Transformers for Trajectory Optimization with Application to Spacecraft Rendezvous

Reliable and efficient trajectory optimization methods are a fundamental need for autonomous dynamical systems, effectively enabling applications including rocket landing, hypersonic reentry, spacecraft rendezvous, and docking. Within such safety-critical application areas, the complexity of the emerging trajectory optimization problems has motivated the application of AI-based techniques to enhance the performance of traditional approaches. However, current AI-based methods either attempt to fully replace traditional control algorithms, thus lacking constraint satisfaction guarantees and incurring in expensive simulation, or aim to solely imitate the behavior of traditional methods via supervised learning. To address these limitations, this paper proposes the Autonomous Rendezvous Transformer (ART) and assesses the capability of modern generative models to solve complex trajectory optimization problems, both from a forecasting and control standpoint. Specifically, this work assesses the capabilities of Transformers to (i) learn near-optimal policies from previously collected data, and (ii) warm-start a sequential optimizer for the solution of non-convex optimal control problems, thus guaranteeing hard constraint satisfaction. From a forecasting perspective, results highlight how ART outperforms other learning-based architectures at predicting known fuel-optimal trajectories. From a control perspective, empirical analyses show how policies learned through Transformers are able to generate near-optimal warm-starts, achieving trajectories that are (i) more fuel-efficient, (ii) obtained in fewer sequential optimizer iterations, and (iii) computed with an overall runtime comparable to benchmarks based on convex optimization.Comment: Presented in 2024 IEEE Aerospace Conferenc

Prevalence and variability of use of home mechanical ventilators, positive airway pressure and oxygen devices in the Lombardy region, Italy

Few studies have analyzed the prevalence and accessibility of home mechanical ventilation (HMV) in Italy. We aimed to investigate the prevalence and prescription variability of HMV as well as of long-term oxygen therapy (LTOT) and continuous positive airway pressure (CPAP), in the Lombardy Region. Prescribing rates of HMV (both noninvasive and tracheostomies), CPAP (auto-CPAP, CPAP/other sleep machines) and LTOT (liquid-O2, O2-gas, concentrators) in the 15 Local Healthcare districts of Lombardy were gathered from billing data for 2012 and compared. Crude rates (per 100,000 population) and rates for the different healthcare districts were calculated. In 2012, 6325 patients were on HMV (crude prescription rate: 63/100,000) with a high variation across districts (8/100,000 in Milano 1 vs 150/100,000 in Pavia). There were 14,237 patients on CPAP (crude prescription rate: 142/100,000; CPAP/other sleep machines 95.3% vs auto-CPAP 4.7%) with also high intra-regional variation (56/100,000 in Mantova vs. 260/100,000 in Pavia). There were 21,826 patients on LTOT (prescription rate: 217/100,000 rate; liquid-O2 94%, O2-gas 2.08%, O2-concentrators 3.8%), with again high intra-regional variation (100/100,000 in Bergamo vs 410/100,000 in Valle Camonica). The crude rate of HMV prescriptions in Lombardy is very high, with a high intra-regional variability in prescribing HMV, LTOT and CPAP which is partly explainable by the accessibility to specialist centers with HMV/sleep-study facilities. Analysis of administrative data and variability mapping can help identify areas of reduced access for an improved standardization of services. An audit among Health Payer and prescribers to interpret the described huge variability could be welcomed

Epithelioid Mesothelioma Patients with Very Long Survival Display Defects in DNA Repair

Aim: DNA repair has an important role in malignant pleural mesothelioma (MPM) tumorigenesis and progression. Prognostic/predictive biomarkers for better management of MPM patients are needed. In the present manuscript, we analyzed the expression of more than 700 genes in a cohort of MPM patients to possibly find biomarkers correlated with survival. Methods: A total of 54 MPM patients, all with epithelioid histology, whose survival follow-up and formalin-fixed paraffin-embedded tumors were available, were included in the study. Gene expression profiles were evaluated using a Nanostring platform analyzing 760 genes involved in different cellular pathways. The percentages of proliferating tumor cells positive for RAD51 and BRCA1 foci were evaluated using an immunofluorescence assay, as a readout of homologous recombination repair status. Results: Patient median survival time was 16.9 months, and based on this value, they were classified as long and short survivors (LS/SS) with, respectively, an overall survival ≥ and <16.9 months as well as very long and very short survivors (VLS/VSS) with an overall survival ≥ than 33.8 and < than 8.45 months. A down-regulation in the DNA damage/repair expression score was observed in LS and VLS as compared to SS and VSS. These findings were validated by the lower number of both RAD51 and BRCA1-positive tumor cells in VLS as compared to VSS. Conclusions: The down-regulation of DNA repair signature in VLS was functionally validated by a lower % of RAD51 and BRCA1-positive tumor cells. If these data can be corroborated in a prospective trial, an easy, cost-effective test could be routinely used to better manage treatment in MPM patients

Platinum sensitivity and DNA repair in a recently established panel of patient-derived ovarian carcinoma xenografts

