We investigate the propagation of two-point density correlations in the one-dimensional Bose-Hubbard Hamiltonian in the thermodynamic limit in terms of the correlation transport distance (CTD), an experimentally measurable magnitude that characterizes the spatial spreading of correlations in time. We confirm that the integrable limits of the model exhibit CTD ballistic growth, while the onset of the chaotic phase leads to the emergence of a pronounced sub-ballistic regime, in agreement with previous results for finite systems. By a meticulous analysis of the spatio-temporal correlation profiles, we show that the correlation front nonetheless propagates ballistically for all interaction strengths, and that the chaos-induced slowdown of the CTD originates from the emergence of long-time distance-dependent correlation tails, together with an enhanced decay of the correlation front amplitude. Our results thus provide a detailed characterization of correlation transport that goes beyond a simple light-cone picture.

Using on-chain Polygon data, we analyze Polymarket's 2024 U.S. Presidential Election market and develop a transaction-level accounting framework with two components: a volume decomposition that separates exchange-equivalent turnover from share minting and burning, and trader-level disagreement measures. Naive aggregation reports $958M of October Trump-market volume, compared with $391M under our decomposition. Market quality improved as arbitrage-deviation half-lives fell from hours to under a minute and Kyle's λ dropped from 0.53 to 0.01. During October's large-account episode, capital flowed into both sides simultaneously, consistent with heterogeneous-beliefs trading rather than one-sided manipulation. The framework generalizes to other tokenized prediction markets.

We present predictions for scattering observables and femtoscopic correlation functions (CFs) of the $I=2$ $Σ_c^{++}π^{+}$, $Σ_c^{0}π^{-}$ systems and its heavy-flavor counterpart $Σ_b^{+}π^{+}$. In both heavy-quark sectors, the strong interaction is formulated within two distinct theoretical frameworks, each constrained to reproduce the lowest-lying odd-parity isoscalar spin-$1/2$ resonances, $Λ_c(2595)$ and $Λ_b(5912)$, respectively. While the $Σ_c^{0}π^{-}$ pair is governed solely by the strong interaction, electrostatic contributions are included in the other two channels involving charged particles through relativistic Coulomb wave functions. We show that the differences observed in the scattering observables between the two strong-interaction models arise mainly from the specific ultraviolet regularization schemes employed. The inclusion of Coulomb effects induces only a very small increase in both the scattering length and the effective range. The resulting CFs in the charm and bottom sectors display analogous global features, in agreement with expectations from heavy-quark flavor symmetry. Both, the $Σ_c^{++}π^+$ and $Σ_b^{+}π^{+}$ CFs, when computed including only the strong interaction, exhibits substantial discriminating power among the different models. However, once Coulomb effects are incorporated, the CFs become largely affected by the repulsive electrostatic interaction, which diminishes their sensitivity to the details of the underlying strong dynamics, thereby reducing the capability to differentiate between theoretical descriptions. Thus, the $Σ_c^{0}π^{-}$ CF-being free from Coulomb effects-provides the most suitable observable for constraining the strong dynamics of the isotensor $Σ_cπ$ system.

Thermal light, including blackbody radiation and spontaneous emission, exhibits photon bunching. Thermal light sources, however, typically yield low spectral densities, limiting their practical utility. Pseudothermal light sources with higher brightness and longer coherence time are often employed instead. While pseudothermal light also exhibits photon bunching, this property may not suffice to fully replicate the behavior of genuine thermal light. Here we demonstrate a method to directly test the Siegert relation for two sources of photon-bunched light, laser light scattered from a rotating ground glass and spontaneously emitted light from a gas discharge lamp, probing a fundamental criterion expected of thermal light.

Quantum error correction (QEC) promises to exponentially suppress qubit noise, but typically assumes spatially-uniform and temporally-constant noise rates. However, real quantum hardware exhibits variation in noise levels over time, which will be amplified by QEC if not addressed. To mitigate this drift in error rates, we leverage transient information readily available in surface code quantum error correction to predict logical error rates (LER) in real time. We infer a prediction model by sampling physical error rates from real hardware, and mapping detector fire rate (DFR), or parity of stabilizer measurements across QEC rounds, to LER. This allows for on-the-fly LER predictions without the typical characterization overhead required to determine LER. This method can easily be extended to other stabilizer codes. Importantly, we observe that this prediction should be accurate yet conservative (i.e. give an upper estimate) to enable appropriately fast responses to real-time physical error changes. That is, responses should be executed marginally ahead of time to allow for their execution to complete, and minimize time spent (ideally none) above intolerable error rates. More importantly, we pair this predictor with a scheme which remaps drifted logical qubits to fresh tiles in a patch-based architecture while their original tiles are recalibrated. Our results demonstrate DFR-based prediction to be an effective LER predictor, and remapping as a spatially efficient and timely mitigation response for small code distances, both of which are significant steps in furthering practical QEC.

