Practical Run-time Checking via Unobtrusive Property Caching

The use of annotations, referred to as assertions or contracts, to describe program properties for which run-time tests are to be generated, has become frequent in dynamic programing languages. However, the frameworks proposed to support such run-time testing generally incur high time and/or space overheads over standard program execution. We present an approach for reducing this overhead that is based on the use of memoization to cache intermediate results of check evaluation, avoiding repeated checking of previously verified properties. Compared to approaches that reduce checking frequency, our proposal has the advantage of being exhaustive (i.e., all tests are checked at all points) while still being much more efficient than standard run-time checking. Compared to the limited previous work on memoization, it performs the task without requiring modifications to data structure representation or checking code. While the approach is general and system-independent, we present it for concreteness in the context of the Ciao run-time checking framework, which allows us to provide an operational semantics with checks and caching. We also report on a prototype implementation and provide some experimental results that support that using a relatively small cache leads to significant decreases in run-time checking overhead.Comment: 30 pages, 1 table, 170 figures; added appendix with plots; To appear in Theory and Practice of Logic Programming (TPLP), Proceedings of ICLP 201

Recommendations to the Social Security Administration on the Design of the Mental Health Treatment Study

Many beneficiaries with mental illness who have a strong desire to work nevertheless continue to seek the protection and security of disability benefits, not only because of the income such benefits provide but also for the health care coverage that comes with it. Further complicating matters is that few jobs available to people with mental illnesses have mental health care coverage, forcing individuals to choose between employment and access to care. These barriers, coupled with the limited treatment options and negative employer attitudes and even discrimination when it comes to employing people with serious metal illness, help "explain" the very rates of low labor force participation among people with psychiatric disabilities

Orbital and Maxillofacial Computer Aided Surgery: Patient-Specific Finite Element Models To Predict Surgical Outcomes

This paper addresses an important issue raised for the clinical relevance of Computer-Assisted Surgical applications, namely the methodology used to automatically build patient-specific Finite Element (FE) models of anatomical structures. From this perspective, a method is proposed, based on a technique called the Mesh-Matching method, followed by a process that corrects mesh irregularities. The Mesh-Matching algorithm generates patient-specific volume meshes from an existing generic model. The mesh regularization process is based on the Jacobian matrix transform related to the FE reference element and the current element. This method for generating patient-specific FE models is first applied to Computer-Assisted maxillofacial surgery, and more precisely to the FE elastic modelling of patient facial soft tissues. For each patient, the planned bone osteotomies (mandible, maxilla, chin) are used as boundary conditions to deform the FE face model, in order to predict the aesthetic outcome of the surgery. Seven FE patient-specific models were successfully generated by our method. For one patient, the prediction of the FE model is qualitatively compared with the patient's post-operative appearance, measured from a Computer Tomography scan. Then, our methodology is applied to Computer-Assisted orbital surgery. It is, therefore, evaluated for the generation of eleven patient-specific FE poroelastic models of the orbital soft tissues. These models are used to predict the consequences of the surgical decompression of the orbit. More precisely, an average law is extrapolated from the simulations carried out for each patient model. This law links the size of the osteotomy (i.e. the surgical gesture) and the backward displacement of the eyeball (the consequence of the surgical gesture)