arXiv subjects

Yue Yu

Publications and source records attributed to Yue Yu.

At least 217 records · Page 12Linked to original sources

arXiv2024

EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records

Large language models (LLMs) have demonstrated exceptional capabilities in planning and tool utilization as autonomous agents, but few have been developed for medical problem-solving. We propose EHRAgent, an LLM agent empowered with a code interface, to autonomously generate and execute code for multi-tabular reasoning within electronic health records (EHRs). First, we formulate an EHR question-answering task into a tool-use planning process, efficiently decomposing a complicated task into a sequence of manageable actions. By integrating interactive coding and execution feedback, EHRAgent learns from error messages and improves the originally generated code through iterations. Furthermore, we enhance the LLM agent by incorporating long-term memory, which allows EHRAgent to effectively select and build upon the most relevant successful cases from past experiences. Experiments on three real-world multi-tabular EHR datasets show that EHRAgent outperforms the strongest baseline by up to 29.6% in success rate. EHRAgent leverages the emerging few-shot learning capabilities of LLMs, enabling autonomous code generation and execution to tackle complex clinical tasks with minimal demonstrations.

cs.CL↗

arXiv2024

BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers

Developing effective biomedical retrieval models is important for excelling at knowledge-intensive biomedical tasks but still challenging due to the deficiency of sufficient publicly annotated biomedical data and computational resources. We present BMRetriever, a series of dense retrievers for enhancing biomedical retrieval via unsupervised pre-training on large biomedical corpora, followed by instruction fine-tuning on a combination of labeled datasets and synthetic pairs. Experiments on 5 biomedical tasks across 11 datasets verify BMRetriever's efficacy on various biomedical applications. BMRetriever also exhibits strong parameter efficiency, with the 410M variant outperforming baselines up to 11.7 times larger, and the 2B variant matching the performance of models with over 5B parameters. The training data and model checkpoints are released at \url{https://huggingface.co/BMRetriever} to ensure transparency, reproducibility, and application to new domains.

cs.CL↗

arXiv2024

MedAdapter: Efficient Test-Time Adaptation of Large Language Models towards Medical Reasoning

Despite their improved capabilities in generation and reasoning, adapting large language models (LLMs) to the biomedical domain remains challenging due to their immense size and corporate privacy. In this work, we propose MedAdapter, a unified post-hoc adapter for test-time adaptation of LLMs towards biomedical applications. Instead of fine-tuning the entire LLM, MedAdapter effectively adapts the original model by fine-tuning only a small BERT-sized adapter to rank candidate solutions generated by LLMs. Experiments demonstrate that MedAdapter effectively adapts both white-box and black-box LLMs in biomedical reasoning, achieving average performance improvements of 25.48% and 11.31%, respectively, without requiring extensive computational resources or sharing data with third parties. MedAdapter also yields superior performance when combined with train-time adaptation, highlighting a flexible and complementary solution to existing adaptation methods. Faced with the challenges of balancing model performance, computational resources, and data privacy, MedAdapter provides an efficient, privacy-preserving, cost-effective, and transparent solution for adapting LLMs to the biomedical domain.

cs.CL↗

arXiv2024

Multi-Designated Detector Watermarking for Language Models

In this paper, we initiate the study of \emph{multi-designated detector watermarking (MDDW)} for large language models (LLMs). This technique allows model providers to generate watermarked outputs from LLMs with two key properties: (i) only specific, possibly multiple, designated detectors can identify the watermarks, and (ii) there is no perceptible degradation in the output quality for ordinary users. We formalize the security definitions for MDDW and present a framework for constructing MDDW for any LLM using multi-designated verifier signatures (MDVS). Recognizing the significant economic value of LLM outputs, we introduce claimability as an optional security feature for MDDW, enabling model providers to assert ownership of LLM outputs within designated-detector settings. To support claimable MDDW, we propose a generic transformation converting any MDVS to a claimable MDVS. Our implementation of the MDDW scheme highlights its advanced functionalities and flexibility over existing methods, with satisfactory performance metrics.

