Miracle-dz/UniFAR-FTUs huggingface dataset:
tal growth and neurobehavioral outcomes in childhood

Using a sample of sibling pairs from a nationally representative U.S. survey, we examine the effects of the fetal growth rate on a set of neurobehavioral outcomes in childhood measured by parent-reported diagnosed developmental disabilities and behavior problems. Based on models that include mother fixed effects, we find that the fetal growth rate, a marker for the fetal environment, is negatively associated with lifetime diagnosis of developmental delay. We also find that the fetal growth rate is negatively associated with disruptive behaviors among male children. These results suggest that developmental disabilities and problem behaviors may play a role in explaining the welldocumented association between birth weight and human capital outcomes measured in adulthood. 2014 Elsevier B.V. All rights reserved. Corresponding author. E-mail address: pchatterji@albany.edu (P. Chatterji).
A Model of Coarse-Graining Effect on DFA

Abstract —Detrended Fluctuation Analysis (DFA) is a scalingmethod used to estimate long-range power-law correlation expo-nents in noisy signals. Data acquisition is an inherent part ofthe method. Continuous signals are converted into discretized,digital signals when the signals are sampled and then quantized,and the digital signals shall be referred to as coarse-grainedresults. In addition, coarse-graining is also a useful means to getrobust information of a dynamical system by mapping originalsignals into symbol sequences. Therefore, it is important tolearn how coarse-graining affects the results of DFA for signals.In this paper, we study the effects of coarse-graining on DFAfor noisy signals with three types of correlation properties, i.e.anti-correlated, long-range correlated and uncorrelated signals.The result shows that the effects of coarse-graining on DFAare closely related to the correlation properties of signals. Tounderstand the scaling behavior of coarse-grained signals, wediscuss the properties of difference signals caused by coarse-graining procedure, and then describe a coarse-grained signal asan additive white noise signal with the original signal. Finally,we build a model for accurately simulating the scaling of coarse-grained signals by means of superposition rule of rms ﬂuctuationfunction.
Collaborative model in construction phase of software

The success of software development projects is still relatively small when compared to others in the field of project success. The quality and speed of software project is highly dependent on the human resource. Therefore in this paper will discuss the improvement of the collaboration model on construction phase of software development in order to obtain the quality of the product (software) better. The method of this collaborative model development is made with the Socio-Technical Systems (STS) approach, which divides a collaborative system into technical system and socio-technical systems that affect each other. The results of this research proposed a collaborative model in construction phase of software and their indicator.
Amphibians and Reptiles of Luzon Island (Philippines), VII: Herpetofauna of Ilocos Norte Province, Northern Cordillera Mountain Range

We report new distribution records for amphibians and reptiles from 20 localities within the northern Cordillera Mountain Range of Ilocos Norte Province, Luzon Island, Philippines. Together with opportunistic collections of specimens from past surveys, our new data result in a total of 58 amphibian and reptile species for Ilocos Norte Province and the extreme northern Cordilleras—all of which constitute major geographic range extensions. We utilize new data and IUCN formalized conservation assessment criteria to revise the conservation status of many species. Our results highlight the degree to which fundamental distribution data are lacking for Luzon amphibians and reptiles and emphasize the manner in which many current species assessments are based on incomplete data and, as a result, may be sorely misleading. The complex biogeography of Luzon’s herpetofauna remains poorly understood, providing opportunities for future research and conservation efforts once distribution patterns and local abundances are properly documented.
Young Stress and Vibration Analyst Competition Finalists

