Productivity and farm size in Australian agriculture: reinvestigating the returns to scale

Higher productivity among large farms is often assumed to be a result of increasing returns to scale. However, using farm-level data for the Australian broadacre industry, it was found that constant or mildly decreasing returns to scale is more typical. On examining the monotonic change in marginal input returns as farm operating size increases, it was found that large farms achieve higher productivity through changes in production technology rather than through changes in scale. The results highlight the disparity between ‘returns to scale’ and ‘returns to size’ in Australian agriculture. They also suggest that improving productivity in smaller farms would depend more on their ability to access advanced technologies than their ability to simply expand. The implications for ongoing structural adjustment in Australian agriculture are discussed.returns to scale, returns to size, production function, technology progress, structural adjustment, Australian agriculture, Agricultural and Food Policy,

Revisiting the Trade-off between Accuracy and Robustness via Weight Distribution of Filters

Adversarial attacks have been proven to be potential threats to Deep Neural Networks (DNNs), and many methods are proposed to defend against adversarial attacks. However, while enhancing the robustness, the clean accuracy will decline to a certain extent, implying a trade-off existed between the accuracy and robustness. In this paper, we firstly empirically find an obvious distinction between standard and robust models in the filters' weight distribution of the same architecture, and then theoretically explain this phenomenon in terms of the gradient regularization, which shows this difference is an intrinsic property for DNNs, and thus a static network architecture is difficult to improve the accuracy and robustness at the same time. Secondly, based on this observation, we propose a sample-wise dynamic network architecture named Adversarial Weight-Varied Network (AW-Net), which focuses on dealing with clean and adversarial examples with a ``divide and rule" weight strategy. The AW-Net dynamically adjusts network's weights based on regulation signals generated by an adversarial detector, which is directly influenced by the input sample. Benefiting from the dynamic network architecture, clean and adversarial examples can be processed with different network weights, which provides the potentiality to enhance the accuracy and robustness simultaneously. A series of experiments demonstrate that our AW-Net is architecture-friendly to handle both clean and adversarial examples and can achieve better trade-off performance than state-of-the-art robust models

Boosting Adversarial Transferability with Learnable Patch-wise Masks

Adversarial examples have raised widespread attention in security-critical applications because of their transferability across different models. Although many methods have been proposed to boost adversarial transferability, a gap still exists in the practical demand. In this paper, we argue that the model-specific discriminative regions are a key factor to cause the over-fitting to the source model, and thus reduce the transferability to the target model. For that, a patch-wise mask is utilized to prune the model-specific regions when calculating adversarial perturbations. To accurately localize these regions, we present a learnable approach to optimize the mask automatically. Specifically, we simulate the target models in our framework, and adjust the patch-wise mask according to the feedback of simulated models. To improve the efficiency, Differential Evolutionary (DE) algorithm is utilized to search for patch-wise masks for a specific image. During iterative attacks, the learned masks are applied to the image to drop out the patches related to model-specific regions, thus making the gradients more generic and improving the adversarial transferability. The proposed approach is a pre-processing method and can be integrated with existing gradient-based methods to further boost the transfer attack success rate. Extensive experiments on the ImageNet dataset demonstrate the effectiveness of our method. We incorporate the proposed approach with existing methods in the ensemble attacks and achieve an average success rate of 93.01% against seven advanced defense methods, which can effectively enhance the state-of-the-art transfer-based attack performance

Mitigating the Accuracy-Robustness Trade-off via Multi-Teacher Adversarial Distillation

