Miracle-dz/UniFAR-FTUs huggingface dataset:
User Association and Interference Management in Massive MIMO HetNets

Two key traits of 5G cellular networks are much higher base station (BS) densities-especially in the case of low-power BSs-and the use of massive MIMO at these BSs. This paper explores how massive MIMO can be used to jointly maximize the offloading gains and minimize the interference challenges arising from adding small cells. We consider two interference management approaches: joint transmission (JT) with local precoding, where users are served simultaneously by multiple BSs without requiring channel state information exchanges among cooperating BSs, and resource blanking, where some macro BS resources are left blank to reduce the interference in the small cell downlink. A key advantage offered by massive MIMO is channel hardening, which enables to predict instantaneous rates a priori. This allows us to develop a unified framework, where resource allocation is cast as a network utility maximization (NUM) problem, and to demonstrate large gains in cell-edge rates based on the NUM solution. We propose an efficient dual subgradient based algorithm, which converges towards the NUM solution. A scheduling scheme is also proposed to approach the NUM solution. Simulations illustrate more than 2x rate gain for 10th percentile users vs. an optimal association without interference management.
Application of Biologically Modeled Chaotic Neural Network to Pattern Recognition in Artificial Olfaction

This paper presents a novel neural network called KIII model for pattern recognition in artificial olfaction, whose topological structure and parameters are based on anatomical and electrophysiology experiments in mammalian olfactory system. Six data sets of three volatile organic compounds in different conditions, each with a wide range of concentrations, are obtained by a signal acquisition system with tin oxide gas sensor array. They are input into Kill model for training and test. Experimental results show that the system had a good classification performance in a wide concentration range while only a few training samples needed
Prediction under Uncertainty in Sparse Spectrum Gaussian Processes with Applications to Filtering and Control

Sparse Spectrum Gaussian Processes (SSGPs) are a powerful tool for scaling Gaussian processes (GPs) to large datasets. Existing SSGP algorithms for regression assume deterministic inputs, precluding their use in many real-world robotics and engineering applications where accounting for input uncertainty is crucial. We address this problem by proposing two analytic moment-based approaches with closed-form expressions for SSGP regression with uncertain inputs. Our methods are more general and scalable than their standard GP counterparts, and are naturally applicable to multi-step prediction or uncertainty propagation. We show that efficient algorithms for Bayesian filtering and stochastic model predictive control can use these methods, and we evaluate our algorithms with comparative analyses and both real-world and simulated experiments.
Changes in physical fitness of a home-based physical exercise program in childhood obesity: A quasi-experimental uncontrolled study

Few studies have evaluated the changes in physical fitness (PF) of obese children and adolescents of a physical activity program for the treatment of obesity, and even fewer have explored the modality of home-based physical exercise. The objective of this study is to evaluate the changes in PF and body composition (BC) of a home-based physical exercise for treating childhood obesity. Thirty-three overweight/obese children and adolescents participated for six months in a home-based intervention that combined aerobics and muscular strength exercises. The results were compared, before and after the intervention, for the different PF components (VO2max, abdominal muscle resistance strength, and lower body explosive strength) and BC (body mass index Z-score (BMI-Z), percentage of body fat, and fat-free mass) variables. A significant reduction was observed in the percentage of body fat (4.7%) and the BMI-Z score (.23), and there was an increase in the fat-free mass of 2.9 kg (p < .001). In addition, the VO2max showed a significant increase (p < .05). The results of the different strength tests also showed significant improvements (p < .05). Our findings support the effectiveness of this program improving not only BC but also PF. However, our results should be interpreted with caution due to lack of control group.
Key predistribution scheme using keyed-hash chain and multipath key reinforcement for wireless sensor networks

In this paper, we present an efficient, effective, and secure mechanism for key-distribution in homogeneous wireless sensor network (WSNs). The scheme is based on multipath key reinforcement. We introduce the concept of a keyed-hash-chain to use a different key in each session between a pair of sensors, without allocating a large amount of memory space to store the keys. First, each sensor node is preloaded with a number of generation keys. Using these generation keys and a publicly known seed value in a pre-defined hash function, distinct key-chains are generated. Multipath Key reinforcement is used to update the number of iterations to achieve a higher level of security.
Proteome analysis of sugar beet leaves under drought stress

