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We give a public key encryption scheme with plausible quasi-exponential security based on the conjectured intractability of two constraint satisfaction problems (CSPs), both of which are instantiated with a corruption rate of $1 - o(1)$. First, we conjecture the hardness of a new large alphabet random predicate CSP (LARP-CSP) defined over an arbitrary but strongly expanding factor graph, where the vast majority of predicate outputs are replaced with random outputs. Second, we conjecture the hardness of the standard $k$XOR problem defined over a random factor graph, again where the vast majority of parity computations are replaced with random bits. In support of our hardness conjecture for LARP-CSPs, we give a variety of lower bounds, ruling out many natural attacks including all known attacks that exploit non-random factor graphs. Our public key encryption scheme is the first to leverage high corruption CSPs while simultaneously achieving a plausible security level far above quasi-polynomial. At the heart of our work is a new method for planting cryptographic trapdoors based on the label extended factor graph for a CSP. Along the way to achieving our result, we give the first unifo

Colloidal perovskite quantum dots (pQDs) are promising quantum light emitters, and investigations at the single pQD scale have so far relied mostly on hot-injection synthesis, which requires precise temperature control and an inert atmosphere. While alternative synthesis routes under milder conditions are often associated with structural and surface defects that may have limited impact in ensemble measurements, demonstrating high optical quality at the level of individual pQDs constitutes the most stringent benchmark for a new synthesis protocol. Here, we demonstrate that a modified ligand-assisted reprecipitation (LARP) approach yields CsPbBr3 pQDs showing state-of-the-art optical properties at the scale of single emitters. By combining an amine-mediated post-synthetic size-trimming strategy with didodecyldimethylammonium bromide (DDAB) ligands for enhanced surface passivation and colloidal stability, we obtain isolated pQDs with stable emission and minimal spectral diffusion at cryogenic temperatures. Micro-photoluminescence experiments resolve the characteristic fine structure of the bright exciton, its low-energy optical phonon replicas, and the trion and biexciton states. Time

Autoregressive (AR) video generative models rely on video tokenizers that compress pixels into discrete token sequences. The length of these token sequences is crucial for balancing reconstruction quality against downstream generation computational cost. Traditional video tokenizers apply a uniform token assignment across temporal blocks of different videos, often wasting tokens on simple, static, or repetitive segments while underserving dynamic or complex ones. To address this inefficiency, we introduce $\textbf{EVATok}$, a framework to produce $\textbf{E}$fficient $\textbf{V}$ideo $\textbf{A}$daptive $\textbf{Tok}$enizers. Our framework estimates optimal token assignments for each video to achieve the best quality-cost trade-off, develops lightweight routers for fast prediction of these optimal assignments, and trains adaptive tokenizers that encode videos based on the assignments predicted by routers. We demonstrate that EVATok delivers substantial improvements in efficiency and overall quality for video reconstruction and downstream AR generation. Enhanced by our advanced training recipe that integrates video semantic encoders, EVATok achieves superior reconstruction and state

Polars and low-accretion rate polars (LARPs) are strongly magnetic cataclysmic variables. Mediated by the magnetic field of the white dwarf, their spin and binary orbit are (mostly) synchronized. They play an important role in our understanding of close binary evolution and the generation of strong magnetic fields in white dwarfs. Thanks to X-ray all-sky surveys, optical variability, and spectroscopic surveys, the number of polars and LARPs has grown from just a few in the 1980s to more than 200 today. Follow-up studies are facilitated by the systematic compilation of these systems presented here, which is also made available as an online resource. Yearly updates are planned, and community input is highly appreciated.

