6,680 research outputs found
Drought impacts on ecosystem functions of the U.S. National Forests and Grasslands: Part I evaluation of a water and carbon balance model
Understanding and quantitatively evaluating the regional impacts of climate change and variability (e.g., droughts) on forest ecosystem functions (i.e., water yield, evapotranspiration, and productivity) and services (e.g., fresh water supply and carbon sequestration) is of great importance for developing climate change adaptation strategies for National Forests and Grasslands (NFs) in the United States. However, few reliable continental-scale modeling tools are available to account for both water and carbon dynamics. The objective of this study was to test a monthly water and carbon balance model, the Water Supply Stress Index (WaSSI) model, for potential application in addressing the influences of drought on NFs ecosystem services across the conterminous United States (CONUS). The performance of the WaSSI model was comprehensively assessed with measured streamflow (Q) at 72 U.S. Geological Survey (USGS) gauging stations, and satellite-based estimates of watershed evapotranspiration (ET) and gross primary productivity (GPP) for 170 National Forest and Grassland (NFs). Across the 72 USGS watersheds, the WaSSI model generally captured the spatial variability of multi-year mean annual and monthly Q and annual ET as evaluated by Correlation Coefficient (R = 0.71–1.0), Nash–Sutcliffe Efficiency (NS = 0.31–1.00), and normalized Root Mean Squared Error (0.06–0.48). The modeled ET and GPP by WaSSI agreed well with the remote sensing-based estimates for multi-year annual and monthly means for all the NFs. However, there were systemic discrepancies in GPP between our simulations and the satellite-based estimates on a yearly and monthly scale, suggesting uncertainties in GPP estimates in all methods (i.e., remote sensing and modeling). Overall, our assessments suggested that the WaSSI model had the capability to reconstruct the long-term forest watershed water and carbon balances at a broad scale. This model evaluation study provides a foundation for model applications in understanding the impacts of climate change and variability (e.g., droughts) on NFs ecosystem service functions
Observation of electric current induced by optically injected spin current
A normally incident light of linear polarization injects a pure spin current
in a strip of 2-dimensional electron gas with spin-orbit coupling. We report
observation of an electric current with a butterfly-like pattern induced by
such a light shed on the vicinity of a crossbar shaped InGaAs/InAlAs quantum
well. Its light polarization dependence is the same as that of the spin
current. We attribute the observed electric current to be converted from the
optically injected spin current caused by scatterings near the crossing. Our
observation provides a realistic technique to detect spin currents, and opens a
new route to study the spin-related science and engineering in semiconductors.Comment: 15 pages, 4 figure
Towards Net-Zero Carbon Emissions in Network AI for 6G and Beyond
A global effort has been initiated to reduce the worldwide greenhouse gas
(GHG) emissions, primarily carbon emissions, by half by 2030 and reach net-zero
by 2050. The development of 6G must also be compliant with this goal.
Unfortunately, developing a sustainable and net-zero emission systems to meet
the users' fast growing demands on mobile services, especially smart services
and applications, may be much more challenging than expected. Particularly,
despite the energy efficiency improvement in both hardware and software
designs, the overall energy consumption and carbon emission of mobile networks
are still increasing at a tremendous speed. The growing penetration of
resource-demanding AI algorithms and solutions further exacerbate this
challenge. In this article, we identify the major emission sources and
introduce an evaluation framework for analyzing the lifecycle of network AI
implementations. A novel joint dynamic energy trading and task allocation
optimization framework, called DETA, has been introduced to reduce the overall
carbon emissions. We consider a federated edge intelligence-based network AI
system as a case study to verify the effectiveness of our proposed solution.
Experimental results based on a hardware prototype suggest that our proposed
solution can reduce carbon emissions of network AI systems by up to 74.9%.
Finally, open problems and future directions are discussed
GBD-TS: Goal-based Pedestrian Trajectory Prediction with Diffusion using Tree Sampling Algorithm
Predicting pedestrian trajectories is crucial for improving the safety and
effectiveness of autonomous driving and mobile robots. However, this task is
nontrivial due to the inherent stochasticity of human motion, which naturally
requires the predictor to generate multi-model prediction. Previous works have
used various generative methods, such as GAN and VAE, for pedestrian trajectory
prediction. Nevertheless, these methods may suffer from problems, including
mode collapse and relatively low-quality results. The denoising diffusion
probabilistic model (DDPM) has recently been applied to trajectory prediction
due to its simple training process and powerful reconstruction ability.
However, current diffusion-based methods are straightforward without fully
leveraging input information and usually require many denoising iterations
leading to a long inference time or an additional network for initialization.
