Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint optimization of SINR and power allocation to relays in cluster-based wireless sensor networks

Wireless relaying has attracted dramatic attention in the last few years due to its advantage in multi-hop communication systems. Cooperation among nodes in a network enhances the overall system performance. Power allocation schemes have been discussed with respect to optimization of signal to noise ratio (SNR) without considering the interference. The contribution of our present approach is to allocate power to relay nodes in presence of interference, for cluster-based cooperative wireless sensor networks (WSNs). In this paper, we consider optimization of the signal-to-interference plus noise ratio (SINR) and allocating the power to relay nodes by using the difference of two convex functions (dc) algorithm. The best relay is selected based on the comparisons of power allocated to relay nodes. Such schemes would be typically applicable to a network where allocation of orthogonal channels to sensor nodes will not be feasible such as marine WSNs, border sensor networks etc. Numerical and simulation results are also provided. Comprehensive analysis and extensive simulations show our scheme to be highly effective and flexible in balancing the cost and quality of service (QoS) performance.
Multiobjective Optimization for Meaningful Metrical Poetry

This paper reports our experiments to properly handle the multiobjective optimization nature of poetry generation — as defined in Manurung (2003) — as a stochastic search that seeks to produce a text that simultaneously satisfies the properties of grammaticality, meaningfulness, and poeticness. In particular, we employ the SPEA2 Algorithm (Zitzler, Laumanns, and Thiele 2001). Various results show that it consistently outperforms our previous system in its ability to generate a meaningful metrical text according to given semantic and metre specifications, and in some cases is able to generate the intended text, whereas our previous system fails to do so. However, it is still unable to reach the goal of generating an entire poem. We conclude with suggestions for further work to address this shortcom-
Transition Breast Cancer and Confers Epithelial-Mesenchymal HOXB 7 , a Homeodomain Protein , Is Overexpressed in Updated

Epithelial-mesenchymal transition (EMT) is increasingly recognized as a mechanism whereby cells in primary noninvasive tumors acquire properties essential for migration and invasion. Microarray analyses of microdissected epithelial cells from bone metastasis revealed a HOXB7 overexpression that was 3-fold higher than in primary breast carcinomas and 18fold higher compared with normal breast. This led us to investigate the role of HOXB7 in neoplastic transformation of breast cells. Expression of HOXB7 in both MCF10A and MadinDarby canine kidney (MDCK) epithelial cells resulted in the acquisition of both phenotypic and molecular attributes typical of EMT. Loss of epithelial proteins, claudin 1 and claudin 7, mislocalization of claudin 4 and E-cadherin, and the expression of mesenchymal proteins, vimentin and A-smooth muscle actin, were observed. MDCK cells expressing HOXB7 exhibited properties of migration and invasion. Unlike MDCK vector–transfected cells, MDCK-HOXB7 cells formed highly vascularized tumors in mice. MDCK-HOXB7 cells overexpressed basic fibroblast growth factor (bFGF), had more active forms of both Ras and RhoA proteins, and displayed higher levels of phosphorylation of p44 and p42 mitogen-activated protein kinase (MAPK; extracellular signal–regulated kinases 1 and 2). Effects initiated by HOXB7 were reversed by specific inhibitors of FGF receptor and the Ras-MAPK pathways. These data provide support for a function for HOXB7 in promoting tumor invasion through activation of Ras/Rho pathway by up-regulating bFGF, a known transcriptional target of HOXB7. Reversal of these effects by HOXB7-specific siRNA further suggested that these effects were mediated by HOXB7. Thus, HOXB7 overexpression caused EMT in epithelial cells, accompanied by acquisition of aggressive properties of tumorigenicity, migration, and invasion. (Cancer Res 2006; 66(19): 9527-34)
Cardiovascular Effects of Nickel in Ambient Air

Background Fine particulate matter (FPM) in ambient air causes premature mortality due to cardiac disease in susceptible populations. Objective Our objective in this study was to determine the most influential FPM components. Methods A mouse model of atherosclerosis (ApoE−/−) was exposed to either filtered air or concentrated FPM (CAPs) in Tuxedo, New York (85 μg/m3 average, 6 hr/day, 5 days/week, for 6 months), and the FPM elemental composition was determined for each day. We also examined associations between PM components and mortality for two population studies: National Mortality and Morbidity Air Pollution Study (NMMAPS) and Hong Kong. Results For the CAPs-exposed mice, the average of nickel was 43 ng/m3, but on 14 days, there were Ni peaks at ~ 175 ng/m3 and unusually low FPM and vanadium. For those days, back-trajectory analyses identified a remote Ni point source. Electrocardiographic measurements on CAPs-exposed and sham-exposed mice showed Ni to be significantly associated with acute changes in heart rate and its variability. In NMMAPS, daily mortality rates in the 60 cities with recent speciation data were significantly associated with average Ni and V, but not with other measured species. Also, the Hong Kong sulfur intervention produced sharp drops in sulfur dioxide, Ni, and V, but not other components, corresponding to the intervention-related reduction in cardiovascular and pulmonary mortality. Conclusions Known biological mechanisms cannot account for the significant associations between Ni with the acute cardiac function changes in the mice or with cardiovascular mortality in people at low ambient air concentrations; therefore, further research is needed.
Training a SVM-based classifier in distributed sensor networks

