arXiv subjects

Cheng Chen

Publications and source records attributed to Cheng Chen.

At least 253 records · Page 14Linked to original sources

arXiv2023

Radiology-Llama2: Best-in-Class Large Language Model for Radiology

This paper introduces Radiology-Llama2, a large language model specialized for radiology through a process known as instruction tuning. Radiology-Llama2 is based on the Llama2 architecture and further trained on a large dataset of radiology reports to generate coherent and clinically useful impressions from radiological findings. Quantitative evaluations using ROUGE metrics on the MIMIC-CXR and OpenI datasets demonstrate that Radiology-Llama2 achieves state-of-the-art performance compared to other generative language models, with a Rouge-1 score of 0.4834 on MIMIC-CXR and 0.4185 on OpenI. Additional assessments by radiology experts highlight the model's strengths in understandability, coherence, relevance, conciseness, and clinical utility. The work illustrates the potential of localized language models designed and tuned for specialized domains like radiology. When properly evaluated and deployed, such models can transform fields like radiology by automating rote tasks and enhancing human expertise.

cs.CL↗

arXiv2023

Can a binary star host three giant circumbinary planets?

We investigate the orbital stability of a tilted circumbinary planetary system with three giant planets. The planets are spaced by a constant number ($Δ$) of mutual Hill radii in the range $Δ=3.4-12.0$ such that the period ratio of the inner pair is the same as the outer pair. A tilted circumbinary planetary system can be unstable even if the same system around a coplanar binary is stable. For an equal mass binary, we find that the stability of a three-planet system is qualitatively similar to that of a two-planet system, but the three-planet system is more unstable in mean motion resonance regions. For an unequal mass binary, there is significantly more instability in the three-planet system as the inner planets can undergo von-Zeipel-Kozai-Lidov oscillations. Generally in unstable systems, the inner planets are more likely to be ejected than the outer planets. The most likely unstable outcome for closely spaced systems, with $Δ\lesssim 8$, is a single remaining stable planet. For more widely separated systems, $Δ\gtrsim 8$, the most likely unstable outcome is two stable planets, only one being ejected. An observed circumbinary planet with significant eccentricity may suggest that it was formed from an unstable system. Consequently, a binary can host three tilted giant planets if the binary stars are close to equal mass and provided that the planets are well spaced and not close to a mean motion resonance.

astro-ph.EP↗

arXiv2023

Multiplicity formula for induced representations: Bessel and Fourier-Jacobi models over Archimedean local fields

This article proves a formula relating the multiplicity of an induced representation and that of the inducing datum for the Bessel and the Fourier-Jacobi models over Archimedean local fields by generalizing the approach of C. Moeglin and J.-L. Waldspurger in [MW12], which was successful for Bessel models of special orthogonal groups over non-Archimedean local fields. As an application, we give a uniform proof of the local Gan-Gross-Prasad conjecture for all classical groups over Archimedean local fields for generic local $L$-parameters based on the tempered basic cases.

math.NT↗

arXiv2023

Treatment Outcome Prediction for Intracerebral Hemorrhage via Generative Prognostic Model with Imaging and Tabular Data

Intracerebral hemorrhage (ICH) is the second most common and deadliest form of stroke. Despite medical advances, predicting treat ment outcomes for ICH remains a challenge. This paper proposes a novel prognostic model that utilizes both imaging and tabular data to predict treatment outcome for ICH. Our model is trained on observational data collected from non-randomized controlled trials, providing reliable predictions of treatment success. Specifically, we propose to employ a variational autoencoder model to generate a low-dimensional prognostic score, which can effectively address the selection bias resulting from the non-randomized controlled trials. Importantly, we develop a variational distributions combination module that combines the information from imaging data, non-imaging clinical data, and treatment assignment to accurately generate the prognostic score. We conducted extensive experiments on a real-world clinical dataset of intracerebral hemorrhage. Our proposed method demonstrates a substantial improvement in treatment outcome prediction compared to existing state-of-the-art approaches. Code is available at https://github.com/med-air/TOP-GPM

