arXiv subjects

Jiahao Wu

Publications and source records attributed to Jiahao Wu.

At least 73 records · Page 4Linked to original sources

arXiv2024

Decoherence in Exchange-Coupled Quantum Spin Qubit Systems: Impact of Multiqubit Interactions and Geometric Connectivity

We investigate the impact of different connectivities on the decoherence time in quantum systems under quasi-static Heisenberg noise. We considered three types of elementary units, including node, stick and triangle and connect them into ring, chain, and tree configurations. We find that rings exhibit greater stability compared to chains, contrary to the expectation that higher average connectivity leads to decreased stability. Additionally, the stick configuration is more stable than the triangle configuration. We also observe similar trends in entanglement entropy and return probability, indicating their potential use in characterizing decoherence time. Our findings provide insights into the interplay between connectivity and stability in quantum systems, with implications for the design of robust quantum technologies and quantum error correction strategies.

quant-ph↗

arXiv2024

Discrete Semantic Tokenization for Deep CTR Prediction

Incorporating item content information into click-through rate (CTR) prediction models remains a challenge, especially with the time and space constraints of industrial scenarios. The content-encoding paradigm, which integrates user and item encoders directly into CTR models, prioritizes space over time. In contrast, the embedding-based paradigm transforms item and user semantics into latent embeddings, subsequently caching them to optimize processing time at the expense of space. In this paper, we introduce a new semantic-token paradigm and propose a discrete semantic tokenization approach, namely UIST, for user and item representation. UIST facilitates swift training and inference while maintaining a conservative memory footprint. Specifically, UIST quantizes dense embedding vectors into discrete tokens with shorter lengths and employs a hierarchical mixture inference module to weigh the contribution of each user--item token pair. Our experimental results on news recommendation showcase the effectiveness and efficiency (about 200-fold space compression) of UIST for CTR prediction.

cs.IR↗

arXiv2023

Disentangled Contrastive Learning for Social Recommendation

Social recommendations utilize social relations to enhance the representation learning for recommendations. Most social recommendation models unify user representations for the user-item interactions (collaborative domain) and social relations (social domain). However, such an approach may fail to model the users heterogeneous behavior patterns in two domains, impairing the expressiveness of user representations. In this work, to address such limitation, we propose a novel Disentangled contrastive learning framework for social Recommendations DcRec. More specifically, we propose to learn disentangled users representations from the item and social domains. Moreover, disentangled contrastive learning is designed to perform knowledge transfer between disentangled users representations for social recommendations. Comprehensive experiments on various real-world datasets demonstrate the superiority of our proposed model.

cs.IR↗

arXiv2023

Enhancing Graph Collaborative Filtering via Uniformly Co-Clustered Intent Modeling

Graph-based collaborative filtering has emerged as a powerful paradigm for delivering personalized recommendations. Despite their demonstrated effectiveness, these methods often neglect the underlying intents of users, which constitute a pivotal facet of comprehensive user interests. Consequently, a series of approaches have arisen to tackle this limitation by introducing independent intent representations. However, these approaches fail to capture the intricate relationships between intents of different users and the compatibility between user intents and item properties. To remedy the above issues, we propose a novel method, named uniformly co-clustered intent modeling. Specifically, we devise a uniformly contrastive intent modeling module to bring together the embeddings of users with similar intents and items with similar properties. This module aims to model the nuanced relations between intents of different users and properties of different items, especially those unreachable to each other on the user-item graph. To model the compatibility between user intents and item properties, we design the user-item co-clustering module, maximizing the mutual information of co-clusters of users and items. This approach is substantiated through theoretical validation, establishing its efficacy in modeling compatibility to enhance the mutual information between user and item representations. Comprehensive experiments on various real-world datasets verify the effectiveness of the proposed framework.

cs.IR↗

arXiv2023

Steady-Hand Eye Robot 3.0: Optimization and Benchtop Evaluation for Subretinal Injection

Subretinal injection methods and other procedures for treating retinal conditions and diseases (many considered incurable) have been limited in scope due to limited human motor control. This study demonstrates the next generation, cooperatively controlled Steady-Hand Eye Robot (SHER 3.0), a precise and intuitive-to-use robotic platform achieving clinical standards for targeting accuracy and resolution for subretinal injections. The system design and basic kinematics are reported and a deflection model for the incorporated delta stage and validation experiments are presented. This model optimizes the delta stage parameters, maximizing the global conditioning index and minimizing torsional compliance. Five tests measuring accuracy, repeatability, and deflection show the optimized stage design achieves a tip accuracy of <30 $μ$m, tip repeatability of 9.3 $μ$m and 0.02°, and deflections between 20-350 $μ$m/N. Future work will use updated control models to refine tip positioning outcomes and will be tested on in vivo animal models.

