arXiv subjects

Ben Wang

Publications and source records attributed to Ben Wang.

At least 37 records · Page 2Linked to original sources

arXiv2025

Superresolution of unequal-brightness thermal sources for stellar interferometry

Resolving high-contrast targets is a fundamental yet highly challenging task in astronomy. Using quantum estimation theory, we demonstrate that the ultimate limit for estimating the separation between two unequal-brightness thermal sources via interferometry remains constant, enabling the potential for superresolution. We give a comparative analysis of two primary stellar interferometric schemes: amplitude interferometry and intensity interferometry. Notably, the nulling strategy employed in amplitude interferometry, a configuration specifically proposed for exoplanet detection by leveraging destructive interference to suppress the brighter source, is quantum optimal for separation estimation. While intensity interferometry is less effective than amplitude interferometry in lossless scenarios and fails to achieve superresolution, it becomes competitive when optical loss in large-scale interferometry is considered. By applying these methodologies to modern stellar interferometry, we highlight the promise of large-scale interferometry for advancing high-resolution astronomical observation.

astro-ph.IM↗

arXiv2025

Approaching the Multiparameter Quantum Cramér-Rao Bound via Classical Correlation and Entangling Measurements

Multiparameter quantum metrology is essential for a wide range of practical applications. However, simultaneously achieving the ultimate precision for all parameters, as prescribed by the quantum Cramér-Rao bound (QCRB), remains a significant challenge. In this work, we propose a scheme termed local operation with entangling measurements (LOEM) strategy, which leverages classically correlated orthogonal pure states combined with entangling measurements to attain the multiparameter QCRB. We experimentally validate this scheme using a quantum photonic system. Additionally, we employ iterative interactions to demonstrate that the LOEM strategy can achieve the precision of Heisenberg scaling. By theoretically and experimentally demonstrating the saturation of the multiparameter QCRB with the LOEM strategy, our work advances the practical applications of quantum metrology in multiparameter estimation.

quant-ph↗

arXiv2025

MARM: Unlocking the Future of Recommendation Systems through Memory Augmentation and Scalable Complexity

Scaling-law has guided the language model designing for past years, however, it is worth noting that the scaling laws of NLP cannot be directly applied to RecSys due to the following reasons: (1) The amount of training samples and model parameters is typically not the bottleneck for the model. Our recommendation system can generate over 50 billion user samples daily, and such a massive amount of training data can easily allow our model parameters to exceed 200 billion, surpassing many LLMs (about 100B). (2) To ensure the stability and robustness of the recommendation system, it is essential to control computational complexity FLOPs carefully. Considering the above differences with LLM, we can draw a conclusion that: for a RecSys model, compared to model parameters, the computational complexity FLOPs is a more expensive factor that requires careful control. In this paper, we propose our milestone work, MARM (Memory Augmented Recommendation Model), which explores a new cache scaling-laws successfully.

cs.IR↗

arXiv2025

Luminous Mid-IR Selected Obscured Quasars at Cosmic Noon in SDSS Stripe82 II: Spectroscopic Diversity and Broad H$α$ Emissions

We present a multiwavelength spectroscopic survey of 23 luminous mid-infrared-selected Type-2 quasars at redshifts z = 0.88 to 3.49. The targets were selected in the SDSS Stripe 82 field based on their bright WISE W4 detections (flux > 5 mJy) and extremely faint or red optical counterparts (e.g., r > 23 or r - W4 > 8.4), designed to identify heavily obscured quasars. Deep near-infrared (Gemini/GNIRS) and optical (Keck/LRIS and KCWI) spectroscopy confirm 23 out of 24 candidates as Type-2 quasars in this redshift range, including 12 objects at z > 2. The spectra exhibit strong rest-frame UV and optical emission lines (Ly-alpha, C IV, [O III], H-alpha) with a wide range of line widths, indicating significant spectral diversity. Approximately one-third of the sample (8 of 23) shows broad H-alpha emission (FWHM > 2000 km/s) despite their Type-2 classification, while the rest have only narrow lines (FWHM < 2000 km/s) characteristic of classical obscured quasars. Notably, these broad-line Type-2 quasars share similar spectral energy distributions with the JWST-discovered "little red dot" (LRD) AGNs, suggesting that our sample could be lower-redshift analogues of the heavily obscured broad-line AGNs uncovered by JWST. We also find that the [O III] 5007 angstrom emission is relatively weak for their high bolometric luminosities, deviating from trends seen in lower-redshift Type-2 QSOs. A new composite spectrum for Type-2 QSOs is built using our sample. Overall, our results demonstrate that mid-IR selection efficiently uncovers a diverse population of obscured quasars and that spectroscopic follow-up is crucial for revealing their true nature. This study provides new insights into heavily obscured SMBH growth at cosmic noon and bridges the gap to the obscured AGN populations now being revealed by JWST.

