pwc-archive/papers-with-abstracts huggingface dataset:
Robust single-shot 3D fluorescence imaging in scattering media with a simulator-trained neural network
Authors: Jeffrey Alido, Joseph Greene, Yujia Xue, Guorong Hu, Yunzhe Li, Mitchell Gilmore, Kevin J. Monk, Brett T. DiBenedictis, Ian G. Davison, Lei Tian
Date: 2023-03-22 00:00:00
Tasks: 3D Reconstruction, Deep Learning

Imaging through scattering is a pervasive and difficult problem in many biological applications. The high background and the exponentially attenuated target signals due to scattering fundamentally limits the imaging depth of fluorescence microscopy. Light-field systems are favorable for high-speed volumetric imaging, but the 2D-to-3D reconstruction is fundamentally ill-posed, and scattering exacerbates the condition of the inverse problem. Here, we develop a scattering simulator that models low-contrast target signals buried in heterogeneous strong background. We then train a deep neural network solely on synthetic data to descatter and reconstruct a 3D volume from a single-shot light-field measurement with low signal-to-background ratio (SBR). We apply this network to our previously developed Computational Miniature Mesoscope and demonstrate the robustness of our deep learning algorithm on scattering phantoms with different scattering conditions. The network can robustly reconstruct emitters in 3D with a 2D measurement of SBR as low as 1.05 and as deep as a scattering length. We analyze fundamental tradeoffs based on network design factors and out-of-distribution data that affect the deep learning model's generalizability to real experimental data. Broadly, we believe that our simulator-based deep learning approach can be applied to a wide range of imaging through scattering techniques where experimental paired training data is lacking.
SMUG: Towards robust MRI reconstruction by smoothed unrolling
Authors: Hui Li, Jinghan Jia, Shijun Liang, Yuguang Yao, Saiprasad Ravishankar, Sijia Liu
Date: 2023-03-14 00:00:00
Tasks: Adversarial Defense, image-classification, Image Classification, Image Reconstruction, MRI Reconstruction, Rolling Shutter Correction

Although deep learning (DL) has gained much popularity for accelerated magnetic resonance imaging (MRI), recent studies have shown that DL-based MRI reconstruction models could be oversensitive to tiny input perturbations (that are called 'adversarial perturbations'), which cause unstable, low-quality reconstructed images. This raises the question of how to design robust DL methods for MRI reconstruction. To address this problem, we propose a novel image reconstruction framework, termed SMOOTHED UNROLLING (SMUG), which advances a deep unrolling-based MRI reconstruction model using a randomized smoothing (RS)-based robust learning operation. RS, which improves the tolerance of a model against input noises, has been widely used in the design of adversarial defense for image classification. Yet, we find that the conventional design that applies RS to the entire DL process is ineffective for MRI reconstruction. We show that SMUG addresses the above issue by customizing the RS operation based on the unrolling architecture of the DL-based MRI reconstruction model. Compared to the vanilla RS approach and several variants of SMUG, we show that SMUG improves the robustness of MRI reconstruction with respect to a diverse set of perturbation sources, including perturbations to the input measurements, different measurement sampling rates, and different unrolling steps. Code for SMUG will be available at https://github.com/LGM70/SMUG.
Don't (fully) exclude me, it's not necessary! Causal inference with semi-IVs
Authors: Christophe Bruneel-Zupanc
Date: 2023-03-22 00:00:00
Tasks: Causal Inference, valid

This paper proposes semi-instrumental variables (semi-IVs) as an alternative to instrumental variables (IVs) to identify the causal effect of a binary (or discrete) endogenous treatment. A semi-IV is a less restrictive form of instrument: it affects the selection into treatment but is excluded only from one, not necessarily both, potential outcomes. Having two semi-IVs, one excluded from the potential outcome under treatment and the other from the potential outcome under control, is sufficient to nonparametrically point identify local average treatment effect (LATE) and marginal treatment effect (MTE) parameters. In practice, semi-IVs provide a solution to the challenge of finding valid IVs because they are easier to find: many selection-specific shocks, policies, costs, or benefits are valid semi-IVs. As an application, I estimate the returns to working in the manufacturing sector using sector-specific semi-IVs.
Self-Aware Transport of Economic Agents
Authors: Andrew Lyasoff
Date: 2023-03-22 00:00:00

