arXiv subjects

Yiwen Wang

Publications and source records attributed to Yiwen Wang.

At least 55 records · Page 3Linked to original sources

arXiv2024

Beware of Overestimated Decoding Performance Arising from Temporal Autocorrelations in Electroencephalogram Signals

Researchers have reported high decoding accuracy (>95%) using non-invasive Electroencephalogram (EEG) signals for brain-computer interface (BCI) decoding tasks like image decoding, emotion recognition, auditory spatial attention detection, etc. Since these EEG data were usually collected with well-designed paradigms in labs, the reliability and robustness of the corresponding decoding methods were doubted by some researchers, and they argued that such decoding accuracy was overestimated due to the inherent temporal autocorrelation of EEG signals. However, the coupling between the stimulus-driven neural responses and the EEG temporal autocorrelations makes it difficult to confirm whether this overestimation exists in truth. Furthermore, the underlying pitfalls behind overestimated decoding accuracy have not been fully explained due to a lack of appropriate formulation. In this work, we formulate the pitfall in various EEG decoding tasks in a unified framework. EEG data were recorded from watermelons to remove stimulus-driven neural responses. Labels were assigned to continuous EEG according to the experimental design for EEG recording of several typical datasets, and then the decoding methods were conducted. The results showed the label can be successfully decoded as long as continuous EEG data with the same label were split into training and test sets. Further analysis indicated that high accuracy of various BCI decoding tasks could be achieved by associating labels with EEG intrinsic temporal autocorrelation features. These results underscore the importance of choosing the right experimental designs and data splits in BCI decoding tasks to prevent inflated accuracies due to EEG temporal autocorrelation.

eess.SP↗

arXiv2024

Multimodal Semantic-Aware Automatic Colorization with Diffusion Prior

Colorizing grayscale images offers an engaging visual experience. Existing automatic colorization methods often fail to generate satisfactory results due to incorrect semantic colors and unsaturated colors. In this work, we propose an automatic colorization pipeline to overcome these challenges. We leverage the extraordinary generative ability of the diffusion prior to synthesize color with plausible semantics. To overcome the artifacts introduced by the diffusion prior, we apply the luminance conditional guidance. Moreover, we adopt multimodal high-level semantic priors to help the model understand the image content and deliver saturated colors. Besides, a luminance-aware decoder is designed to restore details and enhance overall visual quality. The proposed pipeline synthesizes saturated colors while maintaining plausible semantics. Experiments indicate that our proposed method considers both diversity and fidelity, surpassing previous methods in terms of perceptual realism and gain most human preference.

cs.CV↗

arXiv2024

A ThermalKinetic Inductance Detectors Pixel Design for Cosmic Microwave Background Observations at 90/150 GHz bands

The highly sensitive millimeter-wave telescope is an important tool for accurate measurement of Cosmic Microwave Background (CMB) radiation, and its core component is a detector array located in a cryogenic focal plane. The feasibility of utilizing thermal kinetic inductance detectors (TKIDs) for CMB observations has been demonstrated. We propose a pixel design of TKIDs for observing CMB through atmospheric windows for observations in the 90/150 GHz bands. Assuming lossless dielectric, the coupling efficiency of a single pixel is around 90%. This pixel design will be utilized for future large-scale TKIDs array designs for CMB observations.

astro-ph.IM↗

arXiv2023

Market Crowds' Trading Behaviors, Agreement Prices, and the Implications of Trading Volume

It has been long that literature in financial academics focuses mainly on price and return but much less on trading volume. In the past twenty years, it has already linked both price and trading volume to economic fundamentals, and explored the behavioral implications of trading volume such as investor's attitude toward risks, overconfidence, disagreement, and attention etc. However, what is surprising is how little we really know about trading volume. Here we show that trading volume probability represents the frequency of market crowd's trading action in terms of behavior analysis, and test two adaptive hypotheses relevant to the volume uncertainty associated with price in China stock market. The empirical work reveals that market crowd trade a stock in efficient adaptation except for simple heuristics, gradually tend to achieve agreement on an outcome or an asset price widely on a trading day, and generate such a stationary equilibrium price very often in interaction and competition among themselves no matter whether it is highly overestimated or underestimated. This suggests that asset prices include not only a fundamental value but also private information, speculative, sentiment, attention, gamble, and entertainment values etc. Moreover, market crowd adapt to gain and loss by trading volume increase or decrease significantly in interaction with environment in any two consecutive trading days. Our results demonstrate how interaction between information and news, the trading action, and return outcomes in the three-term feedback loop produces excessive trading volume which includes various internal and external causes.

