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Yuchen Guo

Publications and source records attributed to Yuchen Guo.

At least 73 records · Page 4Linked to original sources
arXiv2023

Triggering Boundary Phase Transitions through Bulk Measurements in 2D Cluster States

We investigate the phase diagram at the boundary of an infinite two-dimensional cluster state subject to bulk measurements using tensor network methods. The state is subjected to uniform measurements $M = \cosθZ+\sinθX$ on the lower boundary qubits and in all bulk qubits. Our results show that the boundary of the system exhibits volume-law entanglement at the measurement angle $θ= π/2$ and area-law entanglement for any $θ< π/2$. Within the area-law phase, a phase transition occurs at $θ_c=1.371$. The phase with $θ\in(θ_c,π/2)$ is characterized by a noninjective matrix product state, which cannot be realized as the unique ground state of a one-dimensional local, gapped Hamiltonian. Instead, it resembles a cat state with spontaneous symmetry breaking. These findings demonstrate that the phase diagram of the boundary of a two-dimensional system can be more intricate than that of a standard one-dimensional system.

quant-ph↗
arXiv2023

Composite Quantum Phases in Non-Hermitian Systems

Non-Hermitian systems have attracted considerable interest in recent years owing to their unique topological properties that are absent in Hermitian systems. While such properties have been thoroughly characterized in free fermion models, they remain an open question for interacting bosonic systems. In this work, we present a precise definition of quantum phases for non-Hermitian systems and propose a new family of phases referred to as composite quantum phases. We demonstrate the existence of these phases in a one-dimensional spin-$1$ system and show their robustness against perturbations through numerical simulations. Furthermore, we investigate the phase diagram of our model, indicating the extensive presence of these new phases in non-Hermitian systems. Our work establishes a new framework for studying and constructing quantum phases in non-Hermitian interacting systems, revealing exciting possibilities beyond the single-particle picture.

quant-ph↗
arXiv2023

Modeling Orders of User Behaviors via Differentiable Sorting: A Multi-task Framework to Predicting User Post-click Conversion

User post-click conversion prediction is of high interest to researchers and developers. Recent studies employ multi-task learning to tackle the selection bias and data sparsity problem, two severe challenges in post-click behavior prediction, by incorporating click data. However, prior works mainly focused on pointwise learning and the orders of labels (i.e., click and post-click) are not well explored, which naturally poses a listwise learning problem. Inspired by recent advances on differentiable sorting, in this paper, we propose a novel multi-task framework that leverages orders of user behaviors to predict user post-click conversion in an end-to-end approach. Specifically, we define an aggregation operator to combine predicted outputs of different tasks to a unified score, then we use the computed scores to model the label relations via differentiable sorting. Extensive experiments on public and industrial datasets show the superiority of our proposed model against competitive baselines.

cs.IR↗
arXiv2023

Construction of Non-Hermitian Parent Hamiltonian from Matrix Product States

There are various research strategies used for non-Hermitian systems, which typically involve introducing non-Hermitian terms to pre-existing Hermitian Hamiltonians. It can be challenging to directly design non-Hermitian many-body models that exhibit unique features not found in Hermitian systems. In this Letter, we propose a new method to construct non-Hermitian many-body systems by generalizing the parent Hamiltonian method into non-Hermitian regimes. This allows us to build a local Hamiltonian using given matrix product states as its left and right ground states. We demonstrate this method by constructing a non-Hermitian spin-$1$ model from the asymmetric Affleck-Kennedy-Lieb-Tasaki (AKLT) state, which preserves both chiral order and symmetry-protected topological order. Our approach opens up a new paradigm for systematically constructing and studying non-Hermitian many-body systems, providing guiding principles to explore new properties and phenomena in non-Hermitian physics.

