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Recent advancements in fine-tuning Vision-Language Foundation Models (VLMs) have garnered significant attention for their effectiveness in downstream few-shot learning tasks.While these recent approaches exhibits some performance improvements, they often suffer from excessive training parameters and high computational costs. To address these challenges, we propose a novel Block matrix-based low-rank adaptation framework, called Block-LoRA, for fine-tuning VLMs on downstream few-shot tasks. Inspired by recent work on Low-Rank Adaptation (LoRA), Block-LoRA partitions the original low-rank decomposition matrix of LoRA into a series of sub-matrices while sharing all down-projection sub-matrices. This structure not only reduces the number of training parameters, but also transforms certain complex matrix multiplication operations into simpler matrix addition, significantly lowering the computational cost of fine-tuning. Notably, Block-LoRA enables fine-tuning CLIP on the ImageNet few-shot benchmark using a single 24GB GPU. We also show that Block-LoRA has the more tighter bound of generalization error than vanilla LoRA. Without bells and whistles, extensive experiments demonstrate that Block-LoRA achieves competitive performance compared to state-of-the-art CLIP-based few-shot methods, while maintaining a low training parameters count and reduced computational overhead., Comment: Under Review

According to the theory of group representations, the types of band degeneracy can be divided into accidental degeneracy and essential degeneracy. The essentially degenerate nodal lines (NLs) are typically resided on the high-symmetry lines of the Brillouin zone. Here, we propose a type of NL in two dimension that is essentially degenerate but is hidden within the high-symmetry planes, making it less observable, dubbed a hidden-essential nodal line (HENL). The existence of HENL is guaranteed as long as the system hosts a horizontal glide-mirror symmetry, hence such NLs can be widely found in both non-magnetic and magnetic systems. We perform an exhaustive search over all 528 magnetic layer groups (MLGs) for HENL that can be enforced by glide-mirror symmetry with both spinless and spinfull systems. We find that 122 candidate MLGs host spinless HENL, while 63 candidate MLGs demonstrate spinful HENL. In addition, we reveal that horizontal mirror and time-reversal symmetry in type-II and type-IV MLGs with spin-orbital coupling can enforce HENL formed. The 15 corresponding candidate MLGs have also been presented. Furthemore, we derive a few typical lattice models to characterize the existence for the HENL. For specific electronic fillings in real materials, namely 4$N$+2 in spinless systems (and 2$N$+1 in spinful systems), the presence of the HENLs in candidate MLGs is required regardless of the details of the systems. Using \emph{ab-initio} calculations, we further identify possible material candidates that realize spinless and spinful HENL. Moreover, spinful HENLs exhibit a novel persistent spin texture wih the characteristic of momentum-independent spin configuration. Our findings uncover a new type of topological semimetal state and offer an ideal platform to study the related physics of HENLs.

Passive metal reflectors for communication enhancement have appealing advantages such as ultra low cost, zero energy expenditure, maintenance-free operation, long life span, and full compatibility with legacy wireless systems. To unleash the full potential of passive reflectors for wireless communications, this paper proposes a new passive reflector architecture, termed flexible reflector (FR), for enabling the flexible adjustment of beamforming direction via the FR placement and rotation optimization. We consider the multi-FR aided area coverage enhancement and aim to maximize the minimum expected receive power over all locations within the target coverage area, by jointly optimizing the placement positions and rotation angles of multiple FRs. To gain useful insights, the special case of movable reflector (MR) with fixed rotation is first studied to maximize the expected receive power at a target location, where the optimal single-MR placement positions for electrically large and small reflectors are derived in closed-form, respectively. It is shown that the reflector should be placed at the specular reflection point for electrically large reflector. While for area coverage enhancement, the optimal placement is obtained for the single-MR case and a sequential placement algorithm is proposed for the multi-MR case. Moreover, for the general case of FR, joint placement and rotation design is considered for the single-/multi-FR aided coverage enhancement, respectively. Numerical results are presented which demonstrate significant performance gains of FRs over various benchmark schemes under different practical setups in terms of receive power enhancement., Comment: 13 pages, 15 figures

