Stock Options and Chief Executive Compensation

Although stock options are commonly observed in chief executive officer (CEO) com- pensation contracts, there is theoretical controversy about whether stock options are part of the optimal contract. Using a sample of Fortune 500 companies, we solve an agency model calibrated to the company-specifc data and we find that stock options are almost always part of the optimal contract. This result is robust to alternative assumptions about the level of CEO risk-aversion and the disutility associated with their effort. In a supplementary analysis, we solve for the optimal contract when there are no restrictions on the contract space. We find that the optimal contract (which is characterized as a state-contingent payoff to the CEO) typically has option-like features over the most probable range of outcomes.Stock Options, Incentives, Agency Model

Timing of pair production in time-dependent force fields

We examine the creation and annihilation dynamics for electron-positron pairs in a time-dependent but subcritical electric force using a simplified model system. Numerical and semianalytical solutions to computational quantum field theory show that despite the continuity of the quantum field operator in time, the actual number of created particles can change in a discontinuous way if the field changes abruptly. The number of permanently created particles after the pulse, however, increases continuously with the duration of the electric field pulse, suggesting a transition from an exclusive annihilation to a creation regime

How Potent are Evasion Attacks for Poisoning Federated Learning-Based Signal Classifiers?

There has been recent interest in leveraging federated learning (FL) for radio signal classification tasks. In FL, model parameters are periodically communicated from participating devices, training on their own local datasets, to a central server which aggregates them into a global model. While FL has privacy/security advantages due to raw data not leaving the devices, it is still susceptible to several adversarial attacks. In this work, we reveal the susceptibility of FL-based signal classifiers to model poisoning attacks, which compromise the training process despite not observing data transmissions. In this capacity, we develop an attack framework in which compromised FL devices perturb their local datasets using adversarial evasion attacks. As a result, the training process of the global model significantly degrades on in-distribution signals (i.e., signals received over channels with identical distributions at each edge device). We compare our work to previously proposed FL attacks and reveal that as few as one adversarial device operating with a low-powered perturbation under our attack framework can induce the potent model poisoning attack to the global classifier. Moreover, we find that more devices partaking in adversarial poisoning will proportionally degrade the classification performance.Comment: 6 pages, Accepted to IEEE ICC 202

Higher accuracy output feedback sliding mode control of sampled-data systems

(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.This is the author version of a work accepted for publication in IEEE Transactions on Automatic Control. The definitive version is available via the publisher at 10.1109/TAC.2015.2505303The problem of output feedback sliding mode control for sampled-data systems in the presence of external disturbances is considered. The proposed output feedback control strategy helps obtain a quasi sliding mode with an O(T3) boundary layer, where T is the sampling period. This outperforms the O(T2) result induced by the one-step delayed disturbance approximation method. The proposed scheme is applicable to linear systems which are relative degree one and minimum phase. An example is given to illustrate the efficacy of the new method

Feedback first: the surprisingly weak effects of magnetic fields, viscosity, conduction, and metal diffusion on galaxy formation

Using high-resolution simulations with explicit treatment of stellar feedback physics based on the FIRE (Feedback in Realistic Environments) project, we study how galaxy formation and the interstellar medium (ISM) are affected by magnetic fields, anisotropic Spitzer-Braginskii conduction and viscosity, and sub-grid metal diffusion from unresolved turbulence. We consider controlled simulations of isolated (non-cosmological) galaxies but also a limited set of cosmological "zoom-in" simulations. Although simulations have shown significant effects from these physics with weak or absent stellar feedback, the effects are much weaker than those of stellar feedback when the latter is modeled explicitly. The additional physics have no systematic effect on galactic star formation rates (SFRs) . In contrast, removing stellar feedback leads to SFRs being over-predicted by factors of $\sim 10 -100$. Without feedback, neither galactic winds nor volume filling hot-phase gas exist, and discs tend to runaway collapse to ultra-thin scale-heights with unphysically dense clumps congregating at the galactic center. With stellar feedback, a multi-phase, turbulent medium with galactic fountains and winds is established. At currently achievable resolutions and for the investigated halo mass range $10^{10}-10^{13} M_{\odot}$, the additional physics investigated here (MHD, conduction, viscosity, metal diffusion) have only weak ($\sim10\%$-level) effects on regulating SFR and altering the balance of phases, outflows, or the energy in ISM turbulence, consistent with simple equipartition arguments. We conclude that galactic star formation and the ISM are primarily governed by a combination of turbulence, gravitational instabilities, and feedback. We add the caveat that AGN feedback is not included in the present work

The Rad4TopBP1 ATR-Activation domain functions in G1/S phase in a chromatin-dependent manner

DNA damage checkpoint activation can be subdivided in two steps: initial activation and signal amplification. The events distinguishing these two phases and their genetic determinants remain obscure. TopBP1, a mediator protein containing multiple BRCT domains, binds to and activates the ATR/ATRIP complex through its ATR-Activation Domain (AAD). We show that Schizosaccharomyces pombe Rad4TopBP1 AAD–defective strains are DNA damage sensitive during G1/S-phase, but not during G2. Using lacO-LacI tethering, we developed a DNA damage–independent assay for checkpoint activation that is Rad4TopBP1 AAD–dependent. In this assay, checkpoint activation requires histone H2A phosphorylation, the interaction between TopBP1 and the 9-1-1 complex, and is mediated by the phospho-binding activity of Crb253BP1. Consistent with a model where Rad4TopBP1 AAD–dependent checkpoint activation is ssDNA/RPA–independent and functions to amplify otherwise weak checkpoint signals, we demonstrate that the Rad4TopBP1 AAD is important for Chk1 phosphorylation when resection is limited in G2 by ablation of the resecting nuclease, Exo1. We also show that the Rad4TopBP1 AAD acts additively with a Rad9 AAD in G1/S phase but not G2. We propose that AAD–dependent Rad3ATR checkpoint amplification is particularly important when DNA resection is limiting. In S. pombe, this manifests in G1/S phase and relies on protein– chromatin interactions