quant-ph↗
arXiv2023

Consolidator: Mergeable Adapter with Grouped Connections for Visual Adaptation

Recently, transformers have shown strong ability as visual feature extractors, surpassing traditional convolution-based models in various scenarios. However, the success of vision transformers largely owes to their capacity to accommodate numerous parameters. As a result, new challenges for adapting large models to downstream tasks arise. On the one hand, classic fine-tuning tunes all parameters in a huge model for every task and thus easily falls into overfitting, leading to inferior performance. On the other hand, on resource-limited devices, fine-tuning stores a full copy of parameters and thus is usually impracticable for the shortage of storage space. However, few works have focused on how to efficiently and effectively transfer knowledge in a vision transformer. Existing methods did not dive into the properties of visual features, leading to inferior performance. Moreover, some of them bring heavy inference cost though benefiting storage. To tackle these problems, we propose consolidator to modify the pre-trained model with the addition of a small set of tunable parameters to temporarily store the task-specific knowledge while freezing the backbone model. Motivated by the success of group-wise convolution, we adopt grouped connections across the features extracted by fully connected layers to construct tunable parts in a consolidator. To further enhance the model's capacity to transfer knowledge under a constrained storage budget and keep inference efficient, we consolidate the parameters in two stages: 1. between adaptation and storage, and 2. between loading and inference. On a series of downstream visual tasks, our consolidator can reach up to 7.56 better accuracy than full fine-tuning with merely 0.35% parameters, and outperform state-of-the-art parameter-efficient tuning methods by a clear margin. Code is available at https://github.com/beyondhtx/Consolidator.

cs.CV↗
arXiv2023

Hi Sheldon! Creating Deep Personalized Characters from TV Shows

Imagine an interesting multimodal interactive scenario that you can see, hear, and chat with an AI-generated digital character, who is capable of behaving like Sheldon from The Big Bang Theory, as a DEEP copy from appearance to personality. Towards this fantastic multimodal chatting scenario, we propose a novel task, named Deep Personalized Character Creation (DPCC): creating multimodal chat personalized characters from multimodal data such as TV shows. Specifically, given a single- or multi-modality input (text, audio, video), the goal of DPCC is to generate a multi-modality (text, audio, video) response, which should be well-matched the personality of a specific character such as Sheldon, and of high quality as well. To support this novel task, we further collect a character centric multimodal dialogue dataset, named Deep Personalized Character Dataset (DPCD), from TV shows. DPCD contains character-specific multimodal dialogue data of ~10k utterances and ~6 hours of audio/video per character, which is around 10 times larger compared to existing related datasets.On DPCD, we present a baseline method for the DPCC task and create 5 Deep personalized digital Characters (DeepCharacters) from Big Bang TV Shows. We conduct both subjective and objective experiments to evaluate the multimodal response from DeepCharacters in terms of characterization and quality. The results demonstrates that, on our collected DPCD dataset, the proposed baseline can create personalized digital characters for generating multimodal response.Our collected DPCD dataset, the code of data collection and our baseline will be published soon.

cs.CL↗
arXiv2023

Introducing the Texas Euclid Survey for Lyman Alpha (TESLA) Survey: Initial Study Correlating Galaxy Properties to Lyman-Alpha Emission

We present the Texas Euclid Survey for Lyman-Alpha (TESLA), a spectroscopic survey in the 10 square degree of the Euclid North Ecliptic Pole (NEP) field. Using TESLA, we study how the physical properties of Lyman-alpha emitters (LAEs) correlate with Lyman-alpha emission to understand the escape of Lyman alpha from galaxies at redshifts 2 -- 3.5. We present an analysis of 43 LAEs performed in the NEP field using early data from the TESLA survey. We use Subaru Hyper Suprime-Cam imaging in the grizy-bands, Spitzer/IRAC channels 1 and 2 from the Hawaii 20 square degree (H20) survey and spectra acquired by the Visible Integral-Field Replicable Unit Spectrograph (VIRUS) on the Hobby-Eberly Telescope. We perform spectral energy distribution (SED) fitting to compute the galaxy properties of 43 LAEs, and study correlations between stellar mass, star formation rate (SFR), and dust, to the Lyman-alpha rest-frame equivalent widths (EW). We uncover marginal (1 sigma significance) correlations between stellar mass and Lyman-alpha EW, and star formation rate (SFR) and Lyman-alpha EW, with a Spearman correlation coefficient of -0.$34_{-.14}^{+.17}$ and -0.$37_{-.14}^{+.16}$ respectively. We show that the Lyman-alpha distribution of the 43 LAEs is consistent with being drawn from an exponential distribution with an e-folding scale of 150 Angstrom. Once complete the TESLA survey will enable the study of ~ thousands of LAEs to explore correlations between galaxy properties and Lyman-alpha EW. The large sample size will allow the construction of a predictive model for the Lyman-alpha EW as a function of SED-derived galaxy properties, which could be used to improve Lyman-alpha based constraints on reionization.

