Practical Post-Quantum Few-Time Verifiable Random Function with Applications to Algorand

In this work, we introduce the first practical post-quantum verifiable random function (VRF) that relies on well-known (module) lattice problems, namely Module-SIS and Module-LWE. Our construction, named LB-VRF, results in a VRF value of only 84 bytes and a proof of around only 5 KB (in comparison to several MBs in earlier works), and runs in about 3 ms for evaluation and about 1 ms for verification. In order to design a practical scheme, we need to restrict the number of VRF outputs per key pair, which makes our construction few-time. Despite this restriction, we show how our few-time LB-VRF can be used in practice and, in particular, we estimate the performance of Algorand using LB-VRF. We find that, due to the significant increase in the communication size in comparison to classical constructions, which is inherent in all existing lattice-based schemes, the throughput in LB-VRF-based consensus protocol is reduced, but remains practical. In particular, in a medium-sized network with 100 nodes, our platform records a 1.14x to 3.4x reduction in throughput, depending on the accompanying signature used. In the case of a large network with 500 nodes, we can still maintain at least 24 transactions per second. This is still much better than Bitcoin, which processes only about 5 transactions per second

In Vitro Uptake of 140 kDa Bacillus thuringiensis Nematicidal Crystal Proteins by the Second Stage Juvenile of Meloidogyne hapla

Plant-parasitic nematodes (PPNs) are piercing/sucking pests, which cause severe damage to crops worldwide, and are difficult to control. The cyst and root-knot nematodes (RKN) are sedentary endoparasites that develop specialized multinucleate feeding structures from the plant cells called syncytia or giant cells respectively. Within these structures the nematodes produce feeding tubes, which act as molecular sieves with exclusion limits. For example, Heterodera schachtii is reportedly unable to ingest proteins larger than 28 kDa. However, it is unknown yet what is the molecular exclusion limit of the Meloidogyne hapla. Several types of Bacillus thuringiensis crystal proteins showed toxicity to M. hapla. To monitor the entry pathway of crystal proteins into M. hapla, second-stage juveniles (J2) were treated with NHS-rhodamine labeled nematicidal crystal proteins (Cry55Aa, Cry6Aa, and Cry5Ba). Confocal microscopic observation showed that these crystal proteins were initially detected in the stylet and esophageal lumen, and subsequently in the gut. Western blot analysis revealed that these crystal proteins were modified to different molecular sizes after being ingested. The uptake efficiency of the crystal proteins by the M. hapla J2 decreased with increasing of protein molecular mass, based on enzyme-linked immunosorbent assay analysis. Our discovery revealed 140 kDa nematicidal crystal proteins entered M. hapla J2 via the stylet, and it has important implications in designing a transgenic resistance approach to control RKN

Multi‐Objective Ensemble‐Processing Strategies to Optimize the Simulation of the Western North Pacific Subtropical High in Boreal Summer

The western North Pacific Subtropical High (WNPSH) in boreal summer is a major atmospheric player affecting East Asian climate, but its simulation in state‐of‐the‐art climate models is still largely biased. Here we use a multi‐objective optimization strategy, the Pareto optimality, to incorporate multiple physical constraints in processing multi‐model simulations provided by the Coupled Model Intercomparison Project Phase 6. We aim to improve the simulation of WNPSH by this practice. Sea surface temperatures from three tropical oceanic basins are found highly related to WNPSH, and thus used as constraints. We also present an ameliorated strategy, which takes a subset of the raw Pareto optimality by imposing conditions of smallest errors. Results show that the overestimate of WNPSH is effectively corrected. The two multi‐objective optimization schemes both perform better than the traditional approach, revealing the importance of implementing physically based links in processing multi‐model ensemble simulations

Rhizobia–Legume Symbiosis Increases Aluminum Resistance in Alfalfa

Alfalfa is the most important forage legume with symbiotic nitrogen-fixing nodule in roots, but it is sensitive to aluminum (Al), which limits its plantation in acidic soils. One rhizobia clone of Sinorhizobium meliloti with Al tolerance (AT1) was isolated from the nodule in AlCl3-treated alfalfa roots. AT1 showed a higher growth rate than the standard rhizobia strain Sm1021 under Al-stressed conditions. Alfalfa growth was improved by inoculation with AT1 under Al-stressed conditions, with increased length and fresh weight in shoots and roots. High nitrogenase activity and pink effective nodules were obtained in AT1-inoculated plant roots under Al stress, with increased total nitrogen compared with the non-inoculated control. The application of exogenous NH4+-nitrogen increased the Al resistance in alfalfa. It is suggested that rhizobia’s increase of the Al resistance in alfalfa is associated with its improved nitrogen status. Inoculation with Al-tolerant rhizobia is worth testing in an acidic field for improved alfalfa productivity

Latent Distribution Adjusting for Face Anti-Spoofing

With the development of deep learning, the field of face anti-spoofing (FAS) has witnessed great progress. FAS is usually considered a classification problem, where each class is assumed to contain a single cluster optimized by softmax loss. In practical deployment, one class can contain several local clusters, and a single-center is insufficient to capture the inherent structure of the FAS data. However, few approaches consider large distribution discrepancies in the field of FAS. In this work, we propose a unified framework called Latent Distribution Adjusting (LDA) with properties of latent, discriminative, adaptive, generic to improve the robustness of the FAS model by adjusting complex data distribution with multiple prototypes. 1) Latent. LDA attempts to model the data of each class as a Gaussian mixture distribution, and acquire a flexible number of centers for each class in the last fully connected layer implicitly. 2) Discriminative. To enhance the intra-class compactness and inter-class discrepancy, we propose a margin-based loss for providing distribution constrains for prototype learning. 3) Adaptive. To make LDA more efficient and decrease redundant parameters, we propose Adaptive Prototype Selection (APS) by selecting the appropriate number of centers adaptively according to different distributions. 4) Generic. Furthermore, LDA can adapt to unseen distribution by utilizing very few training data without re-training. Extensive experiments demonstrate that our framework can 1) make the final representation space both intra-class compact and inter-class separable, 2) outperform the state-of-the-art methods on multiple standard FAS benchmarks