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Incomplete scenario is a prevalent, practical, yet challenging setting in Multimodal Recommendations (MMRec), where some item modalities are missing due to various factors. Recently, a few efforts have sought to improve the recommendation accuracy by exploring generic structures from incomplete data. However, two significant gaps persist: 1) the difficulty in accurately generating missing data due to the limited ability to capture modality distributions; and 2) the critical but overlooked visibility bias, where items with missing modalities are more likely to be disregarded due to the prioritization of items' multimodal data over user preference alignment. This bias raises serious concerns about the fair treatment of items. To bridge these two gaps, we propose a novel Modality-Diffused Counterfactual (MoDiCF) framework for incomplete multimodal recommendations. MoDiCF features two key modules: a novel modality-diffused data completion module and a new counterfactual multimodal recommendation module. The former, equipped with a particularly designed multimodal generative framework, accurately generates and iteratively refines missing data from learned modality-specific distribution spaces. The latter, grounded in the causal perspective, effectively mitigates the negative causal effects of visibility bias and thus assures fairness in recommendations. Both modules work collaboratively to address the two aforementioned significant gaps for generating more accurate and fair results. Extensive experiments on three real-world datasets demonstrate the superior performance of MoDiCF in terms of both recommendation accuracy and fairness

The study of the Heisenberg-Kitaev honeycomb ferromagnets has recently drawn attention because of their rich topological properties. Topological phase transitions may arise when there exist two or more distinct topological phases, and they are often revealed by a gap-closing phenomenon. In this work, we investigate the magnonic properties of honeycomb ferromagnets exhibiting Kitaev and DMI interactions in the presence of a Heisenberg exchange and magnetocrystalline anisotropy exposed to a magnetic field. We employ the Self-Consistent Renormalization (SCR) spin wave theory to investigate the effects of magnon-magnon interactions (MMIs) and thermal fluctuations on the properties of magnons. Our findings demonstrate that the magnon system undergoes topological phase transitions driven by temperature and magnetic fields, which are attributed to MMIs. Specifically, as the temperature rises, the magnon band gap at the Dirac points closes and reopens at the critical temperature Tc , which is below the Curie temperature. By showing that the Chern numbers of the magnonic bands are distinct above and below Tc , we confirm that the gap-closing phenomenon is indeed a signature for the topological phase transitions. Furthermore, our analysis indicates that the thermal Hall conductivity in the magnonic system exhibits a sign reversal at Tc , which can serve as an experimental probe of its topological nature.

Water Distribution Networks (WDNs) are vital infrastructures, and contamination poses serious public health risks. Harmful substances can interact with disinfectants like chlorine, making chlorine monitoring essential for detecting contaminants. However, chlorine sensors often become unreliable and require frequent calibration. This study introduces the Dual-Threshold Anomaly and Drift Detection (AD&DD) method, an unsupervised approach combining a dual-threshold drift detection mechanism with an LSTM-based Variational Autoencoder(LSTM-VAE) for real-time contamination detection. Tested on two realistic WDNs, AD&DD effectively identifies anomalies with sensor offsets as concept drift, and outperforms other methods. A proposed decentralized architecture enables accurate contamination detection and localization by deploying AD&DD on selected nodes.

Imperceptible adversarial attacks have recently attracted increasing research interests. Existing methods typically incorporate external modules or loss terms other than a simple $l_p$-norm into the attack process to achieve imperceptibility, while we argue that such additional designs may not be necessary. In this paper, we rethink the essence of imperceptible attacks and propose two simple yet effective strategies to unleash the potential of PGD, the common and classical attack, for imperceptibility from an optimization perspective. Specifically, the Dynamic Step Size is introduced to find the optimal solution with minimal attack cost towards the decision boundary of the attacked model, and the Adaptive Early Stop strategy is adopted to reduce the redundant strength of adversarial perturbations to the minimum level. The proposed PGD-Imperceptible (PGD-Imp) attack achieves state-of-the-art results in imperceptible adversarial attacks for both untargeted and targeted scenarios. When performing untargeted attacks against ResNet-50, PGD-Imp attains 100$\%$ (+0.3$\%$) ASR, 0.89 (-1.76) $l_2$ distance, and 52.93 (+9.2) PSNR with 57s (-371s) running time, significantly outperforming existing methods., Comment: accepted by ICASSP 2025

Compressing integer keys is a fundamental operation among multiple communities, such as database management (DB), information retrieval (IR), and high-performance computing (HPC). Recent advances in \emph{learned indexes} have inspired the development of \emph{learned compressors}, which leverage simple yet compact machine learning (ML) models to compress large-scale sorted keys. The core idea behind learned compressors is to \emph{losslessly} encode sorted keys by approximating them with \emph{error-bounded} ML models (e.g., piecewise linear functions) and using a \emph{residual array} to guarantee accurate key reconstruction. While the concept of learned compressors remains in its early stages of exploration, our benchmark results demonstrate that an SIMD-optimized learned compressor can significantly outperform state-of-the-art CPU-based compressors. Drawing on our preliminary experiments, this vision paper explores the potential of learned data compression to enhance critical areas in DBMS and related domains. Furthermore, we outline the key technical challenges that existing systems must address when integrating this emerging methodology.

Implicit neural representations and 3D Gaussian splatting (3DGS) have shown great potential for scene reconstruction. Recent studies have expanded their applications in autonomous reconstruction through task assignment methods. However, these methods are mainly limited to single robot, and rapid reconstruction of large-scale scenes remains challenging. Additionally, task-driven planning based on surface uncertainty is prone to being trapped in local optima. To this end, we propose the first 3DGS-based centralized multi-robot autonomous 3D reconstruction framework. To further reduce time cost of task generation and improve reconstruction quality, we integrate online open-vocabulary semantic segmentation with surface uncertainty of 3DGS, focusing view sampling on regions with high instance uncertainty. Finally, we develop a multi-robot collaboration strategy with mode and task assignments improving reconstruction quality while ensuring planning efficiency. Our method demonstrates the highest reconstruction quality among all planning methods and superior planning efficiency compared to existing multi-robot methods. We deploy our method on multiple robots, and results show that it can effectively plan view paths and reconstruct scenes with high quality.

