pwc-archive/papers-with-abstracts huggingface dataset:
Inverted Inference and Recursive Bootstrapping: A Primal-Dual Theory of Structured Cognition
Authors: Xin Li
Date: 2024-04-01 00:00:00
Tasks: All, Hippocampus

This paper introduces a unifying framework that links the Context-Content Uncertainty Principle (CCUP) with optimal transport (OT) via primal-dual inference. We propose that cognitive representations are not static encodings but active dual constraints that shape feasible manifolds for learning and inference. Cognition is formalized as the dynamic alignment of high-entropy contexts with low-entropy content, implemented through cycle-consistent inference that minimizes conditional entropy. Central to this framework is the concept of inverted inference: a goal-driven mechanism that reverses the direction of conditioning to simulate latent trajectories consistent with internal goals. This asymmetric inference cycle closes the duality gap in constrained optimization, aligning context (primal variables) with content (dual constraints), and reframing inference as structure-constrained entropy minimization. Temporally, we introduce recursive bootstrapping, where each inference cycle sharpens the structural manifold for the next, forming memory chains that support path-dependent optimization and hierarchical goal decomposition. Spatially, we extend the model via hierarchical spatial bootstrapping, connecting to Hierarchical Navigable Small World (HNSW) graphs to enable sublinear retrieval of goal-consistent latent states. Altogether, this framework provides a computational theory of cognition in which dynamic alignment across time and space supports efficient generalization, abstraction, and adaptive planning. CCUP emerges as a scalable principle for both slow, recursive reasoning and fast, structure-aware recognition through layered primal-dual cycles.
Aardvark weather: end-to-end data-driven weather forecasting
Authors: Anna Vaughan, Stratis Markou, Will Tebbutt, James Requeima, Wessel P. Bruinsma, Tom R. Andersson, Michael Herzog, Nicholas D. Lane, Matthew Chantry, J. Scott Hosking, Richard E. Turner
Date: 2024-03-30 00:00:00
Tasks: Weather Forecasting

Weather forecasting is critical for a range of human activities including transportation, agriculture, industry, as well as the safety of the general public. Machine learning models have the potential to transform the complex weather prediction pipeline, but current approaches still rely on numerical weather prediction (NWP) systems, limiting forecast speed and accuracy. Here we demonstrate that a machine learning model can replace the entire operational NWP pipeline. Aardvark Weather, an end-to-end data-driven weather prediction system, ingests raw observations and outputs global gridded forecasts and local station forecasts. Further, it can be optimised end-to-end to maximise performance over quantities of interest. Global forecasts outperform an operational NWP baseline for multiple variables and lead times. Local station forecasts are skillful up to ten days lead time and achieve comparable and often lower errors than a post-processed global NWP baseline and a state-of-the-art end-to-end forecasting system with input from human forecasters. These forecasts are produced with a remarkably simple neural process model using just 8% of the input data and three orders of magnitude less compute than existing NWP and hybrid AI-NWP methods. We anticipate that Aardvark Weather will be the starting point for a new generation of end-to-end machine learning models for medium-range forecasting that will reduce computational costs by orders of magnitude and enable the rapid and cheap creation of bespoke models for users in a variety of fields, including for the developing world where state-of-the-art local models are not currently available.
Diffusion-Driven Domain Adaptation for Generating 3D Molecules
Authors: Haokai Hong, WanYu Lin, Kay Chen Tan
Date: 2024-04-01 00:00:00
Tasks: Benchmarking, Decoder, Denoising, Domain Adaptation

