Image-Based Planning of Minimally Traumatic Inner Ear Access for Robotic Cochlear Implantation

Objective: During robotic cochlear implantation, an image-guided robotic system provides keyhole access to the scala tympani of the cochlea to allow insertion of the cochlear implant array. To standardize minimally traumatic robotic access to the cochlea, additional hard and soft constraints for inner ear access were proposed during trajectory planning. This extension of the planning strategy aims to provide a trajectory that preserves the anatomical and functional integrity of critical intra-cochlear structures during robotic execution and allows implantation with minimal insertion angles and risk of scala deviation.Methods: The OpenEar dataset consists of a library with eight three-dimensional models of the human temporal bone based on computed tomography and micro-slicing. Soft constraints for inner ear access planning were introduced that aim to minimize the angle of cochlear approach, minimize the risk of scala deviation and maximize the distance to critical intra-cochlear structures such as the osseous spiral lamina. For all cases, a solution space of Pareto-optimal trajectories to the round window was generated. The trajectories satisfy the hard constraints, specifically the anatomical safety margins, and optimize the aforementioned soft constraints. With user-defined priorities, a trajectory was parameterized and analyzed in a virtual surgical procedure.Results: In seven out of eight cases, a solution space was found with the trajectories safely passing through the facial recess. The solution space was Pareto-optimal with respect to the soft constraints of the inner ear access. In one case, the facial recess was too narrow to plan a trajectory that would pass the nerves at a sufficient distance with the intended drill diameter. With the soft constraints introduced, the optimal target region was determined to be in the antero-inferior region of the round window membrane.Conclusion: A trend could be identified that a position between the antero-inferior border and the center of the round window membrane appears to be a favorable target position for cochlear tunnel-based access through the facial recess. The planning concept presented and the results obtained therewith have implications for planning strategies for robotic surgical procedures to the inner ear that aim for minimally traumatic cochlear access and electrode array implantation.

NEST: Neural Soft Type Constraints to Improve Entity Linking in Tables

Matching tables against Knowledge Graphs is a crucial task in many applications. A widely adopted solution to improve the precision of matching algorithms is to refine the set of candidate entities by their type in the Knowledge Graph. However, it is not rare that a type is missing for a given entity. In this paper, we propose a methodology to improve the refinement phase of matching algorithms based on type prediction and soft constraints. We apply our methodology to state-of-the-art algorithms, showing a performance boost on different datasets.

Constraint-following servo control for an underactuated mobile robot under hard constraints

This article formulates the control problem of underactuated mobile robot as servo constraint-following, and develops a novel constraint-following servo control approach for underactuated mobile robot under both servo soft and hard constraints. Servo soft constraints are expressed as equalities, which may be holonomic or non-holonomic. Servo hard constraints are expressed as inequalities. It is required that the underactuated mobile robot motion eventually converges to servo soft constraints, and satisfies servo hard constraints at all times. Diffeomorphism is employed to incorporate hard constraints into soft constraints, yielding new soft constraints to relax hard constraints. By this, we design a constraint-following servo control based on the new servo soft constraints, which drives the system to strictly follow the original servo soft and hard constraints. The effectiveness of the proposed approach is proved by rigorous proof and simulations.

An Intelligent Technique for Solving Timetable Problem

Timetabling problem is complex combinatorial resources allocation problems. There are two hard and soft constraints to be satisfied. The timetable is feasible if all hard constraints are satisfied. Besides, satisfying more of the soft constraints produces a high-quality timetable. Crow Search Algorithm (CSA) as an intelligence technique presents for solving timetable problem. CSA like all meta-heuristic optimization techniques is a nature-inspire of intelligent behavior of crows. The proposed CSA tested using the well-known benchmark of hard timetabling datasets (hdtt). Taguchi’s method used to tune the best parameter combinations for the factors and levels. The tuned parameters of CSA are applied on datasets in separate experiment. The results show that the proposed CSA is superior to generate solutions in reasonable CPU time when compared with other literature techniques.

A new two-stage variable neighborhood search algorithm for the nurse rostering problem

The nurse rostering problem refers to the assignment of nurses to daily shifts according to the required demand of each shift and day, with consideration for the operational requirements and nurses’ preferences. This problem is known to be an NP-hard problem, difficult to be solved using the known exact solution methods especially for large size instances. Mostly, this problem is modeled with soft and hard constraint, and the objective is set to minimize the violations for the soft constraints. In this paper, a new two-stage variable neighborhood search algorithm is proposed for solving the nurse rostering problem. The first stage aims at minimizing the violations of the soft constraints with the higher penalty weights in the objective function. While the second stage considers minimizing the total solution penalty taking into account all the soft constraint. The proposed algorithm was tested using 24 benchmark instances. The test results revealed that the proposed algorithm is able to compete the results of a recent heuristic approach from literature for most of the tested instances.

Exploiting Soft Constraints within Decomposition and Coordination Methods for Sub-hourly Unit Commitment

Unit commitment (UC) is an important problem solved on a daily basis within a strict time limit. While hourly UC problems are currently considered, they may not be flexible enough with the fast-changing demand and the increased penetration of intermittent renewables. Sub-hourly UC is therefore recommended. This, however, will significantly increase problem complexity even under the deterministic setting, and current methods may not be able to obtain good solutions within the time limit. In this paper, deterministic sub-hourly UC is considered, with the innovative exploitation of soft constraints – constraints that do not need to be strictly satisfied, but with predetermined penalty coefficients for their violations. The key idea is the “surrogate optimization” concept that ensures multiplier convergence within “surrogate” Lagrangian relaxation as long as the “surrogate optimality condition” is satisfied without the need to optimally solve the “relaxed problem.” Consequently, subproblems can still be formed and optimized when soft constraints are not relaxed, leading to a drastically reduced number of multipliers and improved performance. To further enhance the method, a parallel version is developed. Testing results on the Polish system demonstrate the effectiveness and robustness of both the sequential and parallel versions at finding high-quality solutions within the time limit.

Chapter 23. MaxSAT, Hard and Soft Constraints

MaxSAT solving is becoming a competitive generic approach for solving combinatorial optimization problems, partly due to the development of new solving techniques that have been recently incorporated into modern MaxSAT solvers, and to the challenge problems posed at the MaxSAT Evaluations. In this chapter we present the most relevant results on both approximate and exact MaxSAT solving, and survey in more detail the techniques that have proven to be useful in branch and bound MaxSAT and Weighted MaxSAT solvers. Among such techniques, we pay special attention to the definition of good quality lower bounds, powerful inference rules, clever variable selection heuristics and suitable data structures. Moreover, we discuss the advantages of dealing with hard and soft constraints in the Partial MaxSAT formalims, and present a summary of the MaxSAT Evaluations that have been organized so far as affiliated events of the International Conference on Theory and Applications of Satisfiability Testing.