astro-ph.GA↗
arXiv2023

CEERS Key Paper V: A triality on the nature of HST-dark galaxies

The new capabilities that JWST offers in the near- and mid-infrared (IR) are used to investigate in unprecedented detail the nature of optical/near-IR faint, mid-IR bright sources, HST-dark galaxies among them. We gather JWST data from the CEERS survey in the EGS, jointly with HST data, and analyze spatially resolved optical-to-mid-IR spectral energy distributions (SEDs) to estimate both photometric redshifts in 2 dimensions and stellar populations properties in a pixel-by-pixel basis. We select 138 galaxies with F150W-F356W>1.5 mag, F356W<27.5 mag. The nature of these sources is threefold: (1) 71% are dusty star-forming galaxies at 2 100 Gyr^-1); (2) 18% are quiescent/dormant (i.e., subject to reignition and rejuvenation) galaxies at 3<z<5, masses log M/M_sun~10 and post-starburst stellar mass-weighted ages (0.5-1 Gyr); and (3) 11% are strong young starbursts with indications of high-EW emission lines (typically, [OIII]+Hbeta) at 6<z<7 and log M/M_sun~9.5. The sample is dominated by disk-like galaxies with a remarkable compactness for XELG-z6 (effective radii smaller than 0.4 kpc). Large attenuations in SFGs, 2<A(V)<5 mag, are found within 1.5 times the effective radius, approximately 2 kpc, while QGs present A(V)~0.2 mag. Our SED-fitting technique reproduces the expected dust emission luminosities of IR-bright and sub-millimeter galaxies. This study implies high levels of star formation activity between z~20 and z~10, where virtually 100% of our galaxies had already formed 10^8 M_sun of their stellar content, 60% of them had assembled 10^9 M_sun, and 10% up to 10^10 M_sun (in situ or ex situ). (abridged)

astro-ph.GA↗
arXiv2023

CEERS: Spatially Resolved UV and mid-IR Star Formation in Galaxies at 0.2 < z < 2.5: The Picture from the Hubble and James Webb Space Telescopes

We present the mid-IR (MIR) morphologies for 64 star-forming galaxies at $0.2 10^{9}~M_\odot}$ using JWST MIRI observations from the Cosmic Evolution Early Release Science survey (CEERS). The MIRI bands span the MIR (7.7--21~$μ$m), enabling us to measure the effective radii ($R_{\rm{eff}}$) and Sérsic indexes of these SFGs at rest-frame 6.2 and 7.7 $μ$m, which contains strong emission from Polycyclic aromatic hydrocarbon (PAH) features, a well-established tracer of star formation in galaxies. We define a ``PAH-band'' as the MIRI bandpass that contains these features at the redshift of the galaxy. We then compare the galaxy morphologies in the PAH-bands to those in rest-frame Near-UV (NUV) using HST ACS/F435W or ACS/F606W and optical/near-IR using HST WFC3/F160W imaging from UVCANDELS and CANDELS, where the NUV-band and F160W trace the profile of (unobscured) massive stars and the stellar continuum, respectively. The $R_{\rm{eff}}$ of galaxies in the PAH-band are slightly smaller ($\sim$10\%) than those in F160W for galaxies with $\rm{M_*\gtrsim10^{9.5}~M_\odot}$ at $z\leq1.2$, but the PAH-band and F160W have a similar fractions of light within 1 kpc. In contrast, the $R_{\rm{eff}}$ of galaxies in the NUV-band are larger, with lower fractions of light within 1 kpc compared to F160W for galaxies at $z\leq1.2$. Using the MIRI data to estimate the $\rm{SFR_{\rm{IR}}}$ surface density, we find the correlation between the $\rm{SFR_{\rm{IR}}}$ surface density and stellar mass has a steeper slope than that of the $\rm{SFR_{\rm{UV}}}$ surface density and stellar mass, suggesting more massive galaxies having increasing amounts of obscured fraction of star formation in their inner regions. This paper demonstrates how the high-angular resolution data from JWST/MIRI can reveal new information about the morphology of obscured-star formation.

