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The growing complexity of safety-relevant systems causes an increasing effort for safety assurance. The reduction of development costs and time-to-market, while guaranteeing safe operation, is therefore a major challenge. In order to enable efficient safety assessment of complex architectures, we present an approach, which combines deductive safety analyses, in form of Component Fault Trees (CFTs), with an Error Effect Simulation (EES) for sanity checks. The combination reduces the drawbacks of both analyses, such as the subjective failure propagation assumptions in the CFTs or the determination of relevant fault scenarios for the EES. Both CFTs and the EES provide a modular, reusable and compositional safety analysis and are applicable throughout the whole design process. They support continuous model refinement and the reuse of conducted safety analysis and simulation models. Hence, safety goal violations can be identified in early design stages and the reuse of conducted safety analyses reduces the overhead for safety assessment.

The Elementary and Secondary Education Act (ESEA) was comprehensively reauthorized by the Every Student Succeeds Act (ESSA; P.L. 114-95) on December 10, 2015. The Title I-A program is the largest grant program authorized under the ESEA and is funded at $15.8 billion for FY2018. Title I-A of the ESEA authorizes aid to Local education agency's (LEAs) for the education of disadvantaged children. Title I-A grants provide supplementary educational and related services to low-achieving and other students attending pre-kindergarten through grade 12 schools with relatively high concentrations of students from low-income families. Title I-A has also become a vehicle to which a number of requirements affecting broad aspects of public K-12 education for all students have been attached as conditions for receiving Title I-A grants. Under Title I-A, funds are distributed to state educational agencies (SEAs) and (LEAs) based on four formulas. The ESSA made few changes to these formulas. One notable change is an increase in the set-aside for the Bureau of Indian Education (BIE) and Outlying Areas from 1.0% to 1.1%. This change will take effect beginning in FY2017 provided the total amount available for state grants would not be less than the amount available in FY2016. All changes to the Title I-A grant allocation process made by the ESSA will take effect beginning in FY2017. This report provides a detailed discussion of the four Title I-A formulas used to determine grants as modified by the ESSA. Table A-1 in Appendix A offers an overview of the key elements included in the four formulas. Appendix B provides an overview of Title I-A appropriations levels in recent years. [Elizabeth Crowe, former Research Assistant at CRS, also contributed to this report.]

The Elementary and Secondary Education Act (ESEA) is the primary source of federal aid to elementary and secondary education. The ESEA was last reauthorized by the Every Student Succeeds Act (ESSA; P.L. 114-95) in 2015. The Title I-A program has always been the largest grant program authorized under the ESEA. Title I-A grants provide supplementary educational and related services to low-achieving and other students attending elementary and secondary schools with relatively high concentrations of students from low-income families. Title I-A grant amounts are primarily driven by the number of "formula children"--principally children from low-income families--in an LEA, although all four formulas also include an expenditure factor based on education expenditures, minimum grant provisions, and hold harmless provisions. Since the initial enactment of Title I-A in 1965, the formula(s) have been criticized for being more favorable to more densely populated and typically urban areas due to how children from low-income families are counted, and for being more favorable to wealthier states due to the inclusion of factors based on education expenditures. This report analyzes issues related to three of the major debates surrounding the Title I-A formulas: (1) the effect of different formula factors and provisions on grant amounts, (2) whether the formulas are more favorable to certain types of LEAs and states, and (3) how effectively the Title I-A formulas target funds on concentrations of poverty. The report is intended to complement CRS Report R44898, "History of the ESEA Title I-A Formulas," which provides a detailed examination of the history of the Title I-A formulas and of the underlying tensions in the policy debates about the design of the formulas from enactment of the original ESEA through enactment of the ESSA.

