pwc-archive/papers-with-abstracts huggingface dataset:
Adam-like Algorithm with Smooth Clipping Attains Global Minima: Analysis Based on Ergodicity of Functional SDEs
Authors: Keisuke Suzuki
Date: 2023-11-29 00:00:00

In this paper, we prove that an Adam-type algorithm with smooth clipping approaches the global minimizer of the regularized non-convex loss function. Adding smooth clipping and taking the state space as the set of all trajectories, we can apply the ergodic theory of Markov semigroups for this algorithm and investigate its asymptotic behavior. The ergodic theory we establish in this paper reduces the problem of evaluating the convergence, generalization error and discretization error of this algorithm to the problem of evaluating the difference between two functional stochastic differential equations (SDEs) with different drift coefficients. As a result of our analysis, we have shown that this algorithm minimizes the the regularized non-convex loss function with errors of the form $n^{-1/2}$, $\eta^{1/4}$, $\beta^{-1} \log (\beta + 1)$ and $e^{- c t}$. Here, $c$ is a constant and $n$, $\eta$, $\beta$ and $t$ denote the size of the training dataset, learning rate, inverse temperature and time, respectively.
Insider trading in discrete time Kyle games
Authors: Christoph Kühn, Christopher Lorenz
Date: 2023-12-01 00:00:00

We present a new discrete time version of Kyle's (1985) classic model of insider trading, formulated as a generalised extensive form game. The model has three kinds of traders: an insider, random noise traders, and a market maker. The insider aims to exploit her informational advantage and maximise expected profits while the market maker observes the total order flow and sets prices accordingly. First, we show how the multi-period model with finitely many pure strategies can be reduced to a (static) social system in the sense of Debreu (1952) and prove the existence of a sequential Kyle equilibrium, following Kreps and Wilson (1982). This works for any probability distribution with finite support of the noise trader's demand and the true value, and for any finite information flow of the insider. In contrast to Kyle (1985) with normal distributions, equilibria exist in general only in mixed strategies and not in pure strategies. In the single-period model we establish bounds for the insider's strategy in equilibrium. Finally, we prove the existence of an equilibrium for the game with a continuum of actions, by considering an approximating sequence of games with finitely many actions. Because of the lack of compactness of the set of measurable price functions, standard infinite-dimensional fixed point theorems are not applicable.
Linker-Tuning: Optimizing Continuous Prompts for Heterodimeric Protein Prediction
Authors: Shuxian Zou, Hui Li, Shentong Mo, Xingyi Cheng, Eric Xing, Le Song
Date: 2023-12-02 00:00:00
Tasks: Protein Structure Prediction

Predicting the structure of interacting chains is crucial for understanding biological systems and developing new drugs. Large-scale pre-trained Protein Language Models (PLMs), such as ESM2, have shown impressive abilities in extracting biologically meaningful representations for protein structure prediction. In this paper, we show that ESMFold, which has been successful in computing accurate atomic structures for single-chain proteins, can be adapted to predict the heterodimer structures in a lightweight manner. We propose Linker-tuning, which learns a continuous prompt to connect the two chains in a dimer before running it as a single sequence in ESMFold. Experiment results show that our method successfully predicts 56.98% of interfaces on the i.i.d. heterodimer test set, with an absolute improvement of +12.79% over the ESMFold-Linker baseline. Furthermore, our model can generalize well to the out-of-distribution (OOD) test set HeteroTest2 and two antibody test sets Fab and Fv while being $9\times$ faster than AF-Multimer.
Functional Magnetic Resonance Imaging Changes and Increased Muscle Pressure in Fibromyalgia: Insights from Prominent Theories of Pain and Muscle Imaging
Authors: Seth Adler, Farzan Vahedifard, Rachel Akers, Christopher Sica, Mehmet Kocak, Edwin Moore, Marc Minkus, Gianna Elias, Nikhil Aggarwal, Sharon Byrd, Mehmoodur Rasheed, Robert S. Katz
Date: 2023-12-04 00:00:00
Tasks: Diagnostic

