Miracle-dz/UniFAR-FTUs huggingface dataset:
Lte/lte-a uplink carrier aggregation using unlicensed spectrum

Methods, systems, and apparatuses are described for wireless communications. In one method, an uplink grant may be received over a licensed spectrum. A clear channel assessment (CCA) may be performed in response to the uplink grant to determine availability of an unlicensed spectrum. The CCA may be performed prior to a transmission associated with the uplink grant. In another method, scheduling information may be received over a licensed spectrum. An uplink grant may be transmitted over the licensed spectrum. The uplink grant may be based at least in part on the scheduling information. The uplink grant may be configured to trigger a CCA to determine availability of an unlicensed spectrum prior to a transmission associated with the uplink grant.
1 0 0 11101000 11110100 00011100 XOR 3 popcount Census Windows Census Transforms Bitstrings Hamming Distance

One major obstacle for a wide spread FPGA usage in general-purpose computing is the development tool flow that requires much higher effort than for pure software solutions. Convey Computer promises a solution to this problem for their HC-1 platform, where the FPGAs are configured to run as a vector processor and the software source code can be annotated with pragmas that guide an automated vectorization process. We investigate this approach for a stereo matching algorithm that has abundant parallelism and a number of different computational patterns. We note that for this case study the automated vectorization in its current state doesn’t hold its productivity promise. However, we also show that using the Vector Personality can yield a significant speedups compared to CPU implementations in two of three investigated phases of the algorithm. Those speedups don’t match custom FPGA implementations, but can come with much reduced development effort. Keywords-Parallel architectures; Reconfigurable architectures; Performance analysis; Stereo image processing
Underwriting Cycle and Ruin Probability June 6 , 2007

This paper presents a model for analyzing the impact of underwriting cycles on an insurer’s surplus. The model allows the insurer to vary its security loading in response to the cycles, with a strategy parameter that indicates the extent to which the insurer follows the loading which prevails in the market. The insurer’s claim rate is also allowed to vary to reflect exposure changes that result from the insurer’s strategy. We analyze ruin probabilities using both simulation and a Lundberg-type upper bound which is developed in the paper. We find that the latter is suitable and convenient for comparing ruin probabilities under the different insurer strategies.
Overexpression of KPNA 2 is associated with unfavorable prognosis in cervical carcinoma

Aims: The present study is to investigate the clinical significance of KPNA2 in the development of cervical cancer. Methods: KPNA2 mRNA levels in 8 paired cervical cancer tissues and the adjacent non-tumor tissues were examined using real-time PCR. The expression and prognostic value of KPNA2 were examined in 94 cervical cancer patients after surgical resection. The overall and recurrent-free survival rates were estimated using Kaplan-Meier method and compared with the log-rank test. The prognostic analysis was carried out with univariate and multivariate Cox regressions models. Results: Compared to non-tumor tissues, KPNA2 mRNA was clearly increased in cervical cancer tissues. High expression of KPNA2 was significantly associated with tumor stage (P = 0.034) and tumor size (P = 0.025) of the disease. Moreover, high expression of KPNA2 was significantly associated with poorer overall (OS) and recurrent free (RFS) survival (P = 0.002 and = 0.004, respectively) of cervical cancer patients. Multivariate analysis suggested that reduced expression of KPNA2 was an independent prognostic marker of cervical cancer (P = 0.038). Conclusion: KPNA2 may serve as oncogene in the development of cervical cancer, and may serve as clinicopathologic biomarkers for prognosis of cervical cancer patients.
Effects of Chilling Temperatures on Photosynthesis