The broadband afterglow of Gamma-ray bursts (GRBs) is usually believed to originate from the synchrotron radiation of electrons accelerated by the external shock of relativistic jets. Therefore, the jet structure should have a significant impact on the GRB afterglow features. The latest observations indicate that the GRB jets may possess intricate structures, such as Gaussian structure, power-law structure, or jet-cocoon structure. Most recently, an abnormal afterglow of GRB 210731A has raised extensive attention, whose optical afterglow exhibites multiple rebrightening phenomena within 4 hours, posing a serious challenge to the standard afterglow model. Here we intend to interpret the characteristics of GRB 210731A afterglows within the framework of non-axisymmetric structured jets, where multiple distinct peaks in the afterglow light curve are caused by the uneven distribution of energy and velocity within the jet in the azimuth angle direction. Through Monte Carlo Markov Chain fitting, we show that a three-component asymmetric structured jet can well explain the multi-band afterglow data. The energy difference among the three components is about 1.5 orders of magnitude, with higher-energy components exhibiting slower speeds. The radiation contribution of each component has sequentially dominated the light curve of the afterglow, resulting in multiple peaks, with the highest peak occurring at the latest time. We suggest that in the future, polarization observations should be conducted on afterglows with multiple brightening signatures, which will help to effectively distinguish the structured jet model from other alternative models, such as energy injection, and ultimately help to determine the true configuration of jets., Comment: 12 pages, 4 figures. Accepted by ApJ for publication

As the most energetic explosion in the universe, gamma-ray bursts (GRBs) are usually believed to be generated by relativistic jets. Some mechanisms (e.g. internal non-uniform magnetic dissipation processes or the precession of the central engine) may generate asymmetric jet structures, which is characterized by multiple fluctuations in the light curve of afterglow. Since the jet's structure introduces asymmetry in radiation around the line of sight (LOS), it is naturally expected that polarization will be observable. In this work, we reveal the polarization characteristics of gamma-ray burst afterglows with a non-axisymmetric structured jet. Our results show that the afterglow signal generally exhibits polarization, with the degree and evolution influenced by the specific jet structure, observing frequency, and the line of sight (LOS). The polarization degree is notably higher when the LOS is outside the jet. This degree fluctuates over time as different regions of radiation alternate in their dominance, which is accompanied by the rotation of the polarization angle and further reflects the intricate nature of the jet. Regarding its evolution over frequency, the polarization degree displays significant fluctuations at spectral breaks, with the polarization angle possibly undergoing abrupt changes. These features may provide strong evidence for future identification of potential GRBs with asymmetric jet structures., Comment: 21 pages, 10 figures. Accepted by ApJ for publication

The production of silicon monoxide (SiO) can be considered as a fingerprint of shock interaction. In this work, we use high-sensitivity observations of the SiO (2-1) and H$^{13}$CO$^{+}$ (1-0) emission to investigate the broad and narrow SiO emission toward 146 massive star-forming regions in the ATOMS survey. We detected SiO emission in 136 regions and distinguished broad and narrow components across the extension of 118 sources (including 58 UC $H_{II}$ regions) with an average angular resolution of 2.5$^{\prime}$$^{\prime}$. The derived SiO luminosity ($L_{SiO}$) across the whole sample shows that the majority of $L_{SiO}$ (above 66$\%$) can be attributed to broad SiO, indicating its association with strong outflows. The comparison of the ALMA SiO images with the filamentary skeletons identified from H$^{13}$CO$^{+}$ and in the infrared data (at 4.5, 8, and 24 $mu$m), further confirms that most SiO emission originates from outflows. However, note that for nine sources in our sample, the observed SiO emission may be generated by expanding UC $H_{II}$ regions. There is a moderate positive correlation between the bolometric luminosity ($L_{bol}$) and $L_{SiO}$ for both components (narrow and broad). The UC $H_{II}$ sources show a weaker positive correlation between $L_{bol}$ and $L_{SiO}$ and higher $L_{SiO}$ compared to the sources without UC $H_{II}$ regions. These results imply that the SiO emission from UC $H_{II}$ sources might be affected by UV-photochemistry induced by UC $H_{II}$ regions., Comment: 23 pages, 14 figures

Altermagnets (AM) are a recently discovered third class of collinear magnets, and have been attracting significant interest in the field of condensed matter physics. Here, based on first-principles calculations and theoretical analysis, we propose four two-dimensional (2D) magnetic materials--monolayer V$_2$Te$_2$O, V$_2$STeO, V$_2$SSeO, and V$_2$S$_2$O--as candidates for altermagnetic materials. We show that these materials are semiconductors with spin-splitting in their nonrelativistic band structures. Furthermore, in the band structure, there are a pair of Dirac-type valleys located at the time-reversal invariant momenta (TRIM) X and Y points. These two valleys are connected by crystal symmetry instead of time-reversal symmetry. We investigate the strain effect on the band structure and find that uniaxial strain can induce valley polarization, topological states in these monolayer materials. Moreover, piezomagnetism can be realized upon finite doping. Our result reveals interesting valley physics in monolayer V$_2$Te$_2$O, V$_2$STeO, V$_2$SSeO, and V$_2$S$_2$O, suggesting their great potential for valleytronics, spintronics, and multifunctional nanoelectronics applications., Comment: 7 pages, 8 figures

Despite having triggered devastating pandemics in the past, our ability to quantitatively assess the emergence potential of individual strains of animal influenza viruses remains limited. This study introduces Emergenet, a tool to infer a digital twin of sequence evolution to chart how new variants might emerge in the wild. Our predictions based on Emergenets built only using 220,151 Hemagglutinnin (HA) sequences consistently outperform WHO seasonal vaccine recommendations for H1N1/H3N2 subtypes over two decades (average match-improvement: 3.73 AAs, 28.40\%), and are at par with state-of-the-art approaches that use more detailed phenotypic annotations. Finally, our generative models are used to scalably calculate the current odds of emergence of animal strains not yet in human circulation, which strongly correlates with CDC's expert-assessed Influenza Risk Assessment Tool (IRAT) scores (Pearson's $r = 0.721, p = 10^{-4}$). A minimum five orders of magnitude speedup over CDC's assessment (seconds vs months) then enabled us to analyze 6,354 animal strains collected post-2020 to identify 35 strains with high emergence scores ($> 7.7$). The Emergenet framework opens the door to preemptive pandemic mitigation through targeted inoculation of animal hosts before the first human infection., Comment: 35 pages, 15 figures

We classify translatively exponential and GL(2,Z) covariant valuations on lattice polygons valued at measurable real functions. A typical example of such valuations is induced by the Laplace transform, but as it turns out there are many more. The argument uses the ergodicity of the linear action of SL(2,Z) on R2, and some elementary properties of the Fibonacci numbers.

