Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Patterning in Adiposity in Adolescence: Prospective Observations from the Chinese Birth Cohort ‘‘Children of 1997’’

Introduction Low early life socio-economic position is more strongly associated with adiposity among women than men. We examined whether the sex difference of social patterning in general and central adiposity exists before adulthood. Methods In Hong Kong’s “Children of 1997” birth cohort, we used multivariable regression to examine the association of parental education, a marker of early life socio-economic position, with body mass index (BMI) (n = 7252, 88% follow-up) and waist-height ratio (n = 5636, 68% follow-up), at 14 years. Results Parental education of Grade 9 or below, compared to Grade 12 or above, was associated with higher waist-height ratio z-score particularly in girls (0.30, 95% confidence interval (CI) 0.19, 0.41) compared to boys (0.12, 95% CI 0.02, 0.22) (p for sex interaction = 0.02). Lower parental education was associated with greater BMI z-score in adolescents of locally born mothers, but not adolescents of migrant mothers, with no difference by sex. Conclusions Different social patterning in different markers of adiposity may imply different sociological and biological mediating pathways. A stronger association between low early life socio-economic position and waist-height ratio in adolescent girls may indicate sex-specific influences of SEP related early life exposures on central adiposity.
ROC Analysis of Example Weighting in Subgroup Discovery

This paper presents two new ways of example weighting for subgroup discovery. The proposed example weighting schemes are applicable to any subgroup discovery algorithm that uses the weighted covering approach to discover interesting subgroups in data. To show the implications that the new example weighting schemes have on subgroup discovery, they were implemented in the APRIORI-SD algorithm. ROC analysis was then used to study their behavior, and the behavior of APRIORI-SD’s original example weighting scheme, both theoretically and practically, by application on the UK Traffic challenge data set. The findings show that the proposed example weighting schemes are a valid alternative to APRIORI-SD’s original example weighting scheme when the goal is to discover fewer subgroups that are either small and highly accurate or large and less accurate.
Automatic evaluation and training in English pronunciation

SRI is developing a system that uses real time speech recognition to diag nose, evaluate and provide training in spoken English. The paper first describes the methods and results of a study of the feasibility of automati cally grading the performance of Japanese students when reading English aloud. Utterances recorded from Japanese speakers were independently rated by expert listeners. Speech grading software was developed from a speaker independent hidden-Markov-model speech recognition system. The auto matic grading procedure first aligned the speech with a model and then com pared the segments of the speech signal with models of those segments that have been developed from a database of speech from native speakers of English. The evaluation study showed that ratings of speech quality by experts are very reliable and automatic grades correlate well (r > 0.8) with those expert ratings. SRI is now extending this technology and integrating it in a spoken-language training system. This effort involves (1) porting SRI's DECIPHER speech recognition system to a microcomputer platform, and (2) extending the speech-evaluation software to more exactly diagnose a learner's pronuncia tion deficits and lead the learner through an appropriate regimen of exer cises.
Software Size Uncertainty : The Effects of Growth and Estimation Variability

1,2 Abstract— Examination of currently-accepted software cost, schedule, and defect estimation algorithms reveals a common acknowledgment that estimated software size is the single most influential independent variable. Unfortunately, “The most important business decisions about a software project are made at the time of minimum knowledge and maximum uncertainty. ” This includes minimum knowledge and maximum uncertainty about a software product’s effective size at the time when most estimating is done. Further complicating the issue of estimate uncertainty, in the author’s opinion, is the lack of a commonly-accepted taxonomy. This paper proposes definitions for and the relationship between two key contributors to software size uncertainty: growth and estimation process variability, both being distributions, the dispersions of which decrease as a function of project progress.
Graph based Metrics for Intrusion Response Measures in Computer Networks

This contribution presents a graph based approach for modelling the effects of both attacks against computer networks and response measures as reactions against the attacks. Certain properties of the model graphs are utilized to quantify different response metrics which are well-kown from the pragmatic view of network security officers. Using these metrics, it is possible to (1) quantify practically relevant properties of a response measure after its application, and (2) estimate these properties for all available response measures prior to their application. The latter case is the basis for the selection of an appropriate reaction to a given attack. Our graph-based model is similar to those used in software reliability analysis and was designed for a scalable granularity in representing properties of the network and its components to be protected. Different examples show the applicability of the model and the resulting metric values.
A backward pass through a CNN using a generative model of its activations

