pwc-archive/papers-with-abstracts huggingface dataset:
Error bounds for maxout neural network approximations of model predictive control
Authors: Dieter Teichrib, Moritz Schulze Darup
Date: 2023-04-18 00:00:00
Tasks: Model Predictive Control

Neural network (NN) approximations of model predictive control (MPC) are a versatile approach if the online solution of the underlying optimal control problem (OCP) is too demanding and if an exact computation of the explicit MPC law is intractable. The drawback of such approximations is that they typically do not preserve stability and performance guarantees of the original MPC. However, such guarantees can be recovered if the maximum error with respect to the optimal control law and the Lipschitz constant of that error are known. We show in this work how to compute both values exactly when the control law is approximated by a maxout NN. We build upon related results for ReLU NN approximations and derive mixed-integer (MI) linear constraints that allow a computation of the output and the local gain of a maxout NN by solving an MI feasibility problem. Furthermore, we show theoretically and experimentally that maxout NN exist for which the maximum error is zero.
Sample-and-Hold Safety with Control Barrier Functions
Authors: Gilbert Bahati, Pio Ong, Aaron D. Ames
Date: 2023-04-18 00:00:00

A common assumption on the deployment of safeguarding controllers on the digital platform is that high sampling frequency translates to a small violation of safety. This paper investigates and formalizes this assumption through the lens of Input-to-State Safety. From this perspective, and leveraging control barrier functions (CBFs), we propose an alternative solution for maintaining safety of sample-and-hold controlled systems without any violation to the original safe set. Our approach centers around modulating the sampled control input in order to guarantee a more robust safety condition. We analyze both the time-triggered and the event-triggered sample-and-hold implementations, including the characterization of sampling frequency requirements and trigger conditions. We demonstrate the effectiveness of our approach in the context of adaptive cruise control through simulations.
Intermittent Safety Filters for Event-Triggered Safety Maneuvers with Application to Satellite Orbit Transfers
Authors: Pio Ong, Aaron D. Ames
Date: 2023-04-18 00:00:00

In balancing safety with the nominal control objectives, e.g., stabilization, it is desirable to reduce the time period when safety filters are in effect. Inspired by traditional spacecraft maneuvers, and with the ultimate goal of reducing the duration when safety is of concern, this paper proposes an event-triggered control framework with switching state-based triggers. Our first trigger in the scheme monitors safety constraints encoded by barrier functions, and thereby ensures safety without the need to alter the nominal controller--and when the boundary of the safety constraint is approached, the controller drives the system to the region where control actions are not needed. The second trigger condition determines if the safety constraint has improved enough for the success of the first trigger. We begin by motivating this framework for impulsive control systems, e.g., a satellite orbiting an asteroid. We then expand the approach to more general nonlinear system through the use of safety filtered controllers. Simulation results demonstrating satellite orbital maneuvers illustrate the utility of the proposed event-triggered framework.
Optimal Robust Network Design: Formulations and Algorithms for Maximizing Algebraic Connectivity
Authors: Neelkamal Somisetty, Harsha Nagarajan, Swaroop Darbha
Date: 2023-04-17 00:00:00
Tasks: Autonomous Vehicles, Position

This paper focuses on designing edge-weighted networks, whose robustness is characterized by maximizing algebraic connectivity, or the second smallest eigenvalue of the Laplacian matrix. This problem is motivated by cooperative vehicle localization, where accurately estimating relative position measurements and establishing communication links are essential. We also examine an associated problem where every robot is limited by payload, budget, and communication to pick no more than a specified number of relative position measurements. The basic underlying formulation for these problems is nonlinear and is known to be NP-hard. Our approach formulates this problem as a Mixed Integer Semi-Definite Program (MISDP), later reformulated into a Mixed Integer Linear Program (MILP) for obtaining optimal solutions using cutting plane algorithms. We introduce a novel upper-bounding algorithm based on principal minor characterization of positive semi-definite matrices and discuss a degree-constrained lower bounding formulation inspired by robust network structures. In addition, we propose a maximum cost heuristic with low computational complexity to identify high-quality feasible solutions for instances involving up to one hundred nodes. We show extensive computational results corroborating our proposed methods.
Long time scales, individual differences, and scale invariance in animal behavior
Authors: William Bialek, Joshua W. Shaevitz
Date: 2023-04-19 00:00:00

