313 research outputs found
Optimizing Wi-Fi Channel Selection in a Dense Neighborhood
In dense neighborhoods, there are often dozens of homes in close proximity.
This can either be a tight city-block with many single-family homes (SFHs), or
a multiple dwelling units (MDU) complex (such as a big apartment building or
condominium). Each home in such a neighborhood (either a SFH or a single unit
in a MDU complex) has its own Wi-Fi access point (AP). Because there are few
(typically 2 or 3) non-overlapping radio channels for Wi-Fi, neighboring homes
may find themselves sharing a channel and competing over airtime, which may
cause bad experience of slow internet (long latency, buffering while streaming
movies, etc.). Wi-Fi optimization over all the APs in a dense neighborhood is
highly desired to provide the best user experience.
We present a method for Wi-Fi channel selection in a centralized way for all
the APs in a dense neighborhood. We describe how to use recent observations to
estimate the potential-pain matrix - for each pair of APs, how much Wi-Fi-pain
would they cause each other if they were on the same channel. We formulate an
optimization problem - finding a channel allocation (which channel each home
should use) that minimizes the total Wi-Fi-pain in the neighborhood. We design
an optimization algorithm that uses gradient descent over a neural network to
solve the optimization problem. We describe initial results from offline
experiments comparing our optimization solver to an off-the-shelf
mixed-integer-programming solver. In our experiments we show that the
off-the-shelf solver manages to find a better (lower total pain) solution on
the train data (from the recent days), but our neural-network solver
generalizes better - it finds a solution that achieves lower total pain for the
test data (tomorrow).Comment: We discussed this work in the 2022 Fall Technical Forum as part of
SCTE Cable-Tec Expo. This paper was published in SCTE Technical Journal. For
citing this work, please cite the original publicatio
ReMotENet: Efficient Relevant Motion Event Detection for Large-scale Home Surveillance Videos
This paper addresses the problem of detecting relevant motion caused by
objects of interest (e.g., person and vehicles) in large scale home
surveillance videos. The traditional method usually consists of two separate
steps, i.e., detecting moving objects with background subtraction running on
the camera, and filtering out nuisance motion events (e.g., trees, cloud,
shadow, rain/snow, flag) with deep learning based object detection and tracking
running on cloud. The method is extremely slow and therefore not cost
effective, and does not fully leverage the spatial-temporal redundancies with a
pre-trained off-the-shelf object detector. To dramatically speedup relevant
motion event detection and improve its performance, we propose a novel network
for relevant motion event detection, ReMotENet, which is a unified, end-to-end
data-driven method using spatial-temporal attention-based 3D ConvNets to
jointly model the appearance and motion of objects-of-interest in a video.
ReMotENet parses an entire video clip in one forward pass of a neural network
to achieve significant speedup. Meanwhile, it exploits the properties of home
surveillance videos, e.g., relevant motion is sparse both spatially and
temporally, and enhances 3D ConvNets with a spatial-temporal attention model
and reference-frame subtraction to encourage the network to focus on the
relevant moving objects. Experiments demonstrate that our method can achieve
comparable or event better performance than the object detection based method
but with three to four orders of magnitude speedup (up to 20k times) on GPU
devices. Our network is efficient, compact and light-weight. It can detect
relevant motion on a 15s surveillance video clip within 4-8 milliseconds on a
GPU and a fraction of second (0.17-0.39) on a CPU with a model size of less
than 1MB.Comment: WACV1
An Automatic Evaluation Framework for Multi-turn Medical Consultations Capabilities of Large Language Models
Large language models (LLMs) have achieved significant success in interacting
with human. However, recent studies have revealed that these models often
suffer from hallucinations, leading to overly confident but incorrect
judgments. This limits their application in the medical domain, where tasks
require the utmost accuracy. This paper introduces an automated evaluation
framework that assesses the practical capabilities of LLMs as virtual doctors
during multi-turn consultations. Consultation tasks are designed to require
LLMs to be aware of what they do not know, to inquire about missing medical
information from patients, and to ultimately make diagnoses. To evaluate the
performance of LLMs for these tasks, a benchmark is proposed by reformulating
medical multiple-choice questions from the United States Medical Licensing
Examinations (USMLE), and comprehensive evaluation metrics are developed and
evaluated on three constructed test sets. A medical consultation training set
is further constructed to improve the consultation ability of LLMs. The results
of the experiments show that fine-tuning with the training set can alleviate
hallucinations and improve LLMs' performance on the proposed benchmark.
