Miracle-dz/UniFAR-FTUs huggingface dataset:
Panoptic Multi-TSDFs: a Flexible Representation for Online Multi-resolution Volumetric Mapping and Long-term Dynamic Scene Consistency

For robotic interaction in environments shared with other agents, access to volumetric and semantic maps of the scene is crucial. However, such environments are inevitably subject to long-term changes, which the map needs to account for. We thus propose panoptic multi-TSDFs as a novel representation for multi-resolution volumetric mapping in changing environments. By leveraging high-level information for 3D reconstruction, our proposed system allocates high resolution only where needed. Through reasoning on the object level, semantic consistency over time is achieved. This enables our method to maintain up-to-date reconstructions with high accuracy while improving coverage by incorporating previous data. We show in thorough experimental evaluation that our map can be efficiently constructed, maintained, and queried during online operation, and that the presented approach can operate robustly on real depth sensors using non-optimized panoptic segmentation as input.
RAIL-KD: RAndom Intermediate Layer Mapping for Knowledge Distillation

Intermediate layer knowledge distillation (KD) can improve the standard KD technique (which only targets the output of teacher and student models) especially over large pre-trained language models. However, intermediate layer distillation suffers from excessive computational burdens and engineering efforts required for setting up a proper layer mapping. To address these problems, we propose a RAndom Intermediate Layer Knowledge Distillation (RAIL-KD) approach in which, intermediate layers from the teacher model are selected randomly to be distilled into the intermediate layers of the student model. This randomized selection enforce that: all teacher layers are taken into account in the training process, while reducing the computational cost of intermediate layer distillation. Also, we show that it act as a regularizer for improving the generalizability of the student model. We perform extensive experiments on GLUE tasks as well as on out-of-domain test sets. We show that our proposed RAIL-KD approach outperforms other state-of-the-art intermediate layer KD methods considerably in both performance and training-time.
On the Three-dimensional Structure of Local Molecular Clouds

We leverage the 1 pc spatial resolution of the Leike et al. three-dimensional (3D) dust map to characterize the 3D structure of nearby molecular clouds (d ≲ 400 pc). We start by “skeletonizing” the clouds in 3D volume density space to determine their “spines,” which we project on the sky to constrain cloud distances with ≈1% uncertainty. For each cloud, we determine an average radial volume density profile around its 3D spine and fit the profiles using Gaussian and Plummer functions. The radial volume density profiles are well described by a two-component Gaussian function, consistent with clouds having broad, lower-density outer envelopes and narrow, higher-density inner layers. The ratio of the outer to inner envelope widths is ≈3:1. We hypothesize that these two components may be tracing a transition between atomic and diffuse molecular gas or between the unstable and cold neutral medium. Plummer-like models can also provide a good fit, with molecular clouds exhibiting shallow power-law wings with density, n, falling off like n −2 at large radii. Using Bayesian model selection, we find that parameterizing the clouds’ profiles using a single Gaussian is disfavored. We compare our results with two-dimensional dust extinction maps, finding that the 3D dust recovers the total cloud mass from integrated approaches with fidelity, deviating only at higher levels of extinction (A V ≳ 2–3 mag). The 3D cloud structure described here will enable comparisons with synthetic clouds generated in simulations, offering unprecedented insight into the origins and fates of molecular clouds in the interstellar medium.
Deviation-Based Learning

This paper proposes a new approach to training recommender systems called deviation-based learning. The recommender and rational users have different knowledge. The recommender learns user knowledge by observing what action users take upon receiving recommendations. Learning eventually stalls if the recommender always suggests a choice: Before the recommender completes learning, users start following the recommendations blindly, and their choices do not reflect their knowledge. The learning rate and social welfare improve substantially if the recommender abstains from recommending a particular choice when she predicts that multiple alternatives will produce a similar payoff.
SDSS J1059+4251, a Highly Magnified z ∼ 2.8 Star-forming Galaxy: ESI Observations of the Rest-frame UV Spectrum

