Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient seismic forward modeling using simultaneous random sources and sparsity

The high cost of simulating densely sampled seismic forward modeling data arises from activating sources one at a time in sequence.Toincreaseefficiency,onecouldleveragerecentinnovations in seismic field-data acquisition and activate several e.g., 2‐6 sources simultaneously during modeling. However, such approaches would suffer from degraded data quality because of the interference between the model’s responses to the simultaneoussources.Twonewefficientsimultaneous-sourcemodeling approachesareproposedthatrelyonthenoveltandemuseofrandomness and sparsity to construct almost noise-free model response to individual sources. In each approach, thefirst step is to measure the model’s cumulative response with all sources activated simultaneously using randomly scaled band-limited impulses or continuous band-limited random-noise waveforms. In the second step, the model response to each individual source is estimated from the cumulative receiver measurement by exploiting knowledge of the random source waveforms and the sparsity of the model response to individual sources in a known transformdomaine.g. ,curveletdomain.Theefficiencyachievable by the approaches is primarily governed by the sparsity of the model response. By invoking results from the field of compressive sensing, theoretical bounds are provided that assert that the approaches would need less modeling time for sparser i.e., simpler or more structured model responses.Asimulated modelingexampleisillustratedthatshowsthatdatacollectedwithas manyas8192sourcesactivatedsimultaneouslycanbeseparated into the 8192 individual source gathers with data quality comparable to that obtained when the sources were activated sequentially.Theproposedapproachescouldalsodramaticallyimprove seismic field-data acquisition efficiency if the source signatures actuallyprobingtheearthcanbemeasuredaccurately.
The Risk Factors and Outcomes of Acute Kidney Injury after Thoracic Endovascular Aortic Repair

Background We aimed to evaluate the incidence, predictive factors, and impact of acute kidney injury (AKI) after thoracic endovascular aortic repair (TEVAR). Methods A total of 53 patients who underwent 57 TEVAR operations between 2008 and 2015 were reviewed for the incidence of AKI as defined by the RIFLE (risk, injury, failure, loss, and end-stage kidney disease risk) consensus criteria. The estimated glomerular filtration rate was determined in the perioperative period. Comorbidities and postoperative outcomes were retrospectively reviewed. Results Underlying aortic pathologies included 21 degenerative aortic aneurysms, 20 blunt traumatic aortic injuries, six type B aortic dissections, five type B intramural hematomas, three endoleaks and two miscellaneous diseases. The mean age of the patients was 61.2±17.5 years (range, 15 to 85 years). AKI was identified in 13 (22.8%) of 57 patients. There was an association of preoperative stroke and postoperative paraparesis and paraplegia with AKI. The average intensive care unit (ICU) stay in patients with AKI was significantly longer than in patients without AKI (5.3 vs. 12.7 days, p=0.017). The 30-day mortality rate in patients with AKI was significantly higher than patients without AKI (23.1% vs. 4.5%, p=0.038); however, AKI did not impact long-term survival. Conclusion Preoperative stroke and postoperative paraparesis and paraplegia were identified as predictors for AKI. Patients with AKI experienced longer average ICU stays and greater 30-day mortality than those without AKI. Perioperative identification of high-risk patients, as well as nephroprotective strategies to reduce the incidence of AKI, should be considered as important aspects of a successful TEVAR procedure.
Character Recognition using Ensemble classifier

To improve the accuracy of data classification systems, several techniques using classifier fusion have been suggested. This paper proposed a model of classifier fusion for character recognition problem. The work presented here aims to tackle the disadvantages and benefit of different classifiers with varying feature sets. In particular, this approach proposes the use of statistical procedures for the selection of the best subgroup among different classification algorithms and the subsequent fusion of the decision of the models in this subgroup with methods like voting, weighted voting. Ensemble classifier is constructed by using Support Vector Machine and K-Nearest Neighbor. Experimental results show that the performance of proposed ensemble classifier is better as compared to other classifiers in character recognition. Keywords-Character Recognition; Ensemble classifier; Support Vector Machine; K-Nearest Neighbor.
Active Control of an Axial Flow Compressor via Pulsed Air Injection

This paper presents the use of pulsed air injection to control the onset of rotating stall in a low-speed, axial flow compressor. By measuring the unsteady pressures near the rotor face, a control algorithm determines the magnitude and phase of the first mode of rotating stall and controls the injection of air in the front of the rotor face. Experimental results show that this technique slightly extends the stall point of the compressor and eliminates the hysteresis loop normally associated with rotating stall. A parametric study is used to determine the optimal control parameters for suppression of stall. Analytic results---using a low-dimensional model developed by Moore and Greitzer combined with an unsteady shift in the compressor characteristic to model the injectors---give further insights into the operation of the controller. Based on this model, we show that the behavior of the experiment can be explained as a change in the bifurcation behavior of the system under nonlinear feedback. A higher fidelity simulation model is then used to further verify some of the specific performance characteristics that are observed in experiments.
Responses of Marine Organisms to Climate Change across Oceans

