pwc-archive/papers-with-abstracts huggingface dataset:
Pedestrian Motion Prediction Using Transformer-based Behavior Clustering and Data-Driven Reachability Analysis
Authors: Kleio Fragkedaki, Frank J. Jiang, Karl H. Johansson, Jonas Mårtensson
Date: 2024-08-09 00:00:00
Tasks: motion prediction

In this work, we present a transformer-based framework for predicting future pedestrian states based on clustered historical trajectory data. In previous studies, researchers propose enhancing pedestrian trajectory predictions by using manually crafted labels to categorize pedestrian behaviors and intentions. However, these approaches often only capture a limited range of pedestrian behaviors and introduce human bias into the predictions. To alleviate the dependency on manually crafted labels, we utilize a transformer encoder coupled with hierarchical density-based clustering to automatically identify diverse behavior patterns, and use these clusters in data-driven reachability analysis. By using a transformer-based approach, we seek to enhance the representation of pedestrian trajectories and uncover characteristics or features that are subsequently used to group trajectories into different "behavior" clusters. We show that these behavior clusters can be used with data-driven reachability analysis, yielding an end-to-end data-driven approach to predicting the future motion of pedestrians. We train and evaluate our approach on a real pedestrian dataset, showcasing its effectiveness in forecasting pedestrian movements.
Situational Awareness in Indian Power Grid using Synchrophasor Data
Authors: Makarand Sudhakar Ballal
Date: 2024-08-09 00:00:00

Wide Area Measurement Systems (WAMS) can guide system operators' to increase their situational awareness by expanding observability of their supervise area and adjoining systems. Power system oscillations in the electrical grid are a matter of contention. It may transform the stable operation of the power system into an unstable system when oscillations grow and if no corrective actions are taken to mitigate them. This paper manifests the utilization of the synchrophasor technology for situational awareness by oscillation detection. An algorithm is proposed for the low-frequency oscillation-related analysis based on Prony and Fast Fourier Transform (FFT) methods. It gives better insight during the grid disturbance and other events related to power system operation. The effectiveness of this algorithm is validated by two case studies in the Western Regional Indian power grid.
Brain functions emerge as thermal equilibrium states of the connectome
Authors: Elkaïoum M. Moutuou, Habib Benali
Date: 2024-08-26 00:00:00

A fundamental paradigm in neuroscience is that cognitive functions -- such as perception, learning, memory, and locomotion -- are shaped by the brain's structural organization. However, the theoretical principles explaining how this physical architecture governs its function remain elusive. Here, we propose an algebraic quantum mechanics (AQM) framework in which the functional states of a structural connectome emerge as thermal equilibrium states of an algebraic quantum system defined on the underlying directed multigraph. These equilibrium states, derived from the Kubo-Martin-Schwinger (KMS) states formalism, capture the contribution of each neuron to the overall information flow. We apply this framework to the connectome of the nematode {\em Caenorhabditis elegans}, providing a detailed description of the KMS states, exploring their functional implications, and predicting functional networks based on anatomical connectivity. Ultimately, our approach reveals functional circuits predicted by the topology of the connectome and illuminates on the mechanisms linking structure to function.
Multi-Task Multi-Fidelity Learning of Properties for Energetic Materials
Authors: Robert J. Appleton, Daniel Klinger, Brian H. Lee, Michael Taylor, Sohee Kim, Samuel Blankenship, Brian C. Barnes, Steven F. Son, Alejandro Strachan
Date: 2024-08-21 00:00:00
Tasks: Materials Screening

Data science and artificial intelligence are playing an increasingly important role in the physical sciences. Unfortunately, in the field of energetic materials data scarcity limits the accuracy and even applicability of ML tools. To address data limitations, we compiled multi-modal data: both experimental and computational results for several properties. We find that multi-task neural networks can learn from multi-modal data and outperform single-task models trained for specific properties. As expected, the improvement is more significant for data-scarce properties. These models are trained using descriptors built from simple molecular information and can be readily applied for large-scale materials screening to explore multiple properties simultaneously. This approach is widely applicable to fields outside energetic materials.
Gravix: Active Learning for Gravitational Waves Classification Algorithms
Authors: Raja Vavekanand, Kira Sam, Vavek Bharwani
Date: 2024-08-18 00:00:00
Tasks: Active Learning, Bayesian Optimization, Classification, Hyperparameter Optimization

