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Qing Luo

Publications and source records attributed to Qing Luo.

At least 19 recordsLinked to original sources

NORmal: Architecting 3D Ferroelectric NOR Flash as Main Memory -- From Co-Fabricated BEOL Arrays to 200B-Class Multi-Agent LLMs in a Smartphone

Smartphone agents increasingly work as teams, in which a large planner directs parallel workers across long sessions. Running such a team on the device requires 200B-class models and contexts that exceed phone DRAM many times over, yet existing tiers treat flash as storage: UFS starves per-token weight streaming, and mobile High-Bandwidth Flash (HBF) needs a redesigned SoC and cannot absorb continuous KV writes. We show that a non-volatile memory can join an unmodified LPDDR6 bus as a plain rank only if its access latency fits the DRAM timing window, a condition no NAND tier meets. We present NORmal, to our knowledge the first main-memory architecture for 3D ferroelectric NOR flash. We have fabricated and experimentally characterized back-end-of-line (BEOL) 3D FeNOR arrays that co-integrate dense 3-bit MLC and fast, high-endurance in-place-writable SLC with sub-7.5 ns sensing. NORmal turns these co-fabricated arrays into a byte-addressable, deterministic-timing load/store rank on an unmodified LPDDR6 interface through an under-array DRAM-emulation bridge, staggered bank reads with zero-cycle weight swizzling, and posted SLC writes. Across this rank and DRAM, NORmal places agent state by mutability and lifetime rather than by hotness, serving 200B-class models and persistent planner contexts in place while dynamically partitioning subchannels across agent phases. In serving-level simulation on production agent traces, NORmal is 8.9$\times$--28.0$\times$ faster than UFS offloading, within 0.4% of an unlimited-DRAM ideal, and 2.8$\times$--6.3$\times$ faster than HBF on sessions beyond 256K tokens, with 8.1$\times$--17.8$\times$ less memory energy per token.

cs.AR↗

HydroJEV: A one-second, training-free screen for cyber-attack and fault attribution in water distribution networks

When a SCADA alarm is raised in a water distribution network, operators must decide quickly whether it reflects a cyberattack, a physical fault, a normal transient or a faulty sensor. Supervised classifiers need labelled incidents that utilities rarely have, and frontier large language models (LLMs) take tens of seconds per decision. We tested whether Jev, a training-free model that returns class probabilities in about one second, can serve as the first tier of this triage. On a four-class cause-attribution benchmark built on the C-Town network in EPANET, Jev was compared with a hand-written rule tree, a supervised classifier and seven cloud LLMs on identical evidence in four sealed, pre-registered rounds. With only a label-free prior correction, Jev matched the rule tree (macro-F1 0.62-0.64 against 0.56-0.61 in distribution) and exceeded the supervised classifier by 0.36-0.42 on event subtypes absent from its labels, in all four rounds, and it outperformed the classifier whenever fewer than about four labelled events per class were available. Jev also decided 20-40 times faster than frontier LLMs. Accepting only benign Jev verdicts confirmed by the rule tree spared an LLM reviewer 35-38% of windows on fresh sealed sets without loss of macro-F1. Transferred unchanged to two further networks, this gated cascade stayed within the non-inferiority margin of its reviewer on all four sets. A fast, training-free screen can therefore take over about a third of the review load in SCADA anomaly triage while preserving the accuracy of deliberate review.

cs.AI↗

Water-network decisions share one hydraulic gradient, and it can now be computed exactly

Calibration, leak localisation and sensor placement on water distribution networks (WDNs) are decisions about continuous parameters, yet the hydraulic engine that defines the physics returns a solution and no derivatives, so practice falls back on derivative-free search or on surrogates whose error the answer inherits. We make the global gradient algorithm itself exactly differentiable: the forward pass reproduces the reference engine's discrete devices, status switching and low-flow linearisation included, and the backward pass solves the implicit adjoint by reusing the forward pass's terminal factorisation, so one extra sparse solve returns every parameter's gradient at once, batched over scenarios on one graphics processor. Across 52 public, synthetic and operational networks and 8,140 simulation frames, every network meets the acceptance criterion, the largest head deviation from EPANET 2.2 is 1.137e-13 ft and 25 agree exactly. One adjoint solve replaces the 906 simulations a finite-difference roughness Jacobian costs on the 905-pipe L-TOWN benchmark, and a leak-inversion training loop runs at 463-470 ms per optimiser step for 256 scenarios, 191 times the prior pipeline. Gradient calibration reaches its endpoint within a median 595 model calls, where the strongest of five tuned metaheuristics needs 8,060 to match it on the training loss and two never do within 20,000. On a 554-link operating network, one adjoint pass audits, pipe by pipe, which roughness parameters the installed sensors can constrain and which sensors to add, on the model the utility already operates.

