Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple Dimension of Urban Well-Being: Evidence from India

This paper addresses differences in outcomes across households residing in slums and non-slum urban areas of India. Using a nationally representative household data set, we undertake a robust multidimensional evaluation of intracity differences in well-being. We first established that if utility is defined as access to public goods such as water and sanitation, then residents in non-slum urban areas are unambiguously better off than slum dwellers. This finding implies that there is justification for slums garnering a sizable portion of the allocation of water and sanitation programs. On the other hand, we found that the distribution of private goods (monthly per capita expenditure and per capita living area) in non-slum areas does not dominate the distribution of these goods in the slums. In fact, at very low levels of MPCE and per capita living area, the distribution of these private goods in slums dominates the distribution in non slums. This important finding implies that non-slum residents are not unequivocally better off than slum residents. Since slums are on an average poorer than other urban areas, it may be more pragmatic, therefore, to target policies at slum development. However, such policies would fail to reach the poorest residents of non-slum areas in both large and small cities. Our results make the case for a more inclusive policy that targets these groups as well.
Pilot hopping in multi-cell massive MIMO systems: Pilot assignment and new results

Massive MIMO promises large spectral and power efficiency. However, pilot contamination, a well known challenge in massive MIMO, limits these aspired gains. In this paper, we propose a pilot hopping sequence along with a pilot reuse strategy for pilot decontamination. A simple hopping scheme is proposed and simulated in an uplink L-cells system where every user is assigned distinct pilot sequences in consecutive time slots during the channel coherent time in order to diversify the contaminating users. The base station uses LS channel estimation and receive beamforming for data detection. The covariance matrix of the channel estimation error is obtained and it is shown that the number of antennas can be greatly reduced when using the pilot hopping compared to the conventional system. It is also shown that the gain achieved by using pilot hopping depends on the pilot reuse factor β. This gain is not monotonic function with β and therefore brings to attention that a careful cell planing is required when applying pilot hopping. Monte Carlo simulations are used to illuminate our results.
Feature engineering and tree modeling for author-paper identification challenge

The ability to search literature and collect/aggregate metrics around publications is a central tool for modern research. Both academic and industry researchers across hundreds of scientific disciplines, from astronomy to zoology, increasingly rely on search to understand what has been published and by whom. Microsoft Academic Search is an open platform, which provides a variety of metrics and experiences for the research community, in addition to literature search. As the covering data came from many sources, the profile of an author with an ambiguous name tends to contain noise, resulting in papers that are incorrectly assigned to others. KDD Cup 2013 Track 1 challenges participants to determine which papers in an author profile were truly written by the given author. In this work, we present how to use tree-base models to accurately predict the paper author. We incorporate feature engineering into the models with the advantages of them. This paper introduces two kinds of tree-base models (GB-DT [4], RGF [5]) and presents in detail the learning algorithm and how features can be generated for the task. The experimental results show the effectiveness of the proposed approach.
Multiresolution foliage for forest rendering

Plants are important objects in virtual environments. High complexity of shape structure is found in plant communities. Level of detail (LOD) of plant geometric models becomes important for interactive forest rendering. We emphasize three major problems in current research: the time consumption in LOD model construction and extraction, the balance between visual effect and data compression, and the time consumption in the communication between Central Processing Unit (CPU) and Graphics Processing Unit (GPU). We present a new foliage simplification framework for LOD model and forest rendering. By an uneven subdivision of the tree crown volume, the cost for LOD model construction is drastically reduced. With a GPU‐oriented design of LOD storage structure for foliage, the costly hierarchical traversal of a binary tree is replaced by a sequential lookup of an array. The structure also decreases the communication between the CPU and the GPU in rendering. In addition, Leaf density is introduced to adapt compression to the local distribution of leaves, so that more visually relevant details are kept. According to foliage nature (broad leaves or needles), higher compression are finally reached using mixed polygon/line models. This framework is implemented on virtual scenes of simulated trees with high detail. Copyright © 2009 John Wiley & Sons, Ltd.
Vertical mode expansion method for transmission of light through a single circular hole in a slab.