cs.RO↗

arXiv2023

Multi-Domain Learning From Insufficient Annotations

Multi-domain learning (MDL) refers to simultaneously constructing a model or a set of models on datasets collected from different domains. Conventional approaches emphasize domain-shared information extraction and domain-private information preservation, following the shared-private framework (SP models), which offers significant advantages over single-domain learning. However, the limited availability of annotated data in each domain considerably hinders the effectiveness of conventional supervised MDL approaches in real-world applications. In this paper, we introduce a novel method called multi-domain contrastive learning (MDCL) to alleviate the impact of insufficient annotations by capturing both semantic and structural information from both labeled and unlabeled data.Specifically, MDCL comprises two modules: inter-domain semantic alignment and intra-domain contrast. The former aims to align annotated instances of the same semantic category from distinct domains within a shared hidden space, while the latter focuses on learning a cluster structure of unlabeled instances in a private hidden space for each domain. MDCL is readily compatible with many SP models, requiring no additional model parameters and allowing for end-to-end training. Experimental results across five textual and image multi-domain datasets demonstrate that MDCL brings noticeable improvement over various SP models.Furthermore, MDCL can further be employed in multi-domain active learning (MDAL) to achieve a superior initialization, eventually leading to better overall performance.

cs.LG↗

arXiv2023

Mechanical transistors for logic-with-memory computing

As a potential revolutionary topic in future information processing, mechanical computing has gained tremendous attention for replacing or supplementing conventional electronics vulnerable to power outages, security attacks, and harsh environments. Despite its potential for constructing intelligent matter towards nonclassical computing systems beyond the von Neumann architecture, most works on mechanical computing demonstrated that the ad hoc design of simple logic gates cannot fully realize a universal mechanical processing framework involving interconnected arithmetic logic components and memory. However, such a logic-with-memory computing architecture is critical for complex and persistent state-dependent computations such as sequential logic. Here we propose a mechanical transistor (M-Transistor), abstracting omnipresent temperatures as the input-output mechanical bits, which consists of a metamaterial thermal channel as the gate terminal driving a nonlinear bistable soft actuator to selectively connect the output terminal to two other variable thermal sources. This M-Transistor is an elementary unit to modularly form various combinational and sequential circuits, such as complex logic gates, registers (volatile memory), and long-term memories (non-volatile memory) with much fewer units than the electronic counterparts. Moreover, they can establish a universal processing core comprising an arithmetic circuit and a register in a compact, reprogrammable network involving periodic read, write, memory, and logic operations of the mechanical bits. Our work contributes to realizing a non-electric universal mechanical computing architecture that combines multidisciplinary engineering with structural mechanics, materials science, thermal engineering, physical intelligence, and computational science.

physics.app-ph↗

arXiv2023

FANS: Fast Non-Autoregressive Sequence Generation for Item List Continuation

User-curated item lists, such as video-based playlists on Youtube and book-based lists on Goodreads, have become prevalent for content sharing on online platforms. Item list continuation is proposed to model the overall trend of a list and predict subsequent items. Recently, Transformer-based models have shown promise in comprehending contextual information and capturing item relationships in a list. However, deploying them in real-time industrial applications is challenging, mainly because the autoregressive generation mechanism used in them is time-consuming. In this paper, we propose a novel fast non-autoregressive sequence generation model, namely FANS, to enhance inference efficiency and quality for item list continuation. First, we use a non-autoregressive generation mechanism to decode next $K$ items simultaneously instead of one by one in existing models. Then, we design a two-stage classifier to replace the vanilla classifier used in current transformer-based models to further reduce the decoding time. Moreover, to improve the quality of non-autoregressive generation, we employ a curriculum learning strategy to optimize training. Experimental results on four real-world item list continuation datasets including Zhihu, Spotify, AotM, and Goodreads show that our FANS model can significantly improve inference efficiency (up to 8.7x) while achieving competitive or better generation quality for item list continuation compared with the state-of-the-art autoregressive models. We also validate the efficiency of FANS in an industrial setting. Our source code and data will be available at MindSpore/models and Github.

