Miracle-dz/UniFAR-FTUs huggingface dataset:
Filter Effects and Filter Artifacts in the Analysis of Electrophysiological Data

In a recent review, VanRullen (2011) concludes that electrophysiological data should not be filtered at all when one is interested in the temporal dynamics or onset latencies of the electrophysiological responses. This conclusion was based on the observation that response onset latency was “smeared out in time for several tens or even hundreds of milliseconds” (p. 6) in a simulated dataset. It is correct that any band limitation in the frequency domain necessarily affects the signal in the time domain resulting in reduced precision and artifacts (cf. e.g., Luck, 2005). Nevertheless, here, we will discuss that the problem is overestimated by about an order of magnitude by the assumptions and analysis parameters used in VanRullen's simulated dataset and advertise the cautious usage of carefully designed filters to be able to also detect small signals.
Inverting the angular correlation function

The two point angular correlation function is an excellent measure of structure in the Universe. To extract from it the three-dimensional power spectrum, one must invert Limber’s equation. Here we perform this inversion using a Bayesian prior constraining the smoothness of the power spectrum. Among other virtues, this technique allows for the possibility that the estimates of the angular correlation function are correlated from bin to bin. The outputs of this technique are estimators for the binned power spectrum and a full covariance matrix. Angular correlations mix small and large scales but after the inversion, small-scale data can be trivially eliminated, thereby allowing for realistic constraints on theories of large-scale structure. We analyse the automated plate measurement (APM) catalogue as an example, comparing our results with previous results. As a by-product of these tests, we find ‐ in rough agreement with previous work ‐ that APM places stringent constraints on cold dark matter inspired models, with the shape parameter constrained to be 0:25 ^ 0:04 (using data with wavenumber k # 0:1 h Mpc 21 ). This range of allowed values uses the full power spectrum covariance matrix, but assumes negligible covariance in the off-diagonal angular correlation error matrix, which is estimated with a large angular resolution of 0.58 (in the range 0.58 and 208).
On the Effectiveness of Contracts as Test Oracles in the Detection and Diagnosis of Race Conditions and Deadlocks in Concurrent Object-Oriented Software

The idea behind Design by Contract (DbC) is that a method defines a contract stating the requirements a client needs to fulfill to use it, the precondition, and the properties it ensures after its execution, the post condition. Though there exists ample support for DbC for sequential programs, applying DbC to concurrent programs presents several challenges. We have proposed a solution to these challenges in the context of Java as programming language and the Java Modeling language as specification language. This paper presents our findings when applying our DbC technique on an industrial case study to evaluate the ability of contract-based, runtime assertion checking code at detecting and diagnosing race conditions and deadlocks during system testing. The case study is a highly concurrent industrial system from the telecommunications domain, with actual faults. It is the first work to systematically investigate the impact of contract assertions for the detection of race conditions and deadlocks, along with functional properties, in an industrial system.
On high-throughput and fair multi-hop wireless ad hoc networks with MIMO

Nowadays, the prevalence of multimedia applications mandates that wireless networks provide higher throughput and increased spatial efficiency. Aimed primarily at achieving this goal, the first IEEE 802.11n draft was recently approved. In the draft, MIMO and frame aggregation are utilized to improve the PHY and MAC layers in single-hop WLANs by achieving high raw rates and goodput. However, in the context of multi-hop wireless ad hoc networks, the hidden terminal, exposed terminal, and deafness problems remain unsolved by IEEE 802.11n. Consequently, this greatly degrades the MAC efficiency in multi-hop wireless ad hoc networks, even in the presence of frame aggregation. In this paper, we propose a novel MAC scheme that utilizes MIMO distributed spatial multiplexing to solve all the aforementioned problems, resulting in high throughput and fairness in multi-hop wireless ad hoc networks. Moreover, our scheme can be integrated with frame aggregation to further enhance the network performance.
Hierarchical Event Selection for Video Storyboards with a Case Study on Snooker Video Visualization

Video storyboard, which is a form of video visualization, summarizes the major events in a video using illustrative visualization. There are three main technical challenges in creating a video storyboard, (a) event classification, (b) event selection and (c) event illustration. Among these challenges, (a) is highly application-dependent and requires a significant amount of application specific semantics to be encoded in a system or manually specified by users. This paper focuses on challenges (b) and (c). In particular, we present a framework for hierarchical event representation, and an importance-based selection algorithm for supporting the creation of a video storyboard from a video. We consider the storyboard to be an event summarization for the whole video, whilst each individual illustration on the board is also an event summarization but for a smaller time window. We utilized a 3D visualization template for depicting and annotating events in illustrations. To demonstrate the concepts and algorithms developed, we use Snooker video visualization as a case study, because it has a concrete and agreeable set of semantic definitions for events and can make use of existing techniques of event detection and 3D reconstruction in a reliable manner. Nevertheless, most of our concepts and algorithms developed for challenges (b) and (c) can be applied to other application areas.
Universal Approximation of Extreme Learning Machine With Adaptive Growth of Hidden Nodes

