Optimal measurements for nonlocal correlations

A problem in quantum information theory is to find the experimental setup that maximizes the nonlocality of correlations with respect to some suitable measure such as the violation of Bell inequalities. The latter has however some drawbacks. First and foremost it is unfeasible to determine the whole set of Bell inequalities already for a few measurements and thus unfeasible to find the experimental setup maximizing their violation. Second, the Bell violation suffers from an ambiguity stemming from the choice of the normalization of the Bell coefficients. An alternative measure of nonlocality with a direct information-theoretic interpretation is the minimal amount of classical communication required for simulating nonlocal correlations. In the case of many instances simulated in parallel, the minimal communication cost per instance is called nonlocal capacity, and its computation can be reduced to a convex-optimization problem. This quantity can be computed for a higher number of measurements and turns out to be useful for finding the optimal experimental setup. Focusing on the bipartite case, in this paper, we present a simple method for maximizing the nonlocal capacity over a given configuration space and, in particular, over a set of possible measurements, yielding the corresponding optimal setup. Furthermore, we show that there is a functional relationship between Bell violation and nonlocal capacity. The method is illustrated with numerical tests and compared with the maximization of the violation of CGLMP-type Bell inequalities on the basis of entangled two-qubit as well as two-qutrit states. Remarkably, the anomaly of nonlocality displayed by qutrits turns out to be even stronger if the nonlocal capacity is employed as a measure of nonlocality.Comment: Some typos and errors have been corrected, especially in the section concerning the relation between Bell violation and communication complexit

Spectral Temporal Contrastive Learning

Learning useful data representations without requiring labels is a cornerstone of modern deep learning. Self-supervised learning methods, particularly contrastive learning (CL), have proven successful by leveraging data augmentations to define positive pairs. This success has prompted a number of theoretical studies to better understand CL and investigate theoretical bounds for downstream linear probing tasks. This work is concerned with the temporal contrastive learning (TCL) setting where the sequential structure of the data is used instead to define positive pairs, which is more commonly used in RL and robotics contexts. In this paper, we adapt recent work on Spectral CL to formulate Spectral Temporal Contrastive Learning (STCL). We discuss a population loss based on a state graph derived from a time-homogeneous reversible Markov chain with uniform stationary distribution. The STCL loss enables to connect the linear probing performance to the spectral properties of the graph, and can be estimated by considering previously observed data sequences as an ensemble of MCMC chains.Comment: Accepted to Self-Supervised Learning - Theory and Practice, NeurIPS Workshop, 202

Long-term air pollution exposure, genome-wide DNA methylation and lung function in the lifelines cohort study

BACKGROUND: Long-term air pollution exposure is negatively associated with lung function, yet the mechanisms underlying this association are not fully clear. Differential DNA methylation may explain this association. OBJECTIVES: Our main aim was to study the association between long-term air pollution exposure and DNA methylation. METHODS: We performed a genome-wide methylation study using robust linear regression models in 1,017 subjects from the LifeLines cohort study to analyze the association between exposure to nitrogen dioxide (NO2) and particulate matter (PM2.5, fine particulate ma

Cognitive behavioural therapy with optional graded exercise therapy in patients with severe fatigue with myotonic dystrophy type 1:a multicentre, single-blind, randomised trial

Background: Myotonic dystrophy type 1 is the most common form of muscular dystrophy in adults and leads to severe fatigue, substantial physical functional impairment, and restricted social participation. In this study, we aimed to determine whether cognitive behavioural therapy optionally combined with graded exercise compared with standard care alone improved the health status of patients with myotonic dystrophy type 1. Methods: We did a multicentre, single-blind, randomised trial, at four neuromuscular referral centres with experience in treating patients with myotonic dystrophy type 1 located in Paris (France), Munich (Germany), Nijmegen (Netherlands), and Newcastle (UK). Eligible participants were patients aged 18 years and older with a confirmed genetic diagnosis of myotonic dystrophy type 1, who were severely fatigued (ie, a score of ≥35 on the checklist-individual strength, subscale fatigue). We randomly assigned participants (1:1) to either cognitive behavioural therapy plus standard care and optional graded exercise or standard care alone. Randomisation was done via a central web-based system, stratified by study site. Cognitive behavioural therapy focused on addressing reduced patient initiative, increasing physical activity, optimising social interaction, regulating sleep–wake patterns, coping with pain, and addressing beliefs about fatigue and myotonic dystrophy type 1. Cognitive behavioural therapy was delivered over a 10-month period in 10–14 sessions. A graded exercise module could be added to cognitive behavioural therapy in Nijmegen and Newcastle. The primary outcome was the 10-month change from baseline in scores on the DM1-Activ-c scale, a measure of capacity for activity and social participation (score range 0–100). Statistical analysis of the primary outcome included all participants for whom data were available, using mixed-effects linear regression models with baseline scores as a covariate. Safety data were presented as descriptives. This trial is registered with ClinicalTrials.gov, number NCT02118779. Findings: Between April 2, 2014, and May 29, 2015, we randomly assigned 255 patients to treatment: 128 to cognitive behavioural therapy plus standard care and 127 to standard care alone. 33 (26%) of 128 assigned to cognitive behavioural therapy also received the graded exercise module. Follow-up continued until Oct 17, 2016. The DM1-Activ-c score increased from a mean (SD) of 61·22 (17·35) points at baseline to 63·92 (17·41) at month 10 in the cognitive behavioural therapy group (adjusted mean difference 1·53, 95% CI −0·14 to 3·20), and decreased from 63·00 (17·35) to 60·79 (18·49) in the standard care group (−2·02, −4·02 to −0·01), with a mean difference between groups of 3·27 points (95% CI 0·93 to 5·62, p=0·007). 244 adverse events occurred in 65 (51%) patients in the cognitive behavioural therapy group and 155 in 63 (50%) patients in the standard care alone group, the most common of which were falls (155 events in 40 [31%] patients in the cognitive behavioural therapy group and 71 in 33 [26%] patients in the standard care alone group). 24 serious adverse events were recorded in 19 (15%) patients in the cognitive behavioural therapy group and 23 in 15 (12%) patients in the standard care alone group, the most common of which were gastrointestinal and cardiac. Interpretation: Cognitive behavioural therapy increased the capacity for activity and social participation in patients with myotonic dystrophy type 1 at 10 months. With no curative treatment and few symptomatic treatments, cognitive behavioural therapy could be considered for use in severely fatigued patients with myotonic dystrophy type 1. Funding: The European Union Seventh Framework Programme

Classification of current anticancer immunotherapies

During the past decades, anticancer immunotherapy has evolved from a promising therapeutic option to a robust clinical reality. Many immunotherapeutic regimens are now approved by the US Food and Drug Administration and the European Medicines Agency for use in cancer patients, and many others are being investigated as standalone therapeutic interventions or combined with conventional treatments in clinical studies. Immunotherapies may be subdivided into “passive” and “active” based on their ability to engage the host immune system against cancer. Since the anticancer activity of most passive immunotherapeutics (including tumor-targeting monoclonal antibodies) also relies on the host immune system, this classification does not properly reflect the complexity of the drug-host-tumor interaction. Alternatively, anticancer immunotherapeutics can be classified according to their antigen specificity. While some immunotherapies specifically target one (or a few) defined tumor-associated antigen(s), others operate in a relatively non-specific manner and boost natural or therapy-elicited anticancer immune responses of unknown and often broad specificity. Here, we propose a critical, integrated classification of anticancer immunotherapies and discuss the clinical relevance of these approaches

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong