Miracle-dz/UniFAR-FTUs huggingface dataset:
Physical activity, screen time, and prevalence of overweight/obesity among adolescents in a creative, problem-solving program.

Certain dietary, physical activity, and sedentary behaviors have been studied extensively and consistently have been found to be associated with childhood overweight and obesity Productive sedentary-to-light activity pursuits may enhance the relationship among positive health behaviors. The purpose of this study was to identify the prevalence of overweight and obesity in a population of adolescents participating in a creative, problem-solving afterschool program and to identify the physical activity, sedentary, and dietary behaviors related to overweight and obesity in this group. This study describes health behaviors thought to be related to weight status for 1679 adolescents in grades 6 12 participating in a competitive, creative, team-oriented, problem-solving afterschool program. Self-reported dietary, physical activity, and sedentary behavior data were collected in this convenience sample based on the 2007 Youth Risk Behavior Survey. Measured height and weight data were collected to calculate BMI percentile. Using multinomial regression, models were constructed to predict the likelihood of being classified as overweight/obese. The prevalence of overweight in the study population was 15.6%, obese was 11.4%. Fruit/vegetable intake, milk consumption, moderate-to-vigorous physical activity, television time, and total screen time were all independently associated with weight status. After controlling for all covariates, male gender, minority racial/ethnic status, and high screen time while not meeting the physical activity recommendation significantly increased the likelihood of overweight/obesity. The participants in the current study are leaner and engage in healthier behaviors compared to national data for adolescents. The interactive variable combining both sedentary and PA measures is a relatively new technique used to provide a better understanding of the relationship between sedentary and physical activity behaviors as they relate to weight status. Even among healthy eating, physically active, low-risk adolescents, the interactive variable of physical activity and screen time strongly was associated with weight status.
Evaluating innovative FCN Networks for energy prices' forecasting

In a competitive electricity market, an accurate forecasting of energy prices is an important activity for all the market participants. This paper proposes a novel approach based on Neural Networks for forecasting energy prices. Two different architectures of Neural Networks are used. In particular, Multi-Layer Perceptron (MLP) and Fully Connected Neural (FCN) networks are designed, calibrated and compared.
Predicting Fine-grained Social Roles with Selectional Preferences

Selectional preferences, the tendencies of predicates to select for certain semantic classes of arguments, have been successfully applied to a number of tasks in computational linguistics including word sense disambiguation, semantic role labeling, relation extraction, and textual inference. Here we leverage the information encoded in selectional preferences to the task of predicting fine-grained categories of authors on the social media platform Twitter. First person uses of verbs that select for a given social role as subject (e.g. I teach . for teacher) are used to quickly build up binary classifiers for that role.
HST Snapshot Survey of Post-AGB Objects

The results from a Hubble Space T elescope (HST ) snapshot survey of post-AGB objects are shown. The aim of the survey is to complement existing HST images of PPN and to connect various types of nebulosities with physical and chemical properties of their central stars. Nebulosities are detected in 15 of 33 sources. Images and photometric and geometric measurements are presented. For sources with nebulosities we see a morphological bifurcation into two groups, DUPLEX and SOLE, as previous studies have found. We find further support to the previous results suggesting that this dichotomy is caused by a difference in optical thickness of the dust shell. The remaining 18 sources are classified as stellar post-AGB objects, because our observations indicate a lack of nebulosity. We show that some stellar sources may in fact be DUPLEX or SOLE based on their infrared colors. The cause of the differences among the groups are investigated. We discuss some evidence suggesting that high progenitor-mass AGB stars tend to become DUPLEX post-AGB objects. Intermediate progenitormass AGB stars tend to be SOLE post-AGB objects. Most of the stellar sources probably have low mass progenitors and do not seem to develop nebulosities during the post-AGB phase and therefore do not become planetary nebulae. Subject headings: planetary nebulae: general — stars: AGB and post-AGB — stars: circumstellar matter — stars: mass loss — reflection nebulae
Color binary correlation descriptor for image retrieval

