Rate of Homogeneous Crystal Nucleation in molten NaCl

We report a numerical simulation of the rate of crystal nucleation of sodium chloride from its melt at moderate supercooling. In this regime nucleation is too slow to be studied with "brute-force" Molecular Dynamics simulations. The melting temperature of ("Tosi-Fumi") NaCl is $\sim 1060$K. We studied crystal nucleation at $T$=800K and 825K. We observe that the critical nucleus formed during the nucleation process has the crystal structure of bulk NaCl. Interestingly, the critical nucleus is clearly faceted: the nuclei have a cubical shape. We have computed the crystal-nucleation rate using two completely different approaches, one based on an estimate of the rate of diffusive crossing of the nucleation barrier, the other based on the Forward Flux Sampling and Transition Interface Sampling (FFS-TIS) methods. We find that the two methods yield the same result to within an order of magnitude. However, when we compare the extrapolated simulation data with the only available experimental results for NaCl nucleation, we observe a discrepancy of nearly 5 orders of magnitude. We discuss the possible causes for this discrepancy

Non-equilibrium dynamics of an active colloidal "chucker"

We report Monte Carlo simulations of the dynamics of a "chucker": a colloidal particle which emits smaller solute particles from its surface, isotropically and at a constant rate k_c. We find that the diffusion constant of the chucker increases for small k_c, as recently predicted theoretically. At large k_c the chucker diffuses more slowly due to crowding effects. We compare our simulation results to those of a "point particle" Langevin dynamics scheme in which the solute concentration field is calculated analytically, and in which hydrodynamic effects can be included albeit in an approximate way. By simulating the dragging of a chucker, we obtain an estimate of its apparent mobility coefficient which violates the fluctuation-dissipation theorem. We also characterise the probability density profile for a chucker which sediments onto a surface which either repels or absorbs the solute particles, and find that the steady state distributions are very different in the two cases. Our simulations are inspired by the biological example of exopolysaccharide-producing bacteria, as well as by recent experimental, simulation and theoretical work on phoretic colloidal "swimmers".Comment: re-submission after referee's comment

Augmenting Group Performance in Target-Face Recognition via Collaborative Brain-Computer Interfaces for Surveillance Applications

This paper presents a hybrid collaborative brain- computer interface (cBCI) to improve group-based recognition of target faces in crowded scenes recorded from surveillance cameras. The cBCI uses a combination of neural features extracted from EEG and response times to estimate the decision confidence of the users. Group decisions are then obtained by weighing individual responses according to these confidence estimates. Results obtained with 10 participants indicate that the proposed cBCI improves decision errors by up to 7% over traditional group decisions based on majority. Moreover, the confidence estimates obtained by the cBCI are more accurate and robust than the confidence reported by the participants after each decision. These results show that cBCIs can be an effective means of human augmentation in realistic scenarios

CES-530: Collaborative Brain-Computer Interface for Aiding Decision-making

We look at the possibility of integrating the percepts from multiple non-communicating observers as a means of achieving better joint perception and better group decisions. Our approach involves the combination of a brain-computer interface with human behavioural responses. To test ideas in controlled conditions, we asked observers to perform a simple matching task involving the rapid sequential presentation of pairs of visual patterns and the subsequent decision as whether the two patterns in a pair were the same or different. We recorded the response times of observers as well as a neural feature which predicts incorrect decisions and, thus, indirectly indicates the confidence of the decisions made by the observers. We then built a composite neuro-behavioural feature which optimally combines the two measures. For group decisions, we uses a majority rule and three rules which weigh the decisions of each observer based on response times and our neural and neuro-behavioural features. Results indicate that the integration of behavioural responses and neural features can significantly improve accuracy when compared with the majority rule. An analysis of event-related potentials indicates that substantial differences are present in the proximity of the response for correct and incorrect trials, further corroborating the idea of using hybrids of brain-computer interfaces and traditional strategies for improving decision making

A Collaborative BCI Trained to Aid Group Decisions in a Visual Search Task Works Well with Similar Tasks

This study tests the possibility of using collaborative brain-computer interfaces (cBCIs) trained with EEG data collected during a decision task to enhance group performance in similar tasks

Improving Group Decision Making with Collaborative Brain-Computer Interfaces

Groups are generally superior to individuals in making decisions. However, time constraints and authoritarian leaders could nullify the potential advantages provided by groups. This thesis proposes a hybrid collaborative Brain-Computer Interface (cBCI) for improving performance in group decision-making. Neural signals recorded via electroencephalography are integrated with other physiological and behavioural measures to predict the likelihood of the user being correct in a decision, i.e., decision confidence. Behavioural responses from multiple users are then weighed according to these confidence estimates to obtain group decisions. The proposed cBCI has been tested with a variety of decision-making tasks, including visual matching, visual search with traditional and realistic stimuli, face recognition from multiple viewpoints, and speech perception. Groups assisted by the cBCI were significantly superior in making decisions than both individuals and traditional equally-sized groups making decisions using the majority method. This thesis also investigates the impact that a constrained form of communication has on individual and group performance in a visual-search experiment. When decision makers are able to exchange information during the experiment, their performance dramatically decreases. However, the cBCI yields superior group decisions even in this context. The confidence estimated by the cBCI is also a more reliable predictor of correctness than the confidence reported by participants after making a decision. When group members were allowed to communicate during visual search, their reported confidence was totally unrelated to the decision correctness, while in a speech perception task reported confidences were very good predictors of correctness. On the contrary, the cBCI?s confidence estimates correlated with correctness in all experiments. When critical decisions involving substantial risks have to be made (e.g., in defence), the proposed cBCI could be a useful tool to reduce the number of erroneous group decisions, thereby saving money and lives

Optimized Collaborative Brain-Computer Interfaces for Enhancing Face Recognition

The aim of this study is to maximize group decision performance by optimally adapting EEG confidence decoders to the group composition. We train linear support vector machines to estimate the decision confidence of human participants from their EEG activity. We then simulate groups of different size and membership by combining individual decisions using a weighted majority rule. The weights assigned to each participant in the group are chosen solving a small-dimension, mixed, integer linear programming problem, where we maximize the group performance on the training set. We therefore introduce optimized collaborative brain-computer interfaces (BCIs), where the decisions of each team member are weighted according to both the individual neural activity and the group composition. We validate this approach on a face recognition task undertaken by 10 human participants. The results show that optimal collaborative BCIs significantly enhance team performance over other BCIs, while improving fairness within the group. This research paves the way for practical applications of collaborative BCIs to realistic scenarios characterized by stable teams, where optimizing the decision policy of a single group may lead to significant long-term benefits of team dynamics

Local structure of liquid carbon controls diamond nucleation

Diamonds melt at temperatures above 4000 K. There are no measurements of the steady-state rate of the reverse process: diamond nucleation from the melt, because experiments are difficult at these extreme temperatures and pressures. Using numerical simulations, we estimate the diamond nucleation rate and find that it increases by many orders of magnitude when the pressure is increased at constant supersaturation. The reason is that an increase in pressure changes the local coordination of carbon atoms from three-fold to four-fold. It turns out to be much easier to nucleate diamond in a four-fold coordinated liquid than in a liquid with three-fold coordination, because in the latter case the free-energy cost to create a diamond-liquid interface is higher. We speculate that this mechanism for nucleation control is relevant for crystallization in many network-forming liquids. On the basis of our calculations, we conclude that homogeneous diamond nucleation is likely in carbon-rich stars and unlikely in gaseous planets