Quantum cognition: A new theoretical approach to psychology

What type of probability theory best describes the way humans make judgments under uncertainty and decisions under conflict? Although rational models of cognition have become prominent and have achieved much success, they adhere to the laws of classical probability theory despite the fact that human reasoning does not always conform to these laws. For this reason we have seen the recent emergence of models based on an alternative probabilistic framework drawn from quantum theory. These quantum models show promise in addressing cognitive phenomena that have proven recalcitrant to modeling by means of classical probability theory. This review compares and contrasts probabilistic models based on Bayesian or classical versus quantum principles, and highlights the advantages and disadvantages of each approach.

A nonconservative Lagrangian framework for statistical fluid registration - SAFIRA

In this paper, we used a nonconservative Lagrangian mechanics approach to formulate a new statistical algorithm for fluid registration of 3-D brain images. This algorithm is named SAFIRA, acronym for statistically-assisted fluid image registration algorithm. A nonstatistical version of this algorithm was implemented, where the deformation was regularized by penalizing deviations from a zero rate of strain. In, the terms regularizing the deformation included the covariance of the deformation matrices Σ and the vector fields (q). Here, we used a Lagrangian framework to reformulate this algorithm, showing that the regularizing terms essentially allow nonconservative work to occur during the flow. Given 3-D brain images from a group of subjects, vector fields and their corresponding deformation matrices are computed in a first round of registrations using the nonstatistical implementation. Covariance matrices for both the deformation matrices and the vector fields are then obtained and incorporated (separately or jointly) in the nonconservative terms, creating four versions of SAFIRA. We evaluated and compared our algorithms' performance on 92 3-D brain scans from healthy monozygotic and dizygotic twins; 2-D validations are also shown for corpus callosum shapes delineated at midline in the same subjects. After preliminary tests to demonstrate each method, we compared their detection power using tensor-based morphometry (TBM), a technique to analyze local volumetric differences in brain structure. We compared the accuracy of each algorithm variant using various statistical metrics derived from the images and deformation fields. All these tests were also run with a traditional fluid method, which has been quite widely used in TBM studies. The versions incorporating vector-based empirical statistics on brain variation were consistently more accurate than their counterparts, when used for automated volumetric quantification in new brain images. This suggests the advantages of this approach for large-scale neuroimaging studies.

Post-quantum key exchange for the TLS protocol from the ring learning with errors problem

Lattice-based cryptographic primitives are believed to offer resilience against attacks by quantum computers. We demonstrate the practicality of post-quantum key exchange by constructing cipher suites for the Transport Layer Security (TLS) protocol that provide key exchange based on the ring learning with errors (R-LWE) problem, we accompany these cipher suites with a rigorous proof of security. Our approach ties lattice-based key exchange together with traditional authentication using RSA or elliptic curve digital signatures: the post-quantum key exchange provides forward secrecy against future quantum attackers, while authentication can be provided using RSA keys that are issued by today's commercial certificate authorities, smoothing the path to adoption. Our cryptographically secure implementation, aimed at the 128-bit security level, reveals that the performance price when switching from non-quantum-safe key exchange is not too high. With our R-LWE cipher suites integrated into the Open SSL library and using the Apache web server on a 2-core desktop computer, we could serve 506 RLWE-ECDSA-AES128-GCM-SHA256 HTTPS connections per second for a 10 KiB payload. Compared to elliptic curve Diffie-Hellman, this means an 8 KiB increased handshake size and a reduction in throughput of only 21%. This demonstrates that provably secure post-quantum key-exchange can already be considered practical.

Enhanced electron lifetime of CdSe/CdS quantum dot (QD) sensitized solar cells using ZnSe core–shell structure with efficient regeneration of quantum Ddots