q-fin.GN↗

arXiv2023

Learn to Sing by Listening: Building Controllable Virtual Singer by Unsupervised Learning from Voice Recordings

The virtual world is being established in which digital humans are created indistinguishable from real humans. Producing their audio-related capabilities is crucial since voice conveys extensive personal characteristics. We aim to create a controllable audio-form virtual singer; however, supervised modeling and controlling all different factors of the singing voice, such as timbre, tempo, pitch, and lyrics, is extremely difficult since accurately labeling all such information needs enormous labor work. In this paper, we propose a framework that could digitize a person's voice by simply "listening" to the clean voice recordings of any content in a fully unsupervised manner and predict singing voices even only using speaking recordings. A variational auto-encoder (VAE) based framework is developed, which leverages a set of pre-trained models to encode the audio as various hidden embeddings representing different factors of the singing voice, and further decodes the embeddings into raw audio. By manipulating the hidden embeddings for different factors, the resulting singing voices can be controlled, and new virtual singers can also be further generated by interpolating between timbres. Evaluations of different types of experiments demonstrate the proposed method's effectiveness. The proposed method is the critical technique for producing the AI choir, which empowered the human-AI symbiotic orchestra in Hong Kong in July 2022.

cs.SD↗

arXiv2022

TT-Net: Dual-path transformer based sound field translation in the spherical harmonic domain

In the current method for the sound field translation tasks based on spherical harmonic (SH) analysis, the solution based on the additive theorem usually faces the problem of singular values caused by large matrix condition numbers. The influence of different distances and frequencies of the spherical radial function on the stability of the translation matrix will affect the accuracy of the SH coefficients at the selected point. Due to the problems mentioned above, we propose a neural network scheme based on the dual-path transformer. More specifically, the dual-path network is constructed by the self-attention module along the two dimensions of the frequency and order axes. The transform-average-concatenate layer and upscaling layer are introduced in the network, which provides solutions for multiple sampling points and upscaling. Numerical simulation results indicate that both the working frequency range and the distance range of the translation are extended. More accurate higher-order SH coefficients are obtained with the proposed dual-path network.

cs.SD↗

arXiv2022

Understanding How Older Adults Comprehend COVID-19 Interactive Visualizations via Think-Aloud Protocol

Older adults have been hit disproportionally hard by the COVID-19 pandemic. One critical way for older adults to minimize the negative impact of COVID-19 and future pandemics is to stay informed about its latest information, which has been increasingly presented through online interactive visualizations (e.g., live dashboards and websites). Thus, it is imperative to understand how older adults interact with and comprehend online COVID-19 interactive visualizations and what challenges they might encounter to make such visualizations more accessible to older adults. We adopted a user-centered approach by inviting older adults to interact with COVID-19 interactive visualizations while at the same time verbalizing their thought processes using a think-aloud protocol. By analyzing their think-aloud verbalizations, we identified four types of thought processes representing how older adults comprehended the visualizations and uncovered the challenges they encountered. Furthermore, we also identified the challenges they encountered with seven common types of interaction techniques adopted by the visualizations. Based on the findings, we present design guidelines for making interactive visualizations more accessible to older adults.

cs.HC↗

arXiv2021

The critical role of the donor polymer in the stability of high-performance non-fullerene acceptor organic solar cells

Driven by the rapid development of non-fullerene electron acceptors (NFAs), the power conversion efficiencies of organic solar cells (OSCs) have reached levels suitable for commercial applications. However, the poor operational stability of high-performance NFA OSCs is a remaining fundamental challenge that must be addressed. Whilst previous studies have primarily focused on the NFA component, we consider here the degradation pathways of both the donor and acceptor materials in the benchmark PM6:Y6 blend. Here, we show that light soaking greatly increases the energetic disorder and trap state density in PM6, with little effect on Y6. This is corroborated by electron paramagnetic resonance spectroscopy, which reveals increased recombination via trapped polarons on PM6 after light soaking. In addition, ultrafast optical spectroscopy studies on light-soaked samples show that PM6 singlet excitons are rapidly converted into interchain polaron pairs on sub-100 fs timescales; this process outcompetes electron transfer to Y6, significantly reducing the charge generation yield of the blend. We make similar observations in the parent polymer, PBDB-T, indicating that this class of donor materials, used in most high-performance OSCs to date, are intrinsically unstable to light soaking. Thus, we reveal that the donor polymer can be a further critical weak link in efficient OSC systems, whose degradation mechanism needs to be addressed collectively with NFAs.

