Visualization of the Digital Divide Among K-12 Students: Open Data, Quantitative Measures, and Policy Implications

Our work utilized a multi-disciplinary approach to assess the digital divide among K-12 students through socio-technical and economic analysis. Results show that access to high-speed internet (broadband) and use continued to be a challenge for children and schools located in disadvantaged communities. Three visualizations were developed to display the digital disparity at the county level across our country and to support decision-making in resource allocation to improve broadband access and utilization

EffectiveSan: Type and Memory Error Detection using Dynamically Typed C/C++

Low-level programming languages with weak/static type systems, such as C and C++, are vulnerable to errors relating to the misuse of memory at runtime, such as (sub-)object bounds overflows, (re)use-after-free, and type confusion. Such errors account for many security and other undefined behavior bugs for programs written in these languages. In this paper, we introduce the notion of dynamically typed C/C++, which aims to detect such errors by dynamically checking the "effective type" of each object before use at runtime. We also present an implementation of dynamically typed C/C++ in the form of the Effective Type Sanitizer (EffectiveSan). EffectiveSan enforces type and memory safety using a combination of low-fat pointers, type meta data and type/bounds check instrumentation. We evaluate EffectiveSan against the SPEC2006 benchmark suite and the Firefox web browser, and detect several new type and memory errors. We also show that EffectiveSan achieves high compatibility and reasonable overheads for the given error coverage. Finally, we highlight that EffectiveSan is one of only a few tools that can detect sub-object bounds errors, and uses a novel approach (dynamic type checking) to do so.Comment: To appear in the Proceedings of 39th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI2018

The University of Hawaii Institute for Astronomy CCD camera control system

The University of Hawaii Institute for Astronomy CCD Camera Control System consists of a NeXT workstation, a graphical user interface, and a fiber optics communications interface which is connected to a San Diego State University CCD controller. The UH system employs the NeXT-resident Motorola DSP 56001 as a real time hardware controller. The DSP 56001 is interfaced to the Mach-based UNIX of the NeXT workstation by DMA and multithreading. Since the SDSU controller also uses the DPS 56001, the NeXT is used as a development platform for the embedded control software. The fiber optic interface links the two DSP 56001's through their Synchronous Serial Interfaces. The user interface is based on the NeXTStep windowing system. It is easy to use and features real-time display of image data and control over all camera functions. Both Loral and Tektronix 2048 x 2048 CCD's have been driven at full readout speeds, and the system is intended to be capable of simultaneous readout of four such CCD's. The total hardware package is compact enough to be quite portable and has been used on five different telescopes on Mauna Kea. The complete CCD control system can be assembled for a very low cost. The hardware and software of the control system has proven to be quite reliable, well adapted to the needs of astronomers, and extensible to increasingly complicated control requirements

Open-closed homotopy algebra in mathematical physics

In this paper we discuss various aspects of open-closed homotopy algebras (OCHAs) presented in our previous paper, inspired by Zwiebach's open-closed string field theory, but that first paper concentrated on the mathematical aspects. Here we show how an OCHA is obtained by extracting the tree part of Zwiebach's quantum open-closed string field theory. We clarify the explicit relation of an OCHA with Kontsevich's deformation quantization and with the B-models of homological mirror symmetry. An explicit form of the minimal model for an OCHA is given as well as its relation to the perturbative expansion of open-closed string field theory. We show that our open-closed homotopy algebra gives us a general scheme for deformation of open string structures ($A_\infty$-algebras) by closed strings ($L_\infty$-algebras).Comment: 38 pages, 4 figures; v2: published versio

Sonographically Guided Popliteus Tendon Sheath Injection

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135538/1/jum2010295775.pd

Embracing additive manufacture: implications for foot and ankle orthosis design

<p>Abstract</p> <p>Background</p> <p>The design of foot and ankle orthoses is currently limited by the methods used to fabricate the devices, particularly in terms of geometric freedom and potential to include innovative new features. Additive manufacturing (AM) technologies, where objects are constructed via a series of sub-millimetre layers of a substrate material, may present the opportunity to overcome these limitations and allow novel devices to be produced that are highly personalised for the individual, both in terms of fit and functionality.</p> <p>Two novel devices, a foot orthosis (FO) designed to include adjustable elements to relieve pressure at the metatarsal heads, and an ankle foot orthosis (AFO) designed to have adjustable stiffness levels in the sagittal plane, were developed and fabricated using AM. The devices were then tested on a healthy participant to determine if the intended biomechanical modes of action were achieved.</p> <p>Results</p> <p>The adjustable, pressure relieving FO was found to be able to significantly reduce pressure under the targeted metatarsal heads. The AFO was shown to have distinct effects on ankle kinematics which could be varied by adjusting the stiffness level of the device.</p> <p>Conclusions</p> <p>The results presented here demonstrate the potential design freedom made available by AM, and suggest that it may allow novel personalised orthotic devices to be produced which are beyond the current state of the art.</p

Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test

Diabetes is a large healthcare burden worldwide. There is substantial evidence that lifestyle modifications and drug intervention can prevent diabetes, therefore, an early identification of high risk individuals is important to design targeted prevention strategies. In this paper, we present an automatic tool that uses machine learning techniques to predict the development of type 2 diabetes mellitus (T2DM). Data generated from an oral glucose tolerance test (OGTT) was used to develop a predictive model based on the support vector machine (SVM). We trained and validated the models using the OGTT and demographic data of 1,492 healthy individuals collected during the San Antonio Heart Study. This study collected plasma glucose and insulin concentrations before glucose intake and at three time-points thereafter (30, 60 and 120 min). Furthermore, personal information such as age, ethnicity and body-mass index was also a part of the data-set. Using 11 OGTT measurements, we have deduced 61 features, which are then assigned a rank and the top ten features are shortlisted using minimum redundancy maximum relevance feature selection algorithm. All possible combinations of the 10 best ranked features were used to generate SVM based prediction models. This research shows that an individual’s plasma glucose levels, and the information derived therefrom have the strongest predictive performance for the future development of T2DM. Significantly, insulin and demographic features do not provide additional performance improvement for diabetes prediction. The results of this work identify the parsimonious clinical data needed to be collected for an efficient prediction of T2DM. Our approach shows an average accuracy of 96.80% and a sensitivity of 80.09% obtained on a holdout set