Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Load Analysis For Automated Request-Quota Controlling In Clouds

The importance of efficient use of IT infrastructure capacities increases, as the growing amount of deployed IT systems increasingly requires resources. On the one hand, companies can outsource their IT demands to Cloud providers in order to only pay for the very amount of required computational power. On the other hand, cloud providers themselves need to be efficient as well, as it may be the deciding factor on their market positioning. This paper proposes a method to accept or refuse incoming requests based on a load estimation and load forecast. We will present the architecture and a general implementation of the so-called Quota-Control-Unit (QCU), followed by results of an extensive simulation. The evaluation will show that the introduced method is a promising approach of securing IT systems from overload while keeping the economic outcome at the possible maximum.
Nonlinear Filtering with Population Codes

In this paper we develop a method for the nonlinear filtering of dynamic population codes with unknown latent dynamics. This method is based on a parameterized function which updates beliefs while keeping the dimension of the belief space constant. In order to optimize this function, we construct a graphical model representation of the belief-latent state dynamics, and show that a certain marginalization of the graphical model has the form of a conditional exponential family harmonium. This marginalization allows us to maximize the likelihood of observations, and thereby optimize the parameterized function, by the method of contrastive divergence minimization. By defining the parameterized function as a multilayer perceptron, we show how the stochastic contrastive divergence gradient may be computed with backpropagation. We conclude by demonstrating the effectiveness of this approach on a filtering problem based on a stochastic pendulum.
Mutant Huntingtin Alters Retrograde Transport of TrkB Receptors in Striatal Dendrites

Huntingtin (HTT), the protein mutated in Huntington's disease (HD), controls transport of the neurotrophin, brain-derived neurotrophic factor (BDNF), within corticostriatal neurons. Transport and delivery of BDNF to the striatum are reduced in disease, which contributes to striatal neuron degeneration. BDNF released by cortical neurons activates TrkB receptors at striatal dendrites to promote striatum survival. However, it remains to be determined whether transport of TrkB, the BDNF receptor, depends on HTT and whether such transport is altered in mutant situation. Here we show that TrkB binds to and colocalizes with HTT and dynein. Silencing HTT reduces vesicular transport of TrkB in striatal neurons. In HD, the polyQ expansion in HTT alters the binding of TrkB-containing vesicles to microtubules and reduces transport. Using a combination of microfluidic devices that isolate dendrites from cell bodies and BDNF coupled to quantum dots, we selectively analyzed TrkB retrograde transport in response to BDNF stimulation at dendrite terminals. We show that the retrograde transport of TrkB vesicles within striatal dendrites and the BDNF/TrkB-induced signaling through ERK phosphorylation and c-fos induction are decreased in neurons from an HD mouse model. Together, our findings demonstrate that HTT is a crucial regulator of TrkB trafficking. Transport defects in HD are not restricted to BDNF transport in cortical neurons but also affect trafficking of its ligand-bound receptor in the striatal neurons. This transport alteration may further impair BDNF-TrkB survival signaling within the corticostriatal connection that is most affected in HD.
Imageability and Category-specificity

In this paper, we report the case of DrO, a patient who has been described as having a selective problem understanding the meaning of abstract words in the auditory modality. We test this claim by means of an on-line semantic priming task, comparing the automatic activation of semantic information in both the auditory and visual modalities. Although DrO showed priming for both abstract and concrete words in the visual modality, there was only priming for concrete words in the auditory modality. However, DrO's reaction times (RTs) and errors in the auditory priming study suggested thathe mighthave a generalised auditory processing impairment. We tested and confirmed this hypothesis in a series of further studies. We propose an account of why a general auditory processing impairment would affect abstract words more than concrete words by appealing to an auditory analogue of Plaut and Shallice's (1993) recent computational model of deep dyslexia.
Optimizing Sparse Linear Algebra for Large-Scale Graph Analytics

