arXiv subjects

Yi Li

Publications and source records attributed to Yi Li.

At least 343 records · Page 19Linked to original sources

arXiv2023

Prefix-diffusion: A Lightweight Diffusion Model for Diverse Image Captioning

While impressive performance has been achieved in image captioning, the limited diversity of the generated captions and the large parameter scale remain major barriers to the real-word application of these systems. In this work, we propose a lightweight image captioning network in combination with continuous diffusion, called Prefix-diffusion. To achieve diversity, we design an efficient method that injects prefix image embeddings into the denoising process of the diffusion model. In order to reduce trainable parameters, we employ a pre-trained model to extract image features and further design an extra mapping network. Prefix-diffusion is able to generate diverse captions with relatively less parameters, while maintaining the fluency and relevance of the captions benefiting from the generative capabilities of the diffusion model. Our work paves the way for scaling up diffusion models for image captioning, and achieves promising performance compared with recent approaches.

cs.CV↗

arXiv2023

Evaluation of transplant benefits with the U.S. Scientific Registry of Transplant Recipients by semiparametric regression of mean residual life

Kidney transplantation is the most effective renal replacement therapy for end stage renal disease patients. With the severe shortage of kidney supplies and for the clinical effectiveness of transplantation, patient's life expectancy post transplantation is used to prioritize patients for transplantation; however, severe comorbidity conditions and old age are the most dominant factors that negatively impact post-transplantation life expectancy, effectively precluding sick or old patients from receiving transplants. It would be crucial to design objective measures to quantify the transplantation benefit by comparing the mean residual life with and without a transplant, after adjusting for comorbidity and demographic conditions. To address this urgent need, we propose a new class of semiparametric covariate-dependent mean residual life models. Our method estimates covariate effects semiparametrically efficiently and the mean residual life function nonparametrically, enabling us to predict the residual life increment potential for any given patient. Our method potentially leads to a more fair system that prioritizes patients who would have the largest residual life gains. Our analysis of the kidney transplant data from the U.S. Scientific Registry of Transplant Recipients also suggests that a single index of covariates summarize well the impacts of multiple covariates, which may facilitate interpretations of each covariate's effect. Our subgroup analysis further disclosed inequalities in survival gains across groups defined by race, gender and insurance type (reflecting socioeconomic status).

stat.ME↗

arXiv2023

Kernel Cox partially linear regression: building predictive models for cancer patients' survival

Wide heterogeneity exists in cancer patients' survival, ranging from a few months to several decades. To accurately predict clinical outcomes, it is vital to build an accurate predictive model that relates patients' molecular profiles with patients' survival. With complex relationships between survival and high-dimensional molecular predictors, it is challenging to conduct non-parametric modeling and irrelevant predictors removing simultaneously. In this paper, we build a kernel Cox proportional hazards semi-parametric model and propose a novel regularized garrotized kernel machine (RegGKM) method to fit the model. We use the kernel machine method to describe the complex relationship between survival and predictors, while automatically removing irrelevant parametric and non-parametric predictors through a LASSO penalty. An efficient high-dimensional algorithm is developed for the proposed method. Comparison with other competing methods in simulation shows that the proposed method always has better predictive accuracy. We apply this method to analyze a multiple myeloma dataset and predict patients' death burden based on their gene expressions. Our results can help classify patients into groups with different death risks, facilitating treatment for better clinical outcomes.

stat.ML↗

arXiv2023

Penalized Deep Partially Linear Cox Models with Application to CT Scans of Lung Cancer Patients

Lung cancer is a leading cause of cancer mortality globally, highlighting the importance of understanding its mortality risks to design effective patient-centered therapies. The National Lung Screening Trial (NLST) employed computed tomography texture analysis, which provides objective measurements of texture patterns on CT scans, to quantify the mortality risks of lung cancer patients. Partially linear Cox models have gained popularity for survival analysis by dissecting the hazard function into parametric and nonparametric components, allowing for the effective incorporation of both well-established risk factors (such as age and clinical variables) and emerging risk factors (e.g., image features) within a unified framework. However, when the dimension of parametric components exceeds the sample size, the task of model fitting becomes formidable, while nonparametric modeling grapples with the curse of dimensionality. We propose a novel Penalized Deep Partially Linear Cox Model (Penalized DPLC), which incorporates the SCAD penalty to select important texture features and employs a deep neural network to estimate the nonparametric component of the model. We prove the convergence and asymptotic properties of the estimator and compare it to other methods through extensive simulation studies, evaluating its performance in risk prediction and feature selection. The proposed method is applied to the NLST study dataset to uncover the effects of key clinical and imaging risk factors on patients' survival. Our findings provide valuable insights into the relationship between these factors and survival outcomes.

