arXiv subjects

Kai Guo

Publications and source records attributed to Kai Guo.

At least 73 records · Page 4Linked to original sources

arXiv2024

Deterministic Computing Power Networking: Architecture, Technologies and Prospects

With the development of new Internet services such as computation-intensive and delay-sensitive tasks, the traditional "Best Effort" network transmission mode has been greatly challenged. The network system is urgently required to provide end-to-end transmission determinacy and computing determinacy for new applications to ensure the safe and efficient operation of services. Based on the research of the convergence of computing and networking, a new network paradigm named deterministic computing power networking (Det-CPN) is proposed. In this article, we firstly introduce the research advance of computing power networking. And then the motivations and scenarios of Det-CPN are analyzed. Following that, we present the system architecture, technological capabilities, workflow as well as key technologies for Det-CPN. Finally, the challenges and future trends of Det-CPN are analyzed and discussed.

cs.NI↗

arXiv2024

Exploring AI-Generated Text in Student Writing: How Does AI Help?

English as foreign language_EFL_students' use of text generated from artificial intelligence_AI_natural language generation_NLG_tools may improve their writing quality. However, it remains unclear to what extent AI-generated text in these students' writing might lead to higher-quality writing. We explored 23 Hong Kong secondary school students' attempts to write stories comprising their own words and AI-generated text. Human experts scored the stories for dimensions of content, language and organization. We analyzed the basic organization and structure and syntactic complexity of the stories' AI-generated text and performed multiple linear regression and cluster analyses. The results show the number of human words and the number of AI-generated words contribute significantly to scores. Besides, students can be grouped into competent and less competent writers who use more AI-generated text or less AI-generated text compared to their peers. Comparisons of clusters reveal some benefit of AI-generated text in improving the quality of both high-scoring students' and low-scoring students' writing. The findings can inform pedagogical strategies to use AI-generated text for EFL students' writing and to address digital divides. This study contributes designs of NLG tools and writing activities to implement AI-generated text in schools.

cs.CL↗

arXiv2023

Preliminary Design of CSNS-II Linac SRF LLRF

China Spallation Neutron Source(CSNS) target power will upgrade to 500 kW(CSNS-II) from 300kW, energy gain of H-Linac will up to 300 MeV from 80 MeV using about 50 superconductor cavities. LLRF is an important device for controlling the amplitude and phase of the SRF cavity field to be less than 0.6% and 0.6 deg. The parameters and requirements for CSNS-II Linac LLRF are presented here. The preliminary design work and algorithm verification progress and results at C-ADS Injector-I are introduced.

physics.acc-ph↗

arXiv2023

Nonlinear dielectric geometric-phase metasurface with simultaneous structure and lattice symmetry design

In this work, we utilize thin dielectric meta-atoms placed on a silver substrate to efficiently enhance and manipulate the third harmonic generation. We theoretically and experimentally reveal that when the structural symmetry of the meta-atom is incompatible with the lattice symmetry of an array, some generalized nonlinear geometric phases appear, which offers new possibilities for harmonic generation control beyond the accessible symmetries governed by the selection rule. The underlying mechanism is attributed to the modified rotation of the effective principal axis of a dense meta-atom array, where the strong coupling among the units gives rise to a generalized linear geometric phase modulation on the pump light. Therefore, nonlinear geometric phases carried by the third-harmonic emissions are the natural result of the wave-mixing process among the modes excited at the fundamental frequency. This mechanism further points out a new strategy to predict the nonlinear geometric phases delivered by the nanostructures according to their linear responses. Our design is simple and efficient, and offers alternatives for the nonlinear meta-devices that are capable of flexible photon generation and manipulation.

physics.optics↗

arXiv2023

EFL Students' Attitudes and Contradictions in a Machine-in-the-loop Activity System

This study applies Activity Theory and investigates the attitudes and contradictions of 67 English as a foreign language (EFL) students from four Hong Kong secondary schools towards machine-in-the-loop writing, where artificial intelligence (AI) suggests ideas during composition. Students answered an open-ended question about their feelings on writing with AI. Results revealed mostly positive attitudes, with some negative or mixed feelings. From a thematic analysis, contradictions or points of tension between students and AI stemmed from AI inadequacies, students' balancing enthusiasm with preference, and their striving for language autonomy. The research highlights the benefits and challenges of implementing machine-in-the-loop writing in EFL classrooms, suggesting educators align activity goals with students' values, language abilities, and AI capabilities to enhance students' activity systems.

