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We investigate stochastic parabolic evolution equations with time-dependent random generators and locally Lipschitz continuous drift terms. Using pathwise mild solutions, we construct an infinite-dimensional stationary Ornstein-Uhlenbeck type process, which is shown to be tempered in suitable function spaces. This property, together with a bootstrapping argument based on the regularizing effect of parabolic evolution families, is then applied to prove the global well-posedness and the existence of a random attractor for reaction-diffusion equations with random non-autonomous generators and nonlinearities satisfying certain growth and dissipativity assumptions., Comment: 32 pages, Comments are welcome!

In this paper, we investigate the scattering of BPS magnetic monopoles through numerical simulations. We present an ansatz for various multi-monopole configurations suitable for analyzing monopole scattering processes. Our study includes planar scattering scenarios involving two, three, and four monopoles, as well as non-planar processes where three and four monopoles form intermediate tetrahedral and cubic states, respectively. Our observations align with the theoretical predictions of the moduli space approximation. Furthermore, we extend our analysis to relativistic velocities and explore parameters beyond the BPS limit., Comment: 14 pages, 9 figures, ancillary video at https://youtu.be/a2wOOo2AAHI

Colloidal two-dimensional lead chalcogenide nanocrystals represent an intriguing new class of materials that push the boundaries of quantum confinement by combining a crystal thickness down to the monolayer with confinement in the lateral dimension. In particular flat PbSe quantum dots exhibit efficient telecommunication band-friendly photoluminescence (1.43 - 0.83 eV with up to 61% quantum yield) that is highly interesting for fiber-optics information processing. By using cryogenic scanning tunneling microscopy and spectroscopy, we probe distinct single layer-defined PbSe quantum dot populations down to a monolayer with in-gap state free quantum dot-like density of states, in agreement with theoretical tight binding calculations. Cryogenic ensemble photoluminescence spectra reveal mono-, bi-, and trilayer contribution, confirming the structural, electronic and theoretical results. From larger timescale shifts and ratio changes in the optical spectra we infer Ostwald ripening in solution and fusing in deposited samples of thinner flat PbSe quantum dots, which can be slowed down by surface passivation with PbI2. By uncovering the interplay between thickness, lateral size and density of states, as well as the synthetic conditions and post-synthetic handling, our findings enable the target-oriented synthesis of two-dimensional PbSe quantum dots with precisely tailored optical properties at telecom wavelengths.

The IR dynamics of SYK is that of the Schwarzian theory, the effective theory of broken reparametrization invariance. In the double scaling limit, SYK is completely solvable by chord diagrams, whose generating functional is a bilocal Liouville theory. At low temperatures a set of modes in this description becomes soft. We interpret them as reparametrization of some twisted time coordinates, and show explicitly that they lead to the nonlinear Schwarzian theory. We further consider deformations of the theory in the double scaling limit, giving rise to diagrams with multiple species of chords, and show that the generating functional is now a Liouville theory with multiple fields. These deformations can be tracked to the IR and we discuss how they affect the Schwarzian., Comment: 47 pages, 6 figures. v2 - updated refs

We study the problem of joint communication and detection of wiretapping on an optical fiber from a quantum perspective. Our system model describes a communication link that is capable of transmitting data under normal operating conditions and raising a warning at the transmitter side in case of eavesdropping. It contributes to a unified modelling approach, based on which established quantum communication paradigms like quantum key distribution can be compared to other approaches offering similar functionality., Comment: 6 pages, 3 figures, to be presented at 2024 IEEE Global Communications Conference

It is a common belief that quantum key distribution systems are the one and only information-theoretically secure physical layer security protocol that enables secure data transmission without a need for the legitimate parties to have any channel knowledge. It is also known that this high security profile results in severe rate restrictions for the parties utilizing the quantum key distribution systems. This observation raises the question of whether quantifying the level of ignorance of the legitimate parties with regard to the channel parameters may enable us to navigate the large gray zone between insecure but highly performant systems on the one side and perfectly secure but highly non-performant systems on the other side. Indeed, by proving a capacity formula for the bosonic compound wiretap channel using the binary phase shift keying alphabet, we are able to quantify in this work exactly how channel uncertainty penalizes data transmission rates., Comment: 6 pages, 2 figures

