Quantum de Finetti theorem under fully-one-way adaptive measurements

We prove a version of the quantum de Finetti theorem: permutation-invariant quantum states are well approximated as a probabilistic mixture of multi-fold product states. The approximation is measured by distinguishability under fully one-way LOCC (local operations and classical communication) measurements. Our result strengthens Brand\~{a}o and Harrow's de Finetti theorem where a kind of partially one-way LOCC measurements was used for measuring the approximation, with essentially the same error bound. As main applications, we show (i) a quasipolynomial-time algorithm which detects multipartite entanglement with amount larger than an arbitrarily small constant (measured with a variant of the relative entropy of entanglement), and (ii) a proof that in quantum Merlin-Arthur proof systems, polynomially many provers are not more powerful than a single prover when the verifier is restricted to one-way LOCC operations.Comment: V2: minor changes. V3: new title, more discussions added, presentation improved. V4: minor changes, close to published versio

PathologyGAN: Learning deep representations of cancer tissue

We apply Generative Adversarial Networks (GANs) to the domain of digital pathology. Current machine learning research for digital pathology focuses on diagnosis, but we suggest a different approach and advocate that generative models could drive forward the understanding of morphological characteristics of cancer tissue. In this paper, we develop a framework which allows GANs to capture key tissue features and uses these characteristics to give structure to its latent space. To this end, we trained our model on 249K H&E breast cancer tissue images, extracted from 576 TMA images of patients from the Netherlands Cancer Institute (NKI) and Vancouver General Hospital (VGH) cohorts. We show that our model generates high quality images, with a Frechet Inception Distance (FID) of 16.65. We further assess the quality of the images with cancer tissue characteristics (e.g. count of cancer, lymphocytes, or stromal cells), using quantitative information to calculate the FID and showing consistent performance of 9.86. Additionally, the latent space of our model shows an interpretable structure and allows semantic vector operations that translate into tissue feature transformations. Furthermore, ratings from two expert pathologists found no significant difference between our generated tissue images from real ones. The code, generated images, and pretrained model are available at https://github.com/AdalbertoCq/Pathology-GANComment: MIDL 2020 final versio

Maximal Privacy Without Coherence

Privacy lies at the fundament of quantum mechanics. A coherently transmitted quantum state is inherently private. Remarkably, coherent quantum communication is not a prerequisite for privacy: there are quantum channels that are too noisy to transmit any quantum information reliably that can nevertheless send private classical information. Here, we ask how much private classical information a channel can transmit if it has little quantum capacity. We present a class of channels N_d with input dimension d^2, quantum capacity Q(N_d) <= 1, and private capacity P(N_d) = log d. These channels asymptotically saturate an interesting inequality P(N) <= (log d_A + Q(N))/2 for any channel N with input dimension d_A, and capture the essence of privacy stripped of the confounding influence of coherence.Comment: 6 pages. Proof of Eq.(13) slightly revise

Coupled-resonator-induced transparency with a squeezed vacuum

We present the first experimental observation of quantum fluctuation spectra in two coupled optical cavities with an injected squeezed vacuum light. The quadrature components of the reflected squeezed vacuum spectra are measured by phase sensitive homodyne detector. The experimental results demonstrate coupled-resonator-induced transparency in the quantum regime, in which electromagnetically-induced-transparency-like characteristic of the absorption and dispersion properties of the coupled optical cavities determines the line-shape of the reflected quantum noise spectra.Comment: 4 pages, 4 figures, appear in Phys. Rev. Let

Intracapsular development and dispersal polymorphism in the predatory gastropod Ocenebra erinaceus (Linnaeus 1758)

