pwc-archive/papers-with-abstracts huggingface dataset:
A review on the charging station planning and fleet operation for electric freight vehicles
Authors: Md Rakibul Alam, Zhaomiao Guo
Date: 2023-11-28 00:00:00
Tasks: Scheduling

Freight electrification introduces new opportunities and challenges for planning and operation. Although research on charging infrastructure planning and operation is widely available for general electric vehicles, unique physical and operational characteristics of EFVs coupled with specific patterns of logistics require dedicated research. This paper presents a comprehensive literature review to gain a better understanding of the state-of-the-art research efforts related to planning (charging station siting and sizing) and operation (routing, charge scheduling, platoon scheduling, and fleet sizing) for EFVs. We classified the existing literature based on the research topics, innovations, methodologies, and solution approaches, and future research directions are identified. Different types of methodologies, such as heuristic, simulation, and mathematical programming approaches, were applied in the reviewed literature where mathematical models account for the majority. We further narrated the specific modeling considerations for different logistic patterns and research goals with proper reasoning. To solve the proposed models, different solution approaches, including exact algorithms, metaheuristic algorithms, and software simulation, were evaluated in terms of applicability, advantages, and disadvantages. This paper helps to draw more attention to the planning and operation issues and solutions for freight electrification and facilitates future studies on EFV to ensure a smooth transition to a clean freight system.
Towards Energysheds: A Technical Definition and Cooperative Framework for Future Power System Operations
Authors: Dakota Hamilton, Samuel Chevalier, Amritanshu Pandey, Mads Almassalkhi
Date: 2023-11-27 00:00:00
Tasks: Decision Making

There is growing interest in understanding how interactions between system-wide objectives and local community decision-making will impact the clean energy transition. The concept of energysheds has gained traction in the areas of public policy and social science as a way to study these relationships. However, development of technical definitions of energysheds that permit system analysis are still largely missing. In this work, we propose a mathematical definition for energysheds, and introduce an analytical framework for studying energyshed concepts within the context of future electric power system operations. This framework is used to develop insights into the factors that impact a community's ability to achieve energyshed policy incentives within a larger connected power grid, as well as the tradeoffs associated with different spatial policy requirements. We also propose an optimization-based energyshed policy design problem, and show that it can be solved to global optimality within arbitrary precision by employing concepts from quasi-convex optimization. Finally, we investigate how interconnected energysheds can cooperatively achieve their objectives in bulk power system operations.
Control of the Power Flows of a Stochastic Power System
Authors: Zhen Wang, Kaihua Xi, Aijie Cheng, Hai Xiang Lin, Jan H. van Schuppen
Date: 2023-11-27 00:00:00

How to determine the vector of power supplies of a stochastic power system for the next short horizon, such that the probability is less than a prespecified value that any phase-angle difference of a power line of the power network exits from a safe set? The power system is modelled such that the differential equation of each frequency is affected by a Brownian motion process. A safe set can be selected to be any subset of the interval $(-\pi/2, ~ +\pi/2)$, which is a sufficient condition for not losing synchronization. That the controlled system has an improved performance is shown by numerical results of three academic examples including a particular eight-node academic network, a twelve-node ring network, and a Manhattan-grid network.
Optimal Observer Design Using Reinforcement Learning and Quadratic Neural Networks
Authors: Soroush Asri, Luis Rodrigues
Date: 2023-11-27 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper introduces an innovative approach based on policy iteration (PI), a reinforcement learning (RL) algorithm, to obtain an optimal observer with a quadratic cost function. This observer is designed for systems with a given linearized model and a stabilizing Luenberger observer gain. We utilize two-layer quadratic neural networks (QNN) for policy evaluation and derive a linear correction term using the input and output data. This correction term effectively rectifies inaccuracies introduced by the linearized model employed within the observer design. A unique feature of the proposed methodology is that the QNN is trained through convex optimization. The main advantage is that the QNN's input-output mapping has an analytical expression as a quadratic form, which can then be used to obtain a linear correction term policy. This is in stark contrast to the available techniques in the literature that must train a second neural network to obtain policy improvement. It is proven that the obtained linear correction term is optimal for linear systems, as both the value function and the QNN's input-output mapping are quadratic. The proposed method is applied to a simple pendulum, demonstrating an enhanced correction term policy compared to relying solely on the linearized model. This shows its promise for addressing nonlinear systems.
Compact Modeling and Rapid Simulation of Silicon Photonic Micro-Disk and Ring Modulators
Authors: Vishal Saxena, Md Jubayer Shawon
Date: 2023-11-23 00:00:00

