General Economics
Do Female Directors Raise ESG Ratings? A Meta-Analysis
Karolina Hozova, Tomas Havranek and Zuzana Irsova
Appointing more women to corporate boards is widely expected to also raise firms' environmental, social, and governance (ESG) performance. We provide the first meta-analysis of this relationship, drawing on 533 estimates from 106 studies that measure ESG performance with Bloomberg or LSEG ratings. The average reported effect of a one-percentage-point increase in board gender diversity is about 0.28 ESG points, but much of it does not survive scrutiny. Correcting for publication bias with a battery of linear and non-linear methods lowers the effect to between roughly 0.08 and 0.17 points. A best-practice estimate that also imposes sound study design puts it near 0.12 for most of the world, markedly higher for the Middle East, and near -0.11 for the Southeast Asian
markets that dominate the Asian evidence. The differences that remain across studies are systematic, driven mainly by geography and by the choice of estimation method rather than by the ESG-rating provider or the controls a study includes. Board gender diversity may be well worth pursuing on its own merits, but the evidence that it reliably raises ESG scores is weaker than the published record suggests.
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Functional Analysis
A Direct Approach to Rough Paths in Time-Weighted Besov Spaces
Atiksh Gupta
We extend the Besov rough path framework of Friz and Seeger to paths lying in Besov spaces that are Muckenhoupt-weighted in time. We establish a weighted Besov sewing lemma, construct Young and rough integrals, and prove local Lipschitz continuity of the Itô-Lyons map in weighted Besov norms. The resulting estimates deliver "location-sensitive" control of errors in RDE solution increments. We illustrate the framework with a
diffusion whose coefficient is singular at a specified time.
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Artificial Intelligence
PAWS: Policy-driven Agentic World Simulation
Tiviatis Sim, Jia Hui Woon, Xinming Gao et al. (+5 more)
Policy interventions propagate through public communication, institutional decisions, and stakeholder responses, yet
datasets for financial multi-
agent simulation rarely connect these processes to temporally aligned historical evidence. We introduce PAWS, a Policy-driven Agentic World Simulation
dataset covering 36 verified U.S. financial and economic policy episodes, 12,727 policy-linked news records, and 65,291 source-grounded stakeholder actions. Each action is linked to its supporting news and represented by a multi-layer event frame capturing its interaction mode, financial-action family and subtype, semantic attributes, and conditional mappings to external taxonomies. Entities are resolved to normalized organizations, and actions are aligned with daily
market-return context to support policy-
agent simulation replay. On 2,522 stratified action samples, independent AI and human reviewers achieved 89.4% initial agreement on interaction mode, with disagreements subsequently adjudicated. Case studies of the 2008 short-selling ban and 2001 decimalization recover documented policy timelines and associated
market patterns across both dense and sparse news settings. A replay study further shows that high accuracy can mask failure to detect rare stakeholder actions, identifying action timing and calibration as central challenges. PAWS provides an auditable substrate for evaluating
agent influence, policy-response cascades, and action-outcome
alignment in historically grounded financial simulations.
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All authors Tiviatis Sim, Jia Hui Woon, Xinming Gao, Chen Gao, Fengbin Zhu, Zheng Huanhuan, Chua Tat Seng, Kenji Kawaguchi
Information Theory
The spectral relation between irreducible cyclic codes and generalized Paley graphs
Ricardo A. Podestá and Denis E. Videla
Let $p$ be a
prime and $\mathbb{F}_q/\mathbb{F}_r$ a finite field extension with $q=p^m$ and $r=p^s$. For any $k\mid q-1$, we consider $r$-ary irreducible cyclic codes (ICC) of the form $C(k,q/r) = \{(Tr_{q/r}(\gamma \omega^{ik})_{i=0}^{n-1})\}_{\gamma \in \mathbb{F}_r}$, with $\omega$ a primitive element of $\mathbb{F}_q$ and $ n= \tfrac{q-1}{k}$, and generalized Paley (GP) graphs $\Gamma(k,q) = Cay(\mathbb{F}_q, \{ x^k : x \in \mathbb{F}_q^* \})$. We show that there is a simple closed formula relating the weight distribution of $C(k,q/r)$ with the spectrum of $\Gamma(k_r,q)$, where $k_r=\gcd(k, \frac{q-1}{r-1})$. Then, we give $Spec(\Gamma(k,q))$ explicitly for those graphs associated with irreducible 2-weight cyclic codes in the semiprimitive and exceptional cases. Finally, we give the weight enumerators of irreducible cyclic codes associated with Hamming GP-graphs.