cs.HC↗

arXiv2023

Cases of EFL Secondary Students' Prompt Engineering Pathways to Complete a Writing Task with ChatGPT

ChatGPT is a state-of-the-art (SOTA) chatbot. Although it has potential to support English as a foreign language (EFL) students' writing, to effectively collaborate with it, a student must learn to engineer prompts, that is, the skill of crafting appropriate instructions so that ChatGPT produces desired outputs. However, writing an appropriate prompt for ChatGPT is not straightforward for non-technical users who suffer a trial-and-error process. This paper examines the content of EFL students' ChatGPT prompts when completing a writing task and explores patterns in the quality and quantity of the prompts. The data come from iPad screen recordings of secondary school EFL students who used ChatGPT and other SOTA chatbots for the first time to complete the same writing task. The paper presents a case study of four distinct pathways that illustrate the trial-and-error process and show different combinations of prompt content and quantity. The cases contribute evidence for the need to provide prompt engineering education in the context of the EFL writing classroom, if students are to move beyond an individual trial-and-error process, learning a greater variety of prompt content and more sophisticated prompts to support their writing.

cs.HC↗

arXiv2023

Understanding EFL Student Idea Generation Strategies for Creative Writing with NLG Tools

Natural language generation (NLG) is a process within artificial intelligence where computer systems produce human-comprehensible language texts from information. English as a foreign language (EFL) students' use of NLG tools might facilitate their idea generation, which is fundamental to creative writing. However, little is known about how EFL students interact with NLG tools to generate ideas. This study explores strategies adopted by EFL students when searching for ideas using NLG tools, evaluating ideas generated by NLG tools and selecting NLG tools for ideas generation. Four Hong Kong secondary school students attended workshops where they learned to write stories comprising their own words and words generated by NLG tools. After the workshops, they answered questions to reflect on their writing experience with NLG tools. In a thematic analysis of the written reflections, we found students may have existing ideas when searching for ideas and evaluating ideas with NLG tools. Students showed some aversion to ideas generated by NLG tools and selected NLG tools that generated a greater quantity of ideas. The findings inform our understanding of EFL students' concerns when using NLG tools for idea generation and can inform educators' instruction to implement NLG tools for classroom creative writing.

cs.CY↗

arXiv2023

The Role of AI in Human-AI Creative Writing for Hong Kong Secondary Students

The recent advancement in Natural Language Processing (NLP) capability has led to the development of language models (e.g., ChatGPT) that is capable of generating human-like language. In this study, we explore how language models can be utilized to help the ideation aspect of creative writing. Our empirical findings show that language models play different roles in helping student writers to be more creative, such as the role of a collaborator, a provocateur, etc

cs.CL↗

arXiv2022

Multi-head Uncertainty Inference for Adversarial Attack Detection

Deep neural networks (DNNs) are sensitive and susceptible to tiny perturbation by adversarial attacks which causes erroneous predictions. Various methods, including adversarial defense and uncertainty inference (UI), have been developed in recent years to overcome the adversarial attacks. In this paper, we propose a multi-head uncertainty inference (MH-UI) framework for detecting adversarial attack examples. We adopt a multi-head architecture with multiple prediction heads (i.e., classifiers) to obtain predictions from different depths in the DNNs and introduce shallow information for the UI. Using independent heads at different depths, the normalized predictions are assumed to follow the same Dirichlet distribution, and we estimate distribution parameter of it by moment matching. Cognitive uncertainty brought by the adversarial attacks will be reflected and amplified on the distribution. Experimental results show that the proposed MH-UI framework can outperform all the referred UI methods in the adversarial attack detection task with different settings.

cs.LG↗

arXiv2022

Gated Recurrent Unit for Video Denoising

Current video denoising methods perform temporal fusion by designing convolutional neural networks (CNN) or combine spatial denoising with temporal fusion into basic recurrent neural networks (RNNs). However, there have not yet been works which adapt gated recurrent unit (GRU) mechanisms for video denoising. In this letter, we propose a new video denoising model based on GRU, namely GRU-VD. First, the reset gate is employed to mark the content related to the current frame in the previous frame output. Then the hidden activation works as an initial spatial-temporal denoising with the help from the marked relevant content. Finally, the update gate recursively fuses the initial denoised result with previous frame output to further increase accuracy. To handle various light conditions adaptively, the noise standard deviation of the current frame is also fed to these three modules. A weighted loss is adopted to regulate initial denoising and final fusion at the same time. The experimental results show that the GRU-VD network not only can achieve better quality than state of the arts objectively and subjectively, but also can obtain satisfied subjective quality on real video.

