Search

We discuss the Josephson vortices in planar superconductor-topological insulator-superconductor (S-TI-S) junctions, where the TI section is narrow and long. We are motivated by recent experiments, especially by those in junctions of Corbino ring geometry, where non-zero critical current was observed at low temperatures even if a non-zero phase winding number (fluxoid) was enforced in the ring by the perpendicular magnetic field. In this paper we focus on the "atomic" limit in which the low-energy bound states of different vortices do not overlap. In this limit we can associate the non-vanishing critical current with the irregularities (disorder) in the junction's width. We also discuss the microwave spectroscopy of the Josephson vortices in the atomic limit and observe particularly simple selection rules for the allowed transitions., Comment: 9 pages, 8 figures, misprints corrected, cosmetic improvements of figures

Synthesizing a novel view from a single input image is a challenging task. Traditionally, this task was approached by estimating scene depth, warping, and inpainting, with machine learning models enabling parts of the pipeline. More recently, generative models are being increasingly employed in novel view synthesis (NVS), often encompassing the entire end-to-end system. In this work, we adapt a modern diffusion model architecture for end-to-end NVS in the pixel space, substantially outperforming previous state-of-the-art (SOTA) techniques. We explore different ways to encode geometric information into the network. Our experiments show that while these methods may enhance performance, their impact is minor compared to utilizing improved generative models. Moreover, we introduce a novel NVS training scheme that utilizes single-view datasets, capitalizing on their relative abundance compared to their multi-view counterparts. This leads to improved generalization capabilities to scenes with out-of-domain content.

We establish an ordinary as well as a logarithmical convexity of the Moment Generating Function (MGF) for the centered random variable and vector (r.v.) satisfying the Kramer's condition. Our considerations are based on the theory of the so-called Grand Lebesgue Spaces.

We derive in this short report the exact exponential decreasing tail of distribution for naturel normed sums of independent centered random variables (r.v.), applying the theory of Grand Lebesgue Spaces (GLS). We consider also some applications into the theory of U statistics, where we deduce alike for the independent variables the refined exponential tail estimates for ones under natural norming sequence.

We construct an optimal exponential tail decreasing confidence region for an unknown density of distribution in the Lebesgue-Riesz as well as in the uniform} norm, built on the sample of the random vectors based of the famous recursive Wolverton-Wagner density estimation.

We offer in this short report the so-called adaptive functional smoothness estimation in the Hilbert space norm sense in the three classical problems of non-parametrical statistic: regression, density and spectral (density) function measurement (estimation).

A sensitive optical diffractometry method is developed and utilized for advanced tomography of laser-induced air plasma formations. Using transverse diffractometry and Supergaussian plasma distribution modelling we extract the main parameters of the plasma being the plasma density, width and shape with 20 micrometer spatial resolution throughout the plasma formation. The experimentally recorded diffraction patterns fitted by the Supergaussian plasma model are found to capture unprecedentedly delicate traits in the evolution of the plasma from its effective birth and on. Key features in the spatial evolution of the plasma such as the 'escape position', the 'turning point' and the refocusing dynamics of the beam are identified and explored in details. Our work provides experimental and theoretical access into the highly nonlinear dynamics of laser-induced air plasma., Comment: Main text - 10 pages, Supplementary Information - 6 pages

In this paper we present two design generations of metasurface-based planar microlenses implemented on Front-Side Illumination SPAD pixels. This kind of microlens is an alternative to conventional reflow microlens. It offers more degrees of freedom in term of design, especially the capability to design off-axis microlens to gather light around the SPAD photodiode. The two generations of microlenses have been fabricated on STMicroelectronics SPAD and characterized. We validated the sensitivity improvement offered by extended metasurface-based microlens. We also confirmed the impact of lithography capability on metasurface performances, highlighting the

Fraud is ubiquitous across applications and involve users bypassing the rule of law, often with the strategic aim of obtaining some benefit that would otherwise be unattainable within the bounds of lawful conduct. However, user fraud can be detrimental. To mitigate the harms of user fraud, we study the problem of policing fraud as a security game between an administrator and users. In this game, an administrator deploys $R$ security resources (e.g., police officers) across $L$ locations and levies fines against users engaging in fraud at those locations. For this security game, we study both payoff and revenue maximization administrator objectives. In both settings, we show that computing the optimal administrator strategy is NP-hard and develop natural greedy algorithm variants for the respective settings that achieve at least half the payoff or revenue as the payoff-maximizing or revenue-maximizing solutions, respectively. We also establish a resource augmentation guarantee that our proposed greedy algorithms with one extra resource, i.e., $R+1$ resources, achieve at least the same payoff (revenue) as the payoff-maximizing (revenue-maximizing) outcome with $R$ resources. Moreover, in the setting when user types are homogeneous, we develop a near-linear time algorithm for the revenue maximization problem and a polynomial time approximation scheme for the payoff maximization problem. Next, we present numerical experiments based on a case study of parking enforcement at Stanford University's campus, highlighting the efficacy of our algorithms in increasing parking permit earnings at the university by over \$300,000 annually. Finally, we study several model extensions, including incorporating contracts to bridge the gap between the payoff and revenue-maximizing outcomes and generalizing our model to incorporate additional constraints beyond a resource budget constraint.