Can we train a molecule generator that can generate 3D molecules from a new domain, circumventing the need to collect data? This problem can be cast as the problem of domain adaptive molecule generation. This work presents a novel and principled diffusion-based approach, called GADM, that allows shifting a generative model to desired new domains without the need to collect even a single molecule. As the domain shift is typically caused by the structure variations of molecules, e.g., scaffold variations, we leverage a designated equivariant masked autoencoder (MAE) along with various masking strategies to capture the structural-grained representations of the in-domain varieties. In particular, with an asymmetric encoder-decoder module, the MAE can generalize to unseen structure variations from the target domains. These structure variations are encoded with an equivariant encoder and treated as domain supervisors to control denoising. We show that, with these encoded structural-grained domain supervisors, GADM can generate effective molecules within the desired new domains. We conduct extensive experiments across various domain adaptation tasks over benchmarking datasets. We show that our approach can improve up to 65.6% in terms of success rate defined based on molecular validity, uniqueness, and novelty compared to alternative baselines.
Grappa -- A Machine Learned Molecular Mechanics Force Field
Authors: Leif Seute, Eric Hartmann, Jan Stühmer, Frauke Gräter
Date: 2024-03-25 00:00:00
Tasks: Computational Efficiency

Simulating large molecular systems over long timescales requires force fields that are both accurate and efficient. In recent years, E(3) equivariant neural networks have lifted the tension between computational efficiency and accuracy of force fields, but they are still several orders of magnitude more expensive than established molecular mechanics (MM) force fields. Here, we propose Grappa, a machine learning framework to predict MM parameters from the molecular graph, employing a graph attentional neural network and a transformer with symmetry-preserving positional encoding. The resulting Grappa force field outperformstabulated and machine-learned MM force fields in terms of accuracy at the same computational efficiency and can be used in existing Molecular Dynamics (MD) engines like GROMACS and OpenMM. It predicts energies and forces of small molecules, peptides, RNA and - showcasing its extensibility to uncharted regions of chemical space - radicals at state-of-the-art MM accuracy. We demonstrate Grappa's transferability to macromolecules in MD simulations from a small fast folding protein up to a whole virus particle. Our force field sets the stage for biomolecular simulations closer to chemical accuracy, but with the same computational cost as established protein force fields.
Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generation
Authors: Odin Zhang, Yufei Huang, Shichen Cheng, Mengyao Yu, Xujun Zhang, Haitao Lin, Yundian Zeng, Mingyang Wang, Zhenxing Wu, Huifeng Zhao, Zaixi Zhang, Chenqing Hua, Yu Kang, Sunliang Cui, Peichen Pan, Chang-Yu Hsieh, Tingjun Hou
Date: 2024-03-15 00:00:00
Tasks: Graph Generation

Most earlier 3D structure-based molecular generation approaches follow an atom-wise paradigm, incrementally adding atoms to a partially built molecular fragment within protein pockets. These methods, while effective in designing tightly bound ligands, often overlook other essential properties such as synthesizability. The fragment-wise generation paradigm offers a promising solution. However, a common challenge across both atom-wise and fragment-wise methods lies in their limited ability to co-design plausible chemical and geometrical structures, resulting in distorted conformations. In response to this challenge, we introduce the Deep Geometry Handling protocol, a more abstract design that extends the design focus beyond the model architecture. Through a comprehensive review of existing geometry-related models and their protocols, we propose a novel hybrid strategy, culminating in the development of FragGen - a geometry-reliable, fragment-wise molecular generation method. FragGen marks a significant leap forward in the quality of generated geometry and the synthesis accessibility of molecules. The efficacy of FragGen is further validated by its successful application in designing type II kinase inhibitors at the nanomolar level.
Score-Based Diffusion Models for Photoacoustic Tomography Image Reconstruction
Authors: Sreemanti Dey, Snigdha Saha, Berthy T. Feng, Manxiu Cui, Laure Delisle, Oscar Leong, Lihong V. Wang, Katherine L. Bouman
Date: 2024-03-30 00:00:00
Tasks: Image Reconstruction