cs.CV↗

arXiv2022

High-performance non-Fermi-liquid metallic thermoelectric materials

Searching for high-performance thermoelectric (TE) materials in the paradigm of narrow-bandgap semiconductors has lasted for nearly 70 years and is obviously hampered by a bottleneck of research now. Here we report on the discovery of a few metallic compounds, TiFexCu2x-1Sb and TiFe1.33Sb, showing the thermopower exceeding many TE semiconductors and the dimensionless figure of merits comparable with the state-of-the-art TE materials. A quasi-linear temperature (T) dependence of electrical resistivity in 2 K - 700 K and the logarithmic T-dependent electronic specific heat at low temperature are also observed to coexist with the high thermopower, highlighting the strong intercoupling of the non-Fermi-liquid (NFL) quantum critical behavior of electrons with TE transports. Electronic structure analysis reveals the existence of fluctuating Fe-eg-related local magnetic moments, Fe-Fe antiferromagnetic (AFM) interaction at the nearest 4c-4d sites, and two-fold degenerate eg orbitals antiferromagnetically coupled with the dual-type itinerant electrons close to the Fermi level, all of which infer to a competition between the AFM ordering and Kondo-like spin compensation as well as a parallel two-channel Kondo effect. These effects are both strongly meditated by the structural disorder due to the random filling of Fe/Cu at the equivalent 4c/4d sites of the Heusler crystal lattice. The magnetic susceptibility deviates from ideal antiferromagnetism but can be fitted well by x(T) = 1/(θ + BTα), seemingly being consistent with the quantum critical scenario of strong local correlation as discussed before. Our work not only breaks the dilemma that the promising TE materials should be heavily-doped semiconductors, but also demonstrates the correlation among high TE performance, NFL quantum criticality, and magnetic fluctuation, which opens up new directions for future research.

cond-mat.str-el↗

arXiv2022

Bioinspired composite learning control under discontinuous friction for industrial robots

Adaptive control can be applied to robotic systems with parameter uncertainties, but improving its performance is usually difficult, especially under discontinuous friction. Inspired by the human motor learning control mechanism, an adaptive learning control approach is proposed for a broad class of robotic systems with discontinuous friction, where a composite error learning technique that exploits data memory is employed to enhance parameter estimation. Compared with the classical feedback error learning control, the proposed approach can achieve superior transient and steady-state tracking without high-gain feedback and persistent excitation at the cost of extra computational burden and memory usage. The performance improvement of the proposed approach has been verified by experiments based on a DENSO industrial robot.

cs.RO↗

arXiv2022

Ternary and Binary Quantization for Improved Classification

Dimension reduction and data quantization are two important methods for reducing data complexity. In the paper, we study the methodology of first reducing data dimension by random projection and then quantizing the projections to ternary or binary codes, which has been widely applied in classification. Usually, the quantization will seriously degrade the accuracy of classification due to high quantization errors. Interestingly, however, we observe that the quantization could provide comparable and often superior accuracy, as the data to be quantized are sparse features generated with common filters. Furthermore, this quantization property could be maintained in the random projections of sparse features, if both the features and random projection matrices are sufficiently sparse. By conducting extensive experiments, we validate and analyze this intriguing property.

cs.CV↗

arXiv2022

Pedestrian Dead Reckoning System using Quasi-static Magnetic Field Detection

Kalman filter-based Inertial Navigation System (INS) is a reliable and efficient method to estimate the position of a pedestrian indoors. Classical INS-based methodology which is called IEZ (INS-EKF-ZUPT) makes use of an Extended Kalman Filter (EKF), a Zero velocity UPdaTing (ZUPT) to calculate the position and attitude of a person. However, heading error which is a key factor of the whole Pedestrian Dead Reckoning (PDR) system is unobservable for IEZ-based PDR system. To minimize the error, Electronic Com-pass (EC) algorithm becomes a valid method. But magnetic disturbance may have a big negative effect on it. In this paper, the Quasi-static Magnetic field Detection (QMD) method is proposed to detect the pure magnetic field and then selects EC algorithm or Heuristic heading Drift Reduction algorithm (HDR) according to the detection result, which implements the complementation of the two methods. Meanwhile, the QMD, EC, and HDR algorithms are integrated into the IEZ framework to form a new PDR solution which is named Advanced IEZ (AIEZ).

