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Xi Chen

Publications and source records attributed to Xi Chen.

At least 523 records · Page 29Linked to original sources
arXiv2024

Self-Reasoning Assistant Learning for non-Abelian Gauge Fields Design

Non-Abelian braiding has attracted substantial attention because of its pivotal role in describing the exchange behaviour of anyons, in which the input and outcome of non-Abelian braiding are connected by a unitary matrix. Implementing braiding in a classical system can assist the experimental investigation of non-Abelian physics. However, the design of non-Abelian gauge fields faces numerous challenges stemmed from the intricate interplay of group structures, Lie algebra properties, representation theory, topology, and symmetry breaking. The extreme diversity makes it a powerful tool for the study of condensed matter physics. Whereas the widely used artificial intelligence with data-driven approaches has greatly promoted the development of physics, most works are limited on the data-to-data design. Here we propose a self-reasoning assistant learning framework capable of directly generating non-Abelian gauge fields. This framework utilizes the forward diffusion process to capture and reproduce the complex patterns and details inherent in the target distribution through continuous transformation. Then the reverse diffusion process is used to make the generated data closer to the distribution of the original situation. Thus, it owns strong self-reasoning capabilities, allowing to automatically discover the feature representation and capture more subtle relationships from the dataset. Moreover, the self-reasoning eliminates the need for manual feature engineering and simplifies the process of model building. Our framework offers a disruptive paradigm shift to parse complex physical processes, automatically uncovering patterns from massive datasets.

cs.LG↗
arXiv2024

LLaST: Improved End-to-end Speech Translation System Leveraged by Large Language Models

We introduces LLaST, a framework for building high-performance Large Language model based Speech-to-text Translation systems. We address the limitations of end-to-end speech translation(E2E ST) models by exploring model architecture design and optimization techniques tailored for LLMs. Our approach includes LLM-based speech translation architecture design, ASR-augmented training, multilingual data augmentation, and dual-LoRA optimization. Our approach demonstrates superior performance on the CoVoST-2 benchmark and showcases exceptional scaling capabilities powered by LLMs. We believe this effective method will serve as a strong baseline for speech translation and provide insights for future improvements of the LLM-based speech translation framework. We release the data, code and models in https://github.com/openaudiolab/LLaST.

cs.CL↗
arXiv2024

Gaussian Process Model with Tensorial Inputs and Its Application to the Design of 3D Printed Antennas

In simulation-based engineering design with time-consuming simulators, Gaussian process (GP) models are widely used as fast emulators to speed up the design optimization process. In its most commonly used form, the input of GP is a simple list of design parameters. With rapid development of additive manufacturing (also known as 3D printing), design inputs with 2D/3D spatial information become prevalent in some applications, for example, neighboring relations between pixels/voxels and material distributions in heterogeneous materials. Such spatial information, vital to 3D printed designs, is hard to incorporate into existing GP models with common kernels such as squared exponential or Matérn. In this work, we propose to embed a generalized distance measure into a GP kernel, offering a novel and convenient technique to incorporate spatial information from freeform 3D printed designs into the GP framework. The proposed method allows complex design problems for 3D printed objects to take advantage of a plethora of tools available from the GP surrogate-based simulation optimization such as designed experiments and GP-based optimizations including Bayesian optimization. We investigate the properties of the proposed method and illustrate its performance by several numerical examples of 3D printed antennas. The dataset is publicly available at: https://github.com/xichennn/GP_dataset.

cs.LG↗
arXiv2024

Composable Generation Strategy Framework Enabled Bidirectional Design on Topological Circuits

