Miracle-dz/UniFAR-FTUs huggingface dataset:
Input shaping designs to account for uncertainty in both frequency and damping in flexible structures

In the modeling of flexible structures, there is generally uncertainty in both the frequencies and damping constants, and it is thus important that the control methods for such systems be robust to these uncertainties. Input shapers have been shown to yield good performance while being insensitive to modeling errors. Most previous input shaping studies, however, have addressed only frequency modeling errors and do not take into account the distributions of the parameter variations. We extend a recently proposed input shaping method (Pao et al. (1997)), that allows the ranges of system parameter values to be weighted according to the expected modeling errors, for designing shapers that accommodate both frequency and damping uncertainty.
CONSISTENT BOUNDARY CONDITIONS FOR COSMOLOGICAL TOPOLOGICALLY MASSIVE GRAVITY AT THE CHIRAL POINT

We show that cosmological topologically massive gravity at the chiral point allows not only Brown–Henneaux boundary conditions as consistent boundary conditions, but also slightly more general ones which encompass the logarithmic primary found in J. High Energy Phys.07 (2008) 134 as well as all its descendants.
A Novel Semi-supervised Method for Named Entity Detection

Machine Learning (ML) based approaches for the Named Entity Recognition (NER) task require Named Entity (NE) annotated data to train the classifier. If amount of NE annotated data is not sufficient, the classifier may not yield good result. NE annotated data is scarce, especially for resource poor languages. But in most cases large raw corpora are available. In this paper we describe a novel approach of making use of additional raw corpus to improve the performance of a Maximum Entropy (MaxEnt) based classifier. The proposed methodology is applied on the NER task in Hindi. Experimental results prove the effectiveness of the proposed approach, which might be helpful in Natural Language Processing (NLP) tasks in resource poor languages.
Nail-patella syndrome—a novel mutation in the LMX1B gene

Nail-patella syndrome (NPS) is an autosomal-dominant pleiotropic disorder characterized by dyplasia of finger nails, skeletal anomalies and frequently renal disease. In the reported case, genetic analysis revealed a new missense mutation in the homeodomain of LMX1B, presumed to abolish DNA binding (c.725T>C, p.Val242Ala). A missense mutation at codon 725 was identified, where thymine was replaced by cytosine which led to the replacement of valine by alanine at position 242. It was not detected in both parents. A 2005 study by Bongers et al. described a significant association between the presence of clinically relevant renal involvement in an NPS patient and a positive family history of nephropathy, which was lacking in our case.
System-Wide Cooperative Optimization for NAND Flash-Based Mobile Systems

NAND flash memory has become an essential storage medium for various mobile devices, but it has some idiosyncrasies, such as out-of-place updates and bulk erase operations, which impair the I/O performance of those devices. In particular, the random write performance is strongly influenced by the overhead of a Flash Translation Layer (FTL) that hides the idiosyncrasies of NAND flash memory. To reduce the FTL overhead, operating systems need to be adapted for FTL, but widely used mobile operating systems still mainly adopt algorithms designed for traditional hard disk drives. Although there have been recent studies on rearranging write patterns into a sequential form in the operating system, these approaches fail to produce sequential write patterns under complicated workloads, and FTL still suffers from significant garbage collection overhead. If the operating system can be made aware of the write patterns that FTL requires, the overhead can be alleviated even under random write workloads. In this paper, we propose a system-wide cooperative optimization scheme, where the operating system communicates with the underlying FTL and generates write patterns that FTL can exploit to reduce the overhead. The proposed scheme was implemented on a real mobile device, and the experimental results show that the proposed scheme constantly improves performance under diverse workloads.
The RoboCup Soccer Humanoid League: Overview and Outlook

—The goal of the RoboCup Federation is that in the year 2050 the human world champion team of soccer is going to play (–and going to loose) against the best robotic team. This best robotic team is going to be a team of humanoid robots. Thus, by the start of the Humanoid League (HL) in 2002 the RoboCup community has crossed an important watershed. Since the start the HL underwent a profound development. Competitions and challenges have changed in various ways; rules maturated in many points and gained more focus on the issues that are essential from a technical point of view; and of course the robots became better. In the RoboCup 2005 for the ﬁrst time regular 2-2 games have been conducted. In 2006 we saw a further improvement of the performance of the teams. Still many open issues exist and are intensely discussed in the responsible committees and the whole Humanoid League community. In order to give an introduction to potential newcomers we give a brief overview of the short history of this league, and its current status. We point out the technical and research challenges and show that the whole league can be seen as a project, a kind of evolutionary optimization process to solve research issues – particular those related to vision processing, reactive behaviors and robust and dynamic walking.
Encapsulating Nutrients to Improve Reproduction and Nitrogen Utilization in Ruminants

