Miracle-dz/UniFAR-FTUs huggingface dataset:
A downlink ICIC method based on priority in LTE-Advanced systems

Downlink ICIC(Inter Cell Interference Coordination) which plays a crucial role in improving cell edge spectrum efficiency in LTE-Advanced system is the process that mitigates the interference from Node(e.g. evolved Node Base(eNB), pico, femeto) to User Equipments (UE) in adjacent cells. In this paper, the limitation of traditional ICIC method is analyzed and an improved ICIC method based on priority is proposed in order to improve efficiency in LTE-A systems.
Overcoming pain thresholds with multilevel models—an example using quantitative sensory testing (QST) data

The assessment of somatosensory function is a cornerstone of research and clinical practice in neurology. Recent initiatives have developed novel protocols for quantitative sensory testing (QST). Application of these methods led to intriguing findings, such as the presence lower pain-thresholds in healthy children compared to healthy adolescents. In this article, we (re-) introduce the basic concepts of signal detection theory (SDT) as a method to investigate such differences in somatosensory function in detail. SDT describes participants’ responses according to two parameters, sensitivity and response-bias. Sensitivity refers to individuals’ ability to discriminate between painful and non-painful stimulations. Response-bias refers to individuals’ criterion for giving a “painful” response. We describe how multilevel models can be used to estimate these parameters and to overcome central critiques of these methods. To provide an example we apply these methods to data from the mechanical pain sensitivity test of the QST protocol. The results show that adolescents are more sensitive to mechanical pain and contradict the idea that younger children simply use more lenient criteria to report pain. Overall, we hope that the wider use of multilevel modeling to describe somatosensory functioning may advance neurology research and practice.
Efficient 3D reconstruction of large-scale urban environments from street-level video

Recovering the 3-dimensional (3D) structure of a scene from 2-dimensional (2D) images is a fundamental problem in computer vision. This technology has many applications in computer graphics, entertainment, robotics, transportation, manufacturing, security, etc. One application is 3D mapping. For example, Google Earth and Microsoft Bing Maps provide a 3D virtual replica of many of the Earth's cities. However, these 3D models are low-detail and lack ground-level realism. Google Street View and Bing Street Side provide high-resolution panoramas captured from the streets of many cities, but these stills cannot provide free navigation through the virtual world. In this dissertation, I will show how to automatically and efficiently create detailed 3D models of urban environments from streetlevel imagery. A major goal of this dissertation is to model large urban areas, even entire cities, which is an enormous challenge due to the sheer scale of the problem. Even a partial data capture of the town of Chapel Hill requires millions of frames of street-level video. The methods presented in this dissertation are highly parallel and use little memory, and can therefore utilize modern graphics hardware (GPU) technology to process video at the recording frame rate. Also, the structure in urban scenes such as planarity, orthogonality, verticality, and texture regularity can be exploited to achieve 3D reconstructions with greater efficiency, higher quality, and lower complexity. By examining the structure of an urban scene, a multiple-direction plane-sweep stereo method is performed on the GPU in real-time. An analysis of stereo precision leads to a view selection strategy that guarantees constant depth resolution and improves bounds on time complexity. Depth measurements are further improved by segmenting the scene into piecewise-planar and non-planar regions, a process which is aided by learned planar surface appearance. Finally, depth measurements are fused and the final 3D surface is recovered using a multi-layer heightmap model that produces clean, complete, and compact 3D reconstructions. The effectiveness of these methods is demonstrated by results from thousands of frames of video from a variety of urban scenes.
Rescue of Social Motility Lost during Evolution of Myxococcus xanthus in an Asocial Environment

ABSTRACT Replicate populations of the social bacterium Myxococcus xanthus underwent extensive evolutionary adaptation to an asocial selective environment (liquid batch culture). All 12 populations showed partial or complete loss of their social (S) motility function after 1,000 generations of evolution. Mutations in the pil gene cluster (responsible for type IV pilus biogenesis and function) were found to be at least partially responsible for the loss of S motility in the majority of evolved lines. Restoration (partial or complete) of S motility in the evolved lines by genetic complementation with wild-type pil genes positively affected their fruiting body development and sporulation while negatively affecting their competitive fitness in the asocial regime. This genetic tradeoff indicates that mutations in the pil region were adaptive in the asocial selective environment. This finding was confirmed by experiments showing that defined deletions of pil gene regions conferred a competitive advantage under asocial conditions. Moreover, an amino acid substitution in an evolved genotype was located in a region predicted by genetic complementation analysis to bear an adaptive mutation.
Finding Names in Trove: Named Entity Recognition for Australian Historical Newspapers

