arXiv subjects

Ting Wu

Publications and source records attributed to Ting Wu.

At least 37 records · Page 2Linked to original sources

arXiv2024

Similarity-driven and Task-driven Models for Diversity of Opinion in Crowdsourcing Markets

The recent boom in crowdsourcing has opened up a new avenue for utilizing human intelligence in the realm of data analysis. This innovative approach provides a powerful means for connecting online workers to tasks that cannot effectively be done solely by machines or conducted by professional experts due to cost constraints. Within the field of social science, four elements are required to construct a sound crowd - Diversity of Opinion, Independence, Decentralization and Aggregation. However, while the other three components have already been investigated and implemented in existing crowdsourcing platforms, 'Diversity of Opinion' has not been functionally enabled yet. From a computational point of view, constructing a wise crowd necessitates quantitatively modeling and taking diversity into account. There are usually two paradigms in a crowdsourcing marketplace for worker selection: building a crowd to wait for tasks to come and selecting workers for a given task. We propose similarity-driven and task-driven models for both paradigms. Also, we develop efficient and effective algorithms for recruiting a limited number of workers with optimal diversity in both models. To validate our solutions, we conduct extensive experiments using both synthetic datasets and real data sets.

stat.AP↗

arXiv2023

Modeling the Q-Diversity in a Min-max Play Game for Robust Optimization

Models trained with empirical risk minimization (ERM) are revealed to easily rely on spurious correlations, resulting in poor generalization. Group distributionally robust optimization (group DRO) can alleviate this problem by minimizing the worst-case loss over pre-defined groups. While promising, in practice factors like expensive annotations and privacy preclude the availability of group labels. More crucially, when taking a closer look at the failure modes of out-of-distribution generalization, the typical procedure of reweighting in group DRO loses efficiency. Hinged on the limitations, in this work, we reformulate the group DRO framework by proposing Q-Diversity. Characterized by an interactive training mode, Q-Diversity relaxes the group identification from annotation into direct parameterization. Furthermore, a novel mixing strategy across groups is presented to diversify the under-represented groups. In a series of experiments on both synthetic and real-world text classification tasks, results demonstrate that Q-Diversity can consistently improve worst-case accuracy under different distributional shifts, outperforming state-of-the-art alternatives.

cs.CL↗

arXiv2023

Enhancing Contrastive Learning with Noise-Guided Attack: Towards Continual Relation Extraction in the Wild

The principle of continual relation extraction~(CRE) involves adapting to emerging novel relations while preserving od knowledge. While current endeavors in CRE succeed in preserving old knowledge, they tend to fail when exposed to contaminated data streams. We assume this is attributed to their reliance on an artificial hypothesis that the data stream has no annotation errors, which hinders real-world applications for CRE. Considering the ubiquity of noisy labels in real-world datasets, in this paper, we formalize a more practical learning scenario, termed as \textit{noisy-CRE}. Building upon this challenging setting, we develop a noise-resistant contrastive framework named as \textbf{N}oise-guided \textbf{a}ttack in \textbf{C}ontrative \textbf{L}earning~(NaCL) to learn incremental corrupted relations. Compared to direct noise discarding or inaccessible noise relabeling, we present modifying the feature space to match the given noisy labels via attacking can better enrich contrastive representations. Extensive empirical validations highlight that NaCL can achieve consistent performance improvements with increasing noise rates, outperforming state-of-the-art baselines.

cs.CL↗

arXiv2023

First Detailed Analysis of a Relatively Deep, Low Mass-ratio Contact Binary: ATO J108.6991+27.8306

We present the first detailed photometric analysis of ATO J108.6991+27.8306 (hereinafter as J108). The short-period close binary J108 was observed by the Nanshan 1 m Wide Field Telescope of the Xinjiang Astronomical Observatory. The obtained BVRI-band light curves were used to determine the photometric solution by using the 2003 version of the Wilson-Devinney code. J108 is a typical deep ( f > 50%), low mass ratio (q < 0.25) overcontact binary system with a mass ratio of q = 0.1501 and a fill-out factor of f = 50.1 %, suggesting that it is in the late evolutionary stage of contact binary systems. We found the target to be a W-type W UMa binary and provided evidence for the presence of starspots on both components. From the temperature-luminosity diagram, the main component is the evolved main sequence star with an evolutionary age of about 7.94 Gyr.

