Miracle-dz/UniFAR-FTUs huggingface dataset:
Institute for Research on Poverty Discussion Papers Child Support, Routine Income Withholding, and Postdivorce Income for Divorced Mothers and Their Children Child Support, Routine Income Withholding, and Postdivorce Income for Divorced Mothers and Their Children Introduction

Child support reform has been proposed as a way of reducing "welfare dependency" and countering the "feminization of poverty" while increasing the well-being of children living in single-parent households. The federal government and some states have already begun to change laws regarding the establishment of child support awards and the collection of payments. Routine income withholding of child support payments, mandated by the 1988 Family Support Act, is designed to increase the proportion of child support collected. This paper assesses its effects on postdivorce income by using data from a demonstration of routine withholding in ten Wisconsin counties. Unfortunately, the data conclude that routine income withholding will have only a small effect on postdivorce income. The authors suggest that to substantially increase postdivorce income, as well as child support, awards in more family court cases must be established, the amount of those awards must be greater, and the collection rate for those who have awards must be improved.
Evaluation of Site Amplification of Erbaa, Tokat (Turkey)

The study area, Erbaa, is one of the largest towns of Tokat with a population of 47000 in the northern part of Turkey. Erbaa is in North Anatolian Fault Zone (NAFZ) and partly located on the Kelkit river plain, also referred to as the Erbaa basin. After the disastrous 1942 earthquake (Ms=7.2), the settlement area was seriously damaged and moved farther southwards of its old place in 1944. Dynamic properties of Erbaa soils were determined and shear wave velocity profiles were prepared to be used in site response analyses as part of a microzonation study. During this process, empirically-based shear wave velocities were calculated and site-specific formulas were proposed. 1-D equivalent linear site response analyses were performed in accordance with site-specific grid model using ProSHAKE (v.1.12) software. The amplification ratio was calculated on the basis of site amplification method using soil/bedrock ratio to obtain amplification factors (AF) for the study area. Amplification factors from 1-D site response analyses mostly vary within a range of approximately 1.5-2.5 in the study area.
Cornell Engineering Research Conference (cerc) Presentations and Poster Session @bullet Our Small World: from Micro-to- Nanoscale Investigations @bullet Computational Modeling and Robotic Systems Sponsors Organizers the following Companies' Name and Logo Must Appear On: ‐promotional Program Poster t

Light refreshments will be provided during the presentation sessions. Free tickets for the reception will be distributed in the oral presentations. All students are welcome to attend. Abstracts On behalf of the Organizing Committee, welcome to the Cornell Engineering Research Conference 2010. This year we are pleased to feature two parallel sessions of Ph.D. and M.Eng. oral presentations in the morning and the afternoon, and an early afternoon poster session. The oral sessions and poster session include best presentation competitions. Student authors of the winning presentations and posters will be recognized in the evening reception. Reception will feature keynote address by Professor Clifford Pollock. We encourage you to participate fully in the day's programming, which highlights graduate research in four strategic areas; We hope that the program will stimulate discussion and a lively exchange of ideas. * Tickets for the reception will be distributed at the oral sessions free of charge. Light refreshments will be provided during the oral presentation sessions. Poster 56 Emily M. Chandler: Heterotypic tumor models to study breast cancer under pathologically relevant conditions Advisor: Claudia Fischbach Poster 57 Alexander J. Veach: UAN polymer nanoparticles encapsulating anti-inflammatory drugs for targeted delivery, imaging, and therapy Advisor: Moonsoo Jin Poster 58 Alycia S. Gailey: A novel approach for wireless communication of in vivo data from freely moving research animals Advisor: Manfred Lindau Poster 59 Todd Anderson: Distributed denitrification in a northeastern agricultural landscape Advisor: Todd Walter 9 ABSTRACTS Ordered alphabetically by the last name of the presenter Conversion of second-generation renewable biomass to useful products is gaining attention as an alternative to traditional sugar and starch-based renewable energy crops. The recalcitrance of resources such as (ligno)cellulose makes biological conversion exceptionally challenging. We studied conversion of fcorn fiber into n-butyrate, which is a biofuel precursor. The bioconversion process, which consists of anaerobic hydrolysis and fermentation, is performed with thermophilic (55 degree Celsius) nondefined mixed microbial cultures. These open microbial systems are less energy intensive than pure culture processes because complete saccharification and sterilization of substrates is unnecessary. Nondefined microbial conversion presents major hurdles to overcome, however, because endproducts are less concentrated and more diverse. To study product selectivity, we operated three identical 5-L anaerobic sequencing batch reactors treating corn fiber for 275 days, each with a different substrate pretreatment. The three pretreatments were performed at 160 degree Celsius for 20 min with the variations: 1. ) 0.5 % w/v H2SO4 (acid); …
On the $L^{p}$ index of spin Dirac operators on conical manifolds

