Miracle-dz/UniFAR-FTUs huggingface dataset:
Ratio of weight to height gain: a useful tool for identifying children at risk of becoming overweight or obese at preschool age

PURPOSE: To analyze the usefulness of the weight gain/height gain ratio from birth to two and three years of age as a predictive risk indicator of excess weight at preschool age. METHODS: The weight and height/length of 409 preschool children at daycare centers were measured according to internationally recommended rules. The weight values and body mass indices of the children were transformed into a z-score per the standard method described by the World Health Organization. The Pearson correlation coefficients (rP) and the linear regressions between the anthropometric parameters and the body mass index z-scores of preschool children were statistically analyzed (alpha = 0.05). RESULTS: The mean age of the study population was 3.2 years (±0.3 years). The prevalence of excess weight was 28.8%, and the prevalence of overweight and obesity was 8.8%. The correlation coefficients between the body mass index z-scores of the preschool children and the birth weights or body mass indices at birth were low (0.09 and 0.10, respectively). There was a high correlation coefficient (rP = 0.79) between the mean monthly gain of weight and the body mass index z-score of preschool children. A higher coefficient (rP = 0.93) was observed between the ratio of the mean weight gain per height gain (g/cm) and the preschool children body mass index z-score. The coefficients and their differences were statistically significant. CONCLUSION: Regardless of weight or length at birth, the mean ratio between the weight gain per g/cm of height growth from birth presented a strong correlation with the body mass index of preschool children. These results suggest that this ratio may be a good indicator of the risk of excess weight and obesity in preschool-aged children.
Transition-Based Generation from Abstract Meaning Representations

This work addresses the task of generating English sentences from Abstract Meaning Representation (AMR) graphs. To cope with this task, we transform each input AMR graph into a structure similar to a dependency tree and annotate it with syntactic information by applying various predefined actions to it. Subsequently, a sentence is obtained from this tree structure by visiting its nodes in a specific order. We train maximum entropy models to estimate the probability of each individual action and devise an algorithm that efficiently approximates the best sequence of actions to be applied. Using a substandard language model, our generator achieves a Bleu score of 27.4 on the LDC2014T12 test set, the best result reported so far without using silver standard annotations from another corpus as additional training data.
Rethinking virtual private networks in the software-defined era

Multi Protocol Label Switching (MPLS) Virtual Private Networks (VPNs) have seen an unparalleled increasing adoption in the last decade. Although their flexibility as transport technology and their effectiveness for traffic engineering are well recognized, VPNs are difficult to set up and manage, due to the complexity of configurations, to the number of involved protocols, and to the limited control and predictability of network behaviors. On the other hand, Software-Defined Networking (SDN) is a consolidated, yet still emerging paradigm by which the control plane logic of a network device is implemented by an arbitrarily programmed software that runs outside the device itself. We conjugate the effectiveness of traditional VPNs with the programmability of SDN, proposing a novel and improved realization of MPLS VPNs based on SDN. With our approach, provisioning and setup of VPNs are accomplished by using a simple and flexible configuration language. Management and troubleshooting are facilitated because only a minimal set of technologies (notably, just MPLS) is retained. Control and predictability of network behaviors are enhanced by the centralized coordination enforced by the SDN controller. Besides illustrating our proposed approach and specifying the configuration language, we describe a prototype implementation of a controller and the outcome of tests we conducted in several configuration scenarios.
The sum capacity of underlay cognitive broadcast channel

We study the sum capacity of the underlay cognitive broadcast (BC) channel with M primary receivers (PRs) and K secondary receivers (SRs). In particular, the sum capacity is investigated under an average transmit power constraint (TPC) on the SRs and multiple average or peak interference power constraints (IPCs) on the PRs. In a fading environment, the average sum capacity is obtained from the solution of a convex optimization problem. The ergodic sum capacity C is then derived from the limiting case where the number of fading state tends to infinity. Further, the asymptotic capacity is investigated in the homogeneous Rayleigh fading system for which two different asymptotic regimes are analyzed.
Node sampling: a robust RTL power modeling approach

We propose a robust RTL power modeling methodology for functional units. Our models are consistently accurate over a wide range of input statistics, they are automatically constructed and can provide pattern-by-pattern power estimates. An additional desirable feature of our modeling methodology is the capability of accounting for the impact of technology variations, library changes and synthesis tools. Our methodology is based on the concept of node sampling, as opposed to more traditional approaches based on input sampling. We analyze the theoretical properties of node sampling and we formally show that it is a statistically sound approach. The superior robustness of our method is due to its limited dependency on pattern based characterization.
A Lie-algebraic analysis of the problem of absolute stability

The absolute stability problem (ASP) entails determining a critical parameter value for which a feedback system, composed of an nth-order linear system and a sector-bounded nonlinear function, loses its stability. The ASP is one of the oldest open problems in the theory of stability and control. Recently, it is attracting considerable interest, as solving it is equivalent to providing a necessary and sufficient condition for the stability of linear switched systems under arbitrary switching. Pyatnitsky pioneered the most promising approach for addressing the ASP. His approach is based on using optimal control techniques for characterizing the “most destabilizing” sector-bounded nonlinear function. This is equivalent to characterizing the “most destabilizing” switching law for a linear switched system under arbitrary switching. In this paper, we develop a new approach to the ASP which is based on a Lie-algebraic analysis of the switching function that determines the optimal control. We show that the finiteness of the associated Lie algebra implies that the switching function itself is the solution of a switched linear system of order at most n2. Furthermore, the switching function has a special and symmetric structure. This makes it possible to obtain an explicit analytic expression for the switching function for low orders of n. We demonstrate this using two examples.
Stable Adaptive Work-Stealing for Concurrent Multi-core Runtime Systems

The proliferation of multi-core architectures has led to explosive development of parallel applications using programming models, such as OpenMP, TBB, and Cilk, etc. With increasing number of cores, however, it becomes harder to efficiently schedule parallel applications on these resources since current multi-core runtime systems still lack efficient mechanisms to support collaborative scheduling of these applications. In this paper, we study feedback-driven adaptive scheduling based on work stealing, which provides an efficient solution for concurrently executing a set of applications on multi-core systems. To dynamically estimate the number of cores desired by each application, a stable feedback algorithm, called A-Deque, is proposed using the length of active deques, which more precisely captures the parallelism variation of the applications. Furthermore, a prototype system is built by extending the Cilk runtime system, and the experimental results show that feedback-driven scheduling algorithms have more advantages for scheduling parallel applications with dynamic changing parallelism, and better overall performances are achieved with more accurate and stable feedback mechanism. Compared with existing algorithms, A-Deque improves the performances by up to 19.13\% and 28.96\% with respect to average response time and processor utilization respectively.
Research of Personalized Learning Resource Recommendation Based on Learner's FDI

The personalized learning resource recommendation, as one higher level of the requirements for the learning based on resources of people in the Internet age, has been paid more attention by researchers. Some existing researches can provide personalized learning resources services for learners, but few of them considers the learner's needs for learning resources from a cognitive style point of view. However, cognitive styles are the relatively steady attitudes and methods of human during the information using process, the differences of learners in cognitive styles can affect their learning results greatly. Therefore, seeing from psychology perspective, Field Dependence-Independence (FDI), one cognitive style, was introduced in the personalized learning resource recommendation in this paper. Then the recommending process of personalized learning resources based on FDI was discussed. The research shows that the learners can be provided with more personalized learning resources, when considering their FDI in the recommending process.
Bayesian Inference of Signaling Network Topology in a Cancer Cell Line

MOTIVATION Protein signaling networks play a key role in cellular function, and their dysregulation is central to many diseases, including cancer. To shed light on signaling network topology in specific contexts, such as cancer, requires interrogation of multiple proteins through time and statistical approaches to make inferences regarding network structure. RESULTS In this study, we use dynamic Bayesian networks to make inferences regarding network structure and thereby generate testable hypotheses. We incorporate existing biology using informative network priors, weighted objectively by an empirical Bayes approach, and exploit a connection between variable selection and network inference to enable exact calculation of posterior probabilities of interest. The approach is computationally efficient and essentially free of user-set tuning parameters. Results on data where the true, underlying network is known place the approach favorably relative to existing approaches. We apply these methods to reverse-phase protein array time-course data from a breast cancer cell line (MDA-MB-468) to predict signaling links that we independently validate using targeted inhibition. The methods proposed offer a general approach by which to elucidate molecular networks specific to biological context, including, but not limited to, human cancers. AVAILABILITY http://mukherjeelab.nki. nl/DBN (code and data).
Regulation of the EphA2 Kinase by the Low Molecular Weight Tyrosine Phosphatase Induces Transformation*