Time-delay interferometry (TDI) is essential in space-based gravitational wave (GW) detectors, effectively reducing laser noise and improving detection precision. As one of the most promising GW detectors, the space-based detectors are able to observe the effects from GW polarizations. The detection of GW additional polarizations carries significant implications, potentially revealing deviations from general relativity and opening avenues to explore alternative gravity theories. In this study, we examine the impacts of second-generation TDI combinations on GW polarization detection by simulating LISA, Taiji, and TianQin, including realistic orbital effects such as link length and angle variations. The detector performance is assessed through sensitivity curves derived from averaged response functions, as well as signal-to-noise ratio (SNR) of binary black holes (BBHs). For massive BBHs, the $\mathcal{A}$ and $\mathcal{E}$ channels typically offer the best sensitivity, while the $X$ channel in TianQin is most effective for detecting additional polarizations. For stellar-mass BBHs, the $\alpha$ channel provides the highest SNR for vector modes in LISA and Taiji specifically for lower-mass systems, while the $\mathcal{A}$ and $\mathcal{E}$ channels are optimal for higher masses or other polarizations. TianQin consistently favors the $X$ channel for additional polarizations. Our findings emphasize the importance of selecting high-sensitivity TDI combinations to enhance detection capabilities across different polarizations, deepening our insight into GW sources and the fundamental nature of spacetime, Comment: 12 pages, 12 figures

This paper examines how spillover effects in A/B testing can impede organizational progress and develops strategies for mitigating these challenges. We identify a phenomenon termed ``seesaw experimentation'', where a firm's overall performance paradoxically deteriorates despite achieving continuous improvements in measured A/B testing metrics. Seesaw experimentation arises when successful innovations in primary metrics generate negative externalities in secondary, unmeasured dimensions. To address this problem, we propose implementing a positive hurdle rate for A/B test approval. We derive the optimal hurdle rate, offering a simple solution that preserves decentralized experimentation while mitigating negative spillovers., Comment: 21 pages, 2 figures

The topological properties observed in iron-based superconductors extend our understanding of vortex Majorana quasiparticle excitations in unexpected ways. Vortex Majorana physics has been extensively studied within the context of the topologically protected surface Dirac state. By employing an in-situ strain device, we demonstrate that uniaxial strain can generate Majorana zero modes out of the topological Dirac semimetal bulk state in LiFeAs. Uniaxial strain along [100] direction is found to enhance the band renormalization of LiFeAs, effectively reducing the energy separation between the Fermi level and the topological Dirac semimetal state, and breaking C4 symmetry. Using scanning tunneling microscopy, we observe the evolution of vortex bound states in the topological Dirac semimetal state region, accompanied by the emergence of Majorana zero modes and vortex bound states contributed by the bulk band. Our work provides a controllable method for experimentally engineering Majorana physics in iron-based superconductors, and offers valuable insights into the topological Dirac semimetal state with intrinsic s-wave superconductivity., Comment: 17 pages, 4 figures

The development of large language models (LLM) has revolutionized various fields and is anticipated to drive the advancement of autonomous systems. In the context of autonomous optical networks, creating a high-level cognitive agent in the control layer remains a challenge. However, LLM is primarily developed for natural language processing tasks, rendering them less effective in predicting the physical dynamics of optical communications. Moreover, optical networks demand rigorous stability, where direct deployment of strategies generated from LLM poses safety concerns. In this paper, a digital twin (DT)-enhanced LLM scheme is proposed to facilitate autonomous optical networks. By leveraging monitoring data and advanced models, the DT of optical networks can accurately characterize their physical dynamics, furnishing LLMs with dynamic-updated information for reliable decision-making. Prior to deployment, the generated strategies from LLM can be pre-verified in the DT platform, which also provides feedback to the LLM for further refinement of strategies. The synergistic interplay between DT and LLM for autonomous optical networks is demonstrated through three scenarios: performance optimization under dynamic loadings in an experimental C+L-band long-haul transmission link, protection switching for device upgrading in a field-deployed six-node mesh network, and performance recovery after fiber cuts in a field-deployed C+L-band transmission link., Comment: 7 pages,6 figures; Accepted by IEEE Communications Magazine, Open call

Current approaches for open-vocabulary scene graph generation (OVSGG) use vision-language models such as CLIP and follow a standard zero-shot pipeline -- computing similarity between the query image and the text embeddings for each category (i.e., text classifiers). In this work, we argue that the text classifiers adopted by existing OVSGG methods, i.e., category-/part-level prompts, are scene-agnostic as they remain unchanged across contexts. Using such fixed text classifiers not only struggles to model visual relations with high variance, but also falls short in adapting to distinct contexts. To plug these intrinsic shortcomings, we devise SDSGG, a scene-specific description based OVSGG framework where the weights of text classifiers are adaptively adjusted according to the visual content. In particular, to generate comprehensive and diverse descriptions oriented to the scene, an LLM is asked to play different roles (e.g., biologist and engineer) to analyze and discuss the descriptive features of a given scene from different views. Unlike previous efforts simply treating the generated descriptions as mutually equivalent text classifiers, SDSGG is equipped with an advanced renormalization mechanism to adjust the influence of each text classifier based on its relevance to the presented scene (this is what the term "specific" means). Furthermore, to capture the complicated interplay between subjects and objects, we propose a new lightweight module called mutual visual adapter. It refines CLIP's ability to recognize relations by learning an interaction-aware semantic space. Extensive experiments on prevalent benchmarks show that SDSGG outperforms top-leading methods by a clear margin., Comment: NeurIPS 2024. Code: https://github.com/guikunchen/SDSGG

Approximate Nearest Neighbor Search (ANNS) is now widely used in various applications, ranging from information retrieval, question answering, and recommendation, to search for similar high-dimensional vectors. As the amount of vector data grows continuously, it becomes important to support updates to vector index, the enabling technique that allows for efficient and accurate ANNS on vectors. Because of the curse of high dimensionality, it is often costly to identify the right neighbors of a single new vector, a necessary process for index update. To amortize update costs, existing systems maintain a secondary index to accumulate updates, which are merged by the main index by global rebuilding the entire index periodically. However, this approach has high fluctuations of search latency and accuracy, not even to mention that it requires substantial resources and is extremely time-consuming for rebuilds. We introduce SPFresh, a system that supports in-place vector updates. At the heart of SPFresh is LIRE, a lightweight incremental rebalancing protocol to split vector partitions and reassign vectors in the nearby partitions to adapt to data distribution shift. LIRE achieves low-overhead vector updates by only reassigning vectors at the boundary between partitions, where in a high-quality vector index the amount of such vectors are deemed small. With LIRE, SPFresh provides superior query latency and accuracy to solutions based on global rebuild, with only 1% of DRAM and less than 10% cores needed at the peak compared to the state-of-the-art, in a billion scale vector index with 1% of daily vector update rate., Comment: SOSP 23