We compute the index of the Dirac operator on a spin Riemannian manifold with conical singularities, acting from Lp(_+) to Lq(_-) with p, q > 1. When 1+n/p-n/q > 0 we obtain the usual Atiyah-Patodi-Singer formula, but with a spectral cut at (n + 1)/2 - n/q instead of 0 in the definition of the eta invariant. In particular we reprove Chou's formula for the L2 index. For 1+n/p-n/q _ 0 the index formula contains an extra term related to the Calderon projector.
Environment, Energy, and Economic Development

Limited Electronic Distribution Rights This document and trademark(s) contained herein are protected by law as indicated in a notice appearing later in this work. This electronic representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of RAND electronic documents to a non-RAND website is prohibited. RAND electronic documents are protected under copyright law. Permission is required from RAND to reproduce, or reuse in another form, any of our research documents for commercial use. For information on reprint and linking permissions, please see RAND Permissions. Skip all front matter: Jump to Page 16 The RAND Corporation is a nonprofit institution that helps improve policy and decisionmaking through research and analysis.
IMAGE SEARCH RESULTS DIVERSIFICATION

In the last years, the volume of data (text, image or multi- media) has grown exponentially. In this context, the information retrieval task has become more and more difficult. This paper aims to present an application that performs image retrieval based on Flickr network image collection. After the user fills in the query, the system is responsible with (1) query processing, (2) image retrieval and (3) results displaying. Before retrieving the results, the system clusters the images based on several cri- terions and shows to the user one representative image for every cluster it creates. In this way, the results displayed to the user are diverse and he still has the possibility to view all the images from a cluster, when clicking on the representative image. The system achieves diversity in two ways: (i) at query processing, when the user adds from WordNet the lexical family corresponding to query keywords, and (ii) when the results are displayed and the clusters are created.
IMPROVED BLOCK BASED FEATURE LEVEL IMAGE FUSION TECHNIQUE USING CONTOURLET WITH NEURAL NETWORK

As multisensory data is made available in many areas such as remote sensing, medical imaging, etc, the sensor fusion has become a new field for research. Multisensor image fusion mainly focuses on combining spatial information of a high resolution panchromatic (PAN) image with spectral information of a low resolution multispectral image (MS) to produce an image with highest spatial content while preserving spectral resolution. A geometrical transform called contourlet transform (CT) is introduced, which represents images containing contours and textures. This paper derived an efficient block based feature level contourlet transform with neural network (BFCN) model for image fusion. The proposed BFCN model integrates CT with neural network (NN), which plays a significant role in feature extraction and detection in machine learning applications. In the proposed BFCN model, the two fusion techniques, CT and NN are discussed for fusing the IRS-1D images using LISS III scanner about the locations Hyderabad, Vishakhapatnam, Mahaboobnagar and Patancheru in Andhra Pradesh, India. Also Landsat 7 image data and QuickBird image data are used to perform experiments on the proposed BFCN model. The features under study are contrast visibility, spatial frequency, energy of gradient, variance and edge information. Feed forward back propagation neural network is trained and tested for classification, since the learning capability of NN makes it feasible to customize the image fusion process. The trained NN is then used to fuse the pair of source images. The proposed BFCN model is compared with other techniques to assess the quality of the fused image. Experimental results clearly prove that the proposed BFCN model is an efficient and feasible algorithm for image fusion.
Journal of Applied Communications vol.98 (4) Full Issue

Journal of Applied Communications vol. 98 (3) Full Issue Creative Commons License This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License. This full issue is available in Journal of Applied Communications: http://newprairiepress.org/jac/vol98/iss3/7 Official Journal of the Association for Communication Excellence in Agriculture, Natural Resources, and Life and Human Sciences Jou rna l o f Ap pli ed Co mm un ica tio ns ISSN 1051-0834© Volume 98 • No. 3 • 2014 1 Ellis: Journal of Applied Communications vol. 98 (3) Full Issue Published by New Prairie Press, 2017
Rate analysis of inexact dual first order methods: Application to distributed MPC for network systems