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Populations and Evolution
Scarlet Fever Dynamics in 19th and 20th Century London
Kevin H. Zhao and David J. D. Earn
Weekly scarlet fever (SF) mortality records for London, UK, from 1842 to 1939, together with notified case records from 1901 to 1939, reveal a strong annual epidemic pattern with peak prevalence in the autumn. In addition to the annual epidemic pattern, this long time series reveals a cyclical envelope with a period that lengthened over the decades. In particular, the period of the envelope increased from about four years to about eight years between 1880 and 1920, coinciding with a dramatic decline in pre-antibiotic-era SF deaths (from about 2300/yr to about 80/yr). We quantify the spectral features of the SF time series using a wavelet transform, and attempt to explain why the frequency structure changed over time, using a mechanistic mathematical model of SF transmission dynamics. We estimate the parameters of the model in part from the literature and in part by fitting mortality and incidence jointly. We use a mechanistic transition analysis (considering both attractors and transients in model solutions) to relate the observed changes in frequency structure to our estimated changes in model parameters. We find that almost all spectral
evolution in the time series can be explained by changes in the effective reproduction number and the amplitude of seasonal forcing. These changes reflect observed variation in birth rates together with inferred changes in transmission, which may in part have arisen through pathogen
evolution.
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High Energy Physics (Phenomenology)
Hyperfine Structure of $B$ and $D$ Mesons in a QCD-Inspired Potential Model
Krishna Kingkar Pathak
We investigate the hyperfine structure of $B$ and $D$
mesons within a QCD-inspired potential model based on the Cornell ansatz. The conventional contact
spin–
spin term is regularized with a Gaussian smearing to remove the short-distance singularity, and analytic wavefunctions are obtained using the Dalgarno–Lewis perturbative method with the Coulomb part as parent. The numerical study has been extended to include explicit 2S hyperfine predictions and results for the $B_c$ system; these additions probe the stability of the potential parameter space (effective $\alpha_s$, confinement strength and smearing width) for unequal-mass systems and radial excitations. We also present a short sensitivity study of the fixed effective coupling $\alpha_s$ and discuss the physical interpretation of the smearing parameter $\sigma$. The combined analytic and phenomenological framework provides compact, testable
benchmarks for upcoming spectroscopy measurements.
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Astrophysics of Galaxies
A sub-100 pc view at z~5 of a multiply imaged massive quiescent galaxy
Richard Pan, Danilo Marchesini, Joseph F. V. Allingham et al. (+34 more)
James Webb Space
Telescope surveys reveal many rapidly assembled
galaxies in the first billion years. Simulations struggle to reproduce their rapid
star formation and abrupt cessation, known as quenching. Testing these models is difficult because high-
redshift galaxies are compact and appear unresolved. We present VENUS-CLJ0152-QG1, a massive post-starburst
galaxy at
redshift 5.18. A foreground cluster lenses it into three images, yielding a physical resolution of about 70 parsecs, enough to probe quenching processes on sub-kiloparsec scales. We find a half-light radius twice as large as expected, a central
stellar mass density three times lower than field analogs, and off-center ionized gas emission. These features suggest it quenched without compaction or an obvious active
galactic nucleus, a departure from unlensed high-
redshift galaxies that
lensing makes visible.
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All authors Richard Pan, Danilo Marchesini, Joseph F. V. Allingham, Seiji Fujimoto, Adi Zitrin, Dan Coe, Vasily Kokorev, Lukas J. Furtak, Jacqueline Antwi-Danso, Yoshihisa Asada, Franz E. Bauer, Maruša Bradač, Larry D. Bradley, Chloe M. Cheng, Pratika Dayal, Andreas L. Faisst, Brenda L. Frye, Norman A. Grogin, Tiger Yu-Yang Hsiao, Gourav Khullar, Anton M. Koekemoer, Conor Larison, Ray A. Lucas, Vladan Markov, Nicholas S. Martis, Minami Nakane, Gaël Noirot, Johan Richard, Massimo Ricotti, Luke Robbins, Fengwu Sun, Francesco Valentino, Eros Vanzella, Chris Willott, Rogier A. Windhorst, Hiroto Yanagisawa, Anita Zanella
Computer Vision
On the Diffusibility of High-Dimensional Latents
Chao Feng, Zhiyang Xu, Bowei Chen et al. (+7 more)
Representation Autoencoders (RAEs) enable
diffusion models to operate in the feature spaces of pretrained visual encoders. However, many off-the-shelf encoders are not optimized for faithful reconstruction, discarding fine-grained visual details. As expected, finetuning these encoders for image reconstruction recovers such details. However, perhaps counterintuitively, this procedure reduces the effective dimensionality of the resulting representation, and the altered geometry has downstream effects on generation. Specifically, we show that using the standard velocity prediction in flow matching in this high-dimensional space requires the model to fit orthogonal noise directions outside the low-dimensional signal
manifold, making optimization inefficient. This motivates using the clean data parameterization ($\boldsymbol{x}_{0}$-prediction) instead, which focuses learning on the underlying signal
manifold. Across experiments with multiple strong-reconstruction encoders, we show that $\boldsymbol{x}_{0}$-prediction consistently improves text-to-image generation performance.