astro-ph.GA↗
arXiv2023

Box-Level Active Detection

Active learning selects informative samples for annotation within budget, which has proven efficient recently on object detection. However, the widely used active detection benchmarks conduct image-level evaluation, which is unrealistic in human workload estimation and biased towards crowded images. Furthermore, existing methods still perform image-level annotation, but equally scoring all targets within the same image incurs waste of budget and redundant labels. Having revealed above problems and limitations, we introduce a box-level active detection framework that controls a box-based budget per cycle, prioritizes informative targets and avoids redundancy for fair comparison and efficient application. Under the proposed box-level setting, we devise a novel pipeline, namely Complementary Pseudo Active Strategy (ComPAS). It exploits both human annotations and the model intelligence in a complementary fashion: an efficient input-end committee queries labels for informative objects only; meantime well-learned targets are identified by the model and compensated with pseudo-labels. ComPAS consistently outperforms 10 competitors under 4 settings in a unified codebase. With supervision from labeled data only, it achieves 100% supervised performance of VOC0712 with merely 19% box annotations. On the COCO dataset, it yields up to 4.3% mAP improvement over the second-best method. ComPAS also supports training with the unlabeled pool, where it surpasses 90% COCO supervised performance with 85% label reduction. Our source code is publicly available at https://github.com/lyumengyao/blad.

cs.CV↗
arXiv2023

X-ReID: Cross-Instance Transformer for Identity-Level Person Re-Identification

Currently, most existing person re-identification methods use Instance-Level features, which are extracted only from a single image. However, these Instance-Level features can easily ignore the discriminative information due to the appearance of each identity varies greatly in different images. Thus, it is necessary to exploit Identity-Level features, which can be shared across different images of each identity. In this paper, we propose to promote Instance-Level features to Identity-Level features by employing cross-attention to incorporate information from one image to another of the same identity, thus more unified and discriminative pedestrian information can be obtained. We propose a novel training framework named X-ReID. Specifically, a Cross Intra-Identity Instances module (IntraX) fuses different intra-identity instances to transfer Identity-Level knowledge and make Instance-Level features more compact. A Cross Inter-Identity Instances module (InterX) involves hard positive and hard negative instances to improve the attention response to the same identity instead of different identity, which minimizes intra-identity variation and maximizes inter-identity variation. Extensive experiments on benchmark datasets show the superiority of our method over existing works. Particularly, on the challenging MSMT17, our proposed method gains 1.1% mAP improvements when compared to the second place.

cs.CV↗
arXiv2023

Dusty Starbursts Masquerading as Ultra-high Redshift Galaxies in JWST CEERS Observations

Lyman Break Galaxy (LBG) candidates at z>10 are rapidly being identified in JWST/NIRCam observations. Due to the (redshifted) break produced by neutral hydrogen absorption of rest-frame UV photons, these sources are expected to drop out in the bluer filters while being well detected in redder filters. However, here we show that dust-enshrouded star-forming galaxies at lower redshifts (z<7) may also mimic the near-infrared (near-IR) colors of z>10 LBGs, representing potential contaminants in LBG candidate samples. First, we analyze CEERS-DSFG-1, a NIRCam dropout undetected in the F115W and F150W filters but detected at longer wavelengths. Combining the JWST data with (sub)millimeter constraints, including deep NOEMA interferometric observations, we show that this source is a dusty star-forming galaxy (DSFG) at z~5.1. We also present a tentative 2.6sigma SCUBA-2 detection at 850um around a recently identified z~16 LBG candidate in the same field and show that, if the emission is real and associated with this candidate, the available photometry is consistent with a z~5 dusty galaxy with strong nebular emission lines despite its blue near-IR colors. Further observations on this candidate are imperative to mitigate the low confidence of this tentative submillimeter emission and its positional uncertainty. Our analysis shows that robust (sub)millimeter detections of NIRCam dropout galaxies likely imply z=4-6 redshift solutions, where the observed near-IR break would be the result of a strong rest-frame optical Balmer break combined with high dust attenuation and strong nebular line emission, rather than the rest-frame UV Lyman break. This provides evidence that DSFGs may contaminate searches for ultra high-redshift LBG candidates from JWST observations.