Short-range reliable and secure communication is a major priority in the tactical, military and disaster response settings where the traditional communication infrastructure is either off-line or prone to interception. Current VHF/UHF radios and software-defined radios are popular but large-sized devices and require lots of power, making them not suitable to be used as lightweight wearable devices with seamless hand-free use. In this paper, the design and theoretical framework of a miniature, LoRa based encrypted intercommunication device that can be used in secure field communication over a range of 1-1.5km and under line-of-sight conditions is provided. The suggested system consists of a voice-activated acquisition block, digital audio compression, an embedded microcontroller processor, and AES-128 encryption followed by a low-power transmission via the LoRa protocol. Through the ability of chirp spread spectrum modulation to utilize the long-range and low-energy properties, the system is guaranteed reliable communications coupled with low power consumption and low electromagnetic footprint. The theoretical analysis of the proposed communication range is justified using a link-budget that justifies the practicability of the communication range in the real propagation conditions. This architecture focuses on infrastructural agnosticism, peer-to-peer security as well as wearable ergonomics. The given scheme shows the possibilities of LoRa technology in the scope of other traditional IoT telemetry, and it can be further extended to include secure tactical voice communication platforms.

Large language models (LLMs) training or inference across multiple nodes introduces significant pressure on GPU memory and interconnect bandwidth. The Compute Express Link (CXL) shared memory pool offers a scalable solution by enabling memory sharing across nodes, reducing over-provisioning and improving resource utilization. We propose \name, a collective communication library, leveraging the CXL shared memory pool to support cross-node GPU operations without relying on traditional RDMA-based networking. Our design addresses the challenges on synchronization, data interleaving, and communication parallelization faced by using the CXL shared memory pool for collective communications. Evaluating on multiple nodes with a TITAN-II CXL switch and six Micron CZ120 memory cards, we show that \name achieves highly efficient collective operations across hosts, demonstrating CXL's potential for scalable, memory-centric GPU communication. Our evaluation demonstrates that \name achieves average performance improvements of 1.34$\times$ for AllGather, 1.84$\times$ for Broadcast, 1.94$\times$ for Gather, and 1.04$\times$ for Scatter, compared to the original RDMA-based implementation over 200 Gbps InfiniBand. \textcolor{dong}{In addition, the evaluation with a case of LLM training shows 1.11$\times$ speedup compared with the InfiniBand while saving production cost by $2.75\times$ in hardware.}

Polyregular functions form a robust class of string-to-string functions with polynomial growth, as evidenced by Bojanczyk (2018). This class admits numerous descriptions and enjoys several closure properties. Most notably, polyregular functions are regularity reflecting (\ie the inverse image of a regular language is regular). In this work, we propose a robust class of string-to-string functions with exponential growth which we call expregular functions. We consider the following three models for describing them: - MSO set interpretations, which extend MSO interpretations (one of the models capturing polyregular functions), by operating on monadic variables instead of tuples of first-order variables; - yield-Hennie machines, which are branching one-tape Turing machines with bounded visit; and - Ariadne transducers, a new model of 2-way pushdown machines with a bounded visit restriction. Our main contribution is a translation from MSO set interpretations to yield-Hennie machines, which are known to be regularity reflecting (Dartois, Nguy\~{ê}n, Peyrat 2026). In particular this establishes that MSO set interpretations are regularity reflecting, which in turn settles a major conjecture about automatic structures: every automatic $ω$-word has a decidable MSO theory. Yield-Hennie machine directly translate to Ariadne transducers, and our second contribution is to prove that Ariadne transducers also translate to MSO set interpretations, thus establishing the equivalence of the three models. This is obtained by showing that Ariadne automata -- the automaton model corresponding to Ariadne transducers -- recognise regular languages.

