pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic modeling of spike count data with Conway-Maxwell Poisson variability
Authors: Ganchao Wei, Ian H. Stevenson
Date: 2022-05-01 00:00:00
Tasks: Hippocampus

In many areas of the brain, neural spiking activity covaries with features of the external world, such as sensory stimuli or an animal's movement. Experimental findings suggest that the variability of neural activity changes over time and may provide information about the external world beyond the information provided by the average neural activity. To flexibly track time-varying neural response properties, here we developed a dynamic model with Conway-Maxwell Poisson (CMP) observations. The CMP distribution can flexibly describe firing patterns that are both under- and over-dispersed relative to the Poisson distribution. Here we track parameters of the CMP distribution as they vary over time. Using simulations, we show that a normal approximation can accurately track dynamics in state vectors for both the centering and shape parameters ($\lambda$ and $\nu$). We then fit our model to neural data from neurons in primary visual cortex and "place cells" in the hippocampus. We find that this method out-performs previous dynamic models based on the Poisson distribution. The dynamic CMP model provides a flexible framework for tracking time-varying non-Poisson count data and may also have applications beyond neuroscience.
A Theory of Natural Intelligence
Authors: Christoph von der Malsburg, Thilo Stadelmann, Benjamin F. Grewe
Date: 2022-04-22 00:00:00
Tasks: Inductive Bias

Introduction: In contrast to current AI technology, natural intelligence -- the kind of autonomous intelligence that is realized in the brains of animals and humans to attain in their natural environment goals defined by a repertoire of innate behavioral schemata -- is far superior in terms of learning speed, generalization capabilities, autonomy and creativity. How are these strengths, by what means are ideas and imagination produced in natural neural networks? Methods: Reviewing the literature, we put forward the argument that both our natural environment and the brain are of low complexity, that is, require for their generation very little information and are consequently both highly structured. We further argue that the structures of brain and natural environment are closely related. Results: We propose that the structural regularity of the brain takes the form of net fragments (self-organized network patterns) and that these serve as the powerful inductive bias that enables the brain to learn quickly, generalize from few examples and bridge the gap between abstractly defined general goals and concrete situations. Conclusions: Our results have important bearings on open problems in artificial neural network research.
Mind the gap: how multiracial individuals get left behind when we talk about race, ethnicity, and ancestry in genomic research
Authors: Daphne O. Martschenko, Hannah Wand, Jennifer L. Young, Genevieve L. Wojcik
Date: 2022-04-29 00:00:00
Tasks: Diversity

It is widely acknowledged that there is a diversity problem in genomics stemming from the vast underrepresentation of non-European genetic ancestry populations. While many challenges exist to address this gap, a major complicating factor is the misalignment between (1) how society defines and labels individuals; (2) how populations are defined for research; and (3) how research findings are translated to benefit human health. Recent conversations to address the lack of clarity in terminology in genomics have largely focused on ontologies that acknowledge the difference between genetic ancestry and race. Yet, these ontological frameworks for ancestry often follow the subjective discretization of people, normalized by historical racial categories; this perpetuates exclusion at the expense of inclusion. In order to make the benefits of genomics research accessible to all, standards around race, ethnicity, and genetic ancestry must deliberately and explicitly address multiracial, genetically admixed individuals who make salient the limitations of discrete categorization in genomics and society. Starting with the need to clarify terminology, we outline current practices in genomic research and translation that fail those who are 'binned' for failing to fit into a specific bin. We conclude by offering concrete solutions for future research in order to share the benefits of genomics research with the full human population.
Heterogeneity of Interaction Strengths and Its Consequences on Ecological Systems
Authors: Zachary Jackson, BingKan Xue
Date: 2022-04-29 00:00:00

Ecosystems are formed by networks of species and their interactions. Traditional models of such interactions assume a constant interaction strength between a given pair of species. However, there is often significant trait variation among individual organisms even within the same species, causing heterogeneity in their interaction strengths with other species. The consequences of such heterogeneous interactions for the ecosystem have not been studied systematically. As a theoretical exploration, we analyze a simple ecosystem with trophic interactions between two predators and a shared prey, which would exhibit competitive exclusion in models with homogeneous interactions. We consider several scenarios where individuals of the prey species differentiate into subpopulations with different interaction strengths. We show that in all these cases, whether the heterogeneity is inherent, reversible, or adaptive, the ecosystem can stabilize at a new equilibrium where all three species coexist. Moreover, the prey population that has heterogeneous interactions with its predators reaches a higher density than it would without heterogeneity, and can even reach a higher density in the presence of two predators than with just one. Our results suggest that heterogeneity may be a naturally selected feature of ecological interactions that have important consequences for the stability and diversity of ecosystems.
Local adaptation, phenotypic plasticity, and species coexistence
Authors: José F. Fontanari, Margarida Matos, Mauro Santos
Date: 2022-04-29 00:00:00
Tasks: Diversity

