arXiv subjects

Yu Pan

Publications and source records attributed to Yu Pan.

At least 19 recordsLinked to original sources

arXiv2026

Simultaneous Real-Color Time-Domain Survey of the Coma Field with the Multi-channel Photometric Survey Telescope (Mephisto)

We present the results from a high-cadence, multi-channel time-domain survey targeting the Coma galaxy cluster, utilizing the newly commissioned 1.6-m Multi-channel Photometric Survey Telescope (Mephisto). Mephisto's pioneering mosaic focal-plane design enables strictly simultaneous imaging in three distinct optical bands ($u/v, g/r, i/z$). This unique capability allows for the extraction of true, instantaneous spectral energy distributions (SEDs) of fast-evolving transients. Our survey continuously monitors a 20-square-degree footprint centered on the Coma cluster. By strategically targeting this massive local structure, we exploit cosmic anisotropy to significantly amplify the volumetric discovery rate of stellar explosions. During the initial six-month pilot operation, we successfully detected and tracked nine new supernovae (SNe). Remarkably, our high-cadence, multi-band strategy allowed us to capture ultra-early rising light curves and pre-discovery signals (e.g., SN 2026dyr) hours to days prior to formal discovery alerts. These instantaneous early-phase colors allow us to reconstruct intrinsic SEDs without temporal interpolation, providing reliable constraints on the physical parameters of early supernova (SN) evolution. Furthermore, our observed supernova yield shows good agreement with theoretical forecasts based on local stellar mass and star-formation rate overdensities. These pilot results validate the efficiency of the Mephisto Coma survey, demonstrating its objective capability to systematically explore the local transient population and provide rapid optical localizations for future multi-messenger events.

astro-ph.HE↗

arXiv2026

Atmospheric Parameter Determination of RR Lyrae Stars With Mephisto Multi-band Photometry

Phase-resolved atmospheric parameters of pulsating stars, including RR Lyrae stars and Cepheids, are essential for studying stellar structure and pulsation physics and for establishing accurate stellar benchmarks for Galactic structure and assembly history. We present a photometric framework that simultaneously infers Teff, log g, and [Fe/H] from Mephisto multi-epoch, multi-band (u, v, g, r, i, z) photometry. A supervised neural-network forward model is built by training on synthetic colors derived from theoretical stellar atmosphere models. It reproduces the synthetic colors to a precision of 0.01 per cent. The model is applied to observed multi-epoch colors to derive phase-dependent atmospheric parameters. We apply the method to six RRab and four RRc stars in three globular clusters observed with Mephisto. We find typical per-phase uncertainties of about 62 K in effective temperature (Teff), 0.10 dex in surface gravity (log g), and 0.16 dex in metallicity ([Fe/H]). Repeated measurements across the pulsation cycle reduce the typical formal uncertainty of the final [Fe/H] estimates to roughly 0.03 dex. A sensitivity analysis indicates that the five Mephisto colors provide distinct and complementary constraints on the three stellar atmospheric parameters: (u-v) provides robust constraints on log g, (v-g) is informative for estimating [Fe/H], and (u-v), (v-g), (g-r), (r-i), and (i-z) all provide information on Teff. The results demonstrate that Mephisto multi-epoch, multi-band photometry can deliver precise, cost-effective phase-resolved atmospheric parameters for RR Lyrae stars, offering an efficient way to construct large samples of stellar benchmarks for studies of stellar pulsation physics and Galactic structure and evolution.

astro-ph.SR↗

arXiv2026

JAL-Turn: Joint Acoustic-Linguistic Modeling for Real-Time and Robust Turn-Taking Detection in Full-Duplex Spoken Dialogue Systems

