Overfreezing Meets Overparameterization: A Double Descent Perspective on Transfer Learning of Deep Neural Networks

We study the generalization behavior of transfer learning of deep neural networks (DNNs). We adopt the overparameterization perspective -- featuring interpolation of the training data (i.e., approximately zero train error) and the double descent phenomenon -- to explain the delicate effect of the transfer learning setting on generalization performance. We study how the generalization behavior of transfer learning is affected by the dataset size in the source and target tasks, the number of transferred layers that are kept frozen in the target DNN training, and the similarity between the source and target tasks. We show that the test error evolution during the target DNN training has a more significant double descent effect when the target training dataset is sufficiently large with some label noise. In addition, a larger source training dataset can delay the arrival to interpolation and double descent peak in the target DNN training. Moreover, we demonstrate that the number of frozen layers can determine whether the transfer learning is effectively underparameterized or overparameterized and, in turn, this may affect the relative success or failure of learning. Specifically, we show that too many frozen layers may make a transfer from a less related source task better or on par with a transfer from a more related source task; we call this case overfreezing. We establish our results using image classification experiments with the residual network (ResNet) and vision transformer (ViT) architectures

The Mw 6.3, 2009 L’Aquila earthquake: source, path and site effects from spectral analysis of strong motion data

The strong motion data of 2009 April 6 L’Aquila (Central Italy) earthquake (Mw = 6.3) and of 12 aftershocks (4.1 ≤ Mw ≤ 5.6) recorded by 56 stations of the Italian strong motion network are spectrally analysed to estimate the source parameters, the seismic attenuation, and the site amplification effects. The obtained source spectra for S wave have stress drop values ranging from 2.4 to 16.8 MPa, being the stress drop of the main shock equal to 9.2 MPa. The spectral curves describing the attenuation with distance show the presence of shoulders and bumps, mainly around 50 and 150 km, as consequence of significant reflected and refracted arrivals from crustal interfaces. The attenuation in the first 50 km is well described by a quality factor equal to Q( f ) = 59 f 0.56 obtained by fixing the geometrical spreading exponent to 1. Finally, the horizontal-to-vertical spectral ratio provides unreliable estimates of local site effects for those stations showing large amplifications over the vertical component of motion

CARPET: a web-based package for the analysis of ChIP-chip and expression tiling data

Summary: CARPET (Collection of Automated Routine Programs for Easy Tiling) is a set of Perl, Python and R scripts, integrated on the Galaxy2 web-based platform, for the analysis of ChIP-chip and expression tiling data, both for standard and custom chip designs. CARPET allows rapid experimental data entry, simple quality control, normalization, easy identification and annotation of enriched ChIP-chip regions, detection of the absolute or relative transcriptional status of genes assessed by expression tiling experiments and, more importantly, it allows the integration of ChIP-chip and expression data. Results can be visualized instantly in a genomic context within the UCSC genome browser as graph-based custom tracks through Galaxy2. All generated and uploaded data can be stored within sessions and are easily shared with other users. Availability: http://bio.ifom-ieo-campus.it/galaxy Contacts: [email protected] lucilla.luzi@if om-ieo-campus.i

Comparison between empirical predictive equations calibrated at global and national scale and the Italian strong-motion data

In Italy in the last years many ground motion prediction equations (hereinafter GMPEs) were calibrated both at national and regional scale using weak and strong motion data recorded in the last 30 years by several networks. Moreover many of the Italian strongest earthquakes were included in global datasets in order to calibrate GMPEs suitable to predict ground-motion at very large scale. In the last decade the Sabetta and Pugliese (1996) relationships represented a reference for the ground motion predictions in Italy. At present all Italian strong-motion data, recorded from 1972 by RAN (Italian Accelerometric Network), and more recently by other regional networks (e.g. RAIS, Strong motion network of Northern Italy), are collected in ITACA (ITalian ACcelerometric Archive). Considering Italian strong-motion data with Mw  4.0 and distance (Joyner-Boore or epicentral) up to 100 km, new GMPEs were developed by Bindi et al. (2009), aimed at replacing the older Italian relationships. The occurrence of the recent 23rd December 2008, Mw 5.4, Parma (Northern Italy) earthquake and the 6th April 2009, Mw 6.3, L’Aquila earthquake, allowed to upgrade the ITACA data set and gave us the possibility to validate the predictive capability of many GMPEs, developed using Italian, European and global data sets. The results are presented in terms of quality of performance (fit between recorded and predicted values) using the maximum likelihood approach as explained in Spudich et al. (1999). Considering the strong-motion data recorded during the L’Aquila sequence the considered GMPEs, in average, overestimate the observed data, showing a dependence of the residuals with distance in particular at higher frequencies. An improvement of fit is obtained comparing all Italian strong-motion data included in ITACA with the European GMPEs calibrated by Akkar and Bommer (2007 a,b) and the global models calibrated by Cauzzi and Faccioli (2008). In contrast, Italian data seem to attenuate faster than the NGA models calibrated by Boore and Atkinson (2008), in particular at higher frequencies

Ground Motion Prediction Equations Derived from the Italian Strong Motion Database

We present a set of ground motion prediction equations (GMPEs) derived for the geometrical mean of the horizontal components and the vertical, considering the latest release of the strong motion database for Italy. The regressions are performed over the magnitude range 4–6.9 and considering distances up to 200 km. The equations are derived for peak ground acceleration (PGA), peak ground velocity (PGV) and 5%-damped spectral acceleration at periods between 0.04 and 2 s. The total standard deviation (sigma) varies between 0.34 and 0.38 log10 unit, confirming the large variability of ground shaking parameters when regional data sets containing small to moderate magnitude events (M < 6) are used. The between-stations variability provides the largest values for periods shorter than 0.2 s while, for longer periods, the between-events and between-stations distributions of error provide similar contribution to the total variabilit

What can we learn from the January 2012 Northern Italy earthquakes?

