arXiv subjects

Yuting Chen

Publications and source records attributed to Yuting Chen.

32 records · Page 2Linked to original sources

arXiv2024

Interplay between ferroelectricity and metallicity in hexagonal YMnO$_3$

We use first-principles density functional theory to investigate how the polar distortion is affected by doping in multiferroic hexagonal yttrium manganite, h-YMnO$_3$. While the introduction of charge carriers tends to suppress the polar distortion in conventional ferroelectrics, the behavior in improper geometric ferroelectrics, of which h-YMnO$_3$ is the prototype, has not been studied to date. Using both background charge doping and atomic substitution, we find an approximately linear dependence of the polar distortion on doping concentration, with hole doping suppressing and electron doping enhancing it. We show that this behavior is a direct consequence of the improper geometric nature of the ferroelectricity. In addition to its doping effect, atomic substitution can further suppress or enhance the polar distortion through changes in the local chemistry and geometry.

cond-mat.mtrl-sci↗

arXiv2023

Synergizing Human-AI Agency: A Guide of 23 Heuristics for Service Co-Creation with LLM-Based Agents

This empirical study serves as a primer for interested service providers to determine if and how Large Language Models (LLMs) technology will be integrated for their practitioners and the broader community. We investigate the mutual learning journey of non-AI experts and AI through CoAGent, a service co-creation tool with LLM-based agents. Engaging in a three-stage participatory design processes, we work with with 23 domain experts from public libraries across the U.S., uncovering their fundamental challenges of integrating AI into human workflows. Our findings provide 23 actionable "heuristics for service co-creation with AI", highlighting the nuanced shared responsibilities between humans and AI. We further exemplar 9 foundational agency aspects for AI, emphasizing essentials like ownership, fair treatment, and freedom of expression. Our innovative approach enriches the participatory design model by incorporating AI as crucial stakeholders and utilizing AI-AI interaction to identify blind spots. Collectively, these insights pave the way for synergistic and ethical human-AI co-creation in service contexts, preparing for workforce ecosystems where AI coexists.

cs.HC↗

arXiv2023

Channel-robust Automatic Modulation Classification Using Spectral Quotient Cumulants

Automatic modulation classification (AMC) is to identify the modulation format of the received signal corrupted by the channel effects and noise. Most existing works focus on the impact of noise while relatively little attention has been paid to the impact of channel effects. However, the instability posed by multipath fading channels leads to significant performance degradation. To mitigate the adverse effects of the multipath channel, we propose a channel-robust modulation classification framework named spectral quotient cumulant classification (SQCC) for orthogonal frequency division multiplexing (OFDM) systems. Specifically, we first transform the received signal to the spectral quotient (SQ) sequence by spectral circular shift division operations. Secondly, an outlier detector is proposed to filter the outliers in the SQ sequence. At last, we extract spectral quotient cumulants (SQCs) from the filtered SQ sequence as the inputs to train the artificial neural network (ANN) classifier and use the trained ANN to make the final decisions. Simulation results show that our proposed SQCC method exhibits classification robustness and superiority under various unknown Rician multipath fading channels compared with other existing methods. Specifically, the SQCC method achieves nearly 90% classification accuracy at the signal to noise ratio (SNR) of 4dB when testing under multiple channels but training under AWGN channel.

eess.SP↗

arXiv2023

InfeRE: Step-by-Step Regex Generation via Chain of Inference

Automatically generating regular expressions (abbrev. regexes) from natural language description (NL2RE) has been an emerging research area. Prior studies treat regex as a linear sequence of tokens and generate the final expressions autoregressively in a single pass. They did not take into account the step-by-step internal text-matching processes behind the final results. This significantly hinders the efficacy and interpretability of regex generation by neural language models. In this paper, we propose a new paradigm called InfeRE, which decomposes the generation of regexes into chains of step-by-step inference. To enhance the robustness, we introduce a self-consistency decoding mechanism that ensembles multiple outputs sampled from different models. We evaluate InfeRE on two publicly available datasets, NL-RX-Turk and KB13, and compare the results with state-of-the-art approaches and the popular tree-based generation approach TRANX. Experimental results show that InfeRE substantially outperforms previous baselines, yielding 16.3% and 14.7% improvement in DFA@5 accuracy on two datasets, respectively. Particularly, InfeRE outperforms the popular tree-based generation approach by 18.1% and 11.3% on both datasets, respectively, in terms of DFA@5 accuracy.