Research on development of efficient passivation materials for high performance and stable quantum dot sensitized solar cells (QDSCs) is highly important. While ZnS is one of the most widely used passivation material in QDSCs, an alternative material based on ZnSe which was deposited on CdS/CdSe/TiO2 photoanode to form a semi-core/shell structure has been found to be more efficient in terms of reducing electron recombination in QDSCs in this work. It has been found that the solar cell efficiency was improved from 1.86% for ZnSe0 (without coating) to 3.99% using 2 layers of ZnSe coating (ZnSe2) deposited by successive ionic layer adsorption and reaction (SILAR) method. The short circuit current density (Jsc) increased nearly 1-fold (from 7.25 mA/cm2 to13.4 mA/cm2), and the open circuit voltage (Voc) was enhanced by 100 mV using ZnSe2 passivation layer compared to ZnSe0. Studies on the light harvesting efficiency (ηLHE) and the absorbed photon-to-current conversion efficiency (APCE) have revealed that the ZnSe coating layer caused the enhanced ηLHE at wavelength beyond 500 nm and a significant increase of the APCE over the spectrum 400−550 nm. A nearly 100% APCE was obtained with ZnSe2, indicating the excellent charge injection and collection process in the device. The investigation on charge transport and recombination of the device has indicated that the enhanced electron collection efficiency and reduced electron recombination should be responsible for the improved Jsc and Voc of the QDSCs. The effective electron lifetime of the device with ZnSe2 was nearly 6 times higher than ZnSe0 while the electron diffusion coefficient was largely unaffected by the coating. Study on the regeneration of QDs after photoinduced excitation has indicated that the hole transport from QDs to the reduced species (S2−) in electrolyte was very efficient even when the QDs were coated with a thick ZnSe shell (three layers). For comparison, ZnS coated CdS/CdSe sensitized solar cell with optimum shell thickness was also fabricated, which generated a lower energy conversion efficiency (η = 3.43%) than the ZnSe based QDSC counterpart due to a lower Voc and FF. This study suggests that ZnSe may be a more efficient passivation layer than ZnS, which is attributed to the type II energy band alignment of the core (CdS/CdSe quantum dots) and passivation shell (ZnSe) structure, leading to more efficient electron−hole separation and slower electron recombination.

Group 14 element-based non-centrosymmetric quantum spin hall insulators with large bulk gap

To date, a number of two-dimensional (2D) topological insulators (TIs) have been realized in Group 14 elemental honeycomb lattices, but all are inversionsymmetric. Here, based on first-principles calculations, we predict a new family of 2D inversion-asymmetric TIs with sizeable bulk gaps from 105 meV to 284 meV, in X2–GeSn (X = H, F, Cl, Br, I) monolayers, making them in principle suitable for room-temperature applications. The nontrivial topological characteristics of inverted band orders are identified in pristine X2–GeSn with X = (F, Cl, Br, I), whereas H2–GeSn undergoes a nontrivial band inversion at 8% lattice expansion. Topologically protected edge states are identified in X2–GeSn with X = (F, Cl, Br, I), as well as in strained H2–GeSn. More importantly, the edges of these systems, which exhibit single-Dirac-cone characteristics located exactly in the middle of their bulk band gaps, are ideal for dissipationless transport. Thus, Group 14 elemental honeycomb lattices provide a fascinating playground for the manipulation of quantum states.

Mapping bound plasmon propagation on a nanoscale stripe waveguide using quantum dots: Influence of spacer layer thickness

In this paper we image the highly confined long range plasmons of a nanoscale metal stripe waveguide using quantum emitters. Plasmons were excited using a highly focused 633 nm laser beam and a specially designed grating structure to provide stronger incoupling to the desired mode. A homogeneous thin layer of quantum dots was used to image the near field intensity of the propagating plasmons on the waveguide. We observed that the photoluminescence is quenched when the QD to metal surface distance is less than 10 nm. The optimised spacer layer thickness for the stripe waveguides was found to be around 20 nm. Authors believe that the findings of this paper prove beneficial for the development of plasmonic devices utilising stripe waveguides.

Fractal and multifractal properties of a family of fractal networks

In this work, we study the fractal and multifractal properties of a family of fractal networks introduced by Gallos et al (2007 Proc. Nat. Acad. Sci. USA 104 7746). In this fractal network model, there is a parameter e which is between 0 and 1, and allows for tuning the level of fractality in the network. Here we examine the multifractal behavior of these networks, the dependence relationship of the fractal dimension and the multifractal parameters on parameter e. First, we find that the empirical fractal dimensions of these networks obtained by our program coincide with the theoretical formula given by Song et al (2006 Nature Phys. 2 275). Then from the shape of the τ(q) and D(q) curves, we find the existence of multifractality in these networks. Last, we find that there exists a linear relationship between the average information dimension 〈D(1)〉 and the parameter e.