Historical newspapers are an important resource in humanities research, providing the source materials about people and places in historical context. The Trove collection in the National Library of Australia holds a large collection of digitised newspapers dating back to 1803. This paper reports on some work to apply named-entity recognition (NER) to data from Trove with the aim of supplying useful data to Humanities researchers using the HuNI Virtual Laboratory. We present an evaluation of the Stanford NER system on this data and discuss the issues raised when applying NER to the 155 million articles in the Trove archive. We then present some analysis of the results including a version published as Linked Data and an exploration of clustering the mentions of certain names in the archive to try to identify individuals.
Leading Indicator Project: Lithuania

The authors present a method for forecasting growth cycles in economic activity, measured as total industrial production. They construct a series which they aggregate into a composite leading indicator to predict the path of the economy in Lithuania. The cycle is the result of the economy's deviations from its long-term trend. A contractionary phase means a decline in the growth rate of the economy, not necessarily an absolute decline in economic activity. The indicator they select for economic activity is usually the Index of Industrial Production, plus a group of variables that, when filtered and adjusted, becomes the composite leading indicator that forecasts the reference series. Variables include economically, and statistically significant financial, monetary, real sector, and business survey data. They base selection of the components of the leading indicator on the forecast efficiency and economic significance of the series. Once selected, the relevant variables are aggregated into a single composite leading indicator, which forecasts the de-trended Index of Industrial Production. They apply the Hodrick-Prescott filter method for de-trending the series. This is a smoothing technique that decomposes seasonally adjusted series, into cyclical and trend components. One advantage of the Hodrick-Prescott filter is that it provides a reasonable estimate of a series' long-term trend. The OECD uses a system of leading indicators to predict growth cycles in the economies of its member countries. These exercises have been very effective in their forecasting ability and accuracy - but for the technique to work it is essential to have an adequate statistical system that provides many economic variables in a precise and timely manner, preferably monthly. The authors extend the OECD technique, and present an application to a country of the former Soviet Union.
Mapping the policy process in Nigeria

How research contributes to the policy process in developing countries in general, and in Nigeria more specifically, is not well understood. Yet such understanding is a critical part of doing effective policy research. This has become especially critical for the International Food Policy Research Institute (IFPRI), which has set up a country office for policy research in Nigeria. A key challenge for IFPRI, and other research organizations in the country, is how to better integrate research results into policy and communicate research results to Nigerian policymakers. To gain some useful insights into how research does, or does not, influence policy in Nigeria, we examined a case involving the process leading up to the adoption in 2006 of Nigeria’s National Fertilizer Policy. Rather than focusing on how research influences policy in general, examining a particular policy allowed us to trace the actual policy process that took place, the actors involved, and the types of links and interactions between them. A diverse group of stakeholders (government, donors, the research community, farmer organizations, and the private sector) undoubtedly debated the content of the fertilizer policy. Thus, its successful formulation and adoption offered a useful opportunity to examine how it came about in spite of competing vested interests (both for and against it) and what role, if any, research-based information played in developing it. The policy covered some highly contentious political issues, most prominently the issue of privatization of the fertilizer sector in place of the large-scale and long-standing subsidy program. How the actors engaged and appeased people with vested interests who would normally oppose the policy, and the degree to which research-based information played a role in policy development, is of interest to IFPRI and others engaged in policy research. To study the policy process that led to the formulation and adoption of the National Fertilizer Policy, we used a network-mapping tool, Net-Map. Drawing on social network approaches, the tool is particularly suitable since it can help highlight the actors and formal and informal interactions involved in the policy process, as well as examine the flows of information from researchers to help determine the pathways of research-based information. In support of the Net-Map method, we also undertook a content analysis of published and grey literature on fertilizer policies in Nigeria in the years prior to the passing of the fertilizer bill. This provided a context for the knowledge-based and policy discussions, who was involved in them, and who funded or drove them.
Reuse-based Analytical Models for Caches Rathijit

We develop a reuse distance/stack distance based analytical modeling framework for efficient, online prediction of cache performance for a range of cache configurations and replacement policies LRU, PLRU, RANDOM, NMRU. Such a predictive framework can be extremely useful in selecting the optimal parameters in a dynamic reconfiguration environment that performs power-shifting or resource reallocation through cache partitioning. Our framework unifies existing cache miss-rate prediction techniques such as Smith’s associativity model, Poisson variants, and hardware way-counter based schemes. We also show how to adapt way-counters to work when the number of sets in the cache changes. We propose a novel low-overhead hardware mechanism to estimate reuse distance/stack distance distributions using a combination of set-sampling and time-sampling. This can be used even in cases where using way-counters is not possible, e.g. RANDOM/NMRU replacement policies.
Evolution over two decades of the tropical clouds in a subsidence area and their relation to large-scale environment

