Miracle-dz/UniFAR-FTUs huggingface dataset:
Divide-and-Conquer Attack: Harnessing the Power of LLM to Bypass the Censorship of Text-to-Image Generation Model

—Text-to-image generative models offer many innovative services but also raise ethical concerns due to their potential to generate unethical images. Most publicly available text-to-image models employ safety filters to prevent unintended generation intents. In this work, we introduce the Divide-and-Conquer Attack to circumvent the safety filters of state-of-the-art text-to-image models. Our attack leverages LLMs as agents for text transformation, creating adversarial prompts from sensitive ones. We have developed effective helper prompts that enable LLMs to break down sensitive drawing prompt into multiple harmless descriptions, allowing them to bypass safety filters while still generating sensitive images. This means that the latent harmful meaning only becomes apparent when all individual elements are drawn together. Our evaluation demonstrates that our attack successfully circumvents the closed-box safety filter of SOTA DALL · E 3 integrated natively into ChatGPT to generate unethical images. This approach, which essentially uses LLM-generated adversarial prompts against GPT-4-assisted DALL · E 3, is akin to using one’s own spear to breach their shield. It could have more severe security implications than previous manual crafting or iterative model querying methods, and we hope it stimulates more attention towards similar efforts. Our code and data are available at: https://github.com/researchcode001/Divide-and-Conquer-Attack.
LLMs Perform Poorly at Concept Extraction in Cyber-security Research Literature

The cybersecurity landscape evolves rapidly and poses threats to organizations. To enhance resilience, one needs to track the latest developments and trends in the domain. It has been demonstrated that standard bibliometrics approaches show their limits in such a fast-evolving domain. For this purpose, we use large language models (LLMs) to extract relevant knowledge entities from cybersecurity-related texts. We use a subset of arXiv preprints on cybersecurity as our data and compare different LLMs in terms of entity recognition (ER) and relevance. The results suggest that LLMs do not produce good knowledge entities that reflect the cybersecurity context, but our results show some potential for noun extractors. For this reason, we developed a noun extractor boosted with some statistical analysis to extract specific and relevant compound nouns from the domain. Later, we tested our model to identify trends in the LLM domain. We observe some limitations, but it offers promising results to monitor the evolution of emergent trends.
UNeR3D: Versatile and Scalable 3D RGB Point Cloud Generation from 2D Images in Unsupervised Reconstruction

In the realm of 3D reconstruction from 2D images, a persisting challenge is to achieve high-precision reconstructions devoid of 3D Ground Truth data reliance. We present UNeR3D, a pioneering unsupervised methodology that sets a new standard for generating detailed 3D reconstructions solely from 2D views. Our model significantly cuts down the training costs tied to supervised approaches and introduces RGB coloration to 3D point clouds, enriching the visual experience. Employing an inverse distance weighting technique for color rendering, UNeR3D ensures seamless color transitions, enhancing visual fidelity. Our model's flexible architecture supports training with any number of views, and uniquely, it is not constrained by the number of views used during training when performing reconstructions. It can infer with an arbitrary count of views during inference, offering unparalleled versatility. Additionally, the model's continuous spatial input domain allows the generation of point clouds at any desired resolution, empowering the creation of high-resolution 3D RGB point clouds. We solidify the reconstruction process with a novel multi-view geometric loss and color loss, demonstrating that our model excels with single-view inputs and beyond, thus reshaping the paradigm of unsupervised learning in 3D vision. Our contributions signal a substantial leap forward in 3D vision, offering new horizons for content creation across diverse applications. Code is available at https://github.com/HongbinLin3589/UNeR3D.
Lightweight high-resolution Subject Matting in the Real World

Existing saliency object detection (SOD) methods struggle to satisfy fast inference and accurate results simultaneously in high resolution scenes. They are limited by the quality of public datasets and efficient network modules for high-resolution images. To alleviate these issues, we propose to construct a saliency object matting dataset HRSOM and a lightweight network PSUNet. Considering efficient inference of mobile depolyment framework, we design a symmetric pixel shuffle module and a lightweight module TRSU. Compared to 13 SOD methods, the proposed PSUNet has the best objective performance on the high-resolution benchmark dataset. Evaluation results of objective assessment are superior compared to U$^2$Net that has 10 times of parameter amount of our network. On Snapdragon 8 Gen 2 Mobile Platform, inference a single 640$\times$640 image only takes 113ms. And on the subjective assessment, evaluation results are better than the industry benchmark IOS16 (Lift subject from background).
Evolving Reservoirs for Meta Reinforcement Learning