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All authors Chao Feng, Zhiyang Xu, Bowei Chen, Yuanjun Xiong, Xiyao Wang, Jui-Hsien Wang, Richard Zhang, Zhe Lin, Andrew Owens, Yijun Li
Numerical Analysis
Hutch#: Optimal non-adaptive Frobenius norm estimation
Tyler Chen, Diana Halikias, Christopher Musco et al. (+1 more)
The Girard–Hutchinson
estimator provides an extremely simple randomized estimate of the Frobenius norm of a
matrix $A$ that can only be accessed implicitly via
matrix-vector products. In particular, if $\Omega$ is a random Gaussian
matrix with $r = O(1/\varepsilon^2)$ columns, than $\frac{1}{r}\|A\Omega\|_F^2$ provides a $(1\pm \varepsilon)$ multiplicative approximation to $\|A\|_F^2$ with high probability. In this work, we introduce a closely related
estimator, given by \begin{align*} {\frac{1}{r}\|A\Omega\|_F^2 + \frac{1}{r}\|\Psi^T A\|_F^2 - \frac{1}{r^2}\|\Psi^T A\Omega\|_F^2}, \end{align*} where $\Psi$ is a second, independent random Gaussian
matrix with $r$ columns. We prove that this
estimator yields a $(1\pm\varepsilon)$ multiplicative approximation to $\|A\|_F^2$ when $r = O(1/\varepsilon)$, a quadratic improvement over Girard–Hutchinson. This dependence on $\varepsilon$ is optimal. Our method, which we call Hutch# (pronounced “Hutch sharp”), matches the complexity of the Hutch++
algorithm [Meyer, Musco, Musco, Woodruff, 2021]. However, unlike Hutch++, Hutch# uses only non-adaptive
matrix-vector products with $A$ and $A^T$ and requires no orthogonalization or other advanced linear
algebra steps. Thus, Hutch# combines the simplicity of the Girard–Hutchinson
estimator and the optimal query complexity of Hutch++.
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All authors Tyler Chen, Diana Halikias, Christopher Musco, David Persson
Computation and Language
Contrastive Learning for Authorship Verification
Peter Kirby
Our results show that contrastive learning outperforms a classification-based approach to authorship verification under the tested settings. We identify loss function, batch size, training duration, pre-trained model, input context length, and random text span data augmentation as important factors of model performance. Based on these considerations, we develop a ModernBERT Bi-Encoder model that achieves 98.4% accuracy on the PAN21 authorship verification task.
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Artificial Intelligence
StudentBench: AI and human tutoring yield equivalent GRE learning gains
Curtis Northcutt, Inaara Hasmani, Kevin Feng et al. (+3 more)
Artificial intelligence offers an unprecedented opportunity to augment human capabilities, yet progress at the frontier has focused primarily on advancing model capabilities. We introduce StudentBench, a suite of AI teaching evaluations and a public platform that enables large-scale data collection with over 175,000 student-AI messages to study whether large
language models (LLMs) produce learning gains equivalent to human tutoring. Using StudentBench, we measured learning gains on Quantitative and Verbal GRE questions across 2,383 human participants receiving AI tutoring, human tutoring, or no tutoring. We establish that AI tutoring is statistically equivalent to expert human tutoring for GRE learning gains (p = .015), and in five of the seven GRE domains, the best performing AI tutor surpassed the human tutor, on average. In a second study, expert human tutors compared LLM-generated lesson plans and practice problems through 2,028 pairwise rubric evaluations. Together, the two studies clearly separate AI tutors across: (1) lesson planning, (2) practice-problem creation, (3) conversational pedagogy, (4) cost, and (5) engagement. Surprisingly, one AI tutor achieved learning gains equivalent to human tutoring (p = .044) at 918 times lower cost (USD 0.0052 for AI versus USD 4.81 for human, per percentage point gained). For Quantitative GRE sessions, faster AI replies correlated with more student messages, more messages with more correct practice, and more correct practice with larger learning gains (all p < .002). To support future research, we open-source the de-identified data collected in our studies.
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All authors Curtis Northcutt, Inaara Hasmani, Kevin Feng, Trevor Khangi, Andreas Plesner, Jonas Mueller
Quantum Physics
Compressed Permutation Oracles Revisited
Joseph Carolan and Christian Majenz
The compressed permutation oracle has been used to analyze the
quantum security of a number of cryptographic constructions which resisted prior techniques. However, these analyses were fundamentally limited by the poor soundness of the method: the technique was proven sound only up to $O(N^{1/12})$ queries to permutations on $N$ elements. We revisit this analysis, improving the soundness bound to a tight $\Omega(N^{1/2})$. In addition to being tighter, our proof is conceptually simpler and more direct, and gives the same bound in the ideal cipher model. The main technical idea is to construct the compression isometry from a simple POVM on the naive purification, a technique which may find wider applications. As immediate applications, our results yield tight, concrete collision and pre-image lower bounds for the sponge hash construction underlying SHA3 and the Davies–Meyer compression function used in SHA1 and SHA2. More broadly, the improved soundness
theorem provides a general-purpose tool for analyzing
quantum security in settings where random permutations or ideal ciphers serve as the underlying primitive.
