As quantum computing moves from isolated experiments toward integration with large-scale workflows, the integration of quantum devices into HPC systems has gained much interest. Quantum cloud providers expose shared devices through first-come first-serve queues where a circuit that executes in 3 seconds can spend minutes to an entire day waiting. Minimizing this overhead while maintaining execution fidelity is the central challenge of quantum cloud scheduling, and existing approaches treat the two as separate concerns. We present Qurator, an architecture-agnostic quantum-classical task scheduler that jointly optimizes queue time and circuit fidelity across heterogeneous providers. Qurator models hybrid workloads as dynamic DAGs with explicit quantum semantics, including entanglement dependencies, synchronization barriers, no-cloning constraints, and circuit cutting and merging decisions, all of which render classical scheduling techniques ineffective. Fidelity is estimated through a unified logarithmic success score that reconciles incompatible calibration data from IBM, IonQ, IQM, Rigetti, AQT, and QuEra into a canonical set of gate error, readout fidelity, and decoherence terms. We evaluate Qurator on a simulator driven by four months of real queue data using circuits from the Munich Quantum Toolkit benchmark suite. Across load conditions from 5 to 35,000 quantum tasks, Qurator stays within 1% of the highest-fidelity baseline at low load while achieving 30-75% queue time reduction at high load, at a fidelity cost bounded by a user-specified target.

Automated proof generation for formal software verification remains largely unresolved despite advances in large language models (LLMs). While LLMs perform well in NLP, vision, and code generation, formal verification still requires substantial human effort. Interactive theorem proving (ITP) demands manual proof construction under strict logical constraints, limiting scalability; for example, verifying the seL4 microkernel required decades of effort. Existing LLM-based approaches attempt to automate this process but remain limited. Most rely on single-shot generation or shallow retrieval, which may work for small proofs but fail to scale to large, interdependent verification tasks with deep structural dependencies. We present PROMISE (PROof MIning via Structural Embeddings), a structure-aware framework that reframes proof generation as a stateful search over proof-state transitions. Instead of surface-level retrieval, PROMISE mines structural patterns from proof states and tactic transitions, enabling retrieval and adaptation of compatible proof fragments during iterative search. We evaluate PROMISE on the seL4 benchmark across multiple LLM backends and compare it with prior systems such as Selene and Rango. PROMISE consistently outperforms prior methods, achieving up to +26 point improvements (186% relative gain) while maintaining robustness across models, demonstrating the effectiveness of structure-aware proof mining for scalable theorem proving.

Scientific software relies on high-precision computation, yet finite floating-point representations can introduce precision errors that propagate in safety-critical domains. Despite the growing use of large language models (LLMs) in scientific applications, their reliability in handling floating-point numerical stability has not been systematically evaluated. This paper evaluates LLMs' reasoning on high-precision numerical computation through two numerical stabilization tasks: (1) detecting instability in numerical expressions by generating error-inducing inputs (detection), and (2) rewriting expressions to improve numerical stability (stabilization). Using popular numerical benchmarks, we assess six LLMs on nearly 2,470 numerical structures, including nested conditionals, high-precision literals, and multi-variable arithmetic. Our results show that LLMs are equally effective as state-of-the-art traditional approaches in detecting and stabilizing numerically unstable computations. More notably, LLMs outperform baseline methods precisely where the latter fail: in 17.4% (431) of expressions where the baseline does not improve accuracy, LLMs successfully stabilize 422 (97.9%) of them, and achieve greater stability than the baseline across 65.4% (1,615) of all expressions. However, LLMs struggle with control flow and high-precision literals, consistently removing such structures rather than reasoning about their numerical implications, whereas they perform substantially better on purely symbolic expressions. Together, these findings suggest that LLMs are effective at stabilizing expressions that classical techniques cannot, yet struggle when exact numerical magnitudes and control flow semantics must be precisely reasoned about, as such concrete patterns are rarely encountered during training.