Climate change is driving changes in the physical and chemical properties of the ocean that have consequences for marine ecosystems. Here, we review evidence for the responses of marine life to recent climate change across ocean regions, from tropical seas to polar oceans. We consider observed changes in calcification rates, demography, abundance, distribution and phenology of marine species. We draw on a database of observed climate change impacts on marine species, supplemented with evidence in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. We discuss factors that limit or facilitate species’ responses, such as fishing pressure, the availability of prey, habitat, light and other resources, and dispersal by ocean currents. We find that general trends in species responses are consistent with expectations from climate change, including poleward and deeper distributional shifts, advances in spring phenology, declines in calcification and increases in the abundance of warm-water species. The volume and type of evidence of species responses to climate change is variable across ocean regions and taxonomic groups, with much evidence derived from the heavily-studied north Atlantic Ocean. Most investigations of marine biological impacts of climate change are of the impacts of changing temperature, with few observations of effects of changing oxygen, wave climate, precipitation (coastal waters) or ocean acidification. Observations of species responses that have been linked to anthropogenic climate change are widespread, but are still lacking for some taxonomic groups (e.g., phytoplankton, benthic invertebrates, marine mammals).
Signaling Role of Prokineticin 2 on the Estrous Cycle of Female Mice

The possible signaling role of prokineticin 2 (PK2) and its receptor, prokineticin receptor 2 (PKR2), on female reproduction was investigated. First, the expression of PKR2 and its co-localization with estrogen receptor (ERα) in the hypothalamus was examined. Sexually dimorphic expression of PKR2 in the preoptic area of the hypothalamus was observed. Compared to the male mice, there was more widespread PKR2 expression in the preoptic area of the hypothalamus in the female mice. The likely co-expression of PKR2 and ERα in the preoptic area of the hypothalamus was observed. The estrous cycles in female PK2-null, and PKR2-null heterozygous mice, as well as in PK2-null and PKR2-null compound heterozygous mice were examined. Loss of one copy of PK2 or PKR2 gene caused elongated and irregular estrous cycle in the female mice. The alterations in the estrous cycle were more pronounced in PK2-null and PKR2-null compound heterozygous mice. Consistent with these observations, administration of a small molecule PK2 receptor antagonist led to temporary blocking of estrous cycle at the proestrous phase in female mice. The administration of PKR2 antagonist was found to blunt the circulating LH levels. Taken together, these studies indicate PK2 signaling is required for the maintenance of normal female estrous cycles.
Highlighting Relevant Passages for Users of the Interactive SPIDER Retrieval System

We report here on our participation on the 1995 TREC-4 conference. This was limited to participation in the interactive searching task due to a re-implementation of our SPIDER retrieval system. We report on two aspects of the SPIDER system that are particulary useful for interactive searching, namely the use of fast query evaluation, and the use of passage highlighting in presenting retrieved documents to users. We also report on the lessons learned from observing people interact with our system and note the challenge faced by researchers wishing to evaluate the interactive use of information retrieval systems
Organic Shape Modeling through Haptic Devices

This paper presents a sketching system for 3D organic shapes modeling and animation using virtual reality devices. On the hardware side, it is based on the Haptic Workstation™ which conveys force feedback on the user arms (upper body limbs), and a head mounted display to present the 3D generated images. On the software side, we use implicit surfaces modeling techniques such as metaballs. In fact, designers feel well comfortable with this kind of primitives due to their ability in the organic shapes creation such as virtual humans. The proposed system provides an efficient alternative to produce advanced 3D shapes sketching
SRIUBC: Simple Similarity Features for Semantic Textual Similarity

We describe the systems submitted by SRI International and the University of the Basque Country for the Semantic Textual Similarity (STS) SemEval-2012 task. Our systems focused on using a simple set of features, featuring a mix of semantic similarity resources, lexical match heuristics, and part of speech (POS) information. We also incorporate precision focused scores over lexical and POS information derived from the BLEU measure, and lexical and POS features computed over split-bigrams from the ROUGE-S measure. These were used to train support vector regressors over the pairs in the training data. From the three systems we submitted, two performed well in the overall ranking, with split-bigrams improving performance over pairs drawn from the MSR Research Video Description Corpus. Our third system maintained three separate regressors, each trained specifically for the STS dataset they were drawn from. It used a multinomial classifier to predict which dataset regressor would be most appropriate to score a given pair, and used it to score that pair. This system underperformed, primarily due to errors in the dataset predictor.
A Canonical Form for The Inclusion Principle of Dynamic Systems

