Simulation Of Emergency Medical Materials Collaborative Distribution Considering Supplier Clustering,
2022
1.China Research Center for Emergency Management, Wuhan University of Technology, Wuhan 430070, China;2.School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan 430070, China;
Simulation Of Emergency Medical Materials Collaborative Distribution Considering Supplier Clustering, Zhe Wang, Hongyuan Shao, Zihao Cong, Wenwen Ma
Journal of System Simulation
Abstract: Aiming at the insufficient local government medical material reserves and low distribution efficiency to public health emergencies, consider supplier clustering a two-stage emergency medical supplies public-private collaborative location and alloation model is designed. In for disaster preparedness, fuzzy clustering algorithm to realize supplier clustering, and a cooperation mechanism with the government is establish realize the joint storage of emergency medical supplies; In the early stage of rescue, relying on the government and various suppliers' material storage and transportation capabilities, a location allocation simulation model to minimize the weighted sum of total logistics cost and demand unsatisfied rate is …
Research On High-Performance Emulation Technology Of Starlink Constellation Based On Cloud Platform,
2022
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China;
Research On High-Performance Emulation Technology Of Starlink Constellation Based On Cloud Platform, Yuan Liu, Xinyi Xue, Xiaofeng Wang
Journal of System Simulation
Abstract: Network emulation on Starlink constellation is an important verification and evaluation tool for the design and construction of low earth orbit constellations in the future. Aiming at the characteristics of large-scale and complex structure of the Starlink network, a high-performance satellite network emulation system is designed. Based on the distributed network emulation architecture of cloud platform, through the development of STK Engine underlying interface, satellite model library storage optimization and asynchronous message transmission technology, the rapid deployment of Starlink constellation is carried out, and has good scalability. The experimental results show that the proposed method can carry out …
Analysis And Research On End-To-End Optical Image Quality Of Large Aperture Off-Axis Space Telescope,
2022
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China;
Analysis And Research On End-To-End Optical Image Quality Of Large Aperture Off-Axis Space Telescope, Zhang Ban, Xiaobo Li, Xun Yang, Yuxi Jiang
Journal of System Simulation
Abstract: In order to evaluate the image quality of space on orbit telescope under the comprehensive constraints, the chain calculation analysis method is adopted. The main error factors of the optical image quality degradation are divided into six static errors and two dynamic errors. After calculation, the optical system wavefront aberration under the static loading error is reduced to 0.057λ on average. Under the influence of the static and dynamic errors, the 80% energy concentration of point spread function increases gradually. The method can be used to analyze and evaluate the influence of image stabilization control and precise temperature …
Agglomerative Hierarchical Clustering With Dynamic Time Warping For Household Load Curve Clustering,
2022
Western University
Agglomerative Hierarchical Clustering With Dynamic Time Warping For Household Load Curve Clustering, Fadi Almahamid, Katarina Grolinger
Electrical and Computer Engineering Publications
Energy companies often implement various demand response (DR) programs to better match electricity demand and supply by offering the consumers incentives to reduce their demand during critical periods. Classifying clients according to their consumption patterns enables targeting specific groups of consumers for DR. Traditional clustering algorithms use standard distance measurement to find the distance between two points. The results produced by clustering algorithms such as K-means, K-medoids, and Gaussian Mixture Models depend on the clustering parameters or initial clusters. In contrast, our methodology uses a shape-based approach that combines Agglomerative Hierarchical Clustering (AHC) with Dynamic Time Warping (DTW) to classify …
An Investigation Of The Reconstruction Capacity Of Stacked Convolutional Autoencoders For Log-Mel-Spectrograms,
2022
Technological University Dublin
An Investigation Of The Reconstruction Capacity Of Stacked Convolutional Autoencoders For Log-Mel-Spectrograms, Anastasia Natsiou, Luca Longo, Seán O'Leary
Conference Papers
In audio processing applications, the generation of expressive sounds based on high-level representations demonstrates a high demand. These representations can be used to manipulate the timbre and influence the synthesis of creative instrumental notes. Modern algorithms, such as neural networks, have inspired the development of expressive synthesizers based on musical instrument timbre compression. Unsupervised deep learning methods can achieve audio compression by training the network to learn a mapping from waveforms or spectrograms to low-dimensional representations. This study investigates the use of stacked convolutional autoencoders for the compression of time-frequency audio representations for a variety of instruments for a single …