astro-ph.GA↗
arXiv2023

DarkVision: A Benchmark for Low-light Image/Video Perception

Imaging and perception in photon-limited scenarios is necessary for various applications, e.g., night surveillance or photography, high-speed photography, and autonomous driving. In these cases, cameras suffer from low signal-to-noise ratio, which degrades the image quality severely and poses challenges for downstream high-level vision tasks like object detection and recognition. Data-driven methods have achieved enormous success in both image restoration and high-level vision tasks. However, the lack of high-quality benchmark dataset with task-specific accurate annotations for photon-limited images/videos delays the research progress heavily. In this paper, we contribute the first multi-illuminance, multi-camera, and low-light dataset, named DarkVision, serving for both image enhancement and object detection. We provide bright and dark pairs with pixel-wise registration, in which the bright counterpart provides reliable reference for restoration and annotation. The dataset consists of bright-dark pairs of 900 static scenes with objects from 15 categories, and 32 dynamic scenes with 4-category objects. For each scene, images/videos were captured at 5 illuminance levels using three cameras of different grades, and average photons can be reliably estimated from the calibration data for quantitative studies. The static-scene images and dynamic videos respectively contain around 7,344 and 320,667 instances in total. With DarkVision, we established baselines for image/video enhancement and object detection by representative algorithms. To demonstrate an exemplary application of DarkVision, we propose two simple yet effective approaches for improving performance in video enhancement and object detection respectively. We believe DarkVision would advance the state-of-the-arts in both imaging and related computer vision tasks in low-light environment.

cs.CV↗
arXiv2023

Noise effects on purity and quantum entanglement in terms of physical implementability

Quantum decoherence due to imperfect manipulation of quantum devices is a key issue in the noisy intermediate-scale quantum (NISQ) era. Standard analyses in quantum information and quantum computation use error rates to parameterize quantum noise channels. However, there is no explicit relation between the decoherence effect induced by a noise channel and its error rate. In this work, we propose to characterize the decoherence effect of a noise channel by the physical implementability of its inverse, which is a universal parameter quantifying the difficulty to simulate the noise inverse with accessible quantum channels. We establish two concise inequalities connecting the decrease of the state purity and logarithmic negativity after a noise channel to the physical implementability of the noise inverse, which is required to be decomposed as mutually orthogonal unitaries or product channels respectively. Our results are numerically demonstrated on several commonly adopted two-qubit noise models. We believe that these relations contribute to the theoretical research on the entanglement properties of noise channels and provide guiding principles for quantum circuit design.

quant-ph↗
arXiv2022

First Look at z > 1 Bars in the Rest-Frame Near-Infrared with JWST Early CEERS Imaging

Stellar bars are key drivers of secular evolution in galaxies and can be effectively studied using rest-frame near-infrared (NIR) images, which trace the underlying stellar mass and are less impacted by dust and star formation than rest-frame UV or optical images. We leverage the power of {\it{JWST}} CEERS NIRCam images to present the first quantitative identification and characterization of stellar bars at $z>1$ based on rest-frame NIR F444W images of high resolution (~1.3 kpc at z ~ 1-3). We identify stellar bars in these images using quantitative criteria based on ellipse fits. For this pilot study, we present six examples of robustly identified bars at $z>1$ with spectroscopic redshifts, including the two highest redshift bars at ~2.136 and 2.312 quantitatively identified and characterized to date. The stellar bars at $z$ ~ 1.1-2.3 presented in our study have projected semi-major axes of ~2.9-4.3 kpc and projected ellipticities of ~0.41-0.53 in the rest-frame NIR. The barred host galaxies have stellar masses ~ $ 1 \times 10^{10}$ to $2 \times 10^{11}$ $M_{\odot}$, star formation rates of ~ 21-295 $M_{\odot}$ yr$^{-1}$, and several have potential nearby companions. Our finding of bars at $z$ ~1.1-2.3 demonstrates the early onset of such instabilities and supports simulations where bars form early in massive dynamically cold disks. It also suggests that if these bars at lookback times of 8-10 Gyr survive out to present epochs, bar-driven secular processes may operate over a long time and have a significant impact on some galaxies by z ~ 0.