Detailed analyses of high-redshift galaxies are challenging because these galaxies are faint, but this difficulty can be overcome with gravitational lensing, in which the magnification of the flux enables spectroscopy with a high signal-to-noise ratio (S/N). We present the rest-frame ultraviolet (UV) Keck Echellette Spectrograph and Imager (ESI) spectrum of the newly discovered z = 2.79 lensed galaxy SDSS J1059+4251. With an observed magnitude F814W = 18.8 and a magnification factor μ = 31 ± 3, J1059+4251 is both highly magnified and intrinsically luminous, about two magnitudes brighter than MUV* at z ∼ 2–3. With a stellar mass M * = (3.22 ± 0.20) × 1010 M ⊙, star formation rate SFR = 50 ± 7 M⊙ yr−1, and stellar metallicity Z * ≃ 0.15–0.5 Z ⊙, J1059+4251 is typical of bright star-forming galaxies at similar redshifts. Thanks to the high S/N and the spectral resolution of the ESI spectrum, we are able to separate the interstellar and stellar features and derive properties that would be inaccessible without the aid of the lensing. We find evidence of a gas outflow with speeds up to −1000 km s−1, and of an inflow that is probably due to accreting material seen along a favorable line of sight. We measure relative elemental abundances from the interstellar absorption lines and find that α-capture elements are overabundant compared to iron-peak elements, suggestive of rapid star formation. However, this trend may also be affected by dust depletion. Thanks to the high data quality, our results represent a reliable step forward in the characterization of typical galaxies at early cosmic epochs.
Enhanced interface interaction between modified carbon nanotubes and magnesium matrix

Abstract Carbon nanotube (CNT)/metal interface interaction is critical to the mechanical properties of CNT-reinforced metal matrix composites (MMCs). In this paper, in order to realize the chemical modification of the interface interaction between CNTs and Mg matrix, different types of defects (monovacancy, carbon and oxygen adatoms, as well as p-type boron and n-type nitrogen substitution) are introduced in CNTs to investigate the effect of the defects on the interface interaction (Eib) between CNT and Mg (0 0 0 1) surface. Moreover, two models (adsorption model and interface model) are compared and validated to investigate the interface interaction. It is revealed that the CNT with the carbon adatom has the highest Eib with the Mg (0 0 0 1), and the effect of boron doping on Eib is superior to the intermediate oxygen which has already been proved experimentally in the enhancement of the interface interaction in MMCs. In terms of the electronic structure analysis, we reveal the micro-mechanism of the increase of Eib under the action of different types of defects, and propose that the presence of holes (boron dopant) and the unsaturated electrons in CNTs can generate the chemical interaction between CNT and Mg matrix effectively. Our results are of great scientific importance to the realization of robust interfacial bonding between CNTs and Mg matrix via the reinforcement modification, so as to enhance the mechanical properties of CNTs reinforced Mg matrix composites.
Fine-grained Meta-Theorems for Vertex Integrity

Vertex Integrity is a graph measure which sits squarely between two more well-studied notions, namely vertex cover and tree-depth, and that has recently gained attention as a structural graph parameter. In this paper we investigate the algorithmic trade-offs involved with this parameter from the point of view of algorithmic meta-theorems for First-Order (FO) and Monadic Second Order (MSO) logic. Our positive results are the following: (i) given a graph $G$ of vertex integrity $k$ and an FO formula $\phi$ with $q$ quantifiers, deciding if $G$ satisfies $\phi$ can be done in time $2^{O(k^2q+q\log q)}+n^{O(1)}$; (ii) for MSO formulas with $q$ quantifiers, the same can be done in time $2^{2^{O(k^2+kq)}}+n^{O(1)}$. Both results are obtained using kernelization arguments, which pre-process the input to sizes $2^{O(k^2)}q$ and $2^{O(k^2+kq)}$ respectively. The complexities of our meta-theorems are significantly better than the corresponding meta-theorems for tree-depth, which involve towers of exponentials. However, they are worse than the roughly $2^{O(kq)}$ and $2^{2^{O(k+q)}}$ complexities known for corresponding meta-theorems for vertex cover. To explain this deterioration we present two formula constructions which lead to fine-grained complexity lower bounds and establish that the dependence of our meta-theorems on $k$ is best possible. More precisely, we show that it is not possible to decide FO formulas with $q$ quantifiers in time $2^{o(k^2q)}$, and that there exists a constant-size MSO formula which cannot be decided in time $2^{2^{o(k^2)}}$, both under the ETH. Hence, the quadratic blow-up in the dependence on $k$ is unavoidable and vertex integrity has a complexity for FO and MSO logic which is truly intermediate between vertex cover and tree-depth.
Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification

Convolutional Neural Networks have demonstrated dermatologist-level performance in the classification of melanoma from skin lesion images, but prediction irregularities due to biases seen within the training data are an issue that should be addressed before widespread deployment is possible. In this work, we robustly remove bias and spurious variation from an automated melanoma classification pipeline using two leading bias unlearning techniques. We show that the biases introduced by surgical markings and rulers presented in previous studies can be reasonably mitigated using these bias removal methods. We also demonstrate the generalisation benefits of unlearning spurious variation relating to the imaging instrument used to capture lesion images. Our experimental results provide evidence that the effects of each of the aforementioned biases are notably reduced, with different debiasing techniques excelling at different tasks.
Computing Complexity-aware Plans Using Kolmogorov Complexity

In this paper, we introduce complexity-aware planning for finite-horizon deterministic finite automata with rewards as outputs, based on Kolmogorov complexity. Kolmogorov complexity is considered since it can detect computational regularities of deterministic optimal policies. We present a planning objective yielding an explicit trade-off between a policy’s performance and complexity. It is proven that maximising this objective is non-trivial in the sense that dynamic programming is infeasible. We present two algorithms obtaining low-complexity policies, where the first algorithm obtains a low-complexity optimal policy, and the second algorithm finds a policy maximising performance while maintaining local (stage-wise) complexity constraints. We evaluate the algorithms on a simple navigation task for a mobile robot, where our algorithms yield low-complexity policies that concur with intuition.
Endogenous Growth Under Multiple Uses of Data

We model a dynamic data economy with fully endogenous growth where agents generate data from consumption and share themwith innovation and production firms. Different from other productive factors such as labor or capital, data are nonrival in their uses across sectors which affect both the level and growth of economic outputs. Despite the vertical nonrivalry, the innovation sector dominates the production sector in data usage and contribution to growth because (i) data are dynamically nonrival and add to knowledge accumulation, and (ii) innovations “desensitize” raw data and enter production as knowledge, which allays consumers’ privacy concerns. Data uses in both sectors interact to generate spillover of allocative distortion and exhibit an apparent substitutability due to labor’s rivalry and complementarity with data. Consequently, growth rates under a social planner and a decentralized equilibrium differ, which is novel in the literature and has policy implications. Specifically, consumers’ failure to fully internalize knowledge spillover when bearing privacy costs, combined with firms’ market power, underprice data and inefficiently limit their supply, leading to underemployment in the innovation sector and a suboptimal long-run growth. Improving data usage efficiency is ineffective in mitigating the underutilization of data, but interventions in the data market and direct subsidies hold promises.
Single Person Pose Estimation: A Survey

Human pose estimation in unconstrained images and videos is a fundamental computer vision task. To illustrate the evolutionary path in technique, in this survey we summarize representative human pose methods in a structured taxonomy, with a particular focus on deep learning models and single-person image setting. Specifically, we examine and survey all the components of a typical human pose estimation pipeline, including data augmentation, model architecture and backbone, supervision representation, post-processing, standard datasets, evaluation metrics. To envisage the future directions, we finally discuss the key unsolved problems and potential trends for human pose estimation.
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