This project explores the integration of Bayesian Optimization (BO) algorithms into a base machine learning model, specifically Convolutional Neural Networks (CNNs), for classifying gravitational waves among background noise. The primary objective is to evaluate whether optimizing hyperparameters using Bayesian Optimization enhances the base model's performance. For this purpose, a Kaggle [1] dataset that comprises real background noise (labeled 0) and simulated gravitational wave signals with noise (labeled 1) is used. Data with real noise is collected from three detectors: LIGO Livingston, LIGO Hanford, and Virgo. Through data preprocessing and training, the models effectively classify testing data, predicting the presence of gravitational wave signals with a remarkable score, of 83.61%. The BO model demonstrates comparable accuracy to the base model, but its performance improvement is not very significant (84.34%). However, it is worth noting that the BO model needs additional computational resources and time due to the iterations required for hyperparameter optimization, requiring additional training on the entire dataset. For this reason, the BO model is less efficient in terms of resources compared to the base model in gravitational wave classification
Symbolic dynamics of joint brain states during dyadic coordination
Authors: Italo Ivo Lima Dias Pinto, Zhibin Zhou, Javier O. Garcia, Ramesh Srinivasan
Date: 2024-08-23 00:00:00
Tasks: EEG, Functional Connectivity

We propose a novel approach to investigate the brain mechanisms that support coordination of behavior between individuals. Brain states in single individuals defined by the patterns of functional connectivity between brain regions are used to create joint symbolic representations of the evolution of brain states in two or more individuals performing a task together. These symbolic dynamics can be analyzed to reveal aspects of the dynamics of joint brain states that are related to coordination or other interactive behaviors. We apply this approach to simultaneous electroencephalographic (EEG) data from pairs of subjects engaged in two different modes of finger-tapping coordination tasks (synchronization and syncopation) under different interaction conditions (Uncoupled, Leader-Follower, and Mutual) to explore the neural mechanisms of multi-person motor coordination. Our results reveal that the dyads exhibit mostly the same joint symbols in different interaction conditions - the most important differences are reflected in the symbolic dynamics. Recurrence analysis shows that interaction influences the dwell time in specific joint symbols and the structure of joint symbol sequences (motif length). In synchronization, increasing feedback promotes stability with longer dwell times and motif length. In syncopation, Leader-Follower interactions enhance stability (increase dwell time and motif length), but Mutual feedback dramatically reduces stability. Network analysis reveals distinct topological changes with task and feedback. In synchronization, stronger coupling stabilizes a few states restricting the pattern of flow between states, preserving a core-periphery structure of the joint brain states. In syncopation, a more distributed flow amongst a larger set of joint brain states reduces the dominance of core joint brain states.
The nonlinear economy (I): How resource constrains lead to business cycles
Authors: Frank Schweitzer, Giona Casiraghi
Date: 2024-08-21 00:00:00

We explore the nonlinear dynamics of a macroeconomic model with resource constraints. The dynamics is derived from a production function that considers capital and a generalized form of energy as inputs. Energy, the new variable, is depleted during the production process and has to be renewed, whereas capital grows with production and decreases from depreciation. Dependent on time scales and energy related control parameters, we obtain steady states of high or low production, but also sustained oscillations that show properties of business cycles. We also find conditions for the coexistence of stable fixed points and limit cycles. Our model allows to specify investment and saving functions for Kaldor's model of business cycles. We provide evidence for an endogenous origin of business cycles if depleting resources are taken into account.
Rapid and accurate mosquito abundance forecasting with Aedes-AI neural networks
Authors: Adrienne C. Kinney, Roberto Barrera, Joceline Lega
Date: 2024-08-28 00:00:00