astro-ph.GA↗
arXiv2022

Ground Plane Matters: Picking Up Ground Plane Prior in Monocular 3D Object Detection

The ground plane prior is a very informative geometry clue in monocular 3D object detection (M3OD). However, it has been neglected by most mainstream methods. In this paper, we identify two key factors that limit the applicability of ground plane prior: the projection point localization issue and the ground plane tilt issue. To pick up the ground plane prior for M3OD, we propose a Ground Plane Enhanced Network (GPENet) which resolves both issues at one go. For the projection point localization issue, instead of using the bottom vertices or bottom center of the 3D bounding box (BBox), we leverage the object's ground contact points, which are explicit pixels in the image and easy for the neural network to detect. For the ground plane tilt problem, our GPENet estimates the horizon line in the image and derives a novel mathematical expression to accurately estimate the ground plane equation. An unsupervised vertical edge mining algorithm is also proposed to address the occlusion of the horizon line. Furthermore, we design a novel 3D bounding box deduction method based on a dynamic back projection algorithm, which could take advantage of the accurate contact points and the ground plane equation. Additionally, using only M3OD labels, contact point and horizon line pseudo labels can be easily generated with NO extra data collection and label annotation cost. Extensive experiments on the popular KITTI benchmark show that our GPENet can outperform other methods and achieve state-of-the-art performance, well demonstrating the effectiveness and the superiority of the proposed approach. Moreover, our GPENet works better than other methods in cross-dataset evaluation on the nuScenes dataset. Our code and models will be published.

cs.CV↗
arXiv2022

Quantum error mitigation via matrix product operators

In the era of noisy intermediate-scale quantum (NISQ) devices, the number of controllable hardware qubits is insufficient to implement quantum error correction (QEC). As an alternative, quantum error mitigation (QEM) can suppress errors in measurement results via repeated experiments and postprocessing of data. Typical techniques for error mitigation, e.g., the quasi-probability decomposition method, ignore correlated errors between different gates. Here, we introduce a QEM method based on the matrix product operator (MPO) representation of a quantum circuit that can characterize the noise channel with polynomial complexity. Our technique is demonstrated on a $\rm{depth}=20$ fully parallel quantum circuit of up to $N_q=20$ qubits undergoing local and global noise. The circuit error is reduced by several times with only a small bond dimension $D^{\prime} = 1$ for the noise channel. The MPO representation increases the accuracy of modeling noise without consuming more experimental resources, which improves the QEM performance and broadens its scope of application. Our method is hopeful of being applied to circuits in higher dimensions with more qubits and deeper depth.

quant-ph↗
arXiv2022

A Long Time Ago in a Galaxy Far, Far Away: A Candidate z ~ 12 Galaxy in Early JWST CEERS Imaging

We report the discovery of a candidate galaxy with a photo-z of z~12 in the first epoch of the JWST Cosmic Evolution Early Release Science (CEERS) Survey. Following conservative selection criteria we identify a source with a robust z_phot = 11.8^+0.3_-0.2 (1-sigma uncertainty) with m_F200W=27.3, and >7-sigma detections in five filters. The source is not detected at lambda < 1.4um in deep imaging from both HST and JWST, and has faint ~3-sigma detections in JWST F150W and HST F160W, which signal a Ly-alpha break near the red edge of both filters, implying z~12. This object (Maisie's Galaxy) exhibits F115W-F200W > 1.9 mag (2-sigma lower limit) with a blue continuum slope, resulting in 99.6% of the photo-z PDF favoring z > 11. All data quality images show no artifacts at the candidate's position, and independent analyses consistently find a strong preference for z > 11. Its colors are inconsistent with Galactic stars, and it is resolved (r_h = 340 +/- 14 pc). Maisie's Galaxy has log M*/Msol ~ 8.5 and is highly star-forming (log sSFR ~ -8.2 yr^-1), with a blue rest-UV color (beta ~ -2.5) indicating little dust though not extremely low metallicity. While the presence of this source is in tension with most predictions, it agrees with empirical extrapolations assuming UV luminosity functions which smoothly decline with increasing redshift. Should followup spectroscopy validate this redshift, our Universe was already aglow with galaxies less than 400 Myr after the Big Bang.

astro-ph.GA↗