In this paper, a novel image descriptor, called Color Binary Correlation (CBC), is proposed for image retrieval. This method defines and describes the structure elements utilizing binary patterns based on colors and edge orientation respectively, and thus integrate texture with the other two properties. Besides, its variants CBCri and CBCu2, which are presented for rotated invariance and “unform” patterns respectively, are presented and analyzed for image retrieval. The experimental results in Corel-1K and Corel-10K datasets demonstrate that our descriptors are more robust and discriminative than other methods and CBC shows better performance than CBCri and CBCu2 because of taking more possible patterns into consideration.
Eigenvalue multiplicity and volume growth

Let $G$ be a finite group with symmetric generating set $S$, and let $c = \max_{R > 0} |B(2R)|/|B(R)|$ be the doubling constant of the corresponding Cayley graph, where $B(R)$ denotes an $R$-ball in the word-metric with respect to $S$. We show that the multiplicity of the $k$th eigenvalue of the Laplacian on the Cayley graph of $G$ is bounded by a function of only $c$ and $k$. More specifically, the multiplicity is at most $\exp((\log c)(\log c + \log k))$. Similarly, if $X$ is a compact, $n$-dimensional Riemannian manifold with non-negative Ricci curvature, then the multiplicity of the $k$th eigenvalue of the Laplace-Beltrami operator on $X$ is at most $\exp(n^2 + n log k)$. The first result (for $k=2$) yields the following group-theoretic application. There exists a normal subgroup $N$ of $G$, with $[G : N] \leq \alpha(c)$, and such that $N$ admits a homomorphism onto the cyclic group $Z_M$, where $M \geq |G|^{\delta(c)}$ and $\alpha(c), \delta(c) > 0$ are explicit functions depending only on $c$. This is the finitary analog of a theorem of Gromov which states that every infinite group of polynomial growth has a subgroup of finite index which admits a homomorphism onto the integers. This addresses a question of Trevisan, and is proved by scaling down Kleiner's proof of Gromov's theorem. In particular, we replace the space of harmonic functions of fixed polynomial growth by the second eigenspace of the Laplacian on the Cayley graph of $G$.
Throwing Pebbles into a Dark CAVE: a Study of Participation and Behaviour in the {Act-KM} Online Community

This article defines online communities and their constituent elements. It provides a framework in which online communities can be discussed and compared irrespective of topic or content. It explores the notions of core and peripheral membership and behaviour and provides a set of metrics for determining the activity profile of the community. Finally it presents a case study of an online community and discusses it using the framework and metrics presented.
Biomechanical Analysis of Force Distribution in Human Finger Extensor Mechanisms

The complexities of the function and structure of human fingers have long been recognised. The in vivo forces in the human finger tendon network during different activities are critical information for clinical diagnosis, surgical treatment, prosthetic finger design, and biomimetic hand development. In this study, we propose a novel method for in vivo force estimation for the finger tendon network by combining a three-dimensional motion analysis technique and a novel biomechanical tendon network model. The extensor mechanism of a human index finger is represented by an interconnected tendinous network moving around the phalanx's dorsum. A novel analytical approach based on the “Principle of Minimum Total Potential Energy” is used to calculate the forces and deformations throughout the tendon network of the extensor mechanism when subjected to an external load and with the finger posture defined by measurement data. The predicted deformations and forces in the tendon network are in broad agreement with the results obtained by previous experimental in vitro studies. The proposed methodology provides a promising tool for investigating the biomechanical function of complex interconnected tendon networks in vivo.
The DEEP2 Galaxy Redshift Survey: AEGIS Observations of a Dual AGN at z = 0.7

