pwc-archive/papers-with-abstracts huggingface dataset:
Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial Libraries
Authors: Boris N. Slautin, Utkarsh Pratiush, Ilia N. Ivanov, Yongtao Liu, Rohit Pant, Xiaohang Zhang, Ichiro Takeuchi, Maxim A. Ziatdinov, Sergei V. Kalinin
Date: 2024-02-03 00:00:00
Tasks: Bayesian Optimization, Dimensionality Reduction, Gaussian Processes, Representation Learning

The rapid growth of automated and autonomous instrumentations brings forth an opportunity for the co-orchestration of multimodal tools, equipped with multiple sequential detection methods, or several characterization tools to explore identical samples. This can be exemplified by the combinatorial libraries that can be explored in multiple locations by multiple tools simultaneously, or downstream characterization in automated synthesis systems. In the co-orchestration approaches, information gained in one modality should accelerate the discovery of other modalities. Correspondingly, the orchestrating agent should select the measurement modality based on the anticipated knowledge gain and measurement cost. Here, we propose and implement a co-orchestration approach for conducting measurements with complex observables such as spectra or images. The method relies on combining dimensionality reduction by variational autoencoders with representation learning for control over the latent space structure, and integrated into iterative workflow via multi-task Gaussian Processes (GP). This approach further allows for the native incorporation of the system's physics via a probabilistic model as a mean function of the GP. We illustrated this method for different modalities of piezoresponse force microscopy and micro-Raman on combinatorial $Sm-BiFeO_3$ library. However, the proposed framework is general and can be extended to multiple measurement modalities and arbitrary dimensionality of measured signals. The analysis code that supports the funding is publicly available at https://github.com/Slautin/2024_Co-orchestration.
Ultrafast jet classification on FPGAs for the HL-LHC
Authors: Patrick Odagiu, Zhiqiang Que, Javier Duarte, Johannes Haller, Gregor Kasieczka, Artur Lobanov, Vladimir Loncar, Wayne Luk, Jennifer Ngadiuba, Maurizio Pierini, Philipp Rincke, Arpita Seksaria, Sioni Summers, Andre Sznajder, Alexander Tapper, Thea K. Aarrestad
Date: 2024-02-02 00:00:00
Tasks: Quantization

Three machine learning models are used to perform jet origin classification. These models are optimized for deployment on a field-programmable gate array device. In this context, we demonstrate how latency and resource consumption scale with the input size and choice of algorithm. Moreover, the models proposed here are designed to work on the type of data and under the foreseen conditions at the CERN LHC during its high-luminosity phase. Through quantization-aware training and efficient synthetization for a specific field programmable gate array, we show that $O(100)$ ns inference of complex architectures such as Deep Sets and Interaction Networks is feasible at a relatively low computational resource cost.
Enriched Physics-informed Neural Networks for Dynamic Poisson-Nernst-Planck Systems
Authors: Xujia Huang, Fajie Wang, Benrong Zhang, Hanqing Liu
Date: 2024-02-01 00:00:00
Tasks: GPU

This paper proposes a meshless deep learning algorithm, enriched physics-informed neural networks (EPINNs), to solve dynamic Poisson-Nernst-Planck (PNP) equations with strong coupling and nonlinear characteristics. The EPINNs takes the traditional physics-informed neural networks as the foundation framework, and adds the adaptive loss weight to balance the loss functions, which automatically assigns the weights of losses by updating the parameters in each iteration based on the maximum likelihood estimate. The resampling strategy is employed in the EPINNs to accelerate the convergence of loss function. Meanwhile, the GPU parallel computing technique is adopted to accelerate the solving process. Four examples are provided to demonstrate the validity and effectiveness of the proposed method. Numerical results indicate that the new method has better applicability than traditional numerical methods in solving such coupled nonlinear systems. More importantly, the EPINNs is more accurate, stable, and fast than the traditional physics-informed neural networks. This work provides a simple and high-performance numerical tool for addressing PNPs with arbitrary boundary shapes and boundary conditions.
Multi-fidelity physics constrained neural networks for dynamical systems
Authors: Hao Zhou, Sibo Cheng, Rossella Arcucci
Date: 2024-02-03 00:00:00

