Miracle-dz/UniFAR-FTUs huggingface dataset:
Visual Reconciliation of Alternative Similarity Spaces in Climate Modeling

Visual data analysis often requires grouping of data objects based on their similarity. In many application domains researchers use algorithms and techniques like clustering and multidimensional scaling to extract groupings from data. While extracting these groups using a single similarity criteria is relatively straightforward, comparing alternative criteria poses additional challenges. In this paper we define visual reconciliation as the problem of reconciling multiple alternative similarity spaces through visualization and interaction. We derive this problem from our work on model comparison in climate science where climate modelers are faced with the challenge of making sense of alternative ways to describe their models: one through the output they generate, another through the large set of properties that describe them. Ideally, they want to understand whether groups of models with similar spatio-temporal behaviors share similar sets of criteria or, conversely, whether similar criteria lead to similar behaviors. We propose a visual analytics solution based on linked views, that addresses this problem by allowing the user to dynamically create, modify and observe the interaction among groupings, thereby making the potential explanations apparent. We present case studies that demonstrate the usefulness of our technique in the area of climate science.
Journal of Geophysical Research: Atmospheres Spatial Representativeness of Surface-Measured Variations of Downward Solar Radiation

When using time series of ground-based surface solar radiation (SSR) measurements in combination with gridded data, the spatial and temporal representativeness of the point observations must be considered. We use SSR data from surface observations and high-resolution (0.05 ∘ ) satellite-derived data to infer the spatiotemporal representativeness of observations for monthly and longer time scales in Europe. The correlation analysis shows that the squared correlation coeﬃcients ( R 2 ) between SSR times series decrease linearly with increasing distance between the surface observations. For deseasonalized monthly mean time series, R 2 ranges from 0.85 for distances up to 25 km between the stations to 0.25 at distances of 500km. A decorrelation length (i.e., the e -folding distance of R 2 ) on the order of 400 km (with spread of 100–600 km) was found. R 2 from correlations between point observations and colocated grid box area means determined from satellite data were found to be 0.80 for a 1 ∘ grid. To quantify the error which arises when using a point observation as a surrogate for the area mean SSR of larger surroundings, we calculated a spatial sampling error (SSE) for a 1 ∘ grid of 8(3) W/m 2 for monthly (annual) time series. The SSE based on a 1 ∘ grid, therefore, is of the same magnitude as the measurement uncertainty. The analysis generally reveals that monthly mean (or longer temporally aggregated) point observations of SSR capture the larger-scale variability well. This ﬁnding shows that comparing time series of SSR measurements with gridded data is feasible for those time scales.
Goal-Driven Autonomy in a Navy Strategy Simulation

Modern complex games and simulations pose many challenges for an intelligent agent, including partial observability, continuous time and effects, hostile opponents, and exogenous events. We present ARTUE (Autonomous Response to Unexpected Events), a domain-independent autonomous agent that dynamically reasons about what goals to pursue in response to unexpected circumstances in these types of environments. ARTUE integrates AI research in planning, environment monitoring, explanation, goal generation, and goal management. To explain our conceptualization of the problem ARTUE addresses, we present a new conceptual framework, goal-driven autonomy, for agents that reason about their goals. We evaluate ARTUE on scenarios in the TAO Sandbox, a Navy training simulation, and demonstrate its novel architecture, which includes components for Hierarchical Task Network planning, explanation, and goal management. Our evaluation shows that ARTUE can perform well in a complex environment and that each component is necessary and contributes to the performance of the integrated system.
pService: Towards Similarity Search on Peer-to-Peer Web Services Discovery

We present the design of pService system for P2P Web services discovery supporting similarity search with its locality-preserving feature based on modified Skip Graph. In pService, the most important property (e.g. service name) of a service is viewed as the indexing key of a peer node. The other properties are extracted as multiattributes which would be mapped into one-dimensional indices using HSFC (Hilbert Space Filling Curve), and assigned to membership vector of the peer node. These two features help to support similarity search in two aspects: peer nodes with similar indexing keys would be distributed neighborly, peer nodes with similar indices will connect to each other through routing table. Our evaluation showed that the pService performs considerable Web services discovery efficiency.
Decomposing data machines