In this paper we propose and analyze two dual methods based on inexact gradient information and averaging that generate approximate primal solutions for smooth convex optimization problems. The complicating constraints are moved into the cost using the Lagrange multipliers. The dual problem is solved by inexact first order methods based on approximate gradients and we prove sublinear rate of convergence for these methods. In particular, we provide, for the first time, estimates on the primal feasibility violation and primal and dual suboptimality of the generated approximate primal and dual solutions. Moreover, we solve approximately the inner problems with a parallel coordinate descent algorithm and we show that it has linear convergence rate. In our analysis we rely on the Lipschitz property of the dual function and inexact dual gradients. Further, we apply these methods to distributed model predictive control for network systems. By tightening the complicating constraints we are also able to ensure the primal feasibility of the approximate solutions generated by the proposed algorithms. We obtain a distributed control strategy that has the following features: state and input constraints are satisfied, stability of the plant is guaranteed, whilst the number of iterations for the suboptimal solution can be precisely determined.
THE MODIFIED OPTIMAL H∞ CONTROL PROBLEM FOR DESCRIPTOR SYSTEMS

The H ∞ control problem is studied for linear constant coefficient descriptor systems. Necessary and sufficient optimality conditions are derived for systems of arbitrary index. These conditions are formulated in terms of deflating subspaces of even matrix pencils containing only the parameters of the original system. It is shown that this approach leads to a more numerically robust and efficient method in computing the optimal value γ in contrast to other methods such as the widely used Riccati- and linear matrix inequality (LMI)-based approaches. The results are illustrated by a numerical example.
A Preliminary Study on Using Code Smells to Improve Bug Localization

Bug localization is a technique that has been proposed to support the process of identifying the locations of bugs specified in a bug report. A traditional approach such as information retrieval (IR)-based bug localization calculates the similarity between the bug description and the source code and suggests locations that are likely to contain the bug. However, while many approaches have been proposed to improve the accuracy, the likelihood of each module having a bug is often overlooked or they are treated equally, whereas this may not be the case. For example, modules having code smells have been found to be more prone to changes and faults. Therefore, in this paper, we explore a first step toward leveraging code smells to improve bug localization. By combining the code smell severity with the textual similarity from IR-based bug localization, we can identify the modules that are not only similar to the bug description but also have a higher likelihood of containing bugs. Our preliminary evaluation on four open source projects shows that our technique can improve the baseline approach by 142.25% and 30.50% on average for method and class levels, respectively.
Electronic Voting System for University Student Elections: The Case of Kabarak University, Kenya

Student elections in higher education institutions have over the years become very expensive and involving exercises. This study examines the challenges facing the conduct of these student elections manually and presents an electronic alternative that demonstrates great advantages especially in introducing efficiency at the vote tallying stage of the election. The electronic voting system developed and tested at Kabarak University is a result of a consultative engagement with the student fraternity and utilizes a very basic but secure approach to guarantee the sanctity of the process and accuracy in the tallying of the results. The system and development process will serve to demonstrate the applicability of electronic voting not only in the higher education context but also in other contexts that require an infusion of efficiency in the voting processes.
You Are How You Drive: Peer and Temporal-Aware Representation Learning for Driving Behavior Analysis

Driving is a complex activity that requires multi-level skilled operations (e.g., acceleration, braking, turning). Analyzing driving behavior can help us assess driver performances, improve traffic safety, and, ultimately, promote the development of intelligent and resilient transportation systems. While some efforts have been made for analyzing driving behavior, existing methods can be improved via representation learning by jointly exploring the peer and temporal dependencies of driving behavior. To that end, in this paper, we develop a Peer and Temporal-Aware Representation Learning based framework (PTARL) for driving behavior analysis with GPS trajectory data. Specifically, we first detect the driving operations and states of each driver from GPS traces. Then, we derive a sequence of multi-view driving state transition graphs from the driving state sequences, in order to characterize a driver's driving behavior that varies over time. In addition, we develop a peer and temporal-aware representation learning method to learn a sequence of time-varying yet relational vectorized representations from the driving state transition graphs. The proposed method can simultaneously model both the graph-graph peer dependency and the current-past temporal dependency in a unified optimization framework. Also, we provide effective solutions for the optimization problem. Moreover, we exploit the learned representations of driving behavior to score driving performances and detect dangerous regions. Finally, extensive experimental results with big trajectory data demonstrate the enhanced performance of the proposed method for driving behavior analysis.
Software Quality through the Eyes of the End-User and Static Analysis Tools: A Study on Android OSS Applications

Source code analysis tools have been the vehicle for measuring and assessing the quality of a software product for decades. However, recently many studies have shown that post-deployment end-user reviews provide a wealth of insight into the quality of a software product and how it should evolve and be maintained. For example, end-user reviews help to identify missing features or inform developers about incorrect or unexpected software behavior. We believe that analyzing end-user reviews and utilizing analysis tools are a crucial step towards understanding the complete picture of the quality of a software product, as well as towards reasoning about the evolution history of it. In this paper, we investigate whether both methods correlate with one another. In other words, we explore if there exists a relationship between user satisfaction and the application's internal quality characteristics. To conduct our research, we analyze a total of 46 actual releases of three Android open source software (OSS) applications on the Google Play Store. For each release, we employ multiple static analysis tools to assess several aspects of the application's software quality. We retrieve and manually analyze the complete reviews after each release of each application from its store page, totaling 1004 reviews. Our initial results suggest that having high or low code quality does not necessary ensure user overall satisfaction.
Convex Bounds for Equation Error in Stable Nonlinear Identification