The emergence of smart low-power devices (motes), which have micro-sensing, on-board processing, and wireless communication capabilities, has impelled research in distributed and on-line learning under communication constraints. In this paper, we show how to perform a classification task in a wireless sensor network using distributed algorithms for Support Vector Machines (SVMs), taking advantage of the sparse representation that SVMs provide for the decision boundaries. We present two energy-efficient algorithms that involve a distributed incremental learning for the training of a SVM in a wireless sensor network, both for stationary and non-stationary sample data (concept drift). Through analytical studies and simulation experiments, we show that the two proposed algorithms exhibit similar performance to the traditional centralized SVM training methods, while being much more efficient in terms of energy cost.
Integration of supra-lexical linguistic models with speech recognition using shallow parsing and finite state transducers

This paper proposes a layered Finite State Transducer (FST) framework integrating hierarchical supra-lexical linguistic knowledge into speech recognition based on shallow parsing. The shallow parsing grammar is derived directly from the full fledged grammar for natural language understanding, and augmented with top-level n-gram probabilities and phrase-level context-dependent probabilities, which is beyond the standard context-free grammar (CFG) formalism. Such a shallow parsing approach can help balance sufficient grammar coverage and tight structure constraints. The context-dependent probabilistic shallow parsing model is represented by layered FSTs, which can be integrated with speech recognition seamlessly to impose early phrase-level structural constraints consistent with natural language understanding. It is shown that in the JUPITER [1] weather information domain, the shallow parsing model achieves lower recognition word error rates, compared to a regular class n-gram model with the same order. However, we find that, with a higher order top-level n-gram model, pre-composition and optimization of the FSTs are highly restricted by the computational resources available. Given the potential of such models, it may be worth pursing an incremental approximation strategy [2], which includes part of the linguistic model FST in early optimization, while introducing the complete model through dynamic composition.
Connecting With Others: Counselor Educator Identity Development in Hybrid Doctoral Programs

ABSTRACT Counselor professional identity development (PID) is an often discussed topic, but limited attention has been given to the PID of doctoral students transitioning to counselor educators. Through multiple rounds of interviews, this grounded theory study examined the PID of doctoral students within the context of hybrid counselor education programs. The findings showed that making connections with others was critical to the overall PID process. The researchers provide implications for doctoral student training and future research.
A dither-less all digital PLL for cellular transmitters

Frequency synthesizer for cellular transmitters demands low phase-noise both in-band and out-of-band. The first is necessary to implement wideband modulations (e.g. WCDMA), while the second to satisfy the challenging emission mask of GSM. Moreover a low level of fractional spurs must be ensured. The paper describes the first dither-less ADPLL capable to satisfy all these requirements. These results are achieved exploiting a highly linear 2-dimension Vernier TDC and a very fine frequency resolution DCO. Both building blocks heavily rely on digital calibration techniques to precisely and efficiently implement two-point modulation and spur cancellation in the presence of many implementation impairments.
Practical Introduction to LTE Radio Planning

This paper reviews basics of radio planning for 3GPP LTE. Both coverage-limited and interference-limited scenarios are considered. For the coverage-limited scenario LTE link budget is compared to that of 3GPP Release 8 HSPA+ with 2x2 MIMO. It is shown that, given the same 5MHz bandwidth, both systems have similar cell ranges but, for a given target bit rate, there exists an optimum LTE system bandwidth that maximizes cell range in both uplink and downlink. For the interference-limited scenario (with random uncoordinated interference) we illustrate the relationship between average network load, cell edge target throughput and cell range, as well as the notion of interference margin for cell range dimensioning. Impact of base station antenna configurations on dual-stream Multiple-Input Multiple-Output (MIMO) performance is demonstrated by means of a real-world measurement example. The impact of advanced LTE radio resource management features are briefly reviewed. Finally, the most important radio parameter planning tasks are introduced.
Line search for averaged operator iteration

Many popular first order algorithms for convex optimization, such as forward-backward splitting, Douglas-Rachford splitting, and the alternating direction method of multipliers (ADMM), can be formulated as averaged iteration of a nonexpansive mapping. In this paper we propose a line search for averaged iteration that preserves the theoretical convergence guarantee, while often accelerating practical convergence. We discuss several general cases in which the additional computational cost of the line search is modest compared to the savings obtained.
Analysis of Genetic Diversity of Two Mangrove Species with Morphological Alterations in a Natural Environment