Efficient and robust turn-taking detection remains a significant challenge in industrial-grade voice AI agents. Existing systems often rely on acoustic or linguistic cues alone, limiting accuracy and stability, whereas directly endowing large language models with full-duplex capability incurs heavy data and deployment costs that hinder real-time usage. We propose JAL-Turn, a lightweight speech-only turn-taking framework based on a joint acoustic-linguistic modeling paradigm, in which a cross-attention module adaptively fuses pre-trained acoustic and linguistic representations for low-latency hold or shift prediction. By sharing a frozen ASR encoder, JAL-Turn enables turn-taking prediction to run fully in parallel with speech recognition, introducing almost no additional end-to-end latency. We further introduce a scalable pipeline that automatically derives reliable turn-taking labels from large-scale real-world dialogue corpora. Experiments on the public Easy-Turn and Smart-Turn benchmarks and an in-house Japanese customer-service dataset show that JAL-Turn achieves competing results among strong baselines in detection accuracy while maintaining superior real-time performance.

cs.CL↗

arXiv2026

Prime-EVC: One-Step Expressive Emotional Voice Conversion via Emotion-Primed Flow Matching

Recent diffusion- and conditional flow matching (CFM)-based emotional voice conversion (EVC) systems achieve strong conversion quality. However, they typically rely on multiple sampling steps, because the conditioning specifies which emotion to impose while its acoustic realization is left to the sampler. In this paper, we propose Prime-EVC, a one-step expressive EVC architecture based on emotion-primed flow matching. Our key idea is to reallocate the emotional conversion between the conditioning and the sampler: a FiLM-fused speaker-emotion style, an emotional variance adaptor, and direct emotion and spectro-temporal supervision assemble an emotion-rich conditioning upstream, rendering the residual conversion left to the CFM decoder attainable within one Euler step. Experiments on the ESD corpus demonstrate that single-step Prime-EVC surpasses five representative EVC systems on every quality metric and exceeds 4- and 100-step DurFlex-EVC in subjective naturalness and emotion while running 3.4--50 times faster than both.

cs.SD↗

arXiv2026

Conditioned in Training, Dispensable at Inference: Typological Conditioning for Multilingual Speech Translation

Multilingual speech translation frontends built on speech large language models typically condition on language identity, as a learned embedding or a prompted language name. Such a signal, however, is learned separately for each language and transfers nothing between typologically related languages. In this paper, we present TypoCast, a frontend that conditions on the typological profile of the source language rather than on its label alone. A typology-informed hierarchical language encoding represents each language by its morphological type, constituent order and family, so that supervision is shared among related languages. A dual-route mixture-of-experts adapter routes inputs through a shared typological route together with a per-language residual, and a typology-aware prompt conveys the same profile to the decoder. Extensive experiments on CVSS-C demonstrate that TypoCast surpasses state-of-the-art speech-to-speech translation systems on French, German and Spanish to English, averaged over the three directions, and outperforms the identity-conditioned S2ST-Omni on every language of a seven-language inventory, with only 228M trainable parameters. Ablation studies further reveal that the conditioning signal acts during training and is dispensable at inference: a model trained without it is consistently worse, whereas removing it from a trained model changes BLEU by less than 0.2, and expert usage is consistent with the signal shaping how adapter capacity is divided among languages.

cs.CL↗

arXiv2026

Boundary-Free Contextual Biasing: Depth-Adaptive Gating and Reading-Space Matching for Unsegmented Languages

Contextual biasing supplies an ASR system with a list of expected words at inference time, but existing methods rely on word boundaries that Japanese and Chinese do not provide. We present a boundary-free biasing decoder for frozen public CTC models, built on a character-level Aho-Corasick automaton, with no training and no second pass. Two evidence-based mechanisms replace the boundary: a depth-adaptive gate that sets how hard to push from match depth, and reading-space matching for when the audio is right but the characters are wrong. On Aishell-1 NE's hard R1 subset we reach 66.5% recall, above the trained CLAS baseline (64%), transferring to WenetSpeech and to a second architecture without retuning. We release the first open Japanese contextual-biasing benchmark, where biasing lifts rare-word recall by 25 points at precision above 97%, and still by 19 and 22 points against 1,000-word lists.

