EFFECTS OF LOW-TEMPERATURE AND PHYSIOLOGICAL AGE ON SUPEROXIDE-DISMUTASE IN WATER HYACINTH (EICHHORNIA-CRASSIPES SOLMS)

Superoxide dismutase activity in water hyacinth leaves was not sensitive to small changes in environmental pH, but declined markedly with greater pH changes. KCN inhibited superoxide dismutase activity, suggesting that the enzyme was mainly composed of the Cu-Zn form. Low temperature (2-degrees-C) treatment caused a decline in superoxide dismutase activity. This effect became more pronounced as the treatment time was prolonged. Furthermore, the decline was much more significant than reductions of glucose-6-phosphate dehydrogenase activity or respiration under comparable conditions. With increasing physiological age, superoxide dismutase activity declined and was significantly lower in old than in young leaves. Therefore, superoxide dismutase activity might be employed as one of physiological parameters in studying leaf senescence

Robust Localization Algorithm Based on Best Length Optimization for Wireless Sensor Networks

In this paper, a robust range-free localization algorithm by realizing best hop length optimization is proposed for node localization problem in wireless sensor networks (WSNs). This algorithm is derived from classic DV-Hop method but the critical hop length between any relay nodes is accurately computed and refined in space WSNs with arbitrary network connectivity. In case of network parameters hop length between nodes can be derived without complicated computation and further optimized using Kalman filtering in which guarantees robustness even in complicated environment with random node communication range. Especially sensor fusion techniques used has well gained robustness, accuracy, scalability, and power efficiency even without accurate distance or angle measurement which is more suitable in nonlinear conditions and power limited WSNs environment. Simulation results indicate it gained high accuracy compared with DV-Hop and Centroid methods in random communication range conditions which proves it gives characteristic of high robustness. Also it needs relatively little computation time which possesses high efficiency. It can well solve localization problem with many unknown nosed in the network and results prove the theoretical analysis

Angle-Aware and Tone-Aware Luminosity Analysis for Paper Model Surface

Luminosity contributes to the paper model surface perception. It has a significant impact on the perception of colour and details. The main purpose of this paper is to study the reflection luminosity of paper model surface which can be of complex or difficult shape surface. The final perception quality of a product, whether it is plain or 3D or other different shape, depends on the surface luminosity perceived by the receptor, such as eyes or measurement instruments. However, the number of parameters and limits of the paper model surface are enormous. It is a time-consuming work to select every parameter by a trial-and-error procedure. For a paper surface under the fixed lighting environment, the most important factors to decide the performance of perception are commonly viewing angles and surface tone. Therefore, the two related terms, perception angle and surface tone, were chosen to work in the analysis process. The final analysis, based on the initial conditions, enabled to predict the perception of paper model surface and to set the optimal perceived angels and tones. It still proposed the next step to model the perception of paper model surface of different shapes in a relatively short period

HC3 Plus: A Semantic-Invariant Human ChatGPT Comparison Corpus

ChatGPT has gained significant interest due to its impressive performance, but people are increasingly concerned about its potential risks, particularly around the detection of AI-generated content (AIGC), which is often difficult for untrained humans to identify. Current datasets utilized for detecting ChatGPT-generated text primarily center around question-answering, yet they tend to disregard tasks that possess semantic-invariant properties, such as summarization, translation, and paraphrasing. Our primary studies demonstrate that detecting model-generated text on semantic-invariant tasks is more difficult. To fill this gap, we introduce a more extensive and comprehensive dataset that considers more types of tasks than previous work, including semantic-invariant tasks. In addition, the model after a large number of task instruction fine-tuning shows a strong powerful performance. Owing to its previous success, we further instruct fine-tuning Tk-instruct and built a more powerful detection system. Experimental results show that our proposed detector outperforms the previous state-of-the-art RoBERTa-based detector

Pre-training with Large Language Model-based Document Expansion for Dense Passage Retrieval

In this paper, we systematically study the potential of pre-training with Large Language Model(LLM)-based document expansion for dense passage retrieval. Concretely, we leverage the capabilities of LLMs for document expansion, i.e. query generation, and effectively transfer expanded knowledge to retrievers using pre-training strategies tailored for passage retrieval. These strategies include contrastive learning and bottlenecked query generation. Furthermore, we incorporate a curriculum learning strategy to reduce the reliance on LLM inferences. Experimental results demonstrate that pre-training with LLM-based document expansion significantly boosts the retrieval performance on large-scale web-search tasks. Our work shows strong zero-shot and out-of-domain retrieval abilities, making it more widely applicable for retrieval when initializing with no human-labeled data.Comment: 10 pages, 3 tables, 4 figures, under revie

