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Positivity bounds in effective field theories (EFTs) can be extracted through the moment problem approach, utilizing well-established results from the mathematical literature. We generalize this formalism using the matrix moment approach to derive positivity bounds for theories with multiple field components. The sufficient conditions for obtaining optimal bounds are identified and applied to several example field theories, yielding results that match precisely the numerical bounds computed using other methods. The upper unitarity bounds can also be easily harnessed in the matrix case. Furthermore, the moment problem formulation also provides a means to reverse engineer the UV spectrum from the EFT coefficients, often uniquely, as explicitly demonstrated in examples such as string amplitudes and the $stu$ kink theory., Comment: 59 pages, 15 figures

We construct static axisymmetric multisolitons in the Einstein-Friedberg-Lee-Sirlin model. This theory features a complex scalar field which gains mass through its interaction with a real scalar field that has a non-zero vacuum expectation value. By performing three-dimensional numerical relativity simulations, we identify stable dipolar boson stars in specific regions of the parameter space. Based on the dipole results, non-rotating odd parity chains with and without gravity can also be constructed when the mass of the real scalar field is sufficiently small. However, chains beyond the dipole case are found to be unstable., Comment: 38 pages, 16 figures

Motivated by deep neural networks, the deep Gaussian process (DGP) generalizes the standard GP by stacking multiple layers of GPs. Despite the enhanced expressiveness, GP, as an $L_2$ regularization prior, tends to be over-smooth and sub-optimal for inhomogeneous subjects, such as images with edges. Recently, Q-exponential process (Q-EP) has been proposed as an $L_q$ relaxation to GP and demonstrated with more desirable regularization properties through a parameter $q>0$ with $q=2$ corresponding to GP. Sharing the similar tractability of posterior and predictive distributions with GP, Q-EP can also be stacked to improve its modeling flexibility. In this paper, we generalize Q-EP to deep Q-EP to enjoy both proper regularization and improved expressiveness. The generalization is realized by introducing shallow Q-EP as a latent variable model and then building a hierarchy of the shallow Q-EP layers. Sparse approximation by inducing points and scalable variational strategy are applied to facilitate the inference. We demonstrate the numerical advantages of the proposed deep Q-EP model by comparing with multiple state-of-the-art deep probabilistic models., Comment: 21 pages, 5 figures

We investigate time delays of gravitational wave and scalar wave scatterings around black hole backgrounds in scalar-Gauss-Bonnet effective field theories of gravity. By requiring infrared causality, we impose a lower bound on the cutoff scale of the theories. With this bound, we further discuss the detectability of scalar-Gauss-Bonnet gravity in gravitational waves from binary black hole mergers. Comparing with the gravitational effective field theories that only contain the two tensor modes, adding a scalar degree of freedom opens up a detectable window in the planned observations., Comment: 13 pages, 4 figures, typos corrected, citations added, figures updated

Using dispersion relations of the scattering amplitudes and semi-definite programming, we calculate causality bounds on the Wilson coefficients in scalar-tensor effective field theories that include parity-violating operators. Particular attention has been paid to the dynamical-Chern-Simons (dCS) and scalar-Gauss-Bonnet (sGB) couplings, along with higher order coefficients, and the interplay between them. For the leading terms, the bounds on the parity-conserving and -violating coefficients are simply projections of the complex coefficients. Some parity-violating coefficients are found to be upper bounded by the the parity-conserving counterparts, or the higher order parity-conserving coefficients. While the observational constraints on parity-violating coefficients are weaker than the parity-conserving counterparts, the causality bounds are of comparable strength and thus may play a more prominent role in constraining strong gravity effects in upcoming observations., Comment: 49 pages, 19 figures, 1 table

Automatic disease diagnosis has become increasingly valuable in clinical practice. The advent of large language models (LLMs) has catalyzed a paradigm shift in artificial intelligence, with growing evidence supporting the efficacy of LLMs in diagnostic tasks. Despite the increasing attention in this field, a holistic view is still lacking. Many critical aspects remain unclear, such as the diseases and clinical data to which LLMs have been applied, the LLM techniques employed, and the evaluation methods used. In this article, we perform a comprehensive review of LLM-based methods for disease diagnosis. Our review examines the existing literature across various dimensions, including disease types and associated clinical specialties, clinical data, LLM techniques, and evaluation methods. Additionally, we offer recommendations for applying and evaluating LLMs for diagnostic tasks. Furthermore, we assess the limitations of current research and discuss future directions. To our knowledge, this is the first comprehensive review for LLM-based disease diagnosis., Comment: 69 pages