Emerging data-intensive applications attempt to process and provide insight into vast amounts of online data. A new class of linear algebra algorithms can efficiently execute sparse matrix-matrix and matrix-vector multiplications on large-scale, shared memory multiprocessor systems, enabling analysts to more easily discern meaningful data relationships, such as those in social networks.
Automatic keyword extraction from medical and healthcare curriculum

Medical and healthcare study programmes are quite complicated in terms of branched structure and heterogeneous content. In logical sequence a lot of requirements and demands placed on students appear there. This paper focuses on an innovative way how to discover and understand complex curricula using modern information and communication technologies. We introduce an algorithm for curriculum metadata automatic processing - automatic keyword extraction based on unsupervised approaches, and we demonstrate a real application during a process of innovation and optimization of medical education. The outputs of our pilot analysis represent systematic description of medical curriculum by three different approaches (centrality measures) used for relevant keywords extraction. Further evaluation by senior curriculum designers and guarantors is required to obtain an objective benchmark.
The TEXES Survey for H2 Emission from Protoplanetary Disks

We report the results of a search for pure rotational molecular hydrogen emission from the circumstellar environments of young stellar objects with disks using the Texas Echelon Cross Echelle Spectrograph (TEXES) on the NASA Infrared Telescope Facility and the Gemini North Observatory. We searched for mid-infrared H2 emission in the S(1), S(2), and S(4) transitions. Keck/NIRSPEC observations of the H2 S(9) transition were included for some sources as an additional constraint on the gas temperature. We detected H2 emission from 6 of 29 sources observed: AB Aur, DoAr 21, Elias 29, GSS 30 IRS 1, GV Tau N, and HL Tau. Four of the six targets with detected emission are class I sources that show evidence for surrounding material in an envelope in addition to a circumstellar disk. In these cases, we show that accretion shock heating is a plausible excitation mechanism. The detected emission lines are narrow (~10 km s−1), centered at the stellar velocity, and spatially unresolved at scales of 0.4″, which is consistent with origin from a disk at radii 10-50 AU from the star. In cases where we detect multiple emission lines, we derive temperatures ≳500 K from ~1 M⊕ of gas. Our upper limits for the nondetections place upper limits on the amount of H2 gas with T > 500 K of less than a few Earth masses. Such warm gas temperatures are significantly higher than the equilibrium dust temperatures at these radii, suggesting that the gas is decoupled from the dust in the regions that we are studying and that processes such as UV, X-ray, and accretion heating may be important.
Anonymization of trajectory data

Trajectories of mobile objects, are automatically collected in huge quantities. Publishing and exploiting such data is essential to improve planning, but it threatens the privacy of individuals: re-identification of the individual behind a trajectory is easy unless precautions are taken. We present two heuristics for privacy-preserving publication of trajectories. Both of them publish only true locations. The first heuristic is based on trajectory microaggregation and on location permutation; it effectively achieves trajectory k-anonymity. The second heuristic is based only on location permutation; it gives up trajectory k-anonymity and aims at a different property named location k-diversity. The advantage of the second heuristic is that it takes into account reachability constraints when computing anonymized trajectories.
Inter-session reproducibility of cerebral blood flow (CBF) in the primary motor cortex as assessed in the VESPA (vendor-specific features of ASL-MRI) study

Poster: "ECR 2013 / B-0894 / Inter-session reproducibility of cerebral blood flow (CBF) in the primary motor cortex as assessed in the VESPA (vendor-specific features of ASL-MRI) study" by: "R. Steketee1, H. -J. M. M. Mutsaerts2, G. Houston1, C. B. L. M. Majoie2, A. J. Nederveen2, M. Smits1; 1Rotterdam/NL, 2Amsterdam/NL"
A new technique for Face Recognition using 2D-Gabor Wavelet Transform with 2D-Hidden Markov Model approach

