pwc-archive/papers-with-abstracts huggingface dataset:
Noise-robust latent vector reconstruction in ptychography using deep generative models
Authors: Jacob Seifert, Yifeng Shao, Allard P. Mosk
Date: 2023-10-18 00:00:00
Tasks: Dimensionality Reduction, Image Reconstruction, Object, Retrieval

Computational imaging is increasingly vital for a broad spectrum of applications, ranging from biological to material sciences. This includes applications where the object is known and sufficiently sparse, allowing it to be described with a reduced number of parameters. When no explicit parameterization is available, a deep generative model can be trained to represent an object in a low-dimensional latent space. In this paper, we harness this dimensionality reduction capability of autoencoders to search for the object solution within the latent space rather than the object space. We demonstrate a novel approach to ptychographic image reconstruction by integrating a deep generative model obtained from a pre-trained autoencoder within an Automatic Differentiation Ptychography (ADP) framework. This approach enables the retrieval of objects from highly ill-posed diffraction patterns, offering an effective method for noise-robust latent vector reconstruction in ptychography. Moreover, the mapping into a low-dimensional latent space allows us to visualize the optimization landscape, which provides insight into the convexity and convergence behavior of the inverse problem. With this work, we aim to facilitate new applications for sparse computational imaging such as when low radiation doses or rapid reconstructions are essential.
Incorporating Preferences Into Treatment Assignment Problems
Authors: Daido Kido
Date: 2023-11-15 00:00:00

This study investigates the problem of individualizing treatment allocations using stated preferences for treatments. If individuals know in advance how the assignment will be individualized based on their stated preferences, they may state false preferences. We derive an individualized treatment rule (ITR) that maximizes welfare when individuals strategically state their preferences. We also show that the optimal ITR is strategy-proof, that is, individuals do not have a strong incentive to lie even if they know the optimal ITR a priori. Constructing the optimal ITR requires information on the distribution of true preferences and the average treatment effect conditioned on true preferences. In practice, the information must be identified and estimated from the data. As true preferences are hidden information, the identification is not straightforward. We discuss two experimental designs that allow the identification: strictly strategy-proof randomized controlled trials and doubly randomized preference trials. Under the presumption that data comes from one of these experiments, we develop data-dependent procedures for determining ITR, that is, statistical treatment rules (STRs). The maximum regret of the proposed STRs converges to zero at a rate of the square root of the sample size. An empirical application demonstrates our proposed STRs.
Locally Asymptotically Minimax Statistical Treatment Rules Under Partial Identification
Authors: Daido Kido
Date: 2023-11-15 00:00:00

Policymakers often desire a statistical treatment rule (STR) that determines a treatment assignment rule deployed in a future population from available data. With the true knowledge of the data generating process, the average treatment effect (ATE) is the key quantity characterizing the optimal treatment rule. Unfortunately, the ATE is often not point identified but partially identified. Presuming the partial identification of the ATE, this study conducts a local asymptotic analysis and develops the locally asymptotically minimax (LAM) STR. The analysis does not assume the full differentiability but the directional differentiability of the boundary functions of the identification region of the ATE. Accordingly, the study shows that the LAM STR differs from the plug-in STR. A simulation study also demonstrates that the LAM STR outperforms the plug-in STR.
The impact of Electricity Blackouts and poor infrastructure on the livelihood of residents and the local economy of City of Johannesburg, South Africa
Authors: Nkosingizwile Mazwi Mchunu, George Okechukwu Onatu, Trynos Gumbo
Date: 2023-11-15 00:00:00

This paper discusses the impact of electricity blackouts and poor infrastructure on the livelihood of residents and the local economy of Johannesburg, South Africa. The importance of a stable electricity grid plays a vital role in the effective functioning of urban infrastructure and the economy. The importance of electricity in the present-day South Africa has not been emphasized enough to be prioritized at all levels of government, especially at the local level, as it is where all socio-economic activities take place. The new South Africa needs to redefine the importance of electricity by ensuring that it is accessible, affordable, and produced sustainably, and most of all, by ensuring that the energy transition initiatives to green energy take place in a planned manner without causing harm to the economy, which might deepen the plight of South Africans. Currently, the City of Johannesburg is a growing spatial entity in both demographic and urbanization terms, and growing urban spaces require a stable supply of electricity for the proper functioning of urban systems and the growth of the local economy. The growth of the city brings about a massive demand for electricity that outstrips the current supply of electricity available on the local grid. The imbalance in the current supply and growing demand for electricity result in energy blackouts in the city, which have ripple effects on the economy and livelihoods of the people of Johannesburg. This paper examines the impact of electricity blackouts and poor infrastructure on the livelihood of residents and the local economy of Johannesburg, South Africa.
Quantity versus quality in publication activity: knowledge production at the regional level
Authors: Timur Gareev, Irina Peker
Date: 2023-11-15 00:00:00