Extreme learning machines (ELMs) have been proposed for generalized single-hidden-layer feedforward networks which need not be neuron-like and perform well in both regression and classification applications. In this brief, we propose an ELM with adaptive growth of hidden nodes (AG-ELM), which provides a new approach for the automated design of networks. Different from other incremental ELMs (I-ELMs) whose existing hidden nodes are frozen when the new hidden nodes are added one by one, in AG-ELM the number of hidden nodes is determined in an adaptive way in the sense that the existing networks may be replaced by newly generated networks which have fewer hidden nodes and better generalization performance. We then prove that such an AG-ELM using Lebesgue p-integrable hidden activation functions can approximate any Lebesgue p-integrable function on a compact input set. Simulation results demonstrate and verify that this new approach can achieve a more compact network architecture than the I-ELM.
High-Performance Liquid Chromatography Assay for Moxifloxacin in Brain Tissue and Plasma: Validation in a Pharmacokinetic Study in a Murine Model of Cerebral Listeriosis

Moxifloxacin is a broad-spectrum antibacterial 8-methoxy-fluoroquinolone. In order to evaluate the pharmacokinetic properties of moxifloxacin in mouse plasma and brain tissue, we developed a high-performance liquid chromatography (HPLC) method. This study was based on single-drug delivery, intravenously dosed in a central listeriosis murine model. The method employed a reversed-phase Lichrospher RP-18 with a precolumn (250 × 4.6 mm) and a mobile phase composed of a mixture of acetonitrile, methanol, and citric buffer (pH = 3.5) with sodium dodecyl sulfate and tetrabutylammonium bromide. Fluorescence detection was performed at an excitation wavelength of 290 nm and an emission wavelength of 550 nm. The relative standard deviation of intra- and inter-day assays was <10%. This validated method led to a short retention time (8.0 min) for moxifloxacin. The standard curves were linear from 5–250 μg/L in plasma and from 0.1–2.5 μg/g of brain tissue. The limits of quantification were 5 μg/L in plasma and 0.1 μg/g in brain tissue. The method enabled the detection of systemic antimicrobial in plasma and in CNS in Listeria-infected mice. Injected moxifloxacin passed through the encephalic barrier within a 30 to 60 min after injection time frame. Moxifloxacin pharmacokinetics are modeled in an infected model compared to control mice.
FOREST MANAGEMENT AND WATER QUALITY IN LATVIA: IDENTIFYING CHALLENGES AND SEEKING SOLUTIONS

Rise in human population, industrialization, urbanization, intensified agriculture and forestry pose considerable risks to water supply and quality both on global and regional scale. While freshwater resources are abundant in Latvia, during recent years increased attention has been devoted to water quality in relation to anthropogenic impacts. Forest cover in Latvia equals 52% and forest management and forest infrastructure building and maintenance are among the activities that may, directly or indirectly, affect water quality in headwater catchments. Sedimentation, eutrophication and export of hazardous substances, especially mercury (Hg), are of highest concern. To address these topics, several initiatives have started recently. In 2011, cooperation programme between Latvian State Forest Research Institute (LSFRI) “Silava” and JSC “Latvia’s State Forests” was launched to evaluate the impact of forest management on the environment. This programme included research on the efficiency of water protection structures used at drainage system maintenance (sedimentation ponds, overland flow) and regeneration felling (bufferzones). In 2016, within the second stage of this cooperation programme, a study on the impact of forest management on water quality (forest road construction, drainage system maintenance, felling) was started on a catchment scale. Since 2016 LSFRI Silava is partner in the Interreg Baltic Sea Region Programme project “Water management in Baltic forests”. By focusing on drainage systems, riparian zones and beaver activity, this project aims at reducing nutrient and Hg export from forestry sites to streams and lakes. While this project mostly has a demonstration character, it will also offer novel results on Hg and methylmercury (MeHg) concentrations in beaver ponds in all participating states. This paper aims at summarizing most important challenges related to the impact of forest management on water quality and corresponding recent initiatives striving to offer solutions. Keywords: forest infrastructure building, forest management, water protection measures, water quality Article DOI: http://doi.org/10.15544/RD.2017. 146
Distributed classifier migration in xcs for classification of electroencephalographic signals