The quantization of the Seiberg-Witten curve of ${\cal N}=2$ super Yang-Mills theory leads to a deformation of one-dimensional quantum mechanics with unconventional behavior. Most notably, quantum tunneling is suppressed at special points in parameter space. In this paper we examine these deformed models in the case of double-well and cubic potentials, and we find that they have a rich phase structure. In what we call the strong coupling phase, the theory behaves like conventional quantum mechanics, instantons are real, and tunneling is not suppressed. In the weak coupling phase, the instantons responsible for tunneling become complex, and tunneling suppression takes place at the so-called Toda lattice points. At the critical point between the two phases, which corresponds to a monopole point in super Yang-Mills theory, the non-perturbative amplitudes display an anomalous scaling as a function of $\hbar$. This phase structure reflects the physics of the underlying super Yang-Mills theory and can be regarded as a physical manifestation of wall-crossing behavior of the BPS spectrum, which we determine in our problem by using resurgent techniques.

A ${}^{10}\mathrm{B}$-coated double-GEM neutron detector (BGEM) was developed as a ${}^{3}\mathrm{He}$-free cold-neutron beamline detector using a single $\mathrm{B}_{4}\mathrm{C}$ converter cathode and a 512-channel APV25 orthogonal-strip readout over an active area of $10 \times 10~\mathrm{cm}^{2}$. The detector was tested at the HANARO Bio-REF beamline with a monochromatic $4.5~\mathring{\mathrm{A}}$ beam ($E_{n}=4.03~\mathrm{meV}$). The absolute detection efficiency relative to a ${}^{6}\mathrm{Li}$-based Ce:LiCAF reference detector was $\varepsilon_{\mathrm{BGEM}}=(8.69 \pm 0.20)\%$ (stat.). The pulse-height spectrum was qualitatively consistent with Geant4 energy-deposition simulations, and Cd-mask imaging yielded a Gaussian-equivalent edge-spread width of $σ= 555 \oplus 102~μ\mathrm{m}$. These results establish a cold-neutron beamline benchmark for a single-converter BGEM detector with full-strip APV25 readout.

The origin of the soft X-ray excess below 2 keV in active galactic nuclei (AGNs) remains debated, with relativistic reflection from the inner accretion disk and warm Comptonization in an optically thick corona being the leading explanations. We investigate the timing and spectral properties of the Seyfert galaxy Mrk 110 using six XMM-Newton observations. A frequency-dependent lag analysis in the 7-9 $\times 10^{-5}$ Hz range reveals a soft X-ray lag of 889-3000s in the combined 2019 data, detected with a significance of 80%. The cross-correlation function analysis, supported by simulations, also detects lags of similar nature. Spectral modeling performed by adopting both proposed black hole masses in the literature for Mrk 110 confirms the presence of a warm corona in all observations, along with a weak relativistic reflection component and the reflection fraction remains low (Rf < 1). Interpreting the measured soft lag in terms of light travel time implies an emission radius 4.5 Rg for a supermassive black hole mass of $M = 1.4 \times 10^8$ solar mass , favoring a reflection scenario. However, if a lower mass of $M = 2 \times 10^7$ solar mass is adopted, the inferred radius increases, and both relativistic reflection and warm Comptonization can plausibly contribute to the observed soft lag. The warm corona radius appears larger in the high accretion state and smaller in a lower accretion state, although no trend can be established. The persistently low reflection fraction suggests an outflowing inner corona in Mrk 110, consistent with the recent detection of jet activity in this source.

Localization is a key component of the wireless ecosystem. Machine learning (ML)-based localization using channel state information (CSI) is one of the most popular methods for achieving high-accuracy localization with low cost. However, to be accurate and robust, ML-based algorithms need to be trained and tested with large amounts of data, covering not only many user equipment (UE)/target locations, but also many different access points (APs) locations to which the UEs connect, in a variety of different environment types. This paper presents a massive-sized CSI dataset, WiLoc (Wi-Fi Localization), and makes it publicly available. WiLoc is obtained by a series of precision measurement campaigns that span three months, and it is massive in all the above-mentioned three dimensions: > 12 million UE locations, > 3,000 APs, covering 16 buildings for indoor localization, and > 30 streets for outdoor use. The paper describes the dataset structure, measurement environments, measurement protocols, and the dataset validations. Comprehensive case studies validate the advantages of large datasets in ML-driven localization strategies for both "standard" and transfer learning. We envision this dataset, which is by far the largest of its kind, to become a standard resource for researchers in the field of ML-based localization.

