GEM-PSO: Particle Swarm Optimization Guided by Enhanced Memory

Particle Swarm Optimization (PSO) is a widely-used nature-inspired optimization technique in which a swarm of virtual particles work together with limited communication to find a global minimum or optimum. PSO has has been successfully applied to a wide variety of practical problems, such as optimization in engineering fields, hybridization with other nature-inspired algorithms, or even general optimization problems. However, PSO suffers from a phenomenon known as premature convergence, in which the algorithm\u27s particles all converge on a local optimum instead of the global optimum, and cannot improve their solution any further. We seek to improve upon the standard Particle Swarm PSO algorithm by fixing this premature convergence behavior. We do so by storing and exploiting increased information in the form of past bests, which we deem enhanced memory. We introduce three types of modifications to each new algorithm (which we call a GEM-PSO: Particle Swarm Optimization, Guided by Enhanced Memory, because our modifications all deal with enhancing the memory of each particle). These are procedures for saving a found best, for removing a best from memory when a new one is to be added, and for selecting one (or more) bests to be used from those saved in memory. By using different combinations of these modifications, we can create many different variants of GEM-PSO that have a wide variety of behaviors and qualities. We analyze the performance of GEM-PSO, discuss the impact of PSO\u27s parameters on the algorithms\u27 performances, isolate different modifications in order to closely study their impact on the performance of any given GEM-PSO variant, and finally look at how multiple modifications perform. Finally, we draw conclusions about the efficacy and potential of GEM-PSO variants, and provide ideas for further exploration in this area of study. Many GEM-PSO variants are able to consistently outperform standard PSO on specific functions, and GEM-PSO variants can be shown to be promising, with both general and specific use cases

Exact solution of the trigonometric SU(3) spin chain with generic off-diagonal boundary reflections

The nested off-diagonal Bethe ansatz is generalized to study the quantum spin chain associated with the $SU_q(3)$ R-matrix and generic integrable non-diagonal boundary conditions. By using the fusion technique, certain closed operator identities among the fused transfer matrices at the inhomogeneous points are derived. The corresponding asymptotic behaviors of the transfer matrices and their values at some special points are given in detail. Based on the functional analysis, a nested inhomogeneous T-Q relations and Bethe ansatz equations of the system are obtained. These results can be naturally generalized to cases related to the $SU_q(n)$ algebra.Comment: published version, 27 pages, 1 table, 1 figur

JOINT SPACE NARROWING CLASSIFICATION BASED ON HAND X-RAY IMAGE

X-ray images have been widely used by radiologists for disease diagnosis. For Rheumatoid Arthritis (RA), Joint Space Narrowing (JSN) is one major symptom that can be read from X-ray images. In this thesis, we investigate the JSN classification for RA diagnosis in terms of methodology, data analysis, neural network models, performance analysis. First, we perform the statistical analysis of X-ray data and design a baseline convolutional neural network (CNN). We show algorithms to extract joint patches. Then we conduct prediction analysis. Second, we design the fusion model to harness the correlation between the same type of joints. Sharing information within one X-ray image would increase the prediction performance. We also compare unified classifiers and separate classifiers. Third, we design the attention map model for joints with complex contexts, which filters out unrelated surroundings. We conclude that our models give good JSN prediction for Rheumatoid Arthritis

Gendered Dimensions of Accountability to Address Health Workforce Shortages in Northern Nigeria

Northern Nigeria has some of the worst health indices in sub‑Saharan Africa. Poor health outcomes are the result of multiple factors, including the lack of front-line health workers in rural and hard-to-reach areas. In 2012, funded by UK aid and DFID, the Women for Health programme was created to address the issue of gendered barriers of access to health education programmes and the subsequent dearth of nurses and midwives. It emerged that a different kind of ‘accountability’ was required to achieve improved maternal health outcomes: holding to account powerful actors within the community for their role in creating barriers of access to education for women and girls, as well as barriers to the retention of female health workers. This article, drawn directly from programme activities in Jigawa, Kano, Katsina, Yobe, and Zamfara states, documents strategies to shift gender norms that limit women’s professional choices and their access to quality maternal health services.Open Society Foundations, Vozes Desiguais/Unequal Voices, Future Health Systems consortium, the Impact Initiative and Health Systems Globa