An efficient method is developed for rigorously analyzing the scattering of light by a layered circular cylindrical object in a layered background, and it is applied to the study of the transmission of light through a subwavelength hole in a metallic film, where the hole may be filled by a dielectric material. The method relies on expanding the electromagnetic field (subtracted by one-dimensional solutions of the layered media) in one-dimensional modes, where the expansion "coefficients" are functions satisfying two-dimensional Helmholtz equations. A system of equations is established on the boundary of the circular cylinder to solve the expansion "coefficients. " The method effectively reduces the original three-dimensional scattering problem to a two-dimensional problem on the boundary of the cylinder.
An Analysis of Cache Sharing in Chip Multiprocessors

We present the effects of L1 and L2 cache sharing on cache miss rates, cache line invalidations, and constuctive and destructive interference. The most important finding of this paper is that a system configuration that shares L2 caches, does not share L1 caches, and does not enforce inclusion between the L1 and L2 caches will produce the highest performance cache and communication hierarchy for a chip multiprocessor. This is due to the relatively high speed of communication through the L2 cache but the low effects of L2 sharing on L1 performance — if inclusion is not enforced. Sharing at the L1 level produces too many conflict misses at this all important resource.
Results from CDF and D0 (everything but the $B^{)}$

With the start of Run II at the Fermilab Tevatron a host of new physics opportunities are opened. In this paper we will review the prospects for physics at the CDF and DO experiments. Topics ranging from QCD, to electro-weak precision measurements, to top-quark physics, to searches for the Higgs boson and signals of physics beyond the Standard Model will be discussed. B-Physics at the Tevatron is covered in a separate contribution to these proceedings. We will outline how upgrades to the accelerator and the detectors make these studies possible with precisions higher than ever achieved previously and will show results from the first data collected in Run II. These results give us confidence in our ability to achieve ambitious physics goals, and point the way toward a bright future for the Tevatron.
The Open University ’ s repository of research publications and other research outputs Elemental abundances of blue compact dwarfs from mid-infrared spectroscopy with Spitzer Journal

We present a study of elemental abundances in a sample of 13 blue compact dwarf (BCD) galaxies, using the 10– 37 mhigh-resolution spectra obtained with Spitzer IRS.We derive the abundances of neon and sulfur for our sample using the infrared fine-structure lines probing regions which may be obscured by dust in the optical and compare our results with similar infrared studies of starburst galaxies from ISO. We find a good correlation between the neon and sulfur abundances, although sulfur is underabundant relative to neon with respect to the solar value. A comparison of the elemental abundances (neon and sulfur) measured from the infrared data with those derived from the optical (neon, sulfur, and oxygen) studies reveals a good overall agreement for sulfur, while the infrared-derived neon abundances are slightly higher than the optical values. This indicates either that the metallicities of dust-enshrouded regions in BCDs are similar to the optically accessible regions, or that if they are different they do not contribute substantially to the total infrared emission of the host galaxy. Subject headingg s: dust, extinction — galaxies: starburst — nuclear reactions, nucleosynthesis, abundances
Mining Social Influence in Microblogging via Tensor Factorization Approach

Microblogging has become an important social media for creating, sharing, or exchanging information and ideas. Social influence analysis in Microblogging is often exploited for different tasks such as information retrieval, recommendations, businesses intelligence. Most existing methods mostly rely on social links between users, failing to take advantage of characteristics of Microblogging. Furthermore, the size of Microblogging's user (i.e. Microblogger) is very large, which makes computing resource for social influence mining approach can't be satisfied by single computer. In this paper, a tensor factorization framework based on cloud computing platform is proposed for mining social influence in Microblogging. The framework has three components: a feature extraction component, a tensor factorization component and a user influence ranking component. In feature extraction component, features are extracted for capturing user social influence quantitatively through statistical analysis on the Microbloggers' relations. In tensor factorization component, tensor factorization based MapReduce model is presented to infer user's implicit user's relations. Finally, a user influence ranking function is constructed for computing user social influence in user influence ranking component. Experiments on Sina weibo dataset (Chinese Microblogging platform) show that our proposal significantly not only improves the prediction accuracy compared with two baseline methods, but also has competitive advantage for processing massive data from Microblogging.
Emotion Recognition from Text based on the Rough Set Theory and the Support Vector Machines

In recent years, several methods on human emotion recognition have been published. But computer application on Chinese natural language processing (NLP) is still on the starting stage. In this paper, we proposed a scheme that emotion recognition from text through classification with the rough set theory and the support vector machines (SVMs). The basic steps are firstly to sample data sets, to build the decisions table, and to find importance of attributions and the simplest form of decisions table according to relative reduction and then the rough set model of system is obtained, finally train the predicting model by the SVMs. Our experiment results show that rough set theory and SVMs method are effective in emotion recognition, and the high recognition rate is resulted.
Influence of Continuous Nursing on the Psychological State and Coping Style of Patients Undergoing Pacemaker Implantation