Fibromyalgia is a complicated and multifaceted disorder marked by widespread chronic pain, fatigue, and muscle tenderness. Current explanations for the pathophysiology of this condition include the Central Sensitization Theory, Cytokine Inflammation Theory, Muscle Hypoxia, Muscle Tender Point Theory, and Small Fiber Neuropathy Theory. The objective of this review article is to examine and explain each of these current theories and to provide a background on our current understanding of fibromyalgia. The medical literature on this disorder, as well as on the roles of functional magnetic resonance imaging (fMRI) and elastography as diagnostic tools, was reviewed from the 1970s to early 2023, primarily using the PubMed database. Five prominent theories of fibromyalgia etiology were examined: 1) Central Sensitization Theory; 2) Cytokine Inflammation Theory; 3) Muscle Hypoxia; 4) Muscle Tender Point Theory; and 5) Small Fiber Neuropathy Theory. Previous fMRI studies of FMS have revealed two key findings. First, patients with FMS show altered activation patterns in brain regions involved in pain processing. Second, the connectivity between brain structures in individuals diagnosed with FMS and healthy controls is different. Both of these findings will be expanded upon in this paper. The article also explores the potential for future research in fibromyalgia due to the advancements in fMRI and elastography techniques, such as shear wave ultrasound. Increased understanding of the underlying mechanisms contributing to fibromyalgia symptoms is necessary for improved diagnosis and treatment, and advanced imaging techniques can aid in this process.
Unsupervised learning on spontaneous retinal activity leads to efficient neural representation geometry
Authors: Andrew Ligeralde, Yilun Kuang, Thomas Edward Yerxa, Miah N. Pitcher, Marla Feller, SueYeon Chung
Date: 2023-12-05 00:00:00
Tasks: Contrastive Learning, image-classification, Image Classification

Prior to the onset of vision, neurons in the developing mammalian retina spontaneously fire in correlated activity patterns known as retinal waves. Experimental evidence suggests that retinal waves strongly influence the emergence of sensory representations before visual experience. We aim to model this early stage of functional development by using movies of neurally active developing retinas as pre-training data for neural networks. Specifically, we pre-train a ResNet-18 with an unsupervised contrastive learning objective (SimCLR) on both simulated and experimentally-obtained movies of retinal waves, then evaluate its performance on image classification tasks. We find that pre-training on retinal waves significantly improves performance on tasks that test object invariance to spatial translation, while slightly improving performance on more complex tasks like image classification. Notably, these performance boosts are realized on held-out natural images even though the pre-training procedure does not include any natural image data. We then propose a geometrical explanation for the increase in network performance, namely that the spatiotemporal characteristics of retinal waves facilitate the formation of separable feature representations. In particular, we demonstrate that networks pre-trained on retinal waves are more effective at separating image manifolds than randomly initialized networks, especially for manifolds defined by sets of spatial translations. These findings indicate that the broad spatiotemporal properties of retinal waves prepare networks for higher order feature extraction.
Effects of a mixed reality headset on the delay of visually evoked potentials
Authors: Víctor Manuel Hidalgo, Carlos Andrés Bazaes, Juan-Carlos Letelier
Date: 2023-12-04 00:00:00
Tasks: Mixed Reality

Virtual and mixed reality (VR, MR) technologies offer a powerful solution for on-the-ground flight training curricula. While these technologies offer safer and cheaper instructional programs, it is still unclear how they impact neuronal brain dynamics. Indeed, MR simulations engage students in a strange mix of incongruous visual, somatosensory and vestibular sensory input. Characterizing brain dynamics during MR simulation is important for understanding cognitive processes during virtual flight training. To this end, we studies the delays introduced in the neuronal stream from the retina to the visual cortex when presented with visual stimuli using a Varjo-XR3 headset. We recorded cortical visual evoked potentials (VEPs) from 6 subjects under two conditions. First, we recorded normal VEPs triggered by short flashes. Second, we recorded VEPs triggered by an internal image of the flashes produced by the Varjo-XR3 headset. All subjects had used the headset before and were familiar with immersive experiences. Our results show mixed-reality stimulation imposes a small, but consistent, 4 [ms] processing delay in the N2-VEP component during MR stimulation as compared to direct stimulation. Also we found that VEP amplitudes during MR stimulation were also decreased. These results suggest that visual cognition during mixed-reality training is delayed, not only by the unavoidalbe hardware/software processing delays of the headset and the attached computer, but also by an extra biological delay induced by the headset's limited visual display in terms of the image intensity and contrast. As flight training is a demanding task, this study measures visual signal latency to better understand how MR affects the sensation of immersion.
Spike distance function as a learning objective for spike prediction
Authors: Kevin Doran, Marvin Seifert, Carola A. M. Yovanovich, Tom Baden
Date: 2023-12-04 00:00:00