The integration of cellular communication with Unmanned Aerial Vehicles (UAVs) extends the range of command and control and payload communications of autonomous UAV applications. Accurate modeling of this air-to-ground wireless environment aids UAV mission planning. Models built on and insights obtained from real-life experiments intricately capture the variations in air-to-ground link quality with UAV position, offering more fidelity for simulations and system design than those that rely on generic theoretical models designed for ground scenarios or ray-tracing simulations. In this work, we conduct aerial flights at the Aerial Experimentation and Research Platform for Advanced Wireless (AERPAW) Lake Wheeler testbed to study the variation in key performance indicators (KPIs) of a private 4G/5G cellular base station (BS) with the UAV's altitude, distance from the BS, elevation, and azimuth relative to the BS. Variations in 4G and 5G physical layer KPIs and application layer throughput are logged and analyzed, using two Android smartphones: a Keysight Nemo device, with enhanced KPI access, through a rooted operating system, and a standard smartphone running a custom application that utilizes open-source Android APIs. The observed signal strength measurements are compared to theoretical predictions from free space path loss models that incorporate the BS antenna radiation patterns. Mathematical model parameters for polynomial curve approximations are derived to fit the observed data. Light machine learning approaches, namely random forests, gradient boosting regressors and neural networks, are used to model KPI behaviour as a function of UAV position relative to the BS. The insights and models generated from real-life experiments in this study can serve as valuable tools in the design, simulation and deployment of cellular communication-based UAV systems.

Diffusion models have emerged as the prevailing approach for text-to-image (T2I) and text-to-video (T2V) generation, yet production platforms must increasingly serve both modalities on shared GPU clusters while meeting stringent latency SLOs. Co-serving such heterogeneous workloads is challenging: T2I and T2V requests exhibit vastly different compute demands, parallelism characteristics, and latency requirements, leading to significant SLO violations in existing serving systems. We present GENSERVE, a co-serving system that leverages the inherent predictability of the diffusion process to optimize serving efficiency. A central insight is that diffusion inference proceeds in discrete, predictable steps and is naturally preemptible at step boundaries, opening a new design space for heterogeneity-aware resource management. GENSERVE introduces step-level resource adaptation through three coordinated mechanisms: intelligent video preemption, elastic sequence parallelism with dynamic batching, and an SLO-aware scheduler that jointly optimizes resource allocation across all concurrent requests. Experimental results show that GENSERVE improves the SLO attainment rate by up to 44% over the strongest baseline across diverse configurations.

Large language model (LLM) decoding is a major inference bottleneck because its low arithmetic intensity makes performance highly sensitive to memory bandwidth. 3D-stacked near-memory processing (NMP) provides substantially higher local memory bandwidth than conventional off-chip interfaces, making it a promising substrate for decode acceleration. However, our analysis shows that this bandwidth advantage also shifts many decode operators on 3D-stacked NMP back into the compute-bound regime. Under the tight area budget of the logic die, the design of the compute substrate itself therefore becomes a first-order challenge. Therefore, we rethink the compute microarchitecture of prior 3D-stacked NMP designs. First, we replace prior MAC tree-based compute units with a more area-efficient systolic array, and we further observe that decode operators exhibit substantial shape diversity, making reconfigurability in both systolic array shape and dataflow essential for sustaining high utilization. Building on this insight, we continue to exploit two key opportunities: the high local memory bandwidth reduces the need for large on-chip buffers, and the existing vector core, originally designed to handle auxiliary tensor computations, already provides much of the control logic and multi-ported buffering required for fine-grained flexibility for systolic array, allowing us to unify the two structures in a highly area-efficient manner. Based on these insights, we present the first compute microarchitecture tailored to 3D-stacked NMP LLM decoding, explicitly designed to satisfy the joint requirements of low area cost, high-bandwidth operation, and fine-grained reconfigurability. We further propose an multi-core scheduling framework. Compared with Stratum, our design achieves an average 2.91x speedup and 2.40x higher energy efficiency across both dense and MoE models.