Physics-constrained neural networks are commonly employed to enhance prediction robustness compared to purely data-driven models, achieved through the inclusion of physical constraint losses during the model training process. However, one of the major challenges of physics-constrained neural networks consists of the training complexity especially for high-dimensional systems. In fact, conventional physics-constrained models rely on singular-fidelity data necessitating the assessment of physical constraints within high-dimensional fields, which introduces computational difficulties. Furthermore, due to the fixed input size of the neural networks, employing multi-fidelity training data can also be cumbersome. In this paper, we propose the Multi-Scale Physics-Constrained Neural Network (MSPCNN), which offers a novel methodology for incorporating data with different levels of fidelity into a unified latent space through a customised multi-fidelity autoencoder. Additionally, multiple decoders are concurrently trained to map latent representations of inputs into various fidelity physical spaces. As a result, during the training of predictive models, physical constraints can be evaluated within low-fidelity spaces, yielding a trade-off between training efficiency and accuracy. In addition, unlike conventional methods, MSPCNN also manages to employ multi-fidelity data to train the predictive model. We assess the performance of MSPCNN in two fluid dynamics problems, namely a two-dimensional Burgers' system and a shallow water system. Numerical results clearly demonstrate the enhancement of prediction accuracy and noise robustness when introducing physical constraints in low-fidelity fields. On the other hand, as expected, the training complexity can be significantly reduced by computing physical constraint loss in the low-fidelity field rather than the high-fidelity one.
Data-driven Policy Learning for Continuous Treatments
Authors: Chunrong Ai, Yue Fang, Haitian Xie
Date: 2024-02-04 00:00:00

This paper studies policy learning for continuous treatments from observational data. Continuous treatments present more significant challenges than discrete ones because population welfare may need nonparametric estimation, and policy space may be infinite-dimensional and may satisfy shape restrictions. We propose to approximate the policy space with a sequence of finite-dimensional spaces and, for any given policy, obtain the empirical welfare by applying the kernel method. We consider two cases: known and unknown propensity scores. In the latter case, we allow for machine learning of the propensity score and modify the empirical welfare to account for the effect of machine learning. The learned policy maximizes the empirical welfare or the modified empirical welfare over the approximating space. In both cases, we modify the penalty algorithm proposed in \cite{mbakop2021model} to data-automate the tuning parameters (i.e., bandwidth and dimension of the approximating space) and establish an oracle inequality for the welfare regret.
Decomposing Global Bank Network Connectedness: What is Common, Idiosyncratic and When?
Authors: Jonas Krampe, Luca Margaritella
Date: 2024-02-04 00:00:00
Tasks: Density Estimation

We propose a novel approach to estimate high-dimensional global bank network connectedness in both the time and frequency domains. By employing a factor model with sparse VAR idiosyncratic components, we decompose system-wide connectedness (SWC) into two key drivers: (i) common component shocks and (ii) idiosyncratic shocks. We also provide bootstrap confidence bands for all SWC measures. Furthermore, spectral density estimation allows us to disentangle SWC into short-, medium-, and long-term frequency responses to these shocks. We apply our methodology to two datasets of daily stock price volatilities for over 90 global banks, spanning the periods 2003-2013 and 2014-2023. Our empirical analysis reveals that SWC spikes during global crises, primarily driven by common component shocks and their short term effects. Conversely, in normal times, SWC is largely influenced by idiosyncratic shocks and medium-term dynamics.
One-inflated zero-truncated Poisson and negative binomial regression models
Authors: Ryan T. Godwin
Date: 2024-02-03 00:00:00
Tasks: regression