Approaches to predicting neuronal spike responses commonly use a Poisson learning objective. This objective quantizes responses into spike counts within a fixed summation interval, typically on the order of 10 to 100 milliseconds in duration; however, neuronal responses are often time accurate down to a few milliseconds, and Poisson models struggle to precisely model them at these timescales. We propose the concept of a spike distance function that maps points in time to the temporal distance to the nearest spike. We show that neural networks can be trained to approximate spike distance functions, and we present an efficient algorithm for inferring spike trains from the outputs of these models. Using recordings of chicken and frog retinal ganglion cells responding to visual stimuli, we compare the performance of our approach to that of Poisson models trained with various summation intervals. We show that our approach outperforms the use of Poisson models at spike train inference.
Minimum-phase property of the hemodynamic response function, and implications for Granger Causality in fMRI
Authors: Leonardo Novelli, Lionel Barnett, Anil Seth, Adeel Razi
Date: 2023-12-04 00:00:00

Granger Causality (GC) is widely used in neuroimaging to estimate directed statistical dependence among brain regions using time series of brain activity. An important issue is that functional MRI (fMRI) measures brain activity indirectly via the blood-oxygen-level-dependent (BOLD) signal, which affects the temporal structure of the signals and distorts GC estimates. However, some notable applications of GC are not concerned with the GC magnitude but its statistical significance. This is the case for network inference, which aims to build a statistical model of the system based on directed relationships among its elements. The critical question for the viability of network inference in fMRI is whether the hemodynamic response function (HRF) and its variability across brain regions introduce spurious relationships, i.e., statistically significant GC values between BOLD signals, even if the GC between the neuronal signals is zero. It has been mathematically proven that such spurious statistical relationships are not induced if the HRF is minimum-phase, i.e., if both the HRF and its inverse are stable (producing finite responses to finite inputs). However, whether the HRF is minimum-phase has remained contentious. Here, we address this issue using multiple realistic biophysical models from the literature and studying their transfer functions. We find that these models are minimum-phase for a wide range of physiologically plausible parameter values. Therefore, statistical testing of GC is plausible even if the HRF varies across brain regions, with the following limitations. First, the minimum-phase condition is violated for parameter combinations that generate an initial dip in the HRF, confirming a previous mathematical proof. Second, the slow sampling of the BOLD signal (in seconds) compared to the timescales of neural signal propagation (milliseconds) may still introduce spurious GC.
Seizure detection from Electroencephalogram signals via Wavelets and Graph Theory metrics
Authors: Paul Grant, Md Zahidul Islam
Date: 2023-11-28 00:00:00
Tasks: Attribute, Seizure Detection

Epilepsy is one of the most prevalent neurological conditions, where an epileptic seizure is a transient occurrence due to abnormal, excessive and synchronous activity in the brain. Electroencephalogram signals emanating from the brain may be captured, analysed and then play a significant role in detection and prediction of epileptic seizures. In this work we enhance upon a previous approach that relied on the differing properties of the wavelet transform. Here we apply the Maximum Overlap Discrete Wavelet Transform to both reduce signal \textit{noise} and use signal variance exhibited at differing inherent frequency levels to develop various metrics of connection between the electrodes placed upon the scalp. %The properties of both the noise reduced signal and the interconnected electrodes differ significantly during the different brain states. Using short duration epochs, to approximate close to real time monitoring, together with simple statistical parameters derived from the reconstructed noise reduced signals we initiate seizure detection. To further improve performance we utilise graph theoretic indicators from derived electrode connectivity. From there we build the attribute space. We utilise open-source software and publicly available data to highlight the superior Recall/Sensitivity performance of our approach, when compared to existing published methods.
Timing decisions as the next frontier for collective intelligence
Authors: Albert B. Kao, Shoubhik Banerjee, Fritz Francisco, Andrew M. Berdahl
Date: 2023-12-01 00:00:00
Tasks: Decision Making