cs.CV↗

arXiv2023

Revisit of tensor-meson nonet in resonance chiral theory

We study the properties of the lowest multiplet of light-flavor tensor meson resonances, i.e. $f_2(1270)$, $a_2(1320)$, $K_2^*(1430)$, and $f_2'(1525)$, within the resonance chiral theory approach. The higher-order resonance chiral operators, including the light-quark mass and $1/N_C$ corrections, are simultaneously incorporated in our study. The use of resonance chiral expressions allows us to analyze not only the relevant experimental data but also in the meantime the lattice results at unphysical quark masses, including the masses of the lowest multiplet of tensor resonances and their decay widths into two pseudoscalar mesons. In addition, the radiative decays of the tensor resonances into one photon plus one pseudoscalar meson and two photons are also studied.

hep-ph↗

arXiv2023

Numerical Simulation of the Impact of Different Cushion Gases on Underground Hydrogen Storage in Aquifers Based on an Experimentally-Benchmarked Equation-of-State

Underground hydrogen storage (UHS) in geological formations is a promising technology for large-scale hydrogen energy storage. Although lessons were learned from similar studies, including geological carbon sequestration and underground gas storage, the unique thermodynamic and physical properties of hydrogen distinguish UHS from the other subsurface storage projects. We developed a two-phase, three components reservoir simulator, which incorporated essential physics based on the fully coupled multi-physics framework of the Delft Advanced Reservoir Simulation (DARSim). Hydrogen rich fingers were observed in the aqueous phase when CO2 was used as the cushion gas, because dissolved CO2 increased brine density, leading to density-driven downward convection which was favorable for hydrogen dissolution into the aqueous phase. The highest purity of produced hydrogen was observed when CO2 was used as the cushion gas, whereas using CH4 and N2 as the cushion gas was favorable for the hydrogen production rate and mobility. This work is the first study that utilizes an EoS based reservoir simulator to investigate hydrogen's flow patterns and interactions with cushion gases in an underground storage system. The developed reservoir simulation tool and research findings from this study will be valuable to support decision making in practical UHS projects.

physics.geo-ph↗

arXiv2023

Semantic Contrastive Bootstrapping for Single-positive Multi-label Recognition

Learning multi-label image recognition with incomplete annotation is gaining popularity due to its superior performance and significant labor savings when compared to training with fully labeled datasets. Existing literature mainly focuses on label completion and co-occurrence learning while facing difficulties with the most common single-positive label manner. To tackle this problem, we present a semantic contrastive bootstrapping (Scob) approach to gradually recover the cross-object relationships by introducing class activation as semantic guidance. With this learning guidance, we then propose a recurrent semantic masked transformer to extract iconic object-level representations and delve into the contrastive learning problems on multi-label classification tasks. We further propose a bootstrapping framework in an Expectation-Maximization fashion that iteratively optimizes the network parameters and refines semantic guidance to alleviate possible disturbance caused by wrong semantic guidance. Extensive experimental results demonstrate that the proposed joint learning framework surpasses the state-of-the-art models by a large margin on four public multi-label image recognition benchmarks. Codes can be found at https://github.com/iCVTEAM/Scob.

cs.CV↗

arXiv2023

REAL: A Representative Error-Driven Approach for Active Learning

Given a limited labeling budget, active learning (AL) aims to sample the most informative instances from an unlabeled pool to acquire labels for subsequent model training. To achieve this, AL typically measures the informativeness of unlabeled instances based on uncertainty and diversity. However, it does not consider erroneous instances with their neighborhood error density, which have great potential to improve the model performance. To address this limitation, we propose $REAL$, a novel approach to select data instances with $\underline{R}$epresentative $\underline{E}$rrors for $\underline{A}$ctive $\underline{L}$earning. It identifies minority predictions as \emph{pseudo errors} within a cluster and allocates an adaptive sampling budget for the cluster based on estimated error density. Extensive experiments on five text classification datasets demonstrate that $REAL$ consistently outperforms all best-performing baselines regarding accuracy and F1-macro scores across a wide range of hyperparameter settings. Our analysis also shows that $REAL$ selects the most representative pseudo errors that match the distribution of ground-truth errors along the decision boundary. Our code is publicly available at https://github.com/withchencheng/ECML_PKDD_23_Real.