Neural networks have shown to be a practical way of building a very complex mapping between a pre-specified input space and output space. For example, a convolutional neural network (CNN) mapping an image into one of a thousand object labels is approaching human performance in this particular task. However the mapping (neural network) does not automatically lend itself to other forms of queries, for example, to detect/reconstruct object instances, to enforce top-down signal on ambiguous inputs, or to recover object instances from occlusion. One way to address these queries is a backward pass through the network that fuses top-down and bottom-up information. In this paper, we show a way of building such a backward pass by defining a generative model of the neural network's activations. Approximate inference of the model would naturally take the form of a backward pass through the CNN layers, and it addresses the aforementioned queries in a unified framework.
Decision-Making Under Conditions of Sleep Deprivation: Cognitive and Neural Consequences

Previous research has presented a mixed picture of the cognitive and neural consequences of sleep deprivation on decision-making. Functional magnetic resonance imaging (fMRI) was used to examine the neural changes associated with simple and integrated decision-making under conditions of sleep deprivation. When rested, regions of the anterior prefrontal cortex (aPFC) were recruited to a greater degree for integrated decision-making (ID). After 24 hours of sleeplessness, there was minimal effect on simple decision-making but a clear vulnerability of ID to sleeplessness that was accompanied by a breakdown in task-specific neural activity in prefrontal cortex that, when rested, correlated with behavioral performance.
Dark-field hyperlens exploiting a planar fan of tips

Metallo-dielectric superlenses transfer subwavelength-scale information without magnification. The so-called hyperlenses additionally magnify, transferring images into traditional far-field optics. We target hyperlenses based on the “canalization” phenomenon in an array of wires, modified to form an open fan, also called “endoscope. ” We use an integrated optics design with silicon wires, fed for instance by grating couplers, accessing gold wire fans. This alleviates the need to care for wire length. We explore a regime where we do not only image a near-field source, but where we image illuminated nano-objects, as done in microscopy, light being fed by a second fan before the object plane. In order to counter the low contrast from illuminated nano-objects, we propose here a dark-field hyperlens concept: We show that the illumination fan can be fed so as to get a dark output for a “void” object field, as occurs in the eponym microscopy method. We obtain, at a wavelength as large as 1200 nm, a well-resolved imaging capability for a scene of two 30 nm silicon particles.
Ensemble Feature Ranking for Shellfish Farm Closure Cause Identification

Shellfish farms must be closed if there is suspected contamination during production to avoid serious health hazards. The authorities monitor a number of environmental and water quality variables through a set of sensors to check the health of shellfish farms and to decide on the closure of the farms. The research presented in this paper aims to develop an ensemble feature ranking algorithm to identify the cause of closure. We have presented and analysed the results obtained using the proposed algorithm to demonstrate its effectiveness.
The importance of liquid evaporation on rectified diffusion processes

This paper unveils the basic mechanisms underlying rectifie dd iffusion processes. The exact solution of the radial transport of species in both phases, gas and liquid, turns to be crucial to accurately predict the growthrate in the context of lithotripsy. The Direct Numerical Simulation of the temporal evolution of an spherical bubble is used to gain new insight on the effect of two phenomena: Transient effects related with the gas diffusion in the liquid and non uniform concentration gradients inside the bubble. The special characteristics of the process impedes a direct application of usual hypothesis usually applied in studies related with bubble growth in ultrasonic fields.
On reachability sets for optimal feedback controllers: Monitoring the approach of a region of attraction

Sums-of-Squares optimization represents an important tool for the direct computation of a local Lyapunov function for a nonlinear dynamic system. Specifically, it can certify a sub-levelset of the cost-to-go from an optimal feedback controller like the Linear Quadratic Regulator (LQR), geometrically an ellipsoid in the state space, as Region of Attraction (ROA) of the closed-loop system. More complex robotic tasks however require switching control to first take the system into the ROA before invoking the LQR stabilizer. In this paper, we propose computationally efficient measures of the ROA distance based on quadratic and conic optimization to effectively supervise such a trajectory as it approaches the ROA. As a one-dimensional condensate of the multi-dimensional state trajectory, monitoring the ROA distance evolution allows us to early detect deviations, e.g. due to input saturation or time delay, in order to quickly take corrective action such as replanning. Importantly, computing the ROA distance adds only a small overhead on top of the ROA calculation itself and can be done concurrently.
Myocyte Enhancer Factor 2C as a Neurogenic and Antiapoptotic Transcription Factor in Murine Embryonic Stem Cells

Cell-based therapies require a reliable source of cells that can be easily grown, undergo directed differentiation, and remain viable after transplantation. Here, we generated stably transformed murine ES (embryonic stem) cells that express a constitutively active form of myocyte enhancer factor 2C (MEF2CA). MEF2C has been implicated as a calcium-dependent transcription factor that enhances survival and affects synapse formation of neurons as well as differentiation of cardiomyocytes. We now report that expression of MEF2CA, both in vitro and in vivo, under regulation of the nestin enhancer effectively produces “neuronal” progenitor cells that differentiate into a virtually pure population of neurons. Histological, electrophysiological, and behavioral analyses demonstrate that MEF2C-directed neuronal progenitor cells transplanted into a mouse model of cerebral ischemia can successfully differentiate into functioning neurons and ameliorate stroke-induced behavioral deficits.
Model predictive control of constrained non-linear time-delay systems