Multimodal large language models (MLLMs) have shown impressive capabilities in document understanding, a rapidly growing research area with significant industrial demand in recent years. As a multimodal task, document understanding requires models to possess both perceptual and cognitive abilities. However, current MLLMs often face conflicts between perception and cognition. Taking a document VQA task (cognition) as an example, an MLLM might generate answers that do not match the corresponding visual content identified by its OCR (perception). This conflict suggests that the MLLM might struggle to establish an intrinsic connection between the information it "sees" and what it "understands." Such conflicts challenge the intuitive notion that cognition is consistent with perception, hindering the performance and explainability of MLLMs. In this paper, we define the conflicts between cognition and perception as Cognition and Perception (C&P) knowledge conflicts, a form of multimodal knowledge conflicts, and systematically assess them with a focus on document understanding. Our analysis reveals that even GPT-4o, a leading MLLM, achieves only 68.6% C&P consistency. To mitigate the C&P knowledge conflicts, we propose a novel method called Multimodal Knowledge Consistency Fine-tuning. This method first ensures task-specific consistency and then connects the cognitive and perceptual knowledge. Our method significantly reduces C&P knowledge conflicts across all tested MLLMs and enhances their performance in both cognitive and perceptual tasks in most scenarios., Comment: Preprint

Rastall theory, originally introduced in 1972, suggests a violation of the usual conservation law. We consider two generalizations of Rastall theory: Brans-Dicke-Rastall theory and the newly established scalar-tensor-Rastall theory, the latter being a further generalization of the former. The field equations in these two generalized theories are studied across different parameter spaces, and the polarization modes of gravitational waves, as a key focus, are subsequently investigated. The results show that the polarization modes of gravitational waves in Brans-Dicke-Rastall theory are the same as those in Brans-Dicke theory; specifically, both theories exhibit the plus, cross, and breathing modes. However, in scalar-tensor-Rastall theory, the polarization modes of gravitational waves depend on the parameter space of the theory. Particularly, over a broad range of the parameter space, regardless of some special values of the parameters, it allows only two tensor modes, just as in general relativity, without introducing any additional degrees of freedom. This indicates that Rastall theory offers a novel approach to constructing modified gravity theories that propagate only two tensor degrees of freedom. In the remaining regions of the parameter space, there is also one scalar mode in addition to the two tensor modes. The scalar mode can be either a mixture of the breathing and longitudinal modes or just a pure breathing mode, depending on the parameter space. These results will play a crucial role in constraining the theoretical parameters through future gravitational wave detection projects, such as LISA, Taiji, and TianQin.

The recently introduced Segment Anything Model (SAM), a Visual Foundation Model (VFM), has demonstrated impressive capabilities in zero-shot segmentation tasks across diverse natural image datasets. Despite its success, SAM encounters noticeably performance degradation when applied to specific domains, such as medical images. Current efforts to address this issue have involved fine-tuning strategies, intended to bolster the generalizability of the vanilla SAM. However, these approaches still predominantly necessitate the utilization of domain specific expert-level prompts during the evaluation phase, which severely constrains the model's practicality. To overcome this limitation, we introduce a novel self-prompting based fine-tuning approach, called SAM-SP, tailored for extending the vanilla SAM model. Specifically, SAM-SP leverages the output from the previous iteration of the model itself as prompts to guide subsequent iteration of the model. This self-prompting module endeavors to learn how to generate useful prompts autonomously and alleviates the dependence on expert prompts during the evaluation phase, significantly broadening SAM's applicability. Additionally, we integrate a self-distillation module to enhance the self-prompting process further. Extensive experiments across various domain specific datasets validate the effectiveness of the proposed SAM-SP. Our SAM-SP not only alleviates the reliance on expert prompts but also exhibits superior segmentation performance comparing to the state-of-the-art task-specific segmentation approaches, the vanilla SAM, and SAM-based approaches., Comment: Under Review

The pursuit for "ferroelectric metal" which combines seemingly incompatible spontaneous electric polarization and metallicity, has been assiduously ongoing but remains elusive. Unlike traditional ferroelectrics with a wide band gap, ferroelectric (FE) metals can naturally incorporate nontrivial band topology near the Fermi level, endowing them with additional exotic properties. Here, we show first-principles evidence that the metallic PtBi2 monolayer is an intrinsic two-dimensional (2D) topological FE metal, characterized by out-of-plane polarization and a moderate switching barrier. Moreover, it exhibits a topologically nontrivial electronic structure with Z2 invariant equal to 1, leading to a significant FE bulk photovoltaic effect. A slight strain can further enhance this effect to a remarkable level, which far surpass that of previously reported 2D/3D FE materials. Our work provides an important step towards realizing intrinsic monolayer topological FE metals and paves a promising way for future nonlinear optical devices., Comment: 7 pages, 5 figures, accepted by Phys. Rev. Lett