eess.SP↗

arXiv2021

Orthogonal Graph Neural Networks

Graph neural networks (GNNs) have received tremendous attention due to their superiority in learning node representations. These models rely on message passing and feature transformation functions to encode the structural and feature information from neighbors. However, stacking more convolutional layers significantly decreases the performance of GNNs. Most recent studies attribute this limitation to the over-smoothing issue, where node embeddings converge to indistinguishable vectors. Through a number of experimental observations, we argue that the main factor degrading the performance is the unstable forward normalization and backward gradient resulted from the improper design of the feature transformation, especially for shallow GNNs where the over-smoothing has not happened. Therefore, we propose a novel orthogonal feature transformation, named Ortho-GConv, which could generally augment the existing GNN backbones to stabilize the model training and improve the model's generalization performance. Specifically, we maintain the orthogonality of the feature transformation comprehensively from three perspectives, namely hybrid weight initialization, orthogonal transformation, and orthogonal regularization. By equipping the existing GNNs (e.g. GCN, JKNet, GCNII) with Ortho-GConv, we demonstrate the generality of the orthogonal feature transformation to enable stable training, and show its effectiveness for node and graph classification tasks.

cs.LG↗

arXiv2021

CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks

Despite the recent advances of graph neural networks (GNNs) in modeling graph data, the training of GNNs on large datasets is notoriously hard due to the overfitting. Adversarial training, which augments data with the worst-case adversarial examples, has been widely demonstrated to improve model's robustness against adversarial attacks and generalization ability. However, while the previous adversarial training generally focuses on protecting GNNs from spiteful attacks, it remains unclear how the adversarial training could improve the generalization abilities of GNNs in the graph analytics problem. In this paper, we investigate GNNs from the lens of weight and feature loss landscapes, i.e., the loss changes with respect to model weights and node features, respectively. We draw the conclusion that GNNs are prone to falling into sharp local minima in these two loss landscapes, where GNNs possess poor generalization performances. To tackle this problem, we construct the co-adversarial perturbation (CAP) optimization problem in terms of weights and features, and design the alternating adversarial perturbation algorithm to flatten the weight and feature loss landscapes alternately. Furthermore, we divide the training process into two stages: one conducting the standard cross-entropy minimization to ensure the quick convergence of GNN models, the other applying our alternating adversarial training to avoid falling into locally sharp minima. The extensive experiments demonstrate our CAP can generally improve the generalization performance of GNNs on a variety of benchmark graph datasets.

cs.LG↗

arXiv2021

Cascaded Compressed Sensing Networks: A Reversible Architecture for Layerwise Learning

Recently, the method that learns networks layer by layer has attracted increasing interest for its ease of analysis. For the method, the main challenge lies in deriving an optimization target for each layer by inversely propagating the global target of the network. The propagation problem is ill posed, due to involving the inversion of nonlinear activations from lowdimensional to high-dimensional spaces. To address the problem, the existing solution is to learn an auxiliary network to specially propagate the target. However, the network lacks stability, and moreover, it results in higher complexity for network learning. In the letter, we show that target propagation could be achieved by modeling the network s each layer with compressed sensing, without the need of auxiliary networks. Experiments show that the proposed method could achieve better performance than the auxiliary network-based method.

cs.LG↗

arXiv2020

Identification of Repurposable Drugs and Adverse Drug Reactions for Various Courses of COVID-19 Based on Single-Cell RNA Sequencing Data

Coronavirus disease 2019 (COVID-19) has impacted almost every part of human life worldwide, posing a massive threat to human health. There is no specific drug for COVID-19, highlighting the urgent need for the development of effective therapeutics. To identify potentially repurposable drugs, we employed a systematic approach to mine candidates from U.S. FDA-approved drugs and preclinical small-molecule compounds by integrating the gene expression perturbation data for chemicals from the Library of Integrated Network-Based Cellular Signatures project with a publicly available single-cell RNA sequencing dataset from mild and severe COVID-19 patients. We identified 281 FDA-approved drugs that have the potential to be effective against SARS-CoV-2 infection, 16 of which are currently undergoing clinical trials to evaluate their efficacy against COVID-19. We experimentally tested the inhibitory effects of tyrphostin-AG-1478 and brefeldin-a on the replication of the single-stranded ribonucleic acid (ssRNA) virus influenza A virus. In conclusion, we have identified a list of repurposable anti-SARS-CoV-2 drugs using a systems biology approach.

q-bio.GN↗