146 research outputs found
Coordinate Quantized Neural Implicit Representations for Multi-view Reconstruction
In recent years, huge progress has been made on learning neural implicit
representations from multi-view images for 3D reconstruction. As an additional
input complementing coordinates, using sinusoidal functions as positional
encodings plays a key role in revealing high frequency details with
coordinate-based neural networks. However, high frequency positional encodings
make the optimization unstable, which results in noisy reconstructions and
artifacts in empty space. To resolve this issue in a general sense, we
introduce to learn neural implicit representations with quantized coordinates,
which reduces the uncertainty and ambiguity in the field during optimization.
Instead of continuous coordinates, we discretize continuous coordinates into
discrete coordinates using nearest interpolation among quantized coordinates
which are obtained by discretizing the field in an extremely high resolution.
We use discrete coordinates and their positional encodings to learn implicit
functions through volume rendering. This significantly reduces the variations
in the sample space, and triggers more multi-view consistency constraints on
intersections of rays from different views, which enables to infer implicit
function in a more effective way. Our quantized coordinates do not bring any
computational burden, and can seamlessly work upon the latest methods. Our
evaluations under the widely used benchmarks show our superiority over the
state-of-the-art. Our code is available at
https://github.com/MachinePerceptionLab/CQ-NIR.Comment: to be appeared at ICCV 202
Privacy-preserving Energy Scheduling for Smart Grid with Renewables
We consider joint demand response and power procurement to optimize the average social welfare of a smart power grid system with renewable sources. The renewable sources such as wind and solar energy are intermittent and fluctuate rapidly. As a consequence, the demand response algorithm needs to be executed in real time to ensure the stability of a smart grid system with renewable sources. We develop a demand response algorithm that converges to the optimal solution with superlinear rates of convergence. In the simulation studies, the proposed algorithm converges roughly thirty time faster than the traditional subgradient algorithm. In addition, it is fully distributed and can be realized either synchronously or in asynchronous manner, which eases practical deployment
Natural Language based Context Modeling and Reasoning with LLMs: A Tutorial
Large language models (LLMs) have become phenomenally surging, since
2018--two decades after introducing context-awareness into computing systems.
Through taking into account the situations of ubiquitous devices, users and the
societies, context-aware computing has enabled a wide spectrum of innovative
applications, such as assisted living, location-based social network services
and so on. To recognize contexts and make decisions for actions accordingly,
various artificial intelligence technologies, such as Ontology and OWL, have
been adopted as representations for context modeling and reasoning. Recently,
with the rise of LLMs and their improved natural language understanding and
reasoning capabilities, it has become feasible to model contexts using natural
language and perform context reasoning by interacting with LLMs such as ChatGPT
and GPT-4. In this tutorial, we demonstrate the use of texts, prompts, and
autonomous agents (AutoAgents) that enable LLMs to perform context modeling and
reasoning without requiring fine-tuning of the model. We organize and introduce
works in the related field, and name this computing paradigm as the LLM-driven
Context-aware Computing (LCaC). In the LCaC paradigm, users' requests, sensors
reading data, and the command to actuators are supposed to be represented as
texts. Given the text of users' request and sensor data, the AutoAgent models
the context by prompting and sends to the LLM for context reasoning. LLM
generates a plan of actions and responds to the AutoAgent, which later follows
the action plan to foster context-awareness. To prove the concepts, we use two
showcases--(1) operating a mobile z-arm in an apartment for assisted living,
and (2) planning a trip and scheduling the itinerary in a context-aware and
personalized manner.Comment: Under revie
EdgeSense: Edge-Mediated Spatial-Temporal Crowdsensing
Edge computing recently is increasingly popular due to the growth of data size and the need of sensing with the reduced center. Based on Edge computing architecture, we propose a novel crowdsensing framework called Edge-Mediated Spatial-Temporal Crowdsensing. This algorithm targets on receiving the environment information such as air pollution, temperature, and traffic flow in some parts of the goal area, and does not aggregate sensor data with its location information. Specifically, EdgeSense works on top of a secured peer-To-peer network consisted of participants and propose a novel Decentralized Spatial-Temporal Crowdsensing framework based on Parallelized Stochastic Gradient Descent. To approximate the sensing data in each part of the target area in each sensing cycle, EdgeSense uses the local sensor data in participants\u27 mobile devices to learn the low-rank characteristic and then recovers the sensing data from it. We evaluate the EdgeSense on the real-world data sets (temperature [1] and PM2.5 [2] data sets), where our algorithm can achieve low error in approximation and also can compete with the baseline algorithm which is designed using centralized and aggregated mechanism
Searching for Radio Outflows from M31* with VLBI Observations
As one of the nearest and most dormant supermassive black holes (SMBHs), M31*
provides a rare but promising opportunity for studying the physics of black
hole accretion and feedback at the quiescent state. Previous Karl G. Jansky
Very Large Array (VLA) observations with an arcsec resolution have detected
M31* as a compact radio source over centimeter wavelengths, but the steep radio
spectrum suggests optically-thin synchrotron radiation from an outflow driven
by a hot accretion flow onto the SMBH. Aiming to probe the putative radio
outflow, we have conducted milli-arcsec-resolution very long baseline
interferometric (VLBI) observations of M31* in 2016, primarily at 5 GHz and
combining the Very Long Baseline Array, Tianma-65m and Shanghai-25m Radio
Telescopes. Despite the unprecedented simultaneous resolution and sensitivity
achieved, no significant () signal is detected at the putative
position of M31* given an RMS level of , thus ruling
out a point-like source with a peak flux density comparable to that
() measured by the VLA observations taken in 2012. We
disfavor the possibility that M31* has substantially faded since 2012, in view
that a 2017 VLA observation successfully detected M31* at a historically-high
peak flux density ( at 6 GHz). Instead, the
non-detection of the VLBI observations is best interpreted as the arcsec-scale
core being resolved out at the milli-arcsec-scale, suggesting an intrinsic size
of M31* at 5 GHz larger than times the Schwarzschild radius. Such
extended radio emission may originate from a hot wind driven by the weakly
accreting SMBH.Comment: 9 pages, 2 figures. Accepted for publication in the Astrophysical
Journa
Ultrasound-induced release of micropallets with cells
Separation of selected adherent live cells attached on an array of microelements, termed micropallets, from a mixed population is an important process in biomedical research. We demonstrated that adherent cells can be safely, selectively, and rapidly released from the glass substrate together with micropallets using an ultrasound wave. A 3.3-MHz ultrasound transducer was used to release micropallets (500 μm × 500 μm × 300 μm) with attached HeLa cells, and a cell viability of 92% was obtained after ultrasound release. The ultrasound-induced release process was recorded by a high-speed camera, revealing a proximate velocity of ∼0.5 m/s
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