10,130 research outputs found
R. A. Markus. — The End of Ancient Christianity, 1990
No full textFontaine Jacques. R. A. Markus. — The End of Ancient Christianity, 1990. In: Revue des Études Anciennes. Tome 94, 1992, n°3-4. pp. 501-503
R. A. Markus. — The End of Ancient Christianity, 1990
No full textFontaine Jacques. R. A. Markus. — The End of Ancient Christianity, 1990. In: Revue des Études Anciennes. Tome 94, 1992, n°3-4. pp. 501-503
End-user challenges to digitalisation and security systems integration: a retail perspective
No full textWith the assumed digitalisation happening on the end-user side, progressive security systems integrators stress the importance of changing the business concept from being installers to being service and solution providers. However, less is known about the details of this process on the end-user side, Markus Lahtinen of LUSAX project explains
Calculating Time and the End of Time in the Carolingian World, c.740-820
No full textThe hopes and fears associated with the imminence of apocalypse acted as catalysts for a number of significant changes in history. Relevant patterns of behaviour are not, however, always consistent. This paper examines the intellectual contexts for the (sometimes quite real) fear that the world might end or be revolutionised in c. AD800 with the advent of the ‘6000th year of the world’. It argues that, in the Carolingian world, apocalyptic belief was widespread but that it centred on an undefined sense of imminence and a concern for reform, rather than a prioritisation of specific dates. Indeed, building on recent developments in the study of computus (‘time-reckoning’), it is clear that chronological systems such as AD-dating were adapted and discussed – at length – for their relevance to paschal reckonings, not apocalypticism. Evidence here also points towards the relative independence of centres such as Auxerre, St Gall and Monte Cassino, where questions about time could be pursued without much or any central direction from figures such as Charlemagne. It is therefore dangerous to posit a relative ‘consensus of silence’ about apocalypticism to explain the thin evidence; and doubly dangerous to extrapolate from it that, for example, Charlemagne’s imperial coronation occurred on Christmas Day AD800 for undocumented apocalyptic reasons rather than for the pressing political concerns indicated in the sources. Apocalypticism was real in eighth-century Europe, but it was more varied than often thought.Peer reviewe
End-to-End Protein Normal Mode Frequency Predictions Using Language and Graph Models and Application to Sonification
No full textThe prediction of mechanical and dynamical properties
of proteins
is an important frontier, especially given the greater availability
of proteins structures. Here we report a series of models that provide
end-to-end predictions of nanodynamical properties of proteins, focused
on high-throughput normal mode predictions directly from the amino
acid sequence. Using neural network models within the family of Natural
Language Processing and graph-based methods, we offer atomistically
based mechanistic predictions of key protein mechanical features.
The models include an end-to-end long short-term memory (LSTM) model,
an end-to-end transformer model, a graph-based transformer model,
and an equivariant graph neural network. All four models show exceptional
performance, with the graph-based transformer architecture offering
the best results but at the cost of requiring a graph structure as
input. Conversely, the LSTM and transformer models offer end-to-end
sequence-to-property prediction capabilities, providing efficient
avenues for protein engineering, analysis, and design. We compare
our results against published data based on a Principal Neighborhood
Aggregation graph neural network, revealing that the transformer model
offers better performance while also being able to predict a large
set of the first 64 normal mode frequencies, simultaneously. The use
of the end-to-end transformer model may facilitate other downstream
applications through the use of transfer learning, and it offers a
comprehensive prediction of dynamical properties without any structural
knowledge, directly from the amino acid sequence. We demonstrate a
potential application in scientific sonification, where the normal
mode frequencies are transposed to generate audible signals for a
detailed analysis of subtle changes of protein sequences
End-to-End Protein Normal Mode Frequency Predictions Using Language and Graph Models and Application to Sonification
No full textThe prediction of mechanical and dynamical properties
of proteins
is an important frontier, especially given the greater availability
of proteins structures. Here we report a series of models that provide
end-to-end predictions of nanodynamical properties of proteins, focused
on high-throughput normal mode predictions directly from the amino
acid sequence. Using neural network models within the family of Natural
Language Processing and graph-based methods, we offer atomistically
based mechanistic predictions of key protein mechanical features.