This paper presents an investigation into combining migration strategies inspired by multi-deme parallel genetic algorithms with the XCS learning classifier system to provide parallel and distributed classifier migration. Migrations occur between distributed XCS classifier sub-populations using classifiers ranked according to numerosity, fitness or randomly selected. The influence of the degree-of-connectivity introduced by fully-connected, bi-directional ring and uni-directional ring topologies is examined. Results indicate that classifier migration is an effective method for improving classification accuracy, improving learning speed and reducing final classifier population size, in the single-step classification of noisy, artefact- inclusive human electroencephalographic signals. The experimental results will be used as part of our larger research effort investigating the feasibility of using EEG signals as an interface to allow paralysed persons to control a powered wheelchair or other devices.
A randomized, controlled trial of the use of pulmonary-artery catheters in high-risk surgical patients.

BACKGROUND Some observational studies suggest that the use of pulmonary-artery catheters to guide therapy is associated with increased mortality. METHODS We performed a randomized trial comparing goal-directed therapy guided by a pulmonary-artery catheter with standard care without the use of a pulmonary-artery catheter. The subjects were high-risk patients 60 years of age or older, with American Society of Anesthesiologists (ASA) class III or IV risk, who were scheduled for urgent or elective major surgery, followed by a stay in an intensive care unit. Outcomes were adjudicated by observers who were unaware of the treatment-group assignments. The primary outcome was in-hospital mortality from any cause. RESULTS Of 3803 eligible patients, 1994 (52.4 percent) underwent randomization. The base-line characteristics of the two treatment groups were similar. A total of 77 of 997 patients who underwent surgery without the use of a pulmonary-artery catheter (7.7 percent) died in the hospital, as compared with 78 of 997 patients in whom a pulmonary-artery catheter was used (7.8 percent)--a difference of 0.1 percentage point (95 percent confidence interval, -2.3 to 2.5). There was a higher rate of pulmonary embolism in the catheter group than in the standard-care group (8 events vs. 0 events, P=0.004). The survival rates at 6 months among patients in the standard-care and catheter groups were 88.1 and 87.4 percent, respectively (difference, -0.7 percentage point [95 percent confidence interval, -3.6 to 2.2]; negative survival differences favor standard care); at 12 months, the rates were 83.9 and 83.0 percent, respectively (difference, -0.9 percentage point [95 percent confidence interval, -4.3 to 2.4]). The median hospital stay was 10 days in each group. CONCLUSIONS We found no benefit to therapy directed by pulmonary-artery catheter over standard care in elderly, high-risk surgical patients requiring intensive care.
Development and prospective validation of a clinical index to predict survival in ambulatory patients referred for cardiac transplant evaluation.

BACKGROUND Risk stratification of patients with end-stage congestive heart failure is a critical component of the transplant candidate selection process. Accurate identification of individuals most likely to survive without a transplant would facilitate more efficient use of scarce donor organs. METHODS AND RESULTS Multivariable proportional hazards survival models were developed with the use of data on 80 clinical characteristics from 268 ambulatory patients with advanced heart failure (derivation sample). Invasive and noninvasive models (with and without catheterization-derived data) were constructed. A prognostic score was determined for each patient from each model. Stratum-specific likelihood ratios were used to develop three prognostic-score risk groups. The models were prospectively validated on 199 similar patients (validation sample) by calculation of the area under the receiver operating characteristic curve for 1-year event-free survival, the censored c-index for event-free survival, and comparison of event-free survival curves for prognostic-score risk strata. Outcome events were defined as urgent transplant or death without transplant. The noninvasive model performed well in both samples, and increased performance was not attained by the addition of catheterization-derived variables. Prognostic-score risk groups derived from the noninvasive model in the derivation sample effectively stratified the risk of an outcome event in both samples (1-year event-free survival for derivation and validation samples, respectively: low risk, 93% and 88%; medium risk, 72% and 60%; high risk, 43% and 35%). CONCLUSIONS Selection of candidates for cardiac transplantation may be improved by use of this noninvasive risk-stratification model.
Disclosure of Adverse Events in the United States and Canada: An Update, and a Proposed Framework for Improvement

