arXiv subjects

Feng Tian

Publications and source records attributed to Feng Tian.

At least 109 records · Page 6Linked to original sources

arXiv2021

HoloBoard: a Large-format Immersive Teaching Board based on pseudo HoloGraphics

In this paper, we present HoloBoard, an interactive large-format pseudo-holographic display system for lecture-based classes. With its unique properties of immersive visual display and transparent screen, we designed and implemented a rich set of novel interaction techniques like immersive presentation, role-play, and lecturing behind the scene that is potentially valuable for lecturing in class. We conducted a controlled experimental study to compare a HoloBoard class with a normal class by measuring students' learning outcomes and three dimensions of engagement (i.e., behavioral, emotional, and cognitive engagement). We used pre-/post- knowledge tests and multimodal learning analytics to measure students' learning outcomes and learning experiences. Results indicated that the lecture-based class utilizing HoloBoard lead to slightly better learning outcomes and a significantly higher level of student engagement. Given the results, we discussed the impact of HoloBoard as an immersive media in the classroom setting and suggest several design implications for deploying HoloBoard in immersive teaching practices.

cs.HC↗

arXiv2021

TeethTap: Recognizing Discrete Teeth Gestures Using Motion and Acoustic Sensing on an Earpiece

Teeth gestures become an alternative input modality for different situations and accessibility purposes. In this paper, we present TeethTap, a novel eyes-free and hands-free input technique, which can recognize up to 13 discrete teeth tapping gestures. TeethTap adopts a wearable 3D printed earpiece with an IMU sensor and a contact microphone behind both ears, which works in tandem to detect jaw movement and sound data, respectively. TeethTap uses a support vector machine to classify gestures from noise by fusing acoustic and motion data, and implements K-Nearest-Neighbor (KNN) with a Dynamic Time Warping (DTW) distance measurement using motion data for gesture classification. A user study with 11 participants demonstrated that TeethTap could recognize 13 gestures with a real-time classification accuracy of 90.9% in a laboratory environment. We further uncovered the accuracy differences on different teeth gestures when having sensors on single vs. both sides. Moreover, we explored the activation gesture under real-world environments, including eating, speaking, walking and jumping. Based on our findings, we further discussed potential applications and practical challenges of integrating TeethTap into future devices.

cs.HC↗

arXiv2021

The Mega-MUSCLES Spectral Energy Distribution Of TRAPPIST-1

We present a 5A-100um Spectral Energy Distribution (SED) of the ultracool dwarf star TRAPPIST-1, obtained as part of the Mega-MUSCLES Treasury Survey. The SED combines ultraviolet and blue-optical spectroscopy obtained with the Hubble Space Telescope, X-ray spectroscopy obtained with XMM-Newton, and models of the stellar photosphere, chromosphere, transition region and corona. A new Differential Emission Measure model of the unobserved extreme-ultraviolet spectrum is provided, improving on the Lyman alpha to EUV relations often used to estimate the 100-911A flux from low-mass stars. We describe the observations and models used, as well as the recipe for combining them into an SED. We also provide a semi-empirical, noise-free model of the stellar ultraviolet spectrum based on our observations for use in atmospheric modelling of the TRAPPIST-1 planets.

astro-ph.SR↗

arXiv2021

CASS: Towards Building a Social-Support Chatbot for Online Health Community

Chatbots systems, despite their popularity in today's HCI and CSCW research, fall short for one of the two reasons: 1) many of the systems use a rule-based dialog flow, thus they can only respond to a limited number of pre-defined inputs with pre-scripted responses; or 2) they are designed with a focus on single-user scenarios, thus it is unclear how these systems may affect other users or the community. In this paper, we develop a generalizable chatbot architecture (CASS) to provide social support for community members in an online health community. The CASS architecture is based on advanced neural network algorithms, thus it can handle new inputs from users and generate a variety of responses to them. CASS is also generalizable as it can be easily migrate to other online communities. With a follow-up field experiment, CASS is proven useful in supporting individual members who seek emotional support. Our work also contributes to fill the research gap on how a chatbot may influence the whole community's engagement.

cs.HC↗

arXiv2021

GEOMScope: Large Field-of-view 3D Lensless Microscopy with Low Computational Complexity

Recent development of lensless imagers has enabled three-dimensional (3D) imaging through a thin piece of optics in close proximity to a camera sensor. A general challenge of wide-field lensless imaging is the high computational complexity and slow speed to reconstruct 3D objects through iterative optimization process. Here, we demonstrated GEOMScope, a lensless 3D microscope that forms image through a single layer of microlens array and reconstructs objects through a geometrical-optics-based pixel back projection algorithm and background suppressions. Compared to others, our method allows local reconstruction, which significantly reduces the required computation resource and increases the reconstruction speed by orders of magnitude. This enables near real-time object reconstructions across a large volume of 23x23x5 mm^3, with a lateral resolution of 40 um and axial resolution of 300 um. Our system opens new avenues for broad biomedical applications such as endoscopy, which requires both miniaturized device footprint and real-time high resolution visualization.