Graph data are ubiquitous in natural sciences and machine learning. In this paper, we consider the problem of quantizing graph structured, bandlimited data to few bits per entry while preserving its information under low-pass filtering. We propose an efficient single-shot noise shaping method that achieves state-of-the-art performance and comes with rigorous error bounds. In contrast to existing methods it allows reliable quantization to arbitrary bit-levels including the extreme case of using a single bit per data coefficient.

Spin-$3/2$ $Δ$ baryon resonances are investigated within AdS/QCD, using Rarita-Schwinger fields. The differential configurational entropy (DCE) and differential configurational complexity (DCC) associated with their bulk energy densities are computed. It yields Regge-like trajectories relating configurational information measures to the radial excitation number and the experimental mass spectrum of the $Δ$ baryons. We then extrapolate the spectrum of heavier $Δ$ baryon resonances beyond the currently established states in the PDG, also comparing them with states in the PDG that are omitted from the summary table. Our results support a relevant interplay among holographic QCD dynamics, configurational information entropy, and baryon spectroscopy in strongly coupled QCD.

The quantum threat to cybersecurity has accelerated the standardization of Post-Quantum Cryptography (PQC). Migrating legacy software to these quantum-safe algorithms is not a simple library swap, but a new software engineering challenge: existing vulnerability detection, refactoring, and testing tools are not designed for PQC's probabilistic behavior, side-channel sensitivity, and complex performance trade-offs. To address these challenges, this paper outlines a vision for a new class of tools and introduces the Automated Quantum-safe Adaptation (AQuA) framework, with a three-pillar agenda for PQC-aware detection, semantic refactoring, and hybrid verification, thereby motivating Quantum-Safe Software Engineering (QSSE) as a distinct research direction.

AI agents that autonomously interact with external tools and environments have shown great promise across real-world applications. However, their reliance on external data exposes them to serious indirect prompt injection attacks, where malicious instructions embedded in third-party content hijack agent behaviors. To mitigate this threat, a growing number of defenses have been proposed and evaluated under existing agent security benchmarks. These benchmarks provide structured environments for comparing attacks and defenses, and have become a key driver for defense design and optimization. However, as agents move toward more complex and open-ended real-world deployments, there is a pressing need for benchmarks to become more adaptive and better reflect the dynamic environments faced by real-world agentic systems. In this work, we reveal three fundamental flaws in the current benchmarks and push the frontier along these dimensions: (i) lack of dynamic open-ended tasks, (ii) lack of helpful instructions, and (iii) simplistic user tasks. To bridge this gap, we introduce AgentDyn, a manually designed benchmark featuring 60 challenging open-ended tasks and 560 injection test cases across Shopping, GitHub, and Daily Life. Unlike prior static benchmarks, AgentDyn requires dynamic planning and incorporates helpful third-party instructions. Our evaluation of ten state-of-the-art defenses suggests that almost all existing defenses are either not secure enough or suffer from significant over-defense, revealing that existing defenses are still far from real-world deployment. Our benchmark is available at https://github.com/leolee99/AgentDyn.

Post-market fairness monitoring is now mandated to ensure fairness and accountability for high-risk employment AI systems under emerging regulations such as the EU AI Act. However, effective fairness monitoring often requires access to sensitive personal data, which is subject to strict legal protections under data protection law. Multi-party computation (MPC) offers a promising technical foundation for compliant post-market fairness monitoring, enabling the secure computation of fairness metrics without revealing sensitive attributes. Despite growing technical interest, the operationalization of MPC-based fairness monitoring in real-world hiring contexts under concrete legal, industrial, and usability constraints remains unknown. This work addresses this gap through a co-design approach integrating technical, legal, and industrial expertise. We identify practical design requirements for MPC-based fairness monitoring, develop an end-to-end, legally compliant protocol spanning the full data lifecycle, and empirically validate it in a large-scale industrial setting. Our findings provide actionable design insights as well as legal and industrial implications for deploying MPC-based post-market fairness monitoring in algorithmic hiring systems.