eess.AS↗

arXiv2026

Local Presentations of Groups: Completion and Smith's Conjecture

For a symmetric subset of a group containing the identity, its universal local presentation records the multiplication relations visible within the subset. We prove that a group is recovered from every such generating subset if and only if it is trivial or cyclic of order two or three, as conjectured by P. A. Smith. The main step is an exact completion theorem: local maps into a target group always extend to generating domains if and only if every target element has a square root. By embedding an arbitrary group in a group with surjective squaring, we deduce that every partial local presentation embeds in a generating one. This gives subgroup inheritance and reduces the classification to finite generation. We also construct obstructions in one fixed countable metabelian target on domains of at most five elements. This bound is sharp even when the target is allowed to vary.

math.GR↗

arXiv2026

Agentsensus: Consensus-Compressed Shared Memory for Multi-Agent Story Worlds

A agentic story world is a dynamic system simulating who learned what, when, and from whom -- yet the standard design gives each character a private memory stream. A shared event is therefore stored once per witness, large duplication will be incurred in terms of storage. We present Agentsensus, a story-world simulation framework in which there is an unified long-term memory. Records of the same event merge into one owned by all its witnesses, and semantically relevant memory records are linked. We evaluate on four worlds -- two classical Chinese novels, Hamlet, and a real-world conflict timeline -- run for 40 to 80 rounds against three per-character memory designs under an equal-granularity protocol. Agentsensus writes 22-44% fewer entries than the closest baseline and is the only design whose memory becomes shared (14-28% of records held by more than one character, some by 10) and linked (94-99%), at judged simulation quality indistinguishable or even better than the baselines. An ablation attributes this to the merge itself: disabling it multiplies the store by 3.1x and takes sharing to exactly zero. Sharing also compounds with the horizon rather than saturating early, rising 6% to 9% to 14% as one world is re-run at 10, 20 and 40 rounds.

cs.AI↗

arXiv2026

Foreground Voice Activity Detection: Learning Speaker Selectivity from Supervision

Voice activity detection (VAD) fronts most voice-agent pipelines, yet production detectors treat all human speech, background talkers included, as valid activity; in crowded settings this floods recognition, stalls turn-taking, and triggers false barge-in. We formalize Foreground VAD (FVAD): a frame-synchronous, enrollment-free task in which only the dominant speaker, defined by sustained presence rather than instantaneous loudness, is positive, and which reduces to conventional VAD when a single speaker is present. We show that foreground selectivity is largely governed by training supervision: the crucial ingredient is an augmentation recipe pairing foreground-only labels with competing-speaker mixing, generated fully automatically without human annotation. To quantify selectivity we introduce the Background False-Alarm Rate (BG-FAR), gated by foreground F1, and build a controlled benchmark, Mix-Interference, complemented by an adapted VOiCES for real-world far-field evaluation. Across equal-size backbones, Mamba and LSTM perform on par while a longer-context attention model is no better, suggesting that training supervision plays a substantially larger role than temporal modeling capacity in achieving foreground selectivity. The resulting lightweight streaming model, Mamba-FVAD, outperforms commercial VADs and enrollment-based speaker-aware systems in foreground selectivity while staying competitive on conventional VAD, at 1-2 ms per-frame CPU latency.

eess.AS↗

arXiv2026

Disturbance Rejection Control under Nested Signal Temporal Logic Specifications: A Recursive Design Approach

For the control synthesis problem under signal temporal logic (STL) specifications, control barrier functions (CBFs) serve as an effective method. However, traditional CBF approaches are severely restricted in expressiveness, particularly failing to encode nested formulas containing multiple temporal operators. While recent methods based on reachability analysis attempt to address this, they incur a heavy computational burden and rely strictly on known system dynamics. To overcome this challenge, this paper investigates a CBF-based recursive control scheme for nested STL specifications under uncertain disturbances. Within this scheme, we introduce a novel recursive CBF design procedure guided by a modified STL tree (sTLT) to yield explicit, parameterized CBFs without heavy computational demands. To render the proposed recursive CBF design applicable to systems subject to uncertain disturbances, we further integrate a novel reconstructed CBF-based quadratic programming (QP) controller. This controller requires no prior knowledge of the disturbances while relaxing initial safety assumptions. The proposed recursive control synthesis framework is proven to effectively encode nested STL specifications while ensuring that the system satisfies the specifications under unknown disturbances, as supported by simulation results.