Understanding the mechanisms of species coexistence has always been a fundamental topic in ecology. Classical theory predicts that interspecific competition may select for traits that stabilize niche differences, although recent work shows that this is not strictly necessary. Here we ask whether adaptive phenotypic plasticity could allow species coexistence (i.e., some stability at an equilibrium point) without ecological differentiation in habitat use. We used individual-based stochastic simulations defining a landscape composed of spatially uncorrelated or autocorrelated environmental patches, where two species with the same competitive strategies, not able to coexist without some form of phenotypic plasticity, expanded their ranges in the absence of a competition-colonization trade-off (a well-studied mechanism for species diversity). Each patch is characterized by a random environmental value that determines the optimal phenotype of its occupants. In such a scenario, only local adaptation and gene flow (migration) may interact to promote genetic variation and coexistence in the metapopulation. Results show that a competitively inferior species with adaptive phenotypic plasticity can coexist in a same patch with a competitively superior, non-plastic species, provided the migration rates and variances of the patches' environmental values are sufficiently large.
Joint Track Machine Learning: An autonomous method for measuring 6DOF TKA kinematics from single-plane x-ray images
Authors: Andrew Jensen, Paris Flood, Lindsey Palm-Vlasak, Will Burton, Paul Rullkoetter, Scott Banks
Date: 2022-04-29 00:00:00

Dynamic radiographic measurement of 3D TKA kinematics has provided important information for implant design and surgical technique for over 30 years. However, current methods of measuring TKA kinematics are too cumbersome or time-consuming for practical clinical application. Even state-of-the-art techniques require human-supervised initialization or human supervision throughout the entire optimization process. Elimination of human supervision could potentially bring this technology into clinical practicality. Therefore, we propose a fully autonomous pipeline for quantifying TKA kinematics from single-plane imaging. First, a convolutional neural network segments the femoral and tibial implants from the image. Second, segmented images are compared to Normalized Fourier Descriptor shape libraries for initial pose estimates. Lastly, a Lipschitzian optimization routine minimizes the difference between the segmented image and the projected implant. This technique reliably reproduces human-supervised kinematics measurements from internal datasets and external validation studies, with RMS differences of less than 0.7mm and 4{\deg} for internal studies and 0.8mm and 1.7{\deg} for external validation studies. This performance indicates that it will soon be practical to perform these measurements in a clinical setting.
Infusing Linguistic Knowledge of SMILES into Chemical Language Models
Authors: Ingoo Lee, Hojung Nam
Date: 2022-04-20 00:00:00
Tasks: Molecular Property Prediction, Property Prediction, Self-Supervised Learning

The simplified molecular-input line-entry system (SMILES) is the most popular representation of chemical compounds. Therefore, many SMILES-based molecular property prediction models have been developed. In particular, transformer-based models show promising performance because the model utilizes a massive chemical dataset for self-supervised learning. However, there is no transformer-based model to overcome the inherent limitations of SMILES, which result from the generation process of SMILES. In this study, we grammatically parsed SMILES to obtain connectivity between substructures and their type, which is called the grammatical knowledge of SMILES. First, we pretrained the transformers with substructural tokens, which were parsed from SMILES. Then, we used the training strategy 'same compound model' to better understand SMILES grammar. In addition, we injected knowledge of connectivity and type into the transformer with knowledge adapters. As a result, our representation model outperformed previous compound representations for the prediction of molecular properties. Finally, we analyzed the attention of the transformer model and adapters, demonstrating that the proposed model understands the grammar of SMILES.
A systematic analysis of biotech startups that went public in the first half of 2021
Authors: Sebastian G. Huayamares, Melissa P. Lokugamage, Alejandro J. Da Silva Sanchez, James E. Dahlman
Date: 2022-05-02 00:00:00

Biotechnologies are being commercialized at historic rates. In 2020, 74 biotech startups went public through an Initial Public Offering (IPO), and 60 went through the IPO process in the first six months of 2021. However, the traits associated with biotech startups obtaining recent IPOs have not been reported. Here we build a database of biotechs that underwent an IPO in the first half of 2021. By analyzing leadership, technological focus, clinical trials, and financing, we found that advanced degrees among the leadership, clinical trials, and intellectual property are important factors for biotech startups. The data also suggest that large private rounds can decrease time-to-IPO and affect post-IPO stock performance. Notably, these traits were often exhibited by the 138 biotech IPOs in 2018-2019, suggesting 2021 data were not driven by COVID.
The Cross-Sectional Intrinsic Entropy. A Comprehensive Stock Market Volatility Estimator
Authors: Claudiu Vinte, Marcel Ausloos
Date: 2022-04-29 00:00:00
Tasks: Time Series Analysis