We present a neural network classification (NNC) method for photometric redshift estimation that produces well-calibrated redshift probability density functions (PDFs). The method discretizes the redshift space into ordered bins and optimizes the Continuous Ranked Probability Score (CRPS), which respects the ordinal nature of redshift and naturally provides uncertainty quantification. Unlike traditional regression approaches that output single point estimates, our method can capture multi-modal posterior distributions arising from color-redshift degeneracies. We apply this method to the DESI Legacy Imaging Surveys Data Release 10 (LSDR10) and Pan-STARRS Data Release 2 (PS1DR2), using an unprecedented spectroscopic training sample from DESI DR1 and SDSS DR19. Our method achieves $σ_{\mathrm{NMAD}} = 0.0153$ and $η= 0.50\%$ on LSDR10, and $σ_{\mathrm{NMAD}} = 0.0222$ and $η= 0.34\%$ on PS1DR2 combined with unWISE infrared photometry. The NNC method outperforms Random Forest, XGBoost, and standard neural network regression. We demonstrate that DESI DR1 significantly improves photo-$z$ performance at $z > 1$, while the combination of deep optical photometry and mid-infrared coverage is essential for achieving high precision across the full redshift range. We provide a unified photometric redshift catalog combining LSDR10 and PS1DR2 with a hierarchical model selection strategy based on available photometry. The well-calibrated PDFs produced by our method are valuable for cosmological studies and can be extended to next-generation surveys such as CSST, Euclid, and LSST.

The peculiar gamma-ray burst GRB 250702B is the longest event ever observed, lasting about one day and exhibiting four prompt-emission flares of $\sim100$ s with irregular recurrence intervals of at least one hour. To explain this hierarchy of timescales, we consider a scenario in which a stellar object undergoes repeated partial tidal disruptions by a black hole (BH). We find that if a white dwarf (WD) is on a highly eccentric orbit ($e\approx0.97$) around an intermediate-mass black hole (BH) with $M_{\rm BH}\lesssim10^{6}\,M_\odot$ and $a = 50\,R_\odot\left(M_{\rm BH}/10^{6}\,M_\odot\right)^{1/3}$, the observed properties of GRB 250702B can be naturally reproduced. In this framework, the duration of each flare is determined by the viscous accretion timescale of material stripped near pericenter, with a typical mass $ΔM \approx 2\times10^{-2}\,M_\odot$. The minimum recurrence time corresponds to the orbital period, while the total activity period is set by the secular orbital evolution timescale leading to the complete disruption of the WD. Furthermore, if $M_{\rm BH}\gtrsim10^{5}\,M_\odot$ and the orbit has a minimum polar angle relative to the BH equatorial plane of $θ_{\rm min}\gtrsim0.12 {\rm rad}$, relativistic frame dragging induces $\gtrsim0.1$ rad precession of the orbital angular momentum between successive pericenter passages, comparable to a typical GRB jet half-opening angle, resulting in intermittent alignment with the observer and irregular flare spacing. The WD experiences $\approx40$ jet-launch episodes before complete disruption, but only four are expected to be observed on-axis. The remaining off-axis jets become visible at late times, enhancing the radio afterglow by about an order of magnitude, providing a testable prediction of this scenario.

The classical Poincaré estimate establishes closedness of the range of the gradient in unweighted $L^2(Ω)$-spaces as long as $Ω\subseteq\mathbb{R}^3$ is contained in a slab, that is, $Ω$ is bounded in one direction. Here, as a main observation, we provide closed range results for the $\operatorname{rot}$-operator, if (and only if) $Ω$ is bounded in two directions. Along the way, we characterise closed range results for all the differential operators of the primal and dual de Rham complex in terms of directions of boundedness of the underlying domain. As a main application, one obtains the existence of a spectral gap near the $0$ of the Maxwell operator allowing for exponential stability results for solutions of Maxwell's equations with sufficient damping in the conductivity. Our results are based on the validity of Gaffney's (in)equality and the transition of the same to unbounded (simple) domains as well as on the stability of closed range results under bi-Lipschitz regular transformations. The latter technique is well-known and detailed in the appendix; for the results concerning Gaffney's estimate, we shall provide accessible, simple proofs using mere standard results. Moreover, we shall present non-trivial examples and a closed range result for $\operatorname{rot}$ with mixed boundary conditions on a set bounded in one direction only.

