Multiorder neurons for evolutionary higher-order clustering and growth

This letter proposes to use multiorder neurons for clustering irregularly shaped data arrangements. Multiorder neurons are an evolutionary extension of the use of higher-order neurons in clustering. Higher-order neurons parametrically model complex neuron shapes by replacing the classic synaptic weight by higher-order tensors. The multiorder neuron goes one step further and eliminates two problems associated with higher-order neurons. First, it uses evolutionary algorithms to select the best neuron order for a given problem. Second, it obtains more information about the underlying data distribution by identifying the correct order for a given cluster of patterns. Empirically we observed that when the correlation of clusters found with ground truth information is used in measuring clustering accuracy, the proposed evolutionary multiorder neurons method can be shown to outperform other related clustering methods. The simulation results from the Iris, Wine, and Glass data sets show significant improvement when compared to the results obtained using self-organizing maps and higher-order neurons. The letter also proposes an intuitive model by which multiorder neurons can be grown, thereby determining the number of clusters in data

Clinical Outcomes Associated With Melanocytic Lesions Assessed Via Ancillary Gene Expression Profiling (GEP)

Aims/Objectives: Compare GEP assay prediction of 434 melanocytic lesions with dermatopathologist interpretation. Methods: Sensitivity and specificity of assay were calculated based on disagreement of assay prediction with dermatopathologist interpretation. Histologic features were recorded in disagreeing cases. Results: Eighty-five percent of lesions (369/434) had sufficient RNA for scoring. 74.2% 274/369 lesions were classified as “benign”, 11.9% (44/369) “indeterminate”, and 13.8% (51/369) “malignant”. 38/51 of lesions rendered “malignant” by dermatopathologists were classified “malignant” by assay (sensitivity = 74.5%). Lesions rendered by assay as “benign” but “malignant” by dermatopathologists were more likely to have rarer cytologic features. (13/51) lesions rendered “malignant” by dermatopathologists were classified by assay as “benign,” (4/13) or “indeterminate” (9/13). 270/318 lesions rendered “benign” by dermatopathologists were “benign” by assay (specificity = 84.9%). Of 44/369 “indeterminate” lesions, dermatopathologists rendered 9/44

Determining global mean-first-passage time of random walks on Vicsek fractals using eigenvalues of Laplacian matrices

The family of Vicsek fractals is one of the most important and frequently-studied regular fractal classes, and it is of considerable interest to understand the dynamical processes on this treelike fractal family. In this paper, we investigate discrete random walks on the Vicsek fractals, with the aim to obtain the exact solutions to the global mean first-passage time (GMFPT), defined as the average of first-passage time (FPT) between two nodes over the whole family of fractals. Based on the known connections between FPTs, effective resistance, and the eigenvalues of graph Laplacian, we determine implicitly the GMFPT of the Vicsek fractals, which is corroborated by numerical results. The obtained closed-form solution shows that the GMFPT approximately grows as a power-law function with system size (number of all nodes), with the exponent lies between 1 and 2. We then provide both the upper bound and lower bound for GMFPT of general trees, and show that leading behavior of the upper bound is the square of system size and the dominating scaling of the lower bound varies linearly with system size. We also show that the upper bound can be achieved in linear chains and the lower bound can be reached in star graphs. This study provides a comprehensive understanding of random walks on the Vicsek fractals and general treelike networks.Comment: Definitive version accepted for publication in Physical Review

Energy invariance in capillary systems

We demonstrate the continuous translational invariance of the energy of a capillary surface in contact with reconfigurable solid boundaries. We present a theoretical approach to find the energy-invariant equilibria of spherical capillary surfaces in contact with solid boundaries of arbitrary shape and examine the implications of dynamic frictional forces upon of a reconfiguration of the boundaries. Experimentally, we realise our ideas by manipulating the position of a droplet in a wedge geometry using lubricant-impregnated solid surfaces, which eliminate the contact-angle hysteresis and provide a test bed for quantifying dissipative losses out of equilibrium. Our experiments show that dissipative energy losses for an otherwise energy-invariant reconfiguration are relatively small, provided that the actuation timescale is longer than the typical relaxation timescale of the capillary surface. We discuss the wider applicability of our ideas as a pathway for liquid manipulation at no potential energy cost in low-pinning, low-friction situations

Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-based Retrofitting

Incorporating factual knowledge in knowledge graph is regarded as a promising approach for mitigating the hallucination of large language models (LLMs). Existing methods usually only use the user's input to query the knowledge graph, thus failing to address the factual hallucination generated by LLMs during its reasoning process. To address this problem, this paper proposes Knowledge Graph-based Retrofitting (KGR), a new framework that incorporates LLMs with KGs to mitigate factual hallucination during the reasoning process by retrofitting the initial draft responses of LLMs based on the factual knowledge stored in KGs. Specifically, KGR leverages LLMs to extract, select, validate, and retrofit factual statements within the model-generated responses, which enables an autonomous knowledge verifying and refining procedure without any additional manual efforts. Experiments show that KGR can significantly improve the performance of LLMs on factual QA benchmarks especially when involving complex reasoning processes, which demonstrates the necessity and effectiveness of KGR in mitigating hallucination and enhancing the reliability of LLMs

Carbohydrate metabolism in grape cultivars that differ in sucrose accumulation

Sugar concentrations and sucrose-metabolism related enzyme activities in berries and leaves were investigated during berry development using grape cultivars with different sucrose concentrations. Sucrose concentration was significantly negatively related to acid invertase activity in berries. Acid invertase showed the lowest activities in berries of high-sucrose cultivars, ‘Honey Juice’ and ‘B180’, and the highest in tracesucrose cultivars, ‘Concord’, ‘Jingxiu’, and ‘Jingya’. Acid invertase activities in berries of low-sucrose cultivar ‘Canadice’ were between high- and trace-sucrose cultivars. There was no significant difference in glucose and fructose concentrations, the activities of neutral invertase, sucrose synthase and sucrose phosphate synthase in berries among high-, low- and trace-sucrose cultivars as acid invertase. Sugar concentrations and sucrose-metabolism related enzymes activities in leaves also did not show such difference among all cultivars. The results suggest that differences in sucrose concentration in berries among grape cultivars mainly be due to acid invertase activity. In addition, the final sucrose concentration in berries at maturity for a grape cultivar might be decided at véraison, and véraison is the key period for sucrose accumulation.

Prediction feedback in intelligent traffic systems

The optimal information feedback has a significant effect on many socioeconomic systems like stock market and traffic systems aiming to make full use of resources. In this paper, we studied dynamics of traffic flow with real-time information provided and the influence of a feedback strategy named prediction feedback strategy is introduced, based on a two-route scenario in which dynamic information can be generated and displayed on the board to guide road users to make a choice. Our model incorporates the effects of adaptability into the cellular automaton models of traffic flow and simulation results adopting this optimal information feedback strategy have demonstrated high efficiency in controlling spatial distribution of traffic patterns compared with the other three information feedback strategies, i.e., vehicle number and flux.Comment: 14 pages, 15 figure