Background: Patients undergoing pacemaker implantation often experience anxiety and fear. As such, studies have focused on the mechanisms that relieve the negative emotions caused by the intervention. Continuous nursing is a safe and effective nursing mode. In this study, continuous nursing intervention was provided for elderly patients undergoing pacemaker implantation and an empirical investigation was performed to determine the effects of their negative emotion and disease-coping ability. Methods: Overall, 114 (68 males and 46 females) elderly patients who were undergoing pacemaker implantation from Harbin City (China), were enrolled in the study. The patients were divided into two groups, namely, the control group and the intervention group, based on different nursing methods. Routine nursing was applied to the control group; continuous nursing support was provided for the intervention group from January 2014 to January 2015. The nursing results of the two groups were compared. These results were also evaluated using self-rating depression scale, self-rating anxiety scale, and trait coping style questionnaire. Result: The effects of depression and anxiety intervention were significant in the intervention group (P<0.05). Compared with the control group, the intervention group did not significantly differ. The coping style of the intervention group elicited significant effects. Compared with the control group, the intervention group was significantly different (P < 0.05). Conclusion: Continuous nursing can relieve the negative emotion and improve the negative coping style of patients undergoing pacemaker implantation.
Estimation of Temporary Overvoltages during Power System Restoration using Artificial Neural Network

The energization of power transformers following a complete or partial collapse of the system is an important issue. This paper presents an Artificial Neural Network (ANN)-based approach to estimate the temporary overvoltages (TOVs) due to transformer energization. In proposed methodology, Levenberg- Marquardt second order method is used to train the multilayer perceptron. The developed ANN is trained with the extensive simulated results, and tested for typical cases. Then the new algorithms are presented and demonstrated for a partial of 39- bus New England test system. The simulated results show that the proposed technique can estimate the peak values and duration of switching overvoltages with good accuracy.
Chronic electrical stimulation homeostatically decreases spontaneous activity, but paradoxically increases evoked network activity.

Neural dynamics generated within cortical networks play a fundamental role in brain function. However, the learning rules that allow recurrent networks to generate functional dynamic regimes, and the degree to which these regimes are themselves plastic, are not known. In this study we examined plasticity of network dynamics in cortical organotypic slices in response to chronic changes in activity. Studies have typically manipulated network activity pharmacologically; we used chronic electrical stimulation to increase activity in in vitro cortical circuits in a more physiological manner. Slices were stimulated with "implanted" electrodes for 4 days. Chronic electrical stimulation or treatment with bicuculline decreased spontaneous activity as predicted by homeostatic learning rules. Paradoxically, however, whereas bicuculline decreased evoked network activity, chronic stimulation actually increased the likelihood that evoked stimulation elicited polysynaptic activity, despite a decrease in evoked monosynaptic strength. Furthermore, there was an inverse correlation between spontaneous and evoked activity, suggesting a homeostatic tradeoff between spontaneous and evoked activity. Within-slice experiments revealed that cells close to the stimulated electrode exhibited more evoked polysynaptic activity and less spontaneous activity than cells close to a control electrode. Collectively, our results establish that chronic stimulation changes the dynamic regimes of networks. In vitro studies of homeostatic plasticity typically lack any external input, and thus neurons must rely on "spontaneous" activity to reach homeostatic "set points. " However, in the presence of external input we propose that homeostatic learning rules seem to shift networks from spontaneous to evoked regimes.
Empirical Modeling of a Squeeze Film Haptic Actuator

In the analysis of squeeze film haptic tactile feedback actuators, attention is focused on establishing an empirical model of the force generated on the user's finger. The model is based on finite-element simulations which take into account the real motion of the actuator. As analytical models exist for very few special cases, a methodology is exposed to establish an empirical model of the force created between a finger and an actuator aided by an optimal design-of-experiment plan. Then, an example of an actuator which generates a sinc profile amplitude of vibration is treated.
SECRET

DRAMs are used as the main memory in most computing systems today. Studies show that DRAMs contribute to a significant part of overall system power consumption. One of the main challenges in low-power DRAM design is the inevitable refresh process. Due to process variation, memory cells exhibit retention time variations. Current DRAMs use a single refresh period determined by the cell with the largest leakage. Since prolonging refresh intervals introduces retention errors, a set of previous works adopt conventional error-correcting code (ECC) to correct retention errors. However, these approaches introduce significant area and energy overheads. In this article, we propose a novel error correction framework for retention errors in DRAMs, called SECRET (selective error correction for refresh energy reduction). The key observations we make are that retention errors are hard errors rather than soft errors, and only few DRAM cells have large leakage. Therefore, instead of equipping error correction capability for all memory cells as existing ECC schemes, we only allocate error correction information to leaky cells under a refresh interval. Our SECRET framework contains two parts: an offline phase to identify memory cells with retention errors given a target error rate and a low-overhead error correction mechanism. The experimental results show that among all test cases performed, the proposed SECRET framework can reduce refresh power by 87.2% and overall DRAM power up to 18.57% with negligible area and performance overheads.
SIMPLE QUERY SUGGESTION UNTUK PENCARIAN ARTIKEL MENGGUNAKAN JACCARD SIMILARITY