Extensive experiments and ablation studies are conducted to validate the
effectiveness and robustness of the proposed framework.Comment: 10 pages, 9figure
Identification of SNPs in chemerin gene and association with carcass and meat quality traits of Qinchuan Cattle
Chemerin is a novel adipokine that regulates adipogenesis and adipocyte metabolism via its own receptor. In this study, two novel SNPs (868A>G in exon 2 and 2692C>T in exon 5) of chemerin gene were identified by PCR-SSCP and DNA sequencing technology. The allele frequencies of the novel SNPs were determined in the genetically diverse bovine breeds including six Chinese indigenous cattle breeds (Caoyuan red, Jiaxian red, Luxi, Nanyang, Qinchuan and Xia’nan cattle). We evaluated the potential association of the SNPs with traits measured by ultrasound measurement in 214 Qinchuan individuals. Furthermore, meat quality traits data gotten from carcass measurement in another 69 Qinchuan individuals were analyzed by the comparison between the genotypes and their phenotypic data. Results showed that SNP 868A>G had a significant association with the ultrasound loin-muscle area (P < 0.05), loin-eye area and water holding capability (P < 0.05). And also revealed significant effects of genotype on the ultrasound backfat thickness (P < 0.05), backfat thickness and water holding capability (P < 0.05) of SNP 2692C>T. It was shown that associations do exist between chemerin gene and carcass and meat quality traits. As a result of the small sample size of this study, it is proposed that further effort is required to validate these findings in larger populations. It could be concluded that ultrasound measurements are similar in accuracy to carcass measurements for predicting carcass and meat quality traits in cattle, and could be a useful predictor of retail yield in live animals.Keywords: Bos bovine, chemerin gene, PCR-SSCP, SNP, meat quality trait
Learning Semantic-Agnostic and Spatial-Aware Representation for Generalizable Visual-Audio Navigation
Visual-audio navigation (VAN) is attracting more and more attention from the
robotic community due to its broad applications, \emph{e.g.}, household robots
and rescue robots. In this task, an embodied agent must search for and navigate
to the sound source with egocentric visual and audio observations. However, the
existing methods are limited in two aspects: 1) poor generalization to unheard
sound categories; 2) sample inefficient in training. Focusing on these two
problems, we propose a brain-inspired plug-and-play method to learn a
semantic-agnostic and spatial-aware representation for generalizable
visual-audio navigation. We meticulously design two auxiliary tasks for
respectively accelerating learning representations with the above-desired
characteristics. With these two auxiliary tasks, the agent learns a
spatially-correlated representation of visual and audio inputs that can be
applied to work on environments with novel sounds and maps. Experiment results
on realistic 3D scenes (Replica and Matterport3D) demonstrate that our method
achieves better generalization performance when zero-shot transferred to scenes
with unseen maps and unheard sound categories
Optimal control towards sustainable wastewater treatment plants based on multi-agent reinforcement learning
Wastewater treatment plants are designed to eliminate pollutants and
alleviate environmental pollution. However, the construction and operation of
WWTPs consume resources, emit greenhouse gases (GHGs) and produce residual
sludge, thus require further optimization. WWTPs are complex to control and
optimize because of high nonlinearity and variation. This study used a novel
technique, multi-agent deep reinforcement learning, to simultaneously optimize
dissolved oxygen and chemical dosage in a WWTP. The reward function was
specially designed from life cycle perspective to achieve sustainable
optimization. Five scenarios were considered: baseline, three different
effluent quality and cost-oriented scenarios. The result shows that
optimization based on LCA has lower environmental impacts compared to baseline
scenario, as cost, energy consumption and greenhouse gas emissions reduce to
0.890 CNY/m3-ww, 0.530 kWh/m3-ww, 2.491 kg CO2-eq/m3-ww respectively. The
cost-oriented control strategy exhibits comparable overall performance to the
LCA driven strategy since it sacrifices environmental bene ts but has lower
cost as 0.873 CNY/m3-ww. It is worth mentioning that the retrofitting of WWTPs
based on resources should be implemented with the consideration of impact
transfer. Specifically, LCA SW scenario decreases 10 kg PO4-eq in
eutrophication potential compared to the baseline within 10 days, while
significantly increases other indicators. The major contributors of each
indicator are identified for future study and improvement. Last, the author
discussed that novel dynamic control strategies required advanced sensors or a
large amount of data, so the selection of control strategies should also
consider economic and ecological conditions
M2C: Towards Automatic Multimodal Manga Complement
Multimodal manga analysis focuses on enhancing manga understanding with
visual and textual features, which has attracted considerable attention from
both natural language processing and computer vision communities. Currently,
most comics are hand-drawn and prone to problems such as missing pages, text
contamination, and aging, resulting in missing comic text content and seriously
hindering human comprehension. In other words, the Multimodal Manga Complement
(M2C) task has not been investigated, which aims to handle the aforementioned
issues by providing a shared semantic space for vision and language
understanding. To this end, we first propose the Multimodal Manga Complement
task by establishing a new M2C benchmark dataset covering two languages. First,
we design a manga argumentation method called MCoT to mine event knowledge in
comics with large language models. Then, an effective baseline FVP-M
using fine-grained visual prompts is proposed to support manga complement.
Extensive experimental results show the effectiveness of FVP-M method for
Multimodal Mange Complement.Comment: EMNLP2023. arXiv admin note: text overlap with arXiv:2210.1546
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