pwc-archive/papers-with-abstracts huggingface dataset:
Quantum gate algorithm for reference-guided DNA sequence alignment
Authors: G. D. Varsamis, I. G. Karafyllidis, K. M. Gilkes, U. Arranz, R. Martin-Cuevas, G. Calleja, P. Dimitrakis, P. Kolovos, R. Sandaltzopoulos, H. C. Jessen, J. Wong
Date: 2023-08-08 00:00:00

Reference-guided DNA sequencing and alignment is an important process in computational molecular biology. The amount of DNA data grows very fast, and many new genomes are waiting to be sequenced while millions of private genomes need to be re-sequenced. Each human genome has 3.2 B base pairs, and each one could be stored with 2 bits of information, so one human genome would take 6.4 B bits or about 760 MB of storage (National Institute of General Medical Sciences). Today most powerful tensor processing units cannot handle the volume of DNA data necessitating a major leap in computing power. It is, therefore, important to investigate the usefulness of quantum computers in genomic data analysis, especially in DNA sequence alignment. Quantum computers are expected to be involved in DNA sequencing, initially as parts of classical systems, acting as quantum accelerators. The number of available qubits is increasing annually, and future quantum computers could conduct DNA sequencing, taking the place of classical computing systems. We present a novel quantum algorithm for reference-guided DNA sequence alignment modeled with gate-based quantum computing. The algorithm is scalable, can be integrated into existing classical DNA sequencing systems and is intentionally structured to limit computational errors. The quantum algorithm has been tested using the quantum processing units and simulators provided by IBM Quantum, and its correctness has been confirmed.
Money Growth and Inflation: A Quantile Sensitivity Approach
Authors: Matteo Iacopini, Aubrey Poon, Luca Rossini, Dan Zhu
Date: 2023-08-10 00:00:00
Tasks: Sensitivity

An innovative method is proposed to construct a quantile dependence system for inflation and money growth. By considering all quantiles and leveraging a novel notion of quantile sensitivity, the method allows the assessment of changes in the entire distribution of a variable of interest in response to a perturbation in another variable's quantile. The construction of this relationship is demonstrated through a system of linear quantile regressions. Then, the proposed framework is exploited to examine the distributional effects of money growth on the distributions of inflation and its disaggregate measures in the United States and the Euro area. The empirical analysis uncovers significant impacts of the upper quantile of the money growth distribution on the distribution of inflation and its disaggregate measures. Conversely, the lower and median quantiles of the money growth distribution are found to have a negligible influence. Finally, this distributional impact exhibits variation over time in both the United States and the Euro area.
Solving the Forecast Combination Puzzle
Authors: David T. Frazier, Ryan Covey, Gael M. Martin, Donald Poskitt
Date: 2023-08-10 00:00:00

We demonstrate that the forecasting combination puzzle is a consequence of the methodology commonly used to produce forecast combinations. By the combination puzzle, we refer to the empirical finding that predictions formed by combining multiple forecasts in ways that seek to optimize forecast performance often do not out-perform more naive, e.g. equally-weighted, approaches. In particular, we demonstrate that, due to the manner in which such forecasts are typically produced, tests that aim to discriminate between the predictive accuracy of competing combination strategies can have low power, and can lack size control, leading to an outcome that favours the naive approach. We show that this poor performance is due to the behavior of the corresponding test statistic, which has a non-standard asymptotic distribution under the null hypothesis of no inferior predictive accuracy, rather than the {standard normal distribution that is} {typically adopted}. In addition, we demonstrate that the low power of such predictive accuracy tests in the forecast combination setting can be completely avoided if more efficient estimation strategies are used in the production of the combinations, when feasible. We illustrate these findings both in the context of forecasting a functional of interest and in terms of predictive densities. A short empirical example {using daily financial returns} exemplifies how researchers can avoid the puzzle in practical settings.
Interpolation of numerical series by the Fermat-Torricelli point construction method on the example of the numerical series of inflation in the Czech Republic in 2011-2021
Authors: Yekimov Sergey
Date: 2023-08-09 00:00:00
Tasks: regression

The use of regression analysis for processing experimental data is fraught with certain difficulties, which, when models are constructed, are associated with assumptions, and there is a normal law of error distribution and variables are statistically independent. In practice , these conditions do not always take place . This may cause the constructed economic and mathematical model to have no practical value. As an alternative approach to the study of numerical series, according to the author, smoothing of numerical series using Fermat-Torricelli points with subsequent interpolation of these points by series of exponents could be used. The use of exponential series for interpolating numerical series makes it possible to achieve the accuracy of model construction no worse than regression analysis . At the same time, the interpolation by series of exponents does not require the statistical material that the errors of the numerical series obey the normal distribution law, and statistical independence of variables is also not required. Interpolation of numerical series by exponential series represents a "black box" type model, that is, only input parameters and output parameters matter.
Statistical Decision Theory Respecting Stochastic Dominance
Authors: Charles F. Manski, Aleksey Tetenov
Date: 2023-08-09 00:00:00

