This paper explores the possibility of extending the capa-bility of pre-trained neural image compressors (e.g., adapting to new data or target bitrates) without breaking back-ward compatibility, the ability to decode bitstreams encoded by the original model. We refer to this problem as continual learning of image compression. Our initial findings show that baseline solutions, such as end-to-end fine-tuning, do not preserve the desired backward compatibility. To tackle this, we propose a knowledge replay training strategy that effectively addresses this issue. We also design a new model architecture that enables more effective continual learning than existing baselines. Experiments are conducted for two scenarios: data-incremental learning and rate-incremental learning. The main conclusion of this paper is that neural image compressors can be fine-tuned to achieve better per-formance (compared to their pre-trained version) on new data and rates without compromising backward compati-bility. The code is publicly available online.

Image compression emerges as a pivotal tool in the efficient handling and transmission of digital images. Its ability to substantially reduce file size not only facilitates enhanced data storage capacity but also potentially brings advantages to the development of continual machine learning (ML) systems, which learn new knowledge incrementally from sequential data. Continual ML systems often rely on storing representative samples, also known as exemplars, within a limited memory constraint to maintain the performance on previously learned data. These methods are known as memory replay-based algorithms and have proven effective at mitigating the detrimental effects of catastrophic forgetting. Nonetheless, the limited memory buffer size often falls short of adequately representing the entire data distribution. In this paper, we explore the use of image compression as a strategy to enhance the buffer's capacity, thereby increasing exemplar diversity. However, directly using compressed exemplars introduces domain shift during continual ML, marked by a discrepancy between compressed training data and uncompressed testing data. Additionally, it is essential to determine the appropriate compression algorithm and select the most effective rate for continual ML systems to balance the trade-off between exemplar quality and quantity. To this end, we introduce a new framework to incorporate image compression for continual ML including a pre-processing data compression step and an efficient compression rate/algorithm selection method. We conduct extensive experiments on CIFAR-100 and ImageNet datasets and show that our method significantly improves image classification accuracy in continual ML settings.

OBJECTIVES: To investigate approaches of reasoning with large language models (LLMs) and to propose a new prompting approach, ensemble reasoning, to improve medical question answering performance with refined reasoning and reduced inconsistency. MATERIALS AND METHODS: We used multiple choice questions from the USMLE Sample Exam question files on 2 closed-source commercial and 1 open-source clinical LLM to evaluate our proposed approach ensemble reasoning. RESULTS: On GPT-3.5 turbo and Med42-70B, our proposed ensemble reasoning approach outperformed zero-shot chain-of-thought with self-consistency on Steps 1, 2, and 3 questions (+3.44%, +4.00%, and +2.54%) and (2.3%, 5.00%, and 4.15%), respectively. With GPT-4 turbo, there were mixed results with ensemble reasoning again outperforming zero-shot chain-of-thought with self-consistency on Step 1 questions (+1.15%). In all cases, the results demonstrated improved consistency of responses with our approach. A qualitative analysis of the reasoning from the model demonstrated that the ensemble reasoning approach produces correct and helpful reasoning. CONCLUSION: The proposed iterative ensemble reasoning has the potential to improve the performance of LLMs in medical question answering tasks, particularly with the less powerful LLMs like GPT-3.5 turbo and Med42-70B, which may suggest that this is a promising approach for LLMs with lower capabilities. Additionally, the findings show that our approach helps to refine the reasoning generated by the LLM and thereby improve consistency even with the more powerful GPT-4 turbo. We also identify the potential and need for human-artificial intelligence teaming to improve the reasoning beyond the limits of the model.

Recent years witnessed a tremendous growth in the area of mobile computing. Users with mobile devices are able to access services customized to their geographical coordinates, and to engage in complex interactions with other users in their proximity. However, in addition to its many benefits, sharing location with service providers and other users also introduces serious privacy threats. If not properly addressed, the loss of location privacy can bring significant harm to mobile users. Currently, there is a low level of awareness among mobile users with respect to the contingent threats on location privacy, and to the approaches available to mitigate such threats. We propose an educational capture-the-flag (CTF) - style tool designed to raise the level of awareness about the dangers of uncontrolled sharing of location data, and to illustrate prominent location protection techniques. The game-based approach represents an effective and engaging educational tool, suitable for high-school and college students, as well as computer-literate general population mobile users.

Exchangeable graph models (ExGM) are a nonparametric approach to modeling network data that subsumes a number of popular models. The key object that defines an ExGM is often referred to as a graphon, or graph kernel. Here, we make three contributions to advance the theory of estimation of graphons. We determine conditions under which a unique canonical representation for a graphon exists and it is identifiable. We propose a 3-step procedure to estimate the canonical graphon of any ExGM that satisfies these conditions. We then focus on a specific estimator, built using the proposed 3-step procedure, which combines probability matrix estimation by Universal Singular Value Thresholding (USVT) and empirical degree sorting of the observed adjacency matrix. We prove that this estimator is consistent. We illustrate how the proposed theory and methods can be used to develop hypothesis testing procedures for models of network data.