In this study, we report a physical model and a Monte Carlo simulation scheme developed to predict the angular distributions of energetic argon atoms and ions as an ion beam passes through a gas-filled volume. The study explores charge-exchange neutralization as a method for generating fast neutral beams suitable for low-damage, high aspect ratio (HAR) etching. The proposed model and simulation code are straightforward and compact, potentially making them useful tools for prototyping.

We derive an Aronson-Bénilan / Li-Yau estimate in the JKO scheme associated to the porous-medium, heat, and fast-diffusion equations, in dimensions $1$ and $2$, and on simple domains (cubes, quarter-space, half-spaces, whole space, and the torus). Our method is based on a maximum principle for the determinant of the Hessian of Brenier potentials, iterated as a one-step improvement along the scheme. As a consequence, we obtain local $L^\infty$ bounds on the density, uniform in the time step, consistent with the continuous-time result. As a byproduct, we rigorously derive the optimality conditions in the fast-diffusion case, filling a gap in the literature.

We study the computational complexity of decomposing finite discrete dynamical systems (FDDSs) in terms of the semiring operations of alternative and synchronous execution, which is useful for the analysis of discrete phenomena in science and engineering. More specifically, we investigate univariate polynomials of the form $P(X) = B$, that is with a constant side, first over the subsemiring of permutations and then over general FDDSs. We find a characterization of injective polynomials $P$ and efficient algorithms for solving the associated equations. Then, we introduce the more general notion of pseudo-injective polynomial, which is based on a condition on the lengths of the limit cycles of its coefficients, and prove that the corresponding equations are also solvable efficiently. These results also apply even when permutations are encoded in an exponentially more compact way.

According to the correspondence principle of Horowitz and Polchinski, many black holes in string theory are continuously deformed to usual quantum systems involving D-branes and fundamental strings when the string coupling becomes sufficiently small. Therefore if we consider a configuration in space-time where the dilaton varies over an appropriate range, then a black hole moving in such a background will smoothly transition from the black hole state to a normal quantum state whose microstates are not hidden behind an event horizon. The possible obstruction to this mechanism comes from the fact that if the dilaton varies too fast then the adiabatic approximation may break down and / or the ambient space-time itself may collapse to a black hole and get hidden from the asymptotic observer. On the other hand, if the dilaton varies too slowly then the time that it takes for the black hole to travel the required distance will exceed the evaporation time of the black hole. We show that by choosing the background appropriately these obstructions can be avoided and a gentle motion towards the weak coupling region will convert the black hole into a normal quantum state without an event horizon.

As large-scale HPC compute clusters increasingly adopt accelerators such as GPUs to meet the voracious demands of modern workloads, these clusters are increasingly becoming power constrained. Unfortunately, modern applications can often temporarily exceed the power ratings of the accelerators ("power spikes"). Thus, current and future HPC systems must optimize for both power and performance together. However, this is made difficult by increasingly diverse applications, which often require bespoke optimizations to run efficiently on each cluster. Traditionally researchers overcome this problem by profiling applications on specific clusters and optimizing, but the scale, algorithmic diversity, and lack of effective tools make this challenging. To overcome these inefficiencies, we propose Minos, a systematic classification mechanism that identifies similar application characteristics via low-cost profiling for power and performance. This allows us to group similarly behaving workloads into a finite number of distinct classes and reduce the overhead of extensively profiling new workloads. For example, when predicting frequency capping behavior for a previously unseen application, Minos reduces profiling time by 89%. Moreover, across 18 popular graph analytics, HPC, HPC+ML, and ML workloads, Minos achieves a mean error of 4% for power predictions and 3% for performance predictions, significantly improving predictions over state-of-the-art approaches by 10%.

