Dynamic Causal Discovery in Imitation Learning

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Using deep reinforcement learning (DRL) to recover expert policies via imitation has been found to be promising in a wide range of applications. However, it remains a difficult task to interpret the control policy learned by the agent. Difficulties mainly come from two aspects: 1) agents in DRL are usually implemented as deep neural networks (DNNs), which are black-box models and lack in interpretability, 2) the latent causal mechanism behind agents’ decisions may vary along the trajectory, rather than staying static throughout time steps. To address these difficulties, in this paper, we propose a self-explaining imitation framework, which can expose causal relations among states and action variables behind its decisions. Specifically, a dynamic causal discovery module is designed to extract the causal graph basing on historical trajectory and current states at each time step, and a causality encoding module is designed to model the interactions among variables with discovered causal edges. After encoding causality into variable embeddings, a prediction model conducts the imitation learning on top of obtained representations. These three components are trained end-to-end, and discovered causal edges can provide interpretations on rules captured by the agent. Comprehensive experiments are conducted on the simulation dataset to analyze its causal discovery capacity, and we further test it on a real-world medical dataset MIMIC-IV. Experimental results demonstrate its potential of providing explanations behind decisions.

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Edge-based fever screening system over private 5G

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Edge computing and 5G have made it possible to perform analytics closer to the source of data and achieve super-low latency response times, which isn’t possible with centralized cloud deployment. In this paper, we present a novel fever screening system, which uses edge machine learning techniques and leverages private 5G to accurately identify and screen individuals with fever in real-time. Particularly, we present deep-learning based novel techniques for fusion and alignment of cross-spectral visual and thermal data streams at the edge. Our novel Cross-Spectral Generative Adversarial Network (CS-GAN) synthesizes visual images that have the key, representative object level features required to uniquely associate objects across visual and thermal spectrum. Two key features of CS-GAN are a novel, feature-preserving loss function that results in high-quality pairing of corresponding cross-spectral objects, and dual bottleneck residual layers with skip connections (a new, network enhancement) to not only accelerate real-time inference, but to also speed up convergence during model training at the edge. To the best of our knowledge, this is the first technique that leverages 5G networks and limited edge resources to enable real-time feature-level association of objects in visual and thermal streams (30 ms per full HD frame on an Intel Core i7-8650 4-core, 1.9GHz mobile processor). To the best of our knowledge, this is also the first system to achieve real-time operation, which has enabled fever screening of employees and guests in arenas, theme parks, airports and other critical facilities. By leveraging edge computing and 5G, our fever screening system is able to achieve 98.5% accuracy and is able to process ∼ 5X more people when compared to a centralized cloud deployment.

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InfoGCL: Information-Aware Graph Contrastive Learning

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InfoGCL: Information-Aware Graph Contrastive Learning Various graph contrastive learning models have been proposed to improve the performance of tasks on graph datasets in recent years. While effective and prevalent, these models are usually carefully customized. In particular, despite all recent work create two contrastive views, they differ in a variety of view augmentations, architectures, and objectives. It remains an open question how to build your graph contrastive learning model from scratch for particular graph tasks and datasets. In this work, we aim to fill this gap by studying how graph information is transformed and transferred during the contrastive learning process, and proposing an information-aware graph contrastive learning framework called InfoGCL. The key to the success of the proposed framework is to follow the Information Bottleneck principle to reduce the mutual information between contrastive parts while keeping task-relevant information intact at both the levels of the individual module and the entire framework so that the information loss during graph representation learning can be minimized. We show for the first time that all recent graph contrastive learning methods can be unified by our framework. Based on theoretical and empirical analysis on benchmark graph datasets, we show that InfoGCL achieves state-of-the-art performance in the settings of both graph classification and node classification tasks.

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Shaping mmWave Wireless Channel via Multi-Beam Design using Reconfigurable Intelligent Surfaces

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Millimeter-wave (mmWave) communications is considered as a key enabler towards the realization of next-generation wireless networks, due to the abundance of available spectrum at mmWave frequencies. However, mmWave suffers from high free-space path-loss and poor scattering resulting in mostly line-of-sight (LoS) channels which result in a lack of coverage. Reconfigurable intelligent surfaces (RIS), as a new paradigm, have the potential to fill the coverage holes by shaping the wireless channel. In this paper, we propose a novel approach for designing RIS with elements arranged in a uniform planar array (UPA) structure. In what we refer to as multi-beamforming, We propose and design RIS such that the reflected beam comprises multiple disjoint lobes. Moreover, the beams have optimized gain within the desired angular coverage with fairly sharp edges avoiding power leakage to other regions. We provide a closed-form low-complexity solution for the multi-beamforming design. We confirm our theoretical results by numerical analysis.

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Magic-Pipe: Self-optimizing video analytics pipelines

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Microservices-based video analytics pipelines routinely use multiple deep convolutional neural networks. We observe that the best allocation of resources to deep learning engines (or microservices) in a pipeline, and the best configuration of parameters for each engine vary over time, often at a timescale of minutes or even seconds based on the dynamic content in the video. We leverage these observations to develop Magic-Pipe, a self-optimizing video analytic pipeline that leverages AI techniques to periodically self-optimize. First, we propose a new, adaptive resource allocation technique to dynamically balance the resource usage of different microservices, based on dynamic video content. Then, we propose an adaptive microservice parameter tuning technique to balance the accuracy and performance of a microservice, also based on video content. Finally, we propose two different approaches to reduce unnecessary computations due to unavoidable mismatch of independently designed, re-usable deep-learning engines: a deep learning approach to improve the feature extractor performance by filtering inputs for which no features can be extracted, and a low-overhead graph-theoretic approach to minimize redundant computations across frames. Our evaluation of Magic-Pipe shows that pipelines augmented with self-optimizing capability exhibit application response times that are an order of magnitude better than the original pipelines, while using the same hardware resources, and achieving similar high accuracy.

