Experimental and Standard Formats for Procedural Instruction: Evaluation of Merging Pictorials and Words

Three methods of training procedural tasks were studied. Forty-five high aptitude and fourth-five low aptitude Naval trainees from the Basic Electronics and Electricity School, Orlando, Florida, were given training with either a programmed instruction text with a pictorial-print information presentation format, or a standard narrative text. The effects of instructional method and aptitude on the performance of a procedural task after 1 1/2 hours of study and after on week\u27s time were evaluated. It was shown that subjects who studied the programmed instruction text with the pictorial-print information presentation format made significantly (p\u3c.0001) fewer performance errors, immediately after study and after one week, than did the subjects who studied the other methods. It was also shown that high aptitude subjects performed significantly (p\u3c.0001) better than low aptitude subjects, regardless of training method. However, it was found that the low aptitude subjects who studied the programmed instruction text with the pictorial-print information presentation format performed significantly (p \u3c 0.1) better than the low aptitude subjects who studied the other materials. These low aptitude subjects who studied the other materials. These low aptitude subjects also performed significantly (p \u3c .01) better than the high aptitude subjects who studied the standard narrative text

Reducing the Attack Surface of Dynamic Binary Instrumentation Frameworks

Malicious applications pose as one of the most relevant issues in today’s technology scenario, being considered the root of many Internet security threats. In part, this owes the ability of malware developers to promptly respond to the emergence of new security solutions by developing artifacts to detect and avoid them. In this work, we present three countermeasures to mitigate recent mechanisms used by malware to detect analysis environments. Among these techniques, this work focuses on those that enable a malware to detect dynamic binary instrumentation frameworks, thus increasing their attack surface. To ensure the effectiveness of the proposed countermeasures, proofs of concept were developed and tested in a controlled environment with a set of anti-instrumentation techniques. Finally, we evaluated the performance impact of using such countermeasures

Innate dynamics and identity crisis of a metal surface unveiled by machine learning of atomic environments

Metals are traditionally considered hard matter. However, it is well known that their atomic lattices may become dynamic and undergo reconfigurations even well below the melting temperature. The innate atomic dynamics of metals is directly related to their bulk and surface properties. Understanding their complex structural dynamics is, thus, important for many applications but is not easy. Here, we report deep-potential molecular dynamics simulations allowing to resolve at an atomic resolution the complex dynamics of various types of copper (Cu) surfaces, used as an example, near the Hüttig (∼1/3 of melting) temperature. The development of deep neural network potential trained on density functional theory calculations provides a dynamically accurate force field that we use to simulate large atomistic models of different Cu surface types. A combination of high-dimensional structural descriptors and unsupervized machine learning allows identifying and tracking all the atomic environments (AEs) emerging in the surfaces at finite temperatures. We can directly observe how AEs that are non-native in a specific (ideal) surface, but that are, instead, typical of other surface types, continuously emerge/disappear in that surface in relevant regimes in dynamic equilibrium with the native ones. Our analyses allow estimating the lifetime of all the AEs populating these Cu surfaces and to reconstruct their dynamic interconversions networks. This reveals the elusive identity of these metal surfaces, which preserve their identity only in part and in part transform into something else under relevant conditions. This also proposes a concept of “statistical identity” for metal surfaces, which is key to understanding their behaviors and properties

Entanglement transfer, accumulation and retrieval via quantum-walk-based qubit-qudit dynamics

The generation and control of quantum correlations in high-dimensional systems is a major challenge in the present landscape of quantum technologies. Achieving such non-classical high-dimensional resources will potentially unlock enhanced capabilities for quantum cryptography, communication and computation. We propose a protocol that is able to attain entangled states of d-dimensional systems through a quantum-walk (QW)-based transfer & accumulate mechanism involving coin and walker degrees of freedom. The choice of investigating QW is motivated by their generality and versatility, complemented by their successful implementation in several physical systems. Hence, given the cross-cutting role of QW across quantum information, our protocol potentially represents a versatile general tool to control high-dimensional entanglement generation in various experimental platforms. In particular, we illustrate a possible photonic implementation where the information is encoded in the orbital angular momentum and polarization degrees of freedom of single photons

Single-photon Calibration of an Integrated Multiarm Interferometer via Neural Netowrks

Technological quantum sensors requires the development of a calibration procedure that is self-consistent and easily adaptable to different scenarios. Neural networks provide a handy solution in particular when dealing with large systems operating in a noisy environment

Enhanced detection techniques of orbital angular momentum states in the classical and quantum regimes

The orbital angular momentum (OAM) of light has been at the center of several classical and quantum applications for imaging, information processing and communication. However, the complex structure inherent in OAM states makes their detection and classification nontrivial in many circumstances. Most of the current detection schemes are based on models of the OAM states built upon the use of Laguerre-Gauss (LG) modes. However, this may not in general be sufficient to capture full information on the generated states. In this paper, we go beyond the LG assumption, and employ hypergeometric-Gaussian (HyGG) modes as the basis states of a refined model that can be used - in certain scenarios - to better tailor OAM detection techniques. We show that enhanced performances in OAM detection are obtained for holographic projection via spatial light modulators in combination with single-mode fibers (SMFs), and for classification techniques based on a machine learning approach. Furthermore, a three-fold enhancement in the SMF coupling efficiency is obtained for the holographic technique, when using the HyGG model with respect to the LG one. This improvement provides a significant boost in the overall efficiency of OAM-encoded single-photon detection systems. Given that most of the experimental works using OAM states are effectively based on the generation of HyGG modes, our findings thus represent a relevant addition to experimental toolboxes for OAM-based protocols in quantum communication, cryptography and simulation

Adaptive two-phase estimation on a photonic integrated device

Efficient adaptive multiphase estimation has been demonstrated experimentally on an integrated three-arm interferometer injected by single photons. Bayesian learning and Sequential Monte Carlo approximation have been employed as machine learning tools to achieve this goal

Experimental Engineering of Arbitrary Qudit States with Discrete-Time Quantum Walks

The capability to generate and manipulate quantum states in high-dimensional Hilbert spaces is a crucial step for the development of quantum technologies, from quantum communication to quantum computation. One-dimensional quantum walk dynamics represents a valid tool in the task of engineering arbitrary quantum states. Here we affirm such potential in a linear-optics platform that realizes discrete-time quantum walks in the orbital angular momentum degree of freedom of photons. Different classes of relevant qudit states in a six-dimensional space are prepared and measured, confirming the feasibility of the protocol. Our results represent a further investigation of quantum walk dynamics in photonics platforms, paving the way for the use of such a quantum state-engineering toolbox for a large range of applications