Scalable Signal Temporal Logic Guided Reinforcement Learning via Value Function Space Optimization

The integration of reinforcement learning (RL) and formal methods has emerged as a promising framework for solving long-horizon planning problems. Conventional approaches typically involve abstraction of the state and action spaces and manually created labeling functions or predicates. However, the efficiency of these approaches deteriorates as the tasks become increasingly complex, which results in exponential growth in the size of labeling functions or predicates. To address these issues, we propose a scalable model-based RL framework, called VFSTL, which schedules pre-trained skills to follow unseen STL specifications without using hand-crafted predicates. Given a set of value functions obtained by goal-conditioned RL, we formulate an optimization problem to maximize the robustness value of Signal Temporal Logic (STL) defined specifications, which is computed using value functions as predicates. To further reduce the computation burden, we abstract the environment state space into the value function space (VFS). Then the optimization problem is solved by Model-Based Reinforcement Learning. Simulation results show that STL with value functions as predicates approximates the ground truth robustness and the planning in VFS directly achieves unseen specifications using data from sensors

Pickering emulsions for stimuli-responsive transdermal drug delivery: effect of rheology and microstructure on performance

This work investigates the design of stimuli-responsive Pickering emulsions (PEs) for transdermal drug delivery applications, by exploring the impact of stabilising microgels size and interactions on their rheological and release properties. Temperature-responsive poly(N-isopropylacrylamide) microgels modified with 1-benzyl-3-vinylimidazolium bromide (pNIPAM-co-BVI) are synthesized in varying sizes and used to stabilise jojoba oil-in-water concentrated emulsions. The results reveals two distinct behaviours: for small microgels (∼300 nm), the PEs exhibit a smooth, uniform structure characterised by a mild yield stress, characteristic of soft glassy systems. Conversely, larger microgels (∼800 nm) induce droplet clustering, resulting in increased elasticity and a more complex yielding process. Interestingly, transdermal delivery tests demonstrate that microstructure, rather than bulk rheology, governs sustained drug release. The release process can be modelled as diffusion-controlled transport through a porous medium with random traps. At room temperature, the trap size corresponds to the droplet size, and the release time scales with the total dispersed phases volume fraction. However, at physiological temperature (37 °C), above the volume-phase transition temperature of the microgels, the release time increases significantly. The trap size approaches the microgel size, suggesting that microgel porosity becomes the dominant factor controlling drug release. Overall, the results highlight the critical role of microstructure design in optimising stimuli-responsive PEs for controlled transdermal drug delivery

On the Evolution of (Hateful) Memes by Means of Multimodal Contrastive Learning

The dissemination of hateful memes online has adverse effects on social media platforms and the real world. Detecting hateful memes is challenging, one of the reasons being the evolutionary nature of memes; new hateful memes can emerge by fusing hateful connotations with other cultural ideas or symbols. In this paper, we propose a framework that leverages multimodal contrastive learning models, in particular OpenAI's CLIP, to identify targets of hateful content and systematically investigate the evolution of hateful memes. We find that semantic regularities exist in CLIP-generated embeddings that describe semantic relationships within the same modality (images) or across modalities (images and text). Leveraging this property, we study how hateful memes are created by combining visual elements from multiple images or fusing textual information with a hateful image. We demonstrate the capabilities of our framework for analyzing the evolution of hateful memes by focusing on antisemitic memes, particularly the Happy Merchant meme. Using our framework on a dataset extracted from 4chan, we find 3.3K variants of the Happy Merchant meme, with some linked to specific countries, persons, or organizations. We envision that our framework can be used to aid human moderators by flagging new variants of hateful memes so that moderators can manually verify them and mitigate the problem of hateful content online.Comment: To Appear in the 44th IEEE Symposium on Security and Privacy, May 22-25, 202

Asymmetric bounded neural control for an uncertain robot by state feedback and output feedback

In this paper, an adaptive neural bounded control scheme is proposed for an n-link rigid robotic manipulator with unknown dynamics. With the combination of the neural approximation and backstepping technique, an adaptive neural network control policy is developed to guarantee the tracking performance of the robot. Different from the existing results, the bounds of the designed controller are known a priori, and they are determined by controller gains, making them applicable within actuator limitations. Furthermore, the designed controller is also able to compensate the effect of unknown robotic dynamics. Via the Lyapunov stability theory, it can be proved that all the signals are uniformly ultimately bounded. Simulations are carried out to verify the effectiveness of the proposed scheme