Describes a new FSM decomposition algorithm which extracts a data register from a given FSM. The algorithm is a generalization of the standard minimization algorithm-it selects output values which, when stored in a separate data register, will make additional states in the control machine equivalent. Experimental results show that the algorithm is both effective and fast. <<ETX>>
Energy-based cloud detection in multispectral images based on the SVM technique

ABSTRACT In this paper, the energy characteristics of Gabor texture are used for cloud detection in high-resolution multispectral images. First, the satellite remote-sensing image is divided into superpixels using simple linear iterative clustering (SLIC), and then, the energy characteristics of Gabor texture and spectral characteristics are computed by extracting the texture features of the superpixels. The features of the cloud superpixels are used as the learning sample of the support vector machine (SVM) classifier, and a classification model is obtained by training the SVM classifier. Finally, a cloud-detection experiment is conducted for various sensor images with three visible bands and one near-infrared band. The experimental results showed that the proposed method provides an excellent average overall accuracy for thick and thin clouds in a complex background of forests, harbours, snow and mountains. The characteristic parameters of this paper are not limited by the image parameters; thus, they provide good results and universality for various types of sensors.
Group-Sparse-Based Joint Power and Resource Block Allocation Design of Hybrid Device-to-Device and LTE-Advanced Networks

In this paper, a joint power and resource block (RB) allocation (JPRBA) algorithm with low complexity is proposed, which addresses the intra-and-inter-cell interference management problem for a multicell device-to-device (D2D) communication underlaying LTE-Advanced network. We first introduce a power control and resource allocation vector (PORAVdm) to each D2D transmitter, and the set of all PORAVdm has two functions: one is to select appropriate reused RBs for each D2D link, whereas the other is to determine the optimal power for D2D transmitters on each selected RB. To obtain the appropriate PORAVdms, we exploit the group sparse structure to formulate a sum rate maximization problem (referred to as the group least absolute shrinkage and selection operator programming). Then we derive the stationary solution by solving its equivalent sparse weighted mean square error minimization problem. Finally, simulation results show that the proposed JPRBA algorithm can efficiently improve the total throughput.
Combining Model-based and Genetics-based Offspring Generation for Multi-objective Optimization Using a Convergence Criterion

In our previous work conducted by Aimin Zhou et. al., (2005), it has been shown that the performance of multi-objective evolutionary algorithms can be greatly enhanced if the regularity in the distribution of Pareto-optimal solutions is used. This paper suggests a new hybrid multi-objective evolutionary algorithm by introducing a convergence based criterion to determine when the model-based method and when the genetics-based method should be used to generate offspring in each generation. The basic idea is that the genetics-based method, i.e., crossover and mutation, should be used when the population is far away from the Pareto front and no obvious regularity in population distribution can be observed. When the population moves towards the Pareto front, the distribution of the individuals will show increasing regularity and in this case, the model-based method should be used to generate offspring. The proposed hybrid method is verified on widely used test problems and our simulation results show that the method is effective in achieving Pareto-optimal solutions compared to two state-of-the-art evolutionary multi-objective algorithms: NSGA-II and SPEA2, and our pervious method in Aimin Zhou et. al., (2005).
Testosterone in ancient hair from an extinct species

