pwc-archive/papers-with-abstracts huggingface dataset:
Resolving Left-Right Ambiguity During Bearing Only Tracking of an Underwater Target Using Towed Array
Authors: Shreya Das, Ranjeet Kumar Tiwari, Shovan Bhaumik
Date: 2023-02-03 00:00:00

In bearing only tracking using a towed array, the array can sense the bearing angle of the target but is unable to differentiate whether the target is on the left or the right side of the array. Thus, the traditional tracking algorithm generates tracks in both the sides of the array which create difficulties when interception is required. In this paper, we propose a method based on likelihood of measurement which along with the estimators can resolve left-right ambiguity and track the target. A case study has been presented where the target moves (a) in a straight line with a near constant velocity, (b) maneuvers with a turn, and observer takes a `U'-like maneuver. The method along with the various estimators has been applied which successfully resolves the ambiguity and tracks the target. Further, the tracking results are compared in terms of the root mean square error in position and velocity, bias norm, \% of track loss and the relative execution time.
Orthogonal-Time-Frequency-Space Signal Design for Integrated Data and Energy Transfer: Benefits from Doppler Offsets
Authors: Jie Hu, Ke Xu, Luping Xiang, Kun Yang
Date: 2023-02-03 00:00:00

Integrated data and energy transfer (IDET) is an advanced technology for enabling energy sustainability for massively deployed low-power electronic consumption components. However, the existing work of IDET using the orthogonal-frequency-division-multiplexing (OFDM) waveforms is designed for static scenarios, which would be severely affected by the destructive Doppler offset in high-mobility scenarios. Therefore, we proposed an IDET system based on orthogonal-time-frequency-space (OTFS) waveforms with the imperfect channel assumption, which is capable of counteracting the Doppler offset in high-mobility scenarios. At the transmitter, the OTFS-IDET system superimposes the random data signals and deterministic energy signals in the delay-Doppler (DD) domain with optimally designed amplitudes. The receiver optimally splits the received signal in the power domain for achieving the best IDET performance. After formulating a non-convex optimisation problem, it is transformed into a geometric programming (GP) problem through inequality relaxations to obtain the optimal solution. The simulation demonstrates that a higher amount of energy can be harvested when employing our proposed OTFS-IDET waveforms than the conventional OFDM-IDET ones in high mobility scenarios.
Joint Scattering Environment Sensing and Channel Estimation for Integrated Sensing and Communication
Authors: Wenkang Xu, Yongbo Xiao, An Liu, MinJian Zhao
Date: 2023-02-03 00:00:00
Tasks: Integrated sensing and communication

This paper considers an integrated sensing and communication system, where some radar targets also serve as communication scatterers. A location domain channel modeling method is proposed based on the position of targets and scatterers in the scattering environment, and the resulting radar and communication channels exhibit a partially common sparsity. By exploiting this, we propose a joint scattering environment sensing and channel estimation scheme to enhance the target/scatterer localization and channel estimation performance simultaneously. Specifically, the base station (BS) first transmits downlink pilots to sense the targets in the scattering environment. Then the user transmits uplink pilots to estimate the communication channel. Finally, joint scattering environment sensing and channel estimation are performed at the BS based on the reflected downlink pilot signal and received uplink pilot signal. A message passing based algorithm is designed by combining the turbo approach and the expectation maximization method. The advantages of our proposed scheme are verified in the simulations.
Closed-Form Solution for Scaling a Wireless Acoustic Sensor Network
Authors: Kashyap Patel, Anton Kovalyov, Issa Panahi
Date: 2023-02-03 00:00:00
Tasks: Form

