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Vim4Path: Self-Supervised Vision Mamba for Histopathology Images

Vim4Path: Self-Supervised Vision Mamba for Histopathology Im...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Ali Nasiri-Sarvi Vincent Quoc-Huy Trinh Hassan Rivaz Mahdi S. Hosseini Department of Computer Science and Software Engineering (CSSE) Concordia University Canada Institute for Research in Immunology and Cancer of the University of Montreal Canada Department of Electrical and Computer Engineering (ECE) Concordia University Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Representation learning from Gigapixel Whole Slide Images (WSI) poses a significant challenge in computational pathology due to the complicated nature of tissue structures and the scarcity of labeled data. Multi-instance learning methods have addressed this challenge, leveraging image patches to classify slides utilizing pretrained models using Self-Supervised Learning (SSL) approaches. The performance of both SSL and MIL methods relies on the architecture of the feature encoder. This paper proposes leveraging the Vision Mamba (Vim) architecture, inspired by state space models, within the DINO framework for representation learning in computational pathology. We evaluate the performance of Vim against Vision Transformers (ViT) on the Camelyon16 dataset for both patch-level and slide-level classification. Our findings highlight Vim’s enhanced performance compared to ViT, particularly at smaller scales, where Vim achieves an 8.21 increase in ROC AUC for models of similar size. An explainability analysis further highlights Vim’s capabilities, which reveals that Vim uniquely emulates the pathologist workflow—unlike ViT. This alignment with human expert analysis highlights Vim’s potential in practical diagnostic settings and contributes significantly to developing effective representation-learning algorithms in computational pathology. We release the codes and pretrained weights at https://***/AtlasAnalyticsLab/Vim4Path.

关键词： Representation learning Training computer vision Adaptation models Computational modeling Self-supervised learning computer architecture

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Electrocoagulation-based AZO DYE (P4R) Removal Rate Prediction Model using Deep Learning

Electrocoagulation-based AZO DYE (P4R) Removal Rate Predicti...

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2023 International Symposium on Green Technologies and Applications, ISGTA 2023

作者： Akoulih, Meryem Tigani, Smail Byoud, Fouzia Rharib, Meryem El Saadane, Rachid Pierre, Samuel Chehri, Abdellah Ghachtouli, Sanae El IME Lab Faculty of Science Hassan 2 University Casablanca20250 Morocco Research and Development Unit Accsellium LLC Fez Morocco Electrical Engineering Department SIRC-LaGeS Hassania School of Public Labors Casablanca20250 Morocco Department of Computer and Software Engineering Polytechnique Montréal MontréalQC Canada Department of Mathematics and Computer Science Royal Military College of Canada KingstonON11 K7K 7B4 Canada

This article presents a study on the effectiveness of electrocoagulation (EC) for the removal of azo dyes from wastewater. The analysis was performed using a combination of statistical methods, including density estimation, correlation analysis, and deep learning for electrocoagulation performance prediction. The results showed that electrocoagulation was able to effectively remove azo dyes from the wastewater, considering the energy consumption and the mass of flocs being important factors in the process. Deep Learning (DL) is used to build our predictive model using the datasets collected during the experimentation stage. Overall, the findings suggest that electrocoagulation is a promising technique for the treatment of wastewater containing azo dyes, and that the use of statistical and machine learning methods can aid in the optimization of the process. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Azo dyes

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Evaluating Propagation Models for IIoT in Underground Mining: an Experimental Comparative Study in Underground Coal Mines

Evaluating Propagation Models for IIoT in Underground Mining...

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IEEE ANDESCON

作者： Fabian Medina Oscar Montañez Cristian Suancha Eduardo Avendaño Sandra Céspedes Systems and Computing Engineering school UPTC Sogamoso Colombia Electronic Engineering school UPTC Sogamoso Colombia Department of Computer Science and Software Engineering Concordia University Montreal Canada

ISBN: (数字)9798350355284

ISBN: (纸本)9798350355291

Planning the Industrial Internet of Things (IIoT) systems for underground mining is critical in guaranteeing communication between nodes (sensor/actuator) and ensuring the effectiveness of their functionalities. Therefore, it is necessary to understand how electromagnetic waves disperse in under- ground environments through models that attempt to reproduce their behavior. This understanding is critical to designing and simulating efficient communications for IIoT systems requiring reliable and continuous data transmission. This paper evaluates two propagation models for underground mining tunnels and emphasizes the analysis of the materials that make up the side walls, the floor, and the roof. The analysis of the models is compared with experimental measurements of received power in the 433 MHz band made in coal mines in Colombia. The results, which have direct practical implications, show that the True Rays model generates a behavior similar to that observed in the experimental measurements, with determination coefficients above 74%. At the same time, we assessed the impact of the standard deviation of surface roughness on the accuracy of the received power predictions in the True Rays model.

