Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hub Matrix Theory and Applications to Wireless Communications

This paper considers communications and network systems whose properties are characterized by the gaps of the leading eigenvalues of for a matrix. It is shown that a sufficient and necessary condition for a large eigen-gap is that is a "hub" matrix in the sense that it has dominant columns. Some applications of this hub theory in multiple-input and multiple-output (MIMO) wireless systems are presented.
Local Position Classification for Pattern Discovery in Multivariate Sequential Data

Traditional sequential data analysis largely depends on the magnitude of the data with the geometric features of individual data points sometimes being regarded as noise to such analysis. To explore whether these geometric features alone carry some useful information for a better understanding of hidden facts contained in the sequential data, a new method called local position classification (LPC) is proposed in this paper. LPC works on extracting local geometric features of individual data points. The correlated geometric features in different variants in the same sequential data are then classified into some LPC clusters for further interpretation. This semi-quantitative method is easy to use and also a simple tool to estimate possible correlation between two categories in the same series. To exclude the unrelated categories from LPC clusters, a selective correlation analysis (SCA) is combined with LPC so as to make both complement with each other. Analysis of email entries over a year in an Australian university demonstrated that LPC and its combination with SCA could become a new effective tool for discovering useful patterns contained in sequential data.
Twin mitochondrial sequence analysis

When applying genome‐wide sequencing technologies to disease investigation, it is increasingly important to resolve sequence variation in regions of the genome that may have homologous sequences. The human mitochondrial genome challenges interpretation given the potential for heteroplasmy, somatic variation, and homologous nuclear mitochondrial sequences (numts). Identical twins share the same mitochondrial DNA (mtDNA) from early life, but whether the mitochondrial sequence remains similar is unclear. We compared an adult monozygotic twin pair using high‐throughput sequencing and evaluated variants with primer extension and mitochondrial preenrichment. Thirty‐seven variants were shared between the twin individuals, and the variants were verified on the original genomic DNA. These studies support highly identical genetic sequence in this case. Certain low‐level variant calls were of high quality and homology to the mtDNA, and they were further evaluated. When we assessed calls in preenriched mtDNA templates, we found that these may represent numts, which can be differentiated from mtDNA variation. We conclude that twin identity extends to mtDNA, and it is critical to differentiate between numts and mtDNA in genome sequencing, particularly as significant heteroplasmy could influence genome interpretation. Further studies on mtDNA and numts will aid in understanding how variation occurs and persists.
DevScope: a nonintrusive and online power analysis tool for smartphone hardware components

Smartphone power modeling is an important technique for users, application developers, and hardware manufacturers to build energy-aware systems. Compared to traditional offline techniques that use external power measurement devices, the online approach uses a built-in BMU (Battery Monitoring Unit), which has the advantage of generating a dynamic power model. However, the very low update rate of a BMU is an issue for online power modeling. In this paper, we introduce an autonomous power modeling tool for smartphones called DevScope, which overcomes the limitations of BMU-based online power modeling. DevScope controls components according to the BMU update rate, and derives the component power model automatically by analyzing the changes of power state. With DevScope, we construct an online and automatic power modeling that reflects both the diversity of users' environments and smartphone hardware complexity. By evaluating the scheme with various smartphones and configurations, we show that DevScope, indeed, generates a flexible and accurate power model.
COMPARISON OF UNMIXING RESULTS DERIVED FROM AVIRIS , HIGH AND LOW RESOLUTION

The availability of low-altitude, high-spatial-resolution AVIRIS (Green et al, 1998) images and high-resolution HYDICE (Basedow et al, 1995) images over Cuprite, Nevada has provided an unprecedented opportunity to compare the radiometric quality, the ability to detect end-members and the application of matched filters to three different data sets in an area that is well-understood mineralogically. An earlier paper (Goetz and Kindel, 1996) compared high-altitude AVIRIS and HYDICE data and showed that, even though there was a 36:1 ratio in pixel area, no new end-members appeared in the HYDICE images that were not seen in the lower resolution AVIRIS data. This result was not surprising in light of the fact that the signal-to-noise ratio (SNR) of the HYDICE data was much lower than that of AVIRIS. In this study, it was possible to remove the uncertainty associated with differing SNR’s because the same AVIRIS sensor was used to acquire both high-resolution and low-resolution images.
A Network Planning and Management Tool for mitigating the impact of spatially correlated failures in infrastructure networks

