pwc-archive/papers-with-abstracts huggingface dataset:
Interpretable models for extrapolation in scientific machine learning
Authors: Eric S. Muckley, James E. Saal, Bryce Meredig, Christopher S. Roper, John H. Martin
Date: 2022-12-16 00:00:00
Tasks: scientific discovery

Data-driven models are central to scientific discovery. In efforts to achieve state-of-the-art model accuracy, researchers are employing increasingly complex machine learning algorithms that often outperform simple regressions in interpolative settings (e.g. random k-fold cross-validation) but suffer from poor extrapolation performance, portability, and human interpretability, which limits their potential for facilitating novel scientific insight. Here we examine the trade-off between model performance and interpretability across a broad range of science and engineering problems with an emphasis on materials science datasets. We compare the performance of black box random forest and neural network machine learning algorithms to that of single-feature linear regressions which are fitted using interpretable input features discovered by a simple random search algorithm. For interpolation problems, the average prediction errors of linear regressions were twice as high as those of black box models. Remarkably, when prediction tasks required extrapolation, linear models yielded average error only 5% higher than that of black box models, and outperformed black box models in roughly 40% of the tested prediction tasks, which suggests that they may be desirable over complex algorithms in many extrapolation problems because of their superior interpretability, computational overhead, and ease of use. The results challenge the common assumption that extrapolative models for scientific machine learning are constrained by an inherent trade-off between performance and interpretability.
Quantifying fairness and discrimination in predictive models
Authors: Arthur Charpentier
Date: 2022-12-19 00:00:00
Tasks: Fairness

The analysis of discrimination has long interested economists and lawyers. In recent years, the literature in computer science and machine learning has become interested in the subject, offering an interesting re-reading of the topic. These questions are the consequences of numerous criticisms of algorithms used to translate texts or to identify people in images. With the arrival of massive data, and the use of increasingly opaque algorithms, it is not surprising to have discriminatory algorithms, because it has become easy to have a proxy of a sensitive variable, by enriching the data indefinitely. According to Kranzberg (1986), "technology is neither good nor bad, nor is it neutral", and therefore, "machine learning won't give you anything like gender neutrality `for free' that you didn't explicitely ask for", as claimed by Kearns et a. (2019). In this article, we will come back to the general context, for predictive models in classification. We will present the main concepts of fairness, called group fairness, based on independence between the sensitive variable and the prediction, possibly conditioned on this or that information. We will finish by going further, by presenting the concepts of individual fairness. Finally, we will see how to correct a potential discrimination, in order to guarantee that a model is more ethical
Sanctions and Imports of Essential Goods: A Closer Look at the Equipo Anova (2021) Results
Authors: Francisco Rodríguez
Date: 2022-12-19 00:00:00
Tasks: counterfactual

We revisit the results of a recent paper by Equipo Anova, who claim to find evidence of an improvement in Venezuelan imports of food and medicines associated with the adoption of U.S. financial sanctions towards Venezuela in 2017. We show that their results are consequence of data coding errors and questionable methodological choices, including the use an unreasonable functional form that implies a counterfactual of negative imports in the absence of sanctions, the omission of data accounting for four-fifths of the country's food imports at the time of sanctions and incorrect application of regression discontinuity methods. Once these errors are corrected, the evidence of a significant improvement in the level and rate of change in imports of essentials disappears.
Dynamic spending and portfolio decisions with a soft social norm
Authors: Knut Anton Mork, Fabian Andsem Harang, Haakon Andreas Trønnes, Vegard Skonseng Bjerketvedt
Date: 2022-12-20 00:00:00

