Publications

Interpretable deep learning architectures for improving drug response prediction performance: myth or reality?
Yihui Li
David Earl Hostallero
Amin Emad
Motivation: Recent advances in deep learning model development have enabled more accurate prediction of drug response in cancer. However, th… (voir plus)e black-box nature of these models still remains a hurdle in their adoption for precision cancer medicine. Recent efforts have focused on making these models interpretable by incorporating signaling pathway information in model architecture. While these models improve interpretability, it is unclear whether this higher interpretability comes at the cost of less accurate predictions, or a prediction improvement can also be obtained. Results: In this study, we comprehensively and systematically assessed four state-of-the-art interpretable models developed for drug response prediction to answer this question using three pathway collections. Our results showed that models that explicitly incorporate pathway information in the form of a latent layer perform worse compared to models that incorporate this information implicitly. Moreover, in most evaluation setups the best performance is achieved using a simple black-box model. In addition, replacing the signaling pathways with randomly generated pathways shows a comparable performance for the majority of these interpretable models. Our results suggest that new interpretable models are necessary to improve the drug response prediction performance. In addition, the current study provides different baseline models and evaluation setups necessary for such new models to demonstrate their superior prediction performance. Availability and Implementation: Implementation of all methods are provided inhttps://github.com/Emad-COMBINE-lab/InterpretableAI_for_DRP. Generated uniform datasets are inhttps://zenodo.org/record/7101665#.YzS79HbMKUk. Contact:amin.emad@mcgill.caSupplementary Information: Online-only supplementary data is available at the journal’s website.
2023-05-31
Bioinformatics (publié)
doi.org
Mixed-Variable PSO with Fairness on Multi-Objective Field Data Replication in Wireless Networks
Dun Yuan
Yujin Nam
Amal Feriani
Abhisek Konar
Di Wu
Seowoo Jang
Xue Liu
Gregory Dudek
Digital twins have shown a great potential in supporting the development of wireless networks. They are virtual representations of 5G/6G sys… (voir plus)tems enabling the design of machine learning and optimization-based techniques. Field data replication is one of the critical aspects of building a simulation-based twin, where the objective is to calibrate the simulation to match field performance measurements. Since wireless networks involve a variety of key performance indicators (KPIs), the replication process becomes a multi-objective optimization problem in which the purpose is to minimize the error between the simulated and field data KPIs. Unlike previous works, we focus on designing a data-driven search method to calibrate the simulator and achieve accurate and reliable reproduction of field performance. This work proposes a search-based algorithm based on mixed-variable particle swarm optimization (PSO) to find the optimal simulation parameters. Furthermore, we extend this solution to account for potential conflicts between the KPIs using a-fairness concept to adjust the importance attributed to each KPI during the search. Experiments on field data showcase the effectiveness of our approach to (i) improve the accuracy of the replication, (ii) enhance the fairness between the different KPIs, and (iii) guarantee faster convergence compared to other methods.
2023-05-31
ICC 2023 - IEEE International Conference on Communications (publié)
doi.org
arxiv.org
Multi-Agent Attention Actor-Critic Algorithm for Load Balancing in Cellular Networks
Jikun Kang
Di Wu
Ju Wang
Ekram Hossain
Xue Liu
Gregory Dudek
In cellular networks, User Equipment (UE) handoff from one Base Station (BS) to another, giving rise to the load balancing problem among the… (voir plus) BSs. To address this problem, BSs can work collaboratively to deliver a smooth migration (or handoff) and satisfy the UEs' service requirements. This paper formulates the load balancing problem as a Markov game and proposes a Robust Multi-agent Attention Actor-Critic (Robust-MA3C) algorithm that can facilitate collaboration among the BSs (i.e., agents). In particular, to solve the Markov game and find a Nash equilibrium policy, we embrace the idea of adopting a nature agent to model the system uncertainty. Moreover, we utilize the self-attention mechanism, which encourages high-performance BSs to assist low-performance BSs. In addition, we consider two types of schemes, which can facilitate load balancing for both active UEs and idle UEs. We carry out extensive evaluations by simulations, and simulation results illustrate that, compared to the state-of-the-art MARL methods, Robust-MA3C scheme can improve the overall performance by up to 45%.