Topological insulators show important properties, such as topological phase transitions and topological edge states. Although these properties and phenomena can be simulated by well-designed circuits, it is undoubtedly difficult to design such topological circuits due to the complex physical principles and calculations involved. Therefore, achieving a framework that can automatically to complete bidirectional design of topology circuits is very significant. Here, we propose an effective bidirectional collaborative design framework with strong task adaptability, which can automatically generate specific results according to our requirements. In the framework, a composable generation strategy is employed, which involves building a shared multimodal space by bridging alignment in the diffusion process. For simplicity, a series of two-dimensional (2D) Su-Schrieffer-Heeger (SSH) circuits are constructed with different structural parameters. The framework at first is applied to find the relationship between the structural information and topological features. Then, the correctness of the results through experimental measurements can be verified by the automatically generated circuit diagram following the manufacture of Printed Circuit Board (PCB). The framework is demonstrated by achieving good results in the reverse design of circuit structures and forward prediction of topological edge states, reaching an accuracy of 94%. Overall, our research demonstrates the enormous potential of the proposed bidirectional deep learning framework in complex tasks and provides insights for collaborative design tasks.

physics.app-ph↗
arXiv2024

LogoSticker: Inserting Logos into Diffusion Models for Customized Generation

Recent advances in text-to-image model customization have underscored the importance of integrating new concepts with a few examples. Yet, these progresses are largely confined to widely recognized subjects, which can be learned with relative ease through models' adequate shared prior knowledge. In contrast, logos, characterized by unique patterns and textual elements, are hard to establish shared knowledge within diffusion models, thus presenting a unique challenge. To bridge this gap, we introduce the task of logo insertion. Our goal is to insert logo identities into diffusion models and enable their seamless synthesis in varied contexts. We present a novel two-phase pipeline LogoSticker to tackle this task. First, we propose the actor-critic relation pre-training algorithm, which addresses the nontrivial gaps in models' understanding of the potential spatial positioning of logos and interactions with other objects. Second, we propose a decoupled identity learning algorithm, which enables precise localization and identity extraction of logos. LogoSticker can generate logos accurately and harmoniously in diverse contexts. We comprehensively validate the effectiveness of LogoSticker over customization methods and large models such as DALLE~3. \href{https://mingkangz.github.io/logosticker}{Project page}.

cs.CV↗
arXiv2024

Mismatch Quest: Visual and Textual Feedback for Image-Text Misalignment

While existing image-text alignment models reach high quality binary assessments, they fall short of pinpointing the exact source of misalignment. In this paper, we present a method to provide detailed textual and visual explanation of detected misalignments between text-image pairs. We leverage large language models and visual grounding models to automatically construct a training set that holds plausible misaligned captions for a given image and corresponding textual explanations and visual indicators. We also publish a new human curated test set comprising ground-truth textual and visual misalignment annotations. Empirical results show that fine-tuning vision language models on our training set enables them to articulate misalignments and visually indicate them within images, outperforming strong baselines both on the binary alignment classification and the explanation generation tasks. Our method code and human curated test set are available at: https://mismatch-quest.github.io/

cs.CL↗
arXiv2024

On the global complexity of a derivative-free Levenberg-Marquardt algorithm via orthogonal spherical smoothing

In this paper, we propose a derivative-free Levenberg-Marquardt algorithm for nonlinear least squares problems, where the Jacobian matrices are approximated via orthogonal spherical smoothing. It is shown that the gradient models which use the approximate Jacobian matrices are probabilistically first-order accurate, and the high probability complexity bound of the algorithm is also given.

math.NA↗
arXiv2024

Pulse-based variational quantum optimization and metalearning in superconducting circuits

Solving optimization problems using variational algorithms stands out as a crucial application for noisy intermediate-scale devices. Instead of constructing gate-based quantum computers, our focus centers on designing variational quantum algorithms within the analog paradigm. This involves optimizing parameters that directly control pulses, driving quantum states towards target states without the necessity of compiling a quantum circuit. In this work, we introduce pulse-based variational quantum optimization (PBVQO) as a hardware-level framework. We illustrate the framework by optimizing external fluxes on superconducting quantum interference devices, effectively driving the wave function of this specific quantum architecture to the ground state of an encoded problem Hamiltonian. Given that the performance of variational algorithms heavily relies on appropriate initial parameters, we introduce a global optimizer as a meta-learning technique to tackle a simple problem. The synergy between PBVQO and meta-learning provides an advantage over conventional gate-based variational algorithms.