cs.LG↗

arXiv2023

Status of the muEDM experiment at PSI

Permanent electric dipole moments (EDMs) are excellent probes of physics beyond the Standard Model, especially on new sources of CP violation. The muon EDM has recently attracted significant attention due to discrepancies in the magnetic anomaly of the muon, as well as potential violations of lepton-flavor universality in B-meson decays. At the Paul Scherrer Institute in Switzerland, we have proposed a muon EDM search experiment employing the frozen-spin technique, where a radial electric field is exerted within a storage solenoid to cancel the muon's anomalous spin precession. Consequently, the EDM signal can be inferred from the upstream-downstream asymmetry of the decay positron count versus time. The experiment is planned to take place in two phases, anticipating an annual statistical sensitivity of $3\times10^{-21}$ $e\cdot$cm for Phase~I, and $6\times10^{-23}$ $e\cdot$cm for Phase~II. Going beyond $10^{-21}$ $e\cdot$cm will enable us to probe various Standard Model extensions.

hep-ex↗

arXiv2023

Monte Carlo Policy Gradient Method for Binary Optimization

Binary optimization has a wide range of applications in combinatorial optimization problems such as MaxCut, MIMO detection, and MaxSAT. However, these problems are typically NP-hard due to the binary constraints. We develop a novel probabilistic model to sample the binary solution according to a parameterized policy distribution. Specifically, minimizing the KL divergence between the parameterized policy distribution and the Gibbs distributions of the function value leads to a stochastic optimization problem whose policy gradient can be derived explicitly similar to reinforcement learning. For coherent exploration in discrete spaces, parallel Markov Chain Monte Carlo (MCMC) methods are employed to sample from the policy distribution with diversity and approximate the gradient efficiently. We further develop a filter scheme to replace the original objective function by the one with the local search technique to broaden the horizon of the function landscape. Convergence to stationary points in expectation of the policy gradient method is established based on the concentration inequality for MCMC. Numerical results show that this framework is very promising to provide near-optimal solutions for quite a few binary optimization problems.

math.OC↗

arXiv2023

Harmonic enhancement using learnable comb filter for light-weight full-band speech enhancement model

With fewer feature dimensions, filter banks are often used in light-weight full-band speech enhancement models. In order to further enhance the coarse speech in the sub-band domain, it is necessary to apply a post-filtering for harmonic retrieval. The signal processing-based comb filters used in RNNoise and PercepNet have limited performance and may cause speech quality degradation due to inaccurate fundamental frequency estimation. To tackle this problem, we propose a learnable comb filter to enhance harmonics. Based on the sub-band model, we design a DNN-based fundamental frequency estimator to estimate the discrete fundamental frequencies and a comb filter for harmonic enhancement, which are trained via an end-to-end pattern. The experiments show the advantages of our proposed method over PecepNet and DeepFilterNet.

eess.AS↗

arXiv2023

AI Coach Assist: An Automated Approach for Call Recommendation in Contact Centers for Agent Coaching

In recent years, the utilization of Artificial Intelligence (AI) in the contact center industry is on the rise. One area where AI can have a significant impact is in the coaching of contact center agents. By analyzing call transcripts using Natural Language Processing (NLP) techniques, it would be possible to quickly determine which calls are most relevant for coaching purposes. In this paper, we present AI Coach Assist, which leverages the pre-trained transformer-based language models to determine whether a given call is coachable or not based on the quality assurance (QA) questions asked by the contact center managers or supervisors. The system was trained and evaluated on a large dataset collected from real-world contact centers and provides an effective way to recommend calls to the contact center managers that are more likely to contain coachable moments. Our experimental findings demonstrate the potential of AI Coach Assist to improve the coaching process, resulting in enhancing the performance of contact center agents.

