Publications

Grow-push-prune: Aligning deep discriminants for effective structural network compression
Qing Tian
Tal Arbel
James J. Clark
2023-05-31
Computer Vision and Image Understanding (published)
doi.org
arxiv.org
HSV-2 triggers upregulation of MALAT1 in CD4+ T cells and promotes HIV latency reversal
Carl A. Pierce
Lip Nam Loh
Holly Steach
Natalia Cheshenko
Paula Preston-Hurlburt
Fengrui Zhang
Stephanie Stransky
Leah Kravets
Simone Sidoli
William Philbrick
Michel Nassar
Smita Krishnaswamy
Kevan C. Herold
Betsy C. Herold
Herpes simplex virus type 2 (HSV-2) coinfection is associated with increased HIV-1 viral loads and expanded tissue reservoirs, but the mecha… (see more)nisms are not well defined. HSV-2 recurrences result in an influx of activated CD4+ T cells to sites of viral replication and an increase in activated CD4+ T cells in peripheral blood. We hypothesized that HSV-2 induces changes in these cells that facilitate HIV-1 reactivation and replication and tested this hypothesis in human CD4+ T cells and 2D10 cells, a model of HIV-1 latency. HSV-2 promoted latency reversal in HSV-2–infected and bystander 2D10 cells. Bulk and single-cell RNA-Seq studies of activated primary human CD4+ T cells identified decreased expression of HIV-1 restriction factors and increased expression of transcripts including MALAT1 that could drive HIV replication in both the HSV-2–infected and bystander cells. Transfection of 2D10 cells with VP16, an HSV-2 protein that regulates transcription, significantly upregulated MALAT1 expression, decreased trimethylation of lysine 27 on histone H3 protein, and triggered HIV latency reversal. Knockout of MALAT1 from 2D10 cells abrogated the response to VP16 and reduced the response to HSV-2 infection. These results demonstrate that HSV-2 contributes to HIV-1 reactivation through diverse mechanisms, including upregulation of MALAT1 to release epigenetic silencing.
2023-05-31
The Journal of Clinical Investigation (published)
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… (see more)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 (published)
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… (see more) 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 (published)
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… (see more)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 (published)
doi.org
arxiv.org
Robust Scuba Diver Tracking and Recovery in Open Water Using YOLOv7, SORT, and Spiral Search
Faraz Lotfi
Khalil Virji
Gregory Dudek
Target tracking is a classic problem in computer vision, with numerous applications in robotics. However, tracking targets underwater presen… (see more)ts additional complications due to the six degrees of freedom nature of the problem and the challenging visual environment. In this paper, we address the problem of robotic underwater tracking of scuba divers by partitioning it into two parts: vision and control. We propose a new approach that exploits a highly-maneuverable underwater robot to perform experiments in open water, coupling sensing and control for improved performance. To evaluate the temporal stability of different tracking paradigms, we introduce a new metric, frame-to-frame vari-ance, which is better suited to assess the smoothness of detections from the vision side. We implement PID controllers for control and a spiral search algorithm for target recovery in case of a tracking failure. Our approach only uses observations in the image plane, eliminating the need for robot localization or camera calibration. Using a tracking-by-detection paradigm that combines YOLOv7 for target detection, a tuned filtering technique for temporal stability, and a spiral search algorithm for target recovery, we demonstrate promising performance for long-term tracking. We evaluate our proposed paradigm on the VDD-C dataset and deploy it on an underwater robot for several experiments in open water. Our outcomes show consistency with the ones in the initial studies, and the spiral search algorithm demonstrates promising performance for recapturing a target after a tracking failure. Our approach delivers promising performance for robust underwater tracking, achieving successful open-water tracking scenarios in the presence of strong water currents.
2023-05-31
Canadian Conference on Computer and Robot Vision (published)
doi.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… (see more)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 (published)
doi.org
arxiv.org
The clinical value of Aspergillus-specific IgG antibody test in the diagnosis of nonneutropenic invasive pulmonary aspergillosis
Yajie Lu
Lulu Liu
Hongxing Li
Bilin Chen
Yu Gu
Li Wang
Chunlai Feng
Cheng Chen
Yanbin Chen
Wenkui Sun
Xuefan Cui
Min Cao
Yujian Tao
Jinjin Zhong
Huanhuan Zhong
Yueyan Ni
Yuchen Cai
Mengyue Song
Xiaoguang Liu
Yi Shi … (see 1 more)
Xin Su
2023-05-31
Clinical Microbiology and Infection (published)
doi.org
The Plausibility of Sampling as an Algorithmic Theory of Sentence Processing
Jacob Louis Hoover
Morgan Sonderegger
Steven T. Piantadosi
Timothy J. O'Donnell
Words that are more surprising given context take longer to process. However, no incremental parsing algorithm has been shown to directly pr… (see more)edict this phenomenon. In this work, we focus on a class of algorithms whose runtime does naturally scale in surprisal—those that involve repeatedly sampling from the prior. Our first contribution is to show that simple examples of such algorithms predict runtime to increase superlinearly with surprisal, and also predict variance in runtime to increase. These two predictions stand in contrast with literature on surprisal theory (Hale, 2001; Levy, 2008a) which assumes that the expected processing cost increases linearly with surprisal, and makes no prediction about variance. In the second part of this paper, we conduct an empirical study of the relationship between surprisal and reading time, using a collection of modern language models to estimate surprisal. We find that with better language models, reading time increases superlinearly in surprisal, and also that variance increases. These results are consistent with the predictions of sampling-based algorithms.
2023-05-31
Open Mind (published)
doi.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… (see more)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 (preprint)
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… (see more)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 (published)
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… (see more)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 (preprint)
doi.org
arxiv.org