pwc-archive/papers-with-abstracts huggingface dataset:
Deep learning acceleration of iterative model-based light fluence correction for photoacoustic tomography
Authors: Zhaoyong Liang, Shuangyang Zhang, Zhichao Liang, Zhongxin Mo, XiaoMing Zhang, Yutian Zhong, Wufan Chen, Li Qi
Date: 2023-12-04 00:00:00

Photoacoustic tomography (PAT) is a promising imaging technique that can visualize the distribution of chromophores within biological tissue. However, the accuracy of PAT imaging is compromised by light fluence (LF), which hinders the quantification of light absorbers. Currently, model-based iterative methods are used for LF correction, but they require significant computational resources due to repeated LF estimation based on differential light transport models. To improve LF correction efficiency, we propose to use Fourier neural operator (FNO), a neural network specially designed for solving differential equations, to learn the forward projection of light transport in PAT. Trained using paired finite-element-based LF simulation data, our FNO model replaces the traditional computational heavy LF estimator during iterative correction, such that the correction procedure is significantly accelerated. Simulation and experimental results demonstrate that our method achieves comparable LF correction quality to traditional iterative methods while reducing the correction time by over 30 times.
Clonal dynamics of surface-driven growing tissues
Authors: Ruslan Mukhamadiarov, Matteo Ciarchi, Fabrizio Olmeda, Steffen Rulands
Date: 2023-12-04 00:00:00

The self-organization of cells into complex tissues relies on a tight coordination of cell behavior. Identifying the cellular processes driving tissue growth is key to understanding the emergence of tissue forms and devising targeted therapies for aberrant growth, such as in cancer. Inferring the mode of tissue growth, whether it is driven by cells on the surface or cells in the bulk, is possible in cell culture experiments, but difficult in most tissues in living organisms (in vivo). Genetic tracing experiments, where a subset of cells is labeled with inheritable markers have become important experimental tools to study cell fate in vivo. Here, we show that the mode of tissue growth is reflected in the size distribution of the progeny of marked cells. To this end, we derive the clone-size distributions using analytical calculations in the limit of negligible cell migration and cell death, and we test our predictions with an agent-based stochastic sampling technique. We show that for surface-driven growth the clone-size distribution takes a characteristic power-law form with an exponent determined by fluctuations of the tissue surface. Our results show how the mode of tissue growth can be inferred from genetic tracing experiments.
The optimization, design and performance of the FBCM23 ASIC for the upgraded CMS beam monitoring system
Authors: Jan Kaplon, Grzegorz Wegrzyn, Konstantin Shibin, Marnix Barendregt
Date: 2023-12-05 00:00:00

We present the development of the FBCM23 ASIC designed for the Phase-II upgrade of the Fast Beam Condition Monitoring (FBCM) system built at the CMS experiment which will replace the present luminometer based on the BCM1F ASIC [1]. The FBCM system should provide reliable luminosity measurement with 1ns time resolution enabling the detection of beam-induced background. The FBCM23 ASIC comprises 6 channels of the fast front-end amplifier working in transimpedance configuration, booster amplifier, and leading edge discriminator. The complete processing chain provides an overall shaping function equivalent to the CR-RC$^3$ filter. The paper will show the optimization of the design, overall architecture, and the detailed implementation in a CMOS 65nm process as well as preliminary electrical performance.
A new promising material for biological applications: multi-level physical modification of AgNPs-decorated PEEK
Authors: Jana Pryjmakova, Daniel Grossberger, Anna Kutova, Barbora Vokata, Miroslav Slouf, Petr Slepicka, Jakub Siegel
Date: 2023-12-02 00:00:00

