pwc-archive/papers-with-abstracts huggingface dataset:
What is a Mathematical Structure of Conscious Experience?
Authors: Johannes Kleiner, Tim Ludwig
Date: 2023-01-26 00:00:00

In consciousness science, several promising approaches have been developed for how to represent conscious experience in terms of mathematical spaces and structures. What is missing, however, is an explicit definition of what a 'mathematical structure of conscious experience' is. Here, we propose such a definition. This definition provides a link between the abstract formal entities of mathematics and the concreta of conscious experience; it complements recent approaches that study quality spaces, qualia spaces or phenomenal spaces; it provides a general method to identify and investigate structures of conscious experience; and it may serve as a framework to unify the various approaches from different fields. We hope that ultimately this work provides a basis for developing a common formal language to study consciousness.
Connectivity based Real-Time fMRI Neurofeedback Training in Youth with a History of Major Depressive Disorder
Authors: Xiaofu He, Diana Rodriguez Moreno, Zhenghua Hou, Keely Cheslack-Postava, Yanni Jiang, Tong Li, Ronit Kishon, Larry Amsel, George Musa, Zhishun Wang, Christina W. Hovena
Date: 2023-01-25 00:00:00
Tasks: Functional Connectivity

Background: Real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf) has proven to be a powerful technique to help subjects to gauge and enhance emotional control. Traditionally, rtfMRI-nf has focused on emotional regulation through self-regulation of amygdala. Recently, rtfMRI studies have observed that regulation of a target brain region is accompanied by connectivity changes beyond the target region. Therefore, the aim of present study is to investigate the use of connectivity between amygdala and prefrontal regions as the target of neurofeedback training in healthy individuals and subjects with a life-time history of major depressive disorder (MDD) performing an emotion regulation task. Method: Ten remitted MDD subjects and twelve healthy controls (HC) performed an emotion regulation task in 4 runs of rtfMRI-nf training followed by one transfer run without neurofeedback conducted in a single session. The functional connectivity between amygdala and prefrontal cortex was presented as a feedback bar concurrent with the emotion regulation task. Participants' emotional state was measured by the Positive and Negative Affect Schedule (PANAS) prior to and following the rtfMRI-nf. Psychological assessments were used to determine subjects' history of depression. Results: Participants with a history of MDD showed a trend of decreasing functional connectivity across the four rtfMRI-nf runs, and there was a marginally significant interaction between the MDD history and number of training runs. The HC group showed a significant increase of frontal cortex activation between the second and third neurofeedback runs. Comparing PANAS scores before and after connectivity-based rtfMRI-nf, we observed a significant decrease in negative PANAS score in the whole group overall, and a significant decrease in positive PANAS score in the MDD group alone.
Reproducibility of health claims in meta-analysis studies of COVID quarantine (stay-at-home) orders
Authors: S. Stanley Young, Warren B. Kindzierski
Date: 2022-12-23 00:00:00

The coronavirus pandemic (COVID) has been an extraordinary test of modern government scientific procedures that inform and shape policy. Many governments implemented COVID quarantine (stay-at-home) orders on the notion that this nonpharmaceutical intervention would delay and flatten the epidemic peak and largely benefit public health outcomes. The overall research capacity response to COVID since late 2019 has been massive. Given lack of research transparency, only a small fraction of published research has been judged by others to be reproducible before COVID. Independent evaluation of published meta-analysis on a common research question can be used to assess the reproducibility of a claim coming from that field of research. We used a p-value plotting statistical method to independently evaluate reproducibility of specific research claims made in four meta-analysis studies related to benefits/risks of COVID quarantine orders. Outcomes we investigated included: mortality, mental health symptoms, incidence of domestic violence, and suicidal ideation (thoughts of killing yourself). Three of the four meta-analyses that we evaluated (mortality, mental health symptoms, incidence of domestic violence) raise further questions about benefits/risks of this form of intervention. The fourth meta-analysis study (suicidal ideation) is unreliable. Given lack of research transparency and irreproducibility of published research, independent evaluation of meta-analysis studies using p-value plotting is offered as a way to strengthen or refute (falsify) claims made in COVID research.
Customer Momentum
Authors: Mykola Pinchuk
Date: 2023-01-26 00:00:00