Flat-band in twisted graphene bilayer has garnered widespread attention, and whether flat-bands can be realized in carbon monolayer is an interesting topic worth exploring in condensed matter physics. In this work, we demonstrate that, based on the theory of compact localized states, a series of two-dimensional carbon allotropes with flat-bands can be achieved. Two of them named as 191-8-66-C-r567x-1 and 191-10-90-C-r567x-1 are confirmed to be dynamically stable carbon phases with isolated or weakly overlapped flat-bands at the Fermi-level. The maximum Fermi velocities of the flat-band electrons are evaluated to be 1x10^4 m/s and 0.786x10^4 m/s, both of which are lower than the Fermi velocity of the flat-band electrons in magic-angle graphene (4x10^4 m/s). Furthermore, 191-8-66-C-r567x-1 has been confirmed to be a flat-band related magnetic half-metal with a magnetic moment of 1.854 miuB per cell, while 191-10-90-C-r567x-1 is a flat-band related magnetic normal metal with a magnetic moment of 1.663 miuB per cell. These results not only show that flat-bands can be constructed in carbon monolayer, but also indicate the potential for achieving metal-free magnetic materials with light elements based on flat-band theory., Comment: 10 pages, 3 figures

This paper examines how AI persuades doctors when their diagnoses differ. Disagreements arise from two sources: attention differences, which are objective and play a complementary role to the doctor, and comprehension differences, which are subjective and act as substitutes. AI's interpretability influences how doctors attribute these sources and their willingness to change their minds. Surprisingly, uninterpretable AI can be more persuasive by allowing doctors to partially attribute disagreements to attention differences. This effect is stronger when doctors have low abnormality detection skills. Additionally, uninterpretable AI can improve diagnostic accuracy when doctors have career concerns.

Learned indexes have attracted significant research interest due to their ability to offer better space-time trade-offs compared to traditional B+-tree variants. Among various learned indexes, the PGM-Index based on error-bounded piecewise linear approximation is an elegant data structure that has demonstrated \emph{provably} superior performance over conventional B+-tree indexes. In this paper, we explore two interesting research questions regarding the PGM-Index: (a) \emph{Why are PGM-Indexes theoretically effective?} and (b) \emph{Why do PGM-Indexes underperform in practice?} For question~(a), we first prove that, for a set of $N$ sorted keys, the PGM-Index can, with high probability, achieve a lookup time of $O(\log\log N)$ while using $O(N)$ space. To the best of our knowledge, this is the \textbf{tightest bound} for learned indexes to date. For question~(b), we identify that querying PGM-Indexes is highly memory-bound, where the internal error-bounded search operations often become the bottleneck. To fill the performance gap, we propose PGM++, a \emph{simple yet effective} extension to the original PGM-Index that employs a mixture of different search strategies, with hyper-parameters automatically tuned through a calibrated cost model. Extensive experiments on real workloads demonstrate that PGM++ establishes a new Pareto frontier. At comparable space costs, PGM++ speeds up index lookup queries by up to $\mathbf{2.31\times}$ and $\mathbf{1.56\times}$ when compared to the original PGM-Index and state-of-the-art learned indexes.

For single image defocus deblurring, acquiring well-aligned training pairs (or training triplets), i.e., a defocus blurry image, an all-in-focus sharp image (and a defocus blur map), is an intricate task for the development of deblurring models. Existing image defocus deblurring methods typically rely on training data collected by specialized imaging equipment, presupposing that these pairs or triplets are perfectly aligned. However, in practical scenarios involving the collection of real-world data, direct acquisition of training triplets is infeasible, and training pairs inevitably encounter spatial misalignment issues. In this work, we introduce a reblurring-guided learning framework for single image defocus deblurring, enabling the learning of a deblurring network even with misaligned training pairs. Specifically, we first propose a baseline defocus deblurring network that utilizes spatially varying defocus blur map as degradation prior to enhance the deblurring performance. Then, to effectively learn the baseline defocus deblurring network with misaligned training pairs, our reblurring module ensures spatial consistency between the deblurred image, the reblurred image and the input blurry image by reconstructing spatially variant isotropic blur kernels. Moreover, the spatially variant blur derived from the reblurring module can serve as pseudo supervision for defocus blur map during training, interestingly transforming training pairs into training triplets. Additionally, we have collected a new dataset specifically for single image defocus deblurring (SDD) with typical misalignments, which not only substantiates our proposed method but also serves as a benchmark for future research., Comment: The source code and dataset are available at https://github.com/ssscrystal/Reblurring-guided-JDRL

Taking into account nucleon-nucleon gravitational interaction, higher-order terms of symmetry energy, pairing interaction, and neural network corrections, a new BW4 mass model has been developed, which more accurately reflects the contributions of various terms to the binding energy. A novel hybrid algorithm and neural network correction method has been implemented to optimize the discrepancy between theoretical and experimental results, significantly improving the model's binding energy predictions (reduced to around 350 keV). At the same time, the theoretical accuracy near magic nuclei has been marginally enhanced, effectively capturing the special interaction effects around magic nuclei and showing good agreement with experimental data.