Drought is one of the major factors limiting the yield of sugar beet (Beta vulgaris L.). The identification of candidate genes for marker‐assisted selection (MAS) could greatly improve the efficiency of breeding for increased drought tolerance. Drought‐induced changes in the proteome could highlight important genes. Two genotypes of sugar beet (7112 and 7219‐P.69) differing in genetic background were cultivated in the field. A line‐source sprinkler irrigation system was used to apply irrigated and water deficit treatments beginning at the four‐leaf stage. At 157 days after sowing, leaf samples were collected from well‐watered and drought‐stressed plants for protein extraction and to measure shoot biomass and leaf relative water content. Changes induced in leaf proteins were studied by two‐dimensional gel electrophoresis and quantitatively analyzed using image analysis software. Out of more than 500 protein spots reproducibly detected and analyzed, 79 spots showed significant changes under drought. Some proteins showed genotype‐specific patterns of up‐ or downregulation in response to drought. Twenty protein spots were analyzed by liquid chromatography‐tandem mass spectrometry (LC‐MS/MS), leading to identification of Rubisco and 11 other proteins involved in redox regulation, oxidative stress, signal transduction, and chaperone activities. Some of these proteins could contribute a physiological advantage under drought, making them potential targets for MAS.
Greedy codes over Z4

In this paper, we consider the construction of lexi-codes by using a B-ordering over the ring Z<sub>4</sub>. This algorithm can be applied to any multiplicative property over Z<sub>4</sub>. It is shown that this construction produces many optimal codes over Z<sub>4</sub> and also good binary codes.
Integrate social simulation content with game designing curriculum to foster computational thinking

Computational thinking is a fundamental analytical ability to solve problems, design systems, and understand human behavior based on the fundamental concepts of computer science. The concept of computational thinking is almost needed on every profession in the computerization of society. Teaching computational thinking to cultivate problem solving ability by computer is a challenge of computer science education, especially for the K-12 level. Although studies introduce computational concepts by programming design, computational thinking and computer science are not computer programming. The study uses social simulation content (i.e. NetLogo, which uses muti-agent technology) to scaffold learning computational thinking concepts, such as simple genetic algorithm, parallel computing, artificial neural net, or deadlock (such as dining philosophers). To stimulate learning transfer onto computational thinking, NetLogo models library will be used to motivate reflection and meta-cognition development. Preliminary experimental results came from both questionnaires and artifacts. In the future, Gero's (1990) function-behaviour-structure (FBS) ontology will be used for the qualitative analysis to explain the outcomes of learning transfer.
Numerical evaluation of the low Reynolds turbulent flow behaviour in a bioreactor

Bioreactors are commonly used in industry, owing to their low energy requirement. These reactors are often operated at reduced flow rates in order to maintain a high retention time, and reduce shear stress on the biomass. An important factor is the mixing between biomass and substrate, and thus the maintenance of turbulence is important. Hence, the aim of this work is the application of the CFD technique to obtain a better understanding of the turbulent behaviour in an anaerobic sequencing batch reactor, with a nominal Reynolds number of about 1,700. This study considered a single-phase approach to evaluate several turbulence models. The obtained results indicate that the flow has little influence on the regions in the transition regime for the conditions evaluated, in spite of the low Reynolds number. Moreover, comparing the use of different models, it was found that the Reynolds stress model does not require longer time for its calculations.
Accurate image based re-lighting through optimization

Image-based relighting represents a class of techniques that apply new lighting conditions to a scene, given a set of basis images. In this sketch we present a relighting technique that, for a single viewpoint, accurately captures the reflectance field of objects, without restrictions on their geometrical complexity or material properties. Once the reflectance field is captured, the objects can be relit under arbitrary lighting conditions. To achieve such accurate results, our method combines the strengths of both the Light Stage [Debevec et al. 2000] and environment matting [Zongker et al. 1999; Chuang et al. 2000] into a single framework.
GazeHorizon: enabling passers-by to interact with public displays by gaze

Public displays can be made interactive by adding gaze control. However, gaze interfaces do not offer any physical affordance, and require users to move into a tracking range. We present GazeHorizon, a system that provides interactive assistance to enable passers-by to walk up to a display and to navigate content using their eyes only. The system was developed through field studies culminating in a four-day deployment in a public environment. Our results show that novice users can be facilitated to successfully use gaze control by making them aware of the interface at first glance and guiding them interactively into the tracking range.
Motor imagery signal classification using semi supervised and unsupervised extreme learning machines

Brain computer interface (BCI) aims at providing a brand new communication approach without brain's traditional output through nerve and muscle. “Electroencephalography” has been widely used for BCI system as it is a non-invasive approach. Recently, various classifiers have been used for the analysis of EEG signals measured under the planning and relaxed state. The major work addressed in the paper is the classification of EEG signals (motor imagery) measured under planning and relaxed state using advanced learning classifiers. The dataset of planning and relaxed state is a benchmark data and it is taken from UCI (University of California, Irvine) repository. Semi supervised ELM (SS-ELM) and unsupervised ELM (US-ELM) are recently developed networks and used for the EEG signal classification task. Both of these algorithms can be fit in to a unified framework and handle multi-class classification or multi-cluster clustering. SS-ELM algorithm performs better than US-ELM and other real valued algorithms in classifying planning and relaxed states. The improvement is due to the use of spectral techniques in embedding and clustering.
A Unified Continuous Greedy Algorithm for Submodular Maximization