To take into account the temporal dimension of uncertainty in stock markets, this paper introduces a cross-sectional estimation of stock market volatility based on the intrinsic entropy model. The proposed cross-sectional intrinsic entropy (CSIE) is defined and computed as a daily volatility estimate for the entire market, grounded on the daily traded prices: open, high, low, and close prices (OHLC), along with the daily traded volume for all symbols listed on The New York Stock Exchange (NYSE) and The National Association of Securities Dealers Automated Quotations (NASDAQ). We perform a comparative analysis between the time series obtained from the CSIE and the historical volatility as provided by the estimators: close-to-close, Parkinson, Garman-Klass, Rogers-Satchell, Yang-Zhang, and intrinsic entropy (IE), defined and computed from historical OHLC daily prices of the Standard & Poor's 500 index (S&P500), Dow Jones Industrial Average (DJIA), and the NASDAQ Composite index, respectively, for various time intervals. Our study uses approximately 6000 day reference points, starting on 1 Jan. 2001, until 23 Jan. 2022, for both the NYSE and the NASDAQ. We found that the CSIE market volatility estimator is consistently at least 10 times more sensitive to market changes, compared to the volatility estimate captured through the market indices. Furthermore, beta values confirm a consistently lower volatility risk for market indices overall, between 50% and 90% lower, compared to the volatility risk of the entire market in various time intervals and rolling windows.
Regulating stochastic clocks
Authors: Zhe Fei, Weixuan Xia
Date: 2022-05-01 00:00:00

Stochastic clocks represent a class of time change methods for incorporating trading activity into continuous-time financial models, with the ability to deal with typical asymmetrical and tail risks in financial returns. In this paper we propose a significant improvement of stochastic clocks for the same objective but without decreasing the number of trades or changing the trading intensity. Our methodology targets any L\'{e}vy subordinator, or more generally any process of nonnegative independent increments, and is based on various choices of regulating kernels motivated from repeated averaging. By way of a hyperparameter linked to the degree of regulation, arbitrarily large skewness and excess kurtosis of returns can be easily achieved. Generic-time Laplace transforms, characterizing triplets, and cumulants of the regulated clocks and subsequent mixed models are analyzed, serving purposes ranging from statistical estimation and option price calibration to simulation techniques. Under specified jump--diffusion processes and tempered stable processes, a robust moment-based estimation procedure with profile likelihood is developed and a comprehensive empirical study involving S\&P500 and Bitcoin daily returns is conducted to demonstrate a series of desirable effects of the proposed methods.
Pricing Path-dependent Options under Stochastic Volatility via Mellin Transform
Authors: Jiling Cao, Jeong-Hoon Kim, Xi Li, Wenjun Zhang
Date: 2022-05-01 00:00:00
Tasks: Form, Sensitivity

In this paper, we derive closed-form formulas of first-order approximation for down-and-out barrier and floating strike lookback put option prices under a stochastic volatility model, by using an asymptotic approach. To find the explicit closed-form formulas for the zero-order term and the first-order correction term, we use Mellin transform. We also conduct a sensitivity analysis on these formulas, and compare the option prices calculated by them with those generated by Monte-Carlo simulation.
Numerical Method for Highly Non-linear Mean-reverting Asset Price Model with CEV-type Process
Authors: Emmanuel Coffie
Date: 2022-05-02 00:00:00

It is well documented from various empirical studies that the volatility process of an asset price dynamics is stochastic. This phenomenon called for a new approach to describing the random evolution of volatility through time with stochastic models. In this paper, we propose a mean-reverting theta-rho model for asset price dynamics where the volatility diffusion factor of this model follows a highly non-linear CEV-type process. Since this model lacks a closed-form formula, we construct a new truncated EM method to study it numerically under the Khasminskii-type condition. We justify that the truncated EM solutions can be used to evaluate a path-dependent financial product.
A portfolio management of a small RES utility with a Structural Vector Autoregressive model of German electricity markets
Authors: Katarzyna Maciejowska
Date: 2022-04-28 00:00:00
Tasks: Management

