Stellar-mass black holes (BHs) surrounded by neutrino-dominated accretion flows (NDAFs) are a leading central engine of gamma-ray bursts (GRBs). In this work, we investigate the electron fraction distribution in NDAFs with or without disk outflows for different accretion rates, BH spins, and outflow rates. As the results, for the cases of the massive disks at relatively low accretion rates, the outer boundary of the disks are predominantly advection-cooled, yielding electron fractions of \(Y_{\rm e} \sim 0.5\), as expected for massive collapsar progenitors. By contrast, in the cases of lower-mass disk at high accretion rates, neutrino cooling becomes highly efficient and mildly electron-degenerate disks emerge, characterized by \(Y_{\rm e} \lesssim 0.38\) at the outer boundary of the disk, even for the strong outflows, which is consistent with materials from compact object merger scenarios. Moreover, we find that these trends remain robust across different BH spins. Consequently, the self-consistent agreement between the electron fraction properties at the outer boundaries of NDAFs and those expected from GRB progenitors provides effectively support for NDAFs serving as the GRB central engines.

Effective interface conditions for a periodically voided thin layer separating two homogeneous bulk regions are derived for the elastic wave equation by taking the simultaneous limit of vanishing layer periodicity and layer thickness. The limit problems are obtained using the unfolding method for thin perforated domains. We consider three different scalings of the material parameters in the layer that characterise its stiffness, each leading to a distinct type of interface condition and requiring the solution of scaling-dependent cell problems. Depending on the scaling, the resulting effective model yields either a membrane equation or a Kirchhoff-Love plate equation. In the critical regime of reduced stiffness, the interface equation additionally depends on the microscopic variable. By selecting appropriate cell problems, this equation can be reformulated as an effective interface condition between the bulk domains.

Solving the discrete logarithm problem (DLP) with quantum computers is a fundamental task with important implications. Beyond Shor's algorithm, many researchers have proposed alternative solutions in recent years. However, due to current hardware limitations, the scale of DLP instances that can be addressed by quantum computers remains insufficient. To overcome this limitation, we propose a distributed quantum discrete logarithm algorithm that reduces the required quantum register size for solving DLPs. Specifically, we design a distributed quantum algorithm to determine whether the solution is contained in a given set. Based on this procedure, our method solves DLPs by identifying the intersection of sets containing the solution. Compared with Shor's original algorithm, our approach reduces the register size and can improve the success probability, while requiring no quantum communication.

This manuscript considers the Jordan-Moore-Gibson-Thompson (JMGT) equation and its linearized equation with an additional weak damping term (proposed by [B. Kaltenbacher, \emph{Inverse Problems} (2025)] firstly) in the whole space $\mathbb{R}^n$. We mainly study the unique existence and large time behavior, including optimal decay estimates and asymptotic profiles, of global in-time Sobolev solutions for any $n\geqslant 1$. This weak damping term leads to diffusion profiles in the sub-critical case $δ>0$ and regularity-loss decay properties in the critical case $δ=0$, which are greatly different from the results for the corresponding classical models without the weak damping term.

We prove that finitely generated amenable groups acting on CAT(0) spaces satisfy the following alternative: either every action on a geodesically complete CAT(0) space with bounded geometry (or finite dimension) has a global fixed point, or the group admits a fixed-point-free action on $\mathbb{R}^n$. As a consequence, finitely generated amenable torsion groups and finitely generated virtually simple amenable groups cannot act nontrivially on geodesically complete CAT(0) spaces with bounded geometry or on finite-dimensional complete CAT(0) spaces. The proof relies on a Kazhdan-type rigidity theorem for groups with the Euclidean fixed point property: if such a group acts on a geodesically complete CAT(0) space of bounded geometry with almost fixed points, then it has a genuine fixed point. This yields several further corollaries, including a rigidity dichotomy for drift and that any finitely generated torsion group acting on a geodesically complete visibility CAT(0) space with bounded geometry must have a global fixed point. These results make substantial progress on the longstanding problem of understanding actions of torsion groups on CAT(0) spaces.