Animals often demonstrate a remarkable ability to adapt to their environments during their lifetime. They do so partly due to the evolution of morphological and neural structures. These structures capture features of environments shared between generations to bias and speed up lifetime learning. In this work, we propose a computational model for studying a mechanism that can enable such a process. We adopt a computational framework based on meta reinforcement learning as a model of the interplay between evolution and development. At the evolutionary scale, we evolve reservoirs, a family of recurrent neural networks that differ from conventional networks in that one optimizes not the synaptic weights, but hyperparameters controlling macro-level properties of the resulting network architecture. At the developmental scale, we employ these evolved reservoirs to facilitate the learning of a behavioral policy through Reinforcement Learning (RL). Within an RL agent, a reservoir encodes the environment state before providing it to an action policy. We evaluate our approach on several 2D and 3D simulated environments. Our results show that the evolution of reservoirs can improve the learning of diverse challenging tasks. We study in particular three hypotheses: the use of an architecture combining reservoirs and reinforcement learning could enable (1) solving tasks with partial observability, (2) generating oscillatory dynamics that facilitate the learning of locomotion tasks, and (3) facilitating the generalization of learned behaviors to new tasks unknown during the evolution phase.
waveSLAM: Empowering Accurate Indoor Mapping Using Off-the-Shelf Millimeter-wave Self-sensing

This paper presents the design, implementation and evaluation of waveSLAM, a low-cost mobile robot system that uses the millimetre wave (mmWave) communication devices to enhance the indoor mapping process targeting environments with reduced visibility or glass/mirror walls. A unique feature of waveSLAM is that it only leverages existing Commercial-Off-The-Shelf (COTS) hardware (Lidar and mmWave radios) that are mounted on mobile robots to improve the accurate indoor mapping achieved with optical sensors. The key intuition behind the waveSLAM design is that while the mobile robots moves freely, the mmWave radios can periodically exchange angle and distance estimates between themselves (self-sensing) by bouncing the signal from the environment, thus enabling accurate estimates of the target object/material surface. Our experiments verify that waveSLAM can archive cm-level accuracy with errors below 22 cm and 20° in angle orientation which is compatible with Lidar when building indoor maps.
Edge Wasserstein Distance Loss for Oriented Object Detection

Regression loss design is an essential topic for oriented object detection. Due to the periodicity of the angle and the ambiguity of width and height definition, traditional L1-distance loss and its variants have been suffered from the metric discontinuity and the square-like problem. As a solution, the distribution based methods show significant advantages by representing oriented boxes as distributions. Differing from exploited the Gaussian distribution to get analytical form of distance measure, we propose a novel oriented regression loss, Wasserstein Distance(EWD) loss, to alleviate the square-like problem. Specifically, for the oriented box(OBox) representation, we choose a specially-designed distribution whose probability density function is only nonzero over the edges. On this basis, we develop Wasserstein distance as the measure. Besides, based on the edge representation of OBox, the EWD loss can be generalized to quadrilateral and polynomial regression scenarios. Experiments on multiple popular datasets and different detectors show the effectiveness of the proposed method.
Millennial processes of population decline, range contraction and near extinction of the European bison

European bison (Bison bonasus) were widespread throughout Europe during the late Pleistocene. However, the contributions of environmental change and humans to their near extinction have never been resolved. Using process-explicit models, fossils and ancient DNA, we disentangle the combinations of threatening processes that drove population declines and regional extinctions of European bison through space and across time. We show that the population size of European bison declined abruptly at the termination of the Pleistocene in response to rapid environmental change, hunting by humans and their interaction. Human activities prevented populations of European bison from rebounding in the Holocene, despite improved environmental conditions. Hunting caused range loss in the north and east of its distribution, while land use change was responsible for losses in the west and south. Advances in hunting technologies from 1500 CE were needed to simulate low abundances observed in 1870 CE. While our findings show that humans were an important driver of the extinction of the European bison in the wild, vast areas of its range vanished during the Pleistocene–Holocene transition because of post-glacial environmental change. These areas of its former range have been climatically unsuitable for millennia and should not be considered in reintroduction efforts.
Spectral wear modelling of rubber friction on a hard substrate with large surface roughness

Soft-hard matter friction is a long-standing tribology problem that remains unclarified, requiring engineers to empirically predict the wear life. To clarify this issue, this study examines the transient running-in regime of rubber friction on a hard rough substrate and models the temporal wear progression using the spectrum curves of surface roughness for both materials. Performing a series of friction tests and three-dimensional surface-height measurements, the time-dependent behaviours of the power spectral densities (PSDs) are divided into two phases, namely the initial non-steady and long-term steady phases. The detailed spectral analyses of worn rubber surfaces in the initial phase lead to a blended PSD function between self-affine and K-correlation surface models, consisting of one variable (the Hurst exponent) that is saturated by the substrate self-affinity. Supported by the Greenwood–Williamson theory concerning rough contact mechanics, the volumetric estimate with the blended PSD function is used to assess the volume rate of wear debris in the steady phase, which is validated experimentally. These findings not only improve the wear predictions of soft materials from the initial measurements of worn surfaces but also help clarify the constrained multiscale mechanism of wear.
Back in black: melanin-rich skin colour associated with increased net diversification rates in birds

