The star cluster mass--galactocentric radius relation: Implications for cluster formation

Whether or not the initial star cluster mass function is established through a universal, galactocentric-distance-independent stochastic process, on the scales of individual galaxies, remains an unsolved problem. This debate has recently gained new impetus through the publication of a study that concluded that the maximum cluster mass in a given population is not solely determined by size-of-sample effects. Here, we revisit the evidence in favor and against stochastic cluster formation by examining the young ($\lesssim$ a few $\times 10^8$ yr-old) star cluster mass--galactocentric radius relation in M33, M51, M83, and the Large Magellanic Cloud. To eliminate size-of-sample effects, we first adopt radial bin sizes containing constant numbers of clusters, which we use to quantify the radial distribution of the first- to fifth-ranked most massive clusters using ordinary least-squares fitting. We supplement this analysis with an application of quantile regression, a binless approach to rank-based regression taking an absolute-value-distance penalty. Both methods yield, within the $1\sigma$ to $3\sigma$ uncertainties, near-zero slopes in the diagnostic plane, largely irrespective of the maximum age or minimum mass imposed on our sample selection, or of the radial bin size adopted. We conclude that, at least in our four well-studied sample galaxies, star cluster formation does not necessarily require an environment-dependent cluster formation scenario, which thus supports the notion of stochastic star cluster formation as the dominant star cluster-formation process within a given galaxy.Comment: ApJ, in press, 39 pages in AAS preprint format, 10 multi-panel figures (some reduced in size to match arXiv compilation routines

Structures of fibrils formed by α-synuclein hereditary disease mutant H50Q reveal new polymorphs.

Deposits of amyloid fibrils of α-synuclein are the histological hallmarks of Parkinson's disease, dementia with Lewy bodies and multiple system atrophy, with hereditary mutations in α-synuclein linked to the first two of these conditions. Seeing the changes to the structures of amyloid fibrils bearing these mutations may help to understand these diseases. To this end, we determined the cryo-EM structures of α-synuclein fibrils containing the H50Q hereditary mutation. We find that the H50Q mutation results in two previously unobserved polymorphs of α-synuclein: narrow and wide fibrils, formed from either one or two protofilaments, respectively. These structures recapitulate conserved features of the wild-type fold but reveal new structural elements, including a previously unobserved hydrogen-bond network and surprising new protofilament arrangements. The structures of the H50Q polymorphs help to rationalize the faster aggregation kinetics, higher seeding capacity in biosensor cells and greater cytotoxicity that we observe for H50Q compared to wild-type α-synuclein

Chain-of-Choice Hierarchical Policy Learning for Conversational Recommendation

Conversational Recommender Systems (CRS) illuminate user preferences via multi-round interactive dialogues, ultimately navigating towards precise and satisfactory recommendations. However, contemporary CRS are limited to inquiring binary or multi-choice questions based on a single attribute type (e.g., color) per round, which causes excessive rounds of interaction and diminishes the user's experience. To address this, we propose a more realistic and efficient conversational recommendation problem setting, called Multi-Type-Attribute Multi-round Conversational Recommendation (MTAMCR), which enables CRS to inquire about multi-choice questions covering multiple types of attributes in each round, thereby improving interactive efficiency. Moreover, by formulating MTAMCR as a hierarchical reinforcement learning task, we propose a Chain-of-Choice Hierarchical Policy Learning (CoCHPL) framework to enhance both the questioning efficiency and recommendation effectiveness in MTAMCR. Specifically, a long-term policy over options (i.e., ask or recommend) determines the action type, while two short-term intra-option policies sequentially generate the chain of attributes or items through multi-step reasoning and selection, optimizing the diversity and interdependence of questioning attributes. Finally, extensive experiments on four benchmarks demonstrate the superior performance of CoCHPL over prevailing state-of-the-art methods.Comment: Release with source cod

Ecosystem multifunctionality and soil microbial communities in response to ecological restoration in an alpine degraded grassland

Linkages between microbial communities and multiple ecosystem functions are context-dependent. However, the impacts of different restoration measures on microbial communities and ecosystem functioning remain unclear. Here, a 14-year long-term experiment was conducted using three restoration modes: planting mixed grasses (MG), planting shrub with Salix cupularis alone (SA), and planting shrub with Salix cupularis plus planting mixed grasses (SG), with an extremely degraded grassland serving as the control (CK). Our objective was to investigate how ecosystem multifunctionality and microbial communities (diversity, composition, and co-occurrence networks) respond to different restoration modes. Our results indicated that most of individual functions (i.e., soil nutrient contents, enzyme activities, and microbial biomass) in the SG treatment were significantly higher than in the CK treatment, and even higher than MG and SA treatments. Compared with the CK treatment, treatments MG, SA, and SG significantly increased the multifunctionality index on average by 0.57, 0.23 and 0.76, respectively. Random forest modeling showed that the alpha-diversity and composition of bacterial communities, rather than fungal communities, drove the ecosystem multifunctionality. Moreover, we found that both the MG and SG treatments significantly improved bacterial network stability, which exhabited stronger correlations with ecosystem multifunctionality compared to fungal network stability. In summary, this study demonstrates that planting shrub and grasses altogether is a promising restoration mode that can enhance ecosystem multifunctionality and improve microbial diversity and stability in the alpine degraded grassland

