pwc-archive/papers-with-abstracts huggingface dataset:
A review of potential barriers to the participation of electric vehicles in German balancing markets
Authors: Périne Cunat
Date: 2023-11-11 00:00:00

As conventional flexibility providers are gradually being replaced by variable renewable energies and electricity demand keeps rising, additional flexibility will become increasingly valuable for the power system. Meanwhile, new sources of flexibility known as distributed energy resources are starting to emerge. Among them electric vehicles have a promising potential to provide short-term frequency-control services. However their integration into balancing markets faces various barriers. Based on the existing literature, this paper aims at identifying which potential barriers exist in the case of the participation of electric vehicles in German balancing markets.
Hub-Based Platoon Formation: Optimal Release Policies and Approximate Solutions
Authors: Alexander Johansson, Ehsan Nekouei, Xiaotong Sun, Karl Henrik Johansson, Jonas Mårtensson
Date: 2023-11-11 00:00:00

This paper studies the optimal hub-based platoon formation at hubs along a highway under decentralized, distributed, and centralized policies. Hubs are locations along highways where trucks can wait for other trucks to form platoons. A coordinator at each hub decides the departure time of trucks, and the released trucks from the hub will form platoons. The problem is cast as an optimization problem where the objective is to maximize the platooning reward. We first show that the optimal release policy in the decentralized case, where the hubs do not exchange information, is to release all trucks at the hub when the number of trucks exceeds a threshold computed by dynamic programming. We develop efficient approximate release policies for the dependent arrival case using this result. To study the value of information exchange among hubs on platoon formation, we next study the distributed and centralized platoon formation policies which require information exchange among hubs. To this end, we develop receding horizon solutions for the distributed and centralized platoon formation at hubs using the dynamic programming technique. Finally, we perform a simulation study over three hubs in northern Sweden. The profits of the decentralized policies are shown to be approximately 3.5% lower than the distributed policy and 8% lower than the centralized release policy. This observation suggests that decentralized policies are prominent solutions for hub-based platooning as they do not require information exchange among hubs and can achieve a similar performance compared with distributed and centralized policies.
Modeling Power Systems Dynamics with Symbolic Physics-Informed Neural Networks
Authors: Huynh T. T. Tran, Hieu T. Nguyen
Date: 2023-11-11 00:00:00

In recent years, scientific machine learning, particularly physic-informed neural networks (PINNs), has introduced new innovative methods to understanding the differential equations that describe power system dynamics, providing a more efficient alternative to traditional methods. However, using a single neural network to capture patterns of all variables requires a large enough size of networks, leading to a long time of training and still high computational costs. In this paper, we utilize the interfacing of PINNs with symbolic techniques to construct multiple single-output neural networks by taking the loss function apart and integrating it over the relevant domain. Also, we reweigh the factors of the components in the loss function to improve the performance of the network for instability systems. Our results show that the symbolic PINNs provide higher accuracy with significantly fewer parameters and faster training time. By using the adaptive weight method, the symbolic PINNs can avoid the vanishing gradient problem and numerical instability.
Nonsmooth-Optimization-Based Bandwidth Optimal Control for Precision Motion Systems
Authors: Jingjie Wu, Lei Zhou
Date: 2023-11-11 00:00:00

Precision motion systems are at the core of various manufacturing equipment. The rapidly increasing demand for higher productivity necessitates higher control bandwidth in the motion systems to effectively reject disturbances while maintaining excellent positioning accuracy. However, most existing optimal control methods do not explicitly optimize for control bandwidth, and the classic loop-shaping method suffers from conservative designs and fails to address cross-couplings, which motivates the development of new control solutions for bandwidth optimization. This paper proposes a novel bandwidth optimal control formulation based on nonsmooth optimization for precision motion systems. Our proposed method explicitly optimizes the system's MIMO control bandwidth while constraining the H-infinity norm of the closed-loop sensitivity function for robustness. A nonsmooth optimization solver, GRANSO, is used to solve the proposed program, and an augmented quadratic programming (QP)--based descent direction search is proposed to facilitate convergence. Simulation evaluations show that the bandwidth optimal control method can achieve a 23% higher control bandwidth than conventional loop-shaping design, and the QP-based descent direction search can reduce iteration number by 60%, which illustrates the effectiveness and efficiency of the proposed approach.
Regret-Optimal Control under Partial Observability
Authors: Joudi Hajar, Oron Sabag, Babak Hassibi
Date: 2023-11-10 00:00:00