This study contributes to the ongoing debate regarding the balance between quality and quantity in research productivity and publication activity. Using empirical regional knowledge production functions, we establish a significant correlation between R&D spending and research output, specifically publication productivity, while controlling for patenting activity and socioeconomic factors. Our focus is on the dilemma of research quantity versus quality, which is analysed in the context of regional thematic specialization using spatial lags. When designing policies and making forecasts, it is important to consider the quality of research measured by established indicators. In this study, we examine the dual effect of research quality on publication activity. We identify two groups of quality factors: those related to the quality of journals and those related to the impact of publications. On average, these factors have different influences on quantitative measures. The quality of journals shows a negative relationship with quantity, indicating that as journal quality increases, the number of publications decreases. On the other hand, the impact of publications can be approximated by an inverse parabolic shape, with a positive decreasing slope within a common range of values. This duality in the relationship between quality factors and quantitative measures may explain some of the significant variations in conclusions found in the literature. We compare several models that explore factors influencing publication activity using a balanced panel dataset of Russian regions from 2009 to 2021. Additionally, we propose a novel approach using thematic scientometric parameters as a special type of proximity measure between regions in thematic space. Incorporating spatial spillovers in thematic space allows us to account for potential cross-sectional dependence in regional data.
The Future of Sustainability in Germany: Areas for Improvement and Innovation
Authors: Mehrnaz Kouhihabibi, Erfan Mohammadi
Date: 2023-11-15 00:00:00
Tasks: Management

This paper reviews the literature on biodegradable waste management in Germany, a multifaceted endeavor that reflects its commitment to sustainability and environmental responsibility. It examines the processes and benefits of separate collection, recycling, and eco-friendly utilization of biodegradable materials, which produce valuable byproducts such as compost, digestate, and biogas. These byproducts serve as organic fertilizers, peat substitutes, and renewable energy sources, contributing to ecological preservation and economic prudence. The paper also discusses the global implications of biodegradable waste management, such as preventing methane emissions from landfills, a major source of greenhouse gas, and reusing organic matter and essential nutrients. Moreover, the paper explores how biodegradable waste management reduces waste volume, facilitates waste sorting and incineration, and sets a global example for addressing climate change and working toward a sustainable future. The paper highlights the importance of a comprehensive and holistic approach to sustainability that encompasses waste management, renewable energy, transportation, agriculture, waste reduction, and urban development.
Managing Biotechnology and Healthcare Innovation Challenges and Opportunities for Startups and Small Companies
Authors: Narges Ramezani, Erfan Mohammadi
Date: 2023-11-15 00:00:00

The biotechnology industry poses challenges and possibilities for startups and small businesses. It is characterized by high charges and complex policies, making it difficult for such agencies to set up themselves. However, it additionally offers avenues for innovation and increase. This paper delves into powerful techniques that can be a resource in managing biotechnology innovation, which includes partnerships, highbrow assets improvement, virtual technologies, customer engagement, and government investment. These strategies are important for fulfillment in an industry that is constantly evolving. By embracing agility and area of interest focus, startups and small companies can successfully compete in this dynamic discipline.
The Use of Symmetry for Models with Variable-size Variables
Authors: Takeshi Fukasawa
Date: 2023-11-15 00:00:00
Tasks: MME

This paper presents a universal representation of symmetric (permutation-invariant) functions with multidimensional variable-size variables. These representations help justify approximation methods that aggregate information from each variable using moments. It further discusses how these findings provide insights into game-theoretic applications, including two-step policy function estimation, Moment-based Markov Equilibrium (MME), and aggregative games. Regarding policy function estimation, under certain conditions, estimating a common policy function as a function of a firm's own state and the sum of polynomial terms (moments) of competitors' states is justified, regardless of the number of firms in a market, provided a sufficient number of moments are included. For MME, this study demonstrates that MME is equivalent to Markov Perfect Equilibrium if the number of moments reaches a certain level and regularity conditions are satisfied. Regarding aggregative games, the paper establishes that any game satisfying symmetry and continuity conditions in payoff functions can be represented as a multidimensional generalized aggregative game. This extends previous research on generalized (fully) aggregative games by introducing multidimensional aggregates.
Structural Advantages for Integrated Builders in MEV-Boost
Authors: Mallesh Pai, Max Resnick
Date: 2023-11-15 00:00:00