astro-ph.GA↗

arXiv2025

Human-Centered Explainability in Interactive Information Systems: A Survey

Human-centered explainability has become a critical foundation for the responsible development of interactive information systems, where users must be able to understand, interpret, and scrutinize AI-driven outputs to make informed decisions. This systematic survey of literature aims to characterize recent progress in user studies on explainability in interactive information systems by reviewing how explainability has been conceptualized, designed, and evaluated in practice. Following PRISMA guidelines, eight academic databases were searched, and 100 relevant articles were identified. A structural encoding approach was then utilized to extract and synthesize insights from these articles. The main contributions include 1) five dimensions that researchers have used to conceptualize explainability; 2) a classification scheme of explanation designs; 3) a categorization of explainability measurements into six user-centered dimensions. The review concludes by reflecting on ongoing challenges and providing recommendations for future exploration of related issues. The findings shed light on the theoretical foundations of human-centered explainability, informing the design of interactive information systems that better align with diverse user needs and promoting the development of systems that are transparent, trustworthy, and accountable.

cs.HC↗

arXiv2025

A Geometric Quantum Speed Limit: Theoretical Insights and Photonic Implementation

Quantum mechanics imposes a lower bound on the time required for a quantum system to reach certain given targets. In this paper, from a geometric perspective, we introduce a new quantum speed limit (QSL) based on the Bloch angle and derive the condition for it to saturate. Experimentally, we demonstrate the feasibility of measuring this QSL using a photonic system through direct Bloch angle measurements via a swap test, bypassing the need for comprehensive quantum state tomography. Compared to the existing Bloch-angle-based QSL mentioned in prior work, our QSL requires fewer computational and experimental resources and provides tighter constraints for specific dynamics. Our work underscores the Bloch angle's effectiveness in providing tighter and experimentally accessible QSLs and advances the understanding of quantum dynamics.

quant-ph↗

arXiv2025

Quantum Geometric Tensor for Mixed States Based on the Covariant Derivative

The quantum geometric tensor (QGT) is a fundamental quantity for characterizing the geometric properties of quantum states and plays an essential role in elucidating various physical phenomena. The traditional QGT, defined only for pure states, has limited applicability in realistic scenarios where mixed states are common. To address this limitation, we generalize the definition of the QGT to mixed states using the purification bundle and the covariant derivative. Notably, our proposed definition reduces to the traditional QGT when mixed states approach pure states. In our framework, the real and imaginary parts of this generalized QGT correspond to the Bures metric and the mean gauge curvature, respectively, endowing it with a broad range of potential applications. Additionally, using our proposed mixed-state QGT (MSQGT), we derive the geodesic equation applicable to mixed states. This work establishes a unified framework for the geometric analysis of both pure and mixed states, thereby deepening our understanding of the geometric properties of quantum states.

quant-ph↗

arXiv2025

Supervised Learning-enhanced Multi-Group Actor Critic for Live Stream Allocation in Feed