The explosion of data on animal behavior in more natural contexts highlights the fact that these behaviors exhibit correlations across many time scales. But there are major challenges in analyzing these data: records of behavior in single animals have fewer independent samples than one might expect; in pooling data from multiple animals, individual differences can mimic long-ranged temporal correlations; conversely long-ranged correlations can lead to an over-estimate of individual differences. We suggest an analysis scheme that addresses these problems directly, apply this approach to data on the spontaneous behavior of walking flies, and find evidence for scale invariant correlations over nearly three decades in time, from seconds to one hour. Three different measures of correlation are consistent with a single underlying scaling field of dimension $\Delta = 0.180\pm 0.005$.
Playing it safe: information constrains collective betting strategies
Authors: Philipp Fleig, Vijay Balasubramanian
Date: 2023-04-18 00:00:00
Tasks: Bayesian Inference

Every interaction of a living organism with its environment involves the placement of a bet. Armed with partial knowledge about a stochastic world, the organism must decide its next step or near-term strategy, an act that implicitly or explicitly involves the assumption of a model of the world. Better information about environmental statistics can improve the bet quality, but in practice resources for information gathering are always limited. We argue that theories of optimal inference dictate that ``complex'' models are harder to infer with bounded information and lead to larger prediction errors. Thus, we propose a principle of ``playing it safe'' where, given finite information gathering capacity, biological systems should be biased towards simpler models of the world, and thereby to less risky betting strategies. In the framework of Bayesian inference, we show that there is an optimally safe adaptation strategy determined by the Bayesian prior. We then demonstrate that, in the context of stochastic phenotypic switching by bacteria, implementation of our principle of ``playing it safe'' increases fitness (population growth rate) of the bacterial collective. We suggest that the principle applies broadly to problems of adaptation, learning and evolution, and illuminates the types of environments in which organisms are able to thrive.
Pulse shape and voltage-dependent synchronization in spiking neuron networks
Authors: Bastian Pietras
Date: 2023-04-19 00:00:00

Pulse-coupled spiking neural networks are a powerful tool to gain mechanistic insights into how neurons self-organize to produce coherent collective behavior. These networks use simple spiking neuron models, such as the $\theta$-neuron or the quadratic integrate-and-fire (QIF) neuron, that replicate the essential features of real neural dynamics. Interactions between neurons are modeled with infinitely narrow pulses, or spikes, rather than the more complex dynamics of real synapses. To make these networks biologically more plausible, it has been proposed that they must also account for the finite width of the pulses, which can have a significant impact on the network dynamics. However, the derivation and interpretation of these pulses is contradictory and the impact of the pulse shape on the network dynamics is largely unexplored. Here, I take a comprehensive approach to pulse-coupling in networks of QIF and $\theta$-neurons. I argue that narrow pulses activate voltage-dependent synaptic conductances and show how to implement them in QIF neurons such that their effect can last through the phase after the spike. Using an exact low-dimensional description for networks of globally coupled spiking neurons, I prove for instantaneous interactions that collective oscillations emerge due to an effective coupling through the mean voltage. I analyze the impact of the pulse shape by means of a family of smooth pulse functions with arbitrary finite width and symmetric or asymmetric shapes. For symmetric pulses, the resulting voltage-coupling is not very effective in synchronizing neurons, but pulses that are slightly skewed to the phase after the spike readily generate collective oscillations. The results unveil a voltage-dependent spike synchronization mechanism in neural networks, which is facilitated by pulses of finite width and complementary to traditional synaptic transmission.
Climate uncertainty impacts on optimal mitigation pathways and social cost of carbon
Authors: Christopher J. Smith, Alaa Al Khourdajie, Pu Yang, Doris Folini
Date: 2023-04-18 00:00:00

