Miracle-dz/UniFAR-FTUs huggingface dataset:
Opportunistic Source/Destination Cooperation in Cooperative Diversity Networks

In this paper, we combine opportunistic transmission and source/destination cooperation in a decode-and-forward (DF)-based two-hop cooperative diversity network consisting of multiple source-destination pairs and a single relay. Firstly, we consider a special scenario with two source-destination pairs. For this network, there are two possible strategies in each hop: no-cooperation or cooperation. Considering the combination of the two strategies in two cascaded hops, we investigate four different end-to-end transmission strategies, and we find that the four strategies complement one another depending on channel conditions in terms of outage performance. To maximize the mutual information, we propose an optimum joint selection of source-destination pair and end-to-end transmission strategy. Then we show that the optimum joint selection scheme can be simplified without loss of outage performance, and derive its exact outage probability in closed-form. Secondly, we generalize the proposed transmission strategy into a scenario with multiple source-destination pairs. For this network, we first propose an optimum end-to-end transmission strategy to maximize the mutual information, then a suboptimum end-to-end transmission strategy to reduce the signaling overhead and computational complexity. For the suboptimum strategy, we derive the outage probability and diversity order.
A next step towards automated modelling of sources of law

The ultimate goal of the research line described here is support for automated modelling of sources of law. One of the first steps is the automatic recognition of norms. In earlier work we presented a categorization of norms or provisions in legislation. We claimed that the categories are characterized by the use of typical sentence structures and that this would enable automatic detection and classification. In this paper we present the results of experiments in such automatic classification of provisions. We have defined fourteen different categories of provisions, and compiled a list of 88 sentence structures for those categories from twenty Dutch laws. Based on these structures, a parser was used to classify the sentences in fifteen different Dutch laws, classifying 91% of 592 sentences correctly. It compares well with other, statistical approaches. An important improvement of our classifier will be the distinction of principal and auxiliary sentences.
Glucose-Raising Genetic Variants in MADD and ADCY5 Impair Conversion of Proinsulin to Insulin

Introduction Recent meta-analyses of genome-wide association studies revealed new genetic loci associated with fasting glycemia. For several of these loci, the mechanism of action in glucose homeostasis is unclear. The objective of the study was to establish metabolic phenotypes for these genetic variants to deliver clues to their pathomechanism. Methods In this cross-sectional study 1782 non-diabetic volunteers at increased risk for type 2 diabetes underwent an oral glucose tolerance test. Insulin, C-peptide and proinsulin were measured and genotyping was performed for 12 single nucleotide polymorphisms (SNP) in or near the genes GCK (rs4607517), DGKB (rs2191349), GCKR (rs780094), ADCY5 (rs11708067), MADD (rs7944584), ADRA2A (rs10885122), FADS1 (rs174550), CRY2 (rs11605924), SLC2A2 (rs11920090), PROX1 (rs340874), GLIS3 (rs7034200) and C2CD4B (rs11071657). Parameters of insulin secretion (AUC Insulin0–30/AUC Glucose0–30, AUC C-peptide0–120/AUC Glucose0–120), proinsulin-to-insulin conversion (fasting proinsulin, fasting proinsulin/insulin, AUC Proinsulin0–120/AUCInsulin0–120) and insulin resistance (HOMA-IR, Matsuda-Index) were assessed. Results After adjustment for confounding variables, the effect alleles of the ADCY5 and MADD SNPs were associated with an impaired proinsulin-to-insulin conversion (p = 0.002 and p = 0.0001, respectively). GLIS3 was nominally associated with impaired proinsulin-to-insulin conversion and insulin secretion. The diabetogenic alleles of DGKB and PROX1 were nominally associated with reduced insulin secretion. Nominally significant effects on insulin sensitivity could be found for MADD and PROX1. Discussion By examining parameters of glucose-stimulated proinsulin-to-insulin conversion during an OGTT, we show that the SNP in ADCY5 is implicated in defective proinsulin-to-insulin conversion. In addition, we confirmed previous findings on the role of a genetic variant in MADD on proinsulin-to-insulin conversion. These effects may also be related to neighboring regions of the genome.
Surplus Liquidity: Implications for Central Banks

