Foundation Expenses and Compensation: Interim Report 2005

Examines the 10,000 largest foundations and identifies characteristics and operating styles that affect levels of expenses and compensation, including foundation type, size, staffing, scope of activity, and direct charitable activities

On Critical Relative Distance of DNA Codes for Additive Stem Similarity

We consider DNA codes based on the nearest-neighbor (stem) similarity model which adequately reflects the "hybridization potential" of two DNA sequences. Our aim is to present a survey of bounds on the rate of DNA codes with respect to a thermodynamically motivated similarity measure called an additive stem similarity. These results yield a method to analyze and compare known samples of the nearest neighbor "thermodynamic weights" associated to stacked pairs that occurred in DNA secondary structures.Comment: 5 or 6 pages (compiler-dependable), 0 figures, submitted to 2010 IEEE International Symposium on Information Theory (ISIT 2010), uses IEEEtran.cl

What Drives Foundation Expenses & Compensation? Results of a Three-Year Study, Highlights

This brief presents key findings from the latest report of the Foundation Expenses and Compensation Project—the first large-scale, longterm, systematic study of independent, corporate, and community foundations' expense and compensation patterns and the factors behind them. Documenting the varying characteristics of the 10,000 largest U.S. grantmaking foundations, the study finds these differences—including foundation type, size, and operating activities—essential for understanding foundation finances. Not surprisingly, hiring staff and taking on staff-intensive activities raise charitable administrative expenditures relative to charitable distributions, while relying on unpaid board and family members and engaging in less-staff-intensive activities lower them. Most foundation operations, however, are somewhere between these poles

Successful treatment of periprosthetic joint infection caused by Granulicatella para-adiacens with prosthesis retention: a case report.

Granulicatella and Abiotrophia spp. are difficult to detect due to their complex nutritional requirements. Infections with these organisms are associated with high treatment failure rates. We report the first implant-associated infection caused by Granulicatella para-adiacens, which was cured with anti-microbial treatment consisting of anti-biofilm-active rifampin and debridement, exchange of mobile parts and retention of the prosthesis. Patient with a history of left hip arthroplasty presented with acute onset of fever, pain and limited range of motion of the left hip. Arthrocentesis of the affected joint yielded purulent fluid and exchange of mobile parts of the prosthesis, but retention of fixed components was performed. Granulicatella para-adiacens grew from preoperative and intraoperative cultures, including sonication fluid of the removed implant. The transesophageal echocardiography showed a vegetation on the mitral valve; the orthopantogram demonstrated a periapical dental abscess. The patient was treated with intravenous penicillin G and gentamicin for 4 weeks, followed by levofloxacin and rifampin for additional 2 months. At discharge and at follow-up 1, 2 and 5 years later, the patient was noted to have a functional, pain-free, and radiologically stable hip prosthesis and the serum C-reactive protein was normal. Although considered a difficult-to-treat organism, we report a successful treatment of the Granulicatella hip prosthesis infection with prosthesis retention and a prolonged antibiofilm therapy including rifampin. The periapical dental abscess is considered the primary focus of hematogenously infected hip prosthesis, underlining the importance treatment of periodontitis prior to arthroplasty and of proper oral hygiene for prevention of hematogenous infection after arthroplasty

Random Coding Bounds for DNA Codes Based on Fibonacci Ensembles of DNA Sequences

We consider DNA codes based on the concept of a weighted 2-stem similarity measure which reflects the ”hybridization potential” of two DNA sequences. A random coding bound on the rate of DNA codes with respect to a thermodynamic motivated similarity measure is proved. Ensembles of DNA strands whose sequence composition is restricted in a manner similar to the restrictions in binary Fibonacci sequences are introduced to obtain the bound

DogOnt - Ontology Modeling for Intelligent Domotic Environments

Abstract. Home automation has recently gained a new momentum thanks to the ever-increasing commercial availability of domotic components. In this context, researchers are working to provide interoperation mechanisms and to add intelligence on top of them. For supporting intelligent behaviors, house modeling is an essential requirement to understand current and future house states and to possibly drive more complex actions. In this paper we propose a new house modeling ontology designed to fit real world domotic system capabilities and to support interoperation between currently available and future solutions. Taking advantage of technologies developed in the context of the Semantic Web, the DogOnt ontology supports device/network independent description of houses, including both “controllable ” and architectural elements. States and functionalities are automatically associated to the modeled elements through proper inheritance mechanisms and by means of properly defined SWRL auto-completion rules which ease the modeling process, while automatic device recognition is achieved through classification reasoning.

PlanT: Explainable Planning Transformers via Object-Level Representations

Planning an optimal route in a complex environment requires efficientreasoning about the surrounding scene. While human drivers prioritize importantobjects and ignore details not relevant to the decision, learning-basedplanners typically extract features from dense, high-dimensional gridrepresentations containing all vehicle and road context information. In thispaper, we propose PlanT, a novel approach for planning in the context ofself-driving that uses a standard transformer architecture. PlanT is based onimitation learning with a compact object-level input representation. On theLongest6 benchmark for CARLA, PlanT outperforms all prior methods (matching thedriving score of the expert) while being 5.3x faster than equivalentpixel-based planning baselines during inference. Combining PlanT with anoff-the-shelf perception module provides a sensor-based driving system that ismore than 10 points better in terms of driving score than the existing state ofthe art. Furthermore, we propose an evaluation protocol to quantify the abilityof planners to identify relevant objects, providing insights regarding theirdecision-making. Our results indicate that PlanT can focus on the most relevantobject in the scene, even when this object is geometrically distant.<br