175 research outputs found
On the performance of an integrated communication and localization system: an analytical framework
Quantifying the performance bound of an integrated localization and
communication (ILAC) system and the trade-off between communication and
localization performance is critical. In this letter, we consider an ILAC
system that can perform communication and localization via time-domain or
frequency-domain resource allocation. We develop an analytical framework to
derive the closed-form expression of the capacity loss versus localization
Cramer-Rao lower bound (CRB) loss via time-domain and frequency-domain resource
allocation. Simulation results validate the analytical model and demonstrate
that frequency-domain resource allocation is preferable in scenarios with a
smaller number of antennas at the next generation nodeB (gNB) and a larger
distance between user equipment (UE) and gNB, while time-domain resource
allocation is preferable in scenarios with a larger number of antennas and
smaller distance between UE and the gNB.Comment: 5 pages, 3 figure
A review on N-doped biochar for oxidative degradation of organic contaminants in wastewater by persulfate activation
The Persulfate-based advanced oxidation process is the most efficient and commonly used technology to remove organic contaminants in wastewater. Due to the large surface area, unique electronic properties, abundant N functional groups, cost-effectiveness, and environmental friendliness, N-doped biochars (NBCs) are widely used as catalysts for persulfate activation. This review focuses on the NBC for oxidative degradation of organics-contaminated wastewater. Firstly, the preparation and modification methods of NBCs were reviewed. Then the catalytic performance of NBCs and modified NBCs on the oxidation degradation of organic contaminants were discussed with an emphasis on the degradation mechanism. We further summarized the detection technologies of activation mechanisms and the structures of NBCs affecting the PS activation, followed by the specific role of the N configuration of the NBC on its catalytic capacity. Finally, several challenges in the treatment of organics-contaminated wastewater by a persulfate-based advanced oxidation process were put forward and the recommendations for future research were proposed for further understanding of the advanced oxidation process activated by the NBC
The impact of filaments on dwarf galaxy properties in the Auriga simulations
With a hydrodynamical simulation using a simple galaxy formation model without taking into account feedback, our previous work has shown that dense and massive filaments at high redshift can provide potential wells to trap and compress gas, and hence affect galaxy formation in their resident low-mass haloes. In this paper, we make use of the Auriga simulations, a suite of high-resolution zoom-in hydrodynamical simulations of Milky Way-like galaxies, to study whether the conclusion still holds in the simulations with a sophisticated galaxy formation model. In agreement with the results of our previous work, we find that, compared to their counterparts with similar halo masses in the field, dwarf galaxies residing in filaments tend to have higher baryonic and stellar fractions. At the fixed parent halo mass, the filament dwarfs tend to have slightly higher star formation rates than those of field ones. But overall we do not find a clear difference in galaxy g - r colours between the filament and field populations. We also show that at high redshifts, the gas components in dwarf galaxies tend to have their spins aligned with the filaments in which they reside. Our results support a picture in which massive filaments at high redshift assist gas accretion and enhance star formation in their resident dwarf-sized dark matter haloes.Peer reviewe
The abundance of dark matter haloes down to Earth mass
We use the Voids-within-Voids-within-Voids (VVV) simulations, a suite of
successive nested N-body simulations with extremely high resolution (denoted,
from low to high resolution, by L0 to L7), to test the Press-Schechter (PS),
Sheth-Tormen (ST), and extended Press-Schechter (EPS) formulae for the halo
abundance over the entire mass range, from mini-haloes of $10^{-6}\
\mathrm{M_\odot}10^{15}\ \mathrm{M_\odot}z=30z=0z=2\delta=010^{11-15}
~\mathrm{M_\odot}\delta<-0.6\lesssim 20\%10^{-6-12.5} ~\mathrm{M_\odot}z \sim 7-15z \sim 30$, the EPS prediction fits the simulations well
again. We further confirm our results by picking more subvolumes from the full
L0 simulation, finding that our conclusions depend only weakly on the size and
overdensity of the chosen region. Since at mean density the EPS reduces to the
PS mass function, its good agreement with the higher-level simulations implies
that the PS (or, even better, the ST) formula gives an accurate description of
the total halo mass function in representative regions of the universe.Comment: 10 pages, 5 figures (additional 2 figures in the appendix
SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects
Despite the progress we have recorded in the last few years in multilingual
natural language processing, evaluation is typically limited to a small set of
languages with available datasets which excludes a large number of low-resource
languages. In this paper, we created SIB-200 -- a large-scale open-sourced
benchmark dataset for topic classification in 200 languages and dialects to
address the lack of evaluation dataset for Natural Language Understanding
(NLU). For many of the languages covered in SIB-200, this is the first publicly
available evaluation dataset for NLU. The dataset is based on Flores-200
machine translation corpus. We annotated the English portion of the dataset and
extended the sentence-level annotation to the remaining 203 languages covered
in the corpus. Despite the simplicity of this task, our evaluation in
full-supervised setting, cross-lingual transfer setting and prompting of large
language model setting show that there is still a large gap between the
performance of high-resource and low-resource languages when multilingual
evaluation is scaled to numerous world languages. We found that languages
unseen during the pre-training of multilingual language models,