This study presents a closed-form solution for localizing and synchronizing an acoustic sensor node with respect to a Wireless Acoustic Sensor Network (WASN). The aim is to allow efficient scaling of a WASN by individually calibrating newly joined sensor nodes instead of recalibrating the entire array. A key contribution is that the sensor to be calibrated does not need to include a built-in emitter. The proposed method uses signals emitted from spatially distributed sources to compute time difference of arrival (TDOA) measurements between the existing WASN and a new sensor. The problem is then modeled as a set of multivariate nonlinear TDOA equations. Through a simple transformation, the nonlinear TDOA equations are converted into a system of linear equations. Then, weighted least squares (WLS) is applied to find an accurate estimate of the calibration parameters. Signal sources can either be known emitters within the existing WASN or arbitrary sources in the environment, thus allowing for flexible applicability in both active and passive calibration scenarios. Simulation results under various conditions show high joint localization and synchronization performance, often comparable to the Cram\'er-Rao lower bound (CRLB).
LEO-to-User Assignment and Resource Allocation for Uplink Transmit Power Minimization
Authors: Hung Nguyen-Kha, Vu Nguyen Ha, Eva Lagunas, Symeon Chatzinotas, Joel Grotz
Date: 2023-02-02 00:00:00

This paper aims to develop satellite-user association and resource allocation mechanisms to minimize the total transmit power for integrated terrestrial and non-terrestrial networks wherein a constellation of LEO satellites provides the radio access services to both terrestrial base stations (BSs) and the satellite-enabled users (SUEs). In this work, beside maintaining the traditional SatCom connection for SUEs, the LEO satellites provide backhaul links to the BSs to upload the data received from their ground customers. Taking the individual SUE traffic demands and the aggregated BS demands, we formulate a mixed integer programming which consists of the binary variables due to satellite association selection, power control and bandwidth allocation related variables. To cope with this challenging problem, an iterative optimization-based algorithm is proposed by relaxing the binary components and alternating updating all variables. A greedy mechanism is also presented for comparison purpose. Then, numerical results are presented to confirm the effectiveness of our proposed algorithms.
CLASH: Contrastive learning through alignment shifting to extract stimulus information from EEG
Authors: Bernd Accou, Hugo Van hamme, Tom Francart
Date: 2023-01-09 00:00:00
Tasks: Contrastive Learning, Denoising, EEG, Electroencephalogram (EEG)

Stimulus-evoked EEG data has a notoriously low signal-to-noise ratio and high inter-subject variability. We propose a novel paradigm for the self-supervised extraction of stimulus-related brain response data: a model is trained to extract similar information between two time-aligned segments of EEG in response to the same stimulus. The extracted information can subsequently be used to obtain better results in downstream tasks that utilize the response to the stimulus. We show the efficacy of our method for a downstream task of decoding the speech envelope from auditory EEG. Our method outperforms other state-of-the-art denoising techniques, improving reconstruction scores by 45\%. Additionally, we show that in contrast to the baseline denoising techniques, our method can be used with data of unseen subjects and stimuli without retraining, improving decoding performance by 19\% and 34\% over raw EEG for two holdout datasets. Finally, the last experiment reveals that the accuracies obtained in the CLASH paradigm are significantly correlated with the percentile of obtained reconstruction correlation on the null distribution. In general, we showed that the proposed paradigm is suitable to train deep learning models to extract stimulus information from EEG while being stimulus feature agnostic.
Made to measure: an introduction to quantification in microscopy data
Authors: Siân Culley, Alicia Cuber Caballero, Jemima J Burden, Virginie Uhlmann
Date: 2023-02-03 00:00:00

Images are at the core of most modern biological experiments and are used as a major source of quantitative information. Numerous algorithms are available to process images and make them more amenable to be measured. Yet the nature of the quantitative output that is useful for a given biological experiment is uniquely dependent upon the question being investigated. Here, we discuss the 3 main types of visual information that can be extracted from microscopy data: intensity, morphology, and object counts or categorical labels. For each, we describe where they come from, how they can be measured, and what may affect the relevance of these measurements in downstream data analysis. Acknowledging that what makes a measurement "good" is ultimately down to the biological question being investigated, this review aims at providing readers with a toolkit to challenge how they quantify their own data and be critical of conclusions drawn from quantitative bioimage analysis experiments.
Transport-based morphometry of nuclear structures of digital pathology images in cancers
Authors: Mohammad Shifat E Rabbi, Natasha Ironside, John A Ozolek, Rajendra Singh, Liron Pantanowitz, Gustavo K Rohde
Date: 2023-02-02 00:00:00
Tasks: Diagnostic