In the era of content creation revolution propelled by advancements in generative models, the field of web design remains unexplored despite its critical role in modern digital communication. The web design process is complex and often time-consuming, especially for those with limited expertise. In this paper, we introduce Web Rendering Parameters Generation (WebRPG), a new task that aims at automating the generation for visual presentation of web pages based on their HTML code. WebRPG would contribute to a faster web development workflow. Since there is no existing benchmark available, we develop a new dataset for WebRPG through an automated pipeline. Moreover, we present baseline models, utilizing VAE to manage numerous elements and rendering parameters, along with custom HTML embedding for capturing essential semantic and hierarchical information from HTML. Extensive experiments, including customized quantitative evaluations for this specific task, are conducted to evaluate the quality of the generated results., Comment: Accepted at ECCV 2024. The dataset and code can be accessed at https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/DocumentUnderstanding/WebRPG

Recently, large language models (LLMs) and multimodal large language models (MLLMs) have demonstrated promising results on document visual question answering (VQA) task, particularly after training on document instruction datasets. An effective evaluation method for document instruction data is crucial in constructing instruction data with high efficacy, which, in turn, facilitates the training of LLMs and MLLMs for document VQA. However, most existing evaluation methods for instruction data are limited to the textual content of the instructions themselves, thereby hindering the effective assessment of document instruction datasets and constraining their construction. In this paper, we propose ProcTag, a data-oriented method that assesses the efficacy of document instruction data. ProcTag innovatively performs tagging on the execution process of instructions rather than the instruction text itself. By leveraging the diversity and complexity of these tags to assess the efficacy of the given dataset, ProcTag enables selective sampling or filtering of document instructions. Furthermore, DocLayPrompt, a novel semi-structured layout-aware document prompting strategy, is proposed for effectively representing documents. Experiments demonstrate that sampling existing open-sourced and generated document VQA/instruction datasets with ProcTag significantly outperforms current methods for evaluating instruction data. Impressively, with ProcTag-based sampling in the generated document datasets, only 30.5\% of the document instructions are required to achieve 100\% efficacy compared to the complete dataset. The code is publicly available at https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/DocumentUnderstanding/ProcTag.

Spacetime inversion symmetry can generate intriguing types of spinless excitations in crystalline materials. Here, we propose a topological phase protected by spacetime inversion symmetry - the crossed real nodal line (RNL) in the phonon spectrum of gold monobromide (AuBr). In AuBr, there exist four straight nodal lines, which are linked by a crossed nodal line formed by two lower bands. Remarkably, each adjacent two of the four straight nodal lines is a pair, forming a crossed RNL with nontrivial real Chern number. Such configuration and pairing mode of RNL have never been reported. The crossed RNL exhibits unique surface and hinge states distinguished from that of the conventional RNLs. The symmetry protection and the transformation under the symmetry-preserving strain of the crossed RNL are also investigated. Our results open the door to a new class of topological states, and predict its realization in experimentally synthesized material.

Despite the rapid progress in the study of planar Hall effect (PHE) in recent years, all the previous works only showed that the PHE is connected to local geometric quantities, such as Berry curvature. Here, for the first time, we point out that the PHE in magnetic Weyl semimetals is directly related to a global quantity, namely, the Chern number of the Weyl point. This leads to a remarkable consequence that the PHE observation predicted here is robust against many system details, including the Fermi energy. The main difference between non-magnetic and magnetic Weyl points is that the latter breaks time-reversal symmetry T, thus generally possessing an energy tilt. Via semiclassical Boltzmann theory, we investigate the PHE in generic magnetic Weyl models with energy tilt and arbitrary Chern number. We find that by aligning the magnetic and electric fields in the same direction, the trace of the PHE conductivity contributed from Berry curvature and orbital moment is proportional to the Chern number and the energy tilt of the Weyl points, resulting in previously undiscovered quantized PHE plateau by varying Fermi energy. We further confirm the existence of PHE plateaus in a more realistic lattice model without T symmetry. By proposing a new quantized physical quantity, our work not only provides a new tool for extracting the topological character of the Weyl points but also suggests that the interplay between topology and magnetism can give rise to intriguing physics., Comment: 15 pages, 5 figures

