Miracle-dz/UniFAR-FTUs huggingface dataset:
Chronic Maternal Low-Protein Diet in Mice Affects Anxiety, Night-Time Energy Expenditure and Sleep Patterns, but Not Circadian Rhythm in Male Offspring

Offspring of murine dams chronically fed a protein-restricted diet have an increased risk for metabolic and neurobehavioral disorders. Previously we showed that adult offspring, developmentally exposed to a chronic maternal low-protein (MLP) diet, had lower body and hind-leg muscle weights and decreased liver enzyme serum levels. We conducted energy expenditure, neurobehavioral and circadian rhythm assays in male offspring to examine mechanisms for the body-weight phenotype and assess neurodevelopmental implications of MLP exposure. C57BL/6J dams were fed a protein restricted (8%protein, MLP) or a control protein (20% protein, C) diet from four weeks before mating until weaning of offspring. Male offspring were weaned to standard rodent diet (20% protein) and single-housed until 8–12 weeks of age. We examined body composition, food intake, energy expenditure, spontaneous rearing activity and sleep patterns and performed behavioral assays for anxiety (open field activity, elevated plus maze [EPM], light/dark exploration), depression (tail suspension and forced swim test), sociability (three-chamber), repetitive (marble burying), learning and memory (fear conditioning), and circadian behavior (wheel-running activity during light-dark and constant dark cycles). We also measured circadian gene expression in hypothalamus and liver at different Zeitgeber times (ZT). Male offspring from separate MLP exposed dams had significantly greater body fat (P = 0.03), less energy expenditure (P = 0.004), less rearing activity (P = 0.04) and a greater number of night-time rest/sleep bouts (P = 0.03) compared to control. MLP offspring displayed greater anxiety-like behavior in the EPM (P<0.01) but had no learning and memory deficit in fear-conditioning assay (P = 0.02). There was an effect of time on Per1, Per 2 and Clock circadian gene expression in the hypothalamus but not on circadian behavior. Thus, transplacental and early developmental exposure of dams to chronic MLP reduces food intake and energy expenditure, increases anxiety like behavior and disturbs sleep patterns but not circadian rhythm in adult male offspring.
TEACHERS’ CORRECTIVE FEEDBACK ON ENGLISH STUDENTS’ WRITING

This investigates the participants’ attitudes towards corrective feedback as well as the types of corrective feedback on learners’ performance by questionnaires for both students and teachers. Fifty–eight 2 nd -year students and 5 teachers of English at a university in the Mekong Delta of Vietnam participated in the study. The results indicated that students had a positive attitude towards teachers’ corrective feedback. Besides, with the analyzed data, correction with comments and teacher correction was considered as the most useful strategy when giving feedback in the learners’ performance. The outcomes of the study suggest a widespread employment of corrective feedback in teaching writing at universities and colleges in the region. Article visualizations:
Visual Analytics in FCA-based Clustering

Visual analytics is a subdomain of data analysis which combines both human and machine analytical abilities and is applied mostly in decision-making and data mining tasks. Triclustering, based on Formal Concept Analysis (FCA), was developed to detect groups of objects with similar properties under similar conditions. It is used in Social Network Analysis (SNA) and is a basis for certain types of recommender systems. The problem of triclustering algorithms is that they do not always produce meaningful clusters. This article describes a specific triclustering algorithm and a prototype of a visual analytics platform for working with obtained clusters. This tool is designed as a testing frameworkis and is intended to help an analyst to grasp the results of triclustering and recommender algorithms, and to make decisions on meaningfulness of certain triclusters and recommendations.
Automatic Resource Scheduling with Latency Hiding for Parallel Stencil Applications on GPGPU Clusters

Overlapping computations and communication is a key to accelerating stencil applications on parallel computers, especially for GPU clusters. However, such programming is a time-consuming part of the stencil application development. To address this problem, we developed an automatic code generation tool to produce a parallel stencil application with latency hiding automatically from its dataflow model. With this tool, users visually construct the workflows of stencil applications in a dataflow programming model. Our dataflow compiler determines a data decomposition policy for each application, and generates source code that overlaps the stencil computations and communication (MPI and PCIe). We demonstrate two types of overlapping models, a CPU-GPU hybrid execution model and a GPU-only model. We use a CFD benchmark computing 19-point 3D stencils to evaluate our scheduling performance, which results in 1.45 TFLOPS in single precision on a cluster with 64 Tesla C1060 GPUs.
Rbpj Cell Autonomous Regulation of Retinal Ganglion Cell and Cone Photoreceptor Fates in the Mouse Retina

