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We study the ground-state phase diagram of a non-Hermitian cluster-XY spin chain in the language of free fermions. By calculating the second derivative of ground-state energy density and various types of order parameters, we establish the global ground-state phase diagram of the model, exhibiting rich quantum phases and corresponding phase transitions. Specially, the results reveal that the non-Hermitian cluster-XY model contains five different phases and two critical regions, i.e., ferromagnetic (FM), antiferromagnetic (AFM), symmetry-protected topological (SPT), paramagnetic (PM), Luttinger liquid-like phase, as well as critical region I and II. The order parameters and critical behaviors are investigated and the correctness of the theory is confirmed.

Direct Preference Optimization (DPO) has emerged as a more computationally efficient alternative to Reinforcement Learning from Human Feedback (RLHF) with Proximal Policy Optimization (PPO), eliminating the need for reward models and online sampling. Despite these benefits, DPO and its variants remain sensitive to hyper-parameters and prone to instability, particularly on mathematical datasets. We argue that these issues arise from the unidirectional likelihood-derivative negative feedback inherent in the log-likelihood loss function. To address this, we propose a novel LLM alignment loss that establishes a stable Bidirectional Negative Feedback (BNF) during optimization. Our proposed BNF loss eliminates the need for pairwise contrastive losses and does not require any extra tunable hyper-parameters or pairwise preference data, streamlining the alignment pipeline to be as simple as supervised fine-tuning. We conduct extensive experiments across two challenging QA benchmarks and four reasoning benchmarks. The experimental results show that BNF achieves comparable performance to the best methods on QA benchmarks, while its performance decrease on the four reasoning benchmarks is significantly lower compared to the best methods, thus striking a better balance between value alignment and reasoning ability. In addition, we further validate the performance of BNF on non-pairwise datasets, and conduct in-depth analysis of log-likelihood and logit shifts across different preference optimization methods., Comment: 20 pages, 9 figures

Alzheimer's disease (AD) stands as the predominant cause of dementia, characterized by a gradual decline in speech and language capabilities. Recent deep-learning advancements have facilitated automated AD detection through spontaneous speech. However, common transcript-based detection methods directly model text patterns in each utterance without a global view of the patient's linguistic characteristics, resulting in limited discriminability and interpretability. Despite the enhanced reasoning abilities of large language models (LLMs), there remains a gap in fully harnessing the reasoning ability to facilitate AD detection and model interpretation. Therefore, we propose a patient-level transcript profiling framework leveraging LLM-based reasoning augmentation to systematically elicit linguistic deficit attributes. The summarized embeddings of the attributes are integrated into an Albert model for AD detection. The framework achieves 8.51\% ACC and 8.34\% F1 improvements on the ADReSS dataset compared to the baseline without reasoning augmentation. Our further analysis shows the effectiveness of our identified linguistic deficit attributes and the potential to use LLM for AD detection interpretation., Comment: accepted to EMBC 2024

Detecting out-of-distribution (OOD) samples is crucial for trustworthy AI in real-world applications. Leveraging recent advances in representation learning and latent embeddings, Various scoring algorithms estimate distributions beyond the training data. However, a single embedding space falls short in characterizing in-distribution data and defending against diverse OOD conditions. This paper introduces a novel Multi-Manifold Embedding Learning (MMEL) framework, optimizing hypersphere and hyperbolic spaces jointly for enhanced OOD detection. MMEL generates representative embeddings and employs a prototype-aware scoring function to differentiate OOD samples. It operates with very few OOD samples and requires no model retraining. Experiments on six open datasets demonstrate MMEL's significant reduction in FPR while maintaining a high AUC compared to state-of-the-art distance-based OOD detection methods. We analyze the effects of learning multiple manifolds and visualize OOD score distributions across datasets. Notably, enrolling ten OOD samples without retraining achieves comparable FPR and AUC to modern outlier exposure methods using 80 million outlier samples for model training., Comment: European Conference on Computer Vision ECCV 2024 BEW Workshop Best Paper

Let $\pi\colon X\to \Delta$ be a proper smooth K\"ahler morphism from a complex manifold $X$ to the unit disc $\Delta$. Suppose the fibers $X_t=\pi^{-1}(t)$ are biholomorphic to $S$ for all $t\neq0$, where $S$ is a given projective manifold. If the canonical line bundle of $S$ is semiample, then we show that the central fiber $X_0$ is also biholomorphic to $S$. As an application, we obtain that, for smooth families over projective curves satisfying that the canonical line bundle of the generic fiber is semiample, birational isotriviality equals to isotriviality. Moreover, we also obtain a new Parshin-Arakelov type isotriviality criterion.