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Instrumentation and Detectors
Fast-Neutron Scintillation with Multi-Quantum-Well 2D Perovskites
Shaun Tan, Zachary Benesh, Seong Eui Chang et al. (+4 more)
Fast-
neutron scintillators must efficiently convert
neutron energy into detectable light while enabling selectivity between
neutrons and the {\gamma}-ray background. Here, we report a synthesis strategy for layered two-dimensional (2D) lead halide perovskite scintillators with independent control of the macroscopic detector geometry and microscopic phase composition. Our synthesis approach produces mm-thick, large-area scintillators with a deliberately engineered multi-
quantum-well energy landscape, creating an effective spectral separation while retaining nanosecond emission dynamics. Our scintillators exhibit an energy resolution of 5.4% at 662 keV under 137Cs irradiation. Under deuterium-deuterium fusion
neutron irradiation, we directly demonstrate
neutron and {\gamma}-ray event separation by pulse shape discrimination, with a figure of merit of 3.51. Taken together, these results establish multi-
quantum-well 2D perovskites as a scalable materials platform that combines fast-
neutron sensitivity and selectivity within a controllable detector form factor.
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All authors Shaun Tan, Zachary Benesh, Seong Eui Chang, Shelby Elder, Haidi Jakovic, Tae-Woo Lee, Moungi G. Bawendi
Robotics
Where Should I Join? Robot Group Joining via Language-Guided Goal Prediction
Zilin Fang, Zishuo Wang, Gim Hee Lee et al. (+1 more)
Social
navigation typically assumes a specified goal and focuses on reaching it while respecting social conventions, whereas
robot group joining requires predicting where to join based on the group's real-time activity and formation. This is a highly semantic task, yet an important capability for applications such as robotic guide dogs and autonomous mobility scooters. We formulate
language-grounded
robot group joining: given an observation and a natural-
language description of a target group, the
robot identifies the relevant group members and predicts socially compliant joining poses. For grounding, we generate structured candidate subsets through recursive spectral partitioning and rank them with a
language-conditioned image–geometry model. Given the grounded group, a goal predictor leverages human-formation priors to produce a
multimodal energy–orientation map over feasible
robot poses. Experiments on conversations, queues, and audiences across varying group sizes, crowd densities, and visual ambiguities show that our method achieves competitive grounding accuracy with sub-second inference and outperforms all baselines in joining-pose prediction. Real-
robot experiments further demonstrate group joining in both static and dynamically changing interactions.
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All authors Zilin Fang, Zishuo Wang, Gim Hee Lee, David Hsu
Computer Vision
The Past Frames the Future: Memory for Autoregressive Video Generation
Harold Haodong Chen, Rongjin Guo, Disen Lan et al. (+22 more)
Advances in generative models have improved
video fidelity, enabling long-horizon generation, interactive world modeling, and evolving visual environments. Autoregressive (AR)
video generation extends visual sequences through causal rollouts. However, a fundamental bottleneck emerges: as the generated sequence expands, practical models must operate under strictly bounded context windows, storage, and computational limits. Consequently, critical historical information, e.g., entity identities, dynamic states, and intervention-induced causal changes, often leaves the active context long before its relevance diminishes. Overcoming this limitation and maintaining temporal persistence constitutes a fundamental
memory problem. We present a systematic and comprehensive review of
memory mechanisms in AR
video generation. We formulate
memory operationally as persistent historical information maintained across outer AR steps, capable of influencing future generation even after the originating evidence is no longer locally accessible. Building upon this unified framework, we organize the literature through five complementary perspectives: (I) Forms, the representational carriers of history; (II) Functions, the specific semantic and physical information requiring preservation; (III) Operations, the lifecycle of writing, reading, updating, managing, and integrating
memory; (IV) Learning, the optimization of
memory behaviors under closed-loop rollouts; and (V) Evaluation, the paradigms for diagnosing genuine
memory capabilities. We conclude by synthesizing open challenges, including composable and resource-aware
memory architectures, trustworthy state updating, self-rollout learning, and standardized evaluation. By bridging representations, mechanisms, and learning paradigms, this paper establishes a structured foundation for developing reliable,
memory-conditioned
video generation systems.
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All authors Harold Haodong Chen, Rongjin Guo, Disen Lan, Wen-Jie Shu, Hongfei Zhang, Hanzhe Hu, Shengtao Yao, Zixin Zhang, Guibin Zhang, Zhefan Rao, Jinxiu Liu, Yexin Liu, Rui Peng, Yuhao Liu, Bin Ren, Shuai Yang, Yukang Chen, Salman Khan, Ying-Cong Chen, Ser-Nam Lim, Rynson W.H. Lau, Nicu Sebe, Yu Cheng, Ming-Hsuan Yang, Qifeng Chen
Algebraic Geometry
Constructing Rational Curves via Jets on Projective Varieties with Non-Nef Canonical Bundle
Bin Dong, Guoxiong Gao, Bin Guo et al. (+3 more)
We give an algebraic proof in characteristic zero of the Miyaoka–Mori criterion: every point of a curve of negative canonical degree on a smooth projective variety lies on a rational curve. Our jet technique gives, in addition, an effective numerical decomposition of the original curve class. For each prescribed point, the decomposition contains a rational curve through that point, with a positive coefficient independent of the point and with anticanonical degree at most $\dim X+1$. Together with BDPP cone duality, this recovers the projective uniruledness criterion over $\mathbb C$. The main result of this paper was obtained using the Pharos system. A detailed report on the use of Pharos and on the Lean~4 formalization of the main arguments is given in the Appendix B, written by Bin Dong, Guoxiong Gao, Zeming Sun, and Bin Wu.