In the context of a short video & live stream mixed recommendation scenario, the live stream recommendation system (RS) decides whether to allocate at most one live stream into the video feed for each user request. To maximize long-term user engagement, it is crucial to determine an optimal live stream policy for accurate live stream allocation. The inappropriate live stream allocation policy can significantly affect the duration of the usage app and user retention, which ignores the long-term negative impact of live stream allocation. Recently, reinforcement learning (RL) has been widely applied in recommendation systems to capture long-term user engagement. However, traditional RL algorithms often face divergence and instability problems, which restricts the application and deployment in the large-scale industrial recommendation systems, especially in the aforementioned challenging scenario. To address these challenges, we propose a novel Supervised Learning-enhanced Multi-Group Actor Critic algorithm (SL-MGAC). Specifically, we introduce a supervised learning-enhanced actor-critic framework that incorporates variance reduction techniques, where multi-task reward learning helps restrict bootstrapping error accumulation during critic learning. Additionally, we design a multi-group state decomposition module for both actor and critic networks to reduce prediction variance and improve model stability. We also propose a novel reward function to prevent overly greedy live stream allocation. Empirically, we evaluate the SL-MGAC algorithm using offline policy evaluation (OPE) and online A/B testing. Experimental results demonstrate that the proposed method not only outperforms baseline methods under the platform-level constraints but also exhibits enhanced stability in online recommendation scenarios.

cs.IR↗

arXiv2025

Luminous Mid-IR Selected Type-2 Quasars at Cosmic Noon in SDSS Stripe82 I: Selection, Composite Photometry, and Spectral Energy Distributions

We analyze 23 spectroscopically confirmed Type-2 quasars (QSOs) selected from the WISE 22$\rm μ$m band in the SDSS Stripe 82 region, focusing on their multi-band photometry and spectral energy distributions (SEDs). These objects were selected to be IR-luminous ($\rm flux_{W4} > 5mJy$, i.e., $12.62 < W4 < 14.62 \rm\ AB \, magnitude$), optically faint ($r > 23$) or with red color ($r - W4 >8.38$). Gemini/GNIRS observations were conducted for all 24 candidates, and 18/24 were also observed with Keck/LRIS. The observations confirm 23 to be real Type-2 QSOs in the redshift range $0.88 - 2.99$ (12 are at $z>2$). We collect multi-band photometry and conduct SED fitting. The composite photometry probes the wavelength from 0.1$\rm μ$m to 10$\rm μ$m at the rest frame. The IR emission is dominated by dust torus implying an average torus luminosity for the sample of $L_{\rm torus} 10^{46.84} \rm erg/s$. The origin of the rest-UV/optical light is not definitive, but we present three possible scenarios: scattered light, stellar emission, and the reddened accretion disk. Assuming an obscured:unobscured ratio of approximately 1:1, our targets have $L_{\rm bol} = 10^{46.28} \rm erg \,s^{-1} - 10^{47.49} \rm erg \,s^{-1}$ and around SMBH masses $\rm 10^{8.18} M_{\odot} - 10^{9.39} M_{\odot}$, assuming they accreate at the Eddington limit. Compared to previous Type-2 AGN SEDs, our targets have a brighter dust torus and redder optical-IR color. By comparing the SED to the results from JWST `little red dots' (LRDs), we find that these IR-selected Type-2 QSOs have similar SED shapes to the LRDs. This pilot Type-2 QSO survey demonstrates that mid-IR selection is an efficient way to find luminous Type-2 QSOs at $z>2$. Finally, the composite photometry and Type-2 QSOs SED model generated by this sample provide a guide for finding more Type-2 QSOs at higher redshift.

astro-ph.GA↗

arXiv2025

xMTF: A Formula-Free Model for Reinforcement-Learning-Based Multi-Task Fusion in Recommender Systems

Recommender systems need to optimize various types of user feedback, e.g., clicks, likes, and shares. A typical recommender system handling multiple types of feedback has two components: a multi-task learning (MTL) module, predicting feedback such as click-through rate and like rate; and a multi-task fusion (MTF) module, integrating these predictions into a single score for item ranking. MTF is essential for ensuring user satisfaction, as it directly influences recommendation outcomes. Recently, reinforcement learning (RL) has been applied to MTF tasks to improve long-term user satisfaction. However, existing RL-based MTF methods are formula-based methods, which only adjust limited coefficients within pre-defined formulas. The pre-defined formulas restrict the RL search space and become a bottleneck for MTF. To overcome this, we propose a formula-free MTF framework. We demonstrate that any suitable fusion function can be expressed as a composition of single-variable monotonic functions, as per the Sprecher Representation Theorem. Leveraging this, we introduce a novel learnable monotonic fusion cell (MFC) to replace pre-defined formulas. We call this new MFC-based model eXtreme MTF (xMTF). Furthermore, we employ a two-stage hybrid (TSH) learning strategy to train xMTF effectively. By expanding the MTF search space, xMTF outperforms existing methods in extensive offline and online experiments.