Current practices of fault-tolerant network design ignore the fact that most network infrastructure faults are localized or spatially correlated (i.e., confined to regions). Network operators require new tools to mitigate the impact of such region based faults on their infrastructures. Utilizing the support from the U.S. Department of Defense, and by consolidating a wide range of theories and solutions developed in the last few years, the authors of this paper have developed an advanced Network Planning and Management Tool (NPMT) that facilitates the design and provisioning of robust and resilient networks. The tool provides multi-faceted network design, evaluation and simulation capabilities for network planners. Future extensions of the tool currently being worked upon not only expand the tool's capabilities, but also extend these capabilities to heterogeneous interdependent networks such as communication, power, water and satellite networks.
View Invariant Movement Recognition by using Adaptive Neural Fuzzy Inference System

Action recognition is an essential in the field of visual surveillance system to identify the motion objects. This paper presents the adaptive neural fuzzy inference system for detecting the various human movements from the motion video sequences. Human body posture prototypes are identified by self organizing map. It also recognizes the continuous human movements in the video sequences. Fuzzy inference system is proposed to identify the action classification. This method maps, a set of input data into a set of desired output and it matches the data from the training and testing phase. Bayesian framework is used to recognize the various kinds of activities and produce the recognition results. The proposed work is able to detect continuous instances of similar action performed by several people in various view points accurately than other existing methods in the recent literature.
Condition number study of graph theory based preconditioners for isogeometric discretization of Poisson equation

We study the preconditioning of the stiffness matrix which arises from the discretization of the Poisson equation using IsoGeometric Method (IGM). A study of condition number of stiffness matrices, resulting from NURBS based IGM, suggests that novel preconditioning techniques are needed for fast and efficient iterative solvers for the resulting linear system. As a first step towards preconditioning the resulting stiffness matrix, we use graph theory based preconditioners, namely, Vaidya’s preconditioners (maximum weight spanning tree) and Gremban and Miller’s preconditioners (support tree). Numerical results show that these preconditioners do not perform satisfactorily for the matrices arising in IGM.
Diffuse Far-Ultraviolet Observations of the Taurus Region

Diffuse far-ultraviolet (FUV; 1370-1670 Å) flux from the Taurus molecular cloud region has been observed with the SPEAR/FIMS imaging spectrograph. An FUV continuum map of the Taurus region, similar to the visual extinction maps, shows a distinct cloud core and halo region. The dense cloud core, where the visual extinction Av > 1.5, obscures the background diffuse FUV radiation, while scattered FUV radiation is seen in and beyond the halo region, where Av < 1.5. The total intensity of H2 fluorescence in the cloud halo is I = 6.5 × 104 photons cm-2 s-1 sr-1 in the 1370-1670 Å wavelength band. A synthetic model of the H2 fluorescent emission fits the present observation best with a hydrogen density nH = 50 cm-3, H2 column density N(H2) = 0.8 × 1020 cm-2, and incident FUV intensity IUV = 0.2. H2 fluorescence is not seen in the core, presumably because the required radiation flux to induce fluorescence is unable to penetrate the core region.
The Role of Working Memory in Measuring Mental Models

There is no agreement on what a mental model is and how to infer the mental model a person has. We are conducting research aimed at solving these problems by developing a model of Mental Model formation. Our basic hypothesis is that a Mental Model is a dynamic representation created in WM by combining information stored in LTM (the Conceptual Model) and characteristics extracted from the environment. Two experiments tested hypotheses derived from the model. Implications for individual and group research are discussed.
A Genetic Approach to Automated Cluster Detection in Point Datasets

The amount of geographic data has rapidly increased throughout the past decade. Impressive data collection capabilities combine with the ability to merge datasets, resulting in a rapid growth in both the number and size of geographic datasets. Meanwhile, the number and urgency of social, environmental, and public safety applications that use these datasets is so great that it has been argued that “there is an undisputed national imperative for analysis” of these datasets (Openshaw 1995, p. 4). One way to analyze these datasets is by detecting clusters. However, many cluster analysis methods do not scale well to large datasets. In addition, some of these methods return many nearly-identical results for each cluster, which unnecessarily adds to the post-processing burden. Therefore, it is necessary to develop new methods and algorithms to perform cluster analysis on large datasets.
Learning conditional probabilities in event bushes with temporal labels

