Learning about compact binary merger: the interplay between numerical relativity and gravitational-wave astronomy

Activities in data analysis and numerical simulation of gravitational waves have to date largely proceeded independently. In this work we study how waveforms obtained from numerical simulations could be effectively used within the data analysis effort to search for gravitational waves from black hole binaries. We propose measures to quantify the accuracy of numerical waveforms for the purpose of data analysis and study how sensitive the analysis is to errors in the waveforms. We estimate that ~100 templates (and ~10 simulations with different mass ratios) are needed to detect waves from non-spinning binary black holes with total masses in the range 100 Msun < M < 400 Msun using initial LIGO. Of course, many more simulation runs will be needed to confirm that the correct physics is captured in the numerical evolutions. From this perspective, we also discuss sources of systematic errors in numerical waveform extraction and provide order of magnitude estimates for the computational cost of simulations that could be used to estimate the cost of parameter space surveys. Finally, we discuss what information from near-future numerical simulations of compact binary systems would be most useful for enhancing the detectability of such events with contemporary gravitational wave detectors and emphasize the role of numerical simulations for the interpretation of eventual gravitational-wave observations.Comment: 19 pages, 12 figure

Turnover, account value and diversification of real traders: evidence of collective portfolio optimizing behavior

Despite the availability of very detailed data on financial market, agent-based modeling is hindered by the lack of information about real trader behavior. This makes it impossible to validate agent-based models, which are thus reverse-engineering attempts. This work is a contribution to the building of a set of stylized facts about the traders themselves. Using the client database of Swissquote Bank SA, the largest on-line Swiss broker, we find empirical relationships between turnover, account values and the number of assets in which a trader is invested. A theory based on simple mean-variance portfolio optimization that crucially includes variable transaction costs is able to reproduce faithfully the observed behaviors. We finally argue that our results bring into light the collective ability of a population to construct a mean-variance portfolio that takes into account the structure of transaction costsComment: 26 pages, 9 figures, Fig. 8 fixe