The past decade has witnessed a dramatically growing interest in collective intelligence - the phenomenon of groups having an ability to make more accurate decisions than isolated individuals. However, the vast majority of studies to date have focused, either explicitly or implicitly, on spatial decisions (e.g., potential nest sites, food patches, or migration directions). We highlight the equally important, but severely understudied, realm of temporal collective decision-making, i.e., decisions about when to perform an action. We argue that temporal collective decision making is likely to differ from spatial decision making in several crucial ways and probably involves different mechanisms, model predictions, and experimental outcomes. We anticipate that research focused on temporal decisions should lead to a radically expanded understanding of the adaptiveness and constraints of living in groups.
Enhancing data-limited assessments with random effects: A case study on Korea chub mackerel (Scomber japonicus)
Authors: KyuHan Kim, Nokuthaba Sibanda, Richard Arnold, Teresa A'mar
Date: 2023-12-04 00:00:00
Tasks: State Space Models

In a state-space framework, temporal variations in fishery-dependent processes can be modeled as random effects. This modeling flexibility makes state-space models (SSMs) powerful tools for data-limited assessments. Though SSMs enable the model-based inference of the unobserved processes, their flexibility can lead to overfitting and non-identifiability issues. To address these challenges, we developed a suite of state-space length-based age-structured models and applied them to the Korean chub mackerel (Scomber japonicus) stock. Our research demonstrated that incorporating temporal variations in fishery-dependent processes can rectify model mis-specification but may compromise robustness, which can be diagnosed through a series of model checking processes. To tackle non-identifiability, we used a non-degenerate estimator, implementing a gamma distribution as a penalty for the standard deviation parameters of observation errors. This penalty function enabled the simultaneous estimation of both process and observation error variances with minimal bias, a notably challenging task in SSMs. These results highlight the importance of model checking and the effectiveness of the penalized approach in estimating SSMs. Additionally, we discussed novel assessment outcomes for the mackerel stock.
Boltzmann's casino and the unbridgeable chasm in emergence of life research
Authors: Elbert Branscomb
Date: 2023-12-01 00:00:00

Notwithstanding its long history and compelling motivation, research seeking to explicate the emergence life (EoL) has throughout been a cacophony of unresolved speculation and dispute; absent still any clear convergence or other inarguable evidence of progress. This notwithstanding that it has also produced a rich and varied supply of putatively promising technical advances. Not surprising then the effort being advanced by some to establish a shared basis in fundamental assumptions upon which a more productive community research effort might arise. In this essay however, I press a case in opposition. First, that a chasm divides the rich fauna of contesting EoL models into two conceptually incommensurate classes; here named "chemistry models" (to which class belongs nearly all thinking and work in the field, past and present) and "engine models" (advanced in various more-or-less partial forms by a marginal minority of voices dating from Boltzmann forward). Second, that contemporary non-equilibrium thermodynamics dictates that 'engine-less' (i.e. 'chemistry') models cannot in principle generate non-equilibrium, organized states of matter and are in consequence inherently incapable of prizing life out of inanimate matter.
Approximation of Intractable Likelihood Functions in Systems Biology via Normalizing Flows
Authors: Vincent D. Zaballa, Elliot E. Hui
Date: 2023-12-04 00:00:00
Tasks: Model Selection