The paper is concerned with the simultaneous solution of a very large number of optimization problems the structure of which is not given in the outset and is dynamically agreed upon while a large number of optimizers work in an orchestra. The proposed new approach to such problems was prompted by the surprising discovery that the common strategy, adopted in a large body of research, for producing time-invariant equilibrium in the classical Aiyagari-Bewley-Huggett model fails to achieve its objective in a widely cited benchmark study, with the implication that a central problem in macroeconomics has been without an adequate solution, and despite recent advances based on novel mathematical techniques borrowed from the theory of mean field games. It is shown that the intrinsic structure of a generic heterogeneous agent incomplete market model imposes connections across time that existing mathematical frameworks cannot capture. The new technique is shown to provide numerically verifiable equilibria in some widely researched, yet still unsolved, concrete instances of heterogeneous agent models. The scope of "the approximate aggregation conjecture" of Krusell and Smith (still an open problem in macroeconomics) is clarified and a new computational strategy, which does not rely on simulation or the need to postulate infinite time horizon, for models with common shocks is developed. New insights about the fluctuations in the population distribution in such models are drawn and some novel closed-form expressions are obtained.
Strategic Ambiguity in Global Games
Authors: Takashi Ui
Date: 2023-03-22 00:00:00

In games with incomplete and ambiguous information, rational behavior depends not only on fundamental ambiguity (ambiguity about states) but also on strategic ambiguity (ambiguity about others' actions), which further induces hierarchies of ambiguous beliefs. We study the impacts of strategic ambiguity in global games and demonstrate the distinct effects of ambiguous-quality and low-quality information. Ambiguous-quality information makes more players choose an action yielding a constant payoff, whereas (unambiguous) low-quality information makes more players choose an ex-ante best response to the uniform belief over the opponents' actions. If the ex-ante best-response action yields a constant payoff, sufficiently ambiguous-quality information induces a unique equilibrium, whereas sufficiently low-quality information generates multiple equilibria. In applications to financial crises, we show that news of more ambiguous quality triggers a debt rollover crisis, whereas news of less ambiguous quality triggers a currency crisis.
Self-Supervised Representations for Singing Voice Conversion
Authors: Tejas Jayashankar, JiLong Wu, Leda Sari, David Kant, Vimal Manohar, Qing He
Date: 2023-03-21 00:00:00
Tasks: Disentanglement, Voice Conversion

A singing voice conversion model converts a song in the voice of an arbitrary source singer to the voice of a target singer. Recently, methods that leverage self-supervised audio representations such as HuBERT and Wav2Vec 2.0 have helped further the state-of-the-art. Though these methods produce more natural and melodic singing outputs, they often rely on confusion and disentanglement losses to render the self-supervised representations speaker and pitch-invariant. In this paper, we circumvent disentanglement training and propose a new model that leverages ASR fine-tuned self-supervised representations as inputs to a HiFi-GAN neural vocoder for singing voice conversion. We experiment with different f0 encoding schemes and show that an f0 harmonic generation module that uses a parallel bank of transposed convolutions (PBTC) alongside ASR fine-tuned Wav2Vec 2.0 features results in the best singing voice conversion quality. Additionally, the model is capable of making a spoken voice sing. We also show that a simple f0 shifting scheme during inference helps retain singer identity and bolsters the performance of our singing voice conversion model. Our results are backed up by extensive MOS studies that compare different ablations and baselines.
Less is More: Unsupervised Mask-guided Annotated CT Image Synthesis with Minimum Manual Segmentations
Authors: Xiaodan Xing, Giorgos Papanastasiou, Simon Walsh, Guang Yang
Date: 2023-03-19 00:00:00
Tasks: Data Augmentation, Image Generation, Medical Image Analysis, Segmentation