Emissions pathways used in climate policy analysis are often derived from integrated assessment models. However, such emissions pathways do not typically include climate feedbacks on socioeconomic systems and by extension do not consider climate uncertainty in their construction. We use a well-known cost-benefit integrated assessment model, the Dynamic Integrated Climate-Economy (DICE) model, with its climate component replaced by the Finite-amplitude Impulse Response (FaIR) model (v2.1). The climate uncertainty in FaIR is sampled with an ensemble that is consistent with historically observed climate and Intergovernmental Panel on Climate Change (IPCC) assessed ranges of key climate variables such as equilibrium climate sensitivity. Three scenarios are produced: a pathway similar to the "optimal welfare" scenario of DICE that has similar warming outcomes to current policies, and pathways that limit warming to "well-below" 2C and 1.5C with low overshoot, in line with Paris Agreement long-term temperature goals. Climate uncertainty alone is responsible for a factor of five variation (5-95% range) in the social cost of carbon in the 1.5C scenario. CO2 emissions trajectories resulting from the optimal level of emissions abatement in all pathways are also sensitive to climate uncertainty, with 2050 emissions ranging from -12 to +14 GtCO2/yr in the 1.5C scenario. Equilibrium climate sensitivity and the strength of present-day aerosol effective radiative forcing are strong determinants of social cost of carbon and mid-century CO2 emissions. This shows that narrowing climate uncertainty leads to more refined estimates for the social cost of carbon and provides more certainty about the optimal rate of emissions abatement. Including climate and climate uncertainty in integrated assessment model derived emissions scenarios would address a key missing feedback in scenario construction.
Gene activity fully predicts transcriptional bursting dynamics
Authors: Po-Ta Chen, Michal Levo, Benjamin Zoller, Thomas Gregor
Date: 2023-04-18 00:00:00

Transcription commonly occurs in bursts, with alternating productive (ON) and quiescent (OFF) periods, governing mRNA production rates. Yet, how transcription is regulated through bursting dynamics remains unresolved. Here, we conduct real-time measurements of endogenous transcriptional bursting with single-mRNA sensitivity. Leveraging the diverse transcriptional activities in early fly embryos, we uncover stringent relationships between bursting parameters. Specifically, we find that the durations of ON and OFF periods are linked. Regardless of the developmental stage or body-axis position, gene activity levels predict individual alleles' average ON and OFF periods. Lowly transcribing alleles predominantly modulate OFF periods (burst frequency), while highly transcribing alleles primarily tune ON periods (burst size). These relationships persist even under perturbations of cis-regulatory elements or trans-factors and account for bursting dynamics measured in other species. Our results suggest a novel mechanistic constraint governing bursting dynamics rather than a modular control of distinct parameters by distinct regulatory processes.
Chirality-Induced Magnetization of Magnetite by an RNA Precursor
Authors: S. Furkan Ozturk, Deb Kumar Bhowmick, Yael Kapon, Yutao Sang, Anil Kumar, Yossi Paltiel, Ron Naaman, Dimitar D. Sasselov
Date: 2023-04-13 00:00:00

Life is homochiral and homochirality is a fundamental feature of living systems on Earth. While the exact mechanism that led to homochirality is still not fully understood, any realistic scenario on the origins of life needs to address the emergence of homochirality. In order to impose and maintain chirality in a prebiotic network, an environmental factor functioning as a chiral agent is demanded. Magnetized surfaces are prebiotically plausible chiral agents, shown to be effective in enantioseparation of ribose-aminooxazoline (RAO), a ribonucleic acid (RNA) precursor, due to the chiral-induced spin selectivity (CISS) effect. As such, mechanisms for breaking the magnetic symmetry of magnetic minerals are of the utmost importance. Here we report the avalanche magnetization of magnetite $(Fe_{3}O_{4})$ by the crystallization of enantiopure RAO. The observed breaking of the magnetic symmetry is induced by the chiral molecules due to the CISS effect and spreads out across the magnetic surface like an avalanche, providing a way to uniformly magnetize a magnetic surface without fully covering it. Considered together with our previous results on enantioseparation by crystallization on a magnetic surface, chirality-induced avalanche magnetization paves the way for a cooperative feedback between chiral molecules and magnetic surfaces. With this feedback, a weak natural bias in the net magnetization can be amplified and spin-selective processes can be accommodated on magnetic minerals on a persistent basis.
The Impact of Industrial Zone:Evidence from China's National High-tech Zone Policy
Authors: Li Han
Date: 2023-04-19 00:00:00

