To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images ... For Now

The recent advances in diffusion models (DMs) have revolutionized the generation of complex and diverse images. However, these models also introduce potential safety hazards, such as the production of harmful content and infringement of data copyrights. Although there have been efforts to create safety-driven unlearning methods to counteract these challenges, doubts remain about their capabilities. To bridge this uncertainty, we propose an evaluation framework built upon adversarial attacks (also referred to as adversarial prompts), in order to discern the trustworthiness of these safety-driven unlearned DMs. Specifically, our research explores the (worst-case) robustness of unlearned DMs in eradicating unwanted concepts, styles, and objects, assessed by the generation of adversarial prompts. We develop a novel adversarial learning approach called UnlearnDiff that leverages the inherent classification capabilities of DMs to streamline the generation of adversarial prompts, making it as simple for DMs as it is for image classification attacks. This technique streamlines the creation of adversarial prompts, making the process as intuitive for generative modeling as it is for image classification assaults. Through comprehensive benchmarking, we assess the unlearning robustness of five prevalent unlearned DMs across multiple tasks. Our results underscore the effectiveness and efficiency of UnlearnDiff when compared to state-of-the-art adversarial prompting methods. Codes are available at https://github.com/OPTML-Group/Diffusion-MU-Attack. WARNING: This paper contains model outputs that may be offensive in nature.Comment: Codes are available at https://github.com/OPTML-Group/Diffusion-MU-Attac

Assessing spatiotemporal bikeability using multi-source geospatial big data:A case study of Xiamen, China

This study focuses on the development of a new framework for evaluating bikeability in urban environments with the aim of enhancing sustainable urban transportation planning. To close the research gap that previous studies have disregarded the dynamic environmental factors and trajectory data, we propose a framework that comprises four sub-indices: safety, comfort, accessibility, and vitality. Utilizing open-source data, advanced deep neural networks, and GIS spatial analysis, the framework eliminates subjective evaluations and is more efficient and comprehensive than prior methods. The experimental results on Xiamen, China, demonstrate the effectiveness of the framework in identifying areas for improvement and enhancing cycling mobility. The proposed framework provides a structured approach for evaluating bikeability in different geographical contexts, making reproducing bikeability indices easier and more comprehensive to policymakers, transportation planners, and environmental decision-makers.</p

Model Sparsification Can Simplify Machine Unlearning

Recent data regulations necessitate machine unlearning (MU): The removal of the effect of specific examples from the model. While exact unlearning is possible by conducting a model retraining with the remaining data from scratch, its computational cost has led to the development of approximate but efficient unlearning schemes. Beyond data-centric MU solutions, we advance MU through a novel model-based viewpoint: sparsification via weight pruning. Our results in both theory and practice indicate that model sparsity can boost the multi-criteria unlearning performance of an approximate unlearner, closing the approximation gap, while continuing to be efficient. With this insight, we develop two new sparsity-aware unlearning meta-schemes, termed `prune first, then unlearn' and `sparsity-aware unlearning'. Extensive experiments show that our findings and proposals consistently benefit MU in various scenarios, including class-wise data scrubbing, random data scrubbing, and backdoor data forgetting. One highlight is the 77% unlearning efficacy gain of fine-tuning (one of the simplest approximate unlearning methods) in the proposed sparsity-aware unlearning paradigm. Codes are available at https://github.com/OPTML-Group/Unlearn-Sparse

Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced Transfer Learning

Massive data is often considered essential for deep learning applications, but it also incurs significant computational and infrastructural costs. Therefore, dataset pruning (DP) has emerged as an effective way to improve data efficiency by identifying and removing redundant training samples without sacrificing performance. In this work, we aim to address the problem of DP for transfer learning, i.e., how to prune a source dataset for improved pretraining efficiency and lossless finetuning accuracy on downstream target tasks. To our best knowledge, the problem of DP for transfer learning remains open, as previous studies have primarily addressed DP and transfer learning as separate problems. By contrast, we establish a unified viewpoint to integrate DP with transfer learning and find that existing DP methods are not suitable for the transfer learning paradigm. We then propose two new DP methods, label mapping and feature mapping, for supervised and self-supervised pretraining settings respectively, by revisiting the DP problem through the lens of source-target domain mapping. Furthermore, we demonstrate the effectiveness of our approach on numerous transfer learning tasks. We show that source data classes can be pruned by up to 40% ~ 80% without sacrificing downstream performance, resulting in a significant 2 ~ 5 times speed-up during the pretraining stage. Besides, our proposal exhibits broad applicability and can improve other computationally intensive transfer learning techniques, such as adversarial pretraining. Codes are available at https://github.com/OPTML-Group/DP4TL.Comment: Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023

How better informed are the institutional investors?