The inclusion principle provides a mathematical framework for comparing behavior of dynamic systems having different dimensions. Our main objective is to derive a canonical form for larger systems (expansions) that are obtained by expanding smaller systems (contractions). The form offers full freedom in selecting appropriate matrices for the expansion-contraction process. We will broaden the form to include feedback and propose an explicit characterization of contractible control laws subject to overlapping information structure constraints.
Meta-management of virtual organizations: toward information technology support

Electronic commerce enables organizations to form virtual organizations. Shifting between different linkages of the partners for satisfying a need is the major characteristic of the virtual organization, and meta‐management beyond the individual organizational level must be applied in order to optimize the benefit for the entire organizational network. This paper proposes a meta‐management support system to support the meta‐management in virtual organizations. Object‐oriented organization modeling and virtual value chain analysis are used to implement the proposed meta‐management support system.
Diagnosis of single‐subject and group fMRI data with SPMd

Except for purely nonparametric methods, statistical methods depend on assumptions about the distribution of the data studied. While these assumptions are easily checked for a single univariate dataset with diagnostic plots, in the massively univariate model used with functional MRI (fMRI) it is impractical to check with a massive number of plots. In previous work we have demonstrated how to diagnose model assumptions and lack‐of‐fit for single‐subject fMRI models using a working assumption of independent errors; our work depended on images and time series of summary statistics that, when simultaneously viewed dynamically, identify problem scans and voxels. In this article we extend our previous work to account for temporal autocorrelation in single‐subject models and show how analogous methods can be used on group models where multiple subjects are studied. We apply these methods to the single‐subject Functional Image Analysis Contest (FIAC) data and find several anomalies, but none that appear to invalidate the results for that subject. With the group FIAC data we find one subject (and possibly two more) that demonstrate a different pattern of activity. None of our conclusions would be arrived at by simply looking at images of t statistics, demonstrating the importance of model assessment through exploration of the data and diagnosis of model assumptions. Hum Brain Mapp 27:442–451, 2006. © 2006 Wiley‐Liss, Inc.
Compliance through direct persuasive appeals: The moderating role of communicator's attractiveness in interpersonal persuasion

It has been argued that the accessibility of persuasion motives elicits distrust in a communicator's underlying motives and leads to decreased persuasion success. However, this research highlights the fact that salient and positive communicator characteristics (here physical attractiveness) can temper consumers' attributions of selfish motives and lead to increased behavioral compliance when recipients are faced with direct persuasive appeals to get them to do something. This experiment demonstrates that recipients were more likely to comply with an attractive communicator's recommendation when she was forthright about her desire to change recipients' behavior than when she was not. The reverse was true for an unattractive communicator, a finding which indicates that the salience of persuasion motives is likely to become a liability when positive peripheral cues are absent. These effects on recipients' behavioral compliance were found to be mediated by the degree of selfish motives attributed to the communicator. This research was supported by a grant from Prof. Dr. Stefan Hormuth, President of the University of Giessen, to the second author (Project‐No: 60000135). We would like to thank Abuzer Dogan and Sibylle Arhold for their extensive help collecting the data. We are further indebted to Lucie Jung, Tanja Becker, Fiona Schinzer, Agnieszka Twardowska, Marta Skrundz, and Adriane Rosmus for their commitment in taking on the role of experimenters in our study. We are also much obliged to Robert Cialdini and two anonymous reviewers for their helpful comments on a first draft of this paper.
Optimal Adaptive Random Multiaccess in Energy Harvesting Wireless Sensor Networks

Wireless sensors can integrate rechargeable batteries and energy-harvesting (EH) devices to enable long-term, autonomous operation, thus requiring intelligent energy management to limit the adverse impact of energy outages. This work considers a network of EH wireless sensors, which report packets with a random utility value to a fusion center (FC) over a shared wireless channel. Decentralized access schemes are designed, where each node performs a local decision to transmit/discard a packet, based on an estimate of the packet's utility, its own energy level, and the scenario state of the EH process, with the objective to maximize the average long-term aggregate utility of the packets received at the FC. Due to the non-convex structure of the problem, an approximate optimization is developed by resorting to a mathematical artifice based on a game theoretic formulation of the multiaccess scheme, where the nodes do not behave strategically, but rather attempt to maximize a common network utility with respect to their own policy. The symmetric Nash equilibrium (SNE) is characterized, where all nodes employ the same policy; its uniqueness is proved, and it is shown to be a local maximum of the original problem. An algorithm to compute the SNE is presented, and a heuristic scheme is proposed, which is optimal for large battery capacity. It is shown numerically that the SNE typically achieves near-optimal performance, within 3% of the optimal policy, at a fraction of the complexity, and two operational regimes of EH-networks are identified and analyzed: an energy-limited scenario, where energy is scarce and the channel is under-utilized, and a network-limited scenario, where energy is abundant and the shared wireless channel represents the bottleneck of the system.
Reducing Operational Costs through Consolidation with Resource Prediction in the Cloud

