Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Horizon Representations with Hierarchical Forward Models for Reinforcement Learning

Learning control from pixels is difficult for reinforcement learning (RL) agents because representation learning and policy learning are intertwined. Previous approaches remedy this issue with auxiliary representation learning tasks, but they either do not consider the temporal aspect of the problem or only consider single-step transitions, which may cause learning inefficiencies if important environmental changes take many steps to manifest. We propose Hierarchical $k$-Step Latent (HKSL), an auxiliary task that learns multiple representations via a hierarchy of forward models that learn to communicate and an ensemble of $n$-step critics that all operate at varying magnitudes of step skipping. We evaluate HKSL in a suite of 30 robotic control tasks with and without distractors and a task of our creation. We find that HKSL either converges to higher or optimal episodic returns more quickly than several alternative representation learning approaches. Furthermore, we find that HKSL's representations capture task-relevant details accurately across timescales (even in the presence of distractors) and that communication channels between hierarchy levels organize information based on both sides of the communication process, both of which improve sample efficiency.
Few-Shot Non-Parametric Learning with Deep Latent Variable Model

Most real-world problems that machine learning algorithms are expected to solve face the situation with 1) unknown data distribution; 2) little domain-specific knowledge; and 3) datasets with limited annotation. We propose Non-Parametric learning by Compression with Latent Variables (NPC-LV), a learning framework for any dataset with abundant unlabeled data but very few labeled ones. By only training a generative model in an unsupervised way, the framework utilizes the data distribution to build a compressor. Using a compressor-based distance metric derived from Kolmogorov complexity, together with few labeled data, NPC-LV classifies without further training. We show that NPC-LV outperforms supervised methods on all three datasets on image classification in low data regime and even outperform semi-supervised learning methods on CIFAR-10. We demonstrate how and when negative evidence lowerbound (nELBO) can be used as an approximate compressed length for classification. By revealing the correlation between compression rate and classification accuracy, we illustrate that under NPC-LV, the improvement of generative models can enhance downstream classification accuracy.
Atomic ordering and phase separation in lateral heterostructures and multijunctions of ternary two-dimensional hexagonal materials

The growth and microstructural properties of ternary monolayers of two-dimensional hexagonal materials are examined, including both individual two-dimensional crystalline grains and in-plane heterostructures, multijunctions, or superlattices. The study is conducted through the development of a ternary phase ﬁeld crystal model incorporating sublattice ordering and the coupling among the three atomic components. The results demonstrate that a transition of compositional pattern or modulation in this type of two-dimensional ternary crystals, from phase separation to geometrically frustrated lattice atomic ordering, can be controlled via the varying degree of energetic preference of heteroelemental neighboring over the homoelemental ones. Eﬀects of growth and system conditions are quantitatively identiﬁed through numerical calculations and analyses of interspecies spatial correlations and the degree of alloy intermixing or disordering. These ﬁndings are applied to simulating the growth of monolayer lateral heterostructures with atomically sharp heterointerface, and via the sequential process of edge-epitaxy, the formation of the corresponding superlattices or structures with multiple heterojunctions, with outcomes consistent with recent experiments of in-plane multi-heterostructures of transition metal dichalcogenides. Also explored is a distinct type of alloy-based lateral heterostructures and multijunctions which integrate ternary ordered alloy domains with the adjoining blocks of binary compounds, providing a more extensive variety of two-dimensional heterostructural materials.
Single-phase deep learning in cortico-cortical networks

The error-backpropagation (backprop) algorithm remains the most common solution to the credit assignment problem in artificial neural networks. In neuroscience, it is unclear whether the brain could adopt a similar strategy to correctly modify its synapses. Recent models have attempted to bridge this gap while being consistent with a range of experimental observations. However, these models are either unable to effectively backpropagate error signals across multiple layers or require a multi-phase learning process, neither of which are reminiscent of learning in the brain. Here, we introduce a new model, Bursting Cortico-Cortical Networks (BurstCCN), which solves these issues by integrating known properties of cortical networks namely bursting activity, short-term plasticity (STP) and dendrite-targeting interneurons. BurstCCN relies on burst multiplexing via connection-type-specific STP to propagate backprop-like error signals within deep cortical networks. These error signals are encoded at distal dendrites and induce burst-dependent plasticity as a result of excitatory-inhibitory top-down inputs. First, we demonstrate that our model can effectively backpropagate errors through multiple layers using a single-phase learning process. Next, we show both empirically and analytically that learning in our model approximates backprop-derived gradients. Finally, we demonstrate that our model is capable of learning complex image classification tasks (MNIST and CIFAR-10). Overall, our results suggest that cortical features across sub-cellular, cellular, microcircuit and systems levels jointly underlie single-phase efficient deep learning in the brain.
The SJTU X-LANCE Lab System for CNSRC 2022