By utilizing the AdS/CFT correspondence and wave optics techniques, we conducted an extensive study of the imaging properties of holographic Einstein rings in the context of Anti-de Sitter (AdS) black holes (BHs) in Horndeski theory. Our results indicate that the optical characteristics of these holographic Einstein rings are significantly influenced by the observer's position, the physical parameters of the BH, the nature of the wave source, and the configuration of the optical system. Specifically, when the observer is positioned at the north pole of the AdS boundary, the holographic image prominently displays a ring structure aligning with the BH's photon sphere. We thoroughly analyzed how various physical parameters -- including the observation position, event horizon radius, temperature, and the parameter $\gamma$ in Horndeski theory -- affect the holographic Einstein rings. These parameters play a crucial role in determining the rings' radius and brightness, with variations potentially causing the ring structures to deform or even transform into bright spots. Furthermore, our comparative analysis between wave optics and geometric optics reveals a strong agreement in predicting the positions and brightnesses of both the photon ring and the Einstein ring. This research offers new insights into the spacetime geometry of BHs in Horndeski theory and proposes a promising framework for exploring the gravitational duals of strongly coupled systems., Comment: 21 pages, 10 figures

In this study, we propose a genuine fourth-order compact finite difference scheme for solving biharmonic equations with Dirichlet boundary conditions in both two and three dimensions. In the 2D case, we build upon the high-order compact (HOC) schemes for flux-type boundary conditions originally developed by Zhilin Li and Kejia Pan [SIAM J. Sci. Comput., 45 (2023), pp. A646-A674] to construct a high order compact discretization for coupled boundary conditions. When considering the 3D case, we modify carefully designed undetermined coefficient methods of Li and Pan to derive the finite difference approximations of coupled boundary conditions. The resultant FD discretization maintains the global fourth order convergence and compactness. Unlike the very popular Stephenson method, the number of unknows do not increase with dimensions. Besides, it is noteworthy that the condition number of the coefficient matrix increases at a rate of $O(h^{-2})$ in both 2D and 3D. We also validate the performance of the proposed genuine HOC methods through nontrivial examples., Comment: 14 pages, 2 figures

Self-supervised learning (SSL) has garnered significant attention in speech processing, excelling in linguistic tasks such as speech recognition. However, jointly improving the performance of pre-trained models on various downstream tasks, each requiring different speech information, poses significant challenges. To this purpose, we propose a progressive residual extraction based self-supervised learning method, named ProgRE. Specifically, we introduce two lightweight and specialized task modules into an encoder-style SSL backbone to enhance its ability to extract pitch variation and speaker information from speech. Furthermore, to prevent the interference of reinforced pitch variation and speaker information with irrelevant content information learning, we residually remove the information extracted by these two modules from the main branch. The main branch is then trained using HuBERT's speech masking prediction to ensure the performance of the Transformer's deep-layer features on content tasks. In this way, we can progressively extract pitch variation, speaker, and content representations from the input speech. Finally, we can combine multiple representations with diverse speech information using different layer weights to obtain task-specific representations for various downstream tasks. Experimental results indicate that our proposed method achieves joint performance improvements on various tasks, such as speaker identification, speech recognition, emotion recognition, speech enhancement, and voice conversion, compared to excellent SSL methods such as wav2vec2.0, HuBERT, and WavLM.

The star formation efficiency (SFE) measures the proportion of molecular gas converted into stars, while the star formation rate (SFR) indicates the rate at which gas is transformed into stars. Here we propose such a model in the framework of a global radial collapse of molecular clouds, where the collapse velocity depends on the density profile and the initial mass-to-radius ratio of molecular clouds, with the collapse velocity accelerating during the collapse process. This simplified analytical model allows us to estimate a lifetime of giant molecular clouds of approximately $0.44-7.36 \times 10^7\, \rm{yr}$, and a star formation timescale of approximately $0.5-5.88 \times 10^6\, \rm{yr}$. Additionally, we can predict an SFE of approximately $1.59\, \%$, and an SFR of roughly $1.85\, \rm{M_{\odot} \, yr^{-1}}$ for the Milky Way in agreement with observations., Comment: 12 pages, 3 figures, 2 tables. Submitted to the ApJL

In this paper, we investigate the intelligent reflecting surface (IRS)/reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system based on sensing mutual information (MI). Specifically, the base station (BS) perceives the sensing target via the reflected sensing signal by the IRS, while communicating with the users simultaneously. Our aim is to maximize the sensing MI, subject to the quality of service (QoS) constraints for all communication users, the transmit power constraint at the BS, and the unit-modulus constraint on the IRS's passive reflection. We solve this problem under two cases: one simplified case assuming a line-of-sight (LoS) channel between the BS and IRS and no clutter interference to sensing, and the other generalized case considering the Rician fading channel of the BS-IRS link and the presence of clutter interference to sensing. For the first case, we show that the dedicated sensing beamformer cannot enhance the sensing MI if the BS-user direct links are blocked, and develop a low-complexity iterative algorithm to jointly optimize the BS and IRS active/passive beamformers. In contrast, for the second case, we propose an alternative iterative algorithm, which can also be applied to the first case, to solve the beamforming design problem under the general setup. Numerical results are provided to validate the performance of the proposed algorithms, as compared to various benchmark schemes.

Gaseous X-ray polarimetry refers to a class of detectors used for measuring the polarization of soft X-rays. The systematic effects of such detectors introduce residual modulation, leading to systematic biases in the polarization detection results of the source. This paper discusses the systematic effects and their calibration and correction using the Gas Microchannel Plate-Pixel Detector (GMPD) prototype for POLAR-2/Low-Energy X-ray Polarization Detector (LPD). Additionally, we propose an algorithm that combines parameterization with Monte Carlo simulation and Bayesian iteration to eliminate residual modulation. The residual modulation after data correction at different energy points has been reduced to below 1%, and a good linear relationship is observed between the polarization degree and modulation degree. The improvement in modulation degree after correction ranges from 2% to 15%, and the results exceed those of the Imaging X-Ray Polarimetry Explorer (IXPE) above 5 keV.

The theory of general relativity (GR) predicts the existence of gravitational waves (GWs) with two tensor modes, while alternative theories propose up to six polarization modes. In this study, we investigate constraints on GW polarization using a model-independent parametrized post-Einsteinian framework and consider both space- and ground-based detectors. By evaluating the capabilities and network performance of LISA, Taiji, TianQin, LIGO, Virgo, KAGRA, and the Einstein Telescope (ET), we analyze their respective contributions. Among space-based detectors, Taiji provides the most stringent constraints compared with LISA and TianQin.Regarding ground-based detectors, LIGO excels in vector modes while ET offers comprehensive constraints across all polarization modes. In network scenarios, LISA+TJm performs best, and ET surpasses second-generation detector combinations. Furthermore, multiband observations effectively mitigate scalar mode degeneracies thereby significantly enhancing the performance of ground-based detectors. Ultimately, combined space- and ground-based observations provide robust constraints on GW polarizations that advance tests for deviations from GR. Our findings underscore the potential of future GW missions in refining our understanding of gravitational physics through precise measurements of polarizations., Comment: 17 pages, 11 figures