Evolutionary biologists have long been interested in understanding the factors that promote diversification in organisms, often focussing on distinct and/or conspicuous phenotypes with direct effects on natural or sexual selection such as body size and plumage coloration. However, multiple traits that potentially influence net diversification are not conspicuous and/or might be concealed. One such trait, the dark, melanin-rich skin concealed beneath the feathers, evolved more than 100 times during avian evolution, frequently in association with white feathers on the crown and UV-rich environments, suggesting that it is a UV-photoprotective adaptation. Furthermore, multiple species are polymorphic, having both light and dark skin potentially aiding occupation in different UV radiation environments. As such these polymorphisms are predicted to occur in species with large latitudinal variation in their distribution. Furthermore, by alleviating evolutionary constraints on feather colour, the evolution of dark skin may promote net diversification. Here, using an expanded dataset on bird skin coloration of 3033 species we found that more than 19% of species had dark skin. In contrast to our prediction, dark skinned birds have smaller distribution ranges. Furthermore, both dark skin and polymorphism in skin coloration promote net diversification. These results suggest that even concealed traits can influence large scale evolutionary events such as diversification in birds.
PlAtg8-mediated autophagy regulates vegetative growth, sporangial cleavage, and pathogenesis in Peronophythora litchii

ABSTRACT Autophagy is a conserved cellular metabolic process in eukaryotes that transports damaged or redundant intracellular organelles and proteins to lysosomes or vacuoles for degradation and recycling. Atg8 is not only essential for autophagosome formation but also required for fungal development and pathogenicity, but its function is unknown in Peronophythora litchii. In this study, PlAtg8, a yeast Atg8 homolog, is deleted by the CRISPR/Cas9 system coupled with PEG-mediated protoplast transformation in P. litchii. Compared with the wild-type strain, the absence of PlAtg8 affects autophagosome formation. The colony growth rate, number of sporangia, and zoospore release rate are significantly decreased, and the oospores are undetected in ΔPlatg8. The pathogenicity test shows that the lesion areas on litchi leaves and fruits caused by ΔPlatg8 are significantly attenuated compared with the wild type, indicating that PlAtg8 is also required for the pathogenicity of P. litchii. Moreover, the cleavage rate of sporangia in ΔPlatg8 is significantly decreased compared to the wild type by FM4-64 staining, which is probably the main reason for the lower zoospores release rate in ΔPlatg8. These results suggest that PlAtg8 not only regulates autophagy but also participates in the growth and development of P. litchii. IMPORTANCE Peronophythora litchii is the pathogen of litchi downy blight, which is the most serious disease in litchi. Autophagy is an evolutionarily conserved catabolic process in eukaryotes. Atg8 is a core protein of the autophagic pathway, which modulates growth and pathogenicity in the oomycete P. litchii. In P. litchii, CRISPR/Cas9-mediated knockout of the PlATG8 impaired autophagosome formation. PlATG8 knockout mutants exhibited attenuated colony expansion, sporangia production, zoospore discharge, and virulence on litchi leaves and fruits. The reduction in zoospore release was likely underpinned by impaired sporangial cleavage. Thus, in addition to governing autophagic flux, PlAtg8 is indispensable for vegetative growth and infection of P. litchii. Peronophythora litchii is the pathogen of litchi downy blight, which is the most serious disease in litchi. Autophagy is an evolutionarily conserved catabolic process in eukaryotes. Atg8 is a core protein of the autophagic pathway, which modulates growth and pathogenicity in the oomycete P. litchii. In P. litchii, CRISPR/Cas9-mediated knockout of the PlATG8 impaired autophagosome formation. PlATG8 knockout mutants exhibited attenuated colony expansion, sporangia production, zoospore discharge, and virulence on litchi leaves and fruits. The reduction in zoospore release was likely underpinned by impaired sporangial cleavage. Thus, in addition to governing autophagic flux, PlAtg8 is indispensable for vegetative growth and infection of P. litchii.
Attitudes toward water resilience and potential for improvement