Mangrove is an ecosystem subjected to tide, salinity and nutrient variations. These conditions are stressful to most plants, except to mangrove plants that are well-adapted. However, many mangrove areas have extremely stressful conditions, such as salt marshes, and the plants nearby usually present morphological alterations. In Sepetiba Bay, two species of mangrove plants, Avicennia schaueriana and Laguncularia racemosa, have poor development near a salt marsh (SM) compared to plants at the riverside (RS), which is considered a favorable habitat in mangroves. The level of genetic diversity and its possible correlation with the morphological divergence of SM and RS plants of both species were assessed by AFLP molecular markers. We found moderate genetic differentiation between A. schaueriana plants from SM and RS areas and depleted genetic diversity on SM plants. On the other hand, Laguncularia racemosa plants had no genetic differentiation between areas. It is possible that a limited gene flow among the studied areas might be acting more intensely on A. schaueriana plants, resulting in the observed genetic differentiation. The populations of Laguncularia racemosa appear to be well connected, as genetic differentiation was not significant between the SM and RS populations. Gene flow and genetic drift are acting on neutral genetic diversity of these two mangrove species in the studied areas, and the observed genetic differentiation of A. schaueriana plants might be correlated with its morphological variation. For L. racemosa, morphological alterations could be related to epigenetic phenomena or adaptive loci polymorphism that should be further investigated.
Hierarchical Multi-Modal Place Categorization

In this paper we present an hierarchical approach to place categorization. Low level sensory data is processed into more abstract concept, named properties of space. The framework allows for fusing information from heterogeneous sensory modalities and a range of derivatives of their data. Place categories are defined based on the properties that decouples them from the low level sensory data. This gives for better scalability, both in terms of memory and computations. The probabilistic inference is performed in a chain graph which supports incremental learning of the room category models. Experimental results are presented where the shape, size and appearance of the rooms are used as properties along with the number of objects of certain classes and the topology of space.
SEMANTIC CLUSTERING WITH CONTEXT ONTOLOGY FOR INFORMATION RETRIEVAL SYSTEM

Nowadays, there are so many increasing amount of information within world-wide web. For these increasing amounts of information, we need efficient and effective index structure when we have to find needed information. Most indexing techniques directly matched terms from the document and terms from query. But there is a problem when matching. That is most system doesn’t consider the meaning of the words. A word can have more than one meaning. But most systems didn’t consider the context (multiple meaning of a word). This paper presents how to construct an index structure using SSTC and context ontology that provides multiple meanings of a word. Context provides extra information to improve search result relevance. This paper produces context semantic cluster to provide indexing of search engine.
The Application of Backfilling in Cluster Systems

As backfilling resolves the fragmentation problem caused by resource reservation and produces significant benefits in scheduling of parallel jobs, it is widely used by most cluster management software suites. This paper introduces typical backfilling algorithms, focuses on analyzing the different implementations of backfilling in some common workload managers and points out the drawbacks of backfilling.
"Family" Ties for Foster Alumni in College: An Open Systems Consideration

Abstract:This exploratory, qualitative, multi-case study sought to understand, from the perspective of successful foster alumni college students, the role and influence of family members. Sampling was purposeful; criteria required participants were in out-of-home placement at age 13 or older. Participants included eight successful, self-identified foster alumni students. Five themes emerged as key in shaping participants' success: unique definitions of family, boundary redefinition, historical family influencers, boundary management, and social media as a boundary management tool. Overall, findings revealed that traditional processes need to be reviewed and reconsidered in light of the wide variety of non-traditional supports upon which students depended.
Evolution of the CFOs along with the Financialization process in France, a study of their reciprocal relationships

Cette these etudie l'evolution des carrieres, des roles et de l’influence des Directeurs Financiers au regard du processus de financiarisation en France. En analysant la financiarisation et les Directeurs Financiers a travers une approche institutionnaliste, cette recherche presente leurs relations reciproques depuis les annees 1980. Alors que le processus de financiarisation a influence l'evolution de la profession de Directeur Financier, ces derniers ont de leur cote contribue a la financiarisation de differentes manieres. Au moyen d'une approche methodologique mixte, basee sur 1 040 Curriculum Vitae de Directeurs Financiers et 58 entretiens, cette these offre une etude approfondie et a grande echelle de l'evolution des Directeurs Financiers au regard du processus de financiarisation en France.
Secure coordination with a two-sided helper