A xenobank of patient-derived (PDX) ovarian tumor samples has been established consisting of tumors with different sensitivity to cisplatin (DDP), from very responsive to resistant. As the DNA repair pathway is an important driver in tumor response to DDP, we analyzed the mRNA expression of 20 genes involved in the nucleotide excision repair, fanconi anemia, homologous recombination, base excision repair, mismatch repair and translesion repair pathways and the methylation patterns of some of these genes. We also investigated the correlation with the response to platinum-based therapy. The mRNA levels of the selected genes were evaluated by Real Time-PCR (RT-PCR) with ad hoc validated primers and gene promoter methylation by pyrosequencing. All the DNA repair genes were variably expressed in all 42 PDX samples analyzed, with no particular histotype-specific pattern of expression. In high-grade serous/endometrioid PDXs, the CDK12 mRNA expression levels positively correlated with the expression of TP53BP1, PALB2, XPF and POLB. High-grade serous/endometrioid PDXs with TP53 mutations had significantly higher levels of POLQ, FANCD2, RAD51 and POLB than high-grade TP53 wild type PDXs. The mRNA levels of CDK12, PALB2 and XPF inversely associated with the in vivo DDP antitumor activity; higher CDK12 mRNA levels were associated with a higher recurrence rate in ovarian patients with low residual tumor. These data support the important role of CDK12 in the response to a platinum based therapy in ovarian patients

Tolerability of Eribulin and correlation between polymorphisms and neuropathy in an unselected population of female patients with metastatic breast cancer: results of the multicenter, single arm, phase IV PAINTER study

Background Metastatic breast cancer (MBC) is an incurable disease and its treatment focuses on prolonging patients' (pts) overall survival (OS) and improving their quality of life. Eribulin is a microtubule inhibitor that increases OS in pre-treated MBC pts. The most common adverse events (AEs) are asthenia, neutropenia and peripheral neuropathy (PN). Methods PAINTER is a single arm, phase IV study, aimed at evaluating the tolerability of eribulin in MBC pts. Secondary objectives were the description of treatment efficacy and safety, the assessment of the incidence and severity of PN and its association with genetic polymorphisms. Genomic DNA was isolated from blood samples and 15 Single Nucleotide Polymorphisms (SNPs) were genotyped by Taqman specific assays. The association between PN and SNPs were evaluated by Fisher exact test. Results Starting from May 2014 until June 2018 180 pts were enrolled in this study by 20 Italian centers. 170 of these pts could be evaluated for efficacy and toxicity and 159 for polymorphisms analysis. The median age of pts was 60 years old and the biological subtypes were luminal type (64.7%), Her2 positive (18.3%) and triple negative (17%). Pts were pretreated with a median of 5 lines for MBC. The median follow up of this study was 15.4 months with a median number of 4.5 cycles administered (minimum-maximum 1-23). The median overall survival was 12 months. 48.8% of pts experienced a dose reduction, mainly for neutropenia (23.9%) and liver toxicity (12%). 65 pts (38.2%) reported at least one severe toxicity. Neutropenia and neurotoxicity were the most frequent severe AEs (15.3% and 14.7%, respectively); other reported toxicities were osteo-muscular, abdominal or tumor site pain (19.4%), liver toxicity (6.6%), pulmonary toxicity (6.5%) and dermatological toxicity (3.6%). Among the 15 evaluated SNPs, an association with PN was found for rs2233335 and rs7214723. Conclusions Eribulin is a well-tolerated treatment option in MBC. Schedule and dosage modifications were common, but toxicity rarely led to treatment discontinuation. SNPs rs2233335 (G/T and T/T) in the NDRG1 gene and rs7214723 (CC and CT) in the CAMKK1 gene were associated with PN. These findings, if validated, could allow a tailored treatment with eribulin in cancer patients. Trial registration: ClinicalTrials.gov ID: NCT02864030

Assessment of few-hits machine learning classification algorithms for low energy physics in liquid argon detectors

The physics potential of massive liquid argon TPCs in the low-energy regime is still to be fully reaped because few-hits events encode information that can hardly be exploited by conventional classification algorithms. Machine learning (ML) techniques give their best in these types of classification problems. In this paper, we evaluate their performance against conventional (deterministic) algorithms. We demonstrate that both Convolutional Neural Networks (CNN) and Transformer-Encoder methods outperform deterministic algorithms in one of the most challenging classification problems of low-energy physics (single- versus double-beta events). We discuss the advantages and pitfalls of Transformer-Encoder methods versus CNN and employ these methods to optimize the detector parameters, with an emphasis on the DUNE Phase II detectors ("Module of Opportunity")

Assessment of few-hits machine learning classification algorithms for low energy physics in liquid argon detectors

The physics potential of massive liquid argon TPCs in the low-energy regime is still to be fully reaped because few-hits events encode information that can hardly be exploited by conventional classification algorithms. Machine learning (ML) techniques give their best in these types of classification problems. In this paper, we evaluate their performance against conventional (deterministic) algorithms. We demonstrate that both Convolutional Neural Networks (CNN) and Transformer-Encoder methods outperform deterministic algorithms in one of the most challenging classification problems of low-energy physics (single- versus double-beta events). We discuss the advantages and pitfalls of Transformer-Encoder methods versus CNN and employ these methods to optimize the detector parameters, with an emphasis on the DUNE Phase II detectors ("Module of Opportunity")