Currently, over 90% of Ethereum blocks are built using MEV-Boost, an auction that allows validators to sell their block-building power to builders who compete in an open English auction in each slot. Shortly after the merge, when MEV-Boost was in its infancy, most block builders were neutral, meaning they did not trade themselves but rather aggregated transactions from other traders. Over time, integrated builders, operated by trading firms, began to overtake many of the neutral builders. Outside of the integrated builder teams, little is known about which advantages integration confers beyond latency and how latency advantages distort on-chain trading. This paper explores these poorly understood advantages. We make two contributions. First, we point out that integrated builders are able to bid truthfully in their own bundle merge and then decide how much profit to take later in the final stages of the PBS auction when more information is available, making the auction for them look closer to a second-price auction while independent searchers are stuck in a first-price auction. Second, we find that latency disadvantages convey a winner's curse on slow bidders when underlying values depend on a stochastic price process that change as bids are submitted.
Incompleteness, Independence, and Negative Dominance
Authors: Harvey Lederman
Date: 2023-11-14 00:00:00

This paper introduces the axiom of Negative Dominance, stating that if a lottery $f$ is strictly preferred to a lottery $g$, then some outcome in the support of $f$ is strictly preferred to some outcome in the support of $g$. It is shown that if preferences are incomplete on a sufficiently rich domain, then this plausible axiom, which holds for complete preferences, is incompatible with an array of otherwise plausible axioms for choice under uncertainty. In particular, in this setting, Negative Dominance conflicts with the standard Independence axiom. A novel theory, which includes Negative Dominance, and rejects Independence, is developed and shown to be consistent.
Parallel Quantum Hough Transform
Authors: Frank Klefenz, Nico Wittrock, Frank Feldhoff
Date: 2023-11-15 00:00:00

Few of the known quantum algorithms can be reliably executed on a quantum computer. Therefore, as an extension, we propose a Parallel Quantum Hough transform (PQHT) algorithm that we execute on a quantum computer. We give its implementation and discuss the results obtained. The PQHT algorithm is conceptually divided into a parallel rotation stage consisting of a set of connected programmable $\texttt{RZ}$ rotation gates, with adjustable node connections of coincidence detectors realized with quantum logic gates. The modules were developed using IBM Quantum Composer and tested using the IBM QASM simulator. Finally, the modules were programmed using the Python package Qiskit and the jobs were sent to distributed IBM Q System One quantum computers. The successful run results on Fraunhofer Q System One in Ehningen will be presented as a proof of concept for the PQHT algorithm.
Degradation Estimation Recurrent Neural Network with Local and Non-Local Priors for Compressive Spectral Imaging
Authors: Yubo Dong, Dahua Gao, Yuyan Li, Guangming Shi, Danhua Liu
Date: 2023-11-15 00:00:00

In the Coded Aperture Snapshot Spectral Imaging (CASSI) system, deep unfolding networks (DUNs) have demonstrated excellent performance in recovering 3D hyperspectral images (HSIs) from 2D measurements. However, some noticeable gaps exist between the imaging model used in DUNs and the real CASSI imaging process, such as the sensing error as well as photon and dark current noise, compromising the accuracy of solving the data subproblem and the prior subproblem in DUNs. To address this issue, we propose a Degradation Estimation Network (DEN) to correct the imaging model used in DUNs by simultaneously estimating the sensing error and the noise level, thereby improving the performance of DUNs. Additionally, we propose an efficient Local and Non-local Transformer (LNLT) to solve the prior subproblem, which not only effectively models local and non-local similarities but also reduces the computational cost of the window-based global Multi-head Self-attention (MSA). Furthermore, we transform the DUN into a Recurrent Neural Network (RNN) by sharing parameters of DNNs across stages, which not only allows DNN to be trained more adequately but also significantly reduces the number of parameters. The proposed DERNN-LNLT achieves state-of-the-art (SOTA) performance with fewer parameters on both simulation and real datasets.
Image complexity based fMRI-BOLD visual network categorization across visual datasets using topological descriptors and deep-hybrid learning
Authors: Debanjali Bhattacharya, Neelam Sinha, Yashwanth R., Amit Chattopadhyay
Date: 2023-11-03 00:00:00

This study proposes a new approach that investigates differences in topological characteristics of visual networks, which are constructed using fMRI BOLD time-series corresponding to visual datasets of COCO, ImageNet, and SUN. A publicly available BOLD5000 dataset is utilized that contains fMRI scans while viewing 5254 images of diverse complexities. The objective of this study is to examine how network topology differs in response to distinct visual stimuli from these visual datasets. To achieve this, 0- and 1-dimensional persistence diagrams are computed for each visual network representing COCO, ImageNet, and SUN. For extracting suitable features from topological persistence diagrams, K-means clustering is executed. The extracted K-means cluster features are fed to a novel deep-hybrid model that yields accuracy in the range of 90%-95% in classifying these visual networks. To understand vision, this type of visual network categorization across visual datasets is important as it captures differences in BOLD signals while perceiving images with different contexts and complexities. Furthermore, distinctive topological patterns of visual network associated with each dataset, as revealed from this study, could potentially lead to the development of future neuroimaging biomarkers for diagnosing visual processing disorders like visual agnosia or prosopagnosia, and tracking changes in visual cognition over time.
Scanning diffraction imaging without stable illumination and scan position information
Authors: Tao Liu, Bingyang Wang, JiangTao Zhao, Maik Kahnt, Fucai Zhang
Date: 2023-10-31 00:00:00
Tasks: Image Registration, Object, Position