cs.AI↗

arXiv2022

Finding similarity of orbits between two discrete dynamical systems via optimal principle

Whether there is similarity between two physical processes in the movement of objects and the complexity of behavior is an essential problem in science. How to seek similarity through the adoption of quantitative and qualitative research techniques still remains an urgent challenge we face. To this end, the concepts of similarity transformation matrix and similarity degree are innovatively introduced to describe similarity of orbits between two complicated discrete dynamical systems that seem to be irrelevant. Furthermore, we present a general optimal principle, giving a strict characterization from the perspective of dynamical systems combined with optimization theory. For well-known examples of chaotic dynamical systems, such as Lorenz attractor, Chua's circuit, R$\rm\ddot{o}$ssler attractor, Chen attractor, L$\rm\ddot{u}$ attractor and hybrid system, with using of the homotopy idea, some numerical simulation results demonstrate that similarity can be found in rich characteristics and complex behaviors of chaotic dynamics via the optimal principle we presented.

math.DS↗

arXiv2022

Exact and approximate computation of the scatter halfspace depth

The scatter halfspace depth (sHD) is an extension of the location halfspace (also called Tukey) depth that is applicable in the nonparametric analysis of scatter. Using sHD, it is possible to define minimax optimal robust scatter estimators for multivariate data. The problem of exact computation of sHD for data of dimension $d \geq 2$ has, however, not been addressed in the literature. We develop an exact algorithm for the computation of sHD in any dimension $d$ and implement it efficiently using C++ for $d \leq 5$, and in R for any dimension $d \geq 1$. Since the exact computation of sHD is slow especially for higher dimensions, we also propose two fast approximate algorithms. All our programs are freely available in the R package scatterdepth.

stat.CO↗

arXiv2021

An Empirical Study on Tensor Shape Faults in Deep Learning Systems

Software developers frequently adopt deep learning (DL) libraries to incorporate learning solutions into software systems. However, misuses of these libraries can cause various DL faults. Among them, tensor shape faults are most prevalent. Tensor shape faults occur when restriction conditions of operations are not met, leading to many system crashes. To support efficient detection and fixing of these faults, we conduct an empirical study to obtain a deep insight. We construct SFData, a set of 146 buggy programs with crashing tensor shape faults (i.e., those causing programs to crash). By analyzing the faults in SFData, we categorize them into four types and get some valuable observations.

cs.SE↗

arXiv2020

Scenario-aware and Mutual-based approach for Multi-scenario Recommendation in E-Commerce

Recommender systems (RSs) are essential for e-commerce platforms to help meet the enormous needs of users. How to capture user interests and make accurate recommendations for users in heterogeneous e-commerce scenarios is still a continuous research topic. However, most existing studies overlook the intrinsic association of the scenarios: the log data collected from platforms can be naturally divided into different scenarios (e.g., country, city, culture). We observed that the scenarios are heterogeneous because of the huge differences among them. Therefore, a unified model is difficult to effectively capture complex correlations (e.g., differences and similarities) between multiple scenarios thus seriously reducing the accuracy of recommendation results. In this paper, we target the problem of multi-scenario recommendation in e-commerce, and propose a novel recommendation model named Scenario-aware Mutual Learning (SAML) that leverages the differences and similarities between multiple scenarios. We first introduce scenario-aware feature representation, which transforms the embedding and attention modules to map the features into both global and scenario-specific subspace in parallel. Then we introduce an auxiliary network to model the shared knowledge across all scenarios, and use a multi-branch network to model differences among specific scenarios. Finally, we employ a novel mutual unit to adaptively learn the similarity between various scenarios and incorporate it into multi-branch network. We conduct extensive experiments on both public and industrial datasets, empirical results show that SAML consistently and significantly outperforms state-of-the-art methods.

cs.IR↗

arXiv2020

On the constructions of $n$-cycle permutations

Any permutation polynomial is an $ n $-cycle permutation. When $n$ is a specific small positive integer, one can obtain efficient permutations, such as involutions, triple-cycle permutations and quadruple-cycle permutations. These permutations have important applications in cryptography and coding theory. Inspired by the AGW Criterion, we propose criteria for $ n $-cycle permutations, which mainly are of the form $ x^rh(x^s) $. We then propose unified constructing methods including recursive ways and a cyclotomic way for $ n $-cycle permutations of such form. We demonstrate our approaches by constructing three classes of explicit triple-cycle permutations with high index and two classes of $ n $-cycle permutations with low index.