Testosterone is a key regulator in vertebrate development, physiology and behaviour. Whereas technology allows extraction of a wealth of genetic information from extant as well as extinct species, complementary information on steroid hormone levels may add a social, sexual and environmental context. Hair shafts have been previously used to sequence DNA from >50 000 14C years old Siberian woolly mammoths (Mammuthus primigenius). Hair‐testing has also been used to measure endogenous steroids in multiple extant species. Here we use small quantities of woolly mammoth hair samples to measure testosterone, and a genomics‐based approach to determine sex, in permafrost‐preserved mammoths dated to c. 10 000–60 000 14C years. Our validated method opens up exciting opportunities to measure multiple steroids in keratinized tissues from extinct populations of mammals. This may be specifically applied to investigating life histories, including the extinct Quaternary megafauna populations whose remains are preserved in the permafrost throughout the northern hemisphere.
Neoliberalism and the polarizing water geographies of the Zambian Copperbelt

An investigation has been made of the impacts of neoliberal reforms on water and sanitation development in the Copperbelt Province of Zambia. Drawing inspiration from the sub-discipline of post-colonial geography, the paper begins by locating the foundations of water inequalities in the early urban planning policies of British colonialism. The paper goes on to make a critical analysis of the recently adopted water commercialization policy in relation to the historically embedded water inequality. Whilst prompting greater water conservation, commercialization is shown to lead to continued water and sanitation poverty and a widening gulf in the quality of water services between the minority water-rich and majority water-poor populations. It is concluded that in order to help achieve the Millennium Development Goals for water and sanitation, policy makers require a more sophisticated consideration of the spatially inscribed material inequalities that characterize many former European colonies in Africa.
The Human Immunodeficiency Virus Type 1 Envelope Spike of Primary Viruses Can Suppress Antibody Access to Variable Regions

ABSTRACT The human immunodeficiency virus type 1 (HIV-1) envelope spike is a heavily glycosylated trimeric structure in which protein surfaces conserved between different HIV-1 isolates are particularly well hidden from antibody recognition. However, even variable regions on the spike tend to be less antigenic and immunogenic than one might have anticipated for external structures. Here we show that the envelope spike of primary viruses has an ability to restrict antibody recognition of variable regions. We show that access to an artificial epitope, introduced at multiple positions across the spike, is frequently limited, even though the epitope has been inserted at surface-exposed regions on the spike. Based on the data, we posit that restricted antibody access may be the result, at least in part, of a rigidification of the epitope sequence in the context of the spike and/or a highly effective flexible arrangement of the glycan shield on primary viruses. Evolution of the HIV envelope structure to incorporate extra polypeptide sequences into nominally accessible regions with limited antibody recognition may contribute to reducing the magnitude of antibody responses during infection and allow the virus to replicate unhindered by antibody pressure for longer periods.
Dendritic cell vaccination for glioblastoma multiforme: review with focus on predictive factors for treatment response

Glioblastoma multiforme (GBM) is the most common and most aggressive type of primary brain cancer. Since median overall survival with multimodal standard therapy is only 15 months, there is a clear need for additional effective and long-lasting treatments. Dendritic cell (DC) vaccination is an experimental immunotherapy being tested in several Phase I and Phase II clinical trials. In these trials, safety and feasibility have been proven, and promising clinical results have been reported. On the other hand, it is becoming clear that not every GBM patient will benefit from this highly personalized treatment. Defining the subgroup of patients likely to respond to DC vaccination will position this option correctly amongst other new GBM treatment modalities, and pave the way to incorporation in standard therapy. This review provides an overview of GBM treatment options and focuses on the currently known prognostic and predictive factors for response to DC vaccination. In this way, it will provide the clinician with the theoretical background to refer patients who might benefit from this treatment.
Biologically valid linear factor models of gene expression

MOTIVATION The identification of physiological processes underlying and generating the expression pattern observed in microarray experiments is a major challenge. Principal component analysis (PCA) is a linear multivariate statistical method that is regularly employed for that purpose as it provides a reduced-dimensional representation for subsequent study of possible biological processes responding to the particular experimental conditions. Making explicit the data assumptions underlying PCA highlights their lack of biological validity thus making biological interpretation of the principal components problematic. A microarray data representation which enables clear biological interpretation is a desirable analysis tool. RESULTS We address this issue by employing the probabilistic interpretation of PCA and proposing alternative linear factor models which are based on refined biological assumptions. A practical study on two well-understood microarray datasets highlights the weakness of PCA and the greater biological interpretability of the linear models we have developed.
A 7-Bit 18th Order 9.6 GS/s FIR Up-Sampling Filter for High Data Rate 60-GHz Wireless Transmitters

