Beyond Hard Samples: Robust and Effective Grammatical Error Correction with Cycle Self-Augmenting

Recent studies have revealed that grammatical error correction methods in the sequence-to-sequence paradigm are vulnerable to adversarial attack, and simply utilizing adversarial examples in the pre-training or post-training process can significantly enhance the robustness of GEC models to certain types of attack without suffering too much performance loss on clean data. In this paper, we further conduct a thorough robustness evaluation of cutting-edge GEC methods for four different types of adversarial attacks and propose a simple yet very effective Cycle Self-Augmenting (CSA) method accordingly. By leveraging the augmenting data from the GEC models themselves in the post-training process and introducing regularization data for cycle training, our proposed method can effectively improve the model robustness of well-trained GEC models with only a few more training epochs as an extra cost. More concretely, further training on the regularization data can prevent the GEC models from over-fitting on easy-to-learn samples and thus can improve the generalization capability and robustness towards unseen data (adversarial noise/samples). Meanwhile, the self-augmented data can provide more high-quality pseudo pairs to improve model performance on the original testing data. Experiments on four benchmark datasets and seven strong models indicate that our proposed training method can significantly enhance the robustness of four types of attacks without using purposely built adversarial examples in training. Evaluation results on clean data further confirm that our proposed CSA method significantly improves the performance of four baselines and yields nearly comparable results with other state-of-the-art models. Our code is available at https://github.com/ZetangForward/CSA-GEC

Synthesizing Diverse Human Motions in 3D Indoor Scenes

We present a novel method for populating 3D indoor scenes with virtual humans that can navigate the environment and interact with objects in a realistic manner. Existing approaches rely on high-quality training sequences that capture a diverse range of human motions in 3D scenes. However, such motion data is costly, difficult to obtain and can never cover the full range of plausible human-scene interactions in complex indoor environments. To address these challenges, we propose a reinforcement learning-based approach to learn policy networks that predict latent variables of a powerful generative motion model that is trained on a large-scale motion capture dataset (AMASS). For navigating in a 3D environment, we propose a scene-aware policy training scheme with a novel collision avoidance reward function. Combined with the powerful generative motion model, we can synthesize highly diverse human motions navigating 3D indoor scenes, meanwhile effectively avoiding obstacles. For detailed human-object interactions, we carefully curate interaction-aware reward functions by leveraging a marker-based body representation and the signed distance field (SDF) representation of the 3D scene. With a number of important training design schemes, our method can synthesize realistic and diverse human-object interactions (e.g.,~sitting on a chair and then getting up) even for out-of-distribution test scenarios with different object shapes, orientations, starting body positions, and poses. Experimental results demonstrate that our approach outperforms state-of-the-art human-scene interaction synthesis frameworks in terms of both motion naturalness and diversity. Video results are available on the project page: https://zkf1997.github.io/DIMOS

Increasing planting density can improve the yield of Tartary buckwheat

Planting densities and nitrogen fertilizer application rates determine the yield of crops. Tartary buckwheat is a pseudocereal crop with great health care and development values. However, little is known about application of nitrogen fertilizer and planting density on the physiological characteristics of Tartary buckwheat. This study aims to clarify the effect of planting density on the senescence and yield of Tartary buckwheat under low nitrogen conditions. A 2-year field experiment was conducted on Tartary buckwheat (Jinqiao 2) to study the effects of different planting densities (8 × 105, 10 × 105, 12 × 105, 14 × 105, and 16 × 105 plants·ha−1) on the root morphology and activity, chlorophyll and malondialdehyde (MDA) contents, antioxidant enzyme activity, photosynthetic characteristics, agronomic traits, and yield of Tartary buckwheat in the absence of nitrogen fertilizer treatment. With the increase in planting density, the root morphological indices and activities; chlorophyll a, chlorophyll b, and carotenoid contents; superoxide dismutase and peroxidase activities; net photosynthetic rate; transpiration rate; intercellular CO2 concentration and transpiration rate; main stem node, branch, and leaf numbers; grain number and weight per plant; and 1000-grain weight of Jinqiao 2 decreased continuously, whereas plant height and leaf MDA content increased continuously. The yield of Tartary buckwheat first increased and then decreased with the increase in planting density. The yield under 14 × 105 plants·ha−1 treatment increased by 68.61%, 44.82%, 11.00%, and 22.36%, respectively, relative to that under 8 × 105, 10 × 105, 12 × 105, and 16 × 105 plants·ha−1treatments. In summary, planting at an appropriately high density (14 × 105 plants·ha−1) can promote the increase in the yield of Tartary buckwheat populations under low nitrogen conditions and is recommended for use in production to achieve the high-yielding and nitrogen saving cultivation of Tartary buckwheat. This research can serve as a theoretical basis to jointly achieve the high yield and nitrogen saving of Tartary buckwheat

Enhancement of Quantum Sensing in a Cavity Optomechanical System around Quantum Critical Point

The precision of quantum sensing could be improved by exploiting quantum phase transitions, where the physical quantity tends to diverge when the system is approaching the quantum critical point. This critical enhancement phenomenon has been applied to the quantum Rabi model in a dynamic framework, showing a promising sensing enhancement without the complex initial state preparation. In this work, we find a quantum phase transition in the coupling cavity-mechanical oscillator system when the coupling strength crosses a critical point, determined by the effective detuning of cavity and frequency of mechanical mode. By utilizing this critical phenomenon, we obtain a prominent enhancement of quantum sensing, such as the position and momentum of the mechanical oscillator. This result provides an alternative method to enhance the quantum sensing of some physical quantities, such as mass, charge, and weak force, in a large mass system

Direct transformation of-alkane into all-conjugated polyene via cascade dehydrogenation

Selective C(sp$^{3}$) −H activation is of fundamental importance in processing alkane feedstocks to produce high-value-added chemical products. By virtue of an on-surface synthesis strategy, we report selective cascade dehydrogenation of n-alkane molecules under surface constraints, which yields monodispersed all-trans conjugated polyenes with unprecedented length controllability. We are also able to demonstrate the generality of this concept for alkyl-substituted molecules with programmable lengths and diverse functionalities, and more importantly its promising potential in molecular wiring

Face mask integrated with flexible and wearable manganite oxide respiration sensor

Face masks are key personal protective equipment for reducing exposure to viruses and other environmental hazards such as air pollution. Integrating flexible and wearable sensors into face masks can provide valuable insights into personal and public health. The advantages that a breath-monitoring face mask requires, including multi-functional sensing ability and continuous, long-term dynamic breathing process monitoring, have been underdeveloped to date. Here, we design an effective human breath monitoring face mask based on a flexible La0.7Sr0.3MnO3 (LSMO)/Mica respiration sensor. The sensor’s capabilities and systematic measurements are investigated under two application scenes, namely clinical monitoring mode and daily monitoring mode, to monitor, recognise, and analyse different human breath status, i.e., cough, normal breath, and deep breath. This sensing system exhibits super-stability and multi-modal capabilities in continuous and long-time monitoring of the human breath. We determine that during monitoring human breath, thermal diffusion in LSMO is responsible for the change of resistance in flexible LSMO/Mica sensor. Both simulated and experimental results demonstrate good discernibility of the flexible LSMO/Mica sensor operating at different breath status. Our work opens a route for the design of novel flexible and wearable electronic devices

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data