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Noninvasive optical imaging modalities can probe patient's tissue in 3D and over time generate gigabytes of clinically relevant data per sample. There is a need for AI models to analyze this data and assist clinical workflow. The lack of expert labelers and the large dataset required (>100,000 images) for model training and tuning are the main hurdles in creating foundation models. In this paper we introduce FoundationShift, a method to apply any AI model from computational pathology without retraining. We show our method is more accurate than state of the art models (SAM, MedSAM, SAM-Med2D, CellProfiler, Hover-Net, PLIP, UNI and ChatGPT), with multiple imaging modalities (OCT and RCM). This is achieved without the need for model retraining or fine-tuning. Applying our method to noninvasive in vivo images could enable physicians to readily incorporate optical imaging modalities into their clinical practice, providing real time tissue analysis and improving patient care.

Atomic beams offer measurement capabilities for hot vapors but often require complex, bulky systems, limiting miniaturization. This study explores hot vapors in thin cells (L=30,5 microns) as a simplified analogy to atomic beams. Coherence is maintained only for atoms moving parallel to the cell walls, as wall collisions randomize spin. By filtering non-parallel atoms, we mimic atomic beam behavior and observe the Faraday-Ramsey effect. Rubidium atoms are optically pumped with linearly polarized resonance radiation, evolving in a perpendicular magnetic field and probed via Faraday rotation. We believe that thin cell technology is paving the way for the miniaturization of atomic beam-based sensors., Comment: This PDF submission, intended for consideration by Optica Quantum, combines the main text and Supplementary Information (SI). The document includes 10 pages and 7 figures of the main text, followed by references and then 6 pages with 4 figures of the SI. This arrangement facilitates easy navigation and aligns with journal requirements

We derive exact, universal, closed-form quantum Monte Carlo estimators for finite temperature energy susceptibility and fidelity susceptibility for essentially arbitrary Hamiltonians. We demonstrate how our method can be used to study quantum phase transitions without knowledge of an order parameter and without the need to design system-specific ergodic quantum Monte Carlo update rules by applying it to a class of random models., Comment: 9 pages (5 main, 2 bibliography, 3 supplement), 2 figures

We experimentally study electron transport across a single planar junction between the indium electrode and MnTe altermagnet candidate. We confirm standard Ohmic behavior with strictly linear current-voltage curves above the indium critical field or temperature, although with high, about 100~kOhm, junction resistance. At low temperatures and in zero magnetic field, we observe a well-developed Andreev curve with the pronounced coherence peaks, which cannot be normally expected for these high values of normal junction resistance. The conclusion on the Andreev reflection is also supported by suppression in magnetic field, as well as by universality of the observed behavior for all of the investigated samples. The experimental results can be explained by specifics of Andreev transport through the disordered region at the superconductor-altermagnet interface. Due to a different set of restrictions on the possibility of Andreev reflection, an altermagnet suffers from the presence of disorder less than a normal spin-degenerate metal, so the conductance enhancement is retained throughout the superconducting gap.

Chest X-rays or chest radiography (CXR), commonly used for medical diagnostics, typically enables limited imaging compared to computed tomography (CT) scans, which offer more detailed and accurate three-dimensional data, particularly contrast-enhanced scans like CT Pulmonary Angiography (CTPA). However, CT scans entail higher costs, greater radiation exposure, and are less accessible than CXRs. In this work we explore cross-modal translation from a 2D low contrast-resolution X-ray input to a 3D high contrast and spatial-resolution CTPA scan. Driven by recent advances in generative AI, we introduce a novel diffusion-based approach to this task. We evaluate the models performance using both quantitative metrics and qualitative feedback from radiologists, ensuring diagnostic relevance of the generated images. Furthermore, we employ the synthesized 3D images in a classification framework and show improved AUC in a PE categorization task, using the initial CXR input. The proposed method is generalizable and capable of performing additional cross-modality translations in medical imaging. It may pave the way for more accessible and cost-effective advanced diagnostic tools. The code for this project is available: https://github.com/NoaCahan/X-ray2CTPA ., Comment: preprint, project code: https://github.com/NoaCahan/X-ray2CTPA

Designing distributed systems to have predictable performance under high load is difficult because of resource exhaustion, non-linearity, and stochastic behaviour. Timeliness, i.e., delivering results within defined time bounds, is a central aspect of predictable performance. In this paper, we focus on timeliness using the DELTA-Q Systems Development paradigm (DELTA-QSD, developed by PNSol), which computes timeliness by modelling systems observationally using so-called outcome expressions. An outcome expression is a compositional definition of a system's observed behaviour in terms of its basic operations. Given the behaviour of the basic operations, DELTA-QSD efficiently computes the stochastic behaviour of the whole system including its timeliness. This paper formally proves useful algebraic properties of outcome expressions w.r.t. timeliness. We prove the different algebraic structures the set of outcome expressions form with the different DELTA-QSD operators and demonstrate why those operators do not form richer structures. We prove or disprove the set of all possible distributivity results on outcome expressions. On our way for disproving 8 of those distributivity results, we develop a technique called properisation, which gives rise to the first body of maths for improper random variables. Finally, we also prove 14 equivalences that have been used in the past in the practice of DELTA-QSD. An immediate benefit is rewrite rules that can be used for design exploration under established timeliness equivalence. This work is part of an ongoing project to disseminate and build tool support for DELTA-QSD. The ability to rewrite outcome expressions is essential for efficient tool support., Comment: In Proceedings ICE 2023, arXiv:2308.08920