physics.optics↗

arXiv2021

"Brilliant AI Doctor" in Rural China: Tensions and Challenges in AI-Powered CDSS Deployment

Artificial intelligence (AI) technology has been increasingly used in the implementation of advanced Clinical Decision Support Systems (CDSS). Research demonstrated the potential usefulness of AI-powered CDSS (AI-CDSS) in clinical decision making scenarios. However, post-adoption user perception and experience remain understudied, especially in developing countries. Through observations and interviews with 22 clinicians from 6 rural clinics in China, this paper reports the various tensions between the design of an AI-CDSS system ("Brilliant Doctor") and the rural clinical context, such as the misalignment with local context and workflow, the technical limitations and usability barriers, as well as issues related to transparency and trustworthiness of AI-CDSS. Despite these tensions, all participants expressed positive attitudes toward the future of AI-CDSS, especially acting as "a doctor's AI assistant" to realize a Human-AI Collaboration future in clinical settings. Finally we draw on our findings to discuss implications for designing AI-CDSS interventions for rural clinical contexts in developing countries.

cs.HC↗

arXiv2021

Possible Atmospheric Diversity of Low Mass Exoplanets, some Central Aspects

Exoplanetary science continues to excite and surprise with its rich diversity. We discuss here some key aspects potentially influencing the range of exoplanetary terrestrial-type atmospheres which could exist in nature. We are motivated by newly emerging observations, refined approaches to address data degeneracies, improved theories for key processes affecting atmospheric evolution and a new generation of atmospheric models which couple physical processes from the deep interior through to the exosphere and consider the planetary-star system as a whole. Using the Solar System as our guide we first summarize the main processes which sculpt atmospheric evolution then discuss their potential interactions in the context of exoplanetary environments. We summarize key uncertainties and consider a diverse range of atmospheric compositions discussing their potential occurrence in an exoplanetary context.

astro-ph.EP↗

arXiv2020

Effect of inter-system coupling on heat transport in a microscopic collision model

In this paper we consider a bipartite system composed of two subsystems each coupled to its own thermal environment. Based on a collision model, we mainly study whether the approximation (i.e., the inter-system interaction is ignored when modeling the system-environment coupling) is valid or not. We also address the problem of heat transport unitedly for both conventional energy-preserving system-environment interactions and non-energy preserving system-environment interactions. For the former interaction, as the inter-system interaction strength increases, at first this approximation gets worse as expected, but then counterintuitively gets better even for a stronger inter-system coupling. For the latter interaction with asymmetry, this approximation gets progressively worse. In this case we realize a perfect thermal rectification, and we can not find apparent rectification effect for the former interaction. Finally and more importantly, our results show that whether this approximation is valid or not is closely related to the quantum correlations between the subsystems, i.e., the weaker the quantum correlations, the more justified the approximation and vice versa.

quant-ph↗

arXiv2020

Estimating the Ultraviolet Emission of M dwarfs with Exoplanets from Ca II and H$α$

M dwarf stars are excellent candidates around which to search for exoplanets, including temperate, Earth-sized planets. To evaluate the photochemistry of the planetary atmosphere, it is essential to characterize the UV spectral energy distribution of the planet's host star. This wavelength regime is important because molecules in the planetary atmosphere such as oxygen and ozone have highly wavelength dependent absorption cross sections that peak in the UV (900-3200 $Å$). We seek to provide a broadly applicable method of estimating the UV emission of an M dwarf, without direct UV data, by identifying a relationship between non-contemporaneous optical and UV observations. Our work uses the largest sample of M dwarf star far- and near-UV observations yet assembled. We evaluate three commonly-observed optical chromospheric activity indices -- H$α$ equivalent widths and log$_{10}$ L$_{Hα}$/L$_{bol}$, and the Mount Wilson Ca II H&K S and R$'_{HK}$ indices -- using optical spectra from the HARPS, UVES, and HIRES archives and new HIRES spectra. Archival and new Hubble Space Telescope COS and STIS spectra are used to measure line fluxes for the brightest chromospheric and transition region emission lines between 1200-2800 $Å$. Our results show a correlation between UV emission line luminosity normalized to the stellar bolometric luminosity and Ca II R$'_{HK}$ with standard deviations of 0.31-0.61 dex (factors of $\sim$2-4) about the best-fit lines. We also find correlations between normalized UV line luminosity and H$α$ log$_{10}$ L$_{Hα}$/L$_{bol}$ and the S index. These relationships allow one to estimate the average UV emission from M0 to M9 dwarfs when UV data are not available.

astro-ph.SR↗

arXiv2020

The High-Energy Radiation Environment Around a 10 Gyr M Dwarf: Habitable at Last?