We consider a family of nonlinear sigma models on $\mathbb{Z}^{d}$ whose target space is the hyperbolic super manifold $H^{2|2n}$, $n >1$, introduced by Crawford as an extension of Zirnbauer's $H^{2|2}$ model for disordered systems. We prove exponential decay of the two-point correlation function in the high-temperature regime $β\leq C n^{-1}$, with $C>0$ a universal constant, for any $n>1$ and any dimension $d\geq 1$, with mass $\log β^{-1}$. We also consider models with long-range interaction and prove fast decay in the same high-temperature regime. The proof is based on the reduction to a marginal fermionic theory and combines a high-temperature cluster expansion, exact combinatorics and bounds derived via Grassmann norms.

Existing approaches for battery health forecasting often rely on extensive cycling histories and continuously monitored cells. In contrast, many real-world scenarios provide only sparse information, e.g. a single diagnostic cycle. In our study, we investigate state of health (SoH)- and remaining useful life (RUL) estimation of previously unseen lithium-ion cells, relying on cycling data from begin of life (BOL) to end of life (EOL) of multiple similar cells by using the publicly available Oxford battery aging dataset. The estimator applies incremental capacity analysis (ICA)-based feature extraction in combination with data-efficient regression methods. Particular emphasis is placed on a multi-model Gaussian process regression ensemble approach (GPRn), which also provides uncertainty quantification. Due to a rather cell invariant behaviour, the mapping of ICA features to SoH estimation is highly precise and points out a normalized mean absolute error (NMAE) of 1.3%. The more cell variant mapping to RUL estimation is challenging, reflecting in a NMAE of 5.3%. Using the estimation results, a RUL monitoring strategy is derived. The objective is to safely operate a battery cell from BOL to EOL by only taking sparse diagnostic measurements. On average, only four diagnostic measurements are required during a cell's lifetime of 3300 to 5000 cycles.

This paper proposes a movable-antenna-based index modulation (MA-IM) framework that exploits the spatial mobility of a single reconfigurable antenna to create additional information-bearing dimensions for next-generation wireless systems. By discretizing the continuous movable region into a dense set of candidate sampling points and selecting representative anchors for indexing, the proposed framework converts spatial degrees of freedom into a practical modulation resource. Building on this framework, we develop a family of anchor-selection strategies with different levels of channel awareness, including geometry-based, SNR-based, max--min channel-domain, and joint constellation-aware designs. For the resulting MA-IM schemes, joint maximum-likelihood (ML) detectors are derived, along with a low-complexity two-stage detector, and unified analytical upper bounds on the average bit error probability (ABEP) are established based on the joint index--modulation constellation. The results reveal that directly indexing all sampling points is generally unreliable, highlighting the necessity of anchor optimization. The performance of MA-IM is shown to depend on key system parameters, including channel richness, spatial correlation, the number of index states, and the modulation order. In particular, increasing the number of index states and increasing the QAM order affect MA-IM in fundamentally different ways, even under the same transmission rate. Among the proposed schemes, the joint constellation-aware anchor design achieves the best error performance, demonstrating that optimizing channel-domain separation alone is insufficient and that effective MA-IM design must account for the geometry of the joint signal constellation. Simulation results further show that, with properly designed anchors, MA-IM can approach or even outperform same-spectral-efficiency QAM baselines.

This paper proposes a novel modulation and coding scheme (MCS) selection framework that integrates mutual information (MI) prediction based on vector similarity search (VSS) for massive multi-user multiple-input multiple-output orthogonal frequency-division multiplexing (MU-MIMO-OFDM) systems with advanced uplink multi-user detection (MUD). The framework performs MCS selection at the transport block (TB)-level MI and establishes the mapping from post-MUD MI to post-decoding block error rate (BLER) using a prediction function generated from extrinsic information transfer (EXIT) curves. A key innovation is the VSS-based MI prediction scheme, which addresses the challenge of analytically predicting MI in iterative detectors such as expectation propagation (EP). In this scheme, an offline vector database (VDB) stores feature vectors derived from channel state information (CSI) and average received signal-to-noise ratio (SNR), together with corresponding MI values achieved with advanced MUD. During online operation, an approximate nearest neighbor (ANN) search on graphics processing units (GPUs) enables ultra-fast and accurate MI prediction, effectively capturing iterative detection gains. Simulation results under fifth-generation new radio (5G NR)-compliant settings demonstrate that the proposed framework significantly improves both system and user throughput, ensuring that the detection gains of advanced MUD are faithfully translated into tangible system-level performance improvements.

