Order protection through delayed messaging

Several financial exchanges have recently introduced messaging delays (e.g., a 350 microsecond delay at IEX and NYSE American) intended to protect ordinary investors from high-frequency traders who exploit stale orders. We propose an equilibrium model of this exchange design as a modification of the standard continuous double auction market format. The model predicts that a messaging delay will generally improve price efficiency and lower transactions cost but will increase queuing costs. Some of the predictions are testable in the field or in a laboratory environment

Hydrostatic Expansion and Spin Changes During Type I X-Ray Bursts

We present calculations of the spin-down of a neutron star atmosphere due to hydrostatic expansion during a Type I X-ray burst. We show that (i) Cumming and Bildsten overestimated the spin-down of rigidly-rotating atmospheres by a factor of two, and (ii) general relativity has a small (5-10%) effect on the angular momentum conservation law. We rescale our results to different neutron star masses, rotation rates and equations of state, and present some detailed rotational profiles. Comparing with recent observations of large frequency shifts in MXB 1658-298 and 4U 1916-053, we find that the spin-down expected if the atmosphere rotates rigidly is a factor of two to three less than the observed values. If differential rotation is allowed to persist, we find that the upper layers of the atmosphere spin down by an amount comparable to the observed values; however, there is no compelling reason to expect the observed spin frequency to be that of only the outermost layers. We conclude that hydrostatic expansion and angular momentum conservation alone cannot account for the largest frequency shifts observed during Type I bursts.Comment: Submitted to the Astrophysical Journal (13 pages, including 4 figures

Developing Culture-Adaptive Competency Through Experiences with Expressive Avatars

Modern Warfighters often find themselves in a variety of non-combat roles such as negotiator, peacekeeper, reconstruction, and disaster relief. They are expected to perform these roles within a culture alien to their own. Each individual they encounter brings their own set of values to the interaction that must be understood and reconciled. To navigate the human terrain of these complex interactions, the Warfighter must not only consider the specifics of the target culture, but also identify the stakeholders, recognize the influencing cultural dimensions, and adapt to the situation to achieve the best possible outcome. Vcom3D is using game-based scenarios to develop culturally adaptive competency. The avatars that represent the stakeholders must be able to portray culturally accurate behavior, display complex emotion, and communicate through verbal and non-verbal cues. This paper will discuss the use of emerging game technologies to better simulate human behavior in cross-cultural dilemmas. Nomenclature: culture, adaptive, values, cultural values dimensions, dilemmas, virtual humans, non-verbal communication

Order Protection Through Delayed Messaging

Several financial exchanges (e.g., IEX and NYSE American) recently introduced messaging delays to protect ordinary investors from high-frequency traders who exploit stale orders. To capture the impact of such delays, we propose a simple parametric model of the continuous double auction market format. The model examines the dynamics of midpoint pegged order queues and finds their steady states. It shows how messaging delays can protect pegged orders and improve investor welfare, but typically increase queuing costs. Recently available field data show that the empirical distribution of queued pegged orders is highly leptokurtotic and resembles the discrete Laplace distribution predicted by the model

Unsupervised Segmentation in Real-World Images via Spelke Object Inference

Self-supervised, category-agnostic segmentation of real-world images is a challenging open problem in computer vision. Here, we show how to learn static grouping priors from motion self-supervision by building on the cognitive science concept of a Spelke Object: a set of physical stuff that moves together. We introduce the Excitatory-Inhibitory Segment Extraction Network (EISEN), which learns to extract pairwise affinity graphs for static scenes from motion-based training signals. EISEN then produces segments from affinities using a novel graph propagation and competition network. During training, objects that undergo correlated motion (such as robot arms and the objects they move) are decoupled by a bootstrapping process: EISEN explains away the motion of objects it has already learned to segment. We show that EISEN achieves a substantial improvement in the state of the art for self-supervised image segmentation on challenging synthetic and real-world robotics datasets.Comment: 25 pages, 10 figure