关键词： Analytical models Accuracy Predictive models Surface roughness Coal mining Rough surfaces Standards Floors Tuning Industrial Internet of Things

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SMATCH-M-LLM: Semantic Similarity in Metamodel Matching With Large Language Models

SMATCH-M-LLM: Semantic Similarity in Metamodel Matching With...

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International Mining software Repositories, MSR

作者： Nafisa Ahmed Hin Chi Kwok Mohammad Hamdaqa Wesley K. G. Assunção Department of Computer and Software Engineering Polytechnique Montréal Montréal Canada Department of Industrial and Systems Engineering Hong Kong Polytechnic University Hong Kong China Department of Computer Science North Carolina State University Raleigh USA

ISBN: (数字)9798331501839

ISBN: (纸本)9798331501846

Metamodel matching plays a crucial role in defining transformation rules in model-driven engineering by identifying correspondences between different metamodels, forming the foundation for effective transformations. Current techniques face significant challenges due to syntactical and structural heterogeneity. To address this, matching techniques often employ semantic similarity to identify correspondences. Traditional semantic matchers, however, rely on ontology matching tools or lexical databases, which often struggle when metamodels use different terminologies or hierarchical structures. Inspired by the contextual understanding capabilities of Large Language Models (LLMs), this paper explores the capability of GPT-4 potentials as a semantic matcher and alternative to existing methods for metamodel matching. However, metamodels can be large, which can overwhelm LLMs if provided in a single prompt, leading to reduced accuracy. Therefore, we propose prompting LLMs with fragments of the source and target metamodels, identifying correspondences through an iterative process. The fragments to be provided in the prompt are identified based on an initial mapping derived from their elements’ definitions. Through experiments with 10 metamodels, our results show that our LLMbased approach improves the accuracy of metamodel matching, achieving an average F-measure of $\approx 91 \%$, outperforming both the baseline and hybrid approaches, which have a maximum average F-measure of $\approx \mathbf{2 9 \%}$ and $\approx \mathbf{7 4 \%}$, respectively. Moreover, our approach surpasses single-prompt LLM-based matching, which has an average $\mathbf{F}$-measure of $\mathbf{8 0 \%}$, by approximately $\mathbf{1 1 \%}$.

关键词： Accuracy Costs Terminology Databases Large language models Semantics Ontologies Model driven engineering software Iterative methods

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Log Loading Automation for Timber-Harvesting Industry

Log Loading Automation for Timber-Harvesting Industry

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Elie Ayoub Heshan Fernando William Larrivée-Hardy Nicolas Lemieux Philippe Giguère Inna Sharf Department of Mechanical Engineering McGill University Montreal Canada FPInnovations Pointe-Claire QC Canada Computer Science and Software Engineering Department Université Laval Quebec Canada

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The timber-harvesting industry is lagging its peer industries, such as mining and agriculture, with respect to deployment of robotic, AI and autonomous technologies. In this paper, we tackle automation of a critical task that arises in transporting logs from the forest to the sawmill: the log loading operation. This work is motivated by the acute shortages of human operators and the need to improve the efficiencies of timber-harvesting processes. To this end, we demonstrate the full autonomy pipeline for the log loading operation with a fixed-base manipulator (a.k.a., the crane), starting with perception of logs around the machine, then grasp planning for where to grasp logs, through motion planning and control of the log loading maneuver. Our main contribution is in the full integration of the necessary elements to achieve a completely autonomous loading cycle, where the crane picks up and loads all logs within its reach on a trailer. Notable features of our implementation are a generalizable perception stack, a grasp planner to pick up multiple logs at a time and an extensive experimental campaign conducted outdoors, on a commercial log loader retrofitted for autonomy. Our results demonstrate an overall 87% success rate of the log loading operation, with primary failure cases due to log segmentation errors and deficiencies in the final height adjustment algorithm for grasping logs. We also present detailed timing results of the main parts of the autonomy pipeline, which support the feasibility of deployment in operational environment.