cond-mat.mtrl-sci↗

arXiv2021

Machine unlearning via GAN

Machine learning models, especially deep models, may unintentionally remember information about their training data. Malicious attackers can thus pilfer some property about training data by attacking the model via membership inference attack or model inversion attack. Some regulations, such as the EU's GDPR, have enacted "The Right to Be Forgotten" to protect users' data privacy, enhancing individuals' sovereignty over their data. Therefore, removing training data information from a trained model has become a critical issue. In this paper, we present a GAN-based algorithm to delete data in deep models, which significantly improves deleting speed compared to retraining from scratch, especially in complicated scenarios. We have experimented on five commonly used datasets, and the experimental results show the efficiency of our method.

cs.LG↗

arXiv2021

Subject Enveloped Deep Sample Fuzzy Ensemble Learning Algorithm of Parkinson's Speech Data

Parkinson disease (PD)'s speech recognition is an effective way for its diagnosis, which has become a hot and difficult research area in recent years. As we know, there are large corpuses (segments) within one subject. However, too large segments will increase the complexity of the classification model. Besides, the clinicians interested in finding diagnostic speech markers that reflect the pathology of the whole subject. Since the optimal relevant features of each speech sample segment are different, it is difficult to find the uniform diagnostic speech markers. Therefore, it is necessary to reconstruct the existing large segments within one subject into few segments even one segment within one subject, which can facilitate the extraction of relevant speech features to characterize diagnostic markers for the whole subject. To address this problem, an enveloped deep speech sample learning algorithm for Parkinson's subjects based on multilayer fuzzy c-mean (MlFCM) clustering and interlayer consistency preservation is proposed in this paper. The algorithm can be used to achieve intra-subject sample reconstruction for Parkinson's disease (PD) to obtain a small number of high-quality prototype sample segments. At the end of the paper, several representative PD speech datasets are selected and compared with the state-of-the-art related methods, respectively. The experimental results show that the proposed algorithm is effective signifcantly.

cs.SD↗

arXiv2021

Lightweight machine unlearning in neural network

In recent years, machine learning neural network has penetrated deeply into people's life. As the price of convenience, people's private information also has the risk of disclosure. The "right to be forgotten" was introduced in a timely manner, stipulating that individuals have the right to withdraw their consent from personal information processing activities based on their consent. To solve this problem, machine unlearning is proposed, which allows the model to erase all memory of private information. Previous studies, including retraining and incremental learning to update models, often take up extra storage space or are difficult to apply to neural networks. Our method only needs to make a small perturbation of the weight of the target model and make it iterate in the direction of the model trained with the remaining data subset until the contribution of the unlearning data to the model is completely eliminated. In this paper, experiments on five datasets prove the effectiveness of our method for machine unlearning, and our method is 15 times faster than retraining.

cs.LG↗

arXiv2021

Controlled Evaluation of Grammatical Knowledge in Mandarin Chinese Language Models

Prior work has shown that structural supervision helps English language models learn generalizations about syntactic phenomena such as subject-verb agreement. However, it remains unclear if such an inductive bias would also improve language models' ability to learn grammatical dependencies in typologically different languages. Here we investigate this question in Mandarin Chinese, which has a logographic, largely syllable-based writing system; different word order; and sparser morphology than English. We train LSTMs, Recurrent Neural Network Grammars, Transformer language models, and Transformer-parameterized generative parsing models on two Mandarin Chinese datasets of different sizes. We evaluate the models' ability to learn different aspects of Mandarin grammar that assess syntactic and semantic relationships. We find suggestive evidence that structural supervision helps with representing syntactic state across intervening content and improves performance in low-data settings, suggesting that the benefits of hierarchical inductive biases in acquiring dependency relationships may extend beyond English.

cs.CL↗

arXiv2021

Tracking Fast Neural Adaptation by Globally Adaptive Point Process Estimation for Brain-Machine Interface