A Discrete Gabor Wavelet Transform (DGWT) based 2D Hidden Markov Model (2DHMM) approach for Face Recognition (FR) is proposed in this paper. To improve the accuracy of the face recognition algorithm, a Gabor Wavelet Transform is used in obtaining the observation sequence vectors. We have conducted extensive experiments ORL database which shows that the proposed method can improve the accuracy significantly, especially when the face image dataset is large with limited training images. Unlike the pervious HMMs used for FR, we propose 2D HMM with Expectation-Maximization (EM)algorithm suitable for almost perfect estimation as feature vectors. This model of 2D HMM shows superior image segmentation for learning process. A recognition rate of 99% is achieved.
Glucagon-Like Peptide-1 Receptor Ligand Interactions: Structural Cross Talk between Ligands and the Extracellular Domain

Activation of the glucagon-like peptide-1 receptor (GLP-1R) in pancreatic β-cells potentiates insulin production and is a current therapeutic target for the treatment of type 2 diabetes mellitus (T2DM). Like other class B G protein-coupled receptors (GPCRs), the GLP-1R contains an N-terminal extracellular ligand binding domain. N-terminal truncations on the peptide agonist generate antagonists capable of binding to the extracellular domain, but not capable of activating full length receptor. The main objective of this study was to use Hydrogen/deuterium exchange (HDX) to identify how the amide hydrogen bonding network of peptide ligands and the extracellular domain of GLP-1R (nGLP-1R) were altered by binding interactions and to then use this platform to validate direct binding events for putative GLP-1R small molecule ligands. The HDX studies presented here for two glucagon-like peptide-1 receptor (GLP-1R) peptide ligands indicates that the antagonist exendin-4[9-39] is significantly destabilized in the presence of nonionic detergents as compared to the agonist exendin-4. Furthermore, HDX can detect stabilization of exendin-4 and exendin-4[9-39] hydrogen bonding networks at the N-terminal helix [Val19 to Lys27] upon binding to the N-terminal extracellular domain of GLP-1R (nGLP-1R). In addition we show hydrogen bonding network stabilization on nGLP-1R in response to ligand binding, and validate direct binding events with the extracellular domain of the receptor for putative GLP-1R small molecule ligands.
Fitting Semiparametric Additive Hazards Models using Standard Statistical Software

The Cox proportional hazards model has become the standard in biomedical studies, particularly for settings in which the estimation covariate effects (as opposed to prediction) is the primary objective. In spite of the obvious flexibility of this approach and its wide applicability, the model is not usually chosen for its fit to the data, but by convention and for reasons of convenience. It is quite possible that the covariates add to, rather than multiply the baseline hazard, making an additive hazards model a more suitable choice. Typically, proportionality is assumed, with the potential for additive covariate effects not evaluated or even seriously considered. Contributing to this phenomenon is the fact that many popular software packages (e.g., SAS, S‐PLUS/R) have standard procedures to fit the Cox model (e.g., proc phreg, coxph), but as of yet no analogous procedures to fit its additive analog, the Lin and Ying (1994) semiparametric additive hazards model. In this article, we establish the connections between the Lin and Ying (1994) model and both Cox and least squares regression. We demonstrate how SAS's phreg and reg procedures may be used to fit the additive hazards model, after some straightforward data manipulations. We then apply the additive hazards model to examine the relationship between Model for End‐stage Liver Disease (MELD) score and mortality among patients wait‐listed for liver transplantation. (© 2007 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Passenger Flow Simulation (PFS) In A Complex Networked Transportation System

Passenger trip time performance is positively correlated with passenger satisfaction, airfare elasticity, and airline profits. Researchers have demonstrated that flight metrics are poor proxy for passenger trip experience. Trip delays experienced by passengers due to missed connections and cancelled flights are not negligible. This paper describes a passenger flow simulation which captures the asymmetric and unique passenger trip on-time performance and reflects the complexity and significance of the impact of a small set of cancelled flights and missed connections on passenger trip delays. It measures the system performance from the flying public's view. Furthermore, it enables researchers to conduct experiments outside the range of historical data. The results of this research provide decision makers with improved metrics for future investment decisions and better tools to manage the system. The passenger flow simulation model also provides the means to perform analysis for proposed changes to the system.
Improved Multilocus Sequence Typing Scheme for Staphylococcus epidermidis

