500 research outputs found
Public Information and the Persistence of Bond Market Volatility
We examine the reaction of daily bond prices to the release of government macroeconomic news. These news releases are of interest because they are released on periodic, preannounced dates and because they cause substantial bond market volatility. The news component of volatility is not positively autocorrelated on these dates, since the news is released at a specific moment in time. We find that (1) expected returns on the short end of the bond market are significantly higher on these announcement dates, and (2) the persistence pattern of daily volatility is quite different around these days.
Extending Transfer Entropy Improves Identification of Effective Connectivity in a Spiking Cortical Network Model
Transfer entropy (TE) is an information-theoretic measure which has received recent attention in neuroscience for its potential to identify effective connectivity between neurons. Calculating TE for large ensembles of spiking neurons is computationally intensive, and has caused most investigators to probe neural interactions at only a single time delay and at a message length of only a single time bin. This is problematic, as synaptic delays between cortical neurons, for example, range from one to tens of milliseconds. In addition, neurons produce bursts of spikes spanning multiple time bins. To address these issues, here we introduce a free software package that allows TE to be measured at multiple delays and message lengths. To assess performance, we applied these extensions of TE to a spiking cortical network model (Izhikevich, 2006) with known connectivity and a range of synaptic delays. For comparison, we also investigated single-delay TE, at a message length of one bin (D1TE), and cross-correlation (CC) methods. We found that D1TE could identify 36% of true connections when evaluated at a false positive rate of 1%. For extended versions of TE, this dramatically improved to 73% of true connections. In addition, the connections correctly identified by extended versions of TE accounted for 85% of the total synaptic weight in the network. Cross correlation methods generally performed more poorly than extended TE, but were useful when data length was short. A computational performance analysis demonstrated that the algorithm for extended TE, when used on currently available desktop computers, could extract effective connectivity from 1 hr recordings containing 200 neurons in ∼5 min. We conclude that extending TE to multiple delays and message lengths improves its ability to assess effective connectivity between spiking neurons. These extensions to TE soon could become practical tools for experimentalists who record hundreds of spiking neurons
Homotopy-initial algebras in type theory
We investigate inductive types in type theory, using the insights provided by homotopy type theory and univalent foundations of mathematics. We do so by introducing the new notion of a homotopy-initial algebra. This notion is defined by a purely type-theoretic contractibility condition which replaces the standard, category-theoretic universal property involving the existence and uniqueness of appropriate morphisms. Our main result characterises the types that are equivalent to W-types as homotopy-initial algebras
A general construction of internal sheaves in algebraic set theory
We present a solution to the problem of defining a counterpart in Algebraic Set Theory of the construction of internal sheaves in Topos Theory. Our approach is general in that we consider sheaves as determined by Lawvere-Tierney coverages, rather than by Grothen-dieck coverages, and assume only a weakening of the axioms for small maps originally introduced by Joyal and Moerdijk, thus subsuming the existing topos-theoretic results
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Scalable High Performance Message Passing over InfiniBand for Open MPI
InfiniBand (IB) is a popular network technology for modern high-performance computing systems. MPI implementations traditionally support IB using a reliable, connection-oriented (RC) transport. However, per-process resource usage that grows linearly with the number of processes, makes this approach prohibitive for large-scale systems. IB provides an alternative in the form of a connectionless unreliable datagram transport (UD), which allows for near-constant resource usage and initialization overhead as the process count increases. This paper describes a UD-based implementation for IB in Open MPI as a scalable alternative to existing RC-based schemes. We use the software reliability capabilities of Open MPI to provide the guaranteed delivery semantics required by MPI. Results show that UD not only requires fewer resources at scale, but also allows for shorter MPI startup times. A connectionless model also improves performance for applications that tend to send small messages to many different processes
Linearly scaling direct method for accurately inverting sparse banded matrices
In many problems in Computational Physics and Chemistry, one finds a special
kind of sparse matrices, termed "banded matrices". These matrices, which are
defined as having non-zero entries only within a given distance from the main
diagonal, need often to be inverted in order to solve the associated linear
system of equations. In this work, we introduce a new O(n) algorithm for
solving such a system, being n X n the size of the matrix. We produce the
analytical recursive expressions that allow to directly obtain the solution, as
well as the pseudocode for its computer implementation. Moreover, we review the
different options for possibly parallelizing the method, we describe the
extension to deal with matrices that are banded plus a small number of non-zero
entries outside the band, and we use the same ideas to produce a method for
obtaining the full inverse matrix. Finally, we show that the New Algorithm is
competitive, both in accuracy and in numerical efficiency, when compared to a
standard method based in Gaussian elimination. We do this using sets of large
random banded matrices, as well as the ones that appear when one tries to solve
the 1D Poisson equation by finite differences.Comment: 24 pages, 5 figures, submitted to J. Comp. Phy
GiViP: A Visual Profiler for Distributed Graph Processing Systems
Analyzing large-scale graphs provides valuable insights in different
application scenarios. While many graph processing systems working on top of
distributed infrastructures have been proposed to deal with big graphs, the
tasks of profiling and debugging their massive computations remain time
consuming and error-prone. This paper presents GiViP, a visual profiler for
distributed graph processing systems based on a Pregel-like computation model.
GiViP captures the huge amount of messages exchanged throughout a computation
and provides an interactive user interface for the visual analysis of the
collected data. We show how to take advantage of GiViP to detect anomalies
related to the computation and to the infrastructure, such as slow computing
units and anomalous message patterns.Comment: Appears in the Proceedings of the 25th International Symposium on
Graph Drawing and Network Visualization (GD 2017
Weak omega-categories from intensional type theory
We show that for any type in Martin-L\"of Intensional Type Theory, the terms
of that type and its higher identity types form a weak omega-category in the
sense of Leinster. Precisely, we construct a contractible globular operad of
definable composition laws, and give an action of this operad on the terms of
any type and its identity types
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