The main objective of this paper is a study of the asymptotic behavior of distributional solutions to the one-dimensional repulsive pressureless Euler-Poisson system. The system is a model for the dynamics of a mass distribution evolving on \mathbb{R} whose masses exert outward forces on one another. A discrete (describing the evolution of finitely many particles) solution is called sticky if, upon collision, particles stick together and move as one for all subsequent time, according to the conservation of mass and momentum principles. We prove results on the total energy (Hamiltonian) of the system and demonstrate the existence and uniqueness of so-called "perfect" states, where the Hamiltonian is constant over all time and the solution converges to equilibrium, a single stationary particle. We provide a necessary and a sufficient condition for finite-time collapse, and present a quadratic envelope within which a solution must remain in order to collapse. We demonstrate various (counter)examples that illustrate the unique behavior of the repulsive scheme with the sticky condition, analytically and with a computer simulation.

This paper proposes a novel numerical method for computing the density of the limit random variable associated with a supercritical Galton-Watson process. This random variable captures the effect of early demographic fluctuations and determines the random amplitude of long-term exponential population growth. While the existence of a non-trivial limit is ensured by the Kesten-Stigum theorem, computing its density in a stable and efficient manner for arbitrary offspring laws remains a significant challenge. The proposed approach leverages a functional equation that characterizes the Laplace-Stieltjes transform of the limit distribution and combines it with a moment-matching method to obtain accurate approximations within a class of linear combinations of Laguerre polynomials with exponential damping. The effectiveness of the approach is validated on several examples in which the offspring generating function is a polynomial of bounded degree.

Achieving long-range, high data rate, concurrent two-way mmWave communication with power-constrained IoT devices is fundamental to scaling future ubiquitous sensing systems, yet the substantial power demands and high cost of mmWave hardware have long stood in the way of practical deployment. This paper presents Armstrong, the first mmWave full-duplex backscatter tag architecture, charting a genuinely low-cost path toward high-performance mmWave connectivity for ISAC systems. Armstrong operates in full duplex at ranges beyond 88m and beyond 200m in downlink alone, delivering 20x the reach of state-of-the-art systems while being over 100x cheaper than existing mmWave backscatter platforms. Enabling this leap is a novel low-power regenerative amplifier that provides 30 dB of gain while consuming only 7.7 mW during active transmission, paired with a regenerative rectifier that achieves state-of-the-art sensitivity down to -60 dBm. We integrate our circuits on a compact PCB and evaluate it across diverse downlink and uplink scenarios, where it achieves 1 Kbps BERs of less than 10^{-1} at 200m and 88m, respectively, demonstrating resilient, high-quality communication even at extended ranges.

External optical feedback via Rayleigh scattering from an integrated microresonator or an optical fiber has been demonstrated to significantly narrow the intrinsic linewidth of semiconductor lasers. Wavelength matching between the lasing cavity and the external high-Q microresonator is required to accumulate Rayleigh scattering based optical feedback. Optical fiber can provide Rayleigh scattering based optical feedback for any lasing wavelength. However, optical fibers hundreds of meters or even kilometers long are required for the accumulation of Rayleigh scattering based optical feedback, hindering the integration of narrow linewidth lasers. Here, we present an integrated scheme that collects distributed feedback signal with weak wavelength dependence by exploiting surface radiation in a silicon waveguide. The effects of waveguide width on the intensities of the surface radiation and distributed optical feedback signal are first numerically analyzed by introducing a collection coefficient. Numerical calculations show that a 1 μm-wide strip waveguide yields optimal performance for excitation and collection of distributed optical feedback, which is also experimentally verified by measuring the feedback signal with an optical frequency-domain reflectometry. Benefitting from the enhanced distributed optical feedback that is 34.72 dB higher than that in a single-mode fiber, the hybrid integrated laser demonstrates an intrinsic linewidth of 1.52 kHz, a side-mode suppression ratio (SMSR) of 74.71 dB, and a frequency noise of 24.44 Hz2/Hz. Furthermore, within a maximum allowable wavelength tuning range of 2.342 nm, the linewidth narrowing ratio depends little on the wavelength for all the waveguides with different widths.