Many socio-economical critical domains (such as sustainability, public health, and disasters) are characterized by highly complex and dynamic systems, requiring data and model-driven simulations to support decision-making. Due to a large number of unknowns, decision-makers usually need to generate ensembles of stochastic scenarios, requiring hundreds or thousands of individual simulation instances, each with different parameter settings corresponding to distinct scenarios, As the number of model parameters increases, the number of potential timelines one can simulate increases exponentially. Consequently, simulation ensembles are inherently sparse, even when they are extremely large. This necessitates a platform for (a) deciding which simulation instances to execute and (b) given a large simulation ensemble, enabling decision-makers to explore the resulting alternative timelines, by extracting and visualizing consistent, yet diverse timelines from continuous-coupled simulation ensembles. In this article, we present DataStorm-EM platform for data- and model-driven simulation ensemble management, optimization, analysis, and exploration, describe underlying challenges and present our solution.

Deep learning techniques have shown promising results in the automatic classification of respiratory sounds. However, accurately distinguishing these sounds in real-world noisy conditions poses challenges for clinical deployment. Additionally, predicting signals with only background noise could undermine user trust in the system. This paper aims to investigate the feasibility and effectiveness of incorporating a deep learning-based audio enhancement preprocessing step into automatic respiratory sound classification systems to improve robustness and clinical applicability. Multiple experiments were conducted using different audio enhancement model structures and classification models. The classification performance was compared to the baseline method of noise injection data augmentation. Experiments were performed on two datasets: the ICBHI respiratory sound dataset, which includes 5.5 hours of recordings, and the Formosa Archive of Breath Sounds (FABS) dataset, comprising 14.6 hours of recordings. Additionally, a physician validation study was conducted by 7 senior physicians to assess the clinical utility of the system.The integration of the audio enhancement pipeline resulted in a 21.88% increase in the ICBHI classification score on the ICBHI dataset and a 4.10% improvement on the FABS dataset in multi-class noisy scenarios. Quantitative analysis from the physician validation study revealed improvements in efficiency, diagnostic confidence, and trust during model-assisted diagnosis, with workflows integrating enhanced audio leading to an 11.61% increase in diagnostic sensitivity and facilitating high-confidence diagnoses. Incorporating an audio enhancement algorithm significantly enhances the robustness and clinical utility of automatic respiratory sound classification systems, improving performance in noisy environments and fostering greater trust among medical professionals., Comment: Demo website: https://rogertzeng.github.io/ReSC-AE/

The gas-phase metallicity distribution within galaxies records critical information about galactic evolution. In this work we investigate how active galactic nuclei (AGN) influence this distribution by measuring the two-point correlation functions of gas-phase metallicity in 95 non-AGN and 37 AGN-host galaxies from the Calar Alto Legacy Integral Field spectroscopy Area integral field spectrographic survey. We measure metallicity using a novel Bayesian method that properly includes both stellar and AGN contributions to emission line fluxes and allows us to measure metallicities in both AGN-host and non-AGN galaxies in a single, consistent framework. We find that the two-point correlation functions of both AGN-host and non-AGN galaxies are well-fit by a simple injection-diffusion model, and that the correlation lengths $l_\mathrm{corr}$ we derive for the non-AGN galaxies are reasonably consistent with those obtained in earlier work. The AGN-host galaxies generally have smaller $l_\mathrm{corr}$ than non-AGN galaxies at fixed stellar mass, but similar $l_\mathrm{corr}$ at fixed star formation rate (SFR), suggesting that the primary effect of hosting an AGN in this sample is a reduction in SFR at fixed stellar mass, and that this in turn suppresses the correlation length. Our findings further indicate that, while both SFR and stellar mass are positively correlated with metallicity correlation length $l_\mathrm{corr}$, the former is more fundamental, implying that fluctuations in the metallicity distribution within galaxies are driven more by short-term responses to physical processes such as star formation that can change much faster than a Hubble time., Comment: 13 pages, 10 figures, submitted to MNRAS

We prove the conjecture that the deformation limit of Moishezon manifolds under a smooth deformation over a unit disk in $\mathbb{C}$ is Moishezon., Comment: All comments are welcome

With some mild assumptions on metric and topology of the central fiber, we prove that the limit of Kahler manifolds under holomorphic deformation is still Kahler.

