Quality Control Considering Assembly Order for Two Stage Processes

Variation reduction in multistage assembly processes is an important but challenging issue for quality control. It is desirable to minimize the final product variance from a system level. Fruitful research has been conducted on this issue based on the fixed assembly order. However, the variability of parts can be affected by different assembly order. In this paper, we propose a quality control strategy that takes into account the assembly order in two stage assembly processes. Specifically, we analyzed the relationship between the final product variation targets and the assembly order. A case study in conducted on a two stage assembly process of vessel diaphragm to illustrate the developed methodology.disastrous

Detection and identification of huwentoxin-IV interacting proteins by biotin-avidin chemistry combined with mass spectrometry

<div><p>Background : Numerous spider toxins are of interest as tools for neurophysiological research or as lead molecules for the development of pharmaceuticals and insecticides. Direct detection and identification of the interacting proteins of a spider toxin are helpful for its action-mechanism analysis and practical application. The present study employed a combinative strategy for the analysis of interacting proteins of huwentoxin-IV (HWTX-IV), a peptidic neurotoxin from the venom of the spiderSelenocosmia huwena.Results : HWTX-IV was first lightly labeled with biotin under the optimized mild experimental conditions and the toxin labeled with a single biotin group (monobiotinylated HWTX-IV) was demonstrated by electrophysiological experiments to retain its original bioactivity and was used in combination with far-western blotting to detect its interacting proteins. Comparative experiments indicated that some membrane proteins from rat neuromuscular junction preparations bind to monobiotinylated HWTX-IV after being transferred onto a PVDF membrane from the SDS-gel. With capillary high performance liquid chromatography-tandem mass spectrometry, several membrane proteins with which HWTX-IV potentially interacted were identified from the preparations and then bioinformatically analyzed.Conclusions : This work has provided not only a new insight into the action mechanism of HWTX-IV but also a reference technology for the relevant researches.</p></div

TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering

Despite thousands of researchers, engineers, and artists actively working on improving text-to-image generation models, systems often fail to produce images that accurately align with the text inputs. We introduce TIFA (Text-to-Image Faithfulness evaluation with question Answering), an automatic evaluation metric that measures the faithfulness of a generated image to its text input via visual question answering (VQA). Specifically, given a text input, we automatically generate several question-answer pairs using a language model. We calculate image faithfulness by checking whether existing VQA models can answer these questions using the generated image. TIFA is a reference-free metric that allows for fine-grained and interpretable evaluations of generated images. TIFA also has better correlations with human judgments than existing metrics. Based on this approach, we introduce TIFA v1.0, a benchmark consisting of 4K diverse text inputs and 25K questions across 12 categories (object, counting, etc.). We present a comprehensive evaluation of existing text-to-image models using TIFA v1.0 and highlight the limitations and challenges of current models. For instance, we find that current text-to-image models, despite doing well on color and material, still struggle in counting, spatial relations, and composing multiple objects. We hope our benchmark will help carefully measure the research progress in text-to-image synthesis and provide valuable insights for further research

Biomedical Image Splicing Detection using Uncertainty-Guided Refinement

Recently, a surge in biomedical academic publications suspected of image manipulation has led to numerous retractions, turning biomedical image forensics into a research hotspot. While manipulation detectors are concerning, the specific detection of splicing traces in biomedical images remains underexplored. The disruptive factors within biomedical images, such as artifacts, abnormal patterns, and noises, show misleading features like the splicing traces, greatly increasing the challenge for this task. Moreover, the scarcity of high-quality spliced biomedical images also limits potential advancements in this field. In this work, we propose an Uncertainty-guided Refinement Network (URN) to mitigate the effects of these disruptive factors. Our URN can explicitly suppress the propagation of unreliable information flow caused by disruptive factors among regions, thereby obtaining robust features. Moreover, URN enables a concentration on the refinement of uncertainly predicted regions during the decoding phase. Besides, we construct a dataset for Biomedical image Splicing (BioSp) detection, which consists of 1,290 spliced images. Compared with existing datasets, BioSp comprises the largest number of spliced images and the most diverse sources. Comprehensive experiments on three benchmark datasets demonstrate the superiority of the proposed method. Meanwhile, we verify the generalizability of URN when against cross-dataset domain shifts and its robustness to resist post-processing approaches. Our BioSp dataset will be released upon acceptance

Kronos: A Secure and Generic Sharding Blockchain Consensus with Optimized Overhead

Sharding enhances blockchain scalability by dividing the network into shards, each managing specific unspent transaction outputs or accounts. As an introduced new transaction type, cross-shard transactions pose a critical challenge to the security and efficiency of sharding blockchains. Currently, there is a lack of a generic sharding consensus pattern that achieves both security and low overhead. In this paper, we present Kronos, a secure sharding blockchain consensus achieving optimized overhead. In particular, we propose a new secure sharding consensus pattern, based on a buffer managed jointly by shard members. Valid transactions are transferred to the payee via the buffer, while invalid ones are rejected through happy or unhappy paths. Kronos is proved to achieve security with atomicity under malicious clients with optimal intra-shard overhead $k\mathcal{B}$ ($k$ for involved shard number and $\mathcal{B}$ for a Byzantine fault tolerance (BFT) cost). Efficient rejection even requires no BFT execution in happy paths, and the cost in unhappy paths is still lower than a two-phase commit. Besides, we propose secure cross-shard certification methods based on batch certification and reliable cross-shard transfer. The former combines hybrid trees or vector commitments, while the latter integrates erasure coding. Handling $b$ transactions, Kronos is proved to achieve reliability with low cross-shard overhead $\mathcal{O}(n b \lambda)$ ($n$ for shard size and $\lambda$ for the security parameter). Notably, Kronos imposes no restrictions on BFT and does not rely on time assumptions, offering optional constructions in various modules. Kronos could serve as a universal framework for enhancing the performance and scalability of existing BFT protocols, supporting generic models, including asynchronous networks, increasing the throughput by several orders of magnitude. We implement Kronos using two prominent BFT protocols: asynchronous Speeding Dumbo (NDSS\u2722) and partial synchronous Hotstuff (PODC\u2719). Extensive experiments (over up to 1000 AWS EC2 nodes across 4 AWS regions) demonstrate Kronos scales the consensus nodes to thousands, achieving a substantial throughput of 320 ktx/sec with 2.0 sec latency. Compared with the past solutions, Kronos outperforms, achieving up to a 12$\times$ improvement in throughput and a 50% reduction in latency when cross-shard transactions dominate the workload