关键词： Cranes Automation Service robots Loading Pipelines Forestry Planning

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Bidirectional RoF 5G Fronthaul Transmission Links with an InAs/InP Quantum Dash Laser

Bidirectional RoF 5G Fronthaul Transmission Links with an In...

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2023 Photonics North, PN 2023

作者： Xie, Xiaoran Liu, Guocheng Zeb, Khan Liu, Jiaren Mao, Youxin Poole, Philip J. Barrios, Pedro Weber, John Vachon, Martin Sabourin, Nicaulas Zhang, Xiupu Huang, Yu Yao, Jianping Wu, Ke Lu, Zhenguo Advanced Electronics and Photonics Research Centre National Research Council Canada OttawaON Canada Concordia University IPhotonics Labs Department of Electrical and Computer Engineering MontrealQC Canada University of Ottawa Microwave Photonics Research Laboratory OttawaON Canada Poly-Grames Research Center Polytechnique Montreal MontrealQC Canada

ISBN: (纸本)9798350326734

We have experimentally demonstrated a bidirectional radio-over-fiber (RoF) 5G new radio (NR) fronthaul transmission link based on an InAs/InP quantum dash mode-locked laser (QD-MLL), which provide low noise and highly coherent optical carriers. Two of the optical carriers are used for heterodyne based millimeter wave (MMW) RoF downlink transmission. The signal of the downlink is a 2-GBaud 16-QAM signal located at 28.951 GHz after heterodyne detection. The third optical carrier is used in uplink. The signal used in the uplink is a 2-GBaud 16-QAM signal located at 4 GHz. The result is verified by error-vector-magnitude (EVM) in real-time. The EVM of the downlink and uplink is up to 9.5% and 7.9% respectively in back-to-back setup. © 2023 IEEE.

关键词： Quantum dash lasers

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A PYNQ-Based Data Acquisition System for HIL Simulations on Low-Cost FPGAs

A PYNQ-Based Data Acquisition System for HIL Simulations on ...

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2023 IEEE International Conference on Industrial Technology, ICIT 2023

作者： Gravey, Thibaut Meddah, Karim Robert, Teo Rutovic, Emmanuel Montheard, Romain Ould-Bachir, Tarek Polytechnique Montréal Motce Laboratory Department of Computer and Software Engineering MontréalQCH3T 1J4 Canada Labèze France AESIM. Tech Montréal Canada

ISBN: (纸本)9798350336504

This paper presents a generic and software configurable overlay for the acquisition and visualization of data generated by embedded processing systems implemented on FPGA. The proposed architecture takes advantage of the coupling and interaction between the PL (programmable logic) and the PS (processing system) offered by the AMD PYNQ open-source framework on Zynq and several other platforms to provide the end-user with a high level abstraction programming interface. Results, cost, performance and design limitations of the overlay are discussed around its use for HIL simulation of high switching frequency power electronic circuits. The paper demonstrates that the overlay footprint is sufficiently small to be used on low-cost FPGA platforms, and that it provides a throughput of hundreds of millions of samples per second over up to eight 16-bit independent channels, while offering the capacity to log up to 128 MB of contiguous frames of data. © 2023 IEEE.

关键词： Python

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openCAESAR: Balancing Agility and Rigor in Model-Based Systems engineering

openCAESAR: Balancing Agility and Rigor in Model-Based Syste...

