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Explainable deep-learning detection of microplastic fibers via polarization-resolved holographic microscopy

arXiv:2601.15769v3 Announce Type: replace Abstract: Reliable identification of microplastic fibers is crucial for environmental monitoring but remains analytically challenging. We report an explainable deep-learning framework for classifying microplastic and natural microfibers using polarization-resolved digital holographic microscopy. From multiplexed holograms, the complex Jones matrix of each fiber was reconstructed to extract polarization eigen-parameters describing optical anisotropy.

Towards Efficient LLMs Annealing with Principled Sample Selection

arXiv:2605.31175v1 Announce Type: new Abstract: The annealing phase is a pivotal convergence stage in LLM pre-training that ultimately determines final model quality. However, effectively selecting training data during this phase remains a key challenge. Current strategies rely on empirical heuristics, such as domain filtering or context extension, which lack a principled grounding in optimization theory.

EU-Handelkommissar fordert neues Instrument zur Diversifizierung von Lieferketten

Wadephuls Kampf um Deutschlands Sitz im UN-Sicherheitsrat

Listen on Spotify Apple Music Amazon Music Für die Bundesregierung steht in New York viel auf dem Spiel. Deutschland will wieder einen der begehrten, nichtständigen Sitze im UN-Sicherheitsrat für die Jahre 2027 und 2028. Aber aus der Bewerbung ist ein knallharter Postenpoker geworden: Weil sich neben Deutschland auch Portugal und Österreich bewerben, droht kommende Woche eine Kampfabstimmung.

CLONE: A 3DGS-Based Closed-Loop Differentiable Optimization Framework for Single-Image Normal Estimation

arXiv:2508.05950v2 Announce Type: replace Abstract: We propose CLONE, a 3DGS-based Closed-Loop differentiable Optimization framework for single-image Normal Estimation. The core idea is to construct an "image-geometry-image" consistency loop that unifies and jointly constrains the limitations of both paradigms: the reliance on explicit supervision without cross-domain geometric constraints in discriminative methods, and the absence of stable differentiable optimization pathways in generative...

Accurate, full-dimensional computations of thousands of complex vibrational eigenstates with tree tensor network states

arXiv:2605.00998v2 Announce Type: replace Abstract: Tree tensor network states (TTNSs) combined with the density matrix renormalization group (DMRG) are emerging as powerful tools for vibrational and vibronic structure simulations in molecules with strong coupling and fluxionality. In this Perspective, we discuss how TTNS methods enable accurate, full-dimensional computations of thousands of eigenstates for molecular systems ranging from quartic-force-field benchmarks to molecules with...