Microdisk or microring modulators (MDMs or MRMs) realize compact electro-optic modulation in active silicon photonics (SiP) foundry platforms. A key advantage of these resonant modulators is that they readily implement dense wavelength division multiplexing (DWDM) in optical interconnects by tuning and locking the MDMs/MRMs to the DWDM wavelength grid. Compact modeling of static and transient dynamics of these modulators is important for co-simulation with CMOS drivers and wavelength stabilization circuits. This work presents the first compact model for microdisk modulators with a novel approach that uses experimental measurements and allows rapid and accurate simulation. This approach employs coupled real-valued differential equations with analytic signals in Verilog-A, leading to a 7X speed up in transient simulation time over the current art while enhancing accuracy. Since the model is generalized, it can also model microring resonators in addition to MDMs. The model also includes thermo-optic tuning for MDM/MRM with an embedded microheater, which is essential for simulations involving resonant wavelength stabilization.
Energy Transformer
Authors: Benjamin Hoover, Yuchen Liang, Bao Pham, Rameswar Panda, Hendrik Strobelt, Duen Horng Chau, Mohammed J. Zaki, Dmitry Krotov
Date: 2023-02-14 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Anomaly Detection, Graph Anomaly Detection, Graph Classification

Our work combines aspects of three promising paradigms in machine learning, namely, attention mechanism, energy-based models, and associative memory. Attention is the power-house driving modern deep learning successes, but it lacks clear theoretical foundations. Energy-based models allow a principled approach to discriminative and generative tasks, but the design of the energy functional is not straightforward. At the same time, Dense Associative Memory models or Modern Hopfield Networks have a well-established theoretical foundation, and allow an intuitive design of the energy function. We propose a novel architecture, called the Energy Transformer (or ET for short), that uses a sequence of attention layers that are purposely designed to minimize a specifically engineered energy function, which is responsible for representing the relationships between the tokens. In this work, we introduce the theoretical foundations of ET, explore its empirical capabilities using the image completion task, and obtain strong quantitative results on the graph anomaly detection and graph classification tasks.
The Quantization Model of Neural Scaling
Authors: Eric J. Michaud, Ziming Liu, Uzay Girit, Max Tegmark
Date: 2023-03-23 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Language Modeling, Language Modelling, model, Quantization

We propose the Quantization Model of neural scaling laws, explaining both the observed power law dropoff of loss with model and data size, and also the sudden emergence of new capabilities with scale. We derive this model from what we call the Quantization Hypothesis, where network knowledge and skills are "quantized" into discrete chunks ($\textbf{quanta}$). We show that when quanta are learned in order of decreasing use frequency, then a power law in use frequencies explains observed power law scaling of loss. We validate this prediction on toy datasets, then study how scaling curves decompose for large language models. Using language model gradients, we automatically decompose model behavior into a diverse set of skills (quanta). We tentatively find that the frequency at which these quanta are used in the training distribution roughly follows a power law corresponding with the empirical scaling exponent for language models, a prediction of our theory.
EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning
Authors: Mohammad Mahdi Rahimi, Hasnain Irshad Bhatti, YoungHyun Park, Humaira Kousar, Jaekyun Moon
Date: 2023-11-13 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Federated Learning

Federated Learning (FL) is a decentralized machine learning paradigm that enables collaborative model training across dispersed nodes without having to force individual nodes to share data. However, its broad adoption is hindered by the high communication costs of transmitting a large number of model parameters. This paper presents EvoFed, a novel approach that integrates Evolutionary Strategies (ES) with FL to address these challenges. EvoFed employs a concept of 'fitness-based information sharing', deviating significantly from the conventional model-based FL. Rather than exchanging the actual updated model parameters, each node transmits a distance-based similarity measure between the locally updated model and each member of the noise-perturbed model population. Each node, as well as the server, generates an identical population set of perturbed models in a completely synchronized fashion using the same random seeds. With properly chosen noise variance and population size, perturbed models can be combined to closely reflect the actual model updated using the local dataset, allowing the transmitted similarity measures (or fitness values) to carry nearly the complete information about the model parameters. As the population size is typically much smaller than the number of model parameters, the savings in communication load is large. The server aggregates these fitness values and is able to update the global model. This global fitness vector is then disseminated back to the nodes, each of which applies the same update to be synchronized to the global model. Our analysis shows that EvoFed converges, and our experimental results validate that at the cost of increased local processing loads, EvoFed achieves performance comparable to FedAvg while reducing overall communication requirements drastically in various practical settings.
Epistemic Limits of Empirical Finance: Causal Reductionism and Self-Reference
Authors: Daniel Polakow, Tim Gebbie, Emlyn Flint
Date: 2023-11-28 00:00:00
Tasks: Causal Inference, Econometrics