quant-ph↗
arXiv2024

Trace reconstruction from local statistical queries

The goal of trace reconstruction is to reconstruct an unknown $n$-bit string $x$ given only independent random traces of $x$, where a random trace of $x$ is obtained by passing $x$ through a deletion channel. A Statistical Query (SQ) algorithm for trace reconstruction is an algorithm which can only access statistical information about the distribution of random traces of $x$ rather than individual traces themselves. Such an algorithm is said to be $\ell$-local if each of its statistical queries corresponds to an $\ell$-junta function over some block of $\ell$ consecutive bits in the trace. Since several -- but not all -- known algorithms for trace reconstruction fall under the local statistical query paradigm, it is interesting to understand the abilities and limitations of local SQ algorithms for trace reconstruction. In this paper we establish nearly-matching upper and lower bounds on local Statistical Query algorithms for both worst-case and average-case trace reconstruction. For the worst-case problem, we show that there is an $\tilde{O}(n^{1/5})$-local SQ algorithm that makes all its queries with tolerance $τ\geq 2^{-\tilde{O}(n^{1/5})}$, and also that any $\tilde{O}(n^{1/5})$-local SQ algorithm must make some query with tolerance $τ\leq 2^{-\tildeΩ(n^{1/5})}$. For the average-case problem, we show that there is an $O(\log n)$-local SQ algorithm that makes all its queries with tolerance $τ\geq 1/\mathrm{poly}(n)$, and also that any $O(\log n)$-local SQ algorithm must make some query with tolerance $τ\leq 1/\mathrm{poly}(n).$

cs.DS↗
arXiv2024

Deep Bag-of-Words Model: An Efficient and Interpretable Relevance Architecture for Chinese E-Commerce

Text relevance or text matching of query and product is an essential technique for the e-commerce search system to ensure that the displayed products can match the intent of the query. Many studies focus on improving the performance of the relevance model in search system. Recently, pre-trained language models like BERT have achieved promising performance on the text relevance task. While these models perform well on the offline test dataset, there are still obstacles to deploy the pre-trained language model to the online system as their high latency. The two-tower model is extensively employed in industrial scenarios, owing to its ability to harmonize performance with computational efficiency. Regrettably, such models present an opaque ``black box'' nature, which prevents developers from making special optimizations. In this paper, we raise deep Bag-of-Words (DeepBoW) model, an efficient and interpretable relevance architecture for Chinese e-commerce. Our approach proposes to encode the query and the product into the sparse BoW representation, which is a set of word-weight pairs. The weight means the important or the relevant score between the corresponding word and the raw text. The relevance score is measured by the accumulation of the matched word between the sparse BoW representation of the query and the product. Compared to popular dense distributed representation that usually suffers from the drawback of black-box, the most advantage of the proposed representation model is highly explainable and interventionable, which is a superior advantage to the deployment and operation of online search engines. Moreover, the online efficiency of the proposed model is even better than the most efficient inner product form of dense representation ...

cs.IR↗
arXiv2024

Thermotropic reentrant isotropy and antiferroelectricity in the ferroelectric nematic realm: Comparing RM734 and DIO