We present an alternative layer to convolution layers in convolutional neural networks (CNNs). Our approach reduces the complexity of convolutions by replacing it with binary decisions. Those binary decisions are used as indexes to conditional distributions where each weight represents a leaf in a decision tree. This means that only the indices to the weights need to be determined once, thus reducing the complexity of convolutions by the depth of the output tensor. Index computation is performed by simple binary decisions that require fewer cycles compared to conventionally used multiplications. In addition, we show how convolutions can be replaced by binary decisions. These binary decisions form indices in the conditional distributions and we show how they are used to replace 2D weight matrices as well as 3D weight tensors. These new layers can be trained like convolution layers in CNNs based on the backpropagation algorithm, for which we provide a formalization. Our results on multiple publicly available data sets show that our approach performs similar to conventional neuronal networks. Beyond the formalized reduction of complexity and the improved qualitative performance, we show the runtime improvement empirically compared to convolution layers., Comment: Will be published in the proceedings oft he AAAI 2020 conference

Eye movements hold information about human perception, intention and cognitive state. Various algorithms have been proposed to identify and distinguish eye movements, particularly fixations, saccades, and smooth pursuits. A major drawback of existing algorithms is that they rely on accurate and constant sampling rates, impeding straightforward adaptation to new movements such as micro saccades. We propose a novel eye movement simulator that i) probabilistically simulates saccade movements as gamma distributions considering different peak velocities and ii) models smooth pursuit onsets with the sigmoid function. This simulator is combined with a machine learning approach to create detectors for general and specific velocity profiles. Additionally, our approach is capable of using any sampling rate, even with fluctuations. The machine learning approach consists of different binary patterns combined using conditional distributions. The simulation is evaluated against publicly available real data using a squared error, and the detectors are evaluated against state-of-the-art algorithms.

Real-time, accurate, and robust pupil detection is an essential prerequisite for pervasive video-based eye-tracking. However, automated pupil detection in realworld scenarios has proven to be an intricate challenge due to fast illumination changes, pupil occlusion, non-centered and off-axis eye recording, as well as physiological eye characteristics. In this paper, we approach this challenge through: I) a convolutional neural network (CNN) running in real time on a single core, II) a novel computational intensive two stage CNN for accuracy improvement, and III) a fast propability distribution based refinement method as a practical alternative to II. We evaluate the proposed approaches against the state-of-the-art pupil detection algorithms, improving the detection rate up to ~9% percent points on average over all data sets (~7% on one CPU core 7ms). This evaluation was performed on over 135,000 images: 94,000 images from the literature, and 41,000 new hand-labeled and challenging images contributed by this work (v1.0)., Comment: Pupil detection, pupil center estimation, image processing, CNN. arXiv admin note: substantial text overlap with arXiv:1601.04902

For single-cell or metagenomic sequencing projects, it is necessary to sequence with a very high mean coverage in order to make sure that all parts of the sample DNA get covered by the reads produced. This leads to huge datasets with lots of redundant data. A filtering of this data prior to assembly is advisable. Titus Brown et al. (2012) presented the algorithm Diginorm for this purpose, which filters reads based on the abundance of their $k$-mers. We present Bignorm, a faster and quality-conscious read filtering algorithm. An important new feature is the use of phred quality scores together with a detailed analysis of the $k$-mer counts to decide which reads to keep. With recommended parameters, in terms of median we remove 97.15% of the reads while keeping the mean phred score of the filtered dataset high. Using the SDAdes assembler, we produce assemblies of high quality from these filtered datasets in a fraction of the time needed for an assembly from the datasets filtered with Diginorm. We conclude that read filtering is a practical method for reducing read data and for speeding up the assembly process. Our Bignorm algorithm allows assemblies of competitive quality in comparison to Diginorm, while being much faster. Bignorm is available for download at https://git.informatik.uni-kiel.de/axw/Bignorm.git

In this paper, the application of a cycle accurate binary translator for rapid prototyping of SoCs will be presented. This translator generates code to run on a rapid prototyping system consisting of a VLIW processor and FPGAs. The generated code is annotated with information that triggers cycle generation for the hardware in parallel to the execution of the translated program. The VLIW processor executes the translated program whereas the FPGAs contain the hardware for the parallel cycle generation and the bus interface that adapts the bus of the VLIW processor to the SoC bus of the emulated processor core., Comment: Submitted on behalf of EDAA (http://www.edaa.com/)