arXiv subjects

Kang Zhou

Publications and source records attributed to Kang Zhou.

At least 55 records · Page 3Linked to original sources

arXiv2025

Note on hidden zeros and expansions of tree-level amplitudes

In this note, we derive and interpret hidden zeros of tree-level amplitudes of various theories, including Yang-Mills, non-linear sigma model, special Galileon, Dirac-Born-Infeld, and gravity, by utilizing universal expansions of tree-level amplitudes which expand amplitudes of different theories to those of bi-adjoint scalar theory. Hidden zeros of above amplitudes are attributed to zeros of bi-adjoint scalar amplitudes which can be easily proved. For unordered amplitudes such as gravitational ones, the kinematic condition for zeros causes potential divergences of propagators. We also show the detailed mechanism of eliminating such divergences.

hep-th↗

arXiv2024

Re-Examine Distantly Supervised NER: A New Benchmark and a Simple Approach

Distantly-Supervised Named Entity Recognition (DS-NER) uses knowledge bases or dictionaries for annotations, reducing manual efforts but rely on large human labeled validation set. In this paper, we introduce a real-life DS-NER dataset, QTL, where the training data is annotated using domain dictionaries and the test data is annotated by domain experts. This dataset has a small validation set, reflecting real-life scenarios. Existing DS-NER approaches fail when applied to QTL, which motivate us to re-examine existing DS-NER approaches. We found that many of them rely on large validation sets and some used test set for tuning inappropriately. To solve this issue, we proposed a new approach, token-level Curriculum-based Positive-Unlabeled Learning (CuPUL), which uses curriculum learning to order training samples from easy to hard. This method stabilizes training, making it robust and effective on small validation sets. CuPUL also addresses false negative issues using the Positive-Unlabeled learning paradigm, demonstrating improved performance in real-life applications.

cs.CL↗

arXiv2024

Understanding zeros and splittings of ordered tree amplitudes via Feynman diagrams

In this paper, we propose new understandings for recently discovered hidden zeros and novel splittings, by utilizing Feynman diagrams. The study focus on ordered tree level amplitudes of three theories, which are ${\rm Tr}(ϕ^3)$, Yang-Mills, and non-linear sigma model. We find three universal ways of cutting Feynman diagrams, which are valid for any diagram, allowing us to separate a full amplitude into two/three pieces. As will be shown, the first type of cuttings leads to hidden zeros, the second one gives rise to $2$-splits, while the third one corresponds to $3$-splits called smooth splittings. Throughout this work, we frequently use the helpful auxiliary technic of thinking the resulting pieces as in orthogonal spaces. However, final results are independent of this auxiliary picture.

hep-th↗

arXiv2024

Terahertz semiconductor laser chaos

Chaos characterized by its irregularity and high sensitivity to initial conditions finds various applications in secure optical communications, random number generations, light detection and ranging systems, etc. Semiconductor lasers serve as ideal light platforms for chaos generations owing to the advantages in on-chip integration and complex nonlinear effects. In near-infrared wavelengths, semiconductor laser based chaotic light sources have been extensively studied and experimentally demonstrated. However, in the terahertz (THz) spectral range, due to the lack of effective THz light sources and high-speed detectors, chaos generation in THz semiconductor lasers, e.g., quantum cascade lasers (QCLs), is particularly challenging. Due to the fast intersubband carrier transitions, single mode THz QCLs resemble Class A lasers, where chaos can be hardly excited, even with external perturbations. In this work, we experimentally show a THz chaos source based on a sole multimode THz QCL without any external perturbations. Such a dynamical regime is characterized by the largest Lyapunov exponent associated to the temporal traces of the measured radio frequency (intermode beatnote) signal of the laser. The experimental results and chaos validation are confirmed by simulations of our model based on effective semiconductor Maxwell-Bloch Equations. To further understand the physical mechanism of the chaos generation in THz QCLs, a reduced model based on two coupled complex Ginzburg-Landau equations is derived from the full model cited above to systematically investigate the effects of the linewidth enhancement factor and group velocity dispersion on the chaotic regime. This model allows us to show that the chaos generation in the THz QCL can be ascribed to the system attaining the defect mediated turbulence regime.