This paper proposes a model predictive control scheme for non-linear time-delay systems with input constraints. Based on the results for systems without delays, asymptotic stability of the closed loop is guaranteed by utilizing an appropriate terminal cost functional and an appropriate terminal region such that the optimal cost for the finite-horizon problem is an upper bound on the optimal cost for the associated infinite-horizon problem. Two structured procedures are presented to determine offline the terminal cost and the terminal region for a class of non-linear time-delay systems. For both procedures, sufficient conditions can be formulated in terms of linear matrix inequalities based on the Jacobi linearization of the system about the origin. The first procedure uses a combination of Lyapunov–Krasovskii and Lyapunov– Razumikhin conditions in order to compute a locally stabilizing controller and a control invariant region. The second procedure only applies Lyapunov–Krasovskii arguments but may yield more complicated control invariant regions. The effectiveness of both schemes is compared for the example of a continuous stirred tank reactor with recycle stream.
Towards a Unified Physical Model for Virtual Environments

To simulate the behaviour apparent in the real world, virtual environments must include a detailed physical model based on the concepts of Newtonian mechanics. Although considerable research in computer graphics has led to the application of physically-based models in animation, the need for realtime performance in a fully interactive environment has hampered their progress in virtual environments. This paper will outline the development of a unified physically-based model suitable for virtual environments. Focusing on the technique of Modal Analysis (Essa et al., 1992), a series of experiments are discussed which demonstrate the applicability of this method for virtual environments.
Dimension Reduction of Large-Scale Second-Order Dynamical Systems via a Second-Order Arnoldi Method

A structure-preserving dimension reduction algorithm for large-scale second-order dynamical systems is presented. It is a projection method based on a second-order Krylov subspace. A second-order Arnoldi (SOAR) method is used to generate an orthonormal basis of the projection subspace. The reduced system not only preserves the second-order structure but also has the same order of approximation as the standard Arnoldi-based Krylov subspace method via linearization. The superior numerical properties of the SOAR-based method are demonstrated by examples from structural dynamics and microelectromechanical systems.
Confidential Enhancement with Multi-code Keying Reconfiguration over Time-Shifted CHPC-based 2D OCDMA Networks

In order to overcome vulnerability to eavesdropping of on-off-keying based OCDMA scheme, a new multi-code keying reconfiguration is presented to enhance confidentiality over two dimensional time-spreading and wavelength-hopping (2D TS/WH) optical code division multiple access (OCDMA) schemes. The time-shifted carrier-hopping prime codes (CHPCs) are proposed and assigned as signature address code. This proposed scheme provides the integration of electronic and optical layer domain, which inherently comes from the data encryption and spectrum-spreading of the transmitted signals, and then achieve data-confidential enhancement in synchronous networks. Moreover, multi-code keying reconfiguration in a set of distinct codeword is assigned to each user and randomly selection by using thin film filter, tunable delay line and optical switch. Here, the proposed scheme using shifted CHPC appears to be a promising coding type for generating OCDMA code space that are large enough to prevent successful brute force code search attacks. In addition, the probability of interception performance in terms of various multi-code key numbers per user is analyzed. The result shows that the confidentiality is enhanced to combat against eavesdropping while signature address code number per uses is increased in proposed multi-code key reconfiguration scheme.
Usability in practice: alternatives to formative evaluations-evolution and revolution

The adoption of user experience methods within companies has followed a similar evolution over the past two decades. Typically organizations originally institute formative lab-based evaluations, and then add field studies and other user experience methods to their repertoire. This evolution typically occurs because the organization recognizes the need for more data on customer profiles, feature requirements, and task flows, along with the ability to iterate quickly among various design ideas and directions. These methods that fall outside of the categories of formative usability evaluations and field studies are addressed in this paper. Although there are a wide variety of methods within this 'alternative' category, a few representative samples will be discussed in more detail here. In actuality, these methods are not 'alternatives,' rather, they are additions to the toolkit of user experience methods that should be used in conjunction with formative usability studies and field studies.
An algorithm for measurement and detection of path cheating in virtual environments

In this paper, we introduce a cheat-free path discovering process for peer-to-peer online games. The algorithm finds the requested path through the active participation of the users, but cheating is detected through a controller. The controller recalculates a path segment when two peers disagree in terms of cost, and identifies the cheater using the trust profile. This eventually lowers the computational cost. There is no false positive while identifying a cheater as the recalculation is performed by the controller.
Bead-Sort: A Natural Sorting Algorithm