The study of combinatorial problems with a submodular objective function has attracted much attention in recent years, and is partly motivated by the importance of such problems to economics, algorithmic game theory and combinatorial optimization. Classical works on these problems are mostly combinatorial in nature. Recently, however, many results based on continuous algorithmic tools have emerged. The main bottleneck of such continuous techniques is how to approximately solve a non-convex relaxation for the sub- modular problem at hand. Thus, the efficient computation of better fractional solutions immediately implies improved approximations for numerous applications. A simple and elegant method, called "continuous greedy", successfully tackles this issue for monotone submodular objective functions, however, only much more complex tools are known to work for general non-monotone submodular objectives. In this work we present a new unified continuous greedy algorithm which finds approximate fractional solutions for both the non-monotone and monotone cases, and improves on the approximation ratio for many applications. For general non-monotone submodular objective functions, our algorithm achieves an improved approximation ratio of about 1/e. For monotone submodular objective functions, our algorithm achieves an approximation ratio that depends on the density of the polytope defined by the problem at hand, which is always at least as good as the previously known best approximation ratio of 1-1/e. Some notable immediate implications are an improved 1/e-approximation for maximizing a non-monotone submodular function subject to a matroid or O(1)-knapsack constraints, and information-theoretic tight approximations for Submodular Max-SAT and Submodular Welfare with k players, for any number of players k. A framework for submodular optimization problems, called the contention resolution framework, was introduced recently by Chekuri et al. [11]. The improved approximation ratio of the unified continuous greedy algorithm implies improved ap- proximation ratios for many problems through this framework. Moreover, via a parameter called stopping time, our algorithm merges the relaxation solving and re-normalization steps of the framework, and achieves, for some applications, further improvements. We also describe new monotone balanced con- tention resolution schemes for various matching, scheduling and packing problems, thus, improving the approximations achieved for these problems via the framework.
Performance analysis of zone routing protocols in Mobile Ad Hoc Networks

In Mobile Ad Hoc Networks (MANETs), routing is a challenging task due to node mobility, traffic and network size. It is very important to analyze the scalability characteristics of the routing protocols with respect to these parameters. Zone Routing Protocol (ZRP) is considered to be one of the most scalable routing protocols due to its multi-scoping and hybridization features. We propose a general, parameterized model for analyzing control overhead of ZRP. A generic probabilistic model for data traffic is also proposed which can be replaced by different traffic models. Our analytical model is validated by comparisons with simulations performed under different network scenarios. In our simulation results, we have observed that the optimal zone radius lies at a point where proactive and reactive overhead components of ZRP are approximately equal as observed in [1]. Further, as the mobility increases the optimal zone radius value decreases, and as the traffic increases the value of optimal zone radius increases. If a node operates away from the optimal zone radius setting then it has to bear additional routing overhead. We show that the additional overhead is around 35% higher under a wide range of mobility scenarios.
Transmission coordination for ad hoc networks

AHNs are wireless networks operating without the benefits of network infrastructure (basestations) or centralized control. AHNs working with limited spectrum perform best when simultaneous transmissions are coordinated to avoid collisions. Optimal transmission coordination is a combinatorial optimization problem that is, in general, intractable for large size networks, even with global information and central control. Constraints on simultaneous transmissions may arise from both transceiver limitations (e.g., half-duplex designs) and from requirements on the signal to interference ratio. We study the transmission coordination optimization problem under a variety of natural communication constraints. Our work identifies particular instances where the problem may be solved by greedy algorithms, and studies the performance of several natural heuristic solutions.
Hypoxia induces downregulation of soluble guanylyl cyclase &bgr;1 by miR-34c-5p

Summary Soluble guanylyl cyclase (sGC) is the principal receptor for nitric oxide (NO) and crucial for the control of various physiological functions. The &bgr;1 subunit of sGC is obligatory for the biological stability and activity of the sGC heterodimer. MicroRNAs (miRNAs) are important regulators of gene expression and exert great influences on diverse biological activities. The aim of the present study was to determine whether or not the expression of sGC&bgr;1 is specifically regulated by miRNAs. We report that miR-34c-5p directly targets sGC&bgr;1 under hypoxia. Bioinformatics analysis of the sGC&bgr;1 3′-untranslated region (3′-UTR) revealed a putative binding site for miR-34b-5p and miR-34c-5p, but only miR-34c-5p inhibited luciferase activity through interaction with sGC&bgr;1 3′-UTR in HEK293T cells. Site-directed mutagenesis of the putative miR-34c-5p binding site abolished the negative regulation of luciferase expression. Overexpression of miR-34c-5p repressed the expression of sGC&bgr;1 in stable cell lines, which was reversed by miR-34c-5p-specific sponge. Inoculation of mouse lung tissues in vitro with lentivirus bearing miR-34c-5p significantly decreased both the expression of sGC&bgr;1 and NO-stimulated sGC activity, which was also rescued by miR-34c-5p-specific sponge. Furthermore, we identified the putative Sp1-binding site in the promoter region of miR-34c-5p. Luciferase reporter constructs revealed that Sp1 directly binds to the wild-type promoter of miR-34c-5p, which was confirmed by chromatin immunoprecipitation. In summary, these findings reveal that miR-34c-5p directly regulates sGC&bgr;1 expression, and they identify the key transcription factor Sp1 that governs miR-34c-5p expression during hypoxia.
Cache-Based Memory Copy Hardware Accelerator for Multicore Systems

