Miracle-dz/UniFAR-FTUs huggingface dataset:
Reinforcement Learning with Action-Free Pre-Training from Videos

Recent unsupervised pre-training methods have shown to be effective on language and vision domains by learning useful representations for multiple downstream tasks. In this paper, we investigate if such unsupervised pre-training methods can also be effective for vision-based reinforcement learning (RL). To this end, we introduce a framework that learns representations useful for understanding the dynamics via generative pre-training on videos. Our framework consists of two phases: we pre-train an action-free latent video prediction model, and then utilize the pre-trained representations for efficiently learning action-conditional world models on unseen environments. To incorporate additional action inputs during fine-tuning, we introduce a new architecture that stacks an action-conditional latent prediction model on top of the pre-trained action-free prediction model. Moreover, for better exploration, we propose a video-based intrinsic bonus that leverages pre-trained representations. We demonstrate that our framework significantly improves both final performances and sample-efficiency of vision-based RL in a variety of manipulation and locomotion tasks. Code is available at https://github.com/younggyoseo/apv.
Federated Learning with Position-Aware Neurons

Federated Learning (FL) fuses collaborative models from local nodes without centralizing users' data. The permutation invariance property of neural networks and the non-i.i. d. data across clients make the locally updated parameters imprecisely aligned, disabling the coordinate-based parameter averaging. Traditional neurons do not explicitly consider position information. Hence, we propose Position-Aware Neurons (PANs) as an alternative, fusing position-related values (i.e., position encodings) into neuron outputs. PANs couple themselves to their positions and minimize the possibility of dislocation, even updating on heterogeneous data. We turn on/off PANs to disable/enable the permutation invariance property of neural networks. PANs are tightly coupled with positions when applied to FL, making parameters across clients pre-aligned and facilitating coordinate-based parameter averaging. PANs are algorithm-agnostic and could universally improve existing FL algorithms. Furthermore, “FL with PANs” is simple to implement and computationally friendly.
Towards Domain Generalization in Object Detection

Despite the striking performance achieved by modern detectors when training and test data are sampled from the same or similar distribution, the generalization ability of detectors under unknown distribution shifts remains hardly studied. Recently several works discussed the detectors' adaptation ability to a specific target domain which are not readily applicable in real-world applications since detectors may encounter various environments or situations while pre-collecting all of them before training is inconceivable. In this paper, we study the critical problem, domain generalization in object detection (DGOD), where detectors are trained with source domains and evaluated on unknown target domains. To thoroughly evaluate detectors under unknown distribution shifts, we formulate the DGOD problem and propose a comprehensive evaluation benchmark to fill the vacancy. Moreover, we propose a novel method named Region Aware Proposal reweighTing (RAPT) to eliminate dependence within RoI features. Extensive experiments demonstrate that current DG methods fail to address the DGOD problem and our method outperforms other state-of-the-art counterparts.
Offline Reinforcement Learning Under Value and Density-Ratio Realizability: the Power of Gaps

We consider a challenging theoretical problem in offline reinforcement learning (RL): obtaining sample-efficiency guarantees with a dataset lacking sufficient coverage, under only realizability-type assumptions for the function approximators. While the existing theory has addressed learning under realizability and under non-exploratory data separately, no work has been able to address both simultaneously (except for a concurrent work which we compare in detail). Under an additional gap assumption, we provide guarantees to a simple pessimistic algorithm based on a version space formed by marginalized importance sampling (MIS), and the guarantee only requires the data to cover the optimal policy and the function classes to realize the optimal value and density-ratio functions. While similar gap assumptions have been used in other areas of RL theory, our work is the first to identify the utility and the novel mechanism of gap assumptions in offline RL with weak function approximation.
Robust Unlearnable Examples: Protecting Data Against Adversarial Learning

The tremendous amount of accessible data in cyberspace face the risk of being unauthorized used for training deep learning models. To address this concern, methods are proposed to make data unlearnable for deep learning models by adding a type of error-minimizing noise. However, such conferred unlearnability is found fragile to adversarial training. In this paper, we design new methods to generate robust unlearnable examples that are protected from adversarial training. We first find that the vanilla error-minimizing noise, which suppresses the informative knowledge of data via minimizing the corresponding training loss, could not effectively minimize the adversarial training loss. This explains the vulnerability of error-minimizing noise in adversarial training. Based on the observation, robust error-minimizing noise is then introduced to reduce the adversarial training loss. Experiments show that the unlearnability brought by robust error-minimizing noise can effectively protect data from adversarial training in various scenarios. The code is available at \url{https://github.com/fshp971/robust-unlearnable-examples}.
Probabilistic Registration for Gaussian Process 3D shape modelling in the presence of extensive missing data