We explore the post-Newtonian dynamics of spinning black hole (BH) binaries in Einstein-ScalarGauss-Bonnet (ESGB) gravity, a theory that modifies general relativity by introducing a massless scalar field coupled nonminimally to gravity via the Gauss-Bonnet term. By employing an effective field theory (EFT) approach, we extend the Routhian formalism to incorporate spin effects in scalar interactions. In this formalism, we derive for the first time the effective two-body potential for spinning BHs up to the third post-Newtonian (3PN) order in generic ESGB gravity theories. This potential is expressed in terms of the sensitivities of the BHs, which are then obtained through a matching procedure using analytic BH solutions derived here within a slow-rotation approximation, accurate to seventh order in the Gauss-Bonnet coupling and fifth order in the BH spin. We also examine the thermodynamic properties of these rotating solutions, which, as shown in previous work, yield important insights into the inspiral phase evolution in ESGB gravity., Comment: 25 pages, 8 figures. Contains ancillary file

We construct and explore the complex structures of boson stars, drawing inspiration from similar configurations of non-topological solitons in Minkowski space. These "molecular states" of boson stars have a multipolar structure and both positive and negative Noether charges within one star, and the opposite charges swap with time. Thanks to the gravitational attraction, they exist even in the case of a free scalar field. We also explore the effects of scalar self-interactions on these complex structures. We propose to use galactic-scale charge-swapping boson stars as a potential solution to the problem of the observed anisotropic distribution of satellite galaxies., Comment: 25 pages, 23 figures

Automatic differential diagnosis (DDx) is an essential medical task that generates a list of potential diseases as differentials based on patient symptom descriptions. In practice, interpreting these differential diagnoses yields significant value but remains under-explored. Given the powerful capabilities of large language models (LLMs), we investigated using LLMs for interpretable DDx. Specifically, we curated the first DDx dataset with expert-derived interpretation on 570 clinical notes. Besides, we proposed Dual-Inf, a novel framework that enabled LLMs to conduct bidirectional inference (i.e., from symptoms to diagnoses and vice versa) for DDx interpretation. Both human and automated evaluation validated its efficacy in predicting and elucidating differentials across four base LLMs. In addition, Dual-Inf could reduce interpretation errors and hold promise for rare disease explanations. To the best of our knowledge, it is the first work that customizes LLMs for DDx explanation and comprehensively evaluates their interpretation performance. Overall, our study bridges a critical gap in DDx interpretation and enhances clinical decision-making., Comment: 16 pages

Graph anomaly detection (GAD) has been widely applied in many areas, e.g., fraud detection in finance and robot accounts in social networks. Existing methods are dedicated to identifying the outlier nodes that deviate from normal ones. While they heavily rely on high-quality annotation, which is hard to obtain in real-world scenarios, this could lead to severely degraded performance based on noisy labels. Thus, we are motivated to cut the edges of suspicious nodes to alleviate the impact of noise. However, it remains difficult to precisely identify the nodes with noisy labels. Moreover, it is hard to quantitatively evaluate the regret of cutting the edges, which may have either positive or negative influences. To this end, we propose a novel framework REGAD, i.e., REinforced Graph Anomaly Detector. Specifically, we aim to maximize the performance improvement (AUC) of a base detector by cutting noisy edges approximated through the nodes with high-confidence labels. (i) We design a tailored action and search space to train a policy network to carefully prune edges step by step, where only a few suspicious edges are prioritized in each step. (ii) We design a policy-in-the-loop mechanism to iteratively optimize the policy based on the feedback from base detector. The overall performance is evaluated by the cumulative rewards. Extensive experiments are conducted on three datasets under different anomaly ratios. The results indicate the superior performance of our proposed REGAD.

Time series self-supervised learning (SSL) aims to exploit unlabeled data for pre-training to mitigate the reliance on labels. Despite the great success in recent years, there is limited discussion on the potential noise in the time series, which can severely impair the performance of existing SSL methods. To mitigate the noise, the de facto strategy is to apply conventional denoising methods before model training. However, this pre-processing approach may not fully eliminate the effect of noise in SSL for two reasons: (i) the diverse types of noise in time series make it difficult to automatically determine suitable denoising methods; (ii) noise can be amplified after mapping raw data into latent space. In this paper, we propose denoising-aware contrastive learning (DECL), which uses contrastive learning objectives to mitigate the noise in the representation and automatically selects suitable denoising methods for every sample. Extensive experiments on various datasets verify the effectiveness of our method. The code is open-sourced., Comment: Accepted to 33rd International Joint Conference on Artificial Intelligence (IJCAI 2024)

We present DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. It comprises 236B total parameters, of which 21B are activated for each token, and supports a context length of 128K tokens. DeepSeek-V2 adopts innovative architectures including Multi-head Latent Attention (MLA) and DeepSeekMoE. MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation. Compared with DeepSeek 67B, DeepSeek-V2 achieves significantly stronger performance, and meanwhile saves 42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximum generation throughput to 5.76 times. We pretrain DeepSeek-V2 on a high-quality and multi-source corpus consisting of 8.1T tokens, and further perform Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) to fully unlock its potential. Evaluation results show that, even with only 21B activated parameters, DeepSeek-V2 and its chat versions still achieve top-tier performance among open-source models.