Environmental stress is an inescapable reality for most plants growing in natural settings. Conditions of sub- or supraoptimal temperatures, water deficit, water logging, salinity, and pollution can have dramatic effects on plant growth and development, and in agricultural settings, yield. In cotton, yield is a product of the number of mature bolls produced in a given area and the amount of cotton produced by each boll. Though cotton is among the most stress-tolerant crop plants, suboptimal environmental conditions limit the yields and quality of fiber and seed. The most significant effects of abiotic stress related to yield are on fertilization, boll retention, and boll filling. Maintenance of photoassimilate supply during fruit development is critical in achieving high yields in cotton. Because photosynthesis is the driving force behind plant productivity, although not the only factor that determines yield, plants have developed numerous mechanisms that serve to protect the photosynthetic apparatus during stressful conditions. Cotton is produced across a wide range of environments and management conditions, from hot and humid subtropical to semiarid environments. Although production is limited by varying environmental conditions across these environments, it is clear that the physiological resilience to abiotic stress is considerable. We present a review of our understanding of low-temperature limitations to photosynthesis and the impact on productivity. Additionally, we use the High Plains region of Texas as a case study to highlight potential key developmental aspects of low-temperature stress on yield.
PR ] 1 6 M ay 2 01 9 Central limit theorems for multilevel Monte Carlo methods

In this work, we show that uniform integrability is not a necessary condition for central limit theorems (CLT) to hold for normalized multilevel Monte Carlo (MLMC) estimators and we provide near optimal weaker conditions under which the CLT is achieved. In particular, if the variance decay rate dominates the computational cost rate (i.e., β > γ), we prove that the CLT applies to the standard (variance minimizing) MLMC estimator. For other settings where the CLT may not apply to the standard MLMC estimator, we propose an alternative estimator, called the mass-shifted MLMC estimator, to which the CLT always applies. This comes at a small efficiency loss: the computational cost of achieving mean square approximation error O(ǫ2) is at worst a factor O(log(1/ǫ)) higher with the mass-shifted estimator than with the standard one.
Incremental Temporal Mining using Incremental TPMiner and Incremental P-TPMiner Algorithms

Temporal data mining is one type of "predictive”. Temporal Association mining is sequential mining. We usually predict what will happen next or what is probability that certain thing happen Sequential pattern mining is one important case of data mining. Most of sequential pattern mining algorithm works on static data which deals with the database should not change. But the databases in real world application do not have static data rather they have incremental database. There are some applications using temporal event data have used to discovering patterns from events. There are two types of interval-based patterns: Temporal pattern and Probabilistic temporal pattern are proposed. This paper attempts to provide two algorithms Incremental Temporal Pattern Miner (TP-Miner) and Probabilistic Temporal Pattern Miner (P-TP Miner). In this project, apply proposed algorithms to real datasets to make the comparison of Incremental temporal mining and Non-incremental temporal mining.
Towards Large-Capacity and Cost-Effective Main Memories

Large, multi-terabyte main memories per processor socket are instrumental to address the continuously growing performance demands of domains like high-performance computing, databases, and big data. It is an important objective to design large-capacity main memories in a way that maximizes their cost-effectiveness and at the same time minimizes performance losses caused by cost-effective tradeoffs. This thesis addresses a number of issues towards this objective. First, parallel memory protocols, that are key to large main memories, have a limited number of pins. This implies that to address future capacities, the protocols would have to multiplex the pins to transfer wider addresses in a greater number of cycles, hurting performance. This thesis contributes with the concept of adaptive row addressing, comprising three techniques, as a general approach to minimize the performance losses of such cost-effective parallel memory protocols, and, in fact, make them as efficient as an idealized protocol with many enough pins to transfer each address in one cycle. Second, emerging Storage-Class Memory (SCM) technologies can potentially revolutionize main memory design by enabling large-capacity and cost-effective hybrid main memories, that combine DRAM and SCM. However, they add multiple dimensions to the design space of main memories. Detailed exploration of such design spaces solely by means of simulation or prototyping is inefficient. This thesis contributes with Crystal, an analytic method for partitioning hybrid-memory area between DRAM and SCM at design time, and Rock, a framework for pruning design spaces of hybrid memories. Crystal and Rock help system architects to quickly and correctly identify the most promising design points for subsequent detailed evaluation. Third, in hybrid main memories, DRAM is the limited resource, and co-running programs compete for it. Fair and at the same time high-performance management of such memories is an important and open issue. To avoid long operating-system overheads, this management has to be performed by hardware. This thesis contributes with ProFess: a probabilistic hybrid main memory management framework for high performance and fairness. ProFess includes two hardware-based mechanisms that cooperate to significantly improve fairness, performance, and energy-efficiency compared to the state-of-the-art.
Using word clouds to support students’ knowledge integration from online inquiry: an investigation of the process and outcome