The current intense study of ferroelectric nematic liquid crystals was initiated by the observation of the same ferroelectric nematic phase in two independently discovered organic rod-shaped mesogenic compounds, RM734 and DIO. We recently reported that the compound RM734 also exhibits a monotropic, low-temperature, antiferroelectric phase having reentrant isotropic symmetry (the $I_A$ phase), the formation of which is facilitated to a remarkable degree by doping with small (below $1\%$) amounts of the ionic liquid ${\rm BMIM-PF}_6$. Here we report similar phenomenology in DIO, showing that this reentrant isotropic behavior is not only a property of RM734 but is rather a more general, material-independent feature of ferroelectric nematic mesogens. We find that the reentrant isotropic phases observed in RM734 and DIO are similar but not identical, adding two new phases to the ferroelectric nematic realm. The two $I_A$ phases exhibit similar, strongly peaked, diffuse x-ray scattering in the WAXS range $(1<q<2 \, Å^{-1})$ indicative of a distinctive mode of short-ranged, side-by-side molecular packing. The scattering of the $I_A$ phases at small $q$ is quite different in the two materials, however, with RM734 exhibiting a strong, single, diffuse peak at $q \sim 0.08 \,Å^{-1}$ indicating mesoscale modulation with $\sim 80 \,Å$ periodicity, and DIO a sharper diffuse peak at $q \sim 0.27 \,Å^{-1} \sim (2π/{\rm molecular length})$, with second and third harmonics, indicating that in the $I_A$ phase of DIO, short-ranged molecular positional correlation is smectic layer-like.

cond-mat.soft↗
arXiv2024

Phy-Diff: Physics-guided Hourglass Diffusion Model for Diffusion MRI Synthesis

Diffusion MRI (dMRI) is an important neuroimaging technique with high acquisition costs. Deep learning approaches have been used to enhance dMRI and predict diffusion biomarkers through undersampled dMRI. To generate more comprehensive raw dMRI, generative adversarial network based methods are proposed to include b-values and b-vectors as conditions, but they are limited by unstable training and less desirable diversity. The emerging diffusion model (DM) promises to improve generative performance. However, it remains challenging to include essential information in conditioning DM for more relevant generation, i.e., the physical principles of dMRI and white matter tract structures. In this study, we propose a physics-guided diffusion model to generate high-quality dMRI. Our model introduces the physical principles of dMRI in the noise evolution in the diffusion process and introduce a query-based conditional mapping within the difussion model. In addition, to enhance the anatomical fine detials of the generation, we introduce the XTRACT atlas as prior of white matter tracts by adopting an adapter technique. Our experiment results show that our method outperforms other state-of-the-art methods and has the potential to advance dMRI enhancement.

eess.IV↗
arXiv2024

Spatially Dependent in-Gap States Induced by Andreev Tunneling through a Single Electronic State

By using low-temperature scanning tunneling microscopy and spectroscopy (STM/STS), we observe in-gap states induced by Andreev tunneling through a single impurity state in a low carrier density superconductor (NaAlSi). The energy-symmetric in-gap states appear when the impurity state is located within the superconducting gap. In-gap states can cross the Fermi level, and they show X-shaped spatial variation. We interpret the in-gap states as a consequence of the Andreev tunneling through the impurity state, which involves the formation or breakup of a Cooper pair. Due to the low carrier density in NaAlSi, the in-gap state is tunable by controlling the STM tip-sample distance. Under strong external magnetic fields, the impurity state shows Zeeman splitting when it is located near the Fermi level. Our findings not only demonstrate the Andreev tunneling involving single electronic state, but also provide new insights for understanding the spatially-dependent in-gap states in low carrier density superconductors.

cond-mat.supr-con↗
arXiv2024

Evaluation of Bias Towards Medical Professionals in Large Language Models

This study evaluates whether large language models (LLMs) exhibit biases towards medical professionals. Fictitious candidate resumes were created to control for identity factors while maintaining consistent qualifications. Three LLMs (GPT-4, Claude-3-haiku, and Mistral-Large) were tested using a standardized prompt to evaluate resumes for specific residency programs. Explicit bias was tested by changing gender and race information, while implicit bias was tested by changing names while hiding race and gender. Physician data from the Association of American Medical Colleges was used to compare with real-world demographics. 900,000 resumes were evaluated. All LLMs exhibited significant gender and racial biases across medical specialties. Gender preferences varied, favoring male candidates in surgery and orthopedics, while preferring females in dermatology, family medicine, obstetrics and gynecology, pediatrics, and psychiatry. Claude-3 and Mistral-Large generally favored Asian candidates, while GPT-4 preferred Black and Hispanic candidates in several specialties. Tests revealed strong preferences towards Hispanic females and Asian males in various specialties. Compared to real-world data, LLMs consistently chose higher proportions of female and underrepresented racial candidates than their actual representation in the medical workforce. GPT-4, Claude-3, and Mistral-Large showed significant gender and racial biases when evaluating medical professionals for residency selection. These findings highlight the potential for LLMs to perpetuate biases and compromise healthcare workforce diversity if used without proper bias mitigation strategies.

