Limits on non-local correlations from the structure of the local state space

The outcomes of measurements on entangled quantum systems can be nonlocally correlated. However, while it is easy to write down toy theories allowing arbitrary nonlocal correlations, those allowed in quantum mechanics are limited. Quantum correlations cannot, for example, violate a principle known as macroscopic locality, which implies that they cannot violate Tsirelson's bound. This work shows that there is a connection between the strength of nonlocal correlations in a physical theory, and the structure of the state spaces of individual systems. This is illustrated by a family of models in which local state spaces are regular polygons, where a natural analogue of a maximally entangled state of two systems exists. We characterize the nonlocal correlations obtainable from such states. The family allows us to study the transition between classical, quantum, and super-quantum correlations, by varying only the local state space. We show that the strength of nonlocal correlations - in particular whether the maximally entangled state violates Tsirelson's bound or not - depends crucially on a simple geometric property of the local state space, known as strong self-duality. This result is seen to be a special case of a general theorem, which states that a broad class of entangled states in probabilistic theories - including, by extension, all bipartite classical and quantum states - cannot violate macroscopic locality. Finally, our results show that there exist models which are locally almost indistinguishable from quantum mechanics, but can nevertheless generate maximally nonlocal correlations.Comment: 26 pages, 4 figures. v2: Document structure changed. Main theorem has been extended. It applies to all quantum states now. v3: new abstrac

A local variable model for entanglement swapping exploiting the detection loophole

In an entanglement swapping process two initially uncorrelated qubits become entangled, without any direct interaction. We present a model using local variables aiming at reproducing this remarkable process, under the realistic assumption of finite detection efficiencies. The model assumes that the local variables describing the two qubits are initially completely uncorrelated. Nevertheless, we show that once conditioned on the Bell measurement result, the local variables bear enough correlation to simulate quantum measurement results with correlation very close to the quantum prediction. When only a partial Bell measurement is simulated, as carried out is all experiments so far, then the model recovers analytically the quantum prediction.Comment: 5 pages, 5 figure

How much measurement independence is needed in order to demonstrate nonlocality?

If nonlocality is to be inferred from a violation of Bell's inequality, an important assumption is that the measurement settings are freely chosen by the observers, or alternatively, that they are random and uncorrelated with the hypothetical local variables. We study the case where this assumption is weakened, so that measurement settings and local variables are at least partially correlated. As we show, there is a connection between this type of model and models which reproduce nonlocal correlations by allowing classical communication between the distant parties, and a connection with models that exploit the detection loophole. We show that even if Bob's choices are completely independent, all correlations obtained from projective measurements on a singlet can be reproduced, with the correlation (measured by mutual information) between Alice's choice and local variables less than or equal to a single bit.Comment: 5 pages, 1 figure. v2 Various improvements in presentation. Results unchange

The definition of multipartite nonlocality

In a multipartite setting, it is possible to distinguish quantum states that are genuinely $n$-way entangled from those that are separable with respect to some bipartition. Similarly, the nonlocal correlations that can arise from measurements on entangled states can be classified into those that are genuinely $n$-way nonlocal, and those that are local with respect to some bipartition. Svetlichny introduced an inequality intended as a test for genuine tripartite nonlocality. This work introduces two alternative definitions of $n$-way nonlocality, which we argue are better motivated both from the point of view of the study of nature, and from the point of view of quantum information theory. We show that these definitions are strictly weaker than Svetlichny's, and introduce a series of suitable Bell-type inequalities for the detection of 3-way nonlocality. Numerical evidence suggests that all 3-way entangled pure quantum states can produce 3-way nonlocal correlations.Comment: Appendix uploade

Advancing NLP with Cognitive Language Processing Signals

When we read, our brain processes language and generates cognitive processing data such as gaze patterns and brain activity. These signals can be recorded while reading. Cognitive language processing data such as eye-tracking features have shown improvements on single NLP tasks. We analyze whether using such human features can show consistent improvement across tasks and data sources. We present an extensive investigation of the benefits and limitations of using cognitive processing data for NLP. Specifically, we use gaze and EEG features to augment models of named entity recognition, relation classification, and sentiment analysis. These methods significantly outperform the baselines and show the potential and current limitations of employing human language processing data for NLP

Partial list of bipartite Bell inequalities with four binary settings

We give a partial list of 26 tight Bell inequalities for the case where Alice and Bob choose among four two-outcome measurements. All tight Bell inequalities with less settings are reviewed as well. For each inequality we compute numerically the maximal quantum violation, the resistance to noise and the minimal detection efficiency required for closing the detection loophole. Surprisingly, most of these inequalities are outperformed by the CHSH inequality.Comment: 6 pages. Other inequalities welcome. Accepted for publication in Phys. Lett.