In this paper, we present a new architecture of the cache-based memory copy hardware accelerator in a multicore system supporting message passing. The accelerator is able to accelerate memory data movements, in particular memory copies. We perform an analytical analysis based on open-queuing theory to study the utilization of our accelerator in a multicore system. In order to correctly model the system, we gather the necessary information by utilizing a full-system simulator. We present both the simulation results and the analytical analysis. We demonstrate the advantages of our solution based on a full-system simulator utilizing several applications: the STREAM benchmark and the receiver-side of the TCP/IP stack. Our accelerator provides speedups from 2.96 to 4.61 for the receiver-side of the TCP/IP stack, reduces the number of instructions from 26 percent to 44 percent and achieves a higher cache hit rate. Utilizing the analytical analysis, our accelerator reduces in the average number of cycles executed per instruction up to 50 percent for one of the CPUs in the multicore system.
Hospital ergonomics: a qualitative study to explore the organizational and cultural factors

The primary objective was to identify the characteristics of the health care industry with respect to organizational and cultural factors and consider how these might impact on the practice of ergonomics. Qualitative methodology was chosen as a suitable approach. This was supported by a middle ground philosophical position. Twenty-one interviews were carried out with academics and practitioners using a questionnaire proforma which developed iteratively over the 18 months of the project. A progressive four stage sampling strategy was used starting with purposive sampling to spread the net. Suggested contacts were then followed up (snowball sampling), before the third stage of intensity sampling to focus on participants with specific experience in hospital ergonomics. A final strategy of analysis sampling sought extreme and deviant cases to achieve theoretical saturation. The analysis resulted in three categories: organizational, staff and patient issues. The organizational issues included both the size and complexity of the National Health Service. For example, three hierarchical lines were identified in the management structure: an administrative line, a professional line and a patient-focused clinical management line. One of the surprising findings for the staff issues was the perceived lack of ergonomic information about female workers as a population group and traditional female employment sectors. The patient issues incorporated three dimensions associated with the caring role: the type of work; expectations; and possible outcomes. The work tends to be dirty and emotional, with a professional subculture to allow the handling of other peoples' bodies. This subculture was linked to a ‘coping’ attitude where staff put the patients' needs and well-being before their own. The change in patient expectations (from being apologetic through to demanding their rights) is mirrored in a changing model of care from paternalism to partnership. A lack of ergonomic research was identified for female workers in the health care industry relating to both the type of work and gender issues.
Enforcing Interaction Properties in AOSD-Enabled Systems

The construction and maintenance of large and complex software systems depend on the existence of global principles describing the structure and the interaction among its various components. Due to their critical nature, such principles have to be explicitly formulated and strictly verified and enforced throughout the lifetime of the software product. Aspect Oriented Software Development (AOSD) provides necessary tools for the formulation of such principles as aspects--called here structural aspects, and for their compile-time verification and dynamic enforcement. This enforcement, however, becomes problematic when aspects are also used as a vehicle for programming the components themselves, due to aspect interference. In this paper we show how the aspects embedded in system's components--called here programming aspects--can interact unfavourably with other components thus invalidating the role of the structural aspects. We present a number of methods that address this problem, and we introduce a DirectedPairWiseWeaver tool we have developed in order to prevent this interference.
High performance zero-crossing based pipelined analog-to-digital converters

In this thesis, I describe a zero-crossing based pipelined ADC. Unlike traditional pipelined ADCs, this work does not use any op-amps in the signal path. The use of zero-crossing based circuits made it possible to achieve a much better figure of merit. The ADC is design to operate at 200MS/s with a resolution of 12 bits. The simulated results suggest that the target performance is achievable with less than 10 mW of power. This design's figure of merit is at least an order of magnitude better than any existing designs that have comparable speed and accuracy performance. The design will be fabricated later to be tested in silicon. Thesis Supervisor: Hae-Seung Lee Title: Professor of Electrical Engineering