We present evidence of a dual active galactic nucleus (AGN) within an early-type galaxy at z = 0.709 in the extended Groth strip. The galaxy lies on the red sequence, with absolute magnitude MB = -21.0 (AB, with h = 0.7) and rest-frame color U - B = 1.38. Its optical spectrum shows strong, double-peaked [O III] emission lines and weak Hβ emission, with Seyfert-like line ratios. The two narrow peaks are separated by 630 km s-1 in velocity and arise from two distinct regions, spatially resolved in the DEIMOS spectrum, with a projected physical separation of 1.2 kpc. HST ACS imaging shows an early-type (E/S0) galaxy with hints of disturbed structure, consistent with the remnant of a dissipationless merger. Multiwavelength photometric information from the AEGIS consortium confirms the identification of a dust-obscured AGN in an early-type galaxy, with detections in X-ray, optical, infrared, and radio wave bands. These data are most readily explained as a single galaxy harboring two AGNs—the first such system to be observed in an otherwise typical early-type galaxy.
Applicability of using internal GPGPUs in industrial control systems

Industrial control systems are continuously increasing in functionality, connectivity, and levels of integration, and as a consequence they require more computational power. At the same time, these systems have specific requirements related to cost, reliability, timeliness, and thermal power dissipation, which put restrictions on the hardware and software used. Today the high-end embedded CPUs not only provide multiple cores, but also integrated graphics processors (GPU) at close to no additional cost. The use of GPUs for general processing have several potential values in industrial control systems; 1) the added computational power and the high parallelism could pave way for new functionality and 2) the integrated GPU could potentially replace other hardware and thereby reduce the overall cost. In this paper we investigate the applicability of using integrated GPUs in industrial control systems. We do this by evaluating the performance of GPUs with respect to computational problem types and sizes typically found in industrial control systems. In the end we conclude that GPUs are no obvious match for industrial control systems and that several hurdles remain before a wide adoption can be motivated.
A Comparative Study of Robot Languages

Efficient communication with industrial robots is a key factor in the success of contemporary programmable automation, but robot languages-the means of communication-have commonly been developed in an ad hoc manner to meet the needs of a particular robot and application. Thus, we currently have almost as many robot languages as robots. To formulate recommendations for an efficient robot language of the future, this article reviews and evaluates many of the RLs now in use or under development in industrial or academic environments. Table I compares the industrial robots for which the languages were developed, and Table 2 contains background information on the 14 selected languages, * which are identified below.
Global Output-Feedback Extremum Seeking Control for Nonlinear Systems with Arbitrary Relative Degree

Abstract An output-feedback sliding mode based extremum seeking controller was recently introduced for linear uncertain systems by using periodic switching functions. Nonlinear systems were also considered but restricted to relative degree one plants as well as the former linear case. Here, generalization is achieved to include more general dynamics with arbitrary relative degree. Global stability properties of the closed-loop system with convergence to a controlled neighborhood of the desired maximum point are also rigorously proved. Simulation results illustrate the performance of the proposed extremum seeking control algorithm.
Regularization using geometric information between sensors capturing features from brain signals

We propose a regularization based on geometric structure for feature extraction in a sensor array for brain data recordings. The purpose of the study is to add a penalty term using distances between sensors as the geometric information for finding spatial weights. The regularization term is derived under the definition of neighbors of sensors. We evaluate the proposed regularization in common spatial pattern (CSP) which is a well-known feature extraction method for EEG based brain computer interface (BCI). We have demonstrated the CSP procedure with the regularization by simulation for artificial signals. The results show that the proposed method works better than standard CSP in extracting of a component generated in a certain brain spot. Moreover, the classification experimental results using dataset of motor imagery based BCI suggest that the proposed method achieved maximum improvement by 27% in the classification accuracy over the standard CSP in a setting of even when we use only five samples.
Quality and Budget Aware Task Allocation for Spatial Crowdsourcing

A major research challenge for spatial crowdsourcing is to improve the expected quality of the results. However, existing research in this field mostly focuses on achieving this objective in volunteer-based spatial crowdsourcing. In this paper, we introduce the budget limitations into the above problem and consider realistic cases where workers are paid unequally based on their trustworthiness. We propose a novel quality and budget aware spatial task allocation approach which jointly considers the workers' reputation and proximity to the task locations to maximize the expected quality of the results while staying within a limited budget.
Political polarization and popularity in online participatory media: an integrated approach