Abstract Non-technical summary There is a global water crisis, brought on by human actions. The ways we make decisions about water must transform to solve it. We focused on the attitudes that people in society hold toward water to understand how close or far away we are from a broadly accepted worldview that supports this transformation (what we call ‘water resilience’). We found that, across six countries in the Global South and North, attitudes showed moderate support for water resilience. Many people also showed potential to increase their support. Technical summary Water in the Anthropocene is threatened. Water governance aligned with the complex, dynamic, and uncertain nature of social–ecological systems (a ‘water resilience’ paradigm) is needed, and requires transformative change. We queried the potential for transformative change from the perspective that societal worldviews/paradigms offer an important leverage point for system change. Our study aimed to identify attitudes about water resilience and the extent to which there was potential for greater endorsement of water resilience. We surveyed individuals in six countries using vignettes to determine their level of water resilience endorsement (n = 2649). Overall water resilience endorsement was moderate (M = 2.86 out of 4). In some countries, a vignette related to a personally relevant water issue resulted in higher water resilience endorsement. More than half of the respondents held the potential for greater water resilience endorsement. Those with the greatest potential were younger, had children, considered religion more important, were more likely to live in urban areas, and lived in the same area for 10+ years. These findings provide guidance how to engage with the public (e.g. age-specific or parent-focused framing) to potentially increase societal water resilience endorsement. Social media summary General public in six countries moderately supports water resilience to address the water crisis, with room to improve.
Cost-effectiveness of Spironolactone for Adult Female Acne (SAFA): economic evaluation alongside a randomised controlled trial

Objective This study aims to estimate the cost-effectiveness of oral spironolactone plus routine topical treatment compared with routine topical treatment alone for persistent acne in adult women from a British NHS perspective over 24 weeks. Design Economic evaluation undertaken alongside a pragmatic, parallel, double-blind, randomised trial. Setting Primary and secondary healthcare, community and social media advertising. Participants Women ≥18 years with persistent facial acne judged to warrant oral antibiotic treatment. Interventions Participants were randomised 1:1 to 50 mg/day spironolactone (increasing to 100 mg/day after 6 weeks) or matched placebo until week 24. Participants in both groups could continue topical treatment. Main outcome measures Cost-utility analysis assessed incremental cost per quality-adjusted life year (QALY) using the EQ-5D-5L. Cost-effectiveness analysis estimated incremental cost per unit change on the Acne-QoL symptom subscale. Adjusted analysis included randomisation stratification variables (centre, baseline severity (investigator’s global assessment, IGA <3 vs ≥3)) and baseline variables (Acne-QoL symptom subscale score, resource use costs, EQ-5D score and use of topical treatments). Results Spironolactone did not appear cost-effective in the complete case analysis (n=126 spironolactone, n=109 control), compared with no active systemic treatment (adjusted incremental cost per QALY £67 191; unadjusted £34 770). Incremental cost per QALY was £27 879 (adjusted), just below the upper National Institute for Health and Care Excellence’s threshold value of £30 000, where multiple imputation took account of missing data. Incremental cost per QALY for other sensitivity analyses varied around the base-case, highlighting the degree of uncertainty. The adjusted incremental cost per point change on the Acne-QoL symptom subscale for spironolactone compared with no active systemic treatment was £38.21 (complete case analysis). Conclusions The results demonstrate a high level of uncertainty, particularly with respect to estimates of incremental QALYs. Compared with no active systemic treatment, spironolactone was estimated to be marginally cost-effective where multiple imputation was performed but was not cost-effective in complete case analysis. Trial registration number ISRCTN registry (ISRCTN12892056).
Intrinsic stress-induced bending as a platform technology for controlled self-assembly of high-Q on-chip RF inductors

This work reports on the modelling and process technology development for the design and fabrication of vertical, 3D, monolithic RF-MEMS inductors based on self-assembly via intrinsic stresses otherwise referred to as residual or internal stresses in thin films. Stress-induced bending in different cantilever designs were modelled at various film thicknesses using finite element analysis method and bending conditions were optimized. Intrinsic stress-induced bending mechanism is verified by fabrication of bi-layer metallic micro cantilever structures with varying stress conditions which reach bending angles of up to 137° and possibly more upon release. By modulating the loading mode (tensile or compressive) along the beam length, complex out-of-plane wavy cantilevers with multiple upward and/or downward bends were realized. The fabrication and modelling results display large overlap which further demonstrates the applicability of intrinsic stress-induced bending as a controllable technology towards fabrication of out-of-plane 3D micro components. Additionally, as a potential application to RF-MEMS inductors, stress-induced self-assembly of patterned thin film stacks into out-of-plane inductor topologies of varying geometries was investigated. Electromagnetic modelling tools were used to study the effect of bending on inductor performance (primarily the Q factor and self-resonance frequency—f SR), and results were evaluated by comparison to planar inductors of the same number of turns and dimensions, which revealed Q factor and f SR improvement of more than 100% upon bending away from substrate surface.