cs.IR↗

arXiv2025

Variational Graphical Quantum Error Correction Codes

Quantum error correction is essential for achieving fault-tolerant quantum computation. However, most typical quantum error-correcting codes are designed for generic noise models, which may fail to accurately capture the intricate noise characteristics of real quantum devices, limiting their practical performance. This work introduces a learning-based framework for the constructing of quantum error-correcting codes, termed Variational Graphical Quantum Error Correction (VGQEC) codes, which adapts to specific noise profiles of different quantum devices, enabling the design of noise-tailored codes. Specifically, inspired by Quon, a graphical language for quantum information, VGQEC codes incorporate tunable parameters embedded within their Quon graphs, allowing dynamic reconfigurations of the graph structures through parameter adjustments. As the first application of this approach, we show that this flexibility in code designs facilitates seamless transitions between various code families, exemplified by the establishment of a bridge between the five-qubit repetition code and the [[5,1,3]] code, thereby combining their respective advantages. Additionally, a VGQEC code derived from the three-qubit repetition code is fine-tuned for the amplitude damping noise, showcasing the approach's ability for noise-specific code design. Moreover, we experimentally demonstrate the effectiveness of the three-qubit VGQEC code in the low-to-medium noise regime with a photonic system, highlighting its potential for real-world applications.

quant-ph↗

arXiv2025

Unleashing the Potential of Two-Tower Models: Diffusion-Based Cross-Interaction for Large-Scale Matching

Two-tower models are widely adopted in the industrial-scale matching stage across a broad range of application domains, such as content recommendations, advertisement systems, and search engines. This model efficiently handles large-scale candidate item screening by separating user and item representations. However, the decoupling network also leads to a neglect of potential information interaction between the user and item representations. Current state-of-the-art (SOTA) approaches include adding a shallow fully connected layer(i.e., COLD), which is limited by performance and can only be used in the ranking stage. For performance considerations, another approach attempts to capture historical positive interaction information from the other tower by regarding them as the input features(i.e., DAT). Later research showed that the gains achieved by this method are still limited because of lacking the guidance on the next user intent. To address the aforementioned challenges, we propose a "cross-interaction decoupling architecture" within our matching paradigm. This user-tower architecture leverages a diffusion module to reconstruct the next positive intention representation and employs a mixed-attention module to facilitate comprehensive cross-interaction. During the next positive intention generation, we further enhance the accuracy of its reconstruction by explicitly extracting the temporal drift within user behavior sequences. Experiments on two real-world datasets and one industrial dataset demonstrate that our method outperforms the SOTA two-tower models significantly, and our diffusion approach outperforms other generative models in reconstructing item representations.

cs.IR↗

arXiv2025

Creator-Side Recommender System: Challenges, Designs, and Applications

Users and creators are two crucial components of recommender systems. Typical recommender systems focus on the user side, providing the most suitable items based on each user's request. In such scenarios, a few items receive a majority of exposures, while many items receive very few. This imbalance leads to poorer experiences and decreased activity among the creators receiving less feedback, harming the recommender system in the long term. To this end, we develop a creator-side recommender system, called DualRec, to answer the following question: how to find the most suitable users for each item to enhance the creators' experience? We show that typical user-side recommendation algorithms, such as retrieval and ranking algorithms, can be adapted into the creator-side versions with just a few modifications. This greatly simplifies algorithm design in DualRec. Moreover, we discuss a unique challenge in DualRec: the user availability issue, which is not present in user-side recommender systems. To tackle this issue, we incorporate a user availability calculation (UAC) module to effectively enhance DualRec's performance. DualRec has already been implemented in Kwai, a short video recommendation system with over 100 millions user and over 10 million creators, significantly improving the experience for creators.