A rapid and label-free dual detection of Hg (II) and cysteine with the use of fluorescence switching of graphene quantum dots

A simple and rapid method of analysis for mercury ions (Hg2+) and cysteine (Cys) was developed with the use of graphene quantum dots (GQDs) as a fluorescent probe. In the presence of GQDs, Hg2+ cations are absorbed on their negatively charged surface by means of electrostatic interactions. Thus, the fluorescence (FL) of the GQDs would be significantly quenched as a result of the FL charge transfer, e.g. 92% quenching at 450 nm occurs for a 5 μmol L−1 Hg2+ solution. However, when Cys was added, a significant FL enhancement was observed (510% at 450 nm for a 8.0 μmol L−1 Cys solution), and Hg2+ combined with Cys rather than with the GQDs in an aqueous solution. This occurred because a strong metalsingle bondthiol bond formed, displacing the weak electrostatic interactions, and this resulted in an FL enhancement of the GQDs. The limits of detection (LOD) for Hg2+ and Cys were 0.439 nmol L−1 and 4.5 nmol L−1, respectively. Also, this method was used successfully to analyze Hg2+ and Cys in spiked water samples.

Optimizing the quantum dot: Plasmon interaction in a nano gap waveguide

Spontaneous emission (SE) of a Quantum emitter depends mainly on the transmission strength between the upper and lower energy levels as well as the Local Density of States (LDOS)[1]. When a QD is placed in near a plasmon waveguide, LDOS of the QD is increased due to addition of the non-radiative decay and a plasmonic decay channel to free space emission[2-4]. The slow velocity and dramatic concentration of the electric field of the plasmon can capture majority of the SE into guided plasmon mode (Гpl ). This paper focused on studying the effect of waveguide height on the efficiency of coupling QD decay into plasmon mode using a numerical model based on finite elemental method (FEM). Symmetric gap waveguide considered in this paper support single mode and QD as a dipole emitter. 2D simulation models are done to find normalized Гpl and 3D models are used to find probability of SE decaying into plasmon mode ( β) including all three decay channels. It is found out that changing gap height can increase QD-plasmon coupling, by up to a factor of 5 and optimally placed QD up to a factor of 8. To make the paper more realistic we briefly studied the effect of sharpness of the waveguide edge on SE emission into guided plasmon mode. Preliminary nano gap waveguide fabrication and testing are already underway. Authors expect to compare the theoretical results with experimental outcomes in the future

Multifractal analyses of daily rainfall time series in Pearl River basin of China

The multifractal properties of daily rainfall time series at the stations in Pearl River basin of China over periods of up to 45 years are examined using the universal multifractal approach based on the multiplicative cascade model and the multifractal detrended fluctuation analysis (MF-DFA). The results from these two kinds of multifractal analyses show that the daily rainfall time series in this basin have multifractal behavior in two different time scale ranges. It is found that the empirical multifractal moment function K(q)K(q) of the daily rainfall time series can be fitted very well by the universal multifractal model (UMM). The estimated values of the conservation parameter HH from UMM for these daily rainfall data are close to zero indicating that they correspond to conserved fields. After removing the seasonal trend in the rainfall data, the estimated values of the exponent h(2)h(2) from MF-DFA indicate that the daily rainfall time series in Pearl River basin exhibit no long-term correlations. It is also found that K(2)K(2) and elevation series are negatively correlated. It shows a relationship between topography and rainfall variability.

Fractal and complex network analyses of protein molecular dynamics

Based on protein molecular dynamics, we investigate the fractal properties of energy, pressure and volume time series using the multifractal detrended fluctuation analysis (MF-DFA) and the topological and fractal properties of their converted horizontal visibility graphs (HVGs). The energy parameters of protein dynamics we considered are bonded potential, angle potential, dihedral potential, improper potential, kinetic energy, Van der Waals potential, electrostatic potential, total energy and potential energy. The shape of the h(q)h(q) curves from MF-DFA indicates that these time series are multifractal. The numerical values of the exponent h(2)h(2) of MF-DFA show that the series of total energy and potential energy are non-stationary and anti-persistent; the other time series are stationary and persistent apart from series of pressure (with H≈0.5H≈0.5 indicating the absence of long-range correlation). The degree distributions of their converted HVGs show that these networks are exponential. The results of fractal analysis show that fractality exists in these converted HVGs. For each energy, pressure or volume parameter, it is found that the values of h(2)h(2) of MF-DFA on the time series, exponent λλ of the exponential degree distribution and fractal dimension dBdB of their converted HVGs do not change much for different proteins (indicating some universality). We also found that after taking average over all proteins, there is a linear relationship between 〈h(2)〉〈h(2)〉 (from MF-DFA on time series) and 〈dB〉〈dB〉 of the converted HVGs for different energy, pressure and volume.

