pwc-archive/papers-with-abstracts huggingface dataset:
Replicating The Log of Gravity
Authors: Mauricio Vargas Sepúlveda
Date: 2024-09-05 00:00:00

This document replicates the main results from Santos Silva and Tenreyro (2006 in R. The original results were obtained in TSP back in 2006. The idea here is to be explicit regarding the conceptual approach to regression in R. For most of the replication I used base R without external libraries except when it was absolutely necessary. The findings are consistent with the original article and reveal that the replication effort is minimal, without the need to contact the authors for clarifications or incur into data transformations or filtering not mentioned in the article.
A General Equilibrium Study of Venture Capitalists' Effort on Entrepreneurship
Authors: Liukun Wu
Date: 2024-09-16 00:00:00

In this paper, I propose a new general equilibrium model that explains stylized facts about venture capitalists' impact on their portfolio firms. Venture capitalists can help increase firms' productivity, yet they face increasing entry costs to enter. I characterize steady state effort choice, entry threshold, and mass of venture capitalists, and show how they are affected by change in upfront investment, interest rate, and entry costs. The key contribution is that public policy to stimulate startups by subsidizing upfront investments or reducing interest cost have limited success if not accompanied by an increasing supply of experts who can improve business ideas.
Balancing Selection Efficiency and Societal Costs in Selective Contests
Authors: Penghuan Yan
Date: 2024-09-15 00:00:00

Selective contests can impair participants' overall welfare in overcompetitive environments, such as school admissions. This paper models the situation as an optimal contest design problem with binary actions, treating effort costs as societal costs incurred to achieve a desired level of selectivity. We provide a characterization for the feasible set of selection efficiency and societal cost in selective contests by establishing their relationship with feasible equilibrium strategies. We find that selection efficiency and contestants' welfare are complementary, i.e. it is almost impossible to improve one without sacrificing the other. We derive the optimal equilibrium outcome given the feasible set and characterize the corresponding optimal contest design. Our analysis demonstrates that it is always optimal for a contest designer who is sufficiently concerned with societal cost to intentionally introduce randomness into the contest. Furthermore, we show that the designer can optimize any linear payoff function by adjusting a single parameter related to the intensity of randomness, without altering the specific structure of the contest.
FLAMO: An Open-Source Library for Frequency-Domain Differentiable Audio Processing
Authors: Gloria Dal Santo, Gian Marco De Bortoli, Karolina Prawda, Sebastian J. Schlecht, Vesa Välimäki
Date: 2024-09-13 00:00:00

We present FLAMO, a Frequency-sampling Library for Audio-Module Optimization designed to implement and optimize differentiable linear time-invariant audio systems. The library is open-source and built on the frequency-sampling filter design method, allowing for the creation of differentiable modules that can be used stand-alone or within the computation graph of neural networks, simplifying the development of differentiable audio systems. It includes predefined filtering modules and auxiliary classes for constructing, training, and logging the optimized systems, all accessible through an intuitive interface. Practical application of these modules is demonstrated through two case studies: the optimization of an artificial reverberator and an active acoustics system for improved response coloration.
Self-Supervised Elimination of Non-Independent Noise in Hyperspectral Imaging
Authors: Guangrui Ding, Chang Liu, Jiaze Yin, Xinyan Teng, Yuying Tan, Hongjian He, Haonan Lin, Lei Tian, Ji-Xin Cheng
Date: 2024-09-16 00:00:00
Tasks: Denoising