Alterations in nuclear morphology are useful adjuncts and even diagnostic tools used by pathologists in the diagnosis and grading of many tumors, particularly malignant tumors. Large datasets such as TCGA and the Human Protein Atlas, in combination with emerging machine learning and statistical modeling methods, such as feature extraction and deep learning techniques, can be used to extract meaningful knowledge from images of nuclei, particularly from cancerous tumors. Here we describe a new technique based on the mathematics of optimal transport for modeling the information content related to nuclear chromatin structure directly from imaging data. In contrast to other techniques, our method represents the entire information content of each nucleus relative to a template nucleus using a transport-based morphometry (TBM) framework. We demonstrate the model is robust to different staining patterns and imaging protocols, and can be used to discover meaningful and interpretable information within and across datasets and cancer types. In particular, we demonstrate morphological differences capable of distinguishing nuclear features along the spectrum from benign to malignant categories of tumors across different cancer tissue types, including tumors derived from liver parenchyma, thyroid gland, lung mesothelium, and skin epithelium. We believe these proof of concept calculations demonstrate that the TBM framework can provide the quantitative measurements necessary for performing meaningful comparisons across a wide range of datasets and cancer types that can potentially enable numerous cancer studies, technologies, and clinical applications and help elevate the role of nuclear morphometry into a more quantitative science. The source codes implementing our method is available at https://github.com/rohdelab/nuclear_morphometry.
Portfolio Optimisation via the Heston Model Calibrated to Real Asset Data
Authors: Jarosław Gruszka, Janusz Szwabiński
Date: 2023-02-03 00:00:00
Tasks: Management

The debate between active and passive investment strategies has been ongoing for many years and is far from being over. In this paper, we show that the choice of an optimal portfolio management strategy depends on an investment climate, which we measure via the parameters of the Heston model calibrated to the real stock market data. Depending on the values of those parameters, the passive strategy may namely outperform the active ones or vice versa. The method is tested on three stock market indices: S\&P500, DAX and WIG20.
Empirical analysis in limit order book modeling for Nikkei 225 Stocks with Cox-type intensities
Authors: Shunya Chomei
Date: 2023-02-03 00:00:00
Tasks: Model Selection

In this paper, we build on the analysis of Muni Toke and Yoshida (2020) and conduct several empirical studies using high-frequency financial data. Muni Toke and Yoshida (2020) showed the consistency and asymptotic behavior of the Cox-type model estimators for relative intensities of orders in the limit order book, and then by using high-frequency trading data for 36 stocks traded on the Paris Stock Exchange, they carry out model selection and trading sign prediction. In this study, we add new covariates and carry out model selection and trading sign prediction using high-frequency trading data for 222 stocks traded on the Tokyo Stock Exchange. We not only show that the Cox-type model performs well in the Japanese market as well as in the Euronext Paris market, but also present the key factors for more accurate estimation. We also suggest how often the covariates should be calibrated.
Understanding and contextualising diffusion models
Authors: Stefano Scotta, Alberto Messina
Date: 2023-01-26 00:00:00

The latest developments in Artificial Intelligence include diffusion generative models, quite popular tools which can produce original images both unconditionally and, in some cases, conditioned by some inputs provided by the user. Apart from implementation details, which are outside the scope of this work, all of the main models used to generate images are substantially based on a common theory which restores a new image from a completely degraded one. In this work we explain how this is possible by focusing on the mathematical theory behind them, i.e. without analyzing in detail the specific implementations and related methods. The aim of this work is to clarify to the interested reader what all this means mathematically and intuitively.
Transformers in Action Recognition: A Review on Temporal Modeling
Authors: Elham Shabaninia, Hossein Nezamabadi-pour, Fatemeh Shafizadegan
Date: 2022-12-29 00:00:00
Tasks: Action Recognition