Algorithm parameters, in particular hyperparameters of machine learning algorithms, can substantially impact their performance. To support users in determining well-performing hyperparameter configurations for their algorithms, datasets and applications at hand, SMAC3 offers a robust and flexible framework for Bayesian Optimization, which can improve performance within a few evaluations. It offers several facades and pre-sets for typical use cases, such as optimizing hyperparameters, solving low dimensional continuous (artificial) global optimization problems and configuring algorithms to perform well across multiple problem instances. The SMAC3 package is available under a permissive BSD-license at https://github.com/automl/SMAC3.
Systematic fitting of crystal-field levels and accurate extraction of quantum magnetic models in triangular-lattice delafossites

Christopher A. Pocs, Peter E. Siegfried, Jie Xing, Athena S. Sefat, Michael Hermele, 3 B. Normand, 5 and Minhyea Lee Department of Physics, University of Colorado, Boulder, Colorado 80309, USA Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA Center for Theory of Quantum Matter, University of Colorado, Boulder, Colorado 80309, USA Paul Scherrer Institute, CH-5232 Villigen-PSI, Switzerland Institute of Physics, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland (Dated: September 22, 2021)
Well Googled is Half Done: Multimodal Forecasting of New Fashion Product Sales with Image-based Google Trends

New fashion product sales forecasting is a challenging problem that involves many business dynamics and cannot be solved by classical forecasting approaches. In this paper, we investigate the effectiveness of systematically probing exogenous knowledge in the form of Google Trends time series and combining it with multi‐modal information related to a brand‐new fashion item, in order to effectively forecast its sales despite the lack of past data. In particular, we propose a neural network‐based approach, where an encoder learns a representation of the exogenous time series, while the decoder forecasts the sales based on the Google Trends encoding and the available visual and metadata information. Our model works in a non‐autoregressive manner, avoiding the compounding effect of large first‐step errors. As a second contribution, we present VISUELLE, a publicly available dataset for the task of new fashion product sales forecasting, containing multimodal information for 5,577 real, new products sold between 2016 and 2019 from Nunalie, an Italian fast‐fashion company. The dataset is equipped with images of products, metadata, related sales, and associated Google Trends. We use VISUELLE to compare our approach against state‐of‐the‐art alternatives and several baselines, showing that our neural network‐based approach is the most accurate in terms of both percentage and absolute error. It is worth noting that the addition of exogenous knowledge boosts the forecasting accuracy by 1.5% in terms of Weighted Absolute Percentage Error (WAPE), revealing the importance of exploiting informative external information. The code and dataset are both available online (at https://github.com/HumaticsLAB/GTM-Transformer).
Self-Supervised Action-Space Prediction for Automated Driving

Making informed driving decisions requires reliable prediction of other vehicles' trajectories. In this paper, we present a novel learned multi-modal trajectory prediction architecture for automated driving. It achieves kinematically feasible predictions by casting the learning problem into the space of accelerations and steering angles - by performing action-space prediction, we can leverage valuable model knowledge. Additionally, the dimensionality of the action manifold is lower than that of the state manifold, whose intrinsically correlated states are more difficult to capture in a learned manner. For the purpose of action-space prediction, we present the simple Feed-Forward Action-Space Prediction (FFW-ASP) architecture. Then, we build on this notion and introduce the novel Self-Supervised Action-Space Prediction (SSP-ASP) architecture that outputs future environment context features in addition to trajectories. A key element in the self-supervised architecture is that, based on an observed action history and past context features, future context features are predicted prior to future trajectories. The proposed methods are evaluated on real-world datasets containing urban intersections and roundabouts, and show accurate predictions, outperforming state-of-the-art for kinematically feasible predictions in several prediction metrics.
A Bayesian hierarchical model for disease mapping that accounts for scaling and heavy-tailed latent effects