As a pragmatic data augmentation tool, data synthesis has generally returned dividends in performance for deep learning based medical image analysis. However, generating corresponding segmentation masks for synthetic medical images is laborious and subjective. To obtain paired synthetic medical images and segmentations, conditional generative models that use segmentation masks as synthesis conditions were proposed. However, these segmentation mask-conditioned generative models still relied on large, varied, and labeled training datasets, and they could only provide limited constraints on human anatomical structures, leading to unrealistic image features. Moreover, the invariant pixel-level conditions could reduce the variety of synthetic lesions and thus reduce the efficacy of data augmentation. To address these issues, in this work, we propose a novel strategy for medical image synthesis, namely Unsupervised Mask (UM)-guided synthesis, to obtain both synthetic images and segmentations using limited manual segmentation labels. We first develop a superpixel based algorithm to generate unsupervised structural guidance and then design a conditional generative model to synthesize images and annotations simultaneously from those unsupervised masks in a semi-supervised multi-task setting. In addition, we devise a multi-scale multi-task Fr\'echet Inception Distance (MM-FID) and multi-scale multi-task standard deviation (MM-STD) to harness both fidelity and variety evaluations of synthetic CT images. With multiple analyses on different scales, we could produce stable image quality measurements with high reproducibility. Compared with the segmentation mask guided synthesis, our UM-guided synthesis provided high-quality synthetic images with significantly higher fidelity, variety, and utility ($p<0.05$ by Wilcoxon Signed Ranked test).
Modeling Barrett's Esophagus Progression using Geometric Variational Autoencoders
Authors: Vivien van Veldhuizen, Sharvaree Vadgama, Onno J. de Boer, Sybren Meijer, Erik J. Bekkers
Date: 2023-03-17 00:00:00
Tasks: Prognosis, Representation Learning

Early detection of Barrett's Esophagus (BE), the only known precursor to Esophageal adenocarcinoma (EAC), is crucial for effectively preventing and treating esophageal cancer. In this work, we investigate the potential of geometric Variational Autoencoders (VAEs) to learn a meaningful latent representation that captures the progression of BE. We show that hyperspherical VAE (S-VAE) and Kendall Shape VAE show improved classification accuracy, reconstruction loss, and generative capacity. Additionally, we present a novel autoencoder architecture that can generate qualitative images without the need for a variational framework while retaining the benefits of an autoencoder, such as improved stability and reconstruction quality.
Longitudinal Performance of Iris Recognition in Children: Time Intervals up to Six years
Authors: Priyanka Das, Naveen G Venkataswamy, Laura Holsopple, Masudul H Imtiaz, Michael Schuckers, Stephanie Schuckers
Date: 2023-03-10 00:00:00
Tasks: Decision Making, Iris Recognition

The temporal stability of iris recognition performance is core to its success as a biometric modality. With the expanding horizon of applications for children, gaps in the knowledge base on the temporal stability of iris recognition performance in children have impacted decision-making during applications at the global scale. This report presents the most extensive analysis of longitudinal iris recognition performance in children with data from the same 230 children over 6.5 years between enrollment and query for ages 4 to 17 years. Assessment of match scores, statistical modelling of variability factors impacting match scores and in-depth assessment of the root causes of the false rejections concludes no impact on iris recognition performance due to aging.
AptSim2Real: Approximately-Paired Sim-to-Real Image Translation
Authors: Charles Y Zhang, Ashish Shrivastava
Date: 2023-03-09 00:00:00
Tasks: Image-to-Image Translation, Translation