2023-05-31
ICC 2023 - IEEE International Conference on Communications (publié)
doi.org
arxiv.org
Policy Reuse for Communication Load Balancing in Unseen Traffic Scenarios
Yi Tian Xu
Jimmy Li
Di Wu
M. Jenkin
Seowoo Jang
Xue Liu
Gregory Dudek
With the continuous growth in communication network complexity and traffic volume, communication load balancing solutions are receiving incr… (voir plus)easing attention. Specifically, reinforcement learning (RL)-based methods have shown impressive performance compared with traditional rule-based methods. However, standard RL methods generally require an enormous amount of data to train, and generalize poorly to scenarios that are not encountered during training. We propose a policy reuse framework in which a policy selector chooses the most suitable pre-trained RL policy to execute based on the current traffic condition. Our method hinges on a policy bank composed of policies trained on a diverse set of traffic scenarios. When deploying to an unknown traffic scenario, we select a policy from the policy bank based on the similarity between the previous-day traffic of the current scenario and the traffic observed during training. Experiments demonstrate that this framework can outperform classical and adaptive rule-based methods by a large margin.
2023-05-31
ICC 2023 - IEEE International Conference on Communications (publié)
doi.org
arxiv.org
Self-Supervised Transformer Architecture for Change Detection in Radio Access Networks
Igor Kozlov
Dmitriy Rivkin
Wei-Di Chang
Di Wu
Xue Liu
Gregory Dudek
Radio Access Networks (RANs) for telecommunications represent large agglomerations of interconnected hardware consisting of hundreds of thou… (voir plus)sands of transmitting devices (cells). Such networks undergo frequent and often heterogeneous changes caused by network operators, who are seeking to tune their system parameters for optimal performance. The effects of such changes are challenging to predict and will become even more so with the adoption of fifth-generation/sixth-generation (5G/6G) networks. Therefore, RAN monitoring is vital for network operators. We propose a self-supervised learning framework that leverages self-attention and self-distillation for this task. It works by detecting changes in Performance Measurement data, a collection of time-varying metrics which reflect a set of diverse measurements of the network performance at the cell level. Experimental results show that our approach outperforms the state of the art by 4% on a real-world based dataset consisting of about hundred thousands time series. It also has the merits of being scalable and generalizable. This allows it to provide deep insight into the specifics of mode of operation changes while relying minimally on expert knowledge.
2023-05-31
ICC 2023 - IEEE International Conference on Communications (publié)
doi.org
arxiv.org
Wuerstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models
Pablo Pernias
Dominic Rampas
Mats Leon Richter
Christopher Pal
Marc Aubreville
We introduce W\"urstchen, a novel architecture for text-to-image synthesis that combines competitive performance with unprecedented cost-eff… (voir plus)ectiveness for large-scale text-to-image diffusion models. A key contribution of our work is to develop a latent diffusion technique in which we learn a detailed but extremely compact semantic image representation used to guide the diffusion process. This highly compressed representation of an image provides much more detailed guidance compared to latent representations of language and this significantly reduces the computational requirements to achieve state-of-the-art results. Our approach also improves the quality of text-conditioned image generation based on our user preference study. The training requirements of our approach consists of 24,602 A100-GPU hours - compared to Stable Diffusion 2.1's 200,000 GPU hours. Our approach also requires less training data to achieve these results. Furthermore, our compact latent representations allows us to perform inference over twice as fast, slashing the usual costs and carbon footprint of a state-of-the-art (SOTA) diffusion model significantly, without compromising the end performance. In a broader comparison against SOTA models our approach is substantially more efficient and compares favorably in terms of image quality. We believe that this work motivates more emphasis on the prioritization of both performance and computational accessibility.
2023-05-31
ArXiv (prépublication)
arxiv.org
arxiv.org
Reinforcement learning for communication load balancing: approaches and challenges
Di Wu
Jimmy Li
Amal Ferini
Yi Tian Xu
M. Jenkin
Seowoo Jang
Xue Liu
Gregory Dudek
The amount of cellular communication network traffic has increased dramatically in recent years, and this increase has led to a demand for e… (voir plus)nhanced network performance. Communication load balancing aims to balance the load across available network resources and thus improve the quality of service for network users. Most existing load balancing algorithms are manually designed and tuned rule-based methods where near-optimality is almost impossible to achieve. Furthermore, rule-based methods are difficult to adapt to quickly changing traffic patterns in real-world environments. Reinforcement learning (RL) algorithms, especially deep reinforcement learning algorithms, have achieved impressive successes in many application domains and offer the potential of good adaptabiity to dynamic changes in network load patterns. This survey presents a systematic overview of RL-based communication load-balancing methods and discusses related challenges and opportunities. We first provide an introduction to the load balancing problem and to RL from fundamental concepts to advanced models. Then, we review RL approaches that address emerging communication load balancing issues important to next generation networks, including 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions for applying RL for communication load balancing.
