130 research outputs found
How Practical Phase-shift Errors Affect Beamforming of Reconfigurable Intelligent Surface?
Reconfigurable intelligent surface (RIS) is a new technique that is able to
manipulate the wireless environment smartly and has been exploited for
assisting the wireless communications, especially at high frequency band.
However, it suffers from hardware impairments (HWIs) in practical designs,
which inevitably degrades its performance and thus limits its full potential.
To address this practical issue, we first propose a new RIS reflection model
involving phase-shift errors, which is then verified by the measurement results
from field trials. With this beamforming model, various phase-shift errors
caused by different HWIs can be analyzed. The phase-shift errors are classified
into three categories: (1) globally independent and identically distributed
errors, (2) grouped independent and identically distributed errors and (3)
grouped fixed errors. The impact of typical HWIs, including frequency mismatch,
PIN diode failures and panel deformation, on RIS beamforming ability are
studied with the theoretical model and are compared with numerical results. The
impact of frequency mismatch are discussed separately for narrow-band and
wide-band beamforming. Finally, useful insights and guidelines on the RIS
design and its deployment are highlighted for practical wireless systems
LLM4DyG: Can Large Language Models Solve Problems on Dynamic Graphs?
In an era marked by the increasing adoption of Large Language Models (LLMs)
for various tasks, there is a growing focus on exploring LLMs' capabilities in
handling web data, particularly graph data. Dynamic graphs, which capture
temporal network evolution patterns, are ubiquitous in real-world web data.
Evaluating LLMs' competence in understanding spatial-temporal information on
dynamic graphs is essential for their adoption in web applications, which
remains unexplored in the literature. In this paper, we bridge the gap via
proposing to evaluate LLMs' spatial-temporal understanding abilities on dynamic
graphs, to the best of our knowledge, for the first time. Specifically, we
propose the LLM4DyG benchmark, which includes nine specially designed tasks
considering the capability evaluation of LLMs from both temporal and spatial
dimensions. Then, we conduct extensive experiments to analyze the impacts of
different data generators, data statistics, prompting techniques, and LLMs on
the model performance. Finally, we propose Disentangled Spatial-Temporal
Thoughts (DST2) for LLMs on dynamic graphs to enhance LLMs' spatial-temporal
understanding abilities. Our main observations are: 1) LLMs have preliminary
spatial-temporal understanding abilities on dynamic graphs, 2) Dynamic graph
tasks show increasing difficulties for LLMs as the graph size and density
increase, while not sensitive to the time span and data generation mechanism,
3) the proposed DST2 prompting method can help to improve LLMs'
spatial-temporal understanding abilities on dynamic graphs for most tasks. The
data and codes will be open-sourced at publication time
A nomogram based on the preoperative neutrophil-to-lymphocyte ratio to distinguish sarcomatoid renal cell carcinoma from clear cell renal cell carcinoma
ObjectiveOur study aimed to assess the predictive value of the preoperative neutrophil-to-lymphocyte ratio(NLR) in distinguishing sarcomatoid renal cell carcinoma (SRCC) from clear cell renal cell carcinoma(CCRCC) and to developing a nomogram based on the preoperative NLR and other factors to distinguish SRCC from CCRCC.Materials and methodsThe database involved 280 patients, including 46 SRCC and 234 CCRCC. logistic analysis was conducted to select the variables associated with identifying SRCC preoperatively, and subgroup analysis was used to further validate the ability of NLR with preoperative identification of SRCC.In addition, The data were randomly separated into a training cohort(n=195) and a validation cohort(n=85). And an NLR-based nomogram was plotted based on the logistic analysis results. The nomogram was evaluated according to its discrimination, consistency, and clinical benefits.ResultsMultivariate analysis indicated that NLR, flank pain, tumor size, and total cholesterol(TC) were independent risk factors for identifying SRCC. The results of subgroup analysis showed that higher NLR was associated with a higher probability of SRCC in most subgroups. The area under the curve(AUC) of the training and validation cohorts were 0.801 and 0.738, respectively. The results of the calibration curve show high consistency between predicted and actual results. Decision Curve Analysis(DCA) showed clinical intervention based on the model was beneficial over most of the threshold risk range.ConclusionNLR is a potential indicator for preoperative differentiation of SRCC and CCRCC, and the predictive model constructed based on NLR has a good predictive ability. The new model could provide suggestions for the early identification of SRCC
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