Multi-Label Temporal Evidential Neural Networks for Early Event Detection

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Early event detection aims to detect events even before the event is complete. However, most of the existing methods focus on an event with a single label but fail to be applied to cases with multiple labels. Another non-negligible issue for early event detection is a prediction with overconfidence due to the high vacuity uncertainty that exists in the early time series. It results in an over-confidence estimation and hence unreliable predictions. To this end, technically, we propose a novel framework, Multi-Label Temporal Evidential Neural Network (MTENN), for multi-label uncertainty estimation in temporal data. MTENN is able to quality predictive uncertainty due to the lack of evidence for multi-label classifications at each time stamp based on belief/evidence theory. In addition, we introduce a novel uncertainty estimation head (weighted binomial comultiplication (WBC)) to quantify the fused uncertainty of a sub-sequence for early event detection. We validate the performance of our approach with state-of-the-art techniques on real-world audio datasets.

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Utility Pole Localization by Learning From Ambient Traces on Distributed Acoustic Sensing

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Utility pole detection and localization is the most fundamental application in aerial-optic cables using distributed acoustic sensing (DAS). The existing pole localization method recognizes the hammer knock signal on DAS traces by learning from knocking vibration patterns. However, it requires many efforts for data collection such as knocking every pole and manually labeling the poles’ locations, making this labor-intensive solution expensive, inefficient, and highly error prone. In this paper, we propose a pole localization solution by learning the ambient data collected from a DAS system, which are vibration patterns excited by random ambient events, such as wind and nearby traffic. In detail, we investigate a universal framework for learning representations of ambient data in the frequency domain by contrastive learning of the similarity of low and high-frequency series. A Gaussian-based data reweighting kernel is employed for eliminating the effect of the label noise. Experimental results demonstrate the proposed methods outperform the existing contrastive learning methods on the real-world DAS ambient dataset.

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Towards Realizing the Value of Labeled Target Samples: a Two-Stage Approach for Semi-Supervised Domain Adaptation

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Semi-Supervised Domain Adaptation (SSDA) is a recently emerging research topic that extends from the widely-investigated Unsupervised Domain Adaptation (UDA) by further having a few target samples labeled, i.e., the model is trained with labeled source samples, unlabeled target samples as well as a few labeled target samples. Compared with UDA, the key to SSDA lies how to most effectively utilize the few labeled target samples. Existing SSDA approaches simply merge the few precious labeled target samples into vast labeled source samples or further align them, which dilutes the value of labeled target samples and thus still obtains a biased model. To remedy this, in this paper, we propose to decouple SSDA as an UDA problem and a semi-supervised learning problem where we first learn an UDA model using labeled source and unlabeled target samples and then adapt the learned UDA model in a semi-supervised way using labeled and unlabeled target samples. By utilizing the labeled source samples and target samples separately, the bias problem can be well mitigated. We further propose a consistency learning based mean teacher model to effectively adapt the learned UDA model using labeled and unlabeled target samples. Experiments show our approach outperforms existing methods.

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Real-Time Blind Source Separation with Integrated Photonics for Wireless Signals

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We demonstrate, for the first time, real-time blind source separation of interfering GHz transmitters using photonic weights controlled by an RF-System-on-Chip FPGA. This analog system achieves multi-antenna signal separation with millisecond execution latency.

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T-Cell Receptor Optimization with Reinforcement Learning and Mutation Polices for Precision Immunotherapy

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T cells monitor the health status of cells by identifying foreign peptides displayed on their surface. T-cell receptors (TCRs), which are protein complexes found on the surface of T cells, are able to bind to these peptides. This process is known as TCR recognition and constitutes a key step for immune response. Optimizing TCR sequences for TCR recognition represents a fundamental step towards the development of personalized treatments to trigger immune responses killing cancerous or virus-infected cells. In this paper, we formulated the search for these optimized TCRs as a reinforcement learning (RL) problem and presented a framework TCRPPO with a mutation policy using proximal policy optimization. TCRPPO mutates TCRs into effective ones that can recognize given peptides. TCRPPO leverages a reward function that combines the likelihoods of mutated sequences being valid TCRs measured by a new scoring function based on deep autoencoders, with the probabilities of mutated sequences recognizing peptides from a peptide-TCR interaction predictor. We compared TCRPPO with multiple baseline methods and demonstrated that TCRPPO significantly outperforms all the baseline methods to generate positive binding and valid TCRs. These results demonstrate the potential of TCRPPO for both precision immunotherapy and peptide-recognizing TCR motif discovery.