Advancements in graphics technology has increased the use of simulated data for training machine learning models. However, the simulated data often differs from real-world data, creating a distribution gap that can decrease the efficacy of models trained on simulation data in real-world applications. To mitigate this gap, sim-to-real domain transfer modifies simulated images to better match real-world data, enabling the effective use of simulation data in model training. Sim-to-real transfer utilizes image translation methods, which are divided into two main categories: paired and unpaired image-to-image translation. Paired image translation requires a perfect pixel match, making it difficult to apply in practice due to the lack of pixel-wise correspondence between simulation and real-world data. Unpaired image translation, while more suitable for sim-to-real transfer, is still challenging to learn for complex natural scenes. To address these challenges, we propose a third category: approximately-paired sim-to-real translation, where the source and target images do not need to be exactly paired. Our approximately-paired method, AptSim2Real, exploits the fact that simulators can generate scenes loosely resembling real-world scenes in terms of lighting, environment, and composition. Our novel training strategy results in significant qualitative and quantitative improvements, with up to a 24% improvement in FID score compared to the state-of-the-art unpaired image-translation methods.
Toward Polar Sea-Ice Classification using Color-based Segmentation and Auto-labeling of Sentinel-2 Imagery to Train an Efficient Deep Learning Model
Authors: Jurdana Masuma Iqrah, Younghyun Koo, Wei Wang, Hongjie Xie, Sushil Prasad
Date: 2023-03-08 00:00:00

Global warming is an urgent issue that is generating catastrophic environmental changes, such as the melting of sea ice and glaciers, particularly in the polar regions. The melting pattern and retreat of polar sea ice cover is an essential indicator of global warming. The Sentinel-2 satellite (S2) captures high-resolution optical imagery over the polar regions. This research aims at developing a robust and effective system for classifying polar sea ice as thick or snow-covered, young or thin, or open water using S2 images. A key challenge is the lack of labeled S2 training data to serve as the ground truth. We demonstrate a method with high precision to segment and automatically label the S2 images based on suitably determined color thresholds and employ these auto-labeled data to train a U-Net machine model (a fully convolutional neural network), yielding good classification accuracy. Evaluation results over S2 data from the polar summer season in the Ross Sea region of the Antarctic show that the U-Net model trained on auto-labeled data has an accuracy of 90.18% over the original S2 images, whereas the U-Net model trained on manually labeled data has an accuracy of 91.39%. Filtering out the thin clouds and shadows from the S2 images further improves U-Net's accuracy, respectively, to 98.97% for auto-labeled and 98.40% for manually labeled training datasets.
Semantic Communication with Memory
Authors: Huiqiang Xie, Zhijin Qin, Geoffrey Ye Li
Date: 2023-03-22 00:00:00
Tasks: Semantic Communication

While semantic communication succeeds in efficiently transmitting due to the strong capability to extract the essential semantic information, it is still far from the intelligent or human-like communications. In this paper, we introduce an essential component, memory, into semantic communications to mimic human communications. Particularly, we investigate a deep learning (DL) based semantic communication system with memory, named Mem-DeepSC, by considering the scenario question answer task. We exploit the universal Transformer based transceiver to extract the semantic information and introduce the memory module to process the context information. Moreover, we derive the relationship between the length of semantic signal and the channel noise to validate the possibility of dynamic transmission. Specially, we propose two dynamic transmission methods to enhance the transmission reliability as well as to reduce the communication overhead by masking some unessential elements, which are recognized through training the model with mutual information. Numerical results show that the proposed Mem-DeepSC is superior to benchmarks in terms of answer accuracy and transmission efficiency, i.e., number of transmitted symbols.
Robust Outdoor Positioning via Ray Tracing
Authors: Vladislav Ryzhov
Date: 2023-03-22 00:00:00
Tasks: CPU, Outdoor Positioning

Positioning problem can be solved with several approaches in 5G. This paper introduces RT (Ray Tracing) technique for small-cell Outdoor positioning and tracking for systems with distributed architecture with multipath processing gain. Proposed approach exploits high-resolution angular spectrum estimation and known radio propagation environment. It solves positioning problem robustly even in NLOS cases via CoMP (Cooperative Multi Point) reception and joint processing at cloud CPU. The aim of this paper is to disclose proposed algorithm for NR and compare it to GNSS measurements.
Explainable Semantic Communication for Text Tasks
Authors: Chuanhong Liu, Caili Guo, Yang Yang, Wanli Ni, Yanquan Zhou, Lei LI, Tony Q. S. Quek
Date: 2023-03-22 00:00:00
Tasks: Question Answering, Semantic Communication, Semantic Compression, Sentiment Analysis, Triplet