The workhorse model for zero-truncated count data (y = 1, 2, ...) is the zero-truncated negative binomial (ZTNB) model. We find it should seldom be used. Instead, we recommend the one-inflated zero-truncated negative binomial (OIZTNB) model developed here. Zero-truncated count data often contain an excess of 1s, leading to bias and inconsistency in the ZTNB model. The importance of the OIZTNB model is apparent given the obvious presence of one-inflation in four datasets that have traditionally championed the standard ZTNB. We provide estimation, marginal effects, and a suite of accompanying tools in the R package oneinfl, available on CRAN.
The general solution to an autoregressive law of motion
Authors: Brendan K. Beare, Massimo Franchi, Phil Howlett
Date: 2024-02-03 00:00:00

We provide a complete description of the set of all solutions to an autoregressive law of motion in a finite-dimensional complex vector space. Every solution is shown to be the sum of three parts, each corresponding to a directed flow of time. One part flows forward from the arbitrarily distant past; one flows backward from the arbitrarily distant future; and one flows outward from time zero. The three parts are obtained by applying three complementary spectral projections to the solution, these corresponding to a separation of the eigenvalues of the autoregressive operator according to whether they are inside, outside or on the unit circle. We provide a finite-dimensional parametrization of the set of all solutions.
Sparse spanning portfolios and under-diversification with second-order stochastic dominance
Authors: Stelios Arvanitis, Olivier Scaillet, Nikolas Topaloglou
Date: 2024-02-02 00:00:00

We develop and implement methods for determining whether relaxing sparsity constraints on portfolios improves the investment opportunity set for risk-averse investors. We formulate a new estimation procedure for sparse second-order stochastic spanning based on a greedy algorithm and Linear Programming. We show the optimal recovery of the sparse solution asymptotically whether spanning holds or not. From large equity datasets, we estimate the expected utility loss due to possible under-diversification, and find that there is no benefit from expanding a sparse opportunity set beyond 45 assets. The optimal sparse portfolio invests in 10 industry sectors and cuts tail risk when compared to a sparse mean-variance portfolio. On a rolling-window basis, the number of assets shrinks to 25 assets in crisis periods, while standard factor models cannot explain the performance of the sparse portfolios.
Machine Learning Analysis of the Impact of Increasing the Minimum Wage on Income Inequality in Spain from 2001 to 2021
Authors: Marcos Lacasa Cazcarra
Date: 2024-02-04 00:00:00

This paper analyzes the impact of the National Minimum Wage from 2001 to 2021. The MNW increased from 505.7/month (2001) to 1,108.3/month (2021). Using the data provided by the Spanish Tax Administration Agency, databases that represent the entire population studied can be analyzed. More accurate results and more efficient predictive models are provided by these counts. This work is characterized by the database used, which is a national census and not a sample or projection. Therefore, the study reflects results and analyses based on historical data from the Spanish Salary Census 2001-2021. Various machine-learning models show that income inequality has been reduced by raising the minimum wage. Raising the minimum wage has not led to inflation or increased unemployment. On the contrary, it has been consistent with increased net employment, contained prices, and increased corporate profit margins. The most important conclusion is that an increase in the minimum wage in the period analyzed has led to an increase in the wealth of the country, increasing employment and company profits, and is postulated, under the conditions analyzed, as an effective method for the redistribution of wealth.
GHG emissions in the EU-28. A multilevel club convergence study of the Emission Trading System and Effort Sharing Decision mechanisms
Authors: María José Presno, Manuel Landajo, Paula Fernández González
Date: 2024-02-01 00:00:00

The European Union is engaged in the fight against climate change. A crucial issue to enforce common environmental guidelines is environmental convergence. States converging in environmental variables are expected to be able to jointly develop and implement environmental policies. Convergence in environmental indicators may also help determine the efficiency and speed of those policies. This paper employs a multilevel club convergence approach to analyze convergence in the evolution of GHG emissions among the EU-28 members, on a search for countries transitioning from disequilibrium to specific steady-state positions. Overall convergence is rejected, with club composition depending on the specific period (1990-2017, 2005-2017) and emissions categories (global, ETS, ESD) analyzed. Some countries (e.g. the United Kingdom and Denmark) are consistently located in clubs outperforming the EU's average in terms of speed of emissions reductions, for both the whole and the most recent periods, and for both ETS and ESD emissions. At the other end, Germany (with a large industrial and export basis), Ireland (with the strongest GDP growth in the EU in recent years) and most Eastern EU members underperform after 2005, almost reversing their previous positions when the study begins in 1990.
Redistribution with Needs
Authors: Ricardo Martinez, Juan D. Moreno-Ternero
Date: 2024-02-05 00:00:00

