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In the intensive care unit, the capability to predict the need for mechanical ventilation (MV) facilitates more timely interventions to improve patient outcomes. Recent works have demonstrated good performance in this task utilizing machine learning models. This paper explores the novel application of a deep learning model with multi-head attention (FFNN-MHA) to make more accurate MV predictions and reduce false positives by learning personalized contextual information of individual patients. Utilizing the publicly available MIMIC-IV dataset, FFNN-MHA demonstrates an improvement of 0.0379 in AUC and a 17.8\% decrease in false positives compared to baseline models such as feed-forward neural networks. Our results highlight the potential of the FFNN-MHA model as an effective tool for accurate prediction of the need for mechanical ventilation in critical care settings.

Let ${\Bbb G}$ be a locally compact quantum group and ${\mathcal T}(L^2({\Bbb G}))$ be the Banach algebra of trace class operators on $L^2({\Bbb G})$ with the convolution induced by the right fundamental unitary of ${\Bbb G}$. We study the space of harmonic operators $\widetilde{\mathcal H}_\omega$ in ${\mathcal B}(L^2({\Bbb G}))$ associated to a contractive element $\omega\in {\mathcal T}(L^2({\Bbb G}))$. We characterize the existence of non-zero harmonic operators in ${\mathcal K}(L^2({\Bbb G}))$ and relate them with some properties of the quantum group ${\Bbb G}$, such as finiteness, amenability and co-amenability.

We summarize the current best polychromatic (10 to 20 % bandwidth) contrast performance demonstrated in the laboratory by different starlight suppression approaches and systems designed to directly characterize exoplanets around nearby stars. We present results obtained by internal coronagraph and external starshade experimental testbeds using entrance apertures equivalent to off-axis or on-axis telescopes, either monolithic or segmented. For a given angular separation and spectral bandwidth, the performance of each starlight suppression system is characterized by the values of raw contrast (before image processing), off-axis (exoplanet) core throughput, and post-calibration contrast (the final 1 sigma detection limit of off-axis point sources, after image processing). To place the current laboratory results in the perspective of the future Habitable Worlds Observatory (HWO) mission, we simulate visible observations of a fiducial Earth/Sun twin system at 12 pc, assuming a 6m (inscribed diameter) collecting aperture and a realistic end-to-end optical throughput. The exposure times required for broadband exoearth detection (20% bandwidth around a wavelength of 0.55 microns) and visible spectroscopic observations (R=70) are then computed assuming various levels of starlight suppression performance, including the values currently demonstrated in the laboratory. Using spectroscopic exposure time as a simple metric, our results point to key starlight suppression system design performance improvements and trades to be conducted in support of HWO exoplanet science capabilities. These trades may be explored via numerical studies, lab experiments, as well as high contrast space-based observations and demonstrations., Comment: 63 pages, 28 pages, submitted to JATIS

The pattern of state changes in a biomedical time series can be related to health or disease. This work presents a principled approach for selecting a changepoint detection algorithm for a specific task, such as disease classification. Eight key algorithms were compared, and the performance of each algorithm was evaluated as a function of temporal tolerance, noise, and abnormal conduction (ectopy) on realistic artificial cardiovascular time series data. All algorithms were applied to real data (cardiac time series of 22 patients with REM-behavior disorder (RBD) and 15 healthy controls) using the parameters selected on artificial data. Finally, features were derived from the detected changepoints to classify RBD patients from healthy controls using a K-Nearest Neighbors approach. On artificial data, Modified Bayesian Changepoint Detection algorithm provided superior positive predictive value for state change identification while Recursive Mean Difference Maximization (RMDM) achieved the highest true positive rate. For the classification task, features derived from the RMDM algorithm provided the highest leave one out cross validated accuracy of 0.89 and true positive rate of 0.87. Automatically detected changepoints provide useful information about subject's physiological state which cannot be directly observed. However, the choice of change point detection algorithm depends on the nature of the underlying data and the downstream application, such as a classification task. This work represents the first time change point detection algorithms have been compared in a meaningful way and utilized in a classification task, which demonstrates the effect of changepoint algorithm choice on application performance., Comment: 24 pages, 2 figures

The Cellular Potts model, also known as the Glazier-Graner-Hogeweg model, is a lattice-based approach by which biological tissues at the level of individual cells can be numerically studied. Traditionally, a square or hexagonal underlying lattice structure is assumed for two-dimensional systems, and this is known to introduce artifacts in the structure and dynamics of the model tissues. That is, on regular lattices, cells can assume shapes that are dictated by the symmetries of the underlying lattice. Here, we developed a variant of this method that can be applied to a broad class of (ir)regular lattices. We show that on an irregular lattice deriving from a fluid-like configuration, two types of artifacts can be removed. We further report on the transition between a fluid-like disordered and a solid-like hexagonally ordered phase present for monodisperse confluent cells as a function of their surface tension. This transition shows the hallmarks of a first-order phase transition and is different from the glass/jamming transitions commonly reported for the vertex and active Voronoi models. We emphasize this by analyzing the distribution of shape parameters found in our state space. Our analysis provides a useful reference for the future study of epithelia using the (ir)regular Cellular Potts model., Comment: This paper has been accepted for publication in Soft Matter as an open-access article, available at the DOI provided. A major revision was made during the review process, changing the method for capturing the transition point using the effective self-diffusion coefficient instead of the mean square displacement scaling exponent. We thank the reviewers for their constructive feedback

This paper proposes a novel single-level robust mathematical approach to model the RES-only Virtual Power Plant (RVPP) bidding problem in the simultaneous Day Ahead Market (DAM) and Secondary Reserve Market (SRM). The worst-case profit of RVPP due to uncertainties related to electricity prices, Non-dispatchable Renewable Energy Sources (ND-RES) production, and flexible demand is captured. In order to find the worst-case profit in a single-level model, the relationship between price and energy uncertainties leads to some non-linear constraints, which are appropriately linearized. The simulation results show the superiority of the proposed robust model compared to those in the literature, as well as its computational efficiency.