Low latency and high data rate performance are essential in wireless communication systems. This paper explores trade-offs between latency and data rates for optical wireless communication. We introduce a latency-optimized model utilizing compound codes as one corner case and a data rate-optimized model employing channel estimation via pilot signals and feedback before data transmission. Trade-offs between the two extremes are displayed. Most importantly, we detail operating points that can only be reached when the receiver side of the link employs optimal quantum measurement strategies. Furthermore, we propose an IoT application in a robot factory as an example scenario. Our findings reveal a trade-off between latency and data rate driven by two basic algorithms: compound codes reduce latency at the cost of data rates, while channel estimation enhances data rates at the cost of latency., Comment: 6 pages, 2 figures

This paper describes an efficient algorithm for solving noisy linear inverse problems using pretrained diffusion models. Extending the paradigm of denoising diffusion implicit models (DDIM), we propose constrained diffusion implicit models (CDIM) that modify the diffusion updates to enforce a constraint upon the final output. For noiseless inverse problems, CDIM exactly satisfies the constraints; in the noisy case, we generalize CDIM to satisfy an exact constraint on the residual distribution of the noise. Experiments across a variety of tasks and metrics show strong performance of CDIM, with analogous inference acceleration to unconstrained DDIM: 10 to 50 times faster than previous conditional diffusion methods. We demonstrate the versatility of our approach on many problems including super-resolution, denoising, inpainting, deblurring, and 3D point cloud reconstruction.

Deep learning is actively being used in biometrics to develop efficient identification and verification systems. Handwritten signatures are a common subset of biometric data for authentication purposes. Generative adversarial networks (GANs) learn from original and forged signatures to generate forged signatures. While most GAN techniques create a strong signature verifier, which is the discriminator, there is a need to focus more on the quality of forgeries generated by the generator model. This work focuses on creating a generator that produces forged samples that achieve a benchmark in spoofing signature verification systems. We use CycleGANs infused with Inception model-like blocks with attention heads as the generator and a variation of the SigCNN model as the base Discriminator. We train our model with a new technique that results in 80% to 100% success in signature spoofing. Additionally, we create a custom evaluation technique to act as a goodness measure of the generated forgeries. Our work advocates generator-focused GAN architectures for spoofing data quality that aid in a better understanding of biometric data generation and evaluation.

We consider the question of how to best achieve the perception of eye contact when a person is captured by camera and then rendered on a 2D display. For single subjects photographed by a camera, conventional wisdom tells us that looking directly into the camera achieves eye contact. Through empirical user studies, we show that it is instead preferable to {\em look just below the camera lens}. We quantitatively assess where subjects should direct their gaze relative to a camera lens to optimize the perception that they are making eye contact.

Given an input painting, we reconstruct a time-lapse video of how it may have been painted. We formulate this as an autoregressive image generation problem, in which an initially blank "canvas" is iteratively updated. The model learns from real artists by training on many painting videos. Our approach incorporates text and region understanding to define a set of painting "instructions" and updates the canvas with a novel diffusion-based renderer. The method extrapolates beyond the limited, acrylic style paintings on which it has been trained, showing plausible results for a wide range of artistic styles and genres., Comment: Project Page: https://inversepainting.github.io

In this study, we developed an Evidence-based Ensemble Neural Network, namely EVENet, for anatomical brain parcellation using diffusion MRI. The key innovation of EVENet is the design of an evidential deep learning framework to quantify predictive uncertainty at each voxel during a single inference. To do so, we design an evidence-based ensemble learning framework for uncertainty-aware parcellation to leverage the multiple dMRI parameters derived from diffusion MRI. Using EVENet, we obtained accurate parcellation and uncertainty estimates across different datasets from healthy and clinical populations and with different imaging acquisitions. The overall network includes five parallel subnetworks, where each is dedicated to learning the FreeSurfer parcellation for a certain diffusion MRI parameter. An evidence-based ensemble methodology is then proposed to fuse the individual outputs. We perform experimental evaluations on large-scale datasets from multiple imaging sources, including high-quality diffusion MRI data from healthy adults and clinically diffusion MRI data from participants with various brain diseases (schizophrenia, bipolar disorder, attention-deficit/hyperactivity disorder, Parkinson's disease, cerebral small vessel disease, and neurosurgical patients with brain tumors). Compared to several state-of-the-art methods, our experimental results demonstrate highly improved parcellation accuracy across the multiple testing datasets despite the differences in dMRI acquisition protocols and health conditions. Furthermore, thanks to the uncertainty estimation, our EVENet approach demonstrates a good ability to detect abnormal brain regions in patients with lesions, enhancing the interpretability and reliability of the segmentation results., Comment: 16 pages, 5 figures