This paper examines customer momentum, defined as a positive relationship between a firm's returns and past returns of its customers. I confirm previous evidence (Cohen and Frazzini 2008) that customer momentum is both statistically and economically significant. Long-short equally-weighted (value-weighted) decile portfolio generates a monthly return of 122 (106) basis points and a t-statistic above 4 (2.8) with respect to Fama-French factor models. The paper reports that customer momentum neither explains nor is explained by price momentum and earnings momentum. Customer momentum is partially driven by the lead-lag relationship between small and large stocks. I find that in the post-discovery sample, customer momentum has a smaller magnitude and loses statistical significance. The results are consistent with the hypothesis that after its discovery, customer momentum decreased due to exploitation by investors.
Matching of Everyday Power Supply and Demand with Dynamic Pricing: Problem Formalisation and Conceptual Analysis
Authors: Thibaut Théate, Antonio Sutera, Damien Ernst
Date: 2023-01-27 00:00:00
Tasks: Decision Making

The energy transition is expected to significantly increase the share of renewable energy sources whose production is intermittent in the electricity mix. Apart from key benefits, this development has the major drawback of generating a mismatch between power supply and demand. The innovative dynamic pricing approach may significantly contribute to mitigating that critical problem by taking advantage of the flexibility offered by the demand side. At its core, this idea consists in providing the consumer with a price signal which is evolving over time, in order to influence its consumption. This novel approach involves a challenging decision-making problem that can be summarised as follows: how to determine a price signal maximising the synchronisation between power supply and demand under the constraints of maintaining the producer/retailer's profitability and benefiting the final consumer at the same time? As a contribution, this research work presents a detailed formalisation of this particular decision-making problem. Moreover, the paper discusses the diverse algorithmic components required to efficiently design a dynamic pricing policy: different forecasting models together with an accurate statistical modelling of the demand response to dynamic prices.
Biomedical Image Reconstruction: A Survey
Authors: Samuel Cahyawijaya
Date: 2023-01-04 00:00:00
Tasks: Deep Learning, Image Reconstruction, Survey

Biomedical image reconstruction research has been developed for more than five decades, giving rise to various techniques such as central and filtered back projection. With the rise of deep learning technology, biomedical image reconstruction field has undergone a massive paradigm shift from analytical and iterative methods to deep learning methods To drive scientific discussion on advanced deep learning techniques for biomedical image reconstruction, a workshop focusing on deep biomedical image reconstruction, MLMIR, is introduced and is being held yearly since 2018. This survey paper is aimed to provide basic knowledge in biomedical image reconstruction and the current research trend in biomedical image reconstruction based on the publications in MLMIR. This survey paper is intended for machine learning researchers to grasp a general understanding of the biomedical image reconstruction field and the current research trend in deep biomedical image reconstruction.
Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image
Authors: Samer Kais Jameel, Sezgin Aydin, Nebras H. Ghaeb, Jafar Majidpour, Tarik A. Rashid, Sinan Q. Salih, P. S. JosephNg
Date: 2022-12-25 00:00:00
Tasks: Deep Learning, Generative Adversarial Network, Medical Diagnosis