Adversarial training is a practical approach for improving the robustness of deep neural networks against adversarial attacks. Although bringing reliable robustness, the performance toward clean examples is negatively affected after adversarial training, which means a trade-off exists between accuracy and robustness. Recently, some studies have tried to use knowledge distillation methods in adversarial training, achieving competitive performance in improving the robustness but the accuracy for clean samples is still limited. In this paper, to mitigate the accuracy-robustness trade-off, we introduce the Multi-Teacher Adversarial Robustness Distillation (MTARD) to guide the model's adversarial training process by applying a strong clean teacher and a strong robust teacher to handle the clean examples and adversarial examples, respectively. During the optimization process, to ensure that different teachers show similar knowledge scales, we design the Entropy-Based Balance algorithm to adjust the teacher's temperature and keep the teachers' information entropy consistent. Besides, to ensure that the student has a relatively consistent learning speed from multiple teachers, we propose the Normalization Loss Balance algorithm to adjust the learning weights of different types of knowledge. A series of experiments conducted on public datasets demonstrate that MTARD outperforms the state-of-the-art adversarial training and distillation methods against various adversarial attacks

Economic reform and the efficiency of Chinese state enterprises : with special reference to energy utilisation

This thesis examines the impact of economic reform on the technical and allocative efficiency of Chinese state enterprises, using survey data for 1980 and for 1984-88. Market-oriented economic reform of state enterprises began in the late 1970s and continued throughout the 1980s. The thesis focuses on two important aspects of reform: the process by which the central planning system was replaced with an open market system; and changes in institutions governing the financial relationship of enterprises and government, the financing of capital and the management of labour. Both aspects were necessary if state enterprises were to operate efficiently in a competitive market environment. The primary purpose of the thesis is to explore the impact of the reforms on the efficiency with which factor inputs, including energy, are used in a sample of state enterprises in China through the reform period. The reform was seriously flawed, particularly in the area of the financial relationship between enterprises and the govemment and banks. The efficiency of state enterprises is analysed within the framework of Komai's soft budget constraint hypothesis, where the key to improving technical and allocative efficiency is to harden sufficiently the budget constraint of state enterprises. According to Komai, at least three major factors contribute to the soft budget constraint of state enterprises in a centrally planned economy: soft prices, soft tax and soft credit. The thesis investigates in detail institutional change in these areas, assesses its impact on the behaviour of sample enterprises and estimates quantitatively its impact on technical and allocative efficiency. Technical efficiency is defined as the gap between maximum potential output and actual output assessed at a given level of inputs; allocative efficiency is defined as the deviation of input mix from the optimal expansion path assessed at the prevailing relative price of inputs. In estimating technical efficiency, the stochastic production frontier, a relatively new econometric approach, is adopted. As existing models are found to be inappropriate to address the issues in the study, a modified model is used to estimate the level of technical efficiency achieved by each enterprise. This model is also used to estimate allocative efficiency. The analysis indicates that, even after economic reform, the budget constraint of state enterprises was unduly soft. The estimation indicates that both technical and allocative efficiency have improved since 1980. Further estimation suggests that the commodity market and labour management reform were successful, contributing on average over 60 per cent of the improvement found in technical efficiency during the period studied. Due to a continuing soft budget constraint, reform in the areas of taxation and financing of capital did not have a positive impact on technical efficiency. The estimates of allocative efficiency indicate that capital expansion in state enterprises was too rapid to be economically rational — another sign of a soft budget constraint. In this study, I incorporate energy into the stochastic production function as an independent input for estimation. The Chinese economy has been characterised by scarcity and inefficient use of energy resources. The industrial sector has always been China's largest and least efficient consumer of energy. Efficiency in the use of energy therefore deserves attention. The efficiency of energy utilisation in state enterprises is discussed in the framework of Komai's soft budget constraint Based on the empirical results, the thesis discusses the implications of marketoriented reform and institutional distortions for China's long-term economic growth, the fragile natural environment and future reform policies

Preventing Unauthorized AI Over-Analysis by Medical Image Adversarial Watermarking

