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We use an independent dataset that is not used during the model training process—the validation dataset—to assess the performance of the model on unseen data to ensure that the model generalizes well to new instances. The Population Stability Index (PSI) is a statistical measure rooted in information theory, assessing the distinction between a given probability distribution and a reference distribution. ArXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Breaking feedback loops and vicious cycles requires comprehensive, holistic responses. As maritime security strategies become more comprehensive and holistic, so too must our attempts to measure progress.

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Schwcizers (1993) finite-element model verified Reference Föhn.Föhns (1987) formula for . Using S’ for artificially lriggered slabs and a variation, S, with for natural avalanches, Föhn rated the combined “success” of S and S’ for discriminating between snow slopes that had and had not avalanched. S or S’ is rated successful when the index is less than 1 and the slab released, or is greater than 1.5 when the slab did not release.

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In our credit-scoring example, PSI is pivotal in identifying discrepancies between the credit-scoring model’s assumptions and the actual data. A model developed during a period of economic prosperity might misjudge the risk of default during a recession when unemployment rates skyrocket and borrowers find it difficult to repay their loans. You can use the PSI metric to compare the distribution of credit scores in the dataset you used to build the model and a more recent validation dataset to gauge the divergence between the two datasets. If you swap the baseline distribution p(x) and sample distribution q(x), you will get the same number.

Stability Index

Instead, we used WPLIS to identify periods of strong WPLI fluctuation, or conversely, WPLI steadying. WPLIS is not a direct measure of network communication, as WPLI is; instead, it measures changes in communication or functional network connectivity. A WPLIS decrease, indicating stabilization, reflects smaller changes in WPLI over time, meaning a more homogenous and temporally uniform processing state. Conversely, a WPLIS increase indicates larger changes in WPLI over time, and a transitioning processing state. The implementation of WPLIS was a necessary first step, but, as Figure 4 indicates, the dynamics of WPLI likely reflect functional connectivity which may be more complex than traditional voltage-amplitude measures.

Weighted Phase Lag Index

Four hundred and twenty-three consecutive admissions to a multidisciplinary ICU were followed daily. Patients classified into higher CCS classes had significantly higher PSI scores (P less than 0.001), and there was a highly significant correlation (P less than 0.001) between PSI and TISS scores. The linear-logistic regression of observed mortality versus PSI was highly significant (P less than 0.0001) and provided an excellent fit. Highly significant differences between survivors and nonsurvivors were observed for PSI scores (P less than 0.001), as well as for composite slopes of the regression of PSI scores versus days of care (P less than 0.001).

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Key informants, including IDP and returnee representatives, community agents and Red Cross volunteers, were interviewed at each location by investigators in January 2024. The predictions with “medical” as input on a feature (use of loan proceeds) increased from 2% to 8%, while the predictions with “vacation” decreased from 23% to 17%. PSI can be used to measure differences between numeric distributions and categorical distributions. The table below shows the FSI for 2024,[5] with comparisons of each country’s current score to previous years’ indices.[6] A higher score (with a maximum of 120) indicates a weaker, more vulnerable, or more fragile situation in the country.

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• Applying Weighted Phase Lag Index across channels we were able to recover a p300-like cognitive response during walking.
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No one should act upon such information without appropriate professional advice after a thorough examination of the particular situation. Hovering over a point on any charted line displays a window that provides further insight into what drives variability at a particular point in time. With an inception date of 2000, this fund is another long-tenured player that’s tracked the index closely over time.

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Panel (g) shows the voltage amplitude response locked to left heel-strike and how significant the walking artifact is. Channels are sorted and numbered by their correlation to the mean voltage signal with higher correlated signals at the bottom. The black box around 300 ms in (b) shows a example component of the p300 negative deflection across clean channels. Where the density at depth v (measured vertically) is which is typical of the Columbia Mountains. For weak layers within 0.7 m of the surface, such penetrations substantially increase the total shear stress on the weak layer, indicating the relevance of adjusting S’ for ski penetration.