This paper presents the design and measurement of a 4 × oversampled 18th order digital low-pass FIR filter. It is a key building block in the proposed digitally enhanced transmitter architecture for 60 GHz wireless high-data rate links. Spectrum mask requirements are fully satisfied for OFDM modulated signals without requiring additional analog filtering. Pipelined CPL adders and TSPC flip-flops are used to enable a very high operation frequency. The core area is 0.1 mm2 in a standard GP 65 nm CMOS process. Measured power consumption is 400 mW at 9.6 GS/s with a 1.4 V power supply voltage.
Automatic Software Deployment in the Azure Cloud

For application providers, cloud computing has the advantage that it reduces the administrative effort required to satisfy processin g and storage requirements. However, to simplify the task of building scalable applications, some of the cloud computing platforms impose constraints on the appli cation architecture, its implementation and tools that may be used in develo pment; Microsoft Azure is no exception. In this paper we show how an existing d rug discovery system --Discovery Bus --can benefit from Azure even thoug h none of its components was built in the . Net framework. Using an approach base d on the “Deployment and Configuration of Component-based Applications Speci fication” (D&C), we were able to assemble and deploy jobs that include different types of process-based tasks. We show how extending D&C deployment models with tempo ral and spatial constraints provided the flexibility needed to move all the com pute-intensive tasks within the Discovery Bus to Azure with no changes to their ori ginal code. © 2010 University of Newcastle upon Tyne. Printed and published by the University of Newcastl e upon Tyne, Computing Science, Claremont Tower, Claremont Road, Newcastle upon Tyne, NE1 7RU, England. Bibliographical details CALA, J., WATSON, P. Automatic Software Deployment in the Azure Cloud [By] J. Cala, P. Watson Newcastle upon Tyne: University of Newcastle upon T y e: Computing Science, 2010. (University of Newcastle upon Tyne, Computing Scienc e, Technical Report Series, No. CS-TR-1206)
Vesselness Features and the Inverse Compositional AAM for Robust Face Recognition Using Thermal IR

Over the course of the last decade, infrared (IR) and particularly thermal IR imaging based face recognition has emerged as a promising complement to conventional, visible spectrum based approaches which continue to struggle when applied in the real world. While inherently insensitive to visible spectrum illumination changes, IR images introduce specific challenges of their own, most notably sensitivity to factors which affect facial heat emission patterns, e.g. emotional state, ambient temperature, and alcohol intake. In addition, facial expression and pose changes are more difficult to correct in IR images because they are less rich in high frequency detail which is an important cue for fitting any deformable model. In this paper we describe a novel method which addresses these major challenges. Specifically, to normalize for pose and facial expression changes we generate a synthetic frontal image of a face in a canonical, neutral facial expression from an image of the face in an arbitrary pose and facial expression. This is achieved by piecewise affine warping which follows active appearance model (AAM) fitting. This is the first publication which explores the use of an AAM on thermal IR images; we propose a pre-processing step which enhances detail in thermal images, making AAM convergence faster and more accurate. To overcome the problem of thermal IR image sensitivity to the exact pattern of facial temperature emissions we describe a representation based on reliable anatomical features. In contrast to previous approaches, our representation is not binary; rather, our method accounts for the reliability of the extracted features. This makes the proposed representation much more robust both to pose and scale changes. The effectiveness of the proposed approach is demonstrated on the largest public database of thermal IR images of faces on which it achieved 100% identification rate, significantly outperforming previously described methods.
Dynamic Indexability: The Query-Update Tradeoff for One-Dimensional Range Queries