The clarion call for causal reduction in the study of capital markets is intensifying. However, in self-referencing and open systems such as capital markets, the idea of unidirectional causation (if applicable) may be limiting at best, and unstable or fallacious at worst. In this work, we critically assess the use of scientific deduction and causal inference within the study of empirical finance and financial econometrics. We then demonstrate the idea of competing causal chains using a toy model adapted from ecological predator/prey relationships. From this, we develop the alternative view that the study of empirical finance, and the risks contained therein, may be better appreciated once we admit that our current arsenal of quantitative finance tools may be limited to ex post causal inference under popular assumptions. Where these assumptions are challenged, for example in a recognizable reflexive context, the prescription of unidirectional causation proves deeply problematic.
Transfer learning for atomistic simulations using GNNs and kernel mean embeddings
Authors: John Falk, Luigi Bonati, Pietro Novelli, Michele Parrinello, Massimiliano Pontil
Date: 2023-06-02 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Transfer Learning

Interatomic potentials learned using machine learning methods have been successfully applied to atomistic simulations. However, accurate models require large training datasets, while generating reference calculations is computationally demanding. To bypass this difficulty, we propose a transfer learning algorithm that leverages the ability of graph neural networks (GNNs) to represent chemical environments together with kernel mean embeddings. We extract a feature map from GNNs pre-trained on the OC20 dataset and use it to learn the potential energy surface from system-specific datasets of catalytic processes. Our method is further enhanced by incorporating into the kernel the chemical species information, resulting in improved performance and interpretability. We test our approach on a series of realistic datasets of increasing complexity, showing excellent generalization and transferability performance, and improving on methods that rely on GNNs or ridge regression alone, as well as similar fine-tuning approaches.
TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials
Authors: Guillem Simeon, Gianni de Fabritiis
Date: 2023-06-10 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Formation Energy

The development of efficient machine learning models for molecular systems representation is becoming crucial in scientific research. We introduce TensorNet, an innovative O(3)-equivariant message-passing neural network architecture that leverages Cartesian tensor representations. By using Cartesian tensor atomic embeddings, feature mixing is simplified through matrix product operations. Furthermore, the cost-effective decomposition of these tensors into rotation group irreducible representations allows for the separate processing of scalars, vectors, and tensors when necessary. Compared to higher-rank spherical tensor models, TensorNet demonstrates state-of-the-art performance with significantly fewer parameters. For small molecule potential energies, this can be achieved even with a single interaction layer. As a result of all these properties, the model's computational cost is substantially decreased. Moreover, the accurate prediction of vector and tensor molecular quantities on top of potential energies and forces is possible. In summary, TensorNet's framework opens up a new space for the design of state-of-the-art equivariant models.
Accelerating Molecular Graph Neural Networks via Knowledge Distillation
Authors: Filip Ekström Kelvinius, Dimitar Georgiev, Artur Petrov Toshev, Johannes Gasteiger
Date: 2023-06-26 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Data Augmentation, Knowledge Distillation, Molecular Property Prediction, Prediction, Property Prediction

Recent advances in graph neural networks (GNNs) have enabled more comprehensive modeling of molecules and molecular systems, thereby enhancing the precision of molecular property prediction and molecular simulations. Nonetheless, as the field has been progressing to bigger and more complex architectures, state-of-the-art GNNs have become largely prohibitive for many large-scale applications. In this paper, we explore the utility of knowledge distillation (KD) for accelerating molecular GNNs. To this end, we devise KD strategies that facilitate the distillation of hidden representations in directional and equivariant GNNs, and evaluate their performance on the regression task of energy and force prediction. We validate our protocols across different teacher-student configurations and datasets, and demonstrate that they can consistently boost the predictive accuracy of student models without any modifications to their architecture. Moreover, we conduct comprehensive optimization of various components of our framework, and investigate the potential of data augmentation to further enhance performance. All in all, we manage to close the gap in predictive accuracy between teacher and student models by as much as 96.7% and 62.5% for energy and force prediction respectively, while fully preserving the inference throughput of the more lightweight models.
Inference for Low-rank Models without Estimating the Rank
Authors: Jungjun Choi, Hyukjun Kwon, Yuan Liao
Date: 2023-11-28 00:00:00
Tasks: valid

This paper studies the inference about linear functionals of high-dimensional low-rank matrices. While most existing inference methods would require consistent estimation of the true rank, our procedure is robust to rank misspecification, making it a promising approach in applications where rank estimation can be unreliable. We estimate the low-rank spaces using pre-specified weighting matrices, known as diversified projections. A novel statistical insight is that, unlike the usual statistical wisdom that overfitting mainly introduces additional variances, the over-estimated low-rank space also gives rise to a non-negligible bias due to an implicit ridge-type regularization. We develop a new inference procedure and show that the central limit theorem holds as long as the pre-specified rank is no smaller than the true rank. In one of our applications, we study multiple testing with incomplete data in the presence of confounding factors and show that our method remains valid as long as the number of controlled confounding factors is at least as large as the true number, even when no confounding factors are present.
Using Multiple Outcomes to Improve the Synthetic Control Method
Authors: Liyang Sun, Eli Ben-Michael, Avi Feller
Date: 2023-11-27 00:00:00

