arXiv subjects

Xinkang Wang

Publications and source records attributed to Xinkang Wang.

9 recordsLinked to original sources

arXiv2026

Alternative Boundary Conditions in Asymptotically Anti-de Sitter Spacetimes

Anti-de Sitter (AdS) spacetimes are not globally hyperbolic, so boundary conditions are generally needed for well-defined dynamics. Ishibashi and Wald (IW) determined boundary conditions yielding well-posed dynamics with conserved positive energy for scalar, electromagnetic, and linearized gravitational fields, using nonlocal potentials and individual spherical harmonic modes. In this paper, we determine which IW conditions are local in the primary fields and which are AdS-invariant. In four spacetime dimensions, the standard AdS-invariant boundary conditions set the rescaled magnetic field to zero for electromagnetism and the rescaled magnetic Weyl tensor to zero for Einstein gravity on the conformal boundary. We generalize these standard boundary conditions by setting arbitrary linear combinations of the electric and magnetic fields, or the corresponding Weyl tensors, to zero, and we conjecture that these exhaust the local, AdS-invariant, conservative boundary conditions in AdS$_{1,3}$. Boundary conditions with non-AdS-invariance and in different spacetime dimensions are also studied. The AdS-invariant conditions in AdS$_{1,3}$ extend to Yang-Mills theory and nonlinear gravity in a general Fefferman-Graham setting. We construct phase spaces for these general boundary conditions. The covariant phase spaces for general conditions are naturally associated with Lagrangians containing Pontryagin terms and, in gravity, a Gauss-Bonnet term. In gravity, except for the standard boundary condition, no nontrivial asymptotic gauge symmetries exist and all conserved charges such as ADM mass vanish identically, as in a closed universe. Spacetimes with Killing fields including AdS then exhibit linearization instability in the sense of Fischer and Marsden and Moncrief. For nonstandard boundary conditions, the modified symplectic structure yields new expressions for the entropy formula of black holes.

hep-th↗

arXiv2026

AFD-INSTRUCTION: A Comprehensive Antibody Instruction Dataset with Functional Annotations for LLM-Based Understanding and Design

Large language models (LLMs) have significantly advanced protein representation learning. However, their capacity to interpret and design antibodies through natural language remains limited. To address this challenge, we present AFD-Instruction, the first large-scale instruction dataset with functional annotations tailored to antibodies. This dataset encompasses two key components: antibody understanding, which infers functional attributes directly from sequences, and antibody design, which enables de novo sequence generation under functional constraints. These components provide explicit sequence-function alignment and support antibody design guided by natural language instructions. Extensive instruction-tuning experiments on general-purpose LLMs demonstrate that AFD-Instruction consistently improves performance across diverse antibody-related tasks. By linking antibody sequences with textual descriptions of function, AFD-Instruction establishes a new foundation for advancing antibody modeling and accelerating therapeutic discovery.

q-bio.QM↗

arXiv2026

CoRe-ECG: Advancing Self-Supervised Representation Learning for 12-Lead ECG via Contrastive and Reconstructive Synergy

Accurate interpretation of electrocardiogram (ECG) remains challenging due to the scarcity of labeled data and the high cost of expert annotation. Self-supervised learning (SSL) offers a promising solution by enabling models to learn expressive representations from unlabeled signals. Existing ECG SSL methods typically rely on either contrastive learning or reconstructive learning. However, each approach in isolation provides limited supervisory signals and suffers from additional limitations, including non-physiological distortions introduced by naive augmentations and trivial correlations across multiple leads that models may exploit as shortcuts. In this work, we propose CoRe-ECG, a unified contrastive and reconstructive pretraining paradigm that establishes a synergistic interaction between global semantic modeling and local structural learning. CoRe-ECG aligns global representations during reconstruction, enabling instance-level discriminative signals to guide local waveform recovery. To further enhance pretraining, we introduce Frequency Dynamic Augmentation (FDA) to adaptively perturb ECG signals based on their frequency-domain importance, and Spatio-Temporal Dual Masking (STDM) to break linear dependencies across leads, increasing the difficulty of reconstructive tasks. Our method achieves state-of-the-art performance across multiple downstream ECG datasets. Ablation studies further demonstrate the necessity and complementarity of each component. This approach provides a robust and physiologically meaningful representation learning framework for ECG analysis.