Intracellular signaling by protein tyrosine phosphorylation is generally understood to govern many aspects of cellular behavior. The biological consequences of this signaling pathway are important because the levels of protein tyrosine phosphorylation are frequently elevated in cancer cells. In the classic paradigm, tyrosine kinases promote tumor cell growth, survival, and invasiveness, whereas tyrosine phosphatases negatively regulate these same behaviors. Here, we identify one particular tyrosine phosphatase, low molecular weight tyrosine phosphatase (LMW-PTP), which is frequently overexpressed in transformed cells. We also show that overexpression of LMW-PTP is sufficient to confer transformation upon non-transformed epithelial cells. Notably, we show that the EphA2 receptor tyrosine kinase is a prominent substrate for LMW-PTP and that the oncogenic activities of LMW-PTP result from altered EphA2 expression and function. These results suggest a role for LMW-PTP in transformation progression and link its oncogenic potential to EphA2.
Abstractions for Planning with State-Dependent Action Costs

Extending the classical planning formalism with state-dependent action costs (SDAC) allows an up to exponentially more compact task encoding. Recent work proposed to use edge-valued multi-valued decision diagrams (EVMDDs) to represent cost functions, which allows to automatically detect and exhibit structure in cost functions and to make heuristic estimators accurately reflect SDAC. However, so far only the inadmissible additive heuristic has been considered in this context. In this paper, we define informative admissible abstraction heuristics which enable optimal planning with SDAC. We discuss how abstract cost values can be extracted from EVMDDs that represent concrete cost functions without adjusting them to the selected abstraction. Our theoretical analysis shows that this is efficiently possible for abstractions that are Cartesian or coarser. We adapt the counterexample-guided abstraction refinement approach to derive such abstractions. An empirical evaluation of the resulting heuristic shows that highly accurate values can be computed quickly.
Allowable span length of submarine pipeline in shallow water

ABSTRACT Because of the complex geological conditions of the seabed, submarine pipelines buried beneath the ocean floor become suspended over the seabed under the long-term scour of waves eroding the surrounding sediment. Further, most oil fields were built in offshore areas while the country was developing. This gives the waves seen in shallow water obvious nonlinear features, and the abnormal characteristics of these waves must be considered when calculating their hydrodynamic forces. Particularly under such conditions, these suspended spans of submarine pipelines are prone to damage caused by the action of the external environment load. Such damages and eventual failures may result not only in great property losses but also pollution of the marine environment. The span length of these areas is a key predictive factor in pipeline damages. Therefore, determining the allowable span length for these submarine pipelines will allow future projects to avoid or prevent damage from excessive suspended span lengths. Expressions of the hydrodynamic loads placed on suspended spans of pipeline were developed in this work based on the first-order approximate cnoidal wave theory and Morison equation. The formula for the allowable free span length was derived for the common forms of free spanning submarine pipeline based on the point where maximum bending stresses remain less than the material’s allowable stress. Finally, the allowable free span length of real-world pipelines was calculated for a subsea pipeline project in Bohai Bay. This research shows that, with consideration for the complicated marine environment, existing suspended spans are within allowable length limitations. However, continuing to limit the length of these submarine pipeline spans in the Nanpu oil field will require ongoing attention.
Optimizing active ranges for consistent dynamic map labeling

Map labeling encounters unique issues in the context of dynamic maps with continuous zooming and panning-an application with increasing practical importance. In consistent dynamic map labeling, distracting behavior such as popping and jumping is avoided. In the model for consistent dynamic labeling that we use, a label becomes a 3d-solid, with scale as the third dimension. Each solid can be truncated to a single scale interval, called its active range, corresponding to the scales at which the label will be selected. The active range optimization (ARO) problem is to select active ranges so that no two truncated solids overlap and the sum of the heights of the active ranges is maximized. The simple ARO problem is a variant in which the active ranges are restricted so that a label is never deselected when zooming in. We investigate both the general and simple variants, for 1d- as well as 2d-maps. The 1d-problem can be seen as a scheduling problem with geometric constraints, and is also closely related to geometric maximum independent set problems. Different label shapes define different ARO variants. We show that 2d-ARO and general 1d-ARO are NP-complete, even for quite simple shapes. We solve simple 1d-ARO optimally with dynamic programming, and present a toolbox of algorithms that yield constant-factor approximations for a number of 1d- and 2d-variants.
High-rate flicker-free screen-camera communication with spatially adaptive embedding