We take an axiomatic approach to study redistribution problems when agents report income and needs. We formalize axioms reflecting ethical and operational principles such as additivity, impartiality and individual rationality. Different combinations of those axioms characterize three focal rules (laissez faire, full redistribution, and need-adjusted full redistribution) as well as compromises among them. We also uncover the structure of those compromises exploring the Lorenz dominance criterion as well as majority voting. Our analysis provides an axiomatic justification for a linear income tax system. We conclude our analysis resorting to Eurostat's Household Budget Survey from where we illustrate the different redistribution patterns accounting for needs across European countries.
Present Value of the Future Consumer Goods Multiplier
Authors: Ihor Kendiukhov
Date: 2024-02-02 00:00:00

In this paper, we derive a formula for the present value of future consumer goods multiplier based on the assumption that a constant share of investment in the production of consumer goods is expected. The present value appears to be an infinite geometric sequence. Moreover, we investigate how the notion of the multiplier can help us in macroeconomic analysis of capital and investment dynamics and in understanding some general principles of capital market equilibrium. Using the concept of this multiplier, we build a macroeconomic model of capital market dynamics which is consistent with the implications of classical models and with the market equilibrium condition but gives additional quantitative and qualitative predictions regarding the dynamics of shares of investment into the production of consumer goods and the production of means of production. The investment volume is modeled as a function of the multiplier: investments adjust when the value of the multiplier fluctuates around its equilibrium value of one. In addition, we suggest possible connections between the investment volume and the multiplier value in the form of differential equations. We also present the formula for the rate of growth of the multiplier. Independently of the implications of capital market dynamics models, the formula for the multiplier itself can be applied for the evaluation of the present value of capital or the estimation of the macroeconomic impact of changes in investment volumes. Our findings show that both the exponential and hyperbolic discounting in combination with empirical evidence available lead to the value of the multiplier that is close to one.
Censored Beliefs and Wishful Thinking
Authors: Jarrod Burgh, Emerson Melo
Date: 2024-02-02 00:00:00

We present a model elucidating wishful thinking, which comprehensively incorporates both the costs and benefits associated with biased beliefs. Our findings reveal that wishful thinking behavior can be characterized as equivalent to superquantile-utility maximization within the domain of threshold beliefs distortion cost functions. By leveraging this equivalence, we establish WT as driving decision-makers to exhibit a preference for choices characterized by skewness and increased risk. Furthermore, we discuss how our framework facilitates the study of optimistic stochastic choice and optimistic risk aversion.
When and Where To Submit A Paper
Authors: Daniel Luo
Date: 2024-01-29 00:00:00

What is the optimal order in which a researcher should submit their papers to journals of differing quality? I analyze a sequential search model without recall where the researcher's expected value from journal submission depends on the history of past submissions. Acceptances immediately terminate the search process and deliver some payoff, while rejections carry information about the paper's quality, affecting the researcher's belief in acceptance probability over future journals. When journal feedback does not change the paper's quality, the researcher's optimal strategy is monotone in their acceptance payoff. Submission costs distort the researcher's effective acceptance payoff, but maintain monotone optimality. If journals give feedback which can affect the paper's quality, such as through \textit{referee reports}, the search order can change drastically depending on the agent's prior belief about their paper's quality. However, I identify a set of \textit{assortative matched} conditions on feedback such that monotone strategies remain optimal whenever the agent's prior is sufficiently optimistic.
Positive and negative sampling strategies for self-supervised learning on audio-video data
Authors: Shanshan Wang, Soumya Tripathy, Toni Heittola, Annamaria Mesaros
Date: 2024-02-05 00:00:00
Tasks: Self-Supervised Learning