We derive sharp non - asymptotical Lebesgue - Riesz as well as Grand Lebesgue Space norm estimations for different norms of matrix martingales through these norms for the correspondent martingale differences and through the entropic dimension of the extremal points of the unit ball for a basic space. These estimates allow us to deduce in particular the exponential decreasing tail of distribution for these norms of matrix martingales. We bring also some examples in order to show the exactness of the obtained estimations.

In this short report we estimate and calculate the exact value of norms of multilinear integral operators having homogeneous kernel, acting between two Grand Lebesgue Spaces.

We obtain results concerning the so-called factorization for the convergence of random variables almost everywhere (almost surely or with probability one), belonging to the classical Lebesgue-Riesz spaces and we extend these results to the Grand Lebesgue Spaces. We also give exact estimates for the parameters involved and provide several examples. We also show that the obtained estimates are, in the general case, essentially non-improvable, of course up to a multiplicative constant.

This paper explores the utility of agent-based simulations in realistically modelling market structures and sheds light on the nuances of optimal dealer strategies. It underscores the contrast between conclusions drawn from probabilistic modelling and agent-based simulations, but also highlights the importance of employing a realistic test bed to analyse intricate dynamics. This is achieved by extending the agent-based model for auction markets by \cite{Chiarella.2008} to include liquidity providers. By constantly and passively quoting, the dealers influence their own wealth but also have ramifications on the market as a whole and the other participating agents. Through synthetic market simulations, the optimal behaviour of different dealer strategies and their consequences on market dynamics are examined. The analysis reveals that dealers exhibiting greater risk aversion tend to yield better performance outcomes. The choice of quote sizes by dealers is strategy-dependent: one strategy demonstrates enhanced performance with larger quote sizes, whereas the other strategy show a better results with smaller ones. Increasing quote size shows positive influence on the market in terms of volatility and kurtosis with both dealer strategies. However, the impact stemming from larger risk aversion is mixed. While one of the dealer strategies shows no discernible effect, the other strategy results in mixed outcomes, encompassing both positive and negative effects.

We extend the classical Lebesgue-Riesz norm estimations for integral operators acting between different classical Lebesgue-Riesz spaces into the Grand Lebesgue Spaces, in the general case. As an example we consider matrix operators acting between finite dimensional Lebesgue-Riesz spaces, especially generated by means of positive magic squares.

We analyze transformers from the perspective of iterative inference, seeking to understand how model predictions are refined layer by layer. To do so, we train an affine probe for each block in a frozen pretrained model, making it possible to decode every hidden state into a distribution over the vocabulary. Our method, the tuned lens, is a refinement of the earlier "logit lens" technique, which yielded useful insights but is often brittle. We test our method on various autoregressive language models with up to 20B parameters, showing it to be more predictive, reliable and unbiased than the logit lens. With causal experiments, we show the tuned lens uses similar features to the model itself. We also find the trajectory of latent predictions can be used to detect malicious inputs with high accuracy. All code needed to reproduce our results can be found at https://github.com/AlignmentResearch/tuned-lens.

We study moment rearrangement invariant spaces, which contain as particular cases the generalized Grand Lebesgue Spaces, and provide norm estimates for some operators, not necessarily linear, acting between some measurable rearrangement invariant spaces. The estimations are formulated in the terms of fundamental functions for these spaces.

We establish imbedding properties between Grand Lebesgue Spaces and (generalized) Lorentz-Zygmund ones. We extend some known previous results concerning imbedding theorems between Grand Lebesgue and classical Lebesgue-Riesz spaces and we show the exactness of the obtained estimates.

We study heat transport in a Weyl semimetal with broken time-reversal symmetry in the hydrodynamic regime. At the neutrality point, the longitudinal heat conductivity is governed by the momentum relaxation (elastic) time, while longitudinal electric conductivity is controlled by the inelastic scattering time. In the hydrodynamic regime this leads to a large longitudinal Lorenz ratio. As the chemical potential is tuned away from the neutrality point, the longitudinal Lorenz ratio decreases because of suppression of the heat conductivity by the Seebeck effect. The Seebeck effect (thermopower) and the open circuit heat conductivity are intertwined with the electric conductivity. The magnitude of Seebeck tensor is parametrically enhanced, compared to the non-interacting model, in a wide parameter range. While the longitudinal component of Seebeck response decreases with increasing electric anomalous Hall conductivity $\sigma_{xy}$, the transverse component depends on $\sigma_{xy}$ in a non-monotonous way. Via its effect on the Seebeck response, large $\sigma_{xy}$ enhances the longitudinal Lorenz ratio at a finite chemical potential. At the neutrality point, the transverse heat conductivity is determined by the Wiedemann-Franz law. Increasing the distance from the neutrality point, the transverse heat conductivity is enhanced by the transverse Seebeck effect and follows its non-monotonous dependence on $\sigma_{xy}$.

We study the anomalous Hall effect in a disordered Weyl semimetal. While the intrinsic contribution is expressed solely in terms of Berry curvature, the extrinsic contribution is given by a combination of the skew scattering and side jump terms. For the model of small size impurities, we are able to express the skew scattering contribution in terms of scattering phase shifts. We identify the regime in which the skew scattering contribution dominates the side-jump contribution: the impurities are either strong or resonant, and at dilute concentration. In this regime, the Hall resistivity $\rho_{xy}$ is expressed in terms of two scattering phases, analogous to the s-wave scattering phase in a non-topological metal. We compute the dependence of $\rho_{xy}$ on the chemical potential, and show that $\rho_{xy}$ scales with temperature as $T^2$ in low temperatures and as $T^{3/2}$ in the high temperature limit.