Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy-based Models are Zero-Shot Planners for Compositional Scene Rearrangement

Language is compositional; an instruction can express multiple relation constraints to hold among objects in a scene that a robot is tasked to rearrange. Our focus in this work is an instructable scene-rearranging framework that generalizes to longer instructions and to spatial concept compositions never seen at training time. We propose to represent language-instructed spatial concepts with energy functions over relative object arrangements. A language parser maps instructions to corresponding energy functions and an open-vocabulary visual-language model grounds their arguments to relevant objects in the scene. We generate goal scene configurations by gradient descent on the sum of energy functions, one per language predicate in the instruction. Local vision-based policies then re-locate objects to the inferred goal locations. We test our model on established instruction-guided manipulation benchmarks, as well as benchmarks of compositional instructions we introduce. We show our model can execute highly compositional instructions zero-shot in simulation and in the real world. It outperforms language-to-action reactive policies and Large Language Model planners by a large margin, especially for long instructions that involve compositions of multiple spatial concepts. Simulation and real-world robot execution videos, as well as our code and datasets are publicly available on our website: https://ebmplanner.github. io.
In-Situ Observations of the Microphysical Properties of Young Cirrus Clouds

Abstract In situ measurements made in cold (−35° to −60°C) cirrus clouds over southern Germany in March 1994 are presented. The clouds appeared to be in an early stage of their life cycle and their properties in many ways resemble those reported for ice fogs. Crystal concentrations were high (median 2.5 cm−3, STP) and sizes small with a diameter of mean mass of typically 16 μm. The cloud on 18 March presents an interesting case for modeling studies of cirrus formation. On that particular day, the bulk properties of the cloud appeared to be connected to wave structures in the vertical wind field consistent with the Brunt–Vaisala frequency, which gave a corresponding wavelength of 40–50 km. Furthermore, analyses of potential temperature and vertical wind suggested that the vertical displacement producing these clouds was less than 100 m. Size distribution measurements of interstitial particles and crystal residues (particles remaining after evaporation of the crystals) show that small aerosol particles (dia.
COSST: Multi-organ Segmentation with Partially Labeled Datasets Using Comprehensive Supervisions and Self-training

Deep learning models have demonstrated remarkable success in multi-organ segmentation but typically require large-scale datasets with all organs of interest annotated. However, medical image datasets are often low in sample size and only partially labeled, i.e., only a subset of organs are annotated. Therefore, it is crucial to investigate how to learn a unified model on the available partially labeled datasets to leverage their synergistic potential. In this paper, we systematically investigate the partial-label segmentation problem with theoretical and empirical analyses on the prior techniques. We revisit the problem from a perspective of partial label supervision signals and identify two signals derived from ground truth and one from pseudo labels. We propose a novel two-stage framework termed COSST, which effectively and efficiently integrates comprehensive supervision signals with self-training. Concretely, we first train an initial unified model using two ground truth-based signals and then iteratively incorporate the pseudo label signal to the initial model using self-training. To mitigate performance degradation caused by unreliable pseudo labels, we assess the reliability of pseudo labels via outlier detection in latent space and exclude the most unreliable pseudo labels from each self-training iteration. Extensive experiments are conducted on one public and three private partial-label segmentation tasks over 12 CT datasets. Experimental results show that our proposed COSST achieves significant improvement over the baseline method, i.e., individual networks trained on each partially labeled dataset. Compared to the state-of-the-art partial-label segmentation methods, COSST demonstrates consistent superior performance on various segmentation tasks and with different training data sizes.
High-frequency Anticipatory Trading and Its Influences: Small Informed Trader vs. Front-runner

In this paper, the interactions between a large informed trader (IT, for short) and a high-frequency trader (HFT, for short) who can anticipate the former’s incoming order are studied in an extended Kyle’s (1985) model. Equilibria under various speciﬁc situations are discussed. Relying on the speed advantage, HFT always trades in the same direction as the large order in advance. However, whether or not she provides liquidity depends on her inventory aversion, the prediction accuracy, and the market activeness. She may supply liquidity back (act as a front-runner) or continue to take it away (in this case we call her a small IT). Small IT always harms the large trader while front-runner may beneﬁt her. Besides, we ﬁnd surprisingly that (1) increasing the noise in HFT’s signal may in fact decrease IT’s proﬁt; (2) although providing liquidity, a front-runner may harm IT more than a small IT.
Surrogate Assisted Generation of Human-Robot Interaction Scenarios