We present a method to convert weather data into probabilistic forecasts of Aedes aegypti abundance. The approach, which relies on the Aedes-AI suite of neural networks, produces weekly point predictions with corresponding uncertainty estimates. Once calibrated on past trap and weather data, the model is designed to use weather forecasts to estimate future trap catches. We demonstrate that when reliable input data are used, the resulting predictions have high skill. This technique may therefore be used to supplement vector surveillance efforts or identify periods of elevated risk for vector-borne disease outbreaks.
Highly Sensitive Differential Microwave Sensor Using Enhanced Spiral Resonators for Precision Permittivity Measurement
Authors: Nastouh Nikkhah, Rasool Keshavarz, Mehran Abolhasan, Justin Lipman, Negin Shariati
Date: 2024-08-29 00:00:00
Tasks: Sensitivity

This paper presents a highly sensitive microwave sensor for dielectric sensing. One of the main disadvantages of microwave resonant-based sensors is cross-sensitivity originated by time-dependent uncontrolled environmental factors such as temperature that affect the material under test (MUT) behavior, leading to undesirable frequency shifts and, hence, lower accuracy. However, this work eliminates the unwanted errors using the differential measurement technique by comparing two transmission resonance frequencies during a unit test setup to measure the permittivity of MUT over time. The proposed structure comprises a spiral resonator with an extended horizontal microstrip line (EH-ML) coupled to a microstrip transmission line (MTL). Creating EH-ML within the structure comprises two primary contributions: enhanced sensitivity resulting from stronger fringing fields generated by increasing the effective area and improved resolution due to higher resonance frequencies caused by a lower total capacitive coupling effect. The proposed sensor is fabricated and tested using MUTs with a permittivity of less than 80 to verify the performance. In this regard, a frequency detection resolution (FDR) of 44MHz and a sensitivity of 0.85% are achieved at a maximum permittivity of 78.3. The results of theoretical analysis, simulation, and measurement are in relatively good agreement. Consequently, the proposed highly sensitive microwave sensor offers significant advantages, such as low complexity in design and fabrication. It also offers high resolution and precision in a wide range of permittivity, which can be an attractive candidate for dielectric sensing in health, chemical and agriculture applications.
Nonlinear memory in cell division dynamics across species
Authors: Shijie Zhang, Chenyi Fei, Jörn Dunkel
Date: 2024-08-26 00:00:00

Regulation of cell growth and division is essential to achieve cell-size homeostasis. Recent advances in imaging technologies, such as ``mother machines" for bacteria or yeast, have allowed long-term tracking of cell-size dynamics across many generations, and thus have brought major insights into the mechanisms underlying cell-size control. However, understanding the governing rules of cell growth and division within a quantitative dynamical-systems framework remains a major challenge. Here, we implement and apply a framework that makes it possible to infer stochastic differential equation (SDE) models with Poisson noise directly from experimentally measured time series for cellular growth and divisions. To account for potential nonlinear memory effects, we parameterize the Poisson intensity of stochastic cell division events in terms of both the cell's current size and its ancestral history. By applying the algorithm to experimentally measured cell size trajectories, we are able to quantitatively evaluate the linear one-step memory hypothesis underlying the popular ``sizer",``adder", and ``timer" models of cell homeostasis. For Escherichia coli and Bacillus subtilis bacteria, Schizosaccharomyces pombe yeast and Dictyostelium discoideum amoebae, we find that in many cases the inferred stochastic models have a substantial nonlinear memory component. This suggests a need to reevaluate and generalize the currently prevailing linear-memory paradigm of cell homeostasis. More broadly, the underlying inference framework is directly applicable to identify quantitative models for stochastic jump processes in a wide range of scientific disciplines.
The untapped power of a general theory of organismal metabolism
Authors: Marko Jusup, Michael R. Kearney
Date: 2024-08-26 00:00:00

