Miracle-dz/UniFAR-FTUs huggingface dataset:
On remote radio head selection for the downlink of backhaul constrained network MIMO systems

A relevant architecture to enable network MIMO in cellular systems consists of a central unit that is connected to remote radio heads (RRHs) via a shared wireless backhaul link. This letter studies the impact of RRH selection in this system under the two extreme scenarios of perfect and stale channel state information (CSI) at the central unit. It is shown that in both cases, RRH selection based on the instantaneous conditions or the long-term statistics of the backhaul links and of the access channels (i.e., from RRHs to mobile stations) significantly improves the sum-rate performance. The presented results also demonstrate that, in the presence of shared backhaul limitations, the classical precoding design based on the maximization of the number of degrees of freedom (DOFs) on the access channels leads to strictly suboptimal performance even when the access channels operate in the high SNR regime.
One-Step 18F Labeling of Non-Peptidic Bivalent Integrin αvβ3 Antagonist for Cancer Imaging

A rapid one-step 18F labeling reaction with fluoridealuminum complex, which is based on chelation chemistry, has received a surge of interest for 18F radiolabeling of peptides. In this study, a non-peptidic bivalent integrin αvβ3 antagonist (bivalent-IA) was conjugated with 1,4,7-triazacyclononane-1,4-diiacetic acid (NODA). A novel 18F labeled radiotracer, 18F-bivalent-IA, was developed via one step 18F–AlF/NODA chelation reaction in aqueous phase with high radiochemical yield (65–75%, decay corrected) and good specific activity (750–850 mCi/μmol). The tumor integrin targeting efficiency and in vivo pharmacokinetic profile of 18F-bivalent-IA were evaluated in U-87 MG (integrin positive) and MDA-MB-231 (integrin negative) models by small-animal PET/CT scan followed by a biodistribution study. The PET/CT and ROI results showed high tumor uptake of 18F-bivalent-IA in U-87 MG tumor-bearing mice from 5 to 120 min p.i. with good contrast, and the U-87 MG tumor uptake values (6.35 ± 0.67%ID/g, at 1 h p.i.) were 6 times higher than those of MDA-MB-231 tumor (1.05 ± 0.12%ID/g, at 1 h p.i.) (P < 0.0001) which correlated with the integrin αvβ3 expression in tumor tissues confirmed by immunohistochemistry. Co-injection of the 18F-bivalent-IA with 6 nmol (6 μg) of nonradioactive bivalent-IA effectively blocked tumor uptake demonstrating the integrin αvβ3-specificity. In conclusion, the first 18F labeled non-peptidic bivalent integrin αvβ3 targeting radiotracer, 18F-bivalent-IA, was developed and proved to be a highly potent and specific PET radiopharmaceutical for noninvasive imaging of integrin αvβ3, which plays a critical role in tumor angiogenesis and metastasis.
Assessing the Competence Assessment Tool

The Capacity Assessment Tool (CAT) proposed by Maria Carney and colleagues is a distinct improvement over many such tools that have been proposed, but it still faces a number of practical and theoretical challenges. In fact, these challenges are daunting enough to call into question all projects of this sort. The CAT is designed to allow primary care physicians to evaluate a patient’s capacity to make a particular healthcare decision. Designed to address the legal standards identified by Appelbaum and Grisso, the CAT involves discussing with patients the decision at issue and assigning patients points based on their performance in the following six areas: (1) ability to communicate, (2) ability to understand the available alternatives, (3) ability to comprehend risks and benefits, (4) ability to show insight into the reasoning behind their decisions, (5) ability to describe factors that influenced these decisions, and (6) ability to foresee and plan for problems with their chosen course of action. Patients whose scores meet minimum requirements are declared capable of making the decision under consideration (although they may be incapable of making other decisions about their healthcare).
DATA MANAGEMENT IN SENSOR NETWORKS USING SEMANTIC WEB TECHNOLOGIES