cs.DC↗

Verifiable abstention makes AI leak diagnosis accountable in urban water distribution networks

Leak localization is usually evaluated as forced-choice prediction, although sparse hydraulic observations may not justify excavation. Here, we quantify a pressure-information limit and use it to recast localization as selective, evidence-gated decision-making. A physics-grounded executor falsifies competing leak, demand, sensor and valve hypotheses in a hydraulic twin. Deterministic code computes every number and every acceptance predicate; an independent large language model auditor may add a rejection but never overturn a failed check. Forced retrieval placed only 95 of 300 leaks in the correct zone. Across 550 mixed events, the gate acted on 223 (214 correct); on a third-party 33-leak benchmark, all four accepted events were correct. In a replay of 194 audited City D repairs, the pressure tier authorized five excavation recommendations, three matching the repaired district, while the district-inflow tier returned the correct district for 85 events. Observability limits with machine-checkable abstention enable auditable utility intervention.

cs.AI↗

Compact Representation of Mipmapped SVBRDFs via Shared Gaussians

Spatially-varying BRDFs (SVBRDFs) are central to material representation in computer graphics, but their high-resolution, multi-channel, mipmapped textures impose a substantial storage burden. Existing compression methods face a fundamental trade-off: block-based compression provides random access and hardware-friendly decoding but exploits redundancy only within local blocks; image codecs offer strong rate-distortion performance but are not designed for direct real-time texture access; and neural texture compression achieves high compression ratios but requires neural inference during decoding, which introduces additional runtime overhead, especially on mobile platforms. We present Gaussian Texture Compression (GTC), a compact 2D Gaussian-based representation for mipmapped SVBRDF texture stacks that delivers high-quality compression with flexible rate-distortion trade-offs. Our method is based on a key observation that there are two dominant sources of redundancy in such data: across mip levels and across material maps. Both share a common underlying structure: the same spatial support is reused, with only level- or map-specific information attached. This property naturally suits 2D Gaussians, since each Gaussian explicitly separates its spatial footprint from the values it carries, allowing the footprint to be shared while the values vary per level and per map. Building on this property, GTC shares Gaussians along both redundancy dimensions and is trained via a progressive optimization pipeline. Experiments show that GTC achieves higher reconstruction quality and lower memory usage than ASTC, the industry-standard GPU texture compression format, while supporting random-access, non-neural decoding suitable for real-time rendering.

cs.GR↗

Alternative Lattice Design for the STCF Collider Rings

The Super Tau-Charm Facility (STCF) is a proposed high-luminosity electron-positron collider operating in the beam energy range of 1-3.5 GeV, targeting a peak luminosity larger than $0.5\times10^{35}\ \mathrm{cm^{-2}s^{-1}}$ at 2 GeV. In this regime, the combination of beam-beam interaction in the crab-waist scheme and low beam energy imposes stringent constraints on dynamic aperture, momentum acceptance, and Touschek lifetime. In this paper, we present an alternative one-fold lattice design for the STCF collider rings, developed within a systematic optimization framework. The approach consists of three stages: (i) lattice-agnostic global parameter optimization using a parameter optimization model that consistently incorporates luminosity performance, beam-beam limits, and collective effects; (ii) optics design based on a compact interaction region with local chromatic correction and crab-waist sextupoles; and (iii) global nonlinear optimization combining analysis-driven methods and tracking-based refinement. The optimized lattice achieves the more ambitious luminosity of $1\times10^{35}\ \mathrm{cm^{-2}s^{-1}}$ while maintaining a Touschek lifetime of about 600 s at 2 GeV, with sufficient dynamic aperture and momentum acceptance for stable operation. The results highlight the critical role of local nonlinear control in the interaction region and demonstrate that the proposed optimization strategy provides an effective and general methodology for the design of high-luminosity low-energy colliders.