The statistical decision theory pioneered by Wald (1950) has used state-dependent mean loss (risk) to measure the performance of statistical decision functions across potential samples. We think it evident that evaluation of performance should respect stochastic dominance, but we do not see a compelling reason to focus exclusively on mean loss. We think it instructive to also measure performance by other functionals that respect stochastic dominance, such as quantiles of the distribution of loss. This paper develops general principles and illustrative applications for statistical decision theory respecting stochastic dominance. We modify the Wald definition of admissibility to an analogous concept of stochastic dominance (SD) admissibility, which uses stochastic dominance rather than mean sampling performance to compare alternative decision rules. We study SD admissibility in two relatively simple classes of decision problems that arise in treatment choice. We reevaluate the relationship between the MLE, James-Stein, and James-Stein positive part estimators from the perspective of SD admissibility. We consider alternative criteria for choice among SD-admissible rules. We juxtapose traditional criteria based on risk, regret, or Bayes risk with analogous ones based on quantiles of state-dependent sampling distributions or the Bayes distribution of loss.
A Guide to Impact Evaluation under Sample Selection and Missing Data: Teacher's Aides and Adolescent Mental Health
Authors: Simon Calmar Andersen, Louise Beuchert, Phillip Heiler, Helena Skyt Nielsen
Date: 2023-08-09 00:00:00

This paper is concerned with identification, estimation, and specification testing in causal evaluation problems when data is selective and/or missing. We leverage recent advances in the literature on graphical methods to provide a unifying framework for guiding empirical practice. The approach integrates and connects to prominent identification and testing strategies in the literature on missing data, causal machine learning, panel data analysis, and more. We demonstrate its utility in the context of identification and specification testing in sample selection models and field experiments with attrition. We provide a novel analysis of a large-scale cluster-randomized controlled teacher's aide trial in Danish schools at grade 6. Even with detailed administrative data, the handling of missing data crucially affects broader conclusions about effects on mental health. Results suggest that teaching assistants provide an effective way of improving internalizing behavior for large parts of the student population.
The Mobilität.Leben Study: a Year-Long Mobility-Tracking Panel
Authors: Allister Loder, Fabienne Cantner, Victoria Dahmen, Klaus Bogenberger
Date: 2023-08-09 00:00:00

The Mobilit\"at.Leben study investigated travel behavior effects of a natural experiment in Germany. In response to the 2022 cost-of-living crisis, two policy measures to reduce travel costs for the population in June, July, and August 2022 were introduced: a fuel excise tax cut and almost fare-free public transport with the so-called 9-Euro-Ticket. The announcement of a successor ticket to the 9-Euro-Ticket, the so-called Deutschlandticket, led to the immediate decision to continue the study. The Mobilit\"at.Leben study has two periods, the 9-Euro-Ticket period and the Deutschlandticket period, and comprises two elements: several questionnaires and a smartphone-based passive waypoint tracking. The entire duration of the study was almost thirteen months. In this paper, we report on the study design, the recruitment strategy, the study participation in the survey, and the tracking parts, and we share our experience in conducting such large-scale panel studies. Overall, 3,080 people registered for our study of which 1,420 decided to use the smartphone tracking app. While the relevant questionnaires in both phases have been completed by 818 participants, we have 170 study participants who completed the tracking in both phases and all relevant questionnaires. We find that providing a study compensation increases participation performance. It can be concluded that conducting year-long panel studies is possible, providing rich information on the heterogeneity in travel behavior between and within travelers.
Dynamic delegation in promotion contests
Authors: Théo Durandard
Date: 2023-08-10 00:00:00

I study how organizations assign tasks to identify the best candidate to promote among a pool of workers. Task allocation and workers' motivation interact through the organization's promotion decisions. The organization designs the workers' careers to both screen and develop talent. When only non-routine tasks are informative about a worker's type and non-routine tasks are scarce, the organization's preferred promotion system is an index contest. Each worker is assigned a number that depends only on his own type. The principal delegates the non-routine task to the worker whose current index is the highest and promotes the first worker whose type exceeds a threshold. Each worker's threshold is independent of the other workers' types. Competition is mediated by the allocation of tasks: who gets the opportunity to prove themselves is a determinant factor in promotions. Finally, features of the optimal promotion contest rationalize the prevalence of fast-track promotion, the role of seniority, or when a group of workers is systemically advantaged.
School Choice with Multiple Priorities
Authors: Minoru Kitahara, Yasunori Okumura
Date: 2023-08-09 00:00:00
Tasks: Fairness