Vertebrate retinal progenitor cells (RPCs) are pluripotent, but pass through competence states that progressively restrict their developmental potential (Cepko et al., 1996; Livesey and Cepko, 2001; Cayouette et al., 2006). In the rodent eye, seven retinal cell classes differentiate in overlapping waves, with RGCs, cone photoreceptors, horizontals, and amacrines forming predominantly before birth, and rod photoreceptors, bipolars, and Müller glia differentiating postnatally. Both intrinsic and extrinsic factors regulate each retinal cell type (for review, see Livesey and Cepko, 2001). Here, we conditionally deleted the transcription factor Rbpj, a critical integrator of multiple Notch signals (Jarriault et al., 1995; Honjo, 1996; Kato et al., 1997; Han et al., 2002), during prenatal mouse retinal neurogenesis. Removal of Rbpj caused reduced proliferation, premature neuronal differentiation, apoptosis, and profound mispatterning. To determine the cell autonomous requirements for Rbpj during RGC and cone formation, we marked Cre-generated retinal lineages with GFP expression, which showed that Rbpj autonomously promotes RPC mitotic activity, and suppresses RGC and cone fates. In addition, the progressive loss of Rbpj−/− RPCs resulted in a diminished progenitor pool available for rod photoreceptor formation. This circumstance, along with the overproduction of Rbpj−/− cones, revealed that photoreceptor development is under homeostatic regulation. Finally, to understand how the Notch pathway regulates the simultaneous formation of multiple cell types, we compared the RGC and cone phenotypes of Rbpj to Notch1 (Jadhav et al., 2006b; Yaron et al., 2006), Notch3, and Hes1 mutants. We found particular combinations of Notch pathway genes regulate the development of each retinal cell type.
INFORMATION FILTERING WITH SUBMAPS FOR INERTIAL AIDED VISUAL ODOMETRY

Abstract. This work is concerned with the fusion of inertial measurements (accelerations and angular velocities) with imagery data (feature points extracted in a video stream) in a recursive bundle adjustment framework for indoor position and attitude estimation. Recursive processing is achieved by a combination of local submaps and the Schur complement. The Schur complement is used to reduce the problem size at regular intervals while retaining the information provided by past measurements. Local submaps provide a way to propagate the gauge constraints and thereby to alleviate the detrimental effects of linearization errors in the prior. Though the presented technique is not real-time capable in its current implementation, it can be employed to process arbitrarily long trajectories. The presented system is evaluated by comparing the estimated trajectory of the system with a reference trajectory of a prism attached to the system, which was recorded by a total station.
Far-field Acoustic Source Localization and Bearing Estimation using Σ ∆ Learners

Localization of acoustic sources using miniature microphone arrays poses a significant challenge due to fundame ntal limitations imposed by the physics of sound propagation. Wi th sub-wavelength distances between the microphones, resolv ing acute localization cues become difficult due to precision ar tifacts. In this paper we propose a framework which overcomes this limitation by integrating signal-measurement (analog-to-digital conversion) with statistical learning (bearing estimation). At the core of the proposed approach is a min-max stochastic optimization of a regularized cost function that embeds manifold learning within Σ∆ modulation. As a result, the algorithm directly produces a quantized sequence of the bearing estim ates whose precision can be improved asymptotically similar to a conventional Σ∆ modulators. In this paper we present a hardware implementation of a miniture acoustic source localizer which comprises of: (a) a common-mode canceling microphone array and (b) a Σ∆ integrated circuit which produces bearing parameters. The parameters are then combined in an estimati on procedure that can achieve a linear range from 0 − 90. Measured results from a prototype fabricated in a0. 5μ CMOS process demonstrate that the proposed localizer can reliab ly estimate the bearing of an acoustic source with a resolutionless than 2 while consuming less than75μW of power.
UDE-Based Robust Control for a Class of Non-Affine Nonlinear Systems

In this paper, the UDE (uncertainty and disturbance estimator) based robust control is investigated for a class of non-affine nonlinear systems in a normal form. Control system design for non-affine nonlinear systems is one of the most difficult problems due to the lack of mathematical tools. This is also true even for the exact known non-affine systems because of the difficulty in explicitly constructing the control law. It is shown that the proposed UDE-based robust control strategy leads to a stable system. The most important features of the approach are that (i) by adding and subtracting the control term u, the original non-affine form is transformed into a semi-affine form, which not only simplifies the control design procedure, but also avoids the singularity problem of the controller; (ii) the employment of UDE makes the estimation of the lumped uncertain term which is a function of control input, states and disturbances possible, rather than states alone; and (iii) it does not require any knowledge (e.g., bounds) about the uncertainties and disturbances, except the information about the bandwidth, during the design process. The stability of the closed-loop system is established. Effectiveness of the proposed approach is demonstrated through application to the hard disk driver control problem.Copyright © 2013 by ASME
The development of modeling or the modeling of development?