Elevated blood pressure, heart rate and body temperature in mice lacking the XL alpha s protein of the Gnas locus is due to increased sympathetic tone

NEW FINDINGS: What is the central question of this study? Previously, we showed that Gnasxl knock-out mice are lean and hypermetabolic, with increased sympathetic stimulation of adipose tissue. Do these mice also display elevated sympathetic cardiovascular tone? Is the brain glucagon-like peptide-1 system involved? What is the main finding and its importance? Gnasxl knock-outs have increased blood pressure, heart rate and body temperature. Heart rate variability analysis suggests an elevated sympathetic tone. The sympatholytic reserpine had stronger effects on blood pressure, heart rate and heart rate variability in knock-out compared with wild-type mice. Stimulation of the glucagon-like peptide-1 system inhibited parasympathetic tone to a similar extent in both genotypes, with a stronger associated increase in heart rate in knock-outs. Deficiency of Gnasxl increases sympathetic cardiovascular tone. Imbalances of energy homeostasis are often associated with cardiovascular complications. Previous work has shown that Gnasxl-deficient mice have a lean and hypermetabolic phenotype, with increased sympathetic stimulation of adipose tissue. The Gnasxl transcript from the imprinted Gnas locus encodes the trimeric G-protein subunit XLαs, which is expressed in brain regions that regulate energy homeostasis and sympathetic nervous system (SNS) activity. To determine whether Gnasxl knock-out (KO) mice display additional SNS-related phenotypes, we have now investigated the cardiovascular system. The Gnasxl KO mice were ∼20 mmHg hypertensive in comparison to wild-type (WT) littermates (P≤ 0.05) and hypersensitive to the sympatholytic drug reserpine. Using telemetry, we detected an increased waking heart rate in conscious KOs (630 ± 10 versus 584 ± 12 beats min(−1), KO versus WT, P≤ 0.05). Body temperature was also elevated (38.1 ± 0.3 versus 36.9 ± 0.4°C, KO versus WT, P≤ 0.05). To investigate autonomic nervous system influences, we used heart rate variability analyses. We empirically defined frequency power bands using atropine and reserpine and verified high-frequency (HF) power and low-frequency (LF) LF/HF power ratio to be indicators of parasympathetic and sympathetic activity, respectively. The LF/HF power ratio was greater in KOs and more sensitive to reserpine than in WTs, consistent with elevated SNS activity. In contrast, atropine and exendin-4, a centrally acting agonist of the glucagon-like peptide-1 receptor, which influences cardiovascular physiology and metabolism, reduced HF power equally in both genotypes. This was associated with a greater increase in heart rate in KOs. Mild stress had a blunted effect on the LF/HF ratio in KOs consistent with elevated basal sympathetic activity. We conclude that XLαs is required for the inhibition of sympathetic outflow towards cardiovascular and metabolically relevant tissues

Decoding EEG brain activity for multi-modal natural language processing

Until recently, human behavioral data from reading has mainly been of interest to researchers to understand human cognition. However, these human language processing signals can also be beneficial in machine learning-based natural language processing tasks. Using EEG brain activity to this purpose is largely unexplored as of yet. In this paper, we present the first large-scale study of systematically analyzing the potential of EEG brain activity data for improving natural language processing tasks, with a special focus on which features of the signal are most beneficial. We present a multi-modal machine learning architecture that learns jointly from textual input as well as from EEG features. We find that filtering the EEG signals into frequency bands is more beneficial than using the broadband signal. Moreover, for a range of word embedding types, EEG data improves binary and ternary sentiment classification and outperforms multiple baselines. For more complex tasks such as relation detection, further research is needed. Finally, EEG data shows to be particularly promising when limited training data is available