When there are multiple outcome series of interest, Synthetic Control analyses typically proceed by estimating separate weights for each outcome. In this paper, we instead propose estimating a common set of weights across outcomes, by balancing either a vector of all outcomes or an index or average of them. Under a low-rank factor model, we show that these approaches lead to lower bias bounds than separate weights, and that averaging leads to further gains when the number of outcomes grows. We illustrate this via a re-analysis of the impact of the Flint water crisis on educational outcomes.
Climate, Crops, and Postharvest Conflict
Authors: David Ubilava
Date: 2023-11-27 00:00:00

I present new evidence of the effects of climate shocks on conflict. Focusing on political violence in Africa, I find that El Ni\~no Southern Oscillation (ENSO) shocks during the crop-growing season affect harvest-related conflict in croplands exposed to this climate phenomenon. Specifically, a 1{\deg}C warming of sea surface temperature in the tropical Pacific Ocean, a proxy for a moderate-strength El Ni\~no event, reduces political violence in exposed locations with crop agriculture, relative to other areas, by approximately three percent, during the early postharvest season. This effect attenuates toward zero as the crop year progresses. This effect can be five times as large after a strong El Ni\~no event, such as that of 1997 or 2015, in highly exposed croplands, such as parts of Southern Africa and the Sahel. Conversely, a La Ni\~na event, which is a counterpart of an El Ni\~no event, has the opposite effect and thus increases conflict in the exposed croplands during the early postharvest season. Because ENSO events can be predicted at least several months in advance, these findings can contribute to creating a platform for early warnings of changes in political violence in predominantly agrarian societies.
An efficiency analysis of Spanish airports
Authors: Adrian Nerja
Date: 2023-11-08 00:00:00
Tasks: Benchmarking

Privatization and commercialization of airports in recent years are drawing a different picture in the aeronautical industry. Airport benchmarking shows the accommodation and performance of airports in the evolution of the market and the new requirements that they have to face. AENA manages a wide and heterogeneous network of airports. There are 46 airports divided into three categories and with particularities due to their geographical location or the competitive environment where they are located. This paper analyzes the technical efficiency and its determinants of the 39 commercial airports of the AENA network between the years 2011-2014. To do this, two benchmarking techniques, SFA and DEA, are used, with a two-stage analysis. The average efficiency of the network is between 75-79\%. The results with the two techniques are similar with a correlation of 0.67. With regard to the commercial part of the network, AENA has a high margin for improvement because it is below the world and European average. AENA must focus on the development of the commercial area and the introduction of competition within the network to improve the technical efficiency of regional airports mainly.
Study of speaker localization under dynamic and reverberant environments
Authors: Daniel A. Mitchell, Boaz Rafaely
Date: 2023-11-28 00:00:00
Tasks: Audio Signal Processing

Speaker localization in a reverberant environment is a fundamental problem in audio signal processing. Many solutions have been developed to tackle this problem. However, previous algorithms typically assume a stationary environment in which both the microphone array and the sound sources are not moving. With the emergence of wearable microphone arrays, acoustic scenes have become dynamic with moving sources and arrays. This calls for algorithms that perform well in dynamic environments. In this article, we study the performance of a speaker localization algorithm in such an environment. The study is based on the recently published EasyCom speech dataset recorded in reverberant and noisy environments using a wearable array on glasses. Although the localization algorithm performs well in static environments, its performance degraded substantially when used on the EasyCom dataset. The paper presents performance analysis and proposes methods for improvement.
Joint Deep Image Restoration and Unsupervised Quality Assessment
Authors: Hakan Emre Gedik, Abhinau K. Venkataramanan, Alan C. Bovik
Date: 2023-11-27 00:00:00
Tasks: Image Quality Assessment, Image Restoration

Deep learning techniques have revolutionized the fields of image restoration and image quality assessment in recent years. While image restoration methods typically utilize synthetically distorted training data for training, deep quality assessment models often require expensive labeled subjective data. However, recent studies have shown that activations of deep neural networks trained for visual modeling tasks can also be used for perceptual quality assessment of images. Following this intuition, we propose a novel attention-based convolutional neural network capable of simultaneously performing both image restoration and quality assessment. We achieve this by training a JPEG deblocking network augmented with "quality attention" maps and demonstrating state-of-the-art deblocking accuracy, achieving a high correlation of predicted quality with human opinion scores.