Tutorial: Neuro-Symbolic Ai For Mental Healthcare,
2022
University of South Carolina - Columbia
Tutorial: Neuro-Symbolic Ai For Mental Healthcare, Kaushik Roy, Usha Lokala, Manas Gaur, Amit Sheth
Publications
Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …
Visual Object Tracking With Discriminative Filters And Siamese Networks: A Survey And Outlook,
2022
Khalifa University of Science and Technology
Visual Object Tracking With Discriminative Filters And Siamese Networks: A Survey And Outlook, Sajid Javed, Martin Danelljan, Fahad Shahbaz Khan, Muhammad Haris Khan, Michael Felsberg, Jiri Matas
Computer Vision Faculty Publications
Accurate and robust visual object tracking is one of the most challenging and fundamental computer vision problems. It entails estimating the trajectory of the target in an image sequence, given only its initial location, and segmentation, or its rough approximation in the form of a bounding box. Discriminative Correlation Filters (DCFs) and deep Siamese Networks (SNs) have emerged as dominating tracking paradigms, which have led to significant progress. Following the rapid evolution of visual object tracking in the last decade, this survey presents a systematic and thorough review of more than 90 DCFs and Siamese trackers, based on results in …
Ps-Arm: An End-To-End Attention-Aware Relation Mixer Network For Person Search,
2022
Mohamed bin Zayed University of Artificial Intelligence
Ps-Arm: An End-To-End Attention-Aware Relation Mixer Network For Person Search, Mustansar Fiaz, Hisham Cholakkal, Sanath Narayan, Rao Anwer, Fahad Shahbaz Khan
Computer Vision Faculty Publications
Person search is a challenging problem with various real-world applications, that aims at joint person detection and re-identification of a query person from uncropped gallery images. Although, previous study focuses on rich feature information learning, it’s still hard to retrieve the query person due to the occurrence of appearance deformations and background distractors. In this paper, we propose a novel attention-aware relation mixer (ARM) module for person search, which exploits the global relation between different local regions within RoI of a person and make it robust against various appearance deformations and occlusion. The proposed ARM is composed of a relation …
Artificial Intelligence And The Situational Rationality Of Diagnosis: Human Problem-Solving And The Artifacts Of Health And Medicine,
2022
CUNY Graduate Center
Artificial Intelligence And The Situational Rationality Of Diagnosis: Human Problem-Solving And The Artifacts Of Health And Medicine, Michael W. Raphael
Publications and Research
What is the problem-solving capacity of artificial intelligence (AI) for health and medicine? This paper draws out the cognitive sociological context of diagnostic problem-solving for medical sociology regarding the limits of automation for decision-based medical tasks. Specifically, it presents a practical way of evaluating the artificiality of symptoms and signs in medical encounters, with an emphasis on the visualization of the problem-solving process in doctor-patient relationships. In doing so, the paper details the logical differences underlying diagnostic task performance between man and machine problem-solving: its principle of rationality, the priorities of its means of adaptation to abstraction, and the effects …
Hyperfast Second-Order Local Solvers For Efficient Statistically Preconditioned Distributed Optimization,
2022
Weierstrass Institute for Applied Analysis and Stochastics
Hyperfast Second-Order Local Solvers For Efficient Statistically Preconditioned Distributed Optimization, Pavel Dvurechensky, Dmitry Kamzolov, Aleksandr Lukashevich, Soomin Lee, Erik Ordentlich, César A. Uribe, Alexander Gasnikov
Machine Learning Faculty Publications
Statistical preconditioning enables fast methods for distributed large-scale empirical risk minimization problems. In this approach, multiple worker nodes compute gradients in parallel, which are then used by the central node to update the parameter by solving an auxiliary (preconditioned) smaller-scale optimization problem. The recently proposed Statistically Preconditioned Accelerated Gradient (SPAG) method [1] has complexity bounds superior to other such algorithms but requires an exact solution for computationally intensive auxiliary optimization problems at every iteration. In this paper, we propose an Inexact SPAG (InSPAG) and explicitly characterize the accuracy by which the corresponding auxiliary subproblem needs to be solved to guarantee …
A Survey On Multimodal Disinformation Detection,
2022
Qatar Computing Research Institute
A Survey On Multimodal Disinformation Detection, Firoj Alam, Stefano Cresci, Tanmoy Chakraborty, Fabrizio Silvestri, Dimitar Dimitrov, Giovanni Da San Martino, Shaden Shaar, Hamed Firooz, Preslav Nakov
