131 research outputs found
Modeling Twitter Engagement in Real-World Events
Twitter offers tremendous opportunities for people to engage with real-world events (e.g., political election) through information sharing and communicating about these events. However, little is understood about the factors that affect peopleâs Twitter engagement (e.g., posting) in such real-world events. This paper examines multiple predictive factors associated with four different perspectives of usersâ Twitter engagement, and quantify their potential influence on predicting the (i) presence; and (ii) degree of the userâs engagement with real-world events. We find that the measures of peopleâs prior Twitter activities, topical interests, geolocation, and social network structures are all variously correlated to their engagement with real-world events.
Online Review Censorship
Ample anecdotal evidence in the media notes that many businesses seek to âsilenceâ negative reviews, e.g., via legal threat. Despite attention toward this issue, we are aware of no systematic analyses addressing it. We address that gap here, leveraging review data from TripAdvisor.com. First, we estimate that ~1% of truthful reviews are deleted within six months of posting and that negative reviews are significantly more likely to be deleted, consistent with a mechanism of censorship. The effect is substantial; we estimate that a 1-star decrease in rating valence is associated with an approximate 25% (0.25pp) increase in the probability of deletion. Second, we examine how freedom of expression (FoE) in a country associates with characteristics of (uncensored) online reviews. We find that FoE associates with larger review volumes, lower review valence, and faster review posting. We discuss implications for online ratings platforms, consumers, and research opportunities
From Capture to Display: A Survey on Volumetric Video
Volumetric video, which offers immersive viewing experiences, is gaining
increasing prominence. With its six degrees of freedom, it provides viewers
with greater immersion and interactivity compared to traditional videos.
Despite their potential, volumetric video services poses significant
challenges. This survey conducts a comprehensive review of the existing
literature on volumetric video. We firstly provide a general framework of
volumetric video services, followed by a discussion on prerequisites for
volumetric video, encompassing representations, open datasets, and quality
assessment metrics. Then we delve into the current methodologies for each stage
of the volumetric video service pipeline, detailing capturing, compression,
transmission, rendering, and display techniques. Lastly, we explore various
applications enabled by this pioneering technology and we present an array of
research challenges and opportunities in the domain of volumetric video
services. This survey aspires to provide a holistic understanding of this
burgeoning field and shed light on potential future research trajectories,
aiming to bring the vision of volumetric video to fruition.Comment: Submitte
Understanding User Behavior in Volumetric Video Watching: Dataset, Analysis and Prediction
Volumetric video emerges as a new attractive video paradigm in recent years
since it provides an immersive and interactive 3D viewing experience with six
degree-of-freedom (DoF). Unlike traditional 2D or panoramic videos, volumetric
videos require dense point clouds, voxels, meshes, or huge neural models to
depict volumetric scenes, which results in a prohibitively high bandwidth
burden for video delivery. Users' behavior analysis, especially the viewport
and gaze analysis, then plays a significant role in prioritizing the content
streaming within users' viewport and degrading the remaining content to
maximize user QoE with limited bandwidth. Although understanding user behavior
is crucial, to the best of our best knowledge, there are no available 3D
volumetric video viewing datasets containing fine-grained user interactivity
features, not to mention further analysis and behavior prediction. In this
paper, we for the first time release a volumetric video viewing behavior
dataset, with a large scale, multiple dimensions, and diverse conditions. We
conduct an in-depth analysis to understand user behaviors when viewing
volumetric videos. Interesting findings on user viewport, gaze, and motion
preference related to different videos and users are revealed. We finally
design a transformer-based viewport prediction model that fuses the features of
both gaze and motion, which is able to achieve high accuracy at various
conditions. Our prediction model is expected to further benefit volumetric
video streaming optimization. Our dataset, along with the corresponding
visualization tools is accessible at
https://cuhksz-inml.github.io/user-behavior-in-vv-watching/Comment: Accepted by ACM MM'2
LiveVV: Human-Centered Live Volumetric Video Streaming System
Volumetric video has emerged as a prominent medium within the realm of
eXtended Reality (XR) with the advancements in computer graphics and depth
capture hardware. Users can fully immersive themselves in volumetric video with
the ability to switch their viewport in six degree-of-freedom (DOF), including
three rotational dimensions (yaw, pitch, roll) and three translational
dimensions (X, Y, Z). Different from traditional 2D videos that are composed of
pixel matrices, volumetric videos employ point clouds, meshes, or voxels to
represent a volumetric scene, resulting in significantly larger data sizes.
