3,999 research outputs found
Bell nonlocality
Bell's 1964 theorem, which states that the predictions of quantum theory
cannot be accounted for by any local theory, represents one of the most
profound developments in the foundations of physics. In the last two decades,
Bell's theorem has been a central theme of research from a variety of
perspectives, mainly motivated by quantum information science, where the
nonlocality of quantum theory underpins many of the advantages afforded by a
quantum processing of information. The focus of this review is to a large
extent oriented by these later developments. We review the main concepts and
tools which have been developed to describe and study the nonlocality of
quantum theory, and which have raised this topic to the status of a full
sub-field of quantum information science.Comment: 65 pages, 7 figures. Final versio
On Verifying Causal Consistency
Causal consistency is one of the most adopted consistency criteria for
distributed implementations of data structures. It ensures that operations are
executed at all sites according to their causal precedence. We address the
issue of verifying automatically whether the executions of an implementation of
a data structure are causally consistent. We consider two problems: (1)
checking whether one single execution is causally consistent, which is relevant
for developing testing and bug finding algorithms, and (2) verifying whether
all the executions of an implementation are causally consistent.
We show that the first problem is NP-complete. This holds even for the
read-write memory abstraction, which is a building block of many modern
distributed systems. Indeed, such systems often store data in key-value stores,
which are instances of the read-write memory abstraction. Moreover, we prove
that, surprisingly, the second problem is undecidable, and again this holds
even for the read-write memory abstraction. However, we show that for the
read-write memory abstraction, these negative results can be circumvented if
the implementations are data independent, i.e., their behaviors do not depend
on the data values that are written or read at each moment, which is a
realistic assumption.Comment: extended version of POPL 201
Generalization of form in visual pattern classification.
Human observers were trained to criterion in classifying compound Gabor signals with sym- metry relationships, and were then tested with each of 18 blob-only versions of the learning set. General- ization to dark-only and light-only blob versions of the learning signals, as well as to dark-and-light blob versions was found to be excellent, thus implying virtually perfect generalization of the ability to classify mirror-image signals. The hypothesis that the learning signals are internally represented in terms of a 'blob code' with explicit labelling of contrast polarities was tested by predicting observed generalization behaviour in terms of various types of signal representations (pixelwise, Laplacian pyramid, curvature pyramid, ON/OFF, local maxima of Laplacian and curvature operators) and a minimum-distance rule. Most representations could explain generalization for dark-only and light-only blob patterns but not for the high-thresholded versions thereof. This led to the proposal of a structure-oriented blob-code. Whether such a code could be used in conjunction with simple classifiers or should be transformed into a propo- sitional scheme of representation operated upon by a rule-based classification process remains an open question
Complexity over Uncertainty in Generalized Representational\ud Information Theory (GRIT): A Structure-Sensitive General\ud Theory of Information
What is information? Although researchers have used the construct of information liberally to refer to pertinent forms of domain-specific knowledge, relatively few have attempted to generalize and standardize the construct. Shannon and Weaver(1949)offered the best known attempt at a quantitative generalization in terms of the number of discriminable symbols required to communicate the state of an uncertain event. This idea, although useful, does not capture the role that structural context and complexity play in the process of understanding an event as being informative. In what follows, we discuss the limitations and futility of any generalization (and particularly, Shannon’s) that is not based on the way that agents extract patterns from their environment. More specifically, we shall argue that agent concept acquisition, and not the communication of\ud
states of uncertainty, lie at the heart of generalized information, and that the best way of characterizing information is via the relative gain or loss in concept complexity that is experienced when a set of known entities (regardless of their nature or domain of origin) changes. We show that Representational Information Theory perfectly captures this crucial aspect of information and conclude with the first generalization of Representational Information Theory (RIT) to continuous domains
Quantum Information Geometry in the Space of Measurements
We introduce a new approach to evaluating entangled quantum networks using
information geometry. Quantum computing is powerful because of the enhanced
correlations from quantum entanglement. For example, larger entangled networks
can enhance quantum key distribution (QKD). Each network we examine is an
n-photon quantum state with a degree of entanglement. We analyze such a state
within the space of measured data from repeated experiments made by n observers
over a set of identically-prepared quantum states -- a quantum state
interrogation in the space of measurements. Each observer records a 1 if their
detector triggers, otherwise they record a 0. This generates a string of 1's
and 0's at each detector, and each observer can define a binary random variable
from this sequence. We use a well-known information geometry-based measure of
distance that applies to these binary strings of measurement outcomes, and we
introduce a generalization of this length to area, volume and
higher-dimensional volumes. These geometric equations are defined using the
familiar Shannon expression for joint and mutual entropy. We apply our approach
to three distinct tripartite quantum states: the GHZ state, the W state, and a
separable state P. We generalize a well-known information geometry analysis of
a bipartite state to a tripartite state. This approach provides a novel way to
characterize quantum states, and it may have favorable scaling with increased
number of photons.Comment: 21 pages, 7 figure
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A tutorial on cue combination and Signal Detection Theory: Using changes in sensitivity to evaluate how observers integrate sensory information
Many sensory inputs contain multiple sources of information (‘cues’), such as two sounds of different frequencies, or a voice heard in unison with moving lips. Often, each cue provides a separate estimate of the same physical attribute, such as the size or location of an object. An ideal observer can exploit such redundant sensory information to improve the accuracy of their perceptual judgments. For example, if each cue is modeled as an independent, Gaussian, random variable, then combining Ncues should provide up to a √N improvement in detection/discrimination sensitivity. Alternatively, a less efficient observer may base their decision on only a subset of the available information, and so gain little or no benefit from having access to multiple sources of information. Here we use Signal Detection Theory to formulate and compare various models of cue-combination, many of which are commonly used to explain empirical data. We alert the reader to the key assumptions inherent in each model, and provide formulas for deriving quantitative predictions. Code is also provided for simulating each model, allowing expected levels of measurement error to be quantified. Based on these results, it is shown that predicted sensitivity often differs surprisingly little between qualitatively distinct models of combination. This means that sensitivity alone is not sufficient for understanding decision efficiency, and the implications of this are discussed
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A conceptual framework for studying collective reactions to events in location-based social media
Events are a core concept of spatial information, but location-based social media (LBSM) provide information on reactions to events. Individuals have varied degrees of agency in initiating, reacting to or modifying the course of events, and reactions include observations of occurrence, expressions containing sentiment or emotions, or a call to action. Key characteristics of reactions include referent events and information about who reacted, when, where and how. Collective reactions are composed of multiple individual reactions sharing common referents. They can be characterized according to the following dimensions: spatial, temporal, social, thematic and interlinkage. We present a conceptual framework, which allows characterization and comparison of collective reactions. For a thematically well-defined class of event such as storms, we can explore differences and similarities in collective attribution of meaning across space and time. Other events may have very complex spatio-temporal signatures (e.g. political processes such as Brexit or elections), which can be decomposed into series of individual events (e.g. a temporal window around the result of a vote). The purpose of our framework is to explore ways in which collective reactions to events in LBSM can be described and underpin the development of methods for analysing and understanding collective reactions to events
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