What makes living things special is how they manage matter, energy, and entropy. A general theory of organismal metabolism should therefore be quantified in these three currencies while capturing the unique way they flow between individuals and their environments. We argue that such a theory has quietly arrived -- 'Dynamic Energy Budget' (DEB) theory -- which conceptualises organisms as a series of macrochemical reactions that use energy to transform food into structured biomass and bioproducts while producing entropy. We show that such conceptualisation is deeply rooted in thermodynamic principles and that, with the help of a small set of biological assumptions, it underpins the emergence of fundamental ecophysiological phenomena, most notably the three-quarter power scaling of metabolism. Building on the subcellular nature of the theory, we unveil the eco-evolutionary relevance of coarse-graining biomass into qualitatively distinct, stoichiometricially fixed pools with implicitly regulated dynamics based on surface area-volume relations. We also show how generalised enzymes called 'synthesising units' and an information-based state variable called 'maturity' capture transitions between ecological and physiological metabolic interactions, and thereby transitions between unicellular and multicellular metabolic organisation. Formal theoretical frameworks make the constraints imposed by the laws of nature explicit, which in turn leads to better research hypotheses and avoids errors in reasoning. DEB theory uniquely applies thermodynamic formalism to organismal metabolism, linking biological processes across different scales through the transformation of matter and energy, the production of entropy, and the exchange of information. We propose ways in which the theory can inform trans-disciplinary efforts at the frontiers of the life sciences.
Model-based and empirical analyses of stochastic fluctuations in economy and finance
Authors: Rubina Zadourian
Date: 2024-08-14 00:00:00

The objective of this work is the investigation of complexity, asymmetry, stochasticity and non-linearity of the financial and economic systems by using the tools of statistical mechanics and information theory. More precisely, this thesis concerns statistical-based modeling and empirical analyses with applications in finance, forecasting, production processes and game theory. In these areas the time dependence of probability distributions is of prime interest and can be measured or exactly calculated for model systems. The correlation coefficients and moments are among the useful quantities to describe the dynamics and the correlations between random variables. However, the full investigation can only be achieved if the probability distribution function of the variable is known; its derivation is one of the main focuses of the present work.
Regional emission dynamics across phases of the EU ETS
Authors: Marco Dueñas, Antoine Mandel
Date: 2024-08-27 00:00:00

This paper explores the relationship between economic growth and CO$_2$ emissions across European regions from 1990 to 2022, specifically concerning the dynamics of emissions growth rates through different phases of the European Union Emissions Trading System (EU ETS). We find that emissions dynamics exhibit significant volatility influenced by changing policy frameworks. Furthermore, the distribution of emissions growth rates is asymmetric and displays fat tails, suggesting the potential for extreme emissions events. We identify marked disparities across regions: less developed regions experience higher emissions growth rates and greater volatility compared to many developed areas, which show a trend of declining emissions and reduced volatility. Our findings highlight the sensitivity of emissions to policy changes and emphasise the need for clear and effective governance in emissions trading schemes.
Toward Mixed Analog-Digital Quantum Signal Processing: Quantum AD/DA Conversion and the Fourier Transform
Authors: YuAn Liu, John M. Martyn, Jasmine Sinanan-Singh, Kevin C. Smith, Steven M. Girvin, Isaac L. Chuang
Date: 2024-08-27 00:00:00