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All authors Bin Dong, Guoxiong Gao, Bin Guo, Zeming Sun, Bin Wu, Song-Yan Xie
Statistics Theory
Spectral Analysis of Gaussian Integral Operators Arising in BHEP Tests
Bruno Ebner, Dominic Edelmann, Norbert Henze et al. (+2 more)
The Baringhaus-Henze-Epps-Pulley (BHEP) tests for multivariate normality are affine-invariant goodness-of-fit tests based on a Gaussian-weighted $L^2$ distance between empirical and Gaussian characteristic functions. In 1990, Henze and Zirkler expressed the limiting null distribution through the
eigenvalues of an integral operator on the standard Gaussian space. In 1997, Henze and Wagner obtained a simpler covariance kernel and raised the problem of calculating the
eigenvalues of the resulting operator on a Gaussian-weighted space. Although subsequent work treated the univariate case and numerical approximations in a few low dimensions, the complete all-dimensional spectral problem remained open. This paper determines both complete spectra for every dimension $d \in \mathbb{N}$ and every smoothing parameter $\beta > 0$. The two operators are shown to have the forms $\mathcal{X}_{\beta,d}^*\mathcal{X}_{\beta,d}$ and $\mathcal{X}_{\beta,d}\mathcal{X}_{\beta,d}^*$ for the same
Hilbert-Schmidt operator $\mathcal{X}_{\beta,d}$. Consequently, their nonzero
eigenvalues agree, including multiplicities, while the null space of the Henze-Zirkler operator is identified exactly. The Gaussian integral operator in the Henze-Wagner decomposition is diagonalized by Mehler's formula, and rotational
symmetry confines the finite-rank correction to the sectors associated with spherical harmonics of degrees $0$, $1$, and $2$. The degree-$1$ and degree-$2$
eigenvalues are characterized by scalar transcendental equations, and the radial
eigenvalues by an explicit pole-safe Fredholm determinant. The paper establishes nonnegativity, multiplicities, eigenfunction reconstruction, completeness, the trace identity, and a complete characterization of all exceptional pole cases.
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All authors Bruno Ebner, Dominic Edelmann, Norbert Henze, Frédéric Ouimet, Donald Richards
Mathematical Finance
Market Completeness and Optional Projections under Restricted Information
Levin David Schwab
In a finite discrete-time
market, trading decisions may be predictable with respect to a filtration that does not adapt asset prices. The first fundamental
theorem then characterizes absence of arbitrage by measures under which the optional projection of discounted prices is a martingale. We examine the corresponding completeness question. For a fixed projection, every claim measurable with respect to its terminal price history is attainable precisely when all equivalent martingale measures of that fixed process agree on the claim sigma-field. We give the finite-dimensional proof, retaining the distinction between the trading filtration and the claim sigma-field. If the projection is common to all optional martingale measures of the original prices, projected completeness implies uniqueness of their restrictions, but the converse fails even in a three-state model with a unique optional martingale measure. For the binomial model under its product martingale measure, a delay of $k$ periods yields a complete projected
market with effective horizon $(T-k)^+$. An explicit replication construction and finite examples distinguish this completeness from replication at the original prices.
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Statistical Methodology
Detecting Structural Changes in High-Dimensional Multivariate Regression Models
Haoran Li
We study structural-change testing in multivariate linear regression when the response dimension is proportional to the sample size and the number of predictors is fixed. Despite its relevance to applications across a broad range of fields, this problem remains underexplored. The alternatives of interest allow multiple unknown changes in a prescribed linear contrast of the predictor effects. We construct a least-squares-based Wald statistic standardized by the residual covariance
estimator and scan it over candidate change-point
segmentations. We propose flexible segment scans for both single and multiple change points, together with a discretized multiscale scan that reduces the computational cost. When the response dimension and sample size diverge proportionally, we establish weak convergence of the normalized statistic process to a centered Gaussian process, yielding implementable critical values for all three scans. We further characterize their asymptotic power under local alternatives, explicitly describing how the signal, design, and high-dimensional aspect ratio determine the limiting power. The finite-sample performance is examined through simulation studies. We apply the proposed methods to filtered and standardized returns from U.S. equity portfolios to investigate structural changes in their exposures to the Fama–French factors.