ABSTRACT We evaluated three multilocus sequence typing (MLST) schemes for Staphylococcus epidermidis and selected the seven most discriminatory loci for the formation of a new, more powerful MLST scheme. This improved scheme gave 31 sequence types (STs) and 5 clonal complexes (CCs), whereas the other schemes delineate 16 to 24 STs and 1 to 3 CCs.
Simulation Results on Selector Adaptation in Behavior Trees

Behavior trees (BTs) emerged from video game development as a graphical language for modeling intelligent agent behavior. However as initially implemented, behavior trees are static plans. This paper adds to recent literature exploring the ability of BTs to adapt to their success or failure in achieving tasks. The "Selector" node of a BT tries alternative strategies (its children) and returns success only if all of its children return failure. This paper studies several means by which Selector nodes can learn from experience, in particular, learn conditional probabilities of success based on sensor information, and modify the execution order based on the learned iformation. Furthermore, a "Greedy Selector" is studied which only tries the child having the highest success probability. Simulation results indicate significantly increased task performance, especially when frequentist probability estimate is conditioned on sensor information. The Greedy selector was ineffective unless it was preceded by a period of training in which all children were exercised.
A Study of Errors and Misconceptions in the Learning of Addition and Subtraction of Directed Numbers in Grade 8

The study focused on the errors and misconceptions that learners manifest in the addition and subtraction of directed numbers. Skemp’s notions of relational and instrumental understanding of mathematics and Sfard’s participation and acquisition metaphors of learning mathematics informed the study. Data were collected from 35 Grade 8 learners’ exercise book responses to directed numbers tasks as well as through interviews. Content analysis was based on Kilpatrick et al.’s strands of mathematical proficiency. The findings were as follows: 83.3% of learners have misconceptions, 16.7% have procedural errors, 67% have strategic errors, and 28.6% have logical errors on addition and subtraction of directed numbers. The sources of the errors seemed to be lack of reference to mediating artifacts such as number lines or other real contextual situations when learning to deal with directed numbers. Learners seemed obsessed with positive numbers and addition operation frames—the first number ideas they encountered in school. They could not easily accommodate negative numbers or the subtraction operation involving negative integers. Another stumbling block seemed to be poor proficiency in English, which is the language of teaching and learning mathematics. The study recommends that building conceptual understanding on directed numbers and operations on them must be encouraged through use of multirepresentations and other contexts meaningful to learners. For that reason, we urge delayed use of calculators.
Clearing supply and demand under bilateral constraints

In a moneyless market, a non storable, non transferable homogeneous commodity is reallocated between agents with single-peaked preferences. Agents are either suppliers or demanders. Transfers between a supplier and a demander are feasible only if they are linked, and the links form an arbitrary bipartite graph. Typically, supply is short in one segment of the market, while demand is short in another. Information about individual preferences is private, and so is information about feasible links: an agent may unilaterally close one of her links if it is in her interest to do so. Our egalitarian transfer solution rations only the long side in each market segment, equalizing the net transfers of rationed agents as much as permitted by the bilateral constraints. It elicits a truthful report of both preferences and links: removing a feasible link is never profitable to either one of its two agents. Together with efficiency, and a version of equal treatment of equals, these properties are characteristic.
Collaborative content distribution for vehicular ad hoc networks