cs.IR↗

arXiv2025

Experimental benchmarking of quantum state overlap estimation strategies with photonic systems

Accurately estimating the overlap between quantum states is a fundamental task in quantum information processing. While various strategies using distinct quantum measurements have been proposed for overlap estimation, the lack of experimental benchmarks on estimation precision limits strategy selection in different situations. Here we compare the performance of four practical strategies for overlap estimation, including tomography-tomography, tomography-projection, Schur collective measurement and optical swap test using photonic quantum systems. We encode the quantum states on the polarization and path degrees of freedom of single photons. The corresponding measurements are performed by photon detection on certain modes following single-photon mode transformation or two-photon interference. We further propose an adaptive strategy with optimized precision in full-range overlap estimation. Our results shed new light on extracting the parameter of interest from quantum systems, prompting the design of efficient quantum protocols.

quant-ph↗

arXiv2024

Achieving the Multi-parameter Quantum Cramér-Rao Bound with Antiunitary Symmetry

The estimation of multiple parameters is a ubiquitous requirement in many quantum metrology applications. However, achieving the ultimate precision limit, i.e. the quantum Cramér-Rao bound, becomes challenging in these scenarios compared to single parameter estimation. To address this issue, optimizing the parameters encoding strategies with the aid of antiunitary symmetry is a novel and comprehensive approach. For demonstration, we propose two types of quantum statistical models exhibiting antiunitary symmetry in experiments. The results showcase the simultaneous achievement of ultimate precision for multiple parameters without any trade-off and the precision is improved at least twice compared to conventional encoding strategies. Our work emphasizes the significant potential of antiunitary symmetry in addressing multi-parameter estimation problems.

quant-ph↗

arXiv2024

Masked Pre-training Enables Universal Zero-shot Denoiser

In this work, we observe that model trained on vast general images via masking strategy, has been naturally embedded with their distribution knowledge, thus spontaneously attains the underlying potential for strong image denoising. Based on this observation, we propose a novel zero-shot denoising paradigm, i.e., Masked Pre-train then Iterative fill (MPI). MPI first trains model via masking and then employs pre-trained weight for high-quality zero-shot image denoising on a single noisy image. Concretely, MPI comprises two key procedures: 1) Masked Pre-training involves training model to reconstruct massive natural images with random masking for generalizable representations, gathering the potential for valid zero-shot denoising on images with varying noise degradation and even in distinct image types. 2) Iterative filling exploits pre-trained knowledge for effective zero-shot denoising. It iteratively optimizes the image by leveraging pre-trained weights, focusing on alternate reconstruction of different image parts, and gradually assembles fully denoised image within limited number of iterations. Comprehensive experiments across various noisy scenarios underscore the notable advances of MPI over previous approaches with a marked reduction in inference time. Code available at https://github.com/krennic999/MPI.

cs.CV↗

arXiv2024

GPT-4o System Card

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.

cs.CL↗

arXiv2024

Directing the Electrode-Electrolyte Interface Towards Active Nickel-Based Electrocatalysts for Oxygen Evolution Reaction

A comprehensive understanding of the electrode-electrolyte interface in energy conversion systems remains challenging due to the complex and multifaceted nature of interfacial processes. This complexity hinders the development of more efficient electrocatalysts. In this work, we propose a hybrid approach to the theoretical description of the OER process on nickel-iron-based oxyhydroxides ($γ$-Ni$_{1-x}$Fe$_x$OOH) electrodes in alkaline media as a model system. Multiple reaction pathways represented by the single- and dual-site mechanisms were investigated by taking into account the realistic structure of the catalyst, the doping, and the solvation effects using a simple and computationally feasible strategy. Accounting for the variable solvation effects considerably affects the predicted overpotential in a roughly linear relationship between overpotential and dielectric constant. By incorporating quantum chemical simulations with kinetic modeling, we demonstrate that tuning the local solvation environment can significantly enhance the OER activity, opening new routine ways for elucidation of the emerging issues of OER processes on transition metal oxide surfaces and design of cost-effective, efficient electrocatalytic systems.

physics.chem-ph↗