eess.SY↗

arXiv2026

Prior-Averaged Ranking of Low-Order Monomial Potentials in Low-Temperature Warm Inflation

We compare the relative prior-averaged weights of the monomial potentials $V_p(ϕ)=λ_pϕ^p/p$, with $p=2,3,4$, in low-temperature warm inflation with the dissipative coefficient fixed to $Υ=C_ϕT^3/ϕ^2$. The dissipative form, background equations, primordial-spectrum prescription, and compressed observable likelihood are held fixed while the monomial power is varied. For each branch, the warm background equations, including radiation backreaction, are solved numerically, and the broadened likelihood for $(A_s,n_s,r_{0.05})$ is integrated over the adopted prior domain to obtain $Z_{\rm eff}^{(A_s,n_s,r)}$. For the reference configuration $N_*=55$, $σ_r=0.005$, and the viability-conditioned prior A, we obtain $Δ\ln Z_{\rm eff}(p=2)=-32.18$ and $Δ\ln Z_{\rm eff}(p=3)=-6.99$ relative to $p=4$. The same ranking is retained for the specific variations of $N_*$, the prior domains, the random seeds, and the smoothing width of the upper-bound penalty examined here. A representative quartic trajectory gives $n_s=0.96420$, $r_{0.05}=0.02663$, $Q_*=4.68\times10^{-3}$, and $T_*/H_*=10.67$. Its scalar spectrum is evaluated using the assumed Bose--Einstein occupation prescription. A component test at this representative point indicates that the occupation term contributes more to the scalar enhancement than the fitted dissipative growth factor. Thus, within the compressed-likelihood setup, the adopted thermal-occupation prescription, and the adopted viability-conditioned priors, the prior-averaged ranking is $p=4>p=3\gg p=2$.

astro-ph.CO↗

arXiv2026

The Intermediate-Mass Black Hole Reverberation Mapping Project: Scientific Overview and Sample Characteristics

Recent discoveries with the James Webb Space Telescope of massive black holes at high redshift have highlighted fundamental questions about black hole seed formation and the coevolution of black holes with their host galaxies. Because the initial seed population cannot yet be observed directly, nearby intermediate-mass black holes provide a complementary fossil record of black hole formation and early growth. Motivated by this opportunity, we present the Intermediate-Mass Black Hole Reverberation Mapping (IMBH-RM) project and construct a homogeneous Sloan Digital Sky Survey sample of active broad-line IMBHs by uniformly reanalyzing literature candidates with consistent spectral decomposition and black hole mass estimation. Our sample contains 192 reliable IMBH candidates at $z\lesssim0.3$ with $\log(M_{\rm BH}/M_\odot)<6$, including four particularly compelling sources with $\log(M_{\rm BH}/M_\odot)<5$. The primary goal of IMBH-RM is to obtain reliable black hole masses from direct measurements and characteristic sizes of the broad-line region and accretion disk for a carefully selected subsample. These measurements will provide robust low-mass anchors for calibrating single-epoch black hole mass estimates and extending black hole--galaxy scaling relations into the IMBH regime. By building a statistically meaningful reverberation-mapped sample spanning $10^4-10^6\,M_\odot$, we aim to constrain the local IMBH mass distribution and place observational constraints on competing black hole seed formation scenarios. The future Multi-Channel Imager aboard the Chinese Space-station Survey Telescope provides a particularly promising platform for achieving these goals.

astro-ph.GA↗

arXiv2026

Rapid Variability and Broadband Spectral Modeling in the Flaring Activity of BL Lacertae

