43 research outputs found
LICSTER -- A Low-cost ICS Security Testbed for Education and Research
Unnoticed by most people, Industrial Control Systems (ICSs) control entire
productions and critical infrastructures such as water distribution, smart grid
and automotive manufacturing. Due to the ongoing digitalization, these systems
are becoming more and more connected in order to enable remote control and
monitoring. However, this shift bears significant risks, namely a larger attack
surface, which can be exploited by attackers. In order to make these systems
more secure, it takes research, which is, however, difficult to conduct on
productive systems, since these often have to operate twenty-four-seven.
Testbeds are mostly very expensive or based on simulation with no real-world
physical process. In this paper, we introduce LICSTER, an open-source low-cost
ICS testbed, which enables researchers and students to get hands-on experience
with industrial security for about 500 Euro. We provide all necessary material
to quickly start ICS hacking, with the focus on low-cost and open-source for
education and research
Critical review of the influences of nanoparticles on biological wastewater treatment and sludge digestion
Fragment-Based Deep Learning for Simultaneous Prediction of Polarizabilities and NMR Shieldings of Macromolecules and Their Aggregates
Simultaneous prediction of the molecular response properties,
such
as polarizability and the NMR shielding constant, at a low computational
cost is an unresolved issue. We propose to combine a linear-scaling
generalized energy-based fragmentation (GEBF) method and deep learning
(DL) with both molecular and atomic information-theoretic approach
(ITA) quantities as effective descriptors. In GEBF, the total molecular
polarizability can be assembled as a linear combination of the corresponding
quantities calculated from a set of small embedded subsystems in GEBF.
In the new GEBF-DLÂ(ITA) protocol, one can predict subsystem polarizabilities
based on the corresponding molecular wave function (thus electron
density and ITA quantities) and DL model rather than calculate them
from the computationally intensive coupled-perturbed Hartree–Fock
or Kohn–Sham equations and finally obtain the total molecular
polarizability via a linear combination equation. As a proof-of-concept
application, we predict the molecular polarizabilities of large proteins
and protein aggregates. GEBF-DLÂ(ITA) is shown to be as accurate enough
as GEBF, with mean absolute percentage error <1%. For the largest
protein aggregate (>4000 atoms), GEBF-DLÂ(ITA) gains a speedup ratio
of 3 compared with GEBF. It is anticipated that when more advanced
electronic structure methods are used, this advantage will be more
appealing. Moreover, one can also predict the NMR chemical shieldings
of proteins with reasonably good accuracy. Overall, the cost-efficient
GEBF-DLÂ(ITA) protocol should be a robust theoretical tool for simultaneously
predicting polarizabilities and NMR shieldings of large systems
Fragment-Based Deep Learning for Simultaneous Prediction of Polarizabilities and NMR Shieldings of Macromolecules and Their Aggregates
Simultaneous prediction of the molecular response properties,
such
as polarizability and the NMR shielding constant, at a low computational
cost is an unresolved issue. We propose to combine a linear-scaling
generalized energy-based fragmentation (GEBF) method and deep learning
(DL) with both molecular and atomic information-theoretic approach
(ITA) quantities as effective descriptors. In GEBF, the total molecular
polarizability can be assembled as a linear combination of the corresponding
quantities calculated from a set of small embedded subsystems in GEBF.
In the new GEBF-DLÂ(ITA) protocol, one can predict subsystem polarizabilities
based on the corresponding molecular wave function (thus electron
density and ITA quantities) and DL model rather than calculate them
from the computationally intensive coupled-perturbed Hartree–Fock
or Kohn–Sham equations and finally obtain the total molecular
polarizability via a linear combination equation. As a proof-of-concept
application, we predict the molecular polarizabilities of large proteins
and protein aggregates. GEBF-DLÂ(ITA) is shown to be as accurate enough
as GEBF, with mean absolute percentage error <1%. For the largest
protein aggregate (>4000 atoms), GEBF-DLÂ(ITA) gains a speedup ratio
of 3 compared with GEBF. It is anticipated that when more advanced
electronic structure methods are used, this advantage will be more
appealing. Moreover, one can also predict the NMR chemical shieldings
of proteins with reasonably good accuracy. Overall, the cost-efficient
GEBF-DLÂ(ITA) protocol should be a robust theoretical tool for simultaneously
predicting polarizabilities and NMR shieldings of large systems
Fragment-Based Deep Learning for Simultaneous Prediction of Polarizabilities and NMR Shieldings of Macromolecules and Their Aggregates
Simultaneous prediction of the molecular response properties,
such
as polarizability and the NMR shielding constant, at a low computational
cost is an unresolved issue. We propose to combine a linear-scaling
generalized energy-based fragmentation (GEBF) method and deep learning
(DL) with both molecular and atomic information-theoretic approach
(ITA) quantities as effective descriptors. In GEBF, the total molecular
polarizability can be assembled as a linear combination of the corresponding
quantities calculated from a set of small embedded subsystems in GEBF.
In the new GEBF-DLÂ(ITA) protocol, one can predict subsystem polarizabilities
based on the corresponding molecular wave function (thus electron
density and ITA quantities) and DL model rather than calculate them
from the computationally intensive coupled-perturbed Hartree–Fock
or Kohn–Sham equations and finally obtain the total molecular
polarizability via a linear combination equation. As a proof-of-concept
application, we predict the molecular polarizabilities of large proteins
and protein aggregates. GEBF-DLÂ(ITA) is shown to be as accurate enough
as GEBF, with mean absolute percentage error <1%. For the largest
protein aggregate (>4000 atoms), GEBF-DLÂ(ITA) gains a speedup ratio
of 3 compared with GEBF. It is anticipated that when more advanced
electronic structure methods are used, this advantage will be more
appealing. Moreover, one can also predict the NMR chemical shieldings
of proteins with reasonably good accuracy. Overall, the cost-efficient
GEBF-DLÂ(ITA) protocol should be a robust theoretical tool for simultaneously
predicting polarizabilities and NMR shieldings of large systems