arXiv subjects

Bo Feng

Publications and source records attributed to Bo Feng.

At least 37 records · Page 2Linked to original sources

arXiv2025

$2$-split from Feynman diagrams and Expansions

In this paper, we investigate the $2$-split behavior of tree-level amplitudes of bi-adjoint scalar (BAS), Yang-Mills (YM), non-linear sigma model (NLSM), and general relativity (GR) theories under certain kinematic conditions. Our approach begins with a proof, based on the Feynman diagram method, of the $2$-split property for tree-level BAS$\oplus$X amplitudes with $\mathrm{X}={\mathrm{YM},\mathrm{NLSM},\mathrm{GR}}$. The proof relies crucially on a particular pattern in the Feynmam rules of various vertices. Building on this, we use the expansion of X amplitudes into BAS$\oplus$X amplitudes to establish the $2$-split behavior. As a byproduct, we derive universal expansions of the resulting pure X currents into BAS currents, which closely parallel the corresponding on-shell amplitude expansions.

hep-th↗

arXiv2025

Driving Accurate Allergen Prediction with Protein Language Models and Generalization-Focused Evaluation

Allergens, typically proteins capable of triggering adverse immune responses, represent a significant public health challenge. To accurately identify allergen proteins, we introduce Applm (Allergen Prediction with Protein Language Models), a computational framework that leverages the 100-billion parameter xTrimoPGLM protein language model. We show that Applm consistently outperforms seven state-of-the-art methods in a diverse set of tasks that closely resemble difficult real-world scenarios. These include identifying novel allergens that lack similar examples in the training set, differentiating between allergens and non-allergens among homologs with high sequence similarity, and assessing functional consequences of mutations that create few changes to the protein sequences. Our analysis confirms that xTrimoPGLM, originally trained on one trillion tokens to capture general protein sequence characteristics, is crucial for Applm's performance by detecting important differences among protein sequences. In addition to providing Applm as open-source software, we also provide our carefully curated benchmark datasets to facilitate future research.

cs.LG↗

arXiv2025

The canonical differential equations of the one-loop-like integrals

Recently, a new approach for high loop integrals has been proposed in \cite{Huang:2024nij}, where the whole parameter integration has been divided into two parts: a one-loop-like integration and the remaining parameter integration. In this paper, we systematically study the one-loop-like integrals. We establish the IBP relations for the integral family and show how to complete the reduction. We find the canonical master integrals and write down the corresponding canonical differential equations.

hep-ph↗

arXiv2025

Physics-Informed Neural Network for Cross-Domain Predictive Control of Tapered Amplifier Thermal Stabilization

Thermally induced laser noise poses a critical limitation to the sensitivity of quantum sensor arrays employing ultra-stable amplified lasers, primarily stemming from nonlinear gain-temperature coupling effects in tapered amplifiers (TAs). To address this challenge, we present a robust intelligent control strategy that synergistically integrates an encoder-decoder physics-informed gated recurrent unit (PI-GRU) network with a model predictive control (MPC) framework. Our methodology incorporates physical soft constraints into the neural network architecture, yielding a predictive model with enhanced physical consistency that demonstrates robust extrapolation capabilities beyond the training data distribution. Leveraging the PI-GRU model's accurate multi-step predictive performance, we implement a hierarchical parallel MPC architecture capable of real-time thermal instability compensation. This hybrid approach achieves cross-domain consistent thermal stabilization in TAs under diverse laser power operations. Remarkably, while trained exclusively on low-power operational data, our system demonstrates exceptional generalization, improving prediction accuracy by 58.2% and temperature stability by 69.1% in previously unseen high-power operating regimes, as experimentally validated. The novel synchronization of physics-informed neural networks with advanced MPC frameworks presented in this work establishes a groundbreaking paradigm for addressing robustness challenges in cross-domain predictive control applications, overcoming conventional modeling limitations.

eess.SY↗

arXiv2025

Breaking Down Video LLM Benchmarks: Knowledge, Spatial Perception, or True Temporal Understanding?