Parallel Spiking Unit for Efficient Training of Spiking Neural Networks

Efficient parallel computing has become a pivotal element in advancing artificial intelligence. Yet, the deployment of Spiking Neural Networks (SNNs) in this domain is hampered by their inherent sequential computational dependency. This constraint arises from the need for each time step's processing to rely on the preceding step's outcomes, significantly impeding the adaptability of SNN models to massively parallel computing environments. Addressing this challenge, our paper introduces the innovative Parallel Spiking Unit (PSU) and its two derivatives, the Input-aware PSU (IPSU) and Reset-aware PSU (RPSU). These variants skillfully decouple the leaky integration and firing mechanisms in spiking neurons while probabilistically managing the reset process. By preserving the fundamental computational attributes of the spiking neuron model, our approach enables the concurrent computation of all membrane potential instances within the SNN, facilitating parallel spike output generation and substantially enhancing computational efficiency. Comprehensive testing across various datasets, including static and sequential images, Dynamic Vision Sensor (DVS) data, and speech datasets, demonstrates that the PSU and its variants not only significantly boost performance and simulation speed but also augment the energy efficiency of SNNs through enhanced sparsity in neural activity. These advancements underscore the potential of our method in revolutionizing SNN deployment for high-performance parallel computing applications

Inhibition of miR-665 alleviates lipopolysaccharide-induced inflammation via up-regulation of SOCS7 in chondrogenic ATDC5 cells

Purpose: To examine the effect and mechanism of action of miR-665 in osteoarthritis.Methods: An in vitro inflammatory injury model of osteoarthritis was established using chondrogenic ATDC5 cells with lipopolysaccharide (LPS) treatment. The expression levels of inflammatory cytokines were determined by enzyme-linked immunosorbent assays (ELISAs) and by quantitative real-time polymerase chain reaction (qRT-PCR). A binding target for miR-665 was predicted using TargetScan and then evaluated using a dual-luciferase reporter assay.Results: Treatment with LPS significantly up-regulated the inflammatory cytokine expressions of interleukin-1β (IL-1β), IL-6, and tumor necrosis factor-alpha (TNF-α), in ATDC5 cells (p < 0.01), and the expression of miRNA-665 was significantly increased in LPS-treated ATDC5 cells (p < 0.01).Knockdown of miR-665 down-regulated the expression levels of these inflammatory cytokines. Suppressor of cytokine signaling 7 (SOCS7) was identified as a target of miR-665. Data from qRT-PCR and western-blot analyses indicated that SOCS7 expression was promoted by miR-665  inhibition and inhibited by miR-665 over-expression. LPS treatment significantly decreased the expression of SOCS7 protein in ATDC5 cells (p < 0.01), and over-expression of SOCS7 attenuated the LPS-stimulated inflammatory injury. In addition, over-expression of miR-655 enhanced the inflammatory injury and reversed the protective effect of SOCS7 against LPS-stimulated inflammation.Conclusion: Inhibition of miR-665 alleviated LPS-stimulated inflammatory injury in ATDC5 cells via the up-regulation of SOCS7, suggesting a potential therapeutic target for osteoarthritis. Keywords: MiR-665, Lipopolysaccharide, Inflammation, SOCS7, Chondrogenic, ATDC

Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes From Text-To-Image Models

State-of-the-art Text-to-Image models like Stable Diffusion and DALLE$\cdot$2 are revolutionizing how people generate visual content. At the same time, society has serious concerns about how adversaries can exploit such models to generate unsafe images. In this work, we focus on demystifying the generation of unsafe images and hateful memes from Text-to-Image models. We first construct a typology of unsafe images consisting of five categories (sexually explicit, violent, disturbing, hateful, and political). Then, we assess the proportion of unsafe images generated by four advanced Text-to-Image models using four prompt datasets. We find that these models can generate a substantial percentage of unsafe images; across four models and four prompt datasets, 14.56% of all generated images are unsafe. When comparing the four models, we find different risk levels, with Stable Diffusion being the most prone to generating unsafe content (18.92% of all generated images are unsafe). Given Stable Diffusion's tendency to generate more unsafe content, we evaluate its potential to generate hateful meme variants if exploited by an adversary to attack a specific individual or community. We employ three image editing methods, DreamBooth, Textual Inversion, and SDEdit, which are supported by Stable Diffusion. Our evaluation result shows that 24% of the generated images using DreamBooth are hateful meme variants that present the features of the original hateful meme and the target individual/community; these generated images are comparable to hateful meme variants collected from the real world. Overall, our results demonstrate that the danger of large-scale generation of unsafe images is imminent. We discuss several mitigating measures, such as curating training data, regulating prompts, and implementing safety filters, and encourage better safeguard tools to be developed to prevent unsafe generation.Comment: To Appear in the ACM Conference on Computer and Communications Security, November 26, 202