Based on the statistical yearbook data and related patent data of 287 cities in China from 2000 to 2020, this study regards the policy of establishing the national high-tech zones as a quasi-natural experiment. Using this experiment, this study firstly estimated the treatment effect of the policy and checked the robustness of the estimation. Then the study examined the heterogeneity in different geographic demarcation of China and in different city level of China. After that, this study explored the possible influence mechanism of the policy. It shows that the possible mechanism of the policy is financial support, industrial agglomeration of secondary industry and the spillovers. In the end, this study examined the spillovers deeply and showed the distribution of spillover effect.
A hybrid model for day-ahead electricity price forecasting: Combining fundamental and stochastic modelling
Authors: Mira Watermeyer, Thomas Möbius, Oliver Grothe, Felix Müsgens
Date: 2023-04-18 00:00:00
Tasks: Decision Making, Management, Scheduling

The accurate prediction of short-term electricity prices is vital for effective trading strategies, power plant scheduling, profit maximisation and efficient system operation. However, uncertainties in supply and demand make such predictions challenging. We propose a hybrid model that combines a techno-economic energy system model with stochastic models to address this challenge. The techno-economic model in our hybrid approach provides a deep understanding of the market. It captures the underlying factors and their impacts on electricity prices, which is impossible with statistical models alone. The statistical models incorporate non-techno-economic aspects, such as the expectations and speculative behaviour of market participants, through the interpretation of prices. The hybrid model generates both conventional point predictions and probabilistic forecasts, providing a comprehensive understanding of the market landscape. Probabilistic forecasts are particularly valuable because they account for market uncertainty, facilitating informed decision-making and risk management. Our model delivers state-of-the-art results, helping market participants to make informed decisions and operate their systems more efficiently.
Doubly Robust Estimators with Weak Overlap
Authors: Yukun Ma, Pedro H. C. Sant'Anna, Yuya Sasaki, Takuya Ura
Date: 2023-04-18 00:00:00

In this paper, we derive a new class of doubly robust estimators for treatment effect estimands that is also robust against weak covariate overlap. Our proposed estimator relies on trimming observations with extreme propensity scores and uses a bias correction device for trimming bias. Our framework accommodates many research designs, such as unconfoundedness, local treatment effects, and difference-in-differences. Simulation exercises illustrate that our proposed tools indeed have attractive finite sample properties, which are aligned with our theoretical asymptotic results.
Risks and opportunities in arbitrage and market-making in blockchain-based currency markets. Part 1 : Risks
Authors: Vittorio Astarita
Date: 2023-04-17 00:00:00

This study provides a practical introduction to high-frequency trading in blockchain-based currency markets. These types of markets have some specific characteristics that differentiate them from the stock markets, such as a large number of trading exchanges (centralized and decentralized), relative simplicity in moving funds from one exchange to another, and the large number of new currencies that have very little liquidity. This study analyzes the possible risks that specifically characterize this type of trading operation, the potential opportunities, and the algorithms that are mostly used, providing information that can be useful for practitioners who intend to operate in these markets by providing (and risking) liquidity.
Respecting Linear Orders for Supermajority Rules
Authors: Yasunori Okumura
Date: 2023-04-19 00:00:00