We explore the implications of a preference ordering for an investor-consumer with a strong preference for keeping consumption above an exogenous social norm, but who is willing to tolerate occasional dips below it. We do this by splicing two CRRA preference orderings, one with high curvature below the norm and the other with low curvature at or above it. We find this formulation appealing for many endowment funds and sovereign wealth funds, including the Norwegian Government Pension Fund Global, which inspired our research. We solve this model analytically as well as numerically and find that annual spending should not only be significantly lower than the expected financial return, but mostly also procyclical. In particular, financial losses should, as a rule, be followed by larger than proportional spending cuts, except when some smoothing is needed to keep spending from falling too far below the social norm. Yet, at very low wealth levels, spending should be kept particularly low in order to build sufficient wealth to raise consumption above the social norm. Financial risk taking should also be modest and procyclical, so that the investor sometimes may want to "buy at the top" and "sell at the bottom". Many of these features are shared by habitformation models and other models with some lower bound for consumption. However, our specification is more flexible and thus more easily adaptable to actual fund management. The nonlinearity of the policy functions may present challenges regarding delegation to professional managers. However, simpler rules of thumb with constant or slowly moving equity share and consumption-wealth ratio can reach almost the same expected discounted utility. However, the constant levels will then look very different from the implications of expected CRRA utility or Epstein-Zin preferences in that consumption is much lower.
Strategic Observational Learning
Authors: Dimitri Migrow
Date: 2022-12-19 00:00:00

We study learning by privately informed forward-looking agents in a simple repeated-action setting of social learning. Under a symmetric signal structure, forward-looking agents behave myopically for any degrees of patience. Myopic equilibrium is unique in the class of symmetric threshold strategies, and the simplest symmetric non-monotonic strategies. If the signal structure is asymmetric and the game is infinite, there is no equilibrium in myopic strategies, for any positive degree of patience.
TTS-Guided Training for Accent Conversion Without Parallel Data
Authors: Yi Zhou, Zhizheng Wu, Mingyang Zhang, Xiaohai Tian, Haizhou Li
Date: 2022-12-20 00:00:00
Tasks: Decoder, text-to-speech, Text to Speech

Accent Conversion (AC) seeks to change the accent of speech from one (source) to another (target) while preserving the speech content and speaker identity. However, many AC approaches rely on source-target parallel speech data. We propose a novel accent conversion framework without the need of parallel data. Specifically, a text-to-speech (TTS) system is first pretrained with target-accented speech data. This TTS model and its hidden representations are expected to be associated only with the target accent. Then, a speech encoder is trained to convert the accent of the speech under the supervision of the pretrained TTS model. In doing so, the source-accented speech and its corresponding transcription are forwarded to the speech encoder and the pretrained TTS, respectively. The output of the speech encoder is optimized to be the same as the text embedding in the TTS system. At run-time, the speech encoder is combined with the pretrained TTS decoder to convert the source-accented speech toward the target. In the experiments, we converted English with two source accents (Chinese and Indian) to the target accent (American/British/Canadian). Both objective metrics and subjective listening tests successfully validate that, without any parallel data, the proposed approach generates speech samples that are close to the target accent with high speech quality.
A Simple Feature Method for Prosody Rhythm Comparison
Authors: Mariana Julião, Alberto Abad, Helena Moniz
Date: 2022-12-20 00:00:00
Tasks: Clustering, Rhythm, Sentence

Of all components of Prosody, Rhythm has been regarded as the hardest to address, as it is utterly linked to Pitch and Intensity. Nevertheless, Rhythm is a very good indicator of a speaker's fluency in a foreign language or even of some diseases. Canonical ways to measure Rhythm, such as $\Delta C$ or $\%V$, involve a cumbersome process of segment alignment, often leading to modest and questionable results. Perceptively, however, rhythm does not sound as difficult, as humans can grasp it even when the text is not fully intelligible. In this work, we develop an empirical and unsupervised method of rhythm assessment, which does not rely on the content. We have created a fixed-length representation of each utterance, Peak Embedding (PE), which codifies the proportional distance between peaks of the chosen Low-Level Descriptors. Clustering pairs of small sentence-like units, we have attained averages of 0.444 for Silhouette Coefficient using PE with Loudness, and 0.979 for Global Separability Index with a combination of PE with Pitch and Loudness. Clustering same-structure words, we have attained averages of 0.196 for Silhouette Coefficient and 0.864 for Global Separability Index for PE with Loudness.
Improving the quality of neural TTS using long-form content and multi-speaker multi-style modeling
Authors: Tuomo Raitio, Javier Latorre, Andrea Davis, Tuuli Morrill, Ladan Golipour
Date: 2022-12-20 00:00:00
Tasks: Form, text-to-speech, Text to Speech