We report a multi-wavelength study of two flaring episodes of the blazar BL Lacertae during MJD 60500-60800 (9 July 2024 - 5 May 2025). The source reached a daily-averaged $γ$-ray flux of $(1.03 \pm 0.05) \times 10^{-5} \, \mathrm{ph \, cm^{-2} \, s^{-1}}$ ($E > 100$ MeV) on MJD 60588 (5 October 2024). Using orbit-binned data from the Large Area Telescope (LAT) onboard the \textit{Fermi Gamma-ray Space Telescope}, we identify a minimum flux halving timescale of $τ= 1.33 \pm 0.29$ hr. This constrains the upper limit on the $γ$-ray emitting region size to $R \le 2.0 \times 10^{15}$ cm, as well as its distance from the central supermassive black hole to $R_\mathrm{H} \le 5.9 \times 10^{16}$ cm, assuming a Doppler factor of $δ= 14.8$ derived from the spectral energy distribution (SED) modeling. We find tentative evidence for sub-minute $γ$-ray variability with a minimum doubling time of $0.7 \pm 0.2$ min ($p$-value = 0.03). This may originate from an extremely compact region with a size of $R \le 1.8 \times 10^{13}$ cm, suggesting that the emission arises from magnetohydrodynamic substructures, such as plasmoids within a magnetic reconnection zone. Spectral analysis reveals a significant ``softer-when-brighter'' trend ($r = 0.96, p = 4.5 \times 10^{-4}$) during the minute-scale flare peaks, indicating a complex interplay between particle acceleration and radiative cooling. The SED is reproduced using a one-zone leptonic model, in which synchrotron self-Compton (SSC) and external Compton (EC) scattering effectively account for the high-energy emissions. The reduced magnetic field strengths and hard electron injection spectral indices observed during the flaring states suggest enhanced particle acceleration efficiency, possibly associated with relativistic magnetic reconnection.

astro-ph.HE↗

arXiv2026

Phase calibration of quantum oscillations in the magnetostrictive coefficient using the topological antiferromagnet YbMnBi$_2$

The Berry phase accumulated along a cyclotron orbit encodes important information about electronic band topology and is commonly inferred from the phase of quantum oscillations. Measurements of the ac magnetostrictive coefficient have recently emerged as a sensitive thermodynamic probe of quantum oscillations, but the phase offset has not been experimentally calibrated. Here, using the topological antiferromagnet YbMnBi$_2$, we calibrate this offset by directly comparing quantum oscillations in magnetization with those in the ac magnetostrictive coefficient. Measurements of both responses on the same single crystal reveal a single fundamental frequency of approximately 160 T in fields up to 14 T, enabling a direct phase comparison free from ambiguities associated with multiple frequencies. We observe an approximately $π/2$ relative phase shift between the two oscillatory responses, consistent with the Maxwell relation linking the magnetostrictive coefficient to the stress derivative of magnetization. Our results establish the appropriate phase needed to extract cyclotron-orbit phase information from quantum oscillations in the ac magnetostrictive coefficient.

cond-mat.str-el↗

arXiv2026

Training a Knowledge Base: Supervised Structure Learning for Agent-Curated Document Stores

Retrieval-augmented generation treats the document store as a frozen input, and the offline pipelines that do build structure over it build it unsupervised -- a whole corpus indexed at uniform effort, with no signal about which structure a question will need. We instead treat the knowledge base as a non-parametric model trained on (question, answer) pairs: a curator agent answers a supervised question against the current store, is shown the gold answer, then edits the store. The store carries forward, and we evaluate the curated store with a test set, on two contamination-free benchmarks: KBGym, a fictional-universe generator we release, and PhantomWiki. Generalization is probed with four question groups of decreasing overlap with the training set: the trained questions themselves, and unseen questions sharing both of their keys with training, one key, or neither. The curated store's advantage grows with overlap -- from parity where no key was shared, through +0.176 F1 where both keys were, to 25% fewer actions at +0.294 F1 on the trained questions, the one cell significant on both benchmarks -- while matching HippoRAG's gains with 1,913 links against its 196,112: per point of corpus covered, 1.5x the action saving and 2.1x the accuracy gain. Accuracy rises steadily with the share of the corpus the indexes cover, so training on more questions widens coverage, and with it the generalization.