As a fundamental structural feature, the symmetry of materials determines the exotic quantum properties in transition metal dichalcogenides (TMDs) with charge density wave (CDW). Breaking the inversion symmetry, the Janus structure, an artificially constructed lattice, provides an opportunity to tune the CDW states and the related properties. However, limited by the difficulties in atomic-level fabrication and material stability, the experimental visualization of the CDW states in 2D TMDs with Janus structure is still rare. Here, using surface selenization of VTe2, we fabricated monolayer Janus VTeSe. With scanning tunneling microscopy, an unusual root13-root13 CDW state with threefold rotational symmetry breaking was observed and characterized. Combined with theoretical calculations, we find this CDW state can be attributed to the charge modulation in the Janus VTeSe, beyond the conventional electron-phonon coupling. Our findings provide a promising platform for studying the CDW states and artificially tuning the electronic properties toward the applications.

We introduce a novel search strategy for heavy top-philic resonances that induce new contributions to four-top production at the LHC. We capitalize on recent advances in top-tagging performance to demonstrate that the final state, that is expected to be boosted based on current limits, can be fully reconstructed and exploited. Notably, our approach promises bounds on new physics cross-sections that are 30 to 60 times stronger than those obtained through traditional searches, showcasing its unprecedented effectiveness in probing top-philic new physics., Comment: 6 pages, 2 figures

We discuss heavy particles that can be used to pin down the faithful Standard Model (SM) gauge group and their patterns in the SM effective field theory (SMEFT). These heavy particles are not invariant under a specific $\mathbb{Z}_6$ subgroup of $SU(3)_c\times SU(2)_L \times U(1)_Y$, which however acts trivially on all the SM particles, hence the faithful SM gauge group remains undetermined. Different realizations of the faithful SM gauge group correspond to different spectra of heavy particles, and they also correspond to distinct sets of line operators with one-form global symmetry acting on them. We show that the heavy particles not invariant under the $\mathbb{Z}_6$ group cannot appear in tree-level ultraviolet completions of SMEFT, this enforces us to consider one-loop UV completions of SMEFT to identify the $\mathbb{Z}_6$ non-invariant heavy particles. We demonstrate with examples that correlations between Wilson coefficients provide an efficient way to examine models with $\mathbb{Z}_6$ non-invariant heavy particles. Finally, we prove that all the scalars that can trigger electroweak symmetry breaking must be invariant under the $\mathbb{Z}_6$ group, hence they cannot be used to probe the faithful SM gauge group., Comment: v3: JHEP version, the original title "The Standard Model Gauge Group, SMEFT, and Generalized Symmetries" is adjusted in the published version, some improvements

Galaxies' stellar masses, gas-phase oxygen abundances (metallicity), and star formation rates (SFRs) obey a series of empirical correlations, most notably the mass-metallicity relation (MZR) and fundamental metallicity relation (FZR), which relates oxygen abundance to a combination of stellar mass and SFR. However, due to the difficulty of measuring oxygen abundances and SFRs in galaxies that host powerful active galactic nuclei (AGN), to date it is unknown to what extent AGN-host galaxies also follow these correlations. In this work, we apply Bayesian methods to the MaNGA integral field spectrographic (IFS) survey that allow us to measure oxygen abundances and SFRs in AGN hosts, and use these measurements to explore how the MZR and FZR differ between galaxies that do and do not host AGN. We find similar MZRs at stellar masses above $10^{10.5} \mathrm{M}_\odot$, but that at lower stellar masses AGN hosts show up to $\sim 0.2$ dex higher oxygen abundances. The offset in the FZR is significantly smaller, suggesting that the larger deviation in the MZR is a result of AGN-host galaxies having systematically lower SFRs at fixed stellar mass. However, within the AGN-host sample there is little correlation between SFR and oxygen abundance. These findings support a scenario in which an AGN can halt efficient gas accretion, which drives non-AGN host galaxies to both higher SFR and lower oxygen abundance, resulting in the galaxy evolving off the star-forming main sequence (SFMS). As a consequence, as the SFR declines for an individual system its metallicity remains mostly unchanged., Comment: 18 pages, 14 figures, accepted for publication in MNRAS

The long-range interaction can fundamentally alter properties in gapped topological phases such as emergent massive edge modes. However, recent research has shifted attention to topological nontrivial critical points or phases, and it is natural to explore how long-range interaction influences them. In this work, we investigate the topological behavior and phase transition of extended Kitaev chains with long-range interactions, which can be derived from the critical Ising model via the Jordan-Wigner transformation in the short-range limit. Specifically, we analytically find the critical edge modes at the critical point remain stable against long-range interaction. More importantly, we observe these critical edge modes remain massless even when long-range interactions become substantially strong. As a byproduct, we numerically find that the critical behavior of the long-range model belongs to the free Majorana fermion universality class, which is entirely different from the long-range universality class in usual long-range spin models. Our work could shed new light on the interplay between long-range interactions (frustrated) and the gapless topological phases of matter., Comment: 13 pages, 11 figures. Any comments or suggestions are welcome !