The increasing availability of small-size sensor devices during the last few years and the large amount of data that they generate has led to the necessity for more efficient methods regarding data management. In this chapter, we review the techniques that are being used for data gathering and information management in sensor networks and the advantages that are provided through the proliferation of Semantic Web technologies. We present the current trends in the field of data management in sensor networks and propose a three-layer flexible architecture which intends to help developers as well as end users to take advantage of the full potential that modern sensor networks can offer. This architecture deals with issues regarding data aggregation, data enrichment and finally, data management and querying using Semantic Web technologies. Semantics are used in order to extract meaningful information from the sensor’s raw data and thus facilitate smart applications development over large-scale sensor networks.
Syntactic Testsuites and Textual Entailment Recognition

We focus on textual entailments mediated by syntax and propose a new methodology to evaluate textual entailment recognition systems on such data. The main idea is to generate a syntactically annotated corpus of pairs of (non-)entailments and to use error mining methodology from the parsing field to identify the most likely sources of errors. To generate the evaluation corpus we use a template based generation approach where sentences, semantic representations and syntactic annotations are all created at the same time. Furthermore, we adapt the error mining methodology initially proposed for parsing to the field of textual entailment. To illustrate the approach, we apply the proposed methodology to the Afazio RTE system (an hybrid system focusing on syntactic entailment) and show how it permits identifying the most likely sources of errors made by this system on a testsuite of 10 000 (non-)entailment pairs which is balanced in term of (non-)entailment and in term of syntactic annotations.
The Center for Global Change - An Update

The University of Alaska Fairbanks is an affirmative action/equal opportunity employer and educational institution. Research (CGCASR) was created in 1990 at a time when campuses nationwide established similar centers to begin to address the problems of climate change and its potential impacts. Faculty felt that there was an unprecedented opportunity for interdisciplinary interaction at UAF that would be facilitated by the creation of the Center. The Center was to provide a campus-wide framework for developing , coordinating, and implementing interdisciplinary research and educational activities that addressed the role of the Arctic in the global earth system. During the 1990s, many changes occurred at UAF. Other organizations and arrangements for addressing global change developed, and global change research became more widespread and integrated into many departments and institutes. The Arctic Institute of North America established the Arctic Roundtable, an ongoing series of public forums which cover global change and other arctic topics. In 1998, the International Arctic Research Center (IARC), dedicated to global change studies, was created. Given this changing environment, in late 2001 the Center staff and Science Steering Committee met to discuss the future directions and activities of the Center. In addition to maintaining the Center's basic structure and core activities, the committee recommended: • Increasing the role of the Center in strengthening social sciences as an integrated part of global change problems, and • Exploring interactions with the International Arctic Research Center (IARC). To begin implementing these recommendations, we reconstituted the Science Steering Committee, replacing outgoing members and adding new members to achieve a better balance among physical, biological and social sciences and engineering. The committee comprises 23 UAF faculty (see box at left). Also included are liaisons to the Alaska (asterisks indicate new members) Wildlife: Temperature and salinity tolerance and sequencing of luxRI of the luminous photobacterium Phosphoreum isolated from Yukon River salmon Dorte Dissing, Department of Forest Sciences: CAPE and the landscape—A story about the influence of landscape properties on convective available potential energy (CAPE) Quaternary Center and the Alaska Data Visualization and Analysis Laboratory. Our thanks to outgoing members John Goering, Brenda Norcross, Don Schell and Al Tyler for their many contributions to the Center. On 12 April, the Center sponsored a one-day workshop on " Human Dimensions of the Arctic System: Opportunities for Collaborative Research Projects Involving Social and Natural Scientists, " chaired by Henry Huntington, Director of the Human Dimensions of the Arctic …
An algorithm for online tensor prediction