Encapsulation involves surrounding food ingredients, nutrients, enzymes, bacteria or drugs with a small capsule. You can encapsulate solids, liquids or gases. It is the process of applying coatings to nutrients or substances to control their interaction with a specific environment. It creates a barrier between the active ingredient and the environment. This barrier is not intended to last indefinitely. It is designed to protect the active ingredient and then release it. Coating thickness and composition can control the rate and time of release. This makes it possible to deliver a nutrient to a specific site in the gastrointestinal tract. The food industry uses encapsulation technology to mask odors and taste, prevent oxidation and prevent enzymatic and microbial degradation of nutrients. The pharmaceutical industry uses encapsulation to deliver drugs to specific sites in the gastrointestinal tract. In the last 6 years, encapsulation technology has begun to be used in the animal feed industry. The present paper will focus on the use of microencapsulation for vitamins, urea and polyunsaturated fatty acids. Use of encapsulated ingredients in animal nutrition will be reviewed with emphasis on animal performance.
Testing for Dependence in Non-Gaussian Time Series Data

This paper provides a general methodology for testing for dependence in time series data, with particular emphasis given to non-Gaussian data. A dynamic model is postulated for a continuous latent variable and the dynamic structure transferred to the non-Gaussian, possibly discrete, observations. Locally most powerful tests for various forms of dependence are derived, based on an approximate likelihood function. Invariance to the distribution adopted for the data, conditional on the latent process, is shown to hold in certain cases. The tests are applied to various financial data sets, and Monte Carlo experiments used to gauge their finite sample properties
Learning Directional Local Pairwise Bases with Sparse Coding

Recently, sparse coding has been receiving much attention in object and scene recognition tasks because of its superiority in learning an effective codebook over k-means clustering. However, empirically, such codebook requires a relatively large number of visual words, essentially bases, to achieve high recognition accuracy. Therefore, due to the combinatorial explosion of visual words, it is infeasible to use this codebook to represent higher-order spatial features which are equally important in capturing distinct properties of scenes and objects. Contrasted with many previous techniques that exploit higher-order spatial features, Local Pairwise Codebook (LPC) is a simple and effective method to learn a compact set of clusters representing pairs of spatially close descriptors with k-means. Based on LPC, this paper proposes Directional Local Pairwise Bases (DLPB) that applies sparse coding to learn a compact set of bases capturing correlation between these descriptors, so to avoid the combinatorial explosion. Furthermore, such bases are learned for each quantized direction thereby explicitly adding directional information to the representation. We have evaluated DLPB with several challenging object and scene category datasets. Our experimental results show that DLPB outperforms the baselines across all datasets and achieves the state-of-the-art performance on some datasets.
Efficient data-race detection with dynamic symbolic execution

This paper presents data race detection using dynamic symbolic execution and hybrid lockset / happens-before analysis. Symbolic execution is used to explore the execution tree of multi-threaded software for FIFO scheduling on a single CPU core. Compared to exploring the joint scheduling and execution tree, the combinatorial explosion is drastically reduced. An SMT solver is used to control a debugger's machine interface for adaptive dynamic instrumentation to drive program execution into desired paths. Data races are detected in concrete execution with available static binary instrumentation using hybrid analysis. State interpolation using unsatisfiable cores is employed for path pruning, to avoid exploration of paths that do not contribute to increasing branch coverage. An implementation in Eclipse CDT is described and evaluated with data race test cases from the Juliet C/C++ test suite for program analyzers.
Active contour projection for mesh segmentation

Active contour methods can be used to segment a 3D mesh into parts by iteratively moving the contour to the mesh region that minimizes the contour energy. However, as the contour moves, it often does not lie on the mesh surface. To address this problem, existing methods use either vertex/edge projection or mesh parameterization to obtain the corresponding contour on the mesh surface. Although vertex/edge projection methods are simple, they may create unwanted loops along the projected contour due to irregular mesh connectivity or modeling noise. Extra operations, which are often complex, are needed to remove such loops. On the other hand, mesh parameterization suffers from distortion and out-of-range problems, which are not trivial to solve. In this paper, we propose a face projection method to address the above problems. Our experiments show that the proposed method produces much smoother and more accurate projected contours than existing methods.
Learning visual attribute from image and text

Visual attributes are the words describing appearance properties of an object. They have an interesting property in that they are linguistic entities and yet bear a strong connection to visual information. In other words, it would be impossible to learn a meaningful representation of visual attributes only from language context. In this paper, we make a preliminary study on the approach to learn the meaning of visual attributes from both image and text. We collect a large scale dataset of images and texts from the real-world on-line marketplace. We then attempt to learn a grounded representation of automatically generated attributes from the dataset using Canonical Correlation Analysis (CCA) projecting both image and text representation into a common subspace. This encodes both visual and semantic meaning of a word. Through empirical study, we show how grounded learning changes the meaning of the attribute word through the multimodal grounding.
Sustainability of recurrent expenditure on public social welfare programmes: expenditure analysis of the free maternal care programme of the Ghana National Health Insurance Scheme.