The proliferation of low-cost wireless connectivity, combined with the growth of distributed peer-to-peer cooperative systems, is changing the way in which next-generation vehicular networks will evolve. In this paper, we address the problem of low-latency content distribution (multicast streaming) to a dense vehicular highway network from roadside infostations, using efficient multihop vehicle-to-vehicle coll aboration. Due to the highly dynamic nature of the underlying vehicular network topology, we depart from architectures requiring centralized coordination, reliable MAC scheduling, or global network state knowledge, and instead adopt a distributed an d minimally coordinated paradigm. We establish the viability of our approach with both analysis and extensive simulations. Our study is motivated by questions such as “what is the network capacity? ” and “how many infostations are needed to realizea target throughput and latency? ” The key ingredient in our approach is the use of multihop randomized network coding to efficiently distribute the content in the vehicular network. Specifically, we show that in the limit of a highly dense network, our decentralized approach can attain a multicast throughput that is up to a factor of 1/e of the throughput which could be achieved by perfectly scheduling all packets in the network. Further, the gains ofusing randomized network coding over classical store-and-forwa rd multihop routing strategies can be significant, as measuredby the throughput received at a vehicle as a function of the dist ance between that vehicle and the nearest infostation. This in tu rn translates into a significant decrease in the number of roads ide infostations required to meet desired throughput and latency constraints for specific streaming applications.
Kernel Distribution Embeddings

Classical kernel methods have addressed the mapping of individual points to feature space. A generalization is towards feature space representations of probability distributions. We collectively refer to such mappings as distribution embeddings. A natural application of kernel distribution embeddings is in testing for similarities between samples from probability distributions. We refer to the distance between two distribution embeddings as the maximum mean discrepancy (MMD). We have formulated a twosample test [1] (of whether two distributions are the same), and showed the independence test in1 (of whether two random variables observed together are statistically independent) is a special case. A further application of the MMD as independence criterion is in feature selection, where we maximize dependence between features and labels [3]. We have further developed alternative independence tests based on space partitioning approaches and classical divergence measures (such as the `1 distance and KL-divergence) [7, 2]. A recent application uses kernel means in visualization. When using a power-of-cosine kernel for distributions on the projective sphere, the kernel mean can be represented as a symmetric tensor. In the context of diffusion MRI, this permits an efficient visual and quantitative analysis of the uncertainty in nerve fiber estimates, which can inform the choice of MR acquisition schemes and mathematical models.2 We have also used the kernel means embedding to develop a variant of an SVM which operates on distributions rather than points [11], permitting modeling of input uncertainties. One can prove a generalized representer theorem for this case, and in the special case of Gaussian input uncertainties and Gaussian kernel SVMs, it leads to a multi-scale SVM, akin to an RBF network with variable widths, which is still trained by solving a quadratic optimization problem. Given that the MMD depends on the particular kernel chosen, we proposed two kernel selection strategies [15, 8], the earlier one relying on a classification interpretation of the MMD, and the later one explicitly minimizing the probability of Type II error of the associated two-sample test (that is, the probability of wrongly accepting that two unlike distributions are the same, given samples from each).
Left Ventricular Diastolic Pressure‐Volume Relations in Rats with Healed Myocardial Infarction: Effects on Systolic Function

To determine the effects of healed myocardial infarction on the diastolic compliance of the left ventricle, we studied 36 rats 26 days after left coronary artery ligation. Peak cardiac output and stroke volume were measured under ether anesthesia during volume loading, and peak left ventricular developed pressure was determined during occlusion of the ascending aorta. During a slow infusion of saline into the potassium-arrested left ventricle, diastolic pressure and volume were measured continuously over the pressure range −5 to 30 mm Hg. Infarct size was determined by planimetry of serial sections taken from each heart at 1-mm intervals from apex to base. In rats with healed infarets, left ventricular volume was increased in proportion to infarct size and the diastolic pressure-volume relationship was shifted ao that at pressures below 2.5 mm Hg volume was increased, resulting in an increased ventricular compliance in this low pressure range. Above this pressure, the slopes of the pressure-volume curves were similar in rats with and without infarctions. Peak cardiac output and pressure-generating capacity were impaired in proportion to infarct size. This impairment of cardiac performance correlated with the infarct size-related increase in diastolic volume, which served to offset the reduction in flow generating capacity caused by systolic dysfunction, while contributing directly to the impairment of pressure generating capacity.