cs.CR↗

arXiv2024

Filtering-Linearization: A First-Order Method for Nonconvex Trajectory Optimization with Filter-Based Warm-Starting

Nonconvex trajectory optimization is at the core of designing trajectories for complex autonomous systems. A challenge for nonconvex trajectory optimization methods, such as sequential convex programming, is to find an effective warm-starting point to approximate the nonconvex optimization with a sequence of convex ones. We introduce a first-order method with filter-based warm-starting for nonconvex trajectory optimization. The idea is to first generate sampled trajectories using constraint-aware particle filtering, which solves the problem as an estimation problem. We then identify different locally optimal trajectories through agglomerative hierarchical clustering. Finally, we choose the best locally optimal trajectory to warm-start the prox-linear method, a first-order method with guaranteed convergence. We demonstrate the proposed method on a multi-agent trajectory optimization problem with linear dynamics and nonconvex collision avoidance. Compared with sequential quadratic programming and interior-point method, the proposed method reduces the objective function value by up to approximately 96\% within the same amount of time for a two-agent problem, and 98\% for a six-agent problem.

math.OC↗

arXiv2024

Optical multi-beam steering and communication using integrated acousto-optics arrays

Optical beam steering enables optical detection and imaging in macroscopic or microscopic scales and long-range communication over free space. It underpins numerous optical applications, including LiDAR, biomedical imaging, and remote sensing. Despite the inherent speed of light, advanced applications increasingly require the ability to steer multiple beams simultaneously to increase imaging throughput, boost communication bandwidth, and control arrays qubits for scalable quantum computing. Therefore, there is a significant demand for non-mechanical, integrated, and scalable multi-beam steering technology. Here, we report a scalable multi-beam steering system comprising an array of acousto-optic beam steering channels and photonic integrated circuits on a thin-film lithium niobate platform. Each channel generates tens of individually controllable beams of visible wavelength by exciting acoustic waves using digitally synthesized multi-tone microwave signals. We demonstrate the system's capabilities through multi-input, multi-output free-space communications, simultaneously transmitting to multiple receivers at megabits/sec data rates. This technology can be readily scaled up to steer hundreds of optical beams from a compact chip, potentially advancing many areas of optical technologies and enabling novel applications.

physics.optics↗

arXiv2024

Constraint Preconditioning and Parameter Selection for a First-Order Primal-Dual Method applied to Model Predictive Control

Many techniques for real-time trajectory optimization and control require the solution of optimization problems at high frequencies. However, ill-conditioning in the optimization problem can significantly reduce the speed of first-order primal-dual optimization algorithms. We introduce a preconditioning technique and step-size heuristic for Proportional-Integral Projected Gradient (PIPG), a first-order primal-dual algorithm. The preconditioning technique, based on the QR factorization, aims to reduce the condition number of the KKT matrix associated with the optimization problem. Our step-size selection heuristic chooses step-sizes to minimize the upper bound on the convergence of the primal-dual gap for the optimization problem. These algorithms are tested on two model predictive control problem examples and show a solve-time reduction of at least 3.6x.

math.OC↗

arXiv2024

Photorefractive and pyroelectric photonic memory and long-term stability in thin-film lithium niobate microresonators

The stability of the integrated photonic circuits is of critical importance for many applications that require high frequency precision or robust operation over time, such as optomechanical sensing, frequency conversion, optical communication, and quantum optics. Photonic memory is useful for low-energy optical computing and interconnects. Thin film lithium niobate (TFLN), as an emerging photonic platform, exhibits complex material properties including pyroelectric (PE) and photorefractive (PR) effects which could lead to intra-device drift and excess noise under different environmental or operating conditions as well as be utilized for building photonic memory. However, the long-term stability and memory effect of its optical properties has not been explored. In this paper, we discovered a long-lived change of optical refractive index as a result of light excitation and temporal temperature variation using Z-cut TFLN microresonators and reveal a strong dependence of the instability with the crystal orientation of the thin film form. The recovery time are measured to be over 10 hours. Leveraging the photonic memory with a long relaxation time, we realize optical trimming of the cavity resonance frequencies. Our result offers insights towards understanding the fundamental noise properties and dynamic behavior of the integrated TFLN material and devices.