High levels of X-ray and UV activity on young M dwarfs may drive rapid atmospheric escape on temperate, terrestrial planets orbiting within the liquid water habitable zone. However, secondary atmospheres on planets orbiting older, less active M dwarfs may be stable and present more promising candidates for biomarker searches. We present new HST and Chandra observations of Barnard's Star (GJ 699), a 10 Gyr old M3.5 dwarf, acquired as part of the Mega-MUSCLES program. Despite the old age and long rotation period of Barnard's star, we observe two FUV ($δ_{130}$ $\approx$ 5000s; $E_{130}$ $\approx$ 10$^{29.5}$ erg each) and one X-ray ($E_{X}$ $\approx$ 10$^{29.2}$ erg) flares, and estimate a high-energy flare duty cycle (defined here as the fraction of the time the star is in a flare state) of $\sim$ 25\%. A 5 A - 10 $μ$m SED of GJ 699 is created and used to evaluate the atmospheric stability of a hypothetical, unmagnetized terrestrial planet in the habitable zone ($r_{HZ}$ $\sim$ 0.1 AU). Both thermal and non-thermal escape modeling indicate (1) the $quiescent$ stellar XUV flux does not lead to strong atmospheric escape: atmospheric heating rates are comparable to periods of high solar activity on modern Earth, and (2) the $flare$ environment could drive the atmosphere into a hydrodynamic loss regime at the observed flare duty cycle: sustained exposure to the flare environment of GJ 699 results in the loss of $\approx$ 87 Earth atmospheres Gyr$^{-1}$ through thermal processes and $\approx$ 3 Earth atmospheres Gyr$^{-1}$ through ion loss processes, respectively. These results suggest that if rocky planet atmospheres can survive the initial $\sim$ 5 Gyr of high stellar activity, or if a second generation atmosphere can be formed or acquired, the flare duty cycle may be the controlling stellar parameter for the stability of Earth-like atmospheres around old M stars.

astro-ph.EP↗

arXiv2020

A Novel Software-based Multi-path RDMA Solutionfor Data Center Networks

In this paper we propose Virtuoso, a purely software-based multi-path RDMA solution for data center networks (DCNs) to effectively utilize the rich multi-path topology for load balancing and reliability. As a "middleware" library operating at the user space, Virtuoso employs three innovative mechanisms to achieve its goal. In contrast to existing hardware-based MP-RDMA solution, Virtuoso can be readily deployed in DCNs with existing RDMA NICs. It also decouples path selection and load balancing mechanisms from hardware features, allowing DCN operators and applications to make flexible decisions by employing the best mechanisms (as "plug-in" software library modules) as needed. Our experiments show that Virtuoso is capable of fully utilizing multiple paths with negligible CPU overheads

cs.NI↗

arXiv2020

FISHING Net: Future Inference of Semantic Heatmaps In Grids

For autonomous robots to navigate a complex environment, it is crucial to understand the surrounding scene both geometrically and semantically. Modern autonomous robots employ multiple sets of sensors, including lidars, radars, and cameras. Managing the different reference frames and characteristics of the sensors, and merging their observations into a single representation complicates perception. Choosing a single unified representation for all sensors simplifies the task of perception and fusion. In this work, we present an end-to-end pipeline that performs semantic segmentation and short term prediction using a top-down representation. Our approach consists of an ensemble of neural networks which take in sensor data from different sensor modalities and transform them into a single common top-down semantic grid representation. We find this representation favorable as it is agnostic to sensor-specific reference frames and captures both the semantic and geometric information for the surrounding scene. Because the modalities share a single output representation, they can be easily aggregated to produce a fused output. In this work we predict short-term semantic grids but the framework can be extended to other tasks. This approach offers a simple, extensible, end-to-end approach for multi-modal perception and prediction.

cs.CV↗

arXiv2020

Feature Super-Resolution Based Facial Expression Recognition for Multi-scale Low-Resolution Faces

Facial Expressions Recognition(FER) on low-resolution images is necessary for applications like group expression recognition in crowd scenarios(station, classroom etc.). Classifying a small size facial image into the right expression category is still a challenging task. The main cause of this problem is the loss of discriminative feature due to reduced resolution. Super-resolution method is often used to enhance low-resolution images, but the performance on FER task is limited when on images of very low resolution. In this work, inspired by feature super-resolution methods for object detection, we proposed a novel generative adversary network-based feature level super-resolution method for robust facial expression recognition(FSR-FER). In particular, a pre-trained FER model was employed as feature extractor, and a generator network G and a discriminator network D are trained with features extracted from images of low resolution and original high resolution. Generator network G tries to transform features of low-resolution images to more discriminative ones by making them closer to the ones of corresponding high-resolution images. For better classification performance, we also proposed an effective classification-aware loss re-weighting strategy based on the classification probability calculated by a fixed FER model to make our model focus more on samples that are easily misclassified. Experiment results on Real-World Affective Faces (RAF) Database demonstrate that our method achieves satisfying results on various down-sample factors with a single model and has better performance on low-resolution images compared with methods using image super-resolution and expression recognition separately.