Neural text-to-speech (TTS) can provide quality close to natural speech if an adequate amount of high-quality speech material is available for training. However, acquiring speech data for TTS training is costly and time-consuming, especially if the goal is to generate different speaking styles. In this work, we show that we can transfer speaking style across speakers and improve the quality of synthetic speech by training a multi-speaker multi-style (MSMS) model with long-form recordings, in addition to regular TTS recordings. In particular, we show that 1) multi-speaker modeling improves the overall TTS quality, 2) the proposed MSMS approach outperforms pre-training and fine-tuning approach when utilizing additional multi-speaker data, and 3) long-form speaking style is highly rated regardless of the target text domain.
Model-Driven End-to-End Learning for Integrated Sensing and Communication
Authors: José Miguel Mateos-Ramos, Christian Häger, Musa Furkan Keskin, Luc Le Magoarou, Henk Wymeersch
Date: 2022-12-20 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) is envisioned to be one of the pillars of 6G. However, 6G is also expected to be severely affected by hardware impairments. Under such impairments, standard model-based approaches might fail if they do not capture the underlying reality. To this end, data-driven methods are an alternative to deal with cases where imperfections cannot be easily modeled. In this paper, we propose a model-driven learning architecture for joint single-target multi-input multi-output (MIMO) sensing and multi-input single-output (MISO) communication. We compare it with a standard neural network approach under complexity constraints. Results show that under hardware impairments, both learning methods yield better results than the model-based standard baseline. If complexity constraints are further introduced, model-driven learning outperforms the neural-network-based approach. Model-driven learning also shows better generalization performance for new unseen testing scenarios.
Joint Relay Selection and Beam Management Based on Deep Reinforcement Learning for Millimeter Wave Vehicular Communication
Authors: Dohyun Kim, Miguel R. Castellanos, Robert W. Heath Jr
Date: 2022-12-19 00:00:00
Tasks: Deep Reinforcement Learning, Management

Cooperative relays improve reliability and coverage in wireless networks by providing multiple paths for data transmission. Relaying will play an essential role in vehicular networks at higher frequency bands, where mobility and frequent signal blockages cause link outages. To ensure connectivity in a relay-aided vehicular network, the relay selection policy should be designed to efficiently find unblocked relays. Inspired by recent advances in beam management in mobile millimeter wave (mmWave) networks, this paper address the question: how can the best relay be selected with minimal overhead from beam management? In this regard, we formulate a sequential decision problem to jointly optimize relay selection and beam management. We propose a joint relay selection and beam management policy based on deep reinforcement learning (DRL) using the Markov property of beam indices and beam measurements. The proposed DRL-based algorithm learns time-varying thresholds that adapt to the dynamic channel conditions and traffic patterns. Numerical experiments demonstrate that the proposed algorithm outperforms baselines without prior channel knowledge. Moreover, the DRL-based algorithm can maintain high spectral efficiency under fast-varying channels.
Recognition and reconstruction of cell differentiation patterns with deep learning
Authors: Robin Dirk, Jonas L. Fischer, Simon Schardt, Markus J. Ankenbrand, Sabine C. Fischer
Date: 2022-12-20 00:00:00
Tasks: Deep Learning, Graph Neural Network