Corneal diseases are the most common eye disorders. Deep learning techniques are used to per-form automated diagnoses of cornea. Deep learning networks require large-scale annotated datasets, which is conceded as a weakness of deep learning. In this work, a method for synthesizing medical images using conditional generative adversarial networks (CGANs), is presented. It also illustrates how produced medical images may be utilized to enrich medical data, improve clinical decisions, and boost the performance of the conventional neural network (CNN) for medical image diagnosis. The study includes using corneal topography captured using a Pentacam device from patients with corneal diseases. The dataset contained 3448 different corneal images. Furthermore, it shows how an unbalanced dataset affects the performance of classifiers, where the data are balanced using the resampling approach. Finally, the results obtained from CNN networks trained on the balanced dataset are compared to those obtained from CNN networks trained on the imbalanced dataset. For performance, the system estimated the diagnosis accuracy, precision, and F1-score metrics. Lastly, some generated images were shown to an expert for evaluation and to see how well experts could identify the type of image and its condition. The expert recognized the image as useful for medical diagnosis and for determining the severity class according to the shape and values, by generating images based on real cases that could be used as new different stages of illness between healthy and unhealthy patients.
Deep SURE for Unsupervised Remote Sensing Image Fusion
Authors: Han V. Nguyen; Magnus O. Ulfarsson; Johannes R. Sveinsson; Mauro Dalla Mura
Date: 2022-10-19 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2022 10
Tasks: Hyperspectral Image Super-Resolution

Image fusion is utilized in remote sensing (RS) due to the limitation of the imaging sensor and the high cost of simultaneously acquiring high spatial and spectral resolution images. Optical RS imaging systems usually provide images of high spatial resolution but low spectral resolution and vice versa. Therefore, fusing those images to obtain a fused image having both high spectral and spatial resolution is desirable in many applications. This article proposes a fusion framework using an unsupervised convolutional neural network (CNN) and Stein’s unbiased risk estimate (SURE). We derive a new loss function for a CNN that incorporates a backprojection mean square error (MSE) with SURE to estimate the projected mse between the fused image and the ground truth. The main motivation is that training a CNN with this SURE loss function is unsupervised and avoids overfitting. Experimental results for two fusion examples, multispectral and hyperspectral (MS–HS) image fusion and multispectral and multispectral (MS–MS) image fusion, show that the proposed method yields high-quality fused images and outperforms the competitive methods. Codes are available at https://github.com/hvn2/Deep-SURE-Fusion .
Hyperspectral Image Denoising Using SURE-Based Unsupervised Convolutional Neural Networks
Authors: Han V. Nguyen; Magnus O. Ulfarsson; Johannes R. Sveinsson
Date: 2020-07-23 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2020 7
Tasks: Denoising, Hyperspectral Image Denoising, Image Denoising

Hyperspectral images (HSIs) are useful for many remote sensing applications. However, they are usually affected by noise that degrades the HSIs quality. Therefore, HSI denoising is important to improve the performance of subsequent HSI processing and analysis. In this article, we propose an HSI denoising method called Stein's unbiased risk estimate-convolutional neural network (SURE-CNN). The method is based on an unsupervised CNN and SURE. The main difference of SURE-CNN from existing supervised learning methods is that the SURE-based loss function can be computed only from noisy data. Since SURE is an unbiased estimate of the mean squared error (MSE) of an estimator, training a CNN using the SURE loss can yield similar results as using the MSE with ground truth in supervised learning. Also, a subspace version of SURE-CNN is proposed to reduce the running time. Extensive experimental results with both simulated and real data sets show that the SURE-CNN method outperforms the competitive methods in both objective and subjective assessments.
Deep Sparse and Low-Rank Prior for Hyperspectral Image Denoising
Authors: Han V. Nguyen; Magnus O. Ulfarsson; Jakob Sigurdsson; Johannes R. Sveinsson
Date: 2022-09-28 00:00:00
Proceeding: IEEE International Geoscience and Remote Sensing Symposium 2022 9
Tasks: Deblurring, Denoising, Hyperspectral Image Denoising, Image Denoising, Super-Resolution