cs.CV↗

arXiv2019

New boundaries for positive definite functions

With view to applications in stochastic analysis and geometry, we introduce a new correspondence for positive definite kernels (p.d.) $K$ and their associated reproducing kernel Hilbert spaces. With this we establish two kinds of factorizations: (i) Probabilistic: Starting with a positive definite kernel $K$ we analyze associated Gaussian processes $V$. Properties of the Gaussian processes will be derived from certain factorizations of $K$, arising as a covariance kernel of $V$. (ii) Geometric analysis: We discuss families of measure spaces arising as boundaries for $K$. Our results entail an analysis of a partial order on families of p.d. kernels, a duality for operators and frames, optimization, Karhunen--Loève expansions, and factorizations. Applications include a new boundary analysis for the Drury-Arveson kernel, and for certain fractals arising as iterated function systems; and an identification of optimal feature spaces in machine learning models.

math.FA↗

arXiv2019

Modeling e-Learners' Cognitive and Metacognitive Strategy in Comparative Question Solving

Cognitive and metacognitive strategy had demonstrated a significant role in self-regulated learning (SRL), and an appropriate use of strategies is beneficial to effective learning or question-solving tasks during a human-computer interaction process. This paper proposes a novel method combining Knowledge Map (KM) based data mining technique with Thinking Map (TM) to detect learner's cognitive and metacognitive strategy in the question-solving scenario. In particular, a graph-based mining algorithm is designed to facilitate our proposed method, which can automatically map cognitive strategy to metacognitive strategy with raising abstraction level, and make the cognitive and metacognitive process viewable, which acts like a reverse engineering engine to explain how a learner thinks when solving a question. Additionally, we develop an online learning environment system for participants to learn and record their behaviors. To corroborate the effectiveness of our approach and algorithm, we conduct experiments recruiting 173 postgraduate and undergraduate students, and they were asked to complete a question-solving task, such as "What are similarities and differences between array and pointer?" from "The C Programming Language" course and "What are similarities and differences between packet switching and circuit switching?" from "Computer Network Principle" course. The mined strategies patterns results are encouraging and supported well our proposed method.

cs.CY↗

arXiv2019

A Kaczmarz algorithm for sequences of projections, infinite products, and applications to frames in IFS $L^{2}$ spaces

We show that an idea, originating initially with a fundamental recursive iteration scheme (usually referred as "the" Kaczmarz algorithm), admits important applications in such infinite-dimensional, and non-commutative, settings as are central to spectral theory of operators in Hilbert space, to optimization, to large sparse systems, to iterated function systems (IFS), and to fractal harmonic analysis. We present a new recursive iteration scheme involving as input a prescribed sequence of selfadjoint projections. Applications include random Kaczmarz recursions, their limits, and their error-estimates.

math.FA↗

arXiv2019

Superposition, reduction of multivariable problems, and approximation

We study reduction schemes for functions of "many" variables into system of functions in one variable. Our setting includes infinite-dimensions. Following Cybenko-Kolmogorov, the outline for our results is as follows: We present explicit reductions schemes for multivariable problems, covering both a finite, and an infinite, number of variables. Starting with functions in "many" variables, we offer constructive reductions into superposition, with component terms, that make use of only functions in one variable, and specified choices of coordinate directions. Our proofs are transform based, using explicit transforms, Fourier and Radon; as well as multivariable Shannon interpolation.

math.FA↗

arXiv2019

On reproducing kernels, and analysis of measures

Starting with the correspondence between positive definite kernels on the one hand and reproducing kernel Hilbert spaces (RKHSs) on the other, we turn to a detailed analysis of associated measures and Gaussian processes. Point of departure: Every positive definite kernel is also the covariance kernel of a Gaussian process. Given a fixed sigma-finite measure $μ$, we consider positive definite kernels defined on the subset of the sigma algebra having finite $μ$ measure. We show that then the corresponding Hilbert factorizations consist of signed measures, finitely additive, but not automatically sigma-additive. We give a necessary and sufficient condition for when the measures in the RKHS, and the Hilbert factorizations, are sigma-additive. Our emphasis is the case when $μ$ is assumed non-atomic. By contrast, when $μ$ is known to be atomic, our setting is shown to generalize that of Shannon-interpolation. Our RKHS-approach further leads to new insight into the associated Gaussian processes, their Itô calculus and diffusion. Examples include fractional Brownian motion, and time-change processes.

math.FA↗