Abstract We agree with many theoretical points presented by Rogers & McClelland (R&M), especially the role of domain-general learning of coherent covariation. Nonetheless, we argue that in failing to be informed by key aspects of development, including the role of labels on categorization and the emergence of constraints on learning, their model fails to capture important features of the ontogeny of knowledge.
Regret vs. Communication: Distributed Stochastic Multi-Armed Bandits and Beyond

In this paper, we consider the distributed stochastic multi-armed bandit problem, where a global arm set can be accessed by multiple players independently. The players are allowed to exchange their history of observations with each other at specific points in time. We study the relationship between regret and communication. When the time horizon is known, we propose the Over-Exploration strategy, which only requires one-round communication and whose regret does not scale with the number of players. When the time horizon is unknown, we measure the frequency of communication through a new notion called the density of the communication set, and give an exact characterization of the interplay between regret and communication. Specifically, a lower bound is established and stable strategies that match the lower bound are developed. The results and analyses in this paper are specific but can be translated into more general settings.
A Software Framework for Translating ECA Sequences from OWL-DL into Java

The semantic Web offers new opportunities to integrate desktop applications seamlessly with one another, but most software applications currently still rely on proprietary RDBMS systems for data storage, and application logic is typically expressed only through general-purpose programming languages without being formally defined. In this article we propose an alternative approach to designing simple applications, using an OWL-DL ontology to define ECA rules for a domain and then use a novel software framework to transform these into a functional Java API that can execute these rules according to the ontology specification. We use the simple case of a turn based word-game to illustrate the framework in action. The project methodology and framework design are discussed and suggestions for further work are proposed.
A Survey on Semi-Supervised Learning Techniques

Semisupervised learning is a learning standard which deals with the study of how computers and natural systems such as human beings acquire knowledge in the presence of both labeled and unlabeled data. Semisupervised learning based methods are preferred when compared to the supervised and unsupervised learning because of the improved performance shown by the semisupervised approaches in the presence of large volumes of data. Labels are very hard to attain while unlabeled data are surplus, therefore semisupervised learning is a noble indication to shrink human labor and improve accuracy. There has been a large spectrum of ideas on semisupervised learning. In this paper we bring out some of the key approaches for semisupervised learning.
Credibility measures in portfolio analysis: From possibilistic to probabilistic models

Abstract. This paper treats risk based on the notions of credibility measure and credibility expected value. Firstly, the paper derives and discusses the credibility expected value. Secondly, the paper presents a new method of analysis of possibilistic portfolios. The new step is a construction by which with a possibilistic portfolio one associates a probabilistic portfolio. The problem solving of possibilistic portfolio gets down to the problem solving of associated probabilistic portfolio. For the latter one a variety of solving methods exist, from which we can choose the most appropriate one for the initial problem. Risk evaluat ion in the context of probabilistic portfolio leads to an understanding of risk for the possibilistic portfolio. The paper presents an application case of a venture capitalist firm, which needs to solve the shares of its budget to be invested to start-up companies. Keywords: credibility measures; risk theory; probability measures; portfolio analysis * Received October 2010. Accepted April 2011
A novel parallel-type double inverted pendulum control method

Car inverted pendulum system is often used as the benchmark for verifying the performance and effectiveness of a new control method, However, it's very unusual to use parallel-type double inverted pendulum. Based on Lagrange equation, this paper presents a novel state space model for the parallel-type double inverted pendulum system. Also, the paper designs a linear quadratic regulator (LQR) controller with the Lagrange modeling method, Simulation results are presented to demonstrate the feasibility and performance of the proposed model and controller.
Balancing State and Institutional Perspectives in the Implementation of Effective State-Level Academic Program Review

Academic program review is analyzed in regard to purposes, process, schedule and timing, criteria, decisions, and the organizational entities that oversee the process of state-level program review. The purposes of program review are linked to budgeting decisions, mission delineation, and potential program improvement. HE 016 472 20 pages MF $1.17 PC $3.70
Audio-visual musical instrument recognition