Photoacoustic tomography (PAT) is a rapidly-evolving medical imaging modality that combines optical absorption contrast with ultrasound imaging depth. One challenge in PAT is image reconstruction with inadequate acoustic signals due to limited sensor coverage or due to the density of the transducer array. Such cases call for solving an ill-posed inverse reconstruction problem. In this work, we use score-based diffusion models to solve the inverse problem of reconstructing an image from limited PAT measurements. The proposed approach allows us to incorporate an expressive prior learned by a diffusion model on simulated vessel structures while still being robust to varying transducer sparsity conditions.
Unified, Verifiable Neural Simulators for Electromagnetic Wave Inverse Problems
Authors: Charles Dove, Jatearoon Boondicharern, Laura Waller
Date: 2024-03-31 00:00:00

Simulators based on neural networks offer a path to orders-of-magnitude faster electromagnetic wave simulations. Existing models, however, only address narrowly tailored classes of problems and only scale to systems of a few dozen degrees of freedom (DoFs). Here, we demonstrate a single, unified model capable of addressing scattering simulations with thousands of DoFs, of any wavelength, any illumination wavefront, and freeform materials, within broad configurable bounds. Based on an attentional multi-conditioning strategy, our method also allows non-recurrent supervision on and prediction of intermediate physical states, which provides improved generalization with no additional data-generation cost. Using this O(1)-time intermediate prediction capability, we propose and prove a rigorous, efficiently computable upper bound on prediction error, allowing accuracy guarantees at inference time for all predictions. After training solely on randomized systems, we demonstrate the unified model across a suite of challenging multi-disciplinary inverse problems, finding strong efficacy and speed improvements up to 96% for problems in optical tomography, beam shaping through volumetric random media, and freeform photonic inverse design, with no problem-specific training. Our findings demonstrate a path to universal, verifiably accurate neural surrogates for existing scattering simulators, and our conditioning and training methods are directly applicable to any PDE admitting a time-domain iterative solver.
Antinetwork among China A-shares
Authors: Peng Liu
Date: 2024-03-21 00:00:00
Tasks: Management, Time Series

The correlation-based financial networks, constructed with the correlation relationships among the time series of fluctuations of daily logarithmic prices of stocks, are intensively studied. However, these studies ignore the importance of negative correlations. This paper is the first time to consider the negative and positive correlations separately, and accordingly to construct weighted temporal antinetwork and network among stocks listed in the Shanghai and Shenzhen stock exchanges. For (anti)networks during the first 24 years of the 21st century, the node's degree and strength, the assortativity coefficient, the average local clustering coefficient, and the average shortest path length are analyzed systematically. This paper unveils some essential differences in these topological measurements between antinetwork and network. The findings of the differences between antinetwork and network have an important role in understanding the dynamics of a financial complex system. The observation of antinetwork is of great importance in optimizing investment portfolios and risk management. More importantly, this paper proposes a new direction for studying complex systems, namely the correlation-based antinetwork.
The impact of geopolitical risk on the international agricultural market: Empirical analysis based on the GJR-GARCH-MIDAS model
Authors: Yun-Shi Dai, Peng-Fei Dai, Wei-Xing Zhou
Date: 2024-04-02 00:00:00

The current international landscape is turbulent and unstable, with frequent outbreaks of geopolitical conflicts worldwide. Geopolitical risk has emerged as a significant threat to regional and global peace, stability, and economic prosperity, causing serious disruptions to the global food system and food security. Focusing on the international food market, this paper builds different dimensions of geopolitical risk measures based on the random matrix theory and constructs single- and two-factor GJR-GARCH-MIDAS models with fixed time span and rolling window, respectively, to investigate the impact of geopolitical risk on food market volatility. The findings indicate that modeling based on rolling window performs better in describing the overall volatility of the wheat, maize, soybean, and rice markets, and the two-factor models generally exhibit stronger explanatory power in most cases. In terms of short-term fluctuations, all four staple food markets demonstrate obvious volatility clustering and high volatility persistence, without significant asymmetry. Regarding long-term volatility, the realized volatility of wheat, maize, and soybean significantly exacerbates their long-run market volatility. Additionally, geopolitical risks of different dimensions show varying directions and degrees of effects in explaining the long-term market volatility of the four staple food commodities. This study contributes to the understanding of the macro-drivers of food market fluctuations, provides useful information for investment using agricultural futures, and offers valuable insights into maintaining the stable operation of food markets and safeguarding global food security.
Heterogeneous Treatment Effects and Causal Mechanisms
Authors: Jiawei Fu, Tara Slough
Date: 2024-04-02 00:00:00