Introduction Composite materials are weak normal to the plane of the laminate due to a lack of through-thickness reinforcement, with strength dictated by the brittle epoxy matrix. Therefore it is important to evaluate the through-thickness load transfer in composite bonded joints because of their discontinuous nature to improve confidence in the join and inform more efficient joint designs. A double butt strap joint (DBSJ), Figure 1, was constructed with 800g/m 2 unidirectional and 450g/m 2 chopped strand matt glass fibre in a [CSM 8 90 4 ] s sequence using Gurit Prime 20lv epoxy resin using the resin infusion process. Araldite 2015 epoxy adhesive was used to bond the adherends. Figure 1: Schematic of Double butt strap joint Meso scale analysis Component strains within the DBSJ were evaluated using 2D DIC. A Canon mp-e65 macro lens connected to a 5Mp LaVsion 5Mp ELite camera was used to image an area of 3.1 mm x 2.6 mm around the discontinuity between adherends. The specimen was mounted in an Instron 5569 test Inner adherend Outer adherend (strap) Adhesive Root of discontinuity Area of analysis Strap length Overlap length 1 machine and loaded at 1mm/min up to failure. Complex localised strain distributions were revealed, shown in Figures 2 and 3. Detailed analysis of the strain fields identifies small, yet critical, through-thickness and shear strains evolving within the joint. To validate the DIC strain values, Thermoelastic Stress Analysis (TSA) was conducted. TSA provides a fast and accurate experimental technique, to capture the complex behaviour around the join at similar length scales as the DIC. The two independent experimental data sets provide sufficient detail to fully inform numerical models. When a material experiences a stress change it is accompanied by a small temperature change [1], ΔT. The relationship between ΔT and the change in the sum of principal stresses, σ 1 , σ 2 is as follows for an orthotropic material () (1) where α 1 and α 2 are the coefficients of thermal expansion in the principal stress directions, ρ is the material density, C p is the specific heat at constant pressure, and T is the specimen temperature. These constants form the 'thermoelastic constants' in the principal stress directions K 1 , K 2 , providing the relationship between the sum of the principal stresses,
Evolutionary weight tuning based on diphone pairs for unit selection speech synthesis

Unit selection text-to-speech (TTS) conversion is an ongoing research issue for the speech synthesis community. This paper is focused on tuning the weights involved in the target and the concatenations cost metrics. We propose a method for automatically tuning these weights simultaneously by means of diphone pairs. This method is based on techniques provided by the evolutionary computation community, taking advantage of their robustness in noisy domains. The experiments and their analysis show its good performance in this problem, overcoming some constrains assumed by previous works and being an interesting framework for further investigations.
A novel extension of Kruskal's algorithm in multicast routing

Multimedia applications are expected to guarantee end-to-end quality of service (QoS) and are characterized by stringent constraints on delay, delay-jitter, bandwidth, cost, etc. In the litterature, we observe that Kruskal's algorithm is limited to minimal (maximal) spanning unconstrained tree. In this paper, we extend Kruskal's algorithm to incorporate the delay bound constraint. Consequently, we propose a novel algorithm, called EKRUS (Extended Kruskal), for constructing multicast trees. The EKRUS' distinguishing features consists in a better management of Kruskal's priority queues, and in the provision of edge priority aggregation. Preliminary results show that our proposed EKRUS algorithm performs as good as the best well-known algorithms (such as the DDMC, DMCTc algorithms) while exhibiting better complexity. Furthermore, we believe that EKRUS is well suited to actual implementation in networks requiring support for multicast communication.
Asac-2005 Conference Goal Setting Transfer of Training Interventions

This study examines the effectiveness of two types of goals (distal outcome, proximal plus distal outcome) and being urged to ‘do your best’ as transfer of training interventions using a sample of government employees (n=58). Results revealed that participants who set distal outcome goals had the highest level of self-efficacy. Workplace performance, as assessed by peers, correlated with generalization and maintenance as assessed by a survey.
Novel Parameter Identification by Using a High-Gain Observer With Application to a Gas Turbine Engine

In this study, a novel identification technique, that is high-gain observer-based identification approach, is proposed for systems with bounded process and measurement noises. For system parameters with abnormal changes, an adaptive change detection and parameter identification algorithm is next presented. The presented technique and algorithm are finally applied to the parameter identification of the gas turbine engine by using the recorded input data from the engine test-bed. The identified parameters and the response curves are desirable. The simulations have proved the effectiveness of the proposed procedure compared with the previous identification approach.
The Relationship between Traditional Chinese Medicine and Modern Medicine