Cell lineage decisions occur in three-dimensional spatial patterns that are difficult to identify by eye. There is an ongoing effort to replicate such patterns using mathematical modeling. One approach uses long ranging cell-cell communication to replicate common spatial arrangements like checkerboard and engulfing patterns. In this model, the cell-cell communication has been implemented as a signal that disperses throughout the tissue. On the other hand, machine learning models have been developed for pattern recognition and pattern reconstruction tasks. We combined synthetic data generated by the mathematical model with deep learning algorithms to recognize and reconstruct spatial cell fate patterns in organoids of mouse embryonic stem cells. A graph neural network was developed and trained on synthetic data from the model. Application to in vitro data predicted a low signal dispersion value. To test this result, we implemented a multilayer perceptron for the prediction of a given cell fate based on the fates of the neighboring cells. The results show a 70% accuracy of cell fate reconstruction based on the nine nearest neighbors of a cell. Overall, our approach combines deep learning with mathematical modeling to link cell fate patterns with potential underlying mechanisms.
Metabolomics of Aging and Alzheimer's Disease: From Single-Omics to Multi-Omics
Authors: Yiming Li, Yuan Luo
Date: 2022-12-19 00:00:00
Tasks: Diagnostic

Aging is a multifactorial process and a key factor of morbidity and mortality. Alzheimer's disease (AD) is an age-related disorder and a main cause of worldwide disability. Both aging and AD can be characterized by metabolic dysfunction. Metabolomics can quantify the complete set of metabolites in a studied sample and is helpful for studying metabolic alterations in aging and AD. In this review, we summarize the metabolomic changes regarding aging and AD, discuss their biological functions, and highlight their potential application as diagnostic biomarkers or therapeutic targets. Recent advances in multi-omics approaches for understanding the metabolic mechanism of aging and AD are also reviewed.
Quantifying Extrinsic Curvature in Neural Manifolds
Authors: Francisco Acosta, Sophia Sanborn, Khanh Dao Duc, Manu Madhav, Nina Miolane
Date: 2022-12-20 00:00:00
Tasks: Dimensionality Reduction, Topological Data Analysis

The neural manifold hypothesis postulates that the activity of a neural population forms a low-dimensional manifold whose structure reflects that of the encoded task variables. In this work, we combine topological deep generative models and extrinsic Riemannian geometry to introduce a novel approach for studying the structure of neural manifolds. This approach (i) computes an explicit parameterization of the manifolds and (ii) estimates their local extrinsic curvature--hence quantifying their shape within the neural state space. Importantly, we prove that our methodology is invariant with respect to transformations that do not bear meaningful neuroscience information, such as permutation of the order in which neurons are recorded. We show empirically that we correctly estimate the geometry of synthetic manifolds generated from smooth deformations of circles, spheres, and tori, using realistic noise levels. We additionally validate our methodology on simulated and real neural data, and show that we recover geometric structure known to exist in hippocampal place cells. We expect this approach to open new avenues of inquiry into geometric neural correlates of perception and behavior.
Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis
Authors: Shaoxiong Sun, Amos A. Folarin, Yuezhou Zhang, NIcholas Cummins, Rafael Garcia-Dias, Callum Stewart, Yatharth Ranjan, Zulqarnain Rashid, Pauline Conde, Petroula Laiou, Heet Sankesara, Faith Matcham, Daniel Leightley, Katie M. White, Carolin Oetzmann, Alina Ivan, Femke Lamers, Sara Siddi, Sara Simblett, Raluca Nica, Aki Rintala, David C. Mohr, Inez Myin-Germeys, Til Wykes, Josep Maria Haro, Brenda W. J. H. Penninx, Srinivasan Vairavan, Vaibhav A. Narayan, Peter Annas, Matthew Hotopf, Richard J. B. Dobson
Date: 2022-12-20 00:00:00