We investigate the problem of secure source coding with a two-sided helper in a game-theoretic framework. Alice (A) and Helen (H) view iid correlated information sequences Xn and Yn respectively. Alice communicates to Bob (B) at rate R, while H broadcasts a message to both A and B at rate RH. Additionally, A and B share secret key K at rate R0 that is independent of (Xn, Yn). An active adversary, Eve (E) sees all communication links while having access to a (possibly degraded) version of the past information. We characterize the rate-payoff region for this problem. We also solve the problem when the link from A to B is private. Our work recovers previous results of Schieler-Cuff and Kittichokechai et al.
Random tree walk toward instantaneous 3D human pose estimation

The availability of accurate depth cameras have made real-time human pose estimation possible; however, there are still demands for faster algorithms on low power processors. This paper introduces 1000 frames per second pose estimation method on a single core CPU. A large computation gain is achieved by random walk sub-sampling. Instead of training trees for pixel-wise classification, a regression tree is trained to estimate the probability distribution to the direction toward the particular joint, relative to the current position. At test time, the direction for the random walk is randomly chosen from a set of representative directions. The new position is found by a constant step toward the direction, and the distribution for next direction is found at the new position. The continual random walk through 3D space will eventually produce an expectation of step positions, which we estimate as the joint position. A regression tree is built separately for each joint. The number of random walk steps can be assigned for each joint so that the computation time is consistent regardless of the size of body segmentation. The experiments show that even with large computation gain, the accuracy is higher or comparable to the state-of-the-art pose estimation methods.
Object Proposal Generation Using Two-Stage Cascade SVMs

Object proposal algorithms have shown great promise as a first step for object recognition and detection. Good object proposal generation algorithms require high object detection recall rate as well as low computational cost, because generating object proposals is usually utilized as a preprocessing step. The problem of how to accelerate the object proposal generation and evaluation process without decreasing recall is thus of great interest. In this paper, we propose a new object proposal generation method using two-stage cascade support vector machines (SVMs), where in the first stage linear filters are learned for predefined quantized scales/aspect-ratios independently, and in the second stage a global linear classifier is learned across all the quantized scales/aspect-ratios for calibration, so that all the windows from the first stage can be compared properly. The windows with highest scores from the second stage are kept as inputs to our new efficient proposal calibration algorithm to improve their localization quality significantly, resulting in our final object proposals. We explain our scale/aspect-ratio quantization scheme, and investigate the effects of combinations of ℓ1 and ℓ2 regularizers in cascade SVMs with/without ranking constraints in learning. Comprehensive experiments on VOC2007 dataset are conducted, and our method is comparable with the current state-of-the-art methods with much better computational efficiency.
A Parallelisable Algorithm for Optimising UnstructuredMesh Partitions

A newmethod is described for optimising graph partitions which arise in mapping unstructuredmesh calculations to parallel computers. The method employs a combination of iterative techniques to both evenly balance the workload and minimise the number and volume of interprocessor communications. It is designed to work efficiently in parallel as well as sequentially and can be applied directly to dynamically refinedmeshes. In addition, when combined with a fast direct partitioning technique (such as the Greedy algorithm) to give an initial partition, the resulting two-stage process proves itself to be both a powerful and flexible solution to the static graph-partitioning problem. A clustering technique can also be employed to speed up the whole process. Experiments, on graphs with up to a million nodes, indicate that the resulting code is up to an order of magnitude faster than existing state-of-the-art techniques such as Multilevel Recursive Spectral Bisection, whilst providing partitions of equivalent quality.
On the Balance of Unrooted Trees

We solve a class of optimization problems for (phylogenetic) $X$-trees or their shapes. These problems have recently appeared in different contexts, e.g. in the context of the impact of tree shapes on the size of TBR neighborhoods, but so far these problems have not been characterized and solved in a systematic way. In this work we generalize the concept and also present several applications. Moreover, our results give rise to a nice notion of balance for trees. Unsurprisingly, so-called caterpillars are the most unbalanced tree shapes, but it turns out that balanced tree shapes cannot be described so easily as they need not even be unique.
Neural network based Iris recognition system using Haralick features

Iris recognition has been paid more attentions due to its high reliability in personal identification recently. In this paper, an iris recognition system has been proposed. The steps of the proposed method include iris localization, normalization, feature extraction and matching of the iris pattern. To describe the iris data GLCM based Haralick features are used and for matching purpose probabilistic neural network is employed. Experiments are performed using iris images obtained from UBIRIS database. The method gives 97.00% correct classification rate.
Efficient Statistical Machine Translation Algorithm based on IBM Model 4

This paper describes our methodologies for NTCIR-7 Patent Translation Task, and reports the official results based on English and Japanese corpus. Our system was a novel combination pattern of machine translation algorithms including classical statistical method -- IBM model and highly efficient decoding algorithm. The result of this new method is relatively decent, and its speed is also fast. It can be considered as a candidate for such situations as people who want to get a kind of quick and simple grasp of the main idea of a text.