We consider linear orders of finite alternatives constructed by aggregating individual preferences. Specifically, we focus on linear orders that respect modified collective preference relations derived from supermajority rules, where modifications are introduced through two procedures if cycles occur. One procedure utilizes the transitive closure, while the other employs the Suzumura-consistent closure, ensuring the elimination of cycles through consistency adjustments. We derive two sets of linear orders that respect these modified collective preference relations derived from all supermajority rules, and show that these sets are generally nonempty. We show that these sets of linear orders closely relate to those obtained by the ranked pairs method and the Schulze method, thereby providing new insights into these influential methods. Finally, we show that any linear order belonging to either set satisfies two important properties: the extended Condorcet criterion and the Pareto principle.
The Economics of Partisan Gerrymandering
Authors: Anton Kolotilin, Alexander Wolitzky
Date: 2023-04-19 00:00:00

We study the problem of a partisan gerrymanderer who assigns voters to equipopulous districts so as to maximize his party's expected seat share. The designer faces both aggregate uncertainty (how many votes his party will receive) and idiosyncratic, voter-level uncertainty (which voters will vote for his party). We argue that pack-and-pair districting, where weaker districts are ``packed'' with a single type of voter, while stronger districts contain two voter types, is typically optimal for the gerrymanderer. The optimal form of pack-and-pair districting depends on the relative amounts of aggregate and idiosyncratic uncertainty. When idiosyncratic uncertainty dominates, it is optimal to pack opposing voters and pair more favorable voters; this plan resembles traditional ``packing-and-cracking.'' When aggregate uncertainty dominates, it is optimal to pack moderate voters and pair extreme voters; this ``matching slices'' plan has received some attention in the literature. Estimating the model using precinct-level returns from recent US House elections indicates that, in practice, idiosyncratic uncertainty dominates and packing opponents is optimal; moreover, traditional pack-and-crack districting is approximately optimal. We discuss implications for redistricting reform and political polarization. Methodologically, we exploit a formal connection between gerrymandering -- partitioning voters into districts -- and information design -- partitioning states of the world into signals.
With a Grain of Salt: Uncertain Veracity of External News and Firm Disclosures
Authors: Jonathan Libgober, Beatrice Michaeli, Elyashiv Wiedman
Date: 2023-04-18 00:00:00
Tasks: Management

We examine how uncertain veracity of external news influences investor beliefs, market prices and corporate disclosures. Despite assuming independence between the news' veracity and the firm's endowment with private information, we find that favorable news is taken ``with a grain of salt'' in equilibrium -- more precisely, perceived as less likely veracious -- which reinforces investor beliefs that nondisclosing managers are hiding disadvantageous information. Hence more favorable external news could paradoxically lead to lower market valuation. That is, amid management silence, stock prices may be non-monotonic in the positivity of external news. In line with mounting empirical evidence, our analysis implies asymmetric price reactions to news and price declines following firm disclosures. We further predict that external news that is more likely veracious may increase or decrease the probability of disclosure and link these effects to empirically observable characteristics.
Multilingual Query-by-Example Keyword Spotting with Metric Learning and Phoneme-to-Embedding Mapping
Authors: Paul M. Reuter, Christian Rollwage, Bernd T. Meyer
Date: 2023-04-19 00:00:00
Tasks: Keyword Spotting, Metric Learning, Word Embeddings

In this paper, we propose a multilingual query-by-example keyword spotting (KWS) system based on a residual neural network. The model is trained as a classifier on a multilingual keyword dataset extracted from Common Voice sentences and fine-tuned using circle loss. We demonstrate the generalization ability of the model to new languages and report a mean reduction in EER of 59.2 % for previously seen and 47.9 % for unseen languages compared to a competitive baseline. We show that the word embeddings learned by the KWS model can be accurately predicted from the phoneme sequences using a simple LSTM model. Our system achieves a promising accuracy for streaming keyword spotting and keyword search on Common Voice audio using just 5 examples per keyword. Experiments on the Hey-Snips dataset show a good performance with a false negative rate of 5.4 % at only 0.1 false alarms per hour.