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The good, the bad and the ugly: watermarks, transferable attacks and adversarial defenses
No full textOptics for terawatt-scale photovoltaics: review and perspectives
No full textPhotovoltaics, a mature technology, is set to play a vital role in
achieving a carbon-free energy system. This article examines the
pivotal role of optics in advancing photovoltaics. We identify key
scientific research areas where the optics community can make
significant contributions. We are guided by the central question: How
can optics facilitate the large-scale deployment of photovoltaics
necessary for decarbonizing our societies
Spectroscopic Ellipsometry of Plasmonic Gratings─Ideal Parameters for Sensing and Subpicometer Measurement Uncertainty
No full textEnhancing Multi-Energy Modeling: The Role of Mixed-Integer Optimization Decisions
No full textThe goal to decarbonize the energy sector has led to increased research in modeling and optimizing multi-energy systems. One of the most promising and popular techniques for modeling and solving (multi-)energy optimization problems is (multi-objective) mixed-integer programming, valued for its ability to represent the complexities of integrated energy systems. While the literature often focuses on deriving mathematical formulations and parameter settings, less attention is given to critical post-formulation decisions. Modeling multi-energy systems as mixed-integer linear optimization programs demands decisions across multiple degrees of freedom. Key steps include reducing a real-world multi energy network into an abstract topology, defining variables, formulating the relevant (in-)equalities to represent technical requirements, setting (multiple) objectives, and integrating these elements into a mixed-integer program (MIP). However, with these elements fixed, the specific transformation of the abstract topology into a graph structure and the construction of the MIP remain non-uniquely. These choices can significantly impact user-friendliness, problem size, and computational efficiency, thus affecting the feasibility and efficiency of modeling efforts. In this work, we identify and analyze the additional degrees of freedom and describe two distinct approaches to address them. The approaches are compared regarding mathematical equivalence, suitability for solution algorithms, and clarity of the underlying topology. A case study on a realistic subarea of Berlin’s district heating network involving tri-objective optimization for a unit commitment problem demonstrates the practical significance of these decisions. By highlighting these critical yet often overlooked aspects, our work equips energy system modelers with insights to improve computational efficiency, scalability, and interpretability in their optimization efforts, ultimately enhancing the practicality and effectiveness of multi-energy system models
Benchmarking of Quantum and Classical Computing in Large-Scale Dynamic Portfolio Optimization Under Market Frictions
No full textQuantum computing is poised to transform the financial industry, yet its advantages over traditional methods have not been evidenced. As this technology rapidly evolves, benchmarking is essential to fairly evaluate and compare different computational strategies. This study presents a challenging yet solvable problem of large-scale dynamic portfolio optimization under realistic market conditions with frictions. We frame this issue as a Quadratic Unconstrained Binary Optimization (QUBO) problem, compatible with digital computing and ready for quantum computing, to establish a reliable benchmark. By applying the latest solvers to real data, we release benchmarks that help verify true advancements in dynamic trading strategies, either quantum or digital computing, ensuring that reported improvements in portfolio optimization are based on robust, transparent, and comparable metrics
The toxicity, uptake, and impact on galectin-3 mediated apoptosis of lactose functionalized PAMAM dendrimers
No full textPoly(amidoamine) (PAMAM) dendrimers functionalized with ligands that are designed to interact with biological receptors are important macromolecules for the elucidation and mediation of biological recognition processes. Specifically, carbohydrate functionalized dendrimers are useful synthetic multivalent systems for the study of multivalent protein–carbohydrate interactions. For example, lactose functionalized glycodendrimers can be used to discern the function of galectins, galactoside-binding proteins that are often over-expressed during cancer progression. In order to effectively interpret cancer cellular assays using glycodendrimers, however, their properties in the presence of cells must first be assessed. Macromolecules that are taken up by cells would be expected to have access to many different cell signaling pathways and modes of action that solely extracellular macromolecules cannot utilize. In addition, macromolecules that display cellular toxicity could not be used as drug delivery vehicles. Here, we report fundamental studies of cellular toxicity, viability, and uptake with four generations of lactose functionalized PAMAM dendrimers. In all cases, the dendrimers are readily taken up by the cells but do not display any significant cellular toxicity. The glycodendrimers also increase cellular apoptosis, suggesting that they may abrogate the antiapoptotic protections afforded by galectin-3 to cancer cells. The results reported here indicate that appropriately functionalized PAMAM dendrimers can be used as nontoxic tools for the study and mediation of both extra and intracellular cancer processes
Rotation dynamics and torque efficiency of cometary nuclei
No full textThe dynamics of a rigid cometary nucleus is described by the evolutions of its center-of-mass and of its rotation state. Solar irradiation that reaches the surface of a cometary nucleus causes the sublimation of volatiles that form the coma around the nucleus. The sublimation process transfers linear momentum and rotational angular momentum from the nucleus to the surrounding space, and thus affects the dynamics via nongravitational forces and nongravitational torques. With the exception of close approaches to planets, these torques exert the dominant influence on the rotation states of cometary nuclei.
The Rosetta mission 2014-2016 accompanying comet 67P/Churyumov-Gerasimenko provides the longest continuous observational data to track its rotation state. In particular, the data set encompasses the direction of the angular velocity, denoted by ω, and the angular frequency |ω|over a time period of approximately 700 days. The observed change of the rotation state is not explained by a low heat conductivity thermophysical model in combination with a homogeneous surface ice coverage of comet 67P. Spatially and/or temporally varying weights for effective active fraction with respect to a prescribed set of surface regions provide a potential solution to this problem.
Here, we present a methodology for classifying the surface based on vectorial efficiency of the torque. On any cometary surface without geometric symmetry, the methodology highlights the decomposition into eight characteristic regions that encode the signs of torque efficiency with respect to all vector components. This decomposition is divided into two subsets of four regions each of which is located in one of both hemispheric regions.
We analyze in detail rotation states close to lowest energy and different thermophysical models, and we discuss how the uncertainties of observations affect the model parameters. We study the occurrence of these regions for an oblate ellipsoid, a nearprolate ellipsoid, a bilobed shape, and a shape model analogous to that of comet 67P. The sensitivity analysis for comet 67P indicates that the observations constrain only one of the eight weights uniquely. The other directions are poorly constrained and show the limitation of the rotational data to determine the regional activity on comet 67P
Self-supervised pre-training with joint-embedding predictive architecture boosts ECG classification performance
No full textAccurate diagnosis of heart arrhythmias requires the interpretation of electrocardiograms (ECG), which capture the electrical activity of the heart. Automating this process through machine learning is challenging due to the need for large annotated datasets, which are difficult and costly to collect. To address this issue, transfer learning is often employed, where models are pre-trained on large datasets and fine-tuned for specific ECG classification tasks with limited labeled data. Self-supervised learning has become a widely adopted pre-training method, enabling models to learn meaningful representations from unlabeled datasets. In this work, we explore the joint-embedding predictive architecture (JEPA) for self-supervised learning from ECG data. Unlike invariance-based methods, JEPA does not rely on hand-crafted data augmentations, and unlike generative methods, it predicts latent features rather than reconstructing input data. We create a large unsupervised pre-training dataset by combining ten public ECG databases, amounting to over one million records. We pre-train Vision Transformers using JEPA on this dataset and fine-tune them on various PTB-XL benchmarks. Our results show that JEPA outperforms existing invariance-based and generative approaches, achieving an AUC of 0.945 on the PTB-XL all statements task. JEPA consistently learns the highest quality representations, as demonstrated in frozen evaluations, and proves advantageous for pre-training even in the absence of additional data