under-represented language families (like Nilotic and Altantic-Congo), and
languages from the regions of Africa, Americas, Oceania and South East Asia,
often have the lowest performance on our topic classification dataset. We hope
our dataset will encourage a more inclusive evaluation of multilingual language
models on a more diverse set of languages. https://github.com/dadelani/sib-200Comment: under submissio
The mass accretion history of dark matter haloes down to Earth mass
We take advantage of the unprecedented dynamical range provided by the
"Cosmic-Zoom" project to study the mass accretion history (MAH) of present-day
dark matter haloes over the entire mass range present in the CDM
paradigm when the dark matter is made of weakly interacting massive particles
of mass . In particular, we complement previous studies by
exploring the MAHs of haloes with mass from
down to Earth mass, . The formation redshift
of low-mass haloes anti-correlates weakly with mass, peaking at for
haloes of mass . Even lower masses are
affected by the free-streaming cutoff in the primordial spectrum of density
fluctuations and form at lower redshift. We compare MAHs in our simulations
with predictions from two analytical models based on the extended
Press-Schechter theory (EPS), and three empirical models derived by fitting and
extrapolating either results from cosmological -body simulations or Monte
Carlo realizations of halo growth. All models fit our simulations reasonably
well over the mass range for which they were calibrated. While the empirical
models match better for more massive haloes, $M>10^{10}\
h^{-1}\mathrm{M_{\odot}}20$ percent at high redshift. We conclude that EPS
theory predicts the hierarchical build-up of dark matter haloes quite well over
the entire mass range
Early detection of pine wilt disease tree candidates using time-series of spectral signatures
Pine wilt disease (PWD), caused by pine wood nematode (PWN), poses a tremendous threat to global pine forests because it can result in rapid and widespread infestations within months, leading to large-scale tree mortality. Therefore, the implementation of preventive measures relies on early detection of PWD. Unmanned aerial vehicle (UAV)-based hyperspectral images (HSI) can detect tree-level changes and are thus an effective tool for forest change detection. However, previous studies mainly used single-date UAV-based HSI data, which could not monitor the temporal changes of disease distribution and determine the optimal detection period. To achieve these purposes, multi-temporal data is required. In this study, Pinus koraiensis stands were surveyed in the field from May to October during an outbreak of PWD. Concurrently, multi-temporal UAV-based red, green, and blue bands (RGB) and HSI data were also obtained. During the survey, 59 trees were confirmed to be infested with PWD, and 59 non-infested trees were used as control. Spectral features of each tree crown, such as spectral reflectance, first and second-order spectral derivatives, and vegetation indices (VIs), were analyzed to identify those useful for early monitoring of PWD. The Random Forest (RF) classification algorithm was used to examine the separability between the two groups of trees (control and infested trees). The results showed that: (1) the responses of the tree crown spectral features to PWD infestation could be detected before symptoms were noticeable in RGB data and field surveys; (2) the spectral derivatives were the most discriminable variables, followed by spectral reflectance and VIs; (3) based on the HSI data from July to October, the two groups of trees were successfully separated using the RF classifier, with an overall classification accuracy of 0.75–0.95. Our results illustrate the potential of UAV-based HSI for PWD early monitoring
Noninvasive electromyometrial imaging of human uterine maturation during term labor
Electromyometrial imaging (EMMI) was recently developed to image the three-dimensional (3D) uterine electrical activation during contractions noninvasively and accurately in sheep. Herein we describe the development and application of a human EMMI system to image and evaluate 3D uterine electrical activation patterns at high spatial and temporal resolution during human term labor. We demonstrate the successful integration of the human EMMI system during subjects\u27 clinical visits to generate noninvasively the uterine surface electrical potential maps, electrograms, and activation sequence through an inverse solution using up to 192 electrodes distributed around the abdomen surface. Quantitative indices, including the uterine activation curve, are developed and defined to characterize uterine surface contraction patterns. We thus show that the human EMMI system can provide detailed 3D images and quantification of uterine contractions as well as novel insights into the role of human uterine maturation during labor progression
PyPose: A Library for Robot Learning with Physics-based Optimization
Deep learning has had remarkable success in robotic perception, but its
data-centric nature suffers when it comes to generalizing to ever-changing
environments. By contrast, physics-based optimization generalizes better, but
it does not perform as well in complicated tasks due to the lack of high-level
semantic information and the reliance on manual parametric tuning. To take
advantage of these two complementary worlds, we present PyPose: a
robotics-oriented, PyTorch-based library that combines deep perceptual models
with physics-based optimization techniques. Our design goal for PyPose is to
make it user-friendly, efficient, and interpretable with a tidy and
well-organized architecture. Using an imperative style interface, it can be
easily integrated into real-world robotic applications. Besides, it supports
parallel computing of any order gradients of Lie groups and Lie algebras and
-order optimizers, such as trust region methods. Experiments
show that PyPose achieves 3-20 speedup in computation compared to
state-of-the-art libraries. To boost future research, we provide concrete
examples across several fields of robotics, including SLAM, inertial
navigation, planning, and control
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