ABSTRACT This study reports the effects of using word clouds to support students’ knowledge integration from online inquiry as demonstrated by blog posts, tags and concept maps. Fifty-four undergraduate students from a medium-sized university in the northwestern United States were randomly assigned into two groups and blogged for five weeks. To support students’ online searching activity, the treatment group was provided a list of concepts in the format of a word cloud. The control group received the same list of concepts in an alphabetic order. Data analyses included comparing participant-attached tags and individual concept maps to the instructor’s keyword list. Results revealed that the word clouds facilitated the construction of schema in a top-down deductive approach, which could be a meaningful and efficient way of learning.
Ectomycorrhizal fungal mycelial dynamics and its role in forest soil carbon cycling

Most boreal tree species rely on root-associated ectomycorrhizal fungi for nutrient acquisition, in exchange the trees allocate part of their photosynthetically fixed carbon (C) to these fungi. This has a feedback on soil C dynamics as mycorrhizal fungi are important in regulating soil C cycling and storage. However, mycorrhizal fungi are often not included in C dynamics models, as mechanistic understanding of their contribution to mycorrhiza-mediated processes are largely lacking. The aim of the work described in this thesis was to address this knowledge gap by studying the contribution of mycorrhizal fungi in regulating soil C fluxes. This was done by quantifying C fluxes associated with the extraradical mycelium (ERM) of mycorrhizal fungi, and examining how the ERM contributes to variations in soil C cycling along a nemoboreal chronosequence of managed Pinus sylvestris forests. Production and turnover of ERM was quantified by determining ERM biomass in sequentially harvested ingrowth mesh bags and by mathematical modelling. Respiration of ERM was measured as CO2 efflux from mesh bags, and carbon use efficiency (CUE) was calculated from ERM production and respiration rates. We assessed soil fungal communities along the chronosequence and investigated correlations between taxonomic composition and enzyme activities. The ERM standing biomass increased despite decreased production along the chronosequence. This contradiction was explained by a drastic decline in biomass turnover, from seven times to one time per year. The CUE decreased with forest age, but increased tenfold from summer (0.019) to autumn (0.200). This seasonal increase in CUE was associated with a decline in gross photosynthetic production, suggesting that variation in photosynthetic C supply regulates seasonal variations in CUE. Relative abundance of ectomycorrhizal taxa increased with forest age, and was dominated by Atheliaceae species in young forests, and by Cortinarius and Russula species in mature forests. Enzyme activities were related to community composition, and seem to be important for maintaining forest productivity, by facilitating organic nutrient mobilisation. This thesis is a first step in parameterizing mycorrhizal mycelial C fluxes to enable explicit inclusion of ERM parameters in forest ecosystem C models.
Information Retrieval System for Determining The Title of Journal Trends in Indonesian Language Using TF-IDF and Na?ve Bayes Classifier

The journal is known as one of the relevant serial literature that can support a researcher in doing his research. In it’s development journal has two formats that can be accessed by library users namely: printed format and digital format. Then from the number of published journals, not accompanied by the growing amount of information and knowledge that can be retrieved from these documents. The TF-IDF method is one of the fastest and most efficient text mining methods to extract useful words as the value of information from a document. This method combines two concepts of weight calculation that is the frequency of word appearance on a particular document and the inverse frequency of documents containing the word. Furthermore, data analysis of journal title is done by Naïve Bayes Classifier method. The purpose of the research is to build a website-based information retrieval system that can help to classify and define trends from Indonesian journal titles. This research produces a system that can be used to classify journal titles in Indonesian language, with system accuracy in determining the classification of 90,6% and 9,4% error rate. The highest percentage result that became the trend of title classification was decision support system category which was 24.7%.
Estimation On Vehicle Motion Based On Video Group 20