Existing video understanding benchmarks often conflate knowledge-based and purely image-based questions, rather than clearly isolating a model's temporal reasoning ability, which is the key aspect that distinguishes video understanding from other modalities. We identify two major limitations that obscure whether higher scores truly indicate stronger understanding of the dynamic content in videos: (1) strong language priors, where models can answer questions without watching the video; and (2) shuffling invariance, where models maintain similar performance on certain questions even when video frames are temporally shuffled. To alleviate these issues, we propose VBenchComp, an automated pipeline that categorizes questions into different domains: LLM-Answerable, Semantic, and Temporal. Specifically, LLM-Answerable questions can be answered without viewing the video; Semantic questions remain answerable even when the video frames are shuffled; and Temporal questions require understanding the correct temporal order of frames. The rest of the questions are labeled as Others. This can enable fine-grained evaluation of different capabilities of a video LLM. Our analysis reveals nuanced model weaknesses that are hidden by traditional overall scores, and we offer insights and recommendations for designing future benchmarks that more accurately assess video LLMs.

cs.CV↗

arXiv2025

Hidden Zeros and $2$-split via BCFW Recursion Relation

In this paper, we provide another angle to understand recent discoveries, i.e., the hidden zeros and corresponding 2-split behavior using the BCFW recursion relation. For the hidden zeros, we show that although the BCFW recursion relation is not directly applicable for computing amplitudes of the non-linear sigma model, we can indeed prove the zeros using the modified BCFW recursion relation. Our work also indicates that for the 2-split to hold, the current should be carefully defined.

hep-th↗

arXiv2025

Generation Function For One-loop Tensor Reduction

For loop integrals, the reduction is the standard method. Having an efficient way to find reduction coefficients is an important topic in scattering amplitudes. In this paper, we present the generation functions of reduction coefficients for general one-loop integrals with arbitrary tensor rank.

hep-ph↗

arXiv2025

Multivariate hypergeometric solutions of cosmological (dS) correlators by $\text{d} \log$-form differential equations

In this paper, we give the analytic expression for the homogeneous part of solutions of arbitrary tree-level cosmological correlators, including massive propagators and time-derivative interaction cases. The solutions are given in the form of multivariate hypergeometric functions. It is achieved by two steps. Firstly, we indicate the factorization of the homogeneous part of solutions, i.e., the homogeneous part of solutions of multiple vertices is the product of the solutions of the single vertex. Secondly, we give the solution to the $\text{d} \log$-form differential equations of arbitrary single vertex integral family. We also show how to determine the boundary conditions for the differential equations. There are two techniques we developed for the computation. Firstly, we analytically solve $\text{d} \log$-form differential equations via power series expansion. Secondly, we handle degenerate multivariate poles in power series expansion of differential equations by blow-up. They could also be useful in the evaluation of multi-loop Feynman integrals in flat spacetime.

hep-th↗

arXiv2024

Magnetized neutral 2SC color superconductivity and possible origin of the inner magnetic field of magnetars

In this paper the neutral 2SC phase of color superconductivity is investigated in the presence of a magnetic field and for diquark coupling constants and baryonic densities that are expected to characterize neutron stars. Specifically, the behavior of the charged gluons Meissner masses is investigated in the parameter region of interest taking into account, in addition, the contribution of a rotated magnetic field. It is found that up to moderately-high diquark coupling constants the mentioned Meissner masses become tachyonic independently of the applied magnetic-field amplitude, hence signalizing the chromomagnetic instability of this phase. To remove the instability, the restructuring of the system ground state is proposed, which now will be formed by vortices of the rotated charged gluons. These vortices boost the applied magnetic field having the most significant increase for relatively low applied magnetic fields. Finally, considering that with the stellar rotational frequency observed for magnetars a field of the order of $10^8$ G can be generated by dynamo effect, we show that by the boosting effect just described the field can be amplified to $10^{17}$ G that is in the range of inner core fields expected for magnetars. Thus, we conclude that the described mechanism could be the one responsible for the large fields characterizing magnetars if the cores of these compact objects are formed by neutral 2SC matter.

hep-ph↗

arXiv2024

General One-loop Generating Function by IBP relations

In this paper we have studied the most general generating function of reduction for one loop integrals with arbitrary tensor structure in numerator and arbitrary power distribution of propagators in denominator. Using IBP relations, we have established the partial differential equations for these generating functions and solved them analytically. These results provide useful guidance for applying generating function method to reductions of higher loop integrals.