We identify a fading AGN SDSS J220141.64+115124.3 from the internal Product Launch-11 (MPL-11) in Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey. The central region with a projected radius of $\sim$2.4 kpc is characterized as LINER-like line ratios while the outskirts extended to $\sim$15 kpc show Seyfert-like line ratios. The [OIII]$\lambda$5007 luminosity of the Seyfert regions is a factor of 37 (2) higher than the LINER regions without (with) dust attenuation correction, suggesting that the AGN activity decreases at least $\sim$8 $\times$ 10$^3$ yrs ($\sim$2.4 kpc/light-speed) ago. We model the emission line spectra in the central region with double Gaussian components (a narrow core and a broad wing) and analyze the properties of each component. The narrow core component mostly co-rotates with the stellar disc, whereas the broad wing component with a median of the velocity dispersion $\sim$300 km s$^{-1}$ is related to a wind outflow. The kinematic position angle (PA) of the ionized gas shows a $\sim$20{\deg} twist from the galaxy center to 1.5 effective radius. The median of the PA difference between the gas and stellar components is as large as $\sim$50{\deg} within 0.4 effective radius. The tidal feature in DESI image and star-gas misalignment suggest this galaxy is a merger remnant. Combining all these observational results as well as public available X-ray and MIR luminosities, we confirm this is a fading AGN, the merger process kick-started the central engine to quasar phase which ionized gas composed of tidal debris, and now the activity of the central black hole decreases. The discontinuity in [OIII]$\lambda$5007 flux and EQW maps is due to multiple AGN outbursts triggered by merger remnant gas inflows., Comment: Accepted for publication in MNRAS. 12 pages, 10 figures, 1 table

We investigate the suppression of star formation in galaxy pairs based on the isolated galaxy pair sample derived from the SDSS survey. By comparing the star formation rate between late-type galaxies in galaxy pairs and those in the isolated environment, we detect the signal of star formation suppression in galaxy pairs at $d_p < 100$kpc and $200$kpc$ < d_p < 350$kpc. The occurrence of star formation suppression in these late-type galaxies requires their companion galaxies to have an early-type morphology ($n_s > 2.5$). Star formation suppression in wide galaxy pairs with $200$kpc$ < d_p < 350$kpc mainly occurs in massive late-type galaxies, while in close galaxy pairs with $d_p < 100$kpc, it only appears in late-type galaxies with a massive companion ( $\log M_\star > 11.0$), nearly independent of their own stellar mass. Based on these findings, we infer that star formation suppression in wide galaxy pairs is actually a result of galaxy conformity, while in close galaxy pairs, it stems from the influence of hot circum-galactic medium surrounding companion galaxies., Comment: 10 pages, 4 figures, accepted for publication in ApJ

By constructing an exactly solvable spin model, we investigate the critical behaviors of transverse field Ising chains interpolated with cluster interactions, which exhibit various types of topologically distinct Ising critical points. Using fidelity susceptibility as an indicator, we establish the global phase diagram, including ferromagnetic, trivial paramagnetic, and symmetry-protected topological phases. Different types of critical points exist between these phases, encompassing both topologically trivial and non-trivial Ising critical points, as well as Gaussian critical points. Importantly, we demonstrate the existence of a Lifshitz transition between these topologically distinct Ising critical points, with central charge and critical exponents determined through finite-size scaling. This work serves as a valuable reference for further research on phase transitions within the gapless quantum phase of matter., Comment: 13 pages, 9 figures. Any comments or suggestions are welcome!