Efficient simulation of Darcy flow in highly heterogeneous porous media requires iterative solvers that remain robust under large permeability contrasts and mixed boundary conditions. Spectral coarse spaces in two-level overlapping Schwarz methods provide such robustness, but their practical use is often limited by an expensive setup phase dominated by many local generalized eigenvalue solves. We propose a purely algebraic, coarse-space approximation that avoids these repeated local eigensolves by using a graph neural network operating on the system-matrix graph. On the analysis side, we introduce a coefficient-weighted subspace-distance measure to quantify the discrepancy between the approximated and target local multiscale coarse spaces, and we derive a condition-number bound for the resulting preconditioned operator in terms of this distance. This bound yields a principled supervised-training objective and links learning error to solver performance. Numerical experiments on 2D and 3D high-contrast Darcy systems with varying mixed boundary conditions demonstrate that the proposed approach substantially reduces setup cost and improves end-to-end time-to-solution, while preserving robust convergence across the tested contrasts and boundary configurations.

This paper studies a fractional attraction-repulsion system with generalized logistic source and nonlinear productions: \begin{equation*} \left\{ \begin{aligned} &u_t = -(-Δ)^αu - χ_1 \nabla \cdot (u \nabla v) + χ_2 \nabla \cdot (u \nabla w) + au - bu^γ, &x \in \mathbb{R}^N, \, t > 0, \\ &0 = Δv - λ_1 v + μ_1 u^k, &x \in \mathbb{R}^N, \, t > 0, \\ &0 = Δw - λ_2 w + μ_2 u^k, &x \in \mathbb{R}^N, \, t > 0. \end{aligned} \right. \end{equation*} We first establish the global boundedness of classical solutions with nonnegative bounded and uniformly continuous initial data in two different cases: $γ\geq k + 1$ and $γ< k + 1$, respectively. Next, we show the asymptotic behavior of the global solutions for both cases $γ= k + 1$ and $γ\neq k + 1$. Finally, we obtain the spreading speed of solutions. In particular, when $γ= k + 1$, the upper bound of the spreading speed increases monotonically with $k$. If the condition of balanced attraction-repulsion intensities is further specified, the spreading speed will be equal to $\frac{a}{N + 2α}$.

The rapid emergence of edge computing platforms and large-scale data centers has made power efficiency a primary design constraint, particularly for data-intensive and AI-driven workloads. Field-programmable gate arrays (FPGAs) are increasingly adopted due to their flexibility and potential for energy-efficient acceleration. However, FPGA supply voltages are typically fixed at design time using conservative margins, limiting the ability to adapt power consumption to runtime conditions. This paper presents VolTune, an open-source runtime voltage control architecture that enables runtime tuning of FPGA supply voltages through FPGA-integrated control logic that abstracts low-level PMBus operations. VolTune provides both hardware-based and software-based control paths, allowing designers to balance deterministic low-latency operation against programmability. In the presented prototype, the hardware-based control path achieves a measured end-to-end voltage transition latency of 2.3 ms, while the controller adds under 2% static power overhead and under 2% FPGA resource overhead. As a representative case study, VolTune is evaluated on the GTX transceiver supply rail of a Kintex-7 platform. The results show that runtime voltage tuning exposes a bounded operating region with clear trade-offs between energy efficiency and reliability, and achieves up to approximately 29.3% rail-power reduction at 10.0 Gbps when allowing BER up to 10e-6. These results show that FPGA-integrated runtime voltage control can provide practical energy savings with low integration overhead.

In this paper, we present a unified theoretical study of fluctuation-dominated transport and transverse thermoelectric response in two-dimensional superconducting films subjected to out-of-plane magnetic fields and electric-field drive. Our approach is based on the time-dependent Ginzburg-Landau equation with Langevin thermal noise, in which interaction effects of fluctuating Cooper pairs are incorporated self-consistently at the Gaussian (Hartree) level. We derive closed-form expressions for the fluctuation-induced Cooper-pair density, the renormalized resistance $R(T,B_\perp)$, and the nonlinear current response $J(E,B_\perp)$, explicitly accounting for the feedback of the electric field on the fluctuation spectrum. A central result is the emergence of an intrinsic S-shaped nonlinear $J$-$E$ (or $I$-$V$) characteristic, featuring a negative-differential segment and multivalued solutions under voltage control. Within this framework, we introduce a physically transparent procedure to identify characteristic instability scales, such as the magnetic field $B^{\ast}$ (or equivalently $B_χ$), which marks the terminal point of the S-shaped instability where the nonlinear response becomes single-valued. In parallel, we analyze the off-diagonal Peltier coefficient $α_{xy}$ as a direct probe of the transverse thermoelectric response of superconducting fluctuations. The theory is validated through systematic comparisons with recent experimental measurements of multi-field $R(T)$ curves, nonlinear $I$-$V$ characteristics, and $α_{xy}$ data across a broad range of thin-film superconducting materials.