cs.AI↗

arXiv2023

Amplitude Basis for Conformal Correlators

We present a classification of conformally-invariant three-point tensor structures in $d$ dimensions that parallels the classification of three-particle scattering amplitudes in $d+1$ dimensions. Using a set of canonically-normalized weight-shifting operators, we construct a basis of three-point structures involving conserved currents or stress tensors and non-conserved spinning operators, directly from their amplitude counterparts. As an application, we also examine the conformal block expansion of the four-point functions of external currents and stress tensors in this amplitude basis. Our results can be useful for conformal bootstrap applications involving spinning correlators as well as Witten diagram computations in anti-de Sitter space.

hep-th↗

arXiv2023

Momentum-space formulae for AdS correlators for diverse theories in diverse dimensions

In this paper, we explore correlators of a series of theories in anti-de Sitter space: we present comprehensive results for interactions involving scalars, gluons, and gravitons in multiple dimensions. One aspect of our investigation is the establishment of an intriguing connection between the kinematic factors of these theories; indeed, such a connection directly relates these theories among themselves and with other theories of higher spin fields. Besides providing several explicit results throughout the paper, we also highlight the interconnections and relationships between these different theories, providing valuable insights into their similarities and distinctions.

hep-th↗

arXiv2022

Cosmological Double-Copy Relations

We present differential double-copy relations between gluon and graviton three-point functions in (A)dS$_{d+1}$. We introduce a set of differential operators in (A)dS that naturally generalize on-shell kinematics of scattering amplitudes in flat space. This provides a way to construct (A)dS correlators by replacing the kinematic variables of amplitudes with the corresponding differential operators and suitably ordering them. By construction, the resulting correlators are manifestly conformally invariant, with the correct flat-space limit, and exhibit a differential double-copy structure.

hep-th↗

arXiv2016

Data encoding efficiency in pixel detector readout with charge information

The average minimum number of bits needed for lossless readout of a pixel detector is calculated, in the regime of interest for particle physics where only a small fraction of pixels have a non-zero value per frame. This permits a systematic comparison of the readout efficiency of different encoding imple- mentations. The calculation is compared to the number of bits used by the FE-I4 pixel readout chip of the ATLAS experiment.

physics.ins-det↗

arXiv2015

Optimizing BAO measurements with non-linear transformations of Lyman-alpha forest

We explore the effect of applying a non-linear transformation to the Lyman-$α$ forest transmitted flux $F=e^{-τ}$ and the ability of analytic models to predict the resulting clustering amplitude. Both the large-scale bias of the transformed field (signal) and the amplitude of small scale fluctuations (noise) can be arbitrarily modified, but we were unable to find a transformation that increases significantly the signal-to-noise ratio on large scales using Taylor expansion up to third order. In particular, however, we achieve a 33% improvement in signal to noise for Gaussianized field in transverse direction. On the other hand, we explore an analytic model for the large-scale biasing of the Ly$α$ forest, and present an extension of this model to describe the biasing of the transformed fields. Using hydrodynamic simulations we show that the model works best to describe the biasing with respect to velocity gradients, but is less successful in predicting the biasing with respect to large-scale density fluctuations, especially for very nonlinear transformations.

astro-ph.CO↗

arXiv2014

Data encoding efficiency in binary strip detector readout

A prescription to calculate the minimum number of bits needed for binary strip detector readout is presented. This permits a systematic analysis of the readout efficiency relative to this theoretical minimum number of bits. Different level efficiencies are defined to include context information and engineering properties needed for reliable transmission, such as DC-balance. A commonly used encoding method is analyzed as an example and found to have an efficiency only of order 50%. A new encoding method called Pattern Overlay Compression is introduced to illustrate how the systematic analysis can guide the construction of more efficient readout methods. Pattern Overlay Compression significantly outperforms the above example in the occupancy range of interest.

physics.ins-det↗