cs.IR↗

arXiv2023

Spectroscopy Study on NV Sensors in Diamond-based High-pressure Devices

Recently, the negatively charged nitrogen-vacancy (NV) center has emerged as a robust and versatile quantum sensor in pressurized environments. There are two popular ways to implement NV sensing in a diamond anvil cell (DAC), which is a conventional workhorse in the high-pressure community: create implanted NV centers (INVs) at the diamond anvil tip or immerse NV-enriched nano-diamonds (NDs) in the pressure medium. Nonetheless, there are limited studies on comparing the local stress environments experienced by these sensor types as well as their performances as pressure gauges. In this work, by probing the NV energy levels with the optically detected magnetic resonance (ODMR) method, we experimentally reveal a dramatic difference in the partially reconstructed stress tensors of INVs and NDs incorporated in the same DAC. Our measurement results agree with computational simulations, concluding that INVs perceive a more non-hydrostatic environment dominated by a uniaxial stress along the DAC axis. This provides insights on the suitable choice of NV sensors for specific purposes and the stress distribution in a DAC. We further propose some possible methods, such as using NDs and nanopillars, to extend the maximum working pressure of quantum sensing based on ODMR spectroscopy, since the maximum working pressure could be restricted by non-hydrostaticity of the pressure environment. Moreover, we explore more sensing applications of the NV center by studying how pressure modifies different aspects of the NV system. We perform a photoluminescence study using both INVs and NDs to determine the pressure dependence of the zero-phonon line, which helps developing an all-optical pressure sensing protocol with the NV center. We also characterize the spin-lattice relaxation ($T_1$) time of INVs under pressure to lay a foundation for robust pulsed measurements with NV centers in pressurized environments.

cond-mat.mes-hall↗

arXiv2022

Microscopic study of optically-stable, coherent color centers in diamond generated by high-temperature annealing

Single color centers in solid have emerged as promising physical platforms for quantum information science. Creating these centers with excellent quantum properties is a key foundation for further technological developments. In particular, the microscopic understanding of the spin bath environments is the key to engineer color centers for quantum control. In this work, we propose and demonstrate a distinct high-temperature annealing (HTA) approach for creating high-quality nitrogen vacancy (NV) centers in implantation-free diamonds. Simultaneously using the created NV centers as probes for their local environment we verify that no damage was microscopically induced by the HTA. Nearly all single NV centers created in ultra-low-nitrogen-concentration membranes possess stable and Fourier-transform-limited optical spectra. Furthermore, HTA strongly reduces noise sources naturally grown in ensemble samples, and leads to more than three-fold improvements of decoherence time and sensitivity. We also verify that the vacancy activation and defect reformation, especially H3 and P1 centers, can explain the reconfiguration between spin baths and color centers. This novel approach will become a powerful tool in vacancy-based quantum technology.

cond-mat.mes-hall↗

arXiv2022

Enabling Fast and Flexible Distributed Deep Learning with Programmable Switches

Deep learning has been used in a wide range of areas and made a huge breakthrough. With the ever-increasing model size and train-ing data volume, distributed deep learning emerges which utilizes a cluster to train a model in parallel. Unfortunately, the performance is often far from linear speedup due to the communication overhead between cluster nodes. To address this challenge, this paper designs and implements Libra, a network aggregator, that utilizes in-network computation to optimize the communication for distributed DL training in two aspects: 1) reduce active connections and 2) aggregate exchanged network packets. We implemented our Libra on Intel Tofino switches, customized a lightweight host stack and integrated it into an open-source training framework PS-lite. The experimental result shows that our Libra can achieve 1.5~4 times speedup.

cs.NI↗

arXiv2021

The derivation of the compressible Euler equation from quantum many-body dynamics

We study the three dimensional many-particle quantum dynamics in mean-field setting. We forge together the hierarchy method and the modulated energy method. We prove rigorously that the compressible Euler equation is the limit as the particle number tends to infinity and the Planck's constant tends to zero. We establish strong and quantitative microscopic to macroscopic convergence of mass and momentum densities up to the 1st blow up time of the limiting Euler equation. We justify that the macroscopic pressure emerges from the space-time averages of microscopic interactions, which are in fact, Strichartz-type bounds. We have hence found a physical meaning for Strichartz type bounds which were first raised by Klainerman and Machedon in this context.

math.AP↗

arXiv2021

LGA-RCNN: Loss-Guided Attention for Object Detection

Object detection is widely studied in computer vision filed. In recent years, certain representative deep learning based detection methods along with solid benchmarks are proposed, which boosts the development of related researchs. However, existing detection methods still suffer from undesirable performance under challenges such as camouflage, blur, inter-class similarity, intra-class variance and complex environment. To address this issue, we propose LGA-RCNN which utilizes a loss-guided attention (LGA) module to highlight representative region of objects. Then, those highlighted local information are fused with global information for precise classification and localization.