​— In this project, we analyzed two different algorithms used to compute optical flow. At the same time, we applied different algorithms and machine learning models to calculate speed depends on optical flow. Except for existing algorithms and model, we also modified the old machine-learning algorithms to try to bring better results and compare the performance of those algorithms.
Two Imported Chikungunya Cases, Taiwan

4. Ong CS, Eisler DL, Alikhani A, Fung VW, Tomblin J, Bowie WR, et al. Novel Cryptosporidium genotypes in sporadic cryptosporidiosis cases: fi rst report of human infections with a cervine genotype. Emerg Infect Dis. 2002;8:263–8. 5. Ong CSL, Isaac-Renton JL. Molecular epidemiological investigations of waterborne cryptosporidiosis outbreaks in Canada. In: Latham SM, Smith HV, Wastling JM, editors. Workshop on the Application of Genetic Fingerprinting for the Monitoring of Cryptosporidium in Humans, Animals and the Environment; Boulder, Colorado, 2003 Aug 3–5. Marlow, Buckinghamshire (UK): Foundation for Water Research; 2004. p. 121–34. 6. Sulaiman IM, Lal AA, Xiao L. A population genetic study of the Cryptosporidium parvum human genotype parasites. J Eukaryot Microbiol. 2001;Suppl:24S– 7S. DOI: 10.1111/j.1550-7408.2001. tb00441.x 7. Trotz-Williams LA, Martin DS, Gatei W, Cama V, Peregrine AS, Martin SW, et al. Genotype and subtype analyses of Cryptosporidium isolates from dairy calves and humans in Ontario. Parasitol Res. 2006;99:346–52. DOI: 10.1007/s00436006-0157-4 8. Ong CSL, Chow S, So PPL, Chen R, Xiao L, Sulaiman I, et al. Identifi cation of two different Cryptosporidium hominis subtypes from cases in the 2001 waterborne cryptosporidiosis outbreak in North Battleford, Saskatchewan. In: Robertson W, Brooks T, editors. Proceedings of the 11th Canadian National Conference and 2nd Policy Forum on Drinking Water; Calgary, Alberta; Apr 2004. Ottawa (Canada): Canadian Water and Wastewater Assoc. , Health Canada & Alberta Environment; 2004. p. 628–39. 9. Glaberman S, Moore JE, Lowery CJ, Chalmers RM, Sulaiman I, Elwin K, et al. Three drinking-water–associated cryptosporidiosis outbreaks, Northern Ireland. Emerg Infect Dis. 2002;8:631–3. 10. Zhou L, Singh A, Jiang J, Xiao L. Molecular surveillance of Cryptosporidium spp. in raw wastewater in Milwaukee: implications for understanding outbreak occurrence and transmission dynamics. J Clin Microbiol. 2003;41:5254–7. DOI: 10.1128/JCM.41. 11.5254-5257.2003
EMMA : A Context-Aware Middleware for Energy Management on Mobile Devices

The rapid increase of smartphones’ sensing, computation and communication capabilities is accompanied by a growing demand for energy. Both, short-term and long-term energy allocation is a bottleneck which severely constrains a mobile device’s capabilities and usability. It is thus one of the most critical challenges for current device development. Despite of numerous hardware improvements, e.g., concerning the energy consumption of sensors and displays, as well as the development of more capable batteries, this issue remains to be solved. Hence, software-based approaches can be used to optimize the energy management of mobile devices according to a user’s preferences, context information and the current energetic state of a device. In this paper, we specify the requirements for a modular energy management middleware architecture coined EMMA, which considers the dynamic and modular integration of existing energy improvement concepts in relation to the device’s current energy status and active services as well as the users context and preferences. Furthermore, we present a prototype application which demonstrates some of EMMA’s core concepts. Keywords–mobile energy management; contextual service provision
2 + 1 dimensional gravity