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Quantum Physics
Purely-logarithmic-time- and constant-space-overhead fault-tolerant quantum computation
Zhengyi Han and Zi-Wen Liu
We prove that constant-space-overhead fault-tolerant
quantum computation can be achieved with provably strictly logarithmic time overhead, improving over the best known results with additional subpolylogarithmic factors. Our main construction uses
polynomial-subrank transversal logical $\CCZ$ gates on good
quantum locally testable codes to implement addressable universal computation by transferring batches of logical
qubits between dense storage and active logical subspaces while reusing the same ancillary workspace. Logical $\CCZ$ gates are implemented directly by the transversal operation, so only stabilizer resource states require separate preparation. Furthermore, we give an alternative construction that also achieves purely logarithmic time overhead based on modifying the
quantum Reed–Solomon magic-state
distillation scheme of Nguyen and Pattison. Recursively applying a fixed
distillation circuit protected by qLTCs of increasing block length eliminates the subpolylogarithmic time factor.
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High Energy Astrophysical Phenomena
First Pre-peak Ultraviolet Spectrum of a Tidal Disruption Event: A Fast Outflow Revealed Prior to Maximum Light in TDE2025aarm
Erica Hammerstein, Ryan Chornock, Jean Somalwar et al. (+32 more)
The
tidal disruption and eventual
accretion of a
star by a massive black hole can lead to the launching of
outflows and winds that encode important information about the
tidal disruption and
accretion processes. However, little is known about the existence and behavior of these
outflows before optical light-curve peak, when processes such as stream-stream collisions and disk formation may be important. Here, we present the first pre-peak ultraviolet (UV) spectrum of a
tidal disruption event (TDE), obtained $\sim 20$ days before optical maximum light of the nearby (
redshift $z=0.0137$) TDE2025aarm, along with quasi-simultaneous infrared (IR) through X-ray observations. Our HST/STIS spectrum shows strong evidence for an early-time
outflow through broad near-UV (NUV) and far-UV (FUV) absorption lines blueshifted by $\sim$10,000 km s$^{-1}$, including two new UV broad absorption features not yet identified in a TDE. We find that the FUV–IR continuum deviates significantly from a blackbody ($f_\lambda \propto \lambda^{-3.08}$), which we interpret as a signature of reprocessing through the
outflow. This deviation implies that the bolometric luminosity in optical TDEs may be underestimated by a significant factor ($\sim$9 in this case) when inferred from single-temperature blackbody fits to NUV–optical photometry alone. This work further confirms that TDEs are capable of launching fast
outflows at very early times and emphasizes the importance of
prompt FUV spectroscopic observations of TDEs that can capture the full continuum emission and energetics.
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All authors Erica Hammerstein, Ryan Chornock, Jean Somalwar, Raffaella Margutti, Brenna Mockler, Olivia Aspegren, Itai Sfaradi, Natalie LeBaron, Jonathan Carney, Alexei V. Filippenko, A.J. Nayana, Igor Andreoni, Thomas Brink, Peter J. Brown, Adam J. Burgasser, S. Bradley Cenko, Suvi Gezari, Xiaoshan Huang, Mansi M. Kasliwal, Jon M. Miller, Sasha Mintz, Sam Rose, Peter Senchyna, Joshua D. Simon, Emma Softich, Robert Stein, Yuhan Yao, WeiKang Zheng, Richard Dekany, Matthew J. Graham, Russ R. Laher, Josiah Purdum, Ben Rusholme, Jesper Sollerman, Stefan van der Walt
Machine Learning
Even Sharper Bounds for Transductive Learning and Its Applications
Yingzhen Yang
We introduce Sharper Transductive Local Complexity (STLC), a localized complexity method for transductive learning under uniform sampling without replacement. The construction starts from a Bernstein-type concentration inequality for the supremum of the test–train empirical process. Its proof uses the modified log-Sobolev inequality for the swap walk and a two-parameter
entropy closure. A peeling argument with a surrogate localization functional then gives excess-risk bounds with the same fixed-point and confidence terms as the classical inductive local Rademacher-complexity bounds, without the additional logarithmic confidence factor in earlier transductive results. For realizable learning over a binary class of VC dimension $\dVC$, with training size $m$, test size $u$, and $u\ge m\ge\dVC$, STLC yields $\cO\{\dVC\log(me/\dVC)/m\}$. This matches the standard inductive rate and, when $m\ge9$, is within a logarithmic factor of the transductive minimax lower bound of order $\dVC/m$. For transductive kernel learning, STLC gives a spectrum-adaptive excess-risk bound without the multiplicative imbalance factors appearing in the earlier local-complexity bound.