We propose a shape fitting/registration method based on a Gaussian Processes formulation, suitable for shapes with extensive regions of missing data. Gaussian Processes are a proven powerful tool, as they provide a unified setting for shape modelling and fitting. While the existing methods in this area prove to work well for the general case of the human head, when looking at more detailed and deformed data, with a high prevalence of missing data, such as the ears, the results are not satisfactory. In order to overcome this, we formulate the shape fitting problem as a multi-annotator Gaussian Process Regression and establish a parallel with the standard probabilistic registration. The achieved method SFGP shows better performance when dealing with extensive areas of missing data when compared to a state-of-the-art registration method and current approaches for registration with pre-existing shape models. Experiments are conducted both for a 2D small dataset with diverse transformations and a 3D dataset of ears.
End-to-End Active Speaker Detection

Recent advances in the Active Speaker Detection (ASD) problem build upon a two-stage process: feature extraction and spatio-temporal context aggregation. In this paper, we propose an end-to-end ASD workflow where feature learning and contextual predictions are jointly learned. Our end-to-end trainable network simultaneously learns multi-modal embeddings and aggregates spatio-temporal context. This results in more suitable feature representations and improved performance in the ASD task. We also introduce interleaved graph neural network (iGNN) blocks, which split the message passing according to the main sources of context in the ASD problem. Experiments show that the aggregated features from the iGNN blocks are more suitable for ASD, resulting in state-of-the art performance. Finally, we design a weakly-supervised strategy, which demonstrates that the ASD problem can also be approached by utilizing audiovisual data but relying exclusively on audio annotations. We achieve this by modelling the direct relationship between the audio signal and the possible sound sources (speakers), as well as introducing a contrastive loss. All the resources of this project will be made available at: https://github.com/fuankarion/end-to-end-asd.
Electromagnetic transition amplitude for Roper resonance from holographic QCD

The Roper resonance, the first excited state of the nucleon, Is one of the best-established baryon resonances. Yet, its properties have not been consistently explained by effective models of QCD, such as the non-relativistic quark model. In this letter, we propose an alternative approach in the Sakai-Sugimoto model that is one of the holographic models of QCD. In particular, we analyze the helicity amplitude of the electromagnetic transitions at the leading of 't~Hooft coupling $1/\lambda$. The model incorporates baryon structure at short distance by non-linear mesons surrounded by meson clouds at long distance. We demonstrate that the recently observed data by CLAS are explained in the present approach.
Balanced Allocations in Batches: Simplified and Generalized

We consider the allocation of m balls (jobs) into n bins (servers). In the Two-Choice process, for each of m sequentially arriving balls, two randomly chosen bins are sampled and the ball is placed in the least loaded bin. It is well-known that the maximum load is m/n + log2 logn + O(1) with high probability. Berenbrink, Czumaj, Englert, Friedetzky and Nagel [7] introduced a parallel version of this process, where m balls arrive in consecutive batches of size b = n each. Balls within the same batch are allocated in parallel, using the load information of the bins at the beginning of the batch. They proved that the gap of this process is O(logn) with high probability. In this work, we present a new analysis of this setting, which is based on exponential potential functions. This allows us to both simplify and generalize the analysis of [7] in different ways: (1) Our analysis covers a broad class of processes. This includes not only Two-Choice, but also processes with fewer bin samples like the (1 + β)-process, processes which can only receive one bit of information from each bin sample and graphical allocation, where bins correspond to vertices in a graph. (2) Balls may be of different weights, as long as their weights are independent samples from a distribution satisfying a technical condition on its moment generating function. (3) For any batch sizes b ≥ n, we prove a gap of is O (b/n·logn). For any b ∈ [n, n3], we improve this to is O (b/n + logn) and show that it is tight for a family of processes. This implies the unexpected result that for e.g. the (1 + β)-process with constant β ∈ (0, 1], the gap is Θ(logn) for all b ∈ [n, n logn]. We also conduct experiments which support our theoretical results, and even hint at a superiority of less powerful processes like (1+ β) for large batch sizes. Full version of the paper at: https://arxiv.org/abs/2203.13902.
Role of the chiral spin configuration in field-free spin–orbit torque-induced magnetization switching by a locally injected spin current