This note focuses on the ground motion recorded during the recent moderate earthquakes occurred in the central part of Northern Italy (panel 1), a region characterized by low seismicity. For this area the Italian seismic hazard map (Stucchi et al., 2011) assigns a maximum horizontal acceleration (rock site) up to 0.2 g (10% probability of exceedance in 50 yrs). In the last 4 years, the region was struck by 9 earthquakes in the magnitude range 4≤Mw≤5.0, with the three largest located in the Northern Apennines (Mw 4.9 and 5.0 Parma events, December 2008 and January 2012) and in the Po plain (Mw 4.9 Reggio Emila event of January 2012). We analyze the strongmotion data (distance < 300 km) from these events recorded by stations belonging to the INGV (RAIS, http://rais.mi.ingv.it; RSNC http://iside.rm.ingv.it) and DPC (RAN, www.protezionecivile.it; http://itaca.mi.ingv.it). The 2008 and 2012 Parma events, both characterized by reverse focal mechanisms (http://cnt.rm.ingv.it/), have depths of 27 and 60 km respectively. The deep event produced a maximum peak ground acceleration (PGA) of 97 cm/s2 at Novellara (NVL, EC8 C class) station (70 km from the epicenter). The 25th January 2012 event (depth of 34 km) produced a maximum PGA of 114 cm/s2 at Sorbolo (SRP) station (7 km from the epicenter). Preliminary analyses show: 1) a peculiar ground-motion attenuation of the deep Parma event with respect to the shallow one. In panel 2, the PGAs for the two Parma events are plotted as a function of hypocentral distance and compared to the global ground motion prediction equation (GMPE) calibrated by Cauzzi and Faccioli (2008) using events with depth < 30 km. The different distance-decay of PGA for the deep event (blue for A class of EC8 and red for B and C classes, CEN 2003) is evident, in particular for distance up to 100 km. On the other hand, the PGAs of the 2008 Parma crustal event (grey) are well explained by this GMPE. In panel 3, the PGAs for the deep 2012 event, grouped for EC8 classes, are compared to the national GMPE calibrated by Bindi et al. (2011) using crustal events and epicentral distance. Also in this case, the GMPE underestimates the PGAs up to 200 km. Although most of the class C sites (red) show the largest PGAs, the underestimation cannot be completely ascribed to site effects. The large PGAs from the Parma deep event, with respect to the shallow one, could be explained in terms of source effects (e.g. large stress drop values enhancing the high-frequency radiation). In addition, as explained by Castro et al. (2008), the different attenuation in the lower and upper crust could explain the large PGAs recorded for the 2012 deep event. 2) seismic amplification at Po Plain sites: In panel 4, the PGAs of the January 25th, Mw 4.9, Reggio Emilia event are plotted as a function of the epicentral distance, together with the Bindi et al. (2011) GMPE. In general, the largest amplitudes occur at the Po plain sites (red), suggesting possible peculiar site response. An overall increase of the PGAs is observed around 100km, in agreement with the results of Bragato et al. (2011) that studied the regional influence of Moho S-wave reflections in the area. An example of site response is shown in panel 5, considering TREG (class C) and ZEN8 (class A) stations (panel 5a), located at 88 km from the Reggio Emila epicentre. The rotational standard spectral ratio (panel 5b) for 10 s of S wave shows polarized amplifications around 2 Hz, detected also at others Po plain sites (not reported), as well as amplification on the vertical component. The points discussed above should to be interpreted as a warning for future applications dealing with ground motion estimation in the aftermath of an earthquake in this area (e.g. ShakeMap calculation): currently used GMPEs, based on different events and sites characteristics could lead to significant bias in the final results

Pan-European ground-motion prediction equations for the average horizontal component of PGA, PGV, and 5 %-damped PSA at spectral periods up to 3.0 s using the RESORCE dataset

This article presents a set of Ground-Motion Prediction Equations (GMPEs) for Europe and the Middle East, derived from the RESORCE strong motion data bank, following a standard regression approach. The parametric GMPEs are derived for the peak ground acceleration, peak ground velocity, and 5 %-damped pseudo-absolute acceleration response spectra computed over 23 periods between 0.02 and 3 s, considering the average horizontal-component ground-motions. The GMPEs are valid for distances less than 300 km, hypocentral depth up to 35 km and over the magnitude range 4–7.6. Two metrics for the source-to-station distance (i.e. Joyner-Boore and hypocentral) are considered. The selected dataset is composed by 2,126 recordings (at a period of 0.1 s) related to 365 earthquakes, that includes strong-motion data from 697 stations.The EC8 soil classification (four classes from A to D) discriminates recording sites and four classes (normal, reverse, strike-slip, and unspecified) describe the style of faulting. A subset which contains only stations with measured Vs30 and earthquakes with specified focal mechanism (1,224 records from 345 stations and 255 earthquakes) is used to test of the accuracy of the median prediction and the variability associated to the broader data set. A random effect regression scheme is applied and bootstrap analyses are performed to estimate the 95 % confidence levels for the parameters. The total standard deviation sigma is decomposed into between-events and within-event components, and the site-to-site component is evaluated as well. The results show that the largest contribution to the total sigma is coming from the within-event component. When analyzing the residual distributions, no significant trends are observed that can be ascribed to the earthquake type (mainshock-aftershock classification) or to the non-linear site effects. The proposed GMPEs have lower median values than global models at short periods and large distances, while are consistent with global models at long periods (T>1) s. Consistency is found with two regional models developed for Turkey and Italy, as the considered dataset is dominated by waveforms recorded in these regions