In data-driven applications, preserving user privacy while enabling valuable computations remains a critical challenge. Technologies like Differential Privacy (DP) have been pivotal in addressing these concerns. The shuffle model of DP requires no trusted curators and can achieve high utility by leveraging the privacy amplification effect yielded from shuffling. These benefits have led to significant interest in the shuffle model. However, the computation tasks in the shuffle model are limited to statistical estimation, making the shuffle model inapplicable to real-world scenarios in which each user requires a personalized output. This paper introduces a novel paradigm termed Private Individual Computation (PIC), expanding the shuffle model to support a broader range of permutation-equivariant computations. PIC enables personalized outputs while preserving privacy, and enjoys privacy amplification through shuffling. We propose a concrete protocol that realizes PIC. By using one-time public keys, our protocol enables users to receive their outputs without compromising anonymity, which is essential for privacy amplification. Additionally, we present an optimal randomizer, the Minkowski Response, designed for the PIC model to enhance utility. We formally prove the security and privacy properties of the PIC protocol. Theoretical analysis and empirical evaluations demonstrate PIC's capability in handling non-statistical computation tasks, and the efficacy of PIC and the Minkowski randomizer in achieving superior utility compared to existing solutions.

Recent studies suggest that filamentary structures are representative of the initial conditions of star formation in molecular clouds and support a filament paradigm for star formation, potentially accounting for the origin of the stellar initial mass function (IMF). Using Herschel imaging observations of the California giant molecular cloud, we aim to further investigate the filament paradigm for low- to intermediate-mass star formation and to better understand the exact role of filaments in the origin of stellar masses. Using the multiscale, multiwavelength extraction method getsf, we identify starless cores, protostars, and filaments in the Herschel data set and separate these components from the background cloud contribution to determine accurate core and filament properties. Both the prestellar core mass function (CMF) and the distribution of filament masses per unit length or filament line mass function (FLMF) are consistent with power-law distributions at the high-mass end, $\Delta N/\Delta {\rm log}M\propto M^{-1.4 \pm 0.2}$ at $M > 1\,M_\odot$ for the CMF and $\Delta N/\Delta {\rm log} {M}_{\rm line} \propto {M}_{\rm line}^{-1.5\pm0.2}$ for the FLMF at $M_{\rm line} > 10\,M_\odot {\rm pc^{-1}}$, which are both consistent with the Salpeter power-law IMF. Based on these results, we propose a revised model for the origin of the CMF in filaments, whereby the global prestellar CMF in a molecular cloud arises from the integration of the CMFs generated by individual thermally supercritical filaments within the cloud. Our findings support the existence a tight connection between the FLMF and the CMF/IMF and suggests that filamentary structures represent a critical evolutionary step in establishing a Salpeter-like mass function., Comment: 16 pages, 22 figures, accepted for publication in Astronomy & Astrophysics

This work describes the motion of photons in black hole (BH) spacetimes within the framework of Horndeski theory. We focus on the shadows, quasinormal modes (QNMs) and optical appearances of BHs surrounded by geometrically thin accretion disks. The QNMs of BHs are calculated by the WKB method and the eikonal limit, respectively. Using Event Horizon Telescope (EHT) observations of $\mathrm{M} 87^*$ and $\mathrm{Sgr} \mathrm{A}^*$, we can constrain the parameter in Horndeski theory to a small range. Based on the constraint, we obtain the frequency ranges of the fundamental modes for $\mathrm{M} 87^*$ and $\mathrm{Sgr} \mathrm{A}^*$ in Horndeski theory. By exploring the optical appearances of BHs, we find that for the current resolution of the EHT, it primarily captures direct emission. This work advances our understanding of the observational characteristics of BHs in Horndeski theory and constrains Horndeski theory by EHT observations of $\mathrm{M} 87^*$ and $\mathrm{Sgr} \mathrm{A}^*$.

With the rapid advancement of large language models (LLMs), the diversity of multi-LLM tasks and the variability in their pricing structures have become increasingly important, as costs can vary greatly between different LLMs. To tackle these challenges, we introduce the \textit{C2MAB-V}, a \underline{C}ost-effective \underline{C}ombinatorial \underline{M}ulti-armed \underline{B}andit with \underline{V}ersatile reward models for optimal LLM selection and usage. This online model differs from traditional static approaches or those reliant on a single LLM without cost consideration. With multiple LLMs deployed on a scheduling cloud and a local server dedicated to handling user queries, \textit{C2MAB-V} facilitates the selection of multiple LLMs over a combinatorial search space, specifically tailored for various collaborative task types with different reward models. Based on our designed online feedback mechanism and confidence bound technique, \textit{C2MAB-V} can effectively address the multi-LLM selection challenge by managing the exploration-exploitation trade-off across different models, while also balancing cost and reward for diverse tasks. The NP-hard integer linear programming problem for selecting multiple LLMs with trade-off dilemmas is addressed by: i) decomposing the integer problem into a relaxed form by the local server, ii) utilizing a discretization rounding scheme that provides optimal LLM combinations by the scheduling cloud, and iii) continual online updates based on feedback. Theoretically, we prove that \textit{C2MAB-V} offers strict guarantees over versatile reward models, matching state-of-the-art results for regret and violations in some degenerate cases. Empirically, we show that \textit{C2MAB-V} effectively balances performance and cost-efficiency with nine LLMs for three application scenarios., Comment: 32 pages, 14 figures, conference

The recently proposed Bayesian Flow Networks~(BFNs) show great potential in modeling parameter spaces, offering a unified strategy for handling continuous, discretized, and discrete data. However, BFNs cannot learn high-level semantic representation from the parameter space since {common encoders, which encode data into one static representation, cannot capture semantic changes in parameters.} This motivates a new direction: learning semantic representations hidden in the parameter spaces to characterize mixed-typed noisy data. {Accordingly, we propose a representation learning framework named ParamReL, which operates in the parameter space to obtain parameter-wise latent semantics that exhibit progressive structures. Specifically, ParamReL proposes a \emph{self-}encoder to learn latent semantics directly from parameters, rather than from observations. The encoder is then integrated into BFNs, enabling representation learning with various formats of observations. Mutual information terms further promote the disentanglement of latent semantics and capture meaningful semantics simultaneously.} We illustrate {conditional generation and reconstruction} in ParamReL via expanding BFNs, and extensive {quantitative} experimental results demonstrate the {superior effectiveness} of ParamReL in learning parameter representation.