For deterministic magnetization switching by spin–orbit torque (SOT) in a perpendicular magnetic anisotropy system, an additional in-plane direction magnetic field is essential to break the lateral symmetry. Realizing chirality in a magnetic ordering system can be one approach for achieving asymmetry in the lateral direction for field-free magnetization switching. However, systematic analysis of the influence of the chiral spin system on deterministic switching is still scarce. We investigate the field-free SOT-induced magnetization switching by using a chiral spin configuration experimentally and theoretically with micromagnetic simulations. We designed a system in which only part of the ferromagnetic layer overlaps with the heavy metal layer in the Pt/Co/MgO structure. Therefore, a spin current exerts only on a local area of the ferromagnetic layer, which results in a Néel-type chiral spin configuration. The induced chiral spin configuration can be stabilized (or destabilized) depending on the sign of the interfacial Dzyaloshinskii–Moriya interaction and the direction of the current. The stabilized spin configuration plays a crucial role in the deterministic switching in the zero field. We expect our findings to widen the perspective on chirality-based all-electrical SOT device applications.
Thin-Plate Spline Motion Model for Image Animation

Image animation brings life to the static object in the source image according to the driving video. Recent works attempt to perform motion transfer on arbitrary objects through unsupervised methods without using a priori knowledge. However, it remains a significant challenge for current unsupervised methods when there is a large pose gap between the objects in the source and driving images. In this paper, a new end-to-end unsupervised motion transfer framework is proposed to overcome such issues. Firstly, we propose thin-plate spline motion estimation to produce a more flexible optical flow, which warps the feature maps of the source image to the feature domain of the driving image. Secondly, in order to restore the missing regions more realistically, we leverage multi-resolution occlusion masks to achieve more effective feature fusion. Finally, additional auxiliary loss functions are designed to ensure that there is a clear division of labor in the network modules, encouraging the network to generate high-quality images. Our method11Our source code is publicly available: https://github.com/yoyo-nb/Thin-Plate-Spline-Motion-Model. can animate a variety of objects, including talking faces, human bodies, and pixel animations. Experiments demonstrate that our method performs better on most benchmarks than the state of the art with visible improvements in motion-related metrics.
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective

The lack of adversarial robustness has been recognized as an important issue for state-of-the-art machine learning (ML) models, e.g., deep neural networks (DNNs). Thereby, robustifying ML models against adversarial attacks is now a major focus of research. However, nearly all existing defense methods, particularly for robust training, made the white-box assumption that the defender has the access to the details of an ML model (or its surrogate alternatives if available), e.g., its architectures and parameters. Beyond existing works, in this paper we aim to address the problem of black-box defense: How to robustify a black-box model using just input queries and output feedback? Such a problem arises in practical scenarios, where the owner of the predictive model is reluctant to share model information in order to preserve privacy. To this end, we propose a general notion of defensive operation that can be applied to black-box models, and design it through the lens of denoised smoothing (DS), a first-order (FO) certified defense technique. To allow the design of merely using model queries, we further integrate DS with the zeroth-order (gradient-free) optimization. However, a direct implementation of zeroth-order (ZO) optimization suffers a high variance of gradient estimates, and thus leads to ineffective defense. To tackle this problem, we next propose to prepend an autoencoder (AE) to a given (black-box) model so that DS can be trained using variance-reduced ZO optimization. We term the eventual defense as ZO-AE-DS. In practice, we empirically show that ZO-AE- DS can achieve improved accuracy, certified robustness, and query complexity over existing baselines. And the effectiveness of our approach is justified under both image classification and image reconstruction tasks. Codes are available at https://github.com/damon-demon/Black-Box-Defense.
AGRONOMICAL ATTRIBUTES OF SOYBEANS AND SOIL RESISTANCE TO PENETRATION IN NO-TILLAGE AND CHISELED SURFACES

Farming systems may affect soil properties and crop production of components, decreasing yields. This study aimed to assess penetration resistance (PR) and agronomic traits of soybeans under no-tillage (NT) and chiseling (CP). The experiment was carried out in a randomized block with two treatments (tillage and scarification) with 12 repetitions; yet soil penetration resistance (PR) was assessed in a factorial block, considering farming systems and sampling sites (row and interrows). PR was measured with an impact penetrometer, sampling in row and interrows within a 22.5 x 0.1 m2 mesh grid up to 0.60 m depth. The obtained values were higher for NT in the 0.10-0.20 m layer. Great PR values in interrows were only registered within the surface layer. This is because there was no soil mobilization by seeder chisel plow. In chiseled soil, compaction was reduced, however, the first tractor passings were able to compact the soil to a depth of 0.50 m. Therefore, scarification reduced the plant stand and penetration resistance, allowing greater growth and soybean yield.
MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering

This paper introduces MedMCQA, a new large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions. More than 194k high-quality AIIMS \&NEET PG entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average token length of 12.77 and high topical diversity. Each sample contains a question, correct answer(s), and other options which requires a deeper language understanding as it tests the 10+ reasoning abilities of a model across a wide range of medical subjects \&topics. A detailed explanation of the solution, along with the above information, is provided in this study.
A Model-Based Approach to Requirements Analysis for Complex Products in Engineering Design