Deep Attentive Time Series Modelling for Quantitative Finance

Mención Internacional en el título de doctorTime series modelling and forecasting is a persistent problem with extensive implications in scientific, business, industrial, and economic areas. This thesis’s contribution is twofold. Firstly, we propose a novel probabilistic time series forecasting methodology that introduces the use of Fourier domain-based attention models, merging classic signal processing spectral filtering techniques with machine learning architectures. Secondly, we take advantage of the abundance of financial intraday high-frequency data to develop deep learning-based solutions for modelling financial time series. Machine learning methods can potentially enhance the performance of traditional methodologies used by practitioners. Deep neural networks’ feature extraction capabilities, which can benefit from the rising accessibility of highfrequency data, and attention mechanisms, which help to model temporal patterns, are mostly to blame for this. Concerning our first major contribution, this thesis empirically demonstrates that spectral domain-based machine learning models can learn the properties of time series datasets and integrate this information to improve the forecasting accuracy. Simultaneously, Fourier domain-based models alleviate some of the inconveniences commonly associated with deep autoregressive models. These architectures, prone to prioritising recent past data, often ignore critical global information not contained in previous time steps. Additionally, they are susceptible to error accumulation and propagation and may not yield illustrative results. The proposed model, the Spectral Attention Autoregressive Model (SAAM), mitigates these problems by combining deep autoregressive models with a Spectral Attention (SA) module. This module uses two attention models operating over the Fourier domain representation of the time series’ embedding. Through spectral filtering, SAAM differentiates between the components of the frequency domain that should be considered noise and subsequently filtered out, and the global patterns that are relevant and should be incorporated into the predictions. Empirical evaluation proves how the proposed Spectral Attention module can be integrated into various deep autoregressive models, consistently improving the results of these base architectures and achieving state-of-the-art performance. Afterwards, this thesis shifts toward showcasing the benefits of machine learning solutions in two different quantitative finance scenarios, proving how attention-based deep learning approaches compare favourably to classic parametric-based models and providing solutions for various algorithmic and high-frequency trading problems. In the context of volatility forecasting, which plays a central role among equity risk measures, we show that Dilated Causal Convolutional-based neural networks offer significant performance gains compared to well-established volatility-oriented parametric models. The proposed model, called DeepVol, showcases how data- driven models can avoid the limitations of classical methods by taking advantage of the abundance of high-frequency data. DeepVol outperforms baseline methods while exhibiting robustness in the presence of volatility shocks, showing its ability to extract universal features and transfer learning to out-of-distribution data. Consequently, data-driven approaches should be carefully considered in the context of volatility forecasting, as they can be instrumental in the valuation of financial derivatives, risk management, and the formation of investment portfolios. Finally, this thesis presents a survival analysis model for estimating the distri- bution of fill times for limit orders posted in the Limit Order Book (LOB). The proposed model, which does not make assumptions about the underlying stochastic processes, employs a convolutional-Transformer encoder and a monotonic neural network decoder to relate the time-varying features of the LOB to the distribution of fill times. It grants practitioners the capability of making informed decisions between market orders and limit orders, which in practice entails a trade-off between immediate execution and price premium. We offer an exhaustive comparison of the survival functions resulting from different order placement strategies, offering insight into the fill probability of orders placed within the spread. Empirical evaluation reveals the superior performance of the monotonic encoder-decoder convolutional- Transformer compared to state-of-the-art benchmarks, leading to more accurate predictions and improved economic value.El modelado y predicción de series temporales es un problema persistente con amplias implicaciones en áreas científicas, comerciales, industriales y económicas. Esta tesis propone una doble contribución en este ámbito. En primer lugar, formulamos una novedosa metodología para la predicción probabilística de series temporales que introduce el uso de modelos de atención basados en el dominio de la frecuencia, con la transformada de Fourier desempeñando un papel fundamental. El modelo propuesto fusiona técnicas clásicas de filtrado