Exploring new Hall effect is always a fascinating research topic. The ordinary Hall effect and the quantum Hall effect, initially discovered in two-dimensional (2D) non-magnetic systems, are the phenomena that a transverse current is generated when a system carrying an electron current is placed in a magnetic field perpendicular to the currents. In this work, we propose the electric counterparts of these two Hall effects, termed as electric Hall effect (EHE) and quantum electric Hall effect (QEHE). The EHE and QEHE emerge in 2D magnetic systems, where the transverse current is generated by applying an electric gate-field instead of a magnetic field. We present a symmetry requirement for intrinsic EHE and QEHE. With a weak gate-field, we establish an analytical expression of the intrinsic EHE coefficient. We show that it is determined by intrinsic band geometric quantities: Berry curvature and its polarizability which consists of both intraband and interband layer polarization. Via first-principles calculations, we investigate the EHE in the monolayer Ca(FeN)$_2$, where significant EHE coefficient is observed around band crossings. Furthermore, we demonstrate that the QEHE can appear in the semiconductor monolayer $\rm BaMn_2S_3$, of which the Hall conductivity exhibits steps that take on the quantized values $0$ and $\pm1$ in the unit of $e^2/h$ by varying the gate-field within the experimentally achievable range. Due to the great tunability of the electric gate-field, the EHE and QEHE proposed here can be easily controlled and should have more potential applications., Comment: 7 pages, 3 figures

There has been a significant focus on real topological systems that enjoy space-time inversion symmetry (PT ) and lack spin-orbit coupling. While the theoretical classification of the real topology has been established, more progress has yet to be made in the materials realization of such real topological systems in three dimensions (3D). To address this crucial issue, by selecting the carbon-based material candidates as targets, we perform high-throughput computing to inspect the real topology in the phonon spectrums of the 3D carbon allotropes in the Samara Carbon Allotrope Database (SACADA). Among 1192 kinds of 3D carbon allotropes, we find 65 real topological systems with a phononic real Chern insulating (PRCI) state, 2 real topological systems with a phononic real nodal line (PRNL) state, 10 real topological systems with a phononic real Dirac point (PRDP) state, and 8 real topological systems with a phononic real triple-point pair (PRTPP) state. This extremely expands the material candidates with real topology, especially for the gapless topological phonons. We exhibit the PRCI, PRNL, PRTPP, and PRDP states of 27-SG. 166-pcu-h, 1081-SG. 194- 4 2T13-CA, 52-SG. 141-gis, and 132-SG. 191-3,4T157 as illustrative examples, and explore the second-order boundary mode, i.e., phononic hinge mode. Among the four examples, the materials 1081-SG. 194-42T13-CA and 52-SG. 141-gis are so ideal that the PRNL and PRTPP in them are well separated from other bands, and the phononic hinge mode can be clearly observed. This study aims to broaden the understanding of 3D topological phonons, and emphasizes the potential of 3D carbon allotropes as a valuable framework for exploring the fascinating physics related to phononic hinge modes and phononic real topology.

Recently, leveraging large language models (LLMs) or multimodal large language models (MLLMs) for document understanding has been proven very promising. However, previous works that employ LLMs/MLLMs for document understanding have not fully explored and utilized the document layout information, which is vital for precise document understanding. In this paper, we propose LayoutLLM, an LLM/MLLM based method for document understanding. The core of LayoutLLM is a layout instruction tuning strategy, which is specially designed to enhance the comprehension and utilization of document layouts. The proposed layout instruction tuning strategy consists of two components: Layout-aware Pre-training and Layout-aware Supervised Fine-tuning. To capture the characteristics of document layout in Layout-aware Pre-training, three groups of pre-training tasks, corresponding to document-level, region-level and segment-level information, are introduced. Furthermore, a novel module called layout chain-of-thought (LayoutCoT) is devised to enable LayoutLLM to focus on regions relevant to the question and generate accurate answers. LayoutCoT is effective for boosting the performance of document understanding. Meanwhile, it brings a certain degree of interpretability, which could facilitate manual inspection and correction. Experiments on standard benchmarks show that the proposed LayoutLLM significantly outperforms existing methods that adopt open-source 7B LLMs/MLLMs for document understanding. The training data of the LayoutLLM is publicly available at https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/DocumentUnderstanding/LayoutLLM, Comment: CVPR 2024