Visual Prompt Learning (VPL) differs from traditional fine-tuning methods in reducing significant resource consumption by avoiding updating pre-trained model parameters. Instead, it focuses on learning an input perturbation, a visual prompt, added to downstream task data for making predictions. Since learning generalizable prompts requires expert design and creation, which is technically demanding and time-consuming in the optimization process, developers of Visual Prompts as a Service (VPaaS) have emerged. These developers profit by providing well-crafted prompts to authorized customers. However, a significant drawback is that prompts can be easily copied and redistributed, threatening the intellectual property of VPaaS developers. Hence, there is an urgent need for technology to protect the rights of VPaaS developers. To this end, we present a method named \textbf{WVPrompt} that employs visual prompt watermarking in a black-box way. WVPrompt consists of two parts: prompt watermarking and prompt verification. Specifically, it utilizes a poison-only backdoor attack method to embed a watermark into the prompt and then employs a hypothesis-testing approach for remote verification of prompt ownership. Extensive experiments have been conducted on three well-known benchmark datasets using three popular pre-trained models: RN50, BIT-M, and Instagram. The experimental results demonstrate that WVPrompt is efficient, harmless, and robust to various adversarial operations., Comment: 11 pages, 7 figures

Let $U$ be a connected open subset of $\mathbb{R}^n$, and let $X=(X_1,X_{2},\ldots,X_m)$ be a system of H\"{o}rmander vector fields defined on $U$. This paper addresses sharp embedding results and geometric inequalities in the generalized Sobolev space $\mathcal{W}_{X,0}^{k,p}(\Omega)$, where $\Omega\subset\subset U$ is a general open bounded subset of $U$. By employing Rothschild-Stein's lifting technique and saturation method, we prove the representation formula for smooth functions with compact support in $\Omega$. Combining this representation formula with weighted weak-$L^p$ estimates, we derive sharp Sobolev inequalities on $\mathcal{W}_{X,0}^{k,p}(\Omega)$, where the critical Sobolev exponent depends on the generalized M\'{e}tivier index. As applications of these sharp Sobolev inequalities, we establish the isoperimetric inequality, logarithmic Sobolev inequalities, Rellich-Kondrachov compact embedding theorem, Gagliardo-Nirenberg inequality, Nash inequality, and Moser-Trudinger inequality in the context of general H\"{o}rmander vector fields., Comment: 43 pages

In this paper, we use the concepts of quantum entanglement and coherence to analyze the Unruh and anti-Unruh effects based on the model of Unruh-DeWitt detector. For the first time, we find that (i) the Unruh effect reduces quantum entanglement but enhances quantum coherence; (ii) the anti-Unruh effect enhances quantum entanglement but reduces quantum coherence. This surprising result refutes the notion that the Unruh effect can only destroy quantum entanglement and coherence simultaneously, and that the anti-Unruh can only protect quantum resources. Consequently, it opens up a new source for discovering experimental evidence supporting the existence of the Unruh and anti-Unruh effects., Comment: 12 pages, 2 figures

We systematically investigate unconventional superfluid phases of fermionic dipolar particles lying in a double-wire setup with laser-assisted interwire tunneling. Our numerical simulations, based on the nonlocal Kohn-Sham Bogoliubov-de Gennes equation, reveal the existence of a large Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) region with a stripe phase under an imbalance of particle densities between two wires. When the laser-assisted interwire tunneling is present, it induces a transition from the FFLO phase to the topological superfluid phase and the associated Majorana zero modes exhibit an oscillation structure, which is significantly enhanced by the long-range nature of the interwire dipolar interaction. This distinguishes itself from the results obtained with usual contact interaction and offers new opportunities for manipulating and reshaping Majorana zero modes by adjusting the degree of the nonlocality and the interwire separation., Comment: 8 pages, 6 figures

Data stream mining aims at extracting meaningful knowledge from continually evolving data streams, addressing the challenges posed by nonstationary environments, particularly, concept drift which refers to a change in the underlying data distribution over time. Graph structures offer a powerful modelling tool to represent complex systems, such as, critical infrastructure systems and social networks. Learning from graph streams becomes a necessity to understand the dynamics of graph structures and to facilitate informed decision-making. This work introduces a novel method for graph stream classification which operates under the general setting where a data generating process produces graphs with varying nodes and edges over time. The method uses incremental learning for continual model adaptation, selecting representative graphs (prototypes) for each class, and creating graph embeddings. Additionally, it incorporates a loss-based concept drift detection mechanism to recalculate graph prototypes when drift is detected., Comment: IEEE World Congress on Computational Intelligence (WCCI) 2024; Keywords: graph streams, concept drift, incremental learning, graph prototypes, nonstationary environments. International Joint Conference on Neural Networks, 2024

In response to the rising interest in large multimodal models, we introduce Cross-Attention Token Pruning (CATP), a precision-focused token pruning method. Our approach leverages cross-attention layers in multimodal models, exemplified by BLIP-2, to extract valuable information for token importance determination. CATP employs a refined voting strategy across model heads and layers. In evaluations, CATP achieves up to 12.1X higher accuracy compared to existing token pruning methods, addressing the trade-off between computational efficiency and model precision.

We present a complete classification of $\operatorname{SL}(n)$ contravariant, $C(\mathbb{R}^n\setminus\{o\})$-valued valuations on polytopes, without any additional assumptions.It extends the previous results of the second author [Int. Math. Res. Not. 2020] which have a good connection with the $L_p$ and Orlicz Brunn-Minkowski theory. Additionally, our results deduce a complete classification of $\operatorname{SL}(n)$ contravariant symmetric-tensor-valued valuations on polytopes.

Scene text recognition is an important and challenging task in computer vision. However, most prior works focus on recognizing pre-defined words, while there are various out-of-vocabulary (OOV) words in real-world applications. In this paper, we propose a novel open-vocabulary text recognition framework, Pseudo-OCR, to recognize OOV words. The key challenge in this task is the lack of OOV training data. To solve this problem, we first propose a pseudo label generation module that leverages character detection and image inpainting to produce substantial pseudo OOV training data from real-world images. Unlike previous synthetic data, our pseudo OOV data contains real characters and backgrounds to simulate real-world applications. Secondly, to reduce noises in pseudo data, we present a semantic checking mechanism to filter semantically meaningful data. Thirdly, we introduce a quality-aware margin loss to boost the training with pseudo data. Our loss includes a margin-based part to enhance the classification ability, and a quality-aware part to penalize low-quality samples in both real and pseudo data. Extensive experiments demonstrate that our approach outperforms the state-of-the-art on eight datasets and achieves the first rank in the ICDAR2022 challenge.