This paper studies online solutions for regret-optimal control in partially observable systems over an infinite-horizon. Regret-optimal control aims to minimize the difference in LQR cost between causal and non-causal controllers while considering the worst-case regret across all $\ell_2$-norm-bounded disturbance and measurement sequences. Building on ideas from Sabag et al., 2023, on the the full-information setting, our work extends the framework to the scenario of partial observability (measurement-feedback). We derive an explicit state-space solution when the non-causal solution is the one that minimizes the $\mathcal H_2$ criterion, and demonstrate its practical utility on several practical examples. These results underscore the framework's significant relevance and applicability in real-world systems.
The Impact of Load Altering Attacks on Distribution Systems with ZIP Loads
Authors: Sajjad Maleki, Shijie Pan, E. Veronica Belmega, Charalambos Konstantinou, Subhash Lakshminarayana
Date: 2023-11-10 00:00:00

Load-altering attacks (LAAs) pose a significant threat to power systems with Internet of Things (IoT)-controllable load devices. This research examines the detrimental impact of LAAs on the voltage profile of distribution systems, taking into account the realistic load model with constant impedance Z, constant current I, and constant power P (ZIP). We derive closed-form expressions for computing the voltages of buses following LAA by making approximations to the power flow as well as the load model. We also characterize the minimum number of devices to be manipulated in order to cause voltage safety violations in the system. We conduct extensive simulations using the IEEE-33 bus system to verify the accuracy of the proposed approximations and highlight the difference between the attack impacts while considering constant power and the ZIP load model (which is more representative of real-world loads).
MatNexus: A Comprehensive Text Mining and Analysis Suite for Materials Discover
Authors: Lei Zhang, Markus Stricker
Date: 2023-11-07 00:00:00
Tasks: Articles, Retrieval, Word Embeddings

MatNexus is a specialized software for the automated collection, processing, and analysis of text from scientific articles. Through an integrated suite of modules, the MatNexus facilitates the retrieval of scientific articles, processes textual data for insights, generates vector representations suitable for machine learning, and offers visualization capabilities for word embeddings. With the vast volume of scientific publications, MatNexus stands out as an end-to-end tool for researchers aiming to gain insights from scientific literature in material science, making the exploration of materials, such as the electrocatalyst examples we show here, efficient and insightful.
Pulsed interactions unify reaction-diffusion and spatial nonlocal models for biological pattern formation
Authors: Eduardo H. Colombo, Ricardo Martinez-Garcia, Justin M. Calabrese, Cristóbal López, Emilio Hernández-García
Date: 2023-11-13 00:00:00

The emergence of a spatially-organized population distribution depends on the dynamics of the population and mediators of interaction (activators and inhibitors). Two broad classes of models have been used to investigate when and how self-organization is triggered, namely, reaction-diffusion and spatially nonlocal models. Nevertheless, these models implicitly assume smooth propagation scenarios, neglecting that individuals many times interact by exchanging short and abrupt pulses of the mediating substance. A recently proposed framework advances in the direction of properly accounting for these short-scale fluctuations by applying a coarse-graining procedure on the pulse dynamics. In this paper, we generalize the coarse-graining procedure and apply the extended formalism to new scenarios in which mediators influence individuals' reproductive success or their motility. We show that, in the slow- and fast-mediator limits, pulsed interactions recover, respectively, the reaction-diffusion and nonlocal models, providing a mechanistic connection between them. Furthermore, at each limit, the spatial stability condition is qualitatively different, leading to a timescale-induced transition where spatial patterns emerge as mediator dynamics becomes sufficiently fast.
Advancing Parsimonious Deep Learning Weather Prediction using the HEALPix Mesh
Authors: Matthias Karlbauer, Nathaniel Cresswell-Clay, Dale R. Durran, Raul A. Moreno, Thorsten Kurth, Boris Bonev, Noah Brenowitz, Martin V. Butz
Date: 2023-09-11 00:00:00
Tasks: Deep Learning

