Simulating the Synchronizing Behavior of High-Frequency Trading in Multiple Markets

Nearly one-half of all trades in financial markets are executed by high-speed, autonomous computer programs -- a type of trading often called high-frequency trading (HFT). Although evidence suggests that HFT increases the efficiency of markets, it is unclear how or why it produces this outcome. Here we create a simple model to study the impact of HFT on investors who trade similar securities in different markets. We show that HFT can improve liquidity by allowing more transactions to take place without adversely affecting pricing or volatility. In the model, HFT synchronizes the prices of the securities, which allows buyers and sellers to find one another across markets and increases the likelihood of competitive orders being filled.Comment: 7 pages, 4 figures, 1 tabl

Imagination and Phenomenal Experience

In this paper I argue that the zombie conceivability argument, as developed by David Chalmers, is unsound. My primary contribution in the paper is a critique of the claim that zombies are positively conceivable. I argue that phenomenal experience (that which zombies purportedly lack) is not something we can imagine; it is only something we can have. We can only imagine the contents of experience, and in doing so we have a new phenomenal experience of the imagined contents. Without being able to imagine phenomenal experience we have no way to determine whether a creature we imagine has or lacks phenomenal experience. Therefore, we have no justification for claiming that zombies are positively conceivable. Chalmers also argues, however, that the negative conceivability of zombies is sufficient for his argument. In response to this claim, I defer to an argument given by Keith Frankish that invokes the notion of anti-zombies (creatures that are physically identical to us, with no non-physical properties, and yet have phenomenal experience). Frankish argues that a parallel argument can be given that goes from the conceivability of an anti-zombie, to the possibility of an anti-zombie, to the truth of materialism. I argue that, without the positive conceivability of zombies, there is no reason to think that zombies (rather than anti-zombies) are negatively conceivable

From Form to Function: Detecting the Affordance of Tool Parts using Geometric Features and Material Cues

With recent advances in robotics, general purpose robots like Baxter are quickly becoming a reality. As robots begin to collaborate with humans in everyday workspaces, they will need to understand the functions of objects and their parts. To cut an apple or hammer a nail, robots need to not just know a tool’s name, but they must find its parts and identify their potential functions, or affordances. As Gibson remarked, “If you know what can be done with a[n] object, what it can be used for, you can call it whatever you please.” We hypothesize that the geometry of a part is closely related to its affordance, since its geometric properties govern the possible physical interactions with the environment. In the first part of this thesis, we investigate how the affordances of tool parts can be predicted using geometric features from RGB-D sensors like Kinect. We develop several approaches to learn affordance from geometric features: using superpixel based hierarchical sparse coding, structured random forests, and convolutional neural networks. To evaluate the proposed methods, we construct a large RGB-D dataset where parts are labeled with multiple affordances. Experiments over sequences containing clutter, occlusions, and viewpoint changes show that the approaches provide precise predictions that can be used in robotics applications. In addition to geometry, the material properties of a part also determine its potential functions. In the second part of this thesis, we investigate how material cues can be integrated into a deep learning framework for affordance prediction. We propose a modular approach for combining high-level material information, or other mid-level cues, in order to improve affordance predictions. We present experiments which demonstrate the efficacy of our approach on an expanded RGB-D dataset, which includes data from non-tool objects and multiple depth sensors. The work presented in this thesis lays a foundation for the development of robots which can predict the potential functions of tool parts, and provides a basis for higher level reasoning about affordance

Securing the Internet of Healthcare

Cybersecurity, which includes the security of information technology (IT), is critical to ensuring that society trusts, and therefore can benefit from, modern technology. Problematically, though, rarely a day goes by without a news story related to how critical data has been exposed, exfiltrated, or otherwise inappropriately used or accessed as a result of supply chain vulnerabilities. From the Russian government’s campaign to influence the 2016 U.S. presidential election to the September 2017 Equifax breach of more than 140 million Americans’ credit reports, cyber risk has become a topic of conversation in boardrooms and the White House, on Wall Street and main street. But these discussions often miss the problems replete in the expansive supply chains on which many of these products and services we depend on are built; this is particularly true in the medical device context. The problem recently made national news with the voluntary recall of more than 400,000 pacemakers that were found to be vulnerable to hackers, necessitating a firmware update. This Article explores the myriad vulnerabilities in the supply chain for medical devices, investigates existing FDA cybersecurity and privacy regulations to identify any potential governance gaps, and suggests a path forward to boost cybersecurity due diligence for manufacturers by making use of new approaches and technologies, including blockchain

IC3: Image Captioning by Committee Consensus

If you ask a human to describe an image, they might do so in a thousand different ways. Traditionally, image captioning models are trained to generate a single "best" (most like a reference) image caption. Unfortunately, doing so encourages captions that are "informationally impoverished," and focus on only a subset of the possible details, while ignoring other potentially useful information in the scene. In this work, we introduce a simple, yet novel, method: "Image Captioning by Committee Consensus" (IC3), designed to generate a single caption that captures high-level details from several annotator viewpoints. Humans rate captions produced by IC3 at least as helpful as baseline SOTA models more than two thirds of the time, and IC3 can improve the performance of SOTA automated recall systems by up to 84%, outperforming single human-generated reference captions, and indicating significant improvements over SOTA approaches for visual description. Code is available at https://davidmchan.github.io/caption-by-committee/Comment: To Appear at EMNLP 202

