Metasurface quantum graphs for generalized Hong-Ou-Mandel interference.

Multiphoton interference and entanglement are fundamental to quantum information science, yet extending these effects to higher-dimensional systems remains challenging given the imperfections and complexity of scaling conventional linear-optical setups. We present a generalized Hong-Ou-Mandel effect using metasurfaces and graph theory, achieving controlled multiphoton bunching, antibunching, and entanglement across parallel Jones matrix-encoded spatial modes-all within a single-layer metasurface. A graph-theoretic dual framework is introduced that simultaneously encodes the metasurface-based multiport interferometer designs and its resulting nonclassical correlations, enabling the direct translation of linear quantum optical networks into a single-layer metasurface.

Adaptive planning depth in human problem-solving.

We humans are capable of solving challenging planning problems, but the range of adaptive strategies that we use to address them is not yet fully characterized. Here, we designed a series of problem-solving tasks that require planning at different depths. After systematically comparing the performance of participants and planning models, we found that when facing problems that require planning to a certain number of subgoals (from 1 to 8), participants make an adaptive use of their cognitive resources-namely, they tend to select an initial plan having the minimum required depth, rather than selecting the same depth for all problems.

Statistical Learning of Incidental Perceptual Regularities Induces Sensory Conditioned Cortical Responses.

Statistical learning of sensory patterns can lead to predictive neural processes enhancing stimulus perception and enabling fast deviancy detection. Predictive processes have been extensively demonstrated when environmental statistical regularities are relevant to task execution. Preliminary evidence indicates that statistical learning can even occur independently of task relevance and top-down attention, although the temporal profile and neural mechanisms underlying sensory predictions and error signals induced by statistical learning of incidental sensory regularities remain unclear.

Normatively irrelevant disgust interferes with decision under uncertainty: Insights from the Iowa gambling task.

This study investigates whether a not informative, irrelevant emotional reaction of disgust interferes with decision-making under uncertainty. We manipulate the Iowa Gambling Task (IGT) by associating a disgust-eliciting image with selections from Disadvantageous/Bad decks (Congruent condition) or Advantageous/Good decks (Incongruent condition). A Control condition without manipulations is also included.

Risky decisions are influenced by individual attributes as a function of risk preference.

It has long been assumed in economic theory that multi-attribute decisions involving several attributes or dimensions - such as probabilities and amounts of money to be earned during risky choices - are resolved by first combining the attributes of each option to form an overall expected value and then comparing the expected values of the alternative options, using a unique evidence accumulation process. A plausible alternative would be performing independent comparisons between the individual attributes and then integrating the results of the comparisons afterwards. Here, we devise a novel method to disambiguate between these types of models, by orthogonally manipulating the expected value of choice options and the relative salience of their attributes.

Humans account for cognitive costs when finding shortcuts: An information-theoretic analysis of navigation.

When faced with navigating back somewhere we have been before we might either retrace our steps or seek a shorter path. Both choices have costs. Here, we ask whether it is possible to characterize formally the choice of navigational plans as a bounded rational process that trades off the quality of the plan (e.

Amortized Bayesian Model Comparison With Evidential Deep Learning.

Comparing competing mathematical models of complex processes is a shared goal among many branches of science. The Bayesian probabilistic framework offers a principled way to perform model comparison and extract useful metrics for guiding decisions. However, many interesting models are intractable with standard Bayesian methods, as they lack a closed-form likelihood function or the likelihood is computationally too expensive to evaluate.

Increased Entropic Brain Dynamics during DeepDream-Induced Altered Perceptual Phenomenology.

In recent years, the use of psychedelic drugs to study brain dynamics has flourished due to the unique opportunity they offer to investigate the neural mechanisms of conscious perception. Unfortunately, there are many difficulties to conduct experiments on pharmacologically-induced hallucinations, especially regarding ethical and legal issues. In addition, it is difficult to isolate the neural effects of psychedelic states from other physiological effects elicited by the drug ingestion.

Normatively Irrelevant Affective Cues Affect Risk-Taking under Uncertainty: Insights from the Iowa Gambling Task (IGT), Skin Conductance Response, and Heart Rate Variability.