Surplus liquidity occurs where cashflows into the banking system persistently exceed withdrawals of liquidity from the market by the central bank. This is reflected in holdings of reserves in excess of the central bank's required reserves. The occurrence of surplus liquidity is widespread, covering many countries around the world. Historically, it has been observed most often in Soviet, wartime and transitional countries. Transitional economies, for example, often attract large capital inflows as the economy opens and undergoes privatisation. The effect of these inflows on liquidity is often magnified by central bank intervention in the foreign exchange market when there is upward pressure on the domestic currency. In the wartime economy, consumption is restricted and large amounts of involuntary savings accumulate until goods and services eventually become more widely available. Soviet-style economies have displayed widespread shortages and administered prices. This creates a situation of repressed inflation, whereby prices are too low relative to the money stock, leaving individuals with excess real balances. The importance of surplus liquidity for central banks is threefold and lies in its potential to influence: (1) the transmission mechanism of monetary policy; (2) the conduct of central bank intervention in the money market, and (3) the central bank's balance sheet and income.
Pin-Align: A New Dynamic Programming Approach to Align Protein-Protein Interaction Networks

To date, few tools for aligning protein-protein interaction networks have been suggested. These tools typically find conserved interaction patterns using various local or global alignment algorithms. However, the improvement of the speed, scalability, simplification, and accuracy of network alignment tools is still the target of new researches. In this paper, we introduce Pin-Align, a new tool for local alignment of protein-protein interaction networks. Pin-Align accuracy is tested on protein interaction networks from IntAct, DIP, and the Stanford Network Database and the results are compared with other well-known algorithms. It is shown that Pin-Align has higher sensitivity and specificity in terms of KEGG Ortholog groups.
Hydrodynamical simulations of galaxy clusters in dark energy cosmologies

In this work we investigate the influence of dark energy on structure formation, within five different cosmological models, namely a concordance $\Lambda$CDM model, two models with dynamical dark energy, viewed as a quintessence scalar field (using a RP and a SUGRA potential form) and two extended quintessence models (EQp and EQn) where the quintessence scalar field interacts non-minimally with gravity (scalar-tensor theories). We adopted for all models the normalization of the matter power spectrum $\sigma_{8}$ to match the CMB data. For each model, we perform hydrodynamical simulations in a cosmological box of $(300 \ {\rm{Mpc}} \ h^{-1})^{3}$ including baryons and allowing for cooling and star formation. We find that, in models with dynamical dark energy, the evolving cosmological background leads to different star formation rates and different formation histories of galaxy clusters, but the baryon physics is not affected in a relevant way. We investigate several proxies for the cluster mass function based on X-ray observables like temperature, luminosity, $M_{gas}$, and $Y_{X}$. We confirm that the overall baryon fraction is almost independent of the dark energy models within few percentage points. The same is true for the gas fraction. This evidence reinforces the use of galaxy clusters as cosmological probe of the matter and energy content of the Universe. We also study the $c-M$ relation in the different cosmological scenarios, using both dark matter only and hydrodynamical simulations. We find that the normalization of the $c-M$ relation is directly linked to $\sigma_{8}$ and the evolution of the density perturbations for $\Lambda$CDM, RP and SUGRA, while for EQp and EQn it depends also on the evolution of the linear density contrast. These differences in the $c-M$ relation provide another way to use galaxy clusters to constrain the underlying cosmology.
STEM+: A Fair and Efficient Algorithm for Scheduling on the DSCH of UMTS Networks