As human-robot interaction (HRI) systems advance, so does the difficulty of evaluating and understanding the strengths and limitations of these systems in different environments and with different users. To this end, previous methods have algorithmically generated diverse scenarios that reveal system failures in a shared control teleoperation task. However, these methods require directly evaluating generated scenarios by simulating robot policies and human actions. The computational cost of these evaluations limits their applicability in more complex domains. Thus, we propose augmenting scenario generation systems with surrogate models that predict both human and robot behaviors. In the shared control teleoperation domain and a more complex shared workspace collaboration task, we show that surrogate assisted scenario generation efficiently synthesizes diverse datasets of challenging scenarios. We demonstrate that these failures are reproducible in real-world interactions.
Pseudo-Hamiltonian neural networks for learning partial differential equations

Pseudo-Hamiltonian neural networks (PHNN) were recently introduced for learning dynamical systems that can be modelled by ordinary differential equations. In this paper, we extend the method to partial differential equations. The resulting model is comprised of up to three neural networks, modelling terms representing conservation, dissipation and external forces, and discrete convolution operators that can either be learned or be given as input. We demonstrate numerically the superior performance of PHNN compared to a baseline model that models the full dynamics by a single neural network. Moreover, since the PHNN model consists of three parts with different physical interpretations, these can be studied separately to gain insight into the system, and the learned model is applicable also if external forces are removed or changed.
Discovering Object-Centric Generalized Value Functions From Pixels

Deep Reinforcement Learning has shown significant progress in extracting useful representations from high-dimensional inputs albeit using hand-crafted auxiliary tasks and pseudo rewards. Automatically learning such representations in an object-centric manner geared towards control and fast adaptation remains an open research problem. In this paper, we introduce a method that tries to discover meaningful features from objects, translating them to temporally coherent"question"functions and leveraging the subsequent learned general value functions for control. We compare our approach with state-of-the-art techniques alongside other ablations and show competitive performance in both stationary and non-stationary settings. Finally, we also investigate the discovered general value functions and through qualitative analysis show that the learned representations are not only interpretable but also, centered around objects that are invariant to changes across tasks facilitating fast adaptation.
Monetary policy and the joint distribution of income and wealth: The heterogeneous case of the euro area

This papers aims to establish the empirical relationship between income, net wealth and their joint distribution in a selected group of euro area countries. I estimate measures of dependence between income and net wealth using a semiparametric copula approach and calculate a bivariate Gini coefficient. By combining structural inference from vector autoregressions on the macroeconomic level with a simulation using microeconomic data, I investigate how conventional and unconventional monetary policy measures affect the joint distribution. Results indicate that effects of monetary policy are highly heterogeneous across different countries, both in terms of the dependence of income and net wealth on each other, and in terms of inequality in both income and net wealth.
JaxPruner: A concise library for sparsity research

This paper introduces JaxPruner, an open-source JAX-based pruning and sparse training library for machine learning research. JaxPruner aims to accelerate research on sparse neural networks by providing concise implementations of popular pruning and sparse training algorithms with minimal memory and latency overhead. Algorithms implemented in JaxPruner use a common API and work seamlessly with the popular optimization library Optax, which, in turn, enables easy integration with existing JAX based libraries. We demonstrate this ease of integration by providing examples in four different codebases: Scenic, t5x, Dopamine and FedJAX and provide baseline experiments on popular benchmarks.
Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification

Clinical notes are assigned ICD codes - sets of codes for diagnoses and procedures. In the recent years, predictive machine learning models have been built for automatic ICD coding. However, there is a lack of widely accepted benchmarks for automated ICD coding models based on large-scale public EHR data. This paper proposes a public benchmark suite for ICD-10 coding using a large EHR dataset derived from MIMIC-IV, the most recent public EHR dataset. We implement and compare several popular methods for ICD coding prediction tasks to standardize data preprocessing and establish a comprehensive ICD coding benchmark dataset. This approach fosters reproducibility and model comparison, accelerating progress toward employing automated ICD coding in future studies. Furthermore, we create a new ICD-9 benchmark using MIMIC-IV data, providing more data points and a higher number of ICD codes than MIMIC-III. Our open-source code offers easy access to data processing steps, benchmark creation, and experiment replication for those with MIMIC-IV access, providing insights, guidance, and protocols to efficiently develop ICD coding models.
Comparisons for carrier-borne epidemics in heterogeneous and homogeneous populations

Two specific models for the spread of a carrier-borne epidemic are considered which allow for individual variability. The implications of differences in the infection or removal rates are investigated by comparing the propagation of the disease for heterogeneous and homogeneous populations. This is achieved by means of the stochastically larger and more variable order relations for random variables. The results obtained extend earlier ones and are illustrated with some numerical examples.
Unpaired Quad-Path Cycle Consistent Adversarial Networks for Single Image Defogging