We analyse a sample of massive disk galaxies selected from the SDSS-IV/MaNGA survey to investigate how the evolution of these galaxies depends on their stellar and halo masses. We applied a semi-analytic spectral fitting approach to the data from different regions in the galaxies to derive several of their key physical properties. From the best-fit model results, together with direct observables such as morphology, colour, and the Mgb/$\langle$Fe$\rangle$ index ratio measured within $1 R_{\rm e}$, we find that for central galaxies both their stellar and halo masses have a significant influence in their evolution. For a given halo mass, galaxies with higher stellar mass accumulate their stellar mass and become chemically enriched earlier than those with smaller stellar mass. Furthermore, at a given stellar mass, galaxies living in more massive halos have longer star-formation timescales and are delayed in becoming chemically enriched. In contrast, the evolution of massive satellite galaxies is mostly determined by their stellar mass. The results indicate that both the assembled halo mass and the halo assembly history impact the evolution of central galaxies. Our spatially resolved analysis indicates that only the galaxy properties in the central region ($0.0$--$0.5 R_{\rm e}$) show the dependencies described above. This fact supports a halo-driven formation scenario since the galaxies' central regions are more likely to contain old stars formed along with the halo itself, keeping a memory of the halo formation process., Comment: 13 pages, 7 figures, MNRAS accepted

The Wilson coefficients of the Standard Model Effective Field Theory are subject to a series of positivity bounds. It has been shown that, while the positivity part of the UV partial wave unitarity leads to the Wilson coefficients living in a convex cone, further including the non-positivity part caps the cone from above. For the Higgs scattering, a capped positivity cone have been obtained using a simplified, linear unitarity conditions and without utilizing the full internal symmetries of the Higgs scattering. Here we further implement the stronger nonlinear unitarity conditions from the UV, which generically gives rise to better bounds. We show that, for the Higgs case in particular, while the nonlinear unitarity conditions per se do not enhance the bounds, the fuller use of the internal symmetries do shrink the capped positivity cone significantly., Comment: 20 pages, 4 figures, invited contribution to JUSTC

Diffeomorphic image registration is a fundamental step in medical image analysis, owing to its capability to ensure the invertibility of transformations and preservation of topology. Currently, unsupervised learning-based registration techniques primarily extract features at the image level, potentially limiting their efficacy. This paper proposes a novel unsupervised learning-based fully convolutional network (FCN) framework for fast diffeomorphic image registration, emphasizing feature acquisition at the image patch level. Furthermore, a novel differential operator is introduced and integrated into the FCN architecture for parameter learning. Experiments are conducted on three distinct T1-weighted magnetic resonance imaging (T1w MRI) datasets. Comparative analyses with three state-of-the-art diffeomorphic image registration approaches including a typical conventional registration algorithm and two representative unsupervised learning-based methods, reveal that the proposed method exhibits superior performance in both registration accuracy and topology preservation.

Recently, it has been found that a $Q$-ball can amplify waves incident upon it, due to rotation in the internal space and the interaction of the two modes in the complex scalar field. While the spherically symmetric 3D case has been investigated previously, here we explore the 3D axi-symmetric case, which is numerically much more challenging. The difficulty comes because a partial wave expansion is needed, and the different partial waves can not be separated, for either the background spinning Q-ball solution or the perturbative scattering on top of it. A relaxation method and a high dimensional shooting method are applied to compute the Q-ball solutions and the amplification factors respectively. We also classify the behavior of the amplification factors and we discuss their bounds and the superradiance criteria., Comment: 20 pages, 14 figures

The physics of individual Q-balls and interactions between multiple Q-balls are well-studied in classical numerical simulations. Interesting properties and phenomena have been discovered, involving stability, forces, collisions and swapping of charge between different components of multi-Q-ball systems. We investigate these phenomena in quantum field theory, including quantum corrections to leading order in a 2PI coupling expansion, the inhomogeneous Hartree approximation. The presence of quantum modes and new decay channels allows the mean-field Q-ball to exchange charge with the quantum modes, and also alters the charge swapping frequencies of the composite Q-balls. It is also observed that the periodic exchanges between the mean-field and quantum modes tend to be quenched by collisions between Q-balls. We illustrate how the classical limit arises through a scaling of the Q-ball potential, making quantum corrections negligible for large-amplitude Q-balls., Comment: 29 pages, 18 figures