We present a parsimonious deep learning weather prediction model to forecast seven atmospheric variables with 3-h time resolution for up to one-year lead times on a 110-km global mesh using the Hierarchical Equal Area isoLatitude Pixelization (HEALPix). In comparison to state-of-the-art (SOTA) machine learning (ML) weather forecast models, such as Pangu-Weather and GraphCast, our DLWP-HPX model uses coarser resolution and far fewer prognostic variables. Yet, at one-week lead times, its skill is only about one day behind both SOTA ML forecast models and the SOTA numerical weather prediction model from the European Centre for Medium-Range Weather Forecasts. We report several improvements in model design, including switching from the cubed sphere to the HEALPix mesh, inverting the channel depth of the U-Net, and introducing gated recurrent units (GRU) on each level of the U-Net hierarchy. The consistent east-west orientation of all cells on the HEALPix mesh facilitates the development of location-invariant convolution kernels that successfully propagate weather patterns across the globe without requiring separate kernels for the polar and equatorial faces of the cube sphere. Without any loss of spectral power after the first two days, the model can be unrolled autoregressively for hundreds of steps into the future to generate realistic states of the atmosphere that respect seasonal trends, as showcased in one-year simulations.
Olfactory learning alters navigation strategies and behavioral variability in C. elegans
Authors: Kevin S. Chen, Anuj K. Sharma, Jonathan W. Pillow, Andrew M. Leifer
Date: 2023-11-13 00:00:00
Tasks: Descriptive

Animals adjust their behavioral response to sensory input adaptively depending on past experiences. The flexible brain computation is crucial for survival and is of great interest in neuroscience. The nematode C. elegans modulates its navigation behavior depending on the association of odor butanone with food (appetitive training) or starvation (aversive training), and will then climb up the butanone gradient or ignore it, respectively. However, the exact change in navigation strategy in response to learning is still unknown. Here we study the learned odor navigation in worms by combining precise experimental measurement and a novel descriptive model of navigation. Our model consists of two known navigation strategies in worms: biased random walk and weathervaning. We infer weights on these strategies by applying the model to worm navigation trajectories and the exact odor concentration it experiences. Compared to naive worms, appetitive trained worms up-regulate the biased random walk strategy, and aversive trained worms down-regulate the weathervaning strategy. The statistical model provides prediction with $>90 \%$ accuracy of the past training condition given navigation data, which outperforms the classical chemotaxis metric. We find that the behavioral variability is altered by learning, such that worms are less variable after training compared to naive ones. The model further predicts the learning-dependent response and variability under optogenetic perturbation of the olfactory neuron AWC$^\mathrm{ON}$. Lastly, we investigate neural circuits downstream from AWC$^\mathrm{ON}$ that are differentially recruited for learned odor-guided navigation. Together, we provide a new paradigm to quantify flexible navigation algorithms and pinpoint the underlying neural substrates.
ReactionT5: a large-scale pre-trained model towards application of limited reaction data
Authors: Tatsuya Sagawa, Ryosuke Kojima
Date: 2023-11-12 00:00:00
Tasks: Prediction

Transformer-based deep neural networks have revolutionized the field of molecular-related prediction tasks by treating molecules as symbolic sequences. These models have been successfully applied in various organic chemical applications by pretraining them with extensive compound libraries and subsequently fine-tuning them with smaller in-house datasets for specific tasks. However, many conventional methods primarily focus on single molecules, with limited exploration of pretraining for reactions involving multiple molecules. In this paper, we propose ReactionT5, a novel model that leverages pretraining on the Open Reaction Database (ORD), a publicly available large-scale resource. We further fine-tune this model for yield prediction and product prediction tasks, demonstrating its impressive performance even with limited fine-tuning data compared to traditional models. The pre-trained ReactionT5 model is publicly accessible on the Hugging Face platform.
STRIDE: Structure-guided Generation for Inverse Design of Molecules
Authors: Shehtab Zaman, Denis Akhiyarov, Mauricio Araya-Polo, Kenneth Chiu
Date: 2023-11-06 00:00:00
Tasks: Drug Design, Drug Discovery