There is consensus that physicians, health professionals and health care organizations should discuss harm that results from health care delivery (adverse events), including the reasons for harm, with patients and their families. Thought leaders and policy makers in the USA and Canada support this goal. However, there are gaps in both countries between patients and physicians in their attitudes about how errors should be handled, and between disclosure policies and their implementation in practice. This paper reviews the state of disclosure policy and practice in the two countries, and the barriers to full disclosure. Important barriers include fear of consequences, attitudes about disclosure, lack of skill and role models, and lack of peer and institutional support. The paper also describes the problem of the second victim, a corollary of disclosure whereby health care workers are also traumatized by the same events that harm patients. The presence of multiple practical and personal barriers to disclosure suggests the need for a comprehensive solution directed at multiple levels of the health care system, including health departments, institutions, local managers, professional staff, patients and families, and including legal, health system and local institutional support. At the local level, implementation could be based on a translating-evidence-into-practice framework. Applying this framework would involve the formation of teams, training, measurement and identification of local barriers to achieving universal disclosure of adverse events. Significance for public health It is inevitable that some patients will be harmed rather than helped by health care. There is consensus that patients and their families must be told about these harmful events. However, there are gaps between patient and physician attitudes about how errors should be handled, and between disclosure policies and their implementation. There are important barriers that impede disclosure, including fear of consequences, attitudes about disclosure, lack of skill, and lack of institutional support. A related problem is that of the second victim, whereby health care workers are traumatized by the same harmful events. This can impair their performance and further compromise safety. The problem is unlikely to be solved by focusing solely on increasing disclosure. A comprehensive solution is needed, directed at multiple levels of the health care system, including health departments, institutions, local managers, professional staff, patients and families, and including legal, health system and local institutional support.
A theoretical bilevel control scheme for power networks with large-scale penetration of distributed renewable resources

In this paper, we present a bilevel control framework to achieve a highly-reliable smart distribution network with large-scale penetration of distributed renewable resources (DRRs). We assume that the power distribution network consists of several residential/commercial communities. In the first level of the proposed control scheme, distributed community-level controllers are designed based on the stochastic model of demand and generation. These controllers utilize the local storage units and DRRs to maintain a certain level of reliability for the community. In order to formulate the residential demand and DRRs, we use the Gaussian white noise added to some periodic signals, formulated as a stochastic process. In the second level of the proposed control framework, we take the advantage of bulk generation units to improve the reliability by a global flow controller. In other words, we get help from a few number of bulk power plants in the grid to improve its reliability in the context of satisfying the residential demand with high probability. The global controller dispatches the available non-renewable power plants between communities to enhance the reliability of each community. Using our stochastic model, we obtain a theoretical low-threshold for the certainty of the demand satisfaction in the smart power distribution network.
Design of logical topologies: A linear formulation for wavelength routed optical networks with no wavelength changers

In this paper we present a exact linear formulation (mixed integer linear program) for designing a logical topology with no wavelength changers. Since the objective of our linear formulation is minimizing the maximum offered load on any link, which is called congestion, the resulting logical topology reflects the traffic intensities between the nodes. Our linear formulation yields a logical topology and routing and wavelength assignment for the logical links. Previously (see IEEE Journal on Selected Areas in Communications, vol.40, no.1, p.840-51, 1996) the problem of logical topology design is considered but the number of wavelengths the fiber can support is not a constraint. In this paper we take into consideration the number of wavelengths the fiber supports, the hop length of a logical link, and show the trade-offs involved in minimizing congestion. Since the whole problem is linearizable the solution obtained by relaxation of the integer constraints yields a lower bound on the congestion. This can be used to compare the efficiency of heuristic algorithms. We solve the problem exactly for a small size network and for large size networks we develop some heuristic algorithms to obtain a feasible solution using the solution obtained by relaxing the integer constraints. We introduce a cutting plane which enables us to reduce the previously obtained upper bounds on congestion. Numerical results for a six node wide area network and the National Science Foundation Network (NSFNET) are presented for various cases.
On-road magnetic emissions prediction of electric cars in terms of driving dynamics using neural networks

This paper presents a novel artificial neural network (ANN) model estimating vehicle-level radiated magnetic emissions of an electric car as a function of the corresponding driving pattern. Real world electromagnetic interference (EMI) experiments have been realized in a semi-anechoic chamber using Renault Twizy. Time-domain electromagnetic interference (TDEMI) measurement techniques have been employed to record the radiated disturbances in the 150 kHz-30 MHz range. Interesting emissions have been found in the range 150 kHz-3.8 MHz approximately. The instantaneous vehicle speed and acceleration have been chosen to represent the vehicle operational modes. A comparative study of the prediction performance between different static and dynamic neural networks has been done. Results showed that a Multilayer Perceptron (MLP) model trained with extreme learning machines (ELM) has achieved the best prediction results. The proposed model has been used to estimate the radiated magnetic field levels of an urban trip carried out with a Think City electric car.