Sensor-based Human Activity Recognition (HAR) underpins many ubiquitous and wearable computing applications, yet current models remain limited by scarce labels, sensor heterogeneity, and weak generalization across users, devices, and contexts. Foundation models, which are generally pretrained at scale using self-supervised and multimodal learning, offer a unifying paradigm to address these challenges by learning reusable, adaptable representations for activity understanding. This survey synthesizes emerging foundation models for sensor-based HAR. We first clarify foundational concepts, definitions, and evaluation criteria, then organize existing work using a lifecycle-oriented taxonomy spanning input design, pretraining, adaptation, and utilization. Rather than enumerating individual models, we analyze recurring design patterns and trade-offs across nine technical axes, including modality scope, tokenization, architectures, learning paradigms, adaptation mechanisms, and deployment settings. From this synthesis, we identify three dominant development trajectories: (1) HAR-specific foundation models trained from scratch on large sensor corpora, (2) adaptation of general time-series or multimodal foundation models to sensor-based HAR, and (3) integration of large language models for reasoning, annotation, and human-AI interaction. We conclude by highlighting open challenges in data curation, multimodal alignment, personalization, privacy, and responsible deployment, and outline directions toward general-purpose, interpretable, and human-centered foundation models for activity understanding. A complete, continuously updated index of papers and models is available in our companion repository: https://github.com/zhaxidele/Foundation-Models-Defining-A-New-Era-In-Human-Activity-Recognition.

Molybdenum sulfides are in the spotlight of materials science thanks to their interesting properties for applications in optoelectronics, nanocomposites, lubricants, and catalysis. The structural characterization of Molybdenum sulfides is a crucial step to understand and tune their properties. Vibrational techniques, such as infrared and Raman spectroscopy, can directly link to structural features, but the experimental literature suffers from large variability. Theoretical calculations are a powerful tool complementing and explaining empirical measurements. The reliability of first-principles calculation depends on the level of approximation made, taking into account disorder, doping, or temperature to yield a good description of the phonon statistics and related measurable quantities, such as the infrared and Raman peaks. In this study we calculate the Raman spectrum of crystalline 2H-MoS2, including broadening and shifts due to thermal and anharmonic effects. Our results demonstrate excellent agreement with experimental measurements; notably, the calculated temperature trends in frequencies and linewidths align with empirical observations. These findings establish a robust computational framework, paving the way for similar studies on amorphous Molybdenum sulfides.

Inferring cell-cell communication (CCC) from single-cell transcriptomics remains fundamentally limited by reliance on curated ligand-receptor databases, which primarily capture co-expression rather than the system-level effects of signaling on cellular states. Here, we introduce QuantumXCT, a hybrid quantum-classical generative framework that reframes CCC as a problem of learning interaction-induced state transformations between cellular state distributions. By encoding transcriptomic profiles into a high-dimensional Hilbert space, QuantumXCT trains parameterized quantum circuits to learn a unitary transformation that maps a baseline non-interacting cellular state to an interacting state. This approach enables the discovery of communication-driven changes in cellular state distributions without requiring prior biological assumptions. We validate QuantumXCT using both synthetic data with known ground-truth interactions and single-cell RNA-seq data from ovarian cancer-fibroblast co-culture model. The QuantumXCT model accurately recovered complex regulatory dependencies, including feedback structures, and identified dominant communication hubs such as the PDGFB-PDGFRB-STAT3 axis. Importantly, the learned quantum circuit is interpretable: its entangling topology was translated into biologically meaningful interaction networks, while post hoc contribution analysis quantified the relative influence of individual interactions on the observed state transitions. Notably, by shifting CCC inference from static interaction lookup to learning data-driven state transformations, QuantumXCT provides a generative framework for modeling intercellular communication. This work establishes a new paradigm for de novo discovery of communication programs in complex biological systems and highlights the potential of quantum machine learning in the context of single-cell biology.