cs.CL↗

arXiv2026

LLM-Powered Predictive Decision-Making for Sustainable Data Center Operations

The growing demand for AI-driven workloads, particularly from Large Language Models (LLMs), has raised concerns about the significant energy and resource consumption in data centers. This work introduces a novel LLM-based predictive scheduling system designed to enhance operational efficiency while reducing the environmental impact of data centers. Our system utilizes an LLM to predict key metrics such as execution time and energy consumption from source code, and it has the potential to extend to other sustainability-focused metrics like water usage for cooling and carbon emissions, provided the data center can track such data. The predictive model is followed by a real-time scheduling algorithm that allocates GPU resources, aiming to improve sustainability by optimizing both energy consumption and queuing delays. With fast inference times, the ability to generalize across diverse task types, and minimal data requirements for training, our approach offers a practical solution for data center scheduling. This framework demonstrates strong potential for advancing sustainability objectives in AI-driven infrastructure. Through our collaboration with a data center, we achieved a 32% reduction in energy consumption and a 30% decrease in waiting time.

cs.LG↗

arXiv2026

On Ruling polynomials of Legendrian links

The ruling polynomial is a Legendrian invariant that is closely related to the augmentation variety of the Legendrian. We characterize all graded and ungraded ruling polynomials, and construct Legendrian links realizing each possible polynomial. The graded augmentation varieties of the Legendrians we construct all have trivial cluster algebra structures. Finally, we construct Legendrian knots admitting $k$ exact Lagrangian fillings with $χ(L)=n$ that are pairwise smoothly non-isotopic for $n\leq 1$, and $k\geq 0.$

math.SG↗

arXiv2026

Spin nematic liquid crystal and scalar spin chirality in tetragonal lattice YbMnBi$_2$

A spin nematic order, analogous to the nematic liquid crystal, characterizes the spontaneous breaking of spin-space rotational symmetry while preserving time-reversal ($T$) symmetry. In contrast, scalar spin chirality (SSC), a composite three-spin order, breaks $T$ symmetry and is known to induce an anomalous Hall effect (AHE). Although a spin nematic phase has been suggested in frustrated magnets and the square-lattice iridate, how it might affect magnetotransport properties is unknown. Here we use polarized neutron scattering to show that tetragonal $A$MnBi$_2$ ($A$ = Ca, Yb) is a strictly $c$-axis-aligned collinear antiferromagnet (C-type), with $T_N \approx 270$ K and 290 K, respectively. On cooling from 450 K to $T_N$, low-energy spin excitations in YbMnBi$_2$ spontaneously change from isotropic to anisotropic in spin space within the tetragonal plane, forming a dynamic spin nematic phase around 400 K due to heavy Yb-induced spin-orbit coupling, before gapping out below $T_N$. Similar measurements on CaMnBi$_2$ reveal isotropic paramagnetic scattering without a spin nematic phase above $T_N$. Under an in-plane magnetic field, the Yb$^{3+}$ moments may interact with the dynamic spin nematic phase to induce nonzero SSC, giving rise to AHE and an anomalous Nernst effect (ANE) in YbMnBi$_2$ that are absent in CaMnBi$_2$ above $T_N$. A symmetry-based Ginzburg-Landau analysis shows that coupling terms between the nematic order and SSC are allowed under an external magnetic field, which could explain the rapid increase of AHE with field in YbMnBi$_2$. Our results provide compelling evidence for dynamic SSC-induced AHE and ANE in the paramagnetic phase of a compensated collinear antiferromagnet, opening a new avenue for the physics of composite spin orders and room-temperature spintronics without magnetic order.

cond-mat.str-el↗