A quasi-current representation for information needs inspired by Two-State Vector Formalism

Recently, a number of quantum theory (QT)-based information retrieval (IR) models have been proposed for modeling session search task that users issue queries continuously in order to describe their evolving information needs (IN). However, the standard formalism of QT cannot provide a complete description for users’ current IN in a sense that it does not take the ‘future’ information into consideration. Therefore, to seek a more proper and complete representation for users’ IN, we construct a representation of quasi-current IN inspired by an emerging Two-State Vector Formalism (TSVF). With the enlightenment of the completeness of TSVF, a “two-state vector” derived from the ‘future’ (the current query) and the ‘history’ (the previous query) is employed to describe users’ quasi-current IN in a more complete way. Extensive experiments are conducted on the session tracks of TREC 2013 & 2014, and show that our model outperforms a series of compared IR models

Quantum-Inspired Interactive Networks for Conversational Sentiment Analysis.

Conversational sentiment analysis is an emerging, yet challenging Artificial Intelligence (AI) subtask. It aims to discover the affective state of each participant in a conversation. There exists a wealth of interaction information that affects the sentiment of speakers. However, the existing sentiment analysis approaches are insufficient in dealing with this task due to ignoring the interactions and dependency relationships between utterances. In this paper, we aim to address this issue by modeling intrautterance and inter-utterance interaction dynamics. We propose an approach called quantum-inspired interactive networks (QIN), which leverages the mathematical formalism of quantum theory (QT) and the long short term memory (LSTM) network, to learn such interaction dynamics. Specifically, a density matrix based convolutional neural network (DM-CNN) is proposed to capture the interactions within each utterance (i.e., the correlations between words), and a strong-weak influence model inspired by quantum measurement theory is developed to learn the interactions between adjacent utterances (i.e., how one speaker influences another). Extensive experiments are conducted on the MELD and IEMOCAP datasets. The experimental results demonstrate the effectiveness of the QIN model

A Quantum-Inspired Multimodal Sentiment Analysis Framework

Multimodal sentiment analysis aims to capture diversified sentiment information implied in data that are of different modalities (e.g., an image that is associated with a textual description or a set of textual labels). The key challenge is rooted on the “semantic gap” between different low-level content features and high-level semantic information. Existing approaches generally utilize a combination of multimodal features in a somehow heuristic way. However, how to employ and combine multiple information from different sources effectively is still an important yet largely unsolved problem. To address the problem, in this paper, we propose a Quantum-inspired Multimodal Sentiment Analysis (QMSA) framework. The framework consists of a Quantum-inspired Multimodal Representation (QMR) model (which aims to fill the “semantic gap” and model the correlations between different modalities via density matrix), and a Multimodal decision Fusion strategy inspired by Quantum Interference (QIMF) in the double-slit experiment (in which the sentiment label is analogous to a photon, and the data modalities are analogous to slits). Extensive experiments are conducted on two large scale datasets, which are collected from the Getty Images and Flickr photo sharing platform. The experimental results show that our approach significantly outperforms a wide range of baselines and state-of-the-art methods

A Quantum-like Multimodal Network Framework for Modeling Interaction Dynamics in Multiparty Conversational Sentiment Analysis

Sentiment analysis in conversations is an emerging yet challenging artificial intelligence (AI) task. It aims to discover the affective states and emotional changes of speakers involved in a conversation on the basis of their opinions, which are carried by different modalities of information (e.g., a video associated with a transcript). There exists a wealth of intra- and inter-utterance interaction information that affects the emotions of speakers in a complex and dynamic way. How to accurately and comprehensively model complicated interactions is the key problem of the field. To fill this gap, in this paper, we propose a novel and comprehensive framework for multimodal sentiment analysis in conversations, called a quantum-like multimodal network (QMN), which leverages the mathematical formalism of quantum theory (QT) and a long short-term memory (LSTM) network. Specifically, the QMN framework consists of a multimodal decision fusion approach inspired by quantum interference theory to capture the interactions within each utterance (i.e., the correlations between different modalities) and a strong-weak influence model inspired by quantum measurement theory to model the interactions between adjacent utterances (i.e., how one speaker influences another). Extensive experiments are conducted on two widely used conversational sentiment datasets: the MELD and IEMOCAP datasets. The experimental results show that our approach significantly outperforms a wide range of baselines and state-of-the-art models

Preparation of PbS Nanoparticles by Phase-Transfer Method and Application to Pb2+-Selective Electrode Based on PVC Membrane

A novel approach to prepare homogeneous PbS nanoparticles by phase-transfer method was developed. The preparatory conditions were studied in detail, and the nanoparticles were characterized by transmission electron microscopy (TEM) and UV-vis spectroscopy. Then a novel lead ion-selective electrode of polyvinyl chloride (PVC) membrane based on these lead sulfide nanoparticles was prepared, and the optimum ratio of components in the membrane was determined. The results indicated that the sensor exhibited a wide concentration range of 1.0×10−5 to 1.0×10−2 mol.L−1. The response time of the electrode was about 10 s, and the optimal pH in which the electrode could be used was from 3.0 to 7.0. Selectivity coefficients indicated that the electrode was selective to the primary ion over the interfering ion. The electrode can be used for at least 3 months without any divergence in potential. It was successfully applied to directly determine lead ions in solution and used as an indicator electrode in potentiometric titration of lead ions with EDTA