Protein rotational kinetics are essential for understanding macromolecular behavior in crowded environments, yet measuring these dynamics at solid-liquid interfaces remains a significant challenge due to low signal strengths. Here, we experimentally demonstrate a label-based optical technique for measuring rotational diffusion kinetics using an all-dielectric multilayer stack that sustains both transverse electric and transverse magnetic polarized surface electromagnetic waves. We introduce the concept of Fluorescence Recovery after Orientational Photobleaching, a rotational analogue to the standard translatory fluorescence recovery after photobleaching technique, which utilizes anisotropic photobleaching via resonant transverse electric excitation followed by real-time monitoring of the orientational relaxation towards isotropy. Our ratiometric analysis of the transverse electric and magnetic polarized fluorescence components allows for a distance-independent estimation of the rotational friction coefficient. Applying this method to covalently bound neutravidin, we observe a rotational friction coefficient (about 5.8E-18 J s) significantly higher than in bulk solutions, highlighting the impact of surface anchoring and molecular crowding. The proposed approach provides a robust, high-sensitivity platform for resolving biomolecular dynamics in complex interfacial environments.

We introduce the universal virtual braid group $UV_n(c)$, which provides a unified algebraic framework for virtual braid--type structures with $c$ types of crossings and admits natural quotient maps onto the standard families in the literature. We prove that $UV_n(c)$ contains a right-angled Artin subgroup of finite index, yielding strong structural consequences: residual finiteness, linearity, solvability of the word and conjugacy problems, and the Tits alternative. For $n\ge 5$, the commutator subgroup $UV_n(c)'$ is perfect, and every non-abelian finite image contains a subgroup isomorphic to the symmetric group $S_n$; in particular, $S_n$ is the smallest non-abelian finite quotient. These rigidity phenomena persist under a broad class of natural quotients, including virtual braid, virtual singular braid, virtual twin and multi-virtual braid groups. We further obtain a complete classification of subgroup separability (LERF) and the Howson property for $UV_n(c)$ and its pure subgroup $PUV_n(c)$, showing that both properties hold precisely for $n\le 3$. We also compute the virtual cohomological dimension, determine the center, prove that the finite-index RAAG subgroup is characteristic, and construct explicit finite quotients of $UV_n(c)$ whose order is strictly larger than $n!$.

We study a composite model of two $1D$ $PXP$ chains with dual constraints, forming a junction that acts as an infinite kinematic barrier to quantum information exchange. Moreover, the hard wall at the junction which acts as a perfect reflector, preventing any quantum information leakage between the two sides of the composite chain, can be made permeable by relaxing the constraint at the junction sites. Multiple frozen junctions shatter the Hilbert space into disjoint Krylov fragments, the number of which increases exponentially with the engineered defects. Furthermore, the energy level statistics in each symmetry-resolved sector are strictly Poissonian, demonstrating that the tensor sum of the disjoint Hamiltonians results in a pure superposition of the chaotic spectra of the sub- $PXP$ chains. We also find that a chirally protected zero-energy mode can exist which has local peaks at the physical edges and within the bulk near the junction sites. This state is protected from hybridization with bulk states induced by any chirality preserving disorder. Due to the tensor product structure of the eigenfunctions, the non-zero energy scar states also multiply in number. Finally, we introduce novel Fock states with spatially tunable thermal and athermal regions. This architecture can be readily realized in programmable Rydberg atom platforms using optical tweezers, addressing beams and facilitation techniques.

We propose a new mechanism for generating a primordial electromagnetic relic during the recombination--decoupling transition, based on the rate-dependent thermodynamics of the cosmic photon gas. Treating the photon sector as an open system coupled to the electron plasma, we show that a finite Thomson relaxation rate generates a departure from instantaneous thermal equilibrium, leading to non-adiabatic mode squeezing. As this relaxation rate rapidly decreases across recombination, the system quickly loses the ability to further amplify the deviation, and the squeezing freezes out at a small but finite value. This dynamics is naturally described as a narrow transition layer between an adiabatic tracking regime and a post-relaxation freeze-out regime. By a canonical transformation, the reduced evolution equation is recast into a forced oscillator with a smooth effective potential, clarifying the origin of the squeezing and the selection of the relic scale. Projecting the resulting non-equilibrium electromagnetic relic onto the magnetic sector, we derive the corresponding spectrum and show that its characteristic peak is controlled not by the squeezing parameter alone but by the weighted combination \(k^3\mathscr S_k\). In representative realizations, the peak corresponds today to scales of order \(10\)--\(20\) Mpc, while the present-day field amplitude remains extremely small. The mechanism is therefore better viewed as a source of a frozen non-equilibrium electromagnetic relic than as a complete explanation of the observed cosmic magnetic fields.