We present a new method for online prediction and learning of tensors ($N$-way arrays, $N >2$) from sequential measurements. We focus on the specific case of 3-D tensors and exploit a recently developed framework of structured tensor decompositions proposed in [1]. In this framework it is possible to treat 3-D tensors as linear operators and appropriately generalize notions of rank and positive definiteness to tensors in a natural way. Using these notions we propose a generalization of the matrix exponentiated gradient descent algorithm [2] to a tensor exponentiated gradient descent algorithm using an extension of the notion of von-Neumann divergence to tensors. Then following a similar construction as in [3], we exploit this algorithm to propose an online algorithm for learning and prediction of tensors with provable regret guarantees. Simulations results are presented on semi-synthetic data sets of ratings evolving in time under local influence over a social network. The result indicate superior performance compared to other (online) convex tensor completion methods.
Low-Rank Tensor Completion by Truncated Nuclear Norm Regularization

Currently, low-rank tensor completion has gained cumulative attention in recovering incomplete visual data whose partial elements are missing. By taking a color image or video as a three-dimensional (3D) tensor, previous studies have suggested several definitions of tensor nuclear norm. However, they have limitations and may not properly approximate the real rank of a tensor. Besides, they do not explicitly use the low-rank property in optimization. It is proved that the recently proposed truncated nuclear norm (TNN) can replace the traditional nuclear norm, as a better estimation to the rank of a matrix. Thus, this paper presents a new method called the tensor truncated nuclear norm (T-TNN), which proposes a new definition of tensor nuclear norm and extends the truncated nuclear norm from the matrix case to the tensor case. Beneficial from the low rankness of TNN, our approach improves the efficacy of tensor completion. We exploit the previously proposed tensor singular value decomposition and the alternating direction method of multipliers in optimization. Extensive experiments on real-world videos and images demonstrate that the performance of our approach is superior to those of existing methods.
Ischemic cortical lesions after permanent occlusion of individual middle cerebral artery branches in rats.

Our study describes the anatomy of the middle cerebral artery (MCA) in 65 Sprague-Dawley rats and the spatial distribution of ischemic cortical lesions caused by occluding major MCA branches. The rats characteristically had at least two major MCA branches, frontal and parietal. Many rats had additional branches supplying the pyriform and temporal cortexes. Permanent occlusion of the frontal or parietal branches combined with 30 minutes of bilateral carotid artery occlusion produced visible Evans blue dye uptake by ischemic cortical areas after 24 hours. No lesions distal to the occlusion were apparent in 38% and 43% of rats with frontal and parietal branch occlusions, respectively; small lesions contiguous with the occlusion site were observed in 38% and 32% of the rats. Only 6% of the frontal and 7% of the parietal branch occlusions produced isolated distal infarcts as expected if these branches were end-arteries. Blood flow was reversed in arteries distal to the occlusion. We conclude that extensive collateral connections of the frontal and parietal MCA branches with other arterial systems protect the anterior and posterior cortical regions. In contrast, occlusions of the pyriform branch of the MCA invariably caused infarcts in the frontopyriform region. In about one third of the rats, frontal or parietal branch occlusions produced lesions involving much of the proximal MCA territory; the frontopyriform region was most consistently affected. Combined, these data suggest that the pyriform MCA branch is an end-artery and that the cortical region it supplies is prone to ischemic damage resulting from any reduction of blood flow through the main MCA trunk. (ABSTRACT TRUNCATED AT 250 WORDS)
Combining models and guided empirical search to optimize for multiple levels of the memory hierarchy

This paper describes an algorithm for simultaneously optimizing across multiple levels of the memory hierarchy for dense-matrix computations. Our approach combines compiler models and heuristics with guided empirical search to take advantage of their complementary strengths. The models and heuristics limit the search to a small number of candidate implementations, and the empirical results provide the most accurate information to the compiler to select among candidates and tune optimization parameter values. We have developed an initial implementation and applied this approach to two case studies, matrix multiply and Jacobi relaxation. For matrix multiply, our results on two architectures, SGI R10000 and Sun UltraSparc IIe, outperform the native compiler, and either outperform or achieve comparable performance as the ATLAS self-tuning library and the hand-tuned vendor BLAS library. Jacobi results also substantially outperform the native compilers.
Brain connectivity at different time-scales measured with EEG