2+1-dimensional gravity is still a fascinating theory. One can study many issues of the four-dimensional theory without its technical complications, and the only other lower dimensional theory, two-dimensional gravity, has an action which is a topological invariant. It has a Chern-Simons formulation [1, 2, 3], and the curvature is constant, proportional to the cosmological constant (positive, negative, or zero). There are no local degrees of freedom, only global topological ones, and only when there are non trivial topologies. However, it does have local exact solutions [4], and admits three-dimensional black holes [5]. Often considered as a “toy model” without physical relevance, 2+1-dimensional gravity has, nevertheless, interesting connections to other theories, e.g. in the context of the Ads-CFT correspondence [6], pure Chern-Simons theory on a manifold with boundary induces, for certain boundary conditions, the WessZumino-Witten model [7] on the boundary [8]. For different boundary conditions, it induces Liouville theory at infinity [9]. Three-dimensional ChernSimons theory can also arise as a string theory [10]. Tullio Regge always liked discrete physics, preferably with very few degrees of freedom, so when I showed him the 1989 Witten preprint on 2+1-dimensional gravity [3] which describes a theory with a finite number of only global topological degrees of freedom, he was very enthusiastic. We worked intensely on 2+1-dimensional gravity from then on, for about five years, and published 16
Ju l 2 01 9 GEMSEC : Graph Embedding with Self Clustering

Modern graph embedding procedures can efficiently process graphs with millions of nodes. In this paper, we propose GEMSEC – a graph embedding algorithm which learns a clustering of the nodes simultaneously with computing their embedding. GEMSEC is a general extension of earlier work in the domain of sequence-based graph embedding. GEMSEC places nodes in an abstract feature space where the vertex features minimize the negative log-likelihood of preserving sampled vertex neighborhoods, and it incorporates known social network properties through a machine learning regularization. We present two new social network datasets and show that by simultaneously considering the embedding and clustering problems with respect to social properties, GEMSEC extracts high-quality clusters competitive with or superior to other community detection algorithms. In experiments, the method is found to be computationally efficient and robust to the choice of hyperparameters.
OCAN : ONE-CLASS ADVERSARIAL NETS 4 . 1 Framework Overview

Many online applications, such as online social networks or knowledge bases, are often attacked by malicious users who commit different types of actions such as vandalism on Wikipedia or fraudulent reviews on eBay. Currently, most of the fraud detection approaches require a training dataset that contains records of both benign and malicious users. However, in practice, there are often no or very few records of malicious users. In this paper, we develop one-class adversarial nets (OCAN) for fraud detection with only benign users as training data. OCAN first uses LSTM-Autoencoder to learn the representations of benign users from their sequences of online activities. It then detects malicious users by training a discriminator of a complementary GAN model that is different from the regular GAN model. Experimental results show that our OCAN outperforms the state-of-the-art one-class classification models and achieves comparable performance with the latest multi-source LSTM model that requires both benign and malicious users in the training phase.
The Basel II IRB approach for credit portfolios : A survey Kjersti Aas

In this report we review the Basel II IRB approach, including the theory used to derive its model foundation, and the interpretation of its parameters. The IRB approach is a hybrid between a very simple statistical model of capital needs for credit risk and a negotiated settlement. It is characterised by its computational simplicity; there is an analytical formula for the calculation of capital, and the model is perfectly additive. However, this strength is also the cause of its weakness. The main assumptions have some negative consequences that are discussed in this report. Hence, the statistical model behind the Basel II IRB approach is not best practice today, and certainly will not become so in the future. Even the Basel Committee itself states that the IRB is work-in-progress, and will remain so long after the implementation of Basel II (Basel Committee on Banking Supervision, 2004b). We give some indications of how the IRB approach may be modified to deal with its weaknesses.