Three-dimensional correlated-fermion phase separation from analysis of the geometric mean of the individual susceptibilities

A quasi-Gaussian approximation scheme is formulated to study the strongly correlated imbalanced fermions thermodynamics, where the mean-field theory is not applicable. The non-Gaussian correlation effects are understood to be captured by the statistical geometric mean of the individual susceptibilities. In the three-dimensional unitary fermions ground state, an {\em universal} non-linear scaling transformation relates the physical chemical potentials with the individual Fermi kinetic energies. For the partial polarization phase separation to full polarization, the calculated critical polarization ratio is $P_C={[1-(1-\xi)^{6/5}]}/{[1+(1-\xi)^{6/5}]}\doteq 0.34$. The $\xi=4/9$ defines the ratio of the symmetric ground state energy density to that of the ideal fermion gas.Comment: Minor changes with typos correcte

Intelligent Case Assignment Method Based on the Chain of Criminal Behavior Elements

The assignment of cases means the court assigns cases to specific judges. The traditional case assignment methods, based on the facts of a case, are weak in the analysis of semantic structure of the case not considering the judges\u27 expertise. By analyzing judges\u27 trial logic, we find that the order of criminal behaviors affects the final judgement. To solve these problems, we regard intelligent case assignment as a text-matching problem, and propose an intelligent case assignment method based on the chain of criminal behavior elements. This method introduces the chain of criminal behavior elements to enhance the structured semantic analysis of the case. We build a BCTA (Bert-Cnn-Transformer-Attention) model to achieve intelligent case assignment. This model integrates a judge\u27s expertise in the judge\u27s presentation, thus recommending the most compatible judge for the case. Comparing the traditional case assignment methods, our BCTA model obtains 84% absolutely considerable improvement under P@1. In addition, comparing other classic text matching models, our BCTA model achieves an absolute considerable improvement of 4% under P@1 and 9% under Macro F1. Experiments conducted on real-world data set demonstrate the superiority of our method

A Two Dimensional Feature Engineering Method for Relation Extraction

Transforming a sentence into a two-dimensional (2D) representation (e.g., the table filling) has the ability to unfold a semantic plane, where an element of the plane is a word-pair representation of a sentence which may denote a possible relation representation composed of two named entities. The 2D representation is effective in resolving overlapped relation instances. However, in related works, the representation is directly transformed from a raw input. It is weak to utilize prior knowledge, which is important to support the relation extraction task. In this paper, we propose a two-dimensional feature engineering method in the 2D sentence representation for relation extraction. Our proposed method is evaluated on three public datasets (ACE05 Chinese, ACE05 English, and SanWen) and achieves the state-of-the-art performance. The results indicate that two-dimensional feature engineering can take advantage of a two-dimensional sentence representation and make full use of prior knowledge in traditional feature engineering. Our code is publicly available at https://github.com/Wang-ck123/A-Two-Dimensional-Feature-Engineering-Method-for-Entity-Relation-Extractio

Coal-fired PM_2.5 induces endothelial cell injury and the expression of atherosclerosis-related adhesion molecules:Involvement of the p38 and JNK signaling pathways

It has been reported that PM2.5 causes endothelial cell injury and promotes atherosclerosis. However, the exact mechanism through which coal-fired PM2.5 induces endothelial cell injury, and the involvement of the p38 and JNK signaling pathways in this process remain unclear. In this study, EA.hy926 cells were exposed to coal-fired PM2.5 at concentrations of 25, 50, and 100 μg/mL, and the toxic effects were observed. The phosphorylation of the JNK and p38 signaling pathways was investigated through western blot analysis. Additionally, the expression of adhesion molecules (ICAM-1 and E-selectin) was assessed using ELISA and flow cytometry. Changes in cellular toxicity and adhesion molecules were evaluated after pretreatment with the p38 inhibitor SB203580 (10 μM) and the JNK inhibitor SP600125 (25 μM). We observed that exposure to coal-fired PM2.5 led to a decrease in cell survival rate and proliferation while promoting apoptosis. Exposure to coal-fired PM2.5 promoted the expression of ICAM-1 and E-selectin, as well as the phosphorylation of p38 and JNK in EA.hy926 cells. After using p38 and JNK inhibitors, there was an observed increase in cell survival rate and proliferation, accompanied by a decrease in apoptosis. The levels of ICAM-1 and E-selectin showed no significant changes with the addition of p38 and JNK inhibitors. Our results indicated that coal-fired PM2.5 caused cellular toxicity and increased the levels of ICAM-1 and E-selectin. The p38 and JNK signaling pathways might play a role in the reduction of cell viability, while the regulation of ICAM-1 and E-selectin might not be influenced by these pathways.</p