stat.ML↗

arXiv2023

Ferromagnetic interlayer coupling in CrSBr crystals irradiated by ions

Layered magnetic materials are becoming a major platform for future spin-based applications. Particularly the air-stable van der Waals compound CrSBr is attracting considerable interest due to its prominent magneto-transport and magneto-optical properties. In this work, we observe a transition from antiferromagnetic to ferromagnetic behavior in CrSBr crystals exposed to high-energy, non-magnetic ions. Already at moderate fluences, ion irradiation induces a remanent magnetization with hysteresis adapting to the easy-axis anisotropy of the pristine magnetic order up to a critical temperature of 110 K. Structure analysis of the irradiated crystals in conjunction with density functional theory calculations suggest that the displacement of constituent atoms due to collisions with ions and the formation of interstitials favors ferromagnetic order between the layers.

cond-mat.mtrl-sci↗

arXiv2023

From Plastic Waste to Treasure: Selective Upcycling through Catalytic Technologies

The huge amount of plastic wastes has become a pressing global environmental problem, leading to severe environmental pollution and resource depletion through conventional downcycling technologies like incineration and landfilling. In contrast, selective upcycling of various plastics offers a promising solution for converting waste plastics into valuable products. This review provides a comprehensive overview of the recent advancements in innovative catalytic technologies, including thermocatalysis, electrocatalysis, and photocatalysis. Special emphasis is placed on elucidating the reaction mechanisms, activating designated chemical bonds for high selectivity, and elaborating the above techniques in terms of reaction conditions and products. Finally, the application prospects and future development trends in plastic catalysis are discussed, providing valuable insights for realizing a sustainable circular plastic economy.

physics.chem-ph↗

arXiv2023

Programmable Real-Time Magnon Interference in Two Remotely Coupled Magnonic Resonators

Magnon interference is a signature of coherent magnon interactions for coherent information processing. In this work, we demonstrate programmable real-time magnon interference, with examples of nearly perfect constructive and destructive interference, between two remotely coupled yttrium iron garnet spheres mediated by a coplanar superconducting resonator. Exciting one of the coupled resonators by injecting single- and double-microwave pulse leads to the coherent energy exchange between the remote magnonic resonators and allows us to realize a programmable magnon interference that can define an arbitrary state of coupled magnon oscillation. The demonstration of time-domain coherent control of remotely coupled magnon dynamics offers new avenues for advancing coherent information processing with circuit-integrated hybrid magnonic networks.

cond-mat.mes-hall↗

arXiv2023

CLIPN for Zero-Shot OOD Detection: Teaching CLIP to Say No

Out-of-distribution (OOD) detection refers to training the model on an in-distribution (ID) dataset to classify whether the input images come from unknown classes. Considerable effort has been invested in designing various OOD detection methods based on either convolutional neural networks or transformers. However, zero-shot OOD detection methods driven by CLIP, which only require class names for ID, have received less attention. This paper presents a novel method, namely CLIP saying no (CLIPN), which empowers the logic of saying no within CLIP. Our key motivation is to equip CLIP with the capability of distinguishing OOD and ID samples using positive-semantic prompts and negation-semantic prompts. Specifically, we design a novel learnable no prompt and a no text encoder to capture negation semantics within images. Subsequently, we introduce two loss functions: the image-text binary-opposite loss and the text semantic-opposite loss, which we use to teach CLIPN to associate images with no prompts, thereby enabling it to identify unknown samples. Furthermore, we propose two threshold-free inference algorithms to perform OOD detection by utilizing negation semantics from no prompts and the text encoder. Experimental results on 9 benchmark datasets (3 ID datasets and 6 OOD datasets) for the OOD detection task demonstrate that CLIPN, based on ViT-B-16, outperforms 7 well-used algorithms by at least 2.34% and 11.64% in terms of AUROC and FPR95 for zero-shot OOD detection on ImageNet-1K. Our CLIPN can serve as a solid foundation for effectively leveraging CLIP in downstream OOD tasks. The code is available on https://github.com/xmed-lab/CLIPN.

cs.CV↗

arXiv2023

Application-Level Validation of Accelerator Designs Using a Formal Software/Hardware Interface