This study considers a model where schools may have multiple priority orders on students, which may be inconsistent with each other. For example, in school choice systems, since the sibling priority and the walk zone priority coexist, the priority orders based on them would be conflicting. We introduce a weaker fairness notion called M-fairness to examine such markets. Further, we focus on a more specific situation where all schools have only two priority orders, and for a certain group of students, a priority order of each school is an improvement of the other priority order of the school. An illustrative example is the school choice matching market with a priority-based affirmative action policy. We introduce a mechanism that utilizes the efficiency adjusted deferred acceptance algorithm and show that the mechanism satisfies properties called responsiveness to improvements and improved-group optimally M-stability, which is stronger than student optimally M-stability.
Tropical Analysis: With an Application to Indivisible Goods
Authors: Nicholas C. Bedard, Jacob K. Goeree
Date: 2023-08-08 00:00:00

We establish the Subgradient Theorem for monotone correspondences -- a monotone correspondence is equal to the subdifferential of a potential if and only if it is conservative, i.e. its integral along a closed path vanishes irrespective of the selection from the correspondence along the path. We prove two attendant results: the Potential Theorem, whereby a conservative monotone correspondence can be integrated up to a potential, and the Duality Theorem, whereby the potential has a Fenchel dual whose subdifferential is another conservative monotone correspondence. We use these results to reinterpret and extend Baldwin and Klemperer's (2019) characterization of demand in economies with indivisible goods. We introduce a simple test for existence of Walrasian equilibrium in quasi-linear economies. Fenchel's Duality Theorem implies this test is met when the aggregate utility is concave, which is not necessarily the case with indivisible goods even if all consumers have concave utilities.
SAR Target Image Generation Method Using Azimuth-Controllable Generative Adversarial Network
Authors: Chenwei Wang, Jifang Pei, Xiaoyu Liu, Yulin Huang, Deqing Mao, Yin Zhang, Jianyu Yang
Date: 2023-08-10 00:00:00
Tasks: Generative Adversarial Network, Image Generation

Sufficient synthetic aperture radar (SAR) target images are very important for the development of researches. However, available SAR target images are often limited in practice, which hinders the progress of SAR application. In this paper, we propose an azimuth-controllable generative adversarial network to generate precise SAR target images with an intermediate azimuth between two given SAR images' azimuths. This network mainly contains three parts: generator, discriminator, and predictor. Through the proposed specific network structure, the generator can extract and fuse the optimal target features from two input SAR target images to generate SAR target image. Then a similarity discriminator and an azimuth predictor are designed. The similarity discriminator can differentiate the generated SAR target images from the real SAR images to ensure the accuracy of the generated, while the azimuth predictor measures the difference of azimuth between the generated and the desired to ensure the azimuth controllability of the generated. Therefore, the proposed network can generate precise SAR images, and their azimuths can be controlled well by the inputs of the deep network, which can generate the target images in different azimuths to solve the small sample problem to some degree and benefit the researches of SAR images. Extensive experimental results show the superiority of the proposed method in azimuth controllability and accuracy of SAR target image generation.
Global in Local: A Convolutional Transformer for SAR ATR FSL
Authors: Chenwei Wang, Yulin Huang, Xiaoyu Liu, Jifang Pei, Yin Zhang, Jianyu Yang
Date: 2023-08-10 00:00:00
Tasks: Few-Shot Learning

Convolutional neural networks (CNNs) have dominated the synthetic aperture radar (SAR) automatic target recognition (ATR) for years. However, under the limited SAR images, the width and depth of the CNN-based models are limited, and the widening of the received field for global features in images is hindered, which finally leads to the low performance of recognition. To address these challenges, we propose a Convolutional Transformer (ConvT) for SAR ATR few-shot learning (FSL). The proposed method focuses on constructing a hierarchical feature representation and capturing global dependencies of local features in each layer, named global in local. A novel hybrid loss is proposed to interpret the few SAR images in the forms of recognition labels and contrastive image pairs, construct abundant anchor-positive and anchor-negative image pairs in one batch and provide sufficient loss for the optimization of the ConvT to overcome the few sample effect. An auto augmentation is proposed to enhance and enrich the diversity and amount of the few training samples to explore the hidden feature in a few SAR images and avoid the over-fitting in SAR ATR FSL. Experiments conducted on the Moving and Stationary Target Acquisition and Recognition dataset (MSTAR) have shown the effectiveness of our proposed ConvT for SAR ATR FSL. Different from existing SAR ATR FSL methods employing additional training datasets, our method achieved pioneering performance without other SAR target images in training.
Semi-Supervised Semantic Segmentation of Cell Nuclei via Diffusion-based Large-Scale Pre-Training and Collaborative Learning
Authors: Zhuchen Shao, Sourya Sengupta, Hua Li, Mark A. Anastasio
Date: 2023-08-08 00:00:00
Tasks: Segmentation, Semantic Segmentation, Semi-Supervised Semantic Segmentation, Unsupervised Pre-training