Natural Language Processing Faculty Publications
Recent years have witnessed the proliferation of offensive content online such as fake news, propaganda, misinformation, and disinformation. While initially this was mostly about textual content, over time images and videos gained popularity, as they are much easier to consume, attract more attention, and spread further than text. As a result, researchers started leveraging different modalities and combinations thereof to tackle online multimodal offensive content. In this study, we offer a survey on the state-of-the-art on multimodal disinformation detection covering various combinations of modalities: text, images, speech, video, social media network structure, and temporal information. Moreover, while some studies focused …
Noisy Label Regularisation For Textual Regression,
2022
University of Melbourne
Noisy Label Regularisation For Textual Regression, Yuxia Wang, Timothy Baldwin, Karin Verspoor
Natural Language Processing Faculty Publications
Training with noisy labelled data is known to be detrimental to model performance, especially for high-capacity neural network models in low-resource domains. Our experiments suggest that standard regularisation strategies, such as weight decay and dropout, are ineffective in the face of noisy labels. We propose a simple noisy label detection method that prevents error propagation from the input layer. The approach is based on the observation that the projection of noisy labels is learned through memorisation at advanced stages of learning, and that the Pearson correlation is sensitive to outliers. Extensive experiments over real-world human-disagreement annotations as well as randomly-corrupted …
Lemurs Optimizer: A New Metaheuristic Algorithm For Global Optimization,
2022
Mohamed Bin Zayed University of Artificial Intelligence
Lemurs Optimizer: A New Metaheuristic Algorithm For Global Optimization, Ammar Kamal Abasi, Sharif Naser Makhadmeh, Mohammed Azmi Al-Betar, Osama Ahmad Alomari, Mohammed A. Awadallah, Zaid Abdi Alkareem Alyasseri, Iyad Abu Doush, Ashraf Elnagar, Eman H. Alkhammash, Myriam Hadjouni
Machine Learning Faculty Publications
The Lemur Optimizer (LO) is a novel nature-inspired algorithm we propose in this paper. This algorithm’s primary inspirations are based on two pillars of lemur behavior: leap up and dance hub. These two principles are mathematically modeled in the optimization context to handle local search, exploitation, and exploration search concepts. The LO is first benchmarked on twenty-three standard optimization functions. Additionally, the LO is used to solve three real-world problems to evaluate its performance and effectiveness. In this direction, LO is compared to six well-known algorithms: Salp Swarm Algorithm (SSA), Artificial Bee Colony (ABC), Sine Cosine Algorithm (SCA), Bat Algorithm …
Artificial Intelligence, Consumers, And The Experience Economy,
2022
Singapore Management University
Artificial Intelligence, Consumers, And The Experience Economy, Hannah H. Chang, Anirban Mukherjee
Research Collection Lee Kong Chian School Of Business
The term Artificial Intelligence (AI) was first used by McCarthy, Minsky, Rochester, and Shannon in a proposal for a summer research project in 1955 (Solomonoff, 1985). It is widely and commonly defined to be “the science and engineering of making intelligent machines” (McCarthy, 2006). Recent technological advances and methodological developments have made AI pervasive in new marketing offerings, ranging from self-driving cars, intelligent voice assistants such as Amazon’s Alexa, to burger-making robots at restaurants and rack-moving robots inside warehouses such as Amazon’s family of robots (Kiva, Pegasus, Xanthus) and delivery drones. There is optimism, and perhaps even over-optimism, of the …
Wave-Vit: Unifying Wavelet And Transformers For Visual Representation Learning,
2022
JD Explore Academy
Wave-Vit: Unifying Wavelet And Transformers For Visual Representation Learning, Ting Yao, Yingwei Pan, Yehao Li, Chong-Wah Ngo, Tao Mei
Research Collection School Of Computing and Information Systems
Multi-scale Vision Transformer (ViT) has emerged as a powerful backbone for computer vision tasks, while the self-attention computation in Transformer scales quadratically w.r.t. the input patch number. Thus, existing solutions commonly employ down-sampling operations (e.g., average pooling) over keys/values to dramatically reduce the computational cost. In this work, we argue that such over-aggressive down-sampling design is not invertible and inevitably causes information dropping especially for high-frequency components in objects (e.g., texture details). Motivated by the wavelet theory, we construct a new Wavelet Vision Transformer (Wave-ViT) that formulates the invertible down-sampling with wavelet transforms and self-attention learning in a unified way. …
Pandemic Time And Tourism In Oecd Countries: Artificial Intelligence And Digital Platforms,
2022
Universita della Campania Luigi Vanvitelli
Pandemic Time And Tourism In Oecd Countries: Artificial Intelligence And Digital Platforms, Alfonso Marino, Paolo Pariso, Michele Picariello