Embedded screen-camera communication techniques encode information in screen imagery that can be decoded with a camera receiver yet remains unobtrusive to the human observer. These techniques have applications in tagging content on screens similar to QR-code tagging for other objects. This paper characterizes the design space for flicker-free embedded screen-camera communication. In particular, we identify an orthogonal dimension to prior work: spatial content-adaptive encoding, and observe that it is essential to combine multiple dimensions to achieve both high capacity and minimal flicker. From these insights, we develop content-adaptive encoding techniques that exploit visual features such as edges and texture to unobtrusively communicate information. These can then be layered over existing techniques to further boost the capacity. Our experimental results show that there is potential to achieve an average goodput of about 22 kbps, significantly outperforming existing work while remaining flicker-free.
Wrist rehabilitation following stroke: initial clinical results

In 1991, a novel robot named MIT-MANUS was introduced as a test bed to study the potential of using robots to assist in and quantify the neuro-rehabilitation of motor function. It introduced a new brand of therapy, offering a highly backdrivable mechanism with a soft and stable feel for the user. MIT-MANUS proved an excellent fit for shoulder and elbow rehabilitation in stroke patients, showing in clinical trials a reduction of impairment in these joints. The greater reduction in impairment was observed in the group of muscles exercised. This suggests a need for additional robots to rehabilitate other target areas of the body. The focus here is a robot for wrist rehabilitation designed to provide three rotational degrees of freedom. A previous paper at ICORR2003 and its companion book described the basic system design and characterization. In this paper we present clinical results from five (5) stroke patients. A comprehensive review of the wrist robot design, characterization, and initial clinical results are being submitted elsewhere (EEEE Transactions on Neural Systems and Rehabilitation Engineering).
Infection with Toxoplasma gondii Bradyzoites Has a Diminished Impact on Host Transcript Levels Relative to Tachyzoite Infection

ABSTRACT Toxoplasma gondii, an intracellular pathogen, has the potential to infect nearly every warm-blooded animal but rarely causes morbidity. The ability for the parasite to convert to the bradyzoite stage and live inside slow-growing cysts that can go unnoticed by the host immune system allows for parasite persistence for the life of the infected host. This intracellular survival likely necessitates host cell modulation, and tachyzoites are known to modify a number of signaling cascades within the host to promote parasite survival. Little is known, however, about how bradyzoites manipulate their host cell. Microarrays were used to profile the host transcriptional changes caused by bradyzoite infection and compared to those of tachyzoite-infected and uninfected hosts cells 2 days postinfection in vitro. Infection resulted in chemokine, cytokine, extracellular matrix, and growth factor transcript level changes. A small group of genes were specifically induced by tachyzoite infection, including granulocyte-macrophage colony-stimulating factor, BCL2-related protein A1, and interleukin-24. Bradyzoite infection yielded only about half the changes seen with tachyzoite infection, and those changes that did occur were almost all of lower magnitude than those induced by tachyzoites. These results suggest that bradyzoites lead a more stealthy existence within the infected host cell.
A method to build a representation using a classifier and its use in a K Nearest Neighbors-based deployment

The K Nearest Neighbors (KNN) is strongly dependent on the quality of the distance metric used. For supervised classification problems, the aim of metric learning is to learn a distance metric for the input data space from a given collection of pair of similar/dissimilar points. A crucial point is the distance metric used to measure the closeness of instances. In the industrial context of this paper the key point is that a very interesting source of knowledge is available : a classifier to be deployed. The knowledge incorporated in this classifier is used to guide the choice (or the construction) of a distance adapted to the situation Then a KNN-based deployment is elaborated to speed up the deployment of the classifier compared to a direct deployment.
Effect on vision of repeated exposure to carbon dioxide. Rep No 566.

Abstract : The visual effects of repeated exposure to CO2 at levels commonly regarded as innocuous were investigated. Exposure to CO2 varied cyclically from . 03 (air) to 3.0%, at 1 atmosphere pressure, every 24 hours for a period of 6 days. A battery of visual tests was administered during this period and in control periods both before and after the CO2 exposure. Among the various tests, night vision sensitivity and color sensitivity for green were the only ones which repeatedly detected impairment of efficiency during the period of exposure. All other visual functions remained normal.