cs.CY↗
arXiv2024

Non-modal growth analysis of high-speed flows over an inclined cone

Spatial optimal responses to both inlet disturbances and harmonic external forcing for hypersonic flows over a blunt cone at nonzero angles of attack are obtained by efficiently solving the direct-adjoint equations with a parabolic approach. In either case, the most amplified disturbances initially take the form of localized streamwise vortices on the windward side and will undergo a two-stage evolution process when propagating downstream: they first experience a substantial algebraic growth by exploiting the Orr and lift-up mechanisms, and then smoothly transition to a quasi exponential-growth stage driven by the crossflow-instability mechanism, accompanied by an azimuthal advection of the disturbance structure towards the leeward side. The algebraic-growth phase is most receptive to the external forcing, whereas the exponential-growth stage relies on the disturbance frequency and can be significantly strengthened by increasing the angle of attack. The wavemaker delineating the structural sensitivity region for the optimal gain is shown to lie on the windward side immediately downstream of the inlet, implying a potent control strategy. Additionally, considerable non-modal growth is also observed for broadband high-frequency disturbances residing in the entropy layer.

physics.flu-dyn↗
arXiv2024

MindStar: Enhancing Math Reasoning in Pre-trained LLMs at Inference Time

Although Large Language Models (LLMs) achieve remarkable performance across various tasks, they often struggle with complex reasoning tasks, such as answering mathematical questions. Recent efforts to address this issue have primarily focused on leveraging mathematical datasets through supervised fine-tuning or self-improvement techniques. However, these methods often depend on high-quality datasets that are difficult to prepare, or they require substantial computational resources for fine-tuning. Inspired by findings that LLMs know how to produce the right answer but struggle to select the correct reasoning path, we propose a purely inference-based searching method -- MindStar (M*). This method formulates reasoning tasks as searching problems and proposes two search ideas to identify the optimal reasoning paths. We evaluate the M* framework on both the GSM8K and MATH datasets, comparing its performance with existing open and closed-source LLMs. Our results demonstrate that M* significantly enhances the reasoning abilities of open-source models, such as Llama-2-13B and Mistral-7B, and achieves comparable performance to GPT-3.5 and Grok-1, but with substantially reduced model size and computational costs.

cs.LG↗
arXiv2024

Shortcut to adiabaticity improvement of STIRAP based qubit rotation

Robust quantum control is essential for the development of quantum computers, which rely on precise manipulation of qubits. One form of quantum control is stimulated Raman adiabatic passage (STIRAP), which ordinarily is a state transfer protocol but was extended by Kis and Renzoni (Phys. Rev. A 65, 032318 (2002)) to perform qubit rotations. Shortcut methods to adiabaticity for STIRAP have been shown to speed up adiabatic processes, beyond the adiabatic criterion, with high fidelity. Here, we apply shortcut to adiabaticity methods to the STIRAP qubit rotation scheme to improve the performance of quantum logic gates. The scheme can be implemented via direct connections between ground states in a 4-level $Λ$ system or effective connections in a 5-level $Λ$ system with modified pulses that implement transitionless quantum driving via the addition of a counterdiabatic driving term. We show that the extended shortcut to adiabaticity method serves to improve the fidelity of qubit rotations in the diabatic regime.

quant-ph↗