We develop a conceptualization of ideology, in which a system of ideas represents social, economic, and political relationships. We use ideology as a lens for understanding and critiquing intersecting social, economic, and political aspects of how 'AI' technologies are being developed. We observe ideological shifts. We question that the present tangling of corporate and university objectives is beneficial to labor, particularly computer science students, and the general public.

We define functions of the sub-Laplacian $Δ$ on the Heisenberg group $\mathbb H^d$ as Fourier multipliers. In this setting, we show that the solution $u$ of the free fractional Schrödinger equation $i\partial_tu + (-Δ)^νu = 0, u|_{t=0} = u_0$, for any $ν> 0$, satisfies the Hardy space estimate that $$ \|u(t,\cdot)\|_{H^p(\mathbb H^d)} \leq C_p (1 + t)^{Q|1/p-1/2|}\|(1-Δ)^{νQ|1/p-1/2|}u_0\|_{H^p(\mathbb H^d)}, $$ with $Q = 2d + 2$, for all $p \in (0,\infty)$, and the corresponding estimate with $p = \infty$ in $\mathrm{BMO}(\mathbb H^d)$. This is done via a general regularity result for parameter dependent sub-Laplacian Fourier multipliers. We prove also that Bessel potential spaces on the Heisenberg group correspond to Sobolev spaces in the same way as in Euclidean space, also for Hardy spaces.

Sustaining high inter-satellite link (ISL) throughput under intermittent solar harvesting is a fundamental challenge for LEO mega-constellations. Existing works impose static power ceilings that ignore real-time battery state and comprehensive onboard power budgets, causing eclipse-period energy crises. Learning-based approaches capture battery dynamics but lack equilibrium guarantees and do not scale beyond small constellations. We propose the \textbf{Hierarchical Battery-Aware Game (HBAG)} algorithm, a unified game-theoretic framework for ISL power allocation that operates identically across finite and mega-constellation regimes. For finite constellations, HBAG converges to a unique variational equilibrium; as constellation size grows, the same distributed update rule converges to the Mean Field Game (MFG) equilibrium without algorithm redesign. Comprehensive experiments on Starlink Shell~A ($M=172$, $θ=0.38$) show that HBAG achieves \textbf{100\% energy sustainability rate} (ESR) in all 10 independent runs, representing a \textbf{+87.4\%} gain over the traditional static-power baseline (SATFLOW-L, ESR\,=\,12.6\%). At the same time, HBAG reduces the flow violation ratio by \textbf{78.3\%} to 7.62\% (below the 10\% industry tolerance). HBAG further maintains ESR $\geq 93.4\%$ across eclipse fractions $θ\in [0,\,0.6]$ and scales linearly to 5{,}000 satellites with less than 75\,ms per-slot runtime, confirming deployment feasibility at full Starlink scale.

We develop a hybrid conservative finite-volume / bounded-interval multiwavelet formulation for the deterministic one-dimensional Buckley--Leverett equation. Because Buckley--Leverett transport is a nonlinear hyperbolic conservation law with entropy-admissible shocks, the saturation update is performed by a conservative finite-volume scheme with monotone numerical fluxes, while the evolving state is represented and reconstructed in a bounded-interval multiwavelet basis. This strategy preserves the correct shock-compatible transport mechanism and simultaneously provides a hierarchical multiresolution description of the solution. Validation against reference Buckley--Leverett profiles for a Berea benchmark shows excellent agreement in probe saturation histories, spatial profiles, front-location diagnostics, and global error measures. The multiwavelet reconstruction also tracks the internal finite-volume state with essentially exact fidelity. The resulting formulation provides a reliable first step toward more native multiwavelet transport solvers for porous-media flow.