Being able to distinguish between safe and risky options is paramount in making functional choices. However, deliberate manipulation of decision-makers emotions can lead to risky behaviors. This study aims at understanding how affective reactions driven by normatively irrelevant affective cues can interfere with risk-taking.

Flexibilities of wavelets as a computational basis set for large-scale electronic structure calculations.

The BigDFT project was started in 2005 with the aim of testing the advantages of using a Daubechies wavelet basis set for Kohn-Sham (KS) density functional theory (DFT) with pseudopotentials. This project led to the creation of the BigDFT code, which employs a computational approach with optimal features of flexibility, performance, and precision of the results. In particular, the employed formalism has enabled the implementation of an algorithm able to tackle DFT calculations of large systems, up to many thousands of atoms, with a computational effort that scales linearly with the number of atoms.

A Bayesian brain model of adaptive behavior: an application to the Wisconsin Card Sorting Task.

Adaptive behavior emerges through a dynamic interaction between cognitive agents and changing environmental demands. The investigation of information processing underlying adaptive behavior relies on controlled experimental settings in which individuals are asked to accomplish demanding tasks whereby a hidden regularity or an abstract rule has to be learned dynamically. Although performance in such tasks is considered as a proxy for measuring high-level cognitive processes, the standard approach consists in summarizing observed response patterns by simple heuristic scoring measures.

Static Polarizabilities at the Basis Set Limit: A Benchmark of 124 Species.

Benchmarking molecular properties with Gaussian-type orbital (GTO) basis sets can be challenging, because one has to assume that the computed property is at the complete basis set (CBS) limit, without a robust measure of the error. Multiwavelet (MW) bases can be systematically improved with a controllable error, which eliminates the need for such assumptions. In this work, we have used MWs within Kohn-Sham density functional theory to compute static polarizabilities for a set of 92 closed-shell and 32 open-shell species.

A Joint Modelling Approach to Analyze Risky Decisions by Means of Diffusion Tensor Imaging and Behavioural Data.

Understanding dependencies between brain functioning and cognition is a challenging task which might require more than applying standard statistical models to neural and behavioural measures to be accomplished. Recent developments in computational modelling have demonstrated the advantage to formally account for reciprocal relations between mathematical models of cognition and brain functional, or structural, characteristics to relate neural and cognitive parameters on a model-based perspective. This would allow to account for both neural and behavioural data simultaneously by providing a joint probabilistic model for the two sources of information.

A State Space Approach to Dynamic Modeling of Mouse-Tracking Data.

Mouse-tracking recording techniques are becoming very attractive in experimental psychology. They provide an effective means of enhancing the measurement of some real-time cognitive processes involved in categorization, decision-making, and lexical decision tasks. Mouse-tracking data are commonly analyzed using a two-step procedure which first summarizes individuals' hand trajectories with independent measures, and then applies standard statistical models on them.

A Dynamic Framework for Modelling Set-Shifting Performances.

Higher-order cognitive functions can be seen as a class of cognitive processes which are crucial in situations requiring a flexible adjustment of behaviour in response to changing demands of the environment. The cognitive assessment of these functions often relies on tasks which admit a dynamic, or longitudinal, component requiring participants to flexibly adapt their behaviour during the unfolding of the task. An intriguing feature of such experimental protocols is that they allow the performance of an individual to change as the task unfolds.

A new nonparametric test for the race model inequality.

The race model inequality (RMI), as first introduced by Miller (Cognitive Psychology, 14, 247-279, 1982), entails an upper bound on the amount of statistical facilitation for reaction times (RTs) attainable by a race model within the redundant-signals paradigm. A violation of RMI may be considered as empirical evidence for a coactivation model rather than a race model. Here, we introduce a novel nonparametric procedure for evaluating the RMI for single participant analysis.

Label-free and non-invasive discrimination of HaCaT and melanoma cells in a co-culture model by hyperspectral confocal reflectance microscopy.

A novel hyperspectral confocal microscopy method to separate different cell populations in a co-culture model is presented here. The described methodological and instrumental approach allows discrimination of different cell types using a non-invasive, label free method with good accuracy with a single cell resolution. In particular, melanoma cells are discriminated from HaCaT cells by hyperspectral confocal imaging, principal component analysis and optical frequencies signing, as confirmed by fluorescence labelling cross check.