Hyperspectral imaging has been widely used for spectral and spatial identification of target molecules, yet often contaminated by sophisticated noise. Current denoising methods generally rely on independent and identically distributed noise statistics, showing corrupted performance for non-independent noise removal. Here, we demonstrate Self-supervised PErmutation Noise2noise Denoising (SPEND), a deep learning denoising architecture tailor-made for removing non-independent noise from a single hyperspectral image stack. We utilize hyperspectral stimulated Raman scattering and mid-infrared photothermal microscopy as the testbeds, where the noise is spatially correlated and spectrally varied. Based on single hyperspectral images, SPEND permutates odd and even spectral frames to generate two stacks with identical noise properties, and uses the pairs for efficient self-supervised noise-to-noise training. SPEND achieved an 8-fold signal-to-noise improvement without having access to the ground truth data. SPEND enabled accurate mapping of low concentration biomolecules in both fingerprint and silent regions, demonstrating its robustness in sophisticated cellular environments.
Adversarial Deep-Unfolding Network for MA-XRF Super-Resolution on Old Master Paintings Using Minimal Training Data
Authors: Herman Verinaz-Jadan, Su Yan, Catherine Higgitt, Pier Luigi Dragotti
Date: 2024-09-14 00:00:00
Tasks: Super-Resolution

High-quality element distribution maps enable precise analysis of the material composition and condition of Old Master paintings. These maps are typically produced from data acquired through Macro X-ray fluorescence (MA-XRF) scanning, a non-invasive technique that collects spectral information. However, MA-XRF is often limited by a trade-off between acquisition time and resolution. Achieving higher resolution requires longer scanning times, which can be impractical for detailed analysis of large artworks. Super-resolution MA-XRF provides an alternative solution by enhancing the quality of MA-XRF scans while reducing the need for extended scanning sessions. This paper introduces a tailored super-resolution approach to improve MA-XRF analysis of Old Master paintings. Our method proposes a novel adversarial neural network architecture for MA-XRF, inspired by the Learned Iterative Shrinkage-Thresholding Algorithm. It is specifically designed to work in an unsupervised manner, making efficient use of the limited available data. This design avoids the need for extensive datasets or pre-trained networks, allowing it to be trained using just a single high-resolution RGB image alongside low-resolution MA-XRF data. Numerical results demonstrate that our method outperforms existing state-of-the-art super-resolution techniques for MA-XRF scans of Old Master paintings.
Integrating Deep Unfolding with Direct Diffusion Bridges for Computed Tomography Reconstruction
Authors: Herman Verinaz-Jadan, Su Yan
Date: 2024-09-14 00:00:00
Tasks: Computed Tomography (CT), Deep Learning

Computed Tomography (CT) is widely used in healthcare for detailed imaging. However, Low-dose CT, despite reducing radiation exposure, often results in images with compromised quality due to increased noise. Traditional methods, including preprocessing, post-processing, and model-based approaches that leverage physical principles, are employed to improve the quality of image reconstructions from noisy projections or sinograms. Recently, deep learning has significantly advanced the field, with diffusion models outperforming both traditional methods and other deep learning approaches. These models effectively merge deep learning with physics, serving as robust priors for the inverse problem in CT. However, they typically require prolonged computation times during sampling. This paper introduces the first approach to merge deep unfolding with Direct Diffusion Bridges (DDBs) for CT, integrating the physics into the network architecture and facilitating the transition from degraded to clean images by bypassing excessively noisy intermediate stages commonly encountered in diffusion models. Moreover, this approach includes a tailored training procedure that eliminates errors typically accumulated during sampling. The proposed approach requires fewer sampling steps and demonstrates improved fidelity metrics, outperforming many existing state-of-the-art techniques.
A Multimodal Approach for Fluid Overload Prediction: Integrating Lung Ultrasound and Clinical Data
Authors: Tianqi Yang, Nantheera Anantrasirichai, Oktay Karakuş, Marco Allinovi, Alin Achim
Date: 2024-09-13 00:00:00
Tasks: imbalanced classification, Management