physics.optics↗

arXiv2024

Weakly-supervised Medical Image Segmentation with Gaze Annotations

Eye gaze that reveals human observational patterns has increasingly been incorporated into solutions for vision tasks. Despite recent explorations on leveraging gaze to aid deep networks, few studies exploit gaze as an efficient annotation approach for medical image segmentation which typically entails heavy annotating costs. In this paper, we propose to collect dense weak supervision for medical image segmentation with a gaze annotation scheme. To train with gaze, we propose a multi-level framework that trains multiple networks from discriminative human attention, simulated with a set of pseudo-masks derived by applying hierarchical thresholds on gaze heatmaps. Furthermore, to mitigate gaze noise, a cross-level consistency is exploited to regularize overfitting noisy labels, steering models toward clean patterns learned by peer networks. The proposed method is validated on two public medical datasets of polyp and prostate segmentation tasks. We contribute a high-quality gaze dataset entitled GazeMedSeg as an extension to the popular medical segmentation datasets. To the best of our knowledge, this is the first gaze dataset for medical image segmentation. Our experiments demonstrate that gaze annotation outperforms previous label-efficient annotation schemes in terms of both performance and annotation time. Our collected gaze data and code are available at: https://github.com/med-air/GazeMedSeg.

cs.CV↗

arXiv2024

Farey tree locking of terahertz semiconductor laser frequency combs

Frequency combs show various applications in molecular fingerprinting, imaging, communications, and so on. In the terahertz frequency range, semiconductor-based quantum cascade lasers (QCLs) are ideal platforms for realizing the frequency comb operation. Although self-started frequency comb operation can be obtained in free-running terahertz QCLs due to the four-wave mixing locking effects, resonant/off-resonant microwave injection, phase locking, and femtosecond laser based locking techniques have been widely used to broaden and stabilize terahertz QCL combs. These active locking methods indeed show significant effects on the frequency stabilization of terahertz QCL combs, but they simultaneously have drawbacks, such as introducing large phase noise and requiring complex optical coupling and/or electrical circuits. Here, we demonstrate Farey tree locking of terahertz QCL frequency combs under microwave injection. The frequency competition between the Farey fraction frequency and the cavity round-trip frequency results in the frequency locking of terahertz QCL combs, and the Farey fraction frequencies can be accurately anticipated based on the downward trend of the Farey tree hierarchy. Furthermore, dual-comb experimental results show that the phase noise of the dual-comb spectral lines is significantly reduced by employing the Farey tree locking method. These results pave the way to deploying compact and low phase noise terahertz frequency comb sources.

physics.optics↗

arXiv2024

On soft factors and transmutation operators

The well known soft theorems state the specific factorizations of tree level gravitational (GR) amplitudes at leading, sub-leading and sub-sub-leading orders, with universal soft factors. For Yang-Mills (YM) amplitudes, similar factorizations and universal soft factors are found at leading and sub-leading orders. Then it is natural to ask if the similar factorizations and soft factors exist at higher orders. In this note, by using transformation operators proposed by Cheung, Shen and Wen, we reconstruct the known soft factors of YM and GR amplitudes, and prove the nonexistence of higher order soft factor of YM or GR amplitude which satisfies the universality.

hep-th↗

arXiv2024

Constructing tree amplitudes of scalar EFT from double soft theorem

The well known Adler zero can fully determine tree amplitudes of non-linear sigma model (NLSM), but fails to fix tree pion amplitudes with higher-derivative interactions. To fill this gap, in this paper we propose a new method based on exploiting the double soft theorem for scalars, which can be applied to a wider range. A remarkable feature of this method is, we only assume the universality of soft behavior at the beginning, and determine the explicit form of double soft factor in the process of constructing amplitudes. To test the applicability, we use this method to construct tree NLSM amplitudes and tree amplitudes those pions in NLSM couple to bi-adjoint scalars. We also construct the simplest pion amplitudes which receive leading higher-derivative correction, with arbitrary number of external legs. All resulted amplitudes are formulated as universal expansions to appropriate basis.

hep-th↗

arXiv2024

Towards tree Yang-Mills and Yang-Mills-scalar amplitudes with higher-derivative interactions