Systems biology relies on mathematical models that often involve complex and intractable likelihood functions, posing challenges for efficient inference and model selection. Generative models, such as normalizing flows, have shown remarkable ability in approximating complex distributions in various domains. However, their application in systems biology for approximating intractable likelihood functions remains unexplored. Here, we elucidate a framework for leveraging normalizing flows to approximate complex likelihood functions inherent to systems biology models. By using normalizing flows in the Simulation-based inference setting, we demonstrate a method that not only approximates a likelihood function but also allows for model inference in the model selection setting. We showcase the effectiveness of this approach on real-world systems biology problems, providing practical guidance for implementation and highlighting its advantages over traditional computational methods.
A generalisation of the method of regression calibration and comparison with Bayesian and frequentist model averaging methods
Authors: Mark P Little, Nobuyuki Hamada, Lydia B Zablotska
Date: 2023-12-03 00:00:00
Tasks: regression

For many cancer sites low-dose risks are not known and must be extrapolated from those observed in groups exposed at much higher levels of dose. Measurement error can substantially alter the dose-response shape and hence the extrapolated risk. Recently, there has been considerable attention paid to methods of dealing with shared errors, which are particularly important in occupational and environmental settings. In this paper we test Bayesian model averaging (BMA) and frequentist model averaging (FMA) methods, the first of these similar to the so-called Bayesian two-dimensional Monte Carlo (2DMC) method, and both fairly recently proposed, against a very newly proposed modification of the regression calibration method, the extended regression calibration (ERC) method. The quasi-2DMC+BMA method performs well when a linear model is assumed, but poorly when a linear-quadratic model is assumed. FMA performs as well as quasi-2DMC+BMA when a linear model is assumed, and generally much better with a linear-quadratic model, although the coverage probability for the quadratic coefficient is uniformly too high. ERC yields coverage probabilities that are too low when shared and unshared Berkson errors are both large (50%), although otherwise it performs well, and coverage is generally better than the quasi-2DMC+BMA or FMA methods, particularly for the linear-quadratic model. The bias of predicted relative risk at a variety of doses is generally smallest for ERC, and largest for quasi-2DMC+BMA and FMA, with standard regression calibration and Monte Carlo maximum likelihood exhibiting bias in predicted relative risk generally somewhat intermediate between ERC and the other two methods. In general ERC performs best in the scenarios presented, and should be the method of choice in situations where there may be substantial shared error, or suspected curvature in the dose response.
Handling nonlinearities and uncertainties of fed-batch cultivations with difference of convex functions tube MPC
Authors: Niels Krausch, Martin Doff-Sotta, Mark Canon, Peter Neubauer, Mariano Nicolas Cruz Bournazou
Date: 2023-12-01 00:00:00
Tasks: Model Predictive Control

Bioprocesses are often characterized by nonlinear and uncertain dynamics. This poses particular challenges in the context of model predictive control (MPC). Several approaches have been proposed to solve this problem, such as robust or stochastic MPC, but they can be computationally expensive when the system is nonlinear. Recent advances in optimal control theory have shown that concepts from convex optimization, tube-based MPC, and difference of convex functions (DC) enable stable and robust online process control. The approach is based on systematic DC decompositions of the dynamics and successive linearizations around feasible trajectories. By convexity, the linearization errors can be bounded tightly and treated as bounded disturbances in a robust tube-based MPC framework. However, finding the DC composition can be a difficult task. To overcome this problem, we used a neural network with special convex structure to learn the dynamics in DC form and express the uncertainty sets using simplices to maximize the product formation rate of a cultivation with uncertain substrate concentration in the feed. The results show that this is a promising approach for computationally tractable data-driven robust MPC of bioprocesses.
Copula-based deviation measure of cointegrated financial assets
Authors: Alexander Shulzhenko
Date: 2023-12-04 00:00:00
Tasks: Informativeness

This study outlines a comprehensive methodology utilizing copulas to discern inconsistencies in the behavior exhibited by pairs of financial assets. It introduces a robust approach to establishing the interrelationship between the returns of these assets, exploring potential measures of dependence among the stochastic variables represented by these returns. Special emphasis is placed on scrutinizing the traditional measure of dependence, namely the correlation coefficient, delineating its limitations. Furthermore, the study articulates an alternative methodology that offers enhanced stability and informativeness in appraising the relationship between financial instrument returns.