The advancement of deep learning has facilitated the integration of Artificial Intelligence (AI) into clinical practices, particularly in computer-aided diagnosis. Given the pivotal role of medical images in various diagnostic procedures, it becomes imperative to ensure the responsible and secure utilization of AI techniques. However, the unauthorized utilization of AI for image analysis raises significant concerns regarding patient privacy and potential infringement on the proprietary rights of data custodians. Consequently, the development of pragmatic and cost-effective strategies that safeguard patient privacy and uphold medical image copyrights emerges as a critical necessity. In direct response to this pressing demand, we present a pioneering solution named Medical Image Adversarial watermarking (MIAD-MARK). Our approach introduces watermarks that strategically mislead unauthorized AI diagnostic models, inducing erroneous predictions without compromising the integrity of the visual content. Importantly, our method integrates an authorization protocol tailored for legitimate users, enabling the removal of the MIAD-MARK through encryption-generated keys. Through extensive experiments, we validate the efficacy of MIAD-MARK across three prominent medical image datasets. The empirical outcomes demonstrate the substantial impact of our approach, notably reducing the accuracy of standard AI diagnostic models to a mere 8.57% under white box conditions and 45.83% in the more challenging black box scenario. Additionally, our solution effectively mitigates unauthorized exploitation of medical images even in the presence of sophisticated watermark removal networks. Notably, those AI diagnosis networks exhibit a meager average accuracy of 38.59% when applied to images protected by MIAD-MARK, underscoring the robustness of our safeguarding mechanism

EfficientTrain: Exploring Generalized Curriculum Learning for Training Visual Backbones

The superior performance of modern deep networks usually comes with a costly training procedure. This paper presents a new curriculum learning approach for the efficient training of visual backbones (e.g., vision Transformers). Our work is inspired by the inherent learning dynamics of deep networks: we experimentally show that at an earlier training stage, the model mainly learns to recognize some 'easier-to-learn' discriminative patterns within each example, e.g., the lower-frequency components of images and the original information before data augmentation. Driven by this phenomenon, we propose a curriculum where the model always leverages all the training data at each epoch, while the curriculum starts with only exposing the 'easier-to-learn' patterns of each example, and introduces gradually more difficult patterns. To implement this idea, we 1) introduce a cropping operation in the Fourier spectrum of the inputs, which enables the model to learn from only the lower-frequency components efficiently, 2) demonstrate that exposing the features of original images amounts to adopting weaker data augmentation, and 3) integrate 1) and 2) and design a curriculum learning schedule with a greedy-search algorithm. The resulting approach, EfficientTrain, is simple, general, yet surprisingly effective. As an off-the-shelf method, it reduces the wall-time training cost of a wide variety of popular models (e.g., ResNet, ConvNeXt, DeiT, PVT, Swin, and CSWin) by >1.5x on ImageNet-1K/22K without sacrificing accuracy. It is also effective for self-supervised learning (e.g., MAE). Code is available at https://github.com/LeapLabTHU/EfficientTrain.Comment: ICCV 202

Avalon's Game of Thoughts: Battle Against Deception through Recursive Contemplation

Recent breakthroughs in large language models (LLMs) have brought remarkable success in the field of LLM-as-Agent. Nevertheless, a prevalent assumption is that the information processed by LLMs is consistently honest, neglecting the pervasive deceptive or misleading information in human society and AI-generated content. This oversight makes LLMs susceptible to malicious manipulations, potentially resulting in detrimental outcomes. This study utilizes the intricate Avalon game as a testbed to explore LLMs' potential in deceptive environments. Avalon, full of misinformation and requiring sophisticated logic, manifests as a "Game-of-Thoughts". Inspired by the efficacy of humans' recursive thinking and perspective-taking in the Avalon game, we introduce a novel framework, Recursive Contemplation (ReCon), to enhance LLMs' ability to identify and counteract deceptive information. ReCon combines formulation and refinement contemplation processes; formulation contemplation produces initial thoughts and speech, while refinement contemplation further polishes them. Additionally, we incorporate first-order and second-order perspective transitions into these processes respectively. Specifically, the first-order allows an LLM agent to infer others' mental states, and the second-order involves understanding how others perceive the agent's mental state. After integrating ReCon with different LLMs, extensive experiment results from the Avalon game indicate its efficacy in aiding LLMs to discern and maneuver around deceptive information without extra fine-tuning and data. Finally, we offer a possible explanation for the efficacy of ReCon and explore the current limitations of LLMs in terms of safety, reasoning, speaking style, and format, potentially furnishing insights for subsequent research.Comment: 40 page