Ptychography has become prominent at synchrotron facilities worldwide for characterizing biological and material specimens' topological structures and properties at the nanometer or atomic scale, due to its lens - less, highly quantitative phase imaging. Its high - resolution imaging depends on accurate lateral scan position info, a large overlap ratio, and a stable probe function. But as research moves to atomic scales, meeting these requirements gets harder, often needing high - precision motion control and manual pre - calibration. This paper presents a new imaging framework. By adding a wavefront modulator and a new phase retrieval workflow, it removes the strict requirements of traditional ptychography. Significantly, our method doesn't need pre - calibration of the wavefront modulator. Optical validation showed the deviation between the recovered position and a top - tier motion stage was less than 10 nm. Sub - pixel position accuracy was achievable even with a 13% overlap ratio. In an X - ray experiment with a spatially unstable probe violating the ptychographic model and an 18% overlap ratio, our method simultaneously and quantitatively retrieved beam spatial deviation, scan position, probe function, and sample transmission function. Notably, it measured probe spatial deviations of 500nm along the X - axis and 50nm along the Y - axis, which is not feasible by previous methods. With these experimentally proven advantages, we anticipate our method to be a powerful imaging tool with auto - quantitative calibration of various parameters. It will simplify scanning diffraction microscopy implementation and expand its application scope, especially in nanometer and atomic scale imaging.
JOSA: Joint surface-based registration and atlas construction of brain geometry and function
Authors: Jian Li, Greta Tuckute, Evelina Fedorenko, Brian L. Edlow, Adrian V. Dalca, Bruce Fischl
Date: 2023-10-22 00:00:00
Tasks: Medical Image Analysis

Surface-based cortical registration is an important topic in medical image analysis and facilitates many downstream applications. Current approaches for cortical registration are mainly driven by geometric features, such as sulcal depth and curvature, and often assume that registration of folding patterns leads to alignment of brain function. However, functional variability of anatomically corresponding areas across subjects has been widely reported, particularly in higher-order cognitive areas. In this work, we present JOSA, a novel cortical registration framework that jointly models the mismatch between geometry and function while simultaneously learning an unbiased population-specific atlas. Using a semi-supervised training strategy, JOSA achieves superior registration performance in both geometry and function to the state-of-the-art methods but without requiring functional data at inference. This learning framework can be extended to any auxiliary data to guide spherical registration that is available during training but is difficult or impossible to obtain during inference, such as parcellations, architectonic identity, transcriptomic information, and molecular profiles. By recognizing the mismatch between geometry and function, JOSA provides new insights into the future development of registration methods using joint analysis of the brain structure and function.
Throughput Maximisation in Ultra-wideband Hybrid-amplified Links
Authors: Henrique Buglia, Eric Sillekens, Lidia Galdino, Robert Killey, Polina Bayvel1
Date: 2023-11-15 00:00:00

A semi-analytical, real-time nonlinear-interference model including ASE noise in hybrid-amplified links is introduced. Combined with particle-swarm optimisation, the capacity of a hybrid-amplified 10.5 THz 117x57 km link was maximised, increasing throughput by 12% versus an EDFAs-only configuration.
RIS Position and Orientation Estimation via Multi-Carrier Transmissions and Multiple Receivers
Authors: Reza Ghazalian, Hui Chen, George C. Alexandropoulos, Gonzalo Seco-Granados, Henk Wymeersch, Riku Jäntti
Date: 2023-11-15 00:00:00
Tasks: Position, State Estimation

Reconfigurable intelligent surfaces (RISs) are considered as an enabling technology for the upcoming sixth generation of wireless systems, exhibiting significant potential for radio localization and sensing. An RIS is usually treated as an anchor point with known position and orientation when deployed to offer user localization. However, it can also be attached to a user to enable its localization in a semi-passive manner. In this paper, we consider a static user equipped with an RIS and study the RIS localization problem (i.e., joint three-dimensional position and orientation estimation), when operating in a system comprising a single-antenna transmitter and multiple synchronized single-antenna receivers with known locations. We present a multi-stage estimator using time-of-arrival and spatial frequency measurements, and derive the Cram\'er-Rao lower bounds for the estimated parameters to validate the estimator's performance. Our simulation results demonstrate the efficiency of the proposed RIS state estimation approach under various system operation parameters.