Task-oriented semantic communication has gained increasing attention due to its ability to reduce the amount of transmitted data without sacrificing task performance. Although some prior efforts have been dedicated to developing semantic communications, the semantics in these works remains to be unexplainable. Challenges related to explainable semantic representation and knowledge-based semantic compression have yet to be explored. In this paper, we propose a triplet-based explainable semantic communication (TESC) scheme for representing text semantics efficiently. Specifically, we develop a semantic extraction method to convert text into triplets while using syntactic dependency analysis to enhance semantic completeness. Then, we design a semantic filtering method to further compress the duplicate and task-irrelevant triplets based on prior knowledge. The filtered triplets are encoded and transmitted to the receiver for completing intelligent tasks. Furthermore, we apply the propsed TESC scheme to two emblematic text tasks: sentiment analysis and question answering, in which the semantic codec is meticulously customized for each task. Experimental results demonstrate that 1) TESC scheme outperforms benchmarks in terms of Top-1 accuracy and transmission efficiency, and 2) TESC scheme enjoys about 150% performance gain compared to the traditional communication method.
Graph Signal Processing: History, Development, Impact, and Outlook
Authors: Geert Leus, Antonio G. Marques, José M. F. Moura, Antonio Ortega, David I Shuman
Date: 2023-03-21 00:00:00
Tasks: Graph Learning

Graph signal processing (GSP) generalizes signal processing (SP) tasks to signals living on non-Euclidean domains whose structure can be captured by a weighted graph. Graphs are versatile, able to model irregular interactions, easy to interpret, and endowed with a corpus of mathematical results, rendering them natural candidates to serve as the basis for a theory of processing signals in more irregular domains. In this article, we provide an overview of the evolution of GSP, from its origins to the challenges ahead. The first half is devoted to reviewing the history of GSP and explaining how it gave rise to an encompassing framework that shares multiple similarities with SP. A key message is that GSP has been critical to develop novel and technically sound tools, theory, and algorithms that, by leveraging analogies with and the insights of digital SP, provide new ways to analyze, process, and learn from graph signals. In the second half, we shift focus to review the impact of GSP on other disciplines. First, we look at the use of GSP in data science problems, including graph learning and graph-based deep learning. Second, we discuss the impact of GSP on applications, including neuroscience and image and video processing. We conclude with a brief discussion of the emerging and future directions of GSP.
Real-time modelling of observation filter in the Remote Microphone Technique for an Active Noise Control application
Authors: Chung Kwan Lai, Bhan Lam, Dongyuan Shi, Woon-Seng Gan
Date: 2023-03-21 00:00:00

The remote microphone technique (RMT) is often used in active noise control (ANC) applications to overcome design constraints in microphone placements by estimating the acoustic pressure at inconvenient locations using a pre-calibrated observation filter (OF), albeit limited to stationary primary acoustic fields. While the OF estimation in varying primary fields can be significantly improved through the recently proposed source decomposition technique, it requires knowledge of the relative source strengths between incoherent primary noise sources. This paper proposes a method for combining the RMT with a new source-localization technique to estimate the source ratio parameter. Unlike traditional source-localization techniques, the proposed method is capable of being implemented in a real-time RMT application. Simulations with measured responses from an open-aperture ANC application showed a good estimation of the source ratio parameter, which allows the observation filter to be modelled in real-time.
On a barrier height problem for RNA branching
Authors: Christine Heitsch, Chi N. Y. Huynh, Greg Johnston
Date: 2023-03-21 00:00:00

The branching of an RNA molecule is an important structural characteristic yet difficult to predict correctly, especially for longer sequences. Using plane trees as a combinatorial model for RNA folding, we consider the thermodynamic cost, known as the barrier height, of transitioning between branching configurations. Using branching skew as a coarse energy approximation, we characterize various types of paths in the discrete configuration landscape. In particular, we give sufficient conditions for a path to have both minimal length and minimal branching skew. The proofs offer some biological insights, notably the potential importance of both hairpin stability and domain architecture to higher resolution RNA barrier height analyses.