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Conference on Model Driven engineering Languages and Systems Companion (MODELS-C)

作者： Maged Elaasar Nicolas Rouquette David Wagner Bentley James Oakes Abdelwahab Hamou-Lhadj Mohammad Hamdaqa NASA Jet Propulsion Lab California Institute of Technology Pasadena USA DIRO Université de Montréal Montreal Canada Computer and Software Engineering Polytechnique Montréal Montreal Canada Electrical and Computer Engineering Concordia University Montreal Canada

Model-Based System engineering (MBSE) employs models and formal languages to support development of complex (systems-of-) systems. NASA Jet Propulsion Laboratory (JPL) sees MBSE as a key approach to managing the complexity of system development. However, balancing agility and rigor in MBSE has been reported as a challenging task not yet addressed by modeling tools and frameworks. This is because existing MBSE approaches may enable agility but compromise rigor, or enhance rigor but impede agility. We discuss the challenges of balancing agility and rigor in MBSE across seven systems engineering architectural functions defined by the JPL Integrated Model-Centric engineering (IMCE) initiative. We demonstrate how openCAESAR, an open-source MBSE methodology and framework created at JPL, can strike a balance between agility and rigor through a case study of the Kepler16b project and discussion of lessons learned from past projects.

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Earth observation payloads on-board a 3U CubeSat and their scientific purpose for monitoring aerosols and surface temperature hotspots 74

Earth observation payloads on-board a 3U CubeSat and their s...

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74th International Astronautical Congress, IAC 2023

作者： Vlassov, David Hudon-Castillo, Yianni Chazbek, Rachad Restrepo-Vermette, Joël Lord-Quintric, Rémi Perraton-Elorza, Amauri Nomezine, Anthony Beltrame, Giovanni 2500 Chem. de Polytechnique MontréalQCH3T 1J4 Canada Computer and Software Engineering Department 2500 Chem. de Polytechnique MontréalQCH3T 1J4 Canada

The technical student society PolyOrbite from the university of polytechnique montreal in Québec, Canada is designing a nanosatellite mission with a homemade 3U CubeSat that will observe the Earth's surface with the help of infrared and visible spectrum cameras. These payloads will give information on aerosols and various surface temperature hotspots on earth. The CubeSat will be able to monitor cities, agricultural lands, wildfires, glaciers and more. The main goal would be to monitor Canadian grounds and work with scientists and students in various fields to help push knowledge about our planet. Besides having a scientific purpose, the CubeSat will also be used for technological demonstration. Two innovative technologies for space missions will be tested: Artificial intelligence with on earth machine learning and model sampling, and compressed sensing. To achieve these goals, the project objectives split into two categories: scientific and engineering. The scientific part will consist in making the link between scientists and engineers. The team will need to know the studied targeted area and what information to get from it. The received data will have to be analysed, making it important to design an easy-to-use platform that will encompass needed information. The engineering part consists of integrating payloads and new technologies into the CubeSat, the main challenges being: • Payload choice: Since budget and time are limited, choosing a cost-effective payload system will be mandatory. Commercial-use cameras are the number one options since they are less expensive than space grade equipment. • Payload calibration: Having a thermal payload, an adequate temperature calibration need to be done to minimize biases on temperature measurement and increase the scientific value of the data. Multiple calibration protocols will be done to assure this task. • Gathering data, filtering data and sending it to the ground station network: The data gathered from both payloads c

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Local Anomaly Detection in Videos Using Object-Centric Adversarial Learning 25th

Local Anomaly Detection in Videos Using Object-Centric Adver...

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25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Roy, Pankaj Raj Bilodeau, Guillaume-Alexandre Seoud, Lama LITIV Department of Computer and Software Engineering Polytechnique Montréal Montréal Canada Institute of Biomedical Engineering Polytechnique Montréal Montréal Canada

ISBN: (纸本)9783030687984

We propose a novel unsupervised approach based on a two-stage object-centric adversarial framework that only needs object regions for detecting frame-level local anomalies in videos. The first stage consists in learning the correspondence between the current appearance and past gradient images of objects in scenes deemed normal, allowing us to either generate the past gradient from current appearance or the reverse. The second stage extracts the partial reconstruction errors between real and generated images (appearance and past gradient) with normal object behaviour, and trains a discriminator in an adversarial fashion. In inference mode, we employ the trained image generators with the adversarially learned binary classifier for outputting region-level anomaly detection scores. We tested our method on four public benchmarks, UMN, UCSD, Avenue and ShanghaiTech and our proposed object-centric adversarial approach yields competitive or even superior results compared to state-of-the-art methods. © 2021, Springer Nature Switzerland AG.

关键词： Anomaly detection

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