How to achieve energy efficiency to run a cloud data center is a major challenge in the era of rising electricity cost and environmental protection. Various techniques have been devised to help reduce energy consumption for cloud data centers that consist of a large number of identical servers, including dynamic allocation of active servers, consolidating diverse applications to run on them, and adjusting the CPU speed of an active server. Leveraging these techniques, we use an Online Coloring Bin Packing problem to model the consolidation problem and devise an effective application-aware approximation algorithm to find a near-optimal solution. We show a 1.7 asymptotic approximation ratio. We then apply a Predictive Bayesian Network model to identify daily workload patterns and adjust resource provisioning accordingly. We evaluate our approaches using traces collected from a real data center and demonstrate that (1) our prediction algorithm is effective in estimating future demands, (2) our coordinated approaches can provide significant savings of energy and operational costs close to the near-optimal offline solution, and (3) our approaches incur little reliability costs in term of wear-and-tear of server components.
Dendritic voltage‐gated K+ conductance gradient in pyramidal neurones of neocortical layer 5B from rats

Voltage‐gated potassium channels effectively regulate dendritic excitability in neurones. It has been suggested that in the distal apical dendrite of layer 5B (L5B) neocortical pyramidal neurones, K+ conductances participate in active dendritic synaptic integration and control regenerative dendritic potentials. The ionic mechanism for triggering these regenerative potentials has yet to be elucidated. Here we used two‐electrode voltage clamp (TEVC) to quantitatively record K+ conductance densities of a sustained K+ conductance in the soma and apical dendrite of L5B neurones of adult rats. We report that the somatic and proximal dendritic sustained voltage‐gated K+ conductance density is more than 10‐fold larger than previous estimates. The results obtained using TEVC were corroborated using current‐clamp experiments in combination with compartmental modelling. Possible error sources, including inaccurate measurement of the passive membrane parameters and unknown axonal and basal dendritic conductance distributions, were shown not to distort the density estimation considerably. The sustained voltage‐gated K+ conductance density was found to decrease steeply along the apical dendrite. The steep negative K+ conductance density gradient along the apical dendrite may help to define a distal, low‐threshold region for amplification of distal synaptic input in L5B pyramidal neurones.
Active queue management design using discrete-event control

Recently, control-theoretic approaches have been studied and employed to manage and control the performance of computing systems. Most of the existing control-theoretic approaches model computing systems as linear systems and apply feedback control. In this paper, we show discrete-event modeling and control techniques can be effectively applied to performance management and control of computing systems. We use active queue management design for Internet routers as a running example. By modeling the router as a queueing system, we formulate the problem of designing the optimal dropping strategy as an optimal queueing control problem under discrete-event control framework. We then derive the optimal controller design using uniformization and value iteration. Through numerical evaluations, we also discuss the effect of various design parameters and workload characteristics on the optimal dropping strategies derived.
A fully-integrated 900-MHz CMOS power amplifier for mobile RFID reader applications

A 900-MHz linear power amplifier has been fabricated for ultra-high-frequency (UHF) radio frequency identification (RFID) reader applications using a 0.25-mum CMOS technology. An on-chip transmission-line transformer is used for output matching network. Input and inter-stage matching components, and RF chokes are fully integrated in the designed amplifier so that no external components are required. The power amplifier provides linear output power of 27 dBm at 920 MHz with a 2.5-V supply. Power-added-efficiency (PAE) at 1-dB-gain-compression point (P1dB) is 28 %. Gain flatness over the full UHF RFID band, which covers from 860 MHz to 960 MHz, is 1 dB
A Higher Order Sensitivity Analysis of Parameter Estimation Problems

The use of model-based simulation to gain knowledge of unknown phenomena and processes behavior is a challenging task in many natural sciences. In order to get a full description of an underlying process, an important issue is to estimate unknown parameters from real but erroneous observations. Thus the whole system is affected by uncertainties and a sensitivity analysis is necessary. Usually one applies first order sensitivity analysis and resulting linearized confidence regions to determine the statistical accuracy of the solution to parameter estimation problems. But especially in highly nonlinear cases linearized regions may not be an adequate representation. In this paper, we suggest quadratic confidence regions