A Multiagent Evolutionary Algorithm for the Resource-Constrained Project Portfolio Selection and Scheduling Problem

A multiagent evolutionary algorithm is proposed to solve the resource-constrained project portfolio selection and scheduling problem. The proposed algorithm has a dual level structure. In the upper level a set of agents make decisions to select appropriate project portfolios. Each agent selects its project portfolio independently. The neighborhood competition operator and self-learning operator are designed to improve the agent’s energy, that is, the portfolio profit. In the lower level the selected projects are scheduled simultaneously and completion times are computed to estimate the expected portfolio profit. A priority rule-based heuristic is used by each agent to solve the multiproject scheduling problem. A set of instances were generated systematically from the widely used Patterson set. Computational experiments confirmed that the proposed evolutionary algorithm is effective for the resource-constrained project portfolio selection and scheduling problem

Mining the Relationship between Emoji Usage Patterns and Personality

Emojis have been widely used in textual communications as a new way to convey nonverbal cues. An interesting observation is the various emoji usage patterns among different users. In this paper, we investigate the correlation between user personality traits and their emoji usage patterns, particularly on overall amounts and specific preferences. To achieve this goal, we build a large Twitter dataset which includes 352,245 users and over 1.13 billion tweets associated with calculated personality traits and emoji usage patterns. Our correlation and emoji prediction results provide insights into the power of diverse personalities that lead to varies emoji usage patterns as well as its potential in emoji recommendation tasks.Comment: To appear at The International AAAI Conference on Web and Social Media (ICWSM) 201

Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors

Visual localization is an attractive problem that estimates the camera localization from database images based on the query image. It is a crucial task for various applications, such as autonomous vehicles, assistive navigation and augmented reality. The challenging issues of the task lie in various appearance variations between query and database images, including illumination variations, dynamic object variations and viewpoint variations. In order to tackle those challenges, Panoramic Annular Localizer into which panoramic annular lens and robust deep image descriptors are incorporated is proposed in this paper. The panoramic annular images captured by the single camera are processed and fed into the NetVLAD network to form the active deep descriptor, and sequential matching is utilized to generate the localization result. The experiments carried on the public datasets and in the field illustrate the validation of the proposed system.Comment: Accepted by ITSC 201

Remote Medication Status Prediction for Individuals with Parkinson's Disease using Time-series Data from Smartphones

Medication for neurological diseases such as the Parkinson's disease usually happens remotely away from hospitals. Such out-of-lab environments pose challenges in collecting timely and accurate health status data. Individual differences in behavioral signals collected from wearable sensors also lead to difficulties in adopting current general machine learning analysis pipelines. To address these challenges, we present a method for predicting the medication status of Parkinson's disease patients using the public mPower dataset, which contains 62,182 remote multi-modal test records collected on smartphones from 487 patients. The proposed method shows promising results in predicting three medication statuses objectively: Before Medication (AUC=0.95), After Medication (AUC=0.958), and Another Time (AUC=0.976) by examining patient-wise historical records with the attention weights learned through a Transformer model. Our method provides an innovative way for personalized remote health sensing in a timely and objective fashion which could benefit a broad range of similar applications.Comment: Accepted to ICDH-2023. Camera ready with supplementary materia

The timing and cause of glacial activity during the last glacial in central Tibet based on Be-10 surface exposure dating east of Mount Jaggang, the Xainza range

Mountain glaciers are sensitive to climate change, and can provide valuable information for inferring former climates on the Tibetan Plateau (TP). The increasing glacial chronologies indicate that the timing of the local Last Glacial Maximum (LGM) recorded across the TP is asynchronous, implying different local influences of the mid-latitude westerlies and Asian Summer Monsoon in triggering glacier advances. However, the well-dated sites are still too few, especially in the transition zone between regions controlled by the two climate systems. Here we present detailed last glacial chronologies for the Mount Jaggang area, in the Xainza range, central Tibet, with forty-three apparent Be-10 exposure-ages ranging from 12.4 +/- 0.8 ka to 61.9 +/- 3.8 ka. These exposure-ages indicate that at least seven glacial episodes occurred during the last glacial cycle east of Mount Jaggang. These include: a local LGM that occurred at similar to 61.9 +/- 3.8 ka, possibly corresponding to Marine Isotope Stage 4 (MIS 4); subsequent glacial advances at similar to 43.2 +/- 2.6 ka and similar to 35.1 +/- 2.1 ka during MIS 3; one glacial re-advance/standstill at MIS3/2 transition (similar to 29.8 +/- 1.8 ka); and three glacial re-advances/standstills that occurred following MIS 3 at similar to 27.9 +/- 1.7 ka, similar to 21.8 +/- 13 ka, and similar to 15.1 +/- 0.9 ka. The timing of these glacial activities is roughly in agreement with North Atlantic millennial-scale climate oscillations (Heinrich events), suggesting the potential correlations between these abrupt climate changes and glacial fluctuations in the Mount Jaggang area. The successively reduced glacial extent might have resulted from an overall decrease in Asian Summer Monsoon intensity over this timeframe. (C) 2018 Elsevier Ltd. All rights reserved

Enhancing Representation in Radiography-Reports Foundation Model: A Granular Alignment Algorithm Using Masked Contrastive Learning

Recently, multi-modal vision-language foundation models have gained significant attention in the medical field. While these models offer great opportunities, they still face a number of challenges, such as the requirement for fine-grained knowledge understanding in computer-aided diagnosis and capability of utilizing very limited or no task-specific labeled data in real-world clinical applications. In this study, we present MaCo, a novel multi-modal medical foundation model that explores masked contrastive learning to achieve granular alignment and zero-shot learning for a variety of medical imaging tasks. MaCo incorporates a correlation weighting mechanism to adjust the correlation between masked image patches and their corresponding reports, thereby enhancing the representation learning capabilities. We evaluate MaCo on six well-known open-source X-ray datasets, and the experimental results show it outperforms seven state-of-the-art approaches for classification, segmentation, and zero-shot phase grounding, demonstrating its great potential to promote a wide range of medical image analysis tasks