Adversarial learning-based image defogging methods have been extensively studied in computer vision due to their remarkable performance. However, most existing methods have limited defogging capabilities for real cases because they are trained on the paired clear and synthesized foggy images of the same scenes. In addition, they have limitations in preserving vivid color and rich textual details in defogging. To address these issues, we develop a novel generative adversarial network, called quad-path cycle consistent adversarial network (QPC-Net), for single image defogging. QPC-Net consists of a Fog2Fogfree block and a Fogfree2Fog block. In each block, there are three learning-based modules, namely, fog removal, color-texture recovery, and fog synthetic, which sequentially compose dual-path that constrain each other to generate high quality images. Specifically, the color-texture recovery model is designed to exploit the self-similarity of texture and structure information by learning the holistic channel-spatial feature correlations between the foggy image with its several derived images. Moreover, in the fog synthetic module, we utilize the atmospheric scattering model to guide it to improve the generative quality by focusing on an atmospheric light optimization with a novel sky segmentation network. Extensive experiments on both synthetic and real-world datasets show that QPC-Net outperforms state-of-the-art defogging methods in terms of quantitative accuracy and subjective visual quality.
Characterizations of amenability through stochastic domination and finitary codings

We establish new characterizations of amenability of graphs through two probabilistic notions: stochastic domination and finitary codings (also called finitary factors). On the stochastic domination side, we show that the plus state of the Ising model at very low temperature stochastically dominates a high density Bernoulli percolation if and only if the underlying graph is nonamenable. This answers a question of Liggett and Steif. We prove a similar result for the ``infinite cluster process'' of Bernoulli percolation, which is of particular interest as this process is not monotone and does not possess any nice form of the domain Markov property. We also prove that the plus states of the Ising model at very low temperatures are stochastically ordered if and only if the graph is nonamenable. This answers a second question of Liggett and Steif. We further show that these stochastic domination results can be witnessed by invariant monotone couplings. On the finitary coding side, we show that the plus state of the Ising model at very low temperature is a finitary factor of an i.i. d. process if and only if the underlying graph is nonamenable (assuming it supports a phase transition). We show a similar result for the infinite cluster process of a high-density Bernoulli percolation. A main technique is to consider diluted processes, for which Holley's condition can be established. In order to compare two Ising models, we apply a dilution mechanism using lattice gas theory. Along the way we develop general tools to establish invariant domination when Holley's condition is satisfied. The finitary factor results are based on the stochastic domination results and a dynamical construction involving bounding chains, along with a new technique to analyze coupling from the past in infinite-range processes via a novel disease spreading model, which may be of independent interest.
Boundary element approach for dynamic poroelastic problems

A Boundary Element approach for dynamic poroelastic problems in the frequency domain is presented. The technique is based on an integral equation formulation in terms of solid displacements and fluid stress. The analogy between dynamic poroelasticity and thermoelasticity in the frequency domain is used to obtain the fundamental solution. The Boundary Elements formulation is developed and the numerical aspects related to the integration of the fundamental solution kernels analysed. A computer code for two-dimensional problems is implemented and validated by solving two basic problems. The obtained numerical results are compared with the corresponding exact solutions.
Chemical Differentiation around Five Massive Protostars Revealed by ALMA: Carbon-chain Species and Oxygen/Nitrogen-bearing Complex Organic Molecules

We present Atacama Large Millimeter/submillimeter Array Band 3 data toward five massive young stellar objects (MYSOs), and investigate relationships between unsaturated carbon-chain species and saturated complex organic molecules (COMs). An HC5N (J = 35–34) line has been detected from three MYSOs, where nitrogen (N)-bearing COMs (CH2CHCN and CH3CH2CN) have been detected. The HC5N spatial distributions show compact features and match with a methanol (CH3OH) line with an upper-state energy around 300 K, which should trace hot cores. The hot regions are more extended around the MYSOs where N-bearing COMs and HC5N have been detected compared to two MYSOs without these molecular lines, while there are no clear differences in the bolometric luminosity and temperature. We run chemical simulations of hot-core models with a warm-up stage, and compare with the observational results. The observed abundances of HC5N and COMs show good agreements with the model at the hot-core stage with temperatures above 160 K. These results indicate that carbon-chain chemistry around the MYSOs cannot be reproduced by warm carbon-chain chemistry, and a new type of carbon-chain chemistry occurs in hot regions around MYSOs.
Inferring Preferences from Demonstrations in Multi-objective Reinforcement Learning: A Dynamic Weight-based Approach