We present an overview of different methods for decomposing a multichannel spontaneous electroencephalogram (EEG) into sets of temporal patterns and topographic distributions. All of the methods presented here consider the scalp electric field as the basic analysis entity in space. In time, the resolution of the methods is between milliseconds (time-domain analysis), subseconds (time- and frequency-domain analysis) and seconds (frequency-domain analysis). For any of these methods, we show that large parts of the data can be explained by a small number of topographic distributions. Physically, this implies that the brain regions that generated one of those topographies must have been active with a common phase. If several brain regions are producing EEG signals at the same time and frequency, they have a strong tendency to do this in a synchronized mode. This view is illustrated by several examples (including combined EEG and functional magnetic resonance imaging (fMRI)) and a selective review of the literature. The findings are discussed in terms of short-lasting binding between different brain regions through synchronized oscillations, which could constitute a mechanism to form transient, functional neurocognitive networks.
OFDM transmission with single antenna interference cancellation

Future mobile communications radio networks, e.g. 3GPP long term evolution (LTE), will typically use an orthogonal frequency division multiplexing (OFDM) based air interface in the downlink. Furthermore, in order to avoid frequency planning, a frequency reuse factor of one is desirable. In this case, system capacity is limited by interference, which is particularly crucial for mobile terminals with a single receive antenna. For a high throughput, interference cancellation algorithms are required in the receiver. In this paper, a single antenna interference cancellation (SAIC) algorithm is introduced for amplitude-shift keying (ASK) modulation schemes used in coded OFDM transmission which achieves high gains in comparison to a conventional coded OFDM transmission employing quadrature amplitude modulation (QAM) in an interference limited scenario. Furthermore, an adaptive least-mean-square (LMS) and a recursive least-squares (RLS) SAIC receiver, respectively, are presented. We show that in particular the RLS solution enables a good tradeoff between performance and complexity and is robust even to multiple interferers and frequency synchronization errors.
Genetic Structure, Nestmate Recognition and Behaviour of Two Cryptic Species of the Invasive Big-Headed Ant Pheidole megacephala

Background Biological invasions are recognized as a major cause of biodiversity decline and have considerable impact on the economy and human health. The African big-headed ant Pheidole megacephala is considered one of the world's most harmful invasive species. Methodology/Principal Findings To better understand its ecological and demographic features, we combined behavioural (aggression tests), chemical (quantitative and qualitative analyses of cuticular lipids) and genetic (mitochondrial divergence and polymorphism of DNA microsatellite markers) data obtained for eight populations in Cameroon. Molecular data revealed two cryptic species of P. megacephala, one inhabiting urban areas and the other rainforests. Urban populations belong to the same phylogenetic group than those introduced in Australia and in other parts of the world. Behavioural analyses show that the eight populations sampled make up four mutually aggressive supercolonies. The maximum distance between nests from the same supercolony was 49 km and the closest distance between two nests belonging to two different supercolonies was 46 m. The genetic data and chemical analyses confirmed the behavioural tests as all of the nests were correctly assigned to their supercolony. Genetic diversity appears significantly greater in Africa than in introduced populations in Australia; by contrast, urban and Australian populations are characterized by a higher chemical diversity than rainforest ones. Conclusions/Significance Overall, our study shows that populations of P. megacephala in Cameroon adopt a unicolonial social structure, like invasive populations in Australia. However, the size of the supercolonies appears several orders of magnitude smaller in Africa. This implies competition between African supercolonies and explains why they persist over evolutionary time scales.
Interaction of the C-Terminal Domains of Sendai Virus N and P Proteins: Comparison of Polymerase-Nucleocapsid Interactions within the Paramyxovirus Family