To address these challenges and promote the application of diffusion models in
trajectory prediction, we propose a novel scene-aware multi-modal pedestrian
trajectory prediction framework called GBD. GBD combines goal prediction with
the diffusion network. First, the goal predictor produces multiple goals, and
then the diffusion network generates multi-modal trajectories conditioned on
these goals. Furthermore, we introduce a new diffusion sampling algorithm named
tree sampling (TS), which leverages common feature to reduce the inference time
and improve accuracy for multi-modal prediction. Experimental results
demonstrate that our GBD-TS method achieves state-of-the-art performance with
real-time inference speed.Comment: Submitted to ICRA 202
Beneath Surface Similarity: Large Language Models Make Reasonable Scientific Analogies after Structure Abduction
The vital role of analogical reasoning in human cognition allows us to grasp
novel concepts by linking them with familiar ones through shared relational
structures. Despite the attention previous research has given to word
analogies, this work suggests that Large Language Models (LLMs) often overlook
the structures that underpin these analogies, raising questions about the
efficacy of word analogies as a measure of analogical reasoning skills akin to
human cognition. In response to this, our paper introduces a task of analogical
structure abduction, grounded in cognitive psychology, designed to abduce
structures that form an analogy between two systems. In support of this task,
we establish a benchmark called SCAR, containing 400 scientific analogies from
13 distinct fields, tailored for evaluating analogical reasoning with structure
abduction. The empirical evidence underlines the continued challenges faced by
LLMs, including ChatGPT and GPT-4, in mastering this task, signifying the need
for future exploration to enhance their abilities.Comment: Accepted to EMNLP 2023 (Findings
Production of doubly charmed hadron and in relativistic heavy ion collisions
Heavy ion collisions provide a unique opportunity for studying the properties
of exotic hadrons with two charm quarks. The production of is
significantly enhanced in nuclear collisions compared to proton-proton
collisions due to the creation of multiple charm pairs. In this study, we
employ the Langevin equation in combination with the Instantaneous Coalescence
Model (LICM) to investigate the production of and
which consists of two charm quarks. We consider as molecular states
composed of and mesons. The Langevin equation is used to calculate
the energy loss of charm quarks and mesons in the hot medium. The
hadronization process, where charm quarks transform into each state as
constituents of production, is described using the coalescence
model. The coalescence probability between and is determined by the
Wigner function, which encodes the information of the wave function.
Our results show that the production varies by approximately one
order of magnitude when different widths in the Wigner function, representing
distinct binding energies of , are considered. This variation offers
valuable insights into the nature of through the analysis of its
wave function. The is treated as a hadronic state produced at
the hadronization of the deconfined matter. Its production is also calculated
as a comparison with the molecular state .Comment: 7 pages, 5 figure
Knowledge Matters: Radiology Report Generation with General and Specific Knowledge
Automatic radiology report generation is critical in clinics which can
relieve experienced radiologists from the heavy workload and remind
inexperienced radiologists of misdiagnosis or missed diagnose. Existing
approaches mainly formulate radiology report generation as an image captioning
task and adopt the encoder-decoder framework. However, in the medical domain,
such pure data-driven approaches suffer from the following problems: 1) visual
and textual bias problem; 2) lack of expert knowledge. In this paper, we
propose a knowledge-enhanced radiology report generation approach introduces
two types of medical knowledge: 1) General knowledge, which is input
independent and provides the broad knowledge for report generation; 2) Specific
knowledge, which is input dependent and provides the fine-grained knowledge for
report generation. To fully utilize both the general and specific knowledge, we
also propose a knowledge-enhanced multi-head attention mechanism. By merging
the visual features of the radiology image with general knowledge and specific
knowledge, the proposed model can improve the quality of generated reports.
Experimental results on two publicly available datasets IU-Xray and MIMIC-CXR
show that the proposed knowledge enhanced approach outperforms state-of-the-art
image captioning based methods. Ablation studies also demonstrate that both
general and specific knowledge can help to improve the performance of radiology
report generation.Comment: Medical Image Analysi
Spontaneously induced general relativity with holographic interior and general exterior
We study the spontaneously induced general relativity (GR) from the
scalar-tensor gravity. We demonstrate by numerical methods that a novel inner
core can be connected to the Schwarzschild exterior with cosmological constants
and any sectional curvature. Deriving an analytic core metric for a general
exterior, we show that all the nontrivial features of the core, including the
locally holographic entropy packing, are universal for the general exterior in
static spacetimes. We also investigate whether the f(R) gravity can accommodate
the nontrivial core.Comment: 16 pages, 5 figures; v3: clarification improved, revised version
accepted by PL
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