CAT: Beyond Efficient Transformer for Content-Aware Anomaly Detection in Event Sequences

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It is critical and important to detect anomalies in event sequences, which becomes widely available in many application domains. Indeed, various efforts have been made to capture abnormal patterns from event sequences through sequential pattern analysis or event representation learning. However, existing approaches usually ignore the semantic information of event content. To this end, in this paper, we propose a self-attentive encoder-decoder transformer framework, Content-Aware Transformer CAT, for anomaly detection in event sequences. In CAT, the encoder learns preamble event sequence representations with content awareness, and the decoder embeds sequences under detection into a latent space, where anomalies are distinguishable. Specifically, the event content is first fed to a content-awareness layer, generating representations of each event. The encoder accepts preamble event representation sequence, generating feature maps. In the decoder, an additional token is added at the beginning of the sequence under detection, denoting the sequence status. A one-class objective together with sequence reconstruction loss is collectively applied to train our framework under the label efficiency scheme. Furthermore, CAT is optimized under a scalable and efficient setting. Finally, extensive experiments on three real-world datasets demonstrate the superiority of CAT.

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A Deep Learning Framework for Detecting and Localizing Abnormal Pedestrian Behaviors at Grade Crossings

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This paper presents a deep learning-based framework to detect and localize the pedestrians’ anomaly behaviors in videos captured at the grade crossing. A skeleton detection and tracking algorithm are employed to capture the key point trajectories of body movements of the pedestrians. A deep recurrent neural network is applied to learn the normal patterns of pedestrians’ movements using dynamics skeleton trajectories features. An anomaly behaviors detection and localization algorithm are developed by analyzing each pedestrian’s reconstructed trajectories. In the experiments, a video dataset involving normal pedestrian behaviors is established by collecting data at multiple grade crossing spots with different camera angles. Then the proposed framework is trained on the dataset to learn the regularity patterns of normal pedestrians and localize the anomaly behaviors during the testing phase. To the best of our knowledge, it is the first attempt to analyze pedestrians’ behavior at a grade crossing. The experimental results show that the proposed framework can detect and localize the anomaly behaviors, such as squatting down, lingering, and other behaviors that may cause safety issues at the grade crossing. Our study also points out the direction for further improvement of the present development to meet the need for real-world applications.

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T-Cell Receptor-Peptide Interaction Prediction with Physical Model Augmented Pseudo-Labeling

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Predicting the interactions between T-cell receptors (TCRs) and peptides is crucial for the development of personalized medicine and targeted vaccine in immunotherapy. Current datasets for training deep learning models of this purpose remain constrained without diverse TCRs and peptides. To combat the data scarcity issue presented in the current datasets, we propose to extend the training dataset by physical modeling of TCR-peptide pairs. Specifically, we compute the docking energies between auxiliary unknown TCR-peptide pairs as surrogate training labels. Then, we use these extended example-label pairs to train our model in a supervised fashion. Finally, we find that the AUC score for the prediction of the model can be further improved by pseudo-labeling of such unknown TCR-peptide pairs (by a trained teacher model), and re-training the model with those pseudo-labeled TCR-peptide pairs. Our proposed method that trains the deep neural network with physical modeling and data-augmented pseudo-labeling improves over baselines in the available two datasets. We also introduce a new dataset that contains over 80,000 unknown TCR-peptide pairs with docking energy scores.

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Rain Intensity Detection and Classification with Pre-existing Telecom Fiber Cables

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For the first time, we demonstrate detection and classification of rain intensity using Distributed Acoustic Sensing (DAS). An artificial neural network was applied for rain intensity classification and high precision of over 96% was achieved.

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Template Matching Method with Distributed Acoustic Sensing Data and Simulation Data

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We propose a new method to detect acoustic signals by matching distributed acoustic sensing data with simulation. In the simulation of the dynamic strain on an optical fiber, the optical fiber layouts and the gauge length are properly incorporated. We apply the proposed method to the acoustic-source localization and demonstrate the method localizes the source accurately even under the layouts which include the straight optical fiber for the sensing points with the large gauge-length settings.

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Simultaneous Fiber Sensing and Communications

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We review recent advances aimed at increasing the reach of distributed fiber optic sensing with simultaneous data transmission. We review two methods based on measurement of accumulated phase on telecom signals, and chirp-pulsed DAS with inline amplification and frequency diversity.

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Field Tests of Impulsive Acoustic Event Detection, Localization, and Classification Over Telecom Fiber Networks

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We report distributed-fiber-optic-sensing results on impulsive acoustic events localization/classification over telecom networks. A deep-learning-based model was trained to classify starter-gun and fireworks signatures with high accuracy of > 99% using fiber-based-signal-enhancer and >97% using aerial coils.

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Evolution of Fiber Infrastructure – From Data Transmission to Network Sensing

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We review multiple use cases over deployed networks including co-existing sensing/data transmission, cable cut prevention and perimeter intrusion detection to realize telecom infrastructure can be sensing backbones instead of the sole function of data transmission.

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Mosaic: Leveraging Diverse Reflector Geometries for Omnidirectional Around-Corner Automotive Radar

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A large number of traffic collisions occur as a result of obstructed sight lines, such that even an advanced driver assistance system would be unable to prevent the crash. Recent work has proposed the use of around-the-corner radar systems to detect vehicles, pedestrians, and other road users in these occluded regions. Through comprehensive measurement, we show that these existing techniques cannot sense occluded moving objects in many important real-world scenarios. To solve this problem of limited coverage, we leverage multiple, curved reflectors to provide comprehensive coverage over the most important locations near an intersection. In scenarios where curved reflectors are insufficient, we evaluate the relative benefits of using additional flat planar surfaces. Using these techniques, we more than double the probability of detecting a vehicle near the intersection in three real urban locations and enable NLoS radar sensing using an entirely new class of reflectors.

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StyleT2I: Towards Compositional and High-Fidelity Text-to-Image Synthesis

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Although progress has been made for text-to-image synthesis, previous methods fall short of generalizing to unseen or underrepresented attribute compositions in the input text. Lacking compositionality could have severe implications for robustness and fairness, e.g., inability to synthesize the face images of underrepresented demographic groups. In this paper, we introduce a new framework, StyleT2I, to improve the compositionality of text-to-image synthesis. Specifically, we propose a CLIP-guided Contrastive Loss to better distinguish different compositions among different sentences. To further improve the compositionality, we design a novel Semantic Matching Loss and a Spatial Constraint to identify attributes’ latent directions for intended spatial region manipulations, leading to better disentangled latent representations of attributes. Based on the identified latent directions of attributes, we propose Compositional Attribute Adjustment to adjust the latent code, resulting in better compositionality of image synthesis. In addition, we leverage the l2 -norm regularization of identified latent directions (norm penalty) to strike a nice balance between image-text alignment and image fidelity. In the experiments, we devise a new dataset split and an evaluation metric to evaluate the compositionality of text-to-image synthesis models. The results show that StyleT2I outperforms previous approaches in terms of the consistency between the input text and synthesized images and achieves higher fidelity

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