While previous works have successfully achieved volumetric video streaming in
video-on-demand scenarios, the live streaming of volumetric video remains an
unresolved challenge due to the limited network bandwidth and stringent latency
constraints. In this paper, we for the first time propose a holistic live
volumetric video streaming system, LiveVV, which achieves multi-view capture,
scene segmentation \& reuse, adaptive transmission, and rendering. LiveVV
contains multiple lightweight volumetric video capture modules that are capable
of being deployed without prior preparation. To reduce bandwidth consumption,
LiveVV processes static and dynamic volumetric content separately by reusing
static data with low disparity and decimating data with low visual saliency.
Besides, to deal with network fluctuation, LiveVV integrates a volumetric video
adaptive bitrate streaming algorithm (VABR) to enable fluent playback with the
maximum quality of experience. Extensive real-world experiment shows that
LiveVV can achieve live volumetric video streaming at a frame rate of 24 fps
with a latency of less than 350ms
GW26-e4017 Intravenous infusion of drag-reducing polymers protects against acute myocardial ischemia and reperfusion injury
Effects of Tea Residue Extracts with Different Molecular Weight on the Pasting Characteristics of Potato Starch
Tea residues are the remaining residue of tea after processing and utilization, which are rich in multiple active components. To investigate the effects of different types and molecular weights of tea residue extracts on the pasting characteristics of potato starch (PS), the ethanol extract (TRE), water extract (TRW) and alkali extract (TRA) of tea residue were obtained by continuous extraction method. On this basis, the different molecular weights of ethanol extract (TRE-1, 30 kDa) and water extract (TRW-1, 100 kDa) were prepared by a membrane separation. The effects of different tea residue extracts on the viscosity properties were investigated, and the microstructure of potato starch added with tea residue extract was observed by scanning electron microscopy (SEM). The results showed that different types and molecular weights of tea residue extracts could significantly (PTRW-2>TRE-2>TRW-1>TRE-1. The peak viscosity of potato starch was gradually decreased with the increase of different extracts. After adding 10% TRA, TRW-2, TRE-2, TRW-1 and TRE-1, the peak viscosity of potato starch was 4624, 5013, 5431, 5911 and 6195 cP, respectively. TRE-2, TRW-2 and TRA could better promote the link between potato starch fragments and result in a more complete and smooth lamellar structure, compared with TRE-1, TRW-1. In summary, the addition of different types and molecular weights of tea residue extracts could effectively inhibit the gelatinization of potato starch, and the inhibitory effect of 10% alkali extract was the best
Principle, Design and Future of Inchworm Type Piezoelectric Actuators
The inchworm type piezoelectric actuator is one novel actuator to ensure a large working stroke with high resolution which has attracted the continuous attentions from researchers all over the world. In this study, the motion principle of the inchworm type piezoelectric is discussed: the âwalkerâ pattern, the âpusherâ pattern and hybrid âwalker-pusherâ pattern. The classification (linear, rotary and multi-DOF) and development are introduced in details, some significant researches are illustrated. Finally, the future direction of inchworm type piezoelectric actuators is pointed out according the development of inchworm type piezoelectric actuators. This study shows the clear principle, design and future of inchworm type piezoelectric actuators which is meaningful for the development of piezoelectric actuators
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