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Instrumentation and Methods for Astrophysics
Differentiable astrophysics at scale: solving and differentiating ODE ensembles on the GPU
A. Spurio Mancini
Astronomers increasingly fit their models with gradient-based methods, such as
Hamiltonian Monte Carlo, which need the derivatives of the model with respect to its parameters. In many analyses a prediction requires solving a small system of ordinary differential equations (ODEs) for thousands to millions of parameter sets, and this ensemble of integrations often sets the cost of the analysis. We introduce to the astronomical community GRADSOLVE, a JAX library that moves this computation to graphics processing units (GPUs): it integrates each member of the ensemble in its own GPU thread with its own step size and returns the derivatives with respect to the parameters in the same pass. We measure the speed-up it provides in three examples from different fields of astronomy,
stellar orbits in the
Galactic potential, the expansion history of a dark-energy cosmology and the
spin precession of binary black holes, with every code held to the same accuracy requirement. For a million trajectories GRADSOLVE runs 8.5 to 1500 times faster than the serial CPU code of each example running on all 128 cores of a CPU, and several orders of magnitude faster than on one core. On the same GPU it is also 11 to 15 times faster than DIFFRAX, the state-of-the-art ODE library in JAX, in all three examples. With GRADSOLVE a million such integrations take seconds or less on one GPU, so gradient-based analyses that need ensembles of this size become routine. The code is publicly available at https://github.com/ECLIPSE-AI4Science/gradsolve.
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High Energy Physics (Phenomenology)
Sterile Neutrino Dark Matter Cries for GeV Heavy Neutral Leptons
Marco Drewes, Yannis Georis, Juraj Klarić et al. (+2 more)
The ordinary and dark matter in the Universe may share a common origin in the framework of a minimal extension of the Standard Model by three right-handed
neutrinos. A pair of such heavy neutral
leptons (HNLs) can give masses to
neutrinos, generate the
baryon asymmetry at the electroweak scale and produce a large
lepton asymmetry at the QCD scale, which is then resonantly transformed into an abundance of the third state that acts as sterile
neutrino dark matter. The minimality of this setup, known as
Neutrino Minimal Standard Model ($\nu$MSM), makes it highly predictive. Earlier attempts to pin down the properties of the HNLs based on the requirement to simultaneously explain the
neutrino masses as well as the baryonic and dark matter abundances were hampered by an incomplete understanding of the
quantum kinetic equations describing the HNLs throughout
cosmic history. We perform the first parameter scan of the $\nu$MSM in which this shortcoming has been overcome. We find that the $\nu$MSM provides a common explanation for all aforementioned phenomena only within a well-defined parameter space that is limited in all directions, with HNL masses kinematically accessible by the SHiP experiment as well as searches at the LHC and future
lepton colliders. Further predictions for the HNL decay branching ratios, CP-violation, and
lepton number violation (LNV) make the model highly testable, bringing a discovery of the common origin of
neutrino masses and all matter in the Universe within reach of existing and planned experiments.
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All authors Marco Drewes, Yannis Georis, Juraj Klarić, Mikhail Shaposhnikov, Inar Timiryasov
High Energy Physics (Theory)
Scalar Gauss-Bonnet Wormholes and the Imaginary Distance Bound
Alex Kehagias and Antonio Riotto
We study $O(4)$ symmetric Euclidean wormholes in four-dimensional
gravity with a massless scalar linearly coupled to the Gauss-Bonnet invariant. At nonzero coupling, the Einstein wormhole becomes a regular complex saddle that satisfies the Kontsevich-Segal-Witten criterion until its branch ends at a finite coupling. In asymptotically flat space, the Gauss-Bonnet term modifies the scalar displacement and the on-shell action for finite wormholes, but the size dependent parts of both corrections vanish in the large throat limit. The original imaginary distance threshold therefore remains unchanged. A negative cosmological constant already lowers the Einstein large throat imaginary distance relative to its flat space value. When the Gauss-Bonnet coupling is also present, the scalar is no longer a modulus as it develops a logarithmic running and its trajectory has infinite length. Subtracting the logarithmic asymptotic behavior leaves a finite regulated displacement. Its large throat value is shifted upward from the Einstein AdS result by the Gauss-Bonnet interaction and determines the leading large charge behavior of the renormalized fixed charge action.
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Combinatorics
On the multicolour Ramsey numbers $R(3,3,k)$
Bruno Andrades, Marcelo Campos and Robert Morris
In this paper we determine the Ramsey number $R(3,3,k)$ up to a constant factor, showing that $$R(3,3,k) = \Theta\bigg( \frac{k^3}{(\log k)^2} \bigg).$$ The proof of the lower bound combines the Hefty-Horn-King-Pfender construction for $R(3,k)$ with the method of Alon and Rödl. Using the same proof, we also determine the $r$-colour Ramsey numbers $R_r(3,\ldots,3,k)$ up to a constant factor for every fixed $r \geqslant 3$.
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Analysis of PDEs
Global well-posedness and scattering for mass-critical Hartree equation
Zuyu Ma, Changxing Miao, Matthew Rosenzweig et al. (+1 more)
In this paper, we prove that the defocusing mass-critical Hartree equation is global well-posed and scatters for any initial data $u_0\in L^2$, and prove the analogous result in the focusing case, provided the mass of $u_0$ is strictly less than that of the ground state $Q$. This result removes the radial restriction from [C. Miao, G. Xu and L. Zhao, J. Math. Pures Appl. (9), 91(2009), 49-79.], thereby proving the scattering
conjecture for the Hartree equation at the scaling-critical regularity.
