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Based on that, we study the tracking control design problem of an input output linearizable system with multiple time-varying output constraints, where the output constraints are encoded as CBFs and the barrier conditions are expressed as hard constraints in a quadratic program (QP) whose feasibility is guaranteed by the control-sharing property of the CBFs.
These constraints are encoded as possible value fillers for the measuredWith slot for a specific protein property.
The constraints are encoded in a cost function and the algorithm can search through the parameter space for different cost thresholds.
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We show how to encode relational graphs as neural activation in WM and how to use constraints that are encoded in synapses, in order to retrieve and process such complex structures.
Studies are providing fundamental insights into the nature of these topological constraints, how they are encoded by the RNA secondary structure, and how they interplay with other interactions, breathing new meaning to RNA secondary structure.
Stakeholder constraints, preferences, and objectives are encoded in this cost function.
Principles are encoded as constraints, which become formulas of a two-layer graded hybrid logic, where the upper layer restricts reconfigurations, and the lower layer constrains the resulting configurations.
The remaining constraints for a labeling to be complete are encoded similarly.
Our ancestors are encoded in our genes.
MBs with high visual sensitivity will be encoded with small quantization parameters, and MBs with low visual sensitivities are encoded with large quantization parameters so that better subjective visual quality could be obtained under the same given bit rate constraint.
In addition, a minimal loop size of 3 is enforced, i.e. ∀ (i, j ) ∈ P : j − i > 3. The user can define three types of constraints: The structure constraint C str is used to provide the explicit and implicit secondary structure constraints, which is encoded in an extended dot-bracket notation.
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