In our recent works, a new approach for constructing tree amplitudes, based on exploiting soft behaviors, was proposed. In this paper, we extend this approach to effective theories for gluons which incorporate higher-derivative interactions. By applying our method, we construct tree Yang-Mills (YM) and Yang-Mills-scalar (YMS) amplitudes with the single insertion of $F^3$ local operator, as well as the YM amplitudes those receive contributions from both $F^3$ and $F^4$ operators. All results are represented as universal expansions to appropriate basis. We also conjecture a compact general formula for tree YM amplitudes with higher mass dimension, which allows us to generate them from ordinary YM amplitudes, and discuss the consistent factorizations of the conjectured formula.

hep-th↗

arXiv2024

Training-free image style alignment for self-adapting domain shift on handheld ultrasound devices

Handheld ultrasound devices face usage limitations due to user inexperience and cannot benefit from supervised deep learning without extensive expert annotations. Moreover, the models trained on standard ultrasound device data are constrained by training data distribution and perform poorly when directly applied to handheld device data. In this study, we propose the Training-free Image Style Alignment (TISA) framework to align the style of handheld device data to those of standard devices. The proposed TISA can directly infer handheld device images without extra training and is suited for clinical applications. We show that TISA performs better and more stably in medical detection and segmentation tasks for handheld device data. We further validate TISA as the clinical model for automatic measurements of spinal curvature and carotid intima-media thickness. The automatic measurements agree well with manual measurements made by human experts and the measurement errors remain within clinically acceptable ranges. We demonstrate the potential for TISA to facilitate automatic diagnosis on handheld ultrasound devices and expedite their eventual widespread use.

eess.IV↗

arXiv2024

Multi-trace YMS amplitudes from soft behavior

Tree level multi-trace Yang-Mills-scalar (YMS) amplitudes have been shown to satisfy a recursive expansion formula, which expresses any YMS amplitude by those with fewer gluons and/or scalar traces. In an earlier work, the single-trace expansion formula has been shown to be determined by the universality of soft behavior. This approach is nevertheless not extended to multi-trace case in a straightforward way. In this paper, we derive the expansion formula of tree-level multi-trace YMS amplitudes in a bottom-up way: we first determine the simplest amplitude, the double-trace pure scalar amplitude which involves two scalars in each trace. Then insert more scalars to one of the traces. Based on this amplitude, we further obtain the double-soft behavior when the trace containing only two scalars is soft. The multi-trace amplitudes with more scalars and more gluons finally follow from the double-soft behavior as well as the single-soft behaviors which has been derived before.

hep-th↗

arXiv2023

Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence Pairs

Unsupervised relation extraction (URE) aims to extract relations between named entities from raw text without requiring manual annotations or pre-existing knowledge bases. In recent studies of URE, researchers put a notable emphasis on contrastive learning strategies for acquiring relation representations. However, these studies often overlook two important aspects: the inclusion of diverse positive pairs for contrastive learning and the exploration of appropriate loss functions. In this paper, we propose AugURE with both within-sentence pairs augmentation and augmentation through cross-sentence pairs extraction to increase the diversity of positive pairs and strengthen the discriminative power of contrastive learning. We also identify the limitation of noise-contrastive estimation (NCE) loss for relation representation learning and propose to apply margin loss for sentence pairs. Experiments on NYT-FB and TACRED datasets demonstrate that the proposed relation representation learning and a simple K-Means clustering achieves state-of-the-art performance.

cs.CL↗

arXiv2023

Recursive construction for expansions of tree Yang-Mills amplitudes from soft theorem

In this paper, we have introduced a fundamentally different approach, based on a bottom-up methodology, to expand tree-level Yang-Mills (YM) amplitudes into Yang-Mills-scalar (YMS) amplitudes and Bi-adjoint-scalar (BAS) amplitudes. Our method relies solely on the intrinsic soft behavior of external gluons, eliminating the need for external aids such as Feynman rules or CHY rules. The recursive procedure consistently preserves explicit gauge invariance at every step, ultimately resulting in a manifest gauge-invariant outcome when the initial expression is already framed in a gauge-invariant manner. The resulting expansion can be directly analogized to the expansions of gravitational (GR) amplitudes using the double copy structure. When combined with the expansions of Einstein-Yang-Mills amplitudes obtained using the covariant double copy method from existing literature, the expansions presented in this note yield gauge-invariant BCJ numerators.