astro-ph.SR↗

arXiv2022

Some properties of Pre-uniform spaces

In this paper, we introduce the notions of pre-uniform spaces and pre-proximities and investigate some basic properties about them, where the definition of pre-uniformity here is different with the pre-uniformities which are studied in \cite{BR2016}, \cite{GM2007} and \cite{K2016} respectively. First, we prove that each pre-uniform pre-topology is regular, and give an example to show that there exists a pre-uniform structure on a finite set such that the pre-uniform pre-topology is not discrete. Moreover, we give three methods of generating (strongly) pre-uniformities, that is, the definition of a pre-base, a family of strongly pre-uniform covers, or a family of strongly pre-uniform pseudometrics. As an application, we show that each strongly pre-topological group is completely regular. Finally, we pose the concept of the pre-proximity on a set and discuss some properties of the pre-proximity.

math.GN↗

arXiv2022

Less is Better: Recovering Intended-Feature Subspace to Robustify NLU Models

Datasets with significant proportions of bias present threats for training a trustworthy model on NLU tasks. Despite yielding great progress, current debiasing methods impose excessive reliance on the knowledge of bias attributes. Definition of the attributes, however, is elusive and varies across different datasets. Furthermore, leveraging these attributes at input level to bias mitigation may leave a gap between intrinsic properties and the underlying decision rule. To narrow down this gap and liberate the supervision on bias, we suggest extending bias mitigation into feature space. Therefore, a novel model, Recovering Intended-Feature Subspace with Knowledge-Free (RISK) is developed. Assuming that shortcut features caused by various biases are unintended for prediction, RISK views them as redundant features. When delving into a lower manifold to remove redundancies, RISK reveals that an extremely low-dimensional subspace with intended features can robustly represent the highly biased dataset. Empirical results demonstrate our model can consistently improve model generalization to out-of-distribution set, and achieves a new state-of-the-art performance.

cs.CL↗

arXiv2022

Decorrelate Irrelevant, Purify Relevant: Overcome Textual Spurious Correlations from a Feature Perspective

Natural language understanding (NLU) models tend to rely on spurious correlations (i.e., dataset bias) to achieve high performance on in-distribution datasets but poor performance on out-of-distribution ones. Most of the existing debiasing methods often identify and weaken these samples with biased features (i.e., superficial surface features that cause such spurious correlations). However, down-weighting these samples obstructs the model in learning from the non-biased parts of these samples. To tackle this challenge, in this paper, we propose to eliminate spurious correlations in a fine-grained manner from a feature space perspective. Specifically, we introduce Random Fourier Features and weighted re-sampling to decorrelate the dependencies between features to mitigate spurious correlations. After obtaining decorrelated features, we further design a mutual-information-based method to purify them, which forces the model to learn features that are more relevant to tasks. Extensive experiments on two well-studied NLU tasks demonstrate that our method is superior to other comparative approaches.

cs.CL↗

arXiv2022

Some properties of Pre-topological groups

In this paper, we pose the concepts of pre-topological groups and some generalizations of pre-topological groups. First, we systematically investigate some basic properties of pre-topological groups; in particular, we prove that each $T_{0}$ pre-topological group is regular and every almost topological group is completely regular which extends A.A. Markov's theorem to the class of almost topological groups. Moreover, it is shown that an almost topological group is $τ$-narrow if and only if it can be embedded as a subgroup of a pre-topological product of almost topological groups of weight less than or equal to $τ$. Finally, the cardinal invariant, the precompactness and the resolvability are investigated in the class of pre-topological groups.

math.GN↗

arXiv2021

Digital Reconstruction of Elmina Castle for Mobile Virtual Reality via Point-based Detail Transfer

Reconstructing 3D models from large, dense point clouds is critical to enable Virtual Reality (VR) as a platform for entertainment, education, and heritage preservation. Existing 3D reconstruction systems inevitably make trade-offs between three conflicting goals: the efficiency of reconstruction (e.g., time and memory requirements), the visual quality of the constructed scene, and the rendering speed on the VR device. This paper proposes a reconstruction system that simultaneously meets all three goals. The key idea is to avoid the resource-demanding process of reconstructing a high-polygon mesh altogether. Instead, we propose to directly transfer details from the original point cloud to a low polygon mesh, which significantly reduces the reconstruction time and cost, preserves the scene details, and enables real-time rendering on mobile VR devices. While our technique is general, we demonstrate it in reconstructing cultural heritage sites. We for the first time digitally reconstruct the Elmina Castle, a UNESCO world heritage site at Ghana, from billions of laser-scanned points. The reconstruction process executes on low-end desktop systems without requiring high processing power, making it accessible to the broad community. The reconstructed scenes render on Oculus Go in 60 FPS, providing a real-time VR experience with high visual quality. Our project is part of the Digital Elmina effort (http://digitalelmina.org/) between University of Rochester and University of Ghana.