This technical report describes the SJTU X-LANCE Lab system for the three tracks in CNSRC 2022. In this challenge, we explored the speaker embedding modeling ability of deep ResNet (Deeper r-vector). All the systems are only trained on the Cnceleb training set and we use the same systems for the three tracks in CNSRC 2022. In this challenge, our system ranks the first place in the fixed track of speaker verification task. Our best single system and fusion system achieve 0.3164 and 0.2975 minDCF respectively. Besides, we submit the result of ResNet221 to the speaker retrieval track and achieve 0.4626 mAP. More importantly, we have helped the wespeaker [1] toolkit reproduce our result: https://github.com/wenet-e2e/wespeaker.
Optimal Covariance Steering for Continuous-Time Linear Stochastic Systems With Multiplicative Noise

In this paper we study the finite-horizon optimal covariance steering problem for a continuous-time linear stochastic system subject to both additive and multiplicative noise. The noise can be continuous or it may contain jumps. Additive noise does not depend on the state or the control, whereas multiplicative noise has a magnitude proportional to the current state. The cost is assumed to be quadratic in both the state and the control. First, the controllability of the state covariance is established under mild assumptions. Then, the optimal control for steering the covariance is provided. Lastly, the existence and uniqueness of the optimal control is shown. In the process, we provide a result of independent interest regarding the maximal interval of existence of the solution to a matrix Riccati differential equation.
Self-Supervised Training with Autoencoders for Visual Anomaly Detection

Recently, deep auto-encoders have been used for the task of anomaly detection in the visual domain. By optimising for the reconstruction error using anomaly-free examples, the common belief is that a corresponding network should fail to accurately reconstruct anomalous regions in the application phase. This goal is typically addressed by controlling the capacity of the network, either by reducing the size of the bottleneck layer or by enforcing sparsity constraints on its activations. However, neither of these techniques does explicitly penalise reconstruction of anomalous signals often resulting in poor detection. We tackle this problem by adapting a self-supervised learning regime that allows the use of discriminative information during training but focuses on the data manifold of normal examples. Precisely, we investigate two different training objectives inspired by the task of neural image inpainting. Our main objective regularises the model to produce locally consistent reconstructions, while replacing irregularities, therefore, acting as a filter that removes anomalous patterns. Our formal analysis shows that under mild conditions the corresponding model resembles a non-linear orthogonal projection of partially corrupted images onto the manifold of uncorrupted (defect-free) examples. This insight makes the reconstruction error a natural choice for defining the anomaly score of a sample according to its distance from a corresponding projection on the data manifold. We emphasise that inference with our approach is very efficient during training and prediction requiring a single forward pass for each input image. Our experiments on the MVTec AD dataset demonstrate high detection and localisation performance. On the texture-subset, in particular, our approach consistently outperforms recent anomaly detection methods by a significant margin.
Quant-BnB: A Scalable Branch-and-Bound Method for Optimal Decision Trees with Continuous Features

Decision trees are one of the most useful and popular methods in the machine learning toolbox. In this paper, we consider the problem of learning optimal decision trees, a combinatorial optimization problem that is challenging to solve at scale. A common approach in the literature is to use greedy heuristics, which may not be optimal. Recently there has been significant interest in learning optimal decision trees using various approaches (e.g., based on integer programming, dynamic programming) -- to achieve computational scalability, most of these approaches focus on classification tasks with binary features. In this paper, we present a new discrete optimization method based on branch-and-bound (BnB) to obtain optimal decision trees. Different from existing customized approaches, we consider both regression and classification tasks with continuous features. The basic idea underlying our approach is to split the search space based on the quantiles of the feature distribution -- leading to upper and lower bounds for the underlying optimization problem along the BnB iterations. Our proposed algorithm Quant-BnB shows significant speedups compared to existing approaches for shallow optimal trees on various real datasets.
Cataloguing MoSi2N4 and WSi2N4 van der Waals Heterostructures: An Exceptional Material Platform for Excitonic Solar Cell Applications