Managing fluid balance in dialysis patients is crucial, as improper management can lead to severe complications. In this paper, we propose a multimodal approach that integrates visual features from lung ultrasound images with clinical data to enhance the prediction of excess body fluid. Our framework employs independent encoders to extract features for each modality and combines them through a cross-domain attention mechanism to capture complementary information. By framing the prediction as a classification task, the model achieves significantly better performance than regression. The results demonstrate that multimodal models consistently outperform single-modality models, particularly when attention mechanisms prioritize tabular data. Pseudo-sample generation further contributes to mitigating the imbalanced classification problem, achieving the highest accuracy of 88.31%. This study underscores the effectiveness of multimodal learning for fluid overload management in dialysis patients, offering valuable insights for improved clinical outcomes.
Evaluating the Quality of Brain MRI Generators
Authors: Jiaqi Wu, Wei Peng, Binxu Li, Yu Zhang, Kilian M. Pohl
Date: 2024-09-13 00:00:00
Tasks: Anatomy

Deep learning models generating structural brain MRIs have the potential to significantly accelerate discovery of neuroscience studies. However, their use has been limited in part by the way their quality is evaluated. Most evaluations of generative models focus on metrics originally designed for natural images (such as structural similarity index and Frechet inception distance). As we show in a comparison of 6 state-of-the-art generative models trained and tested on over 3000 MRIs, these metrics are sensitive to the experimental setup and inadequately assess how well brain MRIs capture macrostructural properties of brain regions (i.e., anatomical plausibility). This shortcoming of the metrics results in inconclusive findings even when qualitative differences between the outputs of models are evident. We therefore propose a framework for evaluating models generating brain MRIs, which requires uniform processing of the real MRIs, standardizing the implementation of the models, and automatically segmenting the MRIs generated by the models. The segmentations are used for quantifying the plausibility of anatomy displayed in the MRIs. To ensure meaningful quantification, it is crucial that the segmentations are highly reliable. Our framework rigorously checks this reliability, a step often overlooked by prior work. Only 3 of the 6 generative models produced MRIs, of which at least 95% had highly reliable segmentations. More importantly, the assessment of each model by our framework is in line with qualitative assessments, reinforcing the validity of our approach.
Impact of Stain Variation and Color Normalization for Prognostic Predictions in Pathology
Authors: Siyu, Lin, Haowen Zhou, Richard J. Cote, Mark Watson, Ramaswamy Govindan, Changhuei Yang
Date: 2024-09-12 00:00:00
Tasks: Color Normalization, Generative Adversarial Network

In recent years, deep neural networks (DNNs) have demonstrated remarkable performance in pathology applications, potentially even outperforming expert pathologists due to their ability to learn subtle features from large datasets. One complication in preparing digital pathology datasets for DNN tasks is variation in tinctorial qualities. A common way to address this is to perform stain normalization on the images. In this study, we show that a well-trained DNN model trained on one batch of histological slides failed to generalize to another batch prepared at a different time from the same tissue blocks, even when stain normalization methods were applied. This study used sample data from a previously reported DNN that was able to identify patients with early stage non-small cell lung cancer (NSCLC) whose tumors did and did not metastasize, with high accuracy, based on training and then testing of digital images from H&E stained primary tumor tissue sections processed at the same time. In this study we obtained a new series of histologic slides from the adjacent recuts of same tissue blocks processed in the same lab but at a different time. We found that the DNN trained on the either batch of slides/images was unable to generalize and failed to predict progression in the other batch of slides/images (AUC_cross-batch = 0.52 - 0.53 compared to AUC_same-batch = 0.74 - 0.81). The failure to generalize did not improve even when the tinctorial difference correction were made through either traditional color-tuning or stain normalization with the help of a Cycle Generative Adversarial Network (CycleGAN) process. This highlights the need to develop an entirely new way to process and collect consistent microscopy images from histologic slides that can be used to both train and allow for the general application of predictive DNN algorithms.
HESSO: Towards Automatic Efficient and User Friendly Any Neural Network Training and Pruning
Authors: Tianyi Chen, Xiaoyi Qu, David Aponte, Colby Banbury, Jongwoo Ko, Tianyu Ding, Yong Ma, Vladimir Lyapunov, Ilya Zharkov, Luming Liang
Date: 2024-09-11 00:00:00
Tasks: Large Language Model