Machine learning and especially deep learning has had an increasing impact on molecule and materials design. In particular, given the growing access to an abundance of high-quality small molecule data for generative modeling for drug design, results for drug discovery have been promising. However, for many important classes of materials such as catalysts, antioxidants, and metal-organic frameworks, such large datasets are not available. Such families of molecules with limited samples and structural similarities are especially prevalent for industrial applications. As is well-known, retraining and even fine-tuning are challenging on such small datasets. Novel, practically applicable molecules are most often derivatives of well-known molecules, suggesting approaches to addressing data scarcity. To address this problem, we introduce $\textbf{STRIDE}$, a generative molecule workflow that generates novel molecules with an unconditional generative model guided by known molecules without any retraining. We generate molecules outside of the training data from a highly specialized set of antioxidant molecules. Our generated molecules have on average 21.7% lower synthetic accessibility scores and also reduce ionization potential by 5.9% of generated molecules via guiding.
Gradual Optimization Learning for Conformational Energy Minimization
Authors: Artem Tsypin, Leonid Ugadiarov, Kuzma Khrabrov, Alexander Telepov, Egor Rumiantsev, Alexey Skrynnik, Aleksandr I. Panov, Dmitry Vetrov, Elena Tutubalina, Artur Kadurin
Date: 2023-11-05 00:00:00
Tasks: Drug Discovery

Molecular conformation optimization is crucial to computer-aided drug discovery and materials design. Traditional energy minimization techniques rely on iterative optimization methods that use molecular forces calculated by a physical simulator (oracle) as anti-gradients. However, this is a computationally expensive approach that requires many interactions with a physical simulator. One way to accelerate this procedure is to replace the physical simulator with a neural network. Despite recent progress in neural networks for molecular conformation energy prediction, such models are prone to distribution shift, leading to inaccurate energy minimization. We find that the quality of energy minimization with neural networks can be improved by providing optimization trajectories as additional training data. Still, it takes around $5 \times 10^5$ additional conformations to match the physical simulator's optimization quality. In this work, we present the Gradual Optimization Learning Framework (GOLF) for energy minimization with neural networks that significantly reduces the required additional data. The framework consists of an efficient data-collecting scheme and an external optimizer. The external optimizer utilizes gradients from the energy prediction model to generate optimization trajectories, and the data-collecting scheme selects additional training data to be processed by the physical simulator. Our results demonstrate that the neural network trained with GOLF performs on par with the oracle on a benchmark of diverse drug-like molecules using $50$x less additional data.
Graph Frequency Features of Cancer Gene Co-Expression Networks
Authors: Radwa Adel, Ercan Engin Kuruoglu
Date: 2023-11-12 00:00:00
Tasks: Functional Connectivity

Complex gene interactions play a significant role in cancer progression, driving cellular behaviors that contribute to tumor growth, invasion, and metastasis. Gene co-expression networks model the functional connectivity between genes under various biological conditions. Understanding the system-level evolution of these networks in cancer is critical for elucidating disease mechanisms and informing the development of targeted therapies. While previous studies have primarily focused on structural differences between cancer and normal cell co-expression networks, this study applies graph frequency analysis to cancer transcriptomic signals defined on gene co-expression networks, highlighting the graph spectral characteristics of cancer systems. Using a range of graph frequency filters, we showed that cancer cells display distinctive patterns in the graph frequency content of their gene transcriptomic signals, effectively distinguishing between cancer types and stages. The transformation of the original gene feature space into the graph spectral space captured more intricate cancer properties, as validated by significantly higher F-statistic scores for graph frequency-filtered gene features compared to those in the original space.
Labor Space: A Unifying Representation of the Labor Market via Large Language Models
Authors: Seongwoon Kim, Yong-Yeol Ahn, Jaehyuk Park
Date: 2023-11-09 00:00:00
Tasks: Decision Making, Language Modeling, Language Modelling, Large Language Model