Volatiles produced by soil-borne endophytic bacteria increase plant pathogen resistance and affect tritrophic interactions.

Volatile organic compounds (VOCs) released by soil microorganisms influence plant growth and pathogen resistance. Yet, very little is known about their influence on herbivores and higher trophic levels. We studied the origin and role of a major bacterial VOC, 2,3-butanediol (2,3-BD), on plant growth, pathogen and herbivore resistance, and the attraction of natural enemies in maize.

Herbivore-induced maize leaf volatiles affect attraction and feeding behavior of Spodoptera littoralis caterpillars.

Plants under herbivore attack emit volatile organic compounds (VOCs) that can serve as foraging cues for natural enemies. Adult females of Lepidoptera, when foraging for host plants to deposit eggs, are commonly repelled by herbivore-induced VOCs, probably to avoid competition and natural enemies. Their larval stages, on the other hand, have been shown to be attracted to inducible VOCs.

Age at Death of Creutzfeldt-Jakob disease in subsequent family generation carrying the E200K mutation of the prion protein gene.

Background: The E200K mutation of the prion protein gene (PRNP) is the most frequent amino acid substitution in genetic Creutzfeldt-Jakob disease and is the only one responsible for the appearance of clustered cases in the world. In the Israel and Slovakian clusters, age of disease onset was reduced in successive generations but the absence of a clear molecular basis raised the possibility that this event was an observational bias. The aim of the present study was to investigate possible selection biases or confounding factors related to anticipation in E200K CJD patients belonging to a cluster in Southern Italy.

Regional risk assessment for contaminated sites part 3: spatial decision support system.

Large scale (e.g. regional or national) assessments of contaminated sites may be very costly in terms of investigation and methodological (i.

Maximum-entropy Monte Carlo method for the inversion of the structure factor in simple classical systems.

We present a method for the evaluation of the interaction potential of an equilibrium classical system starting from the (partial) knowledge of its structure factor. The procedure is divided into two phases, both of which are based on the maximum entropy principle of information theory. First we determine the maximum entropy estimate of the radial distribution function constrained by the information contained in the structure factor.

Dispensing synthetic green leaf volatiles in maize fields increases the release of sesquiterpenes by the plants, but has little effect on the attraction of pest and beneficial insects.

Maize plants respond to feeding by arthropod herbivores by producing a number of secondary plant compounds, including volatile organic compounds (VOCs). These herbivore-induced VOCs are not only known to attract natural enemies of the herbivores, but they may also prime inducible defences in neighbouring plants, resulting in stronger and faster defence responses in these VOC-exposed plants. Among the compounds that cause this priming effect, green leaf volatiles (GLVs) have received particular attention, as they are ubiquitous and rapidly emitted upon damage.

Information-theory-based solution of the inverse problem in classical statistical mechanics.

We present a procedure for the determination of the interaction potential from the knowledge of the radial pair distribution function. The method, realized inside an inverse Monte Carlo simulation scheme, is based on the application of the maximum entropy principle of information theory and the interaction potential emerges as the asymptotic expression of the transition probability. Results obtained for high density monoatomic fluids are very satisfactory and provide an accurate extraction of the potential, despite a modest computational effort.

Retrospective long-term results and prognostic factors of postoperative treatment for UICC stages II and III rectal cancer.

Aims: To retrospectively evaluate 5-year local control, disease-free survival, cancer-specific survival and overall survival rates in patients with UICC stages II and III rectal cancer treated with adjuvant therapy and especially to analyze the impact of some prognostic factors on clinical outcome at univariate and multivariate analyses.

Methods And Materials: We retrospectively reviewed 306 patients treated with postoperative 5-fluorouracil-based chemoradiation (278 patients) or radiotherapy alone (28 patients) after curative surgery. The following prognostic factors were considered at univariate and multivariate analyses: age, sex, tumor location, surgery procedure, pathological stage, histology, tumor grade, surgical margins and radiotherapy technique.