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All authors Zuyu Ma, Changxing Miao, Matthew Rosenzweig, Jiqiang Zheng
Materials Science
Altermagnetism and generalised tensorial properties in the exchange multiplet framework
Paolo G. Radaelli
A representation-theoretic framework for the calculation of time-reversal-odd tensorial properties of
magnetic materials based on the Bertaut–Izyumov exchange-multiplet formalism is developed. In contrast to approaches based on distinct
magnetic point groups or
spin groups for different orientations of the
magnetic order parameter, the exchange-multiplet framework permits the order parameter to be treated as a continuous variable. It is shown that, for constant-moment collinear structures, covariance imposes a simple set of
symmetry constraints on the tensor intertwiners appearing in a Landau–Lifshitz expansion. This leads to canonical intertwiner forms that factorise the
magnetic and crystallographic parts of the tensor construction. Tensor fields can be built to retain a Cartesian representation in the physical variables while acquiring a systematic dependence on the order-parameter direction through
symmetry-adapted
polynomial bases.
Magnetic-point-group selection rules are recovered automatically for arbitrary order-parameter directions, while the framework also provides a natural separation of co-rotating (SOC-free) and non-co-rotating contributions. The approach is applicable to altermagnets and conventional antiferromagnets alike and is particularly suited to experiments and first-principles calculations in which the
magnetic order parameter can be continuously varied.
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Probability
Stochastic Domination of Gaussian Maxima by the Regular Simplex
Abhijeet Mulgund
Let $n\ge2$, and let $X=(X_1,\ldots,X_n)$ be a centered Gaussian vector with $\mathrm{Var}(X_i)=1$ for every $i$. Let $Z_1,\ldots,Z_n$ be independent standard Gaussians, and put $\overline{Z}=(Z_1+\cdots+Z_n)/n$. We prove $\mathbb{P}\{\max_i X_i\le t\}\ge\mathbb{P}\{\sqrt{n/(n-1)}\,\max_i(Z_i-\overline{Z})\le t\}$ for every $t\in\mathbb{R}$, and for each fixed $t>0$ equality holds only when $\mathrm{Cov}(X_i,X_j)=-1/(n-1)$ for all $i\ne j$. The right side is the distribution function of the maximum of the regular simplex vector. Equivalently, among all simplices containing a given centered ball, the regular simplex circumscribed about the ball has the least standard Gaussian measure, as conjectured by Balitskiy, Karasev, and Tsigler. In our preceding paper we proved this comparison after both maxima are smoothed by independent Gaussian noise of variance $1/(n-1)$, which suffices for the Weak Simplex
Conjecture; here we remove the smoothing, which is what probabilities at a single threshold require. As an application we consider $n$ equally likely signals of equal energy in Gaussian noise, where the transmitter may also send nothing. At every positive false-alarm level, and for every law of a common nonnegative random amplitude not concentrated at zero, the regular simplex uniquely maximizes the average probability of correct identification whenever the signal dimension is at least $n-1$. A Lean formalization is available at https://github.com/abhmul/full-simplex-
conjecture-lean.
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Optimization and Control
Inter-Temporal Price Constraints in Dynamic Pricing: Performance Guarantees Under Price Monotonicity and Promotion Fatigue
Weiyuan Li, Paat Rusmevichientong and Huseyin Topaloglu
We study dynamic pricing problems under inter-temporal price constraints. We have resources with limited capacities. At each time period, we decide which products to make available and what prices to charge for the available products. The sale probability for a product depends on its price. If we make a sale for a product, then we collect a revenue reflecting the price and consume the capacities of a combination of resources. We work with two types of inter-temporal constraints. In price monotonicity, the prices charged for a product at different time periods have to be monotone. In promotion fatigue, we can discount a product at most once over each time interval of a fixed length. Computing the optimal policy is intractable. We use fluid approximations to construct policies. Traditionally, policies from fluid approximations make randomized decisions at each time period by following an optimal solution to the fluid approximation, but such randomized decisions easily violate price monotonicity or promotion fatigue constraints. We develop policies that sample price paths according to an optimal solution to the fluid approximation, while satisfying the inter-temporal constraints. Letting $c_{\min}$ be the smallest initial capacity of a resource and $L$ be the maximum number of resources used by a product, our policies have a performance guarantee of $\max\Big\{ \frac{1}{8L}, \, \frac{1}{2} - \sqrt{\frac{\log c_{\min}}{2 \,c_{\min}}} - \frac{L}{c_{\min}}\Big\}$. Thus, under large resource capacities, our policies are guaranteed to obtain at least half of the optimal total expected revenue. The latter performance guarantee is tight in the sense that no policy can, in general, obtain more than half of the optimal objective value of the fluid approximation even under large resource capacities. We unify our approach to open the path for extensions to other inter-temporal price constraints.
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