Are Equivariant Equilibrium Approximators Beneficial?

Recently, remarkable progress has been made by approximating Nash equilibrium (NE), correlated equilibrium (CE), and coarse correlated equilibrium (CCE) through function approximation that trains a neural network to predict equilibria from game representations. Furthermore, equivariant architectures are widely adopted in designing such equilibrium approximators in normal-form games. In this paper, we theoretically characterize benefits and limitations of equivariant equilibrium approximators. For the benefits, we show that they enjoy better generalizability than general ones and can achieve better approximations when the payoff distribution is permutation-invariant. For the limitations, we discuss their drawbacks in terms of equilibrium selection and social welfare. Together, our results help to understand the role of equivariance in equilibrium approximators.Comment: To appear in ICML 202

A Scalable Neural Network for DSIC Affine Maximizer Auction Design

Automated auction design aims to find empirically high-revenue mechanisms through machine learning. Existing works on multi item auction scenarios can be roughly divided into RegretNet-like and affine maximizer auctions (AMAs) approaches. However, the former cannot strictly ensure dominant strategy incentive compatibility (DSIC), while the latter faces scalability issue due to the large number of allocation candidates. To address these limitations, we propose AMenuNet, a scalable neural network that constructs the AMA parameters (even including the allocation menu) from bidder and item representations. AMenuNet is always DSIC and individually rational (IR) due to the properties of AMAs, and it enhances scalability by generating candidate allocations through a neural network. Additionally, AMenuNet is permutation equivariant, and its number of parameters is independent of auction scale. We conduct extensive experiments to demonstrate that AMenuNet outperforms strong baselines in both contextual and non-contextual multi-item auctions, scales well to larger auctions, generalizes well to different settings, and identifies useful deterministic allocations. Overall, our proposed approach offers an effective solution to automated DSIC auction design, with improved scalability and strong revenue performance in various settings.Comment: NeurIPS 2023 (spotlight

Towards Arbitrary-scale Histopathology Image Super-resolution: An Efficient Dual-branch Framework based on Implicit Self-texture Enhancement

Existing super-resolution models for pathology images can only work in fixed integer magnifications and have limited performance. Though implicit neural network-based methods have shown promising results in arbitrary-scale super-resolution of natural images, it is not effective to directly apply them in pathology images, because pathology images have special fine-grained image textures different from natural images. To address this challenge, we propose a dual-branch framework with an efficient self-texture enhancement mechanism for arbitrary-scale super-resolution of pathology images. Extensive experiments on two public datasets show that our method outperforms both existing fixed-scale and arbitrary-scale algorithms. To the best of our knowledge, this is the first work to achieve arbitrary-scale super-resolution in the field of pathology images. Codes will be available

Effect of steam hydration on reactivity and strength of cement-supported calcium sorbents for CO2 capture

Steam hydration was used to reactivate spent cement-supported CO2 sorbent pellets for recycle and the effect of steam hydration on the reactivity of sorbents was investigated in a bubbling fluidised reactor. A specially designed impact apparatus was developed to evaluate the strength of the reactivated pellets as well as determine the effect of “superheating”. It was found that the reactivity of synthetic pellets was significantly elevated over that of raw limestone after steam hydration. The CaO conversion of spent pellets increased from 0.113 to 0.419 after hydration, whereas that of spent limestone ranged from 0.089 to 0.278. The CaO conversions of hydrated samples calcined under different conditions achieved the identical level, proportional to the degree of hydration. As expected, the mechanical strength of synthetic pellets declined severely after reactivation. Large cracks emerged on hydrated limestone as seen in scanning electron microscope images. By contrast, similar cracks were not observed for synthetic pellets after hydration, although hydration did produce higher porosity than seen with limestone and an increased surface area, which enhanced CO2 capacity and was associated with an increase in strength loss. The breakage rate of superheated steam-reactivated limestone derived pellets was about half that of hydrated samples. This demonstrates that superheating treatment (which allows the annealing of stacking faults and mechanical strain produced by hydration) enhances the strength of hydrated pellets. This work demonstrated that combining steam hydration with superheating can both reactivate the spent synthetic pellets and reduce strength decay associated with the hydration process

Separate and Conquer: Decoupling Co-occurrence via Decomposition and Representation for Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation (WSSS) with image-level labels aims to achieve segmentation tasks without dense annotations. However, attributed to the frequent coupling of co-occurring objects and the limited supervision from image-level labels, the challenging co-occurrence problem is widely present and leads to false activation of objects in WSSS. In this work, we devise a 'Separate and Conquer' scheme SeCo to tackle this issue from dimensions of image space and feature space. In the image space, we propose to 'separate' the co-occurring objects with image decomposition by subdividing images into patches. Importantly, we assign each patch a category tag from Class Activation Maps (CAMs), which spatially helps remove the co-context bias and guide the subsequent representation. In the feature space, we propose to 'conquer' the false activation by enhancing semantic representation with multi-granularity knowledge contrast. To this end, a dual-teacher-single-student architecture is designed and tag-guided contrast is conducted, which guarantee the correctness of knowledge and further facilitate the discrepancy among co-contexts. We streamline the multi-staged WSSS pipeline end-to-end and tackle this issue without external supervision. Extensive experiments are conducted, validating the efficiency of our method and the superiority over previous single-staged and even multi-staged competitors on PASCAL VOC and MS COCO. Code is available at https://github.com/zwyang6/SeCo.git.Comment: Accepted by CVPR 202

Attrition study of cement-supported biomass-activated calcium sorbents for CO2 capture

Enhanced CO2 capacity of biomass modified Ca-based sorbent has been reported recently, but undesired attrition resistance has also been observed. Cement was used as a support for biomass-activated calcium sorbent during the granulation process in this study, in order to improve the poor mechanical resistance. Attrition tests were carried out in an apparatus focused on impact breakage to evaluate how the biomass addition and cement support influence the particle strength during Ca-looping. Results showed biomass addition impaired the mechanical strength and cement support could improve it, which is reflected by the breakage probability and size change after impact of pellets experienced calcination and multiple calcination/carbonation cycles. Larger-sized particles suffered more intense attrition. The mechanical strength of sorbents declined significantly after higher temperature calcination but increased after carbonation. After multiple cycles, the mechanical strength of particles was greatly enhanced, but more cracks emerged. A semi-empirical formula for calculating average diameter after attrition based on Rittinger’s surface theory was developed. Observation on the morphology of particles indicated that particles with more porosity and cracks were more prone to breakage

Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets

In online ad markets, a rising number of advertisers are employing bidding agencies to participate in ad auctions. These agencies are specialized in designing online algorithms and bidding on behalf of their clients. Typically, an agency usually has information on multiple advertisers, so she can potentially coordinate bids to help her clients achieve higher utilities than those under independent bidding. In this paper, we study coordinated online bidding algorithms in repeated second-price auctions with budgets. We propose algorithms that guarantee every client a higher utility than the best she can get under independent bidding. We show that these algorithms achieve maximal coalition welfare and discuss bidders' incentives to misreport their budgets, in symmetric cases. Our proofs combine the techniques of online learning and equilibrium analysis, overcoming the difficulty of competing with a multi-dimensional benchmark. The performance of our algorithms is further evaluated by experiments on both synthetic and real data. To the best of our knowledge, we are the first to consider bidder coordination in online repeated auctions with constraints.Comment: 43 pages, 12 figure