Ideally, accelerator development should be as easy as software development. Several recent design languages/tools are working toward this goal, but actually testing early designs on real applications end-to-end remains prohibitively difficult due to the costs of building specialized compiler and simulator support. We propose a new first-in-class, mostly automated methodology termed "3LA" to enable end-to-end testing of prototype accelerator designs on unmodified source applications. A key contribution of 3LA is the use of a formal software/hardware interface that specifies an accelerator's operations and their semantics. Specifically, we leverage the Instruction-Level Abstraction (ILA) formal specification for accelerators that has been successfully used thus far for accelerator implementation verification. We show how the ILA for accelerators serves as a software/hardware interface, similar to the Instruction Set Architecture (ISA) for processors, that can be used for automated development of compilers and instruction-level simulators. Another key contribution of this work is to show how ILA-based accelerator semantics enables extending recent work on equality saturation to auto-generate basic compiler support for prototype accelerators in a technique we term "flexible matching." By combining flexible matching with simulators auto-generated from ILA specifications, our approach enables end-to-end evaluation with modest engineering effort. We detail several case studies of 3LA, which uncovered an unknown flaw in a recently published accelerator and facilitated its fix.

cs.AR↗

arXiv2023

Existence of Solutions to a Class of Kazdan-Warner Equations on Finite Graphs

Let $G=(V, E)$ be a connected finite graph, $h$ be a positive function on $V$ and $λ_{1}(V)$ be the first non-zero eigenvalue of $-Δ$. For any given finite measure $μ$ on $V$, define functionals \begin{eqnarray*} J_{ β}(u)&=&\frac{1}{2}\int_{V}|\nabla u|^{2}d μ-β\log\int_{V}he^{u}d μ, J_{ α,β}(u)&=&\frac{1}{2}\int_{V}\left(|\nabla u|^{2}- αu^{2}\right) d μ-β\log\int_{V}he^{u}d μ\end{eqnarray*} on the functional space $$ {\bf H}= \left\{ u\in{\bf W}^{1,2}(V) \Bigg| \int_{V}u\!\ dμ=0 \right\}. $$ For any $β\in \mathbb{R}$, we show that $J_{ β}(u)$ has a minimizer $u\in{\bf H}$, and then, based on variational principle, the Kazdan-Warner equation $$ Δu=-\frac{βhe^{u}}{\displaystyle{\int_{V}he^{u}d μ}}+\frac{β}{\text{Vol}(V)} $$ has a solution in ${\bf H}$. If $α< λ_{1}(V)$, then for any $β\in \mathbb{R} , J_{ α,β}(u)$ has a minimizer in ${\bf H}$, thus the Kazdan-Warner equation $$ Δu+α\!\ u=-\frac{βhe^{u}}{\displaystyle{\int_{V}he^{u}d μ}}+\frac{β}{\text{Vol}(V)} $$ has a solution in ${\bf H}$. If $α> λ_{1}(V)$, then for any $β\in \mathbb{R}$, $\displaystyle{\inf_{u\in{\bf H}} J_{ α,β}(u) =- \infty}$. When $α=λ_{1}(V)$, the situation becomes complicated: if $β=0$, the corresponding equation is $-Δu=λ_{1}(V)u$ which has a solution in ${\bf H}$ obviously; if $β>0$, then $\displaystyle{\inf_{u\in {\bf H}} J_{α,β}(u) =- \infty}$; if $β<0$, $J_{ α,β}(u)$ has a minimizer in some subspace of ${\bf H}$. Moreover, we consider the same problem where higher eigenvalues are involved.

math.DG↗

arXiv2023

Context-Aware Pseudo-Label Refinement for Source-Free Domain Adaptive Fundus Image Segmentation

In the domain adaptation problem, source data may be unavailable to the target client side due to privacy or intellectual property issues. Source-free unsupervised domain adaptation (SF-UDA) aims at adapting a model trained on the source side to align the target distribution with only the source model and unlabeled target data. The source model usually produces noisy and context-inconsistent pseudo-labels on the target domain, i.e., neighbouring regions that have a similar visual appearance are annotated with different pseudo-labels. This observation motivates us to refine pseudo-labels with context relations. Another observation is that features of the same class tend to form a cluster despite the domain gap, which implies context relations can be readily calculated from feature distances. To this end, we propose a context-aware pseudo-label refinement method for SF-UDA. Specifically, a context-similarity learning module is developed to learn context relations. Next, pseudo-label revision is designed utilizing the learned context relations. Further, we propose calibrating the revised pseudo-labels to compensate for wrong revision caused by inaccurate context relations. Additionally, we adopt a pixel-level and class-level denoising scheme to select reliable pseudo-labels for domain adaptation. Experiments on cross-domain fundus images indicate that our approach yields the state-of-the-art results. Code is available at https://github.com/xmed-lab/CPR.