OBJECTIVE Sustainability of public social welfare programmes has long been of concern in development circles. An important aspect of sustainability is the ability to sustain the recurrent financial costs of programmes. A free maternal care programme (FMCP) was launched under the Ghana National Health Insurance Scheme (NHIS) in 2008 with a start-up grant from the British Government. This article examines claims expenditure under the programme and the implications for the financial sustainability of the programme, and the lessons for donor and public financing of social welfare programmes. METHODS Records of reimbursement claims for services and medicines by women benefitting from the policy in participating facilities in one sub-metropolis in Ghana were analysed to gain an understanding of the expenditure on this programme at facility level. National level financial inflow and outflow (expenditure) data of the NHIS, related to implementation of this policy for 2008 and 2009, were reviewed to put the facility-based data in the national perspective. FINDINGS A total of US$936 450.94 was spent in 2009 by the scheme on FMCP in the sub-metropolis. The NHIS expenditure on the programme for the entire country in 2009 was US$49.25 million, exceeding the British grant of US$10.00 million given for that year. Subsequently, the programme has been entirely financed by the National Health Insurance Fund. The rapidly increasing, recurrent demands on this fund from the maternal delivery exemption programme-without a commensurate growth on the amounts generated annually-is an increasing threat to the sustainability of the fund. CONCLUSIONS Provision of donor start-up funding for programmes with high recurrent expenditures, under the expectation that government will take over and sustain the programme, must be accompanied by clear long-term analysis and planning as to how government will sustain the programme.
Natural Language Feature Selection via Cooccurrence

Specificity is important for extracting collocations, keyphrases, multi-word and index terms [Newman et al. 2012]. It is also useful for tagging, ontology construction [Ryu and Choi 2006], and automatic summarization of documents [Louis and Nenkova 2011, Chali and Hassan 2012]. Term frequency and inverse-document frequency (TF-IDF) are typically used to do this, but fail to take advantage of the semantic relationships between terms [Church and Gale 1995]. The result is that general idiomatic terms are mistaken for specific terms. We demonstrate use of relational data for estimation of term specificity. The specificity of a term can be learned from its distribution of relations with other terms. This technique is useful for identifying relevant words or terms for other natural language processing tasks.
Sampling-Based Approaches to Improve Estimation of Mortality among Patient Dropouts: Experience from a Large PEPFAR-Funded Program in Western Kenya

Background Monitoring and evaluation (M&E) of HIV care and treatment programs is impacted by losses to follow-up (LTFU) in the patient population. The severity of this effect is undeniable but its extent unknown. Tracing all lost patients addresses this but census methods are not feasible in programs involving rapid scale-up of HIV treatment in the developing world. Sampling-based approaches and statistical adjustment are the only scaleable methods permitting accurate estimation of M&E indices. Methodology/Principal Findings In a large antiretroviral therapy (ART) program in western Kenya, we assessed the impact of LTFU on estimating patient mortality among 8,977 adult clients of whom, 3,624 were LTFU. Overall, dropouts were more likely male (36.8% versus 33.7%; p = 0.003), and younger than non-dropouts (35.3 versus 35.7 years old; p = 0.020), with lower median CD4 count at enrollment (160 versus 189 cells/ml; p<0.001) and WHO stage 3–4 disease (47.5% versus 41.1%; p<0.001). Urban clinic clients were 75.0% of non-dropouts but 70.3% of dropouts (p<0.001). Of the 3,624 dropouts, 1,143 were sought and 621 had their vital status ascertained. Statistical techniques were used to adjust mortality estimates based on information obtained from located LTFU patients. Observed mortality estimates one year after enrollment were 1.7% (95% CI 1.3%–2.0%), revised to 2.8% (2.3%–3.1%) when deaths discovered through outreach were added and adjusted to 9.2% (7.8%–10.6%) and 9.9% (8.4%–11.5%) through statistical modeling depending on the method used. The estimates 12 months after ART initiation were 1.7% (1.3%–2.2%), 3.4% (2.9%–4.0%), 10.5% (8.7%–12.3%) and 10.7% (8.9%–12.6%) respectively. Conclusions/Significance Abstract Assessment of the impact of LTFU is critical in program M&E as estimated mortality based on passive monitoring may underestimate true mortality by up to 80%. This bias can be ameliorated by tracing a sample of dropouts and statistically adjust the mortality estimates to properly evaluate and guide large HIV care and treatment programs.
Strategic Trade Policy under Uncertainty