In this paper, a novel approach to define the optimal bidding of renewable-only virtual power plants (RVPPs) in the day-ahead, secondary reserve, and intra-day markets is proposed. To this aim, a robust optimization algorithm is developed to account for the asymmetric nature of the uncertainties that characterize the market prices, as well as the energy production of the RVPP stochastic sources and flexible demand consumption. Simulation results show increased RVPP benefits compared to other existing solutions and demonstrate the potential of renewable sources to further increase their economic competitiveness. The simplicity of the implementation, the computational efficiency, and the flexible robustness are also verified.

Since the advent of Spectre attacks, researchers and practitioners have developed a range of hardware and software measures to counter transient execution attacks. A prime example of such mitigation is speculative load hardening in LLVM, which protects against leaks by tracking the speculation state and masking values during misspeculation. LLVM relies on static analysis to harden programs using slh that often results in over-protection, which incurs performance overhead. We extended an existing side-channel model validation framework, Scam-V, to check the vulnerability of programs to Spectre-PHT attacks and optimize the protection of programs using the slh approach. We illustrate the efficacy of Scam-V by first demonstrating that it can automatically identify Spectre vulnerabilities in real programs, e.g., fragments of crypto-libraries. We then develop an optimization mechanism that validates the necessity of slh hardening w.r.t. the target platform. Our experiments showed that hardening introduced by LLVM in most cases could be significantly improved when the underlying microarchitecture properties are considered., Comment: The paper will appear in ACM AsiaCCS 2024

The Roman Space Telescope will have the first advanced coronagraph in space, with deformable mirrors for wavefront control, low-order wavefront sensing and maintenance, and a photon-counting detector. It is expected to be able to detect and characterize mature, giant exoplanets in reflected visible light. Over the past decade the performance of the coronagraph in its flight environment has been simulated with increasingly detailed diffraction and structural/thermal finite element modeling. With the instrument now being integrated in preparation for launch within the next few years, the present state of the end-to-end modeling is described, including the measured flight components such as deformable mirrors. The coronagraphic modes are thoroughly described, including characteristics most readily derived from modeling. The methods for diffraction propagation, wavefront control, and structural and thermal finite-element modeling are detailed. The techniques and procedures developed for the instrument will serve as a foundation for future coronagraphic missions such as the Habitable Worlds Observatory., Comment: 113 pages, 85 figures, to be published in SPIE Journal of Astronomical Telescopes, Instruments, and Systems

Let $H$ be an ultraspherical hypergroup associated with a locally compact group $G$ and a spherical projector $\pi$ and let $A(H)$ and $B(H)$ denote the Fourier and Fourier-Stieltjes algebras, respectively, associated with $H.$ In this note, we study power boundedness and Ces\`aro boundedness in $B(H)$. We also characterize the power bounded property for both $A(H)$ and $B(H).$

The Nancy Grace Roman Space Telescope (Roman), under development by NASA, will investigate possible causes for the phenomenon of dark energy and detect and characterize extra-solar planets. The 2.4 m space telescope has two main instruments: a wide-field, infra-red imager and a coronagraph. The coronagraph instrument (CGI) is a technology demonstrator designed to help bridge the gap between the current state-of-the-art space and ground instruments and future high-contrast space coronagraphs that will be capable of detecting and characterizing Earth-like planets in the habitable zones of other stars. Using adaptive optics, including two high-density deformable mirrors and low- and high-order wavefront sensing and control, CGI is designed to suppress the star light by up to 9 orders of magnitude, potentially enabling the direct detection and characterization of Jupiter-class exoplanets. Contrast is the measure of starlight suppression, and high contrast is the chief virtue of a coronagraph. But it is not the only important characteristic: contrast must be balanced against acceptance of planet light. The remaining unsuppressed starlight must also have a stable morphology to allow further estimation and subtraction. To achieve all these goals in the presence of the disturbance and radiation environment of space, the coronagraph must be designed and fabricated as a highly optimized system. The CGI error budget is the top level tool used to guide the optimization, enabling trades of various competing errors. The error budget is based on an analytical model which enables rapid calculation and tracking of performance for the numerous and diverse questions that arise in the system engineering process. In this paper we outline the coronagraph system engineering approach and the error budget.

We present Serberus, the first comprehensive mitigation for hardening constant-time (CT) code against Spectre attacks (involving the PHT, BTB, RSB, STL and/or PSF speculation primitives) on existing hardware. Serberus is based on three insights. First, some hardware control-flow integrity (CFI) protections restrict transient control-flow to the extent that it may be comprehensively considered by software analyses. Second, conformance to the accepted CT code discipline permits two code patterns that are unsafe in the post-Spectre era. Third, once these code patterns are addressed, all Spectre leakage of secrets in CT programs can be attributed to one of four classes of taint primitives--instructions that can transiently assign a secret value to a publicly-typed register. We evaluate Serberus on cryptographic primitives in the OpenSSL, Libsodium, and HACL* libraries. Serberus introduces 21.3% runtime overhead on average, compared to 24.9% for the next closest state-of-the-art software mitigation, which is less secure., Comment: Authors' version; to appear in the Proceedings of the IEEE Symposium on Security and Privacy (S&P) 2024