Brain-machine interfaces (BMIs) help the disabled restore body functions by translating neural activity into digital commands to control external devices. Neural adaptation, where the brain signals change in response to external stimuli or movements, plays an important role in BMIs. When subjects purely use neural activity to brain-control a prosthesis, some neurons will actively explore a new tuning property to accomplish the movement task. The prediction of this neural tuning property can help subjects adapt more efficiently to brain control and maintain good decoding performance. Existing prediction methods track the slow change of the tuning property in the manual control, which is not suitable for the fast neural adaptation in brain control. In order to identify the active neurons in brain control and track their tuning property changes, we propose a globally adaptive point process method (GaPP) to estimate the neural modulation state from spike trains, decompose the states into the hyper preferred direction and reconstruct the kinematics in a dual-model framework. We implement the method on real data from rats performing a two-lever discrimination task under manual control and brain control. The results show our method successfully predicts the neural modulation state and identifies the neurons that become active in brain control. Compared to existing methods, ours tracks the fast changes of the hyper preferred direction from manual control to brain control more accurately and efficiently and reconstructs the kinematics better and faster.

eess.SP↗

arXiv2021

EvoK: Connecting loved ones through Heart Rate sharing

In this work, we present EvoK, a new way of sharing one's heart rate with feedback from their close contacts to alleviate social isolation and loneliness. EvoK consists of a pair of wearable prototype devices (i.e., sender and receiver). The sender is designed as a headband enabling continuous sensing of heart rate with aesthetic designs to maximize social acceptance. The receiver is designed as a wristwatch enabling unobtrusive receiving of the loved one's continuous heart rate with multi-modal notification systems.

cs.HC↗

arXiv2017

Quantum interference induced by multiple Landau-Zener transitions in a strongly driven rf-SQUID qubit

We irradiated an rf-SQUID qubit with large-amplitude and high frequency electromagnetic field. Population transitions between macroscopic distinctive quantum states due to Landau-Zener transitions at energy-level avoided crossings were observed. The qubit population on the excited states as a function of flux detuning and microwave power exhibits interference patterns. Some novel features are found in the interference and a model based on rate equations can well address the features.

quant-ph↗

arXiv2017

Quasi-B-mode generated by high-frequency gravitational waves and corresponding perturbative photon fluxes

Interaction of very low-frequency primordial(relic) gravitational waves(GWs) to cosmic microwave background(CMB) can generate B-mode polarization. Here, for the first time we point out that the electromagnetic(EM) response to high-frequency GWs(HFGWs) would produce quasi-B-mode distribution of the perturbative photon fluxes, and study the duality and high complementarity between such two B-modes. Based on this quasi-B-mode in HFGWs, it is shown that the distinguishing and observing of HFGWs from the braneworld would be quite possible due to their large amplitude, higher frequency and very different physical behaviors between the perturbative photon fluxes and background photons, and the measurement of relic HFGWs may also be possible though face to enormous challenge.

gr-qc↗

arXiv2015

Photon-noise limited sensitivity in titanium nitride kinetic inductance detectors

We demonstrate photon-noise limited performance at sub-millimeter wavelengths in feedhorn-coupled, microwave kinetic inductance detectors (MKIDs) made of a TiN/Ti/TiN trilayer superconducting film, tuned to have a transition temperature of 1.4~K. Micro-machining of the silicon-on-insulator wafer backside creates a quarter-wavelength backshort optimized for efficient coupling at 250~\micron. Using frequency read out and when viewing a variable temperature blackbody source, we measure device noise consistent with photon noise when the incident optical power is $>$~0.5~pW, corresponding to noise equivalent powers $>$~3$\times 10^{-17}$ W/$\sqrt{\mathrm{Hz}}$. This sensitivity makes these devices suitable for broadband photometric applications at these wavelengths.

astro-ph.IM↗

arXiv2014

Optical responses of the switching currents in Al and Nb Josephson junctions

We experimentally demonstrated the optical responses of the switching currents in two types of Josephson tunnel junctions: Al/AlOx/Al and Nb/AlOx/Nb. The radiation-induced switching current shifts were measured at ultra-low bath temperature (T~16 mK). It is observed that the Al-junction has a more sensitive optical response than the Nb-junction, which is as expected since Al electrode has a smaller superconducting gap energy. The minimum detectable radiation powers with the present Al-junction and Nb-junction are 8 pW (corresponding to 8*10^5 incoming photons in one measurement cycle) and 2 nW respectively. In addition, we found that the radiation-induced thermal effects are dominant in the observed optical responses. Several methods are proposed to further improve the optical responsivity, so that the josephson junction based devices could be applicable in photon detections.

cond-mat.mes-hall↗