While Reinforcement Learning from Human Feedback (RLHF) significantly enhances the generation quality of Large Language Models (LLMs), recent studies have raised concerns regarding the complexity and instability associated with the Proximal Policy Optimization (PPO) algorithm, proposing a series of order-based calibration methods as viable alternatives. This paper delves further into current order-based methods, examining their inefficiencies in utilizing reward values and addressing misalignment issues. Building upon these findings, we propose a novel \textbf{V}alue-based \textbf{C}ali\textbf{B}ration (VCB) method to better align LLMs with human preferences. Experimental results demonstrate that VCB surpasses existing alignment methods on AI assistant and summarization datasets, providing impressive generalizability, robustness, and stability in diverse settings., Comment: 19 pages, Under review

Mrk 501 is a prototypical high-synchrotron-peaked blazar (HBL) and serves as one of the primary targets for the {\it Imaging X-ray Polarimetry Explorer} ({\it IXPE}). In this study, we report X-ray polarization measurements of Mrk 501 based on six {\it IXPE} observations. The detection of X-ray polarization at a confidence level exceeding 99\% is achieved in four out of the six observations conducted across the entire energy range (2--8 keV) of {\it IXPE}. The maximum polarization degree ($\Pi_{\rm X}$) is measured to be $15.8\%\pm2.8\%$, accompanied by a polarization angle ($\psi_{\rm X}$) of $98.0\deg\pm5.1\deg$ at a confidence level of $5.6 \sigma$. During the remaining two observations, only an upper limit of $\Pi_{\rm X}<$12\% could be derived at the 99\% confidence level. No temporal variability in polarization is observed throughout all six {\it IXPE} observations for Mrk 501. A discernible trend of energy-dependent variation in the polarization degree is detected in optical spectropolarimetry; however, no analogous indication is observed in $\Pi_{\rm X}$. The chromatic behavior of $\Pi$ and the consistent values of $\psi$ across different frequencies from X-rays to radio waves, along with the agreement between $\psi$ and jet position angle, strongly support the interpretation of the energy-stratified model with shock-accelerated particles in the jet of Mrk 501. Additionally, the possibility of the presence of a global helical magnetic field in the jet of Mrk 501 is discussed., Comment: 20 pages, 8 figures, 4 tables, accepted for publication in ApJL

This paper studies the fitting of Hessian or its inverse for stochastic optimizations using a Hessian fitting criterion from the preconditioned stochastic gradient descent (PSGD) method, which is intimately related to many commonly used second order and adaptive gradient optimizers, e.g., BFGS, Gaussian-Newton and natural gradient descent, AdaGrad, etc. Our analyses reveal the efficiency and reliability differences among a wide range of preconditioner fitting methods, from closed-form to iterative solutions, using Hessian-vector products or stochastic gradients only, with Hessian fittings in the Euclidean space, the manifold of symmetric positive definite (SPL) matrices, to a variety of Lie groups. The most intriguing discovery is that the Hessian fitting itself as an optimization problem is strongly convex under mild conditions with a specific yet general enough Lie group. This discovery turns Hessian fitting into a well behaved optimization problem, and facilitates the designs of highly efficient and elegant Lie group sparse preconditioner fitting methods for large scale stochastic optimizations., Comment: 13 pages, 6 figures, 3 tables

Recent artificial intelligence (AI) technologies show remarkable evolution in various academic fields and industries. However, in the real world, dynamic data lead to principal challenges for deploying AI models. An unexpected data change brings about severe performance degradation in AI models. We identify two major related research fields, domain shift and concept drift according to the setting of the data change. Although these two popular research fields aim to solve distribution shift and non-stationary data stream problems, the underlying properties remain similar which also encourages similar technical approaches. In this review, we regroup domain shift and concept drift into a single research problem, namely the data change problem, with a systematic overview of state-of-the-art methods in the two research fields. We propose a three-phase problem categorization scheme to link the key ideas in the two technical fields. We thus provide a novel scope for researchers to explore contemporary technical strategies, learn industrial applications, and identify future directions for addressing data change challenges.

We present a novel approach to accelerate stochastic gradient descent (SGD) by utilizing curvature information obtained from Hessian-vector products or finite differences of parameters and gradients, similar to the BFGS algorithm. Our approach involves two preconditioners: a matrix-free preconditioner and a low-rank approximation preconditioner. We update both preconditioners online using a criterion that is robust to stochastic gradient noise and does not require line search or damping. To preserve the corresponding symmetry or invariance, our preconditioners are constrained to certain connected Lie groups. The Lie group's equivariance property simplifies the preconditioner fitting process, while its invariance property eliminates the need for damping, which is commonly required in second-order optimizers. As a result, the learning rate for parameter updating and the step size for preconditioner fitting are naturally normalized, and their default values work well in most scenarios. Our proposed approach offers a promising direction for improving the convergence of SGD with low computational overhead. We demonstrate that Preconditioned SGD (PSGD) outperforms SoTA on Vision, NLP, and RL tasks across multiple modern deep-learning architectures. We have provided code for reproducing toy and large scale experiments in this paper.