Graphene quantum dots and the resonance light scattering technique for trace analysis of phenol in different water samples

A novel, highly selective resonance light scattering (RLS) method was researched and developed for the analysis of phenol in different types of industrial water. An important aspect of the method involved the use of graphene quantum dots (GQDs), which were initially obtained from the pyrolysis of citric acid dissolved in aqueous solutions. The GQDs in the presence of horseradish peroxidase (HRP) and H2O2 were found to react quantitatively with phenol such that the RLS spectral band (310 nm) was quantitatively enhanced as a consequence of the interaction between the GQDs and the quinone formed in the above reaction. It was demonstrated that the novel analytical method had better selectivity and sensitivity for the determination of phenol in water as compared to other analytical methods found in the literature. Thus, trace amounts of phenol were detected over the linear ranges of 6.00×10−8–2.16×10−6 M and 2.40×10−6–2.88×10−5 M with a detection limit of 2.20×10−8 M. In addition, three different spiked waste water samples and two untreated lake water samples were analysed for phenol. Satisfactory results were obtained with the use of the novel, sensitive and rapid RLS method.

A novel fluorescent probe involving a graphene quantum dot-enzyme hybrid system for the analysis of hydroquinone in the presence of toxic resorcinol and catechol

An efficient method for the analysis of hydroquinone at trace levels in water samples has been developed in the form of a fluorescent probe based on graphene quantum dots (GQDs). The analytical variable, fluorescence quenching, was generated from the formation of benzoquinone intermediates, which formed during the catalytic oxidation of hydroquinone by horseradish peroxidase (HRP). In general, the reaction mechanism involved hydroquinone, as an electron acceptor, which affected the surface state of GQDs via an electron transfer effect. The water-soluble GQDs were directly prepared by the pyrolysis of citric acid and with the use of the mentioned hybrid enzyme system, the detection limit for hydroquinone was as low as 8.4 × 10−8 M. Furthermore, this analysis was almost unaffected by other phenol and quinine compounds, such as phenol, resorcinol and other quinines, and therefore, the developed GQD method produced satisfactory results for the analysis of hydroquinone in several different lake water samples.

Applying POVM to model non-orthogonality in quantum cognition

Much of the work currently occurring in the field of Quantum Interaction (QI) relies upon Projective Measurement. This is perhaps not optimal, cognitive states are not nearly as well behaved as standard quantum mechanical systems; they exhibit violations of repeatability, and the operators that we use to describe measurements do not appear to be naturally orthogonal in cognitive systems. Here we attempt to map the formalism of Positive Operator Valued Measure (POVM) theory into the domain of semantic memory, showing how it might be used to construct Bell-type inequalities.

The critical role of water in spider silk and its consequence for protein mechanics

Due to its remarkable mechanical and biological properties, there is considerable interest in understanding, and replicating, spider silk's stress-processing mechanisms and structure-function relationships. Here, we investigate the role of water in the nanoscale mechanics of the different regions in the spider silk fibre, and their relative contributions to stress processing. We propose that the inner core region, rich in spidroin II, retains water due to its inherent disorder, thereby providing a mechanism to dissipate energy as it breaks a sacrificial amide-water bond and gains order under strain, forming a stronger amide-amide bond. The spidroin I-rich outer core is more ordered under ambient conditions and is inherently stiffer and stronger, yet does not on its own provide high toughness. The markedly different interactions of the two proteins with water, and their distribution across the fibre, produce a stiffness differential and provide a balance between stiffness, strength and toughness under ambient conditions. Under wet conditions, this balance is destroyed as the stiff outer core material reverts to the behaviour of the inner core.