The event bush is a recently developed formalism for knowledge representation op- timized specifically for Earth science knowledge. It graphically represents logical and/or prob- abilistic dependencies between its nodes (events). Numerical data in geosciences often come in the form of time series: therefore, it is a very common situation to have tables with time-related numerical data for the variables which correspond to the events represented in an event bush. In this work, we present a way to use these numerical data to learn conditional probabilities that have to be specified in the intermediate event bush created on the basis of an event bush with temporal labels. The same idea works also if the bush also has spatial labels.
Characterization and reversal of synaptic defects in the amygdala in a mouse model of fragile X syndrome

Fragile X syndrome (FXS), a common inherited form of mental impairment and autism, is caused by transcriptional silencing of the fragile X mental retardation 1 (FMR1) gene. Earlier studies have identified a role for aberrant synaptic plasticity mediated by the metabotropic glutamate receptors (mGluRs) in FXS. However, many of these observations are derived primarily from studies in the hippocampus. The strong emotional symptoms of FXS, on the other hand, are likely to involve the amygdala. Unfortunately, little is known about how exactly FXS affects synaptic function in the amygdala. Here, using whole-cell recordings in brain slices from adult Fmr1 knockout mice, we find mGluR-dependent long-term potentiation to be impaired at thalamic inputs to principal neurons in the lateral amygdala. Consistent with this long-term potentiation deficit, surface expression of the AMPA receptor subunit, GluR1, is reduced in the lateral amygdala of knockout mice. In addition to these postsynaptic deficits, lower presynaptic release was manifested by a decrease in the frequency of spontaneous miniature excitatory postsynaptic currents (mEPSCs), increased paired-pulse ratio, and slower use-dependent block of NMDA receptor currents. Strikingly, pharmacological inactivation of mGluR5 with 2-methyl-6-phenylethynyl-pyridine (MPEP) fails to rescue either the deficit in long-term potentiation or surface GluR1. However, the same acute MPEP treatment reverses the decrease in mEPSC frequency, a finding of potential therapeutic relevance. Therefore, our results suggest that synaptic defects in the amygdala of knockout mice are still amenable to pharmacological interventions against mGluR5, albeit in a manner not envisioned in the original hippocampal framework.
The Development of the Land Lease Market in Rural China

This paper examines the development of the land lease market in rural China. Special attention is paid to productive heterogeneity among farmers and the openness of the labor market in activating the land lease market. A theoretical model is employed to explore the underlying connections. An econometric model is implemented using a two-period panel data set gathered in three counties of China's Zhejiang province. The results show that productive heterogeneity and a freer labor market do promote more land leases. The implications for the institutional change literature are discussed.
Ethical Conduct in Business Organisations: The Opinion of Management Students in Gauteng

This study investigates the perceptions that business and management students have towards ethical practices of businesses organisations in terms of the environment, their employees, community, regulators and competition. This study employed an exploratory survey design to collect primary data via a self-administered questionnaire. Data were collected from 251 business and management students at a national university in Gauteng, South Africa. Apart from reliability statistics and descriptive statistics, one sample t-tests were employed to test stated hypotheses. The findings showed that students perceived certain aspects of organisations’ ethical conduct in a relatively negative light. As the study concluded that business and management student perceived the ethical conduct of business organisations in a somewhat negative light, organisations should take heed and better outwardly promote the interests of all stakeholders. As more than 75% of the students surveyed had working experience, they represent also the opinion of people in the working world. This study reiterates the fact that what whilst organisations profess to act ethically, this does not necessarily seem to be the case. As more research on this issue reaches the same conclusion, this groundswell should effect change in due course.
The RMB Debate: Empirical Analysis on the Effects of Exchange Rate Shocks in China and Japan

For a better understanding of the ongoing debates on the RMB, this paper investigates the effects of exchange rate shocks on output and the current account for China and Japan. We use structural vector auto-regression models and find that yen appreciation reduces current account surpluses while having no strong effect on output in Japan. RMB appreciation, on the other hand, has an insignificant effect on the current account, although it tends to reduce output in China. For China, dollar pricing with vertical trade integration seems responsible for the insignificant effect on the current account.
Improving the BCCT.core model with lateral information