We present a universal parameter-free quantum Monte Carlo (QMC) algorithm designed to simulate arbitrary spin-$1/2$ Hamiltonians. To ensure the convergence of the Markov chain to equilibrium for every conceivable case, we devise a clear and simple automated protocol that produces QMC updates that are provably ergodic and satisfy detailed balance. We demonstrate the applicability and versatility of our method by considering several illustrative examples, including the simulation of the XY model on a triangular lattice, the toric code, and random $k$-local Hamiltonians. We have made our program code freely accessible on GitHub., Comment: 18 pages, 8 figures

We experimentally investigate charge transport in In-GeTe and In-GeTe-In proximity devices, which are formed as junctions between superconducting indium leads and thick single crystal flakes of $\alpha$-GeTe topological semimetal. We observe nonmonotonic effects of the applied external magnetic field, including reentrant superconductivity in In-GeTe-In Josephson junctions: supercurrent reappears at some finite magnetic field. For a single In-GeTe Andreev junction, the superconducting gap is partially suppressed in zero magnetic field, while the gap is increased nearly to the bulk value for some finite field before its full suppression. We discuss possible reasons for the results obtained, taking into account spin polarization of Fermi arc surface states in topological semimetal $\alpha$-GeTe with a strong spin-orbit coupling. In particular, the zero-field surface state spin polarization partially suppresses the superconductivity, while it is recovered due to the modified spin-split surface state configuration in finite fields. As an alternative possible scenario, the transition into the Fulde-Ferrell-Larkin-Ovchinnikov state is discussed. However, the role of strong spin-orbit coupling in forming the nonmonotonic behavior has not been analyzed for heterostructures in the Fulde-Ferrell-Larkin-Ovchinnikov state, which is crucial for junctions involving GeTe topological semimetal., Comment: One reference has been added

We study gain without inversion due to coherence effects in a Doppler-broadened degenerate three-level system of a rubidium-hydrogen mixture in a miniaturized micron scale custom vapor cell. The cell miniaturization gives rise to collisions of atoms with the walls of the cell. This, combined with the high collision rate with the hydrogen buffer gas allows us to observe gain in the absorption spectra. Furthermore, we analyze the role of cell miniaturization in the evolution of the gain profile. In addition to fundamental interest, the observation of gain without inversion in our miniaturized cells paves the way for applications such as miniaturized lasers without inversion., Comment: 15 pages, 8 figures, 1 table

This paper gives an overview of the unconventional dependence of internal phase differences on the external phase difference in superconductor-normal metal-superconductor (SINIS) and superconductor-superconductor-superconductor (SISIS) tunnel double junctions. The results are obtained within the Ginzburg-Landau (GL) approach that includes boundary conditions for the superconductor order parameter in the presence of a Josephson coupling through interfaces. The boundary conditions are derived within the GL theory and substantiated microscopically. The absence of the one-to-one correspondence between external $\phi$ and internal $\chi_{1,2}$ phase differences in the junctions is shown to occur in two qualitatively different ways, both of which result in the range of $\chi_{1,2}$ reduced and prevent the $4\pi$-periodic current-phase relation $j=j_c\sin\frac{\phi}{2}$. In SINIS junctions, the effect of the supercurrent-induced phase incursion $\varphi$ between the end faces of the central electrode of mesoscopic length $L$ can play a crucial role. In SISIS junctions, there occurs the regime of interchanging modes, which is modified as $L$ decreases. The proximity and pair breaking effects in the double junctions with closely spaced interfaces are addressed., Comment: 22 pages, 7 figures, Contribution for the Annals of Physics special issue in memory of Igor Dzyaloshinskii

The crafting of machine learning (ML) based systems requires statistical control throughout its life cycle. Careful quantification of business requirements and identification of key factors that impact the business requirements reduces the risk of a project failure. The quantification of business requirements results in the definition of random variables representing the system key performance indicators that need to be analyzed through statistical experiments. In addition, available data for training and experiment results impact the design of the system. Once the system is developed, it is tested and continually monitored to ensure it meets its business requirements. This is done through the continued application of statistical experiments to analyze and control the key performance indicators. This book teaches the art of crafting and developing ML based systems. It advocates an "experiment first" approach stressing the need to define statistical experiments from the beginning of the project life cycle. It also discusses in detail how to apply statistical control on the ML based system throughout its lifecycle.