DOCTORAL DISSERTATIONS A ato-D D 72 /2 12 The aim of engineering design is to develop good products. The importance of systematic requirements analysis to get quality products is recognized. The product quality has links to customer satisfaction and the amount of profit, which are of business benefit to a company. This thesis presents a systematic requirements analysis process in the conceptual design phase. The approach resides in using the simplest form of rational design method, the checklist to place the requirements analysis process in a systematic and logical format. Rational methods often have similar aims to the creative methods, such as widening the search space for potential solutions and facilitating a multidisciplinary approach. The checklist provides a conscious and systematic transformation of the customer need to design specifications. The method enhances the use of computer modeling and simulation at an abstract level in conceptual design. It creates an important basis for information system management, integration, knowledge re-use and collaboration. W iliam B race
Reducing nitrous oxide emissions from sugarcane soil with legume intercropping

Australian sugarcane cropping has low nitrogen (N) use efficiencies, largely due to a mismatch of earlyseason N fertiliser application and later season peak crop N demand, in combination with poor soils and wet climate. To address the problem of N losses via run-off, leaching and N2O emissions, the sugarcane industry is evaluating several avenues. One approach is to improve N use efficiency (NUE) by reducing the use of vulnerable-to-loss N fertiliser, supplementing crop needs with biologically fixed N via sugarcane-legume intercropping. In an optimised system, decomposing legumes would deliver N to sugarcane, synchronised with sugarcane’s long N accumulation phase. We hypothesised that legume intercropping in combination with lower N fertiliser rates will reduce N losses (N2O emissions were quantified here) but not sugar yields. Here we report on one of several field trials with sugarcane grown as monoculture or intercropped with legumes at full N fertiliser or lowered rates (67 or 41% of full N). In the second year of implementation and compared to full N fertiliser, N2O emissions were reduced by 50 to 70% in the 67% N treatments irrespective of legume presence. Highest sugarcane biomass was achieved with full-N rate, 67% N, and 67% N + soybean intercropping. Sugarcane production was reduced in 67% N + mung bean intercropping, 41% N and zero N treatments. Sugar yield was variable but statistically similar across all treatments. These early results indicate that evaluation across different growing regions, fertiliser rates and planting times are needed to optimise sugarcane-legume intercropping systems.
S2-Net: Self-supervision Guided Feature Representation Learning for Cross-Modality Images

Combining the respective advantages of cross-modality images can compensate for the lack of information in the single modality, which has attracted increasing attention of researchers into multi-modal image matching tasks. Meanwhile, due to the great appearance differences between cross-modality image pairs, it often fails to make the feature representations of correspondences as close as possible. In this letter, we design a cross-modality feature representation learning network, S2-Net, which is based on the recently successful detect-and-describe pipeline, originally proposed for visible images but adapted to work with cross-modality image pairs. To solve the consequent problem of optimization difficulties, we introduce self-supervised learning with a well-designed loss function to guide the training without discarding the original advantages. This novel strategy simulates image pairs in the same modality, which is also a useful guide for the training of cross-modality images. Notably, it does not require additional data but significantly improves the performance and is even workable for all methods of the detect-and-describe pipeline. Extensive experiments are conducted to evaluate the performance of the strategy we proposed, compared to both handcrafted and deep learning-based methods. Results show that our elegant formulation of combined optimization of supervised and self-supervised learning outperforms state-of-the-arts on RoadScene and RGB-NIR datasets.
Brain-inspired Multilayer Perceptron with Spiking Neurons

Recently, Multilayer Perceptron (MLP) becomes the hotspot in the field of computer vision tasks. Without in-ductive bias, MLPs perform well on feature extraction and achieve amazing results. However, due to the simplic-ity of their structures, the performance highly depends on the local features communication machenism. To further improve the performance of MLP, we introduce information communication mechanisms from brain-inspired neu-ral networks. Spiking Neural Network (SNN) is the most famous brain-inspired neural network, and achieve great success on dealing with sparse data. Leaky Integrate and Fire (LIF) neurons in SNNs are used to communicate be-tween different time steps. In this paper, we incorporate the machanism of LIF neurons into the MLP models, to achieve better accuracy without extra FLOPs. We pro-pose a full-precision LIF operation to communicate be-tween patches, including horizontal LIF and vertical LIF in different directions. We also propose to use group LIF to extract better local features. With LIF modules, our SNN-MLP model achieves 81.9%, 83.3% and 83.5% top-1 accuracy on ImageNet dataset with only 4.4G, 8.5G and 15.2G FLOPs, respectively, which are state-of-the-art re-sults as far as we know. The source code will be available at https://gitee.com/mindspore/models/tree/master/research/cv/snn_mlp.