Effects of Layering Milling Technology on Dough Properties of Highland Barley and Bread Qualities

Highland barley (Qingke) is rich in nutrients and has the nutrient composition of “three highs and two lows,” which are high vitamin, high soluble dietary fiber, high β-glucan, low fat, and low sugar. In this paper, it was proposed to remove the layers of different ratios with different peeling rates. Then, different peeled highland barley was milled into flour and added to bread flour in the same proportion to make wheat-highland barley bread. The results showed that the removal of the cortex of highland barley flour was beneficial to its fermentation characteristics, the comprehensive capacity of gas production and gas holding has been improved, and the maximum fermentation height and retention coefficient were both at QK2-35%, while the gas production at QK4-35% is higher than other samples. From QK0-35% to QK5-35%, the  significance of the highland barley bread increased, from 56.31 to 70.88. The results showed that choosing QK4-35% as the best peeling rate of highland barley flour blends could not only retain the nutritional value of highland barley bread but also optimize the quality of bread to a certain extent, which could attract consumers and has a good development prospect

Compositional effect on the pressure derivatives of bulk modulus of silicate melts

Editor: L. Stixrude Keywords: silicate melts density equation of state pressure derivative of bulk modulus composition error propagation Although the bulk moduli (K T0 ) of silicate melts have a relatively narrow range of values, the pressure derivatives of the isothermal bulk modulus (K T0 ′ ) can assume a broad range of values and have an important influence on the compositional dependence of the melt compressibility at high pressure. Based on the melt density data from sink/float experiments at high pressures in the literature, we calculate K T0 ′ using an isothermal equation of state (EOS) (e.g., Birch-Murnaghan EOS and Vinet EOS) with the previously determined values of room-pressure density (ρ 0 ) and room-pressure bulk modulus (K T0 ). The results show that best estimates of K T0 ′ vary considerably from~3 to~7 for different compositions. K T0 ′ is nearly independent of Mg # (molar Mg/(Mg + Fe)), but decreases with SiO 2 content. Hydrous melts have anomalously small K T0 ′ leading to a high degree of compression at high pressures. For anhydrous melts, K T0 ′ is~7 for peridotitic melts,~6 for picritic melts,~5 for komatiitic melts, and~4 for basaltic melts

Monitoring and predicting drought based on multiple indicators in an arid area, China

Droughts are one of the costliest natural disasters. Reliable drought monitoring and prediction are valuable for drought relief management. This study monitors and predicts droughts in Xinjiang, an arid area in China, based on the three drought indicators, i.e., the Standardized Precipitation Index (SPI), the Standardized Soil Moisture Index (SSMI) and the Multivariate Standardized Drought Index (MSDI). Results indicate that although these three indicators could capture severe historical drought events in the study area, the spatial coverage, persistence and severity of the droughts would vary regarding different indicators. The MSDI could best describe the overall drought conditions by incorporating the characteristics of the SPI and SSMI. For the drought prediction, the predictive skill of all indicators gradually decayed with the increasing lead time. Specifically, the SPI only showed the predictive skill at a 1-month lead time, the MSDI performed best in capturing droughts at 1- to 2-month lead times and the SSMI was accurate up to a 3-month lead time owing to its high persistence. These findings might provide scientific support for the local drought management

Denoising Distantly Supervised Named Entity Recognition via a Hypergeometric Probabilistic Model

Denoising is the essential step for distant supervision based named entity recognition. Previous denoising methods are mostly based on instance-level confidence statistics, which ignore the variety of the underlying noise distribution on different datasets and entity types. This makes them difficult to be adapted to high noise rate settings. In this paper, we propose Hypergeometric Learning (HGL), a denoising algorithm for distantly supervised NER that takes both noise distribution and instance-level confidence into consideration. Specifically, during neural network training, we naturally model the noise samples in each batch following a hypergeometric distribution parameterized by the noise-rate. Then each instance in the batch is regarded as either correct or noisy one according to its label confidence derived from previous training step, as well as the noise distribution in this sampled batch. Experiments show that HGL can effectively denoise the weakly-labeled data retrieved from distant supervision, and therefore results in significant improvements on the trained models.Comment: Accepted to AAAI202