hep-th↗

arXiv2023

Expanding single trace YMS amplitudes with gauge invariant coefficients

In this note, we use the new bottom up method based on soft theorems to construct the expansion of single-trace Yang-Mills-scalar amplitudes recursively. The resulted expansion manifests the gauge invariance for any polarization carried by external gluons, as well as the permutation symmetry among external gluons. Our result is equivalent to that found by Clifford Cheung and James Mangan via the so called covariant color-kinematic duality approach.

hep-th↗

arXiv2023

Is ChatGPT a Good Recommender? A Preliminary Study

Recommendation systems have witnessed significant advancements and have been widely used over the past decades. However, most traditional recommendation methods are task-specific and therefore lack efficient generalization ability. Recently, the emergence of ChatGPT has significantly advanced NLP tasks by enhancing the capabilities of conversational models. Nonetheless, the application of ChatGPT in the recommendation domain has not been thoroughly investigated. In this paper, we employ ChatGPT as a general-purpose recommendation model to explore its potential for transferring extensive linguistic and world knowledge acquired from large-scale corpora to recommendation scenarios. Specifically, we design a set of prompts and evaluate ChatGPT's performance on five recommendation scenarios. Unlike traditional recommendation methods, we do not fine-tune ChatGPT during the entire evaluation process, relying only on the prompts themselves to convert recommendation tasks into natural language tasks. Further, we explore the use of few-shot prompting to inject interaction information that contains user potential interest to help ChatGPT better understand user needs and interests. Comprehensive experimental results on Amazon Beauty dataset show that ChatGPT has achieved promising results in certain tasks and is capable of reaching the baseline level in others. We conduct human evaluations on two explainability-oriented tasks to more accurately evaluate the quality of contents generated by different models. And the human evaluations show ChatGPT can truly understand the provided information and generate clearer and more reasonable results. We hope that our study can inspire researchers to further explore the potential of language models like ChatGPT to improve recommendation performance and contribute to the advancement of the recommendation systems field.

cs.IR↗

arXiv2023

New recursive construction for tree NLSM and SG amplitudes, and new understanding of enhanced Adler zero

We propose a new bottom up method to construct tree amplitudes of non-linear sigma model (NLSM) and special Galileon theory (SG), based on assuming the universality of soft behaviors and the double copy structure. We extend the on-shell amplitudes to off-shell ones with two off-shell external legs, which allow the numbers of external legs to be odd. Then the $3$-point and $4$-point off-shell amplitudes can be bootstrapped, and the soft behaviors of $4$-point NLSM and SG amplitudes can be derived from them. The universality of soft behaviors allows us to invert the resulted soft theorems to construct higher-point off-shell amplitudes recursively, and express them in the formula of expansions to tree amplitudes of bi-adjoint scalar theory. We emphasize that the exact forms of universal soft behaviors are derived, rather than assumed as the input. Back to the on-shell limit, amplitudes with odd numbers of external legs vanish automatically, and the enhanced Adler zero emerge. From the bottom up perspective without the aid of a Lagrangian, the enhanced Adler zero are understood as that soft behaviors vanish faster than the degree expected from the naive power counting of soft momentum in the formula of expansions. Interestingly, such "zero" have explicit formulas and can be interpreted naturally. For tree amplitudes of Born-Infeld and Dirac-Born-Infeld theories, our method for construction does not make sense, but the enhanced Adler zero can be studied similarly.

hep-th↗

arXiv2023

Note on tree NLSM amplitudes and soft theorems

We use the universality of single soft behavior, together with the double copy structure, to completely determine the tree amplitudes of non-linear sigma model (NLSM). We first figure out the Adler's zero for $4$-point NLSM amplitudes, by considering kinematics. Then, we assume the universality of the Adler's zero, and use this requirement to construct general tree NLSM amplitudes in the expanded formula, i.e., the formula of expanding NLSM amplitudes to bi-adjoint scalar amplitudes. We also derive double soft factors for tree NLSM amplitudes, based on the resulting expanded formula.

hep-th↗

arXiv2023

Tree and $1$-loop fundamental BCJ relations from soft theorems

We provide a new derivation of the fundamental BCJ relation among double color ordered tree amplitudes of bi-adjoint scalar theory, based on the leading soft theorem for external scalars. Then, we generalize the fundamental BCJ relation to $1$-loop Feynman integrands. We also use the fundamental BCJ relation to understand the Adler's zero for tree amplitudes of non-linear Sigma model and Born-Infeld theories.

hep-th↗