While chiral perturbation theory for mesons is characterized by a momentum expansion in $Q/Λ_χ$ with $Λ_χ\sim 1$ GeV, existing formulations of effective theory for nucleon-nucleon scattering deviate from data at $Q\sim 300$ MeV or lower. We offer heuristic evidence that unsuspected nonanalytic structure exists in the complex momentum plane obstructing the effective field theory expansion in the spin-triplet channels, associated with the peak of the angular momentum barrier whose energy in low partial waves satisfies $k=\sqrt{ME} \sim 300$ MeV. With this motivation, we construct a meromorphic function of $k^2$ we call the $C$-matrix, for which the radius of convergence of its Taylor expansion in $k^2$ is equivalent to that of the momentum expansion of the effective field theory. Thus the range of validity of the effective theory is directly related to the pole structure of the $C$-matrix. We uncover that pole structure and confirm that it is the source of the obstruction. The systematic inclusion of dimer fields as propagating degrees of freedom in the effective theory to account for those poles results in cut-off insensitive fits at order $Q^0$ to most of the lower partial wave phase shifts up to the pion production threshold, using only the one pion exchange part of the long-range nucleon-nucleon interaction. Our theory should be applicable to the singular potentials regularly found in atomic physics as well.

Floquet engineering provides an emerging pathway for tailoring the electronic states of quantum materials through time-periodic drive. A critical step along this direction is achieving light-induced modifications of the dynamical electronic structure, such as avoided-crossing gap at the Floquet Brillouin zone boundary, via efficient coupling of electrons with the coherent light-field. Here, we report robust Floquet-induced gap in bulk graphite that persists despite the presence of interlayer coupling and photo-excitation. Using time- and angle-resolved photoemission spectroscopy with intense mid-infrared pumping, we directly reveal Floquet-induced gaps at resonance points both in the valence and conduction bands, accompanied by coherent Floquet sidebands. The gap and sidebands coexist with photo-excited carriers, yet their distinct timescales allow us to disentangle their origins. Our demonstration of robust Floquet-induced gaps establishes graphite as a platform for coherent manipulation of Dirac fermions and realization of light-engineered quantum phases.

We test the cosmic distance duality relation (CDDR) using two model-independent methods. Method I is based on the PAge parametrization, which characterizes the expansion history in terms of the cosmic age. Parametrizations of possible CDDR violations are constrained using observational data from Type Ia supernovae (SN), baryon acoustic oscillations (BAO), cosmic chronometers, and gamma-ray bursts (GRB), including the latest PantheonPlus and DES Dovekie SN samples and DESI DR2 BAO data. The results support the validity of the CDDR within $1σ$. Different combinations of data sets are further explored to assess the impact of various probes and calibration choices, demonstrating the robustness of this conclusion. Although GRB data extend to higher redshifts, their constraining power is significantly weaker than that of the other low-redshift probes. The PantheonPlus and DES Dovekie samples yield consistent results. Method II uses a non-parametric Gaussian process reconstruction of the luminosity distance from SN data, combined with BAO measurements to construct the observed CDDR violation and constrain its parametrizations. The results are consistent with those from Method I, and we find no evidence for a violation of the CDDR.

Multiple zeta-star values are variants of multiple zeta values which allow equality in the definition. Similar to the theory of continued fractions, every real number which is greater than $1$ can be realized as an unique infinite multiple zeta-star values in a natural way. In this paper, we investigate the arithmetic sums and products of infinite multiple zeta-star values with restricted indices. Moreover, inspired by the theory of continued fractions and Cantor set, we propose a series of conjectures concerning the algebraic points and arithmetic sums and products of infinite multiple zeta-star values with certain indices.