Even over relatively recent epochs, galaxies have evolved significantly in their location in the mass-metallicity plane, which must be telling us something about the latter stages of galaxy evolution. In this paper, we analyse data from the LEGA-C survey using semi-analytic spectral and photometric fitting to determine these galaxies' evolution up to their observed epoch at $z \sim 0.7$. We confirm that, at $z \sim 0.7$, many objects already lie on the present-day mass-metallicity relation, but with a significant tail of high-mass low-metallicity galaxies that is not seen in the nearby Universe. Similar modelling of the evolution of galaxies in the nearby MaNGA survey allows us to reconstruct their properties at $z \sim 0.7$. Once selection criteria similar to those of LEGA-C are applied, we reassuringly find that the MaNGA galaxies populate the mass-metallicity plane in the same way at $z \sim 0.7$. Matching the LEGA-C sample to their mass-metallicity "twins" in MaNGA at this redshift, we can explore the likely subsequent evolution of individual LEGA-C galaxies. Galaxies already on the present-day mass--metallicity relation form few more stars and their disks fade, so they become smaller and more bulge-like. By contrast, the high-mass low-metallicity galaxies grow their disks through late star formation, and evolve rapidly to higher metallicities due to a cut-off in their wind-driven mass loss. There are significant indications that this late cut-off is associated with the belated end of strong AGN activity in these objects., Comment: 17 pages, 13 figures, MNRAS accepted

We consider a model where the interaction between dark matter and the Standard Model particles are mediated by a ghost-free bi-gravity portal. The bi-gravity model invokes a massive spin-2 particle coupled to the usual massless graviton as well as generic bi-metric matter couplings. The cross-sections for dark matter direct detection are computed and confronted with the experimental bounds. The presence of the massive spin-2 mediator resolves the core-cusp problem, which in turn significantly constrains the dark matter coupling in such a bi-gravity theory. Yet, there remains a window of the parameter space where the model can be tested in the upcoming direct detection experiments such as XENONnT and PandaX-30T. The model also predicts a reheating temperature of the order of $10^6$ GeV., Comment: 6 pages, 3 figures

SMEFT Wilson coefficients are subject to various positivity bounds in order to be consistent with the fundamental principles of S-matrix. Previous bounds on dimension-8 SMEFT operators have been obtained using the positivity part of UV partial wave unitarity and form a (projective) convex cone. We derive a set of linear UV unitarity conditions that go beyond positivity and are easy to implement in an optimization scheme with dispersion relations in a multi-field EFT. Using Higgs scattering as an example, we demonstrate how to obtain closed bounds in the space of the three relevant dimension-8 coefficients, making use of the UV unitarity conditions as well as so-called null constraints that arise from full crossing symmetry. Specifically, we show that they are bounded by inequalities schematically going like $C

The effect of dust attenuation on a galaxy's light depends on a number of physical properties, such as geometry and dust composition, both of which can vary across the faces of galaxies. To investigate this variation, we continue analysis on star-forming regions in 29 galaxies studied previously. We analyse these regions using Swift/UVOT and WISE images, as well as SDSS/MaNGA emission line maps to constrain the relationship between the infrared excess (IRX) and the UV spectral index (beta) for each star forming region. This relationship can be used to constrain which dust attenuation law is appropriate for the region. We find that the value of Dn(4000) for a region is correlated with both IRX and beta, and that the gas-phase metallicity is strongly correlated with the IRX. This correlation between metallicity and IRX suggests that regardless of aperture, metal rich regions have steeper attenuation curves. We also find that integrated galactic light follows nearly the same IRX-beta relationship as that found for kiloparsec-sized star forming regions. This similarity may suggest that the attenuation law followed by the galaxy is essentially the same as that followed by the regions, although the relatively large size of our star forming regions complicates this interpretation because optical opacity and attenuation curves have been observed to vary within individual galaxies., Comment: Accepted to MNRAS September 5, 2023