In the case of polymer medical devices, the surface design plays a crucial role in contact with human tissue. The use of AgNPs as antibacterial agents is well known; however, their anchoring into the polymer surface can still be investigated. This work describes the change in surface morphology and behaviour in the biological environment of polyetheretherketone (PEEK) with immobilised AgNPs after different surface modifications. The initial composites were prepared by immobilisation of silver nanoparticles from a colloid solution into the upper surface layers of polyetheretherketone (PEEK). The prepared samples (Ag/PEEK) had a planar morphology and were further modified with a KrF laser, a GaN laser, and Ar plasma. The samples were studied using the AFM method to visualise changes in surface morphology and to obtain information on the height of the structures and other surface parameters. Comparative analysis of the nanoparticles and polymers was performed using FEG-SEM. The chemical composition of the surface of the samples and optical activity were studied by XPS and UV-Vis spectroscopy. Finally, drop plate antibacterial and cytotoxicity tests were performed to determine the role of Ag nanoparticles after modification and suitability of the surface, which are important for the use of the resulting composite in biomedical applications.
Almost Dominance: Inference and Application
Authors: Xiaojun Song, Zhenting Sun
Date: 2023-12-04 00:00:00

This paper proposes a general framework for inference on three types of almost dominances: Almost Lorenz dominance, almost inverse stochastic dominance, and almost stochastic dominance. We first generalize almost Lorenz dominance to almost upward and downward Lorenz dominances. We then provide a bootstrap inference procedure for the Lorenz dominance coefficients, which measure the degrees of almost Lorenz dominances. Furthermore, we propose almost upward and downward inverse stochastic dominances and provide inference on the inverse stochastic dominance coefficients. We also show that our results can easily be extended to almost stochastic dominance. Simulation studies demonstrate the finite sample properties of the proposed estimators and the bootstrap confidence intervals. We apply our methods to the inequality growth in the United Kingdom and find evidence for almost upward inverse stochastic dominance.
A Method of Moments Approach to Asymptotically Unbiased Synthetic Controls
Authors: Joseph Fry
Date: 2023-12-02 00:00:00
Tasks: valid

A common approach to constructing a Synthetic Control unit is to fit on the outcome variable and covariates in pre-treatment time periods, but it has been shown by Ferman and Pinto (2019) that this approach does not provide asymptotic unbiasedness when the fit is imperfect and the number of controls is fixed. Many related panel methods have a similar limitation when the number of units is fixed. I introduce and evaluate a new method in which the Synthetic Control is constructed using a General Method of Moments approach where units not being included in the Synthetic Control are used as instruments. I show that a Synthetic Control Estimator of this form will be asymptotically unbiased as the number of pre-treatment time periods goes to infinity, even when pre-treatment fit is imperfect and the number of units is fixed. Furthermore, if both the number of pre-treatment and post-treatment time periods go to infinity, then averages of treatment effects can be consistently estimated. I conduct simulations and an empirical application to compare the performance of this method with existing approaches in the literature.
Inference on many jumps in nonparametric panel regression models
Authors: Likai Chen, Georg Keilbar, Liangjun Su, Weining Wang
Date: 2023-12-02 00:00:00
Tasks: regression

We investigate the significance of change-points within fully nonparametric regression contexts, with a particular focus on panel data where data generation processes vary across units, and error terms may display complex dependency structures. In our setting the threshold effect depends on one specific covariate, and we permit the true nonparametric regression to vary based on additional (latent) variables. We propose two uniform testing procedures: one to assess the existence of change-points and another to evaluate the uniformity of such effects across units. Our approach involves deriving a straightforward analytical expression to approximate the variance-covariance structure of change-point effects under general dependency conditions. Notably, when Gaussian approximations are made to these test statistics, the intricate dependency structures within the data can be safely disregarded owing to the localized nature of the statistics. This finding bears significant implications for obtaining critical values. Through extensive simulations, we demonstrate that our tests exhibit excellent control over size and reasonable power performance in finite samples, irrespective of strong cross-sectional and weak serial dependency within the data. Furthermore, applying our tests to two datasets reveals the existence of significant nonsmooth effects in both cases.
Identification and Inference for Synthetic Controls with Confounding
Authors: Guido W. Imbens, Davide Viviano
Date: 2023-12-01 00:00:00

