBP and a 1.7 dB Q-factor gain compared with the chromatic dispersion compensation baseline in the experimental dataset. After that, we assess the Q-factor and the impact of hardware utilization when approximating the activation functions of NN using Taylor series, piecewise linear, and look-up table (LUT) approximations. We also show how to mitigate the approximation errors with extra training and provide some insights into possible gradient problems in the LUT approximation. Finally, to evaluate the complexity of hardware implementation to achieve 200G and 400G throughput, fixed-point NN-based equalizers with approximated activation functions are developed and implemented in an FPGA.
ProductGraphSleepNet: Sleep Staging using Product Spatio-Temporal Graph Learning with Attentive Temporal Aggregation

The classification of sleep stages plays a crucial role in understanding and diagnosing sleep pathophysiology. Sleep stage scoring relies heavily on visual inspection by an expert, which is a time-consuming and subjective procedure. Recently, deep learning neural network approaches have been leveraged to develop a generalized automated sleep staging and account for shifts in distributions that may be caused by inherent inter/intra-subject variability, heterogeneity across datasets, and different recording environments. However, these networks (mostly) ignore the connections among brain regions and disregard modeling the connections between temporally adjacent sleep epochs. To address these issues, this work proposes an adaptive product graph learning-based graph convolutional network, named ProductGraphSleepNet, for learning joint spatio-temporal graphs along with a bidirectional gated recurrent unit and a modified graph attention network to capture the attentive dynamics of sleep stage transitions. Evaluation on two public databases: the Montreal Archive of Sleep Studies (MASS) SS3; and the SleepEDF, which contain full night polysomnography recordings of 62 and 20 healthy subjects, respectively, demonstrates performance comparable to the state-of-the-art (Accuracy: 0.867;0.838, F1-score: 0.818;0.774 and Kappa: 0.802;0.775, on each database respectively). More importantly, the proposed network makes it possible for clinicians to comprehend and interpret the learned spatial and temporal connectivity graphs for sleep stages.
Synthetic Data for Object Classification in Industrial Applications

One of the biggest challenges in machine learning is data collection. Training data is an important part since it determines how the model will behave. In object classification, capturing a large number of images per object and in different conditions is not always possible and can be very time-consuming and tedious. Accordingly, this work explores the creation of artificial images using a game engine to cope with limited data in the training dataset. We combine real and synthetic data to train the object classification engine, a strategy that has shown to be beneficial to increase confidence in the decisions made by the classifier, which is often critical in industrial setups. To combine real and synthetic data, we first train the classifier on a massive amount of synthetic data, and then we fine-tune it on real images. Another important result is that the amount of real images needed for fine-tuning is not very high, reaching top accuracy with just 12 or 24 images per class. This substantially reduces the requirements of capturing a great amount of real data.
Category-Level 6D Object Pose Estimation with Flexible Vector-Based Rotation Representation

In this paper, we propose a novel 3D graph convolution based pipeline for category-level 6D pose and size estimation from monocular RGB-D images. The proposed method leverages an efficient 3D data augmentation and a novel vector-based decoupled rotation representation. Specifically, we first design an orientation-aware autoencoder with 3D graph convolution for latent feature learning. The learned latent feature is insensitive to point shift and size thanks to the shift and scale-invariance properties of the 3D graph convolution. Then, to efficiently decode the rotation information from the latent feature, we design a novel flexible vector-based decomposable rotation representation that employs two decoders to complementarily access the rotation information. The proposed rotation representation has two major advantages: 1) decoupled characteristic that makes the rotation estimation easier; 2) flexible length and rotated angle of the vectors allow us to find a more suitable vector representation for specific pose estimation task. Finally, we propose a 3D deformation mechanism to increase the generalization ability of the pipeline. Extensive experiments show that the proposed pipeline achieves state-of-the-art performance on category-level tasks. Further, the experiments demonstrate that the proposed rotation representation is more suitable for the pose estimation tasks than other rotation representations.
4K-NeRF: High Fidelity Neural Radiance Fields at Ultra High Resolutions

