Approaching Tsirelson's bound in a photon pair experiment

Quantum theory introduces a cut between the observer and the observed system, but does not provide a definition of what is an observer. Based on an informational definition of observer, Grinbaum has recently predicted an upper bound on bipartite correlations in the Clauser-Horne-Shimony-Holt (CHSH) Bell scenario equal to 2.82537, which is slightly smaller than the Tsirelson bound of standard quantum theory, but is consistent with all the available experimental results. Not being able to exceed Grinbaum's limit would support that quantum theory is only an effective description of a more fundamental theory and would have a deep impact in physics and quantum information processing. Here we present a test of the CHSH Bell inequality on photon pairs in maximally entangled states of polarization in which a value 2.82759+-0.00051 is observed, violating Grinbaum's bound by 4.3 standard deviations and providing the smallest distance with respect to Tsirelson's bound ever reported, namely, 0.00084+-0.00051. This sets a new lower experimental bound for Tsirelson's bound, strengthening the value of principles that predict Tsirelson's bound as possible explanations of all natural limits of correlations, and has important consequences for cryptographic security, randomness certification, characterization of physical properties in device-independent scenarios, and certification of quantum computation.Comment: 4 pages, 2 figures, 1 number - updated error bars, references, and error discussio

Oblique Polarized Reflectance Spectroscopy for Depth Sensitive Measurements in the Epithelial Tissue

Optical spectroscopy has shown potential as a tool for precancer detection by discriminating alterations in the optical properties within epithelial tissues. Identifying depth-dependent alterations associated with the progression of epithelial cancerous lesions can be especially challenging in the oral cavity due to the variable thickness of the epithelium and the presence of keratinization. Optical spectroscopy of epithelial tissue with improved depth resolution would greatly assist in the isolation of optical properties associated with cancer progression. Here, we report a fiber optic probe for oblique polarized reflectance spectroscopy (OPRS) that is capable of depth sensitive detection by combining the following three approaches: multiple beveled fibers, oblique collection geometry, and polarization gating. We analyze how probe design parameters are related to improvements in collection efficiency of scattered photons from superficial tissue layers and to increased depth discrimination within epithelium. We have demonstrated that obliquely-oriented collection fibers increase both depth selectivity and collection efficiency of scattering signal. Currently, we evaluate this technology in a clinical trial of patients presenting lesions suspicious for dysplasia or carcinoma in the oral cavity. We use depth sensitive spectroscopic data to develop automated algorithms for analysis of morphological and architectural changes in the context of the multilayer oral epithelial tissue. Our initial results show that OPRS has the potential to improve the detection and monitoring of epithelial precancers in the oral cavity.Biomedical Engineerin

The design of a knowledge-based guidance system for an intelligent multiple objective decision support system (IMODSS)

This paper describes a project that extends the multiple objective decision support system (MODSS) by offering knowledge-based guidance to an intelligent multiple objective decision support system (IMODSS). This IMODSS integrates expert system (ES), multiple objective decision-making (MODM) methodologies, graphical user interface (GUI) and decision support systems (DSS) technologies. This IMODSS uses an expert system shell CLIPS to build a knowledge base to guide the decision-makers (DMs) to select the most suitable MODM method(s) from the MODM methodology base in order to solve their particular decision problems. This IMODSS has been implemented and tested. This paper mainly discusses the design and implementation of the knowledge-based intelligent guidance subsystem in IMODSS

Patient level analytics using self-organising maps: a case study on type-1 diabetes self-care survey responses

Survey questionnaires are often heterogeneous because they contain both quantitative (numeric) and qualitative (text) responses, as well as missing values. While traditional, model-based methods are commonly used by clinicians, we deploy Self Organizing Maps (SOM) as a means to visualise the data. In a survey study aiming at understanding the self-care behaviour of 611 patients with Type-1 Diabetes, we show that SOM can be used to (1) identify co-morbidities; (2) to link self-care factors that are dependent on each other; and (3) to visualise individual patient profiles; In evaluation with clinicians and experts in Type-1 Diabetes, the knowledge and insights extracted using SOM correspond well to clinical expectation. Furthermore, the output of SOM in the form of a U-matrix is found to offer an interesting alternative means of visualising patient profiles instead of a usual tabular form

Least-Cost Feed Formulation for Juvenile Macrobrachium rosenbergii (De Man) by Using the Linear Programming Technique

Linear least-cost programming was used in juvenile M. rosenbergii feed formulation using locally available feed ingredients (fish meal, shrimp meal, copra meal, soybean meal, wheat flour and palm oil). The following constraints were established: the essential amino acid contents were closely similar to those of juvenile M. rosenbergii, crude fat 5-10% and gross energy 4,400 cal/g with least cost. Four types offeed were produced with protein ranges from 25% to 50%. Growth responses of juvenile M. rosenbergii fed these formulated feeds showed that the 40% protein feed (P40) supported the best specific growth rate and feed conversion ratio. P40 feed is recommended for juvenile M. rosenbergii

Peri-operative blood pressure changes in normotensive and hypertensive patients

Controversy surrounds the acceptance of hypertension as an independent risk factor for anaesthesia. In an attempt to identify variables that are associated with increased haemodynamic instability during surgery, the blood pressure profiles of 128 patients were analysed. The two variables that contributed most to the instability were pre-operative control of blood pressure and anaesthetic technique. To reduce the fluctuation in blood pressure, it is advisable for patients to be given a regional anaesthetic. Current therapy for hypertension appears to exaggerate the depressant effects of anaesthetic drugs. Care must be taken not only to prevent hypertensive episodes during surgery, but also hypotensio

Movement correction in DCE-MRI through windowed and reconstruction dynamic mode decomposition

Images of the kidneys using dynamic contrast enhanced magnetic resonance renography (DCE-MRR) contains unwanted complex organ motion due to respiration. This gives rise to motion artefacts that hinder the clinical assessment of kidney function. However, due to the rapid change in contrast agent within the DCE-MR image sequence, commonly used intensity-based image registration techniques are likely to fail. While semi-automated approaches involving human experts are a possible alternative, they pose significant drawbacks including inter-observer variability, and the bottleneck introduced through manual inspection of the multiplicity of images produced during a DCE-MRR study. To address this issue, we present a novel automated, registration-free movement correction approach based on windowed and reconstruction variants of dynamic mode decomposition (WR-DMD). Our proposed method is validated on ten different healthy volunteers’ kidney DCEMRI data sets. The results, using block-matching-block evaluation on the image sequence produced by WR-DMD, show the elimination of 99% of mean motion magnitude when compared to the original data sets, thereby demonstrating the viability of automatic movement correction using WR-DMD

A Modified Neutral Point Method for Kernel-Based Fusion of Pattern-Recognition Modalities with Incomplete Data Sets

It is commonly the case in multi-modal pattern recognition that certain modality-specific object features are missing in the training set. We address here the missing data problem for kernel-based Support Vector Machines, in which each modality is represented by the respective kernel matrix over the set of training objects, such that the omission of a modality for some object manifests itself as a blank in the modality-specific kernel matrix at the relevant position. We propose to fill the blank positions in the collection of training kernel matrices via a variant of the Neutral Point Substitution (NPS) method, where the term ”neutral point” stands for the locus of points defined by the ”neutral hyperplane” in the hypothetical linear space produced by the respective kernel. The current method crucially differs from the previously developed neutral point approach in that it is capable of treating missing data in the training set on the same basis as missing data in the test set. It is therefore of potentially much wider applicability. We evaluate the method on the Biosecure DS2 data set