We extend the EKOP model and estimate the probability of informed trading of institutions (SPIN) and individuals (DPIN) respectively. Using a unique dataset of Chinese stock market, we confirm that institutions are better informed by documenting a significantly higher SPIN.Probability of informed trading Institutional investors Individual investors

<b>Gut microbiota and thyroid diseases: a Mendelian randomization study</b>

Background Recent research indicates a potential association between gut flora and thyroid disease. Yet, the precise nature and direction of this hypothetical causal relationship remain elusive. This study aims to explore the potential causal association between gut microorganisms (GM) and various thyroid conditions, including Nontoxic goitre (NTG), Nontoxic multinodular goitre (NTMG), Thyrotoxicosis with toxic multinodular goitre (TTMG), and Thyroid Cancer (THCA). Methods Genome-wide association study (GWAS) statistics were pooled from MiBioGen and FinnGen for the assessment of gut flora and NTG/NTMG/TTMG/THCA. Various statistical methods including Inverse-Variance Weighting (IVW), MR-Egger regression, Weighted Median, Weighted Mode, Simple Mode, cML-MA, and MR-PRESSO were employed to assess the causal relationship between intestinal flora and NTG/NTMG/TTMG/THCA. Reverse Mendelian randomization (MR) analyses were conducted for intestinal flora identified as causally associated with NTG/NTMG/TTMG/THCA in the forward MR analysis. Furthermore, additional MR analysis was conducted after controlling for common confounders. Results Inverse-Variance Weighting revealed that the Rikenellaceae RC9 gut group (OR=1.144, 95% CI: 1.061–1.234, P<0.001, PFDR=0.095) posed a risk for NTG, while Bifidobacterium (OR=0.747, 95% CI: 0.635–0.878, P<0.001, PFDR=0.075) exhibited a protective effect for NTMG. Additionally, Dorea (OR=2.070, 95% CI: 1.343–3.912, P<0.001, PFDR=0.095) and Phascolarctobacterium (OR=2.287, 95% CI: 1.581–3.308, P<0.001, PFDR=0.002) were identified as risk factors for TTMG, while Family XI (OR=0.711, 95% CI: 0.588–0.859, P<0.001, PFDR=0.078) was protective against THCA. Reverse MR analysis showed no significant causal effect of NTG/NTMG/TTMG/THCA on gut microbiota, and no notable heterogeneity or horizontal pleiotropy of instrumental variables was detected. Conclusions This two-sample Mendelian randomization study identified causal associations between Rikenellaceae RC9 gut group, Bifidobacterium, Dorea, Phascolarctobacterium, and Family XI, and NTG/NTMG/TTMG/THCA. Further randomized controlled trials are warranted to clarify the impact of intestinal flora on thyroid disease and its underlying mechanisms.</p

Unveiling the dynamic active site of defective carbon-based electrocatalysts for hydrogen peroxide production

Active sites identification in metal-free carbon materials is crucial for developing practical electrocatalysts, but resolving precise configuration of active site remains a challenge because of the elusive dynamic structural evolution process during reactions. Here, we reveal the dynamic active site identification process of oxygen modified defective graphene. First, the defect density and types of oxygen groups were precisely manipulated on graphene, combined with electrocatalytic performance evaluation, revealing a previously overlooked positive correlation relationship between the defect density and the 2 e- oxygen reduction performance. An electrocatalytic-driven oxygen groups redistribution phenomenon was observed, which narrows the scope of potential configurations of the active site. The dynamic evolution processes are monitored via multiple in-situ technologies and theoretical spectra simulations, resolving the configuration of major active sites (carbonyl on pentagon defect) and key intermediates (*OOH), in-depth understanding the catalytic mechanism and providing a research paradigm for metal-free carbon materials

Unveiling the dynamic active site of defective carbon-based electrocatalysts for hydrogen peroxide production

Active sites identification in metal-free carbon materials is crucial for developing practical electrocatalysts, but resolving precise configuration of active site remains a challenge because of the elusive dynamic structural evolution process during reactions. Here, we reveal the dynamic active site identification process of oxygen modified defective graphene. First, the defect density and types of oxygen groups were precisely manipulated on graphene, combined with electrocatalytic performance evaluation, revealing a previously overlooked positive correlation relationship between the defect density and the 2 e- oxygen reduction performance. An electrocatalytic-driven oxygen groups redistribution phenomenon was observed, which narrows the scope of potential configurations of the active site. The dynamic evolution processes are monitored via multiple in-situ technologies and theoretical spectra simulations, resolving the configuration of major active sites (carbonyl on pentagon defect) and key intermediates (*OOH), in-depth understanding the catalytic mechanism and providing a research paradigm for metal-free carbon materials.</p