This paper extends the Brander-Spencer (1985) model by considering market uncertainty, exploring nonlinear policy, and examining firms' choices of strategic variables. By investigating the interrelationship between trade policy and market conduct, we find that unlike the often-studied linear policy, a nonlinear policy can influence the domestic firm's choice of strategic variables and hece alter the market conduct in favor of the domestic country. Therefore, a nonlinear policy proves strictly superior to a linear one. Copyright 1995 by Blackwell Publishing Ltd.
Cancellation of nonrenormalizable hypersurface divergences and the d-dimensional Casimir piston

Using a multidimensional cut-off technique, we obtain expressions for the cut-off dependent part of the vacuum energy for parallelepiped geometries in any spatial dimension d. The cut-off part yields nonrenormalizable hypersurface divergences and we show explicitly that they cancel in the Casimir piston scenario in all dimensions. We obtain two different expressions for the d-dimensional Casimir force on the piston where one expression is more convenient to use when the plate separation a is large and the other when a is small (a useful a→1/a duality). The Casimir force on the piston is found to be attractive (negative) for any dimension d. We apply the d-dimensional formulas (both expressions) to the two and three-dimensional Casimir piston with Neumann boundary conditions. The 3D Neumann results are in numerical agreement with those recently derived in 0705.0139 using an optical path technique providing an independent confirmation of our multidimensional approach. We limit our study to massless scalar fields.
Redefining the Experiences of Students in Continuation High Schools: A Narrative Profile of a Latino Youth

Abstract:Continuation education programs have increased over the last fifty years and the fact that there is a gap in knowledge on how they serve students is of great concern as they enroll a marginalized student population (Kelly, 1993; Malagon, 2011). This exploratory qualitative case study draws on narrative profiles to illustrate the experiences of Esteban, a Latino male, attending an urban public continuation high school in one of the largest school districts in the nation. This study draws on a larger sociocultural framework and ecological theories to understand how Esteban navigates various ecological spaces (i.e. home, school, and neighborhood) and how they shape the way he engages in continuation school (Bronfenbrenner, 1979; Figueroa, 2016; Smith, 2002; Vygotsky, 1978). Esteban's narrative profile highlights the support and challenges within the various ecological spaces and how they unfold during the process of reintegrating into school. This study contributes to the literature as the narrative of Esteban challenges the deficit frame on continuation schools and it documents the collective action of his family, caring educators, and community partnerships that have advocated for him to reach his academic goals. Further policy, research, and practice implications are explored to support students in alternative schools and programs.
A partial order approach to branching time logic model checking

Partial order techniques enable reducing the size of the state graph used for model checking, thus alleviating the 'state space explosion' problem. These reductions are based on selecting a subset of the enabled operations from each program state. So far, these methods have been studied, implemented and demonstrated for assertional languages that model the executions of a program as computation sequences, in particular the logic LTL (linear temporal logic). The paper shows, for the first time, how this approach can be applied to languages that model the behavior of a program as a tree. We study here partial order reductions for branching temporal logics, e.g., the logics CTL and CTL* (all logics with the next-time operator removed) and process algebras such as CCS. Conditions on the subset of successors from each node to guarantee reduction that preserves CTL properties are given. Provided experimental results show that the reduction is substantial. <<ETX>>
Quantifier spreading in child eye movements: A case of the Russian quantifier kazhdyj ‘every'

Extensive cross-linguistic work has documented that children up to the age of 9–10 make errors when performing a sentence-picture verification task that pairs spoken sentences with the universal quantifier every and pictures with entities in partial one-to-one correspondence. These errors stem from children’s difficulties in restricting the domain of a universal quantifier to the appropriate noun phrase and are referred in the literature as quantifier-spreading ( q -spreading). We adapted the task to be performed in conjunction with eye-movement recordings using the Visual World Paradigm. Russian-speaking 5-to-6-year-old children ( N = 31) listened to sentences like Kazhdyj alligator lezhit v vanne ‘Every alligator is lying in a bathtub’ and viewed pictures with three alligators, each in a bathtub, and two extra empty bathtubs. Non-spreader children ( N = 12) were adult-like in their accuracy whereas q -spreading ones ( N = 19) were only 43% correct in interpreting such sentences compared to the control sentences. Eye movements of q -spreading children revealed that more looks to the extra containers (two empty bathtubs) correlated with higher error rates reflecting the processing pattern of q -spreading. In contrast, more looks to the distractors in control sentences did not lead to errors in interpretation. We argue that q -spreading errors are caused by interference from the extra entities in the visual context, and our results support the processing difficulty account of acquisition of quantification. Interference results in cognitive overload as children have to integrate multiple sources of information, i.e., visual context with salient extra entities and the spoken sentence in which these entities are mentioned in real-time processing. This article is part of the special collection: Acquisition of Quantification