cs.CV↗

arXiv2023

A Camera free fiber speckle wavemeter

Recovering the wavelength from disordered speckle patterns has become an exciting prospect as a wavelength measurement method due to its high resolution and simple design. In previous studies, panel cameras have been used to detect the subtle differences between speckle patterns. However, the volume, bandwidth, sensitivity, and cost (in non-visible bands) associated with panel cameras have hindered their utility in broader applications, especially in high speed and low-cost measurements. In this work, we broke the limitations imposed by panel cameras by using a quadrant detector (QD) to capture the speckle images. In the scheme of QD detection, speckle images are directly filtered by convolution, where the kernel is equal to one quarter of a speckle pattern. First, we proposed an up-sampling algorithm to pre-process the QD data. Then a new convolution neural network (CNN) based algorithm, shallow residual network (SRN), was proposed to train the up-sampled images. The experimental results show that a resolution of 4 fm (~ 0.5 MHz) was achieved at 1550nm with an updating speed of ~ 1 kHz. More importantly, the SRN shows excellent robustness. The wavelength can be precisely reconstructed from raw QD data without any averaging, even where there exists apparent noise. The low-cost, simple structure, high speed and robustness of this design promote the speckle-based wavemeter to the industrial grade. In addition, without the restriction of panel cameras, it is believed that this wavemeter opens new routes in many other fields, such as distributed optical fiber sensors, optical communications, and laser frequency stabilization.

physics.ins-det↗

arXiv2023

Tunable Magnon-Photon Coupling by Magnon Band Gap in a Layered Hybrid Perovskite Antiferromagnet

Tunability of coherent coupling between fundamental excitations is an important prerequisite for expanding their functionality in hybrid quantum systems. In hybrid magnonics, the dipolar interaction between magnon and photon usually persists and cannot be switched off. Here, we demonstrate this capability by coupling a superconducting resonator to a layered hybrid perovskite antiferromagnet, which exhibits a magnon band gap due to its intrinsic Dzyaloshinskii-Moriya interaction. The pronounced temperature sensitivity of the magnon band gap location allows us to set the photon mode within the gap and to disable magnon-photon hybridization. When the resonator mode falls into the magnon band gap, the resonator damping rate increases due to the nonzero coupling to the detuned magnon mode. This phenomena can be used to quantify the magnon band gap using an analytical model. Our work brings new opportunities in controlling coherent information processing with quantum properties in complex magnetic materials.

cond-mat.mtrl-sci↗

arXiv2023

Morphology-inspired Unsupervised Gland Segmentation via Selective Semantic Grouping

Designing deep learning algorithms for gland segmentation is crucial for automatic cancer diagnosis and prognosis, yet the expensive annotation cost hinders the development and application of this technology. In this paper, we make a first attempt to explore a deep learning method for unsupervised gland segmentation, where no manual annotations are required. Existing unsupervised semantic segmentation methods encounter a huge challenge on gland images: They either over-segment a gland into many fractions or under-segment the gland regions by confusing many of them with the background. To overcome this challenge, our key insight is to introduce an empirical cue about gland morphology as extra knowledge to guide the segmentation process. To this end, we propose a novel Morphology-inspired method via Selective Semantic Grouping. We first leverage the empirical cue to selectively mine out proposals for gland sub-regions with variant appearances. Then, a Morphology-aware Semantic Grouping module is employed to summarize the overall information about the gland by explicitly grouping the semantics of its sub-region proposals. In this way, the final segmentation network could learn comprehensive knowledge about glands and produce well-delineated, complete predictions. We conduct experiments on GlaS dataset and CRAG dataset. Our method exceeds the second-best counterpart over 10.56% at mIOU.

cs.CV↗

arXiv2023

Nematic spin correlations pervading the phase diagram of FeSe$_{1-x}$S$_{x}$

We use resonant inelastic X-ray scattering (RIXS) at the Fe-L$_3$ edge to study the spin excitations of uniaxial-strained and unstrained FeSe$_{1-x}$S$_{x}$ ($0\leq x\leq0.21$) samples. The measurements on unstrained samples reveal dispersive spin excitations in all doping levels, which show only minor doping dependence in energy dispersion, lifetime, and intensity, indicating that high-energy spin excitations are only marginally affected by sulfur doping. RIXS measurements on uniaxial-strained samples reveal that the high-energy spin-excitation anisotropy observed previously in FeSe is also present in the doping range $0< x\leq0.21$ of FeSe$_{1-x}$S$_{x}$. The spin-excitation anisotropy persists to a high temperature up to $T>200$ K in $x=0.18$ and reaches a maximum around the nematic quantum critical doping ($x_c\approx0.17$). Since the spin-excitation anisotropy directly reflects the existence of nematic spin correlations, our results indicate that high-energy nematic spin correlations pervade the regime of nematicity in the phase diagram and are enhanced by the nematic quantum criticality. These results emphasize the essential role of spin fluctuations in driving electronic nematicity and open the door for uniaxial strain tuning of spin excitations in quantum materials hosting strong magnetoelastic coupling and electronic nematicity.