cs.MM↗

arXiv2021

Video Sentiment Analysis with Bimodal Information-augmented Multi-Head Attention

Humans express feelings or emotions via different channels. Take language as an example, it entails different sentiments under different visual-acoustic contexts. To precisely understand human intentions as well as reduce the misunderstandings caused by ambiguity and sarcasm, we should consider multimodal signals including textual, visual and acoustic signals. The crucial challenge is to fuse different modalities of features for sentiment analysis. To effectively fuse the information carried by different modalities and better predict the sentiments, we design a novel multi-head attention based fusion network, which is inspired by the observations that the interactions between any two pair-wise modalities are different and they do not equally contribute to the final sentiment prediction. By assigning the acoustic-visual, acoustic-textual and visual-textual features with reasonable attention and exploiting a residual structure, we attend to attain the significant features. We conduct extensive experiments on four public multimodal datasets including one in Chinese and three in English. The results show that our approach outperforms the existing methods and can explain the contributions of bimodal interaction in multiple modalities.

cs.AI↗

arXiv2021

A Data Analysis Study on Human Liver Blood Circulation

The liver has a unique blood supply system and plays an important role in the human blood circulatory system. Thus, hemodynamic problems related to the liver serve as an important part in clinical diagnosis and treatment. Although estimating parameters in these hemodynamic models is essential to the study of liver models, due to the limitations of medical measurement methods and constraints of ethics on clinical studies, it is impossible to directly measure the parameters of blood vessels in livers. Furthermore, as an important part of the systemic blood circulation, livers' studies are supposed to be in conjunction with other blood vessels. In this article, we present an innovative method to fix parameters of an individual liver in a human blood circulation using non-invasive clinical measurements. The method consists of a 1-D blood flow model of human arteries and veins, a 0-D model reflecting the peripheral resistance of capillaries and a lumped parameter circuit model for human livers. We apply the finite element method in fluid mechanics of these models to a numerical study, based on non-invasive blood related measures of 33 individuals. The estimated results of human blood vessel characteristic and liver model parameters are verified from the perspective of Stroke Value Variation, which shows the effectiveness of our estimation method.

math.NA↗

arXiv2021

A Wideband Sliding Correlation Channel Sounder in 65 nm CMOS: Evaluation Board Performance

Emerging applications such as wireless sensing, position location, robotics, and many more are driven by the ultra-wide bandwidths available at millimeter-wave (mmWave) and Terahertz (THz) frequencies. The characterization and efficient utilization of wireless channels at these extremely high frequencies require detailed knowledge of the radio propagation characteristics of the channels. Such knowledge is developed through empirical observations of operating conditions using wireless transceivers that measure the impulse response through channel sounding. Today, cutting-edge channel sounders rely on several bulky RF hardware components with complicated interconnections, large parasitics, and sub-GHz RF bandwidth. This paper presents a compact sliding correlation-based channel sounder baseband built on a monolithic integrated circuit (IC) using 65 nm CMOS, implemented as an evaluation board achieving a 2 GHz RF bandwidth. The IC is the worlds first gigabit-per-second channel sounder baseband implemented in low-cost CMOS. The presented single-board system can be employed at both the transmit and receive baseband to study multipath characteristics and path loss. Thus, the singleboard implementation provides an inexpensive and compact solution for sliding correlation-based channel sounding with 1 ns multipath delay resolution.

eess.SP↗

arXiv2020

Effects of space sizes on the dispersion of cough-generated droplets from a walking person

The dispersion of viral droplets plays a key role in the transmission of COVID-19. In this work, we analyze the dispersion of cough-generated droplets in the wake of a walking person for different space sizes. The air flow is simulated by solving the Reynolds-Averaged Navier-Stokes equations, and the droplets are modelled as passive Lagrangian particles. Simulation results show that the cloud of droplets locates around and below the waist height of the manikin after two seconds from coughing, which indicates that kids walking behind an infectious patient are exposed to higher transmission risk than adults. More importantly, two distinct droplet dispersion modes occupying significantly different contamination regions are discovered. A slight change of space size is found being able to trigger the transition of dispersion modes even though the flow patterns are still similar. This shows the importance of accurately simulating the air flow in predicting the dispersion of viral droplets and implies the necessity to set different safe-distancing guidelines for different environments.

physics.flu-dyn↗

arXiv2020

A Wideband Sliding Correlator based Channel Sounder in 65 nm CMOS: An Evaluation Board Design