Intuition Behind PSI

The next metric in this progression that could be used for reducing movement artifacts in EEG is the Weighted Phase Lag Index (WPLI). It extends Stam’s PLI measure, by introducing a phase-difference weighting normalization. WPLI could negate the need for standard EEG pre-processing techniques like noisy channel removal, noisy epoch removal, or artifact-laden epoch removal by filtering out artifacts on-line.

Teams rely on changes in prediction and feature distributions as a proxy for performance changes. PSI was picked up by the finance industry as a key metric used to measure feature drift and is one of the more stable and useful drift metrics. Figure 3 is limited to the first two minutes (~25%) of the trial for the subject. The same subject’s WPLI response, when viewed across the whole time period as shown in Figure 4a. In addition, the timings of distinct WPLI deflections, were variable across subjects (Figure 4b,c).

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Figure 3 shows that a robust event-locked WPLI deflection exists, even across electrode pairs containing previously defined noisy channels (Figure 3c). Even in the walking case (Figure 3d-f), where the voltage amplitude signal was dominated by artifact, a clear event-locked cognitive response is visible. Figure 3g demonstrates the effectiveness of WPLI in removing movement artifact as the previously strong heel-strike artifacts shown in Figure 2 are mostly removed.

Features¶

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• One alternative approach for reducing EEG artifacts has been to use machine learning techniques such as a neural networks [10] to extract the important signal.
• Furthermore, by removing all EEG signal concurrent with stride, possible brain dynamics linked to stride were also removed [23].
• Helicopter and Snowcat Skiing Operators Association and the Natural Sciences and Engineering Research Council of Canada.
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Refinements to the Skier Stability Index

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Effect of Normal Load on Shear Strength

From 128 profiles at least 0.5 m deep that were observed during the winters of 1993–95, there were 45 with mean slab density less than 160 kg m−3 and 42 with mean slab density greater than 200 kg m−3. For each of these groups of slabs, the densities are averaged at increments of 0.1 m between 0.1 and 0.6 m as shown in Figure 5. Increases in density with depth appear linear and linear regressions yield for the low-density slabs and 167 kgm −4 for the high-density slabs. Since shows little dependence on mean slab density, a nominal value of is subsequently used for all slabs. Note that experimental features are subject to change at any point infuture releases.

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• Four hundred and twenty-three consecutive admissions to a multidisciplinary ICU were followed daily.
• This measure of temporal stability identifies periods of high/low WPLI variability.
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WPLI had more limitations as a measure for long-term data collections and across subjects than WPLIS because of its sensitivity to a robust and yet unexplored range of brain connectivity dynamics. This work represents a logical step towards implementation of EEG-based brain imaging in real-world settings with on-line artifact removal. One alternative approach for reducing EEG artifacts has been to use machine learning techniques such as a neural networks [10] to extract the important signal.

Network approaches recover the functional connectivity of the active brain [25, 26]. Specifically, functional connectivity is the dynamic measured connectivity that reflects the anatomical connectivity and the underlying processes occurring in the brain at a given time. WPLI, in particular, is a functional connectivity measure that was designed to ignore non-brain sources of activity. The fundamental assumption is that stable, 90 degree out-of-phase, signals can only consistently arise from highly complex coupled harmonic oscillator systems (i.e., the brain) and not from external noise and artifact sources. In another recent study [8], the researchers used an event related template to remove stride-synchronous movement artifacts. Gwin et al. [8] collected both EEG and kinematic data, created an artifact template by first time warping stride-locked EEG signals to uniform lengths in time, then averaged them.

This template was subtracted from the EEG activity and the signal was unwarped, leaving only EEG activity and artifact that was not concurrent with each stride. While this method was useful in removing walking artifact, it is limited in its applicability to controlled situations, uncommon in real-world environments. It also does not account for random and unpredictable movement artifacts that may otherwise obscure the cognitive signal. Furthermore, by removing all EEG signal concurrent with stride, possible brain dynamics linked to stride were also removed [23]. The WPLIS measure does not require such an a priori knowledge of the nature of the artifact, nor the inclusion of kinematic movement data.

Symbols indicating slabs that were remotely triggered from a distance of at least 50 m from the displaced slab are surrounded by a square. In a few cases, in which the slab did not fail, there were two weak layers. When time permitted, both layers were tested, resulting in two data points for the same slope on the same day.