The credibility revolution advances the use of research designs that permit identification and estimation of causal effects. However, understanding which mechanisms produce measured causal effects remains a challenge. A dominant current approach to the quantitative evaluation of mechanisms relies on the detection of heterogeneous treatment effects with respect to pre-treatment covariates. This paper develops a framework to understand when the existence of such heterogeneous treatment effects can support inferences about the activation of a mechanism. We show first that this design cannot provide evidence of mechanism activation without an additional, generally implicit, assumption. Further, even when this assumption is satisfied, if a measured outcome is produced by a non-linear transformation of a directly-affected outcome of theoretical interest, heterogeneous treatment effects are not informative of mechanism activation. We provide novel guidance for interpretation and research design in light of these findings.
Estimating Heterogeneous Effects: Applications to Labor Economics
Authors: Stephane Bonhomme, Angela Denis
Date: 2024-04-01 00:00:00

A growing number of applications involve settings where, in order to infer heterogeneous effects, a researcher compares various units. Examples of research designs include children moving between different neighborhoods, workers moving between firms, patients migrating from one city to another, and banks offering loans to different firms. We present a unified framework for these settings, based on a linear model with normal random coefficients and normal errors. Using the model, we discuss how to recover the mean and dispersion of effects, other features of their distribution, and to construct predictors of the effects. We provide moment conditions on the model's parameters, and outline various estimation strategies. A main objective of the paper is to clarify some of the underlying assumptions by highlighting their economic content, and to discuss and inform some of the key practical choices.
Convolution-t Distributions
Authors: Peter Reinhard Hansen, Chen Tong
Date: 2024-04-01 00:00:00

We introduce a new class of multivariate heavy-tailed distributions that are convolutions of heterogeneous multivariate t-distributions. Unlike commonly used heavy-tailed distributions, the multivariate convolution-t distributions embody cluster structures with flexible nonlinear dependencies and heterogeneous marginal distributions. Importantly, convolution-t distributions have simple density functions that facilitate estimation and likelihood-based inference. The characteristic features of convolution-t distributions are found to be important in an empirical analysis of realized volatility measures and help identify their underlying factor structure.
Sequential Synthetic Difference in Differences
Authors: Dmitry Arkhangelsky, Aleksei Samkov
Date: 2024-03-29 00:00:00
Tasks: Imputation

We propose the Sequential Synthetic Difference-in-Differences (Sequential SDiD) estimator for event studies with staggered treatment adoption, particularly when the parallel trends assumption fails. The method uses an iterative imputation procedure on aggregated data, where estimates for early-adopting cohorts are used to construct counterfactuals for later ones. We prove the estimator is asymptotically equivalent to an infeasible oracle OLS estimator within a linear model with interactive fixed effects. This key theoretical result provides a foundation for standard inference by establishing asymptotic normality and clarifying the estimator's efficiency. By offering a robust and transparent method with formal statistical guarantees, Sequential SDiD is a powerful alternative to conventional difference-in-differences strategies.
Multicriteria Analysis Model in Sustainable Corn Farming Area Planning
Authors: Abdul Haris, Muhammad Munawir Syarif, Hamed Narolla, Rachmat Hidayat
Date: 2024-04-02 00:00:00
Tasks: Data Integration