In this paper, we present a novel and effective framework, named 4K-NeRF, to pursue high fidelity view synthesis on the challenging scenarios of ultra high resolutions, building on the methodology of neural radiance fields (NeRF). The rendering procedure of NeRF-based methods typically relies on a pixel-wise manner in which rays (or pixels) are treated independently on both training and inference phases, limiting its representational ability on describing subtle details, especially when lifting to a extremely high resolution. We address the issue by exploring ray correlation to enhance high-frequency details recovery. Particularly, we use the 3D-aware encoder to model geometric information effectively in a lower resolution space and recover fine details through the 3D-aware decoder, conditioned on ray features and depths estimated by the encoder. Joint training with patch-based sampling further facilitates our method incorporating the supervision from perception oriented regularization beyond pixel-wise loss. Benefiting from the use of geometry-aware local context, our method can significantly boost rendering quality on high-frequency details compared with modern NeRF methods, and achieve the state-of-the-art visual quality on 4K ultra-high-resolution scenarios. Code Available at \url{https://github.com/frozoul/4K-NeRF}
PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection

Large-scale data is an essential component of machine learning as demonstrated in recent advances in natural language processing and computer vision research. However, collecting large-scale robotic data is much more expensive and slower as each operator can control only a single robot at a time. To make this costly data collection process efficient and scalable, we propose Policy Assisted TeleOperation (PATO), a system which automates part of the demonstration collection process using a learned assistive policy. PATO autonomously executes repetitive behaviors in data collection and asks for human input only when it is uncertain about which subtask or behavior to execute. We conduct teleoperation user studies both with a real robot and a simulated robot fleet and demonstrate that our assisted teleoperation system reduces human operators' mental load while improving data collection efficiency. Further, it enables a single operator to control multiple robots in parallel, which is a first step towards scalable robotic data collection. For code and video results, see https://clvrai.com/pato
Self-Sustaining Ultrawideband Positioning System for Event-Driven Indoor Localization

Smart and unobtrusive mobile sensor nodes that accurately track their own position have the potential to augment data collection with location-based functions. To attain this vision of unobtrusiveness, the sensor nodes must have a compact form factor and operate over long periods without battery recharging or replacement. This article presents a self-sustaining and accurate ultrawideband (UWB)-based indoor location system with conservative infrastructure overhead. An event-driven sensing approach allows for balancing the limited energy harvested in indoor conditions with the power consumption of UWB transceivers. The presented tag-centralized concept, which combines heterogeneous system design with embedded processing, minimizes idle consumption without sacrificing functionality. Despite modest infrastructure requirements, high-localization accuracy is achieved with error-correcting double-sided two-way ranging and embedded optimal multilateration. Experimental results demonstrate the benefits of the proposed system: the node achieves a quiescent current of 47nA and operates at 1.2 $\mu \text{A}$ while performing energy harvesting and motion detection. The energy consumption for position updates, with an accuracy of 40 cm (2-D) in realistic nonline-of-sight conditions, is 10.84mJ. In an asset tracking case study within a 200m2 multiroom office space, the achieved accuracy level allows for identifying 36 different desk and storage locations with an accuracy of over 95%. The system’s long-time self-sustainability has been analyzed over 700 days in multiple indoor lighting situations.
Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects Even Under One Sided Overlap

As causal inference becomes more widespread the importance of having good tools to test for causal effects increases. In this work we focus on the problem of testing for causal effects that manifest in a difference in distribution for treatment and control. We build on work applying kernel methods to causality, considering the previously introduced Counterfactual Mean Embedding framework (\textsc{CfME}). We improve on this by proposing the \emph{Doubly Robust Counterfactual Mean Embedding} (\textsc{DR-CfME}), which has better theoretical properties than its predecessor by leveraging semiparametric theory. This leads us to propose new kernel based test statistics for distributional effects which are based upon doubly robust estimators of treatment effects. We propose two test statistics, one which is a direct improvement on previous work and one which can be applied even when the support of the treatment arm is a subset of that of the control arm. We demonstrate the validity of our methods on simulated and real-world data, as well as giving an application in off-policy evaluation.