Finite-aperture constraints render array design nontrivial and can undermine the effectiveness of classical sparse geometries. This letter provides universal guidance for fluid antenna array (FAA) design under a fixed aperture. We derive a closed-form Cramér--Rao bound (CRB) that unifies conventional and reconfigurable arrays by explicitly linking the Fisher information to the geometric variance of port locations. We further obtain a closed-form probability density function of the minimum spacing under random FAA placement, which yields a principled lower bound for the minimum-spacing constraint. Building upon these analytical insights, we then propose a gradient-based algorithm to optimize continuous port locations. Utilizing a simple gradient update design, the optimized FAA can achieve about a $30\%$ CRB reduction and a $42.5\%$ reduction in mean-squared error.

Semantic Communication (SemCom), leveraging its significant advantages in transmission efficiency and reliability, has emerged as a core technology for constructing future intellicise (intelligent and concise) wireless networks. However, intelligent attacks represented by semantic eavesdropping pose severe challenges to the security of SemCom. To address this challenge, Semantic Steganographic Communication (SemSteCom) achieves ``invisible'' encryption by implicitly embedding private semantic information into cover modality carriers. The state-of-the-art study has further introduced generative diffusion models to directly generate stega images without relying on original cover images, effectively enhancing steganographic capacity. Nevertheless, the recovery process of private images is highly dependent on the guidance of private semantic keys, which may be inferred by intelligent eavesdroppers, thereby introducing new security threats. To address this issue, we propose an Agentic AI-driven SemSteCom (AgentSemSteCom) scheme, which includes semantic extraction, digital token controlled reference image generation, coverless steganography, semantic codec, and optional task-oriented enhancement modules. The proposed AgentSemSteCom scheme obviates the need for both cover images and private semantic keys, thereby boosting steganographic capacity while reinforcing transmission security. The simulation results on open-source datasets verify that, AgentSemSteCom achieves better transmission quality and higher security levels than the baseline scheme.

We develop a systematic approach to the computation of gauge invariant quartic interactions between reducible massive and massless higher spin fields. Extending the BRST formulation of existing cubic results, we obtain a single constraint for each off-shell quartic vertex that ensures both the gauge invariance of the Lagrangian and associativity of the gauge transformations at quartic order. A solution to these equations is presented. The general equation is then reduced to an on-shell version to reduce complexity. We find example solutions for the off-shell and on-shell quartic vertices in low spin examples relevant to the problem of black hole scattering.

Neutral atoms have become a competitive platform for quantum metrology, simulation, sensing, and computing. Current magic trapping techniques are insufficient to engineer magic trapping conditions for qudits encoded in hyperfine states with $J \neq 0$, compromising qudit coherence. In this paper we propose a scheme to engineer magic trapping conditions for qudits via bichromatic tweezers. We show it is possible to suppress differential light shifts across all magnetic sublevels of the $5s5p$ $\mathrm{^{3}P_2}$ state by using two carefully chosen wavelengths (with comparable tensor light shift magnitude and opposite sign) at an appropriate intensity ratio, thus suppressing light-shift induced dephasing, enabling scalar magic conditions between the ground state and $5s5p$ $\mathrm{^{3}P_2}$, and tensor magic conditions for qudits encoded within it. Furthermore, this technique enables robust operation at the tensor magic angle 54.7$^\circ$ with linear trap polarization via reduced sensitivity to uncertainty in experimental parameters. We expect this technique to enable new loading protocols, enhance cooling efficiency, and enhance nuclear spins' coherence times, thus facilitating qudit-based quantum computing in ${}^{87}$Sr in the $5s5p$ $\mathrm{^{3}P_2}$ manifold.

We study Weyl elements in isotropic reductive groups over commutative rings. Our main result in an explicit formula for squares of such elements. We also classify these elements in rank one groups and prove basic properties of their loci.

Topological states of matter in disordered systems without translation symmetry have attracted great interest in recent years. These states with topological characters are not only robust against certain disorders, but also can be counterintuitively induced by disorders from a topologically trivial phase in the clean limit. In this review, we summarize the current theoretical and experimental progress on disorder-induced topological phases in both condensed-matter and artificial systems. We first introduce the topological Anderson insulators (TAIs) induced by random disorders and their topological characterizations and experimental realizations. We then discuss various extensions of TAIs with unique localization phenomena in quasiperiodic and non-Hermitian systems. We also review the theoretical and experimental studies on the disorder-induced topology in dynamical and many-body systems, including topological Anderson-Thouless pumps, disordered correlated topological insulators and average-symmetry protected topological orders acting as interacting TAI phases. Finally, we conclude the review by highlighting potential directions for future explorations.