Learning Accurate and Interpretable Decision Rule Sets from Neural Networks

This paper proposes a new paradigm for learning a set of independent logical rules in disjunctive normal form as an interpretable model for classification. We consider the problem of learning an interpretable decision rule set as training a neural network in a specific, yet very simple two-layer architecture. Each neuron in the first layer directly maps to an interpretable if-then rule after training, and the output neuron in the second layer directly maps to a disjunction of the first-layer rules to form the decision rule set. Our representation of neurons in this first rules layer enables us to encode both the positive and the negative association of features in a decision rule. State-of-the-art neural net training approaches can be leveraged for learning highly accurate classification models. Moreover, we propose a sparsity-based regularization approach to balance between classification accuracy and the simplicity of the derived rules. Our experimental results show that our method can generate more accurate decision rule sets than other state-of-the-art rule-learning algorithms with better accuracy-simplicity trade-offs. Further, when compared with uninterpretable black-box machine learning approaches such as random forests and full-precision deep neural networks, our approach can easily find interpretable decision rule sets that have comparable predictive performance.Comment: Published at AAAI 202

Learning Accurate and Interpretable Decision Rule Sets from Neural Networks

This thesis proposes a novel way to learn a set of the Boolean rules in disjunctive normal form as an interpretable model for binary classifications. We consider the problem of learning an interpretable decision rule set as training a neural network in a specific architecture and converting each neuron in the network into a set of minimal decision rules. This approach can easily find a set of interpretable decision rules that has similarly high predictive performance as a full-precision fully-connected deep neural network, and the method can balance between accuracy and complexity. Moreover, we prove that the set of decision rules derived from each neuron is minimum, unique, and irredundant with respect to the original neuron. Our method is competitive with several other state-of-the-art rule learning algorithms, even with fewer rules and simpler rule condition

Tabular machine learning using conjunctive threshold neural networks

We propose a novel three-layer neural network architecture with threshold activations for tabular data classification problems. The hidden layer units correspond to trainable neurons with arbitrary weights and biases and a step activation. These neurons are logically equivalent to threshold logic functions. The output layer neuron is also a threshold function that implements a conjunction of the hidden layer threshold functions. This neural network architecture can leverage state-of-the-art network training methods to achieve high prediction accuracy, and the network is designed so that minimal human understandable explanations can be readily derived from the model. Further, we employ a sparsity-promoting regularization approach to sparsify the threshold functions to simplify them, and to sparsify the output neuron so that it only depends on a small subset of hidden layer threshold functions. Experimental results show that our approach outperforms other state-of-the-art interpretable decision models in prediction accuracy

A review on silver-mediated DNA base pairs: methodology and application

Abstract The investigation of the interaction between metal ions and DNA has always attracted much attention in the fields of bioinorganic chemistry, supramolecular coordination chemistry, and DNA nanotechnology. Its mode of action can be simply divided into two aspects. On the one hand, it is non-specific electrostatic adsorption, mainly including Na+, K+, Mg2+, Ca2+ and other physiologically regulating ions; on the other hand, it is specific covalent binding, such as Pt2+, Hg2+, Ag+ and other heavy metal ions. This article focuses on the mechanism of action between Ag+ and DNA mismatch pair C-C, and summarizes its main characterization methods and various applications. It aims to provide a certain reference for the field of biological devices. With the development of cryo-electron microscopy and liquidcell TEM, the structure of C-Ag+-C is expected to be further characterized, which will be more widely used

Navigation-Oriented Topological Model Construction Algorithm for Complex Indoor Space

Indoor space information is the basis of indoor location services such as indoor navigation, path planning, emergency evacuation, etc. Focusing on indoor navigation needs, this paper proposes a fast construction algorithm for a complex indoor space topology model based on disjoint set for the problem of lacking polygon description and topological relationship expression of indoor space entity objects in building plan drawings. Firstly, the Tarjan algorithm is used for identifying the hanging edges existing in the indoor space. Secondly, each edge is stored as two different edges belonging to two adjacent polygons that share the edge. A ring structure is introduced to judge the geometric position of walls, and then an efficient disjoint set algorithm is used to perform set merging. After that, disjoint set is queried to obtain all indoor space contours and external boundary contours, thereby the complete indoor space topological relationship at multiple levels is established. Finally, the connectivity theory of graph is used for solving the problem of a complex isolated polygon in topology information generation. The experimental results show that the proposed algorithm has generality to efficiently complete the automatic construction of a topological model for complex scenarios, and effectively acquire and organize indoor space information, thus providing a good spatial cognition mode for indoor navigation

A Multi-Zone Staged Indoor Emergency Evacuation Algorithm Based on Time Equalization