physics.acc-ph↗

Efficient Decoder Scaling Strategy for Neural Routing Solvers

Construction-based neural routing solvers, typically composed of an encoder and a decoder, have emerged as a promising approach for solving vehicle routing problems. While recent studies suggest that shifting parameters from the encoder to the decoder enhances performance, most works restrict the decoder size to 1-3M parameters, leaving the effects of scaling largely unexplored. To address this gap, we conduct a systematic study comparing two distinct strategies: scaling depth versus scaling width. We synthesize these strategies to construct a suite of 12 model configurations, spanning a parameter range from 1M to ~150M, and extensively evaluate their scaling behaviors across three critical dimensions: parameter efficiency, data efficiency, and compute efficiency. Our empirical results reveal that parameter count is insufficient to accurately predict the model performance, highlighting the critical and distinct roles of model depth (layer count) and width (embedding dimension). Crucially, we demonstrate that scaling depth yields superior performance gains to scaling width. Based on these findings, we provide and experimentally validate a set of design principles for the efficient allocation of parameters and compute resources to enhance the model performance.

cs.LG↗

Crab-waist interaction region design and integration for the Super Tau-Charm Facility

The Super Tau-Charm Facility (STCF) is a new-generation $e^+e^-$ collider proposed in China, designed to operate in the center-of-mass (CoM) energy range of 2-7 GeV. To achieve the design luminosity exceeding 5*10^34 cm^-2s^-1 at the optimal CoM energy of 4 GeV, a large crossing angle combined with the crab-waist correction scheme is adopted. However, this scheme introduces strong nonlinearities in the interaction region (IR) due to the extremely low vertical beta function of beta_y* <=1 mm, which significantly limits dynamic and momentum apertures of the collider ring. This paper presents a comprehensive modular optics design that addresses these challenges through several key features: 1) local chromaticity correction up to third order to enhance momentum bandwidth; 2) exact -I transformation between chromatic sextupole pairs for nonlinear cancellation; 3) minimization of the dispersion invariant along the IR to improve local momentum acceptance; 4) optimized beta functions at crab sextupole locations to reduce strength requirements and associated nonlinearities. Resonance driving terms analysis confirms effective suppression of geometric aberrations while preserving the intended crab-waist effects. When integrated into the collider ring, the design achieves a Touschek lifetime exceeding 300 s at beam energy of 2 GeV, meeting STCF requirements. The impact of fringe fields from superconducting quadrupoles is mitigated using octupole correctors, and detector solenoid effects are fully suppressed via local anti-solenoid compensation. Furthermore, the defined machine-detector interface layout ensures minimal synchrotron radiation background at the IP beryllium chamber, while ultra-high vacuum conditions are required to suppress beam-gas background. This IR design represents the current optimal solution for STCF and has been incorporated into the project's conceptual design report.

physics.acc-ph↗

Unveiling Retention Loss Mechanism in FeFETs with Gate-side Interlayer by Decoupling Trapped Charges and Ferroelectric Polarization

We propose a direct experimental extraction technique for trapped charges and quantitative energy band diagrams in the FeFETs with metal-insulator-ferroelectric-insulator-semiconductor (MIFIS) structure, derived from the physical relationship between Vth and gate-side interlayer (G.IL) thickness. By decoupling trapped charges and ferroelectric polarization, we reveal that: (i) The gateinjected charges and channel-injected charges are excessive and maintain consistent ratios to ferroelectric polarization (~170% and ~130%, respectively). (ii) Retention loss originates from the detrapping of gate-injected charges rather than ferroelectric depolarization. (iii) As the G.IL thickens, the gate-injected charge de-trapping path transforms from gate-side to channel-side. To address the retention loss, careful material design, optimization, and bandgap engineering in the MIFIS structure are crucial. This work advances the understanding of high retention strategies for MIFIS-FeFETs in 3D FE NAND.

physics.app-ph↗

Conceptual Design Report of Super Tau-Charm Facility: The Accelerator

Electron-positron colliders operating in the GeV region of center-of-mass energies or the Tau-Charm energy region, have been proven to enable competitive frontier research, due to its several unique features. With the progress of high energy physics in the last two decades, a new-generation Tau-Charm factory, Super Tau Charm Facility (STCF) has been actively promoting by the particle physics community in China. STCF holds great potential to address fundamental questions such as the essence of color confinement and the matter-antimatter asymmetry in the universe in the next decades. The main design goals of STCF are with a center-of-mass energy ranging from 2 to 7 GeV and a peak luminosity surpassing 5*10^34 cm^-2s^-1 that is optimized at a center-of-mass energy of 4 GeV, which is about 50 times that of the currently operating Tau-Charm factory - BEPCII. The STCF accelerator is composed of two main parts: a double-ring collider with the crab-waist collision scheme and an injector that provides top-up injections for both electron and positron beams. As a typical third-generation electron-positron circular collider, the STCF accelerator faces many challenges in both accelerator physics and technology. In this paper, the conceptual design of the STCF accelerator complex is presented, including the ongoing efforts and plans for technological R&D, as well as the required infrastructure. The STCF project aims to secure support from the Chinese central government for its construction during the 15th Five-Year Plan (2026-2030) in China.