cs.CL↗

arXiv2023

Deep Single Image Deraining using An Asymetric Cycle Generative and Adversarial Framework

In reality, rain and fog are often present at the same time, which can greatly reduce the clarity and quality of the scene image. However, most unsupervised single image deraining methods mainly focus on rain streak removal by disregarding the fog, which leads to low-quality deraining performance. In addition, the samples are rather homogeneous generated by these methods and lack diversity, resulting in poor results in the face of complex rain scenes. To address the above issues, we propose a novel Asymetric Cycle Generative and Adversarial framework (ACGF) for single image deraining that trains on both synthetic and real rainy images while simultaneously capturing both rain streaks and fog features. ACGF consists of a Rain-fog2Clean (R2C) transformation block and a Clean2Rain-fog (C2R) transformation block. The former consists of parallel rain removal path and rain-fog feature extraction path by the rain and derain-fog network and the attention rain-fog feature extraction network (ARFE) , while the latter only contains a synthetic rain transformation path. In rain-fog feature extraction path, to better characterize the rain-fog fusion feature, we employ an ARFE to exploit the self-similarity of global and local rain-fog information by learning the spatial feature correlations. Moreover, to improve the translational capacity of C2R and the diversity of models, we design a rain-fog feature decoupling and reorganization network (RFDR) by embedding a rainy image degradation model and a mixed discriminator to preserve richer texture details in synthetic rain conversion path. Extensive experiments on benchmark rain-fog and rain datasets show that ACGF outperforms state-of-the-art deraining methods. We also conduct defogging performance evaluation experiments to further demonstrate the effectiveness of ACGF.

cs.CV↗

arXiv2023

Inference of relative permeability curves in reservoir rocks with ensemble Kalman method

Multiphase flows through reservoir rocks are a universal and complex phenomenon. Relative permeability is one of the primary determinants in reservoir performance calculations. Accurate estimation of the relative permeability is crucial for reservoir management and future production. In this paper, we propose inferring relative permeability curves from sparse saturation data with an ensemble Kalman method. We represent these curves through a series of positive increments of relative permeability at specified saturation values, which guarantees monotonicity within, and boundedness between, 0 and 1. The proposed method is validated by the inference performances in two synthetic benchmarks designed by SPE and a field-scale model developed by Equinor that includes certain real-field features. The results indicate that the relative permeability curves can be accurately estimated within the saturation intervals having available observations and appropriately extrapolated to the remaining saturations by virtue of the embedded constraints. The predicted well responses are comparable to the ground truths, even though they are not included as the observation. The study demonstrates the feasibility of using ensemble Kalman method to infer relative permeability curves from saturation data, which can aid in the predictions of multiphase flow and reservoir production.

physics.flu-dyn↗

arXiv2023

Unpaired Quad-Path Cycle Consistent Adversarial Networks for Single Image Defogging

Adversarial learning-based image defogging methods have been extensively studied in computer vision due to their remarkable performance. However, most existing methods have limited defogging capabilities for real cases because they are trained on the paired clear and synthesized foggy images of the same scenes. In addition, they have limitations in preserving vivid color and rich textual details in defogging. To address these issues, we develop a novel generative adversarial network, called quad-path cycle consistent adversarial network (QPC-Net), for single image defogging. QPC-Net consists of a Fog2Fogfree block and a Fogfree2Fog block. In each block, there are three learning-based modules, namely, fog removal, color-texture recovery, and fog synthetic, which sequentially compose dual-path that constrain each other to generate high quality images. Specifically, the color-texture recovery model is designed to exploit the self-similarity of texture and structure information by learning the holistic channel-spatial feature correlations between the foggy image with its several derived images. Moreover, in the fog synthetic module, we utilize the atmospheric scattering model to guide it to improve the generative quality by focusing on an atmospheric light optimization with a novel sky segmentation network. Extensive experiments on both synthetic and real-world datasets show that QPC-Net outperforms state-of-the-art defogging methods in terms of quantitative accuracy and subjective visual quality.