The changes in electricity markets expose RES producers and electricity traders to various risks, among which the price and the volume risk play a very important role. In this research, a portfolio building strategies are presented, which allow to dynamically choose a proportion of electricity traded in different electricity markets (day-ahead and intraday) and hence to optimize the behavior of an utility. Two types of approaches are considered: simple, assuming that the proportions are fixed, and data driven, which allows for thier fluctuation. In order to explore the market information, Structural Vector Autoregressive (SVAR) model is applied, which allows to estimate the relationship between variables of interest and to simulate their future distribution. The presented methods are evaluated with data coming from German electricity market. The results indicate that data driven trading strategies allow to increase the utility revenue and at the same time reduce the trading risk, measured by the predictability of the next day income and the revenue Value at Risk. It turns out that the approach based on Sharp Ratio provides the most robust results.
Transient impact from the Nash equilibrium of a permanent market impact game
Authors: Francesco Cordoni, Fabrizio Lillo
Date: 2022-05-01 00:00:00

A large body of empirical literature has shown that market impact of financial prices is transient. However, from a theoretical standpoint, the origin of this temporary nature is still unclear. We show that an implied transient impact arises from the Nash equilibrium between a directional trader and one arbitrageur in a market impact game with fixed and permanent impact. The implied impact is the one that can be empirically inferred from the directional trader's trading profile and price reaction to order flow. Specifically, we propose two approaches to derive the functional form of the decay kernel of the Transient Impact Model, one of the most popular empirical models for transient impact, from the behaviour of the directional trader at the Nash equilibrium. The first is based on the relationship between past order flow and future price change, while in the second we solve an inverse optimal execution problem. We show that in the first approach the implied kernel is unique, while in the second case infinite solutions exist and a linear kernel can always be inferred.
Deep Learning: From Basics to Building Deep Neural Networks with Python
Authors: Milad Vazan
Date: 2022-04-22 00:00:00
Tasks: Deep Learning

This book is intended for beginners who have no familiarity with deep learning. Our only expectation from readers is that they already have the basic programming skills in Python.
Classification of Buildings' Potential for Seismic Damage by Means of Artificial Intelligence Techniques
Authors: Konstantinos Kostinakis, Konstantinos Morfidis, Konstantinos Demertzis, Lazaros Iliadis
Date: 2022-04-06 00:00:00
Tasks: BIG-bench Machine Learning

Developing a rapid, but also reliable and efficient, method for classifying the seismic damage potential of buildings constructed in countries with regions of high seismicity is always at the forefront of modern scientific research. Such a technique would be essential for estimating the pre-seismic vulnerability of the buildings, so that the authorities will be able to develop earthquake safety plans for seismic rehabilitation of the highly earthquake-susceptible structures. In the last decades, several researchers have proposed such procedures, some of which were adopted by seismic code guidelines. These procedures usually utilize methods based either on simple calculations or on the application of statistics theory. Recently, the increase of the computers' power has led to the development of modern statistical methods based on the adoption of Machine Learning algorithms. These methods have been shown to be useful for predicting seismic performance and classifying structural damage level by means of extracting patterns from data collected via various sources. A large training dataset is used for the implementation of the classification algorithms. To this end, 90 3D R/C buildings with three different masonry infills' distributions are analysed utilizing Nonlinear Time History Analysis method for 65 real seismic records. The level of the seismic damage is expressed in terms of the Maximum Interstory Drift Ratio. A large number of Machine Learning algorithms is utilized in order to estimate the buildings' damage response. The most significant conclusion which is extracted is that the Machine Learning methods that are mathematically well-established and their operations that are clearly interpretable step by step can be used to solve some of the most sophisticated real-world problems in consideration with high accuracy.
Brainish: Formalizing A Multimodal Language for Intelligence and Consciousness
Authors: Paul Pu Liang
Date: 2022-04-14 00:00:00
Tasks: Retrieval, Translation

Having a rich multimodal inner language is an important component of human intelligence that enables several necessary core cognitive functions such as multimodal prediction, translation, and generation. Building upon the Conscious Turing Machine (CTM), a machine model for consciousness proposed by Blum and Blum (2021), we describe the desiderata of a multimodal language called Brainish, comprising words, images, audio, and sensations combined in representations that the CTM's processors use to communicate with each other. We define the syntax and semantics of Brainish before operationalizing this language through the lens of multimodal artificial intelligence, a vibrant research area studying the computational tools necessary for processing and relating information from heterogeneous signals. Our general framework for learning Brainish involves designing (1) unimodal encoders to segment and represent unimodal data, (2) a coordinated representation space that relates and composes unimodal features to derive holistic meaning across multimodal inputs, and (3) decoders to map multimodal representations into predictions (for fusion) or raw data (for translation or generation). Through discussing how Brainish is crucial for communication and coordination in order to achieve consciousness in the CTM, and by implementing a simple version of Brainish and evaluating its capability of demonstrating intelligence on multimodal prediction and retrieval tasks on several real-world image, text, and audio datasets, we argue that such an inner language will be important for advances in machine models of intelligence and consciousness.