Andreev reflection at a normal-superconductor interface may be accompanied with an anomalous spatial shift. The studies so far are limited to the top incidence configuration. Here, we investigate this effect in the side incidence configuration, with the interface parallel to the principal axis of superconductor. We find that the shift exhibits rich behaviors reflecting the character of pair potential. It has two contributions: one from the $k$-dependent phase of pair potential, and the other from the evanescent mode. For chiral $p$-wave pairing, the pairing phase contribution is proportional to the chirality of pairing and is independent of excitation energy, whereas the evanescent mode contribution is independent of chirality and is nonzero only for excitation energy below the gap. The two contributions also have opposite parity with respect to the incident angle. For $d_{x^{2}-y^{2}}$-wave pairing, only the evanescent mode contribution exists, and the shift exhibits suppressed zones in incident angles, manifesting the superconducting nodes. The dependence of the shift on other factors, such as the angle of incident plane and Fermi surface anisotropy, are discussed.

Photonic quantum technologies such as effective quantum communication require room temperature (RT) operating single- or few- photon sensors with high external quantum efficiency (EQE) at 1550 nm wavelength. The leading class of devices in this segment is avalanche photodetectors operating particularly in the Geiger mode. Often the requirements for RT operation and for a high EQE are in conflict, resulting in a compromised solution. We have developed a device which employs a two-dimensional (2D) semiconductor material on a co-optimized dielectric photonic crystal substrate to simultaneously decrease the dark current by three orders of magnitude at RT and maintain an EQE of >99%. The device is amenable to avalanching and form a basis for single photon detection with ultra-low dark current and high photodetection efficiency. Harnessing the high carrier mobility of 2D materials, the device has ~ps jitter time and can be integrated into a large 2D array camera.

A real Chern insulator (RCI) featuring a real Chern number and a second-order boundary mode appears in a two-dimensional (2D) system with the space-time inversion symmetry (PT ). Here, we propose a kind of RCI: mirror real Chern insulator (MRCI) which emerges from the system having additional horizontal mirror symmetry Mz. The MRCI generally is characterized by two independent real Chern numbers, respectively defined in the two mirror subsystems of the system. Hence, the MRCI may host the second-order boundary modes different from the conventional RCI. We show that for spinless systems, the definition of the MRCI is straightforward, as PT keeps each mirror subsystem invariant. For the spinful systems with both PT and Mz, the real Chern number for the total system remain well defined, as MzPT = C2zT , and (C2zT )2= 1. However, since C2zT exchanges the two mirror subsystems, the definition of the MRCI in spinful systems requires the help of projective symmetry algebra. We also discuss the MRCIs in 3D systems, where the MRCI is defined on certain mirror-invariant 2D planes. Compared with its 2D counterpart, the 3D MRCI can exhibit more abundant physics when the systems have additional nonsymmorphic operators. Several concrete MRCI models including 2D and 3D, spinless and spinful models are constructed to further demonstrate our ideas.

In three dimensions, quasi-one-dimensional (Q1D) transport has traditionally been associated with systems featuring a Q1D chain structure. Here, based on first-principle calculations, we go beyond this understanding to show that the Q1D transport can also be realized in certain three-dimensional (3D) altermagnetic (AM) metals with a topological nodal net in momentum space but lacking Q1D chain structure in real space, including the existing compounds $\beta$-Fe$_2$(PO$_4$)O, Co$_2$(PO$_4$)O, and LiTi$_2$O$_4$. These materials exhibit an AM ground state and feature an ideal crossed $Z^3$ Weyl nodal line in each spin channel around Fermi level, formed by three straight and flat nodal lines traversing the entire Brillouin zone. These nodal lines eventually lead to an AM $Z^3$ nodal net. Surprisingly, the electronic conductivity $\sigma_{xx}$ in these topological nodal net metals is dozens of times larger than $\sigma_{yy}$ and $\sigma_{zz}$ in the up-spin channel, while $\sigma_{yy}$ dominates transport in the down-spin channel. This suggests a distinctive Q1D transport signature in each spin channel, and the principal moving directions for the two spin channels are orthogonal, resulting in Q1D direction-dependent spin transport. This novel phenomenon cannot be found in both conventional 3D bulk materials and Q1D chain materials. In particular, the Q1D spin transport gradually disappears as the Fermi energy moves away from the nodal net, further confirming its topological origin. Our work not only enhances the comprehension of topological physics in altermagnets but also opens a new direction for the exploration of topological spintronics.