We present a method for generating video sequences with coherent motion between a pair of input key frames. We adapt a pretrained large-scale image-to-video diffusion model (originally trained to generate videos moving forward in time from a single input image) for key frame interpolation, i.e., to produce a video in between two input frames. We accomplish this adaptation through a lightweight fine-tuning technique that produces a version of the model that instead predicts videos moving backwards in time from a single input image. This model (along with the original forward-moving model) is subsequently used in a dual-directional diffusion sampling process that combines the overlapping model estimates starting from each of the two keyframes. Our experiments show that our method outperforms both existing diffusion-based methods and traditional frame interpolation techniques., Comment: Published at ICLR 2025; Project page: https://svd-keyframe-interpolation.github.io/

Near-infrared emitting colloidal two-dimensional (2D) PbX (X=S, Se) nanoplatelets have emerged as interesting materials with strong size quantisation in the thickness dimension. They act as model systems for efficient charge carrier multiplication and hold potential as intriguing candidates for finer-based photonic quantum applications. However, synthetic access to the third family member, 2D PbTe, remains elusive due to a challenging precursor chemistry. Here, we report a direct synthesis for 2D PbTe nanoplatelets (NPLs) with unable photoluminescence (PL, 910-1460 nm (1.36-0.85 eV), PLQY 1-15 %), based on aminophosphine precursor chemistry. Ex-situ transamination of tris(dimethylamino)phosphine telluride with octylamine is confirmed by 31P NMR and yields a reactive tellurium precursor for the formation of 2D PbTe NPLs at temperatures as low as 0 {\deg}C. The PL position of the PbTe NPLs is unable by controlling the Pb:Te ration in the reaction. GIWAXS confirms the 2D geometry of the NPLs and the formation of superlattices. The importance of a post-synthetic passivation of the PbTe NPLs by PbI2 to ensure colloidal stability of the otherwise oxygen sensitive samples is supported by X-ray photoelectron spectroscopy. Our results expand and complete the row of lead chalcogenide-based 2D NPLs, opening up new ways for further pushing the optical properties of 2D NPLs into the infrared and toward technologically relevant wavelengths.

We present Infinite Texture, a method for generating arbitrarily large texture images from a text prompt. Our approach fine-tunes a diffusion model on a single texture, and learns to embed that statistical distribution in the output domain of the model. We seed this fine-tuning process with a sample texture patch, which can be optionally generated from a text-to-image model like DALL-E 2. At generation time, our fine-tuned diffusion model is used through a score aggregation strategy to generate output texture images of arbitrary resolution on a single GPU. We compare synthesized textures from our method to existing work in patch-based and deep learning texture synthesis methods. We also showcase two applications of our generated textures in 3D rendering and texture transfer.

In the last year or so and going back many decades there has been extensive claims by major computational scientists, engineers, and others that AGI, artificial general intelligence, is five or ten years away, but without a scintilla of scientific evidence, for a broad body of these claims. Computers will become conscious, have a theory of mind, think and reason, will become more intelligent than humans, and so on. But the claims are science fiction, not science. This article reviews evidence for the following three propositions using extensive body of scientific research and related sources from the cognitive and neurosciences, evolutionary evidence, linguistics, data science, comparative psychology, self-driving cars, robotics. and the learning sciences. (1) Do computing machines think or reason? (2) Are computing machines sentient or conscious? (3) Do computing machines have a theory of mind?

With the increasing sophistication and capability of quantum hardware, its integration, and employment in high performance computing (HPC) infrastructure becomes relevant. This opens largely unexplored access models and scheduling questions in such quantum-classical computing environments, going beyond the current cloud access model. SCIM MILQ is a scheduler for quantum tasks in HPC infrastructure. It combines well-established scheduling techniques with methods unique to quantum computing, such as circuit cutting. SCIM MILQ can schedule tasks while minimizing the makespan, i.e., the time that elapses from the start of work to the end, improving on average by 25%. Additionally, it reduces the noise in the circuit by up to 10%, increasing the outcome's reliability. We compare it against an existing baseline and show its viability in an HPC environment., Comment: 9 pages, 2 figures, 3 tables, 1 algorithm, submission to Quantum Week 2024