In this paper, we establish a Paley-Wiener type uncertainty principle for Schrödinger equations with bounded electric and magnetic potentials, \begin{align*} i\partial_tu+Δ_Au+V(t,x)u=0,\,\,u(0,x)=u_0(x), \end{align*} where $Δ_A=(\nabla-iA)^2$ denotes the magnetic Schrödinger operator. Specifically, under suitable assumptions on $A$ and $V$, we show that if a solution $u$ exhibits linear exponential decay and support property in one spatial direction at times $t=0$ and $t=1$ respectively, then $u$ must vanish identically. This result extends the theorem of Kenig-Ponce-Vega [Ann. Sci. Éc. Norm. Supér. (4) 47 (2014), 539-557] to the case $A\neq0$. We overcome the difficulty brought by the magnetic potential which breaks the translation invariance in the leading term of Hamiltonian $H=Δ_A+V$. As a direct consequence, we also obtain a uniqueness result for a class of semi-linear Schrödinger equation with electromagnetic potentials.

Standard periodic pilot patterns in orthogonal frequency division multiplexing (OFDM) systems induce severe delay-domain grating lobes, compromising radar sensing. This paper proposes a two-stage framework to design non-periodic pilot patterns that minimize the peak sidelobe level (PSL) while strictly enforcing communication anchor constraints. We black solve this combinatorial problem using a low-complexity hybrid greedy-stochastic cyclic coordinate descent (SCCD) algorithm. This approach shatters cyclic periodicities to suppress deterministic grating lobes beneath the impassable data-to-pilot interference (DPI) noise floor. System-level evaluations demonstrate the performance of the proposed design in resolving the sensing-communication trade-off, showing improved range root mean square error (RMSE) without degrading the primary communication bit error rate (BER).

We study the space of real rational curves of low degree in the quadric of signature $(3,2)$ and provides a classificaton of real rational knots and nodal curves. Apart from the classification, we also study the relationship between the real rational knots in the quadric and the real rational knots in $\mathbb{RP}^{3}$. Furthermore, a construction for the representatives of all the real rational knots of degree $\leq 5$ in the quadric is presented.

Despite widespread use, the complexity class of modern regular expression matching was not well-understood. Previous work proved that regular expression matching with backreferences and lookarounds was PSPACE-complete, but the proof was not mechanized and applied to an abstract regex language. This paper clarifies the question for JavaScript regular expressions. In this paper, we prove the following new results, with most core proofs mechanized in the Rocq proof assistant. We prove that JavaScript regex matching is indeed PSPACE-hard, even without negative lookarounds, and OptP-hard as well; that JavaScript regex matching without lower-bounded quantifiers (i.e. quantifiers with a non-zero minimum number of repetitions) is PSPACE-complete; and that JavaScript regex matching without lower-bounded quantifiers and without lookarounds is OptP-complete.

A sensor that can detect the direction of the incoming light plays a crucial role in further enhancing the versatility of the multifunction sensors for future applications, where the sensor can read multiple pieces of information, similar to the biological senses, like skin. A hybrid sensor based on an n-type ZnO micro-rod with p-type optically active organic polymer (PBTTT-C14) is developed for low-cost, large-area piezoelectric and optical sensing applications for future artificial electronic skin. The multi-functionality of the device is achieved due to the heterostructure configuration of vertically aligned piezoelectric ZnO micro rod arrays and PBTTT-C14 polymer between two gold electrodes. The deposition of the top gold electrode also led to the formation of two regions where it forms a continuous film and isolated gold particles (Au NPs). The isolated NPs, when activated, has shown surface plasmon resonance (SPR) and Förster resonance energy transfer (FRET), which generate a potential opposite to the normal working of the device, depending on the number of excited Au NPs by the incident light. The polarity flipping/opposite potential development can be attributed to the rise in electron density near the top Au contact due to the SPR and FRET mechanism of isolated Au NPs over the PBTTT-C14 which depends on the illumination direction. As a result, direction-dependent photo voltage polarity flipping was realized in the device. The device has produced piezoelectric and direction-dependent photovoltage flipping responses, leading the way for a multifunction sensor that can detect the direction of incident light and touch.