The interannual variability of cloud properties in a tropical subsidence area (South Atlantic Ocean) is examined using 23 years of ISCCP cloud fractions and optical depths, complemented with ISCCP/Meteosat visible reflectance and a four-years comparison with CALIPSO-GOCCP products. The mean seasonal cloud properties are examined in the area, as their interannual evolution. Circulation regimes (characterized with the SST and w500 from NCEP and ERA-Interim) that dominate summer and winter are also examined, and atmospheric situations are classified in five circulation regimes: ascending air masses, and moderate or strong subsidence with warm or cold SSTs. We examine the mean cloud cover, optical depth, and reflectance in each regime and their evolution in time over 23 years. Observational results (mean values and interannual variability) are compared with simulations from the IPSL and CNRM climate models (part of the CMIP5 experiment), using simulators to ensure that differences can be attributed to model defects. It results that regime occurrence strongly depends on the dataset (NCEP or ERA-Interim), as do their evolution in time along 23 years. The observed cloud cover is stable in time and weakly regime-dependent, whereas the cloud optical depth and reflectance are clearly regime-dependent. Some cloud properties trends actually do exist only in some particular regimes. Compared to observations, models underestimate cloud cover and overestimate cloud optical depth and reflectance. Climate models poorly reproduce regime occurrence and their evolution in time, as well as variations in cloud properties associated with regime change. It means that errors in the simulation of clouds from climate models are firstly due to errors in the simulation of the dynamic and thermodynamic environmental conditions.
Power system load switching monitoring using wavelet transform based multiresolution signal decomposition

This paper presents a wavelet transform based multiresolution signal decomposition technique for monitoring power system load switching. The proposed method depends on the standard deviations of wavelet coefficients calculated at different resolution levels. Power system signals are decomposed using discrete wavelet transform based filter bank. The simulation results of the study clearly demonstrate that proposed method can efficiently detect and localize power system load switching in time domain. The method can also give information on the magnitude of the load switched and its distance from the observation point.
3D facial expression recognition algorithm using local threshold binary pattern and histogram of oriented gradient

Facial expression which carries rich information of body behavior is the leading carrier of human affective and the symbol of intelligence. The main purpose of this paper is to recognize 3D human facial expression. The research in this paper includes the expression feature extraction algorithm and fusion with different kinds of feature. To contain more local texture feature information, we proposed a new feature of 3D facial expression named Local Threshold Binary Pattern (LTBP) which based on Local Binary Pattern (LBP). We calculate the difference of gray value standard between neighboring pixels and the center pixel as a threshold to binary instead of the traditional LBP operation which only comparison of size between neighboring pixels and the center pixel. After we get the LTBP feature, we fuse the LTBP and HOG (Histogram of Oriented Gradient) features to get multi-feature fusion for 3D facial expression recognition. Our algorithm of 3D facial expression recognition comprises three steps: (1) extracting two sets of feature vectors and establishing the correlation criterion function between the two sets of feature vectors; (2) solving the two sets canonical projective vectors and extracting their canonical correlation features by the framework of canonical correlation analysis algorithm; (3) doing feature fusion for classification by using proposed strategy. We have performed comprehensive experiments on the BU-3DFE database which is presently the largest available 3D face database. We have achieved verification rates of more than 90% for the 3D facial expression recognition.
Research about the Influence of Oil Price Uncertainty on World Economic Fluctuation

Although many scholars have studied the issues of oil price fluctuation since 1970, only few scholars have studied the issues of oil price uncertainty. Firstly moving standard deviation and GARCH models are adopted to measure the world oil price uncertainty from 1970 to 2008. The results indicate that oil price uncertainty has remarkable leverage effect and in recent years the uncertainty value is obviously lower than the values between 1970s and 1990s. Then taking world and OECD as the research sample, multivariable VAR model is used to make empirical test about the influence of oil price uncertainty on economic fluctuation. Empirical results indicate that oil price uncertainty can obviously reduce GDP growth rate and increase inflation.
Science Times