In the contemporary digital landscape, the continuous generation of extensive streaming data across diverse domains has become pervasive. Yet, a significant portion of this data remains unlabeled, posing a challenge in identifying infrequent events such as anomalies. This challenge is further amplified in non-stationary environments, where the performance of models can degrade over time due to concept drift. To address these challenges, this paper introduces a new method referred to as VAE4AS (Variational Autoencoder for Anomalous Sequences). VAE4AS integrates incremental learning with dual drift detection mechanisms, employing both a statistical test and a distance-based test. The anomaly detection is facilitated by a Variational Autoencoder. To gauge the effectiveness of VAE4AS, a comprehensive experimental study is conducted using real-world and synthetic datasets characterized by anomalous rates below 10\% and recurrent drift. The results show that the proposed method surpasses both robust baselines and state-of-the-art techniques, providing compelling evidence for their efficacy in effectively addressing some of the challenges associated with anomalous sequence detection in non-stationary streaming data., Comment: submitted to IJCNN2024,under review

The holographic entropy bound is discussed in cosmology. We try to define a special class of spatial systems in cosmology, inside which the entropy of matter satisfies the holographic entropy bound throughout the evolution of the universe. We first take a universe composed of black holes as an example and demonstrate that such a special class of systems does exist. Then, from the perspective of entropy inside the co-moving volume, we find that there exists a universal definition for this special class of spatial systems, i.e., the definition is independent of the components of the universe. Finally, we prove that in some specific cosmological models, multiple definitions can arise for this special class of spatial systems, and we analyze the underlying reasons for the emergence of these multiple definitions., Comment: 14 pages; comments are welcome

With the widespread of machine learning models for healthcare applications, there is increased interest in building applications for personalized medicine. Despite the plethora of proposed research for personalized medicine, very few focus on representing missingness and learning from the missingness patterns in time-series Electronic Health Records (EHR) data. The lack of focus on missingness representation in an individualized way limits the full utilization of machine learning applications towards true personalization. In this brief communication, we highlight new insights into patterns of missingness with real-world examples and implications of missingness in EHRs. The insights in this work aim to bridge the gap between theoretical assumptions and practical observations in real-world EHRs. We hope this work will open new doors for exploring directions for better representation in predictive modelling for true personalization.

In flat-band materials, the strong Coulomb interaction between electrons can lead to exotic physical phenomena. Recently, $\alpha$-In$_2$Se$_3$ thin films were found to possess ferroelectricity and flat bands. In this work, using first-principles calculations, we find that for the monolayer, there is a Weyl point at $\Gamma$ in the flat band, where the inclusion of the spin-orbit coupling opens a gap. Shifting the Fermi level into the spin-orbit gap gives rise to nontrivial band topology, which is preserved for the bilayer regardless of the interlayer polarization couplings. We further calculate the Chern number and edge states for both the monolayer and bilayer, for which the results suggest that they become quantum anomalous Hall insulators under appropriate dopings. Moreover, we find that the doping-induced magnetism for In$_2$Se$_3$ bilayer is strongly dependent on the interlayer polarization coupling. Therefore, doping the flat bands in In$_2$Se$_3$ bilayer can also yield multiferroicity, where the magnetism is electrically tunable as the system transforms between different polarization states. Our study thus reveals that multiferroicity and nontrivial band topology can be unified into one material for designing multifunctional electronic devices., Comment: 6 pages, 4 figures

Recommender Systems (RS) have significantly advanced online content discovery and personalized decision-making. However, emerging vulnerabilities in RS have catalyzed a paradigm shift towards Trustworthy RS (TRS). Despite numerous progress on TRS, most of them focus on data correlations while overlooking the fundamental causal nature in recommendation. This drawback hinders TRS from identifying the cause in addressing trustworthiness issues, leading to limited fairness, robustness, and explainability. To bridge this gap, causal learning emerges as a class of promising methods to augment TRS. These methods, grounded in reliable causality, excel in mitigating various biases and noises while offering insightful explanations for TRS. However, there lacks a timely survey in this vibrant area. This paper creates an overview of TRS from the perspective of causal learning. We begin by presenting the advantages and common procedures of Causality-oriented TRS (CTRS). Then, we identify potential trustworthiness challenges at each stage and link them to viable causal solutions, followed by a classification of CTRS methods. Finally, we discuss several future directions for advancing this field.

In this study, we comprehensively characterized and optimized a cryogenic pure CsI (pCsI) detector. We utilized a {$\SI{2}{cm}\times\SI{2}{cm}\times\SI{2}{cm}$} cube crystal coupled with a HAMAMATSU R11065 photomultiplier tube, achieving a remarkable light yield of \SI{35.2}{PE/\keV_{ee}} and an unprecedented energy resolution of \SI{6.9}{\%} at {\SI{59.54}{\keV}}. Additionally, we measured the scintillation decay time of pCsI, which was significantly shorter than that of CsI(Na) at room temperature. Furthermore, we investigated the impact of temperature, surface treatment, and crystal shape on light yield. Notably, the light yield peaked at approximately \SI{20}{\K} and remained stable within the range of \SI{70}--\SI{100}{\K}. The light yield of the polished crystals was approximately 1.5 times greater than that of the ground crystals, whereas the crystal shape exhibited minimal influence on the light yield. These results are crucial for the design of the \SI{10}{\kg} pCsI detector for the future CLOVERS (Coherent eLastic neutrinO(V)-nucleus scattERing at China Spallation Neutron Source (CSNS)) experiment.`

We consider instability and localized patterns arising from long wave-short wave (LWSW) resonance in the non-integrable regime numerically. We study the stability and instability of elliptic-function periodic waves with respect to subharmonic perturbations, whose period is a multiple of the period of the elliptic waves. We thus find the modulational instability (MI) of the corresponding dnoidal waves. Upon varying parameters of dnoidal waves, spectrally unstable ones can be transformed into stable states via the Hamiltonian Hopf bifurcation. For snoidal waves, we find a transition of the dominant instability scenario between the MI and instability with a bubble-like spectrum. For cnoidal waves, we produce three variants of the MI. Evolution of the unstable states is also considered, leading to formation of rogue waves on top of the elliptic-wave and continuous-wave backgrounds., Comment: To be published in Physical Review E