In Universal Mobile Telecommunication Systems (UMTS), the Downlink Shared Channel (DSCH) can be used for providing streaming services. The traffic model for streaming services is different from the commonly used continuously- backlogged model. Each connection specifies a required service rate over an interval of time, k, called the "control horizon". In this paper, our objective is to determine how k DSCH frames should be shared among a set of I connections. We need a scheduler that is efficient and fair and introduce the notion of discrepancy to balance the conflicting requirements of aggregate throughput and fairness. Our motive is to schedule the mobiles in such a way that the schedule minimizes the discrepancy over the k frames. We propose an optimal and computationally efficient algorithm, called STEM+. The proof of the optimality of STEM+, when applied to the UMTS rate sets is the major contribution of this paper. We also show that STEM+ performs better in terms of both fairness and aggregate throughput compared to other scheduling algorithms. Thus, STEM+ achieves both fairness and efficiency and is therefore an appealing algorithm for scheduling streaming connections.
Approximate iterative Least Squares algorithms for GPS positioning

The efficient implementation of positioning algorithms is investigated for Global Positioning System (GPS) and Differential GPS (DGPS). This is particularly important for smart phones with battery limitations. With the help of the information from base stations, Assisted GPS (AGPS) and DGPS can do the positioning more efficiently and more precisely than GPS. In order to do the positioning, the pseudoranges between the receiver and the satellites are required. The most commonly used algorithm for position computation from pseudoranges is non-linear Least Squares (LS) method. Linearization is done to convert the non-linear system of equations into an iterative procedure, which requires the solution of a linear system of equations in each iteration, i.e. linear LS method is applied iteratively. CORDIC-based approximate rotations are used while computing the QR decomposition for solving the LS problem in each iteration. By choosing accuracy of the approximation, e.g. with a chosen number of optimal CORDIC angles per rotation, the LS computation can be simplified. The accuracy of the positioning results is compared for various numbers of required iterations and various approximation accuracies using real GPS data. The results show that very coarse approximations are sufficient for a reasonable positioning accuracy. Therefore, the presented method reduces the computational complexity significantly and is highly suitable for hardware implementation.
Magnetic Nanosystem for Cancer Therapy Using Oncocalyxone A, an Antitomour Secondary Metabolite Isolated from a Brazilian Plant

This paper describes the investigation and development of a novel magnetic drug delivery nanosystem (labeled as MO-20) for cancer therapy. The drug employed was oncocalyxone A (onco A), which was isolated from Auxemma oncocalyx, an endemic Brazilian plant. It has a series of pharmacological properties: antioxidant, cytotoxic, analgesic, anti-inflammatory, antitumor and antiplatelet. Onco A was associated with magnetite nanoparticles in order to obtain magnetic properties. The components of MO-20 were characterized by XRD, FTIR, TGA, TEM and Magnetization curves. The MO-20 presented a size of about 30 nm and globular morphology. In addition, drug releasing experiments were performed, where it was observed the presence of the anomalous transport. The results found in this work showed the potential of onco A for future applications of the MO-20 as a new magnetic drug release nanosystem for cancer treatment.
Constrained deformations based on convolution surfaces

Deformation is a powerful tool for shape modeling and computer animation. In this paper we present a new local deformation model based on convolution surfaces. After a user specifies a series of constraints, which can consist of line segments, arcs and quadratic curves, their effective radii and maximum displacements, the deformation model creates a set of convolution surfaces taking the constraints as the skeletons. Each convolution surface determines a local influence region and a field function associated with the constraint. Compared with the constrained deformation model based on generalized metaballs, our method can reduce the bulges emerged from the deformation. Furthermore, the user can finely control the deformation result by adjusting the weight distribution along the one dimensional convolution skeleton. This deformation model operates on the local space and is independent of the underlying representation of the object to be deformed. Experimental results show that our new deformation model is both efficient and intuitive.
A kernel k-means clustering method for symbolic interval data