U niversity of California, San Francisco researchers recently reported in Science the mutational profiling of 23 initially low-grade gliomas (LGGs) and associated recurrent tumors and profiled a subset of recurrent tumors in temozolomide-treated patients. Three key findings with potentially important clinical implications for LGG management were demonstrated: (1) LGGs and paired recurrent tumors are highly divergent and often only share a few early mutations, thus partly explaining their differential therapeutic responses; (2) mutant isocitrate dehydrogenase-1 (IDH1) may be critical for LGG formation and is a potential therapeutic target; and (3) temozolo-mide therapy may contribute to malignant transformation and affect clinical outcome. Johnson et al 1 determined the genetic profiles of LGGs and associated recurrences. Mutations that are shared or exclusive to the initial tumor or recurrences were characterized for each of 23 initial tumors and recurrences found up to 11 years later, and tumor phylogenies were mapped via evolutionary analyses. Overall, the paired tumors shared a significant percentage of mutations exclusive to the initial tumors in 43% of cases. These findings suggest that gliomas and recurrent tumors share early tumorigenic mutational origins but diverge afterward in tumorigenesis. LGG sequencing also revealed that an IDH1 mutation was present and remained unchanged in all paired tumors, highlighting IDH1 as a potentially critical LGG driver mutation. IDH1 mutants produce 2-hydroxyglutarate R-enantiomer, an interesting tumor metabolism product that inhibits histone enzymes and alters gene expression. Recent work highlights a possible LGG-selective therapeutic opportunity because inhibitors of IDH-1 mutant activity selectively reduce tumor growth rate and stimulate glioma differentiation. 2 This work also reported the effects of temo-zolomide therapy on mutational profiles of recurrent gliomas, especially given that temozo-lomide use in LGG therapy is controversial. Mutational profiles of paired tumors in 10 temozolomide-treated patients were determined. Recurrent tumors from 6 of the 10 patients exhibited hypermutated phenotypes after temo-zolomide therapy, carrying many more mutations per million base pairs compared with their initial tumors. The hypermutated state is likely caused by the propensity of temozolomide to mutate and compromise DNA mismatch repair pathways. Additionally, the authors characterized the unique hypermutated signature and found significant association with high-grade glioma signaling pathways such as retinoblastoma and protein kinase B–mammalian target of rapamycin sig-naling (Figure, B). These results suggest that temozolomide therapy may contribute to malignant transformation of LGGs, and further studies are needed to determine whether this alters clinical outcomes. Figure. Recurrent tumors from patients treated with temozolomide …
Comparing Media Systems Beyond the Western World: How Far Can Media Systems Travel?

Introduction Only a few books in communication studies have triggered as lively a debate in the academic community as Hallin and Mancini's (2004b) Comparing Media Systems . For decades communication scholars have pushed for more comparative research to advance the theoretical foundations of the field and to generate more systematic knowledge of the differences and similarities of systems of communications across time and space (Blumler, McLeod, and Rosengren, 1992; Esser and Pfetsch, 2004). Even though there have been a number of important collections of country studies (see, for example, Gunther and Mughan, 2000; Kaid and Holtz-Bacha, 1995; Kelly, Mazzoleni, and McQuail, 2004), most of these volumes lack a unifying conceptual framework that would allow us to draw generalizable conclusions beyond the individual cases at hand. Hallin and Mancini's “three models of media and politics” therefore provide a much needed intellectual toolkit for understanding the immense variations between systems of public communication in different cultural and political contexts. Since its publication several scholars have tried to apply the framework to countries other than the group of advanced democracies that are covered in Comparing Media Systems (for Eastern Europe, see Jakubowicz and Sukosd, 2008; for Brazil, Albuquerque, 2005; for South Africa, Hadland, 2007). These studies attempt to classify a particular country into one of the three models – Polarized Pluralist, Democratic Corporatist, or Liberal – usually with the conclusion that it is the Polarized Pluralist model that captures best the features of the country under study. With its defining characteristics of journalistic partisanship, political instrumentalization of the media, and uncontrolled commercialization of the media industry, all of which are regarded as problematic and potentially obstructive to a healthy democratic public sphere, the Polarized Pluralist model seems to have become something like a catch-all category for media systems outside the Western world of established democracies. However, it is rather implausible to assume that, whereas eighteen countries in Western Europe and North America are diversified into three distinct constellations of media–politics relationships, all the remaining media systems around the world can be sufficiently understood by only one model. Classifying non-Western media systems as Polarized Pluralist not only implies that these systems are deficient in one way or the other but also that there is no significant variation in institutions and practices except for those that can be observed in the established democracies of the West.
Constraint Handling for Covariance Matrix Adaption with Meta-Models

Evolution strategies are successful black-box optimization methods. But many practical numerical problems are constrained. Whenever the optimum lies in the vicinity of the constraint boundary, the success rates decrease and make successful random mutations almost impossible. Low success rates result in premature step size reduction and ﬁnally lead to premature convergence. We try to draw a consistent picture of recent developments of heuristics that try to increase the success rate situation at the constraint boundary − from simple death penalty of evolution strategies to covariance matrix adaptation. Meta-models of the constraint functions turn out to be a promising approach for savings in computation time both in terms of ﬁtness and constraint function evaluations. We use meta-models to rotate the mutation ellipsoid, to check the feasibility of candidate solutions, and to repair infeasible mutations at the constraint boundary.