Results 231 to 240 of about 5,451,323 (354)
Interprocedurally Analysing Linear Inequality Relations
H. Seidl +2 more
semanticscholar +1 more source
Abstract In today's digital age, misinformation propagates rapidly through digital channels, blurring the lines between truth and fiction, and challenging the foundations of trust in information sources. Although affecting all spheres of life, misinformation poses a significant threat to workers highlighting a critical intersection between ...
Ashwani Malhotra +4 more
wiley +1 more source
The prevalence of metabolic syndrome and its economic inequality in an elderly population. [PDF]
Fotouhi F +7 more
europepmc +1 more source
On Boundness Conditions for the Set of Feasible Points of Systems of Linear Inequalities
E. O. Effanga, Z. Lipcsey, M. E. Nja
openalex +2 more sources
Abstract Many academic libraries aim to improve services for and interactions with patrons and stakeholders who are neurodivergent, that is, those who have neurocognitive functions that differ from what is considered normal. To support this aim, numerous scholarly works have focused on neurodivergence in academic libraries, but such works have yet to ...
Catharina Ochsner, Jesse Dinneen
wiley +1 more source
A Sharp Quantitative Alexandrov Inequality and Applications to Volume Preserving Geometric Flows in 3D. [PDF]
Julin V, Morini M, Oronzio F, Spadaro E.
europepmc +1 more source
No‐regret and low‐regret control for a weakly coupled abstract hyperbolic system
Abstract This paper explores an optimal control problem of weakly coupled abstract hyperbolic systems with missing initial data. Hyperbolic systems, known for their wave‐like phenomena and complexity, become even more challenging with weak coupling between subsystems.
Meriem Louafi +3 more
wiley +1 more source
Bounded Variation Separates Weak and Strong Average Lipschitz. [PDF]
Elperin A, Kontorovich A.
europepmc +1 more source
Risk‐aware safe reinforcement learning for control of stochastic linear systems
Abstract This paper presents a risk‐aware safe reinforcement learning (RL) control design for stochastic discrete‐time linear systems. Rather than using a safety certifier to myopically intervene with the RL controller, a risk‐informed safe controller is also learned besides the RL controller, and the RL and safe controllers are combined together ...
Babak Esmaeili +2 more
wiley +1 more source
Fast Reflected Forward-Backward algorithm: achieving fast convergence rates for convex optimization with linear cone constraints. [PDF]
Boţ RI, Nguyen DK, Zong C.
europepmc +1 more source