Automated semantic segmentation of cell nuclei in microscopic images is crucial for disease diagnosis and tissue microenvironment analysis. Nonetheless, this task presents challenges due to the complexity and heterogeneity of cells. While supervised deep learning methods are promising, they necessitate large annotated datasets that are time-consuming and error-prone to acquire. Semi-supervised approaches could provide feasible alternatives to this issue. However, the limited annotated data may lead to subpar performance of semi-supervised methods, regardless of the abundance of unlabeled data. In this paper, we introduce a novel unsupervised pre-training-based semi-supervised framework for cell-nuclei segmentation. Our framework is comprised of three main components. Firstly, we pretrain a diffusion model on a large-scale unlabeled dataset. The diffusion model's explicit modeling capability facilitates the learning of semantic feature representation from the unlabeled data. Secondly, we achieve semantic feature aggregation using a transformer-based decoder, where the pretrained diffusion model acts as the feature extractor, enabling us to fully utilize the small amount of labeled data. Finally, we implement a collaborative learning framework between the diffusion-based segmentation model and a supervised segmentation model to further enhance segmentation performance. Experiments were conducted on four publicly available datasets to demonstrate significant improvements compared to competitive semi-supervised segmentation methods and supervised baselines. A series of out-of-distribution tests further confirmed the generality of our framework. Furthermore, thorough ablation experiments and visual analysis confirmed the superiority of our proposed method.
Weakly Semi-Supervised Detection in Lung Ultrasound Videos
Authors: Jiahong Ouyang, Li Chen, Gary Y. Li, Naveen Balaraju, Shubham Patil, Courosh Mehanian, Sourabh Kulhare, Rachel Millin, Kenton W. Gregory, Cynthia R. Gregory, Meihua Zhu, David O. Kessler, Laurie Malia, Almaz Dessie, Joni Rabiner, Di Coneybeare, Bo Shopsin, Andrew Hersh, Cristian Madar, Jeffrey Shupp, Laura S. Johnson, Jacob Avila, Kristin Dwyer, Peter Weimersheimer, Balasundar Raju, Jochen Kruecker, Alvin Chen
Date: 2023-08-08 00:00:00
Tasks: object-detection, Object Detection, Transfer Learning

Frame-by-frame annotation of bounding boxes by clinical experts is often required to train fully supervised object detection models on medical video data. We propose a method for improving object detection in medical videos through weak supervision from video-level labels. More concretely, we aggregate individual detection predictions into video-level predictions and extend a teacher-student training strategy to provide additional supervision via a video-level loss. We also introduce improvements to the underlying teacher-student framework, including methods to improve the quality of pseudo-labels based on weak supervision and adaptive schemes to optimize knowledge transfer between the student and teacher networks. We apply this approach to the clinically important task of detecting lung consolidations (seen in respiratory infections such as COVID-19 pneumonia) in medical ultrasound videos. Experiments reveal that our framework improves detection accuracy and robustness compared to baseline semi-supervised models, and improves efficiency in data and annotation usage.
Unleashing the Potential of Reconfigurable Intelligent Surfaces in Martian Communication
Authors: Enes Koktas, Recep A. Tasci, Ibrahim Yildirim, Ertugrul Basar
Date: 2023-08-10 00:00:00

Space exploration has witnessed a steady increase since the 1960s, with Mars playing a significant role in our quest for further knowledge. As the ambition to colonize Mars becomes a reality through the collaboration of private companies and space agencies, the need for reliable and robust communication infrastructures in the Martian environment becomes paramount. In this context, reconfigurable intelligent surface (RIS)-empowered communication emerges as a promising technology to enhance the coverage and improve the communication quality. By considering various Martian scenarios such as canyons, craters, mountains, and plateaus, this article explores of the potential of RISs in increasing the coverage in Martian environments, thereby enabling future Mars missions to be more robust and reliable. The article also investigates the application of RIS-assisted localization in both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios, presenting a general framework for accurate user angle estimation in challenging Martian conditions. The findings and presented framework of this article provide a promising research direction for integrating RISs in deep space communication as well as paving the way for future improvements in interplanetary communication networks.