cs.IT↗

arXiv2018

Probabilistic Semantic Retrieval for Surveillance Videos with Activity Graphs

We present a novel framework for finding complex activities matching user-described queries in cluttered surveillance videos. The wide diversity of queries coupled with unavailability of annotated activity data limits our ability to train activity models. To bridge the semantic gap we propose to let users describe an activity as a semantic graph with object attributes and inter-object relationships associated with nodes and edges, respectively. We learn node/edge-level visual predictors during training and, at test-time, propose to retrieve activity by identifying likely locations that match the semantic graph. We formulate a novel CRF based probabilistic activity localization objective that accounts for mis-detections, mis-classifications and track-losses, and outputs a likelihood score for a candidate grounded location of the query in the video. We seek groundings that maximize overall precision and recall. To handle the combinatorial search over all high-probability groundings, we propose a highest precision subgraph matching algorithm. Our method outperforms existing retrieval methods on benchmarked datasets.

cs.MM↗

arXiv2016

Efficient Training of Very Deep Neural Networks for Supervised Hashing

In this paper, we propose training very deep neural networks (DNNs) for supervised learning of hash codes. Existing methods in this context train relatively "shallow" networks limited by the issues arising in back propagation (e.e. vanishing gradients) as well as computational efficiency. We propose a novel and efficient training algorithm inspired by alternating direction method of multipliers (ADMM) that overcomes some of these limitations. Our method decomposes the training process into independent layer-wise local updates through auxiliary variables. Empirically we observe that our training algorithm always converges and its computational complexity is linearly proportional to the number of edges in the networks. Empirically we manage to train DNNs with 64 hidden layers and 1024 nodes per layer for supervised hashing in about 3 hours using a single GPU. Our proposed very deep supervised hashing (VDSH) method significantly outperforms the state-of-the-art on several benchmark datasets.

cs.CV↗

arXiv2015

Group Membership Prediction

The group membership prediction (GMP) problem involves predicting whether or not a collection of instances share a certain semantic property. For instance, in kinship verification given a collection of images, the goal is to predict whether or not they share a {\it familial} relationship. In this context we propose a novel probability model and introduce latent {\em view-specific} and {\em view-shared} random variables to jointly account for the view-specific appearance and cross-view similarities among data instances. Our model posits that data from each view is independent conditioned on the shared variables. This postulate leads to a parametric probability model that decomposes group membership likelihood into a tensor product of data-independent parameters and data-dependent factors. We propose learning the data-independent parameters in a discriminative way with bilinear classifiers, and test our prediction algorithm on challenging visual recognition tasks such as multi-camera person re-identification and kinship verification. On most benchmark datasets, our method can significantly outperform the current state-of-the-art.

cs.CV↗

arXiv2014

A Novel Visual Word Co-occurrence Model for Person Re-identification

Person re-identification aims to maintain the identity of an individual in diverse locations through different non-overlapping camera views. The problem is fundamentally challenging due to appearance variations resulting from differing poses, illumination and configurations of camera views. To deal with these difficulties, we propose a novel visual word co-occurrence model. We first map each pixel of an image to a visual word using a codebook, which is learned in an unsupervised manner. The appearance transformation between camera views is encoded by a co-occurrence matrix of visual word joint distributions in probe and gallery images. Our appearance model naturally accounts for spatial similarities and variations caused by pose, illumination & configuration change across camera views. Linear SVMs are then trained as classifiers using these co-occurrence descriptors. On the VIPeR and CUHK Campus benchmark datasets, our method achieves 83.86% and 85.49% at rank-15 on the Cumulative Match Characteristic (CMC) curves, and beats the state-of-the-art results by 10.44% and 22.27%.

cs.CV↗

arXiv2014

A Rank-SVM Approach to Anomaly Detection

We propose a novel non-parametric adaptive anomaly detection algorithm for high dimensional data based on rank-SVM. Data points are first ranked based on scores derived from nearest neighbor graphs on n-point nominal data. We then train a rank-SVM using this ranked data. A test-point is declared as an anomaly at alpha-false alarm level if the predicted score is in the alpha-percentile. The resulting anomaly detector is shown to be asymptotically optimal and adaptive in that for any false alarm rate alpha, its decision region converges to the alpha-percentile level set of the unknown underlying density. In addition we illustrate through a number of synthetic and real-data experiments both the statistical performance and computational efficiency of our anomaly detector.

stat.ML↗