Graph anomaly detection (GAD) is a vital task since even a few anomalies can pose huge threats to benign users. Recent semi-supervised GAD methods, which can effectively leverage the available labels as prior knowledge, have achieved superior performances than unsupervised methods. In practice, people usually need to identify anomalies on new (sub)graphs to secure their business, but they may lack labels to train an effective detection model. One natural idea is to directly adopt a trained GAD model to the new (sub)graph for testing. However, we find that existing semi-supervised GAD methods suffer from poor generalization issue, i.e., well-trained models could not perform well on an unseen area (i.e., not accessible in training) of the same graph. It may cause great troubles. In this paper, we base on the phenomenon and propose a general and novel research problem of generalized graph anomaly detection that aims to effectively identify anomalies on both the training-domain graph and unseen testing graph to eliminate potential dangers. Nevertheless, it is a challenging task since only limited labels are available, and the normal background may differ between training and testing data. Accordingly, we propose a data augmentation method named \textit{AugAN} (\uline{Aug}mentation for \uline{A}nomaly and \uline{N}ormal distributions) to enrich training data and boost the generalizability of GAD models. Experiments verify the effectiveness of our method in improving model generalizability., Comment: Accepted to IEEE Transactions on Knowledge and Data Engineering (TKDE). arXiv admin note: substantial text overlap with arXiv:2209.10168

Scalars are widely used in cosmology to model novel phenomena such as the late-time cosmic acceleration. These are effective field theories with highly nonlinear interactions, including Horndeski theory/generalized galileon and beyond. We use the latest fully crossing symmetric positivity bounds to constrain these cosmological EFTs. These positivity bounds, based on fundamental principles of quantum field theory such as causality and unitarity, are able to constrain the EFT coefficients both from above and below. We first map the mass dependence of the fully crossing symmetric bounds, and find that a nonzero mass generically enlarges the positivity regions. We show that fine-tunings in the EFT construction can significantly reduce the viable regions and sometimes can be precarious. Then, we apply the positivity bounds to several models in the Horndeski class and beyond, explicitly listing the ready-to-use bounds with the model parameters, and discuss the implications for these models. The new positivity bounds are found to severely constrain some of these models, in which positivity requires the mass to be parametrically close to the cutoff of the EFT, effectively ruling them out. The examples include massive galileon, the original beyond Horndeski model, and DHOST theory with unity speed of gravity and nearly constant Newton's coupling., Comment: 45 pages, 16 figures, 1 table. To appear in JCAP

Recently, it has been realized that in some systems internal space rotation can induce energy amplification for scattering waves, similar to rotation in real space. Particularly, it has been shown that energy extraction is possible for a Q-ball, a stationary non-topological soliton that is coherently rotating in its field space. In this paper, we generalize the analysis to the case of boson stars, and show that the same energy extraction mechanism still works for boson stars., Comment: 10 pages, 6 figures; v2: refs added and clarified

We compute the causality/positivity bounds on the Wilson coefficients of scalar-tensor effective field theories. Two-sided bounds are obtained by extracting IR information from UV physics via dispersion relations of scattering amplitudes, making use of the full crossing symmetry. The graviton $t$-channel pole is carefully treated in the numerical optimization, taking into account the constraints with fixed impact parameters. It is shown that the typical sizes of the Wilson coefficients can be estimated by simply inspecting the dispersion relations. We carve out sharp bounds on the leading coefficients, particularly, the scalar-Gauss-Bonnet couplings, and discuss how some bounds vary with the leading $(\partial\phi)^4$ coefficient and as well as phenomenological implications of the causality bounds., Comment: 76 pages, 15 figures; to appear in JHEP

Learning rich data representations from unlabeled data is a key challenge towards applying deep learning algorithms in downstream tasks. Several variants of variational autoencoders (VAEs) have been proposed to learn compact data representations by encoding high-dimensional data in a lower dimensional space. Two main classes of VAEs methods may be distinguished depending on the characteristics of the meta-priors that are enforced in the representation learning step. The first class of methods derives a continuous encoding by assuming a static prior distribution in the latent space. The second class of methods learns instead a discrete latent representation using vector quantization (VQ) along with a codebook. However, both classes of methods suffer from certain challenges, which may lead to suboptimal image reconstruction results. The first class suffers from posterior collapse, whereas the second class suffers from codebook collapse. To address these challenges, we introduce a new VAE variant, termed sparse coding-based VAE with learned ISTA (SC-VAE), which integrates sparse coding within variational autoencoder framework. The proposed method learns sparse data representations that consist of a linear combination of a small number of predetermined orthogonal atoms. The sparse coding problem is solved using a learnable version of the iterative shrinkage thresholding algorithm (ISTA). Experiments on two image datasets demonstrate that our model achieves improved image reconstruction results compared to state-of-the-art methods. Moreover, we demonstrate that the use of learned sparse code vectors allows us to perform downstream tasks like image generation and unsupervised image segmentation through clustering image patches., Comment: 21 pages, 23 figures, and 4 tables