hep-ph↗

arXiv2024

Towards Systematic Evaluation of de Sitter Correlators via Generalized Integration-By-Parts Relations

We generalize Integration-By-Parts (IBP) and differential equations methods to de Sitter correlators related to inflation. While massive correlators in de Sitter spacetime are usually regarded as highly intricate, we find they have remarkably hidden concise structures from the perspective of IBP. We find the factorization of the IBP relations of each vertex integral family corresponding to $\mathrm{d} τ_i$ integration. Furthermore, with a smart construction of master integrals, the universal formulas for iterative reduction and $\mathrm{d} \log$-form differential equations of arbitrary vertex integral family are presented and proved. These formulas dominate all tree-level de Sitter correlators and play a kernel role at the loop-level as well.

hep-th↗

arXiv2024

Notes on Selection Rules of Canonical Differential Equations and Relative Cohomology

We give an explanation of the $\mathrm{d}\log$-form of the coefficient matrix of canonical differential equations using the projection of ($n$+1)-$\mathrm{d}\log$ forms onto $n$-$\mathrm{d}\log$ forms. This projection is done using the leading-order formula for intersection numbers. This formula gives a simple way to compute the coefficient matrix. When combined with the relative twisted cohomology, redundancy in computation using the regulator method can be avoided.

hep-th↗

arXiv2024

Revisiting the measurements and interpretations of DLVO forces

The DLVO theory and electrical double layer (EDL) theory are the foundation of colloid and interface science. With the invention and development of surface forces apparatus (SFA) and atomic force microscope (AFM), the measurements and interpretations of DLVO forces (i.e., mainly measuring the EDL force (electrostatic force) FEDL and van der Waals force FvdW, and interpreting the potential ψ, charge density σ, and Hamaker constant H) can be greatly facilitated by various surface force measurement techniques, and would have been very promising in advancing the DLVO theory, EDL theory, and colloid and interface science. However, although numerous studies have been conducted, pervasive anomalous results can be identified throughout the literature, main including: (1) the fitted ψ/σ is normally extremely small (ψ can be close to or (much) smaller than ψζ (zeta potential)) and varies greatly; (2) the fitted ψ/σ can exceed the allowable range of calculation; and (3) the measured FvdW and the fitted H vary greatly. Based on rigorous and comprehensive arguments, we have reasonably explained the pervasive anomalous results in the literature and further speculated that, the pervasive anomalous results are existing but not noticed and questioned owing to the two important aspects: (1) the pervasive unreasonable understandings of EDL theory and (2) the commonly neglected systematic errors. Consequently, we believe that the related studies have been seriously hampered. We therefore call for re-examination and re-analysis of related experimental results and theoretical understandings by careful consideration of the EDL theory and systematic errors. On these bases, we can interpret the experimental results properly and promote the development of EDL theory, colloid and interface science, and many related fields.

physics.chem-ph↗

arXiv2023

AIM: Automatic Interrupt Modeling for Dynamic Firmware Analysis

The security of microcontrollers, which drive modern IoT and embedded devices, continues to raise major concerns. Within a microcontroller (MCU), the firmware is a monolithic piece of software that contains the whole software stack, whereas a variety of peripherals represent the hardware. As MCU firmware contains vulnerabilities, it is ideal to test firmware with off-the-shelf software testing techniques, such as dynamic symbolic execution and fuzzing. Nevertheless, no emulator can emulate the diverse MCU peripherals or execute/test the firmware. Specifically, the interrupt interface, among all I/O interfaces used by MCU peripherals, is extremely challenging to emulate. In this paper, we present AIM -- a generic, scalable, and hardware-independent dynamic firmware analysis framework that supports unemulated MCU peripherals by a novel interrupt modeling mechanism. AIM effectively and efficiently covers interrupt-dependent code in firmware by a novel, firmware-guided, Just-in-Time Interrupt Firing technique. We implemented our framework in angr and performed dynamic symbolic execution for eight real-world MCU firmware. According to testing results, our framework covered up to 11.2 times more interrupt-dependent code than state-of-the-art approaches while accomplishing several challenging goals not feasible previously. Finally, a comparison with a state-of-the-art firmware fuzzer demonstrates dynamic symbolic execution and fuzzing together can achieve better firmware testing coverage.

