Calibrating the Lee-Carter and the Poisson Lee-Carter models via Neural Networks

This paper introduces a neural network approach for fitting the Lee-Carter and the Poisson Lee-Carter model on multiple populations. We develop some neural networks that replicate the structure of the individual LC models and allow their joint fitting by analysing the mortality data of all the considered populations simultaneously. The neural network architecture is specifically designed to calibrate each individual model using all available information instead of using a population-specific subset of data as in the traditional estimation schemes. A large set of numerical experiments performed on all the countries of the Human Mortality Database (HMD) shows the effectiveness of our approach. In particular, the resulting parameter estimates appear smooth and less sensitive to the random fluctuations often present in the mortality rates' data, especially for low-population countries. In addition, the forecasting performance results significantly improved as well

A deep learning integrated Lee-Carter model

In the field of mortality, the Lee–Carter based approach can be considered the milestone to forecast mortality rates among stochastic models. We could define a “Lee–Carter model family” that embraces all developments of this model, including its first formulation (1992) that remains the benchmark for comparing the performance of future models. In the Lee–Carter model, the kt parameter, describing the mortality trend over time, plays an important role about the future mortality behavior. The traditional ARIMA process usually used to model kt shows evident limitations to describe the future mortality shape. Concerning forecasting phase, academics should approach a more plausible way in order to think a nonlinear shape of the projected mortality rates. Therefore, we propose an alternative approach the ARIMA processes based on a deep learning technique. More precisely, in order to catch the pattern of kt series over time more accurately, we apply a Recurrent Neural Network with a Long Short-Term Memory architecture and integrate the Lee–Carter model to improve its predictive capacity. The proposed approach provides significant performance in terms of predictive accuracy and also allow for avoiding the time-chunks’ a priori selection. Indeed, it is a common practice among academics to delete the time in which the noise is overflowing or the data quality is insufficient. The strength of the Long Short-Term Memory network lies in its ability to treat this noise and adequately reproduce it into the forecasted trend, due to its own architecture enabling to take into account significant long-term patterns

Rapid determination of moment magnitude from the near-field spectra: application to the april 6 2009, L'Aquila seismic sequence

On April 6th 2009, a magnitude Mw=6.1 earthquake struck the Abruzzi region in central Italy. Despite its moderate size, the earth- quake caused more than 300 fatalities and partially destroyed the city of L’Aquila and many villages in its surroundings. The main shock was preceded by an earthquake swarm that started at the end of 2008, and, by the end of November 2009, more than 16,000 aftershocks with M> 0.5 have been recorded by the INGV seismic network. Current advances in data transmission and communication yield high quality broadband velocity and strong motion waveforms in near real-time. These data allow for the rapid characterization of earthquake sources in terms of fault geometry, focal depth and seismic moment. Delouis et al. (2009) have developed a methodology for rapid determination of moment magnitude from the near-fields spectra. In this study we test this methodology on the L’Aquila sequence earthquakes for which we have already com- puted the time domain moment tensor solutions (TDMT, Scognamiglio et al., 2010)

l1-Regularization in Portfolio Selection with Machine Learning

In this work, we investigate the application of Deep Learning in Portfolio selection in a Markowitz mean-variance framework. We refer to a l1 regularized multi-period model; the choice of the l1 norm aims at producing sparse solutions. A crucial issue is the choice of the regularization parameter, which must realize a trade-off between fidelity to data and regularization. We propose an algorithm based on neural networks for the automatic selection of the regularization parameter. Once the neural network training is completed, an estimate of the regularization parameter can be computed via forward propagation. Numerical experiments and comparisons performed on real data validate the approach

Deep Learning Forecasting for Supporting Terminal Operators in Port Business Development

Accurate forecasts of containerised freight volumes are unquestionably important for port terminal operators to organise port operations and develop business plans. They are also relevant for port authorities, regulators, and governmental agencies dealing with transportation. In a time when deep learning is in the limelight, owing to a consistent strip of success stories, it is natural to apply it to the tasks of forecasting container throughput. Given the number of options, practitioners can benefit from the lessons learned in applying deep learning models to the problem. Coherently, in this work, we devise a number of multivariate predictive models based on deep learning, analysing and assessing their performance to identify the architecture and set of hyperparameters that prove to be better suited to the task, also comparing the quality of the forecasts with seasonal autoregressive integrated moving average models. Furthermore, an innovative representation of seasonality is given by means of an embedding layer that produces a mapping in a latent space, with the parameters of such mapping being tuned using the quality of the predictions. Finally, we present some managerial implications, also putting into evidence the research limitations and future opportunities