This paper studies inference on treatment effects in panel data settings with unobserved confounding. We model outcome variables through a factor model with random factors and loadings. Such factors and loadings may act as unobserved confounders: when the treatment is implemented depends on time-varying factors, and who receives the treatment depends on unit-level confounders. We study the identification of treatment effects and illustrate the presence of a trade-off between time and unit-level confounding. We provide asymptotic results for inference for several Synthetic Control estimators and show that different sources of randomness should be considered for inference, depending on the nature of confounding. We conclude with a comparison of Synthetic Control estimators with alternatives for factor models.
Generating a Map of Well-being Regions using Multiscale Moving Direction Entropy on Mobile Sensors
Authors: Yukio Ohsawa, Sae Kondo, Yi Sun, Kaira Sekiguchi
Date: 2023-12-05 00:00:00
Tasks: Diversity

The well-being of individuals in a crowd is interpreted as a product of the crossover of individuals from heterogeneous communities, which may occur via interactions with other crowds. The index moving-direction entropy corresponding to the diversity of the moving directions of individuals is introduced to represent such an inter-community crossover. Multi-scale moving direction entropies, composed of various geographical mesh sizes to compute the index values, are used to capture the information flow owing to human movements from/to various crowds. The generated map of high values of multiscale moving direction entropy is shown to coincide significantly with the preference of people to live in each region.
New Facts and Data about Professors and their Research
Authors: Kyle R. Myers, Wei Yang Tham, Jerry Thursby, Marie Thursby, Nina Cohodes, Karim Lakhani, Rachel Mural, Yilun Xu
Date: 2023-12-03 00:00:00

We introduce a new survey of professors at roughly 150 of the most research-intensive institutions of higher education in the US. We document seven new features of how research-active professors are compensated, how they spend their time, and how they perceive their research pursuits: (1) there is more inequality in earnings within fields than there is across fields; (2) institutions, ranks, tasks, and sources of earnings can account for roughly half of the total variation in earnings; (3) there is significant variation across fields in the correlations between earnings and different kinds of research output, but these account for a small amount of earnings variation; (4) measuring professors' productivity in terms of output-per-year versus output-per-research-hour can yield substantial differences; (5) professors' beliefs about the riskiness of their research are best predicted by their fundraising intensity, their risk-aversion in their personal lives, and the degree to which their research involves generating new hypotheses; (6) older and younger professors have very different research outputs and time allocations, but their intended audiences are quite similar; (7) personal risk-taking is highly predictive of professors' orientation towards applied, commercially-relevant research.
How effectual will you be? Development and validation of a scale in higher education
Authors: Alicia Martin-Navarro, Jose Aurelio Medina-Garrido, Felix Velicia-Martin
Date: 2023-12-01 00:00:00
Tasks: Decision Making

The literature on effectual theory offers validated scales to measure effectual or causal logic in entrepreneurs' decision-making. However, there are no adequate scales to assess in advance the effectual or causal propensity of people with an entrepreneurial intention before the creation of their companies. We aim to determine the validity and reliability of an instrument to measure that propensity by first analysing those works that provide recognised validated scales with which to measure the effectual or causal logic in people who have already started up companies. Then, considering these scales, we designed a scale to evaluate the effectual or causal propensity in people who had not yet started up companies using a sample of 230 final-year business administration students to verify its reliability and validity. The validated scale has theoretical implications for the literature on potential entrepreneurship and entrepreneurial intention and practical implications for promoters of entrepreneurship who need to orient the behaviour of entrepreneurs, entrepreneurs of established businesses who want to implement a specific strategic orientation, entrepreneurs who want to evaluate the effectual propensity of their potential partners and workers, and academic institutions interested in orienting the entrepreneurial potential of their students.
How Generative-AI can be Effectively used in Government Chatbots
Authors: Zeteng Lin
Date: 2023-11-29 00:00:00
Tasks: Chatbot, Question Answering