Knowledge-Enhanced Personalized Review Generation with Capsule Graph Neural Network

Personalized review generation (PRG) aims to automatically produce review text reflecting user preference, which is a challenging natural language generation task. Most of previous studies do not explicitly model factual description of products, tending to generate uninformative content. Moreover, they mainly focus on word-level generation, but cannot accurately reflect more abstractive user preference in multiple aspects. To address the above issues, we propose a novel knowledge-enhanced PRG model based on capsule graph neural network~(Caps-GNN). We first construct a heterogeneous knowledge graph (HKG) for utilizing rich item attributes. We adopt Caps-GNN to learn graph capsules for encoding underlying characteristics from the HKG. Our generation process contains two major steps, namely aspect sequence generation and sentence generation. First, based on graph capsules, we adaptively learn aspect capsules for inferring the aspect sequence. Then, conditioned on the inferred aspect label, we design a graph-based copy mechanism to generate sentences by incorporating related entities or words from HKG. To our knowledge, we are the first to utilize knowledge graph for the PRG task. The incorporated KG information is able to enhance user preference at both aspect and word levels. Extensive experiments on three real-world datasets have demonstrated the effectiveness of our model on the PRG task.Comment: Accepted by CIKM 2020 (Long Paper

Loss of Vision Dominance at the Preresponse Level in Tinnitus Patients: Preliminary Behavioral Evidence

At present, the mechanisms underlying changes in visual processing in individuals with tinnitus remain unclear. Therefore, we investigated whether the vision dominance of individuals with tinnitus disappears at the preresponse level through behavioral study. A total of 38 individuals with tinnitus and 31 healthy controls completed a task in which they were asked to attend to either visual or auditory stimuli while ignoring simultaneous stimulus inputs from the other modality. We manipulated three levels of congruency between the simultaneous visual and auditory inputs: congruent (C), incongruent at the preresponse level (PRIC), and incongruent at the response level (RIC). Thus, we differentiated the cross-modal conflict explicitly into the preresponse (PRIC > C) and response (RIC > PRIC) levels. The results revealed no significant difference in the size of the preresponse level conflict between the auditory attention and visual attention conditions in tinnitus group. In brief, the preresponse level of individuals with tinnitus showed a loss in vision dominance. This may be due to the reduced interference of visual information in auditory processing

DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning

Recent advances in natural language processing, primarily propelled by Large Language Models (LLMs), have showcased their remarkable capabilities grounded in in-context learning. A promising avenue for guiding LLMs in intricate reasoning tasks involves the utilization of intermediate reasoning steps within the Chain-of-Thought (CoT) paradigm. Nevertheless, the central challenge lies in the effective selection of exemplars for facilitating in-context learning. In this study, we introduce a framework that leverages Dual Queries and Low-rank approximation Re-ranking (DQ-LoRe) to automatically select exemplars for in-context learning. Dual Queries first query LLM to obtain LLM-generated knowledge such as CoT, then query the retriever to obtain the final exemplars via both question and the knowledge. Moreover, for the second query, LoRe employs dimensionality reduction techniques to refine exemplar selection, ensuring close alignment with the input question's knowledge. Through extensive experiments, we demonstrate that DQ-LoRe significantly outperforms prior state-of-the-art methods in the automatic selection of exemplars for GPT-4, enhancing performance from 92.5% to 94.2%. Our comprehensive analysis further reveals that DQ-LoRe consistently outperforms retrieval-based approaches in terms of both performance and adaptability, especially in scenarios characterized by distribution shifts. DQ-LoRe pushes the boundary of in-context learning and opens up new avenues for addressing complex reasoning challenges. Our code is released at https://github.com/AI4fun/DQ-LoRe}{https://github.com/AI4fun/DQ-LoRe.Comment: Accepted in ICLR 202