Effect of Zr modification on solidification behavior and mechanical properties of Mg–Y–RE (WE54) alloy

AbstractMagnesium alloys containing rare earth elements (RE) have received considerable attention in recent years due to their high mechanical strength and good heat-resisting performance. Among them, Mg–5%Y–4%RE (WE54) magnesium alloy is a high strength sand casting magnesium alloy for use at temperatures up to 300 °C, which is of great interest to engineers in the aerospace industry. In the present work, the solidification behavior of Zr-containing WE54 alloy and Zr-free alloy was investigated by computer-aided cooling curve analysis (CA-CCA) technique. And the solidification microstructure and mechanical properties of them were also investigated comparatively. It is found from the cooling curves and as-cast microstructure of WE54 alloy that the nucleation temperature of α-Mg in WE54 alloy increases after Zr addition, and the as-cast microstructure of the alloy is significantly refined by Zr. While the phase constitution of WE54 alloy is not changed after Zr addition. These phenomena indicate that Zr acts as heterogeneous nuclei during the solidification of WE54 alloy. Due to refined microstructure, the mechanical properties of Zr-containing WE54 alloy is much higher than Zr-free WE54 alloy

Improving Few-shot Generalization of Safety Classifiers via Data Augmented Parameter-Efficient Fine-Tuning

As large language models (LLMs) are widely adopted, new safety issues and policies emerge, to which existing safety classifiers do not generalize well. If we have only observed a few examples of violations of a new safety rule, how can we build a classifier to detect violations? In this paper, we study the novel setting of domain-generalized few-shot learning for LLM-based text safety classifiers. Unlike prior few-shot work, these new safety issues can be hard to uncover and we do not get to choose the few examples. We demonstrate that existing few-shot techniques do not perform well in this setting, and rather we propose to do parameter-efficient fine-tuning (PEFT) combined with augmenting training data based on similar examples in prior existing rules. We empirically show that our approach of similarity-based data-augmentation + prompt-tuning (DAPT) consistently outperforms baselines that either do not rely on data augmentation or on PEFT by 7-17% F1 score in the Social Chemistry moral judgement and 9-13% AUC in the Toxicity detection tasks, even when the new rule is loosely correlated with existing ones

Adaptive robust blind watermarking scheme improved by entropy-based SVM and optimized quantum genetic algorithm

With the intensive study of machine learning in digital watermarking, its ability to balance the robustness and transparency of watermarking technology has attracted researchers’ attention. Therefore, quantum genetic algorithm, which serves as an intelligent optimized scheme combined with biological genetic mechanism and quantum computing, is widely used in various fields. In this study, an adaptive robust blind watermarking algorithm by means of optimized quantum genetics (OQGA) and entropy classification-based SVM (support vector machine) is proposed. The host image was divided into two parts according to the odd and even rows of the host image. One part was transformed by DCT (discrete cosine transform), and then the embedding intensity and position were separately trained by entropy-based SVM and OQGA; the other part was by DWT (discrete wavelet transform), in which the key fusion was achieved by an ergodic matrix to embed the watermark. Simulation results indicate the proposed algorithm ensures the watermark scheme transparency as well as having better resistance to common attacks such as lossy JPEG compression, image darken, Gaussian low-pass filtering, contrast decreasing, salt-pepper noise, and geometric attacks such as rotation and cropping. Received 22 May 2019 Revised 18 Aug 2019 Accepted 17 Sep 2019 Published 28 Oct 201

Microalgae-mediated tandem culture of shrimp and bivalve: an environmental and health co-benefits solution for phosphorus recovery and emission reduction

Phosphorus (P) accumulation in aquaculture systems is damaging our environment beyond acceptable levels. Devising strategies to potentially recover P from aquaculture systems in a reusable bioresource form is paramount and aligns with circular economy policies. In this study, we constructed two culture models, monoculture (Mon) and tandem culture (Tan), using Exopalaemon carinicauda and Mercenaria mercenaria. By monitoring the performance of rearing organisms, P dynamic patterns, and pollutant emissions, we found that: i) Compared to the Mon system, the Tan system demonstrated no differences in the performance of E. carinicauda and M. mercenaria, suggesting that the Tan model was viable in terms of fishery yield; ii) P in the Tan system could be efficiently recovered and removed from water and sediment, as indicated by the lower phosphate concentration in water (0.01 mg L−1), and the decrease in labile P in surface sediment (from 0.04 to 0.02 mg L−1). A combination of assimilatory and dissimilatory processes, mediated by phototrophic (bait-microalgae) and heterotrophic organisms (bivalves), appeared to be the primary mechanism for P utilization and removal; iii) The Tan system reduced pollutant emissions four times lower than the Mon system due to its minimal tailwater discharge (10%, 230 L). The emissions of total P, phosphate, total organic carbon, ammonium, and chemical oxygen demand from the Tan systems were 19 mg m−2 d−1, 2 mg m−2 d−1, 2 g m−2 d−1, 38 mg m−2 d−1, and 11 g m−2 d−1, respectively, 1.3, 1.7, 1.4, 1.3, and 1.2 times lower than those from the Mon systems. The eco-friendly Tan culture model fully exploited the resources of pond culture, a solution with environmental and health co-benefits for P recovery and emission reduction