physics.optics↗

arXiv2024

Sensing Resource Allocation Against Data-Poisoning Attacks in Traffic Routing

Data-poisoning attacks can disrupt the efficient operations of transportation systems by misdirecting traffic flows via falsified data. One challenge in countering these attacks is to reduce the uncertainties on the types of attacks, such as the distribution of their targets and intensities. We introduce a resource allocation method in transportation networks to detect and distinguish different types of attacks and facilitate efficient traffic routing. The idea is to first cluster different types of attacks based on the corresponding optimal routing strategies, then allocate sensing resources to a subset of network links to distinguish attacks from different clusters via lexicographical mixed-integer programming. We illustrate the application of the proposed method using the Anaheim network, a benchmark model in traffic routing that contains more than 400 nodes and 900 links.

math.OC↗

arXiv2024

Efficient Multi-task Prompt Tuning for Recommendation

With the expansion of business scenarios, real recommender systems are facing challenges in dealing with the constantly emerging new tasks in multi-task learning frameworks. In this paper, we attempt to improve the generalization ability of multi-task recommendations when dealing with new tasks. We find that joint training will enhance the performance of the new task but always negatively impact existing tasks in most multi-task learning methods. Besides, such a re-training mechanism with new tasks increases the training costs, limiting the generalization ability of multi-task recommendation models. Based on this consideration, we aim to design a suitable sharing mechanism among different tasks while maintaining joint optimization efficiency in new task learning. A novel two-stage prompt-tuning MTL framework (MPT-Rec) is proposed to address task irrelevance and training efficiency problems in multi-task recommender systems. Specifically, we disentangle the task-specific and task-sharing information in the multi-task pre-training stage, then use task-aware prompts to transfer knowledge from other tasks to the new task effectively. By freezing parameters in the pre-training tasks, MPT-Rec solves the negative impacts that may be brought by the new task and greatly reduces the training costs. Extensive experiments on three real-world datasets show the effectiveness of our proposed multi-task learning framework. MPT-Rec achieves the best performance compared to the SOTA multi-task learning method. Besides, it maintains comparable model performance but vastly improves the training efficiency (i.e., with up to 10% parameters in the full training way) in the new task learning.

cs.IR↗

arXiv2024

Performance Metrics and Loss Mechanisms in Horticulture Luminescent Solar Concentrators

Horticulture Luminescent Solar Concentrators (HLSCs) represent an innovative concept developed in recent years to promote crop yields, building upon the foundation of traditional Luminescent Solar Concentrators (LSCs). HLSCs are characterized by two distinct properties: spectral conversion and light extraction. Unlike traditional LSCs, HLSCs focus on converting energy from one part of the solar spectrum (typically green) to a specific range (usually red) and aim for the converted photons to exit the device from the bottom surface rather than the edge surfaces. In this study, we start by examining the specific requirements of horticulture to clarify the motivation for using HLSCs. We re-evaluate and propose new optical metrics tailored to HLSCs. Additionally, we analyse potential loss channels for direct red emission and converted red emission. Utilizing Monte Carlo ray tracing method and experimental data, we further explore the factors influencing these loss channels. Our work provides a fundamental discussion on HLSCs and offers design guidelines for future HLSC research.