With the rapid development of artificial intelligence and breakthroughs in machine learning and natural language processing, intelligent question-answering robots have become widely used in government affairs. This paper conducts a horizontal comparison between Guangdong Province's government chatbots, ChatGPT, and Wenxin Ernie, two large language models, to analyze the strengths and weaknesses of existing government chatbots and AIGC technology. The study finds significant differences between government chatbots and large language models. China's government chatbots are still in an exploratory stage and have a gap to close to achieve "intelligence." To explore the future direction of government chatbots more deeply, this research proposes targeted optimization paths to help generative AI be effectively applied in government chatbot conversations.
Two is enough: a flip on Bertrand through positive network effects
Authors: Renato Soeiro, Alberto Pinto
Date: 2023-12-05 00:00:00

We discuss price competition when positive network effects are the only other factor in consumption choices. We show that partitioning consumers into two groups creates a rich enough interaction structure to induce negative marginal demand and produce pure price equilibria where both firms profit. The crucial condition is one group has centripetal influence while the other has centrifugal influence. The result is contrary to when positive network effects depend on a single aggregate variable and challenges the prevalent assumption that demand must be micro-founded on a distribution of consumer characteristics with specific properties, highlighting the importance of interaction structures in shaping market outcomes.
Algorithmic collusion under competitive design
Authors: Ivan Conjeaud
Date: 2023-12-05 00:00:00
Tasks: Q-Learning

We study a simple model of algorithmic collusion in which Q-learning algorithms are designed in a strategic fashion. We let players (\textit{designers}) choose their exploration policy simultaneously prior to letting their algorithms repeatedly play a prisoner's dilemma. We prove that, in equilibrium, collusive behavior is reached with positive probability. Our numerical simulations indicate symmetry of the equilibria and give insight for how they are affected by a parameter of interest. We also investigate general profiles of exploration policies. We characterize the behavior of the system for extreme profiles (fully greedy and fully explorative) and use numerical simulations and clustering methods to measure the likelihood of collusive behavior in general cases.
Prior-Free Predictions for Persuasion
Authors: Eric Gao, Daniel Luo
Date: 2023-12-05 00:00:00
Tasks: Robust Design

We analyze prior-free predictions in the design of persuasion games: settings where Receiver contracts their action on Sender's choices of experiment and realized signals about some state. To do so, we characterize robust mechanisms - those which induce the same allocation rules (mappings from the state to actions) regardless of prior beliefs. These mechanisms take a simple form: they (1) incentivize fully revealing experiments, (2) depend only on the induced posterior, and (3) maximally punish pooling deviations. We then highlight a tight connection between ordinal preference uncertainty and prior-dependent predictions - all such rules are implementable if and only if the sender has a state-independent least favorite action. This, in turn, implies all (and only) ordinally monotone allocation rules are robust in binary action problems. We apply our model to school choice and uncover a novel informational justification for deferred acceptance when school preferences depend on students' unknown ability. Finally, we study good allocation settings with externalities and state-dependent outside options and show all efficient allocation rules are robust, even with significant preference heterogeneity.
SEFGAN: Harvesting the Power of Normalizing Flows and GANs for Efficient High-Quality Speech Enhancement
Authors: Martin Strauss, Nicola Pia, Nagashree K. S. Rao, Bernd Edler
Date: 2023-12-04 00:00:00
Tasks: Audio Generation, Speech Enhancement