cs.CV↗

arXiv2020

Reinforcement Learning-based Product Delivery Frequency Control

Frequency control is an important problem in modern recommender systems. It dictates the delivery frequency of recommendations to maintain product quality and efficiency. For example, the frequency of delivering promotional notifications impacts daily metrics as well as the infrastructure resource consumption (e.g. CPU and memory usage). There remain open questions on what objective we should optimize to represent business values in the long term best, and how we should balance between daily metrics and resource consumption in a dynamically fluctuating environment. We propose a personalized methodology for the frequency control problem, which combines long-term value optimization using reinforcement learning (RL) with a robust volume control technique we termed "Effective Factor". We demonstrate statistically significant improvement in daily metrics and resource efficiency by our method in several notification applications at a scale of billions of users. To our best knowledge, our study represents the first deep RL application on the frequency control problem at such an industrial scale.

cs.LG↗

arXiv2020

Spotlight-based 3D Instrument Guidance for Retinal Surgery

Retinal surgery is a complex activity that can be challenging for a surgeon to perform effectively and safely. Image guided robot-assisted surgery is one of the promising solutions that bring significant surgical enhancement in treatment outcome and reduce the physical limitations of human surgeons. In this paper, we demonstrate a novel method for 3D guidance of the instrument based on the projection of spotlight in the single microscope images. The spotlight projection mechanism is firstly analyzed and modeled with a projection on both a plane and a sphere surface. To test the feasibility of the proposed method, a light fiber is integrated into the instrument which is driven by the Steady-Hand Eye Robot (SHER). The spot of light is segmented and tracked on a phantom retina using the proposed algorithm. The static calibration and dynamic test results both show that the proposed method can easily archive 0.5 mm of tip-to-surface distance which is within the clinically acceptable accuracy for intraocular visual guidance.

cs.RO↗

arXiv2020

NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results

This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor 16 based on a set of prior examples of low and corresponding high resolution images. The goal is to obtain a network design capable to produce high resolution results with the best perceptual quality and similar to the ground truth. The track had 280 registered participants, and 19 teams submitted the final results. They gauge the state-of-the-art in single image super-resolution.

eess.IV↗

arXiv2020

paper2repo: GitHub Repository Recommendation for Academic Papers

GitHub has become a popular social application platform, where a large number of users post their open source projects. In particular, an increasing number of researchers release repositories of source code related to their research papers in order to attract more people to follow their work. Motivated by this trend, we describe a novel item-item cross-platform recommender system, $\textit{paper2repo}$, that recommends relevant repositories on GitHub that match a given paper in an academic search system such as Microsoft Academic. The key challenge is to identify the similarity between an input paper and its related repositories across the two platforms, $\textit{without the benefit of human labeling}$. Towards that end, paper2repo integrates text encoding and constrained graph convolutional networks (GCN) to automatically learn and map the embeddings of papers and repositories into the same space, where proximity offers the basis for recommendation. To make our method more practical in real life systems, labels used for model training are computed automatically from features of user actions on GitHub. In machine learning, such automatic labeling is often called {\em distant supervision\/}. To the authors' knowledge, this is the first distant-supervised cross-platform (paper to repository) matching system. We evaluate the performance of paper2repo on real-world data sets collected from GitHub and Microsoft Academic. Results demonstrate that it outperforms other state of the art recommendation methods.

cs.SI↗

arXiv2018

PRUNE: Dynamic and Decidable Dataflow for Signal Processing on Heterogeneous Platforms

The majority of contemporary mobile devices and personal computers are based on heterogeneous computing platforms that consist of a number of CPU cores and one or more Graphics Processing Units (GPUs). Despite the high volume of these devices, there are few existing programming frameworks that target full and simultaneous utilization of all CPU and GPU devices of the platform. This article presents a dataflow-flavored Model of Computation (MoC) that has been developed for deploying signal processing applications to heterogeneous platforms. The presented MoC is dynamic and allows describing applications with data dependent run-time behavior. On top of the MoC, formal design rules are presented that enable application descriptions to be simultaneously dynamic and decidable. Decidability guarantees compile-time application analyzability for deadlock freedom and bounded memory. The presented MoC and the design rules are realized in a novel Open Source programming environment "PRUNE" and demonstrated with representative application examples from the domains of image processing, computer vision and wireless communications. Experimental results show that the proposed approach outperforms the state-of-the-art in analyzability, flexibility and performance.

cs.DC↗