The essence of the traditional Chinese medicine has always been the most advanced and experienced therapeutic approach in the world. It has knowledge that can impact the direction of future modern medical development; still, it is easy to find simple knowledge with mark of times and special cultures. The basic structure of traditional Chinese medicine is composed of three parts: one consistent with modern medicine, one involuntarily beyond modern medicine, and one that needs to be further evaluated. The part that is consistent with modern medicine includes consensus on several theories and concepts of traditional Chinese medicine, and usage of several treatments and prescriptions of traditional Chinese medicine including commonly used Chinese herbs. The part that is involuntarily beyond modern medicine contains several advanced theories and important concepts of traditional Chinese medicine, relatively advanced treatments, formula and modern prescriptions, leading herbs, acupuncture treatment and acupuncture anesthesia of traditional Chinese medicine that affect modern medicine and incorporates massage treatment that has been gradually acknowledged by modern therapy. The part that needs to be further evaluated consists not only the knowledge of pulse diagnosis, prescription, and herbs, but also many other aspects of traditional Chinese medicine.
Marginalized Multi-Instance Kernels

Support vector machines (SVM) have been highly successful in many machine learning problems. Recently, it is also used for multi-instance (MI) learning by employing a kernel that is defined directly on the bags. As only the bags (but not the instances) have known labels, this MI kernel implicitly assumes all instances in the bag to be equally important. However, a fundamental property of MI learning is that not all instances in a positive bag necessarily belong to the positive class, and thus different instances in the same bag should have different contributions to the kernel. In this paper, we address this instance label ambiguity by using the method of marginalized kernels. It first assumes that all the instance labels are available and defines a label-dependent kernel on the instances. By integrating out the unknown instance labels, a marginalized kernel defined on the bags can then be obtained. A desirable property is that this kernel weights the instance pairs by the consistencies of their probabilistic instance labels. Experiments on both classification and regression data sets show that this marginalized MI kernel, when used in a standard SVM, performs consistently better than the original MI kernel. It also outperforms a number of traditional MI learning methods.
Stochastic Dynamics and the Stationary Distribution of Wealth

April, 2005 Abstract. A model with wealth accumulation subject to i.i. d. random shocks is examined. The transfer function shows what kt+1 wealth at t+ 1 would be, given kt, with no shock. It has a positive slope, but indeterminate concavity/convexity. The stationary distribution satis es a Fredholm integral equation and can be examined by direct analysis of the stochastic process or via the Fredholm equation. The shape of the transfer function, particularly any non-linearities, and the distribution of shocks, a¤ect the stationary distribution(s). Economic theory forces consideration of a broad range of cases, including some which violate -convergence. Twin peaks in the stationary distribution cannot be excluded.
Web services enabling context-aware applications: lessons learned by integrating e-learning applications

Context-aware applications assist users' everyday tasks by adapting their behavior based on context information obtained from instrumented environments. Context is any relevant information describing entities in a user-computer interaction (e.g. user location). The design and implementation of high-quality context management software is an area of active research. We have described elsewhere the context kernel, a Web service that manages context information on behalf of applications. In this paper, we present a case study of how the context kernel has been used to integrate a suite of e-learning applications. We show how the Web services approach allowed applications to seamlessly exchange context on the Web through a uniform context representation schema. We also present the lessons we learned from integrating e-learning applications via Web services.
Dyadic intervention for family caregivers and care receivers in early-stage dementia.