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StreetAware: A High-Resolution Synchronized Multimodal Urban Scene Dataset

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Access to high-quality data is an important barrier in the digital analysis of urban settings, including applications within computer vision and urban design. Diverse forms of data collected from sensors in areas of high activity in the urban environment, particularly at street intersections, are valuable resources for researchers interpreting the dynamics between vehicles, pedestrians, and the built environment. In this paper, we present a high-resolution audio, video, and LiDAR dataset of three urban intersections in Brooklyn, New York, totaling almost 8 unique hours. The data were collected with custom Reconfigurable Environmental Intelligence Platform (REIP) sensors that were designed with the ability to accurately synchronize multiple video and audio inputs. The resulting data are novel in that they are inclusively multimodal, multi-angular, high-resolution, and synchronized. We demonstrate four ways the data could be utilized — (1) to discover and locate occluded objects using multiple sensors and modalities, (2) to associate audio events with their respective visual representations using both video and audio modes, (3) to track the amount of each type of object in a scene over time, and (4) to measure pedestrian speed using multiple synchronized camera views. In addition to these use cases, our data are available for other researchers to carry out analyses related to applying machine learning to understanding the urban environment (in which existing datasets may be inadequate), such as pedestrian-vehicle interaction modeling and pedestrian attribute recognition. Such analyses can help inform decisions made in the context of urban sensing and smart cities, including accessibility-aware urban design and Vision Zero initiatives.

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Explore Benefits of Distributed Fiber Optic Sensing for Optical Network Service Providers

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We review various applications of distributed fiber optic sensing (DFOS) and machine learning (ML) technologies that particularly benefit telecom operators’ fiber networks and businesses. By leveraging relative phase shift of the reflectance of coherent Rayleigh, Brillouin and Raman scattering of light wave, the ambient environmental vibration, acoustic effects, temperature and fiber/cable strain can be detected. Fiber optic sensing technology allows optical fiber to support sensing features in addition to its conventional role to transmit data in telecommunications. DFOS has recently helped telecom operators by adding multiple sensing features and proved feasibility of co-existence of sensing and communication systems on same fiber. We review the architecture of DFOS technique and show examples where optical fiber sensing helps enhance network operation efficiency and create new services for customers on deployed fiber infrastructures, such as determination of cable locations, cable cut prevention, perimeter intrusion detection and networked sensing applications. In addition, edge AI platform allows data processing to be conducted on-the-fly with low latency. Based on discriminative spatial-temporal signatures of different events of interest, real-time processing of the sensing data from the DFOS system provides results of the detection, classification and localization immediately.

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RIS-aided mmWave Beamforming for Two-way Communications of Multiple Pairs

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Millimeter‑wave (mmWave) communications is a key enabler towards realizing enhanced Mobile Broadband (eMBB) as a key promise of 5G and beyond, due to the abundance of bandwidth available at mmWave bands. An mmWave coverage map consists of blind spots due to shadowing and fading especially in dense urban environments. Beamformingemploying massive MIMO is primarily used to address high attenuation in the mmWave channel. Due to their ability in manipulating the impinging electromagnetic waves in an energy‑efficient fashion, Reconfigurable Intelligent Surfaces (RISs) are considered a great match to complement the massive MIMO systems in realizing the beamforming task and therefore effectively filling in the mmWave coverage gap. In this paper, we propose a novel RIS architecture, namely RIS‑UPA where the RIS elements are arranged in a Uniform Planar Array (UPA). We show how RIS‑UPA can be used in an RIS‑aided MIMO system to fill the coverage gap in mmWave by forming beams of a custom footprint, with optimized main lobe gain, minimum leakage, and fairly sharp edges. Further, we propose a configuration for RIS‑UPA that can support multiple two‑way communication pairs, simultaneously. We theoretically obtain closed‑form low‑complexity solutions for our design and validate our theoretical findings by extensive numerical experiments.

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A New Hope: AI Research is Conquering Today’s Computer Vision Plateau

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The age of computer vision is upon us, and it’s transforming the way we live, work and interact with the world. Nearly every industry has found a use case to grow revenue, reduce cost, or create exceptional experiences using computer vision. From self-driving cars to retail automation, surgeons to farmers, computer vision is everywhere, providing critical insights that are driving progress in virtually every aspect of our daily lives. Today, images and videos are annotated to train artificial intelligence (AI) models to recognize specific objects, but there is still so much more to be done when it comes to understanding what those objects are doing in real-time.

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Channel Reciprocity Calibration for Hybrid Beamforming in Distributed MIMO Systems

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Time Division Duplex (TDD)-based distributed massive MIMO systems are envisioned as candidate solution for the physical layer of 6G multi-antenna systems supporting cooperative hybrid beamforming that heavily relies on the obtained uplink channel estimates for efficient coherent downlink precoding. However, due to the hardware impairment between the transmitter and the receiver, full channel reciprocity does not hold between the downlink and uplink direction. Such reciprocity mismatch deteriorates the performance of mm-Wave hybrid beamforming and has to be estimated and compensated for, to avoid performance degradation in the co-operative hybrid beamforming. In this paper, we address the channel reciprocity calibration between any two nodes at two levels. We decompose the problem into two sub-problems. In the first sub-problem, we calibrate the digital chain, i.e. obtain the mismatch coefficients of the (DAC/ADC) up to a constant scaling factor. In the second subproblem, we obtain the (PA/LNA) mismatch coefficients. At each step, we formulate the channel reciprocity calibration as a least square optimization problem that can efficiently be solved via conventional methods such as alternative optimization with high accuracy. Finally, we verify the performance of our channel reciprocity calibration approach through extensive numerical experiments.

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