Pencarian artikel merupakan sebuah proses yang digunakan untuk mencari artikel yang relevan sesuai dengan kata kunci yang dimasukkan oleh pengguna. Konsep sederhana dari pencarian artikel adalah pengguna memasukkan kata kunci, kemudian menekan tombol pencarian sehingga memberikan hasil pencarian. Proses pencarian dengan menggunakan konsep sederhana tersebut memerlukan waktu dan pencarian yang diberikan terkadang kurang sesuai dengan artikel yang dicari. Makalah ini merupakan penerapan dari Jaccard Similarity dalam contoh simple query suggestion. Jaccard similarity digunakan sebagai query suggestion karena sistem dapat membandingkan kata kunci yang diberikan dengan data yang terdapat didalam database yang disebut sebagai Jaccard Index . Sistem yang dibangun memberikan suggestion setiap kali user mengetikkan kata kunci dan hasil pencarian akan tampil secara akurat dengan waktu proses 7 mili detik dengan nilai Jaccard Similarity antara 0 hingga 1 pada 492 data artikel yang diambil dari Prosiding KNS&I STIKOM Bali.
Robust deadbeat current control with adaptive predictor for three-phase voltage-source active power filter

This paper is concerned with a deadbeat current control implementation of shunt-type three-phase active power filter (APF), and the method for improving the control accuracy in both a steady and a transient states is presented. To cancel a delay of settling and one more delay caused by DSP execution time, the desired APF compensating current is to be predicted two sampling periods ahead. In the prediction process, an adaptive predictor is newly applied to predict the control error of two sampling periods ahead, and the reference is modified by the predictor output, so that robustness about parameter uncertainties can be brought to the deadbeat control system.
Individual and Collective Resources and Health in Morocco

The interaction between available individual and collective resources in the determination of health is largely ignored in the literature on the relationship between poverty and health in developing countries. We analyse the role public resources play in the perception that rural women in Morocco have of their health. These resources are taken to contribute directly and indirectly to the improvement of individual health by, on the one hand, providing a health-promoting environment and, on the other, improving the individual? s ability to produce health. The empirical results of multilevel models confirm the expected associations between socioeconomic status, individual vulnerability factors and health. Furthermore, the random part of the model suggests that variation in state of health is also associated with the presence of collective resources. However, the higher the level of women? s individual wealth, the less the characteristics of the community in which they live seem to be associated with their health, and the less the potential vulnerability factors seem to constrain their ability to maintain or improve health. Our results suggest that collective investments derived from various areas of activity will be more favourable to improving health, insofar as they are adapted to the initial capacity of women to benefit from them.
A Chimeric HIV-1 gp120 Fused with Vaccinia Virus 14K (A27) Protein as an HIV Immunogen

In the HIV vaccine field, there is a need to produce highly immunogenic forms of the Env protein with the capacity to trigger broad B and T-cell responses. Here, we report the generation and characterization of a chimeric HIV-1 gp120 protein (termed gp120-14K) by fusing gp120 from clade B with the vaccinia virus (VACV) 14K oligomeric protein (derived from A27L gene). Stable CHO cell lines expressing HIV-1 gp120-14K protein were generated and the protein purified was characterized by size exclusion chromatography, electron microscopy and binding to anti-Env antibodies. These approaches indicate that gp120-14K protein is oligomeric and reacts with a wide spectrum of HIV-1 neutralizing antibodies. Furthermore, in human monocyte-derived dendritic cells (moDCs), gp120-14K protein upregulates the levels of several proinflammatory cytokines and chemokines associated with Th1 innate immune responses (IL-1β, IFN-γ, IL-6, IL-8, IL-12, RANTES). Moreover, we showed in a murine model, that a heterologous prime/boost immunization protocol consisting of a DNA prime with a plasmid expressing gp120-14K protein followed by a boost with MVA-B [a recombinant modified vaccinia virus Ankara (MVA) expressing HIV-1 gp120, Gag, Pol and Nef antigens from clade B], generates stronger, more polyfunctional, and greater effector memory HIV-1-specific CD4+ and CD8+ T-cell immune responses, than immunization with DNA-gp120/MVA-B. The DNA/MVA protocol was superior to immunization with the combination of protein/MVA and the latter was superior to a prime/boost of MVA/MVA or protein/protein. In addition, these immunization protocols enhanced antibody responses against gp120 of the class IgG2a and IgG3, together favoring a Th1 humoral immune response. These results demonstrate that fusing HIV-1 gp120 with VACV 14K forms an oligomeric protein which is highly antigenic as it activates a Th1 innate immune response in human moDCs, and in vaccinated mice triggers polyfunctional HIV-1-specific adaptive and memory T-cell immune responses, as well as humoral responses. This novel HIV-1 gp120-14K immunogen might be considered as an HIV vaccine candidate for broad T and B-cell immune responses.