Matching-based Data Valuation for Generative Model

Data valuation is critical in machine learning, as it helps enhance model transparency and protect data properties. Existing data valuation methods have primarily focused on discriminative models, neglecting deep generative models that have recently gained considerable attention. Similar to discriminative models, there is an urgent need to assess data contributions in deep generative models as well. However, previous data valuation approaches mainly relied on discriminative model performance metrics and required model retraining. Consequently, they cannot be applied directly and efficiently to recent deep generative models, such as generative adversarial networks and diffusion models, in practice. To bridge this gap, we formulate the data valuation problem in generative models from a similarity-matching perspective. Specifically, we introduce Generative Model Valuator (GMValuator), the first model-agnostic approach for any generative models, designed to provide data valuation for generation tasks. We have conducted extensive experiments to demonstrate the effectiveness of the proposed method. To the best of their knowledge, GMValuator is the first work that offers a training-free, post-hoc data valuation strategy for deep generative models

TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering

Despite thousands of researchers, engineers, and artists actively working on improving text-to-image generation models, systems often fail to produce images that accurately align with the text inputs. We introduce TIFA (Text-to-Image Faithfulness evaluation with question Answering), an automatic evaluation metric that measures the faithfulness of a generated image to its text input via visual question answering (VQA). Specifically, given a text input, we automatically generate several question-answer pairs using a language model. We calculate image faithfulness by checking whether existing VQA models can answer these questions using the generated image. TIFA is a reference-free metric that allows for fine-grained and interpretable evaluations of generated images. TIFA also has better correlations with human judgments than existing metrics. Based on this approach, we introduce TIFA v1.0, a benchmark consisting of 4K diverse text inputs and 25K questions across 12 categories (object, counting, etc.). We present a comprehensive evaluation of existing text-to-image models using TIFA v1.0 and highlight the limitations and challenges of current models. For instance, we find that current text-to-image models, despite doing well on color and material, still struggle in counting, spatial relations, and composing multiple objects. We hope our benchmark will help carefully measure the research progress in text-to-image synthesis and provide valuable insights for further research

Intrinsically Elastic Organic Semiconductors (IEOSs)

Elastic semiconductors are becoming more and more important to the development of flexible wearable electronic devices, which can be prepared by structural engineering design, blending, and the intrinsic elastification of organic semiconductors (intrinsically elastic organic semiconductor, IEOS). Compared with the elastic semiconductors prepared by structural engineering and blending, the IEOS prepared by organic synthesis has attracted numerous attentions for its solution processability and highly tunable chemical structures. For IEOSs, reasonable designs of synthetic routes and methods are the basis for realizing good mechanical and electrical properties. This brief review begins with a concise introduction of elastic semiconductors, then follows with several synthetic methods of IEOSs, and concludes the characteristics of each method, which provides guidance for the synthesis of IEOSs in the future. Furthermore, the properties of IEOSs are involved from the aspects of electrical, mechanical properties, and the applications of the IEOSs in elastic electronic devices. Finally, the challenge and an outlook which IEOSs are facing are presented in conclusion

Multi-Proxy Wasserstein Classifier for Image Classification

Most widely-used convolutional neural networks (CNNs) end up with a global average pooling layer and a fully-connected layer. In this pipeline, a certain class is represented by one template vector preserved in the feature banks of fully-connected layer. Yet, a class may have multiple properties useful for recognition while the above formulation only captures one of them. Therefore, it is desired to represent a class by multiple proxies. However, directly adding multiple linear layers turns out to be a trivial solution as no improvement can be observed. To tackle this problem, we adopt optimal transport theory to calculate a non-uniform matching flow between the elements in the feature map of a sample and the proxies of a class in a closed way. By doing so, the models are enabled to achieve partial matching as both the feature maps and the proxy set can now focus on a subset of elements from the counterpart. Such formulation also enables us to embed the samples into the Wasserstein metric space, which has many advantages over the original Euclidean space. This formulation can be achieved by a lightweight iterative algorithm, which can be easily embedded into the automatic differentiation framework. Empirical studies are performed on two widely-used classification datasets, CIFAR, and ILSVRC2012, and the substantial improvements on these two benchmarks demonstrate the effectiveness of our method

Effects of Internal Heat Exchanger on Two-Stage Compression Trans-Critical CO₂ Refrigeration Cycle Combined with Expander and Intercooling

Because of the limitations of traditional refrigerants, the application of trans-critical CO2 technology in domestic gas conditioners and other fields is becoming increasingly popular. This paper proposes a new CO2 trans-critical refrigeration system. Combining the internal heat exchanger and expander components, as well as the two-stage compression cycle, we analyzed the effectiveness of the expander, internal heat exchanger, and intercooling on system performance under various operating conditions in terms of energy, exergy analysis, and optimal discharge pressure. The system performance can be changed by changing the cycle conditions and internal heat exchanger effectiveness, which reduces system power consumption and the percentage of exergy losses of gas cooler components. Compared to the single-stage compression with expander cycle, the systems cycle power consumption is reduced by 2–15.7% and the maximum system COP is increased by 2.93–6.93%. From the view of energy effectiveness, the system’s maximum COP increases by 3.9% and the percentage of exergy losses of gas cooler decreases by 22.5% with the effectiveness of internal heat exchanger varying. The addition of an internal heat exchanger has resulted in improved system performance, which is important for providing a relevant cycle model for the application

Resistive Switching Memories: Observation of Conductance Quantization in Oxide‐Based Resistive Switching Memory (Adv. Mater. 29/2012)

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/92357/1/22220_ftp.pd