Breast Cancer Conservative Treatment (BCCT) is considered the gold standard of breast cancer treatment. However, the aesthetic outcome is diverse and very difficult to evaluate in a consistent way partly due to the weak reproducibility of the subjective methods in use. This motivated the research on the objective methods. BCCT.core is a very recent software that objectively and automatically evaluates the aesthetic outcome of BCCT. However, as in other approaches, the system only uses frontal patient information, disregarding volumetric perception on lateral measurements. In the current work we investigate the improvement of the BCCT.core model by introducing lateral information extracted from patients images. We compare the performance of the model currently used on BCCT.core with the model developed in this study. Experimental results suggest that with lateral measurements the model presents better performance, however improvements are not significant. We can conclude that is essential to use robust models on the BCCT, and the input of 3D models will probably help to obtain better results.
Relationship between Motivation and Behavior of SNS User

With 225 SNS network users as the subjects, the research focuses on the influence of SNS network users’ motivation on their behavior. Data are collected through questionnaires and the relationship between motivation and behavior is studied with relative analytical methods. As shown in the study, information and instrument motivation, entertainment and aesthetic motivation, social connection motivation, altruism motivation, ascription and identification motivation, and intrisic motivation have significant positive correlation with usage time, usage frequency and usage level of the users.
Neoliberalism and Social Resilience in the Developed Democracies

Three decades marked by many market-oriented initiatives often labeled as neo-liberal are widely said to have increased income inequality and influenced people’s thinking about the economy and their role in it. Using data from the World Values Survey, this paper explores how popular attitudes to the economy have changed over this period and asks whether inequalities in subjective well-being have also increased in the developed democracies. We find modest changes in attitudes to the economy and increases in inequalities in well-being across income groups as well as substantial variation across countries that we attempt to explain. To do so, we consider the sources of social resilience, understood as the factors that allow social groups to preserve their well-being in the face of challenges. We find that social networks confer resilience, although they are distributed unevenly across the populace, as do trade unions and the redistributive efforts of governments.
Supervised classication of news articles by whether they mention diseases and outbreaks

Global Viral Forecasting is working to use data extracted from humanreadable web documents in order to predict when and where disease outbreaks will happen. As part of this effort, it is necessary to classify documents by whether they are relevant to this prediction process. For this reason, GVF has collected 75,176 manual annotations of web documents. These annotations include which of the following mutually exclusive classes the document falls into:
The Estimation of Pre- and Postpromotion Dips with Store-Level Scanner Data

One of the mysteries of store-level scanner data modeling is the lack of a dip in sales in the weeks following a promotion. Researchers expect to find a postpromotion dip because analyses of household scanner panel data indicate that consumers tend to accelerate their purchases in response to a promotion—that is, they buy earlier and/or purchase larger quantities than they would in the absence of a promotion. Thus, there should also be a pronounced dip in store-level sales in the weeks following a promotion. However, researchers rarely find such dips at either the category or the brand level. Several arguments have been proposed to account for the lack of a postpromotion dip in store-level sales data and to explain why dips may be hidden. Because dips are difficult to detect by traditional models (and by a visual inspection of the data), the authors propose models that can account for a multitude of factors that together cause complex pre- and postpromotion dips. The authors use three alternative distributed lead and lag structures: an Almon model, an unrestricted dynamic effects model, and an exponential decay model. In each model, the authors include four types of price discounts: without any support, with display-only support, with feature-only support, and with feature and display support. The models are calibrated on store-level scanner data for two product categories: tuna and toilet tissue. The authors estimate the dip to be between 4 and 25% of the current sales effect, which is consistent with household-level studies.
Integrating global and local application of naive bayes classifier

Naive Bayes algorithm captures the assumption that every attribute is independent from the rest of the attributes, given the state of the class attribute. In this study, we attempted to increase the prediction accuracy of the simple Bayes model by integrating global and local application of Naive Bayes classifier. We performed a large-scale comparison with other attempts that have tried to improve the accuracy of the Naive Bayes algorithm as well as other state-of-the-art algorithms on 28 standard benchmark datasets and the proposed method gave better accuracy in most cases.
Fully Integrated Differential Distributed VCO in 0.35-$\mu{\hbox {m}}$ SiGe BiCMOS Technology

We present a fully integrated differential distributed voltage-controlled oscillator implemented in a 0.35-mum SiGe BiCMOS technology. The delay variation by a positive feedback tuning technique, adopted from the ring oscillators, is demonstrated as a fine-tuning alternative, which results to an approximately 420-MHz tuning range. The phase noise is -98 dBc/Hz at 1-MHz offset from the 14.25-GHz carrier. An integrated output buffer isolates the oscillator from the measurement equipment. The measured output power is -17.5 dBm and the overall power consumption of the chip is 138.1 mW employing two power supplies of 3.2 and 4.2 V, respectively