Multicomponent crystals are often assumed to form nearly random solid solutions when thermodynamically stable. However, crystal growth proceeds from structurally heterogeneous liquids, raising the possibility that the liquid state may influence which species are incorporated into the growing crystal. Here we demonstrate that liquid-state structural asymmetry can induce species-selective crystallization in multicomponent systems. Using molecular dynamics simulations of a multivalent rocksalt-type model (AgPbBiTe$_3$), we find that cations with higher valence readily form locally crystal-compatible coordination environments in the liquid and are efficiently incorporated into the growing lattice, whereas lower-valence cations exhibit more disordered liquid coordination and attach less efficiently at the crystal-liquid interface. This asymmetry leads to species-selective incorporation and slower crystal growth. Depth-resolved photoelectron spectroscopy measurements on AgPbBiTe$_3$ further reveal enhanced Ag concentration near grain-boundary and surface regions, consistent with the selective incorporation predicted by the simulations. These results demonstrate that structural compatibility between liquid-state structure and the target crystal motif governs selective incorporation during crystallization, providing a general kinetic mechanism by which compositional heterogeneity can emerge during growth of multicomponent crystals.

As the memory channel count is confined by physical dimensions, memory expanders appear to be a promising approach to extending memory capacity and channels by augmenting the existing I/O interface (e.g., PCIe) with memory-semantic protocols like CXL. Unfortunately, the physical constraints of a computing system restrict scalable capacity expansion with memory expanders. In this work, we propose a block-level compression scheme for modern memory expanders, IBEX, to achieve larger effective memory capacity. Given the performance overhead associated with block-level compression algorithms (e.g., LZ77), IBEX employs a promotion-based approach: only cold data is compressed, whereas hot data remains uncompressed. Our key innovation is internal bandwidth-efficient block management that precisely identifies cold pages with minimal metadata access overhead. Still, the promotion-based approach poses several performance-related challenges at the design level. Therefore, we also propose a shadowed promotion scheme that temporarily postpones the deallocation of promoted data, thereby mitigating the performance penalty incurred by demotion (i.e., recompression). Furthermore, we optimize our compression scheme by compacting metadata and co-locating multiple target blocks for efficient bandwidth utilization. Consequently, IBEX achieves an average of 1.28x-1.40x speedups compared to the state-of-the-art promotion-based block-level approaches. We open-source IBEX at https://github.com/relacslab/ibex-ics26.

This paper addresses the scheduling problem for unrelated crowd workers in mobile social networks, where the required service time for each task varies among the assigned crowd workers. The goal is to minimize the total weighted completion time of all tasks. First, in an environment with identical crowd workers, we improve the approximation ratio of the Largest-Ratio-First (LRF) scheduling algorithm and provide an updated competitive ratio for its online version. Next, for the unrelated crowd workers environment, we introduce a randomized approximation algorithm that achieves an expected approximation ratio of 1.45. This result improves upon the 1.5-approximation ratio reported in our previous work. We also present a derandomization method for this algorithm. Furthermore, to improve computational efficiency, we propose an algorithm that leverages the property that the optimal schedule on a single crowd worker arranges tasks in non-increasing order by their Smith ratios. Experimental results demonstrate that our proposed method outperforms three variants of the LRF algorithm.

Achieving reliable communication has long been a fundamental challenge in networked systems. Semantic Error Correction (SEC) leverages the semantic understanding capabilities of language models (LMs) to perform application-layer error correction, complementing conventional channel decoding. While promising, existing SEC approaches rely solely on context captured by LMs at the application layer, ignoring the rich information available at the physical layer. To address this limitation, this paper introduces Cross-Layer SEC (CL-SEC), an LM-empowered error correction framework that integrates cross-layer information from both the physical and application layers to jointly correct corrupted words in text communication. Using a Bayesian combination in product form tailored to this framework, CL-SEC achieves significantly improved performance over methods that process information in isolated layers. CL-SEC shows substantial gains across multiple error-correction metrics, including bit-error rate, word-error rate, and semantic fidelity scores. Importantly, unlike most semantic communication systems that focus solely on recovering the semantic meaning of transmitted messages, CL-SEC aims to reconstruct the original transmitted message verbatim, leveraging the semantic understanding capabilities of LMs for precise reconstruction.