Securing the Internet of Healthcare

Cybersecurity, including the security of information technology (IT), is a critical requirement in ensuring society trusts, and therefore can benefit from, modern technology. Problematically, though, rarely a day goes by without a news story related to how critical data has been exposed, exfiltrated, or otherwise inappropriately used or accessed as a result of supply chain vulnerabilities. From the Russian government\u27s campaign to influence the 2016 U.S. presidential election to the September 2017 Equifax breach of more than 140-million Americans\u27 credit reports, mitigating cyber risk has become a topic of conversation in boardrooms and the White House, on Wall Street and Main Street. But oftentimes these discussions miss the problems replete in the often-expansive supply chains on which many of these products and services we depend on are built; this is particularly true in the medical device context. The problem recently made national news with the FDA-mandated recall of more than 400,000 pacemakers that were found to be vulnerable to hackers necessitating a firmware update. This Article explores the myriad vulnerabilities in the supply chain for medical devices, investigates existing FDA cybersecurity and privacy regulations to identify any potential governance gaps, and suggests a path forward to boost cybersecurity due diligence for manufacturers by making use of new approaches and technologies, including blockchain

Late Cretaceous ammonoids show that drivers of diversification are regionally heterogeneous

Palaeontologists have long sought to explain the diversification of individual clades to whole biotas at global scales. Advances in our understanding of the spatial distribution of the fossil record through geological time, however, has demonstrated that global trends in biodiversity were a mosaic of regionally heterogeneous diversification processes. Drivers of diversification must presumably have also displayed regional variation to produce the spatial disparities observed in past taxonomic richness. Here, we analyse the fossil record of ammonoids, pelagic shelled cephalopods, through the Late Cretaceous, characterised by some palaeontologists as an interval of biotic decline prior to their total extinction at the Cretaceous-Paleogene boundary. We regionally subdivide this record to eliminate the impacts of spatial sampling biases and infer regional origination and extinction rates corrected for temporal sampling biases using Bayesian methods. We then model these rates using biotic and abiotic drivers commonly inferred to influence diversification. Ammonoid diversification dynamics and responses to this common set of diversity drivers were regionally heterogeneous, do not support ecological decline, and demonstrate that their global diversification signal is influenced by spatial disparities in sampling effort. These results call into question the feasibility of seeking drivers of diversity at global scales in the fossil record

Predicting tree distributions in an East African biodiversity hotspot : model selection, data bias and envelope uncertainty

The Eastern Arc Mountains (EAMs) of Tanzania and Kenya support some of the most ancient tropical rainforest on Earth. The forests are a global priority for biodiversity conservation and provide vital resources to the Tanzanian population. Here, we make a first attempt to predict the spatial distribution of 40 EAM tree species, using generalised additive models, plot data and environmental predictor maps at sub 1 km resolution. The results of three modelling experiments are presented, investigating predictions obtained by (1) two different procedures for the stepwise selection of predictors, (2) down-weighting absence data, and (3) incorporating an autocovariate term to describe fine-scale spatial aggregation. In response to recent concerns regarding the extrapolation of model predictions beyond the restricted environmental range of training data, we also demonstrate a novel graphical tool for quantifying envelope uncertainty in restricted range niche-based models (envelope uncertainty maps). We find that even for species with very few documented occurrences useful estimates of distribution can be achieved. Initiating selection with a null model is found to be useful for explanatory purposes, while beginning with a full predictor set can over-fit the data. We show that a simple multimodel average of these two best-model predictions yields a superior compromise between generality and precision (parsimony). Down-weighting absences shifts the balance of errors in favour of higher sensitivity, reducing the number of serious mistakes (i.e., falsely predicted absences); however, response functions are more complex, exacerbating uncertainty in larger models. Spatial autocovariates help describe fine-scale patterns of occurrence and significantly improve explained deviance, though if important environmental constraints are omitted then model stability and explanatory power can be compromised. We conclude that the best modelling practice is contingent both on the intentions of the analyst (explanation or prediction) and on the quality of distribution data; generalised additive models have potential to provide valuable information for conservation in the EAMs, but methods must be carefully considered, particularly if occurrence data are scarce. Full results and details of all species models are supplied in an online Appendix. (C) 2008 Elsevier B.V. All rights reserved

Late Cretaceous ammonoids show that drivers of diversification are regionally heterogeneous

Palaeontologists have long sought to explain the diversification of individual clades to whole biotas at global scales. Advances in our understanding of the spatial distribution of the fossil record through geological time, however, has demonstrated that global trends in biodiversity were a mosaic of regionally heterogeneous diversification processes. Drivers of diversification must presumably have also displayed regional variation to produce the spatial disparities observed in past taxonomic richness. Here, we analyse the fossil record of ammonoids, pelagic shelled cephalopods, through the Late Cretaceous, characterised by some palaeontologists as an interval of biotic decline prior to their total extinction at the Cretaceous-Paleogene boundary. We regionally subdivide this record to eliminate the impacts of spatial sampling biases and infer regional origination and extinction rates corrected for temporal sampling biases using Bayesian methods. We then model these rates using biotic and abiotic drivers commonly inferred to influence diversification. Ammonoid diversification dynamics and responses to this common set of diversity drivers were regionally heterogeneous, do not support ecological decline, and demonstrate that their global diversification signal is influenced by spatial disparities in sampling effort. These results call into question the feasibility of seeking drivers of diversity at global scales in the fossil record