Beyond ChatBots: ExploreLLM for Structured Thoughts and Personalized Model Responses

Large language model (LLM) powered chatbots are primarily text-based today, and impose a large interactional cognitive load, especially for exploratory or sensemaking tasks such as planning a trip or learning about a new city. Because the interaction is textual, users have little scaffolding in the way of structure, informational "scent", or ability to specify high-level preferences or goals. We introduce ExploreLLM that allows users to structure thoughts, help explore different options, navigate through the choices and recommendations, and to more easily steer models to generate more personalized responses. We conduct a user study and show that users find it helpful to use ExploreLLM for exploratory or planning tasks, because it provides a useful schema-like structure to the task, and guides users in planning. The study also suggests that users can more easily personalize responses with high-level preferences with ExploreLLM. Together, ExploreLLM points to a future where users interact with LLMs beyond the form of chatbots, and instead designed to support complex user tasks with a tighter integration between natural language and graphical user interfaces.Comment: 19 pages, 11 figure

Improving Diversity of Demographic Representation in Large Language Models via Collective-Critiques and Self-Voting

A crucial challenge for generative large language models (LLMs) is diversity: when a user's prompt is under-specified, models may follow implicit assumptions while generating a response, which may result in homogenization of the responses, as well as certain demographic groups being under-represented or even erased from the generated responses. In this paper, we formalize diversity of representation in generative LLMs. We present evaluation datasets and propose metrics to measure diversity in generated responses along people and culture axes. We find that LLMs understand the notion of diversity, and that they can reason and critique their own responses for that goal. This finding motivated a new prompting technique called collective-critique and self-voting (CCSV) to self-improve people diversity of LLMs by tapping into its diversity reasoning capabilities, without relying on handcrafted examples or prompt tuning. Extensive empirical experiments with both human and automated evaluations show that our proposed approach is effective at improving people and culture diversity, and outperforms all baseline methods by a large margin.Comment: To appear at EMNLP 2023 main conferenc

Apremilast Ameliorates Experimental Arthritis via Suppression of Th1 and Th17 Cells and Enhancement of CD4+Foxp3+ Regulatory T Cells Differentiation

Apremilast is a novel phosphodiesterase 4 (PDE4) inhibitor suppressing immune and inflammatory responses. We assessed the anti-inflammatory effects of Apremilast in type II collagen (CII)-induced arthritis (CIA) mouse model. To determine whether Apremilast can ameliorate arthritis onset in this model, Apremilast was given orally at day 14 after CII immunization. Bone erosion was measured by histological and micro-computed tomographic analysis. Anti-mouse CII antibody levels were measured by enzyme-linked immunosorbent assay, and Th17, Th1 cells, and CD4+Foxp3+ regulatory T (Treg) cells were assessed by flow cytometry in the lymph nodes. Human cartilage and rheumatoid arthritis (RA) synovial fibroblasts (RASFs) implantation in the severe combined immunodeficiency mouse model of RA were used to study the role of Apremilast in the suppression of RASF-mediated cartilage destruction in vivo. Compared with untreated and vehicle control groups, we found that Apremilast therapy delayed arthritis onset and reduced arthritis scores in the CIA model. Total serum IgG, IgG1, IgG2a, and IgG2b were all decreased in the Apremilast treatment groups. Moreover, Apremilast markedly prevented the development of bone erosions in CIA mice by CT analysis. Furthermore, in the Apremilast treated group, the frequency of Th17 cells and Th1 cells was significantly decreased while Treg cells’ frequency was significantly increased. The high dose of Apremilast (25 mg/kg) was superior to low dose (5 mg/kg) in treating CIA. Apremilast treatment reduced the migratory ability of RASFs and their destructive effect on cartilage. Compared with the model group, Apremilast treatment significantly reduced the RASFs invasion cartilage scores in both primary implant and contralateral implant models. Our data suggest that Apremilast is effective in treating autoimmune arthritis and preventing the bone erosion in the CIA model, implicating its therapeutic potential in patients with RA