Equation error, also known as one-step-ahead prediction error, is a common quality-of-fit metric in dynamical system identification and learning. In this letter, we use Lagrangian relaxation to construct a convex upper bound on equation error that can be optimized over a convex set of nonlinear models that are guaranteed to be contracting, a strong form of nonlinear stability. We provide theoretical results on the tightness of the relaxation, and show that the method compares favorably to established methods on a variety of case studies.
Energy-Efficient Dynamic Comparator with Active Inductor for Receiver of Memory Interfaces

In this paper, we propose a dynamic comparator that improved the operation performance of receiver (RX) with the effort to reduce power consumption. It is implemented via double-tail StrongARM latch comparator with an active inductor and efforts are made to minimize power consumption for high-speed resulting in better energy efficiency at the targeted high frequency. In this regard, our comparator is suitable for memory application RX to satisfy both low-power and high-speed. It is applied to the single-ended RX designed with a continuous-time linear equalizer, a clock generator and a quarter-rate 2-tap decision-feedback equalizer which is appropriate for the high-frequency memory application. Compared to the conventional one, our design, fabricated in 55nm CMOS process, provides an improvement of 7% in unit interval (UI) margin under the same power consumption and receives up to 10Gb/s PRBS15 data at BER < 10-12 with 0.4 UI margin and energy efficiency of 0.67pJ/bit.
RegMutex: Inter-Warp GPU Register Time-Sharing

Registers are the fastest and simultaneously the most expensive kind of memory available to GPU threads. Due to existence of a great number of concurrently executing threads, and the high cost of context switching mechanisms, contemporary GPUs are equipped with large register files. However, to avoid over-complicating the hardware, registers are statically assigned and exclusively dedicated to threads for the entire duration of the thread's lifetime. This decomposition takes into account the maximum number of live registers at any given point in the GPU binary although the points at which all the requested registers are used may constitute only a small fraction of the whole program. Therefore, a considerable portion of the register file remains under-utilized. In this paper, we propose a software-hardware co-mechanism named RegMutex (Register Mutual Exclusion) to share a subset of physical registers between warps during the GPU kernel execution. With RegMutex, the compiler divides the architected register set into a base register set and an extended register set. While physical registers corresponding to the base register set are statically and exclusively assigned to the warp, the hardware time-shares the remaining physical registers across warps to provision their extended register set. Therefore, the GPU programs can sustain approximately the same performance with the lower number of registers hence yielding higher performance per dollar. For programs that require a large number of registers for execution, RegMutex will enable a higher number of concurrent warps to be resident in the hardware via sharing their register allocations with each other, leading to a higher device occupancy. Since some aspects of register sharing orchestration are being offloaded to the compiler, RegMutex introduces lower hardware complexity compared to existing approaches. Our experiments show that RegMutex improves the register utilization and reduces the number of execution cycles by up to 23% for kernels demanding a high number of registers.
Extending Specification Patterns for Verification of Parametric Traces

This article proposes a temporal and parametric specification language (ParTraP) developed for the verification of execution traces. The language extends specification patterns with nested scopes, real-time and first-order quantification over the data inside a JSON trace, while remaining pragmatic. Its design was directed by a case study in the medical field (computer aided surgery). The paper briefly presents the case study and details the design rationale, syntax and semantics of the language. The language has been implemented and several properties have been successfully evaluated over a corpus of 100 surgery traces.
Improving Box Office Result Predictions for Movies Using Consumer-Centric Models

Recent progress in machine learning and related fields like recommender systems open up new possibilities for data-driven approaches. One example is the prediction of a movie's box office revenue, which is highly relevant for optimizing production and marketing. We use individual recommendations and user-based forecast models in a system that forecasts revenue and additionally provides actionable insights for industry professionals. In contrast to most existing models that completely neglect user preferences, our approach allows us to model the most important source for movie success: moviegoer taste and behavior. We divide the problem into three distinct stages: (i) we use matrix factorization recommenders to model each user's taste, (ii) we then predict the individual consumption behavior, and (iii) eventually aggregate users to predict the box office result. We compare our approach to the current industry standard and show that the inclusion of user rating data reduces the error by a factor of 2x and outperforms recently published research.