ABSTRACT Interaction of the C-terminal domains of Sendai virus (SeV) P and N proteins is crucial for RNA synthesis by correctly positioning the polymerase complex (L+P) onto the nucleocapsid (N/RNA). To better understand this mechanism within the paramyxovirus family, we have studied the complex formed by the SeV C-terminal domains of P (PX) and N (NTAIL) proteins by solution nuclear magnetic resonance spectroscopy. We have characterized SeV NTAIL, which belongs to the class of intrinsically disordered proteins, and precisely defined the binding regions within this latter domain and within PX. SeV NTAIL binds with residues 472 to 493, which have a helical propensity (residues 477 to 491) to the surface created by helices α2 and α3 of PX with a 1:1 stoichiometry, as was also found for measles virus (MV). The binding interface is dominated by charged residues, and the dissociation constant was determined to be 57 ± 18 μM under conditions of the experiment (i.e., in 0.5 M NaCl). We have also shown that the extreme C terminus of SeV NTAIL does not interact with PX, which is in contrast to MV, where a second binding site was identified. In addition, the interaction surfaces of the MV proteins are hydrophobic and a stronger binding constant was found. This gives a good illustration of how selection pressure allowed the C-terminal domains of N and P proteins to evolve concomitantly within this family of viruses in order to lead to protein complexes having the same three-dimensional fold, and thus the same function, but with completely different binding interfaces.
On Traffic Long-Range Dependence at the Output of Schedulers with Multiple Service Classes

Long-range dependence (LRD) is a widely verified property of Internet traffic, which severely impacts network performance yielding longer queuing delays. Moreover, LRD is almost ubiquitous and very hard to remove or control. In this work, we investigated by thorough simulation the effect of schedulers on traffic LRD. We analyzed the output traffic of schedulers merging LRD flows according to various service policies, viz. plain FIFO, static-priority, earliest-deadline-first and general processor sharing. First, we noticed that traffic LRD is not affected much by crossing the scheduler, for any type of service policy. Second, we showed that LRD also propagates across different service classes with any policy except balanced GPS, which ensures complete separation between classes. This phenomenon may explain in part why LRD is so widespread in Internet traffic.
Visualization of point-based surfaces with locally reconstructed subdivision surfaces

Point-based surfaces (i.e. surfaces represented by discrete point sets which are either directly obtained by current 3D acquisition devices or converted from other surface representations) are well designed for multiresolution storage and transmission of complex objects. Unfortunately, visualization of point-based surfaces requires to develop specific rendering techniques (e.g. splatting) as point sets are not well adapted to existing graphics hardware which are optimized for polygonal meshes. In this paper, we propose an efficient reconstruction and visualization technique of point-based surfaces that takes full benefit from the whole optimized pipeline implemented in graphics hardware. The basic idea is to generate a set of independent meshes using a local 2D Delaunay triangulation of the point set. These meshes are then glued together to get a " visual continuity" by using a subdivision process.
Learning Virtual Agents for Decision-Making in Business Simulators

In this paper we describe SIMBA, a simulator for business administration, as a Multi-Agent platform for the design, implementation and evaluation of virtual agents. SIMBA creates a complex competitive environment in which intelligent agents play the role of business decision makers. An important issue of SIMBA architecture is that humans can interact with virtual agents. Decision making in SIMBA is a challenge, since it requires handling large and continuous state and action spaces. In this paper, we propose to tackle this problem using Reinforcement Learning (RL) and K-Nearest Neighbors (KNN) approaches. RL requires the use of generalization techniques to be applied in large state and action spaces. We present different combinations in the choice of the generalization method based on Vector Quantization (VQ) and CMAC. We demonstrate that learning agents are very competitive, and they can outperform human expert decision strategies from business literature.
Sedimentation Risk Assessment Using Satellite and Geospatial Data in Lagoa Feia, Brazil

Lagoa Feia is a lake located in Rio de Janeiro, Brazil, which historically experienced sedimentation impacts due to channel ditching which was enacted to manage water resources for agricultural practices and to avoid flooding. This study models the significance of erosion in the lake basin integrated with landuse, soil types, and climatic data using geographic information system (GIS) techniques. The erosion model was built using the above input variables by applying weighted overlay methods, and the vulnerable areas were mapped. Landsat 8 images were utilized for remote sensing analysis, such as image enhancement indices to detect depth, sedimentation and land-use changes over time. The results of the study are useful to implement Best Management Practices to overcome the issue in the lake.