physics.acc-ph↗

Optics design of the Super Tau-Charm Facility collider rings

The Super Tau-Charm Facility (STCF), China's next-generation electron-positron collider, targets an unprecedented luminosity exceeding 5x10^34 cm^-2 s^-1 at a center-of-mass energy of 4 GeV. The implementation of a submillimeter vertical beta function at interaction point (< 1 mm) and crab-waist collision scheme in this low-energy regime introduces critical challenges through severe nonlinear effects that constrain dynamic aperture and degrade Touschek lifetime. To address these constraints, we propose a novel quasi-two-fold symmetric lattice design integrating several synergistic features: Linear optics optimization minimizing the H-invariant around the ring to maximize local momentum acceptance (LMA); Up to third-order of local chromaticity correction in the interaction region combined with second-order achromatic arc optics, enhancing off-momentum beam dynamics; Configured FODO arc structure with interleaved sextupole groups satisfying -I transformation, suppressing third-order geometric aberrations while optimizing Montague function distributions; Advanced final focus system integrating chromatic sextupoles, crab sextupoles, and strategically positioned octupoles to counteract final quadrupole fringe fields. Furthermore, we develop a multi-objective genetic algorithm using the in-house toolkit PAMKIT to simultaneously optimize 46 sextupole families, maximizing both dynamic aperture and momentum bandwidth. Optics performance is evaluated under error conditions with appropriate corrections, ensuring robust beam dynamics.

physics.acc-ph↗

NTIRE 2025 Challenge on UGC Video Enhancement: Methods and Results

This paper presents an overview of the NTIRE 2025 Challenge on UGC Video Enhancement. The challenge constructed a set of 150 user-generated content videos without reference ground truth, which suffer from real-world degradations such as noise, blur, faded colors, compression artifacts, etc. The goal of the participants was to develop an algorithm capable of improving the visual quality of such videos. Given the widespread use of UGC on short-form video platforms, this task holds substantial practical importance. The evaluation was based on subjective quality assessment in crowdsourcing, obtaining votes from over 8000 assessors. The challenge attracted more than 25 teams submitting solutions, 7 of which passed the final phase with source code verification. The outcomes may provide insights into the state-of-the-art in UGC video enhancement and highlight emerging trends and effective strategies in this evolving research area. All data, including the processed videos and subjective comparison votes and scores, is made publicly available at https://github.com/msu-video-group/NTIRE25_UGC_Video_Enhancement.

cs.CV↗

The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Report

This paper presents a comprehensive review of the NTIRE 2025 Challenge on Single-Image Efficient Super-Resolution (ESR). The challenge aimed to advance the development of deep models that optimize key computational metrics, i.e., runtime, parameters, and FLOPs, while achieving a PSNR of at least 26.90 dB on the $\operatorname{DIV2K\_LSDIR\_valid}$ dataset and 26.99 dB on the $\operatorname{DIV2K\_LSDIR\_test}$ dataset. A robust participation saw \textbf{244} registered entrants, with \textbf{43} teams submitting valid entries. This report meticulously analyzes these methods and results, emphasizing groundbreaking advancements in state-of-the-art single-image ESR techniques. The analysis highlights innovative approaches and establishes benchmarks for future research in the field.

cs.CV↗

AIM 2024 Challenge on Compressed Video Quality Assessment: Methods and Results

Video quality assessment (VQA) is a crucial task in the development of video compression standards, as it directly impacts the viewer experience. This paper presents the results of the Compressed Video Quality Assessment challenge, held in conjunction with the Advances in Image Manipulation (AIM) workshop at ECCV 2024. The challenge aimed to evaluate the performance of VQA methods on a diverse dataset of 459 videos, encoded with 14 codecs of various compression standards (AVC/H.264, HEVC/H.265, AV1, and VVC/H.266) and containing a comprehensive collection of compression artifacts. To measure the methods performance, we employed traditional correlation coefficients between their predictions and subjective scores, which were collected via large-scale crowdsourced pairwise human comparisons. For training purposes, participants were provided with the Compressed Video Quality Assessment Dataset (CVQAD), a previously developed dataset of 1022 videos. Up to 30 participating teams registered for the challenge, while we report the results of 6 teams, which submitted valid final solutions and code for reproducing the results. Moreover, we calculated and present the performance of state-of-the-art VQA methods on the developed dataset, providing a comprehensive benchmark for future research. The dataset, results, and online leaderboard are publicly available at https://challenges.videoprocessing.ai/challenges/compressedvideo-quality-assessment.html.