Apoptosis and autophagy of muscle cell during pork postmortem aging

Objective Pork is an important source of animal protein in many countries. Subtle physiochemical changes occur during pork postmortem aging. The changes of apoptosis and autophagy in pork at 6 h to 72 h after slaughter were studied to provide evidence for pork quality. Methods In this article, morphological changes of postmortem pork was observed through Hematoxylin-eosin staining, apoptotic nuclei were observed by TdT-mediated dUTP nick end labeling assay, protein related to apoptosis and autophagy expressions were tested by western blot and LC3 level were expressed according to immunofluorescence assay. Results In this study, we found the occurrence of apoptosis in postmortem pork, and the process was characterized by nucleus condensation and fragmentation, formation of apoptotic bodies, increase in apoptosis-related Bax/Bcl-2 levels, and activation of caspases. Autophagy reached its peak between 24 and 48 h after slaughter, accompanied by the formation of autophagosomes on the cell membrane and expression of autophagy-related proteins beclin-1, P62, LC3-I, LC3-II, and ATG5. Conclusion Obvious apoptosis was observed at 12 h and autophagy reached its peak at 48 h. The present work provides the evidence for the occurrence of apoptosis and autophagy during postmortem aging of pork. In conclusion, the apoptosis and autophagy of muscle cells discovered in this study have important implications for pork in the meat industry

Modelling and optimisation on scroll expander for waste heat recovery organic Rankine cycle

Scroll expander has demonstrated high efficiency at low power range. In this paper, a generic model of a scroll expander has been developed. It can calculate the ideal expander parameters to give the optimal efficiency and prevent under- or over-expansion at any given operating conditions or fluids. The dynamic model was validated by predicting the ideal volumetric expansion ratio with ideal expansion ratio of 4.03 at 0.7 MPa pressure, and showed agreement with experimental data. The results suggested that the rate of scroll increase K in the geometric model has little effect on volumetric expansion ratio or ideal scroll length of the expander, but when expansion ratio is kept constant, lower K value results in lower leakage losses

Conduction modulation of solution-processed two-dimensional materials

Solution-processed two-dimensional (2D) materials hold promise for their scalable applications. However, the random, fragmented nature of the solution-processed nanoflakes and the poor percolative conduction through their discrete networks limit the performance of the enabled devices. To overcome the problem, we report conduction modulation of the solution-processed 2D materials via the Stark effect. Using liquid-phase exfoliated molybdenum disulfide (MoS2) as an example, we demonstrate nonlinear conduction modulation with a switching ratio of >105 by the local fields from the interfacial ferroelectric P(VDF-TrFE). Through density-functional theory calculations and in situ Raman scattering and photoluminescence spectroscopic analysis, we understand the modulation arises from a charge redistribution in the solution-processed MoS2. Beyond MoS2, we show the modulation may be viable for the other solution-processed 2D materials and low-dimensional materials. The effective modulation can open their electronic device applications

Greenhouse gas emissions from U.S. crude oil pipeline accidents:1968 to 2020

Abstract Crude oil pipelines are considered as the lifelines of energy industry. However, accidents of the pipelines can lead to severe public health and environmental concerns, in which greenhouse gas (GHG) emissions, primarily methane, are frequently overlooked. While previous studies examined fugitive emissions in normal operation of crude oil pipelines, emissions resulting from accidents were typically managed separately and were therefore not included in the emission account of oil systems. To bridge this knowledge gap, we employed a bottom-up approach to conducted the first-ever inventory of GHG emissions resulting from crude oil pipeline accidents in the United States at the state level from 1968 to 2020, and leveraged Monte Carlo simulation to estimate the associated uncertainties. Our results reveal that GHG emissions from accidents in gathering pipelines (~720,000 tCO2e) exceed those from transmission pipelines (~290,000 tCO2e), although significantly more accidents have occurred in transmission pipelines (6883 cases) than gathering pipelines (773 cases). Texas accounted for over 40% of total accident-related GHG emissions nationwide. Our study contributes to enhanced accuracy of the GHG account associated with crude oil transport and implementing the data-driven climate mitigation strategies

In-plane uniaxial pressure-induced out-of-plane antiferromagnetic moment and critical fluctuations in BaFe2_2As2_2

A small in-plane external uniaxial pressure has been widely used as an effective method to acquire single domain iron pnictide BaFe$_2$As$_2$, which exhibits twin-domains without uniaxial strain below the tetragonal-to-orthorhombic structural (nematic) transition temperature $T_s$. Although it is generally assumed that such a pressure will not affect the intrinsic electronic/magnetic properties of the system, it is known to enhance the antiferromagnetic (AF) ordering temperature $T_N$ ($<T_s$) and create in-plane resistivity anisotropy above $T_s$. Here we use neutron polarization analysis to show that such a strain on BaFe$_2$As$_2$ also induces a static or quasi-static out-of-plane ($c$-axis) AF order and its associated critical spin fluctuations near $T_N/T_s$. Therefore, uniaxial pressure necessary to detwin single crystals of BaFe$_2$As$_2$ actually rotates the easy axis of the collinear AF order near $T_N/T_s$, and such effect due to spin-orbit coupling must be taken into account to unveil the intrinsic electronic/magnetic properties of the system.Comment: 11 pages, 4 figures, Supplementary information is available upon reques