In vision-based action recognition, spatio-temporal features from different modalities are used for recognizing activities. Temporal modeling is a long challenge of action recognition. However, there are limited methods such as pre-computed motion features, three-dimensional (3D) filters, and recurrent neural networks (RNN) for modeling motion information in deep-based approaches. Recently, transformers success in modeling long-range dependencies in natural language processing (NLP) tasks has gotten great attention from other domains; including speech, image, and video, to rely entirely on self-attention without using sequence-aligned RNNs or convolutions. Although the application of transformers to action recognition is relatively new, the amount of research proposed on this topic within the last few years is astounding. This paper especially reviews recent progress in deep learning methods for modeling temporal variations. It focuses on action recognition methods that use transformers for temporal modeling, discussing their main features, used modalities, and identifying opportunities and challenges for future research.
Real-Time Traffic End-of-Queue Detection and Tracking in UAV Video
Authors: Russ Messenger, Md Zobaer Islam, Matthew Whitlock, Erik Spong, Nate Morton, Layne Claggett, Chris Matthews, Jordan Fox, Leland Palmer, Dane C. Johnson, John F. O'Hara, Christopher J. Crick, Jamey D. Jacob, Sabit Ekin
Date: 2023-01-10 00:00:00

Highway work zones are susceptible to undue accumulation of motorized vehicles which calls for dynamic work zone warning signs to prevent accidents. The work zone signs are placed according to the location of the end-of-queue of vehicles which usually changes rapidly. The detection of moving objects in video captured by Unmanned Aerial Vehicles (UAV) has been extensively researched so far, and is used in a wide array of applications including traffic monitoring. Unlike the fixed traffic cameras, UAVs can be used to monitor the traffic at work zones in real-time and also in a more cost-effective way. This study presents a method as a proof of concept for detecting End-of-Queue (EOQ) of traffic by processing the real-time video footage of a highway work zone captured by UAV. EOQ is detected in the video by image processing which includes background subtraction and blob detection methods. This dynamic localization of EOQ of vehicles will enable faster and more accurate relocation of work zone warning signs for drivers and thus will reduce work zone fatalities. The method can be applied to detect EOQ of vehicles and notify drivers in any other roads or intersections too where vehicles are rapidly accumulating due to special events, traffic jams, construction, or accidents.
Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness
Authors: Yuancheng Xu, Yanchao Sun, Micah Goldblum, Tom Goldstein, Furong Huang
Date: 2023-02-06 00:00:00
Tasks: Adversarial Robustness

The robustness of a deep classifier can be characterized by its margins: the decision boundary's distances to natural data points. However, it is unclear whether existing robust training methods effectively increase the margin for each vulnerable point during training. To understand this, we propose a continuous-time framework for quantifying the relative speed of the decision boundary with respect to each individual point. Through visualizing the moving speed of the decision boundary under Adversarial Training, one of the most effective robust training algorithms, a surprising moving-behavior is revealed: the decision boundary moves away from some vulnerable points but simultaneously moves closer to others, decreasing their margins. To alleviate these conflicting dynamics of the decision boundary, we propose Dynamics-aware Robust Training (DyART), which encourages the decision boundary to engage in movement that prioritizes increasing smaller margins. In contrast to prior works, DyART directly operates on the margins rather than their indirect approximations, allowing for more targeted and effective robustness improvement. Experiments on the CIFAR-10 and Tiny-ImageNet datasets verify that DyART alleviates the conflicting dynamics of the decision boundary and obtains improved robustness under various perturbation sizes compared to the state-of-the-art defenses. Our code is available at https://github.com/Yuancheng-Xu/Dynamics-Aware-Robust-Training.
Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class Incremental Learning
Authors: Yibo Yang, Haobo Yuan, Xiangtai Li, Zhouchen Lin, Philip Torr, DaCheng Tao
Date: 2023-02-06 00:00:00
Tasks: class-incremental learning, Class Incremental Learning, Few-Shot Class-Incremental Learning, Incremental Learning