We present our approach to online popularity and its applications to political science, aiming at the creation of agent-based models that reproduce patterns of popularity in participatory media. We illustrate our approach analyzing a dataset from Youtube, composed of the view statistics and comments for the videos of the U.S. presidential campaigns of 2008 and 2012. Using sentiment analysis, we quantify the collective emotions expressed by the viewers, finding that democrat campaigns elicited more positive collective emotions than republican campaigns. Techniques from computational social science allow us to measure virality of the videos of each campaign, to find that democrat videos are shared faster but republican ones are remembered longer inside the community. Last we present our work in progress in voting advice applications, and our results analyzing the data from choose4greece.com. We show how we assess the policy differences between parties and their voters, and how voting advice applications can be extended to test our agent-based models.
Correlation Power Analysis Against Stream Cipher MICKEY v2

In this paper we discuss correlation power analysis attack against stream cipher MICKEY v2. In such attacks, we use Hamming-Distance model to simulate the power consumption. Hamming-Distance model is a more accurate description to power consumption than other models such as Hamming-Weight, bit model etc. Generally, Hamming-Distance model is used to map the transitions that occur at the cells’ outputs of a CMOS circuit to the values of power consumption. In our attacks, we propose the Hamming-Distance model based on internal nodes of XOR gates considering that the basic structure of MICKEY v2 is a two-input and a three-input XOR gate. We simulate the power which is coming from not only the output of gate but also the internal nodes. This model is more accurate than before ones because we simulate the power consumption of all transistors consumed. Then we designed the attack way to MICKEY v2 by this model. And finally we simulate the result of attacking. The result shows that it needs only few or ten power traces during initialization for us to reveal the secret key by using weakness of MICKEY v2 initialization when resynchronization.
The association of ownership structure and the management earnings forecast: The case of Malaysian initial public offerings (IPOs)

This study investigate the association of different types of ownership structure and the performance of the firm in meeting the earnings forecasts in the context of mandatory earnings forecasts disclosure requirement. The analysis was tested on a sample of 94 IPO prospectuses published from January 2004 to 30th April 2008. With the mean forecast errors of -7.29%, the result indicates that on average, Malaysian IPO companies managed to meet ±10 percent forecast error tolerance level. 59 sample companies (62.7%) are meeting the Bursa Malaysia regulatory limit of ±10 percent tolerance level. OLS regression results exhibits that only management ownership is significantly associated with forecast errors.
Multipacket Hybrid ARQ: Closing Gap to the Ergodic Capacity

In this work, we consider incremental redundancy (IR) hybrid automatic repeat request (HARQ), where the transmission rounds are carried over independent block-fading channels. We analyze the multipacket HARQ, where the transmitter allows two different packets to share the same channel block; this stands in contrast with the conventional HARQ, where each packet occupies the entire block. We then optimally assign resources within the block to each packet and in each transmission round. We study superposition coding and time-sharing encoding strategies, and we optimize their parameters to maximize the throughput. Besides the conventional, one-bit (ACK/NACK) signaling we also consider a multibit feedback. We formulate our problem as a Markov decision process (MDP), where the decisions concerning the encoding strategies and their parameters are taken using additional information obtained from the receiver via feedback channel. In the case of the one-bit (ACK/NACK) signaling, the partial state information Markov decision process (PSI-MDP) framework is used to obtain the optimal policies. Numerical examples obtained in a Rayleigh-fading channel indicate that, the proposed multipacket HARQ outperforms the conventional one, by more than 5 dB for high-spectral efficiencies.
An EM-MCMC algorithm for Bayesian structure learning

Structure of the network plays a central role in applications of Bayesian Network. However, Bayesian Network structure learning is a hard problem to solve. We propose an method, EM-MCMC algorithm for Bayesian Network structure learning in which Expectation Maximization(EM) algorithm is applied to Bayesian parameter learning and Markov chain Monte Carlo(MCMC) method is used to sample for Bayesian structure. Differ from previous ones, we integrate Accepting Rejection Sampling to MCMC acceptance function. We compare our method with EM-EM Bayesian structure learning that uses EM algorithm in parameter learning and structure learning, experimental results demonstrate the effectiveness and feasibility of our algorithm.
Critical Role of Monocyte Chemoattractant Protein-1 Receptor CCR2 on Monocytes in Hypertension-Induced Vascular Inflammation and Remodeling