2D materials van der Waals heterostructures (vdWHs) provide a revolutionary route toward high‐performance solar energy conversion devices beyond the conventional silicon‐based pn junction solar cells. Despite tremendous research progress accomplished in recent years, the searches of vdWHs with exceptional excitonic solar cell conversion efficiency and optical properties remain an open theoretical and experimental quest. Here, this study shows that the vdWH family composed of MoSi2N4 and WSi2N4 monolayers provides a compelling material platform for developing high‐performance ultrathin excitonic solar cells and photonics devices. Using first‐principle calculations, 51 types of MoSi2N4 and WSi2N4‐based [(Mo,W)Si2N4] vdWHs composed of various metallic, semimetallic, semiconducting, insulating, and topological 2D materials are constructed and classified. Intriguingly, MoSi2N4/(InSe, WSe2) are identified as Type II vdWHs with exceptional excitonic solar cell power conversion efficiency reaching well over 20%, which are competitive to state‐of‐the‐art silicon solar cells. The (Mo,W)Si2N4 vdWH family exhibits strong optical absorption in both the visible and UV regimes. Exceedingly large peak UV absorptions over 40%, approaching the maximum absorption limit of a freestanding 2D material, can be achieved in (Mo,W)Si2N4/α2‐(Mo,W)Ge2P4 vdWHs. The findings unravel the enormous potential of (Mo,W)Si2N4 vdWHs in designing ultimately compact excitonic solar cell device technology.
Quantum Many-Body Scars: A Quasiparticle Perspective

Weakly interacting quasiparticles play a central role in the low-energy description of many phases of quantum matter. At higher energies, however, quasiparticles cease to be well defined in generic many-body systems owing to a proliferation of decay channels. In this review, we discuss the phenomenon of quantum many-body scars, which can give rise to certain species of stable quasiparticles throughout the energy spectrum. This goes along with a set of unusual nonequilibrium phenomena including many-body revivals and nonthermal stationary states. We provide a pedagogical exposition of this physics via a simple yet comprehensive example, that of a spin-1 XY model. We place our discussion in the broader context of symmetry-based constructions of many-body scar states, projector embeddings, and Hilbert space fragmentation. We conclude with a summary of experimental progress and theoretical puzzles. Expected final online publication date for the Annual Review of Condensed Matter Physics, Volume 14 is March 2023. Please see http://www.annualreviews. org/page/journal/pubdates for revised estimates.
GACT: Activation Compressed Training for General Architectures

Training large neural network (NN) models requires extensive memory resources, and Activation Compressed Training (ACT) is a promising approach to reduce training memory footprint. This paper presents GACT, an ACT framework to support a broad range of machine learning tasks for generic NN architectures with limited domain knowledge. By analyzing a linearized version of ACT’s approximate gradient, we prove the convergence of GACT without prior knowledge on operator type or model architecture. To make training stable, we propose an algorithm that decides the compression ratio for each tensor by estimating its impact on the gradient at run time. We implement GACT as a PyTorch library that readily applies to any NN architecture. GACT reduces the activation memory for convolutional NNs, transformers, and graph NNs by up to 8.1 × , enabling training with a 4.2 × to 24.7 × larger batch size, with negligible accuracy loss.
A Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization

Recent decades have witnessed great advancements in multiobjective evolutionary algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these progressively improved MOEAs have not necessarily been equipped with scalable and learnable problem-solving strategies for new and grand challenges brought by the scaling-up MOPs with continuously increasing complexity from diverse aspects, mainly, including expensive cost of function evaluations, many objectives, large-scale search space, time-varying environments, and multitask. Under different scenarios, divergent thinking is required in designing new powerful MOEAs for solving them effectively. In this context, research studies on learnable MOEAs with machine learning techniques have received extensive attention in the field of evolutionary computation. This article begins with a general taxonomy of scaling-up MOPs and learnable MOEAs, followed by an analysis of the challenges that these MOPs pose to traditional MOEAs. Then, we synthetically overview recent advances of learnable MOEAs in solving various scaling-up MOPs, focusing primarily on four attractive directions (i.e., learnable evolutionary discriminators for environmental selection, learnable evolutionary generators for reproduction, learnable evolutionary evaluators for function evaluations, and learnable evolutionary transfer modules for sharing or reusing optimization experience). The insight of learnable MOEAs is offered to readers as a reference to the general track of the efforts in this field.
Sensitivity of neutron star observations to three-nucleon forces

Astrophysical observations of neutron stars have been widely used to infer the properties of the nuclear matter equation of state. Beside being a source of information on average properties of dense matter, however, the data provided by electromagnetic and gravitational wave (GW) facilities are reaching the accuracy needed to constrain, for the ﬁrst time, nuclear dynamics in dense matter. In this work we assess the sensitivity of current and future neutron star observations to directly infer the strength of repulsive three-nucleon forces, which are key to determine the stiﬀness of the equation of state. Using a Bayesian approach we focus on the constraints that can be derived on three-body interactions from binary neutron star mergers observed by second and third-generation of gravitational wave interferometers. We consider both single and multiple observations. For current detectors at design sensitivity the analysis suggests that only low mass systems, with large signal-to-noise ratios (SNR), allow to reliably constrain the three-body forces. However, our results show that a single observation with a third-generation interferometer, such as the Einstein Telescope or Cosmic Explorer, will constrain the strength of the repulsive three-body potential with exquisite accuracy, turning third-generation GW detectors into new laboratories to study the nucleon dynamics.
pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models