Few-shot class-incremental learning (FSCIL) has been a challenging problem as only a few training samples are accessible for each novel class in the new sessions. Finetuning the backbone or adjusting the classifier prototypes trained in the prior sessions would inevitably cause a misalignment between the feature and classifier of old classes, which explains the well-known catastrophic forgetting problem. In this paper, we deal with this misalignment dilemma in FSCIL inspired by the recently discovered phenomenon named neural collapse, which reveals that the last-layer features of the same class will collapse into a vertex, and the vertices of all classes are aligned with the classifier prototypes, which are formed as a simplex equiangular tight frame (ETF). It corresponds to an optimal geometric structure for classification due to the maximized Fisher Discriminant Ratio. We propose a neural collapse inspired framework for FSCIL. A group of classifier prototypes are pre-assigned as a simplex ETF for the whole label space, including the base session and all the incremental sessions. During training, the classifier prototypes are not learnable, and we adopt a novel loss function that drives the features into their corresponding prototypes. Theoretical analysis shows that our method holds the neural collapse optimality and does not break the feature-classifier alignment in an incremental fashion. Experiments on the miniImageNet, CUB-200, and CIFAR-100 datasets demonstrate that our proposed framework outperforms the state-of-the-art performances. Code address: https://github.com/NeuralCollapseApplications/FSCIL
Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining
Authors: Zekun Qi, Runpei Dong, Guofan Fan, Zheng Ge, Xiangyu Zhang, Kaisheng Ma, Li Yi
Date: 2023-02-05 00:00:00
Tasks: 3D Point Cloud Classification, 3D Point Cloud Linear Classification, Decoder, Few-Shot 3D Point Cloud Classification, Representation Learning, Zero-Shot Transfer 3D Point Cloud Classification

Mainstream 3D representation learning approaches are built upon contrastive or generative modeling pretext tasks, where great improvements in performance on various downstream tasks have been achieved. However, we find these two paradigms have different characteristics: (i) contrastive models are data-hungry that suffer from a representation over-fitting issue; (ii) generative models have a data filling issue that shows inferior data scaling capacity compared to contrastive models. This motivates us to learn 3D representations by sharing the merits of both paradigms, which is non-trivial due to the pattern difference between the two paradigms. In this paper, we propose Contrast with Reconstruct (ReCon) that unifies these two paradigms. ReCon is trained to learn from both generative modeling teachers and single/cross-modal contrastive teachers through ensemble distillation, where the generative student guides the contrastive student. An encoder-decoder style ReCon-block is proposed that transfers knowledge through cross attention with stop-gradient, which avoids pretraining over-fitting and pattern difference issues. ReCon achieves a new state-of-the-art in 3D representation learning, e.g., 91.26% accuracy on ScanObjectNN. Codes have been released at https://github.com/qizekun/ReCon.
CAB: Empathetic Dialogue Generation with Cognition, Affection and Behavior
Authors: Pan Gao, Donghong Han, Rui Zhou, Xuejiao Zhang, Zikun Wang
Date: 2023-02-03 00:00:00
Tasks: Dialogue Generation

Empathy is an important characteristic to be considered when building a more intelligent and humanized dialogue agent. However, existing methods did not fully comprehend empathy as a complex process involving three aspects: cognition, affection and behavior. In this paper, we propose CAB, a novel framework that takes a comprehensive perspective of cognition, affection and behavior to generate empathetic responses. For cognition, we build paths between critical keywords in the dialogue by leveraging external knowledge. This is because keywords in a dialogue are the core of sentences. Building the logic relationship between keywords, which is overlooked by the majority of existing works, can improve the understanding of keywords and contextual logic, thus enhance the cognitive ability. For affection, we capture the emotional dependencies with dual latent variables that contain both interlocutors' emotions. The reason is that considering both interlocutors' emotions simultaneously helps to learn the emotional dependencies. For behavior, we use appropriate dialogue acts to guide the dialogue generation to enhance the empathy expression. Extensive experiments demonstrate that our multi-perspective model outperforms the state-of-the-art models in both automatic and manual evaluation.