espectral, pertenecientes al campo del procesado de señal, con modelos de aprendizaje automático. En segundo lugar, desarrollamos varias soluciones basadas en aprendizaje profundo para el modelado de datos financieros intradía, aprovechando la cada vez mayor disponibilidad de los mismos. Los métodos de aprendizaje automático poseen el potencial para mejorar los resultados obtenidos por las metodologías clásicas que los profesionales del ámbito de las finanzas cuantitativas acostumbran a utilizar. La capacidad de extracción de características de las redes neuronales, que pueden aprovechar la creciente accesibilidad a los datos financieros de alta frecuencia, y el uso de los mecanismos de atención para el modelado temporal, son los principales responsables de ésto. En lo relativo a la primera de las contribuciones mencionadas anteriormente, es decir, el uso de modelos de aprendizaje automático que operan sobre el dominio de la frecuencia, esta tesis demuestra de manera empírica que los modelos de aprendizaje profundo basados en el dominio espectral pueden aprender de forma más eficiente las propiedades de las series temporales a predecir. De esta manera, logran mejorar la precisión de las predicciones a la vez que solventan varios de los problemas que lastran el rendimiento de los modelos autoregresivos. Estas arquitecturas son propensas a sobreponderar los datos del pasado inmediato, ignorando a menudo valiosa información global que no está contenida en estas observaciones recientes. Además, son susceptibles a la acumulación y propagación de errores. Finalmente, los resultados que producen son difícilmente interpretables. Proponemos un nuevo modelo, llamado “Spectral Attention Autoregressive Model”(SAAM) (Modelo Autorregresivo con Atención Espectral), que mitiga estos problemas combinando modelos autorregresivos basados en aprendizaje profundo con un módulo de Atención Espectral. Dicho módulo contiene dos modelos de atención que operan sobre la representación en el dominio de Fourier del “embedding” obtenido a partir de la serie temporal a predecir. Usando técnicas de filtrado espectral, SAAM diferencia entre los componentes del espectro que deben ser considerados ruido, y por consiguiente deben ser filtrados, y aquellos patrones globales que son relevantes y deben ser incorporados en las predicciones. Mediante una exhaustiva evaluación empírica, demostramos que nuestro modelo de Atención Espectral puede ser integrado en diversos modelos autorregresivos que forman parte del estado del arte actual, mejorando de forma consistente los resultados obtenidos. En lo relativo a la segunda contribución principal de esta tesis doctoral, demostramos los beneficios que las metodologías de aprendizaje automático basadas en modelos de atención pueden aportar en dos problemas propios de las finanzas cuantitativas. Diversos experimentos demuestran cómo este tipo de modelos pueden mejorar los resultados obtenidos por los modelos clásicos empleados en este campo, proporcionando soluciones innovadoras para diversos problemas recurrentes dentro del trading algorítmico de alta frecuencia. La predicción de volatilidad en mercados financieros es el primero de estos problemas en ser abordado en la presente tesis. La estimación de volatilidad desempeña un papel central entre las medidas de riesgo utilizadas en los mercados de renta variable. En esta tesis demostramos que las redes neuronales basadas en “Dilated Causal Convolutions” (Convolucionales Causales Dilatadas) ofrecen ganancias significativas en comparación con los modelos paramétricos clásicos desarrollados única y exclusivamente para predicción de volatilidad. El modelo propuesto, llamado DeepVol, evidencia que el uso de modelos de aprendizaje profundo puede evitar las numerosas limitaciones propias de los métodos clásicos, logrando aprovechar la abundancia de datos de alta frecuencia para aprender las funciones deseadas. DeepVol supera a todos los modelos de referencia usados como comparativa, a la vez que exhibe robustez en períodos que contienen shocks de volatilidad, demostrando su capacidad para extraer características universales comunes a diferentes instrumentos financieros. Los resultados obtenidos en esta parte de la tesis nos llevan a concluir que los modelos de aprendizaje automático deben considerarse cuidadosamente en el contexto de predicción de volatilidad, pudiendo ser especialmente relevantes en la valoración de derivados financieros, gestión del riesgo, y creación de carteras de inversión. Para terminar, esta tesis presenta un modelo de análisis de supervivencia para estimar la distribución de probabilidad de ejecución subyacente a órdenes limitadas publicadas en el conocido como “Limit Order Book” (Libro de Órdenes Limitadas). El modelo propuesto, que no necesita partir de suposiciones sobre los procesos estocásticos subyacentes, emplea una arquitectura codificador/decodificador que utiliza un “Transformer” convolutional para codificar la información del libro de órdenes y una red monotónica que decodifica la función de supervivencia a estimar.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Juan José Murillo Fuentes.- Secretario: Emilio Parrado Hernández.- Vocal: Manuel Gómez Rodrígue