Wide swaths of bandwidth at millimeter-wave (mmWave) and Terahertz (THz) frequencies stimulate diverse applications in wireless sensing, imaging, position location, cloud computing, and much more. These emerging applications motivate wireless communications hardware to operate with multi Gigahertz (GHz) bandwidth, at nominal costs, minimal size, and power consumption. Channel sounding system implementations currently used to study and measure wireless channels utilize numerous commercially available components from multiple manufacturers that result in a complex and large assembly with many costly and fragile cable interconnections between the constituents and commonly achieve a system bandwidth under one GHz. This paper presents an evaluation board (EVB) design that features a sliding correlator-based channel sounder with 2 GHz null-to-null RF bandwidth in a single monolithic integrated circuit (IC) fabricated in 65 nm CMOS technology. The EVB landscape provides necessary peripherals for signal interfacing, amplification, buffering, and enables integration into both the transmitter and receiver of a channel sounding system, thereby reducing complexity, size, and cost through integrated design. The channel sounder IC on the EVB is the worlds first to report gigabit-per-second baseband operation using low-cost CMOS technology, allowing the global research community to now have an inexpensive and compact channel sounder system with nanosecond time resolution capability for the detection of multipath signals in a wireless channel.

eess.SP↗

arXiv2019

Bowen entropy of generic point for fixed-point free flows

Let $(X, ϕ)$ be a compact metric flow without fixed points. We will be concerned with the entropy of flows which takes into consideration all possible reparametrizations of the flows. In this paper, by establishing the Brin-Katok's entropy formula for flows without fixed points in non-ergodic case, we prove the following result: for an ergodic $ϕ$-invariant measure $μ$, $$ h_{top}^{B}(ϕ, G_μ(ϕ))=h_μ(ϕ_{1}),$$ where $G_μ(ϕ)$ is the set of generic points for $μ$ and $h_{top}^{B}(ϕ, G_μ(ϕ))$ is the Bowen entropy on $G_μ(ϕ)$. This extends the classical result of Bowen in 1973 to fixed-point free flows. Moreover, we show that the Bowen entropy can be determined via the local entropies of measures.

math.DS↗

arXiv2019

Second derivative analysis and alternative data filters for multi-dimensional spectroscopies: a Fourier-space perspective

The second derivative image (SDI) method is widely applied to sharpen dispersive data features in multi-dimensional spectroscopies such as angle resolved photoemission spectroscopy (ARPES). Here, the SDI function is represented in Fourier space, where it has the form of a multi-band pass filter. The interplay of the SDI procedure with undesirable noise and background features in ARPES data sets is reviewed, and it is shown that final image quality can be improved by eliminating higher Fourier harmonics of the SDI filter. We then discuss extensions of SDI-like band pass filters to higher dimensional data sets, and how one can create even more effective filters with some a priori knowledge of the spectral features.

physics.data-an↗

arXiv2019

A Wideband Sliding Correlator-Based Channel Sounder with Synchronization in 65 nm CMOS

A programmable ultra-wideband sliding correlator-based channel sounder with high temporal and spatial resolution is designed in standard 65 nm CMOS. The baseband chip can be configured either as a baseband transmitter to generate a pseudorandom spread spectrum signal with flexible sequence lengths, or as a baseband receiver with sliding correlator having an absolute timing reference to obtain power delay profiles of the multipath components of the wireless channel. The sequence achieved a chip rate of one Giga-bit-per-second, resulting in a multipath delay resolution of 1 ns. The baseband chip occupies an area of 0.66 mm x 1 mm with a power dissipation of 6 mA at 1.1 V in 65 nm CMOS. The sliding correlator-based channel sounder in this work is a critical block for future low-cost, miniaturized channel sounding systems used in accurate and efficient channel propagation measurements at millimeter-wave frequencies.

eess.SP↗

arXiv2018

Quantitative principles for precise engineering of sensitivity in carbon-based electrochemical sensors

A major practical barrier for implementing carbon-based electrode arrays with high device-packing density is to ensure large, predictable, and homogeneous sensitivities across the array. Overcoming this barrier depends on quantitative models to predict electrode sensitivity from its material structure. However, such models are currently lacking. Here, we show that the sensitivity of multilayer graphene electrodes increases linearly with the average point defect density, whereas it is unaffected by line defects or oxygen-containing groups. These quantitative relationships persist until the electrode material transitions to a fully disordered sp2 carbon, where sensitivity declines sharply. We show that our results generalize to a variety of graphene production methods and use them to derive a predictive model that guides nano-engineering graphene structure for optimum sensitivity. Our approach achieves reproducible fabrication of miniaturized sensors with extraordinarily higher sensitivity than conventional material. These results lay the foundation for new integrated electrochemical sensor arrays based on nano-engineered graphene.

physics.app-ph↗