This study aims to develop a framework for multicriteria analysis to evaluate alternatives for sustainable corn agricultural area planning, considering the integration of ecological, economic, and social aspects as pillars of sustainability. The research method uses qualitative and quantitative approaches to integrate ecological, economic, and social aspects in the multicriteria analysis. The analysis involves land evaluation, subcriteria identification, and data integration using Multidimensional Scaling and Analytical Hierarchy Process methods to prioritize developing sustainable corn agricultural areas. Based on the results of the RAP-Corn analysis, it indicates that the ecological dimension depicts less sustainability. The AHP results yield weight distribution and highly relevant scores that describe tangible preferences. Priority directions are grouped as strategic steps toward achieving the goals of sustainable corn agricultural area planning.
Shared Hardships Strengthen Bonds: Negative Shocks, Embeddedness and Employee Retention
Authors: Andrew Balthrop, Hyunseok Jung
Date: 2024-03-29 00:00:00
Tasks: Survival Analysis

Jarring events inspiring reflection, known as ``shocks" in the literature, are the motive force in explaining changes in employee embeddedness and retention within the unfolding model of labor turnover. Substantial research effort has examined strategies for insulating valued employees from adverse shocks. However, this paper provides empirical evidence that unambiguously negative shocks can increase employee retention when underlying firm and employee incentives with respect to these shocks are aligned. Using survival analysis on a unique data set of 466,236 communication records and 45,873 employment spells from 21 trucking companies, we show how equipment-related shocks tend to increase the duration of employment. Equipment shocks also generate paradoxically positive sentiments that demonstrate an increase in employees' affective commitment to the firm. Our results highlight the important moderating role aligned incentives have in how shocks ultimately translate into retention. Shared hardships strengthen bonds in employment as in other areas.
Call the Dentist! A (Con-)Cavity in the Value of Information
Authors: Mark Whitmeyer
Date: 2024-04-01 00:00:00

A natural way of quantifying the ``amount of information'' in decision problems yields a globally concave value for information. Another (in contrast, adversarial) way almost never does.
KazEmoTTS: A Dataset for Kazakh Emotional Text-to-Speech Synthesis
Authors: Adal Abilbekov, Saida Mussakhojayeva, Rustem Yeshpanov, Huseyin Atakan Varol
Date: 2024-04-01 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

This study focuses on the creation of the KazEmoTTS dataset, designed for emotional Kazakh text-to-speech (TTS) applications. KazEmoTTS is a collection of 54,760 audio-text pairs, with a total duration of 74.85 hours, featuring 34.23 hours delivered by a female narrator and 40.62 hours by two male narrators. The list of the emotions considered include "neutral", "angry", "happy", "sad", "scared", and "surprised". We also developed a TTS model trained on the KazEmoTTS dataset. Objective and subjective evaluations were employed to assess the quality of synthesized speech, yielding an MCD score within the range of 6.02 to 7.67, alongside a MOS that spanned from 3.51 to 3.57. To facilitate reproducibility and inspire further research, we have made our code, pre-trained model, and dataset accessible in our GitHub repository.
Voice Conversion Augmentation for Speaker Recognition on Defective Datasets
Authors: Ruijie Tao, Zhan Shi, Yidi Jiang, Tianchi Liu, Haizhou Li
Date: 2024-04-01 00:00:00
Tasks: Speaker Recognition, Voice Conversion

Modern speaker recognition system relies on abundant and balanced datasets for classification training. However, diverse defective datasets, such as partially-labelled, small-scale, and imbalanced datasets, are common in real-world applications. Previous works usually studied specific solutions for each scenario from the algorithm perspective. However, the root cause of these problems lies in dataset imperfections. To address these challenges with a unified solution, we propose the Voice Conversion Augmentation (VCA) strategy to obtain pseudo speech from the training set. Furthermore, to guarantee generation quality, we designed the VCA-NN~(nearest neighbours) strategy to select source speech from utterances that are close to the target speech in the representation space. Our experimental results on three created datasets demonstrated that VCA-NN effectively mitigates these dataset problems, which provides a new direction for handling the speaker recognition problems from the data aspect.