Most of the existing staged evacuation algorithms only consider the impact of crowd density on evacuation partition, but do not take into account the influence of the spatial distribution of occupants and the capacity of exits on the total evacuation time (TET). Therefore, a novel indoor emergency evacuation algorithm based on time equalization is proposed in this paper. All factors affecting TET such as the position and size of each group and the capacity of exits are fully considered in the proposed algorithm, which are uniformly converted into the occupancy time of each exit. An improved Dijkstra algorithm is used to generate evacuation zones according to the proximity relationship and the occupancy time of different exits. The strategy of waiting at the starting point is adopted to ensure that all evacuees are free from congestion during the escape process. In addition, the method of group merging is proposed to further increase the balance among all zones during the partitioning process. The objectives of the proposed algorithm include minimizing the TET of all evacuees, the path length of each escape group, avoiding congestion during the escape process. The experimental results show that the proposed algorithm effectively reduces TET and the path length of groups compared with existing algorithms, which improves the efficiency of evacuation and utilization of all exits and can be applied to the various distribution and density of evacuees

Efficacy of Rg1-Oil Adjuvant on Inducing Immune Responses against Bordetella bronchiseptica in Rabbits

Bordetella bronchiseptica (B. bronchiseptica) is an obligately aerobic, oxidase- and catalase-positive, nonfermentative Gram-negative coccobacillus. This study is aimed at examining the immune effects of Rg1, Rg1 plus oil, and other common adjuvants on inactivated B. bronchiseptica vaccine in rabbits. The mechanism underlying the adjuvant effect of Rg1 plus oil on the vaccine was also explored. Rg1 (100 μg) plus oil significantly improved the immune effect of B. bronchiseptica vaccine at both the humoral and cellular levels. Rg1-oil adjuvant increased the levels of IL-2 and IL-4 in rabbits after immunization. Rg1 (100 μg) plus oil also significantly increased TLR2 expression and downregulated NF-κB in splenocytes. Rg1-oil adjuvant may increase the levels of IL-2 and IL-4 via upregulating TLR2, thereby enhancing the immune effect of B. bronchiseptica vaccine. In conclusion, Rg1 plus oil could be used as a potential vaccine adjuvant for rabbit B. bronchiseptica vaccine

Genetic Diversity and Population Structure of Fusarium commune Causing Strawberry Root Rot in Southcentral China

Strawberry plants and fruits are vulnerable to infections by a broad range of pathogens and pests. However, knowledge about the epidemiology of pathogens causing strawberry diseases is limited. In this study, we analyzed Fusarium commune, a major fungal pathogen causing strawberry root rot, from diseased strawberry root tissues in southcentral China. A total of 354 isolates were obtained from 11 locations that spanned about 700 km from both south to north and east to west. Multilocus genotypes of all isolates were obtained using seven polymorphic simple sequence repeat markers developed in this study. Our analyses revealed significant genetic diversity within each of the 11 local populations of F. commune. STRUCTURE analysis revealed that the optimal number of genetic populations for the 354 strains was two, with most local geographic populations containing isolates in both genetic clusters. Interestingly, many isolates showed allelic ancestry to both genetic clusters, consistent with recent hybridization between the two genetic clusters. In addition, though alleles and genotypes were frequently shared among local populations, statistically significant genetic differentiations were found among the local populations. However, the observed F. commune population genetic distances were not correlated with geographic distances. Together, our analyses suggest that populations of F. commune causing strawberry root rot are likely endemic to southcentral China, with each local population containing shared and unique genetic elements. Though the observed gene flow among geographic regions was relatively low, human activities will likely accelerate pathogen dispersals, resulting in the generation of new genotypes through mating and recombination

Geochemical characteristics and genesis of coalbed methane in Baode area on the eastern margin of Ordos Basin

The degree of coalbed methane exploration and development now in Baode area is relatively low. The lack of systematic understanding of the formation of coalbed methane in this area restricts the further exploration and development of coalbed methane. Based on the comprehensive study on the geochemical characteristics of coalbed methane components, hydrocarbon isotopes, water quality detection and hydrogen oxygen isotopes in coal seam in Baode area, the origin of coalbed methane in this area is discussed. According to the research, the hydrocarbon gas in the composition of coalbed methane in Baode area is mainly CH4 and a small amount of ethane. Both of their drying coefficients are more than 0.99, so they belong to the extremely dry coal bed methane. The value of δ13C(CH4) coalbed methane is on the low side and the value of δ13C(CO2) is on the high side, the mean value of δD(CH4) is -247.5‰, which shows the characteristics of terrestrial biogas. The water produced by coal seam is weak alkaline and belongs to the NaHCO3 type of water. which is similar to the surface water ion composition, salinity , δD(H2O) and δ18O(H2O) values, indicating that the hydrodynamic conditions of the coal seam in this area are more active. There is a recharge of external water, which is benificial to the mass reproduction of CH4 producing bacteria and the formation of biogas. In this area, the coalbed methane is a mixture of thermogenic and biological genesis, mainly composed of thermogenic gases and supplemented by biogenic gases generated through carbon dioxide reduction