Galactic Punctuated Equilibrium: How to Undermine Carter's Anthropic Argument in Astrobiology

We investigate a new strategy which can defeat the (in)famous Carter's "anthropic" argument against extraterrestrial life and intelligence. In contrast to those already considered by Wilson, Livio, and others, the present approach is based on relaxing hidden uniformitarian assumptions, considering instead a dynamical succession of evolutionary regimes governed by both global (Galaxy-wide) and local (planet- or planetary system-limited) regulation mechanisms. This is in accordance with recent developments in both astrophysics and evolutionary biology. Notably, our increased understanding of the nature of supernovae and gamma-ray bursts, as well as of strong coupling between the Solar System and the Galaxy on one hand, and the theories of "punctuated equilibria" of Eldredge and Gould and "macroevolutionary regimes" of Jablonski, Valentine, et al. on the other, are in full accordance with the regulation- mechanism picture. The application of this particular strategy highlights the limits of application of Carter's argument, and indicates that in the real universe its applicability conditions are not satisfied. We conclude that drawing far-reaching conclusions about the scarcity of extraterrestrial intelligence and the prospects of our efforts to detect it on the basis of this argument is unwarranted.Comment: 3 figures, 26 page

Tuning the Mammalian Circadian Clock: Robust Synergy of Two Loops

The circadian clock is accountable for the regulation of internal rhythms in most living organisms. It allows the anticipation of environmental changes during the day and a better adaptation of physiological processes. In mammals the main clock is located in the suprachiasmatic nucleus (SCN) and synchronizes secondary clocks throughout the body. Its molecular constituents form an intracellular network which dictates circadian time and regulates clock-controlled genes. These clock-controlled genes are involved in crucial biological processes including metabolism and cell cycle regulation. Its malfunction can lead to disruption of biological rhythms and cause severe damage to the organism. The detailed mechanisms that govern the circadian system are not yet completely understood. Mathematical models can be of great help to exploit the mechanism of the circadian circuitry. We built a mathematical model for the core clock system using available data on phases and amplitudes of clock components obtained from an extensive literature search. This model was used to answer complex questions for example: how does the degradation rate of Per affect the period of the system and what is the role of the ROR/Bmal/REV-ERB (RBR) loop? Our findings indicate that an increase in the RNA degradation rate of the clock gene Period (Per) can contribute to increase or decrease of the period - a consequence of a non-monotonic effect of Per transcript stability on the circadian period identified by our model. Furthermore, we provide theoretical evidence for a potential role of the RBR loop as an independent oscillator. We carried out overexpression experiments on members of the RBR loop which lead to loss of oscillations consistent with our predictions. These findings challenge the role of the RBR loop as a merely auxiliary loop and might change our view of the clock molecular circuitry and of the function of the nuclear receptors (REV-ERB and ROR) as a putative driving force of molecular oscillations

Community structure informs species geographic distributions

This is the author accepted manuscript. The final version is available from Public Library of Science via the DOI in this recordUnderstanding what determines species’ geographic distributions is crucial for assessing global change threats to biodiversity. Measuring limits on distributions is usually, and necessarily, done with data at large geographic extents and coarse spatial resolution. However, survival of individuals is determined by processes that happen at small spatial assembly processes occurring at small scales, and are often available for relatively extensive areas, so could be useful for explaining species distributions. We demonstrate that Bayesian Network Inference (BNI) can overcome several challenges to including community structure into studies of species distributions, despite having been little used to date. We hypothesized that the relative abundance of coexisting species can improve predictions of species distributions. In 1570 assemblages of 68 Mediterranean woody plant species we used BNI to incorporate community structure into Species Distribution Models (SDMs), alongside environmental information. Information on species associations improved SDM predictions of community structure and species distributions moderately, though for some habitat specialists the deviance explained increased by up to 15%. We demonstrate that most species associations (95%) were positive and occurred between species with ecologically similar traits. This suggests that SDM improvement could be because species co-occurrences are a proxy for local ecological processes. Our study shows that Bayesian Networks, when interpreted carefully, can be used to include local conditions into measurements of species’ large-scale distributions, and this information can improve the predictions of species distributionsThis work was funded by FCT Project “QuerCom” (EXPL/AAG-GLO/2488/2013) and the ERA-Net BiodivERsA project “EC21C” (BIODIVERSA/0003/2011). A.M.N. was supported by a Bolsa de Investigacao de Pos-doutoramento (BI_Pos-Doc_UEvora_Catedra Rui Nabeiro_EXPL_AAG-GLO_2488_2013) and postdoctoral fellowships from the Ministry of Economy and Competitivity (FPDI-2013-16266 and IJCI‐2015‐23498). MGM acknowledges support by a Marie Curie Intra-European Fellowship within the 7th European Community Framework Programme (FORECOMM). J. Vicente is supported by POPH/FSE funds and by National Funds through FCT - Foundation for Science and Technology under the Portuguese Science Foundation (FCT) through Post-doctoral grant SFRH/BPD/84044/2012. AE has a postdoctoral contract funded by the project CN-17-022 (Principado de Asturias, Spain). We are grateful to OneGeology for providing the geological data