Activated monocytes are present in the arterial walls of hypertensive patients and animals. Monocyte chemoattractant protein-1 (MCP-1), which controls monocyte function through its receptor (CCR2), is implicated in hypertensive inflammatory changes in the arterial wall. The role of CCR2 expression on monocytes in hypertension-induced vascular remodeling, however, has not been addressed. We hypothesized that CCR2 on monocytes is critical in hypertension-induced vascular inflammation and remodeling. Hypertension was induced by infusion of angiotensin II (Ang II) into wild-type mice, CCR2-deficient (CCR2−/−) mice, and bone marrow-transferred mice with a leukocyte-selective CCR2 deficiency (BMT-CCR2−/−). In wild-type mice, Ang II increased CCR2 intensity in circulating monocytes, which was prevented by an Ang II type-1 (AT1) receptor blocker or blunted in AT1 receptor–deficient mice. Enhanced CCR2 intensity on monocytes was observed in hypertensive patients and rats, and was reduced by treatment with the Ang II receptor blocker, supporting the clinical relevance of the observation in mice. In CCR2−/− and BMT-CCR2−/− mice, Ang II–induced vascular inflammation and vascular remodeling (aortic wall thickening and fibrosis) were blunted as compared with control mice. In contrast, Ang II–induced left ventricular hypertrophy developed in CCR2−/− and BMT-CCR2−/− mice. The present study suggests that CCR2 expression in monocytes has a critical role in vascular inflammation and remodeling in Ang II–induced hypertension, and possibly in other forms of hypertension.
Suppression of interstitial nephritis by auto-anti-idiotypic immunity

Rats immunized with renal tubular antigens were protected from the development of interstitial nephritis by pretreatment with tubular antigen-reactive T lymphoblasts. Protected animals developed anti- idiotypic antibodies against idiotypes primarily within the antigen- binding region of monoclonal antitubular basement membrane antibodies. These studies extend the concept of auto-anti-idiotypic regulation to autoimmune disease, and they also provide an experimental basis for further efforts to develop biologically relevant mechanisms for attenuating the expression of other kidney diseases.
Effects of Dietary Electrolyte Balance on Growth Performance, Nitrogen Metabolism and Some Blood Biochemical Parameters of Growing Rabbits

The effects of different dietary electrolyte balance (DEB) on growth performance, nitrogen (N) metabolism and some blood biochemical parameters were investigated in 2 to 3 months old growing rabbits. A total of 150 growing rabbits of 2 months age were randomly divided into five groups according to average body weight, with 30 rabbits in each group. The DEB levels of the five experimental diets were −154, −3.16, +201, +347, and +500 meq/kg of dry matter (DM), respectively. There was a 7-d adaptation period and a 23-d experimental period. The results showed that the DEB levels had a quadratic affect on the average daily feed intake (ADFI) (p<0.001). The greatest ADFI was achieved when the DEB level was +201 meq/kg DM. Fecal N (FN) content linearly decreased (0.047), while digestible N (DN), retained N (RN), efficiency of intake N converted into digestible N (DN/IN) and the efficiency of intake N converted into retained N (RN/IN) linearly increased with the DEB increase (0.020, 0.004, 0.021, and 0.049, respectively). Serum phosphorus (P) ion content linearly increased with the DEB increase (p = 0.036). The DEB had a quadratic relationship with serum anion gap (AG) (p = 0.002) and serum parathyroid hormone (PTH) content (p = 0.016). The DEB levels quadratically affected base excess (BE) in the plasma (p<0.001). In conclusion, the DEB unaffected growth performance but affected feed intake, N metabolism and some blood biochemical parameters of growing rabbits.