Knowledge tracing (KT) is the task of using students' historical learning interaction data to model their knowledge mastery over time so as to make predictions on their future interaction performance. Recently, remarkable progress has been made of using various deep learning techniques to solve the KT problem. However, the success behind deep learning based knowledge tracing (DLKT) approaches is still left somewhat unknown and proper measurement and analysis of these DLKT approaches remain a challenge. First, data preprocessing procedures in existing works are often private and custom, which limits experimental standardization. Furthermore, existing DLKT studies often differ in terms of the evaluation protocol and are far away real-world educational contexts. To address these problems, we introduce a comprehensive python based benchmark platform, \textsc{pyKT}, to guarantee valid comparisons across DLKT methods via thorough evaluations. The \textsc{pyKT} library consists of a standardized set of integrated data preprocessing procedures on 7 popular datasets across different domains, and 10 frequently compared DLKT model implementations for transparent experiments. Results from our fine-grained and rigorous empirical KT studies yield a set of observations and suggestions for effective DLKT, e.g., wrong evaluation setting may cause label leakage that generally leads to performance inflation; and the improvement of many DLKT approaches is minimal compared to the very first DLKT model proposed by Piech et al. \cite{piech2015deep}. We have open sourced \textsc{pyKT} and our experimental results at https://pykt.org/. We welcome contributions from other research groups and practitioners.
Bayesian model calibration for block copolymer self-assembly: Likelihood-free inference and expected information gain computation via measure transport

We consider the Bayesian calibration of models describing the phenomenon of block copolymer (BCP) self-assembly using image data produced by microscopy or X-ray scattering techniques. To account for the long-range disorder in BCP equilibrium structures we introduce auxiliary variables to represent this aleatory uncertainty. These variables, however, result in an integrated likelihood for high-dimensional image data that is generally intractable to evaluate. We tackle this challenging Bayesian inference problem using a likelihood-free approach based on measure transport together with the construction of summary statistics for the image data. We also show that expected information gains (EIG) from the observed data about the model parameters can be computed with no signiﬁcant additional cost. Lastly, we present a numerical case study based on the Ohta–Kawasaki model for diblock copolymer thin ﬁlm self-assembly and top-down microscopy characterization. For calibration, we introduce several domain-speciﬁc energy- and Fourier-based summary statistics and quantify their informativeness using EIG. We demonstrate the power of the proposed approach to study the eﬀect of various data corruptions and experimental designs on the calibration results. X-ray scattering experimental design and (ii) magniﬁcations of the microscope. These numerical results allow us to investigate the eﬀects of experimental settings on the calibration results for this representative model, and enable further optimal experimental design studies of interest.
Lagrangian stretching reveals stress topology in viscoelastic flows

Significance Viscoelastic flows are common in biological, geological, and industrial processes, where polymer stretching leads to unstable, time-dependent flows and anomalous transport properties. Quantifying the local polymeric stress is essential to forecasting the onset of such flow instabilities as well as their impact on mixing applications, but direct measurements of the stress field are challenging. A Lagrangian analysis of fluid kinematics establishes a fundamental connection with the Eulerian stress field in viscoelastic flows. These results provide a framework to determine the stress topology directly from conventional experimental flow measurements for arbitrary viscoelastic fluids and flows, including 3D geometries and unsteady flows.
Spontaneous breaking of time translation symmetry in a spin-orbit-coupled ﬂuid of light

We study the interplay between intrinsic spin-orbit coupling and nonlinear photon-photon interactions in a nonparaxial, elliptically polarized ﬂuid of light propagating in a bulk Kerr medium. We ﬁnd that in situations where the nonlinear interactions induce birefringence, i.e., a polarization-dependent nonlinear refractive index, their interplay with spin-orbit coupling results in an interference between the two polarization components of the ﬂuid traveling at diﬀerent wave vectors, which entails the spontaneous breaking of translation symmetry along the propagation direction. This phenomenon leads to a Floquet band structure in the Bogoliubov spectrum of the ﬂuid, and to characteristic oscillations of its intensity correlations. We characterize these oscillations in detail and point out their exponential growth at large propagation distances, revealing the presence of parametric resonances. coupling In this supplemental material we provide further details about the calculations leading to the results of the main text, in particular the nonparaxial Bogoliubov action (Sec. I), the paraxial limit and the sound velocity (Sec. II), the solution of the Bogoliubov equations (Sec. III), the choice of the input conjugate momenta (Sec. IV), and the expression of the intensity-intensity correlation function (Sec. V).