Automated Generation of Masked Nonlinear Components: From Lookup Tables to Private Circuits

Masking is considered to be an essential defense mechanism against side-channel attacks, but it is challenging to be adopted for hardware cryptographic implementations, especially for high security orders. Recently, Knichel et al. proposed an automated tool called AGEMA that enables the generation of masked implementations in hardware for arbitrary security orders using composable gadgets. This accelerates the construction and practical application of masking schemes. This article proposes a new automated tool named AGMNC that can generate masked nonlinear components with much better performance. The effectiveness of AGMNC is evaluated in several case studies. The evaluation results show a significant performance improvement, particularly for the first-order secure SKINNY S-box: saving 41$\%$ area, 25$\%$ latency, and 49$\%$ dynamic power. We achieve such a good result by integrating three key techniques: a new composable AND-XOR gadget, an optimization strategy based on the latency asymmetry feature of the AND-XOR gadget, and an implementation optimization for synchronization. Besides, we use the formal verification tool SILVER and FPGA-based practical experiments to confirm the security of the masked implementations generated by AGMNC

Pushing the Limits: Searching for Implementations with the Smallest Area for Lightweight S-Boxes

The area is one of the most important criteria for an S-box in hardware implementation when designing lightweight cryptography primitives. The area can be well estimated by the number of gate equivalent (GE). However, to our best knowledge, there is no efficient method to search for an S-box implementation with the least GE. Previous approaches can be classified into two categories, one is a heuristic that aims at finding an implementation with a satisfying but not necessarily the smallest GE number; the other one is SAT-based focusing on only the smallest number of gates while it ignored that the areas of different gates vary. Implementation with the least gates would usually not lead to the smallest number of GE. In this paper, we propose an improved SAT-based tool targeting optimizing the number of GE of an S-box implementation. Given an S-box, our tool can return the implementation of this S-box with the smallest number of GE. We speed up the search process of the tool by bit-sliced technique. Additionally, our tool supports 2-, 3-, and 4-input gates, while the previous tools cover only 2-input gates. To highlight the strength of our tool, we apply it to some 4-bit and 5-bit S-boxes of famous ciphers. We obtain a better implementation of RECTANGLE\u27s S-box with the area of 18.00GE. What\u27s more, we prove that the implementations of S-boxes of PICCOLO, SKINNY, and LBLOCK in the current literature have been optimal. When using the DC synthesizer on the circuits produced by our tool, the area are much better than the circuits converted by DC synthesizers from the lookup tables (LUT). At last, we use our tool to find implementations of 5-bit S-boxes, such as those used in KECCAK and ASCON

VIGC: Visual Instruction Generation and Correction

The integration of visual encoders and large language models (LLMs) has driven recent progress in multimodal large language models (MLLMs). However, the scarcity of high-quality instruction-tuning data for vision-language tasks remains a challenge. The current leading paradigm, such as LLaVA, relies on language-only GPT-4 to generate data, which requires pre-annotated image captions and detection bounding boxes, suffering from understanding image details. A practical solution to this problem would be to utilize the available multimodal large language models (MLLMs) to generate instruction data for vision-language tasks. However, it's worth noting that the currently accessible MLLMs are not as powerful as their LLM counterparts, as they tend to produce inadequate responses and generate false information. As a solution for addressing the current issue, this paper proposes the Visual Instruction Generation and Correction (VIGC) framework that enables multimodal large language models to generate instruction-tuning data and progressively enhance its quality on-the-fly. Specifically, Visual Instruction Generation (VIG) guides the vision-language model to generate diverse instruction-tuning data. To ensure generation quality, Visual Instruction Correction (VIC) adopts an iterative update mechanism to correct any inaccuracies in data produced by VIG, effectively reducing the risk of hallucination. Leveraging the diverse, high-quality data generated by VIGC, we finetune mainstream models and validate data quality based on various evaluations. Experimental results demonstrate that VIGC not only compensates for the shortcomings of language-only data generation methods, but also effectively enhances the benchmark performance. The models, datasets, and code will be made publicly available

Research on computer vision application in industry field: focus on distribution network engineering

The operation of distribution networks is currently facing potential safety and quality defects that pose significant hazards. One solution to strengthen management, reduce manual workload, and improve efficiency and quality is by applying deep detection networks for dynamic defect detection in distribution network engineering. To start, defects in distribution network engineering are classified. Then, advanced deep detection networks and their applications in dynamic defect detection are researched and analyzed, along with a review of existing research. Key issues and their solutions for deep detection network application in dynamic defect detection in distribution network engineering are summarized. Finally, future research directions are explored to provide valuable references for future studies