physics.optics↗

arXiv2024

Flavor Nernst effects in quantum paramagnets

Recent advances in spin transport research have highlighted the potential of quantum paramagnets as platforms for exploring novel phenomena and developing next-generation technologies. In this paper, we investigate the flavor Nernst effect (FNE) in quantum paramagnets, focusing on the Hall-type thermal spin transport of crystal electric field (CEF) excitations with spin-orbit couplings. As a proof of principle, we investigate the quantum paramagnetic ground state in an effective spin-1 Hamiltonian with Dzyaloshinskii-Moriya interactions and a large hard-axis anisotropy. We employ linear flavor-wave theory to analyze the low-energy excitations, and obtain the flavor Nernst coefficients from the linear response theory. We demonstrate the FNE in a 2D pyrochlore thin film with an all-in-all-out Ising axis configuration, and investigate their dependence on temperature, anisotropy, DM interaction, and external fields. Our results reveal the connection between the FNE and the Berry curvature of the CEF excitations, suggesting potential applications in manipulating thermal spin currents and exploring topological spin transport phenomena in quantum paramagnets.

cond-mat.str-el↗

arXiv2024

Nonlocal Attention Operator: Materializing Hidden Knowledge Towards Interpretable Physics Discovery

Despite the recent popularity of attention-based neural architectures in core AI fields like natural language processing (NLP) and computer vision (CV), their potential in modeling complex physical systems remains under-explored. Learning problems in physical systems are often characterized as discovering operators that map between function spaces based on a few instances of function pairs. This task frequently presents a severely ill-posed PDE inverse problem. In this work, we propose a novel neural operator architecture based on the attention mechanism, which we coin Nonlocal Attention Operator (NAO), and explore its capability towards developing a foundation physical model. In particular, we show that the attention mechanism is equivalent to a double integral operator that enables nonlocal interactions among spatial tokens, with a data-dependent kernel characterizing the inverse mapping from data to the hidden parameter field of the underlying operator. As such, the attention mechanism extracts global prior information from training data generated by multiple systems, and suggests the exploratory space in the form of a nonlinear kernel map. Consequently, NAO can address ill-posedness and rank deficiency in inverse PDE problems by encoding regularization and achieving generalizability. We empirically demonstrate the advantages of NAO over baseline neural models in terms of generalizability to unseen data resolutions and system states. Our work not only suggests a novel neural operator architecture for learning interpretable foundation models of physical systems, but also offers a new perspective towards understanding the attention mechanism.

cs.LG↗

arXiv2024

Order by projection in single-band Hubbard model: a DMRG study

In a Fermi system near or at half-filling, a specific superconducting pairing channel, if not explicitly included in the Hamiltonian, can be boosted by suppressing a competing pairing channel; this is exemplified by the enhancement of extended $s$-wave correlations upon suppressing $s$-wave Cooper pairing. This phenomenon, originally found by the use of generalized uncertainty relations is referred to as \emph{order by projection}. The case of zero on-site Coulomb interaction in the thermodynamic limit, confirms this mechanism through the analytical solution. In this study, we go further and systematically investigate this mechanism for a strongly correlated fermionic Hubbard model, now with finite on-site interaction, on a square lattice with an extended set of hopping parameters. We explore the behaviors of different pairing channels when one of them is suppressed, utilizing density matrix renormalization group calculations. Our findings provide numerical evidence supporting the existence of \emph{order by projection} in the strongly correlated system we studied. We also investigate the effect of the strength of Hubbard $U$, next-nearest neighbor $t'$, hole-doping, as well as finite-size scaling approaching the thermodynamic limit.

cond-mat.str-el↗

arXiv2024

Investigation on a quantum algorithm for linear differential equations

Ref.[BCOW17] introduced a pioneering quantum approach (coined BCOW algorithm) for solving linear differential equations with optimal error tolerance. Originally designed for a specific class of diagonalizable linear differential equations, the algorithm was extended by Krovi in [Kro23] to encompass broader classes, including non-diagonalizable and even singular matrices. Despite the common misconception, the original algorithm is indeed applicable to non-diagonalizable matrices, with diagonalisation primarily serving for theoretical analyses to establish bounds on condition number and solution error. By leveraging basic estimates from [Kro23], we derive bounds comparable to those outlined in the Krovi algorithm, thereby reinstating the advantages of the BCOW approach. Furthermore, we extend the BCOW algorithm to address time-dependent linear differential equations by transforming non-autonomous systems into higher-dimensional autonomous ones, a technique also applicable for the Krovi algorithm.