Spectral and spatial correlation in hyperspectral images (HSIs) can be exploited in HSI processing because it directly induces a sparse and low-rank prior via linear transformations. Researchers have used the sparse and low-rank prior as an image prior for HSI restoration, such as denoising, deblurring, and super-resolution. This paper proposes a HSI denoising method that incorporates a sparse and low-rank prior with a deep image prior (DIP). The sparse and low-rank prior is obtained using the 2-dimensional discrete wavelet transform (2-D DWT), and singular value decomposition (SVD), while the DIP is provided by the structure of a convolutional neural network (CNN). The combination of a sparse and low-rank prior with a DIP views the CNN-based denoising method similar to a model-based method, inheriting the advantages of both model-based and CNN-based methods. Experimental results with simulated and real HSI datasets show that the proposed method outperforms the conventional sparse and low-rank based methods in both quantitative and qualitative performance. Codes are available at https://github.com/hvn2/DIP-SLR
Sentinel-2 Sharpening Using a Single Unsupervised Convolutional Neural Network With MTF-Based Degradation Model
Authors: Han V. Nguyen; Magnus O. Ulfarsson; Johannes R. Sveinsson; Mauro Dalla Mura
Date: 2021-06-24 00:00:00
Proceeding: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021 6
Tasks: Pansharpening, Super-Resolution

The Sentinel-2 (S2) constellation provides multispectral images at 10 m, 20 m, and 60 m resolution bands. Obtaining all bands at 10 m resolution would benefit many applications. Recently, many model-based and deep learning (DL)-based sharpening methods have been proposed. However, the downside of those methods is that the DL-based methods need to be trained separately for the 20 m and the 60 m bands in a supervised manner at reduced resolution, while the model-based methods heavily depend on the hand-crafted image priors. To break the gap, this article proposes a novel unsupervised DL-based S2 sharpening method using a single convolutional neural network (CNN) to sharpen the 20 and 60 m bands at the same time at full resolution. The proposed method replaces the hand-crafted image prior by the deep image prior (DIP) provided by a CNN structure whose parameters are easily optimized using a DL optimizer. We also incorporate the modulation transfer function-based degradation model as a network layer, and add all bands to both network input and output. This setting improves the DIP and exploits the advantage of multitask learning since all S2 bands are highly correlated. Extensive experiments with real S2 data show that our proposed method outperforms competitive methods for reduced-resolution evaluation and yields very high quality sharpened image for full-resolution evaluation.
Data Imputation for Multivariate Time Series Sensor Data with Large Gaps of Missing Data
Authors: Rui Wu, Scott D. Hamshaw, Lei Yang, Dustin W. Kincaid, Randall Etheridge, Amir Ghasemkhani
Date: 2022-06-01 00:00:00
Proceeding: IEEE Sensors Journal 2022 6
Tasks: Imputation, Time Series, Time Series Analysis

Imputation of missing sensor-collected data is often an important step prior to machine learning and statistical data analysis. One particular data imputation challenge is filling large data gaps when the only related data comes from the same sensor station. In this paper, we propose a framework to improve the popular multivariate imputation by chained equations (MICE) method for dealing with missing data. One key strategy we use to improve model accuracy is to reshape the original sensor data to leverage the correlation between the missing data and the observed data. We demonstrate our framework using data from continuous water quality monitoring stations in Vermont. Because of possible irregularly spaced peaks throughout the time series, the reshaped data is split into extreme and normal values and two MICE models are built. We also recommend that sensor-collected data should be transformed to meet the machine learning model assumptions. According to our experimental results, these strategies can improve MICE data imputation model accuracy at least 23% for large data gaps based on R2 values and are promising to be applied for other data imputation algorithms.
Variational Latent Branching Model for Off-Policy Evaluation
Authors: Qitong Gao, Ge Gao, Min Chi, Miroslav Pajic
Date: 2023-01-28 00:00:00
Tasks: model, Off-policy evaluation, Variational Inference