On the Bethe states of the one-dimensional supersymmetric t-J model with generic open boundaries

By combining the algebraic Bethe ansatz and the off-diagonal Bethe ansatz, we investigate the supersymmetric t-J model with generic open boundaries. The eigenvalues of the transfer matrix are given in terms of an inhomogeneous T-Q relation, and the corresponding eigenstates are expressed in terms of nested Bethe states which have well-defined homogeneous limit. This exact solution provides basis for further analyzing the thermodynamic properties and correlation functions of the model.Comment: 17 pages, 2 tables, published versio

Bethe states of the trigonometric SU(3) spin chain with generic open boundaries

By combining the algebraic Bethe ansatz and the off-diagonal Bethe ansatz, we investigate the trigonometric SU(3) model with generic open boundaries. The eigenvalues of the transfer matrix are given in terms of an inhomogeneous T-Q relation, and the corresponding eigenstates are expressed in terms of nested Bethe-type eigenstates which have well-defined homogeneous limit. This exact solution provides a basis for further analyzing the thermodynamic properties and correlation functions of the anisotropic models associated with higher rank algebras.Comment: 17 pages, 3 tables. arXiv admin note: text overlap with arXiv:1705.0947

Study of instantaneous starvation at a finite-length line contact

Starvation phenomenon widely exists in the non-conforming contacts when high-viscosity lubricating oil or greases are used. However, most of the work focuses on the steady state starvation, and the phenomenon of instantaneous starvation is not well explored by scholars. This paper experimentally studies the effect of speed, base oil viscosity and load on instantaneous starvation, and proposes some improvement measures to weaken the instantaneous starvation

A Cascaded Approach for ultraly High Performance Lesion Detection and False Positive Removal in Liver CT Scans

Liver cancer has high morbidity and mortality rates in the world. Multi-phase CT is a main medical imaging modality for detecting/identifying and diagnosing liver tumors. Automatically detecting and classifying liver lesions in CT images have the potential to improve the clinical workflow. This task remains challenging due to liver lesions' large variations in size, appearance, image contrast, and the complexities of tumor types or subtypes. In this work, we customize a multi-object labeling tool for multi-phase CT images, which is used to curate a large-scale dataset containing 1,631 patients with four-phase CT images, multi-organ masks, and multi-lesion (six major types of liver lesions confirmed by pathology) masks. We develop a two-stage liver lesion detection pipeline, where the high-sensitivity detecting algorithms in the first stage discover as many lesion proposals as possible, and the lesion-reclassification algorithms in the second stage remove as many false alarms as possible. The multi-sensitivity lesion detection algorithm maximizes the information utilization of the individual probability maps of segmentation, and the lesion-shuffle augmentation effectively explores the texture contrast between lesions and the liver. Independently tested on 331 patient cases, the proposed model achieves high sensitivity and specificity for malignancy classification in the multi-phase contrast-enhanced CT (99.2%, 97.1%, diagnosis setting) and in the noncontrast CT (97.3%, 95.7%, screening setting)

Liver Tumor Screening and Diagnosis in CT with Pixel-Lesion-Patient Network

Liver tumor segmentation and classification are important tasks in computer aided diagnosis. We aim to address three problems: liver tumor screening and preliminary diagnosis in non-contrast computed tomography (CT), and differential diagnosis in dynamic contrast-enhanced CT. A novel framework named Pixel-Lesion-pAtient Network (PLAN) is proposed. It uses a mask transformer to jointly segment and classify each lesion with improved anchor queries and a foreground-enhanced sampling loss. It also has an image-wise classifier to effectively aggregate global information and predict patient-level diagnosis. A large-scale multi-phase dataset is collected containing 939 tumor patients and 810 normal subjects. 4010 tumor instances of eight types are extensively annotated. On the non-contrast tumor screening task, PLAN achieves 95% and 96% in patient-level sensitivity and specificity. On contrast-enhanced CT, our lesion-level detection precision, recall, and classification accuracy are 92%, 89%, and 86%, outperforming widely used CNN and transformers for lesion segmentation. We also conduct a reader study on a holdout set of 250 cases. PLAN is on par with a senior human radiologist, showing the clinical significance of our results.Comment: MICCAI 2023, code: https://github.com/alibaba-damo-academy/pixel-lesion-patient-networ