Query-as-context Pre-training for Dense Passage Retrieval

Recently, methods have been developed to improve the performance of dense passage retrieval by using context-supervised pre-training. These methods simply consider two passages from the same document to be relevant, without taking into account the possibility of weakly correlated pairs. Thus, this paper proposes query-as-context pre-training, a simple yet effective pre-training technique to alleviate the issue. Query-as-context pre-training assumes that the query derived from a passage is more likely to be relevant to that passage and forms a passage-query pair. These passage-query pairs are then used in contrastive or generative context-supervised pre-training. The pre-trained models are evaluated on large-scale passage retrieval benchmarks and out-of-domain zero-shot benchmarks. Experimental results show that query-as-context pre-training brings considerable gains and meanwhile speeds up training, demonstrating its effectiveness and efficiency. Our code will be available at https://github.com/caskcsg/ir/tree/main/cotmae-qc .Comment: EMNLP 2023 Main Conferenc

Making Multimodal Generation Easier: When Diffusion Models Meet LLMs

We present EasyGen, an efficient model designed to enhance multimodal understanding and generation by harnessing the capabilities of diffusion models and large language models (LLMs). Unlike existing multimodal models that predominately depend on encoders like CLIP or ImageBind and need ample amounts of training data to bridge the gap between modalities, EasyGen is built upon a bidirectional conditional diffusion model named BiDiffuser, which promotes more efficient interactions between modalities. EasyGen handles image-to-text generation by integrating BiDiffuser and an LLM via a simple projection layer. Unlike most existing multimodal models that are limited to generating text responses, EasyGen can also facilitate text-to-image generation by leveraging the LLM to create textual descriptions, which can be interpreted by BiDiffuser to generate appropriate visual responses. Extensive quantitative and qualitative experiments demonstrate the effectiveness of EasyGen, whose training can be easily achieved in a lab setting. The source code is available at https://github.com/zxy556677/EasyGen

Hierarchical Ni-Mn LDHs@CuC\u3csub\u3e2\u3c/sub\u3eO\u3csub\u3e4\u3c/sub\u3e Nanosheet Arrays-Modified Copper Mesh: A Dual-Functional Material for Enhancing Oil/Water Separation and Supercapacitors

The pursuit of superhydrophilic materials with hierarchical structures has garnered significant attention across diverse application domains. In this study, we have successfully crafted Ni-Mn LDHs@CuC2O4 nanosheet arrays on a copper mesh (CM) through a synergistic process involving chemical oxidation and hydrothermal deposition. Initially, CuC2O4 nanosheets were synthesized on the copper mesh, closely followed by the growth of Ni-Mn LDHs nanosheets, culminating in the establishment of a multi-tiered surface architecture with exceptional superhydrophilicity and remarkable underwater superoleophobicity. The resultant Ni-Mn LDHs@CuC2O4 CM membrane showcased an unparalleled amalgamation of traits, including superhydrophilicity, underwater superoleophobicity, and the ability to harness photocatalytic forces for self-cleaning actions, making it an advanced oil-water separation membrane. The membrane’s performance was impressive, manifesting in a remarkable water flux range (70 kL•m-2•h-1) and an efficient oil separation capability for both oil/water mixture and surfactant-stabilized emulsions (below 60 ppm). Moreover, the innate superhydrophilic characteristics of the membrane rendered it a prime candidate for deployment as a supercapacitor cathode material. Evidenced by a capacitance of 5080 mF•cm-2 at a current density of 6 mA cm-2 in a 6MKOH electrolyte, the membrane’s potential extended beyond oil-water separation. This work not only introduces a cutting-edge oil-water separation membrane and supercapacitor electrode but also offers a promising blueprint for the deliberate engineering of hierarchical structure arrays to cater to a spectrum of related applications

Luminance Prediction of Paper Model Surface Based on Non-Contact Measurement

The overall appearance perception is affected by luminance perception accuracy and efficiency mostly. The surface luminance prediction correlated with surface angle and surface tone value was performed by measuring and modeling the paper model surface luminance. First, we used a rotating bracket designed to facilitate to set the paper surface angle. Then, we set the surface angle from 5° to 85° at the interval of 5° using the designed rotating bracket. Additionally, the four primary color scales, cyan, magenta, yellow, and black, were printed and set at the designed angle. The angle-ware and tone-ware luminance was measured using spectroradiometer, CS-2000. Finally, we proposed and evaluated a mathematical model to reveal the relationship between luminance and surface angle and surface tone using the least squares method. The results indicated that the surface luminance of paper model could be predicted and obtained quickly and accurately for any surface angles and surface tone values by the proposed prediction model

Single-Trial EEG-fMRI Reveals the Generation Process of the Mismatch Negativity

Although research on the mismatch negativity (MMN) has been ongoing for 40 years, the generation process of the MMN remains largely unknown. In this study, we used a single-trial electro-encephalography (EEG)-functional magnetic resonance imaging (fMRI) coupling method which can analyze neural activity with both high temporal and high spatial resolution and thus assess the generation process of the MMN. We elicited the MMN with an auditory oddball paradigm while recording simultaneous EEG and fMRI. We divided the MMN into five equal-durational phases. Utilizing the single-trial variability of the MMN, we analyzed the neural generators of the five phases, thereby determining the spatiotemporal generation process of the MMN. We found two distinct bottom-up prediction error propagations: first from the auditory cortex to the motor areas and then from the auditory cortex to the inferior frontal gyrus (IFG). Our results support the regularity-violation hypothesis of MMN generation