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SmartSlice: Dynamic, Self-optimization of Application’s QoS requests to 5G networks

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Applications can tailor a network slice by specifying a variety of QoS attributes related to application-specific performance, function or operation. However, some QoS attributes like guaranteed bandwidth required by the application do vary over time. For example, network bandwidth needs of video streams from surveillance cameras can vary a lot depending on the environmental conditions and the content in the video streams. In this paper, we propose a novel, dynamic QoS attribute prediction technique that assists any application to make optimal resource reservation requests at all times. Standard forecasting using traditional cost functions like MAE, MSE, RMSE, MDA, etc. don’t work well because they do not take into account the direction (whether the forecasting of resources is more or less than needed), magnitude (by how much the forecast deviates, and in which direction), or frequency (how many times the forecast deviates from actual needs, and in which direction). The direction, magnitude and frequency have a direct impact on the application’s accuracy of insights, and the operational costs. We propose a new, parameterized cost function that takes into account all three of them, and guides the design of a new prediction technique. To the best of our knowledge, this is the first work that considers time-varying application requirements and dynamically adjusts slice QoS requests to 5G networks in order to ensure a balance between application’s accuracy and operational costs. In a real-world deployment of a surveillance video analytics application over 17 cameras, we show that our technique outperforms other traditional forecasting methods, and it saves 34% of network bandwidth (over a ~24 hour period) when compared to a static, one-time reservation.

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You Are What and Where You Are: Graph Enhanced Attention Network for Explainable POI Recommendation

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Point-of-interest (POI) recommendation is an emerging area of research on location-based social networks to analyze user behaviors and contextual check-in information. For this problem, existing approaches, with shallow or deep architectures, have two major drawbacks. First, for these approaches, the attributes of individuals have been largely ignored. Therefore, it would be hard, if not impossible, to gather sufficient user attribute features to have complete coverage of possible motivation factors. Second, most existing models preserve the information of users or POIs by latent representations without explicitly highlighting salient factors or signals. Consequently, the trained models with unjustifiable parameters provide few persuasive rationales to explain why users favor or dislike certain POIs and what really causes a visit. To overcome these drawbacks, we propose GEAPR, a POI recommender that is able to interpret the POI prediction in an end-to-end fashion. Specifically, GEAPR learns user representations by aggregating different factors, such as structural context, neighbor impact, user attributes, and geolocation influence. GEAPR takes advantage of a triple attention mechanism to quantify the influences of different factors for each resulting recommendation and performs a thorough analysis of the model interpretability. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed model. GEAPR is deployed and under test on an internal web server. An example interface is presented to showcase its application on explainable POI recommendation.

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Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation

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Recent multilingual pre-trained language models have achieved remarkable zero-shot performance, where the model is only finetuned on one source language and directly evaluated on target languages. In this work, we propose a self-learning framework that further utilizes unlabeled data of target languages, combined with uncertainty estimation in the process to select high-quality silver labels. Three different uncertainties are adapted and analyzed specifically for the cross lingual transfer: Language Heteroscedastic/Homoscedastic Uncertainty (LEU/LOU), Evidential Uncertainty (EVI). We evaluate our framework with uncertainties on two cross-lingual tasks including Named Entity Recognition (NER) and Natural Language Inference (NLI) covering 40 languages in total, which outperforms the baselines significantly by 10 F1 for NER on average and 2.5 accuracy for NLI.

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Recommend for a Reason: Unlocking the Power of Unsupervised Aspect-Sentiment Co-Extraction

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Compliments and concerns in reviews are valuable for understanding users’ shopping interests and their opinions with respect to specific aspects of certain items. Existing review-based recommenders favor large and complex language encoders that can only learn latent and uninterpretable text representations. They lack explicit user-attention and item-property modeling, which however could provide valuable information beyond the ability to recommend items. Therefore, we propose a tightly coupled two-stage approach, including an Aspect-Sentiment Pair Extractor (ASPE) and an Attention-Property-aware Rating Estimator (APRE). Unsupervised ASPE mines Aspect-Sentiment pairs (AS-pairs) and APRE predicts ratings using AS-pairs as concrete aspect-level evidences. Extensive experiments on seven real-world Amazon Review Datasets demonstrate that ASPE can effectively extract AS-pairs which enable APRE to deliver superior accuracy over the leading baselines.

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Retrieval, Analogy, and Composition: A framework for Compositional Generalization in Image Captioning

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Image captioning systems are expected to have the ability to combine individual concepts when describing scenes with concept combinations that are not observed during training. In spite of significant progress in image captioning with the help of the autoregressive generation framework, current approaches fail to generalize well to novel concept combinations. We propose a new framework that revolves around probing several similar image caption training instances (retrieval), performing analogical reasoning over relevant entities in retrieved prototypes (analogy), and enhancing the generation process with reasoning outcomes (composition). Our method augments the generation model by referring to the neighboring instances in the training set to produce novel concept combinations in generated captions. We perform experiments on the widely used image captioning benchmarks. The proposed models achieve substantial improvement over the compared baselines on both composition-related evaluation metrics and conventional image captioning metrics.

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