PURPOSE The Early Diagnosis Dyadic Intervention (EDDI) program provides a structured, time-limited protocol of one-on-one and dyadic counseling for family caregivers and care receivers who are in the early stages of dementia. The goals and procedures of EDDI are based on previous research suggesting that dyads would benefit from an intervention that increases the care receiver's active participation in his or her care plan, develops positive communication patterns between the caregiver and care receiver, increases knowledge and understanding about available services, and assists the dyad through the emotional turbulence of a diagnosis of Alzheimer's disease or other dementing condition. DESIGN AND METHODS EDDI was developed in response to research and clinical findings that suggested that care dyads in the early stages of dementia and dementia care are able to engage in a dialogue about future preferences for care, and that this discussion could address some of the uncertainty and worry experienced by each member of the dyad. As part of a feasibility trial, 31 dyads participated in the EDDI program. Measures were obtained on the intervention's implementation, including the number of sessions attended, caregiver and care receiver ratings of treatment acceptability and effectiveness, and counselor ratings of treatment effectiveness. RESULTS Participant and counselor evaluations of the EDDI protocol indicated that the intervention was acceptable and satisfactory to the caregivers, care receivers, and counselors, and that the intervention's goals and objectives were achievable. IMPLICATIONS These findings indicate that individuals with early-stage dementia and their family caregivers are able to participate in and benefit from a structured intervention that focuses on care planning for future needs.
Skewed line detection and removal preserving handwritten strokes : a new approach

Text overlapping with lines poses serious problems for the optical character recognition systems. The dilemma becomes crucial for skewed and non-uniform thick line present in the word image. Although detection and removal of the straight underlines has been addressed but still skewed lines removal and restoration of the area after removal of lines persists to be a problem of interest. A new method is proposed to detect and remove skewed and straight line at any position inherited in the word image without characters distortion to avoid restoration stage by preserving strokes. The proposed technique is based on connected component analysis and is equally suitable to remove straight and skewed line from printed and handwritten words. Detailed experiments are conducted on manually filled forms of National Institute of Standard and Technology (NIST) special benchmark database19. Comparisons with other methods available in the literature exhibit potential of the new approach with accuracy up to 95.18%.
Real Interest Parity, Dynamic Convergence and the European Monetary System

Is further economic convergence between European Monetary System (EMS) member countries desirable? This paper addresses some of the convergence issues currently being raised in the debate over Economic and Monetary Union (EMU) in Europe, with particular emphasis on the behaviour of real interest rates. The important distinction between static and dynamic convergence is highlighted. A standard analytical framework is presented which illustrates the importance of the components of the real interest differential - namely capital controls, risk premia and the real exchange rate - as endogenous mechanisms for the transmission of policy. As such, variations in the real interest differential are shown potentially to be important for ensuring dynamic convergence to a steady-state EMU.
Creating categories for Wikipedia articles using Self-Organizing Maps

The article presents the results of the experiments performed on selected sub-set of Wikipedia which we categorized automaticly. We analyze two methods of text representation: based on references and word content. Using them we introduced joint representation that has been used to build groups of similar articles based on Kohonen Self-Organizing Maps. To fulfill efficiency of the data processing, we performed dimensionality reduction of raw data using Principal Component Analysis performed on similarity matrix. Changing the granularity of SOM network allows to build hierarchical categories and find significant relations between articles in documents repository.
Targeted Gene Silencing Using RGD-Labeled Chitosan Nanoparticles

Purpose: This study aimed to develop an Arg-Gly-Asp (RGD) peptide-labeled chitosan nanoparticle (RGD-CH-NP) as a novel tumor targeted delivery system for short interfering RNA (siRNA). Experimental Design: RGD peptide conjugated with chitosan by thiolation reaction was confirmed by proton-NMR (H-NMR). Binding of RGD-CH-NP with ανβ3 integrin was examined by flow cytometry and fluorescence microscopy. Antitumor efficacy was examined in orthotopic mouse models of ovarian carcinoma. Results: We show that RGD-CH-NP loaded with siRNA significantly increased selective intratumoral delivery in orthotopic animal models of ovarian cancer. In addition, we show targeted silencing of multiple growth-promoting genes (POSTN, FAK, and PLXDC1) along with therapeutic efficacy in the SKOV3ip1, HeyA8, and A2780 models using siRNA incorporated into RGD-CH-NP (siRNA/RGD-CH-NP). Furthermore, we show in vivo tumor vascular targeting using RGD-CH-NP by delivering PLXDC1-targeted siRNA into the ανβ3 integrin–positive tumor endothelial cells in the A2780 tumor-bearing mice. This approach resulted in significant inhibition of tumor growth compared with controls. Conclusions: This study shows that RGD-CH-NP is a novel and highly selective delivery system for siRNA with the potential for broad applications in human disease. Clin Cancer Res; 16(15); 3910–22. ©2010 AACR.
Knowledge Clusters: Dealing with a Multilevel Phenomenon