University of South Florida (USF) M3 Publishing
Introduction underline the three phases related to sector crisis, Background, starting from literature highlight the importance of what are the main actions implemented in 38 Member States. Methodology, with SPAD, elaborates a qualitative and quantitative set of policy responses that are displayed in Results. Discussions highlight the different approaches within the OECD area, but also the absence of a common strategy to exit to the sector crisis. The conclusion emphasizes that crisis response policies still need to be built and developed in the OECD area, even though initial responses showed strong responses in individual Member States that did not address …
Pros: An Efficient Pattern-Driven Compressive Sensing Framework For Low-Power Biopotential-Based Wearable With On-Chip Intelligence,
2022
Singapore Management University
Pros: An Efficient Pattern-Driven Compressive Sensing Framework For Low-Power Biopotential-Based Wearable With On-Chip Intelligence, Nhat Pham, Hong Jia, Minh Tran, Tuan Dinh, Nam Bui, Young Kwon, Dong Ma, Phuc Nguyen, Cecilia Mascolo, Tam Vu
Research Collection School Of Computing and Information Systems
While the global healthcare market of wearable devices has been growing signi!cantly in recent years and is predicted to reach $60 billion by 2028, many important healthcare applications such as seizure monitoring, drowsiness detection, etc. have not been deployed due to the limited battery lifetime, slow response rate, and inadequate biosignal quality. This study proposes PROS, an e"cient pattern-driven compressive sensing framework for low-power biopotential-based wearables. PROS eliminates the conventional trade-o# between signal quality, response time, and power consumption by introducing tiny pattern recognition primitives and a pattern-driven compressive sensing technique that exploits the sparsity of biosignals. Specifically, we (i) …
Pruning The Communication Bandwidth Between Reinforcement Learning Agents Through Causal Inference: An Innovative Approach To Designing A Smart Grid Power System,
2022
Dalian University of Technology
Pruning The Communication Bandwidth Between Reinforcement Learning Agents Through Causal Inference: An Innovative Approach To Designing A Smart Grid Power System, Xianjie Zhang, Yu Liu, Wenjun Li, Chen Gong
Research Collection School Of Computing and Information Systems
Electricity demands are increasing significantly and the traditional power grid system is facing huge challenges. As the desired next-generation power grid system, smart grid can provide secure and reliable power generation, and consumption, and can also realize the system’s coordinated and intelligent power distribution. Coordinating grid power distribution usually requires mutual communication between power distributors to accomplish coordination. However, the power network is complex, the network nodes are far apart, and the communication bandwidth is often expensive. Therefore, how to reduce the communication bandwidth in the cooperative power distribution process task is crucially important. One way to tackle this problem …
Hierarchical Hourglass Convolutional Network For Efficient Video Classification,
2022
[email protected]
Hierarchical Hourglass Convolutional Network For Efficient Video Classification, Yi Tan, Yanbin Hao, Hao Zhang, Shuo Wang
PhD Student’s Publications Collection
Videos naturally contain dynamic variation over the temporal axis, which will result in the same visual clues (e.g., semantics, objects) changing their scale, position, and perspective patterns between adjacent frames. A primary trend in video CNN is adopting spatial-2D convolution for spatial semantics and temporal-1D convolution for temporal dynamics. Though the direction achieves a favorable balance between efficiency and efficacy, it suffers from misalignment of visual clues with large displacements. Particularly, rigid temporal convolution would fail to capture correct motions when a specific target moves out of the reception field of temporal convolution between adjacent frames.To tackle large visual displacements …
Editing Out-Of-Domain Gan Inversion Via Differential Activations,
2022
Singapore Management University
Editing Out-Of-Domain Gan Inversion Via Differential Activations, Haorui Song, Yong Du, Tianyi Xiang, Junyu Dong, Jing Qin, Shengfeng He
Research Collection School Of Computing and Information Systems
Despite the demonstrated editing capacity in the latent space of a pretrained GAN model, inverting real-world images is stuck in a dilemma that the reconstruction cannot be faithful to the original input. The main reason for this is that the distributions between training and real-world data are misaligned, and because of that, it is unstable of GAN inversion for real image editing. In this paper, we propose a novel GAN prior based editing framework to tackle the out-of-domain inversion problem with a composition-decomposition paradigm. In particular, during the phase of composition, we introduce a differential activation module for detecting semantic …