This paper proposes SEFGAN, a Deep Neural Network (DNN) combining maximum likelihood training and Generative Adversarial Networks (GANs) for efficient speech enhancement (SE). For this, a DNN is trained to synthesize the enhanced speech conditioned on noisy speech using a Normalizing Flow (NF) as generator in a GAN framework. While the combination of likelihood models and GANs is not trivial, SEFGAN demonstrates that a hybrid adversarial and maximum likelihood training approach enables the model to maintain high quality audio generation and log-likelihood estimation. Our experiments indicate that this approach strongly outperforms the baseline NF-based model without introducing additional complexity to the enhancement network. A comparison using computational metrics and a listening experiment reveals that SEFGAN is competitive with other state-of-the-art models.
Kernel Diffusion: An Alternate Approach to Blind Deconvolution
Authors: Yash Sanghvi, Yiheng Chi, Stanley H. Chan
Date: 2023-12-04 00:00:00

Blind deconvolution problems are severely ill-posed because neither the underlying signal nor the forward operator are not known exactly. Conventionally, these problems are solved by alternating between estimation of the image and kernel while keeping the other fixed. In this paper, we show that this framework is flawed because of its tendency to get trapped in local minima and, instead, suggest the use of a kernel estimation strategy with a non-blind solver. This framework is employed by a diffusion method which is trained to sample the blur kernel from the conditional distribution with guidance from a pre-trained non-blind solver. The proposed diffusion method leads to state-of-the-art results on both synthetic and real blur datasets.
Accelerated Parallel Magnetic Resonance Imaging with Compressed Sensing using Structured Sparsity
Authors: Nicholas Dwork, Erin K. Englund
Date: 2023-12-04 00:00:00
Tasks: compressed sensing

Compressed sensing is an imaging paradigm that allows one to invert an underdetermined linear system by imposing the a priori knowledge that the sought after solution is sparse (i.e., mostly zeros). Previous works have shown that if one also knows something about the sparsity pattern (the locations where non-zero entries exist), one can take advantage of this structure to improve the quality of the result. A significant application of compressed sensing is magnetic resonance imaging (MRI), where samples are acquired in the Fourier domain. Compressed sensing allows one to reconstruct a high-quality image with fewer samples which can be collected with a faster scan. This increases the robustness of MRI to patient motion since less motion is possible during the shorter scan. Parallel imaging, where multiple coils are used to gather data, is another an more ubiquitously used method for accelerating MRI. Existing combinations of these acceleration methods, such as Sparse SENSE, yield high quality images with an even shorter scan time than either technique alone. In this work, we show how to modify Sparse SENSE with structured sparsity to reconstruct a high quality image with even fewer samples.
OpEnCam: Lensless Optical Encryption Camera
Authors: Salman S. Khan, Xiang Yu, Kaushik Mitra, Manmohan Chandraker, Francesco Pittaluga
Date: 2023-12-02 00:00:00

Lensless cameras multiplex the incoming light before it is recorded by the sensor. This ability to multiplex the incoming light has led to the development of ultra-thin, high-speed, and single-shot 3D imagers. Recently, there have been various attempts at demonstrating another useful aspect of lensless cameras - their ability to preserve the privacy of a scene by capturing encrypted measurements. However, existing lensless camera designs suffer numerous inherent privacy vulnerabilities. To demonstrate this, we develop the first comprehensive attack model for encryption cameras, and propose OpEnCam -- a novel lensless OPtical ENcryption CAmera design that overcomes these vulnerabilities. OpEnCam encrypts the incoming light before capturing it using the modulating ability of optical masks. Recovery of the original scene from an OpEnCam measurement is possible only if one has access to the camera's encryption key, defined by the unique optical elements of each camera. Our OpEnCam design introduces two major improvements over existing lensless camera designs - (a) the use of two co-axially located optical masks, one stuck to the sensor and the other a few millimeters above the sensor and (b) the design of mask patterns, which are derived heuristically from signal processing ideas. We show, through experiments, that OpEnCam is robust against a range of attack types while still maintaining the imaging capabilities of existing lensless cameras. We validate the efficacy of OpEnCam using simulated and real data. Finally, we built and tested a prototype in the lab for proof-of-concept.