The central idea of this paper is to present the concept of knowledge clusters to understand knowledge integration in inter-organizational projects. The proliferation of such projects combined with the complexity of managing them, motivated this study. The study is based on a single case study which involves four organizations collaborating on a project. Findings suggest that knowledge integration in inter-organizational projects occurs through knowledge clusters and involves the interaction of complementary specialized knowledge bases within a structure and influenced by the clusters’ perceptions towards the project. Implications of the findings and future research opportunities are discussed.
Disease Slices

In this study, we propose a model which may assist in diagnosis of (AD) using T1 weighted MRI brain images. The proposed model involves construction of statistical features from multiple trans-axial slices from hippocampus and amygdala regions, which play a significant role in AD diagnosis. Features from multiple slices are then averaged, which resulted into a smaller set of relevant features. The reduced set of features enhances the performance of decision learning system, and takes less memory and computation time. Effectiveness of the proposed model is compared with recent voxel-basedmorphometry work in terms of sensitivity, specificity and accuracy. Experimental results on a publicly available MRI dataset showed that the proposed method outperforms the recent voxel-based-morphometry model.
Maturation of the Mitochondrial Redox Response to Profound Asphyxia in Fetal Sheep

Fetal susceptibility to hypoxic brain injury increases over the last third of gestation. This study examined the hypothesis that this is associated with impaired mitochondrial adaptation, as measured by more rapid oxidation of cytochrome oxidase (CytOx) during profound asphyxia. Methods: Chronically instrumented fetal sheep at 0.6, 0.7, and 0.85 gestation were subjected to either 30 min (0.6 gestational age (ga), n = 6), 25 min (0.7 ga, n = 27) or 15 min (0.85 ga, n = 17) of complete umbilical cord occlusion. Fetal EEG, cerebral impedance (to measure brain swelling) and near-infrared spectroscopy-derived intra-cerebral oxygenation (ΔHb = HbO2 – Hb), total hemoglobin (THb) and CytOx redox state were monitored continuously. Occlusion was associated with profound, rapid fall in ΔHb in all groups to a plateau from 6 min, greatest at 0.85 ga compared to 0.6 and 0.7 ga (p<0.05). THb initially increased at all ages, with the greatest rise at 0.85 ga (p<0.05), followed by a progressive fall from 7 min in all groups. CytOx initially increased in all groups with the greatest rise at 0.85 ga (p<0.05), followed by a further, delayed increase in preterm fetuses, but a striking fall in the 0.85 group after 6 min of occlusion. Cerebral impedance (a measure of cytotoxic edema) increased earlier and more rapidly with greater gestation. In conclusion, the more rapid rise in CytOx and cortical impedance during profound asphyxia with greater maturation is consistent with increasing dependence on oxidative metabolism leading to earlier onset of neural energy failure before the onset of systemic hypotension.
Design of Low Adder Cost FIR Digital Filters Using Graph Representation

The hardware implementation of digital filters is mainly dominated by the multiplier blocks. Implementing constant coefficient digital FIR filters multiplier block as a network of adders, sub tractors, and shifters will achieve lower power consumption. This paper uses the graph representations to reduce designed hardware complexity. To further reduce the adder cost, we enhance the hardware resources sharing of different filter coefficients. Simulation results show that using the proposed method has reduced adder cost of multiplier blocks.