The models include an end-to-end long short-term memory (LSTM) model,
an end-to-end transformer model, a graph-based transformer model,
and an equivariant graph neural network. All four models show exceptional
performance, with the graph-based transformer architecture offering
the best results but at the cost of requiring a graph structure as
input. Conversely, the LSTM and transformer models offer end-to-end
sequence-to-property prediction capabilities, providing efficient
avenues for protein engineering, analysis, and design. We compare
our results against published data based on a Principal Neighborhood
Aggregation graph neural network, revealing that the transformer model
offers better performance while also being able to predict a large
set of the first 64 normal mode frequencies, simultaneously. The use
of the end-to-end transformer model may facilitate other downstream
applications through the use of transfer learning, and it offers a
comprehensive prediction of dynamical properties without any structural
knowledge, directly from the amino acid sequence. We demonstrate a
potential application in scientific sonification, where the normal
mode frequencies are transposed to generate audible signals for a
detailed analysis of subtle changes of protein sequences
End-to-End Protein Normal Mode Frequency Predictions Using Language and Graph Models and Application to Sonification
No full textThe prediction of mechanical and dynamical properties
of proteins
is an important frontier, especially given the greater availability
of proteins structures. Here we report a series of models that provide
end-to-end predictions of nanodynamical properties of proteins, focused
on high-throughput normal mode predictions directly from the amino
acid sequence. Using neural network models within the family of Natural
Language Processing and graph-based methods, we offer atomistically
based mechanistic predictions of key protein mechanical features.
The models include an end-to-end long short-term memory (LSTM) model,
an end-to-end transformer model, a graph-based transformer model,
and an equivariant graph neural network. All four models show exceptional
performance, with the graph-based transformer architecture offering
the best results but at the cost of requiring a graph structure as
input. Conversely, the LSTM and transformer models offer end-to-end
sequence-to-property prediction capabilities, providing efficient
avenues for protein engineering, analysis, and design. We compare
our results against published data based on a Principal Neighborhood
Aggregation graph neural network, revealing that the transformer model
offers better performance while also being able to predict a large
set of the first 64 normal mode frequencies, simultaneously. The use
of the end-to-end transformer model may facilitate other downstream
applications through the use of transfer learning, and it offers a
comprehensive prediction of dynamical properties without any structural
knowledge, directly from the amino acid sequence. We demonstrate a
potential application in scientific sonification, where the normal
mode frequencies are transposed to generate audible signals for a
detailed analysis of subtle changes of protein sequences
End-to-End Protein Normal Mode Frequency Predictions Using Language and Graph Models and Application to Sonification
No full textThe prediction of mechanical and dynamical properties
of proteins
is an important frontier, especially given the greater availability
of proteins structures. Here we report a series of models that provide
end-to-end predictions of nanodynamical properties of proteins, focused
on high-throughput normal mode predictions directly from the amino
acid sequence. Using neural network models within the family of Natural
Language Processing and graph-based methods, we offer atomistically
based mechanistic predictions of key protein mechanical features.
The models include an end-to-end long short-term memory (LSTM) model,
an end-to-end transformer model, a graph-based transformer model,
and an equivariant graph neural network. All four models show exceptional
performance, with the graph-based transformer architecture offering
the best results but at the cost of requiring a graph structure as
input. Conversely, the LSTM and transformer models offer end-to-end
sequence-to-property prediction capabilities, providing efficient
avenues for protein engineering, analysis, and design. We compare
our results against published data based on a Principal Neighborhood
Aggregation graph neural network, revealing that the transformer model
offers better performance while also being able to predict a large
set of the first 64 normal mode frequencies, simultaneously. The use
of the end-to-end transformer model may facilitate other downstream
applications through the use of transfer learning, and it offers a
comprehensive prediction of dynamical properties without any structural
knowledge, directly from the amino acid sequence. We demonstrate a
potential application in scientific sonification, where the normal
mode frequencies are transposed to generate audible signals for a
detailed analysis of subtle changes of protein sequences
End-to-End Protein Normal Mode Frequency Predictions Using Language and Graph Models and Application to Sonification
No full textThe prediction of mechanical and dynamical properties
of proteins
is an important frontier, especially given the greater availability
of proteins structures. Here we report a series of models that provide
end-to-end predictions of nanodynamical properties of proteins, focused
on high-throughput normal mode predictions directly from the amino
acid sequence. Using neural network models within the family of Natural
Language Processing and graph-based methods, we offer atomistically
based mechanistic predictions of key protein mechanical features.
The models include an end-to-end long short-term memory (LSTM) model,
an end-to-end transformer model, a graph-based transformer model,
and an equivariant graph neural network. All four models show exceptional
performance, with the graph-based transformer architecture offering
the best results but at the cost of requiring a graph structure as
input. Conversely, the LSTM and transformer models offer end-to-end
sequence-to-property prediction capabilities, providing efficient
avenues for protein engineering, analysis, and design. We compare
our results against published data based on a Principal Neighborhood
Aggregation graph neural network, revealing that the transformer model
offers better performance while also being able to predict a large
set of the first 64 normal mode frequencies, simultaneously. The use
of the end-to-end transformer model may facilitate other downstream
applications through the use of transfer learning, and it offers a
comprehensive prediction of dynamical properties without any structural
knowledge, directly from the amino acid sequence. We demonstrate a
potential application in scientific sonification, where the normal
mode frequencies are transposed to generate audible signals for a
detailed analysis of subtle changes of protein sequences
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