Second-harmonic generation in resonant structures is commonly evaluated in terms of intracavity field enhancement at the fundamental and harmonic frequencies. Here, we formulate nonlinear frequency conversion within a symmetry-resolved overlap framework that explicitly separates resonant field buildup from nonlinear mode projection. Using a simple and analytically tractable Fabry--Perot thin-film-on-substrate geometry, we show that, even in the presence of spectrally bright resonances at both $ω$ and $2ω$, the emitted second-harmonic signal can be strongly suppressed when the spatial parity of the pump-induced nonlinear polarization is incompatible with that of the radiating $2ω$ standing-wave mode. This mechanism gives rise to nonlinearity-selective quasi-bound states in the continuum. Beyond providing a compact interpretation of these nonlinear dark states, the framework unifies pump enhancement, harmonic enhancement, and symmetry-controlled modal overlap within a single predictive metric. More broadly, it identifies thickness regimes in which resonant buildup is accompanied by constructive nonlinear coupling, and distinguishes them from regimes in which apparently favorable resonance conditions remain conversion-inactive because the nonlinear source is orthogonal to the radiating harmonic mode.

We present a new elementary proof of a theorem due to Harald Bohr, which states that an unbounded, analytic, and almost periodic function in a half-plane can be written as the sum of two analytic functions: the first is unbounded and periodic, while the second is bounded and almost periodic. The proof is based on a well-known arithmetical property of translation numbers of almost periodic functions.

Angular momentum removal is a fundamental requirement for star and planet formation, yet the mechanisms driving this process remain debated. Magnetohydrodynamic disk winds, launched along magnetic field lines from extended disk regions, offer a promising solution, particularly in regions where magnetorotational turbulence is weak. Here we present high-resolution Atacama Large Millimeter/submillimeter Array observations of the Class 0 protostar HOPS 358, revealing a rotating, nested outflow structure traced by H2CO, SO, and CH3OH emission. The outflow preserves the disk's rotational sense and is aligned with the disk axis, providing direct observational evidence for a magnetically launched disk wind. From the measured kinematics, we derive a dimensionless magnetic lever arm of approximately 2.3 and constrain the wind-launching region to radii of 10-18 astronomical units within the planet-forming zone. These results demonstrate that magnetohydrodynamic disk winds operate during the deeply embedded phase, efficiently extracting angular momentum while shaping disk evolution and establishing initial conditions for planet formation.

In this paper, we introduce a class of twisted multiparameter singular integrals on $\mathbb{R}^{2m}$, motivated by the Cauchy--Szegő projections and the solving operators for $\bar{\partial}_b$ on a broad family of quadratic surfaces of higher codimension in $\mathbb{C}^n$. These surfaces are represented as suitable quotients of products of Heisenberg groups, a framework illustrated by Stein (Notices Amer. Math. Soc., 1998). While classical multiparameter product and flag theories are well-developed, Nagel, Ricci, and Stein observed a critical limitation: the class of product operators is not closed under passage to a quotient subgroup. To handle the geometric reduction that models these quotient structures, we take the first step in developing an adapted real-variable theory. We achieve this by introducing twisted tube systems and tube maximal functions, establishing a reproducing formula, Littlewood--Paley theory, a Journé-type covering lemma, and atomic decompositions. As particular examples, we obtain twisted Fourier multipliers -- which emerge as novel, direction-sensitive, and anisotropic phase-shift converters with potential applications in signal and image processing.

Ultracold Bose gases in one-dimensional optical lattices constitute an important benchmark problem in the study of strongly interacting many-body quantum phases. Here we present a combined experimental and theoretical study of their phase-coherence properties over a wide range of lattice depths. Experimentally, we extract the single-particle correlation function directly from the measured momentum distribution. Theoretically, we perform tensor-network simulations of the Bose-Hubbard model that incorporate all relevant experimental parameters. For deep lattices well within the Mott insulator regime, the experimental results are in good agreement with the expected zero-temperature behavior, with only small temperature-dependent corrections. As the lattice depth is reduced, finite-temperature effects become increasingly important. We find that the experimental data are quantitatively described by an effective temperature extracted from the tensor-network simulations, and that this effective temperature decreases markedly with increasing lattice depth. Rather than indicating actual cooling, we interpret this behavior as evidence of inhibition of thermalization caused by the formation of Mott domains that suppress heat transport. Counterintuitively, the inhibition of thermalization favors the preparation of an effectively low-entropy quantum gas in the trap center for large lattice depths.