Signal processing stands as a pillar of classical computation and modern information technology, applicable to both analog and digital signals. Recently, advancements in quantum information science have suggested that quantum signal processing (QSP) can enable more powerful signal processing capabilities. However, the developments in QSP have primarily leveraged \emph{digital} quantum resources, such as discrete-variable (DV) systems like qubits, rather than \emph{analog} quantum resources, such as continuous-variable (CV) systems like quantum oscillators. Consequently, there remains a gap in understanding how signal processing can be performed on hybrid CV-DV quantum computers. Here we address this gap by developing a new paradigm of mixed analog-digital QSP. We demonstrate the utility of this paradigm by showcasing how it naturally enables analog-digital conversion of quantum signals -- specifically, the transfer of states between DV and CV quantum systems. We then show that such quantum analog-digital conversion enables new implementations of quantum algorithms on CV-DV hardware. This is exemplified by realizing the quantum Fourier transform of a state encoded on qubits via the free-evolution of a quantum oscillator, albeit with a runtime exponential in the number of qubits due to information theoretic arguments. Collectively, this work marks a significant step forward in hybrid CV-DV quantum computation, providing a foundation for scalable analog-digital signal processing on quantum processors.
Grover Adaptive Search for Maximum Likelihood Detection of Generalized Spatial Modulation
Authors: Kein Yukiyoshi, Taku Mikuriya, Hyeon Seok Rou, Giuseppe Thadeu Freitas de Abreu, Naoki Ishikawa
Date: 2024-08-24 00:00:00

We propose a quantum-assisted solution for the maximum likelihood detection (MLD) of generalized spatial modulation (GSM) signals. Specifically, the MLD of GSM is first formulated as a novel polynomial optimization problem, followed by the application of a quantum algorithm, namely, the Grover adaptive search. The performance in terms of query complexity of the proposed method is evaluated and compared to the classical alternative via a numerical analysis, which reveals that under fault-tolerant quantum computation, the proposed method outperforms the classical solution if the number of data symbols and the constellation size are relatively large.
Modeling the Dynamics of Growth in Master-Planned Communities
Authors: Christopher K. Allsup, Irene S. Gabashvili
Date: 2024-08-26 00:00:00
Tasks: Navigate, Time Series, Time Series Forecasting

This paper describes how a time-varying Markov model was used to forecast housing development at a master-planned community during a transition from high to low growth. Our approach draws on detailed historical data to model the dynamics of the market participants, producing results that are entirely data-driven and free of bias. While traditional time series forecasting methods often struggle to account for nonlinear regime changes in growth, our approach successfully captures the onset of buildout as well as external economic shocks, such as the 1990 and 2008-2011 recessions and the 2021 post-pandemic boom. This research serves as a valuable tool for urban planners, homeowner associations, and property stakeholders aiming to navigate the complexities of growth at master-planned communities during periods of both system stability and instability.
Sensitivity Analysis for Dynamic Discrete Choice Models
Authors: Chun Pong Lau
Date: 2024-08-29 00:00:00
Tasks: Discrete Choice Models, Sensitivity

In dynamic discrete choice models, some parameters, such as the discount factor, are being fixed instead of being estimated. This paper proposes two sensitivity analysis procedures for dynamic discrete choice models with respect to the fixed parameters. First, I develop a local sensitivity measure that estimates the change in the target parameter for a unit change in the fixed parameter. This measure is fast to compute as it does not require model re-estimation. Second, I propose a global sensitivity analysis procedure that uses model primitives to study the relationship between target parameters and fixed parameters. I show how to apply the sensitivity analysis procedures of this paper through two empirical applications.
Marginal homogeneity tests with panel data
Authors: Federico Bugni, Jackson Bunting, Muyang Ren
Date: 2024-08-28 00:00:00

A panel dataset satisfies marginal homogeneity if the time-specific marginal distributions are homogeneous or time-invariant. Marginal homogeneity is relevant in economic settings such as dynamic discrete games. In this paper, we propose several tests for the hypothesis of marginal homogeneity and investigate their properties. We consider an asymptotic framework in which the number of individuals n in the panel diverges, and the number of periods T is fixed. We implement our tests by comparing a studentized or non-studentized T-sample version of the Cramer-von Mises statistic with a suitable critical value. We propose three methods to construct the critical value: asymptotic approximations, the bootstrap, and time permutations. We show that the first two methods result in asymptotically exact hypothesis tests. The permutation test based on a non-studentized statistic is asymptotically exact when T=2, but is asymptotically invalid when T>2. In contrast, the permutation test based on a studentized statistic is always asymptotically exact. Finally, under a time-exchangeability assumption, the permutation test is exact in finite samples, both with and without studentization.