This is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this record.Intraspecific polymorphism during development, such as poecilogony or dispersal polymorphism, has rarely been observed in the marine environment. The ecological advantages of this bet-hedging strategy, whereby the offspring from one species exhibit multiple developmental modes, include the potential for rapid colonization of new habitats while simultaneously achieving a degree of gene flow between populations. The muricid gastropod, Ocenebra erinaceus, is a common, shallow-water marine predator found across England and France. Historically, O. erinaceus caused significant damage to shellfisheries, but more recently it has been impacted by TBT-induced imposex. Despite the previous attention given to this species, little is known about its encapsulated development. Studying O. erinaceus egg capsules from the Solent, UK, we describe intracapsular development at 15 °C, the in situ temperature at time of oviposition. Within each capsule, all embryos developed; no nurse eggs were present. Development was categorized into eight ontogenetic stages, although not all individuals displayed every stage; embryos hatched as either swimming late-pediveliger larvae or crawling juveniles after 59–69 days, indicating dispersal polymorphism to occur in this species. Swimming late-pediveliger larvae completed metamorphosis within 72 h of hatching. As O. erinaceus continues to recover from TBT pollution, dispersal polymorphism may facilitate a rapid expansion in both population size and range. If this occurs, O. erinaceus has the potential to, once again, become a serious problem for shellfisheries around Europe.We thank the skipper and crew of RV Callista for their help with sample collection. This work was supported by a grant from the Malacological Society to KES. AJR was supported by a Natural Environment Research Council PhD studentship

Identification of Persistent and Resurgent Sodium Currents in Spiral Ganglion Neurons Cultured from the Mouse Cochlea

In spiral ganglion neurons (SGNs), the afferent single units of the auditory nerve, high spontaneous and evoked firing rates ensure preservation of the temporal code describing the key features of incoming sound. During postnatal development, the spatiotemporal distribution of ion channel subtypes contributes to the maturation of action potential generation in SGNs, and to their ability to generate spike patterns that follow rapidly changing inputs. Here we describe tetrodotoxin (TTX)-sensitive Na+ currents in SGNs cultured from mice, whose properties may support this fast spiking behavior. A subthreshold persistent Na+ current (INaP) and a resurgent Na+ current (INaR) both emerged prior to the onset of hearing and became more prevalent as hearing matured. Navβ4 subunits, which are proposed to play a key role in mediating INaR elsewhere in the nervous system, were immunolocalized to the first heminode where spikes are generated in the auditory nerve, and to perisomatic nodes of Ranvier. ATX-II, a sea anemone toxin that slows classical Na+ channel inactivation selectively, enhanced INaP five-fold and INaR three-fold in voltage clamp recordings. In rapidly-adapting SGNs under current clamp, ATX-II increased the likelihood of firing additional action potentials. The data identify INaP and INaR as novel regulators of excitability in SGNs, and consistent with their roles in other neuronal types, we suggest that these nonclassical Na+ currents may contribute to the control of refractoriness in the auditory nerve

Non-Commutative Resolutions of Toric Varieties

Let R be the coordinate ring of an affine toric variety. We prove, using direct elementary methods, that the endomorphism ring EndR(A), where A is the (finite) direct sum of all (isomorphism classes of) conic R-modules, has finite global dimension equal to the dimension of R. This gives a precise version, and an elementary proof, of a theorem of Spenko and Van den Bergh ˇ implying that EndR(A) has finite global dimension. Furthermore, we show that EndR(A) is a non-commutative crepant resolution if and only if the toric variety is simplicial. For toric varieties over a perfect field k of prime characteristic, we show that the ring of differential operators Dk(R) has finite global dimension

Divergent membrane properties of mouse cochlear glial cells around hearing onset

Spiral ganglion neurons (SGNs) are the primary afferent neurons of the auditory system, and together with their attendant glia, form the auditory nerve. Within the cochlea, satellite glial cells (SGCs) encapsulate the cell body of SGNs, whereas Schwann cells (SCs) wrap their peripherally- and centrally-directed neurites. Despite their likely importance in auditory nerve function and homeostasis, the physiological properties of auditory glial cells have evaded description. Here, we characterized the voltage-activated membrane currents of glial cells from the mouse cochlea. We identified a prominent weak inwardly rectifying current in SGCs within cochlear slice preparations (postnatal day P5-P6), which was also present in presumptive SGCs within dissociated cultures prepared from the cochleae of hearing mice (P14-P15). Pharmacological block by Ba2+ and desipramine suggested that channels belonging to the Kir4 family mediated the weak inwardly rectifying current, and post hoc immunofluorescence implicated the involvement of Kir4.1 subunits. Additional electrophysiological profiles were identified for glial cells within dissociated cultures, suggesting that glial subtypes may have specific membrane properties to support distinct physiological roles. Immunofluorescence using fixed cochlear sections revealed that although Kir4.1 is restricted to SGCs after the onset of hearing, these channels are more widely distributed within the glial population earlier in postnatal development (i.e., within both SGCs and SCs). The decrease in Kir4.1 immunofluorescence during SC maturation was coincident with a reduction of Sox2 expression and advancing neurite myelination. The data suggest a diversification of glial properties occurs in preparation for sound-driven activity in the auditory nerve