Structured pruning is one of the most popular approaches to effectively compress the heavy deep neural networks (DNNs) into compact sub-networks while retaining performance. The existing methods suffer from multi-stage procedures along with significant engineering efforts and human expertise. The Only-Train-Once (OTO) series has been recently proposed to resolve the many pain points by streamlining the workflow by automatically conducting (i) search space generation, (ii) structured sparse optimization, and (iii) sub-network construction. However, the built-in sparse optimizers in the OTO series, i.e., the Half-Space Projected Gradient (HSPG) family, have limitations that require hyper-parameter tuning and the implicit controls of the sparsity exploration, consequently requires intervening by human expertise. To address such limitations, we propose a Hybrid Efficient Structured Sparse Optimizer (HESSO). HESSO could automatically and efficiently train a DNN to produce a high-performing subnetwork. Meanwhile, it is almost tuning-free and enjoys user-friendly integration for generic training applications. To address another common issue of irreversible performance collapse observed in pruning DNNs, we further propose a Corrective Redundant Identification Cycle (CRIC) for reliably identifying indispensable structures. We numerically demonstrate the efficacy of HESSO and its enhanced version HESSO-CRIC on a variety of applications ranging from computer vision to natural language processing, including large language model. The numerical results showcase that HESSO can achieve competitive even superior performance to varying state-of-the-arts and support most DNN architectures. Meanwhile, CRIC can effectively prevent the irreversible performance collapse and further enhance the performance of HESSO on certain applications.
Deep learning-based classification of breast cancer molecular subtypes from H&E whole-slide images
Authors: Masoud Tafavvoghi, Anders Sildnes, Mehrdad Rakaee, Nikita Shvetsov, Lars Ailo Bongo, Lill-Tove Rasmussen Busund, Kajsa Møllersen
Date: 2024-08-30 00:00:00
Tasks: whole slide images

Classifying breast cancer molecular subtypes is crucial for tailoring treatment strategies. While immunohistochemistry (IHC) and gene expression profiling are standard methods for molecular subtyping, IHC can be subjective, and gene profiling is costly and not widely accessible in many regions. Previous approaches have highlighted the potential application of deep learning models on H&E-stained whole slide images (WSI) for molecular subtyping, but these efforts vary in their methods, datasets, and reported performance. In this work, we investigated whether H&E-stained WSIs could be solely leveraged to predict breast cancer molecular subtypes (luminal A, B, HER2-enriched, and Basal). We used 1,433 WSIs of breast cancer in a two-step pipeline: first, classifying tumor and non-tumor tiles to use only the tumor regions for molecular subtyping; and second, employing a One-vs-Rest (OvR) strategy to train four binary OvR classifiers and aggregating their results using an eXtreme Gradient Boosting (XGBoost) model. The pipeline was tested on 221 hold-out WSIs, achieving an overall macro F1 score of 0.95 for tumor detection and 0.73 for molecular subtyping. Our findings suggest that, with further validation, supervised deep learning models could serve as supportive tools for molecular subtyping in breast cancer. Our codes are made available to facilitate ongoing research and development.
OrthoDoc: Multimodal Large Language Model for Assisting Diagnosis in Computed Tomography
Authors: Youzhu Jin, Yichen Zhang
Date: 2024-08-30 00:00:00
Tasks: Computed Tomography (CT), Diagnostic, Language Modeling, Language Modelling, Large Language Model, Multimodal Large Language Model, RAG, Retrieval-augmented Generation

Multimodal large language models (MLLMs) have achieved significant success in the general field of image processing. Their emerging task generalization and freeform conversational capabilities can greatly facilitate medical diagnostic assistance, helping patients better understand their conditions and enhancing doctor-patient trust. Computed Tomography (CT) is a non-invasive imaging technique used to capture the internal mechanisms of a patient's condition and is widely utilized. However, in past research, the complex textural features of this imaging data have made accurate interpretation by algorithms challenging, impeding the performance of general LLMs in diagnostic assistance. To address this, we developed OrthoDoc, a MLLM designed for CT diagnostics. OrthoDoc is trained on 120,000 CT images and diagnostic reports and includes a Retrieval-Augmented Generation (RAG) module capable of effectively mitigating model hallucinations. This module is informed by extensive medical literature, textbooks, and explanatory data. Thus, OrthoDoc not only processes complex CT images but also stores, understands, and reasons over medical knowledge and language. In extensive experiments, OrthoDoc outperforms commercial models led by GPT-4, demonstrating superior diagnostic capabilities and accuracy. Specifically, OrthoDoc significantly surpasses existing models in the diagnosis of common orthopedic conditions such as fractures, arthritis, and tumors. Additionally, OrthoDoc exhibits robust generalization and stability when handling rare and complex cases.
Exploiting Beam-Split in IRS-aided Systems via OFDMA
Authors: P. Siddhartha, L. Yashvanth, Chandra R. Murthy
Date: 2024-09-16 00:00:00
Tasks: Scheduling

In wideband systems operating at mmWave frequencies, intelligent reflecting surfaces (IRSs) equipped with many passive elements can compensate for channel propagation losses. Then, a phenomenon known as the beam-split (B-SP) occurs in which the phase shifters at the IRS elements fail to beamform at a desired user equipment (UE) over the total allotted bandwidth (BW). Although B-SP is usually seen as an impairment, in this paper, we take an optimistic view and exploit the B-SP effect to enhance the system performance via an orthogonal frequency division multiple access (OFDMA). We argue that due to the B-SP, when an IRS is tuned to beamform at a particular angle on one frequency, it also forms beams in different directions on other frequencies. Then, by opportunistically scheduling different UEs on different subcarriers (SCs), we show that, almost surely, the optimal array gain that scales quadratically in the number of IRS elements can be achieved on all SCs in the system. We derive the achievable throughput of the proposed scheme and deduce that the system also enjoys additional multi-user diversity benefits on top of the optimal beamforming gain over the full BW. Finally, we verify our findings via numerical simulations.
Over-the-Air Computation via 2D Movable Antenna Array
Authors: Nianzu Li, Peiran Wu, Boyu Ning, Lipeng Zhu, Weidong Mei
Date: 2024-09-16 00:00:00
Tasks: Position

Movable antenna (MA) has emerged as a promising technology for improving the performance of wireless communication systems, which enables local movement of the antennas to create more favorable channel conditions. In this letter, we advance its application for over-the-air computation (AirComp) network, where an access point is equipped with a two-dimensional (2D) MA array to aggregate wireless data from massive users. We aim to minimize the computation mean square error (CMSE) by jointly optimizing the antenna position vector (APV), the receive combining vector at the access point and the transmit coefficients from all users. To tackle this highly non-convex problem, we propose a two-loop iterative algorithm, where the particle swarm optimization (PSO) approach is leveraged to obtain a suboptimal APV in the outer loop while the receive combining vector and transmit coefficients are alternately optimized in the inner loop. Numerical results demonstrate that the proposed MA-enhanced AirComp network outperforms the conventional network with fixed-position antennas (FPAs).
Enhancing HAP Networks with Reconfigurable Intelligent Surfaces
Authors: Islam M. Tanash, Ayush Kumar Dwivedi, Fatemeh Rafiei Maleki, Taneli Riihonen
Date: 2024-09-16 00:00:00

This paper presents and analyzes a reconfigurable intelligent surface (RIS)-based high-altitude platform (HAP) network. Stochastic geometry is used to model the arbitrary locations of the HAPs and RISs as a homogenous Poisson point process. Considering that the links between the HAPs, RISs, and users are $\kappa$--$\mu$ faded, the coverage and ergodic capacity of the proposed system are expressed. The analytically derived performance measures are verified through Monte Carlo simulations. Significant improvements in system performance and the impact of system parameters are demonstrated in the results. Thus, the proposed system concept can improve connectivity and data offloading in smart cities and dense urban environments.