In topological physics, it is commonly understood that the existence of the boundary states of a topological system is inherently dictated by its bulk. A classic example is that the surface Fermi arc states of a Weyl system are determined by the chiral charges of Weyl points within the bulk. Contrasting with this established perspective, here, we theoretically and experimentally discover a family of topological chiral bulk states extending over photonic Weyl metamaterial waveguides, solely induced by the waveguide boundaries, independently of the waveguide width. Notably, these bulk states showcase discrete momenta and function as wormhole tunnels that connect Fermi-arc surface states living in different two dimensional spaces via a third dimension. Our work offers a magneticfield-free mechanism for robust chiral bulk transport of waves and highlights the boundaries as a new degree of freedom to regulate bulk Weyl quasiparticles.

Few-shot Learning aims to learn and distinguish new categories with a very limited number of available images, presenting a significant challenge in the realm of deep learning. Recent researchers have sought to leverage the additional textual or linguistic information of these rare categories with a pre-trained language model to facilitate learning, thus partially alleviating the problem of insufficient supervision signals. However, the full potential of the textual information and pre-trained language model have been underestimated in the few-shot learning till now, resulting in limited performance enhancements. To address this, we propose a simple but effective framework for few-shot learning tasks, specifically designed to exploit the textual information and language model. In more detail, we explicitly exploit the zero-shot capability of the pre-trained language model with the learnable prompt. And we just add the visual feature with the textual feature for inference directly without the intricate designed fusion modules in previous works. Additionally, we apply the self-ensemble and distillation to further enhance these components. Our extensive experiments conducted across four widely used few-shot datasets demonstrate that our simple framework achieves impressive results. Particularly noteworthy is its outstanding performance in the 1-shot learning task, surpassing state-of-the-art methods by an average of 3.0\% in classification accuracy. \footnote{We will make the source codes of the proposed framework publicly available upon acceptance. }.

Table structure recognition (TSR) aims at extracting tables in images into machine-understandable formats. Recent methods solve this problem by predicting the adjacency relations of detected cell boxes or learning to directly generate the corresponding markup sequences from the table images. However, existing approaches either count on additional heuristic rules to recover the table structures, or face challenges in capturing long-range dependencies within tables, resulting in increased complexity. In this paper, we propose an alternative paradigm. We model TSR as a logical location regression problem and propose a new TSR framework called LORE, standing for LOgical location REgression network, which for the first time regresses logical location as well as spatial location of table cells in a unified network. Our proposed LORE is conceptually simpler, easier to train, and more accurate than other paradigms of TSR. Moreover, inspired by the persuasive success of pre-trained models on a number of computer vision and natural language processing tasks, we propose two pre-training tasks to enrich the spatial and logical representations at the feature level of LORE, resulting in an upgraded version called LORE++. The incorporation of pre-training in LORE++ has proven to enjoy significant advantages, leading to a substantial enhancement in terms of accuracy, generalization, and few-shot capability compared to its predecessor. Experiments on standard benchmarks against methods of previous paradigms demonstrate the superiority of LORE++, which highlights the potential and promising prospect of the logical location regression paradigm for TSR., Comment: arXiv admin note: substantial text overlap with arXiv:2303.03730

The TiSiCO-family monolayer $X_2Y$CO$_2$($X$=Ti, Zr, Hf; $Y$=Si, Ge) is a two-dimensional second-order topological insulator with unique valley-layer coupling in equilibrium condition. In this work, based on the four-band tight-binding (TB) model of monolayer Ti$_2$SiCO$_2$ (ML-TiSiCO) and the Floquet theory, we study the non-equilibrium properties of the ML-TiSiCO under a periodic field of laser and a gate-electric field. We find the interaction between the time-periodic polarized light and the electric field can lead to a variety of intriguing topological phase transitions. By driving the system with only circularly polarized light (CPL), a photoinduced topological phase transition occurs from a second-order topological insulator to a Chern insulator with a Chern number of $C=\pm$2, and the sign of the Chern number $C$ is determined by the chirality of the incident light. Further adding a perpendicular electric field, we find that the ML-TiSiCO exhibits a rich phase diagram, consisting of Chern insulators with different Chern numbers and various topological semimetals. In contrast, since the linearly polarized light (LPL) does not break time-reversal symmetry, the Chern number of the system would not be changed under the irradiation of LPL. However, there still exist many topological phases, including second-order topological insulator, topological semi-Dirac, Dirac and valley-polarized Dirac semimetals under the interaction between the LPL and the electric field. Our results not only enhance the understanding of the fundamental properties of ML-TiSiCO but also broaden the potential applications of such materials in optoelectronic devices.