Following long periods of quiescence, low-mass X-ray binaries can exhibit intense X-ray outbursts triggered by instabilities within the accretion disk. These outbursts can sometimes be detected in optical wavelengths before being detected in X-rays, acting as an early onset warning and enabling a deep study of accretion disk properties informed by the lag between optical and X-ray rise. We explore the potential of Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) to detect these outbursts early through optical observations. We evaluate the capabilities of LSST based on currently planned survey cadence, filter-specific depth, and other observational factors that affect early detection. We develop and apply an extended metric to assess outburst detectability and recovery fraction. We find that despite inherent potential for early detection of XRB outbursts, the currently planned survey strategy makes it challenging to detect early onset of XRBs. Lastly, we demonstrate how this estimate can be used to infer the wider LMXB population in the Galaxy as the LSST progresses.

The electric fields inside thunderstorms can significantly modify the intensity of secondary cosmic ray muons at the ground level, producing measurable variations in their intensity ($Δ$I$_μ$). By utilizing the decade-long observations of thunderstorms (April 2011-December 2020) by the GRAPES-3 muon telescope (G3MT), a directional asymmetry in $Δ$I$_μ$ is observed, with nearly six times more events being detected from the east than the west directions. Using detailed CORSIKA Monte Carlo simulations, it is shown that this asymmetry is caused by the variations of the muon charge ratio R$_μ$ (N$_{μ^+}$/N$_{μ^-}$). The anisotropic R$_μ$ in turn, is caused by the systematic changes in geomagnetic cutoff rigidities, and subsequent selective filtering of predominantly positively charged primary cosmic rays. As a consequence, the R$_μ$ increases systematically from west to east across the G3MT field of view, enhancing the sensitivity of east directions to positively charged thunderstorm top. Monte Carlo simulations with constant R$_μ$ show that the directional asymmetry disappears, demonstrating the muon charge imbalance to be the dominant driver of the observed asymmetry. The dependence of R$_μ$ on the hadronic interaction is also studied by comparing seven combinations high-, and low-energy hadronic interaction generators, which show a $\lesssim$7% spread in R$_μ$, and $\lesssim$14% variation in the derived thunderstorm potentials. These results provide the first quantitative link between the muon charge asymmetry caused by the geomagnetic field, and the directional distribution of thunderstorms, reinforcing the role of muon observations as a probe of gigavolt potentials in atmospheric electrical structures.

The rapid scaling of Protein Language Models (PLMs) has unlocked unprecedented accuracy in protein structure prediction and design, but the quadratic memory growth of the Key-Value (KV) cache during inference remains a prohibitive barrier for single-GPU deployment and high-throughput generation. While 8-bit quantization is now standard, 3-bit quantization remains elusive due to severe numerical outliers in activations. This paper presents TurboESM, an adaptation of Google's TurboQuant to the PLM domain. We solve the fundamental incompatibility between Rotary Position Embeddings (RoPE) and orthogonal transformations by deriving a RoPE-first rotation pipeline. We introduce a head-wise SVD calibration method tailored to the amino acid activation manifold, a dual look-up table (LUT) strategy for asymmetric K/V distributions, and a 1-bit Quantized Johnson-Lindenstrauss (QJL) residual correction. All experiments are conducted on ESM-2 650M, where our implementation achieves a 7.1x memory reduction (330 MB to 47 MB) while maintaining cosine similarity > 0.96 in autoregressive decoding across diverse protein families, including short peptides, transmembrane helices, enzyme active site fragments, and intrinsically disordered regions. We further implement a Triton-based fused decode attention kernel that eliminates intermediate dequantization memory allocations, achieving a 1.96x speedup over the PyTorch two-step path for the KV fetch operation alone; however, TurboESM incurs a prefill overhead of 21-27 ms relative to the original model due to KV quantization and packing, making it most suitable for memory-bound scenarios rather than latency-critical short-sequence workloads. Analysis reveals that PLMs exhibit sharper outlier profiles than large language models (LLMs) due to amino acid vocabulary sparsity, and our method effectively addresses these distributions.