2023-05-30
Frontiers of Computer Science (publié)
doi.org
Responsible Design Patterns for Machine Learning Pipelines
Saud Hakem Al Harbi
Lionel Nganyewou Tidjon
Foutse Khomh
Integrating ethical practices into the AI development process for artificial intelligence (AI) is essential to ensure safe, fair, and respon… (voir plus)sible operation. AI ethics involves applying ethical principles to the entire life cycle of AI systems. This is essential to mitigate potential risks and harms associated with AI, such as algorithm biases. To achieve this goal, responsible design patterns (RDPs) are critical for Machine Learning (ML) pipelines to guarantee ethical and fair outcomes. In this paper, we propose a comprehensive framework incorporating RDPs into ML pipelines to mitigate risks and ensure the ethical development of AI systems. Our framework comprises new responsible AI design patterns for ML pipelines identified through a survey of AI ethics and data management experts and validated through real-world scenarios with expert feedback. The framework guides AI developers, data scientists, and policy-makers to implement ethical practices in AI development and deploy responsible AI systems in production.
2023-05-30
ArXiv (prépublication)
doi.org
arxiv.org
Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior
Ayush K Chakravarthy
Trang M. Nguyen
Anirudh Goyal
Yoshua Bengio
Michael Curtis Mozer
2023-05-30
ArXiv (prépublication)
doi.org
arxiv.org
A graphical user interface for calculating the arterial input function during dynamic positron emission tomography
Y. Daoud
Liam Carroll
S. Enger
Purpose. Dynamic positron emission tomography (dPET) requires the acquisition of the arterial input function (AIF), conventionally obtained … (voir plus)via invasive arterial blood sampling. To obtain the AIF non-invasively, our group developed and combined two novel solutions consisting of (1) a detector, placed on a patient’s wrist during the PET scans to measure the radiation leaving the wrist and (2) a Geant4-based Monte Carlo simulation software. The simulations require patient-specific wrist geometry. The aim of this study was to develop a graphical user interface (GUI) allowing the user to import 2D ultrasound scans of a patient’s wrist, and measure the wrist features needed to calculate the AIF. Methods. The GUI elements were implemented using Qt5 and VTK-8.2.0. The user imports a patient’s wrist ultrasound scans, measures the radial artery and veins’ surface and depth to model a wrist phantom, then specifies the radioactive source used during the dPET scan. The phantom, the source, and the number of decay events are imported into the Geant4-based Monte Carlo software to run a simulation. In this study, 100 million decays of 18F and 68Ga were simulated in a wrist phantom designed based on an ultrasound scan. The detector’s efficiency was calculated and the results were analyzed using a clinical data processing algorithm developed in a previous study. Results. The detector’s total efficiency decreased by 3.5% for 18F and by 51.7% for 68Ga when using a phantom based on ultrasound scans compared to a generic wrist phantom. Similarly, the data processing algorithm’s accuracy decreased when using the patient-specific phantom, giving errors greater than 1.0% for both radioisotopes. Conclusions. This toolkit enables the user to run Geant4-based Monte Carlo simulations for dPET detector development applications using a patient-specific wrist phantom. Leading to a more precise simulation of the developed detector during dPET and the calculation of a personalized AIF.
2023-05-29
Physics in Medicine & Biology (publié)
doi.org
The neuroconnectionist research programme
Adrien Doerig
R. Sommers
Katja Seeliger
Blake Aaron Richards
J. Ismael
Grace W. Lindsay
Konrad Paul Kording
Talia Konkle
M. Gerven
Nikolaus Kriegeskorte
Tim Kietzmann
2023-05-29
Nature Reviews Neuroscience (publié)
doi.org
arxiv.org
Testing Feedforward Neural Networks Training Programs
Houssem Ben Braiek
Foutse Khomh
2023-05-25
ACM Transactions on Software Engineering and Methodology (publié)
doi.org
arxiv.org