Nature is not only a source of minerals and precious stones but is also a mine of algorithms. By observing and studying natural phenomena, computer algorithms can be extracted. In this note, a simple natural phenomenon is used to design a sorting algorithm for positive integers, called here Bead–Sort. The algorithm’s run– time complexity ranges from O(1) to O(S )( S is the sum of the input integers) depending on the user’s perspective. Finally, three possible implementations are suggested.
SDR design for cognitive radio

Software Defined Radio (SDR) is one of possibilities to realize the structure of device with a high mobility, flexibility and reconfigurability. This technology can provide the seamless shifting between existed air-interface standards. Extending the flexibility further, a system capable to sense the spectrum space available for communication and adapt to it is Cognitive Radio. Obviously SDR in Cognitive Radio should be configured not only to independent standards, protocols and services but also to the extensively dynamic nature of bandwidth allocation. Moreover this need of dynamic allocation of Spectrum space is a must to cater to its increased demand. Cognitive radio is envisioned as the ultimate system that can sense, adapt and learn from the environment in which it operates. Sensing the available bandwidth an SDR (Software defined radio) in a Cognitive System, tunes the circuits in the System for transferring data at optimum data rates, permissible by the space available. So it is a must for the SDR to accordingly add processing circuits to maintain the System performance at variable working frequencies. This paper discusses the critical issue of designing the SDR for the Cognitive radio and also presents some useful results obtained to configure the SDR for higher bandwidth available in Cognitive Radio. Results of Frequency Hopping Spread Spectrum (FHSS) implementation, Codec Algorithm modifications, and decoder iterations variation and performance improvement using OQPSK are depicted in the paper.
Inequality, trade liberalisation and growth

There has been a recent resurgence of interest in the relationship between income inequality and growth. We use the WIDER/UNDP World Income Inequality Database to investigate the effects of inequality and trade liberalisation on growth in a sample of exclusively developing countries. Cross-section (long-run) and panel (short run) techniques are used to test for the effect of inequality on growth controlling for other variables (initial GDP, endowments, investment, human capital and natural barriers to trade). We find consistent evidence for a negative effect of inequality on growth in the long run but no significant effect in the short run. Trade liberalisation appears to have a consistent and significant positive association with growth. These results are quite robust to the inclusion of alternative control variables, and we find no evidence of any interaction affect between inequality and trade liberalisation. We also find consistent evidence that countries relatively endowed with land (and thus dependent on primary commodity exports) and/or that face high natural barriers to trade experience lower growth rates. There is also evidence that, controlling for most other variables, sub-Saharan African countries experience below average growth performance; however, accounting for trade restrictions and natural barriers seems to eliminate this effect. The evidence suggests that inequality is an important factor in explaining low growth in developing countries over the long run.
Effect of Incremental Driving Motion on a Vision-Based Path Planning of a Spherical Robot

Path planning of spherical robots is usually carried out based on a continuous motion. However, actuators such as stepping motors are driven by discrete input signals that generate motion in rotational steps. The results would therefore be different from the continuous path planned for the spherical robot. The spherical robot under study incorporates two stepping motors, which generate the rotation increments. This incremental rotation inputs cause error and deviate the robot from the desired path. The result of such discretization of actuating input is studied here. The robot path is simulated using the inverse kinematics relations. As the angular steps decrease with better resolution of motors, the motion approaches the path expected for continuous motion.
Transcription of Genes Involved in Sulfolipid and Polyacyltrehalose Biosynthesis of Mycobacterium tuberculosis in Experimental Latent Tuberculosis Infection

The Influence of trehalose-based glycolipids in the virulence of Mycobacterium tuberculosis (Mtb) is recognised; however, the actual role of these cell-wall glycolipids in latent infection is unknown. As an initial approach, we determined by two-dimensional thin-layer chromatography the sulfolipid (SL) and diacyltrehalose/polyacyltrehalose (DAT/PAT) profile of the cell wall of hypoxic Mtb. Then, qRT-PCR was extensively conducted to determine the transcription profile of genes involved in the biosynthesis of these glycolipids in non-replicating persistent 1 (NRP1) and anaerobiosis (NRP2) models of hypoxia (Wayne model), and murine models of chronic and progressive pulmonary tuberculosis. A diminished content of SL and increased amounts of glycolipids with chromatographic profile similar to DAT were detected in Mtb grown in the NRP2 stage. A striking decrease in the transcription of mmpL8 and mmpL10 transporter genes and increased transcription of the pks (polyketidesynthase) genes involved in SL and DAT biosynthesis were detected in both the NRP2 stage and the murine model of chronic infection. All genes were found to be up-regulated in the progressive disease. These results suggest that SL production is diminished during latent infection and the DAT/PAT precursors can be accumulated inside tubercle bacilli and are possibly used in reactivation processes.