Pattern

Pattern is central in the pattern-focused case conceptualization approach. This chapter highlights the five basic treatment challenges for each of the eight common patterns in everyday clinical practice. It first defines pattern and provides a clinical strategy for quickly identifying and differentiating the basic patterns. Then, it describes eight patterns: avoidant, borderline, dependent, histrionic, narcissistic, obsessive–compulsive, paranoid, and passive–aggressive. Each of these patterns is discussed in the following format: pattern description, pattern development, pattern types and triggers, and treatment challenges. Clinicians who understand patterns and can identify them easily in clients are more likely to be able to explain and guide treatment, as well as anticipate treatment challenges.

A Quantum Circuit Optimization Framework Based on Pattern Matching

In the NISQ era, quantum computers have insufficient qubits to support quantum error correction, which can only perform shallow quantum algorithms under noisy conditions. Aiming to improve the fidelity of quantum circuits, it is necessary to reduce the circuit depth as much as possible to mitigate the coherent noise. To address the issue, we propose PaF , a Pattern matching-based quantum circuit rewriting algorithm Framework to optimize quantum circuits. The algorithm framework finds all sub-circuits satisfied in the input quantum circuit according to the given external pattern description, then replaces them with better circuit implementations. To extend the capabilities of PaF , a general pattern description format is proposed to make rewriting patterns in existing work become machine-readable. In order to evaluate the effectiveness of PaF , we employ the BIGD benchmarks in QUEKO benchmark suite to test the performance and the result shows that PaF provides a maximal speedup of [Formula: see text] by using few patterns.

Differential Microbial Pattern Description in Subjects with Autoimmune-Based Thyroid Diseases: A Pilot Study

The interaction between genetic susceptibility, epigenetic, endogenous, and environmental factors play a key role in the initiation and progression of autoimmune thyroid diseases (AITDs). Studies have shown that gut microbiota alterations take part in the development of autoimmune diseases. We have investigated the possible relationship between gut microbiota composition and the most frequent AITDs. A total of nine Hashimoto’s thyroiditis (HT), nine Graves–Basedow’s disease (GD), and 11 otherwise healthy donors (HDs) were evaluated. 16S rRNA pyrosequencing and bioinformatics analysis by Quantitative Insights into Microbial Ecology and Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) were used to analyze the gut microbiota. Beta diversity analysis showed that gut microbiota from our groups was different. We observed an increase in bacterial richness in HT and a lower evenness in GD in comparison to the HDs. GD showed a significant increase of Fusobacteriaceae, Fusobacterium and Sutterella compared to HDs and the core microbiome features showed that Prevotellaceae and Prevotella characterized this group. Victivallaceae was increased in HT and was part of their core microbiome. Streptococcaceae, Streptococcus and Rikenellaceae were greater in HT compared to GD. Core microbiome features of HT were represented by Streptococcus, Alistipes, Anaerostipes, Dorea and Haemophilus. Faecalibacterium decreased in both AITDs compared to HDs. PICRUSt analysis demonstrated enrichment in the xenobiotics degradation, metabolism, and the metabolism of cofactors and vitamins in GD patients compared to HDs. Moreover, correlation studies showed that some bacteria were widely correlated with autoimmunity parameters. A prediction model evaluated a possible relationship between predominant concrete bacteria such as an unclassified genus of Ruminococcaceae, Sutterella and Faecalibacterium in AITDs. AITD patients present altered gut microbiota compared to HDs. These alterations could be related to the immune system development in AITD patients and the loss of tolerance to self-antigens.

Communicating Compositional Patterns

How do people perceive and communicate structure? We investigate this question by letting participants play a communication game, where one player describes a pattern, and another player redraws it based on the description alone. We use this paradigm to compare two models of pattern description, one compositional (complex structures built out of simpler ones) and one noncompositional. We find that compositional patterns are communicated more effectively than noncompositional patterns, that a compositional model of pattern description predicts which patterns are harder to describe, and that this model can be used to evaluate participants’ drawings, producing humanlike quality ratings. Our results suggest that natural language can tap into a compositionally structured pattern description language.

Towards the Ensemble: IPCBR Model in Investigating Financial Bubbles

Asset value predictability remains a major research concern in financial market especially when considering the effect of unprecedented market fluctuations on the behaviour of market participants. This paper presents preliminary results toward the building a reliable forward problem on ensemble approach IPCBR model, that leverages the capabilities of Case based Reasoning(CBR) and Inverse Problem Techniques (IPTs) to describe and model abnormal stock market fluctuations (often associated with asset bubbles) using datasets from historical stock market prices. The framework uses a rich set of past observations and geometric pattern description and then applies a CBR to formulate the forward problem, Inverse Problem formulation is then applied to identify a set of parameters that can statistically be associated with the occurrence of the observed patterns. This research work presents a formative strategy aimed to determine the causes of behaviour, rather than predict future time series points which brings a novel perspective to the problem of asset bubbles predictability, and a deviation from the existing research trend. The results depict the stock dynamics and statistical fluctuating evidence associated with the envisaged bubble problem.

Case Study of Spatial Pattern Description, Identification and Application Methodology

In this case study the authors created and tested a configurable and expandable spatial patterns (SP) description, identification, and application methodology (SPDIAM) and an SP identification algorithm. SPDIAM allows urban planning and design (UPD) practitioners to describe SP in a computerized manner, identify SP automatically and then apply them in the UPD domain. SPDIAM is based on the space syntax (SS) method and normalized spatial and non-spatial measures and can be used with the statistical social, economic, and environmental indicators, which are related to the urban sustainability and spatial capital. The goal of the case study experiment was to proof a concept of SPDIAM and to identify the rules and the values of the measures used for the SP identification. For this City Layout SP was identified in the vector data of 12 European, North American, and African cities. The experiment results confirmed that SPDIAM is appropriate to describe SP and identify them automatically. The use of the normalized measures enables the comparison of different SP and reduces the degree of the subjectivity of the UPD solutions. SPDIAM no longer relies on statistical information but forms SP based on the probabilistic complex modelling of a city, which lets SPDIAM indicate possible directions of SP future transformation. SPDIAM uses the newly offered measures CENTER and URBAN COMPACTNESS INDEX to identify SP automatically and can add quantitative and qualitative improvement to the spatial network analysis tools in Geographic Information Systems.

Communicating compositional patterns

How do people perceive and communicate structure? We investigate this question by letting participants play a communication game, where one player describes a pattern, and another player redraws it based on the description alone. We use this paradigm to compare two models of pattern description, one compositional (complex structures built out of simpler ones) and one non-compositional. We find that compositional patterns are communicated more effectively than non-compositional patterns, that a compositional model of pattern description predicts which patterns are harder to describe, and that this model can be used to evaluate participants’ drawings, producing human-like quality ratings. Our results suggest that natural language can tap into a compositionally structured pattern description language.