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For highly efficient ultrathin solar cells, layered indium selenide (InSe), a van der Waals solid, has shown a great promise. In this paper, we study the coherent dynamics of charge carriers generation in {\gamma}-InSe single crystals. We employ ultrafast transient absorption spectroscopy to examine the dynamics of hot electrons after resonant photoexcitation. To study the effect of excess kinetic energy of electrons after creating A exciton (VB1 to CB transition), we excite the sample with broadband pulses centered at 600, 650, 700 and 750 nm, respectively. We analyze the relaxation and recombination dynamics in {\gamma}-InSe by global fitting approach. Five decay associated spectra with their associated lifetimes are obtained, which have been assigned to intraband vibrational relaxation and interband recombination processes. We extract characteristic carrier thermalization times from 1 to 10 ps. To examine the coherent vibrations accompanying intraband relaxation dynamics, we analyze the kinetics by fitting to exponential functions and the obtained residuals are further processed for vibrational analysis. A few key phonon coherences are resolved and ab-initio quantum calculations reveal the nature of the associated phonons. The wavelet analysis is employed to study the time evolution of the observed coherences, which show that the low-frequency coherences last for more than 5 ps. Associated calculations reveal that the contribution of the intralayer phonon modes is the key determining factor for the scattering between free electrons and lattice. Our results provide fundamental insights into the photophysics in InSe and help to unravel their potential for high-performance optoelectronic devices.

Most existing Secure Multi-Party Computation (MPC) protocols for privacy-preserving training of decision trees over distributed data assume that the features are categorical. In real-life applications, features are often numerical. The standard ``in the clear'' algorithm to grow decision trees on data with continuous values requires sorting of training examples for each feature in the quest for an optimal cut-point in the range of feature values in each node. Sorting is an expensive operation in MPC, hence finding secure protocols that avoid such an expensive step is a relevant problem in privacy-preserving machine learning. In this paper we propose three more efficient alternatives for secure training of decision tree based models on data with continuous features, namely: (1) secure discretization of the data, followed by secure training of a decision tree over the discretized data; (2) secure discretization of the data, followed by secure training of a random forest over the discretized data; and (3) secure training of extremely randomized trees (``extra-trees'') on the original data. Approaches (2) and (3) both involve randomizing feature choices. In addition, in approach (3) cut-points are chosen randomly as well, thereby alleviating the need to sort or to discretize the data up front. We implemented all proposed solutions in the semi-honest setting with additive secret sharing based MPC. In addition to mathematically proving that all proposed approaches are correct and secure, we experimentally evaluated and compared them in terms of classification accuracy and runtime. We privately train tree ensembles over data sets with 1000s of instances or features in a few minutes, with accuracies that are at par with those obtained in the clear. This makes our solution orders of magnitude more efficient than the existing approaches, which are based on oblivious sorting.

In this paper, we present a secure logistic regression training protocol and its implementation, with a new subprotocol to securely compute the activation function. To the best of our knowledge, we present the fastest existing secure Multi-Party Computation implementation for training logistic regression models on high dimensional genome data distributed across a local area network.

We demonstrate a simple method for the determination of the magnetic field in an ion trap using laser-cooled Be+ ions. The method is not based on magnetic resonance and thus does not require delivering radiofrequency (RF) radiation to the trap. Instead, stimulated Raman spectroscopy is used, and only an easily generated optical sideband of the laser cooling wave is required. The d.c. magnetic vector, averaged over the Be+ ion ensemble, is determined. Furthermore, the field strength can be minimized and an upper limit for the field gradient can be determined. The resolution of the method is 0.04 G at present. The relevance for precision rovibrational spectroscopy of molecular hydrogen ions is briefly discussed.