Model-based methods have recently shown great potential for off-policy evaluation (OPE); offline trajectories induced by behavioral policies are fitted to transitions of Markov decision processes (MDPs), which are used to rollout simulated trajectories and estimate the performance of policies. Model-based OPE methods face two key challenges. First, as offline trajectories are usually fixed, they tend to cover limited state and action space. Second, the performance of model-based methods can be sensitive to the initialization of their parameters. In this work, we propose the variational latent branching model (VLBM) to learn the transition function of MDPs by formulating the environmental dynamics as a compact latent space, from which the next states and rewards are then sampled. Specifically, VLBM leverages and extends the variational inference framework with the recurrent state alignment (RSA), which is designed to capture as much information underlying the limited training data, by smoothing out the information flow between the variational (encoding) and generative (decoding) part of VLBM. Moreover, we also introduce the branching architecture to improve the model's robustness against randomly initialized model weights. The effectiveness of the VLBM is evaluated on the deep OPE (DOPE) benchmark, from which the training trajectories are designed to result in varied coverage of the state-action space. We show that the VLBM outperforms existing state-of-the-art OPE methods in general.
CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans
Authors: Jieneng Chen, Yingda Xia, Jiawen Yao, Ke Yan, Jianpeng Zhang, Le Lu, Fakai Wang, Bo Zhou, Mingyan Qiu, Qihang Yu, Mingze Yuan, Wei Fang, Yuxing Tang, Minfeng Xu, Jian Zhou, Yuqian Zhao, Qifeng Wang, Xianghua Ye, Xiaoli Yin, Yu Shi, Xin Chen, Jingren Zhou, Alan Yuille, Zaiyi Liu, Ling Zhang
Date: 2023-01-28 00:00:00
Proceeding: ICCV 2023 1
Tasks: Diagnostic, Organ Segmentation, Representation Learning, Semantic Segmentation

Human readers or radiologists routinely perform full-body multi-organ multi-disease detection and diagnosis in clinical practice, while most medical AI systems are built to focus on single organs with a narrow list of a few diseases. This might severely limit AI's clinical adoption. A certain number of AI models need to be assembled non-trivially to match the diagnostic process of a human reading a CT scan. In this paper, we construct a Unified Tumor Transformer (CancerUniT) model to jointly detect tumor existence & location and diagnose tumor characteristics for eight major cancers in CT scans. CancerUniT is a query-based Mask Transformer model with the output of multi-tumor prediction. We decouple the object queries into organ queries, tumor detection queries and tumor diagnosis queries, and further establish hierarchical relationships among the three groups. This clinically-inspired architecture effectively assists inter- and intra-organ representation learning of tumors and facilitates the resolution of these complex, anatomically related multi-organ cancer image reading tasks. CancerUniT is trained end-to-end using a curated large-scale CT images of 10,042 patients including eight major types of cancers and occurring non-cancer tumors (all are pathology-confirmed with 3D tumor masks annotated by radiologists). On the test set of 631 patients, CancerUniT has demonstrated strong performance under a set of clinically relevant evaluation metrics, substantially outperforming both multi-disease methods and an assembly of eight single-organ expert models in tumor detection, segmentation, and diagnosis. This moves one step closer towards a universal high performance cancer screening tool.
Training Full Spike Neural Networks via Auxiliary Accumulation Pathway
Authors: Guangyao Chen, Peixi Peng, Guoqi Li, Yonghong Tian
Date: 2023-01-27 00:00:00

Due to the binary spike signals making converting the traditional high-power multiply-accumulation (MAC) into a low-power accumulation (AC) available, the brain-inspired Spiking Neural Networks (SNNs) are gaining more and more attention. However, the binary spike propagation of the Full-Spike Neural Networks (FSNN) with limited time steps is prone to significant information loss. To improve performance, several state-of-the-art SNN models trained from scratch inevitably bring many non-spike operations. The non-spike operations cause additional computational consumption and may not be deployed on some neuromorphic hardware where only spike operation is allowed. To train a large-scale FSNN with high performance, this paper proposes a novel Dual-Stream Training (DST) method which adds a detachable Auxiliary Accumulation Pathway (AAP) to the full spiking residual networks. The accumulation in AAP could compensate for the information loss during the forward and backward of full spike propagation, and facilitate the training of the FSNN. In the test phase, the AAP could be removed and only the FSNN remained. This not only keeps the lower energy consumption but also makes our model easy to deploy. Moreover, for some cases where the non-spike operations are available, the APP could also be retained in test inference and improve feature discrimination by introducing a little non-spike consumption. Extensive experiments on ImageNet, DVS Gesture, and CIFAR10-DVS datasets demonstrate the effectiveness of DST.