Kernel k-means algorithms have recently been shown to perform better than conventional k-means algorithms in unsupervised classification. In this paper we present is an extension of kernel k-means clustering algorithm for symbolic interval data. To evaluate this method, experiments with synthetic and real interval data sets were performed and we have been compared our method with a dynamic clustering algorithm with adaptive distance. The evaluation is based on an external cluster validity index (corrected Rand index) and the overall error rate of classification (OERC). These experiments showed the usefulness of the proposed method and the results indicate that kernel clustering algorithm gives markedly better performance on data sets considered.
Using Linked Open Data Sources for Entity Disambiguation

Within the framework of RepLab 2013, the filtering task try to discover if one tweet is related to one certain entity or not. Our work tries to take advantages of the Web of Data in order to create a context for every entity, extracted from the available Linked Data Sources. The context in Natural Lan- guage Processing (NLP) is the outstanding issue able to distinguish the con- tained semantics in a message by analyzing the frame in which the words are embedded.
Full-Rate Full-Diversity Linear Quasi-Orthogonal Space-Time Codes for Any Number of Transmit Antennas

We construct a class of linear quasi-orthogonal space-time block codes that achieve full diversity over quasistatic fading channels for any transmit antennas. These codes achieve a normalized rate of one symbol per channel use. Constellation rotation is shown to be necessary for the full-diversity feature of these codes. When the number of transmit antennas is a power of 2, these codes are also delay "optimal. " The quasi-orthogonal property of the code makes one half of the symbols orthogonal to the other half, and we show that this allows each half to be decoded separately without any loss of performance. We give an iterative construction of these codes with a practical decoding algorithm. Numerical simulations are presented to evaluate the performance of these codes in terms of capacity as well as probability of error versus SNR curves. For some special cases, we compute the pairwise probability of error averaged over all the channel states as a single integral that shows the diversity and coding gain more clearly.
Automatic Data Normalization and Parameterization for Optical Motion Tracking

Methods for optical motion capture often require timeconsuming manual processing before the data can be used for subsequent tasks such as retargeting or character animation. These processing steps restrict the applicability of motion capturing especially for dynamic VR-environments with real time requirements. To solve these problems, we present two additional, fast and automatic processing stages based on our motion capture pipeline presented in [HSK05]. A normalization step aligns the recorded coordinate systems with the skeleton structure to yield a common and intuitive data basis across different recording sessions. A second step computes a parameterization based on automatically extracted main movement axes to generate a compact motion description. Our method does not restrict the placement of marker bodies nor the recording setup, and only requires a short calibration phase.
Study of Normalization and Aggregation Approaches for Consensus Network Estimation

Inferring gene regulatory networks from expression data is a very difficult problem that has raised the interest of the scientific community. Different algorithms have been proposed to try to solve this issue, but it has been shown that the different methods have some particular biases and strengths, and none of them is the best across all types of data and datasets. As a result, the idea of aggregating various network inferences through a consensus mechanism naturally arises. In this paper, a common framework to standardize already proposed consensus methods is presented, and based on this framework different proposals are introduced and analyzed in two different scenarios: Homogeneous and Heterogeneous. The first scenario reflects situations where the networks to be aggregated are rather similar because the are obtained with inference algorithms working on the same data, whereas the second scenario deals with very diverse networks because various sources of data are used to generate the individual networks. A procedure for combining multiple network inference algorithms is analyzed in a systematic way. The results show that there is a very significant difference between these two scenarios, and that the best way to combine networks in the Heterogeneous scenario is not the most commonly used. We show in particular that aggregation in the Heterogeneous scenario can be very beneficial if the individual networks are combined with our new proposed method Scale LSum.
Hypocretin-2 Saporin Lesions of the Ventrolateral Periaquaductal Gray (vlPAG) Increase REM Sleep in Hypocretin Knockout Mice

