Practical algorithms and experimentally validated incentives for equilibrium-based fair division (A-CEEI)

Approximate Competitive Equilibrium from Equal Incomes (A-CEEI) is an equilibrium-based solution concept for fair division of discrete items to agents with combinatorial demands. In theory, it is known that in asymptotically large markets: 1. For incentives, the A-CEEI mechanism is Envy-Free-but-for-Tie-Breaking (EF-TB), which implies that it is Strategyproof-in-the-Large (SP-L). 2. From a computational perspective, computing the equilibrium solution is unfortunately a computationally intractable problem (in the worst-case, assuming $\textsf{PPAD}\ne \textsf{FP}$). We develop a new heuristic algorithm that outperforms the previous state-of-the-art by multiple orders of magnitude. This new, faster algorithm lets us perform experiments on real-world inputs for the first time. We discover that with real-world preferences, even in a realistic implementation that satisfies the EF-TB and SP-L properties, agents may have surprisingly simple and plausible deviations from truthful reporting of preferences. To this end, we propose a novel strengthening of EF-TB, which dramatically reduces the potential for strategic deviations from truthful reporting in our experiments. A (variant of) our algorithm is now in production: on real course allocation problems it is much faster, has zero clearing error, and has stronger incentive properties than the prior state-of-the-art implementation.Comment: To appear in EC 202

Federated Linear Contextual Bandits with User-level Differential Privacy

This paper studies federated linear contextual bandits under the notion of user-level differential privacy (DP). We first introduce a unified federated bandits framework that can accommodate various definitions of DP in the sequential decision-making setting. We then formally introduce user-level central DP (CDP) and local DP (LDP) in the federated bandits framework, and investigate the fundamental trade-offs between the learning regrets and the corresponding DP guarantees in a federated linear contextual bandits model. For CDP, we propose a federated algorithm termed as \robin and show that it is near-optimal in terms of the number of clients $M$ and the privacy budget $\varepsilon$ by deriving nearly-matching upper and lower regret bounds when user-level DP is satisfied. For LDP, we obtain several lower bounds, indicating that learning under user-level $(\varepsilon,\delta)$-LDP must suffer a regret blow-up factor at least {$\min\{1/\varepsilon,M\}$ or $\min\{1/\sqrt{\varepsilon},\sqrt{M}\}$} under different conditions.Comment: Accepted by ICML 202

A novel untrained SSVEP-EEG feature enhancement method using canonical correlation analysis and underdamped second-order stochastic resonance

ObjectiveCompared with the light-flashing paradigm, the ring-shaped motion checkerboard patterns avoid uncomfortable flicker or brightness modulation, improving the practical interactivity of brain-computer interface (BCI) applications. However, due to fewer harmonic responses and more concentrated frequency energy elicited by the ring-shaped checkerboard patterns, the mainstream untrained algorithms such as canonical correlation analysis (CCA) and filter bank canonical correlation analysis (FBCCA) methods have poor recognition performance and low information transmission rate (ITR).MethodsTo address this issue, a novel untrained SSVEP-EEG feature enhancement method using CCA and underdamped second-order stochastic resonance (USSR) is proposed to extract electroencephalogram (EEG) features.ResultsIn contrast to typical unsupervised dimensionality reduction methods such as common average reference (CAR), principal component analysis (PCA), multidimensional scaling (MDS), and locally linear embedding (LLE), CCA exhibits higher adaptability for SSVEP rhythm components.ConclusionThis study recruits 42 subjects to evaluate the proposed method and experimental results show that the untrained method can achieve higher detection accuracy and robustness.SignificanceThis untrained method provides the possibility of applying a nonlinear model from one-dimensional signals to multi-dimensional signals

UWAT-GAN: Fundus Fluorescein Angiography Synthesis via Ultra-wide-angle Transformation Multi-scale GAN

Fundus photography is an essential examination for clinical and differential diagnosis of fundus diseases. Recently, Ultra-Wide-angle Fundus (UWF) techniques, UWF Fluorescein Angiography (UWF-FA) and UWF Scanning Laser Ophthalmoscopy (UWF-SLO) have been gradually put into use. However, Fluorescein Angiography (FA) and UWF-FA require injecting sodium fluorescein which may have detrimental influences. To avoid negative impacts, cross-modality medical image generation algorithms have been proposed. Nevertheless, current methods in fundus imaging could not produce high-resolution images and are unable to capture tiny vascular lesion areas. This paper proposes a novel conditional generative adversarial network (UWAT-GAN) to synthesize UWF-FA from UWF-SLO. Using multi-scale generators and a fusion module patch to better extract global and local information, our model can generate high-resolution images. Moreover, an attention transmit module is proposed to help the decoder learn effectively. Besides, a supervised approach is used to train the network using multiple new weighted losses on different scales of data. Experiments on an in-house UWF image dataset demonstrate the superiority of the UWAT-GAN over the state-of-the-art methods. The source code is available at: https://github.com/Tinysqua/UWAT-GAN.Comment: 26th International Conference on Medical Image Computing and Computer Assisted Interventio

Experimental Study, Characterization and Application of Starch-Graft-Acrylamide Gel for Plugging