cs.CR↗

arXiv2023

Intersection theory rules symbology

We propose a novel method to determine the structure of symbols for any family of polylogarithmic Feynman integrals. Using the d log-bases and simple formulas for the leading order and next-to-leading contributions to the intersection numbers, we give a streamlined procedure to compute the entries in the coefficient matrices of canonical differential equations, including the symbol letters and the rational coefficients. We also provide a selection rule to decide whether a given matrix element must be zero. The symbol letters are deeply related to the poles of the integrands and also have interesting connections to the geometry of Newton polytopes. Our method can be applied to many cutting-edge multi-loop calculations. The simplicity of our results also hints at the possible underlying structure in perturbative quantum field theories.

hep-th↗

arXiv2023

TrojText: Test-time Invisible Textual Trojan Insertion

In Natural Language Processing (NLP), intelligent neuron models can be susceptible to textual Trojan attacks. Such attacks occur when Trojan models behave normally for standard inputs but generate malicious output for inputs that contain a specific trigger. Syntactic-structure triggers, which are invisible, are becoming more popular for Trojan attacks because they are difficult to detect and defend against. However, these types of attacks require a large corpus of training data to generate poisoned samples with the necessary syntactic structures for Trojan insertion. Obtaining such data can be difficult for attackers, and the process of generating syntactic poisoned triggers and inserting Trojans can be time-consuming. This paper proposes a solution called TrojText, which aims to determine whether invisible textual Trojan attacks can be performed more efficiently and cost-effectively without training data. The proposed approach, called the Representation-Logit Trojan Insertion (RLI) algorithm, uses smaller sampled test data instead of large training data to achieve the desired attack. The paper also introduces two additional techniques, namely the accumulated gradient ranking (AGR) and Trojan Weights Pruning (TWP), to reduce the number of tuned parameters and the attack overhead. The TrojText approach was evaluated on three datasets (AG's News, SST-2, and OLID) using three NLP models (BERT, XLNet, and DeBERTa). The experiments demonstrated that the TrojText approach achieved a 98.35\% classification accuracy for test sentences in the target class on the BERT model for the AG's News dataset. The source code for TrojText is available at https://github.com/UCF-ML-Research/TrojText.

cs.CL↗

arXiv2023

First-order perturbation theory of trust-region subproblems

Trust-region subproblem (TRS) is an important problem arising in many applications such as numerical optimization, Tikhonov regularization of ill-posed problems, and constrained eigenvalue problems. In recent decades, extensive works focus on how to solve the trust-region subproblem efficiently. To the best of our knowledge, there are few results on perturbation analysis of the trust-region subproblem. In order to fill in this gap, we focus on first-order perturbation theory of the trust-region subproblem. The main contributions of this paper are three-fold. First, suppose that the TRS is in \emph{easy case}, we give a sufficient condition under which the perturbed TRS is still in easy case. Second, with the help of the structure of the TRS and the classical eigenproblem perturbation theory, we perform first-order perturbation analysis on the Lagrange multiplier and the solution of the TRS, and define their condition numbers. Third, we point out that the solution and the Lagrange multiplier could be well-conditioned even if TRS is in {\it nearly hard case}. The established results are computable, and are helpful to evaluate ill-conditioning of the TRS problem beforehand. Numerical experiments show the sharpness of the established bounds and the effectiveness of the proposed strategies.

math.NA↗

arXiv2023

On convergence of the generalized Lanczos trust-region method for trust-region subproblems

The generalized Lanczos trust-region (GLTR) method is one of the most popular approaches for solving large-scale trust-region subproblem (TRS). Recently, Jia and Wang [Z. Jia and F. Wang, \emph{SIAM J. Optim., 31 (2021), pp. 887--914}] considered the convergence of this method and established some {\it a prior} error bounds on the residual, the solution and the Largrange multiplier. In this paper, we revisit the convergence of the GLTR method and try to improve these bounds. First, we establish a sharper upper bound on the residual. Second, we give a new bound on the distance between the approximation and the exact solution, and show that the convergence of the approximation has nothing to do with the associated spectral separation. Third, we present some non-asymptotic bounds for the convergence of the Largrange multiplier, and define a factor that plays an important role on the convergence of the Largrange multiplier. Numerical experiments demonstrate the effectiveness of our theoretical results.

math.NA↗