Robust Classification via Support Vector Machines

Classification models are very sensitive to data uncertainty, and finding robust classifiers that are less sensitive to data uncertainty has raised great interest in the machine learning literature. This paper aims to construct robust support vector machine classifiers under feature data uncertainty via two probabilistic arguments. The first classifier, Single Perturbation, reduces the local effect of data uncertainty with respect to one given feature and acts as a local test that could confirm or refute the presence of significant data uncertainty for that particular feature. The second classifier, Extreme Empirical Loss, aims to reduce the aggregate effect of data uncertainty with respect to all features, which is possible via a trade-off between the number of prediction model violations and the size of these violations. Both methodologies are computationally efficient and our extensive numerical investigation highlights the advantages and possible limitations of the two robust classifiers on synthetic and real-life insurance claims and mortgage lending data, but also the fairness of an automatized decision based on our classifier

Broad-spectrum coronavirus 3C-like protease peptidomimetic inhibitors effectively block SARS-CoV-2 replication in cells: Design, synthesis, biological evaluation, and X-ray structure determination

Despite the approval of vaccines, monoclonal antibodies and restrictions during the pandemic, the demand for new efficacious and safe antivirals is compelling to boost the therapeutic arsenal against the COVID-19. The viral 3-chymotrypsin-like protease (3CLpro) is an essential enzyme for replication with high homology in the active site across CoVs and variants showing an almost unique specificity for Leu-Gln as P2–P1 residues, allowing the development of broad-spectrum inhibitors. The design, synthesis, biological activity, and cocrystal structural information of newly conceived peptidomimetic covalent reversible inhibitors are herein described. The inhibitors display an aldehyde warhead, a Gln mimetic at P1 and modified P2–P3 residues. Particularly, functionalized proline residues were inserted at P2 to stabilize the β-turn like bioactive conformation, modulating the affinity. The most potent compounds displayed low/sub-nM potency against the 3CLpro of SARS-CoV-2 and MERS-CoV and inhibited viral replication of three human CoVs, i.e. SARS-CoV-2, MERS-CoV, and HCoV 229 in different cell lines. Particularly, derivative 12 exhibited nM-low μM antiviral activity depending on the virus, and the highest selectivity index. Some compounds were co-crystallized with SARS-CoV-2 3CLpro validating our design. Altogether, these results foster future work toward broad-spectrum 3CLpro inhibitors to challenge CoVs related pandemics

Developing Islamic Economic Production

There are rules, manner, behaviours or arrangements in Islam in respects to production. Some muslims and Islamic economic agents have been practicing the rules and the regulations. Lack of primary needs production as food has threatened the stability of the life in this world, especially in the underdeveloped and the developing countries in which most muslims live. Shortages of foods may cause unstable life. Muslim and Islamic economist and leaders have to take economic (production) problems into serious consideration if they don’t want to be the victim of world non-muslim economic domination. Muslim must not be consumer or just be a sale agent of the products of others, but must select and develop appropriate technology suitable with their human and natural potentials. Agricultural Industry is suitable for Indonesia and for some other muslim contries.. Our Prophet encouraged us to generate the production by cultivating the idle land (ihya al-mawat) to yield crops for foods

Eribulin in male patients with breast cancer: The first report of clinical outcomes

Background. Evidence on the management and treatment of male breast cancer is scant. We report the analysis of a multicenter Italian series of patients with male breast cancer treated with eribulin. To our knowledge, this is the first report on the use or eribulin in this setting. Patients and Methods. Patients were retrospectively identified in 19 reference centers. All patients received eribulin treatment, according to the standard practice of each center. Data on the identified patients were collected using a standardized form and were then centrally reviewed by two experienced oncologists. Results. A total of 23 patients (median age, 64 yearsrange, 42–80) were considered. The median age at the time of diagnosis of breast cancerwas 57 years (range, 42–74).HER2 status was negative in 14 patients (61%), and 2 patients (9%) had triple-negative disease. The most common metastatic sites were the lung (n 5 1461%) and bone (n 5 1356%). Eribulin was administered for a median of 6 cycles (range, 3–15). All patients reported at least stable diseasetwo complete responses (9%) were documented. Eribulin was well-tolerated, with only four patients (17%) reporting grade 3 adverse events and two (9%) with treatment interruptions because of toxicity. Eight subjects (35%) did not report any adverse event during treatment. For patients with a reported fatal event, the median overall survival from the diagnosis of metastatic disease was 65 months (range, 22–228). Conclusion. Although hampered by all the limitations of any retrospective case series, the results of the present study suggest, for the first time, the use of eribulin as therapy for male breast cancer