cond-mat.str-el↗

arXiv2023

Sparsity-Dimension Trade-Offs for Oblivious Subspace Embeddings

An oblivious subspace embedding (OSE), characterized by parameters $m,n,d,ε,δ$, is a random matrix $Π\in \mathbb{R}^{m\times n}$ such that for any $d$-dimensional subspace $T\subseteq \mathbb{R}^n$, $\Pr_Π[\forall x\in T, (1-ε)\|x\|_2 \leq \|Πx\|_2\leq (1+ε)\|x\|_2] \geq 1-δ$. When an OSE has $s\le 1/2.001ε$ nonzero entries in each column, we show it must hold that $m = Ω\left(d^2/( ε^2s^{1+O(δ)})\right)$, which is the first lower bound with multiplicative factors of $d^2$ and $1/ε$, improving on the previous $Ω\left(d^2/s^{O(δ)}\right)$ lower bound due to Li and Liu (PODS 2022). When an OSE has $s=Ω(\log(1/ε)/ε)$ nonzero entries in each column, we show it must hold that $m = Ω\left((d/ε)^{1+1/4.001εs}/s^{O(δ)}\right)$, which is the first lower bound with multiplicative factors of $d$ and $1/ε$, improving on the previous $Ω\left(d^{1+1/(16εs+4)}\right)$ lower bound due to Nelson and Nguyen (ICALP 2014). This second result is a special case of a more general trade-off among $d,ε,s,δ$ and $m$.

cs.DS↗

arXiv2023

$\ell_p$-Regression in the Arbitrary Partition Model of Communication

We consider the randomized communication complexity of the distributed $\ell_p$-regression problem in the coordinator model, for $p\in (0,2]$. In this problem, there is a coordinator and $s$ servers. The $i$-th server receives $A^i\in\{-M, -M+1, \ldots, M\}^{n\times d}$ and $b^i\in\{-M, -M+1, \ldots, M\}^n$ and the coordinator would like to find a $(1+ε)$-approximate solution to $\min_{x\in\mathbb{R}^n} \|(\sum_i A^i)x - (\sum_i b^i)\|_p$. Here $M \leq \mathrm{poly}(nd)$ for convenience. This model, where the data is additively shared across servers, is commonly referred to as the arbitrary partition model. We obtain significantly improved bounds for this problem. For $p = 2$, i.e., least squares regression, we give the first optimal bound of $\tildeΘ(sd^2 + sd/ε)$ bits. For $p \in (1,2)$,we obtain an $\tilde{O}(sd^2/ε+ sd/\mathrm{poly}(ε))$ upper bound. Notably, for $d$ sufficiently large, our leading order term only depends linearly on $1/ε$ rather than quadratically. We also show communication lower bounds of $Ω(sd^2 + sd/ε^2)$ for $p\in (0,1]$ and $Ω(sd^2 + sd/ε)$ for $p\in (1,2]$. Our bounds considerably improve previous bounds due to (Woodruff et al. COLT, 2013) and (Vempala et al., SODA, 2020).

cs.DS↗

arXiv2023

Fragile superconductivity in a Dirac metal

Studying superconductivity in Dirac semimetals is an important step in understanding quantum matter with topologically non-trivial order parameters. We report on the properties of the superconducting phase in single crystals of the Dirac material LaCuSb2 prepared by the self-flux method. We find that chemical and hydrostatic pressure drastically suppress the superconducting transition. Furthermore, due to large Fermi surface anisotropy, magnetization and muon spin relaxation measurements reveal Type-II superconductivity for applied magnetic fields along the $a$-axis, and Type-I superconductivity for fields along the $c$-axis. Specific heat confirms the bulk nature of the transition, and its deviation from single-gap $s$-wave BCS theory suggests multigap superconductivity. Our tight-binding model points to an anisotropic gap function arising from the spin-orbital texture near the Dirac nodes, providing an explanation for the appearance of an anomaly in specific heat well below $T_c$. Given the existence of superconductivity in a material harboring Dirac fermions, LaCuSb2 proves an interesting material candidate in the search for topological superconductivity.

cond-mat.supr-con↗