The labor market is a complex ecosystem comprising diverse, interconnected entities, such as industries, occupations, skills, and firms. Due to the lack of a systematic method to map these heterogeneous entities together, each entity has been analyzed in isolation or only through pairwise relationships, inhibiting comprehensive understanding of the whole ecosystem. Here, we introduce $\textit{Labor Space}$, a vector-space embedding of heterogeneous labor market entities, derived through applying a large language model with fine-tuning. Labor Space exposes the complex relational fabric of various labor market constituents, facilitating coherent integrative analysis of industries, occupations, skills, and firms, while retaining type-specific clustering. We demonstrate its unprecedented analytical capacities, including positioning heterogeneous entities on an economic axes, such as `Manufacturing--Healthcare'. Furthermore, by allowing vector arithmetic of these entities, Labor Space enables the exploration of complex inter-unit relations, and subsequently the estimation of the ramifications of economic shocks on individual units and their ripple effect across the labor market. We posit that Labor Space provides policymakers and business leaders with a comprehensive unifying framework for labor market analysis and simulation, fostering more nuanced and effective strategic decision-making.
High Dimensional Binary Choice Model with Unknown Heteroskedasticity or Instrumental Variables
Authors: Fu Ouyang, Thomas Tao Yang
Date: 2023-11-13 00:00:00
Tasks: Density Estimation, Dimensionality Reduction, Variable Selection

This paper proposes a new method for estimating high-dimensional binary choice models. The model we consider is semiparametric, placing no distributional assumptions on the error term, allowing for heteroskedastic errors, and permitting endogenous regressors. Our proposed approaches extend the special regressor estimator originally proposed by Lewbel (2000). This estimator becomes impractical in high-dimensional settings due to the curse of dimensionality associated with high-dimensional conditional density estimation. To overcome this challenge, we introduce an innovative data-driven dimension reduction method for nonparametric kernel estimators, which constitutes the main innovation of this work. The method combines distance covariance-based screening with cross-validation (CV) procedures, rendering the special regressor estimation feasible in high dimensions. Using the new feasible conditional density estimator, we address the variable and moment (instrumental variable) selection problems for these models. We apply penalized least squares (LS) and Generalized Method of Moments (GMM) estimators with a smoothly clipped absolute deviation (SCAD) penalty. A comprehensive analysis of the oracle and asymptotic properties of these estimators is provided. Monte Carlo simulations are employed to demonstrate the effectiveness of our proposed procedures in finite sample scenarios.
Design-based Estimation Theory for Complex Experiments
Authors: Haoge Chang
Date: 2023-11-12 00:00:00

This paper considers the estimation of treatment effects in randomized experiments with complex experimental designs, including cases with interference between units. We develop a design-based estimation theory for arbitrary experimental designs. Our theory facilitates the analysis of many design-estimator pairs that researchers commonly employ in practice and provide procedures to consistently estimate asymptotic variance bounds. We propose new classes of estimators with favorable asymptotic properties from a design-based point of view. In addition, we propose a scalar measure of experimental complexity which can be linked to the design-based variance of the estimators. We demonstrate the performance of our estimators using simulated datasets based on an actual network experiment studying the effect of social networks on insurance adoptions.
Quasi-Bayes in Latent Variable Models
Authors: Sid Kankanala
Date: 2023-11-12 00:00:00

Latent variable models are widely used to account for unobserved determinants of economic behavior. This paper introduces a quasi-Bayes approach to nonparametrically estimate a large class of latent variable models. As an application, we model U.S. individual log earnings from the Panel Study of Income Dynamics (PSID) as the sum of latent permanent and transitory components. Simulations illustrate the favorable performance of quasi-Bayes estimators relative to common alternatives.