In disease mapping, the relative risk of a disease is commonly estimated across different areas within a region of interest. The number of cases in an area is often assumed to follow a Poisson distribution whose mean is decomposed as the product between an offset and the logarithm of the disease's relative risk. The log risk may be written as the sum of fixed effects and latent random effects. The BYM2 model decomposes each latent effect into a weighted sum of independent and spatial effects. We build on the BYM2 model to allow for heavy-tailed latent effects and accommodate potentially outlying risks, after accounting for the fixed effects. We assume a scale mixture structure wherein the variance of the latent process changes across areas and allows for outlier identification. We propose two prior specifications for this scale mixture parameter. These are compared through simulation studies and in the analysis of Zika cases from the first (2015-2016) epidemic in Rio de Janeiro city, Brazil. The simulation studies show that, in terms of the model assessment criterion WAIC and outlier detection, the two proposed parametrisations perform better than the model proposed by Congdon (2017) to capture outliers. In particular, the proposed parametrisations are more efficient, in terms of outlier detection, than Congdon's when outliers are neighbours. Our analysis of Zika cases finds 19 out of 160 districts of Rio as potential outliers, after accounting for the socio-development index. Our proposed model may help prioritise interventions and identify potential issues in the recording of cases.
Observational signatures of the surviving donor star in the double-detonation model of Type Ia supernovae

The sub-Chandrasekhar-mass double-detonation (DDet) scenario is a contemporary model for Type Ia supernovae (SNe Ia). The donor star in the DDet scenario is expected to survive the explosion and to be ejected at the high orbital velocity of a compact binary system. For the ﬁrst time, we consistently perform 3D hydrodynamical simulations of the interaction of supernova ejecta with a helium (He) star companion within the DDet scenario. We map the outcomes of 3D impact simulations into 1D stellar evolution codes and follow the long-term evolution of the surviving He-star companions. Our main goal is to provide the post-impact observable signatures of surviving He-star companions of DDet SNe Ia, which will support the search for such companions in future observations. Such surviving companions are ejected with high velocities of up to about 930kms − 1 . We ﬁnd that our surviving He-star companions become signiﬁcantly overluminous for about 10 6 yr during the thermal re-equilibration phase. After the star re-establishes thermal equilibrium, its observational properties are not sensitive to the details of the ejecta-donor interaction. We apply our results to the hypervelocity star US 708, which is one of the fastest unbound stars in our Galaxy; it travels with a velocity of about 1200kms − 1 , making it a natural candidate for an ejected donor remnant of a DDet SN Ia. We ﬁnd that a He-star donor with an initial mass of (cid:38) 0 . 5 M (cid:12) is needed to explain the observed properties of US 708. Based on our detailed binary evolution calculations, however, a progenitor system with such a massive He-star donor cannot get close enough at the moment of the SN explosion to explain the high velocity of US 708. Instead, if US 708 is indeed the surviving He-star donor of a DDet SN Ia, it would require the entire pre-supernova progenitor binary to travel at a velocity of about 400kms − 1 . It could, for example, have been ejected from a globular cluster in the direction of the current motion of the surviving donor star.
DS-Net++: Dynamic Weight Slicing for Efficient Inference in CNNs and Transformers

Dynamic networks have shown their promising capability in reducing theoretical computation complexity by adapting their architectures to the input during inference. However, their practical runtime usually lags behind the theoretical acceleration due to inefficient sparsity. Here, we explore a hardware-efficient dynamic inference regime, named dynamic weight slicing, which adaptively slice a part of network parameters for inputs with diverse difficulty levels, while keeping parameters stored statically and contiguously in hardware to prevent the extra burden of sparse computation. Based on this scheme, we present dynamic slimmable network (DS-Net) and dynamic slice-able network (DS-Net++) by input-dependently adjusting filter numbers of CNNs and multiple dimensions in both CNNs and transformers, respectively. To ensure sub-network generality and routing fairness, we propose a disentangled two-stage optimization scheme with training techniques such as in-place bootstrapping (IB), multi-view consistency (MvCo) and sandwich gate sparsification (SGS) to train supernet and gate separately. Extensive experiments on 4 datasets and 3 different network architectures demonstrate our method consistently outperforms state-of-the-art static and dynamic model compression methods by a large margin (up to 6.6%). Typically, DS-Net++ achieves 2-4x computation reduction and 1.62x real-world acceleration over MobileNet, ResNet-50 and Vision Transformer, with minimal accuracy drops (0.1-0.3%) on ImageNet. Code release: https://github.com/changlin31/DS-Net