A history and theory of textual event detection and recognition

There is large and growing amounts of textual data that contains information about human activities. Mining interesting knowledge from this textual data is a challenging task because it consists of unstructured or semistructured text that are written in natural language. In the field of artificial intelligence, event-oriented techniques are helpful in addressing this problem, where information retrieval (IR), information extraction (IE) and graph methods (GMs) are three of the most important paradigms in supporting event-oriented processing. In recent years, due to information explosions, textual event detection and recognition have received extensive research attention and achieved great success. Many surveys have been conducted to retrospectively assess the development of event detection. However, until now, all of these surveys have focused on only a single aspect of IR, IE or GMs. There is no research that provides a complete introduction or a comparison of IR, IE, and GMs. In this article, a survey about these techniques is provided from a broader perspective, and a convenient and comprehensive comparison of these techniques is given. The hallmark of this article is that it is the first survey that combines IR, IE and GMs in a single frame and will therefore benefit researchers by acting as a reference in this field.</p

On Information Coverage for Location Category Based Point-of-Interest Recommendation

Point-of-interest(POI) recommendation becomes a valuable service in location-based social networks. Based on the norm that similar users are likely to have similar preference of POIs, the current recommendation techniques mainly focus on users' preference to provide accurate recommendation results. This tends to generate a list of homogeneous POIs that are clustered into a narrow band of location categories(like food, museum, etc.) in a city. However, users are more interested to taste a wide range of flavors that are exposed in a global set of location categories in the city.In this paper, we formulate a new POI recommendation problem, namely top-K location category based POI recommendation, by introducing information coverage to encode the location categories of POIs in a city.The problem is NP-hard. We develop a greedy algorithm and further optimization to solve this challenging problem. The experimental results on two real-world datasets demonstrate the utility of new POI recommendations and the superior performance of the proposed algorithms

Optimal Route Search with the Coverage of Users' Preferences

The preferences of users are important in route search and planning. Users may also weight their preferences differently. For example, when a user plans a trip within a city, their preferences can be expressed as keywords shopping mall, restaurant, and museum, with weights 0.5, 0.4, and 0.1, respectively. The resulting route should best satisfy their weighted preferences. In this paper, we take into account the weighted user preferences in route search, and present a keyword coverage problem, which finds an optimal route from a source location to a target location such that the keyword coverage is optimized and that the budget score satisfies a specified constraint. We prove that this problem is NP-hard. To solve the complex problem, we propose the optimal route search by adapting the A* algorithm. An admissible heuristic function is developed to preserve the solution optimality. The experiments conducted on real-world datasets demonstrate both the efficiency and accuracy of our proposed algorithms

On Information Coverage for Location Category Based Point-of-Interest Recommendation

Point-of-interest (POI) recommendation becomes a valuable service in location-based social networks. Based on the norm that similar users are likely to have similar preference of POIs, the current recommendation techniques mainly focus on users’ preference to provide accurate recommendation results. This tends to generate a list of homogeneous POIs that are clustered into a narrow band of location categories (like food, museum, etc.) in a city. However, users are more interested to taste a wide range of flavors that are exposed in a global set of location categories in the city. In this paper, we formulate a new POI recommendation problem, namely top-K location category based POI recommendation, by introducing information coverage to encode the location categories of POIs in a city. The problem is NP-hard. We develop a greedy algorithm and further optimization to solve this challenging problem. The experimental results on two real-world datasets demonstrate the utility of new POI recommendations and the superior performance of the proposed algorithms

Named Entity Recognition Based on Multi-scale Attention

The accuracy of named entity recognition (NER) task will promote the research of multiple downstream tasks in natural language field. Due to a large number of nested semantics in text, named entities are recognized difficultly. Recognizing nested semantics becomes a difficulty in natural language processing. Previous studies have single scale of extracting feature and under-utilization of the boundary information. They ignore many details under different scales and then lead to the situation of entity recognition error or omission. Aiming at the above problems, a multi-scale attention method for named entity recognition (MSA-NER) is proposed. Firstly, the BERT model is used to obtain representation vector containing context information, and then the BiLSTM network is used to strengthen the context representation of text. Secondly, the representation vectors are enumerated and concatenated to form span information matrix. The direction information is fused to obtain richer interactive information. Thirdly, multi-head attention is used to construct multiple subspaces. Two-dimensional convolution is used to optionally aggregate text information at different scales in each subspace, so as to implement multi-scale feature fusion in each attention layer. Finally, the fused matrix is used for span classification to identify named entities. Experimental results show that the [F1] score of the proposed method reaches 81.7% and 86.8% on GENIA and ACE2005 English datasets, respectively. The proposed method demonstrates better recognition performance compared with existing mainstream models