Large-scale industrial recommenders typically use a fixed multi-stage pipeline (recall, ranking, re-ranking) and have progressed from collaborative filtering to deep and large pre-trained models. However, both multi-stage and so-called One Model designs remain essentially static: models are black boxes, and system improvement relies on manual hypotheses and engineering, which is hard to scale under heterogeneous data and multi-objective business constraints. We propose an Agentic Recommender System (AgenticRS) that reorganizes key modules as agents. Modules are promoted to agents only when they form a functionally closed loop, can be independently evaluated, and possess an evolvable decision space. For model agents, we outline two self-evolution mechanisms: reinforcement learning style optimization in well-defined action spaces, and large language model based generation and selection of new architectures and training schemes in open-ended design spaces. We further distinguish individual evolution of single agents from compositional evolution over how multiple agents are selected and connected, and use a layered inner and outer reward design to couple local optimization with global objectives. This provides a concise blueprint for turning static pipelines into self-evolving agentic recommender systems.

AutoModel is an agent based architecture for the full lifecycle of industrial recommender systems. Instead of a fixed recall and ranking pipeline, AutoModel organizes recommendation as a set of interacting evolution agents with long term memory and self improvement capability. We instantiate three core agents along the axes of models, features, and resources: AutoTrain for model design and training, AutoFeature for data analysis and feature evolution, and AutoPerf for performance, deployment, and online experimentation. A shared coordination and knowledge layer connects these agents and records decisions, configurations, and outcomes. Through a case study of a module called paper autotrain, we show how AutoTrain automates paper driven model reproduction by closing the loop from method parsing to code generation, large scale training, and offline comparison, reducing manual effort for method transfer. AutoModel enables locally automated yet globally aligned evolution of large scale recommender systems and can be generalized to other AI systems such as search and advertising.

Dark energy, the main constituent in our expanding universe, responsible for its acceleration, is currently being observed with unprecedented precision through various experiments. While several cosmological models can fit this latest data, deriving some of them from string theory would provide a valuable theoretical prior, with information on the nature of dark energy. This article reviews the efforts towards such a derivation, namely the options from string theory to get a cosmological constant (a de Sitter solution) or a dynamical dark energy (via a quintessence model). After providing a brief historical perspective, we first review proven or conjectured constraints on obtaining dark energy from string theory, in classical or asymptotic regimes. Circumventing such obstructions, by changing regime or ansatz, one can try to construct a de Sitter solution: we present a long list of such attempts, and the difficulties encountered. Among them, we discuss in detail efforts towards classical de Sitter solutions. Then, we review quintessence from string theory, focusing on single-field exponential models. Related topics are discussed, including the coupling to matter, the comparison to observational data, and the absence of a cosmological event horizon.

Minimal Kitaev chains provide a unique platform to engineer Majorana states in quantum dots interacting via normal tunneling and crossed Andreev reflection specified by their amplitudes $|η_{n,a}|$. Here we analyze fluctuations of electric currents in a double quantum dot Kitaev chain using the differential effective charge $q$, that is the ratio of the differential shot noise and conductance. At low bias voltages $V$ we find that $q=e/2$ in a very narrow vicinity of the point $|η_n|=|η_a|$ whereas $q=3e/2$ almost in the whole sweet spot region and marks the range where the poor man's Majorana states largely govern the fluctuations. At high $V$ we show that the sweet spot region is still characterized by $q=3e/2$ uniquely identifying the poor man's Majorana states using the high voltage tails. For $|η_n|=0$ or $|η_a|=0$ we obtain $q=e$ at any $V$. Remarkably, before the asymptotic value $q=e$ is reached for very high $V$, the maximal value $q=2e$ is formed at $|eV|=2\sqrt{|η_n|^2+|η_a|^2}$. The unique nature and potentially rich fluctuation behavior revealed in this work provide a stimulating ground for the next generation experiments on nonequilibrium shot noise in minimal Kitaev chains.