Electrodeposition from supercritical fluids

Recent studies have shown that it is possible to electrodeposit a range of materials, such as Cu, Ag and Ge, from various supercritical fluids, including hydrofluorocarbons and mixtures of CO2 with suitable co-solvents. In this perspective we discuss the relatively new field of electrodeposition from supercritical fluids. The perspective focuses on some of the underlying physical chemistry and covers both practical and scientific aspects of electrodeposition from supercritical fluids. We also discuss possible applications for supercritical fluid electrodeposition and suggest some key developments that are required to take the field to the next stage

Treatment of missing data in Bayesian network structure learning : an application to linked biomedical and social survey data

The authors acknowledge the Research/Scientific Computing teams at The James Hutton Institute and NIAB for providing computational resources and technical support for the “UK’s Crop Diversity Bioinformatics HPC” (BBSRC grant BB/S019669/1), use of which has contributed to the results reported within this paper. Access to this was provided via the University of St Andrews Bioinformatics Unit which is funded by a Wellcome Trust ISSF award (grant 105621/Z/14/Z and 204821/Z/16/Z). XK was supported by a World-Leading PhD Scholarship from St Leonard’s Postgraduate School of the University of St Andrews. VAS and KK were partially supported by HATUA, The Holistic Approach to Unravel Antibacterial Resistance in East Africa, a three-year Global Context Consortia Award (MR/S004785/1) funded by the National Institute for Health Research, Medical Research Council and the Department of Health and Social Care. KK is supported by the Academy of Medical Sciences, the Wellcome Trust, the Government Department of Business, Energy and Industrial Strategy, the British Heart Foundation Diabetes UK, and the Global Challenges Research Fund [Grant number SBF004\1093]. KK is additionally supported by the Economic and Social Research Council HIGHLIGHT CPC- Connecting Generations Centre [Grant number ES/W002116/1].Background Availability of linked biomedical and social science data has risen dramatically in past decades, facilitating holistic and systems-based analyses. Among these, Bayesian networks have great potential to tackle complex interdisciplinary problems, because they can easily model inter-relations between variables. They work by encoding conditional independence relationships discovered via advanced inference algorithms. One challenge is dealing with missing data, ubiquitous in survey or biomedical datasets. Missing data is rarely addressed in an advanced way in Bayesian networks; the most common approach is to discard all samples containing missing measurements. This can lead to biased estimates. Here, we examine how Bayesian network structure learning can incorporate missing data. Methods We use a simulation approach to compare a commonly used method in frequentist statistics, multiple imputation by chained equations (MICE), with one specific for Bayesian network learning, structural expectation-maximization (SEM). We simulate multiple incomplete categorical (discrete) data sets with different missingness mechanisms, variable numbers, data amount, and missingness proportions. We evaluate performance of MICE and SEM in capturing network structure. We then apply SEM combined with community analysis to a real-world dataset of linked biomedical and social data to investigate associations between socio-demographic factors and multiple chronic conditions in the US elderly population. Results We find that applying either method (MICE or SEM) provides better structure recovery than doing nothing, and SEM in general outperforms MICE. This finding is robust across missingness mechanisms, variable numbers, data amount and missingness proportions. We also find that imputed data from SEM is more accurate than from MICE. Our real-world application recovers known inter-relationships among socio-demographic factors and common multimorbidities. This network analysis also highlights potential areas of investigation, such as links between cancer and cognitive impairment and disconnect between self-assessed memory decline and standard cognitive impairment measurement. Conclusion Our simulation results suggest taking advantage of the additional information provided by network structure during SEM improves the performance of Bayesian networks; this might be especially useful for social science and other interdisciplinary analyses. Our case study show that comorbidities of different diseases interact with each other and are closely associated with socio-demographic factors.PostprintPublisher PDFPeer reviewe