quant-ph↗

arXiv2024

Dissipationless topological quantum computation for Majorana objects in sparse-dense mixed encoding process

Topological quantum computation based on Majorana objects is subject to a significant challenge because at least some of the two-qubit quantum gates rely on the fermion (either charge or spin) parity of the qubits. This dependency renders the quantum operations involving these gates probabilistic when attempting to advance quantum processes within the quantum circuit model. Such an approach leads to significant information loss whenever measurements yield the undesired fermion parity. To resolve the problem of wasting information, we devise topological operations that allow for the non-dissipative correction of information from undesired fermion parity to the desired one. We will use the sparse-dense mixed encoding process for the controlled-NOT gate as an example to explain how corrections can be implemented without affecting the quantum information carried by the computational qubits. This correction process can be applied {to} either the undesired input qubits or the fermion parity-dependent quantum gates, and it works for both Majorana-zero-mode-based and Majorana-edge-mode-based topological quantum computation.

quant-ph↗

arXiv2024

Non-Fermi Liquid Behavior of the $t$-$J$ Model in the Strange Metal Phase: $U(1)$ Gauge Theory Consistent with Local Constraints

In the slave particle representation with $U(1)$ gauge symmetry, local constraints on physical states characterized by various mean field solutions belong to Dirac's second-class ones. Although constrained systems are extensively investigated, realistic methods to solve the gauge theory problem with second-class constraints are yet to be developed. We formulate a Becchi-Rouet-Stora-Tyutin (BRST) quantization theory, called consistent $U(1)$ gauge theory, that is consistent with both first- and second-class local constraints for strongly correlated condensed matter systems. In our consistent $U(1)$ gauge theory, the redundant gauge degrees of freedom are removed by proper gauge fixing conditions while the constraints are exactly retained and the gauge invariance is guaranteed by the BRST symmetry. Furthermore, the gauge fixing conditions endow the gauge field with dynamics. This turns the strongly correlated electron model into a weakly coupled slave boson model, so most of the system's physical properties can be calculated by the conventional quantum many-body perturbation method. We focus on the property of the strange metal phase in the $t$-$J$ model. The electron momentum distribution and the spectral function are calculated, and the non-Fermi liquid behavior agrees with the angle-resolved photoemission spectroscopy measurements for cuprate materials. We also study the electromagnetic responses of the strange metal state. The observed non-Fermi liquid anomalies are captured by our calculations. Especially, we find that the Hall resistivity decreases as temperature increases, and the sign of the Hall resistivity varies from negative to positive when the dopant concentration varies from optimal doping to underdoping in the strange metal regime.

cond-mat.str-el↗

arXiv2024

RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records

We present RAM-EHR, a Retrieval AugMentation pipeline to improve clinical predictions on Electronic Health Records (EHRs). RAM-EHR first collects multiple knowledge sources, converts them into text format, and uses dense retrieval to obtain information related to medical concepts. This strategy addresses the difficulties associated with complex names for the concepts. RAM-EHR then augments the local EHR predictive model co-trained with consistency regularization to capture complementary information from patient visits and summarized knowledge. Experiments on two EHR datasets show the efficacy of RAM-EHR over previous knowledge-enhanced baselines (3.4% gain in AUROC and 7.2% gain in AUPR), emphasizing the effectiveness of the summarized knowledge from RAM-EHR for clinical prediction tasks. The code will be published at \url{https://github.com/ritaranx/RAM-EHR}.

cs.CL↗