cs.CV↗

arXiv2023

Toward Integrated Sensing and Communications in IEEE 802.11bf Wi-Fi Networks

As Wi-Fi becomes ubiquitous in public and private spaces, it becomes natural to leverage its intrinsic ability to sense the surrounding environment to implement groundbreaking wireless sensing applications such as human presence detection, activity recognition, and object tracking. For this reason, the IEEE 802.11bf Task Group is defining the appropriate modifications to existing Wi-Fi standards to enhance sensing capabilities through 802.11-compliant devices. However, the new standard is expected to leave the specific sensing algorithms open to implementation. To fill this gap, this article explores the practical implications of integrating sensing into Wi-Fi networks. We provide an overview of the physical and medium access control layers sensing enablers, together with the application layer perspective. We analyze the impact of communication parameters on sensing performance and detail the main research challenges. To make our evaluation replicable, we pledge to release all of our dataset and code to the community.

cs.NI↗

arXiv2023

Role of electron-phonon coupling in excitonic insulator candidate Ta2NiSe5

Electron-hole bound pairs, or excitons, are common excitations in semiconductors. They can spontaneously form and ``condense'' into a new insulating ground state -- the so-called excitonic insulator -- when the energy of electron-hole Coulomb attraction exceeds the band gap. In the presence of electron-phonon coupling, a periodic lattice distortion often concomitantly occurs with this exciton condensation. However, similar structural transition can also be induced by electron-phonon coupling itself, therefore hindering the clean identification of bulk excitonic insulators based on reductionistic reasoning (e.g. which instability is the ``driving force'' of the phase transition). Using high-resolution synchrotron x-ray diffraction and angle-resolved photoemission spectroscopy techniques, we identify key electron-phonon coupling effects in a leading excitonic insulator candidate Ta2NiSe5. These include an extensive unidirectional lattice fluctuation and an electronic pseudogap in the normal state, as well as a negative electronic compressibility in the charge-doped broken-symmetry state. In combination with first principles and model calculations, we determine a minimal lattice model and the corresponding interaction parameters that capture the experimental observations. More importantly, we show how the Coulomb and electron-phonon coupling effects can be separated on the level of lattice model, and demonstrate a general framework beyond the reductionist approach in the investigation of correlated systems with intertwined orders.

cond-mat.str-el↗

arXiv2023

Adversary-Aware Partial label learning with Label distillation

To ensure that the data collected from human subjects is entrusted with a secret, rival labels are introduced to conceal the information provided by the participants on purpose. The corresponding learning task can be formulated as a noisy partial-label learning problem. However, conventional partial-label learning (PLL) methods are still vulnerable to the high ratio of noisy partial labels, especially in a large labelling space. To learn a more robust model, we present Adversary-Aware Partial Label Learning and introduce the $\textit{rival}$, a set of noisy labels, to the collection of candidate labels for each instance. By introducing the rival label, the predictive distribution of PLL is factorised such that a handy predictive label is achieved with less uncertainty coming from the transition matrix, assuming the rival generation process is known. Nonetheless, the predictive accuracy is still insufficient to produce an sufficiently accurate positive sample set to leverage the clustering effect of the contrastive loss function. Moreover, the inclusion of rivals also brings an inconsistency issue for the classifier and risk function due to the intractability of the transition matrix. Consequently, an adversarial teacher within momentum (ATM) disambiguation algorithm is proposed to cope with the situation, allowing us to obtain a provably consistent classifier and risk function. In addition, our method has shown high resiliency to the choice of the label noise transition matrix. Extensive experiments demonstrate that our method achieves promising results on the CIFAR10, CIFAR100 and CUB200 datasets.

cs.LG↗