Ten years ago the sleep disorder narcolepsy was linked to the neuropeptide hypocretin (HCRT), also known as orexin. This disorder is characterized by excessive day time sleepiness, inappropriate triggering of rapid-eye movement (REM) sleep and cataplexy, which is a sudden loss of muscle tone during waking. It is still not known how HCRT regulates REM sleep or muscle tone since HCRT neurons are localized only in the lateral hypothalamus while REM sleep and muscle atonia are generated from the brainstem. To identify a potential neuronal circuit, the neurotoxin hypocretin-2-saporin (HCRT2-SAP) was used to lesion neurons in the ventral lateral periaquaductal gray (vlPAG). The first experiment utilized hypocretin knock-out (HCRT-ko) mice with the expectation that deletion of both HCRT and its target neurons would exacerbate narcoleptic symptoms. Indeed, HCRT-ko mice (n = 8) given the neurotoxin HCRT2-SAP (16.5 ng/23nl/sec each side) in the vlPAG had levels of REM sleep and sleep fragmentation that were considerably higher compared to HCRT-ko given saline (+39%; n = 7) or wildtype mice (+177%; n = 9). However, cataplexy attacks did not increase, nor were levels of wake or non-REM sleep changed. Experiment 2 determined the effects in mice where HCRT was present but the downstream target neurons in the vlPAG were deleted by the neurotoxin. This experiment utilized an FVB-transgenic strain of mice where eGFP identifies GABA neurons. We verified this and also determined that eGFP neurons were immunopositive for the HCRT-2 receptor. vlPAG lesions in these mice increased REM sleep (+79% versus saline controls) and it was significantly correlated (r = 0.89) with loss of eGFP neurons. These results identify the vlPAG as one site that loses its inhibitory control over REM sleep, but does not cause cataplexy, as a result of hypocretin deficiency.
An improvement for singularity in 2-D corrective smoothed particle method with application to Poisson problem

The corrective smoothed particle method (CSPM) is an improvement to the conventional smoothed particle hydrodynamics (SPH) method. It overcomes the problems of tensile instability and boundary deficiency in SPH. As a specially designed numerical method for time-dependent problems, it has been applied to transient heat conduction and structural dynamics problems. In this paper the 2-D CSPM is used to solve time-independent problems, where an extra numerical inversion needs special attention. To permit an invertible matrix for small smoothing length and hence enhance the computational efficiency, an improvement is proposed to the original CSPM. The accuracy and efficiency of the CSPM and the proposed modification is demonstrated by the numerical example.
An exact and two heuristic strategies for truthful bidding in combinatorial transport auctions

To support a freight carrier in a combinatorial transport auction, we proposes an exact and two heuristic strategies for bidding on subsets of requests. The exact bidding strategy is based on the concept of elementary request combinations. We show that it is sufficient and necessary for a carrier to bid on each elementary request combination in order to guarantee the same result as bidding on each element of the powerset of the set of tendered requests. Both heuristic bidding strategies identify promising request combinations. For this, pairwise synergies based on saving values as well as the capacitated p-median problem are used. The bidding strategies are evaluated by a computational study that simulates an auction. It is based on 174 benchmark instances and therefore easily extendable by other researchers. On average, the two heuristic strategies achieve 91 percent and 81 percent of the available sales potential while generating 36 and only 4 percent of the bundle bids of the exact strategy. Therefore, the proposed bidding strategies help a carrier to increase her chance to win and at the same time reduce the computational burden to participate in a combinatorial transport auction.
A fuzzy logic based approach to the SLAM problem using pseudolinear models with two sensors data association

This paper presents an alternative solution to simultaneous localization and mapping (SLAM) problem by applying a fuzzy Kalman filter using a pseudolinear measurement model of nonholonomic mobile robots. Takagi-Sugeno fuzzy model based on observation for nonlinear system is adopted to represent the process and measurement models of the vehicle-landmarks system. The complete system of the vehicle-landmarks model is decomposed into several linear models. Using the Kalman filter theory, each local model is filtered to find the local estimates. The linear combination of these local estimates gives the global estimate for the complete system. The simulation results prove that the new approach results in more anticipated performances, though nonlinearity is directly involved in the Kalman filter equations, compared to the conventional approach.