Many decision-making problems feature multiple objectives. In such problems, it is not always possible to know the preferences of a decision-maker for different objectives. However, it is often possible to observe the behavior of decision-makers. In multi-objective decision-making, preference inference is the process of inferring the preferences of a decision-maker for different objectives. This research proposes a Dynamic Weight-based Preference Inference (DWPI) algorithm that can infer the preferences of agents acting in multi-objective decision-making problems, based on observed behavior trajectories in the environment. The proposed method is evaluated on three multi-objective Markov decision processes: Deep Sea Treasure, Traffic, and Item Gathering. The performance of the proposed DWPI approach is compared to two existing preference inference methods from the literature, and empirical results demonstrate significant improvements compared to the baseline algorithms, in terms of both time requirements and accuracy of the inferred preferences. The Dynamic Weight-based Preference Inference algorithm also maintains its performance when inferring preferences for sub-optimal behavior demonstrations. In addition to its impressive performance, the Dynamic Weight-based Preference Inference algorithm does not require any interactions during training with the agent whose preferences are inferred, all that is required is a trajectory of observed behavior.
Convergence of Adam Under Relaxed Assumptions

In this paper, we provide a rigorous proof of convergence of the Adaptive Moment Estimate (Adam) algorithm for a wide class of optimization objectives. Despite the popularity and efficiency of the Adam algorithm in training deep neural networks, its theoretical properties are not yet fully understood, and existing convergence proofs require unrealistically strong assumptions, such as globally bounded gradients, to show the convergence to stationary points. In this paper, we show that Adam provably converges to $\epsilon$-stationary points with $\mathcal{O}(\epsilon^{-4})$ gradient complexity under far more realistic conditions. The key to our analysis is a new proof of boundedness of gradients along the optimization trajectory of Adam, under a generalized smoothness assumption according to which the local smoothness (i.e., Hessian norm when it exists) is bounded by a sub-quadratic function of the gradient norm. Moreover, we propose a variance-reduced version of Adam with an accelerated gradient complexity of $\mathcal{O}(\epsilon^{-3})$.
Noise Is Not the Main Factor Behind the Gap Between SGD and Adam on Transformers, but Sign Descent Might Be

The success of the Adam optimizer on a wide array of architectures has made it the default in settings where stochastic gradient descent (SGD) performs poorly. However, our theoretical understanding of this discrepancy is lagging, preventing the development of significant improvements on either algorithm. Recent work advances the hypothesis that Adam and other heuristics like gradient clipping outperform SGD on language tasks because the distribution of the error induced by sampling has heavy tails. This suggests that Adam outperform SGD because it uses a more robust gradient estimate. We evaluate this hypothesis by varying the batch size, up to the entire dataset, to control for stochasticity. We present evidence that stochasticity and heavy-tailed noise are not major factors in the performance gap between SGD and Adam. Rather, Adam performs better as the batch size increases, while SGD is less effective at taking advantage of the reduction in noise. This raises the question as to why Adam outperforms SGD in the full-batch setting. Through further investigation of simpler variants of SGD, we find that the behavior of Adam with large batches is similar to sign descent with momentum.
Why magnetic monopole becomes dyon in topological insulators

The Witten effect predicts that a magnetic monopole acquires a fractional electric charge inside topological insulators. In this work, we give a microscopic description of this phenomenon, as well as an analogous two-dimensional system with a vortex. We solve the Dirac equation of electron field both analytically in continuum and numerically on a lattice, by adding the Wilson term and smearing the gauge field within a finite range to regularize the short-distance behavior of the system. Our results reveal that the Wilson term induces a strong positive mass shift, creating a domain-wall around the monopole/vortex. This small, yet finite-sized domain-wall localizes the chiral zero modes and ensures their stability through the Atiyah-Singer index theorem, whose cobordism invariance is crucial in explaining why the electric charge is fractional.
When Do Graph Neural Networks Help with Node Classification: Investigating the Homophily Principle on Node Distinguishability

Homophily principle, i.e., nodes with the same labels are more likely to be connected, was believed to be the main reason for the performance superiority of Graph Neural Networks (GNNs) over Neural Networks (NNs) on Node Classiﬁcation (NC) tasks. Recently, people have developed theoretical results arguing that, even though the homophily principle is broken, the advantage of GNNs can still hold as long as nodes from the same class share similar neighborhood patterns [29], which questions the validity of homophily. However, this argument only considers intra-class Node Distinguishability (ND) and ignores inter-class ND, which is insufﬁcient to study the effect of homophily. In this paper, we ﬁrst demonstrate the aforementioned insufﬁciency with examples and argue that an ideal situation for ND is to have smaller intra-class ND than inter-class ND. To formulate this idea and have a better understanding of homophily, we propose Contextual Stochastic Block Model for Homophily (CSBM-H) and deﬁne two metrics, Probabilistic Bayes Error (PBE) and Expected Negative KL-divergence (ENKL), to quantify ND, through which we can also ﬁnd how intra- and inter-class ND inﬂuence ND together. We visualize the results and give detailed analysis. Through experiments, we veriﬁed that the superiority of GNNs is