eess.IV↗

AIM 2024 Challenge on Video Super-Resolution Quality Assessment: Methods and Results

This paper presents the Video Super-Resolution (SR) Quality Assessment (QA) Challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2024. The task of this challenge was to develop an objective QA method for videos upscaled 2x and 4x by modern image- and video-SR algorithms. QA methods were evaluated by comparing their output with aggregate subjective scores collected from >150,000 pairwise votes obtained through crowd-sourced comparisons across 52 SR methods and 1124 upscaled videos. The goal was to advance the state-of-the-art in SR QA, which had proven to be a challenging problem with limited applicability of traditional QA methods. The challenge had 29 registered participants, and 5 teams had submitted their final results, all outperforming the current state-of-the-art. All data, including the private test subset, has been made publicly available on the challenge homepage at https://challenges.videoprocessing.ai/challenges/super-resolution-metrics-challenge.html

eess.IV↗

Technique Report of CVPR 2024 PBDL Challenges

The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies. By leveraging the principles of physics to inform and enhance deep learning models, we can develop more robust and accurate vision systems. Physics-based vision aims to invert the processes to recover scene properties such as shape, reflectance, light distribution, and medium properties from images. In recent years, deep learning has shown promising improvements for various vision tasks, and when combined with physics-based vision, these approaches can enhance the robustness and accuracy of vision systems. This technical report summarizes the outcomes of the Physics-Based Vision Meets Deep Learning (PBDL) 2024 challenge, held in CVPR 2024 workshop. The challenge consisted of eight tracks, focusing on Low-Light Enhancement and Detection as well as High Dynamic Range (HDR) Imaging. This report details the objectives, methodologies, and results of each track, highlighting the top-performing solutions and their innovative approaches.

cs.CV↗

Longitudinal beam dynamics design fpr Super Tau-Charm Facility

The project of Super Tau-Charm Facility (STCF) proposed in China, as a new-generation high-luminosity $e^+e^-$ collider in the low-energy region with the center-of-mass energy of 2-7 GeV, is well underway. The luminosity is targeted at $1.0\times10^{35} cm^{-2}s^{-1}$ at the optimized beam energy of 2 GeV. Longitudinal beam dynamics becomes of great importance for the STCF due to the constraints from the novel beam-beam effect called coherent X-Z instability and severe beam collective effects. In this paper, we will develop an iterative optimization model for the STCF longitudinal beam dynamics design, which takes into account the influence of transverse dynamics, coherent X-Z instability, and collective effects.

physics.acc-ph↗

Binary stochasticity enabled highly efficient neuromorphic deep learning achieves better-than-software accuracy

Deep learning needs high-precision handling of forwarding signals, backpropagating errors, and updating weights. This is inherently required by the learning algorithm since the gradient descent learning rule relies on the chain product of partial derivatives. However, it is challenging to implement deep learning in hardware systems that use noisy analog memristors as artificial synapses, as well as not being biologically plausible. Memristor-based implementations generally result in an excessive cost of neuronal circuits and stringent demands for idealized synaptic devices. Here, we demonstrate that the requirement for high precision is not necessary and that more efficient deep learning can be achieved when this requirement is lifted. We propose a binary stochastic learning algorithm that modifies all elementary neural network operations, by introducing (i) stochastic binarization of both the forwarding signals and the activation function derivatives, (ii) signed binarization of the backpropagating errors, and (iii) step-wised weight updates. Through an extensive hybrid approach of software simulation and hardware experiments, we find that binary stochastic deep learning systems can provide better performance than the software-based benchmarks using the high-precision learning algorithm. Also, the binary stochastic algorithm strongly simplifies the neural network operations in hardware, resulting in an improvement of the energy efficiency for the multiply-and-accumulate operations by more than three orders of magnitudes.

cs.NE↗