A number of challenges exist for the analysis of mHealth data: maintaining participant engagement over extended time periods and therefore understanding what constitutes an acceptable threshold of missing data; distinguishing between the cross-sectional and longitudinal relationships for different features to determine their utility in tracking within-individual longitudinal variation or screening individuals at high risk; and understanding the heterogeneity with which depression manifests itself in behavioral patterns quantified by the passive features. From 479 participants with MDD, we extracted 21 features capturing mobility, sleep, and smartphone use. We investigated the impact of the number of days of available data on feature quality using the intraclass correlation coefficient and Bland-Altman analysis. We then examined the nature of the correlation between the 8-item Patient Health Questionnaire (PHQ-8) depression scale (measured every 14 days) and the features using the individual-mean correlation, repeated measures correlation, and linear mixed effects model. Furthermore, we stratified the participants based on their behavioral difference, quantified by the features, between periods of high (depression) and low (no depression) PHQ-8 scores using the Gaussian mixture model. We demonstrated that at least 8 (range 2-12) days were needed for reliable calculation of most of the features in the 14-day time window. We observed that features such as sleep onset time correlated better with PHQ-8 scores cross-sectionally than longitudinally, whereas features such as wakefulness after sleep onset correlated well with PHQ-8 longitudinally but worse cross-sectionally. Finally, we found that participants could be separated into 3 distinct clusters according to their behavioral difference between periods of depression and periods of no depression.
The Role of RNA Condensation in Reducing Gene Expression Noise
Authors: Alex Mayer, Grace McLaughlin, Sierra Cole, Amy Gladfelter, Marcus Roper
Date: 2022-12-20 00:00:00

Biomolecular condensates have been shown to play a fundamental role in localizing biochemistry in a cell. RNA is a common constituent of condensates, and can determine their biophysical properties. Functions of biomolecular condensates are varied including activating, inhibiting, and localizing reactions. Recent theoretical work has shown that the phase separation of proteins into droplets can diminish cell to cell variability in protein abundance. However, the extent to which phase separation involving mRNAs may also buffer noise has yet to be explored. In this paper, we introduce a phenomenological model for the phase separation of mRNAs into RNP condensates, and quantify noise suppression as a function of gene expression kinetic parameters. Through stochastic simulations, we highlight the ability for condensates formed from just a handful of mRNAs to regulate the abundance and suppress the fluctuations of proteins. We place particular emphasis on how this mechanism can facilitate efficient transcription by reducing noise even in the situation of infrequent bursts of transcription by exploiting the physics of a concentration-dependent, deterministic phase separation threshold. We investigate two biologically relevant models in which phase separation acts to either "buffer" noise by storing mRNA in inert droplets, or "filter" phase separated mRNAs by accelerating their decay, and quantify expression noise as a function of kinetic parameters. In either case the most efficient expression occurs when bursts produce mRNAs close the phase separation threshold, which we find to be broadly consistent with observations of an RNP-droplet forming cyclinin multinucleate Ashbya gossypii cells. We finally consider the contribution of noise in the phase separation threshold, and show that protein copy number noise can be suppressed by phase separation threshold fluctuations in certain conditions.
Vagus nerve manipulation and microglial plasticity in the prenatal brain
Authors: Marc Courchesne, Colin Wakefield, Karen Nygard, Patrick Burns, Gilles Fecteau, Andre Desrochers, Mingju Cao, Martin G. Frasch
Date: 2022-12-19 00:00:00

The efferent and afferent effects of the vagus nerve on the developing brain have remained enigmatic. Here we review the evidence of such effects on microglial plasticity in the sheep model of human fetal development, one of the most recognized and deployed models of human fetal physiology. We show that vagotomy alters microglial phenotype and that this effect is hormetic under conditions of mild systemic inflammation, as may occur antepartum with chorioamnionitis. We present the methodology to assess not only biomarker-based microglial activation but also the morphometric features of the microglia. Together, these assessments provide a more comprehensive toolbox of glial phenotypical characterizations, especially in the context of investigating the locoregional vagal control of glial function. The presented findings support the earlier discoveries in preclinical and clinical models of adult physiology whereby vagotomy appeared neuroprotective for Parkinson disease, explained, at least in part, by the effects on microglia. In addition, we present the approach to measure and the findings on regional cerebral blood flow changes in relation to vagus nerve manipulation. In summary, the body of evidence underscores the importance of both the efferent and the afferent vagal pathways, via the vagus nerve, in the programming of microglial phenotype in the developing brain. The significance of these relationships for developing and treating early susceptibility to neuroinflammatory and neurodegenerative disorders in later life requires further studies.