The B-tree is a fundamental secondary index structure that is widely used for answering one-dimensional range reporting queries. Given a set of $N$ keys, a range query can be answered in $O(\log_B \nm + \frac{K}{B})$ I/Os, where $B$ is the disk block size, $K$ the output size, and $M$ the size of the main memory buffer. When keys are inserted or deleted, the B-tree is updated in $O(\log_B N)$ I/Os, if we require the resulting changes to be committed to disk right away. Otherwise, the memory buffer can be used to buffer the recent updates, and changes can be written to disk in batches, which significantly lowers the amortized update cost. A systematic way of batching up updates is to use the logarithmic method, combined with fractional cascading, resulting in a dynamic B-tree that supports insertions in $O(\frac{1}{B}\log\nm)$ I/Os and queries in $O(\log\nm + \frac{K}{B})$ I/Os. Such bounds have also been matched by several known dynamic B-tree variants in the database literature. In this paper, we prove that for any dynamic one-dimensional range query index structure with query cost $O(q+\frac{K}{B})$ and amortized insertion cost $O(u/B)$, the tradeoff $q\cdot \log(u/q) = \Omega(\log B)$ must hold if $q=O(\log B)$. For most reasonable values of the parameters, we have $\nm = B^{O(1)}$, in which case our query-insertion tradeoff implies that the bounds mentioned above are already optimal. Our lower bounds hold in a dynamic version of the {\em indexability model}, which is of independent interests.
Ginkgo biloba for preventing cognitive decline in older adults: a randomized trial.

CONTEXT The herbal product Ginkgo biloba is taken frequently with the intention of improving cognitive health in aging. However, evidence from adequately powered clinical trials is lacking regarding its effect on long-term cognitive functioning. OBJECTIVE To determine whether G. biloba slows the rates of global or domain-specific cognitive decline in older adults. DESIGN, SETTING, AND PARTICIPANTS The Ginkgo Evaluation of Memory (GEM) study, a randomized, double-blind, placebo-controlled clinical trial of 3069 community-dwelling participants aged 72 to 96 years, conducted in 6 academic medical centers in the United States between 2000 and 2008, with a median follow-up of 6.1 years. INTERVENTION Twice-daily dose of 120-mg extract of G. biloba (n = 1545) or identical-appearing placebo (n = 1524). MAIN OUTCOME MEASURES Rates of change over time in the Modified Mini-Mental State Examination (3MSE), in the cognitive subscale of the Alzheimer Disease Assessment Scale (ADAS-Cog), and in neuropsychological domains of memory, attention, visual-spatial construction, language, and executive functions, based on sums of z scores of individual tests. RESULTS Annual rates of decline in z scores did not differ between G. biloba and placebo groups in any domains, including memory (0.043; 95% confidence interval [CI], 0.034-0.051 vs 0.041; 95% CI, 0.032-0.050), attention (0.043; 95% CI, 0.037-0.050 vs 0.048; 95% CI, 0.041-0.054), visuospatial abilities (0.107; 95% CI, 0.097-0.117 vs 0.118; 95% CI, 0.108-0.128), language (0.045; 95% CI, 0.037-0.054 vs 0.041; 95% CI, 0.033-0.048), and executive functions (0.092; 95% CI, 0.086-0.099 vs 0.089; 95% CI, 0.082-0.096). For the 3MSE and ADAS-Cog, rates of change varied by baseline cognitive status (mild cognitive impairment), but there were no differences in rates of change between treatment groups (for 3MSE, P = .71; for ADAS-Cog, P = .97). There was no significant effect modification of treatment on rate of decline by age, sex, race, education, APOE*E4 allele, or baseline mild cognitive impairment (P > .05). CONCLUSION Compared with placebo, the use of G. biloba, 120 mg twice daily, did not result in less cognitive decline in older adults with normal cognition or with mild cognitive impairment. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT00010803.