During underbalance drilling, completion and workover wells, plugging channeling, blocking preformation and plugging formation water are inevitable problems. Gel is one of the most effective and convenient method to solve the problem. In this study, modified starch gel is synthesized, investigated experimentally and improved for efficient oil and gas field applications. The gel slurry is composed of starch (3.6 wt.%), initiator (0.02 wt.%), acrylamide (14.4 wt.%), cross-linking agent (4.7 wt.%), all of the components are mixed together with water at pH 10 – 11 which viscosity is as low as 35 – 82 mPa.s and desired to form gel. Here the effects of the components, reaction temperature and pH on gelation time and gel viscosity are systematically investigated, and the results showed that the gelation can be controlled in a wide range 30 – 120 min efficiently by pH and initiator. Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscope (SEM) are employed to study the molecular structure and microstructure of the gel, respectively. A compact three-dimensional network structure was formed in the gel, which contribute to a good adhesion. The gel has been successfully used in shale gas field which provides a reference for sealing other similar high formation pressure under unbalanced workover treatment. DOI: http://dx.doi.org/10.5755/j01.ms.24.4.18565</p

Engineering Heterologous Production of Salicylate Glucoside and Glycosylated Variants

Salicylate 2-O-β-D-glucoside (SAG) is a plant-derived natural product with potential utility as both an anti-inflammatory and as a plant protectant compound. Heterologous biosynthesis of SAG has been established in Escherichia coli through metabolic engineering of the shikimate pathways and introduction of a heterologous biosynthetic step to allow a more directed route to the salicylate precursor. The final SAG compound resulted from the separate introduction of an Arabidopsis thaliana glucosyltransferase enzyme. In this study, a range of heterologous engineering parameters were varied (including biosynthetic pathway construction, expression plasmid, and E. coli strain) for the improvement of SAG specific production in conjunction with a system demonstrating improved plasmid stability. In addition, the glucoside moiety of SAG was systematically varied through the introduction of the heterologous oliose and olivose deoxysugar pathways. Production of analogs was observed for each newly constructed pathway, demonstrating biosynthetic diversification potential; however, production titers were reduced relative to the original SAG compound

Revolutionizing Fuel Cell Efficiency with Non-Metallic Catalysts for Oxygen Reduction Reactions

Platinum-based catalysts are widely used in oxygen reduction reactions, but platinum’s high cost and low reserves have restricted their sustainable development. With continuous in-depth research, it has been found that metal-free catalysts also have better catalytic activity in oxygen reduction reactions and have great potential for development due to the low cost and abundant reserves of metal-free catalysts, which has become a hot research direction. This paper reviews the application of metal-free catalysts in oxygen reduction reactions, including heteroatom-doped carbon-based catalysts, polymeric nitrogen catalysts, and emerging carbon catalysts. This work provides insights into developing non-platinum catalysts for oxygen reduction reactions by comparing the catalytic activity, selectivity, and prolonged stability

Recent Progress of Non-Pt Catalysts for Oxygen Reduction Reaction in Fuel Cells

In recent years, non-Pt-based ORR catalysts have been developing rapidly and have achieved performance comparable to or even surpassing Pt precious metal catalysts in specific reactions, offering new possibilities for Pt-based catalyst replacement and showing great promise for application. This paper reviews the recent research progress of non-Pt-based fuel cell ORR catalysts. The latest research progress of non-Pt-based ORR SACs (including single metal active site ORR SACs, multi-metal active site ORR SACs, and non-Pt-based noble metal catalyst ORR SACs), non-metallic ORR catalysts, alloy-based ORR catalysts, high-entropy alloy ORR catalysts, and other non-Pt-based fuel cell ORR catalysts are presented in detail. This paper discusses in detail the synthesis methods, characterization means, optimization of performance, and application prospects of these non-Pt-based ORR catalysts. In addition, this review details the excellent performance of these catalysts in terms of compositional and structural controllability, electrical conductivity, and chemical stability, as well as their ability to exhibit ORR activity comparable to that of commercial Pt/C catalysts. This field is full of opportunities and challenges. In summary, non-Pt-based fuel cells show great potential in ORR. With the continuous improvement of preparation and characterization technologies, catalysts have broad application and market prospects. In addition, the development trend of non-precious metal fuel cell catalysts is reviewed

Recent Progress of Non-Pt Catalysts for Oxygen Reduction Reaction in Fuel Cells

In recent years, non-Pt-based ORR catalysts have been developing rapidly and have achieved performance comparable to or even surpassing Pt precious metal catalysts in specific reactions, offering new possibilities for Pt-based catalyst replacement and showing great promise for application. This paper reviews the recent research progress of non-Pt-based fuel cell ORR catalysts. The latest research progress of non-Pt-based ORR SACs (including single metal active site ORR SACs, multi-metal active site ORR SACs, and non-Pt-based noble metal catalyst ORR SACs), non-metallic ORR catalysts, alloy-based ORR catalysts, high-entropy alloy ORR catalysts, and other non-Pt-based fuel cell ORR catalysts are presented in detail. This paper discusses in detail the synthesis methods, characterization means, optimization of performance, and application prospects of these non-Pt-based ORR catalysts. In addition, this review details the excellent performance of these catalysts in terms of compositional and structural controllability, electrical conductivity, and chemical stability, as well as their ability to exhibit ORR activity comparable to that of commercial Pt/C catalysts. This field is full of opportunities and challenges. In summary, non-Pt-based fuel cells show great potential in ORR. With the continuous improvement of preparation and characterization technologies, catalysts have broad application and market prospects. In addition, the development trend of non-precious metal fuel cell catalysts is reviewed