Developing efficient single-photon sources is fundamental to advancing photonic quantum technologies. In particular, achieving scalable, cost-effective, stable, high-rate, and high-purity single-photon emission at ambient conditions is paramount for free-space quantum communication. However, fulfilling all the requirements simultaneously under ambient conditions has remained a significant challenge. Here, the scalable, cost-effective ambient condition synthesis of nickel doped (Ni doped) CsPbBr3 perovskite quantum dots (NPQDs) is presented using a modified ligand-assisted reprecipitation (LARP) method. The resulting individual NPQDs demonstrate remarkable photostability, sustaining their performance for over 10 minutes under ambient conditions with environment humidity of ~55%, and exhibit exceptional single-photon purity (>99%) with a narrow emission linewidth (~70 meV). The remarkable photostability could be attributed to the spatial localization of exciton by Ni atoms on the surface of the nanocrystal, reducing its interaction with the environment. Our results demonstrated that NPQDs with outstanding combinations of quantum emitting properties can be both synthesized and oper

This paper presents DeRA, a novel 1D video tokenizer that decouples the spatial-temporal representation learning in video tokenization to achieve better training efficiency and performance. Specifically, DeRA maintains a compact 1D latent space while factorizing video encoding into appearance and motion streams, which are aligned with pretrained vision foundation models to capture the spatial semantics and temporal dynamics in videos separately. To address the gradient conflicts introduced by the heterogeneous supervision, we further propose the Symmetric Alignment-Conflict Projection (SACP) module that proactively reformulates gradients by suppressing the components along conflicting directions. Extensive experiments demonstrate that DeRA outperforms LARP, the previous state-of-the-art video tokenizer by 25% on UCF-101 in terms of rFVD. Moreover, using DeRA for autoregressive video generation, we also achieve new state-of-the-art results on both UCF-101 class-conditional generation and K600 frame prediction.

Lead halide perovskite nanocrystals (LHP-NCs) embedded in polymeric hosts are gaining attention as scalable and low-cost scintillation detectors for technologically relevant applications. Despite rapid progress, little is currently known about the scintillation properties and stability of LHP-NCs prepared by the ligand assisted reprecipitation (LARP) method, which allows mass scalability at room temperature unmatched by any other type of nanostructure, and the implications of incorporating LHP-NCs into polyacrylate hosts are still largely debated. Here, we show that LARP-synthesized CsPbBr3 NCs are comparable to particles from hot-injection routes and unravel the dual effect of polyacrylate incorporation, where the partial degradation of LHP-NCs luminescence is counterbalanced by the passivation of electron-poor defects by the host acrylic groups. Experiments on NCs with tailored surface defects show that the balance between such antithetical effects of polymer embedding is determined by the surface defect density of the NCs and provide guidelines for further material optimization.

We propose a novel neural network approach, LARP (Learned Articulated Rigid body Physics), to model the dynamics of articulated human motion with contact. Our goal is to develop a faster and more convenient methodological alternative to traditional physics simulators for use in computer vision tasks such as human motion reconstruction from video. To that end we introduce a training procedure and model components that support the construction of a recurrent neural architecture to accurately simulate articulated rigid body dynamics. Our neural architecture supports features typically found in traditional physics simulators, such as modeling of joint motors, variable dimensions of body parts, contact between body parts and objects, and is an order of magnitude faster than traditional systems when multiple simulations are run in parallel. To demonstrate the value of LARP we use it as a drop-in replacement for a state of the art classical non-differentiable simulator in an existing video-based reconstruction framework and show comparative or better 3D human pose reconstruction accuracy.

The realization of Urban Air Mobility (UAM) necessitates scalable global path planning algorithms capable of ensuring safe navigation within complex urban environments. This paper proposes a multi-scale risk-aware cell decomposition method that efficiently partitions city-scale airspace into variable-granularity sectors, assigning each cell an analytically estimated risk value based on obstacle proximity and expected risk. Unlike uniform grid approaches or sampling-based methods, our approach dynamically balances resolution with computational speed by bounding cell risk via Mahalanobis distance projections, eliminating exhaustive field sampling. Comparative experiments against classical A*, Artificial Potential Fields (APF), and Informed RRT* across five diverse urban topologies demonstrate that our method generates safer paths with lower cumulative risk while reducing computation time by orders of magnitude. The proposed framework, Larp Path Planner, is open-sourced and supports any map provider via its modified GeoJSON internal representation, with experiments conducted using OpenStreetMap data to facilitate reproducible research in city-wide aerial navigation.