In Self-Supervised Learning (SSL), Audio-Visual Correspondence (AVC) is a popular task to learn deep audio and video features from large unlabeled datasets. The key step in AVC is to randomly sample audio and video clips from the dataset and learn to minimize the feature distance between the positive pairs (corresponding audio-video pair) while maximizing the distance between the negative pairs (non-corresponding audio-video pairs). The learnt features are shown to be effective on various downstream tasks. However, these methods achieve subpar performance when the size of the dataset is rather small. In this paper, we investigate the effect of utilizing class label information in the AVC feature learning task. We modified various positive and negative data sampling techniques of SSL based on class label information to investigate the effect on the feature quality. We propose a new sampling approach which we call soft-positive sampling, where the positive pair for one audio sample is not from the exact corresponding video, but from a video of the same class. Experimental results suggest that when the dataset size is small in SSL setup, features learnt through the soft-positive sampling method significantly outperform those from the traditional SSL sampling approaches. This trend holds in both in-domain and out-of-domain downstream tasks, and even outperforms supervised classification. Finally, experiments show that class label information can easily be obtained using a publicly available classifier network and then can be used to boost the SSL performance without adding extra data annotation burden.
Automatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networks
Authors: Adrián Vázquez-Romero, Ascensión Gallardo-Antolín
Date: 2024-02-05 00:00:00
Tasks: Classification, Ensemble Learning

This paper proposes a speech-based method for automatic depression classification. The system is based on ensemble learning for Convolutional Neural Networks (CNNs) and is evaluated using the data and the experimental protocol provided in the Depression Classification Sub-Challenge (DCC) at the 2016 Audio-Visual Emotion Challenge (AVEC-2016). In the pre-processing phase, speech files are represented as a sequence of log-spectrograms and randomly sampled to balance positive and negative samples. For the classification task itself, first, a more suitable architecture for this task, based on One-Dimensional Convolutional Neural Networks, is built. Secondly, several of these CNN-based models are trained with different initializations and then the corresponding individual predictions are fused by using an Ensemble Averaging algorithm and combined per speaker to get an appropriate final decision. The proposed ensemble system achieves satisfactory results on the DCC at the AVEC-2016 in comparison with a reference system based on Support Vector Machines and hand-crafted features, with a CNN+LSTM-based system called DepAudionet, and with the case of a single CNN-based classifier.
ToMoBrush: Exploring Dental Health Sensing using a Sonic Toothbrush
Authors: Kuang Yuan, Mohamed Ibrahim, Yiwen Song, Guoxiang Deng, Suvendra Vijayan, Robert Nerone, Akshay Gadre, Swarun Kumar
Date: 2024-02-02 00:00:00

Early detection of dental disease is crucial to prevent adverse outcomes. Today, dental X-rays are currently the most accurate gold standard for dental disease detection. Unfortunately, regular X-ray exam is still a privilege for billions of people around the world. In this paper, we ask: "Can we develop a low-cost sensing system that enables dental self-examination in the comfort of one's home?" This paper presents ToMoBrush, a dental health sensing system that explores using off-the-shelf sonic toothbrushes for dental condition detection. Our solution leverages the fact that a sonic toothbrush produces rich acoustic signals when in contact with teeth, which contain important information about each tooth's status. ToMoBrush extracts tooth resonance signatures from the acoustic signals to characterize varied dental health conditions of the teeth. We evaluate ToMoBrush on 19 participants and dental-standard models for detecting common dental problems including caries, calculus, and food impaction, achieving a detection ROC-AUC of 0.90, 0.83, and 0.88 respectively. Interviews with dental experts validate ToMoBrush's potential in enhancing at-home dental healthcare.
Introduction to speech recognition
Authors: Gabriel Dauphin
Date: 2024-02-01 00:00:00
Tasks: Dynamic Time Warping, speech-recognition, Speech Recognition

This document contains lectures and practical experimentations using Matlab and implementing a system which is actually correctly classifying three words (one, two and three) with the help of a very small database. To achieve this performance, it uses speech modeling specificities, powerful computer algorithms (dynamic time warping and Dijktra's algorithm) and machine learning (nearest neighbor). This document introduces also some machine learning evaluation metrics.