In 2008, a humanoid robot developed by Honda conducted a symphonic orchestra in front of a live audience. It perfectly imitated the pre-recorded actions of the orchestra’s human conductor. Despite its realistic movements, it was suggested that the robot could not listen to nor interact with the orchestra as a true conductor would. For a robot to truly direct an orchestra, it would need to hear, distinguish, and respond to the sounds of different instruments. As a first step to making a musically trained robot, we implement monophonic instrument recognition on Hearbo, developed by HRI-JP for audio-based human-robot interaction. Until now, musical instrument recognition has been limited to audio recording analysis. In the field of solo musical instrument recognition, acoustic features such as MFCCs and LPCC’s [1] have been widely studied. Classifiers like k-nearest neighbors [1], SVM and GMM [2] to classify these features have also been examined. Using a priori musical knowledge, Martin’s classification system in [3] used a hierarchy of musical instrument classes, such as grouping string instruments like guitar and violin together. Both his and Klapuri and Eronen’s [4] work showed better results classifying at the instrument family level rather than at the specific instrument. Brown [5] investigated the use of features such as attack and decay to distinguish between four similar woodwind instruments. Indeed, a common problem comes down to distinguishing instruments of the same family. So far, no one has yet exploited the visual differences between instruments to overcome this problem.
Knowing the cost of everything, the value of nothing? Challenges in measuring NHS output growth

An index of health sector outputs should capture the valuable things that the health system produces. Standard national accounting practice is to weight health service outputs by their cost so that they can be aggregated into a single index. Consequently no account is taken of quality changes, defined as changes in the characteristics of health service output which are valued by patients. We propose a method of measuring growth in NHS output that incorporate adjustment for quality. An “ideal” value weighted output index has two fundamental features. First, the value attached to each output reflects its relative contribution to health outcomes and, second, the value of other important characteristics of health care, such as the process of care delivery, can be readily incorporated. Comprehensive calculation of this index for the NHS is currently unfeasible because of a lack of data. We demonstrate how health and waiting time adjusted output indices can be calculated for a limited set of activities for which data on health outcomes exist. The influence of different assumptions about the form of quality adjustments is assessed. Indices are sensitive to the choice of valuation method when (1) there are large discrepancies in the weights produced by alternative bases for valuation (2) there are substantial changes in output mix over time.
Optimized Database for Crack Reconstruction by Neural Network

The non-destructive evaluation of in-material defects is carried out using neural network. The network is trained with the eddy current testing data measured for suitably selected defect prototypes, so that this selection constitutes a consistent representation of the forward problem. It is shown that in presence of noise this network performs in defect reconstruction better, than those networks trained with randomly or regularly selected defect prototypes of about the same number. The estimation of the parameters of cracks is a frequent objective of the eddy current testing (ECT). The forward model that allows to calculate the data measured by the ECT probe from the crack parameters in fact maps the crack parameter space to the data space. This mapping is often complex since the modelling of such ECT configurations generally requires the use of numerical methods. It results that an inverse mapping cannot be obtained explicitly. One possible way to carry out the inversion is then to build a database containing the measured data of some selected crack parameter prototypes. This database is then used to adjust the degrees of freedom of a behavioural inverse model linking the data space to the parameter space. The selection of the database examples is of considerable importance since the consistency of the inverse model depends on them. The regular mapping of the parameter space is generally speaking not an effective solution since it does not take into account that the sensitivity of the probe is often not uniform on the whole range of the parameter space. In this paper we present a database generation method based on the meshing of the parameter space with elements whose node degrees of freedom contain the measured data of the actual crack prototype (1). We attempt to find an optimal sampling of the forward operator, such that the interpolation error of data retrieval is kept below a prescribed bound all over the model space, while using the lowest possible number of data points. The optimization of the database is carried out in the framework of an adaptive meshing. First an initial mesh is spanned over the acceptable region of the model space using a coarse uniform sampling. Then it is being successively refined by inserting new data points into the mesh (where needed) until the criterion on interpolation error satisfies everywhere. Note that we use directional refinement based on edge bisection technique, which may result in a so-called anisotropic mesh. This procedure acts as a systematic exploration of the model space and allows the examples to be more concentrated in the high-sensitivity parameter regions of the probe. The resulting database is referred as optimised mesh
SCOPE : An Open-Source , C + + Implementation for Calculation of Protein Energetics from First Principles

SCOPE (Semi Classical Open Source Protein Energy) is an open-source program that has been implemented in the Object-Oriented C++ language, capable of computing none-bonded energies for protein structures from first principles. SCOPE is also capable of manipulating protein structures within the Rotamer space instead of the typical Cartesian space. This approach simplifies calculation of the transitional force field through elimination of unnecessary terms such as bond lengths, bond angles, and other peptide geometrical constraints. Elimination of unnecessary force calculation is beneficial in improving computational performance while the OO approach results in better program maintenance and customization for other projects. Finally, the calculation of forces has been compared and confirmed with respect to other commonly used programs such as CHARMM and Xplor-NIH. Further development of SCOPE can be very beneficial in refinement of computationally modeled structures, or potentially Ab-Initio calculation of structures from first principles without any reliance on homology modeling.